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CONTENTS ORIGINAL PAPERS

INVESTIGATION OF SO2 RESULTS WITH PASSIVE DIFFUSIVE SAMPLERS 3 AROUND 18 MART CAN THERMAL POWER PLANT IN CANAKKALE Nesimi Ozkurt and Mehmet Karpuzcu YTTRIUM - ACCUMULATION, TRANSLOCATION AND 11 DISTRIBUTION IN YOUNG SUNFLOWER PLANTS (Helianthus annuus L.) Ivana Maksimović, Rudolf Kastori, Marina Putnik-Delić and Tijana Zeremski

REMOVAL OF STRONTIUM IONS FROM AQUEOUS SOLUTION 19 BY ADSORPTION ONTO SODIUM TRITITANATE WHISKER Zhixin Yu, Jiangdong Dai, Ruijun Zhao, Longcheng Xu and Yongsheng Yan MICROSCOPICAL CHANGING OF THE MAIN WOOD ANATOMICAL 26 ELEMENTS OF PEDUNCULATE AND SESSILE OAKS DUE TO ATTACK OF THE WHITE ROT FUNGUS Chondrostereum purpureum (Pers. ex Fr.) Pouz. Milenko Mirić, Snežana Ivković, Snežana Rajković and Miroslava Marković

OCCURRENCE AND DISTRIBUTION OF EPIPELIC AND 31 EPILITHIC DIATOMS IN BATMAN STREAM (TURKEY) Feray Sonmez and Keziban Asan DESIGN STRATEGIES FOR REVITALIZATION 36 ON THE FİLYOS COASTLINE, ZONGULDAK, TURKEY Bülent Cengiz, Mehmet Sabaz, Banu Bekci and Canan Cengiz ESTIMATION OF ENVIRONMENTAL CAPACITY OF 48 PETROLEUM HYDROCARBONS IN JIAOZHOU BAY, CHINA Guo-dong Qian, Xiao-yong Shi, Ke-qiang Li, Xiu-lin Wang, Sheng-kang Liang and Xu-dong Qiao THE EFFECTS OF REACTIVE BLACK 5 TEXTILE DYE ON PEROXIDASE ACTIVITY, LIPID 54 PEROXIDATION AND TOTAL CHLOROPHYLL CONTENT OF PHASEOLUS VULGARIS L. CV. “GINA” Armagan Kaya, Emel Yigit and Gulcin Beker Akbulut HISTOPATHOLOGICAL ALTERATIONS IN 61 GOBIUS NIGER (BLACK GOBY) DUE TO POLLUTION OF THE IZMIR BAY Selma Katalay, Ersin Minareci, Ibrahim Tuğlu and Helmut Segner MUTUAL RELATIONSHIP OF HENRY’S LAW CONSTANTS AND 68 AQUEOUS PHASE CONCENTRATIONS FOR BENZENE, TOLUENE AND O-XYLENE AT 30 º C Roman Tandlich and Bongumusa M. Zuma


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OPTIMIZATION OF ARSENIC SLUDGE IMMOBILIZATION PROCESS 76 IN CEMENT – NATURAL ZEOLITE – LIME BLENDS USING ARTIFICIAL NEURAL NETWORKS AND MULTI-OBJECTIVE CRITERIA FUNCTIONS Tomislav Bolanča, Juraj Šipušić, Šime Ukić, Mario Šiljeg and Magdalena Ujević Bošnjak STUDIES ON REMOVAL OF NAPROXEN SODIUM 84 BY ADSORPTION ONTO ACF IN BATCH AND COLUMN Çiğdem Sarıcı-Özdemir, Yunus Önal, Selim Erdoğan, Canan Akmil-Başar

ELEMENTAL AND RADIOACTIVITY ANALYSES OF MOSSES COLLECTED 94 IN HATILA VALLEY NATIONAL PARK - EASTERN BLACK SEA REGION OF TURKEY Bahadir Koz, Ugur Cevik and Necati Celik THE ADSORPTION OF NONYLPHENOL BY ALGAE 101 Zhange Peng, Jing Li, Jinmei Feng, Zhaochun Wu and Nansheng Deng

SHORT COMMUNICATION

DEBROMINATION OF HEXABROMOCYCLODODECANE 107 IN AQUEOUS SOLUTIONS BY UV-C IRRADIATION Danna Zhou, Liang Chen, Feng Wu, Jie Wang and Fan Yang

INDEX 112


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INVESTIGATION OF SO2 RESULTS WITH PASSIVE DIFFUSIVE SAMPLERS AROUND

18 MART CAN THERMAL POWER PLANT IN CANAKKALE

Nesimi Ozkurt1,* and Mehmet Karpuzcu 2

1 TUBITAK Marmara Research Center, Environment Institute, 41470 Kocaeli, Turkey 2 Gebze Institute of Technology, Kocaeli, Turkey

ABSTRACT

In Turkey, one of the major air pollution sources is energy utilization due to the combustion of lignite. In-creasing energy consumption and use of domestic lignite causes the unavoidable increase of SO2 pollution. Espe-cially around large combustion plants (i.e. coal-fired ther-mal power plants), monitoring cost is critical for evaluation of environmental impacts, since their impact area is very large, depending on measurement techniques.

Since passive diffusion samplers have the potential to provide a cheap and effective means of determining atmos-pheric trace gas concentrations, they were preferred for monitoring of SO2 concentrations in the impact area of Can Thermal Power Plant. For more accurate and continu-ous measurements of air pollutants, an automated continu-ous measurement analyzer using UV fluorescence was also used. The passive diffusion samplers were co-located with the available AQMS (Air Quality Monitoring Stations), and during the year 2006, the continuous analyzer was co-located with the AQMS in Bayramic County, near the Can Thermal Power Plant. The two different measurement tech-niques were used to monitor and compare monthly meas-urements using least-square regression.

The temporal variations observed with the passive samplers and AQMS data for SO2 concentrations were similar. The 8-months comparison of 2006, between pas-sive sampler technique and automated analyzer measure-ment suggests that, in most cases, SO2 results correlate fairly well.

KEYWORDS: Monitoring, passive diffusion sampler, comparison, regression

* Corresponding author

1. INTRODUCTION

In most countries, many power plants driven by fossil fuels are in service today. In the last ten years, many power generation companies have paid attention to proc-ess improvement in steam power plants by taking meas-ures to improve the plant efficiencies and to minimize the environmental impact. Majority of the lignite-fired ther-mal power plants do not have a desulphurization system. Therefore, it is crucial to decide on the optimal place and technology for the future thermal power plants, and to equip the currently operating plants with newer technolo-gies that will reduce amount of contaminants released into the air. Most of lignite-fired thermal power plants have been run by conventional methods and constructed in places very close to residential areas [1].

Turkey is undergoing rapid industrialization, urbani-zation and population growth. Electric power demand of Turkey has been growing steadily, with an average annual growth of 9 % over the past 30 years. The Ministry of Energy and Natural Resources of Turkey predicts 7 % annual growth until 2020 [2]. Although the recent increas-ing interest in energy production from renewable sources, polluting fossil-fuelled power plants will continue to pro-vide most of the electricity in coming decades [3]. It is not viable to design and implement energy production and transmission plans without considering the environmental impacts. Use of fossil fuel is responsible for many envi-ronmental problems [2].

In Turkey, one of the major air pollution sources is energy utilization due to the combustion of lignite. In-creasing energy consumption and use of domestic lignite causes the unavoidable increase of SO2 pollution. Accord-ing to national regulation, in the impact area of a plant, air pollutants should continuously be monitored in long term.

For investigation of temporal and spatial variations of SO2, continuous monthly measurements can be undertaken with passive diffusion samplers. Passive diffusion samplers present a means of explaining a lot of measurement issues in air pollution and atmospheric chemistry. Passive diffu-sion samplers of gaseous compounds are finding increased


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environmental application [4-9]. They provide a cost-effective way to monitor air pollutant species at both local and regional scales. Compared with conventional meth-ods, they can be deployed unattended for extended peri-ods, and do not require electricity [10].They can be used: i) to increase the spatial resolution of measurements; ii) in screening studies to evaluate monitoring site locations; and iii) to aid measurement programs by providing a means to increase data completion [11]. Passive diffusion samplers are preferred, since they have the potential to provide a cheap and effective means of determining atmospheric trace gas concentrations [12]. Other benefits of passive diffusion samplers include simplicity of sampling and high correla-tion results as compared to continuous monitors [13]. Different air pollutants have been investigated by using passive diffusive samplers in many studies [14-24].

In this study, we present 2006 measurements of SO2 at 10 sites in Canakkale using passive diffusion samplers. At first, the levels and variations of SO2 were shown, and the sources and other factors affecting the concentrations of SO2 were discussed. For verification of passive diffu-sive sampler technique, QA/QC was implemented by cross-calibration of SO2 passive diffusion samplers with an UV fluorescent SO2 analyzer in an Air Quality Monitoring Station (AQMS) at the same location around the power plant.

2. MATERIALS AND METHODS

2.1. Passive Diffusion Samplers

During the long-term measurements around Can Ther-mal Power Plant, for investigating the temporal and spatial variations of SO2, as one of the comparable techniques, passive diffusion samplers were used. The European Com-mittee for Standardization defines a passive diffusion sam-pler as follows: “A device that is capable of taking sam-ples of gases or vapours from the atmosphere at a rate con-trolled by a physical process such as gaseous diffusion through a static air layer or a porous material and/or per-meation through a membrane, but which does not involve active movement of air through the device” [25].

Passive diffusion samplers can be broadly classified into tube, radial, badge and cartridge-type samplers [5]. Different types of samplers can have different uptake rates, which make them more or less suitable to certain applications [26]. For example, tube-type samplers have generally lower uptake rates due to longer axial diffusion path and smaller cross-sectional diffusion area, which make them suitable for assessing relatively long-term (e.g. monthly mean) ambient air quality levels [27].

In the sampler, gas molecules are transported by mo-lecular diffusion. During the site measurements, the used samplers operate on the principle of molecular diffusion, with molecules of a gas diffusing from a region of high concentration (open end of the sampler) to a region of low concentration (absorbent end of the sampler). The ambient

concentration measured by the exposed tube was calcu-lated using the Eq. (1):

(1)

where: C = the concentration of gas in the atmosphere (µg m-3); Sr = the sampling rate of gas (m3 h-1); m = mass of ions in tube (µg; in this study for SO2, mass of SO4 ions); t = the time of exposure (h).

From the tube dimensions and the diffusion coeffi-cient of gas in air, the sampling rate was calculated and treated as a constant given in Eq. (2):

(2)

where: a = the cross sectional area of the tube; l = the length of the tube; D12 = diffusion coefficient of gas 1 through gas 2 – in this case gas pollutant through air.

Since the tube dimensions vary depending on the manufacturer, it is important to calculate the sampling rate based upon the actual dimensions of the tubes being used. The sampling rate also depends on the diffusion coeffi-cient D. A recent research, reported in 1998, suggests that the best estimate of D is 0.1361 cm2 s-1 at STP, i.e. 0 Celsius (273 K) and 1 atmosphere (101.3 kPa) [14].

The used SO2 samplers were Palmes-type diffusion samplers (PDSs) provided from Gradko Environmental Ltd., England. The producer firm also has a laboratory which has accreditation certificate for analyzing these samplers. The operating principle and analytical procedures of PDSs have been described elsewhere [22]

2.2. Measurements of AQMS

To investigate a second technique for SO2 measure-ments, an AF 21M-LCD UV fluorescence analyzer (Envi-ronment S.A co) was used in the AQMS.

The automated SO2 analyzer in AQMS was placed in Bayramic county, Canakkale province. The measurement location is shown in Fig. 1.

The measurement principle of the automated SO2 analyzer can be explained by Beer-Lambert law and is given in Eq. (3):

(3)

where, Io denotes the intensity at the chamber inlet, α is the characteristic SO2 absorption coefficient, and c [SO2] is the concentration of the gas to be analyzed.

3. RESULTS AND DISCUSSION

When the objectives of the sampling network are in-vestigated, some considerations should be linked, such as pollutants of interest and possible interference by other pollutants, or the scale of the sampler network, which


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FIGURE 1 - Measurement locations of SO2 and CTPP in Canakkale.

may include large spatial variation in conditions (tem-perature, humidity, wind speed, solar radiation, interfering pollutants). The availability of sites for co-location with instrumental air-quality monitors is necessary for QA/QC and cross-calibration. These should be combined with good, relevant, meteorological and geospatial data, espe-cially if sampler sensitivity to specific environmental factors has to be factored into the calculations [23].

3.1. Verification of PDSs results with measurements of auto-mated analyzer

Although PDSs were proven to be reliable for ambi-ent SO2 monitoring in other studies, they have not previ-ously applied to the sampling and transport conditions in Canakkale. Therefore, comparisons between PDSs and in-situ continuous active analyzer in AQMS were carried out at Bayramic (station 7) site. In the scope of this study, QA/QC was implemented by cross-calibration of SO2 passive diffusion samplers with UV fluorescence SO2 analyzer in AQMS at the same location. One of the pas-sive diffusion sampler measurement sites was placed in an

open area near the AQMS, within the restrictions imposed by the site location type, allowing free circulation of air around the tube. The tubes were placed at a height of 3 m above the ground level.

The passive diffusion samplers for SO2 were used for monthly results during the year 2006. AQMS data for these parameters were arranged also as monthly average for comparison. Monthly average values of sulphur diox-ide passive sampler and SO2 automated analyzer placed in AQMS are given in Table 1.

For SO2, the measurement results of two different techniques were compared using the least-square regres-sion model.

The graphical illustration of two different measure-ment results for sulphur dioxide parameter is given in Fig. 2. By using least square regression model, “α” and “β” values for sulphur dioxide results were calculated and found to be 0.98 and 3.23, respectively. In this case, the coefficient of determination “r2” was 0.92 and correlation coefficient “r” was 0.96 (Fig. 3).


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TABLE 1 - Passive diffusive sampler and automated analyzer’s data with uncertainities of SO2 for the year 2006.

January February March April May June July August September October November December AQMS SO2 (µg/m3) 42.7±0.43 12.8±0.13 22.2±0.22 4.6±0.05 3.5±0.04 2.6±0.03 1.9±0.02 6.7±0.07 3.4±0.03 7.9±0.08 25.2±0.25 30.1±0.30 PDSs SO2 (µg/m3) 44.05±5.29 21.65±2.60 23.2±2.78 8.63±1.04 4.41±0.53 1.86±0.22 1.86±0.22 10.92±1.31 4.14±0.50 19.45±2.33 26.67±3.20 33.76±4.05

FIGURE 2- SO2 results of two different techniques (AQMS & Passive sampler).

FIGURE 3- Regression of AQMS & Passive sampler data for SO2.


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When the coefficient of determination and correlation coefficient were investigated for the measurement results of sulphur dioxide, it can be seen that there is a highly positive correlation between AQMS data and PDS data.

3.2. Investigation of monthly variation of measured concen-trations at 10 stations

The 10 sites in Canakkale province were selected to provide information on the regional aspects of the ambi-ent levels of sulfur dioxide around 18 Mart Can Thermal Power Plant. The details of the sampling stations are summarized in Table 2. Two suburban monitoring sta-tions, Can and Bayramic, are located near the city centers. There were 8 rural stations; 5 of them (Mallikoy, Kocaa-gacli, Hamdibey, Kizilelma, Osmaniye) located near vil-lages but 3 of them (Katran, Yosunlucesme, Fatmaoluk) over mountains near Can-Bayramic region.

Average concentrations were assumed to be the monthly averages of SO2 shown in Table 3. It is obvious that the concentrations varied among different sites and also in different time periods, as shown in Fig. 4. The highest monthly SO2 concentration was observed at Can county (st. 3), northeast of Canakkale, with 226.07±27.13 µg/m3 on December 2006. SO2 concentration varied from 0.34±0.04 µg/m3 to 226.07±27.13 µg/m3 depending on locations of stations and sampling period. The seasonal variability of SO2 concentrations observed in the study area during the sampling period showed generally higher SO2 levels in winter months at all 10 monitoring sites. Due to burning of sulfur-rich coal, the SO2 levels in Can county and surrounding regions were considerably high. Moreover, 18 Mart Can Thermal Power Plant may have also contributed to the observed level of SO2.

TABLE 2 - Monitoring stations

St.No Description of station Coordinates Altitude

1 Kocaagacli N 40° 01΄58.8˝ E026° 49΄ 56.9˝ 256 m

2 Mallikoy N 40° 03΄ 42.4˝ E027° 01΄ 47.1˝ 323 m

3 Can city center N 40° 01΄ 756˝ E027° 02΄ 915˝ 102 m

4 Hamdibey N 39° 52΄ 44.0˝ E027° 17΄ 03.0˝ 553 m

5 Kizilelma N 39° 53΄ 03.4˝ E026° 59΄ 19.6˝ 423 m

6 Osmaniye N 39° 52΄ 15.1˝ E026° 48΄ 56.0˝ 666 m

7 Bayramic N 39° 48΄ 38.7˝ E026 °35΄ 51.9˝ 115 m

8 Katran mountain N 39° 51΄ 31.2˝ E027° 03΄ 03.1˝ 713 m

9 Yosunlucesme N 39° 42΄ 56.2˝ E026° 47΄ 64.1˝ 1150 m

10 Fatmaoluk

N 39° 41΄ 62.6˝ E026° 42΄ 08.1˝ 763 m

TABLE 3 - Temporal variance of PDS data with uncertainities of SO2 at ten stations for year 2006

St. January February March April May June July August September October November December

1 11.53±1.38 1.18±0.14 2.17±0.26 1.35±0.16 2.79±0.33 3.56±0.43 1.52±0.18 2.48±0.30 2.73±0.33 3.59±0.43 67.76±8.13 4.03±0.48

2 16.15±1.94 5.58±0.67 5.56±0.67 5.41±0.65 8.81±1.06 3.14±0.38 2.54±0.30 8.51±1.02 4.41±0.53 5.29±0.63 21.91±2.63 15.85±1.90

3 186.82±22.42 184.2±22.10 219.16±26.30 42.79±5.13 19.15±2.30 3.88±0.47 1.69±0.20 10.19±1.22 6.4±0.77 38.9±4.67 140.85±16.90 226.07±27.13

4 13.84±1.66 2.2±0.26 1.45±0.17 1.52±0.18 3.56±0.43 2.79±0.33 0.68±0.08 2.88±0.35 1.15±0.14 2.11±0.25 10.84±1.30 21.75±2.61

5 11.07±1.33 2.54±0.30 1.21±0.15 1.01±0.12 3.88±0.47 8.81±1.06 0.85±0.10 2.78±0.33 1.6±0.19 10.99±1.32 8.19±0.98 5.27±0.63

6 2.54±0.30 26.72±3.21 0.97±0.12 2.2±0.26 3.83±0.46 19.15±2.30 2.2±0.26 5.41±0.65 4.87±0.58 3.17±0.38 10.7±1.28 8.98±1.08

7 44.05±5.29 21.65±2.60 23.2±2.78 8.63±1.04 4.41±0.53 1.86±0.22 1.86±0.22 10.92±1.31 4.14±0.50 19.45±2.33 26.67±3.20 33.76±4.05

8 0.76±0.09 18.1±2.17 0.72±0.09 0.34±0.04 3.14±0.38 4.41±0.53 0.85±0.10 3.57±0.43 1.62±0.19 2.11±0.25 9.06±1.09 5.05±0.61

9 0.76±0.09 11.67±1.40 1.45±0.17 0.51±0.06 3.11±0.37 4.41±0.53 2.54±0.30 1.29±0.15 0.75±0.09 1.48±0.18 8.39±1.01 1.52±0.18

10 0.85±0.10 18.77±2.25 1.21±0.15 0.68±0.08 4.41±0.53 3.11±0.37 0.85±0.10 2.22±0.27 1.24±0.15 1.9±0.23 8.47±1.02 4.66±0.56


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FIGURE 4 - Temporal variations of SO2 at 10 stations.


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4. CONCLUSION

Atmospheric concentrations of gaseous SO2 at 10 sites in Canakkale were determined during the year 2006 using passive diffusive sampling technique, followed by labora-tory analysis. The observed concentrations showed large geographical differences. The SO2 concentration varied from 0.34±0.04 µg/m3 to 226.07±27.13 µg/m3 around 18 Mart Can Thermal Power Plant in Canakkale.

At most sites, the SO2 levels reached their maxima in

winter and minima in summer. This type of seasonal cycle can be explained by higher emissions in winter due to heating, wet deposition, and seasonal changes in mixing layer height.

The temporal variation of passive sampler’s and

AQMS data for SO2 concentrations were determined to be similar. There is a strong correlation between different measurement techniques of SO2.

Results from this study help to demonstrate that dif-

fusive samplers are ideal for measurements at remote sites, for screening studies, mapping concentrations around large plants, siting of more advanced stations, etc. The main advantages are as follows: the samplers are small, silent, do not need electricity; the measurements are made in situ (without inlet tubing); the measurement range is very large; technical personnel are not needed at the sampling site; field calibration is not needed; 100% time coverage can be obtained; data are simple to deploy and mail. The drawbacks are that only gases can be moni-tored, and peak values of short time intervals are not re-solved.

The temporal variation of passive sampler and

AQMS data for SO2 concentrations were determined both to be similar. The comparisons between passive sampler technique and automated analyzer measuements suggest tha,t in most cases, SO2 results correlate fairly well during 8 months of the year 2006. Finally, these results demon-strate that, for long-term (monthly average) monitoring of air pollutants around large combustion plants like power plants, the passive diffusion sampler tubes can effectively be used.

ACKNOWLEDGEMENTS

This work was supported by grants from TUBITAK Marmara Research Center. The authors would like to thank the EUAS (Electricity Generation Incorporated Company) for data support.

The authors have declared no conflict of interest.

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[11] Carmichael, G. R., Ferm, M., Thongboonchoo, N., Woo, J.H., Chan, L.Y., Murano, K., Viet, P.H., Mossberg, C., Bala, R., Boonjawat, J., Upatum, P., Mohan, M., Adhikary, S.P., Shrestha, A.B., Pienaar, J.J., Brunke, E.B., Chen, T., Jie, T., Guoan, D., Peng, L.C., Dhiharto, S., Harjanto, H., Jose, A.M., Kimani, W., Kirouane, A., Lacaux, J.P., Richard, S., Barturen, O., Cerda, J.C., Athayde, A., Tavares, T., Cotrina, J.S. and Bilici, E. (2003) Measurements of sulfur dioxide, ozone and ammonia concentrations in Asia, Africa, and South America using passive samplers. Atmospheric Envi-ronment 37, 1293-1308.

[12] Ayers, G.P., Keywood, M.D., Gillett, R., Manins, P.C. , Mal-froy, H. and Bardsley, T.(1998) Validation of passive diffu-sion samplers for SO2 and NO2. Atmospheric Environment 32, 3587-3592.

[13] Lozano, A., Usero, J., Vanderlinden, E., Raez, J. , Contreras, J. and Navarrete, B.(2009) Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain. Microchemical Journal 93, 164-172.

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[15] Ergenekon, P., Ozturk, N.K. and Tavsan, S. (2009) Environ-mental air levels of volatile organic compounds by thermal desorption-gas chromatography in an industrial region. Fre-senius Environmental Bulletin 18, 1999-2003.


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[16] Angiuli, L., Bruno, P., Caputi, M., Caselli, M., Gennaro, G. and Rienzo, M.(2003) Radial passive samplers for air quality monitoring in field comparison with a BTEX automatic ana-lyser preliminary results. Fresenius Environmental Bulletin 12, 1167-1172.

[17] Ballesta, P.P., Connolly, R., Boix, A. and Cancelinha, S.J. (2001) Assessment of urban background concentrations of aromatic compounds in air by means of diffusive sampling. Fresenius Environmental Bulletin 10, 46-53.

[18] Ozkurt, N. (2011) Spatio-temporal distributions of Nitrogen Dioxide around Can-Bayramic region in Canakkale: a com-parative exposure and model study. Fresenius Environmental Bulletin 20, 1570-1578.

[19] Carmichael, G. R., Ferm, M., Adikary, S., Ahmad, J., Mohan, M., Hong, M.S., Chen, L., Fook, L., C.M., Soedomo, M., Tran, G., Suksomsank, K., Zhao, D., Arndt, R. and Chen, L.L. (1995) Observed regional distribution of sulfur dioxide in Asia. Water, Air and Soil Pollution 85, 2289-2294.

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[24] Meng, Z.Y., Xu, B.X., Wang, T., Zhang, X.Y., Yu, X.L., Wang, S.F., Lin, W.L., Chen, Y.Z., Jiang, Y.A. and An, X.Q. (2010) Ambient sulfur dioxide, nitrogen dioxide, and ammo-nia at ten background and rural sites in China during 2007-2008. Atmospheric Environment 44, 2625-2631.

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Received: March 29, 2011 Revised: August 12, 2011 Accepted: September 30, 2011 CORRESPONDING AUTHOR

Nesimi Özkurt TUBITAK Marmara Research Center Environment Institute 41470 Gebze Kocaeli TURKEY Phone: +90 262 677 29 36 Fax: +90 262 641 23 09 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 3 – 10


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YTTRIUM - ACCUMULATION, TRANSLOCATION AND DISTRIBU-TION IN YOUNG SUNFLOWER PLANTS (Helianthus annuus L.)

Ivana Maksimović1,*, Rudolf Kastori1, Marina Putnik-Delić1 and Tijana Zeremski2

1Universiy of Novi Sad, Faculty of Agriculture, Trg D. Obradovića 8, 21000 Novi Sad, Serbia 2Institute of Field and Vegetable Crops, M. Gorkog 30, 21000 Novi Sad, Serbia

ABSTRACT

Even though yttrium (Y), similarly to the other rare earth elements, is widely present in the soils and plants, there is little experimental data describing its effects on plant growth and metabolism. Therefore, the aim of this work was to examine the effect of 10-5, 10-4 and 10-3 M Y on growth, photosynthetic pigments, Y accumulation and transfer factors in young sunflower plants grown in semi-controlled conditions, in water cultures.

In the presence of Y, growth of young sunflower was reduced. Plant dry weight, water content, height, leaf area, concentration of chlorophylls a, b and carotenoids declined significantly at 10-4 M Y. Concentration of Y and transfer factor were the highest in roots at 10-3 and 10-5 M Y re-spectively, and the lowest in leaves. At 10-3 M Y plant growth and development practically stopped.

Mechanisms by which omnipresent Y affects plant metabolism remain to be studied profoundly in the future.

KEYWORDS: yttrium, sunflower, growth, accumulation, transfer factor

1. INTRODUCTION

Yttrium (Y) is one of the rare earth elements (REEs). The REEs are widely distributed and present in all parts of the biosphere [1]. REEs are required in industry, agri-culture, medicine, biotechnology; they may cause envi-ronmental problems and affect many other fields. Posi-tive, negative or nil effects of REEs on plant growth and crop yield were observed through experiments done under controlled or field conditions in many countries [1, 2]. Yttrium, as well as the other REEs, according to current knowledge, is not essential for higher plants and - wider- for living organisms. Numerous papers report that, under particular conditions and in some plant species, elements which are not essential for higher plants can stimulate * Corresponding author

their growth and development. Stimulating effect of low concentrations of fluorine [3], titanium [4], lead [5], chro-mium [6], silicon [7] and the other elements on growth, development, physiological and biochemical processes in plants were demonstrated.

When Johann Gadolin first discovered Y, it seemed interesting but not very important. Besides being a metal alloy, Y has many common uses today. The first experi-ments on the uptake of Y in plants were done in the mid-dle of the last century. The mankind was than for the first time confronted with the danger of the movement of ra-dioactive fission products through the food chain to hu-mans by ingestion of contaminated plants [8, 9]. In nu-clear fission reactions various isotopes of Y are produced in rather high amounts. Twenty six unstable Y isotopes have been characterized [10]. Because of this, the investi-gations of the uptake of Y in plants gained importance in the last century, in particular after Chernobyl disaster in 1986 [11]. Mineral and organic fertilizers may sometimes contain Y and by their application Y can be introduced to agricultural soils [12]. The atmospheric deposition of REEs is another path of entry into soils [13].

Data accumulated in the last decades give evidence that Y is present in various rocks, soils, drinking water, food, coal, mineral fertilizers, atmospheric deposit etc. and in relatively high amounts in every living organism [14]. Y is known to be incorporated mainly as Y3+ in several minerals, of which silicate, phosphate, and oxide forms are most frequent. Y is naturally present in minerals gado-linite and xenotime, mostly in the form of YPO4. Y forms insoluble compounds with oxalate and hydroxide, while it is soluble with chloride, nitrate and sulphate. Y forms com-plexes with compounds of biological importance [14].

The concentration of Y in the soil may vary within wide range, depending on soil type, parent substrate on which the soil was made, and the other features of the par-ticular site. Thus, in the different types of soil in Alaska Y concentration ranged from less than 4 to 100 ppm (DW) [15]. The most important anthropogenic sources of Y are the electronic industry, application and production of min-eral fertilizers [16], atmospheric deposition of REEs [13],


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radioactive contamination [11], etc. [17] investigated 71 plant species which belong to 29 families and found Y in each of 1100 samples analyzed. However, in spite of quite high presence of Y in the biosphere, there is quite little data in the literature regarding the single effect of certain REEs elements on higher plants, which goes for Y too [12, 14, 18-20].

Having in mind ubiquity of Y in the biosphere and

therefore its ecological importance, as well as the fact that non-essential elements can affect plant metabolism, it is of interest to examine the effect of various concentrations of Y on plant biomass production and Y accumulation and distribution in plants. The intention was to perform the experiment with the crop species, which already at young stage has well differentiated aboveground parts (stem and leaves) and which intensively accumulates microelements in vegetative parts when concentrations of those elements in the environment are elevated [21].


2.1. Plant material and growth conditions

Sunflower (Helianthus annuus L. hybrid NS-H-4001) seeds were surface sterilized and germinated in the dark, at 25o C, on sterilized quartz sand and watered daily with demineralized water. Uniform 5 days old seedlings were transferred on half-strength Hoagland solution [22] to which was added either 0 (control), 10-5, 10-4 or 10-3 mol Y L-1. Yttrium was added as Y(NO)3 5H2O (Merck). Each variant was set in twelve replications, with six plants in each replication. The plants were grown in the greenhouse, under 12-h photoperiod (irradiance of 200-300 µmol quanta m-2 s-1 day/night temperature of 24 ± 2/15 ± 2 °C, relative humidity of 65-75 %). Nutrient solution was changed every day and plants were aerated regularly. Plants were grown for 22 days and they were in the stage of about 2 pairs of leaves when they were harvested. Sun-flower growth in the presence of 10-3 M Y was severely impaired and therefore it was not possible to analyze sepa-rately features of cotyledons, the 1st pair of leaves and leaves above the first pair. Therefore, those leaves were analyzed together for dry weight, water content, total leaf area, concentration of chloroplast pigments, yttrium con-centration and transfer factor. It is clearly marked in the Results and Discussion section, where are shown both the results for different types of leaves in the presence of 0, 10-5 and 10-4 M Y and all together for 10-3 M Y.

2.2. Growth analysis

Roots, stems, cotyledons, and first and second pair of leaves of each treatment were separated before the analy-ses. Fresh matter was measured and then dry matter was measured after drying the samples at 70 °C to constant mass. Total leaf area was measured by an automatic area meter (LI-3000, Li-Cor, Lincoln, USA). Height of each plant was measured by ruler.

2.3. Concentration of chloroplast pigments

Concentrations of chlorophylls a and b and carotenoids were determined spectrophotometrically, in the acetone extract of freshly harvested leaves, using molar extinction coefficients of [23, 24].

2.4. Concentration of yttrium and transfer factor

Total concentrations of Y in plant tissues were deter-mined by an ICP-OES (Varian, Vista-Pro) after digestion in a mixture of 10ml of HNO3 (65%) and 2 ml of H2O2 (30%) using the microwave technique. Transfer factor (TF), the measure of transport of Y from the nutrient solu-tion to sunflower tissues, was calculated using the follow-ing equation: Transfer factor (TF) = Y concentration in the plant tissue / Y concentration in the nutrient solution.

2.5. Statistical analysis

Statistical analysis was performed using STATIS-TICA 9.0 (StatSoft, University Licence, University of Novi Sad, 2010) and Excell (Microsoft Inc.) software packages. Means of replicates and evaluation of significance of differences between analysed parameters were determinated with descriptive statistics and one-way ANOVA analysis, followed by LSD post hoc test (α=0.05).


3.1. Plant growth in the presence of Y

In this study, the highest applied Y concentration, 10-3 M, strongly reduced growth of young sunflower plants. Plant height and leaf area declined significantly. How-ever, Y affected growth of different plant organs to differ-ent extent. Sunflower root DW significantly declined only in the presence of 10-3 M Y, whereas stem and leaf DW were significantly reduced in the presence of 10-4 and 10-3 M Y. DW of cotyledons and the first pair of leaves were not significantly changed in the presence of Y, but DW of leaves above the first pair was significantly reduced at 10-4 M Y (Figure 1). At 10-3 M Y leaf growth was nearly im-paired and hence biomass production was very low.

Water content in sunflower roots and leaves was sig-nificantly reduced in the presence of 10-3 M Y (Figure 2). The reduction was the highest in leaves, nearly 25%, whereas in roots and stems it was about 13% and 3%, respectively.

Total leaf area of the leaves above the first pair was significantly reduced in the presence of 10-4 M Y (Figure 3). However, although the area of the first pair of leaves and the area of cotyledons declined in the presence of Y, those differences were not significantly different. It might be explained by the fact that those leaves developed be-fore Y treatment was applied.

Plant height was significantly reduced in the presence of 10-3 and 10-4 M Y. The differences were also statisti-cally significant between 10-3 and 10-4 M Y treatments.


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FIGURE 1 - Dry weight (DW) of sunflower in the presence of yt-trium. 1st pair, the first pair of sunflower leaves; Above 1st pair, all leaves except for cotyledons and the 1st pair of leaves. The values marked with the same letter do not differ significantly at P < 0.05.

FIGURE 2 - Water content in sunflower in the presence of yttrium. 1st pair, the first pair of sunflower leaves; Above 1st pair, all leaves except for cotyledons and the 1st pair of leaves. The values marked with the same letter do not differ significantly at P < 0.05.

FIGURE 3 - Total leaf area of sunflower in the presence of yttrium. 1st pair, the first pair of sunflower leaves; Above 1st pair, all leaves except for cotyledons and the 1st pair of leaves. The values marked with the same letter do not differ significantly at P < 0.05.

FIGURE 4 - Height of sunflower plants in the presence of yttrium. The values marked with the same letter do not differ significantly at P < 0.05.

Sunflower height was at the level of the control in the presence of 10-5 M Y (Figure 4).


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Reduced productivity of photosynthesis, which as a consequence has reduction of plant dry weight, can be explained by significant reduction of leaf area and con-centration of photosynthetic pigments in the presence of 10-3 and 10-4 M Y. Reduction of DW of leaves above the first pair and not of cotyledons and the first pair of leaves can be explained by the fact that the second pair of leaves developed after the exposure of plants to Y. Similarly as for the leaf DW, Y did not have the effect on leaf area of leaves which developed before the treatment. So far there are no data that can help to explain the direct mechanism of Y action on plant growth. The data available in the literature [25, 26] suggest that the interaction of Y within the cell, cell organelles and tissues is limited to the outer surface of the plasma membrane, for which Y shows high affinity and binding capacity. In animal systems, the bind-ing capacity of Y to bone is explained by its binding to mucopolysaccharides, phosphorus-containing compounds, fluoride complexes, and nucleic acids [19]. However, this remains to be studied in plant systems.

Research results about the effect of the other REEs on plant growth are somewhat contradictory. The findings mostly point out the inhibitory effect of these elements on plant growth, first of all of lanthanum [27, 28]. In most papers published lately, a stimulating effect of REEs on growth and yield of some cultivated plants was recorded [2]. As Y is not biogenic element for higher plants, there is little data in the literature on its effect on growth, devel-opment and life processes of plants. The first paper about the influence of Y on plant growth was published at the beginning of the last century [29]. It was found that Y provoked a diminution in cell division with irregular cell arrangements by hyacinth (Hyacynthus sp.) rootlets. Cyto-logical studies of the effect of Y sulfate on root meristem of anion (Allium cepa) showed a common effect of col-chicine type mitosis [30]. Application of Y on sandy loam soil stimulated growth of grass timothy (Phleum prat-ense). Yttrium chlorides combined with boron either had no effect or showed a slight inhibitory effect on pollen germination and on growth of pollen micella [31].

REEs in some areas have important position among the essential elements of plant growth and for a long time they have been used as growth promoters, especially in East Asia. Even the lowest Y concentration applied in the present study, 10-5 M, did not demonstrate any stimulative effect on sunflower growth and it can be considered as still too high for sunflower. The same concentration of Y (10-5 M) was found to be optimal for content of chloro-phyll as well as the activities of SOD, Cu-Zn SOD and the POD in cucumber [32].

3.2. Chloroplast pigments

Concentration of chlorophyll a and b as well as caro-tenoids was significantly reduced in the presence of 10-4 M Y both with respect to the control and plants treated with 10-5 M Y (Figure 5). Nevertheless, when chlorophyll a+b are considered together, no statistically significant

difference was recorded between the treatments. The ratio chl. a+b/car nearly doubled in the presence of 10-4 M Y with respect to both 10-5 M Y treatment and the control whereas the ratio chl. a/chl. b practically remained con-stant (Figure 5).

FIGURE 5 - Concentration of photosynthetic pigments and their ratios in sunflower leaves in the presence of yttrium. Chl a, chloro-phyll a; Chl b, chlorophyll b; Car, carotenoids. The values marked with the same letter do not differ significantly at P < 0.05.

Hu et al. [2] in their review paper cite many authors

who found that plants of different species, grown in the presence of RREs, had higher concentration of chloroplast pigments as compared to the control. In our previous work [33] it was shown that in sunflower grown in the presence of RREs concentration of chlorophylls a and b and carotenoids increased. In this study, concentrations of both chlorophyll a and b were reduced and the rate of their reduction was practically the same. Similarly to chlorophylls, concentration of carotenoids declined as well. Although the rate of the reduction of concentrations of chl a, b and carotenoids was very similar (56, 57 and 50 % respectively) in the presence of 10-4 M Y with respect to the control, the ratio chl a+b/car nearly doubled suggest-ing that the concentration of carotenoids was more stable in the presence of 10-4 M Y than concentration of chloro-phylls. In soybeans, REEs increased concentration of chlo-rophyll a while chlorophyll b and carotenoids were not affected [34]. REEs and Y probably did not exert a specific effect on the biosynthesis or degradation of chloroplast pigments in this experiment, since similar phenomenon was observed in the case of deficiency or excess of some other elements which are not involved in their metabolism. There is evidence that some REEs, such as Ce3+, can enter


15

chloroplast and substitute Mg2+ ion in the chlorophyll molecule, forming Ce-chlorophyll [35]. Such feature of Y remains to be studied but one may suppose that such change in chlorophyll structure may contribute to the im-pairment of photosynthesis in the presence of Y.

3.3. Y concentration in sunflower and transfer factor

Concentration of Y in sunflower depended strongly on its concentration in the nutrient medium. It was sig-nificantly the highest in roots of plants grown in the pres-ence of 10-3 M Y of all the other treatments and plant parts. In the presence of 10-3 M Y, Y concentration in stems was significantly higher than in the leaves and it was also significantly higher than Y concentration in the roots grown in the presence of 10-4 M Y (Figure 6). Between the control and 10-5 M Y treatments there were no statisti-cally significant differences in Y concentration, and the same stands for the difference in Y concentration between the control and above ground parts of 10-5 M Y treated plants. Y concentration was analyzed separately in coty-ledons, the 1st pair of leaves and leaves above the first pair. The highest concentration of Y was recorded in the 1st pair of leaves, than in cotyledons and the least in the leaves above the first pair (Figure 6). Although Y concen-tration increased with an increase in Y concentration in the nutrient medium in all types of leaves, significant increase in Y concentration with respect to the control was recorded in the leaves of the 1st pair (at both 10-5 and 10-4 M Y) and in cotyledons (at 10-4 M Y). Uptake of mineral elements

and their distribution and concentration in plant tissues, apart from the concentration and availability of a particu-lar element (i.e. Y), depends strongly on the presence and concentrations of the other ions [36, 37].

Based on what we know, it can be said that Y is

abundant in plant world [38]. The content of Y in edible plants from tropical region ranges from 0.01 to 3.5 mg/kg DW. In forest plants in Germany, Y concentration ranges between 0.15 and 0.25 mg/kg DW [39], in grass (Agrostis capillaris) leaves is in average 0.030 [40], in cabbage between 0.0032 and 0.01 mg/kg DW [41] and in moss carpets of south Sweden between 0.120 and 1.134 mg/kg DW [25]. Compared to the other plant species, ferns and lichens and species of the phylum Mycophytpphyta have a high content of Y [42, 18]. Yttrium concentration in mosses and bryophytes is in the range 2-200 mg/kg (DW) [43]. Rather lower values for the same plants are stated by Bowen [44]: for lichens from 0.2 to 2 mg/kg and for bryo-phytes from 1.3 to 7.5 mg/kg (DW). These results confirm that beside the genetic specificity of the species and probably the genotype, an important role in Y accumulation belongs to ecological factors which may explain significant differences in Y concentration determined in the same species in different environments by certain authors. Also, Y and the other RRE in mosses of densely inhabited areas most probably reflect a contribution from less specific human sources of pollution [25]. Roots usually have higher concentration of RRE than the other plant parts. This was

FIGURE 6 - Concentration of yttrium in sunflower organs in the presence of yttrium in the nutrient medium. 1st pair, the first pair of sun-flower leaves; Above 1st pair, all leaves except for cotyledons and the 1st pair of leaves. The values marked with the same letter do not differ significantly at P < 0.05.


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FIGURE 7 - Transfer factor (TF) of yttrium to different sunflower organs. 1st pair, the first pair of sunflower leaves; Above 1st pair, all leaves except for cotyledons and the 1st pair of leaves

the result of the present study as well as sunflower roots had at least two orders of magnitude higher Y concen-tration than the other plant parts. An increase in Y con-centration with the increase in Y concentration in the nutrient solution was higher in the roots than in the stems and leaves. However, the decreasing order of Y con-centration in the sunflower was root >stem>leaf which is a bit different than the results published by [45-48] who found the inverse order for stems and leaves in maize, wheat, rice and paprika.

TF of Y was the highest in root, in the presence of 10-5 M Y (1325.57) and the lowest in the leaves above the 1st pair in the presence of 10-4 M Y (1.39). In roots and leaves TF declined with an increase in Y concentration whereas in the stems it increased. The exception was TF in leaves at 10-3 M Y which was nearly double in com-parison to the control and over 10 times higher in com-parison to 10-4 M Y treatment (Figure 7). The increase in TF in different leaves at 10-5 M Y with respect to the control and decline at 10-4 M Y is shown in Figure 7.

Transfer factor (TF), which is parameter for the ac-cumulation of Y in relation to the medium or soil, were much higher in root than in stems and leaves. This is in line with finding of [49] that uptake rate of RREs from soil to root is much higher than the translocation rate from root to shoot. In general, TF declined with an increase of Y concentration in the nutrient medium (the only excep-

tion being TF in stems at 10-4 M Y in comparison with 10-5 M Y), probably due to the saturation of plant capacities for its uptake and transport. With an increase in Y concentra-tion, TF was less different between roots and shoots: in roots TF declined and in shoots increased. For example, TF in roots:stems was 53:1 at 10-5 M Y, 14:1 at 10-4 M Y and 1.6:1 at 10-3 M Y. The distribution of Y in sunflower root tissues remains to be studied although it can be as-sumed that there are similarities with distribution of the other REEs. There is evidence, for example, that La and Yb are mostly located in the root cortex and ferric plaque on the root surface, xylem or endoderm [50, 51].

The uptake mechanisms of Y and REEs were investi-gated by Ozaki et al. [52]. After them, some plant species, such as autumn fern, are RREs accumulator species. They found that Y uptake by REEs non-accumulator species (e.g. sunflower) was much higher compared to the other REEs and in those plants the addition of chelating agents did not affect Y uptake.

4. CONCLUSIONS

Yttrium did not show any stimulating effect on growth of young sunflower plants. Even the lowest applied con-centration, 10-5 M, reduced plant growth. However, sig-nificant reduction of sunflower DW, height, leaf area and


17

concentration of photosynthetic pigments occurred at 10-4 M, whereas at 10-3 M Y plant growth was practically arrested. Having in mind omnipresence of Y available to plants and the lack of data on its effect on various physiological processes, its probable genotype-specific and growth stage-specific effects, it would be important to study further mechanisms by which Y may affect plant life.

ACKNOWLEDGEMENTS

We thank Ministry of Education and Science of the Republic of Serbia for financial support and Ms. Dušica Živković for linguistic revision.

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18

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Received: June 27, 2011 Revised: August 19, 2011 Accepted: September 09, 2011 CORRESPONDING AUTHOR

Ivana Maksimović Faculty of Agriculture Trg D. Obradovića 8 21000 Novi Sad SERBIA Phone: +381214853341 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 11 – 18


19

REMOVAL OF STRONTIUM IONS FROM AQUEOUS SOLUTION BY ADSORPTION ONTO SODIUM TRITITANATE WHISKER

Zhixin Yu1, Jiangdong Dai1, Ruijun Zhao2, Longcheng Xu1 and Yongsheng Yan1,*

1School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, P.R. China 2 Zhenjiang Vocational Technical College, Zhenjiang 212003, P.R. China

ABSTRACT

Utilization of solid-phase extraction (SPE) to remove aqueous strontium ions by adsorption onto activated so-dium trititanate whisker (STW) was investigated in this work under the conditions of various pH values, STW amount, and shaking as well as contact time by flame atomic absorption spectrometry (FAAS). The optimum con-ditions obtained were: pH value = 5.0, STW amount = 0.2 g, shaking time = 5.0 min, and contact time = 3.0 h, for the remove of 2.0 mg⋅L-1 strontium(II). The adsorption of Sr(II) on activated STW follows pseudo-second order kinetics, and the adsorption isotherm could be fitted by Langmuir isotherm, with maximum adsorption capacity Q being 8.37 mg⋅g-1 at 25 oC. Thermodynamic parameters includ-ing ∆Hº, ∆Gº and ∆Sº were calculated, and it is indicated that Sr(II) adsorption on activated STW by ion exchange is exothermic, spontaneous, and a physical adsorption reac-tion. Moreover, according to the dimensionless separation factor RL (RL<1), it indicates that it is a highly favorable adsorption. Finally, a detection limit (3σ) of 0.030 µg⋅ml-1

(RSD = 0.93%; n=10) for 1.0 µg⋅ml-1 Sr(II) was obtained.

KEYWORDS: Solid-phase extraction (SPE); sodium trititanate whisker; Sr(II); adsorption; thermodynamics

1. INTRODUCTION

There is a great concern about global warning among the scientific and political communities. This “inconven-ient truth” is the impetus driving development of alterna-tive energies with minimal carbon dioxide emissions. As with no CO2 emissions, nuclear energy is a cheap energy form among many sources of power supply. Unfortunately, nuclear energy is controversial because it can release radio-nuclides which are environmentally harmful [1]. Strontium is the concerned contaminant due to its high yield in spent nuclear fuel and long half-life of 27.7 years. Strontium can get into the food-chain through soil, water (directly or in- * Corresponding author

directly), and was accumulated in the bones, muscles, not readily available in the course of human body metabolism, seriously endangering human health [2, 3]. So, the removal of strontium nuclides from aqueous solution is more and more significant. It has been traditionally carried out by chemical precipitation, ion exchange, membrane processes, which are expensive and inefficient, especially for low-strength wastewaters [4, 5]. In the last few years, adsorp-tion has been shown to be an economically feasible alter-native method for removing trace metals from wastewa-ters and water supplies [6, 7]. Although strontium nuclide is easily spread by aqueous medium, its concentration can be significantly reduced by adsorption onto surfaces of po-rous materials [8-10]. In this study, the feasibility of using STW as porous material to remove strontium from aqueous solutions is estimated. STW is a special member of new high-tech composite materials with excellent ability to en-dure high temperature, high fever. And it also has good corrosion resistance, and compatibility of inorganic and organic groups. Moreover, the porous structure determined it to be very suitable to remove metal ions from aqueous solutions.

Among various separation techniques, the most widely used one for strontium is SPE due to the following advan-tages [11]: (1) higher enrichment factors, (2) absence of emulsion, (3) safety with respect to hazardous samples, (4) minimal costs due to low consumption of reagents, (5) flexibility, and (6) ease of automation. An efficient solid-phase extractant should consist of a stable and insoluble porous matrix.

Since the adsorption materials play a very important role in SPE, the current researches in SPE are mainly fo-cused on the development of new sorbents and new meth-ods. In this study, SPE combined with FAAS, to determine whether STW can remove strontium from aqueous solu-tions efficiently or not, was investigated. A series of batch experiments were conducted as a function to find out the effects of pH, sorbent amount, shaking time, adsorption kinetics, the maximum adsorption capacity, and the re-generation in detail. The results were satisfactory and showed to be promising for the removal of strontium from aqueous solution.


20

2. MATERIAL AND METHODS

2.1. Reagents and samples

Sodium trititanate whisker (Shanghai whisker com-posite material Ltd.) was activated with 3.0 mol/L HNO3. Sr(II) was used in the SrCl2·6H2O form. The wavelength of Sr selected was 460.8 nm. All the other chemicals used were of analytical grade. Double-deionized water (DDW) was used throughout this work. Standard solution of Sr(II): 1.0 g/L; when used, diluted to 10 mg/L, step by step of the standard solution.

2.2. Apparatus

TAS-986, atomic absorption spectrophotometer (Bei-jing general analysis instrument Co. Ltd.): pHS-3C type of acidity; 802 centrifugal precipitators (Shanghai surgical instruments factory); DHG-9140A electric thermostat blast oven (Shanghai Heng Technology Co., Ltd.); SHZ-D(III) circulating water pumps; Mastersizer 2000 grainsize ana-lyzer; electronic balance (BS124S) (Beijing Sartorius In-strument System Co., Ltd.). The X-ray diffraction (XRD) pattern was measured in a D/Max-RA diffractometer with Cu Ka radiation (λ=1.5406Å) at 40 kV and 100 mA. Scan-ning electron microscopy (SEM) images were obtained at 15.0 kV on the field emission scanning electron microscope (SIRION, USA) after gold-plating.

2.3. Activated sodium trititanate whisker

The titanate whisker belonging to the monoclinic sys-tem is a kind of tiny fibrous micron material with high performance. The titanate whisker mainly includes titanium dioxide as well as potassium titanate and sodium titanate whisker (Na2Ti3O7) [12-14]. The laminate structure of Na2Ti3O7 is shown in Fig. 1 [15].

The granular STW should be activated in order to im-prove its properties. It was washed with 3.0 mol⋅L-1 nitric acid and DDW, oven-dried at 110 oC, ground and sized with an 100-mesh sieve, and stored for further use.

FIGURE 1 - The structure of sodium trititanate whisker.

2.4. Batch adsorption experiments

Adsorption studies were carried out using the batch technique to obtain the effects of pH, activated STW amount, shaking time and contact time on the adsorption of Sr(II). The adsorption rate and the equilibrium data were also investigated. The batch adsorption experiments were

performed in 50 ml of the buffer solutions containing 2.0 mg⋅L-1 of Sr(II) under constant conditions. The solu-tion pH was adjusted using diluted hydrochloric acid and sodium hydroxide. The solution added to the sorbent was shaken vigorously for 5.0 min to facilitate adsorption of the Sr(II) ions onto the sorbent. After centrifuging the solution, the concentration of Sr(II) ions in the solution was deter-mined by FAAS. The rate of adsorption efficiency, A%, and adsorption quantity, Q (mg/g), were calculated by the following equations:

0 e

0

(C - C )A% = C

(1)

0 e(C - C )VQ = m

(2)

where C0 and Ce represent initial and equilibration concentration of Sr(II),respectively. V is the volume of solution, and m is the weight of the sorbent.


3.1. Characterization of activated STW

The SEM photos for surface and bulk structures (Fig. 2) indicate that activated STW seems to possess sheet and layered structures with rough surfaces; meanwhile, it has a large number of mesopores on its surface, which facilitate metal-ion adsorption on the STW but also speed up the mass transfer rate of releasing and rebinding of metal ions.

FIGURE 2 - SEM photograph of activated STW.

Fig. 3 shows the X-ray diffraction (XRD) pattern of the sodium trititanate whisker. All diffraction peaks can be per-fectly indexed as the titanate Na2Ti3O7 (JCPDS 31-1329). The sharp and strong peak marked at 10.65° is assigned to prominent peak of Na2Ti3O7. No diffraction peaks from any other impurities are detected. As shown in Fig. 4, the particle diameter of activated STW was between 1.79-

Na+

Na+

( Ti3O7 ) 2-

( Ti3O7 ) 2-


21

10 15 20 25 30 35 40 45 50 55

2θ (degree)

1.6 1.8 2.0 2.2 2.41

2

3

4

5

6

7

8

Num

ber

perc

ent%

Diam.(µm)

0.1 0.2 0.3 0.4 0.5

81

84

87

90

93

A%

Amount of STW

0 2 4 6 8 1088

89

90

91

92

93

94

A%

shaken time (min)

2.28 µm, and 2.02 µm was the average diameter of the sorbent.

FIGURE 3 - XRD pattern of activated STW.

FIGURE 4 - Distribution of particle diameter of activated STW.

3.2. Discussion of adsorption mechanism

According to the structure of sodium trititanate whisker (Fig. 1), its mechanism of heavy metal ion adsorption may be as follows: the sodium ions took up the interlayer, and the lay paralleled with crystals at axis direction; thus, radionuclide ion Sr(II) can exchange with the sodium ion as Sr(II) ions could come into its structure to form a stable crystal SrTi3O7. Dealing with SrTi3O7, radionuclide ion Sr(II) can be achieved to remove [18]. Simple adsorption process for Sr(II) can be expressed as follows:

n(Na2Ti3O7)+nSr2+ = n[SrTi3O7]+ 2nNa+ (3)

3.3. The effect of pH

The effect of pH on the adsorption of STW for Sr(II) was investigated. As can be seen (Fig. 5), the adsorption

of Sr(II) onto STW increased as the pH increased, from a low value of 88.20% at pH 1.0 to its maximum of 93.15% at pH 5.0. Therefore, pH 5.0 was chosen for further ad-sorption performance.

FIGURE 5 - Effect of pH on the removal of 2 mg/L Sr(II) onto 0.2 g of STW for 3 h.

3.4. The effect of activated STW amount

The effect of varying STW amount on Sr(II) remov-ing was tested utilizing the batch procedure with 0.1, 0.2, 0.3, 0.4 and 0.5 g of STW, respectively, under pH 5.0 and 25 oC. The test results are shown in Fig. 6 indicating that the adsorption of Sr(II) increased as the sorbent amount in-creased. When the sorbent amount was 0.2 g, the equilibrium was achieved, and the adsorption efficiency was 93.20%. So,

FIGURE 6 - Effect of STW amount on the removal of 2 mg/L Sr(II) at pH 5.0 for 3h.


22

0 2 4 6 8 10

88

89

90

91

92

93

94

A%

pH

0 1 2 3 4 51

2

3

4

5

6

7

8

298.15K 308.15K

Q(m

g/g)

t (h)

0 1 2 3 4 5-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

lo

g(q e

-qt)

t (h)

y=-0.1881+0.5932

R2=0.930

0 1 2 3 4 5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

t/qt(

hg/m

g)

t (h)

y=0.1237x+0.0285

R2=0.999

0.2 g of sorbent was chosen for further adsorption per-formance.

3.5. The effect of shaking time

The shaking time of the solution was another factor impacting the adsorption of Sr(II) onto activated STW (Fig. 7). When the shaking time was 5.0 min, the adsorp-tion efficiency can reach 93%.

FIGURE 7 - Effect of shaking time on the removal of 2 mg⋅L-1 Sr(II) at pH 5.0 for 3h.

3.6. Adsorption kinetics

The kinetics of Sr(II) adsorption onto STW was in-vestigated. The effect of contact time on the synthesized sorbent adsorption from solution at pH 5.0, 25 oC and 35 oC, respectively, are given in Fig. 8. As shown in Fig. 8, the Sr(II) adsorbed by STW was successful and rapid (equi-librium achieved within 3 h). So, 3.0 h of equilibration time was chosen as optimum contact time. When kinetic consid-erations are taken into account, the rapid adsorption kinet-ics can be attributed to the porous structure of the STW.

It can be noticed that the process consists of two steps (initial fast step due to rapid diffusion of ions from the solution to the external activated STW surface; then, a slow step attributed to the equilibrium of adsorption). In order to analyze the adsorption of Sr(II) onto activated STW at 25 oC, the pseudo first-order and second-order kinetic models were applied, and the Lagergreen equation [16] given by the following equation as the pseudo first-order rate equation was employed in Eq. (3):

1e t e

klog(q - q )= logq - t 2.302

(3)

where, qe is the experimentally determined adsorbed amount of Sr(II) (mg/g) under equilibrium conditions; qe is the amount of Sr(II) adsorbed at time t (mg/g); the rate constants of adsorption k1 have been determined by plot-ting log(qe-qt) vs. t for each parameter. The results are presented in Fig. 9.

FIGURE 8 - Adsorption kinetics on the removal of 2 mg/L Sr(II) at pH 5.0.

FIGURE 9 - Pseudo first-order kinetics plot for the adsorption of Sr(II) at 25 .

FIGURE 10 - Pseudo-second-order kinetics plot for the adsorption of Sr(II) at 25.


23

The pseudo-second-order kinetic model applied herein is expressed as in Eq. (4):

t 2 e e

t 1 1( ) = ( ) + ( ) t q k q 2 q

(4)

where, k2 is the rate constant for the pseudo second-order adsorption of Sr(II) (g/mg⋅h), and all variables in Eq. (4) are as described for Eq. (3). A linear relationship between t/q on the y-axis and t on the x-axis in a kinetic isotherm would fit the pseudo-second-order kinetic model described by Eq. (4). The results can be seen in Fig. 10

The pseudo first-order and pseudo second-order ki-netic variables determined via Eqs. (3) and (4) for Sr(II) adsorption by the activated STW, respectively, are pre-sented in Table 1. Compared to the linear line-fit in the pseudo first-order kinetic isotherm (Fig. 10), supported by a low computed qe value of 3.92 mg/g (with respect to an experimental qe value of 8.37 mg/g) as seen in Table 1, the pseudo second-order kinetic model in Fig. 11 seems to best address the kinetics of the overall Sr(II) adsorption by the activated STW because the experimental data points correlating t/q with t in the kinetic isotherm, yields a linear plot with an R2 value of 0.999, and the computed qe value (8.08mg/g) is quite close to the experimental qe value. The k2 rate constant was, therefore, computed, based on the pseudo-second order kinetic model by solv-ing Eq. (4) for the intercept and slope of the linear plot in Fig.11, to be 0.54 g/mg⋅h. By fitting kinetic results with pseudo second-order equation, the rate constants at 25 and 35 oC would be estimated. These rate constants could further be used to compute the activation energy (Ea) of adsorption (kJ/mol) by the following equation:

2

1 2 1

( ) 1 1ln( ) ( )( )k T Eak T R T T= − − (5)

where, R is the ideal gas constant (8.314 J/mol), and k(T1) and k(T2) are rate constants under 25 and 35 oC. According to the computed values of k(T1) and k(T2) in Table 1, the computed value of Ea can be 11.35 (kJ/mol). By considering the Ea values, it is recognized that Sr(II) adsorption on STW is physisorption, in which van der Waals forces associated with weak electrostatic forces induce ion-exchange reactions.

TABLE 1 - Pseudo first and second-order kinetic constants deter-mined for Sr(II) adsorption on STW.

Experimental qe 8.37 mg/g Pseudo first-order kinetic qe k1 R2

3.92 mg/g 0.43 h-1 0.930

Pseudo second-order kinetic(25 ) qe k2 R2

Pseudo second-order kinetic(35 ) qe k2 R2

8.08 mg/g 0.537 g/mg·h 0.999 7.89 mg/g 0.623 g/mg·h 0.999

Adsorption isotherm and thermodynamic parameters

A certain amount of STW was added into a series of 50-ml colorimetric tubes where concentrations of Sr(II) were 2.0, 10, 50, 100, 150, 200, 250 and 300 g/L, respec-tively, and pH was 5.0 at 25 oC. FAAS was used to de-termine Sr(II) residues after adsorption equilibrium, and the results are shown in Fig. 11. As can be seen, when the equilibrium concentration of Sr(II) reached 200 mg/L, adsorption isotherm tended to saturation (maximum Sr(II) adsorption capacity of 8.37 mg/g was reached).

The equilibrium data were used to fit the Freundlich adsorption isotherm [17] which is an empirical equation employed to describe heterogeneous systems. The linear form of Freundlich adsorption isotherm takes the follow-ing form:

1log log loge f eq K Cn

= + (6)

where, qe is the amount of Sr(II) adsorbed per unit weight of sorbent at equilibrium (mg/g), and Ce represents the equilibrium concentration of Sr(II) ions in solution (mg/L). The Freundlich isotherm constants Kf and n are incorporating factors affecting the adsorption process like adsorption capacity and intensity of adsorption. The con-stants Kf and n calculated from Eq. (6) were 0.866 and 2.36, respectively. The Freundlich plot is shown in Fig. 12.

The equilibrium data were also studied to fit the Langmuir adsorption isotherm, and the equation was expressed as follows [18]:

1 ( )

e e

e m L m

C Cq q K q

= + (7)

where, qe and Ce in Eq. (7) are as described for Eq. (6), qm represents the maximum adsorption capacity of the sorbent (mg/g), and KL is the affinity constant (L/mg). From Fig. 13, linear equation was y = 0.11182x+6.5613, with a linear correlation coefficient of 0.985, the com-puted qm value was 8.94 mg⋅g-1, and KL was 0.017 L/mg. The experimental value of qm (8.37 mg/g) is close to the computed value qm of 8.94 mg/g. From above, it is indi-cated that Sr(II) adsorption is better fitted to Langmuir than Freundlich adsorption isotherm. Therefore, adsorp-tion of Sr(II) onto activated STW preferably follows the monolayer adsorption process. The Langmuir parameters can be used to predict the affinity between the sorbate and the sorbent using the dimensionless separation factor, RL, defined by Dogan et al.[19] as follows:

LL 0

1R =1+ K C

(8)

The value of RL for adsorption of Sr(II) on activated STW is 0.97 indicating a highly favorable adsorption (RL<1).

Further studies determine the thermodynamic pa-rameters of Sr(II) adsorption onto STW. By determining the Kd values in advance from the differences of equilib-rium and initial concentrations, thermodynamic parame-ters of enthalpy, Gibbs energy and entropy of Sr(II) ad-sorption can be obtained using Eqs. 9-12 [20, 21] as shown in Table 2:


24

0 50 100 150 200 250 300

0

2

4

6

8

Q(m

g/g)

Equilibrium concentration (mg/L)

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

logq

e

logCe

y=-0.06258+0.39353x

R2=0.975

0 50 100 150 200 250 3000

5

10

15

20

25

30

35

40

45

Ce/

q e (g

/L)

Ce (mg/L)

y=0.11182x+6.5613

R2=0.985

( / )ed

e

qK L gC= (9)

2 1 2

1 1 2

( ) ( )ln ( / )( ) ( - )

d

d

K T T TH R kJ molK T T T

∆ ° = × (10)

ln ( / )dG RT K kJ m ol∆ ° = − (11)

( / )H GS J molKT

∆ ° − ∆ °∆ ° = (12)

The negative enthalpy ∆Hº<0, with an average value around -22kJ/mol, shows that Sr(II) adsorption is exother-mic. This observation is consistent with the kinetics results, indicating that forward reaction is favored at low tempera-tures. The magnitude of enthalpy change implies that an ion-exchange reaction is driven by van der Waals forces. The negative Gibbs energy (about -8.0 and -7.6 kJ/mol for 25 and 35 oC, respectively) indicates that the Sr(II) adsorp-tion reaction is spontaneous. Moreover, an ion-exchange mechanism is suggested by considering the changes in Gibbs energy [22]. Negative entropy was the result of a stable arrangement of adsorbed Sr(II) on the STW surface, whereas relatively less negative entropy at elevated tem-perature was attributed to the increased desorption.

FIGURE 11 - Adsorption isotherm of Sr(II) adsorption by STW at pH5.0 and 25 .

FIGURE 12 - Freundlich plot for the adsorption of Sr(II) onto activated STW at pH5.0 and 25 .

FIGURE 13 - Linear plots of Ce/qe versus Ce..

TABLE 2 - The computed thermodynamic parameters ∆Hº, ∆Gº and ∆Sº.

Kd(L/g)(25/35) ∆Hº(kJ/mol) ∆Gº(kJ/mol) (25 /35 )

∆Sº(J/molK) (25 /35 )

26.0/19.5 -22 -8.0/-7.6 -47.0/-46.5

Detection limit and RSD

A series of Sr(II) standard solutions (c = 0.050, 0.100, 0.500, 1.000, 1.500, 2.000, 3.000, 4.000 and 5.000 mg/L) was prepared to determine their adsorption. The results showed that Sr(II) concentrations between 0.050-1.500 mg/L achieved a good linear relationship, and the linear equation was [c] = 0.4814[A]-0.0052 with linear correla-tion coefficient 0.99965. The detection limit (3σ) based on 3-fold standard deviation of the blanks by 8 replicates was 0.030 mg/L. Measuring 1.0 mg/L of Sr(II) 10 times, the relative standard deviation (RSD) for 1.0 mg/L Sr(II) was 0.93%.

4. CONCLUSIONS

In this work, removal of aqueous Sr(II) by adsorption onto activated sodium trititanate whisker (STW) was investigated, particularly paid to aspects of kinetic and isotherm adsorption, activation energy Ea and the dimen-sionless separation factor RL. Kinetic studies showed that equilibrium is reached within 3 h under the given condi-tions. Fitting isotherm results by using Langmuir model showed that adsorption is favored at low temperatures, as the temperature was found to have an inverse effect on the adsorption capacity of Sr(II). Calculated thermodynamic parameters indicate that Sr(II) adsorption onto activated STW is exothermic and spontaneous. The activation en-ergy Ea, calculated using the Arrhenius equation, was found to be 11.35 kJ/mol. The type of adsorption of Sr(II) onto activated STW according to the value obtained is physical. The dimensionless separation factor RL has


25

shown that activated STW can be used for removal of Sr(II), and its value indicates a highly favorable adsorp-tion (RL<1). The activated STW could be reused 5 times without apparent decrease in the adsorption capacity (8-9%).

ACKNOWLEDGEMENT

This work was financially supported by the China National Science Foundation (No.20877036), Science and Technology Ministry of China (N0.05C26213100474), and Jiangsu University Talent Foundation (No. 04JDG017).

REFERENCES

[1] El-Kamash, A.M., El-Naggar, M.R., El-Dessouky, M.I. (2006). Immobilization of cesium and strontium radionu-clides in zeolite-cement blends. J. Hazard. Mater. 136, 310-16.

[2] Kocherginsky, N.M., Zhang, Y.K. and Stucki, J.W. (2002). D2EHPA based strontium removal from strongly alkaline nu-clear waste. Desalination, 144, 267–272

[3] Roy, K., Mohapatra, P.K. Rawat, N., Pal, D.K., Basu, S. and Manchanda, V.K. (2004). Separation of 90Y from 90Sr using zirconium vanadate as the ion exchanger. Appl. Radiat. Isot., 60, 621-624

[4] Roane, J.E., Devol, T.A., Leyba, J.D. and Fjeld, R.A. (2003). The use of extraction chromatography resins to concentrate actinides and strontium from soil for radiochromatographic analyses. J. Environ. Radioactivity, 66, 227–245.

[5] Shawabkeh, R.A., Rockstaw, D.A. and Bhada, R.K. (2002). Copper and strontium adsorption by a novel carbon material manufactured from pecan shells. Carbon, 40, 781-786.

[6] Iwaida, T., Nagasaki, S. and Tanaka, S. (2001) Sorption be-havior of strontium onto C–S–H (calcium–silicate–hydrate phases). Studies Surf. Sci. Catalysis, 132, 901-904.

[7]

Shrivastav, P., Menon, S.K., Agrawal, Y.K. (2001). Selective extraction and inductively coupled plasma atomic emission pectrophotometric determination of thorium using a chro-mogenic crown ether. J.Radioanal.Nucl.Chem. 250, 459-464

[8] Osmanlioglu, A.E. (2006) Treatment of radioactive liquid waste by sorption on natural zeolite in Turkey, J. Hazard. Mater. 137, 332–335.

[9] Murathan, A. (2005). Removal of heavy metal ions from aqueous solutions in fixed beds by using horse chestnut and Oak valonia. Fresen. Environ. Bull. 14 (4), 296-299.

[10] Murathan, A.S. (2004) Removal Of Strontium, Aluminium, Manganese And Iron Ions From Aqueous Solutions In Packed Beds. Fresen. Environ. Bull. 13 (6), 481-484.

[11] Daniel, S., Praveen, R.S. and Rao, T.P. (2006). Ternary ion-association complex based ion imprinted polymers (IIPs) for trace determination of palladium(II) in environmental sam-ples. Anal. Chim. Acta 570, 79-87.

[12] Cardoso, V., Souza, A. P., Sartoratto, S. and Nunes, L. (2004). The ionic exchange process of cobalt, nickel and copper (II) in alkaline and acid layered titanates. Colloids Surf., A: Physicochem. Eng. Aspects, 248, 145-149.

[13] Yang, J., Li, D., Wang, H. Z., Wang, X., Yang, X. J. and Lu, L. D. (2001). Effect of particle size of starting material TiO2 on morphology and properties of layered titanates. Mater Lett, 50, 230-234

[14] Papp, S., Korosi, L., Meynen, V., Cool, P., Vansant, F. and Dekany, I. (2005). The influence of temperature on the struc-tural behavior of sodium triand hexatitanates and their proto-nated forms. J. Solid State Chem., 178, 1614-1619.

[15] Shripal, Maurya D., Shalini. (2007). Dielectric-spectroscopic and AC conductivity studies in iron doped layered Na2 Ti3O7 ceramics. Mater. Sci. and Eng. B, 136, 5-10

[16] Ho, Y.S. and McKay, G. (1999).Pseudo-second order model for sorption processes. Proc. Biochem. 34, 451-465.

[17] Niu, C, Wu, W., Wang, Z., Li, S. and Wang, J. (2007). Ad-sorption of heavy metal ions from aqueous solution by crosslinked carboxymethyl konjac glucomannan, J. Hazard. Mater. 141, 209–214.

[18] Benhammou, A. L., Yaacoubi, Nibou, B. and Tanouti, B. (2005). Adsorption of metal ions onto Moroccan stevensite: kinetic and isotherm studies. J. Colloid. Interface Sci., 282, 320–324.

[19] Dogan, M. and Alkan, M. (2003). Removal of methyl violet from aqueous solution by perlite. J. Colloid Interface. Sci., 267, 32–41.

[20] Shahwan, T., Akar, D. and Eroglu, A.E. (2005). Physico-chemical characterization of the retardation of aqueous Cs+ ions by natural kaolinite and clinoptilolite minerals, J.Colloid Interface Sci. 285, 9–17

[21] Shahwan, T., Erten, H.N. and Unugur, S. (2006). A charac-terization study of some aspects of the adsorption of aqueous Co2+ on a natural bentonite clay, J.Colloid Interface Sci. 300, 447–452.

[22] Shahwan, T. and Erten, H.N. (2002) Thermodynamic pa-rameters of Cs+ on natural clays, J. Radioanal. Nucl. Chem. 253,115–120.

Received: June 28, 2011 Accepted: September 14, 2011 CORRESPONDING AUTHOR

Yongshang Yan School of Chemistry and Chemical Engineering Jiangsu University 212013 Zhengjiang P.R. CHINA Phone: +86-0511-88790683 Fax: +86-0511-88791800 E-mail: [email protected]; [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 19 – 25


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MICROSCOPICAL CHANGING OF THE MAIN WOOD ANATOMICAL ELEMENTS OF PEDUNCULATE AND SESSILE OAKS DUE TO ATTACK OF THE WHITE ROT FUNGUS Chondrostereum purpureum (Pers. ex Fr.) Pouz.

Milenko Mirić1, Snežana Ivković1, Snežana Rajković2 and Miroslava Marković2,*

1 Faculty of Forestry, Kneza Višeslava 1, 11030 Belgrade, Serbia 2 Institute of Forestry, Kneza Višeslava 3, 11030 Belgrade, Serbia

ABSTRACT

Samples from sessile and pedunculate oak sap- and heartwood have been exposed to impact of a tested fungus during 8 weeks. After that period, micro-preparations have been made for the analysis by using a light microscope providing normal, UV, blue fluorescence and polarizing light, as well as by using SEM (scanning electron micros-copy). Chondrostereum purpureum in sapwood zones of pedunculate oak damaged parenchyma cell-walls, and con-sumed the polyphenolic content of some cells in heartwood zones, or caused thinning and corrosion of walls.

Normal wood fibers have also been damaged in sap-wood zone. All built elements of wooden tissues of ses-sile oak have been damaged, mainly in sapwood zone. In heartwood of sessile oak, the fungus partially consumed the content of parenchyma cells of medullary rays, and caused their corrosion and alveolar damages of normal wood fibers. Distribution of damaged zones inside the wood was irregular and disordered.

KEYWORDS: Oak, Quercus robur, Quercus petraea, Chondros-tereum purpureum, decay.

1. INTRODUCTION

In the structure of wood cell walls, some 97-99% rep-resent cellulose (40-50%), hemicellulose (15-35%) and lignin (20-35%) [1].

Wood-decaying fungi can utilize each of those con-stituents for nutrition in amounts which define them as causers of brown, soft or white rots [2]. Their destructive action can start in stems and continue (or start) in felled timber. * Corresponding author

The oaks are among the most important species - es-pecially sessile (Quercus petraea agg.) and pedunculate oak (Quercus robur L.), due to their mechanical, physical and esthetical properties [3-6]. The studies on durability of the both oak species have been recently carried out in the laboratory as well as in ground and above-ground field trials [7].

The presence of certain anatomical elements in Euro-pean broad-leaved species vary: vessel parts around 10-30%, fibrillar parts 40-75%, and parenchymatic ones 10-30% [1]. All of these elements play their own role in wood structure, and depending on their quantity, organization and fungal enzymatic systems, they are endangered in different amounts.

One of the most frequent and pioneer species on oak wood after felling, but also on weakened or injured stems, is the white rot fungus Chondrostereum purpureum (Pers. ex Fr.) Pouz. [8, 9], which is widespread in Europe, Asia and North America. Ch. purpureum appears as saprophyte on felled timber of beech, oak and birch wood etc., but sometimes also on coniferous wood. On fruit-trees, it causes "Silver leaf disease" as well as decay of branches and stems. It is noted on more than 150 plants so far.

The aim of this paper is to discover microscopical changes of oak wood anatomical elements due to Chon-drostereum purpureum impact, and to order their consump-tion and deterioration.


In the analysis of damages of anatomical elements caused by Chondrostereum purpureum, 2-dimensional type preparations (5 µm thick) were used for light micros-copy which provides various types of lights, and 3-dimensional preparations in the shape of wooden pris-matic forms (mm size) for SEM analysis.

Wood samples have been taken out from wood blocks (60 x 15 x 5 mm) with the longer side in radial and shorter


27

one in tangential direction, out from the same trunk and border of sap- and heartwood, including both sapwood as well as heartwood zone.

Before exposing to the impact of the fungus, samples were sterilized with the fumigant propylene oxide to pre-vent deformation of wood that could happen in the event of sterilization by heat in the autoclave.

Then, the wood samples of sessile- and pedunculate oak have been exposed during 8 weeks to dicariotic my-celia of the tested fungi in 90-mm Petri containing ca. 20 ml of malt (2%)/agar (2%) medium, in the laboratory under controlled conditions (temperature=21±1°C, rela-tive humidity=70±5%).

Eight-week incubation was performed in order to provide partial decomposition i.e. to prevent total decay of wooden tissue (In this case, all stages of deterioration should not be clearly visible). For the same reason, incu-bations were performed at 21 ± 1 °C, to slow down the speed of wood tissue deterioration. Nevertheless, our previous results showed that the optimal temperature for growth of Ch. purpureum was about 24 °C (near the tem-perature we used), and could not significantly affect the growth of fungal mycelia, but could slightly slow down the process of decay.

After this period, the preparations for microscopic analysis have been prepared in a special way (without ap-pearance of hyphens collapsing or mangling of decayed wood parts).

This unique technique provides the fixation of all tis-sue parts, or their content, regardless of their delicate struc-ture i.e. fungal hyphens in plant vessels or cell-lumens, and stability of their positions and dimensions.

By using specific reagents in combination with vari-ous types of light (provided by universal microscope with blue illumination, cross filter for lengths of 450-490 nm, chromatic diffusion lenses FT 510 and filter LP 520), the different parts of tissue show a different color.

After 8 weeks of incubation, samples were cut into prismatic shape dimensions 2x2x4 mm for slice-cutting on a rotating microtome knife, and 8x6x4 mm for SEM analysis.

Wood samples were then fixed in 4% buffered formol for 24 h in order to prevent collapsing of hyphens or par-tially decomposed wood cells.

After that, all samples were carefully dried in ethanol and impregnated with a solution of 2-hydroxyethyl metacrylate (HEMA, glycol-metacrylate). Then, samples were transferred into special Teflon forms with a mixture of HEMA and hardener (ratio 15:1). After polymeriza-tion, plasticized wooden blocks were fixed on plastic holders and prepared for cuting on the rotating micro-tome-knife. For general observation, micro preparations have been stained in 20% Giemza solution, while for fluorescent staining a 0.1% aqueous solution of acridine-orange was used.

For light microscopy analysis, 120 preparations for each wood species (Q. petraea and Q. robur) were done, for each zone (heartwood and sapwood) as well as for each section (radial, longitudinal and cross section).

For SEM analysis, 3 prismatic wood samples (8x6x4 mm) for each wood species and wood-zone were pre-pared, originating from wooden blocks (60x15x5 mm) that have been exposed for 8 weeks to Ch. purpureum.

Afterwards, samples were dried step by step, at first in ethanol and then in amyl acetate. Dried samples have been placed on holders for scanning, the critical drying point has been estimated, and samples were coated with a golden layer in an accelerator.

Samples have been analyzed in details (SEM) from different angles till the maximal possible depth, with magnification of about 400-11,000 times.

Hyphen appearance, directions and places of penetra-tion, changes in wood fibre cell-walls, tracheas, paren-chyma cells, tension wood-cells etc., have been observed.


Order of damaging of single anatomical elements of examined wood (Table 1) has been determined based on presence of damaged and undamaged tissues in each certain single preparation, so that order of disintegration of elements seemed to be clear and logical.

TABLE 1 n- Order of decomposition of anatomical elements of pedunculate- (Q. robur) and sessile oak (Q. petraea) due to impact of fungus Chondrostereum purpureum after 8 weeks of exposure.

Wood species Wood zone Hyphens Polyphenolic content Tension wood (G – layers)

Parenchyma cell walls

Tracheas Wood fibres

Sapwood More presented Totally consumed Visible hard damages

Visible hard damages

Visible damages Visible damages Quercus robur L. Heartwood Less presented Partially consumed Thinned and

corroded walls Thinned and corroded walls

Thinned and corroded walls

Locally thinned and corroded walls

Sapwood More presented Totally consumed Visible hard damages



Visible hard Damages

Quercus petraea agg. Heartwood Less presented Partially consumed Corrosion of walls Corrosion of walls Corrosion of walls Locally corro-

sion of Walls

ORDER OF DECOMPOSITION

1. 2. 3. 4. 5.


28

Inside pedunculate oak wood exposed to the mycelia of Ch. purpureum, in heartwood, hyphens have been less presented in comparison with sapwood where destroyed walls of wood-fibres (Figure 1) and parenchyma cells (Figures 2 and 3) could be observed. In heartwood, poly-phenolic compounds of parenchyma cells have been par-tially consumed, and cells with thinned and corroded walls have been notified.

FIGURE 1 - Hyphens of Ch. purpureum inside the wood fibers of Q. robur sapwood (SEM, 1800 x).

FIGURE 2 - Hyphens of Ch. purpureum and corrosion of walls inside the parenchyma cells of Q. robur sapwood (SEM, 1600 x).

FIGURE 3 - Corrosion of parenchyma cell wall of Q. robur sapwood affected by Ch. purpureum (SEM 5400 x).

FIGURE 4 - Destroyed walls of all elements of wooden tissue of Q. petraea sapwood due to attack of Ch. purpureum (polarized light / Gimza, 140 x).

In the case of sessile oak, hyphens were more pre-

sented in sapwood than in heartwood zone. In sapwood zone, the damages of all types of wooden cells have been clearly visible (Figure 4). In comparison with the other analyzed preparations, in this case, the intensity of dam-aging of wood cell-walls has been extremely emphasized. Vessels in sapwood of sessile oak have been colonized by


29

mycelia of Ch. purpureum (Figure 5). In heartwood (Fig-ure 6), polyphenols of parenchyma cells of medullar rays have been partially consumed. At the same time, the cor-rosion of walls of wooden cells as well as of parenchyma cells of medullary rays have been clearly visible.

FIGURE 5 - Hyphens of Ch. purpureum inside the vessels of Q. petraea sapwood (SEM, 260 x / 520 x).

FIGURE 6 - Hyphens of Ch. purpureum in Q. petraea heartwood (SEM, 170 x / 860 x).

The endangering of oaks is exposed just immediately after felling of stems, and sapwood is especially sensitive [10, 11]. The heartwood, which lost a basic physiological function, is significantly more resistant owing to the pres-ence of inhibitors (like tannins), as well as the deficiency of moisture, oxygen and easily assimilative nutrients. However, some species of fungi attack and destroy just this zone, regardless of weather they are resistant to those polyphenols, or they are defined as tanninophilic species (Xylobolus frustulatus, Laetiporus sulphureus etc.).

There are not much available published data relating on impact of Ch. purpureum on anatomical elements of oak wood so far. Spiers and Hopcroft [12] observed that Ch. purpureum use to decompose all layers of wooden cells in Salix humboldtiana var. pyramidalis including the middle lamella as well. On the cell-walls, there have been notified numerous alveolar damages being characteristi-cally for white rot fungi and showing the possibility of uniform thinning of cell-walls in direction from the lumen to the middle lamella, by formation of alveolus and with expansion through the pits - poruses [12].

Besides penetration of cell walls, significant consum-ing of polyphenolic content of parenchyma cells of Willow has been noticed by hyphens of Ch. purpureum as well.

In general, the tested fungus caused greater damages to the anatomical elements of sessile oak in comparison with pedunculate one. In both cases, the sapwood was more damaged than heartwood. The notified damages of the heartwood due to impact of Chondrostereum pur-pureum have not been obtained so far. The most affected elements of both oak species were the fibres of tension wood (G-layers) and parenchyma cells, while tracheas and normal wood fibres, especially in the heartwood zone, were far more resistant against the fungal hyphens.

In all tested combinations where damages were noti-fied, the fungus had primarily consumed the content of the parenchyma cells and then destroyed the cell-walls of the tension wood fibres, parenchyma cells, and tracheal walls close to pit zones.

However, normal wood fibres, especially in heart-wood zone, were the least damaged or undamaged ones.

During the wall thinning, the damaging advanced from cell lumen in direction of middle lamella. Hyphens, however, have not been noted in the intercellular spaces, except in the final stages of wood-cell damaging.

Considering the irregular and disordered distribution and appearance of damaged zones inside the wood, the conclusions relating to endangered species, or wood zones, need not to be considered in the quantitative, but exclu-sively in the qualitative sense.

4. CONCLUSIONS

Hyphens of Chondrostereum purpureum have been notified in sapwood as well as in heartwood of both pe-dunculate and sessile oak.


30

Ch. purpureum hyphens in sapwood of pedunculate oak (Q. robur) caused deterioration of parenchyma cell walls, while in the case of heartwood, just partially con-sumed polyphenolic content and unitary-caused thinning or corrosion of the walls of parenchyma cells could be observed. The walls of normal wood fibres in sapwood zone have been destroyed after 8 weeks.

In the case of sessile oak (Q. petraea), all built ele-ments of wood have been damaged, especially in sap-wood. In heartwood zone of sessile oak, the fungus par-tially consumed the content of parenchyma cells of me-dullary rays, parallel to their corrosion as well as damages of wood fibres.

Generally, the tested fungus caused stronger damages of anatomical elements of sessile oak wood than in the case of pedunculate oak. Sapwood of both wood species was much more damaged than heartwood.

Among the anatomical elements, in both wood spe-cies, the most endangered ones have been tension wood fibres (G-layers) and parenchyma cells, while tracheas and normal wood fibres, especially in heartwood zone, have had higher resistance against fungal impact.

In all tested combinations where the damages have been notified, the fungus previously used to consume the content of parenchyma cells, then destroyed walls of tension wood fibres as well as parenchyma cell walls, and finally, the walls of tracheas close to pit zones. Normal wood fibres, especially in heartwood zone, have been the least damaged or undamaged ones.

Considering the irregular and disordered distribution and appearance of damaged zones inside the wood, the conclusion relating to endangered oak species or wood zones need not to be considered in the quantitative, but exclusively in the qualitative sense.

ACKNOWLEDGEMENTS

This paper was realized as a part of the project “Studying climate change and its influence on the envi-ronment: impact, adaptation and mitigation” - (43007) financed by the Ministry of Education and Science of the Republic of Serbia within the framework of integrated and interdisciplinary research for the period 2011-2014.

REFERENCES [1] Grosser, D. (1985) Pflanzliche und tierische Bau- und

Werkholz-Schädlinge. DRW Verlag, Leinfelden – Echterdin-gen, 160 p.

[2] Schmidt, O. and Kerner - Gang, N. (1986) Natural materials. In: Rehm, H.J. and Reed, G. (Ed.) Microbial degradations, Vol. 8. Biotechnology. VCH Verlagsgesellschaft, Weinheim, 557-582 pp.

[3] Jovanović, B. (1991) Dendrology and principles of phytocoe-nology. Scientific Book, Belgrade, 106-138 p (in Serbian)

[4] Wagenführ, R. and Scheiber, C. (1974) Holzatlas. VEB Fachbuchverlag, Leipzig, 690 p.

[5] Ugrenović, A. and Horvat, I. (1950) Wood Technology, Pub-lishing Institute of Croatia, Zagreb, 502 p. (in Croatian)

[6] Rajković, S., Tabaković-Tosić, M., Marković, M., Milovano-vić, J. and Mitić, D. (2010) Application of AQ-10 biofungi-cide on Quercus robur L. seedlings. Fresen. Environ. Bull. 19 (12a), 2987-2992

[7] Brischke, C., Welzbacher, C. R., Rapp, A. O., Augusta, U. and Brandt, K. (2009) Comparative studies on the in – ground and above – ground durability of European oak heartwood (Quercus petraea Liebl. and Quercus robur L.), Eur. J. Wood Prod. 67 (3), 329 – 338.

[8] Butin, H. and Kowalski, T. (1983) Die natürliche Astre-inigung und ihre biologischen Voraussetzungen. II Die Pil-zflora der Stieleiche (Quercus robur L.), Eur. Jour. For. Path. 13 (7), 428-439.

[9] Rypaček, V. (1966) Biologie holzzerstörender Pilze. VEB Gustav Fischer Verlag, Jena, 211 p.

[10] Mirić, M. (1993) Bioecological investigations on the most important fungi from genus Stereum, causers of decay of Oak wood, Ph.D. Thesis. Faculty of Forestry, Belgrade, 144 p. (in Serbian)

[11] Marković, M., Rajković, S., Mirić, M., Mitić, D., Milovano-vić and Tabaković-Tošić, M. (2011) Colonization of the sub-strate of wood – decaying fungi Fomitopsis pinicola (Sw.:Fr.) P. Karst. isolated from beech and fir under con-trolled temperature and pH conditions. Fresen. Environ. Bull. 20 (3), 583-589

[12] Spiers, A. G. and Hopcroft, D. H. (1988) Factors affecting Chondrostereum purpureum infection of Salix, Eur. Jour. of For. Path. 18 (5), 257 – 278.

[13] Wilcox, W. W. (1968) Changes in wood microstructure through progressive stages of decay, US For. Res. Paper FPL 70, 1-46 pp.

Received: June 28, 2011 Revised: September 07, 2011 Accepted: September 30, 2011 CORRESPONDING AUTHOR

Miroslava Marković Institute for Forestry Kneza Višeslava 3 11030 Belgrade SERBIA

Phone +381 69 1999 116 Fax 381 11 2545 969 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 26 – 30


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OCCURRENCE AND DISTRIBUTION OF EPIPELIC AND EPILITHIC DIATOMS IN BATMAN STREAM (TURKEY)

Feray Sonmez* and Keziban Asan

Fırat University, Fisheries Faculty, Department of Basic Aquatic Sciences, 23119, Elazig, Turkey

ABSTRACT

In this study, the seasonal variations in distribution and abundance of epipelic and epilithic diatoms were in-vestigated at 4 stations selected on Batman Stream between April and September 2009. Sampling was performed monthly, and water temperature, electrical conductivity, pH and concentrations of total dissolved solid matter, dissolved oxygen, total hardness, organic matter, nitrite, nitrate, total nitrogen, phosphate, total phosphorus, sulphate, silica and chlorophyll-a were measured and analysed.

Diatoms (Bacillariophyta) were the most important al-gal group in Batman Stream, in terms of number of species and abundance. A total of 49 diatom taxa were identified. Navicula, Cymbella, Nitzschia and Gomphonema were richest in species numbers. The taxon numbers in epipelon were higher than those recorded in epilithon. Cymbella affinis, Cyclotella ocellata and Diatoma vulgare occurred with higher individual numbers in epilithon whilst C. affinis, Tabularia fasciculata and Ctenophora pulchella were more conspicuous in epipelon. Batman River has got Class I in water quality according to the Water Quality Criteria.

KEYWORDS: Epilithon, epipelon, diatom, Batman Stream, Turkey

1. INTRODUCTION

In streams, diatoms are distributed widely on stream-bed substrata, such as surfaces of rocks, stones and plants. These diatom communities play an important role as primary producers that support food webs in stream eco-system. Diatoms often dominate in these environments and were affected from seasonal variations of chemical and physical factors. Numerous studies over the last dec-ade have demonstrated the sensitivity of diatoms to envi-ronmental variations [1-8]. Therefore, diatoms were rec-ognized as an indicator of water quality. * Corresponding author

Algological studies on inland waters of Turkey started in the 1980s. Algological researches on the running waters of Turkey have increased in recent years. However, data on algae occurring in running waters of Turkey are still scant [9-17]. Batman River is one of the three major tributaries of Tigris River which is one of the longest rivers of Tur-key. Batman River is 170 km in length, and approxi-mately 115 km of the river flows through Batman Prov-ince. The river shows an irregular flow regime. The aim of this study was to examine the seasonal occurrence (persis-tence and stability) and abundance of epilithic and epipelic diatoms in Batman Stream, in relation to some environ-mental factors. The findings of the present study are natu-rally expected to contribute to water quality determination of the stream and the database of diatoms identified in Turkish freshwaters.


Four sampling sites were selected within 4-5 km dis-tance from Batman Stream (Fig. 1), and samples were taken at monthly intervals between April and September 2009.

Water temperature, pH, total dissolved solid matter, dissolved oxygen and electrical conductivity of the stream were measured directly by means of an oxygen meter, a pH meter and a conductivity meter, respectively. Organic matter and total hardness were determined by titration analysis. Sulphate, silica, nitrite, nitrate, total nitrogen, total phosphorus and chlorophyll a were analysed spectro-photometrically [18].

Water samples for chemical analysis were collected by using a Nansen bottle (ocean-water sampler). The epilithic diatoms were scrapped from the rocks, gravels and stones, and washed into plastic containers. The epipelic diatoms were collected from the surface of the mud along the streambed. The relative abundance method was applied for individual numbers of epilithic and epipelic diatoms, and results were expressed as “% organism’’, since it was hard to identify the living cells during the counting proc-ess [19].

Subsamples were treated with acid solution to digest organic materials of the diatom cells and to prepare per-


32

FIGURE 1 - The sampling stations and localization of Batman Stream.

manent slides. The samples were boiled on a hot plate for 15 min to expedite the digestion process, and subsequently left to cool. Samples were neutralized by rinsing with dis-tilled water, and a few drops of it were dried on a coverslip. Coverslips were glued with Entellan to glass slides. Spe-cies were observed through a research microscope (Nikon Eclipse 80i) and identified according to Krammer & Lange-Bertalot [20-23]. The relationship between taxon numbers and the frequency of occurrence of conspicuous diatom species with physical and chemical parameters were tested with the Spearman`s Rank Correlation Coef-ficient using Minitab Statistical Software Release 10 [24].


There were no significant differences with respect to concentrations and/or values of physical and chemical parameters between the stations in Batman Stream during the study. However, higher concentrations of hardness and total dissolved solids at station IV as well as SiO2 and chlorophyll a at station II were noticeable. Concentrations and values of physical and chemical parameters at the stations are given in Tables 1 and 2.

Surface water temperature of the stream varied be-tween 20 oC (April) and 28.9 oC (June) at the stations, and

mean water temperature was 25.0±0.78 oC (Table 1). In the stream, pH values were between 7.1 (station I in spring) and 8.7 (during summer months at stations III and IV). The mean pH was 8.0±0.33 throughout the study. Electrical conductivity was observed to be highest in September (610 mV) at station IV, while the minimum (280 mV) was observed in July at station II. The mean electrical conduc-tivity was 396.42±7.69 mV (Table 1).

Dissolved oxygen was measured between 7 and 11.4 mg/L being lower in summer but higher in spring and autumn. The mean dissolved oxygen concentration was 9.04±0.42. Total hardness changed between 27 and 220 mg CaCO3/L (mean 123.58±8.70 mg CaCO3/L). Minimum organic matter concentration was measured as 0.6 mg O2/L in May at station IV, and maximum level (8.7 mg O2/L) in May at station I (mean 2.37±1.33 mg O2/L). The maximum amount of total solid matter (300 mg/L) was observed in September at station IV, whilst the minimum value of 140 mg/L was recorded in July at station II in Batman Stream. The mean total solid matter concentration was 191.5±1.32 mg O2/L (Table 2).

Concentrations of nitrite varied between 0.01 and 0.31 mg NO2-N/L whilst the nitrate concentrations ranged from 0.52 to 3.54 mg NO3-N/L. Minimum nitrite was recorded in June at station IV, and maximum in August at station I. Similarly, minimum nitrate concentration was

TABLE 1 - Variations of some physical and chemical parameters in Batman Stream.

Water temperature

(oC)

pH Electrical conductivity

(mV)

Dissolved oxygen

(mg O2/L)

Total hardness (mg CaCO3/L)

Organic matter (mg O2/L)

Total dissolved solid matter

(mg/L) April 20.5±0.29 7.33±0.10 380.50±4.80 11.1±0.69 120.50±5.00 4.75±1.00 180.50±1.62 May 23.5±0.48 8.23±0.53 410.50±8.12 8.90±0.64 115.50±9.57 2.25±1.50 190.50±1.02 June 28.5±0.70 8.33±0.51 437.50±8.16 8.42±0.40 140.50±9.57 1.25±0.50 210.00±1.20 July 27.5±0.71 8.28±0.22 375.00±5.23 7.81±0.32 130.00±5.77 1.75±1.29 192.50±1.38 August 28.0±0.68 8.08±0.22 350.00±7.56 7.50±0.12 120.00±8.16 1.85±1.50 160.50±1.43 September 21.5±1.83 7.77±0.42 425.00±12.24 10.5±0.33 115.00±14.14 2.35±2.16 215.00±1.24 Mean 25.0±0.78 8.00±0.33 396.42±7.69 9.04±0.42 123.58±8.70 2.37±1.33 191.5±1.32


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TABLE 2 - Variations in degrees/concentrations of some chemical parameters in Batman Stream.

Nitrite (mg NO2

-N/L) Nitrate

(mg NO3-N/L)

Total nitrogen (mg /L)

Total phosphorus (mg /L)

Sulphate (mg SO4/L)

Silica (mg SiO2/L)

Chlorophyll a (µg/L)

April 0.02±0.01 5.25±0.74 2.00±1.00 0.02±0.01 22.53±1.95 0.40±0.62 2.00±1.05 May 0.05±0.02 2.30±0.84 2.00±1.00 0.03±0.02 17.95±1.45 0.80±0.72 2.50±1.25 June 0.02±0.01 1.30±1.42 2.05±1.10 0.03±0.01 14.80±0.96 1.70±1.76 3.50±1.75 July 0.03±0.01 1.60±0.89 2.04±1.10 0.04±0.02 16.53±1.15 1.80±1.60 3.25±1.50 August 0.30±0.01 1.70±1.73 2.05±1.20 0.04±0.02 18.78±1.21 2.10±1.96 3.00±1.40 September 0.15±0.01 1.80±1.07 2.00±1.00 0.03±0.01 17.28±0.97 0.60±0.30 3.00±1.40 Mean 0.10±0.01 2.33±1.11 2.02±1.10 0.03±0.01 17.98±1.28 1.23±1.16 2.87±1.39

recorded in June at station IV. However, its maximum was found in April, at station II. Total nitrogen was ob-served to be highest in August (2.4 mg/L) at station II (minimum value 1.4 mg/L, in May at station III). The mean concentrations of nitrite, nitrate and total nitrogen were 0.10±0.01, 2.33±1.11 and 2.02±1.10 mg/L, respectively (Table 2).

Total phosphorus varied slightly changing between 0.02 and 0.05 mg PO4-P/L being lower in almost every season at all stations but higher during late spring-early summer. The mean total phosphorus concentration was 0.03±0.01 mg/L. Concentrations of sulphate varied slightly throughout the sampling period. Maximum (23.5 mg SO4/L) and minimum (13.3 mg SO4/L) values of sulphate were measured in April and June, respectively. The mean sulphate concentration was 17.98±1.28 mg SO4/L. The maximum concentration of silica (2.4 mg SiO2/L) was ob-served in August, whilst the minimum value (0.3 mg SiO2/L) was recorded in April (mean 1.23±1.16 mg SiO2/L). The maximum (6.13 µg/L) and minimum (0.66 µg/L) values of chlorophyll a were measured in June and April, respec-tively (mean 2.87±1.39 µg/L; Table 2).

A total of 49 diatom taxa were identified in the epilithon and epipelon communities (Table 3) of Batman Stream. Navicula spp. (8) were the most important genus in terms of species number, occurrence of frequency and cell numbers both in epilithon and epipelon, followed by Cymbella (6 spp.), Nitzschia (5 spp.) and Gomphonema (5 spp.) at all stations (Table 3). Some researches [25-30] stated that Navicula and Nitzschia were cosmopolitan species in freshwater ecosystems. Species of Navicula and Nitzschia were also reported to be common in Turkey [9-17]. The findings herein also support the above studies as Navicula and Nitzschia together with Cymbella and Gom-phonema were found to be represented by more species than other diatom genera in the epilithon and epipelon. However, relative abundance of Navicula and Nitzschia representatives were not as noticeable as their species diversity.

A total of 44 taxa were recorded in the epipelon. Cymbella affinis, Tabularia fasciculata, Synedra pulchella and Cyclotella ocellata were the most common and abun-dant diatom species at all stations. Species diversity of epipelic diatoms was the richest in May and June. In fact, Cymbella affinis was important in the epipelon, especially during spring (in May at stations 1, 3 and 4; in April at

station 2), with relative abundance varying between 20 and 56%. Tabularia fasciculata was also recorded both abundantly and frequently at all stations. Cyclotella ocel-lata was the only centric form among epipelic diatoms in the epipelon. This diatom was important in terms of rela-tive abundance and frequency of occurrence in the epipelon, particularly in April (station 1) and June (stations 2 and 3) (Table 3).

Epilithic diatom community was presented with 39 diatom taxa during the sampling period. The community was dominated by pennate forms, particularly by Cym-bella affinis, Diatoma vulgare, Navicula cincta, Gom-phoneis olivacea and Encyonema minutum. These dia-toms were also important during spring and, similarly, Cyclotella ocellata was the most conspicuous centric form as in the case of the epipelon. Cymbella affinis was the most common and abundant diatom species with rela-tive abundance of 70.85%, and frequency of occurrence of 23.7%. Halamphora veneta was another noticeable diatom in terms of occurrence frequency (16%) and rela-tive abundance (23%) (Table 3).

The species composition and numbers of diatom in-dividuals are related to a variety of environmental factors in the aquatic environment; water temperature and trans-parency are among the most important physical factors affecting species composition and numbers of individuals [25-30]. In general, species composition of epipelic dia-toms was found to be rich in summer. However, in the present study, the richest numbers of species at all stations coincided both with spring and summer.

Both negative and positive correlations were observed between relative abundance of diatoms and the concentra-tions of dissolved nutrients at the stations. A positive and strong correlation was observed between relative abun-dance of some epilithic diatoms (at st. 4) and the concen-trations of total nitrogen at all stations (r = 0.74-0.97). In contrast, a negative and strong correlation was observed between the most abundant diatoms, such as Cyclotella ocellata (r = -0.93) and Navicula cincta (r = -0.99) with the concentrations of total phosphorus (at st. 2).

The mean values determined for pH (8), dissolved oxygen (9 mg/L), water temperature (25 oC), total phos-phorus (0.03 mg/L) and total nitrogen (2.02 mg/L) in Batman Stream clearly underlined that the river has got Class I water quality.


34

TABLE 3 - The mean frequency of occurrence and mean relative density of epilithic and epipelic diatoms of Batman Stream.

EPILITHON EPIPELON

TAXA Mean Relative Density (%)

Mean Frequency of Occurrence (%)

Mean Relative Density (%)

Mean Frequency of Occurrence (%)

BACILLARIOPHYTA Centrales Cyclotella ocellata Pantocksek 14.64 70.85 19.05 61.10 Cyclotella radiosa (Grunow in van Heurck) Lemmermann 1.00 16.70 - -

Pennales Achnanthes exigua Grunow 8.18 33.30 1.16 25.00 Achnanthes laevis var. quadratarea (Østrup) Lange-Bertalot - - 5.10 16.70

Cocconeis disculus (Schumann) Cléve 0.70 16.70 6.88 25.03 Cocconeis pediculus Ehrenberg 4.48 27.80 16.95 33.35 Cocconeis placentula Ehrenberg - - 1.70 16.70 Ctenophora pulchella (Ralfs ex Kützing) D.M.Williams & Round 5.42 33.35 6.80 66.66

Cymatopleura solea (Brébisson) W. Smith 1.15 16.70 0.78 25.03 Cymbella affinis Kützing 23.70 70.85 20.63 87.48 Cymbella cistula (Hemprich) O. Kirchner 0.70 16.70 1.48 22.23 Cymbella cuspidata Kützing 7.10 16.70 - - Cymbella helvetica Kützing 3.30 33.40 1.78 33.30 Cymbella naviculiformis (Auerswald) Cléve 3.00 16.70 2.45 16.70

Cymbella tumida (Brébisson) Van Heurck - - 2.27 25.00

Diatoma tenue C. Agardh 1.40 25.00 8.78 22.23 Diatoma vulgare Bory de Saint-Vincent 11.20 66.70 9.13 55.57 Encyonema minutum (Hilse) D. G. Mann 12.14 50.00 7.88 33.35 Encyonema prostratum (Berkeley) Kützing 1.50 16.70 0.60 16.70

Fragilaria capitata (Ehrenberg) Lange-Bertalot - - 0.60 16.70 Fragilaria construens (Ehrenberg) Grunow 14.47 25.00 0.60 16.70

Gomphonema acuminatum Ehrenberg 0.70 16.70 - - Gomphonema gracile Ehrenberg 3.33 25.00 5.80 16.70 Gomphonema intricatum Kützing 3.95 38.90 1.30 16.70 Gomphonema olivaceum (Hornemann) Brébisson 1.26 50.00 4.46 58.33

Gomphonema truncatum Ehrenberg 5.23 41.65 5.10 41.65 Gyrosigma acuminatum (Kützing) Rabenhorst 8.43 25.00 4.84 41.70

Halamphora veneta (Kützing) Levkov 23.00 16.70 8.28 41.65 Hantzschia amphioxys (Ehrenberg) Grunow 4.48 22.20 5.60 20.85

Navicula cincta (Ehrenberg) Ralfs 8.80 58.35 12.40 44.43 Navicula cryptocephala Kützing 2.36 33.30 6.95 54.18 Navicula minuscula Grunow 9.00 22.20 22.00 27.80 Navicula pseudohalophila Cholnoky 5.96 33.30 5.88 27.80 Navicula rhynchocephala Kützing 1.40 16.70 1.60 16.70 Navicula salinarum Grunow 1.65 16.70 20.10 16.70 Navicula trivialis Lange-Bertalot 22.00 16.70 5.75 33.33 Navicula viridula (Kützing) Ehrenberg 1.50 33.30 3.50 33.30 Nitzschia amphibia Grunow 4.26 25.00 4.60 22.23 Nitzschia intermedia Hantzsch ex Cleve & Grunow 0.50 16.70 - -

Nitzschia palea (Kützing) W. Smith - - 0.50 16.70 Nitzschia romana Grunow 1.80 16.70 4.80 25.00 Nitzschia umbonata (Ehrenberg) Lange-Bertalot 1.80 16.70 - - Pinnularia microstauron (Ehrenberg) Cleve - - 0.50 16.70

Rhoicosphenia abbreviata (C. Agardh) Lange-Bertalot - - 2.00 16.70

Sellaphora pupula (Kützing) Mereschkovsky - - 29.9 16.70

Stauroneis legumen (Ehrenberg) Kützing - - 1.20 25.00 Stauroneis pygmeae Krieger - - 0.50 16.70 Tabularia fasciculata (C. Agardh) D.M.Williams & Round 1.80 33.30 7.52 75.00

Ulnaria ulna (Nitzsch) P. Compère 3.45 16.70 10.28 22.23


35

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[2] Biggs, B.J.F. (1996). Patterns in benthic algae of streams. In Ste-venson, R.J., M.L. Bothwell & R.L. Lowe (eds.) Algal ecology: Freshwater benthic ecocystems. Academic Press Inc., San Diego: 31-56.

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[9] Yildiz, K. (1987). An investigation on Altınapa Dam Lake and algae communities of Meram Stream get from this lake. Cum-huriyet University, Science and art faculty, J Sci, 5, 191-207. (in Turkish)

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[15] Sen, B., Alp, M.T., Ozrenk, F., Ercan, Y. and Yildirim,V. (1999). A study on the amounts of plant nutrients and organic matter car-ried into Lake Hazar (Elazig/Turkey). Fresen Environ Bull, 8, 272-280.

[16] Cetin A.K. and Yildirim, V. (2006). Distribution and occurrence of the diatom community in Goksu Stream, Adiyaman, Turkey. Fresenius Environ Bull, 16 (5), 555-560.

[17] Bingol, N.A., Ozyurt, M.S., Dayioglu, H., Yamik, A. and Solak, C.N. (2007). Epilithic diatoms of Up Porsuk River (Kutahya). J Ecol. 15 (62), 23-39. (in Turkish)

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[20] Krammer, K. and Lange-Bertalot, H. (1986). Süsswasserflora von mitteleuropa. Bacillariophyceae, Band 2/1, 1. Teil: Naviculaceae, 1-876, Spectrum Academicher Verlag, Berlin.

[21] Krammer, K. and Lange-Bertalot, H. (1991a). Süsswasserflora von mitteleuropa. Bacillariophyceae, Band 2/3, 3. Teil: Centrales, Fragillariaceae, Eunoticeae, 1-576. Gustav Fischer Verlag, Stutt-gart.

[22] Krammer, K. and Lange-Bertalot, H. (1991b). Süsswasserflora von mitteleuropa. Bacillariophyceae, Band 2/4, 4. Teil: Achnan-thaceae 1-436. Kritische Erganzungenzu Navicula (Lineolatae) und Gomphonema Gesamt literature verzeichnis. Gustav Fischer Verlag, Stuttgart.

[23] Krammer, K. and Lange-Bertalot, H. (1999). Süsswasserflora von mitteleuropa. Bacillariophyceae, Band 2/2, 2. Teil: Bacillario-phyceae, Epithemiaceae, Surirellaceae, 1-610. Spectrum Aca-demicher Verlag, Berlin.

[24] Fowler, J. and Cohen, L. (1992). Practical statistics for field biol-ogy. John Wiley & Sons, London, 227 p.

[25] Round, F. E. (1953). An investigation of two benthic algal com-munities in Malharm Tarn, Yorkshire, J Ecol., 41, 97-174.

[26] Whitton, B.A. (1975). River ecology. Blackwell Scientific Publi-cations, London. 615 p.

[27] Odum, E. P. (1971). Fundamentals of ecology. V.B. Sunders Comp., Philedelphia and London, 547 s.

[28] Chessman, B.C. (1986). Diatom flora of an Australian River Sys-tem: Spatial patterns and environmental relationships. Freshwater Biol, 16, 805-819.

[29] Round, F. E. (1981). The Ecology of algae, Cambridge Univer-sity press. U.S.A. 653 p.

[30] Lowe, R.L. and Pan, Y. (1996). Algal ecology. Benthic algal communities as biological monitors. Academic Pres. 705-739.

Received: July 04, 2011 Accepted: September 20, 2011 CORRESPONDING AUTHOR

Feray Sönmez Fırat University Fisheries Faculty Department of Basic Aquatic Sciences 23119 Elazığ TURKEY Phone: +90 4242370000/4566 Fax : +90 4242386287 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 31 – 35


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DESIGN STRATEGIES FOR REVITALIZATION ON THE FİLYOS COASTLINE, ZONGULDAK, TURKEY

Bülent Cengiz1,*, Mehmet Sabaz1, Banu Bekci1 and Canan Cengiz1

1Department of Landscape Architecture, Faculty of Forestry, Bartın University, 74200 Bartın, Turkey.

ABSTRACT

Filyos, which is a coastal town located at the Western Black Sea region of Turkey, in Çaycuma, Zonguldak, has a significant archeological past, and is home of important archeological ruins. Especially during the 4th and 3rd cen-turies B.C., Filyos had a strategic role in its region, and today it is classified as a historic site. Beside the ancient city of Filyos which is located at the east side, the town is also important for its city walls, the chapel and ruins of an ancient harbor, all from the Byzantine period. A thorough design process that considers the ecological features of the region, users’ demands and the archeological features is crucial for the landscape projects in the town. This study aims to reshape the coastline of Filyos to attract touristic activities and to meet the recreational demands of people, while preserving the archeological and ecological features of the region. In this paper, design principles for the use and organization of Filyos coastline are presented, and a survey is conducted to determine users’ demand. In the conclusion section, design stages are prepared and strate-gies for revitalization on the Filyos coastline are dis-cussed.

KEYWORDS: Filyos coastline, revitalization, landscape design, users’ demand.

1. INTRODUCTION

The pieces of land located along coastlines (attached to the shorelines where water meets the land), are impor-tant for their natural resources, use values and the eco-nomic value they create [1].

Coastal areas are important points of attraction for people, because of their natural, economic and micro-climatic features. They are also among the most pre-ferred places due to different recreational activities they provide to meet holiday and entertainment needs of peo-ple [2]. * Corresponding author

Total length of the coastal regions all over the world is 600,000 km. Width of these regions can vary between 100 and 1000 m. Approximately 18% of the Earth’s sur-face is covered by coastal regions [3]. Due to their socio-economic activities, such as tourism, recreation, transpor-tation and industry, coastal regions host 60% of the world’s population today. Clark [4] underlined that changes in socioeconomic structure, such as rises in population, ecological disturbances, or climate changes, can have sig-nificant negative effects on coastal areas, which can also affect a significant portion of the world’s population.

All over the world, coastlines are focal points of in-terest for their intensive use; they especially grab the attention of people for their potential of touristic and re-creational activities [5]. Due to the rapid population in-crease, human factor is among the most effective vari-ables that change the landscape. Together with the rise in population, the increase in the expectations of human demands on coastal resources led to more intensive use of coastal regions. Pressures on the ecological structure due to this intensive use caused deteriorations in nature and biodiversity [4]. This process is exacerbated with the ab-sence of sufficient laws which could protect the densely populated coastal regions. Natural processes, such as tectonic and geological instability, tsunamis, volcanism, attrition, wave and tidal action, sea level rise or storm events, can have destructive effects on coastal regions. But human related processes, such as coastline devel-opment, offshore dredging, coral mining or sand mining, are also among the destructive forces that threaten the global coastal archaeological record [6].

Today, current waterfront development projects of cities follow a similar path all over the world which is based on reconciling the past projects with contempo-rary objectives of revitalization and economic devel-opment. For this aim, these projects transform water-fronts into places of public entertainment, while setting new regulations that secure visual and physical public access to these regions and to the water. In a broader sense, they aim at contributing to the quality of socioeco-nomic and cultural life, through creating spaces to work, to live and to play [7].

Because of the projects which neglected the needs of ecological structure, the number of deteriorated coastal


37

regions has increased recently. This necessitates a refor-mation in the planning and design processes that are re-lated to these regions through considering the usage-pro-tection balance and maintaining the sustainability of the nature and the culture [8].

Coastal destruction is the most common form of natu-ral deterioration. Fill areas that are formed in these re-gions cause destruction of the fauna through filling of the sea. Therefore, after the construction of fortifications, designs should be developed to reestablish the balance between the sea and the flora, considering the natural species of that region. Together with these vegetation projects, landscape projects can be employed to meet the recreational user demands. In this sense, a design of ecol-ogically privileged, qualified and functional green spaces in rural and urban spaces that will meet users’ needs is possible by only a thorough planning and an efficient implementation of landscape projects.

Dramstad et al. [9] defined some basic planning and design rules that are related to the problems emerging from the changes in landscape caused by plants, water, materials, energy, flood and other spatial pressures. Clark [4] defined strategic planning processes for integrated coastal zone management (ICZM), including scope, prob-lems and solutions. Karaşah et al. [10] conducted a survey to determine the effects of landscape design on users by looking at ecological planning and design processes. This survey also included suggestions for natural designs that can satisfy the aesthetical and functional needs of the citizens. Şahin et al. [12] analyzed the sustainable land-scape planning possibilities for Mersin/Tarsus coastline and developed a model for tourism that can be used for other coastal regions. Kelkit and Ak [13] prepared a plan-ning proposal to be used for the Çanakkale coastline. The techniques of GIS (Geographic Information System) and Remote Sensing facilitate the identification and the analy-sis of data using a multi-disciplinary approach in studies of coastal area planning [8; 13].

This paper focuses on the revitalization of the about 2 km long coastline located on the east of Filyos, which includes ruins of a city wall and a harbor from the Byzan-tine period. In this sense, preservation of the ecologic and archeological features, integration of these features with touristic activities, and reorganization of them to meet the recreational needs of people are the main goals of this study.


The main material of this study is the Filyos coastline (town in Çaycuma, City of Zonguldak). Filyos’ master plan (1/1000 scale), shoreline fill plan and report, as well as archaeological site reports are among the other materi-als used for the study. Information related to the climate, flora, population, history and settlements of the area are gathered through the investigation of reports, previous

studies, fieldwork and interviews with Filyos Town Gov-ernment officers. Landscape design project is prepared through several graphics editing and drawing programs, such as AutoCAD 2010, Adobe Photoshop CS5 and Autodesk 3D Studio Max 2010.

According to Booth [14], the method of this study

consists of 4 stages. Survey for landscape project with a scale of 1:1000, concept plan, preliminary master plan, final project, section-elevation, 3D Max are prepared (Fig. 1).

2.1. Survey Plan

Analysis of the current use of the study area and de-termination of the needs are done according to Maslow [15] theory on hierarchy of needs. These analyses are also supported by a questionnaire conducted with the users. The questionnaire has two stages: collecting the data and analyzing the data.

Data collection: The factors that affect design on the

Filyos Coastline and its environs (socio-demographic structure of the population, topographic structure, flora, climate, transportation, archeological history) were analyzed. Also, to determine the desires of the Filyos people toward land use of the research area, a questionnaire was made. The studies of Kelkit and Ak [12] and Kapuci [16] formed the basis for the questionnaire forms. The questionnaire was conducted with 50 people in Filyos Town on May 2011. MS Excel was used to categorize the data collected through the questionnaire.

Data analysis: The data collected from questionnaires

were grouped by Microsoft Excel by age, sex, education, job (occupation), monthly income (EURO), transporta-tion, reason of use, density of use, activities, pleasurable possibilities, problems that are encountered, desire for rearrangement, and ideas about the historical firebrick fac-tory and its surroundings rearrangement. SPSS software (SPSS 11) was used for the statistical analysis of the data according to the mentioned parameters.

2.2. Concept Plan

Design principles for the Filyos coastline landscape planning are developed according to the previous studies by Memlük et al. [1], Yaşar and Koçhan [17] and Fisher et al. [7].

2.3. Preliminary Master Plan

Studies Harris and Dinnes [18] and Neufert [19] are used for spatial standards. Preliminary master plan con-sists of three stages (stage I: fisherman’s port and vicinity; stage II: coastline erosion area and vicinity; stage III: archeological preservation site).

2.4. Final Project

Final project is supported with section-elevation and 3D Max.


38

FIGURE 1 - Methods flow chart.

3. RESULTS

Site Description

Filyos Town is 27 km away from Zonguldak City and located at the Zonguldak-Bartın border, at the west of the delta where Filyos river flows into the Black Sea. The area of Filyos Town is roughly 15 km2 (Fig. 2).

Study area defines the fisherman’s port which is lo-cated at the middle of Filyos coastline, its vicinity and the west-east extensions of the coastline on two sides of the port. At the east of Filyos coast lays the Ancient City of Filyos (Tios). Some parts of the project area remain within the borders of priority-I and priority-II archaeological pres-ervation sites which are determined by the April 04, 1996 degree 4573 issued by Ankara Cultural and Natural Heri-tage Preservation Board [20]. As an important ancient city in Zonguldak, Filyos is also known as Teion (or Tion). The ruins in the region have traces of Hellenistic, Roman,

Byzantine and Genoese architecture. Ruins of the ancient harbor, castle, aqueducts amphitheatre and church are important pieces of heritage from these periods [21]. In 1997, the shoreline, which has a relatively narrow mor-phologic structure with a low beach quality, changed after the construction of the jetty, due to the sea activities. Width of coastline towards the sea exceeds 50 m and, in some places, it is above 100 m [22]. Total area of the project is approximately 190,000 m2, including a parking lot, a wed-ding hall, fish restaurant, fisherman’s ports, parks, play-grounds, walking tracks, sea fills, archeological preserva-tion sites and some plantations (Acer sp., Pinus brutia, Pinus nigra, Platanus orientalis etc.). The ecological and cultural features of Filyos Town are summarized in Fig. 3.

I. Survey Plan

Studies to evaluate the actual use of the study area and the list of needs that are completed according to Mas-


39

FIGURE 2 - Study area.

FIGURE 3 - Ecological and cultural features of Filyos Town


40

TABLE 1 - Definition of the study area’ actual situation according to Maslow’s [15] hierarchy of needs.

Criteria of Maslow’s [15] hierarchy of needs

Evaluation of the Filyos’ actual

situation

Analysis of Filyos’ actual situation

Physiologic Needs (Eating, drinking, accommodation, etc.) √√

Filyos Town can meet the needs of residents and visitors to certain extend. There is a fish restaurant, bed and breakfast, green space and a beach in the area.

Safety Needs (Self-confidence and being away from danger)

√√√ Since it is a small town, Filyos is a safe place. However, due to the rise in population during summer, extra attention is needed for security.

Love and Belonging (Communication with others and belong-ing to a certain place)

√ Although users can meet their needs in the area, they cannot have the comfort that they desire.

Esteem (Respect by others, achievement and confidence)

√√√

With its natural beauties, the beach and the archeological ruins, Filyos Town provides various opportunities for cultural tourists. Therefore, the ancient harbor, chapel, ancient amphitheatre and aqueduct are still important for the town.

Self-Actualization (Self-satisfaction and achievement) √

Filyos’ touristic area includes important values. But the low beach quality, negligence of the restoration projects of the archeological ruins, continuum of the water pollution, low visual and utilization quality of the green spaces (lack of maintenance for the green spaces) have negative effects on the potentials of the space.

√√√ very good; √√ good; √ bad

low’s [15] hierarchy of needs (Table 1). These studies are also supported by the survey results that are conducted to the users.

The design, which will be developed according to the current needs, aims at promoting more efficient use of the fill areas, improving the perception of the sea by the us-ers, rehabilitating the existing green spaces, providing more recreation services for the users, reorganizing the coastal regions and developing a land-use design for the common good. In this sense, history of the Filyos coastline, its ecological and cultural structure will be preserved, and the coastline will be redesigned to meet the recreational needs of the users.

Before starting project design, a comprehensive sur-vey study was completed to determine the suitable areas for the project [24].

Determination of users’ need for Filyos coastline

through survey: SPSS11 software was used to perform the statistical analysis of the data according to the pa-rameters of the questionnaires. The results of the analysis were evaluated to understand the recreational land-use patterns of the Filyos’ residents in the coastal area. • 60% of the interviewees are permanent residents of

Filyos (24% living in town for 7-10 years; the rest are temporary residents of Filyos).

• 58% of the interviewees are male and 42% of them are female.

• 36% of the interviewees are between the ages of 41 and 50, 24% of them between 31 and 40, 26% of them between 21 and 30, and the rest of them were children and the elderly citizens.

• 68% of the interviewees are married and 32% are single. 34% of the married interviewees have no chil-

dren, 30% have 2 children, 16% have either 1 child or 3 children, and 4% have more than 3 children.

• 58% of the interviewees are high school graduates, 24% have a college degree, 10% have only middle school degree, and 8% have only primary school degree. Among the residents who can be in the workforce, 26% of the interviewees are workers, 22% are state officers, 14% unemployed, 10% are retired, 8% are housewives or students, and 6% are either employed as manager or self-employed.

• 30% of the users have a monthly income of 650 EURO, 20% have an income between 425-650 EURO, 22 % have an income between 320-425 EURO, 14% have an income between 212-320 EURO, and the rest have an income less than 212 EURO. Above data show that users of the area consist of mostly mid-aged people.

• Analysis of the areal use of the resident in Filyos shows that most intensive use of the research space occurs during June-July and August periods (74%). This indicates that the research space is mostly visited during the summer season.

• According to the questionnaires, existing space meets only 50% of the need for green spaces. While Filyos residents access to the area mostly by walking, visitors from outside the town usually use their cars. Only 4% of the visitors prefer public transportation to access to the area, 44% of the users usually bring their families to the area, while 24% go with their friends, and 26% prefer being alone during their visit to the area.

• 47.1% of users visit Filyos coastline during the summer season for its sea and beach. 19.5% of the visitors go to coast to be away from the city. 16.1% prefer being there for the ruins and their architectural heritage (Table 2).

• 27.8% of the users use the area for walking or run-ning. 26.2% of them go for enjoying the sea and the


41

green spaces, 15.9% go for swimming, 10.3% go for fishing and for picnic, and 4.8% go for riding their bikes or walking their pets (Table 3).

• 74% of the users encounter with several problems while they use the space. 87% of the users think that the existing area does not fully meet their needs. 11.6% think that archeological and historic structures at the coastline are not highlighted enough, 9.2% think that beach is not well maintained, 6% complain about the insufficiency of the fitness materials, and 4.4% bring the insufficient maintenance of the coastline as a problem (Table 4). Based on these problems, users have several expectations from the coastal area.

• 16.5% of the users declared that they need terraces for resting and enjoying scenic view, 14.5% declared that

they need tea gardens, restaurants and kiosks, 9% re-ferred to the need for integration of the archeological sites to tourism, 8.5% declared their need for a swim-ming pool and water sports area available the whole year, and 7.5% declared their need for ordered picnic areas (Table 5).

• Besides, 27% of the users think that the firebrick fac-tory and its surroundings, which are located at the coastline, should be transformed into a hotel. 16% of the users suggest allocation of these areas for hand-crafts classes or exhibition areas. 12% of them suggest transformation of the building into a museum that will include archives and photographs to represent the sig-nificance of the factory in history. 4% suggest an in-dustrial park for the area.

TABLE 2 - Distribution of the number of interviewees based on their reasons for visiting Filyos coastline.

Users’ reasons for preferring Filyos coastline n Percentage (%) To be away from the city 17 19.5 For the beach and the sea 41 47.1 Socialization 8 9.2 For commercial purposes 4 4.6 For its historic and archeological features 14 16.1 It is closed to the center, for transportation purposes 3 3.5 Total 87 100

Since more than one choice is marked, percentages were calculated according to “n”.

TABLE 3 - Distribution of the interviewees based on their recreational activities at Filyos coastline.

Recreational activities at Filyos coastline n Percentage (%) Resting in the open area through enjoying the sea and the green spaces 33 26.2 Walking or running 35 27.8 Riding a bike 6 4.8 Going to the playing grounds 1 0.8 Picnic 13 10.3 Taking pets for a walk 5 4 Fishing 13 10.3 Swimming 20 15.9 Total 126 100


TABLE 4 - Distribution of the users according to the problems they refer in the Filyos coastline.

Problems that can be encountered in the Filyos coastline n Percentage (%) Other 110 44 Insufficient maintanance 11 4.4 Security problems related to the park 2 0.8 Water pfllution 10 4 Lack of fitness areas 15 6 Insufficiency of the vegetation 1 0.4 Lack of playgrounds 1 0.4 It is being archeological preservation area 6 2.4 Lack of order and maintenance in the coastline filling areas and the jetty 5 2 Insufficient number of services in the park area (restaurant, café, tea garden, etc.) 8 3.2 Historic and archeological structures are not highlighted enough 29 11.6 Absence of informative and/or directive signs 6 2.4 Lack of alternative uses which can lead to different activities 6 2.4 Insufficiency of the ordered green open spaces 6 2,4 Insufficient number of trashcans, benches and street lights 4 1.6 No allocated space for sports 6 2.4 Insufficient use and maintenance of beach 23 9.2 Shoreline erosion 1 0.4 Total 250 100



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TABLE 5 - Distribution of the interviewees to their needs in the Filyos coastline.

Needs of the users in the Filyos coastline n Percentage (%) Other 37 18.5 Resting and scenic view areas 33 16.5 Ordered picnic areas 15 7.5 Well maintained and clean wide green spaces 10 5 Exhibition areas for handcrafts and concert areas 9 4.5 Walking and running tracks 8 4 Integration of the archeological sites to tourism 18 9 Playgrounds 3 1.5 Areas allocated for sportive activities 12 6 Tea garden, restaurant and kiosks 29 14.5 A swimming pool and place for water activities which will be available all year long 17 8.5 Beach facilities 9 4.5 Total 200 100


II. Concept Plan

Based on the questionnaire, user’s needs were deter-mined and the design process started. The design princi-ples for the landscape design of Filyos coastaline are developed according to the studies conducted by Memlük et al. [1], Yaşar and Koçhan [17] and Fisher et al. [7].

As the first step, survey of the area was completed. During the design processes that were developed to pre-sent alternative solutions for the area, components, such as form, scale, color and texture, were transformed into the drawings in the form of models and plans. At first, the alternative solutions for the area were developed. For each planning process, different design principles (conti-nuity, balance, mass-volume, etc.) were produced to get the results. Among different sketches, the project that can define the area best was chosen to be the project design alternative. In project designs, the most suitable places for settlements were chosen according to their accessibility, comfort and safety. Problems determined through the results of survey studies (orientation of the buildings, vegetation to be preserved, sun-shading orientation, mass-volume-space relationship in vegetation, etc.) were discussed in detail at the landscape design stage. The areal criteria that are used for the design of the project are as follows:

Aim and the scope of the project: Program and capac-ity. Limitations: Ecologic, aesthetics, technical, legal and economic. Needs: Physical (transportation, orientation, lighting, noise control, etc.), human (biological, anthropometric, psychological, socio-cultural), technical (usability, dura-bility, safety, aesthetics, etc.) and users’ demand. Existing landscape features: Existing natural environ-ment (mountainous, sea sides, forested, etc.), topographi-cal structure (sloped, flat), geological structure (hard, soft, slippery, earthquake zone), vegetation (the most common), climate (mild, precipitation), dense/non-dense settlement, vertical/horizontal urbanization, form/ratio/scale, functional distribution (what exists more, what is missing), historical features (characteristics of the period, etc.), landmark-symbolic features (jetty, castle, firebrick fac-tory and its backyard, etc.).

Based on the problems that were determined in the area, the principles and the aim were defined, and then the necessary activities were placed in the project area through design. The proposed design focuses on how a user can spend his day in the area efficiently. After determination of the problems of the project area, a list of needs for the area

FIGURE 4 - Concept plan.


43

was prepared based on the information provided by the city government officers. Restaurants, a hotel, shopping units, sports facilities (tennis, volleyball, basketball, walking and running trails, etc.), a tea garden, playground, picnic areas and parking lots are among these needs (Fig. 4). III. Preliminary Master Plan

At this stage, the effects of the problems on the users of the space, interrelations between the possible and ac-

tual usage of space were analyzed, and possible solutions were discussed with respect to these problems. Needs that had been determined to be used in the design were placed in the sketch plan and, based on the last planning deci-sions, a preliminary master plan was prepared in consid-eration with the spatial standards [18, 19].

The preliminary master plan is discussed in three

stages: most densely used area by the users (Stage I), sea

FIGURE 5 - Analysis of the stages I, II and III of the Filyos Coastline Project.


44

fill at the shoreline (Stage II), archeological preserved site (Stage III). Design was conceptualized as a whole but each stage was analyzed separately (Fig. 5). Users’ de-mands vary in each stage. Each space constructed by this project is determined by a main axis. Although it is diffi-cult to determine the human perceptions as to the interre-lations between space and function by experimental methods, it is possible to collect the data related to the aim. In this study, effects of users’ visual experiences on their perception of existing organization of space are presented through an experimental study [24]. After the design decisions, zoning, formation, hierarchical ordering and spatialization of activities were completed. Then, the current design was elaborated and enriched through sports areas, scenic terraces, surface elements, connecting ele-ments, refining elements, etc. (Fig. 6).

IV. Final project

After the interview with the Filyos government offi-cers, decisions developed during the preliminary master

plan were transferred to the final project (Fig. 7). For the planting design that will be applied throughout the pro-ject, the study of Cengiz et al. [25] was used. Based on this study, functional, aesthetical and economic features of the plants are used and the natural vegetation of the Western Black Sea region is taken into consideration. In this context, proposed plant list consists of Acer campestre, Buxus sempervirens, Carpinus orientalis, Castanea sativa, Crataegus monogyna, Fraxinus angustifolia, Laurus nobilis, Pinus nigra, Platanus orientalis, Prunus avium, Prunus laurocerasus, Rhododendron ponticum, Rosa sp., Syringa vulgaris, Tilia tomentosa, etc. The plant diversity in urban landscape areas plays a certain role in urban nature conservation and the determination of plan-ning and policies. But, it is also important for the design of cities [26].

Design of the final project was supported by section-

elevation, (Fig. 8) and 3D Max images (Fig. 9).

FIGURE 6 - Preliminary master plan.

FIGURE 7 - Final project.


45

FIGURE 8 - Sections elevations from the final project.

FIGURE 9 - 3D Max images.


46

4. CONCLUSIONS

According to the questionnaires, users prefer Filyos coastline mostly for the sea, the beach, and the historic and archeological features of the region. The major prob-lems in the region encountered by the users are the igno-rance of architectural and historic structures, low mainte-nance of the beach and the insufficient number of beach facilities. The current project of the Filyos Municipal Government focuses on recreational activities (walking and running tracks on the shoreline, playgrounds, etc.) which will be available throughout the whole year. Eating and drinking facilities in the area were also renovated and opened to public service according to the current needs.

Filyos coastline landscape design project is developed to meet the recreation needs of the users by considering the design principles for coastal park planning. o Proposals for Stage I: Fisherman’s port and jetty will

continue their current activities. Parks are proposed for coastline sea fills.

o Proposals for Stage II: There is an ongoing fortifica-tion process for the existing erosion at the coastline which is due to the sea waves. The design for this area was prepared, especially to be allocated for beach use. The fortification area, which has the shortest distance to the sea, can also be utilized for vegetation purposes by using native plants, in order to reestablish the har-mony between the flora and the sea.

o Proposals for Stage III: Reprogramming of the old Filyos Firebrick Factory as a hotel and/or an industry park is proposed, which is the first factory in the ar-chaeological preservation area of the region. Besides, the sunken harbor is proposed to be redesigned for diving activities. With the ruins of city walls and the ancient harbor

from the Byzantine period, Filyos is a significant attrac-tion point of the region, also located at intersection points of important roads, sea lines and airport connections. This study aims at planning and revitalizing the 2 km long coastal region to open for public access. Preservation of ecological values of Filyos Town, contribution to the tourism of the town, and meeting the recreational needs of people are also among the goals of this project. This pro-ject was prepared by considering the public use of coastal regions. Besides meeting the recreational needs of the people, it can also contribute to the economic develop-ment of the region. It is also an example for those projects which aim at sustainable and participatory coastal land-scape planning and design processes in developing coun-tries, such as Turkey.

ACKNOWLEDGMENTS

For their efforts and contributions to the Filyos Coast-line Landscape Design Project during the 2010-2011 fall-spring semesters, we would like to express our gratitude to the graduate students of Bartın University, Department of Landscape Architecture, Faculty of Forestry. Those are Gonca Kaya (PhD. student), Bahadır Erkişi (graduate stu-dent), Temel Olğun (graduate student), Y. Alper Topkaya (undergraduate student), M. Cemil Aktaş (undergraduate student) and İsmail Çakır (undergraduate student).

REFERENCES

[1] Memlük, Y., Erdoğan, E., Çalık, E., Cengiz, T., and Kılıç, N. (2000) The Marmara coastal usage and management, case of Sinop. The Marmara Sea 2000 Symposium. İstanbul, Turkey. (in Turkish)

[2] Malkoç, E., Kılıçaslan, C., and Özkan, M.B. (2010) Visual landscape analysis of urban open spaces: a case study of the coastline of Göcek Settlement, Muğla, Türkiye. Indoor Built Environment, 19(5): 520-537.

[3] Özcan, H., Akbulak, C., Kelkit, A., Tosunoğlu, M. and Uy-sal, T. (2009) Ecotourism potential and management of Kavak Delta (northwest Turkey). Journal of Coastal Re-search, 25(3): 781-787.

[4] Clark, J.R. (1996) Coastal zone management handbook. Lewis Publishers. Boca Raton, Florida USA.

[5] Buanes, A., Jentoft, S., Karlsen, G.R., Maurstad, A. and Søreng, S. (2004) In whose interest? An exploratory analysis of stakeholders in Norwegian coastal zone planning. Ocean & Coastal Management, 47(5-6): 207-223.

[6] Rick, T.C. and Fitzpatrick, S.M. (2011) Cataclysmic events in coastal archaeology. Journal of Coastal Conservation: Planning and Management, Special Issue, 14(4).

[7] Fisher, B., Gordon D.L.A., Holst, L., Krieger, A., McMillan, G., Rafferty, L. and Schiffman, E.S. (2004) Remaking the urban waterfront. Urban Land Institute, Washington, DC.

[8] Cengiz, C. (2009) Ecological planning in coastal areas: Ya-lova-Armutlu case study. Ankara University, Graduate School of Natural and Applied Sciences, Department of Landscape Architecture, Ph.D. thesis, Ankara, Turkey. (in Turkish).

[9] Dramstad E.W., Olson, J.D. and Forman, R.T.T. (1996) Landscape ecology principles in landscape architecture and land use planning. Harvard University Graduate School of Design, Island Press and American Society of Landscape Ar-chitects. Washington, DC.

[10] Karaşah, B., Sarı, D. and Güneroğlu, N. (2010) Evaluation of urban coastlines in the respect of landscape architecture: The sample of Trabzon City, Turkey. III. National Black Sea For-estry Congress (Artvin, Turkey), pp. 1504-1512. (in Turkish).

[11] Şahin, Ş., Çabuk, A. and Dilek, F. (2001) Assessment of coastal region of Mersin/Tarsus in terms of tourism within the context of sustainable landscape planning. Ankara Uni-versity Project of Research Funds, Project No: 98-11-04-001. Ankara, Turkey. (in Turkish).


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[12] Kelkit, A. and Ak, T. (2006) Coastal land use planning: a case study of Kordonboyu (Çanakkale). Journal of Coastal Research, 22(4): 951-957.

[13] Özyavuz, M. (2010) Analysis of changes in vegetation using multitemporal satellite imagery, the case of Tekirdağ coastal town. Journal of Coastal Research, 26(6): 1038–1046.

[14] Booth, N.K. (1983) Basic elements of landscape architectural design. Waveland Press, Inc. USA.

[15] Maslow, A.H. (1943) A Theory of human motivation. Psy-chological Review, 50: 370-396.

[16] Kapuci, C. (2004) The effect of historical city structure on the tourism potential case study: İznik City. Zonguldak Karaelmas University, Graduate School of Natural and Ap-plied Sciences, Department of Landscape Architecture, M.Sc. thesis, Bartın, Turkey. (in Turkish).

[17] Yaşar, Y. and Koçhan, A. (2001) Studios of design in Archi-tecture Education. Three different semesters and three differ-ent subjects. Journal of Architecture, Culture, and Art. YAPI, 235, June, s: 46-51. (in Turkish).

[18] Harris, C.W. and Dinnes, N.T. (1998) Time-Saver standards for landscape architecture. McGraw Hill Publishing Com-pany. USA.

[19] Neufert, E. (2008) Knowledge of building design. İstanbul, Turkey: translation from 35. press- II. Turkish press. Beta Publishing Company. İstanbul. (in Turkish).

[20] Anonymous (2008) Ancient City of Filyos (Tios), borders of 1. and 2. Degree Archaeological Preservation Site. Septem-ber 09, 2008 degree and 960 issued by Regional Directorate of Karabük Cultural and Natural Heritage Preservation. Sa-franbolu, Turkey. (in Turkish).

[21] Anonymous (2006) Environmental status report of Zonguldak-2006. Zonguldak, Turkey: Governorship Provin-cial Directorate of Environment Zonguldak Press. (in Turk-ish).

[22] Anonymous (2010) Filyos Municipality park and recreation area shoreline fill plan. Filyos Municipality publication. Filyos, Turkey. (in Turkish).

[23] Davis, P.H.; Mill R.R., and Tan, K. (1988). Flora of Turkey and the East Aegean Islands. Edinburg University Press, Vol. 10. Edinburg, UK.

[24] Kazancıoğlu, B. (2001). A research on the application of a harmony between personal space and architectural space into a case study Like Park. Black Sea Technical University, Graduate School of Natural and Applied Sciences, Department of Landscape Architecture, M.Sc. thesis, Trabzon, Turkey. (in Turkish).

[25] Cengiz, B., Sabaz, M. and Sarıbaş, M. (2011) The use of some natural Crataegus L. (Hawthorn) taxa from Western Black Sea Region of Turkey for landscape applications. Fre-senius Environmental Bulletin, 20(3): 656-664.

[26] Acar, C., Acar, H. and Eroğlu, E. (2007) Evaluation of orna-mental plant resources to urban biodiversity and cultural changing: A case study of residential landscapes in Trabzon city (Turkey). Building and Environment, 42: 218-229.


Bülent Cengiz Bartın University Faculty of Forestry Department of Landscape Architecture 74200 Bartın TURKEY Phone: +90 (378) 223 51 22 Fax: +90 (378) 223 50 62 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 36 – 47


48

ESTIMATION OF ENVIRONMENTAL CAPACITY OF PETROLEUM HYDROCARBONS IN JIAOZHOU BAY, CHINA

Guo-dong Qian1, 2, Xiao-yong Shi2, 3, Ke-qiang Li2, 3,*, Xiu-lin Wang2, 3, Sheng-kang Liang2, 3 and Xu-dong Qiao2, 3

1 College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, P. R. China 2 Key Lab of Marine Chemistry Theory and Technology of Ministry of Education, Ocean University of China, Qingdao 266100, P. R. China

3 College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, P. R. China

ABSTRACT

In recent years, petroleum hydrocarbons (PHs) have become one of the most important factors that affect the water quality of Jiaozhou Bay, which is mainly from land-based discharges and ship wastewater discharges. Based on the investigation of terrigenous pollution (rivers and sewage treatment plants) and seawater in August, October and November 2007, a transport, transformation and hy-drodynamic model is constructed to estimate the marine environmental capacity of PHs in Jiaozhou Bay. Accord-ing to the model, the environmental capacity of PHs in one year is approximately 1570 tons when the target of seawater quality criterion (Grade I/II, 50 µg·L-1) is set to be satisfied. Thus, our research provides necessary scientific foundation to the total amount control efforts in this area.

KEYWORDS: Environmental capacity; petroleum hydrocarbons; transport, transformation and hydrodynamic model; Jiaozhou bay.

1. INTRODUCTION

Pollutant total amount control has been used for water quality management and water environment protection in many countries, such as Total Maximum Daily Load (TMDL) in USA [1], Total Pollutant Load Control System (TPLCS) in Japan [2], and also in European [3] and other countries. With the rapid development of economy and society of China, more and more pollutants are discharged into the coastal waters and have caused serious water pollution problems [4, 5]. Pollutant total amount control is adopted by the environmental protection departments in China in recent years, and has proven to be an effective way for water environment management [6, 7]. As the base of pollutant total amount control, calculation of wa-ter environmental capacity plays an important role. * Corresponding author

Environmental capacity is the ability of environment to accommodate a particular activity or rate of activity without unacceptable impact [8]. When applied to coastal areas, the concept can be defined as the capacity of the system allowing pollutants, without causing long-term de-terioration or pollution [9]. The research on the environ-mental capacity of pollutants is necessary for maintaining the good water quality and sustainable use of coastal natu-ral resources. Modeling is often used to estimate the marine environmental capacity, which has been further applied to evaluate plans for water quality management [10-13].

Jiaozhou Bay (Fig. 1) is a semi-enclosed bay in the West Yellow Sea, and located in Qingdao City of Shan-dong Province, China. The area of the bay is approximately 320 km2, and the mean depth is about 7 m. As shown in Fig. 1, rivers and streams, such as the Lianwan (R1), Yang (R2), Dagu (R3), Moshui (R4), Loushan (R5), Banqiaofang (R6), Licun (R7), and Haibo (R8), flow into it. Polluted water from sewage treatment plants flows in through Lian-wan (W1), Tuandao (W2), Licun (W3), and Haibo (W4). As in many coastal regions near major urban areas, the bay is used for a variety of purposes: aquiculture and fish stock-ing, shipping, recreational boating, and as a repository for sewage effluent and dredged sediments [14].

The negative effects of petroleum hydrocarbons (PHs) pollution on the marine environment have been widely studied [15, 16]. Mainly from land-based discharges (do-mestic and industrial sources) and ship wastewater dis-charges, the water quality in Jiaozhou bay has been gradu-ally getting worse [17, 18], although the loads are decreas-ing attributed to many measures taken in recent years. There is lack of understanding the environmental capacity and, therefore, no effective way to determine the policy for pollutant total amount control. A dynamic model of transport and transformation of PHs in multiphase envi-ronment has been constructed to estimate the environ-mental capacity of PHs in Jiaozhou bay [19], but in this model, the Jiaozhou Bay seemed to be a mixed box, and the process of hydrodynamic transport cannot be reflected in detail, and the environmental capacity is estimated under the conditions of the average concentration of PHs


49

achieving the water quality standard, and cannot be allo-cated to each point source for pollutant reduction. In this study, a transport, transformation and hydrodynamic model is established and used to quantify PHs environmental capacity and reduction in Jiaozhou Bay.


2.1. Data analysis and field surveys

Terrigenous pollution (rivers and sewage treatment plants) was investigated at 12 points (R1–R8 and W1–W4), and seawater samples were collected at 22 stations (1–22) in August, October and November 2007 (Fig. 1). Table 1 shows the observed values of fluxes and concen-trations of PHs in drain outlets of rivers and sewage treatment plants. The loads of PHs from rivers and sew-age treatment plants to Jiaozhou Bay can be obtained according to the fluxes and concentrations measured. The observed values of surface PHs in Jiaozhou Bay in October and November are used to calibrate and validate the transport and transformation model.

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2

36.25

1

2

3

45

6

7

8

9

10

11

12

1314

15

16

17

18

19

20

21

22

R1 & W1

R2

R3

R4

R5

R6

R7 & W3

R8 & W4

W2Huangdao

Hongdao

Qingdao

FIGURE 1 - Research sites for input sources and seawater in Jiaozhou Bay, China (R1–R8, W1-W4, land-based pollution sources, such as rivers, streams, and treated wastewater; 1–22, seawater survey stations).

TABLE 1 - The observed values of petroleun hydrocarbons (PHs) in drain outlets of rivers and sewage treatment plants.

August October November Name flux

(104 m3·day-1) concentration (mg·dm-3)

flux (104 m3·day-1)

concentration (mg·dm-3)

flux (104 m3·day-1)

concentration (mg·dm-3)

R1 12.1 0.15 12.1 1.52 7.1 1.53 R2 123.6 0.01 7.3 2.36 3.4 1.69 R3 1641.6 0.01 66.9 1.66 52.9 1.07 R4 173.0 0.20 23.4 1.39 19.5 2.59 R5 5.2 0.30 40.0 2.02 34.6 1.75 R6 7.8 0.50 2.2 1.45 1.8 2.67 R7 36.0 0.70 39.6 1.40 20.3 1.51 R8 15.6 0.70 7.9 1.48 19.3 0.54 W1 4.0 0.10 2.7 0.10 2.6 0.10 W2 5.0 0.05 4.5 0.20 3.7 0.16 W3 9.0 0.05 9.0 0.30 9.0 0.21 W4 8.0 0.05 7.5 0.40 7.0 0.19

2.2. Applied model

Based on the systematical integration of a hydrody-namic model for water flow simulation and a transport and transformation model for water quality simulation [20], a coupled biochemical transport, transformation and hydrodynamic model is developed. The water quality compartments could be predicted by the biochemical transport and transformation model using residual current as a flow field, which is simulated by the hydrodynamic model [21, 22]. Then, based on the pollution source re-sponse field, a linear programming model is used to esti-mate the environmental capacity of pollutions [23].

2.2.1. Transport, transformation and hydrodynamic model

The main process which contributes to the concentra-tion variation of PHs has been discussed based on a PHs`

transport and transformation model in multiphase envi-ronment in Jiaozhou bay, including water dynamic trans-port, volatilization from water to atmosphere, and micro-biological degradation [9]. Combined with the three-dimensional hydrodynamic model [21], a transport, trans-formation and hydrodynamic model for PHs is developed to evaluate the concentration variation of PHs in Jiaozhou bay.

The concentrations variation of standing stock C at a given time is:

bioatmdifadv tC

tC

tC

tC

tC )()()()(

∂∂

+∂

∂+

∂∂

+∂

∂=

∂∂ (1)

where, C is the concentration of PHs; t is time;

advtC )(

∂∂ and

diftC )(

∂∂ are the concentration variations caused

by advection and diffusion of seawater, respectively;


50

atmtC)(

∂∂

is the concentration variation caused by volatiliza-

tion from water to the atmosphere, and biotC )(

∂∂

is by micro-biological degradation. The standing stock of each com-partment is predicted with temporal and spatial changes by entering velocity components calculated by hydrody-namic modeling into the biochemical transport and trans-formation model. Detailed descriptions and formulation of compartments are available in Li [20].

2.2.2. Linear programming model

Linear programming method has been widely applied to the estimation and allocation of water environmental capacity for rivers and lakes [24, 25]. In this paper, linear programming model is used for the maximization of total pollutant load, subject to the satisfaction of environmental standards imposed on the seawater quality control points, with the optimization target function and water quality constraint equations. The formulas and definitions have been described in detail in Han et al. [26].

2.3. Modeling input data

From Table 1, we can conclude that the total river loads of PHs to Jiaozhou Bay in October and November 2007 are approximately 3.3 and 2.3 t/d, respectively, among which Dagu (R3), Licun (R7), Loushan (R5) and Moshui (R4) contribute the highest loads in the bay. Treated wastewater loads of PHs in October and November 2007 are approximately 0.07 and 0.04 t/d, respectively, among which Licun sewage treatment plant (W3) is the highest. Since the Lianwan (R1), Licun (R7), and Haibo (R8) rivers have the same inlets with the Lianwan (W1), Licun (W3) and Haibo (W4) sewage treatment plants around Jiaozhou Bay, respectively, we integrate them into 3 point sources as Lianwan (R1 & W1), Licun (R7 & W3), and Haibo (R8 & W4). And as the river flux of Banqiaofang (R6) is small and nearby Loushan (R5) river, we also integrate them into 1 point source as Loushan (R5 & R6). Hence, Lianwan (R1 & W1), Tuandao (W2), Yang (R2), Dagu (R3), Moshui (R4), Loushan (R5 & R6), Licun (R7 & W3), and Haibo (R8 & W4) represent 8 point sources. The shipping input, which is about two times of river input to Jiaozhou bay [9], is also allocated to the nearby point sources, respectively.

Major parameter and input value definitions of the model were available in Li [20], and the parameters were optimized by the simulation results of surface PHs in Jiaozhou Bay in October and November 2007 (Table 2).

After the simulation results correlated well with the observed data, calibration and verification were done, and

a quasi-steady state was obtained, 180 days after the be-ginning of the calculation [22].


3.1. Water quality simulation

Figure 2 shows the observed values of surface PHs in Jiaozhou Bay in October and November 2007. The aver-age concentration of PHs in the surface seawater of Jiaozhou Bay in October was about 25 µg·L-1, which is under the water quality standard of grade I (PHs = 50 µg·L-1) defined by the criteria for seawater quality in China [27]. The spatial distribution of PHs shows that it is higher in the northeastern but decreases gradually towards the inner bay and southwestern. The concentrations of PHs exceeded 10 µg·L-1 in the most area of the bay, and especially near the outlet of Haibo river, the concentration was more than 50 µg·L-1. In November, the average con-centration of PHs in Jiaozhou Bay was about 60 µg·L-1, exceeding the water quality standard of grades I and II. The spatial distribution of PHs shows that it is higher in the northeastern, and decreases gradually towards the inner bay and southwestern, which is similar as the distri-bution in October. The concentration of PHs exceeded 40 µg·L-1 in the most area of the bay, and especially near the outlets of Moshui and Loushan river, the concentration was more than 80 µg·L-1.

The simulation results of PHs in the surface seawater of Jiaozhou Bay based on the model are shown in the right side of Fig. 2. The results show that the values and the spatial distributions of PHs match the observed values reasonably well.

In this study, the relative standard deviation (RSD), correlation coefficient (r) and similarity index (SI) are used to evaluate the reliability and accuracy of the simula-tion results. RSD is used to determine the numerical dif-ference between the observed and simulated values, and the other two for the degree of agreement in spatial distri-bution [28, 29]. The quantitative comparison between the observed and simulated values of surface PHs in Jiaozhou Bay in October and November 2007 is shown in Table 3. The results show that RSD values were 43 and 30% in October and November 2007, respectively. The correla-tion coefficients of the observed and simulated values in October and November were both >0.7 (p<0.01), and the similarity indices were also both close to 0.7. In this pre-sent case, the agreement between observed and simulated values of PHs suggests that the results of the ecological model are acceptable

TABLE 2 - Model parameters.

Symbol Units Value Parameters ∆H kJ·mol-1 30 Vaporization heat of PHs λ m·d-1 0.1 Rate constant for PHs volatilization

Kbd d-1 0.02 Biodegration rate constant for PHs


51

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 I

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 I

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 II

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 II

FIGURE 2 - The observed values (left) and simulation results (right) of surface PHs in Jiaozhou Bay in October (I) and November (II) 2007 (µg·L-1).

TABLE 3 - The quantitative comparison between the observed and simulated values of surface PHs in Jiaozhou Bay in October and Novem-ber 2007(n = 422).

Value Spatial distribution

Date the average of ob-served values (µg·L-1)

the average of simulation results (µg·L-1)

RSD (%) Correlation coefficients (p<0.01)

Similar indexes (SI)

October 25 28 43.0 0.73 0.70 November 61 43 30.5 0.77 0.68

3.2. Coastal water quality response

The water quality of the research field is the summary of the individual response fields of all the pollution sources. So, concentration of PHs in conditions of sepa-rate discharge (1 mg/s) of 8 pollutant sources (W1, W2, R2, R3, R4, R5 & R6, R7 & W3, R8 & W4) was simu-lated, respectively. And the response fields of 8 pollutant sources are shown in Fig. 3.

The result shows that, under conditions of individual discharge of each pollutant source, the response fields of the concentration of PHs were higher in the outlet of pollutant sources, and decreased gradually towards the surrounding waters. Especially to Moshui river (R4), with the bad water exchange and terrigenous pollutant emis-sion continuously, the area of high concentration is cen-tralized nearby the river outlet.

3.3. Estimation of pollutant load environmental capacity

The environmental capacity of PHs in Jiaozhou bay was estimated based on the model constructed in this study. The calculation of environmental capacity was closely related to the process of choosing the water qual-ity control point and the target water quality criteria. Con-cerning on the locations of the pollutant sources and the area of the pollutant mixing zone, 34 water quality control points were set covering the entire area of Jiaozhou bay. Based on environmental state, types of water usage, and management policy, grade I/II seawater quality (PHs <50 µg·L-1) were set as the target water quality criteria of Jiaozhou bay.

Total load was allocated to the 8 pollution sources to satisfy the chosen target using the linear programming model (Fig. 4). The maximum load allowed, which is the environmental capacity, was approximately 1570 tons for


52

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R1 & W1

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 W2

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R2

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R3

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R4

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R5 & R6

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R7 & W3

120.1 120.15 120.2 120.25 120.3 120.35 120.4

35.9

35.95

36

36.05

36.1

36.15

36.2 R8 & W4

FIGURE 3 - PH response fields of the 8 pollution sources (W1, W2, R2, R3, R4, R5 & R6, R7 & W3, R8 & W4). (µg·L-1).

PHs per year. It indicates that Tuandao sewage treat-ment plant (W2) accounted for the largest load (about 51.4%). Dagu River (R3), Haibo River and Haibo sewage treatment plant (R8 & W4), Lianwan River and Lianwan sewage treatment plant (R1 & W1), Yang River (R2), Loushan River and Banqiaofang River (R5 & R6), and Licun River and Licun sewage treatment plant (R7 & W3) accounted for 16.1, 7.7, 7.2, 5.8, 5.1 and 3.5%, respec-tively. Moshui River (R4) accounted for the least (3.1%).

R1&W1 R2 R3 R4 R5&R6 R7&W3 R8&W4 W2 EC0

200

400

600

800

1000

1200

1400

1600

Tons

Sources

PHs

FIGURE 4 - The allocated loads of the 8 pollution sources and the environmental capacity of PHs in Jiaozhou Bay, China.

4. CONCLUSION

Based on the investigation of terrigenous pollution (rivers and sewage treatment plants) and seawater in Au-gust, October and November 2007, a transport, transfor-

mation and hydrodynamic model was constructed to esti-mate the marine environmental capacity of PHs in Jiaozhou Bay. According to the model, if the target is set to achieve water quality (grade II) in Jiaozhou Bay, the environmental capacity of PHs for one year was approximately 1570 tons. The accounts of the 8 point sources were W2 (51.4%), R3 (16.1%), R8 & W4 (7.7%), R1 & W1 (7.2%), R2 (5.8%), R5 & R6 (5.1%), R7 & W3 (3.5%), and R4 (3.1%). Thus, our research provides necessary scientific foundation to the total load control efforts in this area.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. Xianwen Bao for providing us with the hydrodynamic model (ECOMSED) in Jiaozhou Bay. The authors would like to thank Dr. Xiu-quan Wan for his assistance in modeling. We would like to express our sincere thanks. The work is supported by the Fundamental Research Funds for the Central Universities (No.201013015), Science Fund projects of Shandong Province (No.ZR2010DM005), the Scientific and Techni-cal Projects of Shandong Province on Environmental Pro-tection “the source, capacity and technology study of total control of pollutants in Shandong Province”, and National Key Technology Research and Development Program (No. 2010BAC69B01).

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Ke-qiang Li Key Lab of Marine Chemistry Theory and Technol-ogy of Ministry of Education Ocean University of China Qingdao 266100 P.R. CHINA Phone: +86-532-8203 2479 Fax: +86-532-8203 1799 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 48 – 53


54

THE EFFECTS OF REACTIVE BLACK 5 TEXTILE DYE ON PEROXI-DASE ACTIVITY, LIPID PEROXIDATION AND TOTAL CHLORO-PHYLL CONTENT OF PHASEOLUS VULGARIS L. CV. “GINA”

Armagan Kaya1, Emel Yigit2,* and Gulcin Beker Akbulut2

1 Adiyaman University, Science and Art Faculty, Department of Biology, Adiyaman, Turkey 2 Inonu University, Science and Art Faculty, Department of Biology, 44280, Malatya, Turkey

ABSTRACT

This study examined the effects of reactive black 5 (RB5) on peroxidase, lipid peroxidation and total chloro-phyll content of Phaseolus vulgaris L. cv. “Gina”. The beans were exposed to 200, 260, 338, 439, 571, 743, 965 and 1255 mg/L of RB5. Significant increase in peroxidase activity was identified in RB5-treated plants. The effects of RB5 on lipid peroxidation were analyzed. MDA con-tent of RB5-treated plants was found to be higher in all application groups, except that of 439 mg/L. In addition, it was found that total chlorophyll content increased in all applications when compared to control group. Our find-ings indicate that RB5 causes significant changes in per-oxidase activity, MDA content, and pigment system.

KEYWORDS: Lipid peroxidation, peroxidase, textile dye (Reac-tive Black 5), total chlorophyll, Phaseolus vulgaris L.

1. INTRODUCTION

At least eight more ranges of reactive dyes have been introduced by 1988. Currently reactive dyes are enor-mously used in textile industry to colour chiefly cotton, wool and polyamide fibres due to their wide variety of colour shades, high wet fastness, ease of application and brilliant colours [1]. Textile wastewaters create a potential contaminant effect on organisms when they are dis-charged to the nature without treatment. The use of wastewaters in agriculture will inevitably pose a risk for the organisms in the environment. Furthermore, dye resi-dues in wastewaters change their structure and reduce light permeability. Thus, they have negative effects on not only terrestrial organisms, but also aqueous organisms. Due to their high color density, the dyes are considered to be contaminants, even in low concentrations, and they have toxic effects on the environment [2, 3]. * Corresponding author

The production of reactive oxygen species (ROS) in plant metabolism is encouraged in stress conditions [4-6]. ROS can damage or cause complete degradation of essen-tial complex molecules in the cells, including fat mole-cules (i.e. lipids), proteins and DNA [7]. The cellular anti-oxidant system serves as a sensor of accumulating ROS [8, 9]. Any perturbation of the balance between formation and scavenging of ROS affects cell redox homeostasis. The activity of antioxidant enzymes, reflecting the ROS pool, is often used as a biomarker for various abiotic stresses [10-12].

Peroxidases (POD), (E.C. 1.11.1.7) are heme-containing enzymes able to oxidise a wide range of organic and inor-ganic compounds, using H2O2 as a co-substrate. PODs are non-specific enzymes as they can use a broad variety of electron donor substrates [13]. POD activity usually in-creases in plant tissues under various stress conditions, such as the influence of toxic elements [14-16], or attack by parasitic organisms [17].

At cellular level, lipid peroxidation is the most sig-nificant damage caused by ROS. The MDA (product of lipid peroxidation) level is regarded to be a biochemical marker for injury mediated by ROS [18-20].

Chlorophyll is a natural pigment that absorbs light energy for photosynthesis [21]. This energy used by the plants was to synthesize glucose from carbon dioxide and water. Differences in leaf chlorophyll content can be an indicator of plant vigour, and its capacity for photo-synthesis, strongly dependent on chlorophyll content [22].

The aim of this work was to estimate if textile waste-water may evoke a defense reaction of Phaseolus vul-garis. In the present study, we investigated the changes in total chlorophyll content, peroxidase activity and lipid peroxidation in Phaseolus vulgaris L. cv. Gina caused by RB5 applied in different concentrations.


2.1. Preparation of Plant Samples In this study, textile dye reactive black 5 (RB5),

Phaseolus vulgaris L. cv. “Gina” as culture plant, and


55

perlite as plant growth medium were used. The seeds of Phaseolus vulgaris L. cv. “Gina” were obtained from May Seed Company. The samples were grown in perlite containing pots using Hoagland’s solution [23]. The stud-ies were carried out in a climate room (23±2 0C, humidity 60%). Application groups were prepared in 8 different concentrations (200, 260, 338, 439, 571, 743, 965 and 1255 mg/L). The trials were performed with 3 replica-tions. The plants were irrigated every 3 days during plant growth period. In the first 3 irrigations, the plants were treated with a test solution containing the dye, and Hoa-gland’s solution was used in the subsequent irrigations. The composition of Hoagland culture solution was pre-pared according to Hoagland and Arnon. Collected sam-ples were frozen in liquid nitrogen and kept at -80 oC.

2.2. Identification of Peroxidase Activity

Polyvinylpyrrolidone (PVP; 0.5 g)) was added to 0.5 g leaf sample and homogenized in 3 ml 66 mM potassium phosphate buffer and 3 ml 100 mM KCl [24]. The homo-genate was centrifuged at 10,000 rpm and 4 oC for 10 min. To prepare a homogeneous solution, a mixture of 3 ml 0.1 M (pH 6.0) potassium phosphate buffer, 0.04 ml 0.03 M H2O2 and 0.05 ml 0.2 M guaiacol was vortexed. To 0.9 ml of this solution, 0.1 ml extract was added to initiate the reaction. The change in enzyme activity was measured spectrophotometrically for 1 min at 436 nm [25].

2.3. Malondialdehyde (MDA) Analysis

The method was performed according to Heath and Packer [26]. Leaf tissue (0.5 g) was homogenized in 1.0% 5 ml trichloroacetic acid (TCA), and the homogenate was centrifuged at 10,000 rpm for 5 min. A solution aliquot (3 ml) was boiled in a water-bath with 0.5% thiobarbi-turic acid (TBA) at 95 oC for 30 min (TBA was prepared in 20% TCA). After boiling, the specimens were cooled in an ice-bath. The final mixture was centrifuged at 10,000 rpm for 15 min, and absorbance of the supernatant was measured at 532 and 600 nm. The measurements made at 600 nm were deduced from those at 532 nm, and with 155 mM-1 cm-1 extinction coefficient, MDA amount was calculated.

2.4. Pigment Extraction and Determination

De Kok and Graham’s [27] method was employed in pigment extraction. The samples were ground in a blender for extraction, and 1 g of each was collected considering 3 repetitions for each concentration. They were ground in a glass mortar with 50 ml acetone (100%, Merck) for 10 min to be homogenized and later, they were placed into Er-lenmeyer flasks covered with foils, not to be exposed to light. Then, 100 ml of acetone was added and Erlenmeyer flasks were sealed with paraffin and placed into a shaking oven to be homogenized for 30 min. Then, they were placed in a refrigerator at 4 °C for 24 h. The samples were filtered after they had been removed from the refrigerator, and water was added at a ratio of 1/5. These samples were re-homogenized in the shaking oven for 15 min. Thereafter,

they were filtered and centrifuged for 10 min at 3,000 rpm to be homogenized. Absorbance values of the centrifuged samples were read according to Lichtenthaler and Wel-burn [28] at 662, 645 and 470 nm.

2.5. Identification of Total Soluble Protein

Total soluble protein amount was identified by Brad-ford method [29], using a microplate reader system (Mo-lecular Devices Corp., Versamax). To each well of the microplate 5 µl prepared homogenate and 250 µl Brad-ford reagent were added, and kept at a dark place and room temperature (25 oC) for 15 min. Later, absorbance change depending on color change was measured at 595 nm. Obtained OD values were compared with standard graphic values drawn using bovine serum albumin (BSA). To-tal protein amounts in the specimens were calculated using package program (Slide). The measurements were performed in three replications.

2.6. Statistical Analysis

Statistical analyses were performed using SPSS 10.0 for Windows. Duncan’s Multiple Range Test was em-ployed to determine the statistical significance of differ-ences among the means [30].

3. RESULTS

In all plant groups treated with RB5, peroxidase activ-ity significantly increased, and this increase was found to be statistically significant (p<0.05). The lowest peroxidase activity was obtained from control with 1.49 U/ mg protein, while the highest ones were from 439 and 1255 mg/L appli-cation groups with 5.70 U/ mg protein (Fig. 1).

Excluding 439 mg/L application group, MDA amount significantly increased when compared to control, and this increase was found to be statistically significant (p<0.05). The highest MDA level was 6.15 µmol MDA/g fresh weight in 1255 mg/L application group, and the lowest MDA level was 2.01 µmol MDA/g fresh weight in 439 mg/L application group. Plant growth was found to de-crease at concentrations >439 mg/L (Fig. 2).

There was also an increase in total chlorophyll of the groups treated with dye. The highest chlorophyll level was 16.65 µg/g in 965 mg/L application group while the lowest total chlorophyll amount was 14.27 µg/g in control group. There was a statistically significant change be-tween total chlorophyll content of control group and dif-ferent concentrations of RB5 (p<0.05) but no significant difference among 260 (16.36 µg/g), 439 (16.19 µg/g), 571 (16.20 µg/g) and 743 (16.15 µg/g) mg/L application groups, in terms of total chlorophyll amount (p<0.05) (Fig. 3).

In general, MDA contents increased in plant groups exposed to dye in comparison to control group. There was an increase in both peroxidase activity and MDA con-tents. These results illustrated the relationship between


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FIGURE 1 - Changes of peroxidase activity in P. vulgaris L. cv. “Gina” leaves exposed to RB5 textile dye (Vertical bars represent standard error of average of 3 replications; data followed by different letters are significantly different from each other (P<0.05) according to Duncan’s test).

FIGURE 2 - Changes in MDA levels of P. vulgaris L. cv. “Gina” leaves exposed to RB5 textile dye (Vertical bars represent standard error of average of 3 replications; data followed by different letters are significantly different from each other (P<0.05) according to Duncan’s test).

FIGURE 3 - Changes in total chlorophyll content of P. vulgaris L. cv. “Gina” leaves exposed to RB5 textile dye (Vertical bars represent standard error of average of 3 replications; data followed by different letters are significantly different from each other (P<0.05) according to Duncan’s test).


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peroxidase activity and MDA content in plants of RB5 applications. Besides, dye usage significantly increased the total chlorophyll content in bean plant leaves elucidat-ing that there is a relationship between peroxidase activ-ity, MDA and total chlorophyll content (Figs. 1-3).

4. DISCUSSION

The dyes used in textile industry undoubtedly have certain direct and indirect effects on biotic and abiotic en-vironment. The wastes disposed from the factories contain some toxic organic and inorganic substances. To control harmful chemicals in aqueous environment, some physical, chemical and biochemical parameters are used. Chemical methods are not sufficient alone to identify the potential harmful effects of the chemicals in aqueous environment. Their toxic effects in wastewaters or their synergistic effects can only be identified with toxicity tests. A toxic-ity study was carried out in a chemical dye production facility of Turkey, and it was reported that the wastewater collected from this facility had a potential toxicity due to its chemical composition. It was reported that the color of the dye in wastewater and the increase in Pb, Cr, Fe+2, Cd, Zn and total hydrocarbon levels caused this toxicity [31]. Moawad et al. [32] evaluated the effects of textile wastes on root length, genotoxicity and germination criteria in plants. Their results showed that high concentrations of dyes were more toxic to seed germination as compared with lower ones. However, the low concentrations of the tested dyes adversely and significantly affected the shoot-ing percentage.

Carias et al [33] studied the effects of antioxidant and detoxification enzymes of Phragmites australis, in the degradation of an azo dye, acid orange 7 (AO7). A sub-surface vertical flow-constructed wetland, planted with P. australis was used to test the plants response to AO7 exposure at two different concentrations (130 and 700 mg L-1). POD activity was not greatly affected during these experiments [33]. The studies on reactions of industrial wastes on plants did not mainly focus on the evaluation of the reactions to textile wastes.

The present study evaluated the effects of different RB5 dye levels on plant growth, peroxidase activity, MDA and total chlorophyll content in Phaseolus vulgaris L. cv. “Gina”. In plant groups treated with RB5, plant growth of 200 mg/L treatment was found to be close to that in con-trol group; however, in the groups treated with 260 and 338 mg/L dye, a lower growth was observed compared to control. In the group treated with 439 mg/L dye, growth was higher than that of control group but growth was lower at concentrations >439 mg/L (Figs. 4 and 5).

Antioxidant system activity increases in many stress types depending on the resistance. As a response to abiotic stress, antioxidant enzymes were found to be at high con-centrations, and these enzymes have a general role in gain-ing tolerance to different environmental conditions by the plants. Under stress conditions, high antioxidant level can prevent damage. Peroxidase is one of the antioxidant plant enzymes (heme-containing glycoprotein), and its activity increases in response to various stress conditions which is a reaction generally shown by the plants [34, 35].

FIGURE 4 - Changes in growth levels of P. vulgaris L. cv. “Gina” exposed to RB5 textile dye (0 (control) -439 mg/L).


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FIGURE 5 - Changes in growth levels of P. vulgaris L. cv. “Gina” exposed to RB5 textile dye (571-1255 mg/L).

In the present study, peroxidase activity in application

groups significantly increased when compared to control. This increase was found to be statistically significant (p<0.05) (Fig. 1). In a study analyzing the effect of tem-perature on peroxidase activity of strawberry plant, it was found that temperature stress increased peroxidase activ-ity. High temperature-stress causes some physiologic, biochemical and molecular changes like protein degrada-tion in plant metabolism, irregularity of membrane inte-gration, or degradation of lipids. The researchers associ-ated peroxidase enzyme with the emergence of physiol-ogic damage created in plants by the temperature stress, and reported that peroxidase activity increased with high temperature stress [34]. These results are in parallel to the findings herein, using RB5 (Fig 1).

MDA formation is a general determinant of lipid per-oxidation. MDA content was found to be higher than the control, except for 439 mg/L, and this increase was found to be statistically significant (p<0.05) (Fig. 2). In a num-ber of researches, fluctuations of MDA content under stress conditions were evaluated [36, 37]. Similar to our findings, these studies analyzed the effect of 40 µM chromium on MDA level at 48, 96 and 144 h. Obtained values were not in parallel with the increase of Cr applica-tion time. At 48 h, MDA content was found to be 7.77 mmol g-1 fresh weight; this value increased to 10.07 mmol g-1 fresh weight after 96 h, and again decreased to 6.65 mmol g-1 fresh weight at 144 h [37].

When the effect of RB5 on pigment system was eval-uated, it was found that total chlorophyll amount signifi-

cantly increased (Fig. 3). This situation is also important in terms of morphology where the dyes give different reactions to plant growth at different levels. Zengin [38] reported that an increase of proline in the leaves of bean seedlings exposed to nickel and chromium occurred, as well as a decrease of chlorophyll (a+b) and total protein contents. In another study, Doğanlar et al. [39] deter-mined that pigment and total soluble protein contents of all tomato cultivars were significantly decreased by salt stress depending on time intervals and salt concentrations. Decreases of pigment and total soluble protein contents were more evident in Hazera under short-time salt expo-sure. Pigment content of Argy plants were less affected by salt concentration and exposure time [39]. The researchers who questioned the effect of chromium on chlorophyll applied 10, 40, 80 and 160 µM chromium and analyzed the change after 48 h. The values at 10 µM concentration were found to be higher than that of control. However, a decrease in chlorophyll content after 40 µM was observed [37]. Our findings show parallelism with this study.

4. CONCLUSION

These results pointed out that there is a relationship between MDA content and peroxidase activity. Thus, the used RB5 is considered to be an important stress factor. Considering the negative effects of increasing RB5 con-centrations on plant growth, it becomes evident that the organizations in textile industry should show due dili-gence to avoid direct disposal of dye wastes to environ-


59

ment without treatment. This study indicates that further studies should be carried out in testing the effects of wastewaters containing dyes disposed to the nature.

ACKNOWLEDGEMENTS

This work (Project No. BAP 2005-59) was financially supported by Inonu University.

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[20] Monterio, M.S., Santos, C., Soares, A.M.V.M. and Mann, R.M. (2009) Assessment of biomarkers of cadmium stress in lettuce. Ecotoxicol. Environ. Saf. 72, 811-818.

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[32] Moawad, H., Abd El-Rahim, W.M. and Khalafallah, M.A. (2003) Evaluation of biotoxicity of textile dyes using two bi-oassay tests. J. Basic Microb. 43(3), 218-229.

[33] Carias, C.C., Novais, J.M. and Susete Martins-Dias (2008) Are Phragmites australis enzymes involved in the degrada-tion of the textile azo dye acid orange 7? Bioresource Tech-nology 99 243-251.

[34] Gulen, H. and Eris, A. (2004) Effect of heat stress on per-oxidase activity and total protein content in strawberry plants. Plant Sci. 166, 739-744.

[35] Candan, N. and Tarhan, L. (2003) Changes in chlorophyll-carotenoid contents, antioxidant enzyme activities and lipid peroxidation levels in Zn-stressed Mentha pulegium, Turk. J. Chem. 27, 21-30.

[36] Gulen H., Cetinkaya C, Kadıoglu M, Kesici M., Cansev A and Eris A. (2008) Peroxidase activity and lipid peroxidation in strawberry (Fragaria X ananassa) plants under low tem-perature. J. Biol. Environ. Sci., 2(6), 95-100.

[37] Sinha, S., Saxena, R. and Singh, S. (2005) Chromium in-duced lipid peroxidation in the plants of Pistia stratiotes L.: role of antioxidants and antioxidant enzymes, Chemosphere. 58, 595-604.

[38] Zengin, F.K. (2009) The effects of nickel and chromium on the contents of chlorophyll, protein, abscisic acid and proline in bean seedlings. Fresenius Environ. Bull. 18(12) 2301-2305.

[39] Doganlar, Z.B., Demir, K., Basak, H., Gul, I. (2010) Effects of salt stress on pigment and total soluble protein contents of three different tomato cultivars. Afr. J. Agric. Res. 5(15), 2056-2065.

Received: July 18, 2011 Revised: September 07, 2011 Accepted: September 30, 2011 CORRESPONDING AUTHOR

Emel Yigit Inonu University Department of Biology Science and Art Faculty 44280 Malatya TURKEY Phone: (422) 377 3763 Fax: (422) 341 0037 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 54 - 60


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HISTOPATHOLOGICAL ALTERATIONS IN GOBIUS NIGER

(BLACK GOBY) DUE TO POLLUTION OF THE IZMIR BAY

Selma Katalay1,*, Ersin Minareci1, Ibrahim Tuğlu2 and Helmut Segner3

1 Celal Bayar University, Science and Art Faculty, Department of Biology, 45140 Muradiye- Manisa, Turkey. 2 Celal Bayar University, Medicine Faculty, Department of Histology-Embriology, 45030 Manisa, Turkey.

3 University of Bern Center for Fish and Wildlife Medicine Dept. Animal Pathology Länggass-Strasse 122, 3012 Bern Switzerland.

ABSTRACT

The present study aims to investigate the possible im-pact of aquatic pollution of Izmir Bay on resident fish. Gobius niger were sampled from two stations of the Izmir Bay, and gills and liver were examined histopathologi-cally. The presence of pathological lesions in gills and liver of the fish as possible result of pollutant exposure was evaluated by semiquantitative analyses. The only histopa-thological changes found were hyperplasia, hypertrophy (12.5%, 18.8%), epithelial degeneration (18.8%, 25%) in gill and fatty livers (12.5%, 25%) of the fishes from both study sites, respectively. The moderate manifestation of pathological lesions in organs of Gobius niger from the presumably Bostanlı site may be either the result of sub-stantial protective and detoxification capabilities of this fish species, or from recent measures to reduce water pollution in Izmir Bay such as the installment of wastewater treat-ment plants.

KEYWORDS: Gobius niger, liver, gill, histopathology 1. INTRODUCTION

Human population increases and industrial develop-ment has been the major cause of coastal contamination around the world during recent decades [1]. The subse-quent accumulation of xenobiotic chemicals in sediment, seawater or aquatic organisms has been shown to be di-rectly linked to adverse health effects in biota and people [2, 3]. Many of the pollutants can build up in the food chain and are responsible for adverse effects in aquatic organisms [4].

Fish are relatively sensitive to changes in their sur-rounding environment. Fish health may therefore reflect and give a good indication of the health status of a spe-cific aquatic ecosystem. Therefore, indicator fish are widely used to evaluate the health of aquatic ecosystems and * Corresponding author

physiological changes serve as biomarkers of environ-mental pollution [5]. In the present study, we used a ma-rine teleost, the black goby Gobius niger, a small species widespread over the Mediterranean as an indicator of pollution of Izmir Bay. In order to conduct a proper histo-logical investigation on a specific organ of an exposed specimen, it is important to examine in parallel the histol-ogy of the same organ of healthy, unexposed (control) specimen, assumed to reflect the normal histological structure of that organ. This enables to identify deviations or abnormal occurrences in a histological analogy [6]. Early toxic effects of pollution may only be evident on cellular or tissue level before significant changes can be identified in fish behavior or external appearance. Histo-logical analysis appears to be a very sensitive parameter and is crucial in determining cellular changes that may occur in target organs, such as the gill, liver, kidney, mus-cle and intestine.

Gills are the first target of waterborne pollutants as they are directly exposed to the external environment. It is well known that changes in fish gill morphology and physiology are among the most commonly recognized responses to environmental pollutants [7, 8]. Secondary lamellar fusion and epithelial cell hyperplasia were de-tected in the gills of fish exposed to toxicants. Moreover, hyperplasia of mucous and chloride cells, deformed bron-chial cartilage, severe and diffuse aneurysms of lamellae, and edema at the base of the secondary lamellae were also found in gill of fish from heavily polluted areas. The liver, as the major organ of metabolism, is a major accumula-tion and/or target organ for many toxicants absorbed from the environment and, thus, liver lesions are often associ-ated with aquatic pollution. Several histopathological alterations including inflammation, glycogen deficiency, macrophage aggregates, and diffuse fatty change were observed in the liver of many fish species from contami-nated aquatic habitats. Because of the growing evidence of a causal relationship between environmental contami-nation and the occurrence of toxicopathic liver lesions in fish [9, 10], studies on liver histopathology in fish have increasingly been incorporated in national marine biologi-cal effects monitoring programs [10, 11]. Fish liver his-tology could therefore serve as a tool for studying the


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interactions between environmental factors such as pol-lutants and hepatic structures and functions [12-14]. The toxic effects of the pollution on the kidney can also lead to dilation of glomerular capillaries, mesangial edema, an adhesion between visceral and parietal layers of Bow-man's capsule, filling of Bowman's space, shrinkage of glomeruli and destruction or disruption of the nephric tubular cells [15]. Erosion of the top plate of intestinal villi, enlargement and degeneration of epithelial cells including their membrane pathology are accepted as his-topathological alterations [12, 13].

Izmir Bay is a coastal system located on Aegean Sea. The bay has a large surface area and water capacity, a total length of 64 km and opens in the Aegean Sea. The depth of water in the outer bay is about 70 m and de-creases towards to the Inner Bay. The bay has been di-vided into three sections as outer, middle and inner, ac-cording to the physical characteristics of the different water masses [16, 17]. The inner bay is considerably small in area (57 km2) and shallow in depth (max. 15 m). It had received the majority of domestic and industrial wastewaters before the construction of wastewater treat-ment plants. This section of the bay still receives some inflow of fresh water from several creeks which are mostly polluted by industrial wastewaters. Because of limited water exchange with the Outer Bay and Aegean Sea, pollution of the Inner Bay had reached unacceptable levels. Eutrophication of the Inner and the Middle Bay had started and spread progressively to the outer part of the Bay. Red-tide occurrence was reported to have in-crease in frequency in last decade [18]. Especially waste-water treatment improves the water quality, but sediment

does not respond to this treatment as fast as water column. Improvement in the quality of bottom water and sediment is the evidence of the recovery of the whole ecosystem of the Izmir Bay [18].

During a recent fish kill at Izmir Bay, biochemical investigations on feral fish showed high 7-ethoxy-resorufin-O-deethylase (EROD) activities and elevated glutathione (GSH) levels, probably due to the presence of organic pollutants [19]. However, histopathological inves-tigations were not done yet, although this could provide a more integrative assessment of the pollution impact than the more selective biochemical markers. The aim of this study, which was part of an integrated environmental evaluation of the Izmir Bay, to perform histopathological examination of several organs such as gills, liver, kidney, muscle and intestine of fish from Izmir Bay in order to reveal possible relationships between environmental pol-lution and biological effects .


In this study, Gobius niger, known as an indicator species in the polluted areas of the Mediterranean Sea, was chosen as study species [20]. A total of 32 Gobius niger (16 fish from each station) aging between 1-3 years were caught at two stations, Bostanlı (inner bay) and Urla (outer bay) in the Izmir Bay during four seasons between October 2005 and October 2006 (Fig. 1). Gills, liver were dissected and fixed in buffered 10% formalin solution for approximately 12 h, dehydrated through a increasing ethanol series, immersed in xylene and were finally em-

FIGURE 1 - The Izmir Bay of Turkey and localization of the sites where Gobius niger were collected.


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bedded in paraffin wax at 56oC. Paraffine blocks were cut serially 5µm using a rotary microtome (RM 2145, Leica Co., Nussloch Germany). Sections were stained with hematoxylene-eosine and analyzed under light micros-copy. Pathological lesions were semi-quantitatively scored as no (+1), few (+2), moderate (+3), strong (+4) and se-vere (+5) damage to the tissue. Data are presented as mean±standard deviation. Analyses were done by Graph-pad statistical software [21-22].


The obtained results of morphometric were presented that gills from both site mostly had normal structure of lamellae, wide interlamellar space, filament, secondary lamellae with basal epithelial thickness. The lamellae was lined by a squamous epithelium and between the lamellae, the filament is lined by a thick stratified epithelium con-stituted by different cellular types. There were no clear histopathologic alterations in gills, neither in fish from the Urla and the Bostanlı site (Fig. 2). Typical gill lesions such as changes in cytoplasm and nucleus, accumulation of deposits, vacuolization, atrophy, necrosis, inflamma-tion, hyperplasia, hypertrophy, hemorrhagia and edema were not seen. We found gill hyperplasia and hypertrophy

in 2 out of 16 fish from the Urla site and 3 out of 16 fish of the Bostanlı site. Moderate epithelial degeneration was seen for 3 out of 16 gills from fish of the Urla site whe-reas this was observed with 4 out of 16 gills from the Bostanlı site.

The hepatic morphology was similar in fishes from the both site (Fig. 3). At both sites, we could not find histopathological alterations such as variable glycogen depletion, fat vacuolation and hepatocellular necrosis. There was only one macroscopically fatty liver in the Bostanlı site. Microscopically, 2 out of 16 gobies from the Urla site and 4 out of 16 gobies of the Bostanlı site showed fatty liver.

Semi-quantitative analyses showed that the intensity of histopathological changes in both liver and gills at the two study sites were similar to each other. (Fig. 4)

The present study investigated whether Black Goby from a Bostanlı site in Izmir Bay display more or more severe histoptahological changes of liver and gills than conspecifics from a Urla site in Izmir Bay. Since Black Goby is living on sediment, this species is of potential use to reflect sediment contamination by anthropogenic chemi-cals. Further, Black Goby is helpful indicator for the eco-nomic species. Gills and liver were selected as target organs, since they are the main sites for heavy metal

FIGURE 2 - The microscopic gill of Gobius niger samples from Urla site (a, b) and Bostanlı site (c,d). Scale bar 10 µm.


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FIGURE 3 - The microscopic liver of Gobius niger samples from Urla site (a, b) and Bostanlı site (c,d). Scale bar 10 µm.

FIGURE 4 - The toxic effect of pollutant on the gill and liver from Gobius niger. There was not significant (p>0.05) changes in tissues be-tween Urla and Bostanlı sites.


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accumulation and effect. The main findings from this study are (a) that only few and mild pathological changes were present, and (b) that prevalence and intensity of pathological changes are not different between Black Goby from the Bostanlı and the Urla site.

Fish gills are very sensitive to changes in the compo-sition of the environment and are an important indicator of waterborne toxicants. Consequently, injury to gill epi-thelium is a common response observed in fish exposed to a variety of contaminants. Gill lesions as indicators of exposure to contaminants have previously been used in numerous laboratory and field studies around the world [23]. The severity of damage to the gills depends on the concentration of the toxicant and the period of exposure [24-25]. In general, it is known that the most common tissue alteration due to pollution is the displacement of the epithelial layer of the secondary lamellae. The epithelial lifting is often accompanied by edematous condition. Cellular hyperplasia and ballooning dilatation of the sec-ondary lamellae described by previous studies are impor-tant features in gills affected by pollution. Circulatory anomalies and hemorrhage are also manifested in affected gills with an indication by severe congestion of blood spaces by erythrocytes. Sometimes presence of leukocytes particularly in the lamellae could be observed together with the presence of mucin, depicting an inflammatory reaction [26]. The gill epithelium is the site of gas ex-change, ionic regulation, acid-base balance, and nitroge-nous waste excretion by fishes. Various environmental pollutants especially a wide variety of aquatic pollutants such as organochlorines, petroleum compounds, organo-phosphates, carbonates, miscellaneous herbicides, acidifi-cation, nitrogenous compounds, heavy metal salts, and organic xenobiotics have been found to affect the mor-phology of the gill epithelium. Gill morphological pa-thologies under adverse water conditions commonly in-clude hyperplasia with lamellar fusion, epithelial hyper-trophy, telangiectasia marked dilation of terminal blood vessels, edema with epithelial separation from basement membranes, general necrosis, and/or epithelial desquama-tion. Also, less severe morphological changes have been described such as partially detached, swollen and degen-erating chloride cells, decreased height of lamellar cell ridges, and appearance of vacuolated epithelial cells [27]. Goby from Izmir Bay did not show such changes in the gills, so that we have no indication of adverse water con-ditions impacting the fish. This finding was unexpected since previous studies on water quality of Izmir Bay re-vealed significant pollution by both xenobiotics and met-als. Again, what is needed are integrated studies measur-ing contaminant burdens as well as tissue histopathology especially for gill in order to better understand the relation between pollution and fish health in Izmir Bay.

The present study also investigated the pollution ef-fect on the liver of the G. niger. Liver as a main site for heavy metal accumulation was used as a target for pollu-tion. Fish liver histology has a characteristic normal struc-

ture. Light microscope observations show that hepato-cytes in fish livers are arranged as tubules or cords. Be-tween the neighboring sinusoids, the hepatocytes are arranged as plates. The hepatic parenchyma of fish is not displaying acini or lobules as known from mammalian liver, and the hepatocytes are polygonal shaped cells, appearing hexagonal, often weakly basophilic. The cell membrane of individual hepatocytes is clearly visible and normal liver is pleomorphic. It was found in literature that the most common lesions in the liver as a toxic effect were vacuolar degeneration, focal areas of necrosis and aggregations of inflammatory cells in the hepatocytes, dilation and thrombosis formation in central veins, and congestion in blood sinusoids, hemolysis in hepatic blood vessels. The prevalence of hepatic lesions involving cellu-lar alteration registered in this study is not similar to those reported by previous studies [28-29]. Species in Izmir Bay point to significant chemical exposure of fishes in the Bay. Hepatic lesions of fish under toxicant exposure are species dependent [29]. Liver hemosiderosis in fish has been associated with the presence of organic pollutants in the environment [30]. High fat content in hepatocytes with nuclei displaced to the cell periphery, as observed in the present study, may result from toxic impact [31-32] but can also be the consequence of nutrition or water temperature, or may even be a species-specific feature in goby [33-34]. Symptoms like lymphocyte infiltration and pycnosis as reported repeatedly to be associated with toxic insult to fish liver [35-36] were not found in our fish from both study sites. Liver damage involving cellular degeneration and granuloma, as well as an increase in the number of hepatocytes and hyperemia were not found neither. Cellular alteration foci as preneoplastic lesions or precursors in hepatic neoplasms histogenesis were not seen in this study. There was only a parasite infection in one liver from Bostanlı site. The degree of association between contaminants and lesion prevalence, and ex-plained variance, reported in previous studies were differ-ent depending on circumstances occurred study area. Therefore, care has to be taken to conclude from the pres-ence of liver pathological lesions on pollution impact as cause of the lesions [9]. The low to moderate prevalence and intensities of liver lesions as observed in our study do not alert for significant pollution stress to goby at the two study sites in Izmir Bay.

Izmir Bay is considered to experience substantial

chemical pollution. For instance, it has been shown that the level of hydrocarbons in sediment of Izmir Bay was found as low or moderate and some metals were found high due to industrial and domestic waste [16]. Küçük-sezgin et al. determined metal concentrations ranged between Hg: 0.05-1.3, Cd: 0.005-0.82, Pb: 14-113 and Cr: 29-316 Ag g-1 in the sediments [16]. Their results showed significant enrichments during sampling periods from Inner Bay. Outer and middle bays show low levels of heavy metal enrichments. Also, the mean concentrations showed ranges of 0.01-0.19 and 0.01-10 µM for phos-


66

phate, 0.10-1.8 and 0.12-27 µM for nitrate and nitrite, and 0.30-5.8 and 0.43-39 µM for silicate in the outer and middle–inner bays, respectively [16].

Some physico-chemical parameters in the vicinity of Urla Harbour Seasonal temperature were measured by Durallı and Egemen [37] as follows: temperature 13.5-27.5°C, dissolved oxygen 5.8-9 mg L-1, salinity ‰ 32.18-38.40, pH 8.01-8.30, turbidity 2.85-7.88 m, nitrite 0-1.08 µg.at L-1, nitrate 0-7.16 µg.at L-1, ammonium 0-7.97 µg.at L-1, silicate 0-11 µg.at L-1, phosphate 0-1.72 µg.at L-1, chlorophyll-a 1.59-11.89 µg L-1, anionic detergent 0.01-0.05 mg L-1, burnable substancel in sediment %1.59-11.

PAHs are known to induce liver lesions in fish [9]. In a study by Moore et al. [38], goby and sturgeon showed a relation between cytochrome P4501A (CYP1A) expres-sion, tissue histopathology and contaminant burdens in fish and sediment. Gobius niger in our study, however, did not show evidence of contaminant-related histopa-thologic in the livers. We could not find these types of severe symptoms in our liver from both sites. Major histo-pathological alterations as reported for fish from polluted sites in other studies such as variable glycogen depletion, fat vacuolation and occasional hepatocellular necrosis near parasitic lesions, basophilic hepatocytes and hepato-cellular necrosis were not seen in our samples. There was not severe inflammatory infiltrates throughout liver pa-renchyma neither. We found some of these symptoms in some fishes less than 25% of all samples from the both sites. Our samples were usually mature females. All these observations showed that there were minor changes in liver from both sites. However, previous studies in many edible fish in the Izmir Bay showed high EROD and GSH activity related to the presence of organic pollutants [19]. Unfortunately, those studies did not include histological investigations. In contrast, in our study we do not have the information on body burdens or EROD activities. Thus, we cannot conclude whether the absence of liver patho-logical lesions in Black Goby of the polluted site is due to absence of significant contaminant or other factors. The discrepancy in our observation an the general believe on high PAH pollution in Izmir Bay should trigger further studies to better understand the impact of organic pollu-tion on the fish fauna in the Bay, which has been declared a refuge for the economic fishes.

4. CONCLUSION

Our study aimed to investigate the histological status of gill and liver of the Gobius as an indicator of possible biological effects of pollution in Izmir Bay. The present study represents an important observation on toxic effect of pollution on fish liver from Izmir bay showing that this is not the case [16, 39]. This observation may be ex-plained by either very effective detoxification ability of the fish or cleaning of the Bay by the water waste treat-ment plant. However, the present histopathological results

need to be verified by chemical analytical studies on wa-ter, sediment and fish as well as by analyses of molecular and biochemical biomarkers. Still, this study provides baseline data for histopathology in fish from the Izmir Bay that can be compared to contaminant levels in the future. These results require further confirmation with advanced molecular techniques to understand the safety of economic fish on human health.

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[38] Moore, M.J., Mitrofanov, I.V., Valentini, S.S., Volkov, V.V., Kurbskiy, A.V., Zhimbey, E.N., Eglinton, L.B. and Stege-man, J.J. (2003) Cytochrome P4501A expression, chemical con-taminants and histopathology in roach, goby and sturgeon and chemical contaminants in sediments from the Caspian Sea, Lake Balkhash and the Ily River Delta, Kazakhstan. Mar. Pollut. Bull. 46, 107–119.

[39] Uluturhan, E. and Kucuksezgin, F. (2007) Heavy metal contami-nants in Red Pandora (Pagellus erythrinus) tissues from the East-ern Aegean Sea, Turkey. Water Res. 41, 1185–1192.

Received: March 28, 2011 Revised: September 06, 2011 Accepted: September 30, 2011 CORRESPONDING AUTHOR

Selma Katalay Celal Bayar University Science and Art Faculty Department of Biology 45140 Muradiye- Manisa TURKEY E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 61 - 67


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MUTUAL RELATIONSHIP OF HENRY’S LAW CONSTANTS AND AQUEOUS PHASE CONCENTRATIONS FOR BENZENE,

TOLUENE AND O-XYLENE AT 30 º C

Roman Tandlich1,* and Bongumusa M. Zuma1

1Environmental Health and Biotechnology Research Group, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, P.O. Box 94, Rhodes University, Grahamstown 6140, South Africa

ABSTRACT

Dimensionless Henry's law constant in water at 30 °C ranged from 0.272 ± 0.015 to 0.340 ± 0.008 for benzene as the equilibrium aqueous phase concentrations ranged from 0.546 ± 0.003 to 329 ± 1 mg/dm3. A decrease in the Henry’s law constant to 0.190 ± 0.002 was recorded be-tween 329 ± 1 and 1370 ± 3 mg/dm3, but the results of the Dixon test at 1 % level of significance indicated that this value was a statistically significant outlier. Therefore Henry’s law constant of benzene was independent on the dissolved aqueous phase of benzene and the average value of 0.312 ± 0.022 is recommended for practical purposes. For toluene, the equilibrium aqueous phase concentration ranged from 3.42 ± 0.05 to 420 ± 4 mg/dm3 and dimen-sionless Henry's law constants varied between 0.170 ± 0.011 and 0.321 ± 0.007. In the case of o-xylene, equilib-rium aqueous phase concentrations ranged from 3.03 ± 0.03 to 123.9 ± 0.3 mg/dm3 and Henry's law constants ranged from 0.120 ± 0.004 to 0.268 ± 0.008. Using Kruskal-Wallis analysis of variance by ranks and the Mack-Wolfe test for ordered alternatives, the Henry's law constant increased up to 6.78 ± 0.04 mg/dm3 for toluene. For o-xylene, Henry's law constant was independent of dissolved concentration up to 6.96 ± 0.05 mg/dm3. Henry’s law constants decreased with increasing equilibrium aqueous phase concentration at higher values for toluene and o-xylene. Hydrogen bonding between water molecules and the aromatic hydrocarbon molecules provides a possible explanation for the observed trends.

KEYWORDS: aromatic hydrocarbon; concentration dependence; volatilisation


1. INTRODUCTION

Benzene, toluene and o-xylene are intermediates of cumene production [1]; they are found in fossil fuels [2] and in gasoline and diesel exhausts [3]. Contributions from anthropogenic sources, such as leakages from fuel storage tanks [4] and evaporation from soil [5], account for the majority of the compounds’ presence in the environment and human exposure. Inhalation is a major route of human exposure to all three hydrocarbons [6-8]. Relevant toxico-logical predictions are often based on the use of different forms of the Henry’s law constant [9]. Its first form is de-fined in Equation (1).

SAT

SAT

CPH = (1)

In Equation (1), H is the thermodynamic Henry’s law constant (Pa.m3.mol-1), while PSAT is the vapour pressure of the studied compound at a given temperature (Pa) and CSAT is its aqueous solubility at the same temperature (mol. m-3). In environmental sciences, the dimensionless form of the Henry’s law constant (KAW) is often preferred. Equation (2) contains the definition of this parameter.

ABSAW TR

HK×

= (2)

In Equation (2), R is the universal gas constant equal to 8.314 J.K-1.mol-1; and TABS is the absolute temperature at which the measurement is conducted (K). As the satu-rated vapour pressure and the compound aqueous solubil-ity are functions of temperature [10], i.e. H and KAW change with varying temperature [10]. Most environmental pre-dictions and modelling work to date have been done using Henry’s law constants at 25 ºC. After an extensive litera-ture review, Shiu and Ma [10] recommended that the fol-lowing H values be used in modelling and other applica-tions: 551 Pa.m3.mol-1 for benzene, 660 Pa.m3.mol-1 for toluene; and 557 Pa.m3.mol-1 for o-xylene. Using Equation (2), the corresponding values of KAW are equal to 0.222 for benzene, 0.266 for toluene and 0.225 for o-xylene.


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South Africa is a semi-arid country where the ambi-ent temperatures often surpass at 25 ºC [11]. Taking 30 ºC as an example and using the review of Shiu and Ma [10], the H values at 30 ºC have been reported to range from 638 to 741 Pa.m3.mol-1 for benzene, from 774 to 835 Pa.m3.mol-1 for toluene; and from 634 to 637 Pa.m3.mol-1 for o-xylene. Using Equation (1) and Equation (2), the respective KAW values were calculated to be between 0.253 and 0.294 for benzene, between 0.307 and 0.331 for toluene; and be-tween 0.251 and 0.252 for o-xylene. Comparing these values with the Henry’s law constant values mentioned above, it can be seen that the increase of 5 °C leads to and increase in the KAW values of 13-32 % for benzene, of 15-24 % for toluene; and approximately of 12 % for o-xylene. Therefore human exposure prediction made using data at 25 ºC may be inaccurate for ambient conditions in South Africa, thus H and KAW measured at higher tem-peratures are required.

According to Shiu and Ma [10], the CSAT values at 30 °C have been shown to vary from 21.6 to 29.1 mol/m3 for ben-zene and from 6.5 to 10.0 mol/m3 for toluene. No aqueous solubility was found for o-xylene at 30 °C. This leads to a 34 % variation in the solubility data for benzene and a 53 % variation for toluene, depending on the source of information. At the same time, the PSAT values will de-pend on the information source and the equations used to derive it/calculate it [10]. Therefore if H and KAW at 30 ºC are estimated based on the literature data for PSAT and CSAT, using Equations 1 and 2, the results can be highly variable and potentially lead to erroneous human exposure predic-tions in South Africa.

Methods used for the measurement of H and KAW in-clude the vacuum-line methods of O’Farrell and Wag-horne [12], the extended Equilibrium Partitioning in Closed Systems (EPICS) [13], EPICS with solid-phase microextrac-tion [14]; the headspace-concentration method of Cheng et al. [15], the differential headspace gas chromatography method of Chai and Zhu [16], the static-headspace-phase-ratio method [17], the direct phase analysis [18]; and the constant headspace composition method of Lodge and Danso [19]. Variability in the published Henry’s law con-stants for the three hydrocarbons studied here originates from poor quality assurance during the measurement of H/KAW in many studies. An example can include the man-ual sampling of the vial headspace with a syringe that has not been pre-heated to the temperature at which H or KAW are measured [19]. Thus to maximise the precision and accuracy of the Henry’s law constant values and related toxicological predictions, the measurement method used must allow for maximum quality control throughout the measurement process. Therefore the authors chose the method of Cheng et al. [15] for their experimental work in this study, after some modification and careful considera-tion of available options.

Other factors besides the measurement technique and the temperature can influence Henry’s law constant val-ues. The aqueous phase concentration of the analyte is one

such parameter [20, 21]; and its influence is often ne-glected in experimental data gathering, due to the assump-tion of the validity of the Henry’s law [19]. In an ideal case, this assumption should be verified at several dis-solved and vapour-phase concentrations over the solubil-ity range of the studied compound [21]. Reports on this topic are, however, scarce to date. An automated version of the technique of Cheng et al. [15] allows for the as-sessment of the relationship of the aqueous phase concen-tration and the H/KAW values. The current study presents results of such an assessment at 30 °C as a model ambient temperature in South Africa for a wide range of aqueous phase concentrations of benzene, toluene and o-xylene.


2.1. Theory

In the method of Cheng et al. [15], a known total amount of an aromatic hydrocarbon (mT) is spiked into a gas chromatography (GC) headspace vial with known volumes of the aqueous and gas phases. The vial is imme-diately sealed and incubated at 30 ºC. At equilibrium, the hydrocarbon concentration is measured in the gas phase (Cg); while KAW and the aqueous phase concentration (CL) are subsequently calculated using the compound’s mass balance. Therefore for a given hydrocarbon, the equilib-rium distribution inside a given GC headspace vial is ex-pressed using Equation (3).

LLggT VCVCm ×+×= (3)

In Equation (3), mT is the total amount of benzene, to-luene or o-xylene spiked into the particular GC headspace vial during sample preparation (mg). Cg is the equilibrium gas phase concentration (mg/dm3) and CL is the equilib-rium aqueous phase concentration of the hydrocarbon in question (mg/dm3). Vg,L are the volumes of the gas phase/headspace (subscript g; equal to 0.012 dm3) and the aqueous phase (subscript L; equal to 0.010 dm3), respec-tively. The GC headspace vial is leak proof and so mT is constant throughout the experiment. The KAW values are defined in Equation (4).

L

gAW C

CK = (4)

KAW can be expressed as a function of experimental values by substituting Equation (4) into Equation (3), as shown in Equation (5).

ggT

LgAW VCm

VCK×−

×= (5)

The value of mT is known, constant and verified using liquid-liquid extraction, and Vg and VL are known. Values of Cg are determined using GC/FID. Equation (3) can be then used to calculate CL and Equation (5) can be used to calculate the values of KAW.


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2.2.Chemicals and consumables

The following chemicals and consumables were pur-chased from Sigma-Aldrich (Johannesburg, South Africa): benzene, toluene, o-xylene, methanol, ethyl acetate, Na2SO4, 10 cm3 glass pipettes, 2 cm3 clear glass GC vials with screw caps and the PTFE-lined silicone septa, 10 mm3 Hamilton syringe, 10 cm3 Fortuna optima glass syringe, 10 and 20 cm3 GC headspace vials (total volumes cali-brated at 12.0 and 22.0 cm3). The MilliQ water purifica-tion system was purchased from Microsep (Port Eliza-beth/Johannesburg, South Africa). Anatech Technologies (Sloane Park, South Africa) was the supplier of stainless steel magnetic crimp top caps with the PTFE-lined silicone septa (referred to as crimp caps in further text). Gases for the GC/FID analyses were of instrument grade (purity was 99.999%) and they were purchased from Afrox-Linde Gas (Port Elizabeth, RSA).

2.3. Preparation of samples for KAW measurement

Stock solutions of individual hydrocarbons were pre-pared by accurately weighing out and completely dissolv-ing the required amount of the neat compound in metha-nol. Then 10.00 (± 0.03) cm3 of MilliQ water was accu-rately pipetted into a 20 cm3 GC headspace vial and the neck of the vial was covered with aluminum foil. Vials were placed on ice for 15 minutes and the aqueous phase was spiked with the stock solution of the individual hy-drocarbon using a 10 mm3 Hamilton syringe. The vials were immediately sealed with a crimp cap and covered with aluminum foil to prevent photodegradation of hydro-carbons. The working solution concentration ranges were as follows: 0.94 - 1580 mg/dm3 for benzene, 4.58 - 511 mg/dm3 for toluene, and 3.94 - 148 mg/dm3 for o-xylene. Five replicates of two concentrations within these inter-vals were prepared to determine of the time period re-quired for the establishment of the gas-liquid distribution equilibrium. The same number of samples was prepared at each concentration level (10-14 concentration levels in total) for the measurement of the KAW values and the determination of the spiking accuracy of mT. All solutions were allowed to equilibrate at room temperature for 4 hours before use. Concentration of methanol in all working solutions was always below 0.1 % (w/v).

To verify the spiking accuracy of mT, 8 cm3 of MilliQ water and 1.8 cm3 of ethyl acetate were added to the working solutions with a 10 cm3 Fortuna syringe through the septum. The vial content was vigorously hand-shaken for 1 minute; and the excess pressure was released by quickly piercing the septum with a disposable syringe nee-dle. The needle was then removed and no leakage of liq-uid from the vials was observed during extraction. After phase separation at room temperature, the ethyl acetate layer was removed and immediately transferred into a 2 cm3 clear glass GC vial. This was then sealed with a screw cap and a PTFE-lined silicone septum. Hundred milligrams of an-hydrous Na2SO4 was added to the vial and this was shaken gently and the crystals were allowed to settle. The pres-

ence of the crystals did not lead to any sorption losses of for any of the three hydrocarbons studied.

Two mm3 of the ethyl acetate solution were then in-jected into the 3800 Varian GC and analysed for the con-tent of the given BTEX compound (see below). The aver-age spiking accuracy for mT was 116 ± 4 % (average of duplicate extraction). These values are in the range com-parable to the U. S. EPA analyte recoveries for volatile organic compounds [22], i.e. no losses of any analyte were observed and mT remained constant throughout all experi-ments. Values of mT in the working solutions were corrected for the spiking accuracy of the given compound. Calibration curves were prepared by dissolving the neat BTEX com-pound in ethyl acetate and injecting 2 mm3 into the 3800 Varian GC as described below.

2.4. Measurement of gas-liquid equilibrium period and the KAW values

After the 4 hour equilibration period, all samples were placed on the headspace tray of the CombiPal autosampler (CTC Analytics/Anatech Technologies, Sloane Park, South Africa), attached to the 3800 Varian GC equipped with a flame ionization detector (SMM Instruments, Johannes-burg, South Africa). Vials were incubated one at the time at 30 ºC and 500 rpm for 5 to 60 minutes. Septa of GC vials were then pierced with a 1000 mm3 headspace sy-ringe (attached to the CombiPal autosampler). Subse-quently, 150 mm3 of the gas phase was removed for GC analyses as described in the next section. During the gas phase sampling, the headspace syringe was heated to 40 ºC to prevent condensation of the analyte molecules on the syringe material.

The headspace sample was removed at 50 mm3/s and immediately injected into the gas chromatograph at 300 mm3/s. These two speeds were optimised to minimise the losses of analytes due to condensation and the sam-pling process, i.e. to address the usual problems of the gas chromatographic measurements of the KAW values [19]. In all cases the gas/liquid equilibrium was established after 15 minutes, as the GC peak height of all hydrocarbons remained constant at longer incubation periods. The incu-bation period for the KAW measurement was therefore set to 15 minutes. All of the KAW measurements were per-formed in the same way as the kinetics of the gas-liquid equilibrium, but the GC signal of BTEX compounds were converted into Cg values using the calibration curves.

2.5. Calibration of the headspace GC/FID response

Ten cm3 GC headspace vials were cooled on ice for 10 minutes, with the necks covered with aluminum foil to prevent moisture condensation inside. Approximately 2 mm3 of the hydrocarbon stock solutions was spiked onto the walls of the pre-cooled vials which were immediately sealed with crimp caps. The vials were again covered with aluminum foil and equilibrated at room temperature for 30 minutes, before being placed into the headspace tray of the CombiPal autosampler. The calibration vials were in-


71

cubated at 30 ºC as described in the previous paragraph. To check the spiking accuracy of Cg, 10 cm3 of neat me-thanol was spiked into selected 10 cm3 headspace GC vials. These were then hand-shaken for 2 minutes. The 10 cm3 headspace vials had the same geometry as the headspace in the 20 cm3 vials. The hydrocarbon concen-tration in the methanolic extract was determined using the GC method and the calibration curve from the liquid extractions (see below).

For spiking accuracy of the headspace calibration mix-tures, one replicate for each concentration was extracted before the 15 minute incubation and one after it. These two values were averaged out to give the spiking accuracies of 97 ± 15 %. This is inside the recovery range listed for volatile organic compounds by the U. S. EPA [22]. The calibration curve for headspace concentrations was con-structed as the dependence of the peak height, and the headspace hydrocarbon concentration which had been corrected for the respective spiking accuracy of Cg. The limits of detection for Cg were equal to 0.1 mg/dm3 for benzene, 1.5 mg/dm3 for toluene and 1.7 mg/dm3 for o-xylene. Conditions of the GC/FID are outlined in the next paragraph.

GC/FID analyses

All GC analyses were conducted on the 3800 Varian GC equipped with a DB-VRX capillary column (30 m × 0.25 mm × 0.53 µm; SMM Instruments, Johannesburg, South Africa) and an FID detector. The temperature of the column was held at 50 ºC for 2 minutes, ramped to 140 ºC at 10 ºC/min and held for 2 minutes. The injector and detector temperatures were set to 180 and 200 ºC, respec-tively. Unless otherwise stated, all injections were done in the splitless mode.


Henry’s law constants as functions of the aqueous phase concentrations are summarised in Figs. 1-3. The CL values of benzene ranged from 0.546 ± 0.003 to 1370 ± 3 mg/dm3, while the values of KAW ranged from 0.190 ± 0.002 to 0.340 ± 0.008. Coefficient of variation of the KAW values ranged from 0.5 to 5.6 %. For toluene, the CL values were inside the following interval: 3.42 ± 0.05 - 420 ± 4 mg/dm3, while KAW ranged from 0.170 ± 0.011 to 0.321 ± 0.007 (coefficients of variation = 2.4-11 %). In the case of o-xylene, the CL values was found to vary between 3.03 ± 0.03 and 123.9 ± 0.3 mg/dm3; and the KAW values ranged from 0.120 ± 0.004 to 0.268 ± 0.008. Coefficient of variation of the KAW values ranged from 1.6 to 15 %.

The experimental KAW values at 30 °C have been re-ported to be equal to 0.280 ± 0.003 for benzene [14] and to 0.281 ± 0.029 for toluene [18]. For o-xylene, only a reference value of KAW was found at 35 °C and it was equal to 0.3048 [17]. Based on the data of Shiu and Ma [10], the KAW values at 30 ºC can be estimated to be in-side the following intervals: 0.253-0.294 for benzene, from 0.307 to 0.331 for toluene; and between 0.251 and 0.252 for o-xylene (see Introduction). Cheng et al. [15] measured the effect of the equilibrium aqueous concentra-tion of benzene and toluene on KAW at 27 °C and 32 °C for CL from 0.48 ± 0.01 to 19.20 ± 0.19 mg/dm3. At 27 °C, the KAW values ranged from 0.205-0.243 for benzene and from 0.221-0.230 for toluene. On the other hand, intervals between 0.259 and 0.270 for benzene, and from 0.226 up to 0.261 for toluene, were recorded at 32 °C. The values of KAW were independent on the aqueous phase concentrations at both temperatures [15].

0 200 400 600 800 1000 1200 14000.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

0.34

0.36

KAW

(dim

ensi

onle

ss)

CL (mg/dm3)

benzene

FIGURE 1 - Dimensionless Henry’s law constant (KAW) as a function of the equilibrium aqueous phase concentration (CL) for benzene at 30 °C. Data at individual concentration levels are presented as the arithmetic mean ± one standard deviation.


72

0 100 200 300 400 500

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

0.34

KAW

(dim

ensi

onle

ss)

CL (mg/dm3)

toluene

FIGURE 2 - Dimensionless Henry’s law constant (KAW) as a function of the equilibrium aqueous phase concentration (CL) for toluene at 30 °C. Data at individual concentration levels are presented as the arithmetic mean ± one standard deviation.

0 20 40 60 80 100 120 1400.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

KAW

(dim

nesi

onle

ss)

CL (mg/dm3)

o-xylene

FIGURE 3 - Dimensionless Henry’s law constant (KAW) as a function of the equilibrium aqueous phase concentration (CL) for o-xylene at 30 °C. Data at individual concentration levels are presented as the arithmetic mean ± one standard deviation.

Thus the KAW results from this study thus span a wid-

er interval than literature data and estimates based on such data. At the same time, the aqueous phase concentration seems to have a strong influence on the Henry’s law con-stants. This observation is contradictory to most literature data, as the evaporation of benzene, toluene and o-xylene from water has been shown to follow first-order kinetics; and the Henry’s law was found to be valid by several authors [10, 21]. On the other hand, there are several

problems with the measurement techniques that have been used by various authors to date (see Introduction). If data from this study are correct then the Henry’s law constant will change with the CL values. However, the possible influence of the measurement technique on the results obtained must be tested. These two questions were an-swered using the Kruskal-Wallis analysis of variance by ranks and the Dixon test after normalisation of the data. Results are outlined below.


73

Whether the KAW values changed with CL in this study was tested using the one-way Kruskal-Wallis analy-sis of variance at 1 % level of significance (Past statistical software package version 2.0, Paleontological Museum, Oslo, Norway and Geological Museum, Copenhagen, Den-mark). Based on the number of concentrations and repli-cates, the large sample approximation applies and so the Kruskal-Wallis statistic follows the χ2 distribution [23]. If CL has an influence on KAW then at least one of the KAW values measured would have to be different from the others and the null hypothesis should be rejected in the Kruskal-Wallis analysis. The Kruskal-Wallis statistic was equal to 38.14 for benzene (critical value from the χ2 distribution = 4.107), to 25.85 for toluene and to 27.65 for o-xylene (critical value from the χ2 distribution = 2.088 for both compounds). Thus at least one of the KAW values was different from the others for all three compounds studied (p-values ranged from 0.0003 to 0.0022). There-fore the Kruskal-Wallis analysis indicates that KAW of benzene, toluene and o-xylene changes as a function of CL in the current study.

If the measurement technique used had any influence on the KAW values, then some of these values would be outliers. Only one non-parametric test for outliers exists and it is the Walsh test [24]. To achieve 1 % level of sig-nificance a sample size of 220 or more would be needed, which was beyond the financial sources available to the authors. Therefore the KAW values were normalised by the square-root transformation and the Dixon test at 1 % level of significance was used to test for outliers [25]. All cal-culations were performed using the Microsoft Excel soft-ware package (Microsoft Corporation, Johannesburg, South Africa). The test static for the benzene results showed that the KAW value measured at 1370 ± 3 mg/dm3, i.e. 0.190 ± 0.002, was an outlier (test statistic r10 = 0.587; critical value = 0.488 see Table I on page 142 in [25] for details). No other outliers were detected in the benzene dataset of KAW.

If the KAW value of 0.190 ± 0.002 is omitted from the dataset as an outlier, then the average KAW for benzene is equal to 0.312 ± 0.022. The coefficient of variation is equal to 6.9 %. This suggests higher variability than re-ported by Görgényi et al. [14]. However, the error of the KAW estimates based on the data compiled by Shiu and Ma [10] is equal to 16 %. Therefore it is unlikely that the CL has a significant influence on the Henry’s law constant of benzene at 30 °C. For practical human exposure and toxi-cological modelling, the average value of 0.312 ± 0.022 should be used at ambient temperatures in South Africa

Results of the Dixon test did not indicate the presence of any outliers among the KAW values for toluene or o-xylene. The respective test statistic values were always equal to or smaller than 0.079 for toluene and 0.107 for o-xylene, while the critical values were equal to 0.568 or higher (see Tables I-IV in [25] for details). At the same time, the shape of the dependence in Figures 2 and 3 is different from that of benzene in Figure 1, clearly sug-

gesting a decreasing trend in Henry’s law constants with increasing CL. Thus the values of the KAW values for tolu-ene and o-xylene are not influenced by the measurement technique used; and there is a strong indication that the Henry’s law constant changes as a function with the CL values for both compounds.

Visual examination of the data trends in Figures 2 and 3 indicates that maximum values of the KAW values inside the both CL intervals. Detection of maximum/peak values of KAW was conducted using the Mack-Wolfe test [26, 27], using the Microsoft Excel (Microsoft Corpora-tion, Johannesburg, South Africa). If there is a maximum value of KAW inside the CL interval, then the null hypothe-sis should be rejected in the Mack-Wolfe test for a given BTEX compound [26]. At 1 % level of significance, this was the case for toluene and the maximum KAW value was equal to 0.321 ± 0.007 at CL of 6.78 ± 0.04 mg/dm3 (test statistic value = 7.638 and the critical value = 2.802). The KAW values increase with increasing CL below the peak CL value and decrease with increasing CL above it. For o-xylene, the maximum value was equal to 0.252 ± 0.012 and it was reached at CL of 3.03 ± 0.03 mg/dm3 (test sta-tistic value = 4.439 and the critical value = 2.802). How-ever, the data in Figure 3 suggest that the maximum KAW value was equal to 0.268 ± 0.008 and it was measured when CL was equal to 6.96 ± 0.05 mg/dm3. This means that KAW of o-xylene probably remains independent on the equilibrium aqueous phase concentration from 1.7 up to 6.96 ± 0.05 mg/dm3. The KAW values decreased with increasing CL value above the peak equilibrium and aque-ous phase concentration.

Results of the statistical testing indicate that dimen-sionless Henry’s law constants vary significantly with the CL values for toluene and o-xylene. Aromatic hydrocar-bons have been shown to form π-H bonds in water [28] which play an important role in their hydration [29]. KAW has been shown to decrease with increasing CL values for the hydrogen-bonding compounds, such as isopropyl alcohol [20]. Therefore the influence of hydrogen bonding could provide an explanation for the KAW dependence on CL above 6.78 ± 0.04 mg/dm3 for toluene and above 6.96 ± 0.05 mg/dm3 for o-xylene. Presence of hydrogen bonds indicates deviation from the ideal solution behaviour, i.e. molecules of toluene and o-xylene will not be randomly distributed throughout the volume of their aqueous solu-tions above 6.78 ± 0.04 mg/dm3 for toluene and above 6.96 ± 0.05 mg/dm3 for o-xylene. Findings of this study are significant, but further research will have to be con-ducted to investigate the relationship between the Henry’s law constants of other aromatic hydrocarbons, their sub-stitution pattern and their CL.

4. CONCLUSIONS

Precision of measurements and the KAW values ob-tained in this study are comparable to the literature data.


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The method used is fast and is ideal for the rapid assess-ment of the relationship between KAW and CL of aromatic hydrocarbons up to approximately 400 mg/dm3. Hydro-gen bonding between water molecules and molecules of toluene and o-xylene provides some explanation for the observed trends of the KAW dependence on CL. Further studies will have to focus on ascertaining whether hydro-gen bonding is the only non-covalent interaction with an influence on the KAW values of the studied compounds. The results of this study provide novel insight into the air/water partitioning of aromatic hydrocarbons and stress the importance of assessing the influence of CL on the KAW values.

ACKNOWLEDGEMENTS

The authors would like to thank the Joint Research Committee of Rhodes University for funding the study (Grant No. 35215/2010).

REFERENCES

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[2] Romanow, S. (2008) Fuel trends report: Gasoline 1995–2005, Compliance and Innovative Strategies Division. Office of Transportation and Air Quality, US Environmental Protec-tion Agency, EPA 420-R-08-002.

[3] Montero, L., Duane, M., Manfredi, U., Astorga, C., Martini, G., Carriero, M., Krasenbrink, A. and Larsen, B.R. (2010) Hydrocarbon emission fingerprints from contemporary vehi-cle/engine technologies with conventional and new fuels. Atmospheric Environment 44, 2167-2175.

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[6] Pohl, H.R., Chou, C.H.S.J., Ruiz, P. and Holler, J.S. (2010) Chemical risk assessment and uncertainty associated with ex-trapolation across exposure duration. Regulatory Toxicology and Pharmacology 57, 18-23.

[7] Matsuoka, M. (2007) Neurotoxicity of organic solvents-recent findings. Brain Nerve 59, 591-596.

[8] Straube, S., Westphal, G.A. and Hallier, E. (2010) Comment on: Implications of latency period between benzene exposure and development of leukaemia-A synopsis of literature. Chemico-Biological Interactions 186, 248-249.

[9] Prevedouros, K., Jones, K.C. and Sweetman, A.J. (2004) Modelling the atmospheric fate and seasonality of polycyclic aromatic hydrocarbons in the UK. Chemosphere 56, 195-208.

[10] Shiu, W.-Y., and Ma, K.-C. (2000) Temperature dependence of physical-chemical properties of selected chemicals of en-vironmental interest. I. mononuclear and polynuclear aro-matic hydrocarbons. Journal of Physical and Chemical Ref-erence Data 29, 41-130.

[11] South African Weather Service (SAWS, 2010). Daily extreme temperatures and rainfall over South Africa. Available from: http://metzone.weathersa.co.za/images/PDF_docs/nr_extremes. pdf?1267994497687 (website accessed on 7th March 2010).

[12] O’Farrell, C.E., Waghorne, W.E. (2010) Henry’s law con-stants of organic compounds in water and n-octane at T = 293.2 K. Journal of Chemical & Engineering Data 55, 1655-1658.

[13] Lau, K., Rogers, T.N. and Chesney, D.J. (2010) Measuring the aqueous Henry’s law constant at elevated temperatures using an extended EPICS Technique. Journal of Chemical & Engineering Data 55, 5144-5148.

[14] Görgényi, M., Dewulf, J., van Langenhove, H. and Héberger, K. (2006) Aqueous salting-out effect of inorganic cations and anions on non-electrolytes. Chemosphere 65, 802-810.

[15] Cheng, W.H., Chu, F.S. and Liou, J.J. (2003) Air–water in-terface equilibrium partitioning coefficients of aromatic hy-drocarbons. Atmospheric Environment 37, 4807-4815.

[16] Falabella, J.B., and Teja, A.S. (2008) Air–water partitioning of gasoline components in the presence of sodium chloride. Energy & Fuels 22, 398-401.

[17] Gao, H., Blanford, W.J. and Birdwell, J. (2009) The pseudo-phase approach to assessing chemical partitioning in air-water-cyclodextrin systems. Environmental Science & Tech-nology 43, 2943-2949.

[18] Helburn, R., Albritton, J., Howe, G., Michael, L. and Franke, D. (2008) Henry’s law constants for fragrance and organic solvent compounds in aqueous industrial surfactants. Journal of Chemical & Engineering Data 53, 1071-1079.

[19] Lodge, K.B., Danso, D. (2007). The measurement of fugacity and the Henry’s law constant for volatile organic compounds containing chromophores. Fluid Phase Equilibria 253, 74-79.

[20] Cheng, W.H., Chou, M.S., Perng, C.H. and Chu, F.S. (2004) Determining the equilibrium partitioning coefficients of vola-tile organic compounds at an air–water interface. Chemos-phere 54, 935-942.

[21] MacKay, D., Shiu, W.Y., Sutherland, R.S. (1979). Determi-nation of Air-Water Henry’s Law Constants for Hydrophobic Pollutants. Environmental Science & Technology 13, 333-337.

[22] U.S. Environmental Protection Agency (2006). Volatile or-ganic compounds by vacuum distillation in combination with Gas Chromatography/Mass Spectrometry (VD/GC/MS). Available at: http://www.epa.gov/osw/hazard/testmethods/ pdfs/8261a.pdf (website accessed on 8th August 2011).

[23] Howell, D.C. (2007) Fundamental statistics for the behav-ioral sciences, 6th Ed. Thomson Wadsworth, 467.

[24] Fundamentals of Statistics. (2010) Walsh test for outliers. Available at: http://www.statistics4u.info/fundstat_eng/ee_ walsh_outliertest.html (website accessed on 12th August 2011).

[25] Rorabacher, J. (1991) Statistical treatment for rejection of deviant values: critical values of Dixon’s “q” parameter and related subrange ratios at the 95% confidence level. Analyti-cal Chemistry 63, 139-146.

[26] Mack, G.A. and Wolfe, D.A. (1981) K-Sample rank tests for umbrella alternatives. Journal of the American Statistical As-sociation 76, 175-181.

[27] Magel, R.C. and Qin, L. (2003) A non-parametric test for umbrella alternatives based on ranked-set sampling. Journal of Applied Statistics 30, 925-937.


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[28] Tarakeshwar, P., Choi, H.S., Lee, S.J., Lee, J.Y. and Kim, K.S. (1999) A theoretical investigation of the nature of the π-H interaction in ethene-H2O, benzene-H2O, and benzene-(H2O)2. Journal of Chemical Physics 111, 5838-5850.

[29] Makhatadze, G.I. and Privalov, P.L. (1994) Energetics of in-teractions of aromatic hydrocarbons with water. Biophysical Chemistry 50, 285-291.

Received: May 13, 2011 Revised: August 16, 2011 Accepted: September 28, 2011 CORRESPONDING AUTHOR

Roman Tandlich Environmental Health and Biotechnology Research Group Division of Pharmaceutical Chemistry Faculty of Pharmacy Rhodes University P.O. Box 94 Grahamstown 6140 SOUTH AFRICA Phone: 00-27-46-603-8825 Fax 00-27-46-636-1205 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 68 – 75


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OPTIMIZATION OF ARSENIC SLUDGE IMMOBILIZATION PROCESS IN CEMENT – NATURAL

ZEOLITE – LIME BLENDS USING ARTIFICIAL NEURAL NETWORKS AND MULTI-OBJECTIVE CRITERIA FUNCTIONS

Tomislav Bolanča1,*, Juraj Šipušić1, Šime Ukić1, Mario Šiljeg2, and Magdalena Ujević Bošnjak3

1University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000 Zagreb, Croatia 2Vodotehnika, Koturaška 49, 10000 Zagreb, Croatia

3Croatian National Institute for Public Health, Rockefellerova 7, 10000 Zagreb, Croatia

ABSTRACT

This work focuses on optimization of arsenic sludge immobilization process in cement – natural zeolite – lime blends using artificial neural networks and multi-objective criteria functions. The developed artificial neural network model describes relations between solidified/stabilized ce-ment formulation and its mechanical (compressive strength) and ecological properties (arsenic and iron release). It is proven that developed artificial neural network solidified/ stabilized model has satisfactory performance characteris-tics (R2 >0.9031 without presence of systematic error; based on an external validation experimental data set). Four multi- objective optimization criteria functions, different in terms of mathematical formulation and ecological interpretation, were developed. The developed criteria functions were used in combination with the artificial neural network so-lidified/stabilized model, providing optimal cement for-mulation. Finally, this study describes an efficient and cost-effective alternative in ecological material formulation process.

KEYWORDS: Solidification/stabilization; cement; natural zeolite; arsenic sludge; artificial neural network

1. INTRODUCTION

Arsenic has been known as a common and notorious contaminant in water environment. In recent years, many approaches have been developed for removal of arsenic from water including adsorption, ion exchange, reverse osmosis, nanofiltration, coagulation process, membrane permeation, biological methods and photocatalytic oxida-tion [1-9]. Among these methods, nanofiltration/reverse * Corresponding author

osmosis and adsorption are highly efficient and economic techniques. Various adsorption materials, such as activated carbon, activated alumina, lanthanum compounds and iron hydroxides, have been used [10-13]. However, after the ad-sorbent medium is completely exhausted, the disposal of the used material becomes a major consideration, since it contains toxic levels of arsenic which may leach out into the environment. Therefore, it has to be disposed safely according to the prevailing environmental regulations. Otherwise, it runs the risk for groundwater as well as for surface water contamination with the leaching from the exhausted bed (arsenic).

Waste immobilization techniques, such as portland cement processes, lime-based technology, bituminization, emulsified asphalt processes, polyethylene extrusion and verification [14-19], are the recent technologies that are widely used to prevent the free movements of the con-taminants in waste and surrounding media. Currently, ce-ment-based solidification/stabilization (S/S) treatment was recognized as the “best demonstrated available technology (BDAT)” by the US Environmental Protection Agency (USEPA) for the land disposal of most toxic elements [20]. Because of it, this is the most widely used approach among all hazardous waste management alternatives.

A wide range of processes has been used in attempts to successfully fix arsenic. These processes include mixing the arsenic with various combinations of cement, lime, iron, silicates, and fly ash [21-24]. One additive that might result with more efficient immobilization is natural zeo-lite, since it can be successfully applied in arsenic re-moval from water process, and its applications are usually economically justified [25-28]. Unfortunately, all above listed additives have not been systematically investigated at the same or similar additive/waste ratios, or with simi-lar arsenic compounds. This limits the generality of many of the conclusions that can be drawn from previous re-searches. Moreover, due to the complex chemistry of arse-nic, the successfulness of any S/S process to treat arsenic


77

wastes appears to be highly dependent upon the particular arsenic waste and not merely upon the varying arsenic concentrations [22, 29]. Therefore, it is obvious that op-timization of immobilization is curtail stage of overall S/S process. Various reports have shown that leaching of con-taminants from the cement-based waste is mostly a diffu-sion-controlled process [23, 30]. Other properties, like compressive strength, might be even more significant for successfulness of waste S/S treatment and, therefore, should be included in the global optimization process. This indi-cates empirical modeling as a reasonable choice for the computer-assisted optimization process. Artificial neural network (ANN) modeling might be a promising approach.

The aim of this work is optimization of arsenic sludge immobilization in cement –natural zeolite – lime blends using ANNs. Feed forward error back propagated ANN model was developed to model cement properties (com-pressive strength, amount of released arsenic and iron) in relation with cement formulation (cement – lime – natural zeolite – sludge – water ratio). Developed ANN model was optimized and validated using an external experimen-tal data set. In addition, four different multi-objective criteria functions were developed and applied for calcula-tion of optimal formulation.

1.1. Artificial neural networks (ANNs)

ANNs are usually defined as structures comprised of densely interconnected adaptive simple processing ele-ments (called artificial neurons). These structures are capa-ble of performing massively parallel computations for data processing and knowledge representation. The ANNs were invented as imitations of biological neurons, but the basic idea was not to replicate the operation of the biological systems but to make use of what is known about the func-tionality of the biological networks for solving complex problems [31]. Fig. 1 illustrates the similarity in structure and functioning among biological neural network (Fig. 1A) and its artificial imitation (Fig. 1B). The biological neural network consists of highly interconnected nerve cells (neu-rons) [32]. The dendrites of each neuron collect input sig-nals x (a stimulation from environment or the outputs from post-synaptic regions of neighboring neurons), and send them forward to cell body (soma) to be processed. The processed signal is than flowing trough nerve fiber, called axon, to the pre-synaptic area, and more forward across synaptic junctions, as input y to dendrites of next neuron. The differences in synaptic strength, w, enable various priorities of neuron connections. The same principles are effective in case of artificial networks. The differences in synaptic strength are replaced by connection weights, w, which assign the data priorities. The signal processing in neuron cell body is compensated by summation of input signals, multiplied by corresponding weights. And fi-nally, this signal, I, is activated by appropriate activation function, f(I).

Backpropagation (BP) networks [32, 33] are one of the most widely used neural networks. Generally, they are

comprised of the input layer with neurons representing model input variables, the output layer with neurons rep-resenting the dependent variables, and one or more hidden layers. Hidden layers also contain neurons that are help-ing in capture of data nonlinearity. Through supervised learning [34], these networks can learn the mapping from one data space to another by using experimental exam-ples. BP is based on searching the minimum of an error surface (error as a function of ANN weights) using gradi-ent descent algorithm (GDF). Each of iterations in BP con-stitutes of two sweeps: forward signal activation produces a solution, and the computed error backward propagation modifies the network weights [31]. However, the most recommended techniques for training BP networks are the quasi-Newton algorithm with Broyden-Fletcher-Goldfarb- Shanno adaptation (BFGS) and conjugate gradient algo-rithm (CG) [35]. These methods perform significantly better than the more traditional algorithms (such as GDF) but they are, generally speaking, more memory-intensive and com-putationally demanding. Nevertheless, these techniques may require a smaller number of iterations to train a given neural network due to their fast convergence rate and more intelligent search criterion.

FIGURE 1 - Comparison of biological (A) and artificial neural net-work (B).

Number of issues should be addressed in develop-

ment of ANN models. The parameters that should be carefully selected and/or optimized are database size and partitioning, data preprocessing, balancing and enrichment, data normalization, input/output representation, network weight initialization, transfer function, convergence crite-ria, number of training cycles, training algorithms and hid-den layer size.


2.1. Experimental design and materials

The basis for the experimental design was a multi-level full factorial scheme, adopted by considering the mechanic properties of the cement (some of the experi-mental points were omitted due to their low compressive strength). The investigated factors included mass of cement,


78

TABLE 1 - The composition of different S/S matrices. Cumulative mass of binder and filler is constantly 450 g.-

Binder Filler Cement

m/g Lime m/g

Natural zeolite m/g

Arsenic sludge m/g

Water m/g

270 135 0 45 180 270 135 45 0 180 315 90 0 45 180 315 90 45 0 180 360 45 0 45 180 360 45 45 0 180 405 0 0 45 180 405 0 45 0 135 405 0 45 0 180 405 0 45 0 225 270 90 45 45 180 270 90 90 0 135 270 90 90 0 180 270 90 90 0 225 315 45 0 90 180 315 45 45 45 180 315 45 90 0 135 315 45 90 0 180 315 45 90 0 225 360 0 0 90 180 360 0 45 45 180 360 0 90 0 135 360 0 90 0 180 360 0 90 0 225 270 45 45 90 180 270 45 90 45 180 270 45 135 0 135 270 45 135 0 180 270 45 135 0 225 315 0 45 90 180 315 0 90 45 180 315 0 135 0 135 315 0 135 0 180 315 0 135 0 225 270 0 90 90 180 270 0 135 45 180 270 0 180 0 135 270 0 180 0 180 270 0 180 0 225

lime, zeolite and sludge (altogether 450 g), and added water. In total, 39 experimental data points were obtained (Table 1).

All the experiments were carried out with double-distilled water, sludge generated from arsenic flocculation process (town of Osijek, Eastern Slavonia, Croatia) con-tained 0.5% arsenic and 23.7% iron; natural zeolite was technologically treated by the Institute for Technology of Nuclear and Other Mineral Raw Materials (ITNMS), Belgrade, and it contained 71.1% of clinoptilolite.

The binder materials used for S/S studies were SPE-CIAL portland cement (minimal 80% clinker) [brand: CEMII/A-M-(S-V) 42.5 N, Našicecement, Croatia] and hydrated lime [brand: SPEZI VAPNO, Istarska tvornica vapna d.d. Croatia]. The binders were mixed with natural zeolite, hazardous arsenic sludge and water (see Table 1) to give 40×40×160 mm test tubes. Experimental condi-tions were 20±2 °C, with air humidity of ≥50%. Obtained

test tubes were stored in a chamber at 90% air humidity for 24±0.25 h, before experimental testing.

2.2. Testing procedures

Compressive strength was measured for each prepared test tube which were placed in a hydraulic squeezer, in quadratic form (40 mm l/w) so that applied force covers 1600 mm2 of testing area. Compression rate was 1.5 MPa.s-1, and compressive strength was expressed as ratio between applied force and covered area.

Toxicity characteristic leaching procedure (TCLP) test provides a means of determining the potential for solid material to release chemical contaminants into a landfill environment (U.S. EPA 1996) [36]. The TCLP was applied to test tubes after solidification/stabilization (S/S) process. Specifically, the test tubes were extracted with fluid (5.7 ml of acetic acid added to 500 ml double-distilled water, plus 64.5 ml of 1 mol/L NaOH), and then diluted to 1 L (pH


79

4.39±0.05; liquid/solid ratio = 20). The extraction was achieved by shaking the system for 18 h, after which the liquid phase was separated off (0.45-µm cellulose acetate filter) and analyzed for arsenic and iron by ICP-OES. The samples were acidified with ultrapure HNO3 (1:100, v/v; Merck, Darmstadt, Germany) and stored at 4 °C.

A HG-ICP-OES (Iris Intrepid II; XSP, Thermo Elec-tron) was used for the determination of As concentrations. Hydride generation was achieved online by mixing the samples with a solution of 3 M HCl and allowing them to react for approx. 10 seconds before mixing with a solution of 1.5 % NaBH4 (w/v; Acros Organics, Gell, Belgium) and stabilizing in 0.1% NaOH (w/v; T.T.T., Sv. Nedelja, Croatia). The ICP-OES apparatus was equipped with an ultrasonic nebulizer for the determination of iron accord-ing to Croatian norm: HRN EN ISO 11885:1998, Water quality – Determination of 33 elements by inductively coupled plasma atomic emission spectroscopy (ISO 11885:1996; EN ISO 11885:1997).

2.3. Development of ANN S/S model

The neural networks applied in this paper were three layer feed forward neural networks. The input layer of all used networks consisted of 5 neurons; each neuron repre-senting one of the input parameters: cement, lime, natural zeolite, arsenic sludge, or water ratio. The output layer consisted of 3 neurons, representing compressive strength of the testing tube and the amounts of leached arsenic and iron.

A two-phase training procedure was used for all cal-culations. During the first phase of 100 iteration steps, the GDF training algorithm was used; the intention was to converge to global minimum region at the error surface. In order to achieve faster and more accurate convergence during the second training phase, three different training algorithms (GDF, CG and BFGS) were used and com-pared. The second phase training procedure was repeated until global minimum at the error surface was found.

The number of neurons in hidden layer was also var-ied: 5, 10, 15, 20, 25, and 30. Thirty experimental data points were used for network training, while 9 experimen-tal data points were used as external validation set.

As connection among the neurons of input and hidden layer, three different activation functions were tested: hyperbolic tangent (Eq. 1), logistic sigmoid (Eq. 2) and exponential function (Eq. 3).

( )−

−

−=

+

x x

x x

e ef xe e

(1)

1( )1 −=

− xf xe

(2)

( ) −= xf x e (3)

For computation of output activities, only linear transfer function was employed (Eq. 4):

( ) =f x x (4)

The second phase training algorithm, the activation function among input and hidden layer, and the number of neurons in hidden layer needed to be optimized in order to obtain the network with best predictive ability.

Statistical analysis was used to calculate agreement between measured and predicted values. The indicators of the agreement are given as determination coefficient (R2) and confidence intervals for intercept and slope. All cal-culations were performed in the Statistica 7.0 (StatSoft Inc. USA) environment.

2.4. Development of S/S multi objective optimization criteria functions

Four multi-objective criteria functions (CR1, CR2, CR3 and CR4) were developed in order to find optimal cement formulation. The criteria function differs in its mathematical formulation as well as in its chemical inter-pretation, and they can be expressed as follows:

1 1CR1 (sludge)(As) (Fe)

= ⋅σ ⋅ ⋅mc c

(5)

2 14 3 1 1CR2 (sludge)

(As) (Fe)

= ⋅σ ⋅ ⋅

mc c

(6)

1 1CR3 (sludge)(As) (Fe)

= + σ + +mc c

(7)

2 14 3 1 1CR4 (sludge)

(As) (Fe)

= + σ + +

mc c

(8)


Development of artificial neural network solidifica-tion/stabilization model (Fig. 2) shows that ANN (S/S) models obtained by using GDF training algorithm have lower predictive ability than those developed using CG and BFGS training algorithms. The reasons for this lies in the fact that GDF training is based on first-order error surface information, and the search for global minimum on the error surface could be easily trapped in local minimum. On the contrary, CG and BFGS training algorithms pro-vide S/S models with higher predictive ability: CG when the higher number of hidden layer neurons is used, and BFGS when the lower number of hidden layer neurons is used. This is due to different complexity of applied search routines. A significant advantage of the BFGS approach over the CG method is that the line search does not need to be performed with such great accuracy, since it does not form a critical factor in the algorithm. For conjugate gradients, the line minimizations need to be performed accurately in order to ensure that the system of conjugate directions and orthogonal gradients is set up correctly. Moreover, the global maximum performance is obtained using BFGS algorithm and 5 neurons in hidden layer reducing any chance of overtraining by using BFGS algo-


80

FIGURE 2 - Development of ANN S/S model. Correlation coefficient vs training algorithm vs activation function for A) 5, B) 10, C) 15, D) 20, E) 25 and F) 30 neurons in hidden layer. TANH, LOG, and EXP, respectively, represent hyperbolic tangent, logistic sigmoid and exponential activation function.

rithm and, at the same time, indicating 5 neurons in hid-den layer as optimal selection for development of S/S model. In addition, from Fig. 2 can be observed that logis-tic sigmoid activation function provides the best perform-ance at higher number of hidden layer neurons, hyper-bolic tangent function at lower number of hidden layer neurons while exponential function proves to be not ex-actly suitable for modeling of S/S properties. Again, the

global maximum of performance characteristics is obtained using hyperbolic tangent activation function and 5 neu-rons in hidden layer indicating selection of this particular activation function to be optimal for the S/S model devel-opment process.

The predictive ability of developed ANN S/S model

was tested by employing statistical calculations (Table 2).


81

TABLE 2 - Performance characteristics of developed ANN S/S model.

Slope Intercept Parameter Value <95% >95% Value <95% >95% R2

Compressive strength 1.0001 0.9907 1.0095 0.0001 -0.1942 0.1943 0.9470 As 1.0040 0.9800 1.0280 0.0090 -0.3245 0.3335 0.9101 Fe 1.0100 0.9790 1,0410 0.0087 -0.4001 0.4088 0.9032

The relationships between simulated parameters (y)

against measured parameters (x) were investigated. If there were no modeling errors, no measurement random errors, and no bias, this would yield the relationship y=x, exist. When random errors were made, at least, the coefficients of linear relationship were different (intercept different from zero and/or slope different from one). The intercepts and slopes of optimized artificial neural network models are not statistically (95% confidence) different from the theoretic ones: 0 and 1, respectively. Considering Table 2, it can be stated that there is no systematic error in opti-mized artificial neural network models for all parameters (compressive strength, released arsenic and iron). Because determination coefficient is a measure of the joint varia-tion between two variables, it represents the strength of the proposed linear relationship between predicted and meas-ured parameters. The determination coefficients (R2) have satisfactory values (0.9032 - 0.9470), and strong linear relationships between predicted and measured values exist for all parameters. On the basis of the conducted statistical tests, it can be concluded that the ANN S/S models devel-oped for this investigation have good generalization abil-ity.

The developed ANN S/S model was used in combina-tion with 4 S/S multi-objective optimization criteria func-tions separately, in order to optimize cement formulation and to test performance characteristics of the used multi-objective criteria functions. Fig. 3 presents the character-istics of optimal S/S cement formulation. One can observe that criteria CR1 and CR2 as well as criteria CR3 and CR4 provide cement formulations with similar compressive strength and leaching characteristics. Mathematical for-mulation differs between CR1, CR3 and CR2, CR4, in terms of exponential factors. The aim of introducing expo-nential factors was to emphasize significance of particular factors (higher exponent means more significance). The re-quests in optimization of cement formulation, interpreted with the above criteria (Eqs. 5-8), can be described as follows:

• Mass of the S/S sludge is the most important factor; this is the aim of this work.

• Compressive strength is the second important factor: if there is no enough compressive strength in the S/S ce-ment, the ecological difference between arsenic sludge and arsenic-cement dust is not justified.

• Leached arsenic is the third important factor which is important for the formulation not to release arsenic into the environment.

• Leached iron is the least important factor; iron in comparison with the above-mentioned factors does not represent a significant ecological issue.

FIGURE 3 - Calculation of optimal S/S cement formulation using 4 developed criteria functions. Characteristics of optimal formulation: A) compressive strength, B) leached As, and C) leached Fe.

According to above explanations and results presented

in Fig. 3, it can be concluded that addressing the signifi-cance through exponential factors does not extract any useful information from the investigated system. On the contrary, significant difference can be observed if a crite-


82

rion which multiplies factors is compared with a criterion which sums these factors. Mathematical formulation of CR1 and CR2, which multiplies factors, has more sensi-tivity; in other words, they most probably can map the slight change of particular factors. In order to verify this assumption, the information that describes optimal ce-ment formulation could be curtailed. Table 3 presents the optimal cement formulation obtained by using criteria CR1, CR2 and CR3, CR4. It can be seen that CR1 and CR2 formulation stabilizes five times more arsenic sludge than that obtained by using CR3 and CR4. Moreover, the arsenic release from CR1 and CR2 formulation has more than a satisfactory value, and it is significantly lower than arsenic release obtained from CR3 and CR4 formulation. However, compressive strength of CR1 and CR2 formula-tion is lower and iron release is higher than in the case of CR3 and CR4 formulation. Since compressive strength value of the CR1 and CR2 is over 40 MPa (more than satisfactory), and release of iron is not nearly significant in comparison with the arsenic release, the optimal S/S ce-ment formulation is obtained by using CR1 and CR2 (Ta-ble 3).

TABLE 3 - Formulations of optimal S/S cements.

Parameter CR1, CR2 CR3, CR4 Cement (m/g) 300 405

Lime (m/g) 15 0 Natural zeolite (m/g) 60 45 Arsenic sludge (m/g) 50 10

Water (m/g) 195 135 4 CONCLUSIONS

This work describes optimization of cement formula-tion in arsenic sludge S/S treatment process using ANN. The developed ANN S/S model was optimized in terms of training algorithm, activation function and hidden layer neurons. Optimized ANN S/S model was validated with an external experimental data set. It is shown that devel-oped ANN S/S model can predict the S/S formulation without presence of either proportional or absolute syste-matic error. Moreover, the satisfactory performance charac-teristic was proven by corresponding correlation coeffi-cients: 0.9470 for compressive strength; 0.9101 for arse-nic release, and 0.9032 for iron release (based on external validation data). The ANN S/S model was successfully used in combination with 4 in-house developed multi-criteria functions for optimization of cement formulation process. The criteria CR1 and CR2 prove to be most effi-cient providing cement formulation with high sludge ratio, and satisfactory mechanical and ecological properties. Finally, this study shows that application of modeling procedure in ecology of new material formulations could be a valuable help, providing useful information in efficient manner without losing time for unnecessary experimenta-tion.

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Received: June 01, 2011 Accepted: September 14, 2011 CORRESPONDING AUTHOR

Tomislav Bolanca University of Zagreb Faculty of Chemical Engineering and Technology Marulicev trg 19 10000 Zagreb CROATIA E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 76 - 83


84

STUDIES ON REMOVAL OF NAPROXEN SODIUM BY ADSORPTION ONTO ACF IN BATCH AND COLUMN

Çiğdem Sarıcı-Özdemir*, Yunus Önal, Selim Erdoğan, Canan Akmil-Başar

Inonu Universty, Faculty of Engineering, Department of Chemical Engineering, 44280, Malatya, Turkey

ABSTRACT

In this study, activated carbon fibers were prepared from textile waste by chemical activation with ZnCl2 (and coded IPZN1, IPZN2, and IPZN3). After preparation they were characterized by analyses using the BET surface area, FT-IR, and XRD methods. The ability of IPZN1, to re-move naproxen sodium from effluent solutions by adsorp-tion was studied. Results were analyzed by the Langmuir, Freundlich, Dubinin-Radushkevich (D-R), Temkin, Frum-kin, Halsey and Henderson equations using linearized cor-relation coefficients at 298 K. The value of Q0 was deter-mined as 294.11 mg.g–1 and the ∆G value of -21.46 kJ.mol-1 for adsorption of naproxen sodium. The fixed-bed adsorption system was used for study of the adsorption of naproxen sodium onto IPZN1. Experiments were conducted to study the effect of flow rate of naproxen sodium. Decreasing the flow rate was found to enhance capacity. The breakthrough data obtained for naproxen sodium was adequately de-scribed by the Thomas and Yoon-Nelson adsorption mod-els. This study revealed that ACF is suitable for use as an effective adsorbent for the adsorption of naproxen sodium.

KEYWORDS: Activated carbon; Naproxen sodium; Isotherm; Column adsorption

1. INTRODUCTION

Pharmaceuticals are emerging as a class of environ-mental contaminants that are extensively and increasingly being used in human medicine. They vary greatly in their chemical structures and are subjected to variations in their concentrations and loads. Naproxen is a member of the arylacetic acid group of non-steroidal anti-inflammatory drugs of which naproxen sodium is one. Naproxen so-dium leaves the human organism unmetabolized via urine or sludge, and can affect water quality as it has the poten-tial to impact on drinking water supplies and health of the ecosystem [1-5]. * Corresponding author

Activated carbon is a microporous adsorbent that can be produced from a various carbonaceous materials, includ-ing wood, coal, lignin, coconut shells, and sugar [6-9]. Its high porosity makes activated carbons the most widely used material for adsorption of toxic substances. Acti-vated carbon is also manufactured by carbonization and activation of fabrics made of several polymeric materials such as nylon, phenolic resin, cellulose etc. [10]. This cate-gory of activated carbon is known as activated carbon fiber (ACF) and is manufactured in two presentations: as cloth and as felt. ACF’s pore structure is composed mainly of micropores.

Fixed-bed adsorption columns are widely used in wa-ter treatment. This technique has proved effective in re-moving organic contaminants. The major part of the ad-sorption process at any time takes place in a relatively narrow adsorption column. As the solution continues to flow, the mass transfer zero which is S-shaped, moves down the column. The total capacity of the bed tower, if the entire bed comes to equilibrium with the feed, can be shown to be proportional to the area between the curve and a line at C/C0 = 1. The total shaded area represents the total- or stoichiometric capacity of the bed as follows [11- 13].

∫∞

−=0 0

).1( dtCCt t (1)

where tt is the time equivalent to the total capacity. Co and C (mg.L–1) are inlet- and outlet concentrations, re-spectively, of the contaminant solutions. The usable capac-ity of the bed up to the break point time tb is the cross-hatched area,

∫ −=bt

u dtCCt

0 0

).1( (2)

where tu is the time equivalent to the usable capacity. The value of tu is usually very close to that of tb.

Of the total bed length of HT, cm, HB is the length of bed used up to the break point;

Tt

uB H

tt

H .= (3)


85

The length of unused bed HUNB is then the unused fraction of the total length;

Tt

uUNB H

tt

H ).1( −= (4)

In this study, ACFs were prepared from textile waste which is the type of waste we investigated by chemical activation with ZnCl2. The aim of this work was to test adsorption behaviour of ACF for removal of naproxen sodium from aqueous solutions. Adsorption isotherms and column parameters were investigated for the adsorption of naproxen sodium onto ACF.


2.1. Materials

Anhydrous naproxen sodium (chemical formula = C14H13O3Na, MW = 252.24 g.mol–1, 99.8 % purity, 16086488) supplied by Abdi İbrahim Drug (İstanbul/ Turkey) was used in the experiments without further pu-rification or processing. One thousand milligrams per liter of stock solution was prepared by dissolving the required amount of naproxen sodium in distilled water and its con-centration determined by UV-VIS spectrophotometry at 230 nm in water.

ACF was produced from textile waste and was used to form felt made from nylon and cotton. The raw materi-als needed were supplied by G.A.P. A.Ş. (Malatya/ Turkey).

2.2. Preparation of Activated Carbon Fiber

In the first step of activation, the starting material was mixed with ZnCl2 in the ratios (of ZnCl2: starting mate-rial) 1:1, 1:2, and 1:3 by weight (and the samples were coded IPZN1, IPZN2, and IPZN3, respectively); the mix-tures were then shaken with distilled water. These mix-tures were then dried at 110oC to prepare the impregnated sample.

In the second step, the impregnated sample was placed on a quartz dish which was then inserted into a quartz tube (i.d.= 60 mm). The quartz tube with the sample was heated in a stream of flowing N2 (100 mL. min–1) at the rate of 10 oC.min-1 to the activation temperature (500oC) and held for 1 h. After activation, the sample was cooled in a N2 flow stream and 0.5 N HCl was added to the cooled sam-ple. To remove residual chemicals, the activated sample was washed sequentially several times with hot distilled water until it ceased to react with chloride AgNO3. The washed sample was then dried at 110 oC to prepare the activated carbon [14].

2.3. Characterization of pore structure of activated carbon

A Tri Star 3000 automated gas adsorption analyser (Mi-cromeritics, USA) was used to measure the nitrogen ad-sorption isotherm at 77 K in the range of relative pres-sures 10-6 to 1. Before measurement all samples were flushed out with gas at 300oC for 2 h. The BET (Branuer-

Emmett-Teller) method was employed to calculate the sur-face area of samples assuming that the surface area occupied by each physisorbed nitrogen molecule was 0.162 nm2. The total pore volumes were estimated to be the liquid volume of N2 at relative pressure (P/P0) of 0.9814. The t-plot method was applied to calculate the micropore vol-ume and mesopore surface area; the mesopore volume was determined by subtracting the micropore volume from total pore volume. The average pore radius was estimated from the BET surface area and the total pore volume by the BJH (Barrett-Joyner-Halenda) method, assuming an open-ended cylindrical pore model without pore Networks [15].

2.4. Characterization of ACF samples

Each sample was mixed with potassium bromide in the ratio of 0.1 wt % and used for FT-IR analysis. Sam-ples were dried under vacuum at 110oC prior to mixing with KBr powder. The mixture was finally ground and then vacuum dried at approximately 110oC, XRD patterns were obtained by a Rigaku Giegerflex D-Max/B powder diffractometer with Cu Kα radiation. Elemental analysis of samples was performed in a Leco CHNS 932 (USA) analyzer.

2.5 Adsorption Experiments

Naproxen sodium solutions were prepared in distilled water at desired concentrations. Adsorption experiments were carried out by agitating 0.1 g of IPZN1 with 50-mL solutions of desired concentration in a thermostatic bath operated at 400 rpm. The amount of naproxen adsorbed onto ACF, qt (mg.g-1) was calculated by mass balance relationship Eq.(5);

WVCCq tt ).( 0 −= (5)

where C0 and Ct are initial- and time t liquid phase concentrations of naproxen sodium (mg.L–1), respectively. V is the volume of the solution (L) and w is the weight of the dry ACF used (g). All experiments were carried out at 25oC. The spectrophotometric determination of naproxen sodium was done on a Shimadzu UV/vıs spectrophotome-ter (model UV-2100S, Japan) at 230 nm.

2.6. Column Studies

Adsorption studies were conducted in a glass column made of Pyrex glass tube of 1.5 cm inner diameter and 50 cm height. The bed length used in the experiments was 7 cm. In a typical experiment, naproxen sodium of 400 mg.L–1 was pumped at a fixed flow rate (20, 30, 40 ml. min–1) and filled with known bed height of adsorbent. The pumped solutions were collected at the outlet of the col-umn at regular time intervals and their concentration meas-ured using a UV-visible spectophotometer at 230 nm. All experiments were carried out at 25oC.

The total naproxen sodium quantity adsorbed, qtotal (mg.g–1), in the column for a given inlet concentration and the flow rate were calculated from Eq.6 [16].


86

0

200

400

600

800

1000

0 0,2 0,4 0,6 0,8 1 1,2

P/P0

Ads

orba

t vol

ume

(cm

3.g-1

)

IPZN1IPZN2IPZN3

∫=

=

=totaltt

tadtotal dtCQq

0

.1000

(6)

where Cad (mg.L–1) is the adsorbed naproxen sodium concentration, Q is the volumetric flow rate (mL. min–1), and ttotal is the total flow time (min).

The equilibrium naproxen sodium uptake in the col-umn or maximum capacity of the column (qeq) was de-fined by Eq. (7) as the total amount of naproxen sodium adsorbed (qtotal) per g of the adsorbent (X) at the end of the total flow time.

Xq

q totaleq = (7)

The breakthrough is usually defined as the phenome-non occurring when effluent concentration from the col-umn is about 3–5 % of the influent concentration [16-19]. The number of bed volumes (BV) is defined as:

EBRTtimeoperating

bedadsorbentofvolumetreatedsolutionofvolumevolumesbedofnumber

==

(8)

The adsorbent empty bed residence time (EBRT) is the time required for the liquid to fill the empty column.

liquidtheofrateflowvolumetricvolumebedEBRT

= (9)

The adsorbent exhaustion rate is the mass of adsorb-ent used per volume of liquid treated at breakthrough

ghbreakthrouattreatedvolumecolumninadsorbentofmassrateexhaustionadsorbent

= (10)


3.1. Ultimate Analysis

The results obtained of ultimate- and proximate analy-sis of the ACFs and raw material are shown in Table 1.

It can be seen that the raw material has a relatively high carbon content (44.03 %) and low ash content (3.55 %), which are desirable characteristics for a good adsorbent. After activation, carbon content rose to 88.14 % and the hydrogen (H) and oxygen (O) content declined progres-sively due to the continual release of volatile matter.

3.2. Pore structure characterization of the prepared ACFs

Nitrogen adsorption is the standard procedure adopted for characterizing the porosity texture of carbonaceous ad-sorbents. The adsorption isotherm is the source of infor-mation about the porous structure of the adsorbent, heat of adsorption, characteristics of its physics and chemistry and so on. It can be seen from Fig.1 that the nitrogen ad-sorption isotherm of ACFs shows a Type I isotherm in the BDDT classification. The structure reveals decreasing mi-croporosity. Physical adsorption is possible due to the appearance of small diameter mesopores. Table 1 lists the results obtained for BET surface area (SBET), external surface area (SEXT), microporous surface area (SMİC), total pore volume (Vt), and average pore diameter (Dp) obtained by applying the BET equation to N2 adsorption at 77 K and the D-R equation also at 77 K. It is seen that the high-est micropore area of 22% was found in sample IPZN1.

TABLE 1 - Elemental analysis and ash content of the raw material and ACF’s and surface properties of the ACF’s

Samples C (wt. %d.b.)

H (wt. % d.b.)

N (wt. % d.b.)

O (wt. % d.b.)

Ash (%)

SBET (m2.g–1) SEXT (m2.g–

1) SMİC (m2.g–1) VT

(cm3.g-1) Dp (nm)

RM 44.03 6.25 0.1 49.62 3.55 - - - - - IPZN1 70.58 2.79 - 26.63 15.49 1426 1107 319 0.830 2.37 IPZN2 88.14 2.51 - 9.35 4.98 1866 1751 116 1.093 2.63 IPZN3 60.87 4.21 - 34.92 9.14 1243 1224 18 0.959 3.09

R.M. Raw Material. d.b. dry bases.

FIGURE 1 - Adsorption isotherm


87

FIGURE 2 - FT-IR spectra of ACF’s.

3.3. Structure characterization of the activated carbon

The FT-IR spectrum of ACF samples is presented in Fig. 2 in which the samples show many peaks belonging to different functional groups. The peaks seen at 3000 cm–1 belong to aliphatic C-H bands. The peaks which appear at 3625 cm-1 indicate free –OH structure [20, 21] The ap-pearance of bands between 1300 and 1000 cm-1 could be attributed to C-O stretching vibrations of ACFs. The C-O-

H band absorption peaks were observed to shift to 900 and 675 cm -1 [22].

XRD diffractograms of ACF samples are shown in Fig. 3. XRD technique is widely and commonly used for the purpose of both clarifying the crystal structure of activated carbon and determining the ash content. The ACF samples have amorphous structures between the 2θ = 20 and 30 ranges [23].

FIGURE 3 - X-Ray diffraction patterns of activated carbon samples.


88

0

20

40

60

80

100

0 200 400 600 800 1000 1200

Naproxen sodium concentration(mg.L-1)

Perc

enta

ge n

apro

xen

sodi

um

rem

oval

(%)

1 h2 h

FIGURE 4 - Effect of concentration and contact time for naproxen sodium adsorption(pH=7, T=298 K, m=0.1 gr).

3.4. Effect of initial concentration of naproxen sodium on adsorption

Effect of initial naproxen sodium concentration on ad-sorption was investigated at concentrations ranging from 200 to 1000 mg.L–1 at 298 K. Fig.4 illustrates the effect of initial naproxen sodium concentration on ACF; the uptake is almost 93 % at 200 mg.L–1 concentration and 54-80 % at the other concentrations. The percentage of naproxen sodium removal declined with increase in concentration.

3.5. Effect of contact time on naproxen sodium adsorption

Effect of contact time for removal of naproxen so-dium by IPZN1 is presented in Fig.4. At a concentration of 200 mg.L–1, the percentage of naproxen sodium re-moved rose from 85 to 93 as the contact time was in-creased from 1 to 2 h. That there is an increase in adsorp-tion with increase in contact time is an indication of the increased mobility of the molecules of naproxen sodium.

3.6. Adsorption Isotherms

In order to determine the mechanism of naproxen so-dium adsorption onto IPZN1, the experimental data were applied to the Langmuir, Freundlich, D-R, Temkin, Frum-kin, Halsey and Henderson isotherm equations. The con-stant parameters of the isotherm equations for this adsorp-tion process were calculated by regression using the linear form of the isotherm equations. The constant parameters and correlation coefficients (R2) are listed in Table 2.

The Langmuir adsorption isotherm has been succes-fully applied to many real sorption processes. The lin-earized Langmuir isotherms are represented by the fol-lowing equation [24]:

00 .1

QC

bQqC e

e

e += (11)

where Ce is the naproxen concentration at equilibrium (mg.L–1), qe the adsorption capacity in equilibrium (mg.g–1), b the Langmuir adsorption constant (L.mg–1), and Q0 sig-

nifies the adsorption capacity (mg.g–1). The value of Q0 was determined as 294.11 mg.g–1 in Table 2. To determine whether or not the adsorption process is favourable, iso-therms can be classified by a term ‘RL’, a dimensionless constant separation factor, which is defined as below [25]

011bC

RL += (12)

where C0 is the initial naproxen concentration (mg.L-1).

The RL values are found in the range of 0.08-0.29 showing favorable adsorption.

The Freundlich adsorption isotherm can be expressed [26] as

efe Cn

kq log1loglog += (13)

where kf (L.g-1) and n are isotherm constants which indicate the capacity and intensity of adsorption, respec-tively. The linear plot of logqe versus logCe shows that adsorption of naproxen sodium also follows the Freundlich isotherm. Table 2 shows the Freundlich adsorption isotherm constant and correlation coefficient. The value of n for the Freundlich isotherm was found to be greater than 1, hence naproxen sodium was favourably adsorbed by IPZN1.

The D-R equation can be expressed [27] as

).exp( 2' εKqq me −= (14)

where ε (Polanyi potential) is equal to eC

RT 11ln( + ),

qe is the amount of the naproxen sodium adsorbed per unit activated carbon fiber (mol.g-1), qm the theoretical mono-layer saturation capacity (mol.g-1), Ce equilibrium concen-tration of the naproxen sodium solution (mol.L-1), K′ a con-stant of the adsorption energy (mol2.kJ-2), R gas constant (kJ.mol-1.K-1), and T is the temperature (K). The linear form of the D-R isotherm is,


89

TABLE 2 - Isotherm constants for naproxen sodium adsorpsion onto IPZN1.

Adsorption İsotherms Constants Langmuir İsotherm Qo (mg.g–1) b (L.mg–1) R2

294.11 0.012 0.961

Freundlich İsotherm kf n R2

4.84 3.22 0.976

Dubinin-Radushkevich İsotherm qm (mg.g–1) K’ (mol. kJ–1)2 E (kJ. mol–1) R2

0.002 0.003 12.71 0.957

Temkin İsotherm B KT R2

50.20 0.314 0.916

Frumkin İsotherm a lnk ∆G (kJ. mol–1) R2

-2.36 8.66 -21.46 0.882

Halsey İsotherm k n R2

3.08 3.22 0.976

Henderson İsotherm k.1011

n R2

4.8 3.41 0.976

2' .lnln εKqq me −= (15)

K′ is related to adsorption energy (E, kJ.mol-1) as [28]

'21K

E = (16)

The constants calculated for the D-R equation are shown in Table 2. The mean adsorption energy (E) gives information about chemical- and physical adsorption. It was found to be 12.71 kJ.mol-1, which is lower than the energy range of adsorption reaction 8-16 kj.mol-1. This type of adsorption of naproxen sodium (onto IPZN1) is defined as physical adsorption [29].

The heat of adsorption and the adsorbate-adsorbate in-teraction on adsorption isotherms were studied by Temkin and Pyzhev [30]. The Temkin isotherm equation is given as,

).ln(. eTe CKb

RTq = (17)

Eq. (17) can be linearized as,

eTe CBKBq ln.ln. 1.1 += (18)

where B1=RT/b, T is the absolute temperature in K, R universal gas constant, 8.314 J.mol-1.K-1, KT the equilib-rium binding constant (L.mg-1), and B1 is related to the heat of adsorption.

The constants obtained for Temkin isotherms are shown in Table 2.

Another popular equation for analysis of isotherm of a high degree of rectangularity is that proposed by Frum-kin. The Frumkin isotherm takes into account interaction between adsorbed species [31] and can be expressed as

ea Cke ..

)1(2 =

−−

θθ

(19)

where θ is the fractional occupation (θ=qe/qm) and Ce is the concentration of naproxen sodium on the IPZN1 sample at equilibrium (mol.L-1); the linearized form is

θθ

θ akC e

2ln1.1

ln +=

− (20)

The parameters a and k are obtained from the slope

and intercept of the plot

− eC1.

1ln

θθ versus θ. The

constant k is related to adsorption equilibrium

RTGk ∆−

=ln (21)

The parameter a is the interaction coefficient. The isotherm constants are summarized in Table 2. The ∆G value of -21.46 kJ.mol-1 for adsorption of naproxen so-dium is because the adsorption process is spontaneous.


90

Halsey [32] and Handerson [33] adsorption isotherm can be expressed respectively as,

ee Cn

kn

q ln1ln1ln

−

= (22)

and

[ ] ee qnkC lnln)1ln(ln +=−− (23)

These equations are suitable for multilayer adsorp-tion; moreover, the these equation can be fitted for heter-oporous solids [34] The Halsey and Henderson adsorption isotherm constants and correlation coefficients are sum-marized in Table 2.

3.7. Column Studies

The results of naproxen sodium adsorption on the IPZN1 using a continuous system were determined in the form of breakthrough curves which showed the loading behaviours of naproxen sodium as adsorbed from the solution and were expressed in terms of normalized con-centration defined as the ratio of the outlet concentration to the inlet concentration of naproxen sodium as a func-tion of time (C/Co vs. t).

3.7.1. Effect of solution flow rate

The effect of the flow rate on the adsorption of na-proxen sodium onto activated carbon was determined by varying the flow rate (20, 30, and 40 mL. min–1) with a constant adsorbent bed height of 7 cm and initial na-

proxen sodium concentration of 400 mg.L-1, as shown by the breakthrough curve in Fig. 5.

As shown in Table 3, the breakthrough occurs (td) at 56, 35, and 25 min and HB investigated at 3.81, 4.2, and 3.06 cm for flow rates of 20, 30, and 40 mL. min–1, respec-tively. As can be seen in Fig. 5, the bed capacities de-creased with the increase in flow rate. The maximum bed capacities for flow rates 20, 30, and 40 mL. min–1 were found to be 224, 210, and 200 mg.g-1, respectively. As the flow rate increases, the breakthrough curve becomes steeper and the break point time and adsorbed ion concen-tration decrease. This behaviour can be explained as fol-lows: the residence time of the solute in the column is not long enough for adsorption equilibrium to be read at that flow rate as the naproxen sodium solution leaves the col-umn before equilibrium occurs [35-36]. Table 3 shows that EBRT are 0.61, 0.41, and 0.31; BV are 91.80, 85.36, and 80.74; adsorbent exhaustion rates are 3.5, 5.7, and 8 (%) for flow rate 20, 30, and 40 mL.min-1, respectively. Also, as the flow rate increased adsorbent exhaustion rate in-creased. At higher EBRT, naproxen sodium ions had more time to contact IPZN1, which resulted in greater removal of naproxen sodium ions in fixed-bed columns.

3.7.2 Mathematical Description

Various mathematical models can be used to describe fixed bed adsorption. These mathematical models have been developed to predict the dynamic behaviour of the column and this work used the following model character-izing fixed bed performance.

0

0,2

0,4

0,6

0,8

1

0 10 20 30 40 50 60 70

t (min)

C/C

o

20 ml/min30 ml/min40ml/min

FIGURE 5 - Effect of flow rate on breakthrough curve( Co = 400 mg.L–1, bed height= 7 cm (2 g), 25 oC)

TABLE 3 - Column data and parameters obtained at different flow rates.

Flow rate (mL. min–1) td (min) tt (min) tb (min) HB (cm) HUNB (cm) qe (mg.g–1) EBRT BV Exh. Rate (%)

20 56 38.5 21 3.81 3.18 224 0.61 91.80 3.5 30 35 25 15 4.20 2.80 210 0.41 85.36 5.7 40 25 16 7 3.06 3.93 200 0.31 80.74 8.0


91

0

0,2

0,4

0,6

0,8

1

0 10 20 30 40 50 60 70

t (min)

C/C o

20 mL/min theo.20 ml/min30 mL/min theo.30 ml/min40 mL/min theo.40ml/min

0

0,2

0,4

0,6

0,8

1

0 10 20 30 40 50 60 70

t (min)

C/Co

20 mL/min theo.20 ml/min30 mL/min theo.30 ml/min40 mL/min theo.40ml/min

(a)

(b)

FIGURE 6 - Comparasion of the experimental, Thomas model (a) and Yoon-Nelson model (b) breakthrough curves.

3.7.2.1. Thomas model

The Thomas model is simple and is widely used for fixed-bed adsorption. The column sorption data obtained at different flow rates were fitted using the Thomas model [16,37]. The model has the following form;

))..((11

effooTho VCXqkCC

−ΕΧΡ+= (24)

where kTh is the Thomas rate constant ( mL.min-1.mg-1) and qo is the maximum solid-phase concentration of the solute (mg.g-1). The linearized form of the Thomas model is as follows;

effoThoTho V

QCk

QXqk

CC ...

)1ln( −=− (25)

The kinetic coefficient kTh and the adsorption ca-pacity of the bed qo can be determined from a plot of ln[(Co/C)-1] against t at given flow rates. Comparisons of experimentally determined breakthrough curves with those predicted by the Thomas model can be seen in Fig. 6. From experimental results and data regression the model pro-posed by Thomas provides a good correlation of the ef-fects of flow rates. Table 4 summarizes the Thomas model

parameters obtained at different flow rates. With increasing flow rate, the bed capacity qo decreased and the Thomas constant kTh increased. Similar results have been noted in literature [16, 19, 38].

3.7.2.2 Yoon and Nelson Model

This model is based on assumption that the rate of decrease in the probability of adsorption for each adsor-bate ion is proportional to the probability of adsorbate adsorption and probability of adsorbate breakthrough on the adsorbent [16].

The Yoon and Nelson model can be expressed as fol-lows;

YNYNo

ktkCC

C ..ln τ−=−

(26)

where kYN is the rate constant (min-1), τ the time re-quired for 50 % adsorbate breakthrough (min) and t is the breakthrough time (min). The values of kYN and τ were determined from plots of ln[C/(Co-C)] against t at differ-ent flow rates. The values obtained can be seen in Table 4; with increasing flow rate, the rate constant kYN increased and τ decreased. and τ values are very similar to those ob-


92

TABLE 4 - Thomas [16] and Yoon- Nelson [16] model parameters at different flow rates.

Flow rate (mL. min–1)

kTH

( mL. mg–1.min–1)

qo (mg.g–1)

kYN

(min–1) τcal.

(min) τexp.

(min) εTH

(%) εYN

(%) 20 0.659 151 0.263 37.6 40 14.20 11,23 30 0.963 145 0.385 24.1 21 16.81 15,26 40 1.204 140 0.481 17.4 16 18.67 17,60

tained as experimental results. The theoretical curves are compared with the corresponding experimental data in Fig. 6. From experimental results and data regression, the model proposed by Yoon-Nelson provided a good corre-lation of the effects of flow rates.

The average percentage errors (ε %) are used to indi-cate the fit between the experimental data and theoretical values of C/Co used for plotting breakthrough curves; they are calculated using the following equation [16].

100*])//())/()/[(( exp

1exp

N

CCCCCC otheoo

N

io −

=∑

=ε (27)

where N is the number of measurements.

It is clear from figures and average percentage er-rors(<18.67 %) in Table 4 that there is a good agreement between the experimental and predicted values from the experimental results and data regression. The model pro-posed by Yoon-Nelson provided a good correlation of the effect of inlet naproxen sodium concentration and flow rate

4. CONCLUSION

The results of this work are summarized as follows: - The highest BET surface area of ACFs investigated

was 1866 m2.g-1. -The Freundlich, Langmuir, D-R, Temkin, Frumkin,

Halsey and Henderson adsorption models were used for adsorption of naproxen sodium onto activated carbon fibers and the isotherm constants were determined.

- The adsorption of naproxen sodium is dependent on the flow rate and the breakpoint time and naproxen so-dium removal decreases with increasing flow rate.

- The Thomas and the Yoon-Nelson models were in-vestigated by linear regression techniques and are rec-ommended for use in column design.

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Received: July 07, 2011 Revised: August 24, 2011 Accepted: September 08, 2011 CORRESPONDING AUTHOR

Çiğdem Sarıcı-Özdemir Inonu Universty Faculty of Engineering Department of Chemical Engineering 44280 Malatya TURKEY Phone: +90 422 3410010–4757, Fax: +90 422 3410046 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 84 – 93


94

ELEMENTAL AND RADIOACTIVITY ANALYSES OF MOSSES COLLECTED IN HATILA VALLEY NATIONAL PARK - EASTERN BLACK SEA REGION OF TURKEY

Bahadir Koz1,*, Ugur Cevik2 and Necati Celik3

1 Giresun University, Elementary Teaching Department, 28049 Giresun, Turkey 2 Karadeniz Technical University, Department of Physics, 61080 Trabzon, Turkey 3Gümüşhane University, Department of Physics Engineering, Gümüşhane, Turkey

ABSTRACT

This study was performed in one of the most impor-tant parks of Turkey, the Hatila national park, where a gold mining activity is planned to be conducted. For this purpose, 40 moss samples were collected for heavy metal and radioactivity measurements. A radioisotope excited Xray fluorescence analysis using the method of multiple standard additions was applied for the elemental analysis of mosses collected in Hatila valley national park in the Eastern Black Sea region of Turkey. An annular 50 mCi 241Am radioactive source and annular 50 mCi 55Fe radio-active sources were used for excitation of characteristic K-X rays. An Si(Li) detector which had a 147eV full width at half maximum for 5.9 keV photons was used for inten-sity measurements. A qualitative analysis of spectral peaks showed that the samples contained potassium, calcium, titanium, iron, tin and barium with average concentrations of 230, 5340, 450, 70, 4 and 33 mg.kg-1, respectively. 137Cs and 40K using gamma-ray spectroscopy were deter-mined in 40 moss species collected from the Hatila valley national park. Average activity concentrations of 40K and 137Cs were found to be 103 and 272 Bq.kg-1, respectively. As the region was shown to be heavily contaminated by the Chernobyl accident, the high concentration of 137Cs could be due to this accident. The results are compared with those obtained in different parts of the world where gold mining activities are being conducted. The possible consequences of these results are briefly discussed from the point of po-tential hazards to ecology and human health.

KEYWORDS: Hatila valley, moss, gold mining, energy dispersive X-ray fluorescence (EDXRF), Chernobyl, radiocaesium


1. INTRODUCTION

Heavy metals are classified among the most danger-ous groups of anthropogenic environmental pollutants due to their toxicity and persistence in the environment [1]. Mining and industrial processing are among the main sources of heavy metal contamination in the environment. Mining activities, through milling operations coupled with grinding, concentrating ores and disposal of tailings, pro-vide obvious sources of metal contamination in the envi-ronment [2]. Increasing anthropogenic influences on the environment, especially pollution loadings, have caused negative changes in natural ecosystems: decreased biodi-versity, simplified structure and lowered productivity. These degradation processes can be seen especially in forest ecosystems [3]. Consequently, it is imperative to continu-ally assess and monitor the levels of heavy metals in the environment due to anthropogenic activities, including mining activities, for evaluation of human exposure and sustainable environment.

Natural and artificial radionuclides are the main sources of radiation exposure to human beings in the environment [4]. The natural sources are largely due to the primordial radionuclides, mainly 238U and 232Th and their decay prod-ucts, as well as 40K. These radionuclides are present in various degrees in all media in the environment, including the human body itself. Besides naturally occurring ones, many radionuclides of artificial origin have been released into the environment by different processes. The isotope 137Cs is one of these anthropogenic radionuclides produced by several types of nuclear activities including past testing of nuclear weapons, accidents in nuclear facilities, reproc-essing of spent nuclear fuel and nuclear power reactors. Until the Chernobyl event (26 April 1986), the activity of 137Cs occurred mostly as a result of global fallout from atmospheric nuclear weapon tests. Around 9.6×1015 Bq of 137Cs was injected into the stratosphere from past tests [5]. After the Chernobyl incident, 137Cs atmospheric activity in central and northern Europe is expected to be strongly controlled by the Chernobyl influence [6]. Turkey, espe-


95

cially the northern part of it, was one of the countries which were contaminated by the Chernobyl accident [7]. In the present work, natural (40K) and artificial (137Cs) radionuclides have been measured in moss samples.

Metal biomonitoring with naturally growing mosses has been a powerful tool, and it was firstly used by Ruh-ling and Tyler [8]. It has become, over 30 years, a valid technique used in Europe [9, 10]. The accumulation abil-ity of bryophytes is of such importance because they take up metals to levels far above their expected physiological needs. They have a high surface-to-volume ratio enabling particles to be trapped, a high cation exchange capacity, and a lack of a well-developed cuticle in their tissues, lead-ing to accumulation of large amounts of elements [11, 12].

Unlike higher plants, mosses have no root system or cu-ticle layer; hence, mineral adsorption occurs over their entire surface. Mineral uptakes from soil play a minor role, and the adsorption of heavy metals is mainly derived from atmos-pheric flux on the surfaces of the moss. Therefore, mosses are excellent biomonitors for trace elements in air [13].

Mosses are sensitive bioindicators of heavy metal contamination and have several advantages as indicator organisms: (1) many species have a vast geographical distribution, and they grow abundantly in various natural habitats, even in industrial and urban agglomerations, (2) they have no epidermis or cuticle, therefore, their cell walls are easily penetrable by metal ions, (3) they have no or-gans for uptake of minerals from substrate, and obtain them mainly from precipitation, (4) transport of minerals between segments is poor because of lack of vascular tis-sues, (5) mosses accumulate metals in a passive way, acting as ion exchangers, and (6) mosses show the con-centrations of the most metals as a function of the amount of atmospheric deposition [14].

The Hatila Valley National Park is one of the 33 na-tional parks in Turkey. It has been under protection since 1994. It is located in the Eastern Black Sea region of Tur-key at the border of the Artvin province. In the valley, the generous vegetation contains over 500 plant species and a rich fauna. The most frequently observed species are bear, wild pork, fox, badger, wild goat, weasel, hawk, eagle, coyote, wild cock, Hopa viper and salmon trout. The most particular future of the vegetation is that it reflects Mediter-ranean vegetation future as well as endemic plant species.

This region is known for its rich gold deposit/ miner-alization; therefore, gold mining is planned in this region in the coming years. The results showed that the region is relatively clean with respect to heavy metals compared to its surroundings, since it has been under protection as national park since 1994. It is believed that the region could be contaminated by potential mining activities [15]. In spite of the anticipated economic benefit that may be derived from gold mining, concerns have been raised that the extraction of natural and hazardous materials during the mining process would degrade this protected area. Therefore, this study is planned and conducted in order to

determine the heavy metal level of the valley before min-ing activity. This work would serve as a basic study, pro-viding information on the background heavy metal level of the valley so that possible contamination of the area in the future, as a result of the proposed mining activity, could be assessed and monitored.


In our measurements, maximum relative errors due to the counting system were of the order ~0.5-5%. Errors originating from sample weighing, source intensity and system geometry were about 4%. The combined relative error in our results was accordingly of the order of 8%.

Errors associated with the activity concentrations come from the net peak area evaluation, determination of mass of the samples and efficiency determination of the counting system. Uncertainty coming from the net peak area is given automatically by the software GammaVision sup-plied with the detection system.

2.1. Sampling and preparation

Moss samples were collected from Hatila Valley Na-tional Park located in the Eastern Black Sea of Turkey (Fig. 1). All samples were dried in a Heraeus furnace and then ground in a spex mill. To reduce particle size effect, the powders obtained were sieved using a 400-mesh sieve and then stirred for 25 min to obtain a well-mixed sample. Forty mg of this powder was pressed in 13-mm diameter pellets for elemental analyses. Then, the rest of the oven-dried samples was put into plastic bottles for radioactivity analyses.

FIGURE 1 - Study area

2.2. Elemental analyses

Quantitative determination of elements is an impor-tant task in industrial, chemical, environmental, minera-logical, physical and medical science as well as in other


96

fields. There are many elemental analysis techniques, such as atomic absorption spectrometry, neutron activa-tion analysis and X-ray fluorescence (XRF). Energy dis-persive X-ray fluorescence (EDXRF) offers several ad-vantages [16]. It allows simultaneous detection and de-termination of several elements; it is sensitive and repro-ducible as well. Sample preparation is usually simple and fast. Also, the equipment cost is much cheaper than that of the conventional wavelength X-ray fluorescence tech-niques, especially if a radioisotope is used instead of an X-ray tube for excitation. The EDXRF method gives also a possibility of trace analysis of biological and geological samples. In recent years, EDXRF method has been used for elemental analysis by several authors [17-19].

Quantitative analysis for the elements was carried out using the standard addition method. The method involves the addition of known quantities of the analyte to the specimen. If an analyte is presented at low levels and no suitable standards are available, standard addition may prove to be an alternative, especially if the analyst is in-terested in only one element. Certain amounts of the ele-ments to be analyzed are added to samples. The detection limit of the system can be calculated as follows:

i

ii S

IC =

where, Ci is the concentration (mg/kg), Ii is the char-acteristic X-ray intensity (cps), and Si is the elemental sensitivity (cps/(mg.kg-1)) for the element i. The detection limit was calculated as follows:

tBGI

SDL i

ii

)(3=

where, DLi is detection limit (mg/kg), Ii(BG) is the background intensity (cps) under element I peak, and t is the counting time (s).

In elemental analysis using X-ray fluorescence technique, matrix effects are known to distort the linearity of “photo peak area versus concentration” graphs for analyses [20]. To minimize or to eliminate matrix effects, we proceeded as follows: (i) the Kα net peak areas for iron, tin and bar-ium obtained from sample spectra were normalized by dividing them by Compton net peak areas, whilst the Kα net peak areas for potassium, calcium, titanium obtained from sample spectra were normalized by dividing them by MnKα peak areas, and (ii) to obtain an ideal grain size for the samples the ground material was sieved using a 400-mesh sieve.

Samples were irradiated by 59.5 keV photons emitted by an annular 50 mCi 241Am radioactive source for iron, tin and barium determination and irradiated by 5.9 keV pho-tons emitted by an annular 50 mCi 55Fe radioactive source for potassium, calcium and titanium. A PGT Si(Li) detec-tor having 147 eV full width at half maximum (FWHM) for 5.9 keV was used for the experiments. 2048 channels

of a multi-channel analyzer were employed in data acqui-sition. In qualitative analysis, characteristic X-rays emit-ted by excited atoms of the sample were registered for 2000 seconds.

2.3. Determination of 40K and 137Cs activity concentrations

Activity concentrations of 40K and 137Cs radionuclides were determined using HPGe computer-controlled detec-tor having the resolution of 2 keV for the 1332 keV en-ergy line of 60Co with conventional electronics and 55% relative efficiency and Genie 2000 as software. The detec-tor was shielded with a 10-cm thick lead layer to reduce the background due to the cosmic rays and the radiation nearby the system.

Full energy peak efficiencies were determined using Standard Reference Material [21] for the moss samples prepared by International Atomic Energy Agency. Decay corrections were performed to the sampling date.

The activity concentrations of 40K and 137Cs were de-termined using their 1460 keV and 661 keV gamma-ray lines, respectively. The specific activity of each sample was then calculated by using the following formula.

)/( tmICA net γε=

where, Cnet is the net area of the total absorption line, A is the activity of the isotope in Bq/ kg, Iγ is the absolute intensity of the transition, t is the sample measurement time,ε is the full energy peak efficiency, and m is the mass of the sample.

For a good statistical analysis, each sample was measured for 86400 seconds. In this way, the statistical error was less than 1 %.


The concentrations of 6 elements in moss samples, their range and mean values (K, Ca, Ti, Fe, Sn, Ba) are shown in Table 1. As can be seen, while Bryum palles-cens, Eurhynchium striatulum, Schistidium apocarpum, Schistidium apocarpum and Dicranella heteromalla have the highest values of K, Ca, Ti, Fe, Sn and Ba; Neckera crispa, Schistidium apocarpum, Neckera crispa, Homalothe-cium lutescens, Drepanocladus uncinatus and Schistidium apocarpum have the lowest ones for K, Ca, Ti, Fe, Sn and Ba, respectively. Fe and Sn are heavy metals. K, Ca, Ti, Fe and Ba are naturally present in the earth crust. Ca is predominantly an indicator for soil dust. Except Sn, K, Ca, Ti, Fe and Ba are present in the earth crust naturally.

The activity concentration results of 40K and 137Cs are given in Table 2. 40K is a naturally occurring radioactive element while 137Cs is released into the atmosphere artifi-cially [5]. Radioactivity concentrations of 40K and 137Cs range from 32 to 384 and from 83 to 826 Bq/kg, with average values of 103 and 272 Bq/kg, respectively. The presence of radiocaesium is probably due to the Cherno-byl accident: The radioactive plume from the accident


97

reached Turkey by 5 May 1986, substantially contaminat-ing various regions and ecosystems of the country. During the emergency, Cekmece Nuclear Research and Training Center (ÇANEM) performed an analysis of various sub-stances. In their report, it has been noted that the surface soil 137Cs activity concentration of the eastern part of the Black Sea mountains was around 4-4.5 kBq/kg at the 0.5-

cm soil in the year 1988 [22]. Also, the level of 137Cs activity concentration in Turkish tea plant was found to be the maximal 44 kBq/kg for the 1986 products by Ge-dikoglu and Sipahi [23]. Recent publications have shown that radiocaesium is still eminent in the eastern part of the Black Sea Region of Turkey [24-27].

TABLE 1 - Hatila valley elemental concentrations (mg/g).

Species K Ca Ti Fe Sn Ba 1 Mnium hornum 2.8 28.6 12.2 2.3 0.03 0.5 1 Schistidium apocarpum 1.4 11.2 22.6 2.7 0.03 0.1 1 Ryhnchostegium murale 3.9 36.8 2.5 0.3 0.03 0.3 1 Hypnum cupressiforme 2.1 19.5 BDL 0.5 0.03 0.3 1 Homalothecium lutescens BDL 93.5 1.2 0.1 0.03 0.2 1 Homalothecium sericeum BDL 117.7 1.2 0.4 0.04 0.2 1 Dicranum scoparium 2.1 63.3 4.3 0.5 0.03 0.2 1 Hylocomium splendens 5.1 43.4 2.6 0.3 0.08 0.3 1 Hypnum jutlandicum 1.8 31.6 2.8 0.3 0.03 0.2 1 Rhytidiadelphus squarrosus 1.5 40.4 6.9 0.6 0.04 0.3 1 Brachythecium glaerosum 3.4 59.5 3.4 0.3 0.03 0.4 1 Bryum rubens 1 90.8 3.8 0.4 0.03 0.4 1 Mnium marginatum 2.3 47.6 5.0 0.5 0.03 0.3 1 Dicranum mildeanum 1.9 37.5 5.0 0.4 0.02 0.3 1 Rhynchostegium riparioides 1.2 42.2 2.6 0.2 0.08 0.2 1 Ctenidium molluscum 1.0 39.4 2.3 0.3 0.04 0.2 1 Neckera crispa 0.8 72.2 1 0.2 0.06 0.1 1 Pleurozium schreberi 6.5 41.7 7.4 0.7 0.03 0.4 1Tortula subulata 4.7 32.8 10.9 2.4 0.03 0.3 1 Grimmia hartmanii 1.7 26.1 6.9 1.5 0.07 0.4 1 Hylocomium brevirostre 1.0 41.8 4 0.5 0.07 0.2 1 Rhytidiadelphus squarrosus 1.8 47.1 4 0.8 0.07 0.1 1 Eurhynchium pulchellum 2.3 59.8 2.6 0.3 0.03 0.4 1 Dicranella heteromalla 2.3 66.8 2.7 0.3 0.1 0.2 1 Tortula subulata 2.0 60.7 2.6 0.3 0.2 0.2 1 Drepanocladus uncinatus 2.2 86.8 6.2 1.1 0.2 0.2 1 Eurhynchium striatum 4.0 51.7 2.4 0.3 BDL 0.3 2 Brachythecium albicans 2.3 66.7 6.3 0.9 0.05 0.3 2 Brachythecium salebrosum 2.0 43.4 5.8 0.7 BDL 0.4 1 Eurhynchium hians 2.4 39.6 4.1 0.7 0.04 0.3 1 Dicranum majus 2.3 43.7 1.7 0.2 0.04 0.2 1 Isothecium myurum 1.6 36.0 5.2 0.7 0.08 0.4 2 Brachythecium populeum 5.1 37.4 2.7 0.3 BDL 0.2 1 Scleropodium purum 3.1 30.6 6 0.7 0.07 1 1 Habrodon perpusillus 1.5 35.4 4.7 0.6 0.07 0.3 2 Bryum pallescens 8.5 29.0 9.4 1.4 BDL 0.7 1 Tortella fragilis 0.9 88.8 7.8 1 BDL 0.3 1 Amblystegium varium 1.9 37.2 9.5 0.7 0.05 0.4 1 Leucodon sciuroides 2.9 46.1 5 0.4 0.04 0.7 1 Eurhynchium striatulum 1.2 172.2 8.1 0.9 BDL 0.4

Average 2.3 53.4 4.5 0.7 0.04 0.33

Min.-Max. Values 0.8-8.5 11.2-172.2 1-22.6 0.1-2.7 0.02-0.01 0.1-1 1 on soil; 2 on tree; BDL: Below Dedection Limit


98

TABLE 2 - Activity concentration results of 40K and 137Cs (Bq/kg).

Species 137Cs 40K 1 Mnium hornum 47±4 97±4 1 Schistidium apocarpum 50±5 212±8 1 Ryhnchostegium murale 102±9 808±28 1 Hypnum cupressiforme 54±5 168±6 1 Homalothecium lutescens 51±5 194±7 1 Homalothecium sericeum 45±4 430±17 1 Dicranum scoparium 44±4 543±21 1 Hylocomium splendens 92±9 537±21 1 Hypnum jutlandicum 73±7 BDL 1 Rhytidiadelphus squarrosus 132±11 526±21 1 Brachythecium glaerosum 32±3 172±7 2 Bryum rubens 75±7 111±4 1 Mnium marginatum 117±9 BDL 1 Dicranum mildeanum 113±10 242±9 1 Rhynchostegium riparioides 348±31 251±10 1 Ctenidium molluscum 72±7 187±7 1 Neckera crispa 54±5 164±6 1 Pleurozium schreberi 145±12 91±4 1 Tortula subulata 35±4 142±6 1 Grimmia hartmanii 37±4 826±33 1 Hylocomium brevirostre 50±5 380±15 1 Rhytidiadelphus squarrosus 50±5 363±15 1 Eurhynchium pulchellum 45±4 BDL 1 Dicranella heteromalla 99±9 108±4 1 Tortula subulata 74±7 249±9 1 Drepanocladus uncinatus 85±9 417±17 1 Eurhynchium striatum 149±13 238±10 2 Brachythecium albicans 168±15 462±18 2 Brachythecium salebrosum 79±7 352±13 1 Eurhynchium hians 66±5 322±12 1 Dicranum majus 89±8 376±15 1 Isothecium myurum 253±23 806±32 2 Brachythecium populeum 120±11 127±5 1 Scleropodium purum 146±13 215±9 1 Habrodon perpusillus 136±11 387±15 2 Bryum pallescens 44±6 121±5 1 Tortella fragilis 70±7 87±4 1 Amblystegium varium 56±5 87±3 1 Leucodon sciuroides 182±17 83±4 1 Eurhynchium striatulum 68±6 177±7

Average 103 272 Min.-Max. values 32-348 83-826 1 on soil; 2 on tree

In Table 3, a comparison of elemental concentrations in different references with the current study area has been introduced. Data presented in Table 3 show that the elemental concentrations in the present study are quite lower than those given in the literature showing that the study area is relatively clean with respect to its surrounding area. This can be correlated with the fact that the valley has been under protection as national park since 1994.

K, Ca and Fe are naturally present in the earth crust, sometimes in the soil in high concentration. In moss sam-

ples, the concentrations could be low which could be attributed to the fact that mosses have no developed root system like other plants. They cannot take elements from the soil but absorb the air in their environment. Therefore, mosses are perfect bioindicators for environmental studies [13].

In Table 4, a literature study on elemental concentra-tions conducted in gold mining areas is summarized. Ac-cording to Boamponsen et al. [28], Sb, Mn, Cu, V, Co, Hg, As, Cd and Th are heavy metals found in a gold mining


99

TABLE 3 - Comparison of the present elemental analysis (mg/g) results with previously published data.

Sampling Region K Ca Ti Fe Ba

Sarp-Samsun Highway19 7.33 38.1 11.60 43.30 0.41

Giresun and Ordu Province32 46.6 54.4 1.04 37.25 1.96

Akçaabat tobacco33 200 61.3 0.26 1.83 0.17

Present study 2.3 53.4 4.5 0.7 0.33

TABLE 4 - Heavy metal concentrations (mg/kg) found in gold and other mining areas of different parts in the world.

Sampling Region Mn Cu Zn Co Hg As Pb Cd Tarkwa gold mining area Ghana28

383

39

1

0.5

1

1

Tapajos gold mining reserve Para State, Brazil29

3

The mining of Pb-Zn ores Aran valley, Spain30

239 208 3371 87 17 1099

As-contaminated abandoned mines Myoung-bong, Korea31

24 59 1064 40

3

area in Ghana. In the current study, none of these heavy metals have been detected in Hatila Valley. It is expected that some of these heavy metals listed above could be detected after the planned mining activity. In reference [29], high Hg concentration was reported in gold mining re-serve in Brazil. Another study [30] shows that Mn, Cu, Zn, Co, As and Pb are found in the Aran valley, Spain. It has also been shown that Cu, Zn, As and Pb heavy metals are found in the mines in Korea [31].

As shown in Table 4, heavy metal contamination is seen in the areas where the gold mining activities are performed. This study shows that the area under study is clean in terms of heavy metals. This could be because the area is a national park and has been protected for many years. However, a gold mining activity is planned to be carried out in this area. If this study is repeated when the gold mining activity is started, any heavy metal contami-nation can then be attributed to this activity. Therefore, the data presented herein represent a basic data for future studies.

4. CONCLUSION

The results of elemental and radioactivity concentra-tion in moss species of Hatila National Valley have been presented. The valley has been under protection as national park since 1994. However, due to its rich gold deposit/ mineralization, plans have been put in place to start gold mining in this region in the coming years. It is well-known that mining activity releases a number of heavy metals into the environmental causing serious pollution to the environ-ment. The current study shows that elemental concentra-tions in the valley are relatively lower than those in its surroundings. If a mining activity starts in this region, it is expected that the region will be contaminated. This study

is, therefore, important since the data presented herein are baseline data for future studies. Also 40K and 137Cs activ-ity concentrations have been determined in this region. 40K is a naturally occurring radionuclide. Therefore, it is pre-sent everywhere. The presence of 137Cs could be correlated with the Chernobyl accident since it is well-documented that the Eastern Black Sea region of Turkey was heavily contaminated by this accident.

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[2] Adriano, D.C. (1986) Trace Elements in the Terrestrial Envi-ronment, Springer-Verlag, New York, p.533.

[3] Shparyk, Y.S. and Parpan, V.I. (2004) Heavy metal pollution and forest health in the Ukrainian Carpathians. Environ-mental Pollution, 130, 55-63.

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[6] Kulan, A. (2006) Seasonal 7Be and 137 Cs activities in sur-face air before and after the Chernobyl event, Journal of En-vironmental Radioactivity, 90, 140-150.

[7] Turkish Atomic Energy Authority (TAEK) (April 1988) Türkiye’de Çernobil Sonrası Radyasyon ve Radyasyon Çal-ışmaları, TAEK Report (İnTurkish).

[8] Ruhling, A. and Tyler, G. (1968) An ecological approach to the lead problem. Botaniska Notiser, 122, 248-342.


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[9] Genoni, P., Parco, V. and Santagostino, A.S. (2000) Metal biomonitoring with mosses in the surroundings of an oil-fired power plant in Italy. Chemosphere, 41, 729-733.

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[11] Uyar, G., Ören, M. & İnce, M. (2007) Atmospheric Heavy Metal Deposition in Düzce Province by Using Mosses as Biomonitors. Fresenius Environmental Bulletin, 16, 145-153.

[12] Zechmeister, H.G., Grodzinska, K. and Szarek-Lukaszewska, G. (2003) Bryophytes. In: Markert, B.A., Breure A.M. Zechmeister, H.G. (eds.): Bioindicators/Biomonitors (princi-ples, assessment, concepts). Elsevier. Amsterdam, pp.339-375.

[13] Lee, S.L.,Li, X.D., Zhang, L., Zhang, S.H., Qi,S.H., Peng,X.Z. (2005) Biomonitoring of trace metals in the at-mosphere using moss (Hypnum plumaeforme) in the Nanling Mountains and the Pearl River Delta Southern China. At-mospheric Environment39, 397-407.

[14] Grodzinska, K. and Szarek-Lukaszewska, G. (2001) Re-sponse of mosses to teh heavy metal deposition in Poland – an overview. Environmental Pollution, 114, 443-451.

[15] MTA (2010) Artvin ili maden ve enerji kaynakları http//www.mta.gov.tr/v1.0/turkiye_maden/maden_potansiyel_2010/Artvin_madenler (10 June 2011)

[16] Van Grieken, R.E. and Markowicz, A.A. (1993) Handbook of X-ray spectrometry. Bassel, Hong Kong, New York. Mar-cel Dekker Inc. p. 314.

[17] Aslan, A., Budak, G. and Karabulut, A. (2004) The amounts Fe, Ba, Sr, K, Ca and Ti in some lichens growing in Erzurum province (Turkey). Journal of Quantitative Spectroscopy and Radiative Transfer, 88, 423-431.

[18] Çevik, U., Kopya, AI., Karal, H. and Sahin, Y. (1995) Quan-titative analysis of sea-bed sediments from Eastern Black Sea by EDXRF spectrometry. Journal of Radioanalitical and Nu-clear Chemistry Letters, 201, 241-249

[19] Koz, B., Cevik, U., Ozdemir, T., Duran, C., Kaya, S., Gun-dogdu, A. and Celik, N. (2008) Analysis of mosses along Sarp-Samsun highway in Turkey. Journal of Hazardous Ma-terials, 153, 646-654.

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[22] Unlu, M.Y., Topcuoğlu, S., Kucukcezzar, R., Varinlioglu, A., Gungor, N., Bulut, A.M. and Gungor, E. (1995) Natural effec-tive half-life of 137Cs in tea plants, Health Physics 65, 94–99.

[23] Gedikoglu, A. and Sipahi, B.L. (1989) Chernobyl Radioactiv-ity in Turkish Tea. Health Physics 56, 97-11.

[24] Celik, N., Cevik, U., Celik, A. and Kucukomeroglu, B (2008) Determination of indoor radon and soil radioactivity levels in Giresun, Turkey. Journal of Environmental Radioactivity, 99, 1348-1354.

[25] Celik, N., Cevik, U., Celik, A. and Koz., B. (2008) Natural and Artificial Radioactivity Measurements in Eastern Black Sea region of Turkey. Journal of Hazardous Materials 162 (1) 146-153

[26] Celik, N., Cevik, U., Celik, A. and Koz, B. (2008) 137Cs and 40K activity concentration measurements and elemental anal-ysis in lichen samples collected from the Giresun province of northeastern Turkey. Isotopes in Environmental and Health Studies, 44(3), 1-9.

[27] Cevik, U., and Celik, N. (2009) Ecological half-life of 137Cs in mosses and lichens in the Ordu povinve, Turkey. Journal of Environmental Radioactivity, 100, 23-28

[28] Boamponsen, L.K., Adam, J.I., Dampare, S.B., Nyarko, B.J.B.,Essumang, D.K. (2010) Assessment of atmospheric heavy metal deposition in the Tarkwa gold mining area of Ghana using epiphytic lichens. Nuclear Instruments and Me-thods in Physics Research B, (in press).

[29] Egler, E.S., Rodrigues-Filho, S., Villas-Boas, R.C., Beinhoff, C. (2006) Evaluation of mercury pollution in cultivated and wild plants from two small communities of the Tapajos gold mining reserve, Para State, Brazil. Science of the Total Envi-ronment, 368, 424-433.

[30] Marques, A.F., Queralt,I., Carvalho, M.L., Bordalo, M. (2003) Total reflection X-ray fluorescence and energy-dispersive X-ray fluorescence analysis of runoff water and vegetation from abandoned mining of Pb-Zn ores. Spectro-chimica Acta Part B 58, 2191-2198.

[31] Chang, J.-S., Yoon, I.H., Kim, K.-W. (2009) Heavy metal and arsenic accumulating fern species as potential ecological indicators in As-contaminated abandoned mines. Ecological Indicators, 9, 1275-1279.

[32] Aslan, A., Budak, G., Tıraşoğlu, E. and Karabulut, A. (2006) Determination of elements in some lichens growing in Gire-sun and Ordu province (Turkey) using energy dispersive X-ray fluorescence spectrometry. Journal of Quantitative Spec-trometry and Radiative Transfer, 97, 10-19.

[33] Çevik,U., Ergen, E., Budak, G., Karabulut, A., Tiraşoğlu, E., Apaydin, G. and Kopya, A.I. (2003) Elemental analysis of Açaabat tobacco and its ash by EDXRF spectrometry. Jour-nal of Quantitative Spectrometry and Radiative Transfer, 78, 409-415.

Received: July 15, 2011 Revised: September 19, 2011 Accepted: October 17, 2011 CORRESPONDING AUTHOR

Bahadir Koz Giresun University Elementary Teaching Department 28049 Giresun TURKEY Phone: +904542153355 Fax: +904542155375 E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 94 - 100


101

THE ADSORPTION OF NONYLPHENOL BY ALGAE

Zhange Peng1,*, Jing Li2, Jinmei Feng1, Zhaochun Wu1 and Nansheng Deng3

1College of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai 201418, P.R. China 2Central-South Architectural Design Institute Co., Ltd, Wuhan 430071, P.R. China

3School of Resources and Environmental Science, Wuhan University, Wuhan 430079, P.R.China

ABSTRACT

Nonylphenol (NP) has received much attention due to its disrupting effect and occurrence in the aquatic environ-ment. The adsorption characteristics of NP by algal Chlor-ella vulgaris (CV) suspensions were investigated. Experi-mental parameters affecting the adsorption process, such as pH level, contact time and effect of solvent were studied. The experimental data had been analyzed using Langmuir and Freundlich models. The results showed that the data was well described by Langmuir adsorption model. Results indicated that NP adsorption by CV was high during the first 30 min, and the equilibrium was achieved after a con-tact time of 60 min. The maximum adsorption capacity was 2.78 mg/(109 cells), adsorption constant was 0.1221 L/mg, and adsorption of NP was pH-dependent. As pH value de-creased, the adsorption capacity of NP increased. The fluorescence spectra of NP and algal suspensions were also investigated. The fluorescence intensity of NP decreased and the fluorescence peak of NP moved to infrared side when algae were added.

KEYWORDS: Nonylphenol, algae, adsorption capacity, fluorescence spectrum

1. INTRODUCTION

At present, more and more surface-active compounds are discharged into domestic and industrial wastewaters. They have become ubiquitous in the environment. Alkyl-phenol polyethoxylates (APEOs) are commercially impor-tant surfactants that have been used for many years [1]. APEOs consist mainly of NP ethoxylates (80%) and oc-tylphenol ethoxylates (15-20%), which are transformed into short-chain homologues, alkylphenol carboxylic acids and alkylphenols (APs) in sewage treatment plants [2-4]. These compounds have received considerable attention due to their toxicity and ability to disrupt the endocrine system [5, 6].

NP, one of the alkylphenol polyethoxylates, is wide-spread in the aquatic environment [7, 8]. The concentration * Corresponding author

of NP in Haihe River (Tianjing, China) had been found to be in the range of 106-296 ng/L [9]. Low levels in drink-ing water were reported in the USA, with a total concen-tration of alkylphenols of almost 1 µg/L [10]. La Guardia et al. [11] studied the biosolids that derived from all 11 U.S. wastewater treatment plants, and results showed that NPs were the major components in biosolids, and their concen-trations ranged from 5.4 to 887 mg/kg (dry weight). So, it is important to investigate the fate of NP in the water.

Surface waters contain significant concentrations of algae and bacteria, both of which have cell walls that contain organic acid functional groups which are capable of adsorbing protons and aqueous metal cations [12]. Al-gae, primarily marine macroalgae, play an important role in the research and development of new biosorption mate-rials due to their high capacities and their availability in nearly unlimited amounts from the ocean [13, 14].

Algal surface contains a large number of polysaccha-rides, proteins and lipids. With certain degree of negative charge, and a larger surface area and viscosity, algae can cause the adsorption and deposition of some metal ions on their surface. Many researchers studied the biosorption of metals by algae [15-18]. Klimmek et al. [19] studied the absorption of cadmium, lead, nickel and zinc by several blue-green algae, and fitted with the Langmuir adsorption model to calculate the maximum adsorption capacity. The results showed that algae had increasing adsorption ca-pacity for 4 metals at low pH. Tuzen et al. [20] investi-gated the biosorption of selenium from aqueous solution by green algae. Their results indicated that the maximum biosorption capacity for Se(IV) was found at contact time of 60 min and temperature 20 °C. The biosorption per-centage decreased with increase in temperature during the equilibrium time.

Based on the current literature, most research was in-terested in the adsorption of metal ions by algae. A few researchers reported on the biosorption of organic com-pounds by algae. Ozer et al. [21] showed that the dyes were adsorbed by algae. Safarikova et al. [22] reported the sorption of dye by modified Chlorella vulgaris cells. Rubn et al. [23] indicated the adsorption of methylene blue on chemically modified algal biomass. Also, the adsorption of algae maybe has some indirect effects on the chemical transformation behavior of organic pollutants [24-26]. So,


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it is of great importance to study the adsorption of organic compounds by algae. The aim of this study was to inves-tigate the adsorption of NP by algae.


2.1. Chemicals and reagents

Nonylphenol (CAS NO. 84852-15-3, 99%) was pur-chased from Sigma Chemical Company, Inc. Methanol was of HPLC-grade (Fish Company, USA). HCl, NaOH, FeCl3·6H2O, CaCl2·2H2O, Ca(H2PO4)2·2H2O, CaSO4·H2O, CuSO4·5H2O, H3BO3, KCl, K2HPO4, KH2PO4, MgSO4 7H2O, MnCl2·4H2O, NaCl, Na2CO3, NaHCO3, (NH4)6Mo7O24 ·6H2O, NaNO3, (NH4)2SO4, ZnSO4·7H2O, were of analytical grade. HCl and NaOH were used to adjust the pH values of solutions. Acetonitrile was of ana-lytical grade (Lingfeng Chemical Reagent Co., Shanghai, PR China). A 200 mg/L stock solution of NP (acetonitrile/ deionized water mixture, 70/30, v/v) with acetonitrile as solvent was prepared to be used in the experiments.

2.2. Preparation of algae

The alga Chlorella vulgaris (CV) used in the experi-ments was obtained from the Wuhan Hydrobiology Insti-tute of Chinese Academy of Sciences (Wuhan, PR China). The mixture of algae and axenic culture medium was put into a conical flask covered with filter paper. Then, the flask was placed in an incubator box at 25 ºC under 12-h light/12-h dark cycles. The axenic culture medium con-sisted of (mg/L): 200 (NH4)2SO4, 30 [Ca(H2PO4)2·2H2O + CaSO4·H2O], 80 MgSO4·7H2O, 100 NaHCO3, 25 KCl, 0.15 ml/L FeCl3 (1%), 2.86 H3BO3, 1.81 MnCl2·4H2O, 0.222 ZnSO4·7H2O, 0.039 (NH4)6Mo7O24·6H2O, 0.079 CuSO4·5H2O, 0.5 ml/L soil extract (the raw soil 0.5 kg mixed with 1000 ml double-distilled water was put into a beaker and heated 2 h in boiling water, then it was fil-trated in axenic conditions after cooling, and the super-natant was used as soil extract). The pH of the axenic cul-ture medium was adjusted to 7.0-7.2 by 0.1 mol/L Na2CO3.

When the algae were growing in a logarithmic growth phase and the density of algae was high (normally 15-20 days), they were taken for use in experiments after being washed with deionized water for 3-4 times. Different concentrations of algae were gained through diluting washed algae with deionized water. The cell counting was carried out with microscope and haemacytometer, and the density of algae (cells/L) could be calculated.

2.3. Adsorption experiments

Different algal concentrations and NP solutions were mixed, and suspensions were shaken continuously. The pH values of sample solutions were adjusted fast before shaken with HCl and NaOH depending on desired values. All ex-periments were conducted at room temperature in the dark.

In the experiment on adsorption equilibrium of NP and CV, the concentration of NP was 4 mg/L and that of

CV was 4×109 cells/L (pH 6). The solution was shaken continuously for 5-6 h in the oscillator. Sampling intervals were 0, 10, 20, 30, 45, 60, 120, 180, 240 and 300 min.

In the experiment investigating the adsorption charac-teristics of NP on CV, the levels of NP were 2, 4, 6, 8 and 10 mg/L, respectively, and the concentration of CV was 3×109 cells/L (pH 6). For investigating the influence of initial pH on the adsorption, suspensions of NP and CV were prepared, in which the concentration of NP was 4 mg/L, and that of CV 3×109 cells/L. The pH values were 3, 4, 5, 6, 7 and 8, respectively. Suspensions were shaken continuously for 60 min.

At different time intervals, samples with NP and algae were collected and centrifuged at 3,100 rpm for 22 min in a LD5A-2A centrifuge (Beijing, PR China). Then, NP con-centration of supernatant was analyzed by HPLC-UV.

The following equation was used to compute the ad-sorption capacity of NP on algae:

0 0

lg lg

( )eq eq

a ae a ae

V C C C Cq

V C C− −

= =⋅

(1)

where, q is the adsorption capacity of NP on algae (mg/109 cells), C0 and Ceq are the beginning and the equi-librium concentrations of NP in the solution (mg/L), re-spectively, V is the solution volume (L), and Calgae (109

cells /L) is the concentration of the algae. The adsorption data points presented herein were the

average of duplicate experimental results, and the devia-tion was within 6%.

2.4. Fluorescence spectra experiments

In a series of experiments on the fluorescence spectra of NP, NP solutions were prepared by diluting 200 mg/L NP stock solution with deionized water. The range of concentrations of NP changed between 0-3 mg/L, and pH was 6. Then, the solution was scanned at the fluorescence spectrophotometer.

In the experiments on the fluorescence spectra of NP and algal suspensions, the concentration of NP was 2.0 mg/L, and algal concentrations were 1×109, 3×109 and 9 ×109 cells/L, respectively (pH 6; acetonitrile concentration 1.6%, v/v). The solution was shaken continuously for 1 h on the oscillator. After shaking, samples with NP and algae were scanned.

2.5. Analysis and error

The UV absorbance spectra of NP solution were re-corded spectrophotometrically (UV-1601, Shimadzu, Ja-pan). The characteristic absorbance peaks were at 224 and 280 nm, respectively. NP in aqueous solution was detected by HPLC [Shimadzu LC-10ATVP pump, Akzonobel KR100-3.5 C18 column (150 × 4.6 mm, 5 µm)] with a flow-rate of 1.0 ml/min and UV detector (Waters 481 de-tector) at 280 nm. The mobile phase was a methanol/water mixture (93/7, v/v), and injection volume was 20 µl. The retention time of NP was about 7 min. Under these analy-sis conditions, no NP was detected in deionized water.


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0.2 0.4 0.6 0.8 1.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

q-1 [(

mg/

109 ce

lls)-1

]

C-1(mg-1)

The fluorescence spectra of NP with algal solution were recorded with a fluorescence spectrophotometer F-4500 (Hitachi, Japan). Excitation wavelength: EX 277 nm, scan speed: 240 nm/min, EX(EM) slit: 5-10 nm, Scan range: 280-500 nm.

2.6. Model to fit experimental data

To determine the maximum adsorption capacity (qmax) to the cells, the equilibrium isotherm of NP sorption was described using Langmuir and Freundlich models. The Langmuir isotherm equation is in the form of a hyperbolic function as follows:

max

1eq

eq

q bCq

bC=

+ (2)

where, Ceq (mg/L) is the equilibrium NP concentra-tion, qmax (mg/(10 9cells)) is the maximum amount of NP per unit 109cells of alga, and b is a constant related to the affinity of the binding sites.

For the fitting of experimental data, the Langmuir equation was linearized as follows:

max max

1 1 1

eqq q q bC= + (3)

The Freundlich expression is an empirical equation based on adsorption on a heterogeneous surface, and is commonly presented as equation (4):

lg lg lg eqq k n C= + (4) 3. RESULTS AND DISCUSSIONS

3.1. Adsorption of NP onto algae 3.1.1. Experiment on adsorption equilibrium

The rate of adsorption is a very important factor. The effect of contact time on the adsorption of NP on CV was investigated. The results are shown in Fig. 1. The adsorp-tion rate was quite fast during the first 30 min but then fluctuated. The equilibrium was achieved at about a con-tact time of 60 min. The results indicated that the adsorp-tion process had two stages: a very rapid surface adsorp-tion and a slow intracellular diffusion.

3.1.2. The adsorption characteristics of NP onto CV

The analysis of equilibrium data is important for de-veloping an equation that can be used for investigating the adsorption characteristics of NP on CV. The suspensions of NP and CV were studied. The adsorption data were modeled using Freundlich and Langmuir adsorption iso-therms. According to the fitted results, the equilibrium adsorption data was well described by Langmuir adsorp-tion isotherm equation. There was a good linear relation-ship between 1/C and 1/q, as being shown in Fig. 2. This phenomenon implies that monolayer adsorption condi-tions exist under the experimental conditions used. The Langmuir adsorption isotherm was used to calculate the maximum adsorption capacity qmax and the adsorption

constant Kad. The adsorption isotherm equation was ob-tained as follows:

Cq94587.2359.01

+= , ( r =0.98838)

where C is the equilibrium concentration of NP. The maximum adsorption capacity qmax is 2.78 mg/109 cells. The adsorption constant Kad is 0.1221 L/mg.

0 50 100 150 200 250 300

0.35

0.40

0.45

0.50

0.55

0.60

q [m

g/(1

09 cells

)]

time (min)

FIGURE 1 The adsorption contact time of NP onto CV. [NP]0 = 4 mg/L, [CV]0 = 4 × 109 cells/L.

FIGURE 2 - The adsorption behavior of NP by CV. [CV] 0 = 3 ×109 cells/L.

3.2. Effect of pH on NP uptake adsorption

Many researchers studied the effect of pH on sorption and the results indicated that pH values of solution could influence adsorption significantly. The influence of initial pH on the adsorption characteristics of NP using algae was investigated. The suspensions of NP and CV were prepared and studied.

The results are shown in Fig. 3. As pH increased from 3 to 8, the adsorption capacity decreased substantially. This phenomenon showed that acidic condition was fa-vorable for algae to adsorb NP. The acid condition could


104

increase cell membrane permeability [27], and low pH value could enhance the diffusion. Vigneault et al. [27] researched the permeability changes in model and phyto-plankton membranes in the presence of aquatic humic substances. Their results indicated that aquatic humic acids can increase the permeability of biological membranes to lipophilic solutes, and pH values have potential implica-tions for the uptake and regulation of toxic and essential solutes by the phytoplankton community.

3 4 5 6 7 8

0.45

0.50

0.55

0.60

0.65

0.70

0.75

[NP]0 = 4 mg/Lq [m

g/(1

09 cells

)]

pHFIGURE 3 - Effect of pH on algal NP adsorption.

250 300 350 400 450 5000

200

400

600

800

1000

1200

1400

1600

The concentration of NP

Fluo

resc

ence

inte

nsity

ΕΜ λ (nm)

Without algae, 1.6% acetonitrile(v/v)

A: 0.5 mg/LB: 1.0 mg/LC: 2.0 mg/LD: 3.0 mg/LE: 0.0 mg/L

A

B

C

D

E

FIGURE 4 - The fluorescence spectra of NP.

3.3. Fluorescence analysis of the mixture of NP with algae 3.3.1. The fluorescence spectra of NP solution

Fluorescence is a good tool for the study of organic matter in water. The fluorescence spectra of NP solution were investigated in the present work. The results are shown in Fig. 4. According to the figure, there is an obvi-ous peak at 306 nm. The peak area and peak height in-crease with increasing NP concentration. It is confirmed that the fluorescence peak is the emission spectrum peak of NP under the present excitation wavelength of 277 nm. This is in accordance with Benmansour et al. [28] who investigated the fluorescence determination of some en-docrine disrupting compounds. Their results showed that

the fluorescence emission wavelengths are about 300 nm for phenolic compounds. They also indicated that the excitation/emission wavelength of NP was 277/300 nm and NP had a rather high fluorescence quantum yield.

Because the stock solution of NP was prepared using acetonitrile as solvent, its effect was also investigated. As shown in Fig. 5, the result indicated that the effect can be ignored when acetonitrile ranged from 0.35-2.35% (v/v).

250 300 350 400 450 500

0

200

400

600

800

1000

1200

ΕΜ λ (nm)

[NP]= 2.0 mg/L(without algae)

A: 0.35% acetonitrileB: 0.85% acetonitrileC: 1.35% acetonitrileD: 1.85% acetonitrileE: 2.35% acetonitrile

Fluo

resc

ence

inte

nsity

The concentration of acetonitrile(v/v)

AB,C,D,E

FIGURE 5 - Effect of solvent acetonitrile on NP.

250 300 350 400 450 500

0

200

400

600

800

1000

1200

ΕΜ λ (nm)

Concentration of CV

[NP] = 2 mg/L1.6 % acetonitrile(v/v)

A: 0 × 109cells/LB: 1 × 109cells/LC: 3 × 109cells/LD: 9 × 109cells/L

Fluo

resc

ence

inte

nsity

A

B

C

D

FIGURE 6 - Effect of algal concentration change on NP.

3.3.2. The effect of algal concentration on the fluorescence spectra of NP

The effect of algal concentration on fluorescence spectra of NP was investigated. As can be seen in Fig. 6, the fluorescence intensity of NP solution was reduced when the 1×109 cells/L algae was added. With more algae, the fluorescence peak of NP moved to infrared side. It is observed that the red shifts became obvious when algal concentration increased from 3×109 to 9×109 cells/L, while there is little change in fluorescence intensity of the solu-tion. This phenomenon implies a binding interaction be-tween NP and algae in water. Wolfbeis [29] reported that the tyrosine-like and tryptophan-like fluorescence had the blue shift due to their binding in proteins. The spectra of NP and algal suspensions shift to longer wavelengths,


105

which maybe is a possible promotion for NP to obtain light to be degraded.

4. CONCLUSIONS

The present research confirms that the adsorption of NP on algae was high. Adsorption equilibrium was achieved at about a contact time of 60 min. The adsorption data of NP on algae is well described by Langmuir adsorption iso-therm equation. This phenomenon implies that mono-layer adsorption conditions exist under the experimental conditions of NP and algal suspensions. Adsorption of NP onto algae was obviously influenced by pH, and acidic con-ditions promoted NP adsorption onto algae.

The tool of fluorescence is an effective method for the characterization of NP and algal suspensions. The fluores-cence spectrum of NP solution showed that there was an obvious peak at 306 nm, and peak area and peak height increased with increasing NP concentration. The fluores-cence spectra of algae and NP suspensions indicated the interaction of NP and algae in water. The fluorescence in-tensity of NP solution decreased when algae were added. The fluorescence peak of NP moved to infrared side when algal concentration increased.

ACKNOWLEDGEMENTS

This work was financed by the School Foundation of Shanghai Institute of Technology (No.YJ2011-12). We thank the supporter from Wuhan University. Special thanks to Dr. Wu for his helpful suggestions.

REFERENCES

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[2] Ahel, M. and Giger, W. (1993) Aqueous solubility of alkyl-phenols and alkylphenol polyethoxylates. Chemosphere 26(8), 1461-1470.

[3] Renner, R. (1997) European bans on surfactant trigger trans-atlantic debate. Environ. Sci. Technol. 31(7), 316A-320A.

[4] Ying, G.G., Williams, B. and Kookana, R. (2002) Environ-mental fate of alkylphenols and alkylphenol ethoxylates-a re-view. Environ. Int. 28(3), 215-226.

[5] Chapin, R.E., Delaney, J., Wang, Y., Lanning, L., Davis, B., Collins, B., Mintz, N. and Wolfe, G. (1999) The effects of 4-nonylphenol in rats: a multigeneration reproduction study. Toxicol. Sci. 52(1), 80-91.

[6] Staples, C., Mihaich, E., Carbone, J., Woodburn, K. and Klecka, G. (2004) A weight of evidence analysis of the chronic ecotoxicity of nonylphenol ethoxylates, nonylphenol ether carboxylates, and nonylphenol. Hum. Ecol. Risk As-sess. 10(6), 999-1017.

[7] Céspedes, R., Lacorte, S., Raldúa, D., Ginebreda, A., Bar-celó, D. and Piña, B. (2005) Distribution of endocrine disrup-tors in the Llobregat River Basin (Catalonia, NE Spain). Chemosphere 61 (11), 1710-1719.

[8] Soares, A., Guieysse, B., Jefferson, B., Cartmell, E. and Les-ter, J.N. (2008) Nonylphenol in the environment: A critical review on occurrence, fate, toxicity and treatment in waste-waters. Environ. Int. 34(7), 1033-1049.

[9] Jin, X., Jiang, G., Huang, G., Liu, J. and Zhou, Q. (2004) De-termination of 4-tert-octylphenol, 4-nonylphenol and bisphe-nol A in surface waters from the Haihe River in Tianjin by gas chromatography-mass spectrometry with selected ion monitoring. Chemosphere 56(11), 1113-1119.

[10] Clark, L.B., Rosen, R.T., Hartman, T.G., Louis, J.B., Suffet, I.H., Lippincott, R.L. and Rosen, J.D. (1992) Determination of alkylphenol ethoxylates and their acetic acid derivates in drinking water by particle beam liquid chromatography/mass spectrometry. Int. J. Environ. Anal. Chem. 47(3), 167-180.

[11] La Guardia, M.J., Hale, R.C., Harvey, E. and Matteson Mai-nor, T. (2001) Alkylphenol ethoxylate degradation products in land-applied sewage sludge (biosolids). Environ. Sci. Technol. 35(24), 4798-4804.

[12] Kaulbach, E.S., Szymanowski, J.E.S. and Fein, J.B. (2005) Surface complexation modeling of proton and Cd adsorption onto an algal cell wall. Environ. Sci. Technol. 39 (11), 4060-4065.

[13] Leusch, A., Holan, Z.R., Volesky, B.(1995) Biosorption of heavy metals (Cd, Cu, Ni, Pb, Zn) by chemically-reinforced biomass of marine algae. J. Chem. Technol. Biotechnol. 62(3), 279-288.

[14] Liu, Y., Cao, Q., Luo, F. and Chen, J. (2009) Biosorption of Cd2+, Cu2+, Ni2+ and Zn2+ ions from aqueous solutions by pre-treated biomass of brown algae. J. Hazard. Mater. 163(2-3), 931-938.

[15] Davis, T.A., Volesky, B. and Mucci, A. (2003) A review of the biochemistry of heavy metal biosorption by brown algae. Water Res. 37(18), 4311-4330.

[16] Karthikeyan, S., Balasubramanian, R. and Iyer, C.S.P. (2007) Evaluation of the marine algae Ulva fasciata and Sargassum sp. for the biosorption of Cu(II) from aqueous solutions. Bio-resour. Technol. 98(2), 452-455.

[17] Sari, A. and Tuzen, M. (2009) Equilibrium, thermodynamic and kinetic studies on aluminum biosorption from aqueous solution by brown algae (Padina pavonica) biomass. J. Haz-ard. Mater. 171(1-3), 973-979.

[18] Montazer-Rahmati, M.M., Rabbani, P., Abdolali, A. and Keshtkar, A.R. (2011) Kinetics and equilibrium studies on biosorption of cadmium, lead, and nickel ions from aqueous solutions by intact and chemically modified brown algae. J. Hazard. Mater. 185(10), 401-407.

[19] Klimmek, S., Stan, H.J., Wilke, A., Bunke, G. and Buchholz R. (2001) Comparative analysis of the biosorption of cad-mium, lead, nickel, and zinc by algae. Environ. Sci. Technol. 35(21), 4283-4288.

[20] Tuzen, M. and Sari, A. (2010) Biosorption of selenium from aqueous solution by green algae (Cladophora hutchinsiae) biomass: Equilibrium, thermodynamic and kinetic studies. Chem. Eng. J. 158(2), 200-206.

[21] Ozer, A., Turabik, M. and Akkaya, G. (2009) Biosorption of acid dyes by brown alga Dictyota dichotoma: equilibrium, kinetic and thermodynamic studies. Fresenius Environ. Bull. 18(10), 1798-1808.


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[22] Safarikova, M., Pona, B.M.R., Mosiniewicz-Szablewska E., Weyda, F. and Safarik, I. (2008) Dye adsorption on magneti-cally modified Chlorella vulgaris cells. Fresenius Environ. Bull. 17(4), 486-492.

[23] Rubn, E., Rodrguez, P., Herrero, R. and Sastre de Vicente, M.E. (2010) Adsorption of methylene blue on chemically modified algal biomass: equilibrium, dynamic, and surface data. J. Chem. Eng. Data 55 (12), 5707-5714.

[24] Ge, L., Chen, P., Deng, H. and Deng, N. (2005) Photodegra-dation of 17 beta-estradiol in aqueous solutions induced by the alga microcystus aeruginosa kutz. Fresen. Environ. Bull. 14(2), 138-141.

[25] Ge, L., Deng, H., Wu, F. and Deng, N. (2009) Microalgae-promoted photodegradation of two endocrine disrupters in aqueous solutions. J. Chem. Technol. Biotechnol. 84(3), 331-336.

[26] Peng, Z., Yang, H., Wu, F. and Deng N. (2010) Mechanism analysis of bisphenol A photodegradation induced by algae. Fresenius Environ. Bull. 19(2), 266-270.

[27] Vigneault, B., Percot, A., Lafleur, M. and Campbell, P.G.C. (2000) Permeability changes in model and phytoplankton membranes in the presence of aquatic humic substances. En-viron. Sci. Technol. 34(18), 3907-3913.

[28] Benmansour, B., Stephan, L., Cabon, J.Y., Deschamps, L. and Giamarchi, P. (2011) Spectroscopic properties and laser induced fluorescence determination of some endocrine dis-rupting compounds. J. Fluoresc. 21(3), 843-850.

[29] Wolfbeis, O.S. (1985) The fluorescence of organic natural products. In: Schulman, S.G. (Ed.), Molecular luminescence spectroscopy, methods and applications. Wiley-Interscience. New York 77(Part I), 167-370.

Received: August 03, 2011 Revised: August 17, 2011 Accepted: September 08, 2011 CORRESPONDING AUTHOR

Zhange Peng College of Urban Construction and Safety Engineering Shanghai Institute of Technology Haiquan Road No.100 Shanghai, 201418 P.R. CHINA E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 101 – 106


107

DEBROMINATION OF HEXABROMOCYCLODODECANE IN AQUEOUS SOLUTIONS BY UV-C IRRADIATION

Danna Zhou1,*, Liang Chen2, Feng Wu2,*, Jie Wang2 and Fan Yang1

1 College of Material Science and Chemical Engineering, China University of Geosciences, Wuhan 430074, PR China 2 Department of Environmental Science, Wuhan University, Wuhan 430079, PR China

ABSTRACT

In this paper, photolysis of Hexabromocyclododecane (HBCD) in aqueous solutions containing methanol as co-solvent was studied under irradiation of 15 W UV-C lamp (λmax: 254 nm and 185 nm) and debromination of HBCD was observed. Effects of initial pH (3 - 9) and initial con-centration of HBCD (0.79 – 3.16 µmol/L) on the debro-mination of HBCD were investigated. Results showed that debromination of 1.58 µmol/L HBCD achieved higher efficiency at acidic or alkalic pH values than that at neutral pH (6.5). The rate of debromination of HBCD increased with the increase of initial concentration of HBCD. The increase of the UV absorption of aqueous solutions con-taining HBCD (15.8 µmol/L) during photolysis was attributed to the production of Br-. And pH decreased with the debromination and appeared to have 1:1 stoichiometric production of HBr. Any product of cyclododecene, cyclo-dodecadiene or cyclododecatriene was not detected by high pressure liquid chromatography (HPLC) at wavelength of 200-220 nm. Hydroxylized products of the debrominated HBCD was proposed as intermediates of photolysis.

KEYWORDS: Debromination, Hexabromocyclododecane (HBCD), UV-C light, Photolysis, Hydroxylation.

1. INTRODUCTION

Persistent Organic Pollutants (POPs) are chemical sub-stances that get the most concern in the current environ-mental research and public media. Because of its recalcitrant nature, the pollution control of POPs has been the hotspot among the research topics. In recent years, Advanced Oxidation Processes (AOPs) have been applied in the degradation of refractory organic pollutants due to its higher oxidative potential and lower secondary pollution than traditional physical and biological processes [1, 2]. * Corresponding author

Hexabromocyclododecane (HBCD, C12H18Br6, CAS No. 3194-55-6) is a cycloaliphatic flame retardant of high bromine content, which was identified as persistent, bio-accumulative toxic substances (PBTs) by European Chemicals Agency (ECHA) [3]. In May 2009, HBCD could be added to the Stockholm Convention’s annexes as new persistent organic pollutants (POPs), and immedi-ately HBCD has been a hot research target [4]. HBCD is one of the brominated flame retardants (BFRs) which is widely used in industrial production and consumer goods, including electronics, textiles, furniture, fireproof materi-als, decorative materials, plastic products, etc. [5]. It can increase the ignition point to reduce risk of fire [6]. Cur-rently, the environmental behaviors and fates of three other types of BFRs including polybrominated diphenyl ethers (PBDEs), polybrominated biphenyls (PBBs) and tetra-bromobisphenol A (t-BBPA) have been investigated ex-tensively, while there has been very few work on the envi-ronmental chemistry of HBCDs.

Photolysis, an important abiological transformation, of HBCD in actual or simulated environmental system has not been reported, except for a theoretical study not dwelling on debromination [7]. The light-induced debromination of PBDEs was reported [8]. Light-induced debromination can happen in water [9], soil minerals (including kaolinite and montmorillonite, etc.) and sediments [10, 11], toluene [12], polymer materials [13], and other systems. Compared with PBDEs, HBCD has no benzene and other conjugated structure, so solar radiation arriving at the earth is not ab-sorbed by HBCD and no direct photolysis of HBCD occurs in surface water. Considering the indirect photolysis, hydroxyl radicals can be added onto the benzene ring of PBDEs. However, because of the hydrocarbon ring structure of HBCD, hydrogen abstraction can take place. Therefore, the knowledge of photochemical transformation of PBDEs can not be simply extended to HBCD.

In this paper, photolysis of HBCD from the aspect of debromination was investigated under UV-C light con-taining radiation at 185 nm and 254 nm mainly. Since the water solubility of HBCD is about micrograms per litre, methanol was added as cosolvent in aqueous solutions for the photolysis of HBCD at concentrations higher than solubility. Effects of initial pH and concentration of HBCD


108

were studied and the preliminary study on debromination mechanism was performed simply by determination of UV spectrum, pH, conductance and HPLC. This work demonstrated the feasibility of debromination of HBCD in water by UV-C light as AOPs and helped us understand the environmental fate of HBCD.


2.1. Reagents

Technical grade HBCD (> 95%) and Trans, Trans, Cis-1, 5, 9-Cyclododecatriene (98%) were purchased from Sigma-Aldrich Co. (Shanghai, PRC) and used without purification. Methanol of HPLC grade was from Fisher Scientific (Springfield, US) and was used for cosolvent and for the preparation of the mobile phase for HPLC analysis. KBr was used as the source of bromide ions. HCl and NaOH were used to adjust the pH values of the solutions. All chemicals were analytical grade. Pure water of 18 MΩ·cm resistivity, prepared with an ultrapure water system (Liyuan Electric Instrument Co. Beijng, China), was used throughout the experiments.

2.2. Photochemical reaction

Irradiation under UV light was performed in the photo-chemical reactor shown in Figure 1 with a 15W low-pressure mercury lamp (Yuelu Illumination Instrument Co., Changsha, PRC), which has two main irradiation wave-lengths of 185 and 254 nm [2]. The lamp was directly im-mersed in 400 mL reaction solution of HBCD loaded in the reaction vessel at a constant stirring rate of 300 rpm. During the illumination, a constant temperature was main-tained at about 25 oC. The concentration of methanol as cosolvent was 1% (v/v) of 1.58 µmol/L HBCD and pH values of the reactant solutions were adjusted with HCl or

NaOH depending on the desired values. During the irradiation, Br- ion concentration was determined. In the control experiments, photolysis of HBCD under UV-A light was performed at 18 w UV-A lamp (λmax: 365 nm, Guangdong Cnlight Co., Guangzhou, PRC), but no de-bromination was observed.

2.3. Analysis

Bromide ion was detected by the Bromide Ion-Selective Electrode (Model pBr-1, Luosu Sci & Tech Co., Shanghai) with detection range of 10-7 – 10-4 mol/L [9]. UV absorption of HBCD solution was recorded by 1600 UV-Vis Spectrometer (Shimadzu, Japan). Measurements were made twice in each experiment with errors less than 5%. In order to monitor the debromination process, the pH value and electrical conductance of the reactant solution were measured during photolysis experiment by using a Mettler Delta 320 pH-meter (Shanghai, China) and a conductance meter DDS-11 (Shanghai, China) respectively. HPLC analysis was performed on Shimadzu LC-10AT system with C18 column and the UV detector at 200, 210 and 220 nm respectively. The mobile phase was a mixture of methanol/ water (90:10, v/v) at a flow rate of 1 mL/min. The inject volume of sample was 200 µL.


3.1. Effect of initial pH

To examine the pH effect on the debromination of HBCD, the photolysis of 1.58 µmol/L HBCD was con-ducted in aqueous solutions at initial pH 3.0, 5.0, 6.5 and 9.0 respectively. Without adding any acid or alkali, the pH of HBCD solution containing 1% methanol was 6.5. From Figure 2, we can see after 4 h of irradiation the debromination efficiency of HBCD in acidic or alkalic

FIGURE 1 - Scheme of photochemical reactor

Magnetic stirrer

Reaction vessel

Immersed UV-C lamp

Reaction solution

Cooling water inlet

Cooling water outlet

Stirring Bar


109

0 1 2 3 40

20

40

Deb

rom

inat

ion

effic

ienc

y (%

)

time (h)

pH 3.0 pH 5.0 pH 6.5 pH 9.0

FIGURE 2 - Debromination efficiency of HBCD (1.58 µmol/L) at different initial pH under UV-C light.

solutions (29.0%-35.6%) was higher than that in near neutral solution (22.9%). Different from phenol, HBCD is very stable and has no tendency of hydrolysis (half life is about 106 year at pH 7 and 105 year at pH 8), pH could not change the form of HBCD and the light absorbance of HBCD in aqueous solutions. Therefore, pH effect was not significant and might be attributed to the change of ionic strength in the reactant solution. Since pH has not sig-nificant effect on the debromination of HBCD, pH was not adjusted in the following photolysis experiments.

3.2. Effect of Initial concentration of HBCD

Photolysis of HBCD at different initial concentration in the range of 0.79-3.16 µmol/L was undertaken to examine the effect of initial concentration of HBCD on the debromination. Since higher concentration of HBCD could not be prepared with only 1% methanol, the initial concentration was set at 3.16 µmol/L. Results were shown in Figure 3. The results indicated that the effect of the initial concentration of HBCD was not obvious during the first 2 hours. However, after 4 h of irradiation, the pro-duction of bromide ions increased with the increase of the initial concentration of HBCD.

3.3. Variation of UV spectrum

The variation of UV absorption spectra was recorded to show the possible products during the photolysis. Be-cause of the low absorbance of 1.58 µmol/L HBCD, we increased the initial concentration of HBCD to 15.8 µmol/L by adding 25% methanol as cosolvent, so that the variation of UV spectrum could be significant. Results in Figure 4 showed that UV absorption increased during photolysis in 40 min, which indicated that some products appeared in the solution during irradiation. In Figure 4, we also presented the UV spectrum of the solution containing 100 µmol/L bromide ions, this spectrum overlapped over 200-210 nm with the spectrum of HBCD solution after irradiation for 40 min. If 15.8 µmol/L HBCD was completely debrominated, the concentration of bromide

ions would be 94.8 µmol/L which is very close to 100 µmol/L. So the UV spectrum of HBCD after 40 min demonstrated the debromination efficiency was close to 100% and the enhancement of UV absorbance was mainly due to production of bromide ions. By using HPLC analysis of the reactant solutions, no accumulative products, such as cyclododecene, cyclododecadiene or cyclododecatriene were detected at wavelength of 200, 210 or 220 nm. So the enhanced UV absorption was not because of cycloalkene.

0 1 2 3 40

2

4

6

8

10

Con

cent

ratio

n of

Br- (µ

mol

/L)

time (h)

C0 = 0.79 µmol/L C0 = 1.58 µmol/L C0 = 2.37 µmol/L C0 = 3.16 µmol/L

FIGURE 3 - Debromination efficiency of HBCD of different initial concentration in aqueous solutions at pH 6.5 under UV-C light.

200 250 3000.0

0.2

0.4

0.6

0.8100 µmol/L KBr

40 min20 min10 min 5 min 0 min

Abs

Wavelength (nm)FIGURE 4 - UV-Vis spectrum during photolysis of HBCD (15.8 µmol/L, pH 6.5)

3.4. Variation of pH and electric conductance

To determine the variation of pH and electric con-ductance of HBCD solutions, initial concentration of HBCD was set at 15.8 µmol/L with 25% methanol. Results in Figure 5 showed pH decreased and conductance in-creased with irradiation time. The variation of the con-centration of bromide ions was presented in Figure 5 as well in terms of pBr (-log[Br-]). It is very interesting to see that pH values were almost identical to pBr values during the whole irradiation for 40 min. That indicated the


110

production of Br- and H+ was at 1:1 stoichiometric ratio in the same step during photolysis. In another word, de-bromination took place with dehydrogenation of HBCD. The conductance of the final reactant solutions (35 µS) after 40 min irradiation was higher than that of 100 µmol/L KBr solution (14 µS), which implied H+ was produced during photolysis.

0 10 20 30 403.5

4.0

4.5

5.0

5.5

6.0

6.5

pH o

r pB

r

time (min)

pH pBr

0

10

20

30

40

Con

duct

ance

(µS)

FIGURE 5 - Variations of pH, pBr and conductance during photolysis of HBCD (15.8 µmol/L, pH 6.5)

3.5. Debromination mechanism

Usually, there are three possible photolysis mechanisms in the UV-C irradiation. The first is ozonation. But in the presence of water, the production of ozone is inhibited,

which makes ozonation impossible to take responsibility for debromination of HBCD. The second is direct pho-tolysis through breaking C-Br bond of HBCD. HBCD, con-taining C-C, C-H and C-Br single bond, can not absorb light of wavelengths longer than 240nm [7], so HBCD can only absorb UV-C irradiation at 185 nm for debromination through direct photoionization. HBCD debromination in an anaerobic biological conversion was proposed through successive two-dehalogenation reaction, each step reaction between two adjacent C atoms simultaneously removes the two bromine atoms and forms double bonds [14]. How-ever, reductive stepwise debromination by UV-C photo-lysis to produce cycloalkene could not account for the large part of the debromination of HBCD because cyclo-dodecene, cyclododecadiene or cyclododecatriene were not detected as accumulative products. The third is in-direct photooxidation by hydroxyl radicals produced by photolysis of dissolved oxygen in the presence of water. Hydroxylation of HBCD produced cyclic alcohol like 5,6,9,10-tetrabromocyclododecane-1,2-diol, 9,10-dibromo-cyclododecane-1,2,5,6-tetraol and cyclododecane-1,2,5, 6,9,10-hexaol. These cyclic alcohol could be split and oxi-dized to carboxylic acids like 4,5,8,9-tetrabromododeca-nedioic acid, 4,5-dibromooctanedioic acid and succinic acid. The preliminary debromination pathway was pro-posed in Figure 6.

BrBr

Br

Br Br

Br

Br

Br Br

Br

2HBr

Br

Br

2HBr 2HBr

Tetrabromocyclododecene Dibromocyclododecadiene 1,5,9-cyclododecatriene

HBCD

Br

Br

2HBr 2HBr

2HBr 2HBr

HO OH

HO

HO OH

OH

HO OH

HO

HO

HO

HO Br

Br

O OOHHO

O

OHO

OHO

OBr

Br Br

Br

O OOHHO

9,10-dibromocyclododecane-1,2,5,6-tetraol cyclododecane-1,2,5,6,9,10-hexaol

4,5,8,9-tetrabromododecanedioic acid succinic acid4,5-dibromooctanedioic acid

Br

Br

HO OH

Br

Br

5,6,9,10-tetrabromocyclododecane-1,2-diol

FIGURE 6 - Proposed stepwise debromination of HBCD during photolysis. Solid line represents major pathway of reactions, while dashed line represents minor pathway of reactions.


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5. CONCLUSIONS

The UV-C light induced photolysis of HBCD in aqueous solutions in the presence of methanol as cosol-vent resulted in debromination and dehydrogenation of HBCD, the generated HBr decreased pH and increased electric conductance of the solution simultaneously. This phenomenon implies that UV-C irradiation can remove HBCD from water. Hydroxylated cyclododecane rather than cycloalkene might be the major intermediate pro-ducts. More work is needed to reveal the environmental photochemical behaviors of HBCD.

ACKNOWLEDGEMENTS

This work was supported by the Natural Science Foundation of China (No. 41103066). Comments from the anonymous reviewers are also appreciated.

REFERENCES

[1] Huang, W.Y., Wang, B.B., Guo, L., Wu, F. and Deng, N. S. (2009) Photochemical Processes and the Related Advanced Oxidation Technology: A Minireview. Fresenius Environ. Bull. 18, 2259-2267.

[2] Zhu, J.Y., Lu, G.F., Li, S.F., Zhu, F.T. and Deng, L. (2010) Oxidation of Melamine in Aqueous Solutions by UV Illumi-nation. Fresenius Environ. Bull. 19, 271-274.

[3] ECHA, (2008) Member state committee Support document for identification of Hexabromocyclododecane and all major Diastereoisomers identified as a substance of very high con-cern. SVHC Support Document, Adopted on 8 October 2008.http://echa.europa.eu/doc/candidate_list/svhc_supdoc_hbccd_publication.pdf

[4] Giles, J. (2004) Treaty calls time on long-term pollutants. Nature 427, 768.

[5] de Wit C.A. (2002) An overview of brominated flame retar-dants in the environment. Chemosphere 46, 583−674.

[6] Gerecke, A.C., Giger, W., Hartmann, P.C., Heeb, N.V., Koh-ler, H.E., Schmid, P., Zennegg M. and Kohler M. (2006) An-aerobic degradation of brominated flame retardents in sewage sludge. Chemosphere 64, 311−317.

[7] Zhao, Y.Y., Zhang, X.H. and Sojinu, O.S.S. (2010) Thermo-dynamics and photochemical properties of alpha, beta, and gamma-hexabromocyclododecanes: A theoretical study. Chemosphere 80, 150-156.

[8] Bendig, P. and Vetter, W. (2010) Photolytical Transforma-tion Rates of Individual Polybrominated Diphenyl Ethers in Technical Octabromo Diphenyl Ether (DE-79). Environ. Sci. Technol. 44, 1650–1655.

[9] Sánchez-Prado, L., Lores, M., Llompart, M., García-Jares, C., Bayona, J. M. and Cela, R. (2006) Natural sunlight and sun simulator photolysis studies of tetra- to hexa-brominated diphenyl ethers in water using solid-phase micro extraction. J. Chromatogr. A 1124, 157–166.

[10] Söderström, G., Sellström, U. and de Wit, C.A.; Tysklind, M. (2004) Photolytic debromination of decabromodiphenyl ether (BDE 209). Environ. Sci. Technol. 38, 127–132.

[11] Ahn, M.Y., Filley, T.R., Jafvert, C.T., Nies, L., Hua, I. and Bezares-Cruz, J. (2006) Photodegradation of decabromodi-phenyl ether adsorbed onto clay minerals, metal oxides, and sediment. Environ. Sci. Technol. 40, 215–220.

[12] da Rosa, M.B., Kruger, H.U., Thomas, S. and Zetzsch, C. (2003) Photolytic debromination and degradation of decab-romodiphenyl ether, an exploratory kinetic study in toluene. Fresenius Environ. Bull. 12, 940-945.

[13] Antos, K. and Sedlar, J. (2005) Influence of aromatic bromi-nated flame retardants on alkane photo-oxidation: A model and polymer study. Polym. Degrad. Stabil. 90, 180-187.

[14] Davis, J.W., Gonsior, S.J. Markham, D.A., Friederich, U., Hunziker, R.W. and Ariano J.M. (2006) Biodegradation and product identification of [14C] hexabromocyclododecane in wastewater sludge and freshwater aquatic sediment. Environ. Sci. Technol. 40, 5395-5401.

Received: August 08, 2011 Revised: August 31, 2011 Accepted: September 08, 2011 CORRESPONDING AUTHOR

Danna Zhou College of Material Science and Chemical Engineering China University of Geosciences Wuhan 430074 P. R. CHINA Phone: +86-27-87408871. E-mail: [email protected]

FEB/ Vol 21/ No 1/ 2012 – pages 107 - 111


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SUBJECT INDEX

A accumulation 11 activated carbon 84 adsorption capacity 101 adsorption 19 algae 101 aromatic hydrocarbon 68 arsenic sludge 76 artificial neural network 76

B Batman Stream 31

C cement 76 Chernobyl 84 Chondrostereum purpureum 26 column adsorption 84 comparison 3 concentration dependence 68

D debromination 107 decay 26 diatom 31

E energy dispersive X-ray fluorescence (EDXRF) 84 Environmental capacity 48 epilithon 31 epipelon 31

F Filyos coastline 36 fluorescence spectrum 101

G gill 61 Gobius niger 61 gold mining 94 growth 11

H Hatila valley 94 hexabromocyclododecane (HBCD) 107 histopathology 61 hydroxylation 107

I isotherm 84

J Jiaozhou bay 48

L landscape design 36 lipid peroxidation 54 liver 61

M monitoring 3 moss 94

N naproxen sodium 84 natural zeolite 76 nonylphenol 101

O oak 26

P passive diffusion sampler 3 peroxidase 54 petroleum hydrocarbons 48 Phaseolus vulgaris L 54 photolysis 107

Q Quercus petraea 26 Quercus robur 26

R radiocaesium 84 regression 3 revitalization 36

S sodium trititanate whisker 19 solidification/stabilization 76 Solid-phase extraction (SPE) 19 Sr(II) 19 sunflower 11

T textile dye (Reactive Black 5) 54 thermodynamics 19 total chlorophyll 54 transfer factor 11 transformation and hydrodynamic model 48 transport 48 Turkey 31

U users’ demand 36 UV-C light 107


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V volatilization 68

Y yttrium 11

subject-index

AUTHOR INDEX

A Akmil-Başar, Canan 84

Asan, Keziban 31

B Bekci, Banu 36 Beker Akbulut, Gulcin 54 Bolanča, Tomislav 76

C Celik, Necati 94 Cengiz, Bülent 36 Cengiz, Canan 36 Cevik, Ugur 94 Chen, Liang 107

D Dai, Jiangdong 19 Deng, Nansheng 101

E

Erdoğan, Selim 84

F Feng, Jinmei 101

I Ivković, Snežana 26

K Karpuzcu, Mehme 3t Kastori, Rudolf 10 Katalay, Selma 61 Kaya, Armagan 54 Koz, Bahadir 94

L Li, Jing 101 Li, Ke-qiang 48 Liang, Sheng-kang 48

M Maksimović, Ivana 10 Marković, Miroslava 26

Minareci, Ersin 61 Mirić, Milenko 26

O Önal, Yunus 84 Ozkurt, Nesimi 3

P Peng, Zhange 101 Putnik-Delić, Marina 10


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Q Qian, Guo-dong 48 Qiao, Xu-dong 48

R Rajković, Snežana 26

S Sabaz, Mehmet 36 Sarıcı-Özdemir, Çiğdem 84 Segner, Helmut 61 Shi, Xiao-yong 48 Šiljeg, Mario 76 Šipušić, Juraj 76 Sonmez, Feray 31

T Tandlich, Roman 68 Tuğlu, Ibrahim 61

U Ujević Bošnjak, Magdalena 76 Ukić, Šime 76

W Wang, Jie 107 Wang, Xiu-lin 48 Wu, Feng 107 Wu, Zhaochun 101

X Xu, Longcheng 19

Y Yan, Yongsheng 19 Yang, Fan 107 Yigit, Emel 54 Yu, Zhixin 19

Z Zeremski, Tijana 10

Zhao, Ruijun 19 Zhou, Danna 107 Zuma, Bongumusa M. 68

author-index

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