USING SATELLITE AEROSOL PRODUCTS FOR MONITORING … · 2018. 5. 15. · USING SATELLITE AEROSOL...

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USING SATELLITE AEROSOL PRODUCTS FOR MONITORING NATIONAL AND REGIONAL AIR QUALITY IN AUSTRIA Robert Höller (1) , Philippe Garnesson (2) , Christian Nagl (1) , and Thomas Holzer-Popp (3) (1) Federal Environment Agency, Spittelauer Lände 5, 1090 Wien, Austria, Email: [email protected] (2) ACRI-ST, 260, route du Pin Montard - B.P. 234, 06904 Sophia Antipolis Cedex, France, Email: [email protected] (3) DLR-DFD, Climate and Atmospheric Products Department, Oberpfaffenhofen, 82234 Wessling, Germany, Email: [email protected] ABSTRACT We evaluate the potential of satellite aerosol data for monitoring national and regional air quality in Austria. This work was performed within the framework of the GSE project PROMOTE, and applies aerosol data derived from MERIS onboard ENVISAT. MERIS aerosol data, which are based on the ESA standard algorithm, are provided with a time delay between data acquisition by the satellite to the delivery of the aerosol product of about one day. MERIS local overpass time is about a.m. 10:30. Data are also provided temporally aggregated (monthly, seasonally, and annually). We present a preliminary assessment of the MERIS aerosol dataset, as well as comparisons with the national ground-based in-situ air quality monitoring network. Advantages and limitations of satellite aerosol datasets for air quality monitoring are discussed. Future work will evaluate also aerosol data from other sensors and compare them to MERIS. 1. INTRODUCTION Concentrations of most classic air pollutants were continuously reduced within the last decades in Austria. Per capita emissions of SO 2 and NO x are among the lowest of all EU member states. Pollution levels are generally lower than the limit values for lead, benzene, and CO. SO 2 exceedances are rare and mainly caused by transboundary air pollution. Nevertheless, limit values for PM10 concentrations (50 μg m -3 daily average not to be exceeded more than 35 times per year; 40 μg m -3 yearly average) are frequently exceeded in agglomerations, but are also exceeded in rural regions, particularly in the north-eastern part of the country as well as in Alpine valleys and basins. Statistical analyses of back trajectories showed that a considerable amount of the total PM10 levels in the eastern part of Austria is caused by long-range transport, but also local sources add substantially to PM10 levels. Therefore, due to its geographic situation, Austria is strongly affected by local, regional, and long-range air pollution. Further emission reduction strategies will be necessary both on national and international levels [1]. The legal framework for monitoring air quality in Austria are the national air quality protection act, ozone act, clean air act for steam boilers, as well as international obligations, such as the EC Air Quality Framework Directive and daughter directives, and the UNECE Convention of Long-Range Transboundary Air Pollution (CLRTAP) [2]. Fig. 1 shows the air quality monitoring sites in Austria that are operated by the Austrian Federal Environment Agency (Umweltbundesamt; UBA). The total number of sites in Austria, including the sites operated by the Federal Provinces, already exceeded 90 in 2003. For an analysis of long-term trends, unfortunately only TSP measurements exist, monitoring of PM10 did not start until 2001. More recently, sites measuring PM2.5 are added to the monitoring network. Figure 1. Air quality monitoring sites in Austria operated by UBA [1]. For an analysis of the state of the atmosphere over Austria, besides the legally required monitoring and analysis methods, UBA is currently also evaluating the potential of satellite remote sensing methods. Within the _____________________________________________________ Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

Transcript of USING SATELLITE AEROSOL PRODUCTS FOR MONITORING … · 2018. 5. 15. · USING SATELLITE AEROSOL...

Page 1: USING SATELLITE AEROSOL PRODUCTS FOR MONITORING … · 2018. 5. 15. · USING SATELLITE AEROSOL PRODUCTS FOR MONITORING NATIONAL AND REGIONAL AIR QUALITY IN AUSTRIA Robert Höller(1),

USING SATELLITE AEROSOL PRODUCTS FOR MONITORING NATIONAL AND

REGIONAL AIR QUALITY IN AUSTRIA

Robert Höller(1)

, Philippe Garnesson(2)

, Christian Nagl(1)

, and Thomas Holzer-Popp(3)

(1)Federal Environment Agency, Spittelauer Lände 5, 1090 Wien, Austria,

Email: [email protected]

(2)

ACRI-ST, 260, route du Pin Montard - B.P. 234, 06904 Sophia Antipolis Cedex, France,

Email: [email protected]

(3)DLR-DFD, Climate and Atmospheric Products Department, Oberpfaffenhofen, 82234 Wessling, Germany,

Email: [email protected]

ABSTRACT

We evaluate the potential of satellite aerosol data for monitoring national and regional air quality in Austria. This work was performed within the framework of the GSE project PROMOTE, and applies aerosol data derived from MERIS onboard ENVISAT. MERIS aerosol data, which are based on the ESA standard algorithm, are provided with a time delay between data acquisition by the satellite to the delivery of the aerosol product of about one day. MERIS local overpass time is about a.m. 10:30. Data are also provided temporally aggregated (monthly, seasonally, and annually). We present a preliminary assessment of the MERIS aerosol dataset, as well as comparisons with the national ground-based in-situ air quality monitoring network. Advantages and limitations of satellite aerosol datasets for air quality monitoring are discussed. Future work will evaluate also aerosol data from other sensors and compare them to MERIS.

1. INTRODUCTION

Concentrations of most classic air pollutants were continuously reduced within the last decades in Austria. Per capita emissions of SO2 and NOx are among the lowest of all EU member states. Pollution levels are generally lower than the limit values for lead, benzene, and CO. SO2 exceedances are rare and mainly caused by transboundary air pollution. Nevertheless, limit values for PM10 concentrations (50 µg m-3 daily average not to be exceeded more than 35 times per year; 40 µg m-3 yearly average) are frequently exceeded in agglomerations, but are also exceeded in rural regions, particularly in the north-eastern part of the country as well as in Alpine valleys and basins. Statistical analyses of back trajectories showed that a considerable amount of the total PM10 levels in the eastern part of Austria is caused by long-range transport, but also local sources add substantially to PM10 levels. Therefore, due to its geographic situation, Austria is strongly affected by local, regional, and long-range air pollution. Further

emission reduction strategies will be necessary both on national and international levels [1]. The legal framework for monitoring air quality in Austria are the national air quality protection act, ozone act, clean air act for steam boilers, as well as international obligations, such as the EC Air Quality Framework Directive and daughter directives, and the UNECE Convention of Long-Range Transboundary Air Pollution (CLRTAP) [2]. Fig. 1 shows the air quality monitoring sites in Austria that are operated by the Austrian Federal Environment Agency (Umweltbundesamt; UBA). The total number of sites in Austria, including the sites operated by the Federal Provinces, already exceeded 90 in 2003. For an analysis of long-term trends, unfortunately only TSP measurements exist, monitoring of PM10 did not start until 2001. More recently, sites measuring PM2.5 are added to the monitoring network.

Figure 1. Air quality monitoring sites in Austria

operated by UBA [1].

For an analysis of the state of the atmosphere over Austria, besides the legally required monitoring and analysis methods, UBA is currently also evaluating the potential of satellite remote sensing methods. Within the

_____________________________________________________

Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

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framework of the GMES Service Element (GSE) project PROMOTE [3], several data products are evaluated and, if necessary, optimized to the user’s requirements. The mission of PROMOTE is to deliver the Atmosphere GMES Service Element by constructing and delivering a sustainable and reliable operational service to support informed decisions on atmospheric policy issues. The aim is an incremental enhancement of services during the lifetime of the project [4]. 2. MERIS AEROSOL DATA

MERIS aerosol data are delivered within a Service Level Agreement (SLA) between ACRI-ST and UBA in the framework of PROMOTE. The service lasts for the full period of the project, with a continuous delivery of aerosol products, that is, the aerosol optical depth (AOD) and the Ångstöm exponent, as well as RGB images of the same area. The time delay between data acquisition by the satellite and the delivery of the aerosol product and the RGBs is about one day. Part of the SLA is also access to archive data (2003 to present), and assistance to exploit the data products. The resolution of the aerosol product is 1 km, with latitude coverage of 44N - 50N, and longitude coverage of 8E - 20E. A more detailed description of the standard MERIS aerosol algorithm is given in Santer et al. [5] and the MERIS aerosol algorithm ATBD [6, 7].

Figure 2. Aerosol optical depth (AOD) distribution

around Austria on 19 August 2006.

A validation of satellite-derived AOD against AERONET was recently performed by Kokhanovsky et.al. [8] for different algorithms and sensors for one scene over central Europe, where they showed that MERIS provided the most accurate AOD retrievals compared to other datasets (POLDER, MODIS, AATSR, MISR, SCIAMACHI). Nevertheless, large

differences were found in the retrieved AOD, even when applying two different algorithms to the same MERIS scene. Validation of the MERIS AOD with ground-based AOD was not performed in this work, as this was already done in several other works [5, 7, 8]. 3. RESULTS

Fig. 2 shows an example of the aerosol optical depth distribution around Austria, as measured on 19 August 2006.

Figure 3. Monthly mean AOD distribution around

Austria (February 2006)

Figure 4. Monthly mean AOD distribution around

Austria (September 2006)

Typically, only part of Austria is covered due to the swath width of MERIS and cloud coverage. During the winter months coverage is even lower due to snow and ice surfaces. Cloudy areas are screened out with a very

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strict cloud rejection (cloud threshold: 5 pixels), but it is obvious in Fig. 2 that the cloud-screening still needs to be improved due to higher aerosol concentrations in the vicinity of clouds. We analyzed several daily swath scenes and found frequent cloud contamination at cloud edges, and in broken cloud fields. Fig. 3 and 4 show average images of the AOD during winter (February 2006) and summer (September 2006) over Austria and surroundings. In both figures it can be seen that even in a monthly average image no full coverage of the area can be achieved. During winter the situation is worse due to frequent cloud coverage and due to the fact that the dark-target method of the MERIS algorithm cannot retrieve data over highly reflecting surfaces such as snow and ice. During summer, nearly full coverage of the area can be achieved, except for some high mountainous regions and urban areas (Fig. 4). But even in summer only a small number of measurements is made per pixel and month (typically less than 15). The higher AOD values in the south-eastern part of Austria might be an artefact due to cloud contamination, but could also be due to higher aerosol concentrations in this area. Fig. 5 shows the yearly average (year 2006) AOD distribution for Austria, and Fig. 6 depicts the number of measurements for the same period. Similar AOD distributions were obtained for the years 2004 and 2005 (not shown here). The number of measurements per pixel and year is generally less than 100 per year, which means less than one observation every three days in the optimum case.

Figure 5. Yearly mean AOD distribution around

Austria (Year 2006)

Yet, it has to be emphasized that the values shown by such a yearly composite image are biased towards the summer, when more retrievals are possible than in the winter months. It is, therefore, clear that the monthly average values from satellite measurements are

calculated from a much smaller number of measurements than the values that are retrieved from ground-based in-situ stations that measure the aerosol concentration every half-hour during day and night. The MERIS AOD dataset has less than 1% temporal coverage compared to the ground-based in-situ measurements when considering half-hourly measurements, and less than 30% temporal coverage when considering daily gravimetric measurements. Composite images therefore have to be treated with great care.

Figure 6. Number of AOD measurements for the year

2006

Several spatial patterns that appear in the composite images do not seem to be real spatial distributions of the AOD, e.g. lines around latitude 46 and 48, as well as longitude 16 (Fig. 5) appear to be artefacts in the MERIS dataset, which should be looked at into more detail. Realistic spatial patterns in the yearly composite are the elevated AOD values in the Po valley, as well as Alpine valleys have clearly visible higher AOD values compared to the surrounding mountains. 4. COMPARISON WITH GROUND-BASED PM

MEASUREMENTS

Several groups already performed comparisons of AOT data from satellite measurements with ground-based PM data [9, 10, 11, 12]. Such simple comparisons are known to be problematic as the PBL height and humidity effect is not taken into account, and also elevated aerosol layers (such as from long range transport of dust) are a problem, as they are observed from the satellite but cannot be measured at the ground. Yet, such comparisons give a first insight if a correlation between datasets exists at all, and which can be improved on with further corrections. Therefore, as such comparisons of satellite AOD with ground-based

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PM only makes sense for sites without local pollutions sources nearby, five background sites in Austria were chosen, that is, Pillersdorf, Illmitz, Enzenkirchen, Zöbelboden, and Vorhegg. All of these locations are rural sites and only slightly influenced by local air pollution (Fig. 1), and some are part of the EMEP network.

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MERIS AOD measurements(red dots) at Illmitz.

Fig. 7 shows the gravimetrically measured daily average PM10 concentrations (blue line) at Illmitz in the eastern part of Austria, near to the Hungarian border (see Fig. 1). Also shown are the retrieved AOT values from the MERIS aerosol product (red dots). The two datasets compare reasonable good, but the amount of data is still too small to make a more detailed assessment.

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Plots for the other four sites show similar patterns. Far-off AOD values could be either elevated aerosol layers or cloud contamination. Fig. 8 shows a scatterplot of the MERIS AOD vs. the gravimetrically measured PM10 values (24h averages) at Illmitz. Although not

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Fig. 9 shows a scatterplot of the MERIS AOD vs. the continuously measured PM10 values (at 10:30 a.m.) at Illmitz, at the time of the overpass of MERIS. Also here, a correlation between the two datasets is obvious, but not much better than the one between the 24h average PM values and the satellite AOD. Also shown is a correlation between the MERIS AOD and the gravimetrically measured (24h averages) PM2.5. As satellite retrievals in the visible part of the spectrum such as the MERIS AOD are more sensitive to fine particles, a higher correlation between PM2.5 than PM10 concentrations with the AOD is assumed. A comparison of Fig. 8 with Fig. 10 suggests a slightly better correlation between in the latter case, but the difference is not really strong.

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5. CONCLUSIONS

From this first evaluation of the ENVISAT/MERIS aerosol product from the viewpoint of air quality assessment, several lessons could be learnt. The advantage of satellite data compared to the operational in-situ monitoring network clearly is the spatial coverage of data. Especially in a country with a complex topography such as Austria, this could provide valuable additional information for areas where no ground-based monitoring stations exist. Satellite data can also give valuable information about long-range transport of pollution, and a synoptic view of the area of interest. Main disadvantages of LEO satellite aerosol data – such as MERIS - for air quality monitoring are missing data for cloudy pixels, and the low temporal frequency of data compared to ground-based data. The data give only a snapshot of the aerosol at the time of the overpass of the satellite. The applicability of LEO satellite data for AQ monitoring is therefore limited, as the diurnal variability is not covered due to fixed local time of measurement. Also, the dark-target approach of the MERIS aerosols algorithm does not provide aerosol information above higher reflecting surfaces such as snow and ice, which limits the usefulness of data in the winter period. Unfortunately, during the winter usually the highest PM10 levels are observed, and additional satellite data would me most valuable. The under-sampling problem, particularly in winter, might be overcome by using different satellite data (GEO data, UV channels) and/or improved algorithms that are able to retrieve aerosols over bright surfaces. Cloud contamination was found to be a serious problem, especially at cloud edges and in broken cloud fields. There might be a physical explanation for higher AOD values in the vicinity of clouds due to the higher humidity there, but for air quality applications such effects are not of interest and should be screened out. Higher AOD values due to cloud contamination influences the daily measurements of AOD, and also has a strong impact on the monthly, seasonal, and yearly composite data. Cloud contamination produces spatial patterns in the AOD composites that have no correspondence in reality. Moreover, yearly composites have the tendency to be biased towards the summer months, as during the winter months only few observations are made (due to snow cover and higher cloudiness in winter). Composites are also biased towards dark surfaces, as more measurements are taken there. Images of the spatial distribution of the number of measurements are very useful to understand such influences on the spatial distribution of the AOD. We conclude that further analyses of the MERIS dataset, such as trend analyses, and conversion to ground-PM using PLB heights and humidity values will only make sense after improvements of the MERIS AOD.

6. REFERENCES

1. Schneider, J., et al. (Umweltbundesamt, Ed.), Schwebestaub in Österreich, pp. 410, 2005 (in German).

2. State of the Environment in Austria (Umweltbundesamt, Ed.), Vol. 7, pp. 472, 2003.

3. PROMOTE webpage: www.gse-promote.org 4. Holzer-Popp, T., Paliouras, E., van Oss, R., van der

A, R., Hoeller, R., and the PROMOTE team, PROMOTE-2 – The GMES Service Element “Atmosphere”, this issue.

5. Santer, R., V. Carrere, P. Dubuisson, and J. C. Roger, Atmospheric corrections over land for MERIS, Int. J. of Rem. Sens., 20, 1819-1840, 1999.

6. Santer, R, Carrere, V., Dessailly, D., Dubuisson, P., and J.-C. Roger, MERIS Algorithm theoretical basis document, ATBD 2.15, Atmospheric corrections over land.

7. Ramon, D., and R. Santer, MERIS Algorithm theoretical basis document, ATBD 2.19, Atmospheric corrections over land: correction of directional effects over DDV.

8. Kokhanovsky, A., F.-M. Breon, A. Cacciari, E. Carbou, D. Diner, W. Di Nicolantonio, R.G. Grainger, W.M.F. Grey, R. Höller, K.-H. Lee, P.R.J. North, A. Sayer, G. Thomas, and W. von Hoyningen-Huene, Aerosol remote sensing over land: satellite retrievals using different algorithms and instruments, Atmos. Research, in press.

9. Chu, D. A., et al. , Global monitoring of air pollution over land from the Earth Observing System –Terra Moderate Resolution Imaging Spectroradiometer (MODIS), J. Geophys. Res., 108(D21), 4661, doi: 10.1029/2002JD003179, 2003.

10. Engel-Cox, J. A., R. A. Hoff, and A. D. J. Haymet, Recommendations on the use of satellite remote-sensing data for urban air quality, J. Air & Waste

Manage. Assoc., 54, 1360-1371, 2004. 11. Hutchison, K.D., Smith, S., and S. J. Faruqui,

Correlating MODIS aerosol optical thickness data ground-based PM2.5 observations across Texas for use real-time air quality prediction system, Atmos.

Environ., 39, 7190–7203, 2005. 12. Koelemejer, R.B.A., C.D. Homan, and J.

Matthijsen, Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe, Atmos. Environ., 40, 5304-5315, 2006.

Acknowledgement

This work is funded by the ESA GMES Service Element (GSE) project PROMOTE. Data are provided by the European Space Agency and ACRI-ST.