Integration of satellite and ground measurements for mapping particulate matter in alpine regions

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1 Integration of satellite and ground measurements for mapping particulate matter in alpine regions Emili E. 1,2 Petitta M. 2 Popp C. 3 Tetzlaff A. 2 Riffler M. 1 Wunderle S. 1 1 University of Bern, Institute of Geography, Hallerstrasse 12, CH EURAC, European Academy, Institute of Applied Remote Sensing, Viale Druso 1, IT Bolzano. 3 Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf, CH Abstract The objective of this study is to integrate satellite and in-situ measurements of particulate matter (PM10), in order to provide operationally daily PM10 maps in Switzerland and South Tyrol (Italy). Knowledge of reliable PM distributions is highly demanded by the local authorities to protect population health. Usage of satellite data to derive PM has been widely investigated over the past years, showing a moderate potential. Its real effectiveness can, however, be tested only by means of a comparison with the well established networks of in-situ measurements. Data assimilation may finally permit to get a further improvement in PM mapping from the integration of both observational systems. Herein, we apply an AOD product of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) to derive maps of PM10. The retrieval of PM10 concentration at ground level requires an estimation of the aerosol vertical distribution. Scaling the AOD with the meteorological boundary layer height and relative humidity profile from a numerical model produces reasonable correlations (>0.6) between satellite data and ground PM. A linear model is established analysing 2 years of AOD, PM and meteorological time series and is further used to compute SEIVIRI PM maps. Validation of such maps reveals higher accuracy over flat areas (r>0.6, RMSE< 10µg/m 3 ) than in alpine valleys and elevated sites. On the other hand an inverse distance interpolation of in-situ measurements is able to produce more accurate (r>0.8, RMSE< 6µg/m 3 ) maps on the domain. An assimilation scheme has been developed considering the interpolation of in-situ measurements as first guess field and updating them with satellite observations wherever they are available. The results show that satellite data is of limited benefit in the considered region, due to the good coverage of the ground networks and the difficulties inherent to the satellite PM retrieval. The situations is reversed when a number of measurement sites are not considered. It can be concluded that satellite data can be of higher interests for countries which possess less dense distribution of measurement sites. 1 Introduction PM is composed of a mixture of solid or liquid airborne particles (aerosols), with an aerodynamic diameter smaller than 10 µm (PM 10 ) or 2.5 µm (PM 2.5 ). Several clinical studies (e.g. Brunekreef and Holgate, 2002) showed that PM can impact human health. The European Community (Community, 1999) established regulations to reduce PM concentration caused by human activities and set reliable PM concentration limits. In this framework, a network of ground measurement sites represents the basis of monitoring the variability of PM concentrations in space and time. Ground-based monitoring networks are, however, unable to provide complete coverage of an area of interest, because of the local character of the measurements. 1

2 Remote sensing data from satellites might provide an additional tool for PM mapping due to their large spatial coverage and repeated measurements. A frequently derived variable from satellite observations is the aerosol optical depth (AOD), which quantifies the extinction of electromagnetic radiation due to aerosols in an atmospheric column at a given wavelength. The major challenge in making use of space-borne AOD products for PM mapping is to predict the ground mass concentrations from a vertically integrated quantity (AOD) (Hoff and Christopher, 2009). Another difficulty is related to the AOD retrievals itself, which implies clear sky observations, numerous assumptions and a correct estimation of surface reflectance to be derived. This analysis is devoted to assess the effective value of PM 10 retrieved from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European geostationary METEOSAT Second Generation (MSG) satellites. In a previous work (Emili et al., 2010) the potential of this sensor for PM 10 mapping was explored, unraveling similar capabilities to other space-borne sensors (e.g. MODIS). As most of the countries already possess a well established ground network of measurements sites, we need to quantify which are the improvements in the knowledge of PM spatial distribution due to usage of satellite data. To face this question, the accuracy of PM 10 maps obtained independently from both satellite data and in-situ measurements is assessed. Even if one of the two approach results to be less accurate in average, there is still chance to improve the PM 10 spatial distribution knowledge by merging the two datasets, according to their relative uncertainties (assimilation). The area of interest in this study is the European alpine region (Switzerland and South Tyrol, Italy). The highly variable topography and environmental conditions in this region represent an additional point of interest, because of the consequent strong variability of PM (e.g. big differences between valleys and elevated sites and between winter and summer conditions). 2 Data 2.1 Satellite Data The AOD at 0.55 µm is retrieved with the SEVIRI s visible channel (0.6 µm and spatial resolution of 5 km in central Europe), using a multi-temporal approach (Popp et al., 2007). A spatial filter is applied to the retrieved AOD in order to reduce noise such that, finally, each pixel represents the AOD of an area of km 2. The AOD product, with an original time resolution of 15 min, has been aggregated in daily averages. The largest errors in the space-borne AOD products are expected to be due to over- or underestimation of surface reflectance and undetected cloud or snow contamination (Popp et al., 2007), which might pose a particular challenge in the study region due to SEVIRI s low spatial resolution. Comparing the satellite derived AOD with ground truth AERONET (Holben et al., 1998) sun-photometer measurements permits to assess the uncertainties on the satellite product. Preliminary evaluation showed that more than 70% of SEVIRI AOD (Popp et al., 2009) over land surfaces (Europe) fall within error of τ = ±0.05 ± 0.15τ (MODIS pre-launch 1-σ expected error). A validation of the product on the alpine region (3 AERONET sites) showed that more than 80% of the match-ups falls within τ in relatively flat locations while the accuracy decreases to 60% of the points inside alpine valleys (Emili et al., 2010). Knowledge of the uncertainties in AOD observations is necessary for the further estimation of errors in satellite derived PM. 2.2 Meteorological Data We used the Boundary Layer Height (BLH) and the vertical profile of Relative Humidity (RH) provided by the European Centre for Medium Range Weather Forecasts (ECMWF) operational archive with a 2

3 spatial grid resolution of 0.25 x0.25. The RH profile is taken at the vertical levels of hpa. The time resolution of the meteorological products is 3 hours. Meteorological fields have been aggregated in daily averages, referring to an approximate time window of AOD observations (6-18 UTC). 2.3 PM 10 data The Swiss National Air Pollution Monitoring Network (NABEL, EMPA, 2008) consists of 14 sites (Fig. 1) where daily measurements of PM 10 are routinely performed. The sites are located in different environments: urban, sub-urban, rural near highway, rural, rural at more than 1000 m of altitude. The Environmental Agency of South Tyrol (APPA) maintains 13 sites, mainly located in valleys and cities (except for a background site at 1700 m elevation), where daily PM 10 measurements are performed. The measurement method is gravimetric for 24h averages and follows the guidelines of the European Community. 3 Methodology and results 3.1 Spatialization of ground observations The first step of the analysis consists in examining the degree of accuracy of PM 10 maps obtained from the sole in-situ measurements. A number of different methods for spatialization of point measurements into the domain of interest has been tested (Inverse Distance ID, Inverse Distance with variable radius of influence, Kriging). The standard approach used to estimate the accuracy of the interpolation technique is the Boot Strap (BS) validation: daily PM 10 maps have been computed for the entire period ( ), excluding routinely one of the measurement sites. Values of PM 10 are extracted from such maps and compared to the values measured in the excluded site. For each site is then possible to compute an error statistics (Root Mean Square Error, Bias and Standard Deviation). The ID method is found to produce the lowest average RMSE in the considered region (Tab. 1) and will be considered for the further analysis. Using an approach based only on horizontal vicinity, such as the ID interpolator, the estimation is problematic for mountainous sites (Tab. 1). Large biases are in fact found in winter months, when the reduced vertical mixing of PM implies very low values on elevated sites. The remaining sites show an average RMSE of 6µg/m 3, a bias of 3µg/m 3 and a standard deviation of 4µg/m 3. Excluding high altitude sites we can assume a relative error of 20% (Tab. 1) as a rough estimation of the accuracy of PM 10 maps derived from in-situ measurements. 3.2 Satellite derived PM 10 maps In this section the procedure used to derive PM 10 from satellite observations is described and the related uncertainties are discussed. Numerous studies investigated the relation of ground concentration of PM with the satellite observed columnar extinction (AOD) (Hoff and Christopher, 2009). The two variables are only linearly related in the simple case of a homogenous/exponential vertical distribution of aerosols. Moreover, in case of high ambient relative humidity, the AOD could increase significantly due to water absorption, remaining the aerosol dry mass concentration (PM) unchanged. The most widely used approach is to estimate empirically the coefficients of a linear model between AOD and PM, based on historical time series of these variables. Previous works (Koelemeijer et al., 2006; Emili et al., 2010) showed that inclusion of meteorological data could enhance the applicability of such linear models. The most robust daily AOD-PM model for SEVIRI data in Switzerland was found to be 3

4 the following: P M 10 = α AOD BLH + β (1) where α and β are the empirical coefficients obtained via linear regression and BLH is the meteorological Boundary Layer Height obtained from a numerical weather prediction model. The 95% confidence level for predicting daily PM (in 2008) was found to vary between 7 and 19 µg/m 3 depending on the site, with lower values for flat sites than for mountainous ones (Emili et al., 2010). Further analysis, conducted with ground based Lidar observations, showed that the ECMWF RH vertical profile can be used as a qualitative proxy for estimating the vertical distribution of aerosol extinction. The original algorithm has been then modified with the following empirical correction: the profile of log(1 RH) is integrated from 500 m to the 700 hpa level (approx. 3 km). The resulting area (C), normalized to the total area of the profile, is used to remove the amount of AOD above the first 500 m layer of the troposphere, which represents noise in the PM estimation (AOD scaled = AOD(1 C)). Using the scaled AOD in the model (1) leads to a slight decrease of the confidence levels (1-2 µg/m 3 ) and then to a better ground PM estimation. Linear regressions between AOD scaled /BLH and P M 10 over the historical time series of the 27 sites showed a noticeable variability of the correlation coefficient, confidence level and regression parameters over the domain. Sites with moderately high correlations ( 0.7) show however similar regressions parameters. Subsequently, the coefficients are defined on the basis of the most confident regressions (α = 150 µg km/m 3, β = 15 µg/m 3 ). We developed a data processing chain based on this approach and computed maps of daily PM 10 for the period on the same domain of the spatialization analysis (Sec. 3.1). An example of a daily PM 10 map derived from SEVIRI AOD is depicted in Fig. 2. Values of SEVIRI PM 10 maps are finally extracted on the grid points nearest to the measurement sites and then compared to the ground measurements. The validation statistics is depicted in Tab. 2. A comparison with the validation statistics of interpolated maps shows that the accuracy of the satellite product is generally lower due to higher standard deviation (RMSE 10µg/m 3, STDEV 10µg/m 3 ). On mountain and valley sites the accuracy is even lower (RMSE> 15µg/m 3 ) due to the coarse resolution of the AOD and meteorological products Satellite maps uncertainties Once a model P M = f(x i ) is fixed, a theoretical uncertainty can be computed using standard error propagation: P M = ( ) f 2 x i (2) x i i where x i represents the standard 1-σ error of the different terms in the model (e.g. AOD, BLH, α, β for the model (1)). All the variables are assumed to be independent and normally distributed. This approach assumes also that the model we use correctly represents the data. Emili et al., 2010 showed that the error on the AOD and on the slope (α) describes a consistent part of the observed confidence levels of predicted PM. We use the same approach here to compute a PM error map for each satellite PM retrieval. As the AOD product (Sec. 2) is less accurate over rugged terrain we consider the error on the AOD value to be τ = ±0.05 ± 0.3τ if the standard deviation of the terrain height in a 25x25 km 2 pixel is higher than 500 m otherwise τ = ±0.05 ± 0.15τ (a DEM of 100 m spatial resolution is used for the computation of the STDEV). The standard error of the slope α is considered to be 20% of the value. This error is a result of the variability of aerosol microphysical properties and uncertainties in the regressions (Emili et al., 2010). For each SEVIRI PM 10 map we then have a correspondent map of estimated variance. 4

5 3.3 Assimilation of satellite data into ground maps The necessary ingredients for data assimilation are: a first guess of the PM field (also called background field) and the related uncertainty, a set of PM observations and their uncertainties. In our case we deal with two kind of measurements, the satellite and the ground based ones. The most natural way to transfer this data in terms of an assimilation problem is to choose the maps obtained by the interpolation of ground measurements as our background fields. Due to missing retrieval, the satellite maps are generally affected by gaps of data, whereas ground maps can be always computed for the entire domain, even if some sites have missing data. In this way it is also natural to think about satellite s data as a possible improvement to the knowledge of the PM field and to test their effectiveness against the ground network measurements. All the maps of PM described in the previous section were already projected to the same 10 km resolution lat-lon grid to assure spatial coincidence of PM products (satellite and background). In a multidimensional problem, like a 2D field of PM concentration, the covariance between errors at different locations also contributes to the assimilation. Following the notation in Kalnay (2003) we specified the background covariance matrix B (with a dimension of n 2 pix, n pix = n lat n lon ) and the observation covariance matrix R (with a dimension of n 2 sat) where the diagonal terms are the variance of the single values and correspond to the errors on the PM maps, previously described. An assimilation procedure based on the exact minimization of the cost function J has been developed and implemented. The small extent of the domain and the limited number of observations permits in fact the inversion of the weights matrix in a reasonable computer time. As a first attempt the non diagonal terms of the covariance matrices are all set to zero. This approximation neglects the correlations of errors between different grid points. In this case the assimilation consists of a simple weighted averages of the background field value and the observed value, where the weights are the respective variances. Modifications of the background field are not propagated in grid points which are not covered by satellite observations (Fig. 2) Validation of merged maps To test if the assimilation of satellite derived PM is effective, a BS validation is performed for the period for a representative subset of the sites. The ground maps, computed excluding routinely a measurement site, are used as background field and merged with satellite PM maps according to the assimilation procedure. Values of PM are then extracted from the merged maps on the location of the excluded site and compared to the measured value of PM. The comparison is done only for days with satellite data, to avoid statistical weight of unmodified PM values. Error statistics (Tab. 3) shows that the satellite data do not bring in average any improvement to the determination of PM 10 distribution but even reduce the accuracy of the background field. Manual tuning of the weight factor in the assimilation average did also not lead to a clear improvement at all sites. To test the methodology in the case of a less developed network of in-situ measurements (e.g. developing countries) a number of sites have been removed (Fig. 1) and the interpolation has been repeated. With the new configuration the estimation of PM is clearly improved in Switzerland (RMSE is reduced by more than 1 µg/m 3, Tab. 3). Very accurate interpolated maps in South Tyrol are still produced, even when only three distanced PM sites are retained, and satellite data, due to the low accuracy in the valleys, is found to be still not able to lead to any improvement. 4 Conclusions and outlook In this study, the potential of a PM 10 product from the geostationary SEVIRI instrument over the complex terrain of the European Alps has been addressed. The effectiveness of satellite data has 5

6 been tested by means of an assimilation schema between maps obtained from ground measurements and satellite derived PM 10 : 1. With the actual distribution of PM 10 measurement sites in the alpine area, a simple interpolation schema (Inverse Distance) produces reliable PM 10 maps in the Alpine valleys and on flat regions (RMSE 6 µg/m 3 ) but very inaccurate predictions on elevated sites (> 1000m); 2. SEVIRI derived PM 10 maps have a lower accuracy than ID maps on flat sites (RMSE 10 µg/m 3 ) and are even worse on both valley and mountain sites (RMSE> 15 µg/m 3 ); 3. When assimilating SEVIRI observations in the interpolated maps using a weighted average based on the relative errors of both maps no significant average improvements are observed. It is concluded that, in the examined region, the assimilation of satellite derived PM does not improve the knowledge of the PM 10 field. We can argue that, given the density of sites, the map derived from in-situ measurements are too accurate to be improved by satellite data. Such analysis is, of course, not generally valid because it depends strictly on the given network of PM 10 sites and the space-time variability of PM 10 fields. We finally consider that, at present, satellite derived PM can be more useful for countries which do not have a well established network for monitoring PM. 5 Acknowledgements This research was supported by the armasuisse, Science & Technology and the autonomous province of Bolzano (Italy). The authors would like to thank NABEL for providing PM 10 data, EUMETSAT for providing SEVIRI data. References Brunekreef, B., Holgate, S., Air pollution and health. The Lancet 360 (9341), Community, E., Council Directive 1999/39/EC of 22 April 1999 regulating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air. Official Journal of the European Communities L163, Emili, E., Popp, C., Petitta, M., Riffler, M., Wunderle, S., Zebisch, M., Pm10 remote sensing from geostationary seviri and polar-orbiting modis sensors over the complex terrain of the european alpine region. Remote Sensing of Environment 114 (11), EMPA, Technischer Bericht zum Nationalen Beobachtungsnetz für Luftfremdstoffe (NABEL). Hoff, R., Christopher, S., Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? Journal of the Air & Waste Management Association (1995) 59 (6), Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., et al., AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment 66 (1), Kalnay, E., Atmospheric modeling, data assimilation, and predictability. Cambridge Univ Pr. Koelemeijer, R., Homan, C., Matthijsen, J., Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment 40 (27), Popp, C., Hauser, A., Foppa, N., Wunderle, S., Remote sensing of aerosol optical depth over central Europe from MSG-SEVIRI data and accuracy assessment with ground-based AERONET measurements. Journal of Geophysical Research 112, D24S11. Popp, C., Riffler, M., Emili, E., Petitta, M., Wunderle, S., Evaluation of Operationally Derived Aerosol Optical Depth from MSG-SEVIRI over Central Europe. Geophysical Research Abstracts 11 (9362). 6

7 6 Tables r Bias Site N Rmse Stdev Rel points µg/m 3 µg/m 3 µg/m 3 Err % Average Avg (mountain) Avg (flat) Avg (valley) Table 1: Average statistics of validation for Inverse Distance interpolation performed with Boot Strap analysis (Root Mean Square Error, Correlation, Bias, Standard Deviation and Relative Error). The results have also been averaged considering the following classification for the measurement sites: mountains (>1000m), flat, alpine valley. r Bias Site N Rmse Stdev Rel points µg/m 3 µg/m 3 µg/m 3 Err % Average Avg (mountain) Avg (flat) Avg (valley) Table 2: Statistics of validation (as in Tab. 1) for SEVIRI derived PM 10 maps. Same differentiation of sites as in Tab. 1 has been adopted. r Bias Site N Rmse Stdev Rel points µg/m 3 µg/m 3 µg/m 3 Err % Average: Avg (flat): Avg (valley): Average: Avg (flat): Avg (valley): Table 3: Differences between the validation statistics (as in Tab. 1) for the maps after assimilation being performed and background maps (statistics is restricted to days with available satellite data). A subsample of flat and valley sites has been used for validation. Positive values indicate improvement in PM determination due to the assimilation. The first three rows depict the validation statistics computed with the whole set of PM sites used for the interpolation. The second three rows show the results with a restricted number of sites used for the interpolation (Sec ). 7

8 7 Figures Figure 1: Digital Elevation Model of the study area. The colored squares indicate the location of the PM10 sampling sites. Yellow sites indicate the subsample of measuring sites which have been used to simulate the network configuration of an hypothetical developing country (Sec ). Figure 2: Example of PM10 maps for 19th, October UL: SEVIRI derived PM10 map (Sec. 3.2). UR: confidence level (σ) for SEVIRI PM10 map (Sec ). ML: PM10 map from interpolation of ground measurements (ID). MR: confidence level (σ) for the interpolated PM10 map (Sec. 3.1). BL: PM10 map after data assimilation. BR: difference between assimilated map and background map (ML). 8