SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM2.5 and in situ PM2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO2, SO2, AOD, PM2.5 and PM10). For the Czech Republic inclusion of satellite data improved mapping of NO2 in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO2. The use of satellites based AOD or PM2.5 improved mapping results for PM2.5 and PM10. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urbanscale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO2 data with a direct comparison against high-resolution TROPOMI NO2 as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regionaland urban-scale air quality monitoring. Remote Sens. 2021, 13, 2219. https://doi.org/10.3390/rs13112219 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2219 2 of 23

[1]  Dietrich Althausen,et al.  Modification of Local Urban Aerosol Properties by Long-Range Transport of Biomass Burning Aerosol , 2018, Remote. Sens..

[2]  Liang-pei Zhang,et al.  Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks , 2017, Journal of Geophysical Research: Atmospheres.

[3]  R. Koelemeijer,et al.  Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe , 2006 .

[4]  Jungho Im,et al.  Estimating ground-level particulate matter concentrations using satellite-based data: a review , 2020 .

[5]  A. Gorai,et al.  A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges , 2020, Asia-Pacific Journal of Atmospheric Sciences.

[6]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[7]  B. Denby,et al.  Spatial mapping of air quality for European scale assessment , 2006 .

[8]  Steffen Beirle,et al.  Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project , 2018, Atmospheric Measurement Techniques.

[9]  Yong Xue,et al.  Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data , 2012 .

[11]  Yong Xue,et al.  Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci) , 2016, Remote. Sens..

[12]  Robert C. Levy,et al.  Optimal estimation for global ground‐level fine particulate matter concentrations , 2013 .

[13]  Pieter Valks,et al.  Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method , 2021, Remote. Sens..

[14]  Heikki Saari,et al.  The ozone monitoring instrument , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Iwona S. Stachlewska,et al.  Effect of Heat Wave Conditions on Aerosol Optical Properties Derived from Satellite and Ground-Based Remote Sensing over Poland , 2017, Remote. Sens..

[16]  Kerstin Stebel,et al.  SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe , 2021, Remote. Sens..

[17]  D. Jacob,et al.  Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. , 2005, Environmental science & technology.

[18]  S. Christopher,et al.  Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? , 2009, Journal of the Air & Waste Management Association.

[19]  Iwona S. Stachlewska,et al.  Near-Real-Time Application of SEVIRI Aerosol Optical Depth Algorithm , 2020, Remote. Sens..

[20]  J. Schmid,et al.  The SEVIRI Instrument , 2000 .

[21]  Zhaokun Hu,et al.  Pinpointing nitrogen oxide emissions from space , 2019, Science Advances.

[22]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[23]  Lorraine A. Remer,et al.  MODIS 3 km aerosol product: algorithm and global perspective , 2013 .

[24]  Georg A. Grell,et al.  Fully coupled “online” chemistry within the WRF model , 2005 .

[25]  P. Kyriakidis A Geostatistical Framework for Area-to-Point Spatial Interpolation , 2004 .

[26]  D. Gesch,et al.  Global multi-resolution terrain elevation data 2010 (GMTED2010) , 2011 .

[27]  Luca Vogt Statistics For Spatial Data , 2016 .

[28]  Doina Nicolae,et al.  A neural network aerosol-typing algorithm based on lidar data , 2018, Atmospheric Chemistry and Physics.

[29]  K. F. Boersma,et al.  S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI , 2020, Atmospheric Measurement Techniques.

[30]  K. F. Boersma,et al.  Quantification of nitrogen oxides emissions from build-up of pollution over Paris with TROPOMI , 2019, Scientific Reports.

[31]  David G. Streets,et al.  Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015 , 2015 .

[32]  Paul Ingmann,et al.  Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and -5p , 2012 .

[33]  Bruce Denby,et al.  Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale , 2008 .

[34]  Marion Schroedter-Homscheidt,et al.  Improvements of synergetic aerosol retrieval for ENVISAT , 2008 .

[35]  No-Wook Park,et al.  Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables , 2013 .

[36]  Zhanqing Li,et al.  MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis , 2020, Atmospheric Environment.

[37]  P. Schneider,et al.  Satellite data inclusion and kernel based potential improvements in NO 2 mapping ETC / , 2018 .

[38]  Lin Sun,et al.  Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei , 2019, Sensors.

[39]  R. Park,et al.  Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea , 2018, Atmospheric Chemistry and Physics.

[40]  P. Atkinson,et al.  Spatial Scale Problems and Geostatistical Solutions: A Review , 2000 .

[41]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[42]  Lianfa Li,et al.  A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 , 2020, Remote. Sens..

[43]  John P. Burrows,et al.  Atmospheric aerosol load as derived from space , 2006 .

[44]  N. Krotkov,et al.  Anthropogenic and volcanic point source SO2 emissions derived from TROPOMI on board Sentinel-5 Precursor: first results , 2020 .

[45]  A.J.H. Visschedijk,et al.  TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling , 2014 .

[46]  Philip Watts,et al.  Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory , 2010 .

[47]  M. Mishchenko,et al.  Calculation of the amplitude matrix for a nonspherical particle in a fixed orientation. , 2000, Applied optics.

[48]  Henk Eskes,et al.  TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications , 2012 .

[49]  K. Shadan,et al.  Available online: , 2012 .

[50]  B. Holben,et al.  Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) , 2003 .

[51]  D. Winker,et al.  Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms , 2009 .

[52]  Olga Zawadzka,et al.  Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data , 2014, Remote. Sens..

[53]  Jia-ling Wang,et al.  Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing , 2018, Scientific Reports.

[54]  Xavier Ceamanos,et al.  AERUS‐GEO: A newly available satellite‐derived aerosol optical depth product over Europe and Africa , 2014 .

[55]  W. Lahoz,et al.  Data assimilation: making sense of Earth Observation , 2014, Front. Environ. Sci..

[56]  M. Gauß,et al.  The EMEP MSC-W chemical transport model -- technical description , 2012 .