Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument
We determine and interpret fine particulate matter (PM 2.5 ) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of...
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ftcopernicus:oai:publications.copernicus.org:acp30315 2023-05-15T13:06:37+02:00 Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument Xu, J.-W. Martin, R. V. van Donkelaar, A. Kim, J. Choi, M. Zhang, Q. Geng, G. Liu, Y. Ma, Z. Huang, L. Wang, Y. Chen, H. Che, H. Lin, P. Lin, N. 2018-09-19 application/pdf https://doi.org/10.5194/acp-15-13133-2015 https://www.atmos-chem-phys.net/15/13133/2015/ eng eng doi:10.5194/acp-15-13133-2015 https://www.atmos-chem-phys.net/15/13133/2015/ eISSN: 1680-7324 Text 2018 ftcopernicus https://doi.org/10.5194/acp-15-13133-2015 2019-12-24T09:52:56Z We determine and interpret fine particulate matter (PM 2.5 ) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM 2.5 . The simulated PM 2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM 2.5 versus the ground-level PM 2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM 2.5 and in situ observations in both annual averages ( r 2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM 2.5 reveals that secondary inorganics (SO 4 2- , NO 3 - , NH 4 + ) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM 2.5 over eastern China for 2013 is 53.8 μg m −3 , with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization. Text Aerosol Robotic Network Copernicus Publications: E-Journals Atmospheric Chemistry and Physics 15 22 13133 13144 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
We determine and interpret fine particulate matter (PM 2.5 ) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM 2.5 . The simulated PM 2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM 2.5 versus the ground-level PM 2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM 2.5 and in situ observations in both annual averages ( r 2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM 2.5 reveals that secondary inorganics (SO 4 2- , NO 3 - , NH 4 + ) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM 2.5 over eastern China for 2013 is 53.8 μg m −3 , with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization. |
format |
Text |
author |
Xu, J.-W. Martin, R. V. van Donkelaar, A. Kim, J. Choi, M. Zhang, Q. Geng, G. Liu, Y. Ma, Z. Huang, L. Wang, Y. Chen, H. Che, H. Lin, P. Lin, N. |
spellingShingle |
Xu, J.-W. Martin, R. V. van Donkelaar, A. Kim, J. Choi, M. Zhang, Q. Geng, G. Liu, Y. Ma, Z. Huang, L. Wang, Y. Chen, H. Che, H. Lin, P. Lin, N. Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
author_facet |
Xu, J.-W. Martin, R. V. van Donkelaar, A. Kim, J. Choi, M. Zhang, Q. Geng, G. Liu, Y. Ma, Z. Huang, L. Wang, Y. Chen, H. Che, H. Lin, P. Lin, N. |
author_sort |
Xu, J.-W. |
title |
Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
title_short |
Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
title_full |
Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
title_fullStr |
Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
title_full_unstemmed |
Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument |
title_sort |
estimating ground-level pm2.5 in eastern china using aerosol optical depth determined from the goci satellite instrument |
publishDate |
2018 |
url |
https://doi.org/10.5194/acp-15-13133-2015 https://www.atmos-chem-phys.net/15/13133/2015/ |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
eISSN: 1680-7324 |
op_relation |
doi:10.5194/acp-15-13133-2015 https://www.atmos-chem-phys.net/15/13133/2015/ |
op_doi |
https://doi.org/10.5194/acp-15-13133-2015 |
container_title |
Atmospheric Chemistry and Physics |
container_volume |
15 |
container_issue |
22 |
container_start_page |
13133 |
op_container_end_page |
13144 |
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1766013485943095296 |