Is the efficacy of satellite-based inversion of SO2 emission model dependent?
Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is qua...
| 出版年: | Environmental Research Letters |
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| 主要な著者: | , , , , , , , , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
IOP Publishing
2021-01-01
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1088/1748-9326/abe829 |
| _version_ | 1850297519768076288 |
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| author | Nan Li Keqin Tang Yi Wang Jun Wang Weihang Feng Haoran Zhang Hong Liao Jianlin Hu Xin Long Chong Shi Xiaoli Su |
| author_facet | Nan Li Keqin Tang Yi Wang Jun Wang Weihang Feng Haoran Zhang Hong Liao Jianlin Hu Xin Long Chong Shi Xiaoli Su |
| author_sort | Nan Li |
| collection | DOAJ |
| container_title | Environmental Research Letters |
| description | Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO _2 emissions from global GEOS-Chem adjoint model and OMI SO _2 data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO _2 and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO _2 emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions. |
| format | Article |
| id | doaj-art-2fcda913104f441bbf8a412b9e770ef6 |
| institution | Directory of Open Access Journals |
| issn | 1748-9326 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| spelling | doaj-art-2fcda913104f441bbf8a412b9e770ef62025-08-19T23:32:36ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-0116303501810.1088/1748-9326/abe829Is the efficacy of satellite-based inversion of SO2 emission model dependent?Nan Li0Keqin Tang1Yi Wang2Jun Wang3https://orcid.org/0000-0002-7334-0490Weihang Feng4Haoran Zhang5Hong Liao6Jianlin Hu7Xin Long8Chong Shi9Xiaoli Su10Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaCenter of Global and Regional Environmental Research, Department of Chemical and Biochemical Engineering, University of Iowa , Iowa, IA, United States of AmericaCenter of Global and Regional Environmental Research, Department of Chemical and Biochemical Engineering, University of Iowa , Iowa, IA, United States of AmericaJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology , Nanjing 210044, People’s Republic of ChinaSchool of Environment Science and Engineering, Southern University of Science and Technology , Shenzhen 518055, People’s Republic of ChinaNational Institute for Environmental Studies, Center for Global Environmental Research , Tsukuba, Ibaraki, JapanKey Laboratory of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences , Xi’an 710061, People’s Republic of ChinaSatellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO _2 emissions from global GEOS-Chem adjoint model and OMI SO _2 data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO _2 and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO _2 emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions.https://doi.org/10.1088/1748-9326/abe829OMIMODISsatellite-based inversionGEOS-ChemWRF-ChemCMAQ |
| spellingShingle | Nan Li Keqin Tang Yi Wang Jun Wang Weihang Feng Haoran Zhang Hong Liao Jianlin Hu Xin Long Chong Shi Xiaoli Su Is the efficacy of satellite-based inversion of SO2 emission model dependent? OMI MODIS satellite-based inversion GEOS-Chem WRF-Chem CMAQ |
| title | Is the efficacy of satellite-based inversion of SO2 emission model dependent? |
| title_full | Is the efficacy of satellite-based inversion of SO2 emission model dependent? |
| title_fullStr | Is the efficacy of satellite-based inversion of SO2 emission model dependent? |
| title_full_unstemmed | Is the efficacy of satellite-based inversion of SO2 emission model dependent? |
| title_short | Is the efficacy of satellite-based inversion of SO2 emission model dependent? |
| title_sort | is the efficacy of satellite based inversion of so2 emission model dependent |
| topic | OMI MODIS satellite-based inversion GEOS-Chem WRF-Chem CMAQ |
| url | https://doi.org/10.1088/1748-9326/abe829 |
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