Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China
Near-surface NO<sub>2</sub> (NS-NO<sub>2</sub>) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO<sub>2</sub> using satellite-based NO<sub>2</sub> column measurement...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9557824/ |
| _version_ | 1818675267100475392 |
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| author | Donghui Li Kai Qin Jason Cohen Qin He Shuo Wang Ding Li Xiran Zhou Xiaolu Ling Yong Xue |
| author_facet | Donghui Li Kai Qin Jason Cohen Qin He Shuo Wang Ding Li Xiran Zhou Xiaolu Ling Yong Xue |
| author_sort | Donghui Li |
| collection | DOAJ |
| description | Near-surface NO<sub>2</sub> (NS-NO<sub>2</sub>) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO<sub>2</sub> using satellite-based NO<sub>2</sub> column measurements, there still exist significant defects, resulting in a low overall fit and significant amount of bias. This article combines GOME-2B and OMI satellite data to estimate daily NS-NO<sub>2</sub> with a spatial resolution of 0.1° × 0.1° from 2014 to 2018 over Mainland China, using a machine learning method. The estimated result has four important characteristics. First, the sample-based cross validation with surface observations shows a good result with <italic>R</italic><sup>2</sup> = 0.80 and RMSE = 9.0 <italic>μ</italic>g/m<sup>3</sup>. Second, the underestimation in high concentration areas and overestimation in low concentration areas are both reduced, compared with the case of using OMI data alone. Third, the estimated NS-NO<sub>2</sub> is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. Furthermore, the population-weighted NO<sub>2</sub>-based estimated dataset shows a significant decline of pollution exposure levels from 2014 to 2018. |
| first_indexed | 2024-12-17T08:24:52Z |
| format | Article |
| id | doaj.art-ab7634f69ef646c8936b56c786c50ce1 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-17T08:24:52Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-ab7634f69ef646c8936b56c786c50ce12022-12-21T21:56:48ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-0114102691027710.1109/JSTARS.2021.31173969557824Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland ChinaDonghui Li0https://orcid.org/0000-0001-6414-8504Kai Qin1Jason Cohen2https://orcid.org/0000-0002-9889-8175Qin He3Shuo Wang4Ding Li5https://orcid.org/0000-0003-0010-200XXiran Zhou6https://orcid.org/0000-0002-2567-0313Xiaolu Ling7Yong Xue8School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaNear-surface NO<sub>2</sub> (NS-NO<sub>2</sub>) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO<sub>2</sub> using satellite-based NO<sub>2</sub> column measurements, there still exist significant defects, resulting in a low overall fit and significant amount of bias. This article combines GOME-2B and OMI satellite data to estimate daily NS-NO<sub>2</sub> with a spatial resolution of 0.1° × 0.1° from 2014 to 2018 over Mainland China, using a machine learning method. The estimated result has four important characteristics. First, the sample-based cross validation with surface observations shows a good result with <italic>R</italic><sup>2</sup> = 0.80 and RMSE = 9.0 <italic>μ</italic>g/m<sup>3</sup>. Second, the underestimation in high concentration areas and overestimation in low concentration areas are both reduced, compared with the case of using OMI data alone. Third, the estimated NS-NO<sub>2</sub> is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. Furthermore, the population-weighted NO<sub>2</sub>-based estimated dataset shows a significant decline of pollution exposure levels from 2014 to 2018.https://ieeexplore.ieee.org/document/9557824/Global ozone monitoring experiment (GOME-2B)Nitrogen Dioxide (NO_2)OMIpopulation-weightedrandom forest (RF) |
| spellingShingle | Donghui Li Kai Qin Jason Cohen Qin He Shuo Wang Ding Li Xiran Zhou Xiaolu Ling Yong Xue Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Global ozone monitoring experiment (GOME-2B) Nitrogen Dioxide (NO_2) OMI population-weighted random forest (RF) |
| title | Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China |
| title_full | Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China |
| title_fullStr | Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China |
| title_full_unstemmed | Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China |
| title_short | Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China |
| title_sort | combing gome 2b and omi satellite data to estimate near surface no sub 2 sub of mainland china |
| topic | Global ozone monitoring experiment (GOME-2B) Nitrogen Dioxide (NO_2) OMI population-weighted random forest (RF) |
| url | https://ieeexplore.ieee.org/document/9557824/ |
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