Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018
Exposure to ambient PM2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412021003512 |
| _version_ | 1831586975433359360 |
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| author | Qingqing He Kai Gao Lei Zhang Yimeng Song Ming Zhang |
| author_facet | Qingqing He Kai Gao Lei Zhang Yimeng Song Ming Zhang |
| author_sort | Qingqing He |
| collection | DOAJ |
| description | Exposure to ambient PM2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM2.5 data for environmental monitoring and PM2.5-related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM2.5 concentrations from 2000 to 2018 over China at a fine scale of 1 km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R2 and root-mean-square-deviation values were 0.59 and 27.18 μg/m3, respectively. The resultant monthly (R2 = 0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM2.5 levels experienced a rapid increase in 2001–2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM2.5 at both a large and a fine scale in China. |
| first_indexed | 2024-12-17T21:46:28Z |
| format | Article |
| id | doaj.art-1a3358da3592462b93aa2daa23dce476 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-12-17T21:46:28Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-1a3358da3592462b93aa2daa23dce4762022-12-21T21:31:26ZengElsevierEnvironment International0160-41202021-11-01156106726Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018Qingqing He0Kai Gao1Lei Zhang2Yimeng Song3Ming Zhang4School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China; Corresponding author at: School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road No.129, Wuhan, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China; Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaExposure to ambient PM2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM2.5 data for environmental monitoring and PM2.5-related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM2.5 concentrations from 2000 to 2018 over China at a fine scale of 1 km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R2 and root-mean-square-deviation values were 0.59 and 27.18 μg/m3, respectively. The resultant monthly (R2 = 0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM2.5 levels experienced a rapid increase in 2001–2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM2.5 at both a large and a fine scale in China.http://www.sciencedirect.com/science/article/pii/S0160412021003512Fine particulate matter (PM2.5)Satellite remote sensingAdaptive spatiotemporal modelingLong-term trendHigh spatiotemporal resolution |
| spellingShingle | Qingqing He Kai Gao Lei Zhang Yimeng Song Ming Zhang Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 Environment International Fine particulate matter (PM2.5) Satellite remote sensing Adaptive spatiotemporal modeling Long-term trend High spatiotemporal resolution |
| title | Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 |
| title_full | Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 |
| title_fullStr | Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 |
| title_full_unstemmed | Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 |
| title_short | Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018 |
| title_sort | satellite derived 1 km estimates and long term trends of pm2 5 concentrations in china from 2000 to 2018 |
| topic | Fine particulate matter (PM2.5) Satellite remote sensing Adaptive spatiotemporal modeling Long-term trend High spatiotemporal resolution |
| url | http://www.sciencedirect.com/science/article/pii/S0160412021003512 |
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