Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020
Fine particulate matter (PM2.5) seriously affects the environment, climate, and human health. Over the past decades, the Beijing–Tianjin–Hebei region (BTH) has been severely affected by pollutant gas and PM2.5 emissions caused by heavy industrial production, topography, and other factors and has bee...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.842237/full |
| _version_ | 1819282061816496128 |
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| author | Xiaohui Yang Xiaohui Yang Xiaohui Yang Dengpan Xiao Dengpan Xiao Dengpan Xiao Huizi Bai Jianzhao Tang Wei Wang Wei Wang |
| author_facet | Xiaohui Yang Xiaohui Yang Xiaohui Yang Dengpan Xiao Dengpan Xiao Dengpan Xiao Huizi Bai Jianzhao Tang Wei Wang Wei Wang |
| author_sort | Xiaohui Yang |
| collection | DOAJ |
| description | Fine particulate matter (PM2.5) seriously affects the environment, climate, and human health. Over the past decades, the Beijing–Tianjin–Hebei region (BTH) has been severely affected by pollutant gas and PM2.5 emissions caused by heavy industrial production, topography, and other factors and has been one of the most polluted areas in China. Currently, the long-term, large-scale, and high spatial resolution monitoring PM2.5 concentrations ([PM2.5]) using satellite remote sensing technology is an important task for the prevention and control of air pollution. The aerosol optical depth (AOD) retrieved by satellites combined with a variety of auxiliary information was widely used to estimate [PM2.5]. In this study, a two-stage statistical regression [linear mixed effects (LME) + geographically weighted regression (GWR)] model, combined with the latest high spatial resolution (1 km) AOD product and meteorological and land use parameters, was constructed to estimate [PM2.5] in BTH from 2013 to 2020. The model was fitted annually, and the ranges of coefficient of determination (R2), root mean square prediction errors (RMSPE), and relative prediction error (RPE) for the model cross-validation were 0.85–0.95, 7.87–29.90 μg/m3, and 19.19%–32.71%, respectively. Overall, the model obtained relatively good performance and could effectively estimate [PM2.5] in BTH. The [PM2.5] showed obvious temporal characteristic within a year (high in winter and low in summer) and spatial characteristic (high in the southern plain and low in the northern mountain). During the investigated period of 2013–2020, the high pollutant areas ([PM2.5] > 75 μg/m3) in 2020 significantly narrowed compared to 2013, and the annual average [PM2.5] in BTH fell below 55 μg/m3, with a drop of 54.04%. In particular, the [PM2.5] in winter season dropped sharply from 2015 to 2017 and declined steadily after 2017. Our results suggested that significant achievements have been made in air pollution control over the past 8 years, and they still need to be maintained. The research can provide scientific basis and support for the prevention and control of air pollution in BTH and beyond. |
| first_indexed | 2024-12-24T01:09:36Z |
| format | Article |
| id | doaj.art-9e72a0a4fb6d4bbd872218b1f784f7a1 |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-12-24T01:09:36Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-9e72a0a4fb6d4bbd872218b1f784f7a12022-12-21T17:23:04ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2022-03-011010.3389/fenvs.2022.842237842237Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020Xiaohui Yang0Xiaohui Yang1Xiaohui Yang2Dengpan Xiao3Dengpan Xiao4Dengpan Xiao5Huizi Bai6Jianzhao Tang7Wei Wang8Wei Wang9College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang, ChinaInstitute of Geographical Sciences, Hebei Academy of Sciences, Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang, ChinaInstitute of Geographical Sciences, Hebei Academy of Sciences, Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang, ChinaInstitute of Geographical Sciences, Hebei Academy of Sciences, Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang, ChinaInstitute of Geographical Sciences, Hebei Academy of Sciences, Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang, ChinaFine particulate matter (PM2.5) seriously affects the environment, climate, and human health. Over the past decades, the Beijing–Tianjin–Hebei region (BTH) has been severely affected by pollutant gas and PM2.5 emissions caused by heavy industrial production, topography, and other factors and has been one of the most polluted areas in China. Currently, the long-term, large-scale, and high spatial resolution monitoring PM2.5 concentrations ([PM2.5]) using satellite remote sensing technology is an important task for the prevention and control of air pollution. The aerosol optical depth (AOD) retrieved by satellites combined with a variety of auxiliary information was widely used to estimate [PM2.5]. In this study, a two-stage statistical regression [linear mixed effects (LME) + geographically weighted regression (GWR)] model, combined with the latest high spatial resolution (1 km) AOD product and meteorological and land use parameters, was constructed to estimate [PM2.5] in BTH from 2013 to 2020. The model was fitted annually, and the ranges of coefficient of determination (R2), root mean square prediction errors (RMSPE), and relative prediction error (RPE) for the model cross-validation were 0.85–0.95, 7.87–29.90 μg/m3, and 19.19%–32.71%, respectively. Overall, the model obtained relatively good performance and could effectively estimate [PM2.5] in BTH. The [PM2.5] showed obvious temporal characteristic within a year (high in winter and low in summer) and spatial characteristic (high in the southern plain and low in the northern mountain). During the investigated period of 2013–2020, the high pollutant areas ([PM2.5] > 75 μg/m3) in 2020 significantly narrowed compared to 2013, and the annual average [PM2.5] in BTH fell below 55 μg/m3, with a drop of 54.04%. In particular, the [PM2.5] in winter season dropped sharply from 2015 to 2017 and declined steadily after 2017. Our results suggested that significant achievements have been made in air pollution control over the past 8 years, and they still need to be maintained. The research can provide scientific basis and support for the prevention and control of air pollution in BTH and beyond.https://www.frontiersin.org/articles/10.3389/fenvs.2022.842237/fullPM2.5 concentrationsaerosol optical depthtwo-stage statistical regression modelspatiotemporal distributionBeijing–Tianjin–Hebei regionTianjin–Hebei region |
| spellingShingle | Xiaohui Yang Xiaohui Yang Xiaohui Yang Dengpan Xiao Dengpan Xiao Dengpan Xiao Huizi Bai Jianzhao Tang Wei Wang Wei Wang Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 Frontiers in Environmental Science PM2.5 concentrations aerosol optical depth two-stage statistical regression model spatiotemporal distribution Beijing–Tianjin–Hebei region Tianjin–Hebei region |
| title | Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 |
| title_full | Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 |
| title_fullStr | Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 |
| title_full_unstemmed | Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 |
| title_short | Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 |
| title_sort | spatiotemporal distributions of pm2 5 concentrations in the beijing tianjin hebei region from 2013 to 2020 |
| topic | PM2.5 concentrations aerosol optical depth two-stage statistical regression model spatiotemporal distribution Beijing–Tianjin–Hebei region Tianjin–Hebei region |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.842237/full |
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