Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method
Ground-level ozone (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/19/4871 |
| _version_ | 1827722285241335808 |
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| author | Junming Li Jing Xue Jing Wei Zhoupeng Ren Yiming Yu Huize An Xingyan Yang Yixue Yang |
| author_facet | Junming Li Jing Xue Jing Wei Zhoupeng Ren Yiming Yu Huize An Xingyan Yang Yixue Yang |
| author_sort | Junming Li |
| collection | DOAJ |
| description | Ground-level ozone (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) concentrations, representing population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations have partial spatial spillover effects. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage. |
| first_indexed | 2024-03-10T21:35:55Z |
| format | Article |
| id | doaj.art-8a0ebaa9bf0b4bafb82b120aaa8abc27 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T21:35:55Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8a0ebaa9bf0b4bafb82b120aaa8abc272023-11-19T15:01:09ZengMDPI AGRemote Sensing2072-42922023-10-011519487110.3390/rs15194871Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference MethodJunming Li0Jing Xue1Jing Wei2Zhoupeng Ren3Yiming Yu4Huize An5Xingyan Yang6Yixue Yang7School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaSchool of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaDepartment of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USAState Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100045, ChinaSchool of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaSchool of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaSchool of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaSchool of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan 030006, ChinaGround-level ozone (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>) concentrations, representing population exposure to ambient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula>, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations have partial spatial spillover effects. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>W</mi><mi>G</mi><mi>L</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></semantics></math></inline-formula> concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.https://www.mdpi.com/2072-4292/15/19/4871ground-level ozoneatmospheric remote sensingBayesian spatiotemporal LASSO regressionspatiotemporal causal inferencespatiotemporal propensity score matching |
| spellingShingle | Junming Li Jing Xue Jing Wei Zhoupeng Ren Yiming Yu Huize An Xingyan Yang Yixue Yang Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method Remote Sensing ground-level ozone atmospheric remote sensing Bayesian spatiotemporal LASSO regression spatiotemporal causal inference spatiotemporal propensity score matching |
| title | Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method |
| title_full | Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method |
| title_fullStr | Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method |
| title_full_unstemmed | Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method |
| title_short | Insighting Drivers of Population Exposure to Ambient Ozone (<i>O</i><sub>3</sub>) Concentrations across China Using a Spatiotemporal Causal Inference Method |
| title_sort | insighting drivers of population exposure to ambient ozone i o i sub 3 sub concentrations across china using a spatiotemporal causal inference method |
| topic | ground-level ozone atmospheric remote sensing Bayesian spatiotemporal LASSO regression spatiotemporal causal inference spatiotemporal propensity score matching |
| url | https://www.mdpi.com/2072-4292/15/19/4871 |
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