Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method
文献类型：期刊论文
| 作者 | Li, Junming; Xue, Jing; Wei, Jing3; Ren, Zhoupeng2; Yu, Yiming; An, Huize; Yang, Xingyan; Yang, Yixue |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 15期号:19页码:4871 |
| 关键词 | ground-level ozone atmospheric remote sensing Bayesian spatiotemporal LASSO regression spatiotemporal causal inference spatiotemporal propensity score matching |
| DOI | 10.3390/rs15194871 |
| 文献子类 | Article |
| 英文摘要 | Ground-level ozone (O-3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O-3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O-3 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 O-3 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 O-3 (PWGLO(3)) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO(3) pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO(3) concentrations have partial spatial spillover effects. The PWGLO(3) concentrations in a region can be driven by this region's surrounding areas' economic factors, wind speed, and PWGLO(3) 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 PM2.5 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. |
| WOS关键词 | YANGTZE-RIVER DELTA ; SURFACE OZONE ; POLLUTION ; NOX ; AIR ; VARIABILITY ; PRECURSORS ; REGRESSION ; VEGETATION ; EMISSION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001084023400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200902]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst L, Beijing 100045, Peoples R China 2.Univ Maryland, Dept Atmospher & Ocean Sci, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA 3.Shanxi Univ Finance & Econ, Sch Stat, 696 Wucheng Rd, Taiyuan 030006, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Junming,Xue, Jing,Wei, Jing,et al. Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method[J]. REMOTE SENSING,2023,15(19):4871. |
| APA | Li, Junming.,Xue, Jing.,Wei, Jing.,Ren, Zhoupeng.,Yu, Yiming.,...&Yang, Yixue.(2023).Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method.REMOTE SENSING,15(19),4871. |
| MLA | Li, Junming,et al."Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method".REMOTE SENSING 15.19(2023):4871. |
入库方式： OAI收割
来源：地理科学与资源研究所
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