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Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

文献类型： 外文期刊

作者： Zhan, Yu 1 ; Luo, Yuzhou 4 ; Deng, Xunfei 5 ; Zhang, Kaishan 1 ; Zhang, Minghua 4 ; Grieneisen, Michael L. 4 ; Di, Bao 1 ;

作者机构： 1.Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China

2.Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610200, Sichuan, Peoples R China

3.Sichuan Univ, Sino German Ctr Water & Hlth Res, Chengdu 610065, Sichuan, Peoples R China

4.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA

5.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

期刊名称：ENVIRONMENTAL SCIENCE & TECHNOLOGY （ 影响因子：9.028； 五年影响因子：9.922 ）

ISSN： 0013-936X

年卷期： 2018 年 52 卷 7 期

页码：

收录情况： SCI

摘要： A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO2 concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R-2 = 0.62 (RMSE = 13.3 g/m(3)) for daily and R-2 = 0.73 (RMSE = 6.5 g/m(3)) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 +/- 11.7 mu g/m(3) (mean +/- standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 +/- 0.38 mu g/m(3)/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 mu g/m(3)/year, while the Beijing-Tianjin Metro did not show a temporal trend (P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 +/- 10.3 mu g/m(3)) and lowest in Southwest China (24.9 +/- 9.4 mu g/m(3)). Approximately 25% of the population lived in nonattainment areas with annual-average NO2 > 40 g/m(3). A piecewise linear function with an abrupt point around 100 people/km(2) characterized the relationship between the population density and the NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2 in China, and provides essential data for epidemiological research and air quality management.

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