气候变化领域集成服务门户(CCPortal): Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach

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DOI	10.1016/j.atmosenv.2021.118930
	Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach
	Liu Y.; Li C.; Liu D.; Tang Y.; Seyler B.C.; Zhou Z.; Hu X.; Yang F.; Zhan Y.
发表日期	2022
ISSN	1352-2310
卷号	271
英文摘要	High ambient concentrations of fine particulate matter (PM2.5) increase the hazardousness of air pollution. Aerosol optical depth (AOD) retrieved by sun-synchronous or geostationary satellites is valuable for monitoring large-scale air quality. This study sought to combine the AOD of the Himawari-8 and the Visible Infrared Imaging Radiometer Suite (VIIRS) to derive the spatiotemporal distributions of hourly PM2.5 concentrations at ∼1 km resolution for China's Sichuan Basin by using a machine-learning approach. Two random forest submodels were developed to fill the daytime gaps in these two AOD datasets, which were then used as predictors in another random-forest submodel predicting the hourly PM2.5 concentrations in daytime. One more random-forest submodel was developed to predict the hourly PM2.5 in nighttime based on the auxiliary variables excluding the AOD data. Leveraging the complementary information of Himawari-8 and VIIRS, this approach demonstrated heightened predictive performance, with cross-validation R2 of 0.840 and RMSE of 15.9 μg/m3. The ability to predict nighttime PM2.5 (R2 from 0.826 to 0.858) was comparable to daytime PM2.5 (R2 from 0.831 to 0.853). In the Sichuan Basin, the sustained high concentrations of PM2.5 were mainly attributed to the stagnant meteorological conditions associated with the basin topography. Based on this full-coverage dataset, we characterized the spatiotemporal distributions of PM2.5 across the basin while demonstrating the regional diurnal patterns and the “weekend effect” for a central urban area. Provisioning comprehensive datasets of hourly PM2.5 as demonstrated in this study is essential for air quality management and environmental epidemiological analyses. © 2022 Elsevier Ltd
关键词	Aerosol optical depth Data fusion Fine particulate matter Hourly PM2.5 Machine learning Sichuan basin
语种	英语
scopus关键词	Air quality; Data fusion; Decision trees; Geostationary satellites; Machine learning; Meteorology; Particles (particulate matter); Random forests; Thermography (imaging); Topography; Aerosol optical depths; Fine particulate matter; Hourly PM2.5; Machine learning approaches; PM 2.5; Random forests; Sichuan Basin; Spatiotemporal distributions; Submodels; Visible infrared imaging radiometer suites; Forecasting
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248080
作者单位	Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China; Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing, 100055, China; Chengdu Academy of Environmental Sciences, Chengdu, 610072, China; Chengdu Research Institute of Big Data Industrial Technology Co., Chengdu, 610072, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu, Sichuan 610065, China
推荐引用方式 GB/T 7714	Liu Y.,Li C.,Liu D.,et al. Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach[J],2022,271.
APA	Liu Y..,Li C..,Liu D..,Tang Y..,Seyler B.C..,...&Zhan Y..(2022).Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach.ATMOSPHERIC ENVIRONMENT,271.
MLA	Liu Y.,et al."Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach".ATMOSPHERIC ENVIRONMENT 271(2022).