Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China

doi:10.1007/s00477-017-1497-6

IGSNRR OpenIR

	Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China
	Yang, Dongyang 1; Lu, Debin 1,2; Xu, Jianhua 1; Ye, Chao 1; Zhao, Jianan1 ; Tian, Guanghui 3; Wang, Xinge 4; Zhu, Nina 1
	2018-08-01
Source Publication	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
Volume	32 Issue:8 Pages:2445-2456
Corresponding Author	Xu, Jianhua(Jhxu@geo.ecnu.edu.cn)
Abstract	The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R-2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R-2 of 0.86 and a small RMSE of 8.15 (mu g/m(3)). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km x week, which would contribute to accurately assessing the potential health effects of air pollution.
Keyword	PM2.5 Spatio-temporal modeling Weekly average PM2.5 concentrations Yangtze River Delta
DOI	10.1007/s00477-017-1497-6
WOS Keyword	AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; FINE PARTICULATE MATTER ; POLLUTION MESA AIR ; USE REGRESSION ; ROAD INTERSECTION ; CARBON-MONOXIDE ; VARIABILITY ; EXPOSURES ; MODEL
Indexed By	SCI
Language	英语
WOS Research Area	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
WOS Subject	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS ID	WOS:000440089100016
Publisher	SPRINGER
Citation statistics
Document Type	期刊论文
Identifier	http://ir.igsnrr.ac.cn/handle/311030/54517
Collection	中国科学院地理科学与资源研究所
Corresponding Author	Xu, Jianhua
Affiliation	1.East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China 2.Tongren Univ, Dept Tourism & Geog, Tongren 554300, Guizhou, Peoples R China 3.Henan Univ, Coll Environm & Planning, Kaifeng 475004, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resource Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
Recommended Citation GB/T 7714	Yang, Dongyang,Lu, Debin,Xu, Jianhua,et al. Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,2018,32(8):2445-2456.
APA	Yang, Dongyang.,Lu, Debin.,Xu, Jianhua.,Ye, Chao.,Zhao, Jianan.,...&Zhu, Nina.(2018).Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,32(8),2445-2456.
MLA	Yang, Dongyang,et al."Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 32.8(2018):2445-2456.

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