National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging
Science of the Total Environment 655 (2019) 423–433 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging Hao Xu a,b, Matthew J. Bechle c,MengWangd,e, Adam A. Szpiro f,SverreVedale, Yuqi Bai a,b,⁎, Julian D. Marshall c,⁎⁎ a The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China b Joint Center for Global Change Studies (JCGCS), Beijing 100875, China c Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States d Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States e Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States f Department of Biostatistics, University of Washington, Seattle, WA 98195, United States HIGHLIGHTS GRAPHICAL ABSTRACT • First high spatial resolution national LUR models for both NO2 and PM2.5 in China • Satellite data and kriging are comple- mentary in making predictions more ac- curate. • Variable selection models perform simi- lar or better than PLS models. • 1km2 resolution prediction maps will be publicly available for future research. article info abstract Article history: Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in Received 27 July 2018 China. China is the most populous country in the world and also has the largest number of air pollution deaths Received in revised form 8 November 2018 per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively Accepted 8 November 2018 low.
[Show full text]