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Sci Total Environ. 2019 Mar 10;655:423-433. doi: 10.1016/j.scitotenv.2018.11.125. Epub 2018 Nov 12.

National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging.

Author information

1
The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies (JCGCS), Beijing 100875, China.
2
Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States.
3
Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States.
4
Department of Biostatistics, University of Washington, Seattle, WA 98195, United States.
5
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States.
6
The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Joint Center for Global Change Studies (JCGCS), Beijing 100875, China. Electronic address: yuqibai@tsinghua.edu.cn.
7
Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States. Electronic address: jdmarsh@uw.edu.

Abstract

Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). Land use, traffic and meteorological variables were included for model building. We tested the resulting models in several ways, including (1) comparing models developed using forward variable selection vs. partial least squares (PLS) variable reduction, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), Leave-One-Province-Out CV (LOPO-CV), and Leave-One-City-Out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Variable-selection models performed similarly to PLS models in 10-fold CV, but better in LOPO-CV. Our best models employed forward variable selection and UK, with 10-fold CV R2 of 0.89 (for both 2014 and 2015) for PM2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO2. Population-weighted concentrations during 2014-2015 decreased for PM2.5 (58.7 μg/m3 to 52.3 μg/m3) and NO2 (29.6 μg/m3 to 26.8 μg/m3). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, >80% of the Chinese population lived in areas that exceeded the Chinese national PM2.5 standard, 35 μg/m3. Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.

KEYWORDS:

Air pollution; China; Land use regression; Satellite data; Universal kriging

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