MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China

Fengchao Liang; Qingyang Xiao; Yujie Wang; Alexei Lyapustin; Guoxing Li; Dongfeng Gu; Xiaochuan Pan; Yang Liu

doi:10.1016/j.scitotenv.2017.10.155

MAIAC-based long-term spatiotemporal trends of PM_2.5 in Beijing, China

Sci Total Environ. 2018 Mar:616-617:1589-1598. doi: 10.1016/j.scitotenv.2017.10.155. Epub 2017 Oct 19.

Authors

Fengchao Liang¹, Qingyang Xiao², Yujie Wang³, Alexei Lyapustin⁴, Guoxing Li⁵, Dongfeng Gu⁶, Xiaochuan Pan⁷, Yang Liu⁸

Affiliations

¹ Department of Occupational and Environmental Health, School of Public Health, Peking University, Beijing 100191, China; Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; Department of Epidemiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
² Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. Electronic address: qingyang.xiao@emory.edu.
³ NASA Goddard Space Flight Center, Greenbelt, MD, USA; University of Maryland Baltimore County, Baltimore, MD, USA. Electronic address: yujie.wang-1@nasa.gov.
⁴ NASA Goddard Space Flight Center, Greenbelt, MD, USA. Electronic address: alexei.i.lyapustin@nasa.gov.
⁵ Department of Occupational and Environmental Health, School of Public Health, Peking University, Beijing 100191, China. Electronic address: liguoxing@bjmu.edu.cn.
⁶ Department of Epidemiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
⁷ Department of Occupational and Environmental Health, School of Public Health, Peking University, Beijing 100191, China. Electronic address: xcpan@bjmu.edu.cn.
⁸ Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. Electronic address: yang.liu@emory.edu.

PMID: 29055576
DOI: 10.1016/j.scitotenv.2017.10.155

Abstract

Satellite-driven statistical models have been proven to be able to provide spatially resolved PM_2.5 estimates worldwide. The North China Plain has been suffering from severe PM_2.5 pollution in recent years. An accurate assessment of the spatiotemporal characteristics of PM_2.5 levels in this region is crucial to design effective air pollution control policy. Our objective is to estimate daily PM_2.5 concentrations at 1km spatial resolution from 2004 to 2014 in Beijing and its surrounding areas using the Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD). A high-performance three-stage model was developed with AOD, meteorological, demographic and land use variables as predictors, which includes a custom-designed PM_2.5 gap-filling method. The 11-year average annual coverage increased from 177days to 279days and annual PM_2.5 prediction error decreased from 14.1μg/m³ to 8.3μg/m³ after gap-filling techniques were applied. Results show that the 11-year overall mean of predicted PM_2.5 was 67.1μg/m³ in our study domain. The cross-validation R² value of our model is 0.82 in 2013 and 0.79 in 2014. In addition, the models predicted historical PM_2.5 concentrations with relatively high accuracy at the seasonal and annual levels (R² ranged from 0.78 to 0.86). Our long-term PM_2.5 prediction filled the gaps left by ground monitors, which would be beneficial to PM_2.5 related epidemiological studies in Beijing.

Keywords: Gap-filling; Long-term trend; MAIAC AOD; North China Plain; PM(2.5).

MeSH terms

Air Pollutants / analysis*
Air Pollution / statistics & numerical data*
Beijing
China
Environmental Monitoring*
Meteorology
Models, Statistical
Particulate Matter / analysis*

Substances

Air Pollutants
Particulate Matter