Journal article
Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model
Atmospheric pollution research, Vol.11(3), pp.482-490
03/2020
DOI: 10.1016/j.apr.2019.11.020
Abstract
PM2.5 estimates solely based on the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products may lead to a substantial bias because of non-random AOD sampling deficiency in cloudy conditions and swap-gap regions. Furthermore, this non-random sampling issue can be exacerbated in polluted regions where heavy aerosol loadings are likely misclassified into clouds. Here, to mitigate non-random sampling deficiency in MODIS AOD product for surface-level PM2.5 estimates, we have combined Bayesian maximum entropy (BME) method with the Linear Mixed-Effects (LME) model, for the first time, to produce more spatiotemporally complete and precise AOD products and thereafter PM2.5 estimates. This combined BME-LME approach was applied to MODIS and sunphotometer AOD products over the North China Plain. Relative to the standard MODIS AOD product, the integration of MODIS and sunphotometer AOD through BME showed increases in both spatiotemporal completeness (up to 96%) and the quality. The resultant monthly PM2.5 estimates from the BME-LME had a bias of 3.5 μg m−3 and a root mean square error (RMSE) of 5.5 μg m−3, showing substantial improvement over PM2.5 estimations from original MODIS AOD product (a bias of 84.1 and a RMSE of 112.1 μg m−3). Merging sunphotomter and satellite AOD observations with BME-LME is a prospective method to simultaneously improve AOD and PM2.5 estimates.
•The Bayesian Maximum Entropy method is introduced to fill missing AOD.•Incorporation of sunphotometer AOD improves the BME performance.•Systematic lower seasonal PM2.5 is produced if missing AODs are not considered.•PM2.5 estimation is remarkably improved by the BME method.
Details
- Title: Subtitle
- Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model
- Creators
- Disong Fu - LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaZijue Song - LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaXiaoling Zhang - Chengdu University of Information Technology, Chengdu, 610225, ChinaXiangao Xia - LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaJun Wang - Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, The University of Iowa, Iowa City, IA, 52241, USAHuizheng Che - Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, ChinaHuangjian Wu - College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaXiao Tang - LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaJinqiang Zhang - LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaMinzheng Duan - LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- Resource Type
- Journal article
- Publication Details
- Atmospheric pollution research, Vol.11(3), pp.482-490
- DOI
- 10.1016/j.apr.2019.11.020
- ISSN
- 1309-1042
- eISSN
- 1309-1042
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/501100001809, name: National Science Foundation of China, award: 91644217, 41475138; DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2016YFC0200403, 2017YFA0603504; DOI: 10.13039/100008893, name: University of Iowa
- Language
- English
- Date published
- 03/2020
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984066100602771
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