气候变化领域集成服务门户(CCPortal): 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

CCPortal

DOI	10.1016/j.apr.2019.11.020
	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
	Fu D.; Song Z.; Zhang X.; Xia X.; Wang J.; Che H.; Wu H.; Tang X.; Zhang J.; Duan M.
发表日期	2020
ISSN	13091042
卷号	11 期号:3
英文摘要	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. © 2020 Turkish National Committee for Air Pollution Research and Control
英文关键词	AERONET; BME; CARSNET; LME; MODIS; North China plain; PM2.5
语种	英语
来源期刊	Atmospheric Pollution Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120578
作者单位	LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China; Chengdu University of Information Technology, Chengdu, 610225, China; Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, The University of Iowa, Iowa City, IA 52241, United States; Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing, 100081, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China; LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Guanghua School of Management, Peking University, Beijing, 100029, China
推荐引用方式 GB/T 7714	Fu D.,Song Z.,Zhang X.,et al. 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[J],2020,11(3).
APA	Fu D..,Song Z..,Zhang X..,Xia X..,Wang J..,...&Duan M..(2020).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,11(3).
MLA	Fu D.,et al."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 11.3(2020).