气候变化领域集成服务门户(CCPortal): Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models

CCPortal

DOI	10.1016/j.atmosenv.2020.117921
	Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models
	Xiao Q.; Geng G.; Cheng J.; Liang F.; Li R.; Meng X.; Xue T.; Huang X.; Kan H.; Zhang Q.; He K.
发表日期	2021
ISSN	13522310
卷号	244
英文摘要	Approximately half of satellite aerosol retrievals are missing that limits the application of satellite data in PM2.5 pollution monitoring. To obtain spatiotemporally continuous PM2.5 distributions, various gap-filling methods have been developed, but have rarely been evaluated. Here, we reviewed and summarized four types of gap-filling strategies, and applied them to a random forest PM2.5 prediction model that incorporated ground observations, chemical transport model (CTM) simulations, and satellite AOD for predicting daily PM2.5 concentrations at a 1-km resolution in 2013 in the Beijing-Tianjin-Hebei region and the Yangtze River Delta. The model out-of-bag predictions were compared with national station measurements and external measurements to assess the performance of different gap-filling methods. We also conducted a by-city cross-validation and characterized the spatial distributions of PM2.5 prediction when the AOD coverage was low. We found that the methods filling in missing data by regression, i.e. multiple imputation and decision tree, performed robustly to characterizing PM2.5 variation at a high spatial resolution and the method filling in missing PM2.5 predictions with decision tree overcame the problem of time-consuming computations. The method using spatiotemporal trends to fill in missing data, i.e. ordinary kriging and generalized additive mixed model, may be overrated in statistical evaluation tests, and predicted artificially oversmoothed PM2.5 spatial distributions. We also revealed that CTM simulations benefited the prediction of PM2.5 spatial distribution in all the models with various gap-filling strategies with higher prediction accuracy in the by-city cross-validation. We noticed that the PM2.5 prediction was not sensitive to the resolution of CTM simulations and even the 12-km resolution CTM simulations benefited the high-resolution PM2.5 prediction. © 2020 Elsevier Ltd
英文关键词	CMAQ; Gap-filling approaches; PM2.5; Random forest; Satellite data
语种	英语
scopus关键词	Decision trees; Filling; Forecasting; River pollution; Satellites; Spatial distribution; Beijing-tianjin-hebei regions; Chemical transport models; Generalized additives; High spatial resolution; Prediction accuracy; Spatiotemporal trends; Statistical evaluation; Yangtze river delta; Predictive analytics; aerosol; concentration (composition); model; model validation; particulate matter; pollution monitoring; prediction; satellite data; spatial distribution; spatiotemporal analysis; article; China; cross validation; decision tree; kriging; particulate matter 2.5; prediction; random forest; river; simulation; China
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144856
作者单位	State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; Key Laboratory of Cardiovascular Epidemiology, Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China; School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, & Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China; Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
推荐引用方式 GB/T 7714	Xiao Q.,Geng G.,Cheng J.,et al. Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models[J],2021,244.
APA	Xiao Q..,Geng G..,Cheng J..,Liang F..,Li R..,...&He K..(2021).Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models.Atmospheric Environment,244.
MLA	Xiao Q.,et al."Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models".Atmospheric Environment 244(2021).