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Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model
Li, Runkui1,2; Ma, Tianxiao1,3,4; Xu, Qun5,6; Song, Xianfeng1,2,3,4
2018-12-01
Source PublicationENVIRONMENTAL POLLUTION
ISSN0269-7491
Volume243Pages:501-509
Corresponding AuthorSong, Xianfeng(xfsong@ucas.ac.cn)
AbstractAccurate spatial information of PM2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM2.5. However, the predicted PM2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km x 1 km MAIAC AOD and observed ground PM2.5 in Beijing, and then seasonal PM2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM2.5 concentration at seasonal level (R-2 >= 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R-2 >= 0.81 in model fitting and testing). PM2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordSpatial pattern Fine particulate matter Land use regression model MAIAC AOD Beijing
DOI10.1016/j.envpol.2018.09.026
WOS KeywordAEROSOL OPTICAL DEPTH ; AIR-POLLUTION ; UNITED-STATES ; PARTICULATE MATTER ; SATELLITE DATA ; VARIABILITY ; VARIABLES ; CHINA ; PM10 ; CITY
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFB0503605] ; Program of Science and Technology Service Network Initiative (STS) of Chinese Academy of Sciences[Y88Q0300YD] ; China Medical Board[15-230] ; National Natural Science Foundation of China[41771435] ; China Scholarship Council[201704910297] ; CAMS Innovation Fund for Medical Sciences[2017-I2M-1-009]
Funding OrganizationNational Key Research and Development Program of China ; Program of Science and Technology Service Network Initiative (STS) of Chinese Academy of Sciences ; China Medical Board ; National Natural Science Foundation of China ; China Scholarship Council ; CAMS Innovation Fund for Medical Sciences
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000449891800054
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52549
Collection中国科学院地理科学与资源研究所
Corresponding AuthorSong, Xianfeng
Affiliation1.Univ Chinese Acad Sci, Coll Resources & Environm, 19A Yuquan Rd, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Sinodanish Coll, Beijing 100049, Peoples R China
4.Univ Chinese Acad Sci, Sinodanish Educ & Res Ctr, Beijing 100190, Peoples R China
5.Chinese Acad Med Sci, Inst Basic Med Sci, Sch Basic Med, Dept Epidemiol & Biostat,Peking Union Med Coll, Beijing 100005, Peoples R China
6.Chinese Acad Med Sci, Peking Union Med Coll, Ctr Environm & Hlth Sci, Beijing 100005, Peoples R China
Recommended Citation
GB/T 7714
Li, Runkui,Ma, Tianxiao,Xu, Qun,et al. Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model[J]. ENVIRONMENTAL POLLUTION,2018,243:501-509.
APA Li, Runkui,Ma, Tianxiao,Xu, Qun,&Song, Xianfeng.(2018).Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model.ENVIRONMENTAL POLLUTION,243,501-509.
MLA Li, Runkui,et al."Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model".ENVIRONMENTAL POLLUTION 243(2018):501-509.
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