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Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China
Zhang, Zhi-Hong1,2; Hu, Mao-Gui2,5; Ren, Jing2; Zhang, Zi-Yin3; Christakos, George4; Wang, Jin-Feng2
2017-11-01
Source PublicationATMOSPHERIC POLLUTION RESEARCH
ISSN1309-1042
Volume8Issue:6Pages:1143-1150
Corresponding AuthorHu, Mao-Gui(humg@lreis.ac.cn) ; Wang, Jin-Feng(wangjf@lreis.ac.cn)
AbstractAir pollution has become more serious in many developing countries. Heavy particulate matter (PM) air pollution is a major threat to people's respiratory and cardiopulmonary health. It is an important problem for public health research to accurately estimate the spatial distribution of high PM concentrations from a limited number of monitoring stations. In this study, a maximum entropy (MaxEnt) model was adopted to obtain the probability distribution map of high PM10 concentrations. Daily PM10 concentration data from 19 air quality monitoring stations from the years 2008-2011 were collected. Land cover, road density, and meteorological data were selected as explanatory variables entered in the model. A receiver operating characteristic (ROC) analysis was used to evaluate the performance of the MaxEnt model. The area under the ROC curve (AUC) shows that the MaxEnt model fits well in the four year period. AUC is 0.78 in 2008, 0.79 in 2009, 0.81 in 2010, and 0.80 in 2011. A probability distribution map of high PM10 concentration indicates high human health risks in regions of Beijing in areas with dense roads and buildings. During the entire research period from 2008 to 2011, the distribution of high PM10 concentration is relatively stable and it indicates that the trend of high concentration has not changed significantly during the four years. Traffic and land cover are the two most important factors that can explain more than 80% variance of PM10 from 2008 to 2011. (C) 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
KeywordPM10 Probability distribution of high concentrations Road density Maximum entropy Public health
DOI10.1016/j.apr.2017.04.006
WOS KeywordAIR-POLLUTION ; QUALITY ; REGRESSION ; FUTURE ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Environmental Protection Public Welfare Science and Technology Research Program of China[201509027] ; National Natural Science Foundation of China[41301425] ; National Natural Science Foundation of China[41531179]
Funding OrganizationNational Environmental Protection Public Welfare Science and Technology Research Program of China ; National Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000417392800014
PublisherTURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56791
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHu, Mao-Gui; Wang, Jin-Feng
Affiliation1.Chinese Acad Environm Planning, Ctr Environm Risk & Damage Assessment, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.Beijing Meteorol Bur, Beijing, Peoples R China
4.San Diego State Univ, Dept Geog, San Diego, CA 92182 USA
5.Chinese Ctr Dis Control & Prevent, Key Lab Surveillance & Early Warnings Infect Dis, Beijing 102206, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Zhi-Hong,Hu, Mao-Gui,Ren, Jing,et al. Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China[J]. ATMOSPHERIC POLLUTION RESEARCH,2017,8(6):1143-1150.
APA Zhang, Zhi-Hong,Hu, Mao-Gui,Ren, Jing,Zhang, Zi-Yin,Christakos, George,&Wang, Jin-Feng.(2017).Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China.ATMOSPHERIC POLLUTION RESEARCH,8(6),1143-1150.
MLA Zhang, Zhi-Hong,et al."Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China".ATMOSPHERIC POLLUTION RESEARCH 8.6(2017):1143-1150.
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