Estimation of residential fine particulate matter infiltration inShanghai, China

doi:10.1016/j.envpol.2017.10.054

CORC > 地球环境研究所 > 中国科学院地球环境研究所 > 粉尘与环境研究室

	Estimation of residential fine particulate matter infiltration inShanghai, China
	Li,HC(Li,Huichu)4; Yang,CY(Yang,Changyuan)4; Zhao, A(Zhao,Ang)2,4; Wang, CC(Wang, Cuicui)4; Chen, RJ(Chen, Renjie)4; Cai, J(Cai, Jing)1,4; Zhao, Y(Zhao, Yan)6; Zhou, XD(Zhou, Xiaodan)4,5; Kan,Haidong,Xu,Huihui ; Xu,HH(Xu,Huihui)5
刊名	Environmental Pollution
	2018-02
卷号	233 期号:2018 页码:494-500
关键词	Pm2.5 Exposure Infiltration Factor Model Prediction Seasonal Variation
DOI	10.1016/j.envpol.2017.10.054
文献子类	期刊论文
英文摘要	Ambient concentrations of fine particulate matter (PM2.5) concentration is often used as an exposure surrogate to estimate PM2.5 health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM2.5 infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM2.5 infiltration factor (Finf) for each individual residence, we aimed to build models for residential PM2.5Finf prediction and to evaluate seasonal Finf variations among residences. We repeated collected paired indoor and outdoor PM2.5 filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM2.5-bound sulfur on the filters was measured by X-ray fluorescence for PM2.5Finf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R2) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM2.5Finf across all observations was 0.83 (±0.18). Finf was found higher and more varied in transitional season (12-25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%-68.4% of the variance in 7-day averages of Finf, The LOOCV analysis showed an R2 of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM2.5Finf between seasons and across residences within season indicated the important source of outdoor-generated PM2.5 exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM2.5 exposure.
项目编号	201502003 ; 91543114 ; 81222036 ; 15GWZX0201 ; 20124377
资助机构	Public Welfare Research Program of National Health andFamily Planning Commission of China ; Public Welfare Research Program of National Health andFamily Planning Commission of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; KeyDiscipline of Public Health in Shanghai ; KeyDiscipline of Public Health in Shanghai ; Shanghai Health and Family Planning Commission ; Shanghai Health and Family Planning Commission
内容类型	期刊论文
源URL	[http://ir.ieecas.cn/handle/361006/5272]
专题	地球环境研究所_粉尘与环境研究室
通讯作者	Kan,Haidong,Xu,Huihui
作者单位	1.Shanghai Key Laboratory of Meteorology and Health, Shanghai, China 2.Environmental & Occupational Health Evaluation Department, Shanghai Municipal Center for Disease Control & Prevention, Shanghai, China 3.Institute of Earth Environment, Chinese Academy of Sciences, Xian, China 4.School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, Key Laboratory of Health Technology Assessment of theMinistry of Health, Fudan University, Shanghai, China 5.Environmental Health Department, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China 6.Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
推荐引用方式 GB/T 7714	Li,HC,Yang,CY,Zhao, A,et al. Estimation of residential fine particulate matter infiltration inShanghai, China[J]. Environmental Pollution,2018,233(2018):494-500.
APA	Li,HC.,Yang,CY.,Zhao, A.,Wang, CC.,Chen, RJ.,...&Liu,SX.(2018).Estimation of residential fine particulate matter infiltration inShanghai, China.Environmental Pollution,233(2018),494-500.
MLA	Li,HC,et al."Estimation of residential fine particulate matter infiltration inShanghai, China".Environmental Pollution 233.2018(2018):494-500.