國家衛生研究院 NHRI:Item 3990099045/11599
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    题名: Developing land-use regression models to estimate PM2.5-bound compound concentrations
    作者: Hsu, CY;Wu, CD;Hsiao, YP;Chen, YC;Chen, MJ;Lung, SCC
    贡献者: National Institute of Environmental Health Sciences
    摘要: Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used frequently to estimate individual levels of exposures to ambient air pollution. This paper used remote sensing and geographical information system (GIS) tools to develop ten regression models for PM2.5-bound compound concentration based on measurements of a six-year period including NH 4 +, SO4 2-, NO 3 -, OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R2) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM2.5-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM2.5 sources like temples collected from GIS and remote sensing data were main variables. In particular, while they were much less used, this study showcased the necessity of remote sensing data of greenness in future LUR studies for reducing the exposure bias. In terms of local residents' health outcome or health effect indicators, this study further offers much-needed support for future air epidemiological studies. The results provide important insights into expanding the application of GIS and remote sensing on exposure assessment for PM2.5-bound compounds.
    日期: 2018-12-06
    關聯: Remote Sensing. 2018 Dec 6;10(12):Article number 1971.
    Link to: http://dx.doi.org/10.3390/rs10121971
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2072-4292&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000455637600113
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85058899609
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