Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations
National Institute of Environmental Health Sciences National Health Research Institutes, Miaoli 350, Taiwan
Department of Forestry and Natural Resources, National Chiayi University, Chiayi 600, Taiwan
Department of Geomatics, National Cheng Kung University, Tainan 701, Taiwan
Department of Occupational Safety and Health, China Medical University, Taichung 404, Taiwan
Research Center for Environmental Changes, Academia Sinica, Taipei 115, Taiwan
Department of Atmospheric Sciences, National Taiwan University, Taipei 106, Taiwan
Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei 100, Taiwan
Authors to whom correspondence should be addressed.
Received: 26 October 2018 / Revised: 4 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
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
, 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
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Hsu, C.-Y.; Wu, C.-D.; Hsiao, Y.-P.; Chen, Y.-C.; Chen, M.-J.; Lung, S.-C.C. Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations. Remote Sens. 2018, 10, 1971.
Hsu C-Y, Wu C-D, Hsiao Y-P, Chen Y-C, Chen M-J, Lung S-CC. Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations. Remote Sensing. 2018; 10(12):1971.
Hsu, Chin-Yu; Wu, Chih-Da; Hsiao, Ya-Ping; Chen, Yu-Cheng; Chen, Mu-Jean; Lung, Shih-Chun C. 2018. "Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations." Remote Sens. 10, no. 12: 1971.
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