| Summary: | 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 PM<sub>2.5</sub>-bound compound concentration based on measurements of a six-year period including <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi>NH</mi> </mrow> <mn>4</mn> <mo>+</mo> </msubsup> <mo>,</mo> <msubsup> <mrow> <mrow> <mo> </mo> <mi>SO</mi> </mrow> </mrow> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> <mo>,</mo> <msubsup> <mrow> <mrow> <mo> </mo> <mi>NO</mi> </mrow> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics> </math> </inline-formula>, OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R<sup>2</sup>) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM<sub>2.5</sub>-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM<sub>2.5</sub> 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 PM<sub>2.5</sub>-bound compounds.
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