Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations
| العنوان: | Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations |
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| المؤلفون: | Chih Da Wu, Yu-Cheng Chen, Chin Yu Hsu, Mu Jean Chen, Shih-Chun Candice Lung, Ya Ping Hsiao |
| المصدر: | Remote Sensing Volume 10 Issue 12 Pages: 1971 Remote Sensing, Vol 10, Iss 12, p 1971 (2018) |
| بيانات النشر: | MDPI AG, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Pollutant, 010504 meteorology & atmospheric sciences, land-use regression (LUR), Regression analysis, 010501 environmental sciences, Explained variation, 01 natural sciences, Regression, temples, Health effect, Remote sensing (archaeology), fine particulate matter (PM2.5), Statistics, General Earth and Planetary Sciences, Environmental science, lcsh:Q, Spatial variability, compounds, lcsh:Science, culture-specific PM2.5 sources, 0105 earth and related environmental sciences, Exposure assessment |
| الوصف: | 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 + , SO 4 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. |
| وصف الملف: | application/pdf |
| تدمد: | 2072-4292 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b42f4c5402dafa831d8bf9241b0c66a9Test https://doi.org/10.3390/rs10121971Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b42f4c5402dafa831d8bf9241b0c66a9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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