Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China
文献类型：SCI/SSCI论文
| 作者 | Wu J. S.; Li, J. C.; Peng, J.; Li, W. F.; Xu, G.; Dong, C. C. |
| 发表日期 | 2015 |
| 关键词 | Land use regression Fine particulate matter PM2.5 Spatiotemporal variation Outdoor exposure Air pollution Beijing particulate air-pollution new-york-city ambient fine particulate long-term exposure source apportionment urban area matter concentrations ultrafine particles sulfur-dioxide united-states |
| 英文摘要 | Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 mu g/m(3) (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R (2) of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 mu g/m(3). This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models. |
| 出处 | Environmental Science and Pollution Research |
| 卷 | 22 |
| 期 | 9 |
| 页 | 7045-7061 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0944-1344 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38947]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Wu J. S.,Li, J. C.,Peng, J.,et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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