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Spatial-temporal heterogeneity of air pollution: The relationship between built environment and on-road PM2.5 at micro scale

机译:空气污染的时空异质性:微观环境下建筑环境与道路PM2.5的关系

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Research on the relationship between built environment and PM2.5 has attracted notable attention during the past decades. However, previous studies were less to test the spatial-temporal heterogeneity of on-road PM2.5 and its related factors at micro scale. To this end, collecting high resolution PM2.5 data by mobile monitoring along different roads in Guangzhou, China, this paper explored the spatial-temporal heterogeneity of the relationship between built environment and on-road PM2.5 during the morning (7-9 am) and evening (7-9 pm) rush hours. Semi-variogram method and geographically weighted regression (GWR) model were utilized to reveal the non-stationarity associations among the large spatial dataset. In terms of temporal heterogeneity, the results showed that the spatial independent radii of on-road PM2.5 were 17 m and 21 m for morning and evening rush hours respectively. The aggregated median value of PM2.5 in the morning rush hours was 34.95 mu g/m(3), while the evening was up to 55.49 mu g/m(3). There were more significant factors of street conditions impact on on-road PM2.5 in the morning while more significant factors of land use and centrality that reflecting the cumulative effect of daily human activities with smaller buffer thresholds in the evening. In terms of spatial heterogeneity, GWR models achieved much better performance than the global ones of multivariate regression models with lower AICc, RMSE and higher adjusted R-2, explaining 10-69% of variance across different roads and rush hours. There was a high degree of spatial heterogeneity that the leading factors were different along various roads on rush hours. The results indicated that the policies and interventions should be more targeted to improve the on-road air environment and reduce personal exposure according to the spatial-temporal geographical context. It can be adopted to provide more realistic and practical guides for urban planning and environmental pollution control.
机译:在过去的几十年中,关于建筑环境与PM2.5之间关系的研究引起了极大的关注。但是,以前的研究很少在微观尺度上测试道路PM2.5及其相关因素的时空异质性。为此,通过在中国广州市不同道路上通过移动监测收集高分辨率PM2.5数据,本文探讨了早晨(7-9)建筑环境与道路PM2.5之间关系的时空异质性。上午)和傍晚(晚上7-9点)。利用半变异函数方法和地理加权回归(GWR)模型来揭示大型空间数据集之间的非平稳关联。就时间异质性而言,结果表明,道路上PM2.5的空间独立半径在早上和晚上高峰时间分别为17 m和21 m。早上高峰时段PM2.5的总中位数为34.95μg / m(3),而晚上则高达55.49μg / m(3)。早晨,街道状况对道路上的PM2.5的影响更大,而在晚上,缓冲阈值较小的情况下,土地使用和集中度的重要影响则反映了日常人类活动的累积影响。就空间异质性而言,GWR模型的性能要优于具有较低AICc,RMSE和较高调整R-2的多元回归模型的全局性能,这说明了不同道路和高峰时间的方差为10-69%。在高峰时段,不同道路上的主导因素存在很大的空间异质性。结果表明,应根据时空地理环境,更加针对性地改善政策和干预措施,以改善道路上的空气环境并减少个人暴露。它可以用来为城市规划和环境污染控制提供更现实和实用的指南。

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