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Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh PA

机译:宾夕法尼亚州匹兹堡的PM2.5和黑碳聚焦反演与24小时整合样本的空间变化

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摘要

A growing literature explores intra-urban variation in pollution concentrations. Few studies, however, have examined spatial variation during “peak” hours of the day (e.g., rush hours, inversion conditions), which may have strong bearing for source identification and epidemiological analyses. We aimed to capture “peak” spatial variation across a region of complex terrain, legacy industry, and frequent atmospheric inversions. We hypothesized stronger spatial contrast in concentrations during hours prone to atmospheric inversions and heavy traffic, and designed a 2-year monitoring campaign to capture spatial variation in fine particles (PM2.5) and black carbon (BC). Inversion-focused integrated monitoring (0600–1100 hours) was performed during year 1 (2011–2012) and compared with 1-week 24-h integrated results from year 2 (2012–2013). To allocate sampling sites, we explored spatial distributions in key sources (i.e., traffic, industry) and potential modifiers (i.e., elevation) in geographic information systems (GIS), and allocated 37 sites for spatial and source variability across the metropolitan domain (~388 km2). Land use regression (LUR) models were developed and compared by pollutant, season, and sampling method. As expected, we found stronger spatial contrasts in PM2.5 and BC using inversion-focused sampling, suggesting greater differences in peak exposures across urban areas than is captured by most integrated saturation campaigns. Temporal variability, commercial and industrial land use, PM2.5 emissions, and elevation were significant predictors, but did not more strongly predict concentrations during peak hours.
机译:越来越多的文献探讨了城市内部污染浓度的变化。但是,很少有研究检查一天中“高峰”时段(例如高峰时间,反演条件)的空间变化,这可能对来源识别和流行病学分析有很大影响。我们旨在捕获复杂地形,传统产业和频繁的大气反转区域中的“高峰”空间变化。我们假设在容易发生大气倒置和交通繁忙的几个小时内,浓度的空间对比更强,并设计了一项为期2年的监测活动,以捕获细颗粒(PM2.5)和黑碳(BC)的空间变化。在第一年(2011-2012年)内进行了以反演为重点的综合监测(0600-1100小时),并将其与第二年(2012-2013年)的1周24小时综合结果进行了比较。为了分配采样点,我们探索了地理信息系统(GIS)的关键源(即交通,工业)和潜在修正量(即高程)的空间分布,并为大都市域中的空间和源变化分配了37个点(〜 388 km 2 )。开发了土地利用回归(LUR)模型,并通过污染物,季节和抽样方法进行了比较。不出所料,我们使用反演为重点的采样发现了PM2.5和BC的空间对比更强,这表明与大多数综合饱和运动相比,市区内的峰值暴露差异更大。时间变化,商业和工业用地,PM2.5排放和海拔高度是重要的预测因素,但没有更强烈地预测高峰时段的浓度。

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