Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, USA.
Int J Environ Res Public Health. 2018 Sep 10;15(9):1968. doi: 10.3390/ijerph15091968.
Despite advances in monitoring and modelling of intra-urban variation in multiple pollutants, few studies have attempted to separate spatial patterns by time of day, or incorporated organic tracers into spatial monitoring studies. Due to varying emissions sources from diesel and gasoline vehicular traffic, as well as within-day temporal variation in source mix and intensity (e.g., rush-hours vs. full-day measures), accurately assessing diesel-related air pollution within an urban core can be challenging. We allocated 24 sampling sites across downtown Pittsburgh, Pennsylvania (2.8 km²) to capture fine-scale variation in diesel-related pollutants, and to compare these patterns by sampling interval (i.e., "rush-hours" vs. "work-week" concentrations), and by season. Using geographic information system (GIS)-based methods, we allocated sampling sites to capture spatial variation in key traffic-related pollution sources (i.e., truck, bus, overall traffic densities). Programmable monitors were used to collect integrated work-week and rush-hour samples of fine particulate matter (PM), black carbon (BC), trace elements, and diesel-related organics (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), in summer and winter 2014. Land use regression (LUR) models were created for PM, BC, total elemental carbon (EC), total organic carbon (OC), elemental (Al, Ca, Fe), and organic constituents (total PAHs, total hopanes), and compared by sampling interval and season. We hypothesized higher pollution concentrations and greater spatial contrast in rush-hour, compared to full work-week samples, with variation by season and pollutant. Rush-hour sampling produced slightly higher total PM and BC concentrations in both seasons, compared to work-week sampling, but no evident difference in spatial patterns. We also found substantial spatial variability in most trace elements and organic compounds, with comparable spatial patterns using both sampling paradigms. Overall, we found higher concentrations of traffic-related trace elements and organic compounds in rush-hour samples, and higher concentrations of coal-related elements (e.g., As, Se) in work-week samples. Mean bus density was the strongest LUR predictor in most models, in both seasons, under each sampling paradigm. Within each season and constituent, the bus-related terms explained similar proportions of variance in the rush-hour and work-week samples. Rush-hour and work-week LUR models explained similar proportions of spatial variation in pollutants, suggesting that the majority of emissions may be produced during rush-hour traffic across downtown. Results suggest that rush-hour emissions may predominantly shape overall spatial variance in diesel-related pollutants.
尽管在监测和建模城市内部多种污染物的时空变化方面取得了进展，但很少有研究试图按一天中的时间分离空间模式，或在空间监测研究中纳入有机示踪剂。由于柴油和汽油车辆交通的排放源不同，以及源混合和强度的日内时间变化（例如，高峰时段与全天测量），准确评估城市核心区的与柴油相关的空气污染可能具有挑战性。我们在宾夕法尼亚州匹兹堡市中心（2.8 平方公里）分配了 24 个采样点，以捕捉与柴油相关污染物的细尺度变化，并通过采样间隔（即“高峰时段”与“工作日”浓度）和季节来比较这些模式。使用基于地理信息系统 (GIS) 的方法，我们分配采样点以捕捉关键交通相关污染源（即卡车、公共汽车、整体交通密度）的空间变化。可编程监测器用于在 2014 年夏季和冬季收集细颗粒物 (PM)、黑碳 (BC)、微量元素和与柴油相关的有机物（多环芳烃 (PAHs)、藿烷、甾烷）的综合工作日和高峰时段样本。创建了 PM、BC、总元素碳 (EC)、总有机碳 (OC)、元素 (Al、Ca、Fe) 和有机成分（总 PAHs、总藿烷）的土地利用回归 (LUR) 模型，并通过采样间隔和季节进行了比较。我们假设与整个工作日样本相比，高峰时段的污染浓度更高，空间对比度更大，且受季节和污染物的影响而变化。与工作日采样相比，两种季节的高峰时段采样都产生了稍高的总 PM 和 BC 浓度，但空间模式没有明显差异。我们还发现大多数微量元素和有机化合物的空间变化很大，两种采样模式下的空间模式相似。总体而言，我们发现高峰时段的交通相关微量元素和有机化合物浓度更高，而工作日样本中与煤相关的元素（如 As、Se）浓度更高。在每个季节和成分中，在每个采样范例下，公交车密度都是大多数模型中最强的 LUR 预测因子。在每个季节和成分中，与公交车相关的术语解释了高峰时段和工作日样本中相似比例的方差。每个季节和成分中，与公交车相关的术语解释了高峰时段和工作日样本中相似比例的方差。每个季节和成分中，与公交车相关的术语解释了高峰时段和工作日样本中相似比例的方差。每个季节和成分中，与公交车相关的术语解释了高峰时段和工作日样本中相似比例的方差。
