Spatial Variation in Particulate Matter Components over a Large Urban Area.
Spatial Variation in Particulate Matter Components over a Large Urban Area.
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DOI：
10.1016/j.atmosenv.2013.10.063
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发表时间：
2014-02-01
期刊：
影响因子：
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通讯作者：
Avol E
中科院分区：
文献类型：
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作者：
Fruin S;Urman R;Lurmann F;McConnell R;Gauderman J;Rappaport E;Franklin M;Gilliland FD;Shafer M;Gorski P;Avol E
为了表征暴露于颗粒物（PM）及其组分，我们进行了一个大规模的采样研究的小尺度空间变化的大小分辨颗粒物的质量和成分。在100至1000米的尺度上收集粒径范围为< 0.2、0.2至2.5和2.5至10 μm的PM，以捕获当地来源。在南加州的8个社区中，从2008年11月到2009年12月，在一年中的两个不同时间，相隔6个月，对多达29个地点进行了为期一个月的旋转综合期采样。在每个社区的区域监测站进行了额外的采样，以提供采样活动期间的时间覆盖范围。住宅抽样地点的选择是基于一种新的设计分层的高和低预测的交通排放量和位置，从以前的分散模型和抽样比较预测。主要的车辆排放成分，如元素碳（EC），表现出更强的模式与交通比污染物与显着的二次形成，如PM2.5或水溶性有机碳。在一年中较冷的时候（10月到3月），这种联系也更强。初级污染物也表现出更大的社区内的空间变化相比，污染物与次级形成的贡献。例如，冷季群落EC的平均值和标准差（SD）分别为1.1和0.17微克/立方米，变异系数（CV）为18%。对于PM2.5，平均值和SD分别为14和1.3 μg/m3，CV为9%。我们的结论是，社区内的空间差异是重要的，准确的交通相关污染物的暴露评估。
To characterize exposures to particulate matter (PM) and its components, we performed a large sampling study of small-scale spatial variation in size-resolved particle mass and composition. PM was collected in size ranges of < 0.2, 0.2-to-2.5, and 2.5-to-10 μm on a scale of 100s to 1000s of meters to capture local sources. Within each of eight Southern California communities, up to 29 locations were sampled for rotating, month-long integrated periods at two different times of the year, six months apart, from Nov 2008 through Dec 2009. Additional sampling was conducted at each community’s regional monitoring station to provide temporal coverage over the sampling campaign duration. Residential sampling locations were selected based on a novel design stratified by high- and low-predicted traffic emissions and locations over- and under-predicted from previous dispersion model and sampling comparisons. Primary vehicle emissions constituents, such as elemental carbon (EC), showed much stronger patterns of association with traffic than pollutants with significant secondary formation, such as PM2.5 or water soluble organic carbon. Associations were also stronger during cooler times of the year (Oct through Mar). Primary pollutants also showed greater within-community spatial variation compared to pollutants with secondary formation contributions. For example, the average cool-season community mean and standard deviation (SD) for EC were 1.1 and 0.17 μg/m3, respectively, giving a coefficient of variation (CV) of 18%. For PM2.5, average mean and SD were 14 and 1.3 μg/m3, respectively, with a CV of 9%. We conclude that within-community spatial differences are important for accurate exposure assessment of traffic-related pollutants.