Spatial variation in particulate matter components over a large urban area
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- 作者：Scott Fruin ; Robert Urman ; Fred Lurmann ; Rob McConnell ; James Gauderman ; Edward Rappaport ; Meredith Franklin ; Frank D. Gillil ; Martin Shafer ; Patrick Gorski ; Edward Avol
- 关键词：Air pollution ; Particulate matter ; Traffic emissions ; Spatial variability
- 刊名：Atmospheric Environment
- 出版年：February, 2014
- 年：2014
- 卷：83
- 期：Complete
- 页码：211-219
- 全文大小：875 K
文摘
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聽渭m 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聽渭g聽m鈭?, respectively, giving a coefficient of variation (CV) of 18%. For PM2.5, average mean and SD were 14 and 1.3聽渭g聽m鈭?, respectively, with a CV of 9%. We conclude that within-community spatial differences are important for accurate exposure assessment of traffic-related pollutants.