To characterize exposures to particulate matter (PM) and its components we

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. 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 APY29 PM2.5 or water soluble organic carbon. Associations were also stronger during cooler occasions 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 common cool-season community mean and standard deviation (SD) for EC were 1.1 and APY29 0.17 μg/m3 respectively giving a coefficient of variation (CV) of 18%. For PM2.5 average mean and SD were 14 and APY29 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. Keywords: Air pollution Particulate matter Traffic emissions Spatial variability 1 Introduction To evaluate the potential health effects in children of long-term exposures to poor air quality the Southern California Children’s Health Study (CHS) was launched in 1992 (Peters et al. 1999 CHS research has shown that regional levels of ambient air pollution are associated with reduced rates of lung function growth (Gauderman et al. 2004 At APY29 a finer spatial scale statistically significant associations have been observed between residential proximity to busy roads (< 75 m) and asthma prevalence (Gauderman et al. 2005 McConnell et al. 2006 as well as between residential proximity to freeways (< 500 m) and both asthma (Gauderman et al. 2005 and reduced rates of lung function growth (Gauderman et al. 2007 These findings complement emerging evidence suggesting residential near-road traffic-related pollutant (TRP) exposures are linked to respiratory infections and allergy (Brauer et al. 2002 Janssen et al. 2003 asthma and wheeze (Venn et al. 2001 and other health outcomes (Wjst et al. 1993 vehicle Vliet et al. 1997 British et al. 1999 Venn et al. 2000 Nicolai et al. 2003 Kim et al. 2004 Zmirou et al. 2004 Gauderman et al. 2005 Nevertheless the reported organizations between residential closeness to busy highways and years as a child asthma are inconsistent (HEI 2010 recommending roadway APY29 proximity may possibly Rabbit polyclonal to PELI3. not be a sufficiently APY29 sufficient proxy for TRP publicity. To even more accurately estimation TRP exposures fine-scale spatial variability of traffic-related atmosphere contaminants (TRPs) should be better realized. This is demanding because TRP focus gradients tend to be steep with several-fold focus variations observed in significantly less than 100 m (Rodes and Holland 1981 Zhu et al. 2002 Spatially thick measurements are therefore necessary and also have been only accomplished using low-cost passive samplers for NOX historically. However NOX could be just a surrogate for several TRPs rather than the broader selection of TRPs which may be traveling the adverse wellness effects associated with living near visitors such as for example diesel particulate matter (dark carbon) (Janssen et al. 2011 and ultrafine contaminants (Delfino et al. 2005 Furthermore these TRPs may have different spatial patterns than those readily captured by NOX. This study likened within-community variant in size-resolved PM and PM parts at “middle size” (100 to 500 m) and “community size” (500 m to 4 kilometres) to between-community variations over “metropolitan scales” of 4 to 100 kilometres these scales becoming described by USEPA to characterize regions of influence of varied sources of primary and secondary PM (Watson et al. 1997 The primary objective of the study was to investigate spatial differences in traffic-related particulate emissions and their components at the neighborhood scale in the eight currently active communities in the USC Children’s Health Study. The eventual goal was to develop a database suitable for estimation of long-term exposure to different components of traffic-related PM in an effort to identify components most responsible for the adverse health effects identified in the CHS. This article details the methods used preliminary results (both seasonal and annual) and provides comparisons of spatial variability over different scales. By measuring specific components of PM in several size fractions our goal was to develop a.