Land use regression modeling of intra-urban variability in multiple traffic-related air pollutants

Harvard School of Public Health, Department of Environmental Health, Landmark Center 4th Floor West, PO Box 15677, Boston, MA 02215, USA.
Environmental Health (Impact Factor: 3.37). 02/2008; 7(1):17. DOI: 10.1186/1476-069X-7-17
Source: PubMed


There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques.
We measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations.
PM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56).
Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods.

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    • "Spatial saturation monitoring and land use regression (LUR) modeling are standard exposure assessment methodologies for characterizing intra-urban variability in air pollution concentrations [1,4-6,11] and pollution source apportionment [15]. For spatial saturation studies, Geographic Information System (GIS)-based indicators of local air pollution sources are used to systematically allocate monitoring locations to saturate hypothesized pollution concentration gradients across complex domains. "
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    • "Additionally, several studies have found asthma or other respiratory outcomes in children to be associated with GIS variables calculated with a similar buffer size or pollutant concentrations estimated with similar buffer sizes (Brauer et al., 2007; Chang et al., 2009; Clark et al., 2010; Gehring et al., 2010; Maantay, 2007; Morgenstern et al., 2007; Ryan et al., 2007). Associations also have been found for buffer sizes in the range of 50–100 m (Brauer et al., 2007; Clougherty et al., 2008; Gilbert et al., 2005; Gordian et al., 2006; McConnell et al., 2010; Ryan et al., 2007). Thus, in this study, we may have underestimated associations of some traffic sources with respiratory outcomes. "
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