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.

Download full-text


Available from: Jonathan Levy
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.
    Full-text · Article · Apr 2014 · Environmental Health
  • Source
    • "The clinical events associated with air pollution (especially cardiorespiratory diseases) affect the population and interfere with normal activities, sometimes causing restricted activities and increased absenteeism from work (Ostro, 1983; Hausmann et al., 1984; Ostro, 1984, 1987; Ostro and Rothschild, 1989; Hansen and Selte, 2000). Previous studies have also indicated an increased risk of the occurrence of the above-mentioned problems associated with the proximity to traffic routes (Weiland et al., 1994; van Wijnen and van der Zee, 1998; Hoek et al., 2002; Clougherty et al., 2008; HEI, 2010). Thus, a fraction of the population is at a greater and growing risk due to different exposure conditions associated with longer transit time. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study aims to estimate the health risks and economic losses due to the effects of air pollution, with a focus on traffic controllers, and to estimate the associated costs through the use of work loss days (WLD) as an indicator of morbidity in São Paulo from 2000 to 2007. The association between traffic controllers’ absenteeism and air pollution was determined by generalized linear models (GLM). The increase in relative risk for WLD was 2.08 (95% CI: 2.04–2.12) per 10 μg/m3 PM10, which in monetary terms represented 9,430 USD/year, equivalent to 133 absences per 1,308 traffic controllers annually that are attributable to air pollution (accumulated total cost was USD 75,439, which was 19% of the company’s operational expenses during the period). These results were extrapolated for the economically active population, and we found that air pollution resulted in 129,832 absences/year and a cost of USD 6,472,686 (77% related to lost wages) per 3,555,237 workers. The estimated values are relevant for planning environmental policies, and are sufficient to promote corrective and preventive actions to avoid this externality.
    Full-text · Article · Oct 2012 · Aerosol and Air Quality Research
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Exposures to ambient air traffic-related pollutants and their sources have been associated with respiratory and asthma morbidity in children. However, longitudinal investigation of the effects of traffic-related exposures during early childhood is limited. We examined associations of residential proximity and density of traffic and stationary sources of air pollution with wheeze, asthma, and immunoglobulin (Ig) E among New York City children between birth and age 5 years. Subjects included 593 Dominican and African American participants from the Columbia Center for Children's Environmental Health cohort. Prenatally, through age 5 years, residential and respiratory health data were collected every 3-6 months. At ages 2, 3, and 5 years, serum IgE was measured. Spatial data on the proximity and density of roadways and built environment were collected for a 250 m buffer around subjects' homes. Associations of wheeze, asthma, total IgE, and allergen-specific IgE with prenatal, earlier childhood, and concurrent exposures to air pollution sources were analyzed using generalized estimating equations or logistic regression. In repeated measures analyses, concurrent residential density of four-way intersections was associated significantly with wheeze (odds ratio: 1.26; 95% confidence interval [CI]: 1.01, 1.57). Age 1 exposures also were associated with wheeze at subsequent ages. Concurrent proximity to highway was associated more strongly with total IgE (ratio of the geometric mean levels: 1.25; 95% CI: 1.09, 1.42) than were prenatal or earlier childhood exposures. Positive associations also were observed between percent commercial building area and asthma, wheeze, and IgE and between proximity to stationary sources of air pollution and asthma. Longitudinal investigation suggests that among Dominican and African American children living in Northern Manhattan and South Bronx during ages 0-5 years, residence in neighborhoods with high density of traffic and industrial facilities may contribute to chronic respiratory morbidity, and concurrent, prenatal, and earlier childhood exposures may be important. These findings may have broad implications for other urban populations that commonly have high asthma prevalence and exposure to a high density of traffic and stationary air pollution sources.
    Full-text · Article · Aug 2011 · Environmental Research
Show more