Integrated exposure modeling: A model using GIS and GLM

Division of Biostatistics, Department of Epidemiology and Public Health, Yale School of Medicine, New Haven, CT 06520, USA.
Statistics in Medicine (Impact Factor: 2.04). 01/2009; 29(1):116-29. DOI: 10.1002/sim.3732
Source: PubMed

ABSTRACT Traffic exhaust is a source of air contaminants that have adverse health effects. Quantification of traffic as an exposure variable is complicated by aerosol dispersion related to variation in layout of roads, traffic density, meteorology, and topography. A statistical model is presented that uses Geographic Information Systems (GIS) technology to incorporate variables into a generalized linear model that estimates distribution of traffic-related pollution. Exposure from a source is expressed as an integral of a function proportional to average daily traffic and a nonparametric dispersion function, which takes the form of a step, polynomial, or spline model. The method may be applied using standard regression techniques for fitting generalized linear models. Modifiers of pollutant dispersion such as wind direction, meteorology, and landscape features can also be included. Two examples are given to illustrate the method. The first employs data from a study in which NO(2) (a known pollutant from automobile exhaust) was monitored outside of 138 Connecticut homes, providing a model for estimating NO(2) exposure. In the second example, estimated levels of nitrogen dioxide (NO(2)) from the model, as well as a separate spatial model, were used to analyze traffic-related health effects in a study of 761 infants.

Download full-text


Available from: Keita Ebisu, Dec 17, 2014
  • Source
    • "Other approaches can be used to estimate concentrations at locations without monitors, thereby addressing spatial misalignment problems introduced by use of ambient monitors. These methods include the use of air quality modeling (Bell, 2006; Touma et al., 2006; Pun and Seigneur, 2008; Hogrefe et al., 2009); land-use regression and traffic modeling (Brauer et al., 2008; Karr et al., 2009; Von Klot et al., 2009; Holford et al., 2010); and other approaches including biomarkers, proximity to sources, and hybrid approaches (Zou et al., 2009; Baxter et al., 2010). Yet reliance on ambient monitoring networks to generate exposure estimates is likely to continue given the substantial resources already invested in developing these networks, the relative ease of their use, and the benefit of using actual measurements as opposed to estimated values. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Studies of the health impacts of airborne particulates' chemical constituents typically assume spatial homogeneity and estimate exposure from ambient monitors. However, factors such as local sources may cause spatially heterogeneous pollution levels. This work examines the degree to which constituent levels vary within communities and whether exposure misclassification is introduced by spatial homogeneity assumptions. Analysis considered PM(2.5) elemental carbon (EC), organic carbon matter, ammonium, sulfate, nitrate, silicon, and sodium ion (Na(+)) for the United States, 1999-2007. Pearson correlations and coefficients of divergence were calculated and compared to distances among monitors. Linear modeling related correlations to distance between monitors, long-term constituent levels, and population density. Spatial heterogeneity was present for all constituents, yet lower for ammonium, sulfate, and nitrate. Lower correlations were associated with higher distance between monitors, especially for nitrate and sulfate, and with lower long-term levels, especially for sulfate and Na(+). Analysis of colocated monitors revealed measurement error for all constituents, especially EC and Na(+). Exposure misclassification may be introduced into epidemiological studies of PM(2.5) constituents due to spatial variability, and is affected by constituent type and level. When assessing health effects of PM constituents, new methods are needed for estimating exposure and accounting for exposure error induced by spatial variability.
    Journal of Exposure Science and Environmental Epidemiology 07/2011; 21(4):372-84. DOI:10.1038/jes.2010.24 · 3.05 Impact Factor
  • Source
    • "Future research could investigate actual observed air pollutants, rather than estimated values, and other environmental stressors in relation to urban land-use and respiratory symptoms. Additionally, although the NO 2 estimation model performs well, it could be improved with consideration of seasonal traffic volume and non-highway roads for which data were unavailable, and wind direction which was not incorporated in the exposure model (Holford et al., 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Children's respiratory health has been linked to many factors, including air pollution. The impacts of urban land-use on health are not fully understood, although these relationships are of key importance given the growing populations living in urban environments. We investigated whether the degree of urban land-use near a family's residence is associated with severity of respiratory symptoms like wheeze among infants. Wheeze occurrence was recorded for the first year of life for 680 infants in Connecticut for 1996-1998 from a cohort at risk for asthma development. Land-use categories were obtained from the National Land Cover Database. The fraction of urban land-use near each subject's home was related to severity of wheeze symptoms using ordered logistic regression, adjusting for individual-level data including smoking in the household, race, gender, and socio-economic status. Nitrogen dioxide (NO(2)) exposure was estimated using integrated traffic exposure modeling. Different levels of urban land-use intensity were included in separate models to explore intensity-response relationships. A buffer distance was selected based on the log-likelihood value of models with buffers of 100-2000 m by 10 m increments. A 10% increase in urban land-use within the selected 1540 m buffer of each infant's residence was associated with 1.09-fold increased risk of wheeze severity (95% confidence interval, 1.02-1.16). Results were robust to alternate buffer sizes. When NO(2), representing traffic pollution, was added to the model, results for urban land-use were no longer statistically significant, but had similar central estimates. Higher urban intensity showed higher risk of prevalence and severity of wheeze symptoms. Urban land-use was associated with severity of wheeze symptoms in infants. Findings indicate that health effect estimates for urbanicity incorporate some effects of traffic-related emissions, but also involve other factors. These may include differences in housing characteristics or baseline healthcare status.
    Environmental Research 07/2011; 111(5):677-84. DOI:10.1016/j.envres.2011.04.004 · 3.95 Impact Factor
  • Source
    • "In an approach described by Holford et al. (2010) "
    [Show abstract] [Hide abstract]
    ABSTRACT: An integrated exposure model was developed that estimates nitrogen dioxide (NO(2)) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO(2) taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO(2) measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO(2) levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.
    Atmospheric Environment 12/2010; 44(39):5156-5164. DOI:10.1016/j.atmosenv.2010.08.058 · 3.06 Impact Factor
Show more