Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment

By Molitor, John; Jerrett, Michael et al. | Environmental Health Perspectives, August 2007 | Go to article overview

Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment


Molitor, John, Jerrett, Michael, Chang, Chih-Chieh, Molitor, Nuoo-Ting, Gauderman, Jim, Berhane, Kiros, McConnell, Rob, Lurmann, Fred, Wu, Jun, Winer, Arthur, Thomas, Duncan, Environmental Health Perspectives


Leading researchers have identified the development of models for assessing air pollution exposure within cities as a priority for future research (Brauer et al. 2003; Brunekreef and Holgate 2002; National Research Council 2002). In the present article we compare and evaluate four spatial models for assigning air pollution exposure at the within-community or intraurban scale. We assess how each model predicts exposure and affects health risks in the context of the Southern California Children's Health Study (CHS; Peters et al. 1999a, 1999b). The CHS study assessed childhood lung function in 12 communities selected to represent a range of exposures. Effects of a correlated group of pollutants, including particulate exposure and nitrogen dioxide were associated with deficits in forced vital capacity (FVC, a measurement of lung volume) and forced expiratory volume in 1 sec ([FEV.sub.1], a measurement of flow rate) (Gauderman et al. 2004, 2007; Molitor et al. 2006; Peters et al. 1999b). The data allow us to examine the effect of incorporating spatial residual errors into the modeling framework of Molitor et al. (2006), potentially explaining a spatial structure not accounted for by the exposure predictors. Therefore, the data serve as a foundation on which to test different exposure models with and without spatially distributed errors and to examine the role of exposure measurement error in air pollution studies.

Interest in assessing exposure at the intraurban scale has grown for a variety of reasons, including early evidence of the large adverse health effects that may emerge from this scale of analysis. For example, Hoek et al. (2002) reported a near doubling of cardiopulmonary mortality [relative risk = 1.95; 95% confidence interval (CI), 1.09-3.52] for Dutch subjects living near major roads in a cohort of 5,000 people, after control of many confounding variables. Although these findings may be robust, the basic exposure models used in these analyses may misclassify exposure because they treat the continuous air pollution field as a discrete entity, that is, either within or outside a specified distance from a road (Jerrett et al. 2005a, 2005b). Thus, questions remain about the validity of results from health effects studies that use exposure surrogates such as road buffers.

Other factors have heightened interest in assessing the relation between air pollution and adverse health effects at the intraurban scale. Empirical exposure studies have shown that for some pollutants associated with traffic, such as [NO.sub.2] and ultrafine particles, variation within cities may exceed variations among central monitoring locations in different cities. Earlier studies from the United Kingdom indicate 2-to 3-fold differences in [NO.sub.2] within distances of [less than or equal to] 50 m of a major road (Hewitt 1991), whereas U.S. studies suggest ultrafine particle concentrations are higher than background until about 300 m from highways during daytime hours (Zhu et al. 2002). The preliminary evidence of large health effects at the intraurban scale and the empirical findings that air pollution exposure varies more within than between communities imply that the most meaningful exposure gradient for research on the adverse health effects of air pollution may occur at the intraurban scale.

Assessing pollution distributions at the intraurban scale has proved challenging because of the lack of routinely collected data, but a new class of models (Jerrett et al. 2005a) that uses geographic information systems (GIS) to integrate existing information now shows promise. These models combine available data on monitoring concentrations, land use, meteorology, time-activity patterns, and emissions. Calibrated exposure models based on this information can identify variation in air pollution concentrations within small areas. Resulting pollution surfaces can then be overlaid on georeferenced study data to assign exposure to individuals at their place of residence, work, or some combination of these microenvironments. …

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