Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges.

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors, and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions (UEIs) before implementing them. Spatial agent-based modeling (ABM) can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This article discusses model architectures and methodological challenges for successfully modeling UEIs using spatial ABM. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; and strategies for model calibration. Major challenges for a successful application of ABM to UEI assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

Modeling exposures to the oxidative potential of PM10.

Differences in the toxicity of ambient particulate matter (PM) due to varying particle composition across locations may contribute to variability in results from air pollution epidemiologic studies. Though most studies have used PM mass concentration as the exposure metric, an alternative which accounts for particle toxicity due to varying particle composition may better elucidate whether PM from specific sources is responsible for observed health effects. The oxidative potential (OP) of PM < 10 µm (PM(10)) was measured as the rate of depletion of the antioxidant reduced glutathione (GSH) in a model of human respiratory tract lining fluid. Using a database of GSH OP measures collected in greater London, U.K. from 2002 to 2006, we developed and validated a predictive spatiotemporal model of the weekly GSH OP of PM(10) that included geographic predictors. Predicted levels of OP were then used in combination with those of weekly PM(10) mass to estimate exposure to PM(10) weighted by its OP. Using cross-validation (CV), brake and tire wear emissions of PM(10) from traffic within 50 m and tailpipe emissions of nitrogen oxides from heavy-goods vehicles within 100 m were important predictors of GSH OP levels. Predictive accuracy of the models was high for PM(10) (CV R(2)=0.83) but only moderate for GSH OP (CV R(2) = 0.44) when comparing weekly levels; however, the GSH OP model predicted spatial trends well (spatial CV R(2) = 0.73). Results suggest that PM(10) emitted from traffic sources, specifically brake and tire wear, has a higher OP than that from other sources, and that this effect is very local, occurring within 50-100 m of roadways.

Predictors of personal polycyclic aromatic hydrocarbon exposures among pregnant minority women in New York City.

As part of a multiyear birth-cohort study examining the roles of pre- and postnatal environmental exposures on developmental deficits and asthma among children, we measured personal exposures to polycyclic aromatic hydrocarbons (PAHs) among 348 pregnant women in northern Manhattan and the South Bronx, New York. Nonsmoking African-American or Dominican women were identified and recruited into the study. During the third trimester of pregnancy, each subject wore a personal air monitor for 48 hr to determine exposure levels to nine PAH compounds. In this study, we examined levels of exposures to PAHs and tested for associations with potential predictor variables collected from questionnaires addressing socioeconomic factors and day-to-day activities during pregnancy as well as activities and environmental exposures during the 48-hr monitoring period. Reliable personal monitoring data for women who did not smoke during the monitoring period were available for 344 of 348 subjects. Mean PAH concentrations ranged from 0.06 ng/m3 for dibenz[a,h]anthracene to 4.1 ng/m3 for pyrene; mean benzo[a]pyrene concentration was 0.50 ng/m3. As found in previous studies, concentrations of most PAHs were higher in winter than in summer. Multiple linear regression analysis revealed associations between personal PAH exposures and several questionnaire variables, including time spent outdoors, residential heating, and indoor burning of incense. This is the largest study to date characterizing personal exposures to PAHs, a ubiquitous class of carcinogenic air contaminants in urban environments, and is unique in its focus on pregnant minority women.