Browsing by Author "Marshall, Julian D."
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- Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of prediction model parsimony in integrated empirical geographic regressionKim, Sun-Young; Bechle, Matthew J.; Hankey, Steven C.; Sheppard, Lianne; Szpiro, Adam A.; Marshall, Julian D. (PLOS, 2020-02-01)National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.
- Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting CitiesHankey, Steven C.; Lindsey, Greg; Marshall, Julian D. (Environmental Health Perspectives, 2016-10-07)Background: Providing infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied. Objectives: Our goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment. Methods: We employed facility–demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600–1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., “walkability” or “bikeability”) to assess exposure. Results: Active travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20–42% of active travel occurs on blocks with high population-level exposure). Only 2–3% of blocks (3–8% of total active travel) are “sweet spots” (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use. Conclusions: Public health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.
- Racial-ethnic exposure disparities to airborne ultrafine particles in the United StatesSaha, Provat K.; Presto, Albert A.; Hankey, Steven C.; Marshall, Julian D.; Robinson, Allen L. (IOP Publishing, 2022-10)Ultrafine particles ('UFP'; <100 nm in diameter) are a subset of fine particulate matter (PM2.5); they have different sources and spatial patterns. Toxicological studies suggest UFP may be more toxic per mass than PM2.5. Racial-ethnic exposure disparities for PM2.5 are well documented; national exposure disparities for UFP remain unexplored due to a lack of national exposure estimates. Here, we combine high-spatial-resolution (census block level) national-scale estimates of long-term, ambient particle number concentrations (PNC; a measure of UFP) with publicly available demographic data (census block-group level) to investigate exposure disparities by race-ethnicity and income across the continental United States. PNC exposure for racial-ethnic minorities (Asian, Black, Hispanic) is 35% higher than the overall national mean. The magnitudes of exposure disparities vary spatially. Disparities are generally larger in densely populated metropolitan areas. The magnitudes of disparities are much larger for PNC than for PM2.5; PM2.5 exposure for racial-ethnic minorities is 9% higher than the overall national mean. Our analysis shows that PNC exposure disparities cannot be explained by differences in income. Whites of all incomes, including low-income Whites, have substantially lower average PNC exposures than people of color of all incomes. A higher proportion of traffic and other PNC sources are located near many minority communities. This means that the exposure disparities are structural and strongly tied to where certain subsets of the population live and that simply reducing PNC emissions nationwide will not reduce these disparities.