Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States

Abstract

BackgroundChronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 μm; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5–10 μm; PM10–2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10–2.5 concentrations for the northeastern and midwestern United States.MethodsFor PM2.5, we developed models for two periods: 1988–1998 and 1999–2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1). PM10–2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model.ResultsPredictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 μg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10–2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 μg/m3). PM10–2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10–2.5.ConclusionsWe provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10–2.5 for estimating exposures of populations living in the northeastern and midwestern United States.

Structural equation models in occupational health: an application to exposure modelling

“…These variables have a clear theoretical basis for inclusion, and there is strong evidence to suggest that diesel exposures will be elevated in more active terminals (greater number of drivers) and those located closer to major road traffic, in addition to the regional differences that were apparent in the SEM. These now insignificant variables have also been shown in the literature to play an important role in traffic-related occupational and population exposures,17 and this lack of robustness in the OLS coefficients is a common problem in misspecified models. However, the insignificant results and sign changes may also be the result of multicollinearity if the included predictors do not provide enough independent information to predict personal-level exposures.…”

Air Pollution and Risk of Uterine Leiomyomata

“…These predictions are generated at the specific address level from nationwide expansions of previously validated spatiotemporal models. 24,25 The models use monthly average PM 10 and/or PM 2.5 data from USEPA’s Air Quality System, a nationwide network of continuous and filter-based monitors, as well as monitoring data from various other sources. The models also incorporate a number of geographic information system (GIS) based predictors such as population density, land use, elevation, distance to road, PM point sources as well as meteorology.…”

Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States

“…Land use (LU) regression exposure models are commonly used in health studies, yet since the LU terms are generally not time varying, their temporal resolution tends to be limited, and based on the spatial resolution of the available PM 2.5 monitoring network (15–18). Land use terms capture traffic and point sources, but spatial smoothing is required to capture variation in secondary aerosols.…”