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

Background Chronic 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. Methods For 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. Results Predictive 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. Conclusions We 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.

[1]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[2]  Halûk Özkaynak,et al.  Relationships between Aerosol Extinction Coefficients Derived from Airport Visual Range Observations and Alternative Measures of Airborne Particle Mass , 1985 .

[3]  Alan Y. Chiang,et al.  Generalized Additive Models: An Introduction With R , 2007, Technometrics.

[4]  W. Mcdonnell,et al.  Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. , 1999, American journal of respiratory and critical care medicine.

[5]  Joel Schwartz,et al.  Spatio-temporal modeling of chronic PM10 exposure for the Nurses' Health Study. , 2008, Atmospheric environment.

[6]  M. Katze,et al.  Functional Genomics Highlights Differential Induction of Antiviral Pathways in the Lungs of SARS-CoV–Infected Macaques , 2007, PLoS pathogens.

[7]  R. Burnett,et al.  Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. , 2002, JAMA.

[8]  D. Dockery,et al.  Health effects of acid aerosols on North American children: air pollution exposures. , 1996, Environmental health perspectives.

[9]  R. Burnett,et al.  Spatial Analysis of Air Pollution and Mortality in Los Angeles , 2005, Epidemiology.

[10]  R. Burnett,et al.  Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease , 2003, Circulation.

[11]  Francine Laden,et al.  Submitted to the Annals of Applied Statistics PRACTICAL LARGE-SCALE SPATIO-TEMPORAL MODELING OF PARTICULATE MATTER CONCENTRATIONS By , 2016 .

[12]  B. Brunekreef,et al.  Epidemiological evidence of effects of coarse airborne particles on health , 2005, European Respiratory Journal.

[13]  D. Dockery,et al.  An association between air pollution and mortality in six U.S. cities. , 1993, The New England journal of medicine.

[14]  R. B. Husar,et al.  Maps of PM2.5 over the U.S. derived from regional PM2.5 and surrogate visibility and PM10 monitoring data , 1998 .

[15]  Dave K Verma,et al.  Relation between income, air pollution and mortality: a cohort study. , 2003, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[16]  Westone,et al.  Home Page , 2004, 2022 2nd International Conference on Intelligent Cybernetics Technology &amp; Applications (ICICyTA).

[17]  S L Zeger,et al.  Exposure measurement error in time-series studies of air pollution: concepts and consequences. , 2000, Environmental health perspectives.

[18]  D. Allen,et al.  Transport of Atmospheric Fine Particulate Matter: Part 1—Findings from Recent Field Programs on the Extent of Regional Transport within North America , 2008, Journal of the Air & Waste Management Association.

[19]  J. Wakefield,et al.  The interpretation of exposure effect estimates in chronic air pollution studies , 2007, Statistics in medicine.

[20]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[21]  Bert Brunekreef,et al.  Estimating Long-Term Average Particulate Air Pollution Concentrations: Application of Traffic Indicators and Geographic Information Systems , 2003, Epidemiology.

[22]  L. Sheppard,et al.  Long-term exposure to air pollution and incidence of cardiovascular events in women. , 2007, The New England journal of medicine.

[23]  D. Dockery,et al.  Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. , 1995, American journal of respiratory and critical care medicine.