title: A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.
abstract: Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.
authors.affiliation: Division of Environmental Health Sciences, University of California, Berkeley, Berkeley, California 94720-1900, United States. beckerman@berkeley.edu
authors.forename: Bernardo S
authors.initials: BS
authors.lastname: Beckerman
authors.forename: Michael
authors.initials: M
authors.lastname: Jerrett
authors.forename: Marc
authors.initials: M
authors.lastname: Serre
authors.forename: Randall V
authors.initials: RV
authors.lastname: Martin
authors.forename: Seung-Jae
authors.initials: SJ
authors.lastname: Lee
authors.forename: Aaron
authors.initials: A
authors.lastname: van Donkelaar
authors.forename: Zev
authors.initials: Z
authors.lastname: Ross
authors.forename: Jason
authors.initials: J
authors.lastname: Su
authors.forename: Richard T
authors.initials: RT
authors.lastname: Burnett
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D001499:Bayes Theorem; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D018986:Motor Vehicles; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D012044:Regression Analysis; D058998:Remote Sensing Technology; D062211:Spatio-Temporal Analysis; D014481:United States
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter