A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020.

ABSTRACT: Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m

SUBMITTER: Martenies SE 

PROVIDER: S-EPMC8313050 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature