Open AccessA Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009–2020
Author(s) -
Sheena E. Martenies,
Jaime Keller,
Sherry WeMott,
Grace Kuiper,
Zev Ross,
William B. Allshouse,
John L. Adgate,
Anne P. Starling,
Dana Dabelea,
Sheryl Magzamen
Publication year - 2021
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.0c06451
Subject(s) - environmental science , metropolitan area , air pollution , predictive modelling , mean squared error , air quality index , sampling (signal processing) , exposure assessment , spatial ecology , statistics , meteorology , geography , mathematics , computer science , ecology , archaeology , filter (signal processing) , computer vision , biology
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 R 2 of 0.83 and a root-mean-square error of 0.15 μg/m 3 for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.