title: Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.
abstract: Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.
authors: †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States.
authors: Colleen E
authors: CE
authors: Reid
authors: †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States.|¶Environmental Health Sciences Department, Fielding School of Public Health, University of California, Los Angeles, California 90095, United States.
authors: Michael
authors: M
authors: Jerrett
authors: ‡Epidemiology Division, School of Public Health, University of California, Berkeley, California 94720, United States.|§Biostatistics Division, School of Public Health, University of California, Berkeley, California 94720, United States.
authors: Maya L
authors: ML
authors: Petersen
authors: ∥Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, Colorado 80301, United States.
authors: Gabriele G
authors: GG
authors: Pfister
authors: ⊥National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, D.C. 20460, United States.
authors: Philip E
authors: PE
authors: Morefield
authors: ‡Epidemiology Division, School of Public Health, University of California, Berkeley, California 94720, United States.
authors: Ira B
authors: IB
authors: Tager
authors: #Sonoma Technology, Inc., Petaluma, California 94954, United States.
authors: Sean M
authors: SM
authors: Raffuse
authors: †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States.|∇Department of Medicine, University of California, San Francisco, California 94143, United States.
authors: John R
authors: JR
authors: Balmes
mesh_terms: D000336:Aerosols / Q000032:analysis; D000393:Air Pollutants / Q000032:analysis; D000465:Algorithms; D001185:Artificial Intelligence; D002140:California; D005390:Fires; D008962:Models, Theoretical; D052638:Particulate Matter / Q000032:analysis*; D011237:Predictive Value of Tests; D012906:Smoke / Q000032:analysis
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter; D012906:Smoke