Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM 2.5

• Published in: Environmental science & technology 2020-11, Vol.54 (21), p.13439-13447
• Main Authors: Cleland, Stephanie E, West, J Jason, Jia, Yiqin, Reid, Stephen, Raffuse, Sean, O'Neill, Susan, Serre, Marc L
• Format: Article
• Language: English
• Subjects: Air Pollutants - analysis; Air Pollution - analysis; Bayes Theorem; California; Entropy; Environmental Monitoring; Fires; Humans; Particulate Matter - analysis; Smoke - analysis; Wildfires
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.0c03761

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate ( = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM ≥ 150.5 μg/m ).