Open AccessEstimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5
Author(s) -
Stephanie E. Cleland,
Jason West,
Yunfeng Jia,
Stephen Reid,
S. M. Raffuse,
Susan O’Neill,
Marc L. Serre
Publication year - 2020
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.0c03761
Subject(s) - environmental science , satellite , smoke , chemical transport model , meteorology , air quality index , kriging , remote sensing , atmospheric sciences , statistics , geography , mathematics , engineering , geology , aerospace engineering
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 2.5 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 R 2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate ( R 2 = 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 2.5 ≥ 150.5 μg/m 3 ).