Modeling Wildfire Smoke Pollution by Integrating Land Use Regression and Remote Sensing Data: Regional Multi-Temporal Estimates for Public Health and Exposure Models

• Published in: Atmosphere Vol. 9; no. 9; p. 335
• Main Authors: Mirzaei, Mojgan, Bertazzon, Stefania, Couloigner, Isabelle
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2018
• Subjects: Aerosol optical depth; Air pollution; AOD (aerosol optical depth); Atmosphere; Carbon; Epidemiology; Fires; Forest & brush fires; Greenhouse gases; Humidity; Land pollution; Land use; LUR (land use regression); Nitrogen dioxide; Optical analysis; Optical thickness; Outdoor air quality; Particulate emissions; Particulate matter; particulate matter PM2.5; Particulate matter sources; Pollutants; Pollution sources; Public health; Regression analysis; Remote sensing; Rural areas; Rural land use; Smoke; spatial analysis; Suspended particulate matter; wildfire smoke; Wildfires; Canada; United States > US
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos9090335

To understand the health effects of wildfire smoke, it is important to accurately assess smoke exposure over space and time. Particulate matter (PM) is a predominant pollutant in wildfire smoke. In this study, we develop land-use regression (LUR) models to investigate the impact that a cluster of wildfires in the northwest USA had on the level of PM in southern Alberta (Canada), in the summer of 2015. Univariate aerosol optical depth (AOD) and multivariate AOD-LUR models were used to estimate the level of PM2.5 in urban and rural areas. For epidemiological studies, it is also important to distinguish between wildfire-related PM2.5 and PM2.5 originating from other sources. We therefore subdivided the study period into three sub-periods: (1) Pre-fire, (2) during-fire, and (3) post-fire. We then developed separate models for each sub-period. With this approach, we were able to identify different predictors significantly associated with smoke-related PM2.5 verses PM2.5 of different origin. Leave-one-out cross-validation (LOOCV) was used to evaluate the models’ performance. Our results indicate that model predictors and model performance are highly related to the level of PM2.5, and the pollution source. The predictive ability of both uni- and multi-variate models were higher in the during-fire period than in the pre- and post-fire periods.