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

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