Statistical modelling of air quality impacts from individual forest fires in New South Wales, Australia

Wildfires and hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 mu m in diameter (PM2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. Fire management agencies need to understand and predict PM2.5 levels associated with a particular fire so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM2.5 pollution. We modelled PM2.5, measured at air quality stations in New South Wales (Australia) from similar to 1400 d when individual fires were burning near air quality stations, as a function of fire and weather variables. Using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite hotspots, we identified days when one fire was burning within 150 km of at least 1 of 48 air quality stations. We extracted ERAS grid-ded weather data and daily active fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM2.5 levels to understand drivers of and predict PM2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also being important. There was a strong increase in PM2.5 with decreasing distance, with a sharp increase when the fire was within 20 km. The models improve our understanding of the drivers of PM2.5 from individual fires and demonstrate a promising approach to PM2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM2.5 that mean further model development would be required for the models to be deployed operationally.