10 Jun 2022
10 Jun 2022
Status: this preprint is open for discussion.

Statistical modelling of air quality impacts from 1500 individual forest fires in NSW, Australia

Michael A. Storey1,2 and Owen F. Price1,2 Michael A. Storey and Owen F. Price
  • 1NSW Bushfire Risk Management Research Hub, Wollongong, NSW 2522, Australia
  • 2Department of Earth, Atmospheric and Life Sciences, University of Wollongong, NSW 2522, Australia

Abstract. Wildfires and controlled hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm 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. It is important for forest-fire management agencies 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 NSW Australia, from 1500 historical individual fires as a function of fire and weather variables. Using VIIRS satellite hotspots, we identified days where one fire was burning within 150 km of one of 48 air quality station. We extracted ERA5 gridded weather data and fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM2.5 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 important. There was a strong increase in PM2.5 modelled with decreasing distance, with a sharp increase when the fire was within 15 km. The models improve understanding of 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.

Michael A. Storey and Owen F. Price

Status: open (until 22 Jul 2022)

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Michael A. Storey and Owen F. Price

Michael A. Storey and Owen F. Price


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Short summary
Models are needed to understand and predict pollutant output from forest fires so fire agencies can reduce smoke-related risks to human health. We modelled air quality (PM2.5) based on fire area and weather variables. We found fire area and boundary layer height were influential predictors, with distance, temperature, wind speed and relative humidity also important. The model predicted reasonably accurately in comparison to other existing methods, but would benefit from further development.