Development of an innovative model to simulate the impact of forest fires

Wildfires pose a significant threat to both natural ecosystems and human health due to the widespread dispersion of pollutants and the generation of intense smoke plumes. This thesis focuses on addressing the complex challenges of modeling wildfire impacts on air quality through the development of an innovative plume rise model, which has been integrated into the existing SPRAY-WEB framework—a Lagrangian particle model for simulating atmospheric dispersion. Extensive analyses were carried out to evaluate the sensitivity of the plume rise model to key free parameters, such as the drag coefficient and the grid resolution used for plume representation. The findings indicate that while employing a more sophisticated drag coefficient formulation can yield slight improvements in accuracy, uncertainties in the estimation of emissive factors appear to exert a more significant influence on the results. Additionally, the research emphasizes the necessity of selecting a grid cell size that is sufficiently large to contain enough particles while remaining smaller than the actual source dimensions to accurately capture the spatial variability of the plume. A new plume rise algorithm was introduced to simulate the mixing of multiple plumes, a scenario frequently encountered during wildfires with multiple ignition points. This innovative approach overcomes the limitations of earlier methods, delivering physically consistent results as validated by comparisons with Briggs’ analytical formulas, experimental data, and advanced simulations using WRF-Fire. The new plume rise model is well suited also to simulate the dispersion of pollutants from accidental fires and it has been embedded in SAPERI, a fast-response, user-friendly modeling chain designed for emergency scenarios, to rapidly assess the impact of accidental pollutant releases. A case study of a fire happened in a plastic waste deposit demonstrated SAPERI’s potential, with its simulated outcomes showing favorable agreement with observational measurements. The work done in collaboration with the UK Met Office also highlights the critical role of incorporating the direct effects of biomass burning aerosols on meteorology and air quality, which further enhances forecasting accuracy. Overall, the advancements presented in this thesis contribute to a more robust and computationally efficient framework for predicting the environmental and public health impacts of wildfires and accidental fires, offering valuable insights for both scientific research and emergency response planning.