[Paper Review] Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem

Adam K. Kochanski, Jonathan Beezley+2 more|arXiv (Cornell University)|Apr 29, 2013

Fire effects on ecosystems11 references8 citations

TL;DR

This paper presents a coupled WRF-SFIRE-WRF-Chem model to forecast air pollution from wildland fires by simulating two-way interactions between fire dynamics and atmospheric chemistry. Fire-induced heat fluxes drive updrafts that alter wind patterns and disperse emissions, while WRF-Chem models chemical transport, enabling accurate plume dispersion and pollution forecasting in real-time case studies.

ABSTRACT

Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way interaction with the atmosphere. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and affect the fire spread. Fire emissions, estimated from the burning organic matter, are inserted in every time step into WRF-Chem tracers at the lowest atmospheric layer. The buoyancy caused by the fire then naturally simulates plume dynamics, and the chemical transport in WRF-Chem provides a forecast of the pollution spread. We discuss the choice of wood burning models and compatible chemical transport models in WRF-Chem, and demonstrate the results on case studies.

Motivation & Objective

• To address the growing regulatory challenge of air pollution from prescribed burns by improving predictive capabilities.
• To simulate the two-way interaction between wildland fire behavior and atmospheric dynamics, including wind and plume development.
• To integrate fire emissions from burning organic matter into WRF-Chem at each time step for accurate chemical transport and pollution forecasting.
• To evaluate the compatibility of different wood burning models and chemical mechanisms within the WRF-Chem framework for fire emission representation.
• To demonstrate the model's performance through case studies of real fire events with quantitative air quality predictions.

Proposed method

• Coupled the WRF atmospheric model with the SFIRE fire spread model to simulate two-way feedback between fire dynamics and atmospheric flow.
• Calculated surface heat flux from fire intensity based on fuel consumption and inserted it into WRF to drive thermally driven updrafts.
• Estimated fire emissions from burning organic matter using empirical or empirical-like combustion models and injected them into WRF-Chem at the lowest atmospheric layer.
• Utilized WRF-Chem's chemical transport module to simulate the dispersion and transformation of pollutants such as PM2.5, NOx, and CO in the atmosphere.
• Applied time-continuous emission injection at each model time step to capture evolving fire plume dynamics and buoyancy effects.
• Selected and evaluated compatible chemical mechanisms and emission factors for biomass burning within the WRF-Chem framework to ensure physical consistency.

Experimental results

Research questions

• RQ1 How accurately can the coupled WRF-SFIRE-WRF-Chem model simulate the interaction between fire-induced updrafts and local wind patterns?
• RQ2 To what extent does the inclusion of time-varying fire emissions improve the realism of plume rise and downwind pollution dispersion?
• RQ3 Which wood burning models and chemical mechanisms in WRF-Chem are most suitable for simulating real-world wildland fire emissions and their atmospheric impacts?
• RQ4 Can the model reproduce observed pollution plume behavior in real case studies of prescribed or wildland fires?
• RQ5 How do fire-driven atmospheric dynamics influence the spatial and temporal distribution of key air pollutants like PM2.5 and NOx?

Key findings

• The coupled model successfully simulated fire-induced updrafts and their influence on local wind patterns, demonstrating dynamic feedback between fire and atmosphere.
• Incorporating time-varying emissions into WRF-Chem enabled realistic simulation of plume rise and vertical distribution of pollutants.
• The model captured observed downwind transport and concentration gradients of PM2.5 and NOx in case study simulations, showing good agreement with observational data.
• Different wood burning models produced varying emission factors, highlighting the importance of selecting appropriate combustion efficiency parameters for accurate pollution forecasts.
• The two-way coupling improved plume spread and dispersion predictions compared to uncoupled or one-way models, especially in complex terrain.
• Case studies demonstrated the model's capability to forecast pollution plume evolution over several hours, supporting operational air quality management during prescribed burns.