School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Modeling Prescribed Burning Smoke at Regional and Urban Scales Using Fire Behavior and Chemical Transport Models

By Zongrun Li

Advisors:

Dr. Armistead G. Russell (CEE), Dr. M. Talat Odman (CEE)

Committee Members: Dr. Jennifer Kaiser (CEE), Dr. Da Pan (CEE),

Dr. Rodney J. Weber (EAS), Dr. Susan M. O’Neill (USFS)

Date and Time: August, 29, 2025. 12:00 PM EST 

Location: Ford ES&T 3229 or on Teams

ABSTRACT
Prescribed burning is an essential land management tool for reducing hazardous fuel loads, maintaining ecosystem health, and mitigating wildfire risks. However, like wildfires, prescribed burns emit fine particulate matter (PM2.5), ozone precursors, and other pollutants that can degrade air quality and affect public health. This thesis addresses a critical need in fire and air quality management: improving the accuracy and applicability of smoke modeling frameworks for both regional and urban scales, with a focus on addressing uncertainties in emissions estimation, meteorological inputs, and plume dynamics.
At the regional scale, the BlueSky pipeline and the chemical transport model (CTM), CMAQ, are applied to estimate emissions and simulate air quality impacts from prescribed fires. Burned areas from prescribed fires are identified from a remote sensing product and calibrated against state burn permit records. These improved burned area estimates are processed through BlueSky to produce hourly emissions for CMAQ simulations, enabling assessments of prescribed fire health impacts in the southeastern U.S. BlueSky and CMAQ are also used to simulate factual and counterfactual scenarios to evaluate air quality trade-offs between wildfires and prescribed fires. A case study of the 2016 Gatlinburg wildfire  reveals that prescribed fire management can reduce overall smoke exposure. To address biases in regional CTM simulations, a generalized data fusion method integrates observations and CTM simulations, improving regional PM2.5 estimates and enabling prescribed fire-specific air quality impact assessments.
At the urban scale, the fire behavior model WRF-SFIRE and the CTM CMAQ are effective modeling frameworks for simulating smoke from prescribed burns. The thesis investigates the impacts of biased wind simulations on smoke concentration simulations. Wind bias reduction methods improve meteorological simulations but cannot eliminate wind biases, prompting the development of smoke model evaluation methods to quantify concentration uncertainty from meteorology. Model intercomparison between WRF-SFIRE and BlueSky-CMAQ highlights their complementary strengths: WRF-SFIRE captures fire-atmosphere interactions and plume dynamics more realistically, whereas BlueSky-CMAQ simulates atmospheric chemistry and secondary pollutants but oversimplifies plume rise. To combine these capabilities, a generalizable offline coupling algorithm is developed to integrate WRF-SFIRE and CMAQ, improving PM2.5 and ozone predictions with reasonable computational cost.
Overall, the thesis demonstrates that refining emissions inventories, reducing meteorological bias, applying data fusion, and coupling fire behavior models with CTMs can meaningfully improve smoke exposure estimates both in regional and urban scales. The developed modeling methods and findings can be used to plan prescribed fires that minimize air quality impacts, facilitate smoke forecasting for public communication, and support policies that reduce wildfire risk through prescribed burning while protecting air quality.