Smoke plumes associated with wildland fires are difficult to characterize due to the non-linear behavior of the variables involved. Plume chemistry is largely modeled using emission factors to represent the relative trace gas and aerosol species emitted. Plume dynamics are modeled based on assumptions of plume vertical distribution and atmospheric dispersion. In the studies presented here, near and in-source measurements of emissions from prescribed burns are used to characterize the variability of emission factors from low-intensity fires. Emissions factors were found to be in the same range as those from other, similar studies in the literature and it appears that the emission factors may be sensitive to small differences in surface conditions such as fuel moisture, surface wind speed, and the ratio of live to dead fuels. We also used two coupled fire atmosphere models, which utilize the Weather Research and Forecasting (WRF) model called WRF-Fire and WRF-Sfire, to investigate the role that atmospheric stability plays in influencing plume rise as well as developing a technique for assessing plume rise and the vertical distribution of pollutants in regional air quality models. Plume heights, as well as rate of growth of the fire, were found to be sensitive to atmospheric stability while fire rate of spread was not. The plume center-of-mass technique was demonstrated to work well but has slightly low estimates compared to observations.

Eulerian chemical transport models are extensively used to steer environmental policy, forecast air quality and study atmospheric processes. However, the ability of these models to simulate concentrated atmospheric plumes, including fire-related smoke, may be limited. Wildland fires are important sources of air pollutants and can significantly affect air quality. Emissions released in wildfires and prescribed burns have been known to substantially increase the air pollution burden at urban locations across large regions. Air quality forecasts generated with numerical models can provide valuable information to environmental regulators and land managers about the potential impacts of fires. Eulerian models present an attractive framework to simulate the transport and transformation of fire emissions. Still, the limitations inherent to chemical transport models when applied to replicate smoke plumes must be identified and well understood to adequately interpret results and further improve the models' predictive skills. Here, a modeling framework centered on the Community Multiscale Air Quality modeling system (CMAQ) is used to simulate several fire episodes that occurred in the Southeastern U.S. and investigate the sensitivity of fine particulate matter concentration predictions to various model inputs and parameters. Significant sources of uncertainty in the model are identified and discussed, including the spatiotemporal allocation of fire emissions and meteorological drivers. In addition, special attention is given to model grid resolution. Adaptive grid modeling is explored as a strategy to simulate fire-related plumes. An adaptive version of CMAQ, capable of dynamically restructuring the grid on which solution fields are estimated and providing refinement at the regions where accuracy is most dependent on resolution, is presented. The fully adaptive three-dimensional modeling technique can be applied to reach unprecedented levels of grid resolution and provide insight into plume dynamics unattainable with static grid models. Through this work the capability of current chemical transport models to replicate fire-related air quality impacts is evaluated, key research needs to achieve effective simulations are identified, and numerical tools designed to improve model performance are developed.