Fine particulate matter (PM2.5) has been associated with a variety of problems that include adverse health effects, reduction in visibility, damage to buildings and crops, and possible interactions with climate. Although stringent air quality regulations are in place, policy makers need efficient tools to test a wide range of control strategies. Sensitivity analysis provides predictions on how the interdependent concentrations of various PM2.5 components and also gaseous pollutant species will respond to specific combinations of precursor emission reductions. The Community Multiscale Air Quality Model (CMAQ) was outfitted with the Decoupled Direct Method in 3D for calculating sensitivities of particulate matter (DDM-3D/PM). This method was evaluated and applied to high PM2.5 episodes in the Southeast United States. Sensitivities of directly emitted particles as well as those formed in the atmosphere through chemical and physical processing of emissions of gaseous precursors such as SO2, NOx, VOCs, and NH3 were calculated. DDM-3D/PM was further extended to calculate receptor oriented sensitivities or the Area of Influence (AOI). AOI analysis determines the geographical extent of relative air pollutant precursor contributions to pollutant levels at a specific receptor of interest. This method was applied to Atlanta and other major cities in Georgia. The tools developed here (DDM-3D/PM and AOI) provide valuable information to those charged with air quality management.

Effective pollution mitigation is the key to successful air quality management. Although states invest millions of dollars to predict future air quality, the regulatory modeling and analysis process to inform pollution control strategy remains uncertain. Traditionally deterministic ‘bright-line’ tests are applied to evaluate the sufficiency of a control strategy to attain an air quality standard. A critical part of regulatory attainment demonstration is the prediction of future pollutant levels using photochemical air quality models. However, because models are uncertain, they yield a false sense of precision that pollutant response to emission controls is perfectly known and may eventually mislead the selection of control policies. These uncertainties in turn affect the health impact assessment of air pollution control strategies. This thesis explores beyond the conventional practice of deterministic attainment demonstration and presents novel approaches to yield probabilistic representations of pollutant response to emission controls by accounting for uncertainties in regional air quality planning. Computationally-efficient methods are developed and validated to characterize uncertainty in the prediction of secondary pollutant (ozone and particulate matter) sensitivities to precursor emissions in the presence of uncertainties in model assumptions and input parameters. We also introduce impact factors that enable identification of model inputs and scenarios that strongly influence pollutant concentrations and sensitivity to precursor emissions. We demonstrate how these probabilistic approaches could be applied to determine the likelihood that any control measure will yield regulatory attainment, or could be extended to evaluate probabilistic health benefits of emission controls, considering uncertainties in both air quality models and epidemiological concentration–response relationships. Finally, ground-level observations for pollutant (ozone) and precursor concentrations (oxides of nitrogen) have been used to adjust probabilistic estimates of pollutant sensitivities based on the performance of simulations in reliably reproducing ambient measurements. Various observational metrics have been explored for better scientific understanding of how sensitivity estimates vary with measurement constraints. Future work could extend these methods to incorporate additional modeling uncertainties and alternate observational metrics, and explore the responsiveness of future air quality to project trends in emissions and climate change.