Browsing by Author "Digar, Antara"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Constraining ozone-precursor responsiveness using ambient measurements(American Geophysical Union, 2013) Digar, Antara; Cohan, Daniel S.; Xiao, Xue; Foley, Kristen M.; Koo, Bonyoung; Yarwood, GregThis study develops probabilistic estimates of ozone (O3) sensitivities to precursor emissions by incorporating uncertainties in photochemical modeling and evaluating model performance based on ground-level observations of O3 and oxides of nitrogen (NOx). Uncertainties in model formulations and input parameters are jointly considered to identify factors that strongly influence O3 concentrations and sensitivities in the Dallas-Fort Worth region in Texas. Weightings based on a Bayesian inference technique and screenings based on model performance and statistical tests of significance are used to generate probabilistic representation of O3 response to emissions and model input parameters. Adjusted (observation-constrained) results favor simulations using the sixth version of the carbon bond chemical mechanism (CB6) and scaled-up emissions of NOx, dampening the overall sensitivity of O3 to NOx and increasing the sensitivity of O3 to volatile organic compounds in the study region. This approach of using observations to adjust and constrain model simulations can provide probabilistic representations of pollutant responsiveness to emission controls that complement the results obtained from deterministic air-quality modeling.Item Uncertainty in Regional Air Quality Modeling(2012-09-05) Digar, Antara; Cohan, Daniel S.; Griffin, Robert J.; Cox, Dennis D.; Bell, Michelle L.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.