Uncertainty in the emission estimates is one the main reasons for shortcomings in the Chemistry Transport Models (CTMs) which can reduce the confidence level of impact assessment of anthropogenic activities on air quality and climate. This dissertation focuses on understating the uncertainties within the CTMs and reducing these uncertainties by improving emission estimates
The first part of this dissertation focuses on reducing the uncertainties around the emission estimates from oil and Natural Gas (NG) operations by using various observations and high-resolution CTMs. To achieve this goal, we used Weather Research and Forecasting with Chemistry (WRF-Chem) model in conjunction with extensive measurements from two major field campaigns in Colorado. Ethane was used as the indicator of oil and NG emissions to explore the sensitivity of ethane to different physical parametrizations and simulation set-ups in the WRF-Chem model using the U.S. EPA National Emission Inventory (NEI-2011). The sensitivity analysis shows up to 57.3% variability in the modeled ethane normalized mean bias (NMB) across the simulations, which highlights the important role of model configurations on the model performance.
Comparison between airborne measurements and the sensitivity simulations shows a model-measurement bias of ethane up to -15ppb (NMB of -80%) in regions close to oil and NG activities. Under-prediction of ethane concentration in all sensitivity runs suggests an actual under-estimation of the oil and NG emissions in the NEI-2011 in Colorado. To reduce the error in the emission inventory, we developed a three-dimensional variational inversion technique. Through this method, optimal scaling factors up to 6 for ethane emission rates were calculated. Overall, the inversion method estimated between 11% to 15% higher ethane emission rates in the Denver-Julesburg basin compared to the NEI-201. This method can be extended to constrain oil and NG emissions in other regions in the US using the available measurement datasets.
The second part of the dissertation discusses the University of Iowa high-resolution chemical weather forecast framework using WRF-Chem designed for the Lake Michigan Ozone Study (LMOS-2017). LMOS field campaign took place during summer 2017 to address high ozone episodes in coastal communities surrounding Lake Michigan. The model performance for clouds, on-shore flows, and surface and aircraft sampled ozone and NOx concentrations found that the model successfully captured much of the observed synoptic variability of onshore flows. Selection of High-Resolution Rapid Refresh (HRRR) model as initial and boundary condition, and the Noah land surface model, significantly improved comparison of meteorology variables to both ground-based and aircraft data. Model consistently underestimated the daily maximum concentration of ozone. Emission sensitivity analysis suggests that increase in Hydrocarbon (HC). Variational inversion method and measurements by GeoTAS and TROPOMI instruments and airborne and ground-based measurements can be used to constrain NOx emissions in the region.