Sensitivity analysis based on a chemical transport model (CTM) serves as an important approach towards better understanding the relationship between trace contaminant levels in the atmosphere and emissions, chemical and physical processes. Previous studies on ozone control identified the high-order Decoupled Direct Method (HDDM) as an efficient tool to conduct sensitivity analysis. Given the growing recognition of the adverse health effects of fine particulate matter (i.e., particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5)), this dissertation presents the development of a HDDM sensitivity technique for particulate matter and its implementation it in a widely used CTM, CMAQ. Compared to previous studies, two new features of the implementation are 1) including sensitivities of aerosol water content and activity coefficients, and 2) tracking the chemical regimes of the embedded thermodynamic model. The new features provide more accurate sensitivities especially for nitrate and ammonium. Results compare well with brute force sensitivities and are shown to be more stable and computationally efficient. Next, this dissertation explores the applications of HDDM. Source apportionment analysis for the Houston region in September 2006 indicates that nonlinear responses accounted for 3.5% to 33.7% of daily average PM2.5, and that PM2.5 formed rapidly during night especially in the presence of abundant ozone and under stagnant conditions. Uncertainty analysis based on the HDDM found that on average, uncertainties in the emissions rates led to 36% uncertainty in simulated daily average PM2.5 and could explain much, but not all, of the difference between simulated and observed PM2.5 concentrations at two observations sites. HDDM is then applied to assess the impact of flare VOC emissions with temporally variable combustion efficiency. Detailed study of flare emissions using the 2006 Texas special inventory indicates that daily maximum 8-hour ozone at a monitoring site can increase by 2.9 ppb when combustion is significantly decreased. The last application in this dissertation integrates the reduced form model into an electricity generation planning model, and enables representation of geospatial dependence of air quality-related health costs in the optimization process to seek the least cost planning for power generation. The integrated model can provide useful advice on selecting fuel types and locations for power plants.

Air Quality Models are defined for prediction and simulation of air pollutant concentrations over a certain period of time. The predictions can be used in setting limits for the emission levels of industrial facilities. The input data for the air quality models are very large and encompass various environmental conditions like wind speed, turbulence, temperature and cloud density. Most air quality models are based on advection-diffusion equations. These differential equations are moderately stiff and require appropriate techniques for fast integration over large intervals of time. Implicit time stepping techniques for solving differential equations being unconditionally stable are considered suitable for the solution. However, implicit time stepping techniques impose certain data dependencies that can cause the parallelization of air quality models to be inefficient. The current approach uses Runge Kutta Chebyshev explicit method for solution of advection diffusion equations. It is found that even if the explicit method used is computationally more expensive in the serial execution, it takes lesser execution time when parallelized because of less complicated data dependencies presented by the explicit time-stepping. The implicit time-stepping on the other hand cannot be parallelized efficiently because of the inherent complicated data dependencies. / Master of Science

In order to meet the US EPA's National Ambient Air Quality Standards as set under the provisions of the Clean Air Act, states and regions throughout the United States are designing cap and trade programs aimed at reducing the emissions of the two dominant precursors for ozone, nitrogen oxides (NOx) and Volatile Organic Compounds (VOCs). While emission cap and trade programs are becoming more common, relatively few analyses have examined the air quality implications of moving emissions from one location to another (due to trading of emissions between facilities), from one sector to another (due to the use of technologies such as Plug-in Electric Hybrid Vehicles - PHEVs), and changing the temporal distribution of emissions (through emissions trading among facilities with different temporal profiles). This thesis will examine, in detail, the air quality implications of two emission cap and trade programs. The first program is a NOx trading program that covers Electricity Generating Units (EGUs) in the Northeastern United States. Results show that refining the temporal limits on this cap and trade program, by charging facilities more to emit NOx on days when ozone is most likely to form, has the potential to significantly reduce NOx emissions and ozone concentrations. Additionally, this research also shows that, for this region, the spatial redistribution of NOx emissions due to trading leads to greater ozone reductions than similar amounts of NOx emission reductions applied evenly across all facilities. Analyses also indicate that displacing emissions from the on-road mobile sector (vehicles) to the EGU sector through the use of PHEVs decreases ozone in most areas, but some highly localized areas show increases in ozone concentration. The second trading program examined in this thesis is limited to Houston, Texas, where a VOC trading program is focused on a sub-set of four Highly Reactive Volatile Organic Compounds (HRVOCs), which have been identified as having substantial ozone formation potential. Work presented in this thesis examined whether this trading program, in its current form or in an expanded form, could lead to air pollution hot spots, due to spatial reallocation of emissions. Results show that the program as currently designed is unlikely to lead to ozone hot spots, so no further spatial limitations are required for this program. Expanding the trading to include Other VOCs, fugitive emissions and chlorine emissions, based on reactivity weighted trading, is also unlikely to lead to the formation of ozone hot spots, and could create more flexibility in a trading market that is currently not very active. Based on these air quality modeling results, policy suggestions are provided that may increase participation in the trading market. These case studies demonstrate that use of detailed air analyses can provide improved designs for increasingly popular emission cap and trade programs, with improved understanding of the impacts of modifying spatial and temporal distributions of emissions. / text

Ground level ozone is one of the most ubiquitous air pollutants in urban areas, and is generated by photochemical reactions of oxides of nitrogen (NOx) and volatile organic compounds (VOCs). The effectiveness of emission reduction strategies for ozone precursors is typically evaluated using gridded, photochemical air quality models. One of the underlying assumptions in these models is that industrial emissions are nearly constant, since many industrial facilities operate continuously at a constant rate of output. However, recent studies performed in the Houston-Galveston-Brazoria area indicate that some industrial emission sources exhibit high temporal emission variability that can lead to very rapid ozone formation, especially when emissions are composed of highly reactive volatile organic compounds. This work evaluates the impact of variable emissions from industrial sources on ground-level ozone formation in Houston area, utilizing a unique hourly emission inventory, known as the 2006 Special Inventory, created as a part of the second Texas Air Quality Study. Comparison of the hourly emissions inventory data with ambient measurements indicated that the impact of the variability of industrial source emissions on ozone can be significant. Photochemical modeling predictions showed that the variability in industrial emissions can lead to differences in local ozone concentrations of as much as 27 ppb at individual ozone monitor locations. The hourly emissions inventory revealed that industrial source emissions are highly variable in nature with diverse temporal patterns and stochastic behavior. Petrochemical and chemical manufacturing flares, which represent the majority of emissions in the 2006 Special Inventory, were grouped into categories based on industrial process, chemical composition of the flared gas, and the temporal patterns of their emissions. Stochastic models were developed for each categorization of flare emissions with the goal of simulating the characterized temporal emission variability. The stochastic models provide representative temporal profiles for flares in the petrochemical manufacturing and chemical manufacturing sectors, and as such serve as more comprehensive input for photochemical air quality modeling. / text