OBSERVATIONS AND EMISSIONS OF ENERGY-ASSOCIATED OZONE PRECURSORS IN THE MID-ATLANTIC UNITED STATES

Surface ozone is formed in the presence of NOx (NO + NO2) and volatile organic compounds (VOCs) and is hazardous to human health. A better understanding of these precursors is needed for developing effective policies to improve air quality. To evaluate the year-to-year changes in source contributions to total VOCs, Positive Matrix Factorization (PMF) was used to perform source apportionment using available hourly observations from June through August at a Photochemical Assessment Monitoring Station (PAMS) in Essex, MD for each year from 2007-2015. Results suggest that while gasoline and vehicle exhaust emissions have fallen, the contribution of natural gas sources to total VOCs has risen. To investigate this increasing natural gas influence, ethane measurements from PAMS sites in Essex, MD and Washington, D.C. were examined. Following a period of decline, daytime ethane concentrations have increased significantly after 2009. This trend appears to be linked with the rapid shale gas production in upwind, neighboring states, especially Pennsylvania and West Virginia. Back-trajectory analyses similarly show that ethane concentrations at these monitors were significantly greater if air parcels had passed through counties containing a high density of unconventional natural gas wells. In addition to VOC emissions, the compressors and engines involved with hydraulic fracturing operations also emit NOx and particulate matter (PM). The Community Multi-scale Air Quality (CMAQ) Model was used to simulate air quality for the Eastern U.S. in 2020, including emissions from shale gas operations in the Appalachian Basin. Predicted concentrations of ozone and PM show the largest decreases when these natural gas resources are hypothetically used to convert coal-fired power plants, despite the increased emissions from hydraulic fracturing operations expanded into all possible shale regions in the Appalachian Basin. While not as clean as burning natural gas, emissions of NOx from coal-fired power plants can be reduced by utilizing post-combustion controls. However, even though capital investment has already been made, these controls are not always operated at optimal rates. CMAQ simulations for the Eastern U.S. in 2018 show ozone concentrations decrease by ~5 ppb when controls on coal-fired power plants limit NOx emissions to historically best rates.

Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

Effects of Information and Time of Use Pricing on Irish Electricity Demand and Supply

In this dissertation I quantify residential behavior response to interventions designed to reduce electricity demand at different periods of the day. In the first chapter, I examine the effect of information provision coupled with bimonthly billing, monthly billing, and in-home displays, as well as a time-of-use (TOU) pricing scheme to measure consumption over each month of the Irish Consumer Behavior Trial. I find that time-of-use pricing with real time usage information reduces electricity usage up to 8.7 percent during peak times at the start of the trial but the effect decays over the first three months and after three months the in-home display group is indistinguishable from the monthly treatment group. Monthly and bi-monthly billing treatments are not found to be statistically different from another. These findings suggest that increasing billing reports to the monthly level may be more cost effective for electricity generators who wish to decrease expenses and consumption, rather than providing in-home displays. In the following chapter, I examine the response of residential households after exposure to time of use tariffs at different hours of the day. I find that these treatments reduce electricity consumption during peak hours by almost four percent, significantly lowering demand. Within the model, I find evidence of overall conservation in electricity used. In addition, weekday peak reductions appear to carry over to the weekend when peak pricing is not present, suggesting changes in consumer habit. The final chapter of my dissertation imposes a system wide time of use plan to analyze the potential reduction in carbon emissions from load shifting based on the Ireland and Northern Single Electricity Market. I find that CO2 emissions savings are highest during the winter months when load demand is highest and dirtier power plants are scheduled to meet peak demand. TOU pricing allows for shifting in usage from peak usage to off peak usage and this shift in load can be met with cleaner and cheaper generated electricity from imports, high efficiency gas units, and hydro units.