Source Attribution of Hydrocarbons with Potential for Impacts on Climate and Human Health from Oil and Natural Gas Production

Oil and natural gas (O&G) production in the United States has increased sharply the past decade, raising questions about the environmental impacts of production activities, including their greenhouse gas and air toxics emissions. This dissertation first presents results from a methane apportionment study in the Fayetteville Shale dry gas basin. Ethane and methane were measured with fast-response analyzers aboard an aircraft and a ground-based mobile laboratory to collect regional and natural gas ethane-to-methane signatures, respectively. Representative area-scale and natural gas ethane-to-methane enhancement ratios were used in a two-source model to estimate the portion of methane emissions from natural gas production. The dissertation then focuses on the air toxic and carcinogen, benzene, which can be emitted from O&G equipment. Concerns about benzene emissions have grown with increasing O&G development near residential areas in the northern Colorado Front Range. Results from the first set of continuous benzene measurements with instrumentation onboard a mobile laboratory stationed adjacent to a multi-well pad in the Triple Creek residential community in Greeley are presented. A multispecies analysis characterizing benzene sources and ambient benzene mixing ratio variability using hydrocarbon, combustion tracer, and meteorological data is presented. Day-to-day and diurnal variability of the ambient benzene mixing ratio were demonstrated and the impact of local meteorology assessed. Benzene observations from this study are compared with those from other studies in O&G-impacted areas in the Front Range. Meteorological data and enhancement ratios (determined from linear regression slopes) between chemical species were used to characterize O&G influence on ambient air at the site. Data from the same campaign were used with a Gaussian plume dispersion model, AERMOD, to estimate benzene emissions rates from the pad. A unit emissions rate (1 g/s) and two wind datasets, from Triple Creek and a nearby monitoring tower, were used in AERMOD to predict daily average benzene concentrations at the measurement location. Campaign benzene emissions rates were estimated through linear regression of background-corrected observed concentrations versus model results using 15-minute average wind data. Results were not significantly affected by the choice of source configuration for the pad, but were sensitive to the assumed emissions release height