DOWNWIND QUANTIFICATION OF METHANE EMISSIONS: MODELS UNCERTAINTIES AND VARIABILITY ACROSS OIL AND GAS AND NON-OIL-AND-GAS SOURCES

Abstract

Methane (CH₄) is a potent greenhouse gas, and accurate quantification of emissions across oil and gas and non–oil and gas sectors is critical for climate mitigation and inventory reconciliation. This dissertation investigates methane emissions using two complementary objectives: (1) evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuously monitoring fence-line sensors, and (2) a comparison of bottom-up, measurement-based and EPA inventory methane emission estimates for non–oil and gas sources in the Denver–Julesburg basin, Colorado, while assessing seasonal variability and consistency with existing inventories.For Objective 1, controlled-release experiments were conducted to test the performance of closed-path eddy covariance (EC), backward Lagrangian stochastic (bLs) modeling, and Gaussian plume inverse method (GPIM). The closed-path EC system was unsuitable due to instrumentation limitations, including non-synchronous data logging, low sampling frequency, and a slow analyzer response, resulting in invalid flux estimates. The GPIM and bLs models demonstrated higher quantification accuracy for single-release single-point (SRSP) emissions, with bLs achieving slopes near unity and moderate R² values, but both methods showed reduced accuracy for multi-release single-point (MRSP) scenarios due to plume interference and complex flow conditions. These results highlight the feasibility of GPIM and bLs for isolated point sources while emphasizing that method performance strongly depends on source complexity, wind conditions, and data quality. Objective 2 focused on generating measurement-based emission factors (EFs) and annualized CH₄ inventories for concentrated animal feeding operations (CAFOs), municipal solid waste (MSW) landfills, wastewater treatment plants (WWTPs), and lakes/reservoirs. Annualized emissions totaled 123 ± 21 Gg CH₄ yr⁻¹, closely aligning with the EPA’s bottom-up estimate of 119 Gg CH₄ yr⁻¹. CAFOs were identified as the dominant non–oil and gas source, contributing 78% of total emissions using study-derived EFs, while MSW landfills and WWTPs contributed 11% and 1%, respectively. Seasonal variability was evident for lakes/reservoirs, and landfill emissions but inconsistent for CAFOs and WWTPs, indicating that representative measurements at any time of year can reliably inform annual inventories for the latter sectors. This study’s measurement-based framework differed from the EPA’s approach in that it relied on direct, field-based quantification rather than process-based activity data and static emission factors. By integrating weighted probabilities within Monte Carlo simulations, this study produced annualized EFs that reflect the distribution of both large, episodic emissions and numerous smaller, continuous sources, rather than assuming uniform emission behavior. Despite these methodological differences, the resulting annualized inventory reconciled closely with the EPA’s estimate, underscoring the robustness of both approaches when temporal and spatial variability are appropriately captured. This convergence suggests that measurement-informed weighting techniques can enhance existing inventory frameworks without necessitating entirely new models, ultimately improving transparency and confidence in methane reporting across non–oil and gas sectors. This dissertation provides several key contributions: (1) a systematic, field-tested comparison of CH₄ quantification methods under controlled-release oil and gas scenarios; (2) improved evidence-based guidance on practical methane quantification, including the strengths and limitations of each method and conditions under which accuracy and uncertainty are optimized; (3) a detailed, field-data-derived methane inventory for non–oil and gas sources in the DJ Basin, providing new emission factors, seasonal insights, and identification of primary emission drivers; and (4) a field-based methodology for scaling snapshot measurements to annual scales for inventory comparisons. Finally, this research emphasizes a system-of-systems perspective for methane mitigation, highlighting the importance of integrating measurements, models, and teams across sectors to assess total emissions and track reduction progress. By combining methodological rigor with field-based inventories, this work informs both sector-specific mitigation strategies and broader global efforts to reduce atmospheric methane growth.