A new mobile methane emissions inspection approach, Other Test Method (OTM) 33A, was used to quantify short-term emission rates from 210 oil and gas production pads during eight two-week field studies in Texas, Colorado, and Wyoming from 2010 to 2013. Emission rates were log-normally distributed with geometric means and 95% confidence intervals (CIs) of 0.33 (0.23, 0.48), 0.14 (0.11, 0.19), and 0.59 (0.47, 0.74) g/s in the Barnett, Denver-Julesburg, and Pinedale basins, respectively. This study focused on sites with emission rates above 0.01 g/s and included short-term (i.e., condensate tank flashing) and maintenance-related emissions. The results fell within the upper ranges of the distributions observed in recent onsite direct measurement studies. Considering data across all basins, a multivariate linear regression was used to assess the relationship of methane emissions to well age, gas production, and hydrocarbon liquids (oil or condensate) production. Methane emissions were positively correlated with gas production, but only approximately 10% of the variation in emission rates was explained by variation in production levels. The weak correlation between emission and production rates may indicate that maintenance-related stochastic variables and design of production and control equipment are factors determining emissions.
[1] In this study, a source-oriented version of the Community Multiscale Air Quality (CMAQ) model was developed and used to quantify the contributions of five major local emission source types in Southeast Texas (vehicles, industry, natural gas combustion, wildfires, biogenic sources), as well as upwind sources, to regional primary and secondary formaldehyde (HCHO) concentrations. Predicted HCHO concentrations agree well with observations at two urban sites (the Moody Tower [MT] site at the University of Houston and the Haden Road #3 site operated by Texas Commission on Environmental Quality). However, the model underestimates concentrations at an industrial site (Lynchburg Ferry). Throughout most of Southeast Texas, primary HCHO accounts for approximately 20-30% of total HCHO, while the remaining portion is due to secondary HCHO (30-50%) and upwind sources (20-50%). Biogenic sources, natural gas combustion, and vehicles are important sources of primary HCHO in the urban Houston area, respectively, accounting for 10-20%, 10-30%, and 20-60% of total primary HCHO. Biogenic sources, industry, and vehicles are the top three sources of secondary HCHO, respectively, accounting for 30-50%, 10-30%, and 5-15% of overall secondary HCHO. It was also found that over 70% of PAN in the Houston area is due to upwind sources, and only 30% is formed locally. The model-predicted source contributions to HCHO at the MT generally agree with source apportionment results obtained from the Positive Matrix Factorization (PMF) technique.
Teague, Aarin, Philip B. Bedient, and Birnur Guven, 2011. Targeted Application of Seasonal Load Duration Curves Using Multivariate Analysis in Two Watersheds Flowing Into Lake Houston. Journal of the American Water Resources Association (JAWRA) 47(3):620‐634. DOI: 10.1111/j.1752‐1688.2011.00529.x
Abstract: Water quality is a problem in Lake Houston, the primary source of drinking water for the City of Houston, Texas, due to pollutant loads coming from the influent watersheds, including Spring Creek and Cypress Creek. Statistical analysis of the historic water quality data was developed to understand the source characterization and seasonality of the watershed. Multivariate analysis including principal component, cluster, and discriminant analysis provided a custom seasonal assessment of the watersheds so that loading curves may be targeted for season specific pollutant source characterization. The load duration curves have been analyzed using data collected by the U.S. Geologic Survey with corresponding City of Houston water quality data at the sites to characterize the behavior of the pollutant sources and watersheds. Custom seasons were determined for Spring Creek and Cypress Creek watersheds and pollutant source characterization compared between the seasons and watersheds.
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