Estimating Regional Methane Emission Factors from Energy and Agricultural Sector Sources Using a Portable Measurement System: Case Study of the Denver–Julesburg Basin

Abstract

Methane (CH4), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and standard emission factors (EF) are used to generate GHG emission inventories. Many of the EFs are globally uniform and do not account for regional variability in industrial or agricultural practices and/or regulation. Regional EFs can be derived from top–down emissions measurements and used to make bespoke regional GHG emission inventories that account for geopolitical and social variability. However, most large-scale top–down approaches campaigns require significant investment. To address this, lower-cost driving surveys (DS) have been identified as a viable alternative to more established methods. DSs can take top–down measurements of many emission sources in a relatively short period of time, albeit with a higher uncertainty. To investigate the use of a portable measurement system, a 2260 km DS was conducted throughout the Denver–Julesburg Basin (DJB). The DJB covers an area of 8000 km2 north of Denver, CO and is densely populated with CH4 emission sources, including oil and gas (O and G) operations, agricultural operations (AGOs), lakes and reservoirs. During the DS, 157 individual CH4 emission sources were detected; 51%, 43% and 4% of sources were AGOs, O and G operations, and natural sources, respectively. Methane emissions from each source were quantified using downwind concentration and meteorological data and AGOs and O and G operations represented nearly all the CH4 emissions in the DJB, accounting for 54% and 37% of the total emission, respectively. Operations with similar emission sources were grouped together and average facility emission estimates were generated. For agricultural sources, emissions from feedlot cattle, dairy cows and sheep were estimated at 5, 31 and 1 g CH4 head−1 h−1, all of which agreed with published values taken from focused measurement campaigns. Similarly, for O and G average emissions for well pads, compressor stations and gas processing plants (0.5, 14 and 110 kg CH4 facility−1 h−1) were in reasonable agreement with emission estimates from intensive measurement campaigns. A comparison of our basin wide O and G emissions to measurements taken a decade ago show a decrease of a factor of three, which can feasibly be explained by changes to O and G regulation over the past 10 years, while emissions from AGOs have remained constant over the same time period. Our data suggest that DSs could be a low-cost alternative to traditional measurement campaigns and used to screen many emission sources within a region to derive representative regionally specific and time-sensitive EFs. The key benefit of the DS is that many regions can be screened and emission reduction targets identified where regional EFs are noticeably larger than the regional, national or global averages.

Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado

“…A number of top‐down methane flux studies have been completed for the DM/NFR region. These include flux estimates based on two NOAA flights in late May 2012 (Pétron et al., 2014), two NOAA flights in March 2015 (Peischl et al., 2018), two University of Colorado (CU) and University of Maryland (UMD) flights in early October 2021 (Fried et al., 2022), a driving survey in the summer of 2021 (Riddick et al., 2022), and a University of Arizona satellite methane inversion analysis for June‐July and September‐October 2021 (Cusworth et al., 2022). The inversions used column‐average methane concentrations retrieved from the TROPOspheric Monitoring Instrument (TROPOMI).…”

“…These reductions occurred even though oil production increased by 343% and gas production by 297% from 2012 to 2020. In contrast, when the results from several short‐term, top‐down methane flux studies for the DJ Basin are considered on a timeline, they show a slight declining trend from 2012 to 2021 (Cusworth et al., 2022; Fried et al., 2022; Peischl et al., 2018; Pétron et al., 2014; Riddick et al., 2022). Possible reasons for discrepancies in methane trends will be discussed.…”

“…Townsend-Small et al (2016 report a 50% contribution to total methane from biogenic sources during July and August 2014. Based on the analysis ofReddy and Taylor (2022a) for June-August 2012-2020, non-O&G sources would represent 30% of total methane in 2012 and 68% in 2020, with an assumption of no annual trend in non-O&G emissions based on CDPHE estimates (Colorado Department of Public Health and Environment, 2021) Riddick et al (2022). found a 63% contribution for 10 days in July 2021 Cusworth et al (2022).…”

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

“…With the development of new extraction techniques, including the combination of horizontal drilling and hydraulic fracturing, new wells have continued to proliferate within the Julesburg Basin [ 41 ] ( Figures S3 and S4 ). Methane leaks from O&G infrastructure are treated as a point source, and are often analyzed using a Gaussian plume dispersion model [ 42 ]. Many of these wells are in close proximity to large population centers within this region, including the cities of Greeley, Cheyenne, and Denver.…”

Potential Underestimate in Reported Bottom-up Methane Emissions from Oil and Gas Operations in the Delaware Basin

“…Currently, bottomup methods estimate that 6 Tg of methane is lost from producing, processing, transmitting, storing, and distributing natural gas in the US each year [3]. Recent studies have shown differences between top-down and bottom-up methane emission estimates [4][5][6][7][8], and studies suggest bottom up approaches may miss some sources [6] or that emission factors derived decades ago may now be unrepresentative of current emissions processes [7,9,10].…”