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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7410
• Main Authors: Riddick, Stuart N., Cheptonui, Fancy, Yuan, Kexin, Mbua, Mercy, Day, Rachel, Vaughn, Timothy L., Duggan, Aidan, Bennett, Kristine E., Zimmerle, Daniel J.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29-09-2022; MDPI
• Subjects: Agricultural practices; agriculture; Aircraft; Case studies; emission factors; energy; Estimates; GHG inventory; Greenhouse gases; Meteorological data; Methane; Methods; mobile survey; Portability; Remote sensing; Satellites; Variability; Wind; United Kingdom; United States > US; Colorado
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197410

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.