Top-down constraints on methane point source emissions from animal agriculture and waste based on new airborne measurements in the U.S. Upper Midwest

Authors: YU, XUEYING - University Of Minnesota; MILLET, DYLAN - University Of Minnesota; WELLS, KELLEY - University Of Minnesota; GRIFFIS, TIMOTHY - University Of Minnesota; CHEN, XIN - University Of Minnesota; Baker, John; CONLEY, STEPHEN - Oxford University; SMITH, MACKENZIE - National Oceanic & Atmospheric Administration (NOAA); GVAKHARIA, ALEXANDER - University Of Michigan; KORT, ERIC - University Of Michigan; PLANT, GENEVIEVE - University Of Michigan; WOOD, JEFFREY - University Of Missouri

Submitted to: Journal of Geophysical Research-Biogeosciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2019
Publication Date: 12/18/2019
Citation: Yu, X., Millet, D.B., Wells, K.C., Griffis, T.J., Chen, X., Baker, J.M., Conley, S.A., Smith, M.L., Gvakharia, A., Kort, E.A., Plant, G., Wood, J.D. 2019. Top-down constraints on methane point source emissions from animal agriculture and waste based on new airborne measurements in the U.S. Upper Midwest. Journal of Geophysical Research-Biogeosciences. 125(1). https://doi.org/10.1029/2019JG005429.
DOI: https://doi.org/10.1029/2019JG005429

Interpretive Summary: Methane(CH4), is a potent greenhouse gas, with a global warming potential nearly 30 times that of CO2 on a molecule per molecule basis. Agriculture is one of the largest anthropogenic sources of methane, but there is much uncertainty about the relative magnitudes of the various agricultural sources. In this study we employed aircraft measurements (top-down estimates) to better measure methane emissions from a number of major agricultural sources and to compare those measurements against state-of-science bottom-up estimates based on EPA methodology. Flights were conducted during 3 seasons over 11 different facilities considered to be major methane sources. These included 7 large confined animal feeding operations (CAFOS) and 2 sugar beet processing facilities. The CAFOS included 5 large dairies, 2 beef feedlots, and 2 large hog operations. We found that for beef feedlots, the measurements agreed reasonably well with the bottom-up estimates, but the measured results were significantly lower for both dairies and sugar beet plants. Results for the hog operations were variable. The results suggest that the emission factors used for bottom up estimates of methane emissions from dairies and beet sugar factories should be reexamined, which ultimately should result in more accurate attribution of methane sources.

Technical Abstract: Agriculture and waste together represent the largest anthropogenic methane source in current inventories. However, these bottom-up estimates contain inherent uncertainties from extrapolating limited in-situ measurements to larger scales. Here, we employ new airborne methane measurements over the US Upper Midwest to better quantify emissions from an array of agriculture and waste point sources. Nine of the largest concentrated animal feeding operations (CAFOs) in the region and two sugar processing plants were measured, with multiple revisits during summer (08/2017), winter (01/2018), and spring (05-06/2018). Results reveal temporal variability in facility-level emissions: the median normalized difference between top-down estimates for in-season repeat visits is 25% in winter and 62% in spring. We further compare the top-down fluxes with state-of-science bottom-up estimates informed by Environmental Protection Agency (EPA) methodology and site-level animal population and management practices. Top-down facility-level emissions are consistent with bottom-up estimates for beef CAFOs, but significantly lower for dairies (by 37% on average) and for sugar plants (by 80% on average). Swine facility results are more variable. The strong seasonality for dairy methane emissions assumed in bottom-up inventories is not seen in the aircraft measurements, with potential implications for seasonal source misattribution in atmospheric inverse modeling.