Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest

Abstract

The methane (CH4) budget and its source partitioning are poorly constrained in the Midwestern United States. We used tall tower (185 m) aerodynamic flux measurements and atmospheric scale factor Bayesian inversions to constrain the monthly budget and to partition the total budget into natural (e.g., wetlands) and anthropogenic (e.g., livestock, waste, and natural gas) sources for the period June 2016 to September 2017. Aerodynamic flux observations indicated that the landscape was a CH4 source with a mean annual CH4 flux of +13.7 ± 0.34 nmol m−2 s−1 and was rarely a net sink. The scale factor Bayesian inversion analyses revealed a mean annual source of +12.3 ± 2.1 nmol m−2 s−1. Flux partitioning revealed that the anthropogenic source (7.8 ± 1.6 Tg CH4 yr−1) was 1.5 times greater than the bottom‐up gridded United States Environmental Protection Agency inventory, in which livestock and oil/gas sources were underestimated by 1.8‐fold and 1.3‐fold, respectively. Wetland emissions (4.0 ± 1.2 Tg CH4 yr−1) were the second largest source, accounting for 34% of the total budget. The temporal variability of total CH4 emissions was dominated by wetlands with peak emissions occurring in August. In contrast, emissions from oil/gas and other anthropogenic sources showed relatively weak seasonality.

Anthropogenic Methane Emission and Its Partitioning for the Yangtze River Delta Region of China

“…The monthly average concentration footprints are also shown in Figure S4. Previous studies have defined the most sensitive zone as the source areas with a footprint smaller than 10 −4 ppm m 2 s/μmol (Chen et al, , ; Kim et al, ). C. Hu, Liu, et al () and C. Hu, Griffis, et al () quantified a threshold of 10 −3 ppm m 2 s/μmol as the intense source area defined where 80% of the CO 2 enhancement was realized.…”

“…The Stochastic Time‐Inverted Lagrangian Transport (STILT) model is a receptor‐oriented model and has been widely applied in the transport simulation of many trace gases including CH 4 (Chen et al, ; Verhulst et al, ), CO 2 (Graven et al, ; C. Hu, Griffis, et al, ; C. Hu, Liu, et al, ), N 2 O (Chen et al, ; Griffis et al, ), and CO (Kim et al, ). The STILT model can accurately simulate the source footprint (influence‐weighting functions) for a given receptor.…”

“…The main parameters in the STILT model setup include number of released particles, back trajectory time, and location and height of the receptor. Following the setup used in our previous work (Chen et al, ; Griffis et al, ; C. Hu, Griffis, et al, ), 500 particles were released per hour from the same location of our CH 4 /CO 2 observation site (34‐m height, 32°12′N, 118°43′E). These particles were then tracked backwards in time for 7 days.…”

“…The CO 2 background concentration data are from the CarbonTracker global CO 2 distributions (spatial resolution of 3° × 2° and 3‐hourly intervals), which was simulated using the TM5 transport model with optimized CO 2 flux (Peters et al, ). Since CH 4 mixing ratio distributions are not available for China during our study period, we followed the strategy of Chen et al () and selected NOAA flask sites as background values (Chen et al, ; Dlugokencky et al, ). Since the prevailing winds during winter are from north China, we selected flask sites WLG (36°17′N, 100°54′E, 3,810‐m height) and UUM (44°27′N, 111°01′E, 1,007‐m height) to define the background CH 4 mixing ratio (https://www.esrl.noaa.gov/gmd/dv/data/).…”

Top‐Down Constraints on Anthropogenic CO2 Emissions Within an Agricultural‐Urban Landscape

“…Previous work used a threshold of 1e −4 ppm m 2 · s/μmol, to define the most sensitive zone (shown as red) for the investigation of CO, N 2 O, and CH 4 (Kim et al, ; Chen et al, , ). At our same site, Chen et al concluded that about 30% of the CH 4 enhancement was contributed by an area defined by a footprint function threshold of 1e −4 ppm m 2 · s/μmol, and most of the U.S. Corn Belt is contained within the most sensitive and intense footprint zone and, thereby, included the dominant influence of agricultural crops (mainly corn and soybean). For the case of CO 2 , the most sensitive zone is quite different because of the strongly heterogeneous distribution of anthropogenic CO 2 emissions.…”