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

Abstract

Anthropogenic carbon dioxide (CO2) emissions dominate the atmospheric greenhouse gas radiative forcing budget. However, these emissions are poorly constrained at the regional (102–106 km2) and seasonal scales. Here we use a combination of tall tower CO2 mixing ratio and carbon isotope ratio observations and inverse modeling techniques to constrain anthropogenic CO2 emissions within a highly heterogeneous agricultural landscape near Saint Paul, Minnesota, in the Upper Midwestern United States. The analyses indicate that anthropogenic emissions contributed 6.6, 6.8, and 7.4 μmol/mol annual CO2 enhancements (i.e., departures from the background values) in 2008, 2009, and 2010, respectively. Oil refinery, the energy industry (power and heat generation), and residential emissions (home heating and cooking) contributed 2.9 (42.5%), 1.4 (19.8%), and 1.1 μmol/mol (15.8%) of the total anthropogenic enhancement over the 3‐year period according to a priori inventories. The total anthropogenic signal was further partitioned into CO2 emissions derived from fuel oil, natural gas, coal, gasoline, and diesel consumption using inverse modeling and carbon isotope ratio analyses. The results indicate that fuel oil and natural gas consumption accounted for 52.5% of the anthropogenic CO2 sources in winter. Here the a posteriori CO2 emission from natural gas was 79.0 ± 4.1% (a priori 20.0%) and accounted for 63% of the total CO2 enhancement including both biological and anthropogenic sources. The a posteriori CO2 emission from fuel oil was 8.4 ± 3.8% (a priori 32.5%)—suggesting a more important role of residential heating in winter. The modeled carbon isotope ratio of the CO2 source (δ13Cs , −29.3 ± 0.4‰) was relatively more enriched in 13C‐CO2 compared to that derived from Miller‐Tans plot analyses (−35.5‰ to −34.8‰), supporting that natural gas consumption was underestimated for this region.

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

“…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. We used a similar approach to define the most intense source area for CH 4 emissions.…”

“…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.…”

“… footprint i , j is the footprint in each grid cell ( j ) for each hour ( i ), and flux i , j is the flux corresponding to the same grid cell and hour. The background mixing ratio, (CO 2 ) background , was calculated following the method of C. Hu, Liu, et al () and C. Hu, Griffis, et al (). They tracked the air flow of released particles to the outermost boundary, and the CO 2 background fields were obtained from CarbonTracker (section 2.3).…”

“…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 Verhulst et al, 2017), CO 2 (Graven et al, 2018;C. Hu, Griffis, et al, 2018;C.…”