scholarly journals Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

2020 ◽  
Author(s):  
Xueying Yu ◽  
Dylan B. Millet ◽  
Kelley C. Wells ◽  
Daven K. Henze ◽  
Hansen Cao ◽  
...  
Keyword(s):  
Prairie Pothole Region ◽  
Source Region ◽  
Methane Emissions ◽  
Field Application ◽  
Anthropogenic Source ◽  
Prairie Pothole ◽  
Upper Midwest ◽  
Bottom Up ◽  
Airborne Measurements ◽  
Great Lakes Coastal Wetlands

Abstract. We apply airborne measurements across three seasons (summer, winter, spring 2017–2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14–17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with Gridded EPA estimates during spring (to within 7 %), but are ~ 25 % higher during summer/winter. Spatial analysis further shows good top-down/bottom-up agreement for beef facilities, but larger (~ 30 %) seasonal discrepancies for dairies and hog farms. Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

scholarly journals Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

2021 ◽  
Vol 21 (2) ◽  
pp. 951-971
Author(s):  
Xueying Yu ◽  
Dylan B. Millet ◽  
Kelley C. Wells ◽  
Daven K. Henze ◽  
Hansen Cao ◽  
...  
Keyword(s):  
Prairie Pothole Region ◽  
Source Region ◽  
Methane Emissions ◽  
Field Application ◽  
Anthropogenic Source ◽  
Prairie Pothole ◽  
Upper Midwest ◽  
Bottom Up ◽  
Airborne Measurements ◽  
Great Lakes Coastal Wetlands

Abstract. We apply airborne measurements across three seasons (summer, winter and spring 2017–2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14–17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature–hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are ∼ 25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (∼ 30 %) seasonal discrepancies for dairies and hog farms (with > 40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

scholarly journals Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends

2019 ◽  
Vol 6 (8) ◽  
pp. 473-478 ◽  
Author(s):  
Jianxiong Sheng ◽  
Shaojie Song ◽  
Yuzhong Zhang ◽  
Ronald G. Prinn ◽  
Greet Janssens-Maenhout
Keyword(s):  
Coal Mine ◽  
Methane Emissions ◽  
Emission Factors ◽  
Bottom Up ◽  
Coal Mine Methane

Evident cooling effects of surface wetlands to mitigate climate change - a study of the Prairie Pothole Region

2021 ◽  
Author(s):  
Zhe Zhang ◽  
Fei Chen ◽  
Michael Barlage ◽  
Lauren E Bortolotti ◽  
James Famiglietti ◽  
...  
Keyword(s):  
Climate Change ◽  
Prairie Pothole Region ◽  
Prairie Pothole ◽  
Cooling Effects ◽  
Mitigate Climate Change

scholarly journals Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

2014 ◽  
Vol 7 (3) ◽  
pp. 1211-1224 ◽  
Author(s):  
W. Zhang ◽  
Q. Zhang ◽  
Y. Huang ◽  
T. T. Li ◽  
J. Y. Bian ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Rice Variety ◽  
Scale Up ◽  
Methane Emissions ◽  
Data Availability ◽  
Scale Model ◽  
Anthropogenic Source ◽  
Rice Paddies ◽  
Data Scarcity

Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5–89.3% of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44–7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3% of the total emissions, ranging from 18.3–28.0% for early, late and middle rice ecosystems. The 95% confidence interval of the estimation was 4.5–8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

scholarly journals Carbonaceous aerosols in China: top-down constraints on primary sources and estimation of secondary contribution

2011 ◽  
Vol 11 (10) ◽  
pp. 28219-28272 ◽  
Author(s):  
T.-M. Fu ◽  
J. J. Cao ◽  
X. Y. Zhang ◽  
S. C. Lee ◽  
Q. Zhang ◽  
...  
Keyword(s):  
Eastern China ◽  
Western China ◽  
Spatiotemporal Variability ◽  
Anthropogenic Source ◽  
Carbonaceous Aerosols ◽  
Top Down ◽  
Bottom Up ◽  
Secondary Sources ◽  
Secondary Formation ◽  
Emission Estimate

Abstract. We simulate elemental carbon (EC) and organic carbon (OC) aerosols in China and compare model results to surface measurements at Chinese rural and background sites, with the goal of deriving "top-down" emission estimates of EC and OC, as well as better quantifying the secondary sources of OC. We include in the model state-of-the-science Chinese "bottom-up" emission inventories for EC (1.92 Tg C yr−1) and OC (3.95 Tg C yr−1), as well as updated secondary OC formation pathways. The average simulated annual mean EC concentration at rural and background site is 1.1 μg C m−3, 56% lower than the observed 2.5 μg C m−3. The average simulated annual mean OC concentration at rural and background sites is 3.4 μg C m−3, 76% lower than the observed 14 μg C m−3. Multiple regression to fit surface monthly mean EC observations at rural and background sites yields best estimate of Chinese EC source of 3.05 ± 0.78 Tg C yr−1. Based on the top-down EC emission estimate and observed seasonal primary OC/EC ratios, we estimate Chinese OC total emissions to be 6.67 ± 1.30 Tg C yr−1. Using these top-down estimates, the simulated average annual mean EC concentration at rural and background sites significantly improved to 1.9 μg C m−3. However, the model still significantly underestimates observed OC in all seasons (simulated average annual mean OC at rural and background sites is 5.4 μg C m−3), with little skill in capturing the spatiotemporal variability. Secondary formation accounts for 21% of Chinese annual mean surface OC in the model, with isoprene being the most important precursor. In summer, as high as 62% of the observed surface OC may be due to secondary formation in eastern China. Our analysis points to three shortcomings in the current bottom-up inventories of Chinese carbonaceous aerosols: (1) the anthropogenic source is severely underestimated, particularly for OC; (2) there is a missing source in western China, likely associated with the use of biofuels or other low-quality fuels for heating; and (3) sources in fall are not well represented, either because the seasonal shifting of emissions and/or secondary formation are poorly captured or because specific fall emission events are missing. More regional measurements with better spatiotemporal coverage are needed to resolve these shortcomings.

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