Estimation of Anthropogenic CH4 and CO2 Emissions in Taiyuan‐Jinzhong Region: One of the World's Largest Emission Hotspots. Issue 8 (15th April 2023)

• Record Type: Journal Article
• Authors: Hu, Cheng; Xiao, Wei; Griffis, Timothy J.; Xiao, Qitao; Wang, Shumin; Zhang, Yuzhong; Wang, Weifeng; Zhu, Lingyun; Chen, Xin; Yu, Xueying; Lee, Xuhui
• Is Part Of: Journal of geophysical research. Volume 128:Issue 8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-15
• Subjects: greenhouse gas -- coal mining -- atmospheric inversion -- industrial cities; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD037915
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Coal mining ranks as the largest anthropogenic CH4 source in China with emission factors (EFs) varying up to 30‐fold among inventories when applied to different provinces. The lack of independent evaluation of coal mining CH4 EFs in China is one of the main uncertainties in estimating national total CH4 emissions. Shanxi province, which supplies 25% of the national coal production, is the largest coal mining CH4 emission region in China and even among the world's largest coal production regions. This area is also a significant anthropogenic CO2 source because of high‐density power and industrial activities. Given the large uncertainties in CH4 and CO2 inventories from provincial to city scales, questions remain whether state‐of‐the‐art inventories have accurately estimated these emission hotspots. Here, we evaluate CH4 and CO2 emissions from one of the world's largest coal production regions near Taiyuan City, the capital of Shanxi province, China. CH4 and CO2 concentrations were measured from March 2018 to February 2019 from a 30‐m tower. These data were used within an inverse modeling framework to simulate both CH4 and CO2 concentrations and to evaluate EFs for this region. Results show generally good agreement between observed and simulated CH4 concentrations. However, the CO2 simulations were much lower compared to the observations. Given the minor role of NEE‐induced CO2 enhancements, we believe that the large difference is attributed to the underestimation of anthropogenic CO2 emissions. In general, the derived posteriori anthropogenic CH4 emissions were 85.2(±18.1)% of a priori emissions, where fugitive CH4 from coal mining accounted for ∼92.7% of total anthropogenic emissions. The derived coal mining EF was 23.2(±4.9) m 3 CH4 /ton coal, close to the default value of high CH4 ‐content coal, but twofold the province average that were reported by previous observation‐based studies in Shanxi province, indicating large spatial inhomogeneity in the coal mining CH4 EF. The posteriori CO2 emissions were 1.6‐fold of the a priori emissions, highlighting underestimation of CO2 emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Finally, we also emphasize the use of satellite observations and denser tower‐based networks are essential in resolving the spatial inhomogeneity of greenhouse gas emissions. Plain Language Summary: The understanding of anthropogenic CH4 and CO2 emissions is basis for climate mitigation especially for global top emitting countries, but the largest issue before addressing above question is that many previous studies have found considerable bias of greenhouse gas emission for almost all inventories from city to regional scales. These facts hindered the government to make and evaluate corresponding mitigation policies. Here, to quantify CH4 and CO2 emissions at one of global largest CH4 and CO2 hotspot in China, we conducted 1 year tower‐based atmospheric CH4 and CO2 concentration measurements and used atmospheric inversion method to constrain and evaluate their emissions, we found coal mining CH4 emission factor has less bias but CO2 emissions were underestimated by 1.6‐fold, highlighting underestimation of CO2 emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Our findings indicate more work is needed for urban government to fully understand their greenhouse gases emissions. Key Points: CH4 emission factor for coal mining was 23.2(±4.9) m 3 CH4 /ton coal, close to the default value of high CH4 ‐content coal in China The posteriori CO2 emissions were 1.6‐fold of the a priori emissions, indicating CO2 emissions in industrial cities were largely underestimated Some large CO2 emissions are missing and more work is needed for urban government to fully understand their greenhouse gases emissions