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The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary: In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points: Poyang Lake as a CO2 source or sink significantly depends on water levelPoyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003Dam explains 22% of differences in CO2 fluxes in autumn impoundment period [ABSTRACT FROM AUTHOR]

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) m3 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) m3 CH4/ton coal, close to the default value of high CH4‐content coal in ChinaThe posteriori CO2 emissions were 1.6‐fold of the a priori emissions, indicating CO2 emissions in industrial cities were largely underestimatedSome large CO2 emissions are missing and more work is needed for urban government to fully understand their greenhouse gases emissions [ABSTRACT FROM AUTHOR]

Developed regions of the world represent a major atmospheric methane (CH4) source, but these regional emissions remain poorly constrained. The Yangtze River Delta (YRD) region of China is densely populated (about 16% of China's total population) and consists of large anthropogenic and natural CH4 sources. Here, atmospheric CH4 concentrations measured at a 70-m tall tower in the YRD are combined with a scale factor Bayesian inverse (SFBI) modeling approach to constrain seasonal variations in CH4 emissions. Results indicate that in 2018 agricultural soils (AGS, rice production) were the main driver of seasonal variability in atmospheric CH4 concentration. There was an underestimation of emissions from AGS in the a priori inventories (EDGAR—Emissions Database for Global Atmospheric Research v432 or v50), especially during the growing seasons. Posteriori CH4 emissions from AGS accounted for 39% (4.58 Tg, EDGAR v432) to 47% (5.21 Tg, EDGAR v50) of the total CH4 emissions. The posteriori natural emissions (including wetlands and water bodies) were 1.21 Tg and 1.06 Tg, accounting for 10.1% (EDGAR v432) and 9.5% (EDGAR v50) of total emissions in the YRD in 2018. Results show that the dominant factor for seasonal variations in atmospheric concentration in the YRD was AGS, followed by natural sources. In summer, AGS contributed 42% (EDGAR v432) to 64% (EDGAR v50) of the CH4 concentration enhancement while natural sources only contributed about 10% (EDGAR v50) to 15% (EDGAR v432). In addition, the newer version of the EDGAR product (EDGAR v50) provided more reasonable seasonal distribution of CH4 emissions from rice cultivation than the old version (EDGAR v432). [ABSTRACT FROM AUTHOR]

The atmospheric carbon dioxide (CO 2) mixing ratio and its carbon isotope (δ13 C-CO 2) composition contain important CO 2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO 2 and δ13 C-CO 2 can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13 C-CO 2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO 2 and δ13 C-CO 2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO 2 budget and investigate the main factors that dominated δ13 C-CO 2 variations for the Yangtze River delta (YRD) region, one of the largest anthropogenic CO 2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v4.3.2 CO 2 inventories to simulate hourly CO 2 mixing ratios and δ13 C-CO 2 , evaluated these simulations with observations, and constrained the total anthropogenic CO 2 emission. We show that (1) top-down and bottom-up estimates of anthropogenic CO 2 emissions agreed well (bias < 6 %) on an annual basis, (2) the WRF-STILT model can generally reproduce the observed diel and seasonal atmospheric δ13 C-CO 2 variations, and (3) anthropogenic CO 2 emissions played a much larger role than ecosystems in controlling the δ13 C-CO 2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13 C-CO 2 seasonality increased from 1.53 ‰ to 1.66 ‰. (4) Atmospheric transport processes in summer amplified the cement CO 2 enhancement proportions in the YRD area, which dominated monthly δs (the mixture of δ13 C-CO 2 from all regional end-members) variations. These findings show that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities. [ABSTRACT FROM AUTHOR]

China is the largest CO 2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO 2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO 2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO 2 emissions. Continuous atmospheric CO 2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO 2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1–40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO 2 reductions during COVID-19. Further, emissions in 2021 were 14.3–14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions. [Display omitted] • A 40% decrease of anthropogenic CO 2 emissions was observed during the 2020 COVID-19 pandemic. • Power industry accounted for 73.9% of total CO 2 reductions during COVID-19. • Anthropogenic CO 2 emissions in 2021 were 14% larger than in 2019. • The recovery of CO 2 emissions between year 2021 and 2019 indicates a fast economic rebound. • Point source location mismatches should be evaluated in city scale emission inversion. [ABSTRACT FROM AUTHOR]

China is the largest CO 2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO 2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO 2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO 2 emissions. Continuous atmospheric CO 2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO 2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO 2 reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions., (Copyright © 2022 Elsevier Ltd. All rights reserved.)