Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-3009-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-3009-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Mar 2024
Research article |
| 08 Mar 2024
| 08 Mar 2024
Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Fan Lu
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jason Blake Cohen
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Related authors
Hongrui Gao, Qin He, Kai Qin, Jhoon Kim, Diego Loyola, Pravash Tiwari, Lingxiao Lu, and Jason B. Cohen
Earth Syst. Sci. Data, 18, 7883–7914, https://doi.org/10.5194/essd-18-7883-2026, https://doi.org/10.5194/essd-18-7883-2026, 2026
Short summary
Short summary
This study provides a continuous daytime-hourly tropospheric nitrogen dioxide dataset over four Asian pollution hotspots from 2019 to 2024, reconstructed using satellite observations, meteorological reanalysis, and physics-aware machine learning. The dataset fills observational gaps, captures fine-scale spatial and diurnal dynamics, and supports regional air-quality and health impact studies.
Ye Feng, Jason Blake Cohen, Xiaolu Li, Lingxiao Lu, Shuo Wang, Zhewen Liu, Lei Wang, Jian Liu, and Kai Qin
Atmos. Chem. Phys., 26, 15139–15159, https://doi.org/10.5194/acp-26-15139-2026, https://doi.org/10.5194/acp-26-15139-2026, 2026
Short summary
Short summary
Carbon monoxide pollution is difficult to track across Central Asia, where monitoring is limited. We used satellite observations and weather data to estimate emissions from 2019 to 2024 and assessed how uncertainty in the observations influenced the results. Average emissions fell by 37.9%, while the strength of major pollution hotspots declined by 40.8%. The study also identified emissions linked to persistent coal fires and changing industries, helping improve pollution records and monitoring.
Wei Hu, Jason Blake Cohen, Lingxiao Lu, Bo Zheng, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Jun Li, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2026-4468, https://doi.org/10.5194/egusphere-2026-4468, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study presents a perturbation experiment to investigate the impact of satellite data uncertainty which are not fully removed using a fast and physically-based divergence operator. The shape of the uncertainty distribution governs error propagation and thus effects the stability of derived fluxes. Coal-based emissions sources are reliably captured. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions.
Xinqi Wei, Qin He, and Kai Qin
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, 1477–1485, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1477-2026, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1477-2026, 2026
Huiru Zhong, Lu Shen, Fengwei Wan, Meng Qu, and Kai Qin
Atmos. Meas. Tech., 19, 4759–4778, https://doi.org/10.5194/amt-19-4759-2026, https://doi.org/10.5194/amt-19-4759-2026, 2026
Short summary
Short summary
Methane is a potent greenhouse gas, yet existing satellite and ground observations in China are too sparse to accurately quantify emissions, highlighting the need for an expanded ground network. Using Bayesian inversion and simulated annealing, we identify optimal locations for new sites. Results show that prioritizing stations in eastern and southwestern China would be most effective. Expanding the network with 50 stations could nearly double the current emission constraint capability.
Pravash Tiwari, Jason Blake Cohen, Hongrui Gao, Lingxiao Lu, Jun Wang, Oleg Dubovik, and Kai Qin
Atmos. Chem. Phys., 26, 9149–9180, https://doi.org/10.5194/acp-26-9149-2026, https://doi.org/10.5194/acp-26-9149-2026, 2026
Short summary
Short summary
Black carbon's climate impact is highly uncertain because its radiative effect depends on particle size, mixing state, and column loading. This study combines satellite data, physics-based simulations, and machine learning to estimate black carbon forcing across contrasting regions. The same black carbon amount can warm or cool the atmosphere depending on local aerosol properties. The machine learning framework provides a fast, transferable tool for regional climate assessment.
Luoyao Guan, Jason Blake Cohen, Shuo Wang, Pravash Tiwari, Zhewen Liu, Zhengqiang Li, and Kai Qin
Atmos. Chem. Phys., 26, 3107–3123, https://doi.org/10.5194/acp-26-3107-2026, https://doi.org/10.5194/acp-26-3107-2026, 2026
Short summary
Short summary
This study examines how black carbon particles from the coal industry influence regional climate by absorbing sunlight. Based on ground measurements and modeling, we find that conventional approaches, which oversimplify particle size and structure, which could leading a bias in terms of their warming effect. Our results highlight that more realistic particle characterizations are crucial for improving climate predictions in industrial regions.
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
Atmos. Chem. Phys., 26, 1931–1946, https://doi.org/10.5194/acp-26-1931-2026, https://doi.org/10.5194/acp-26-1931-2026, 2026
Short summary
Short summary
This study provides TROPOMI (TROPOspheric Monitoring Instrument) with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.
Zhewen Liu, Jason B. Cohen, Pravash Tiwari, Luoyao Guan, Shuo Wang, Zhengqiang Li, and Kai Qin
Earth Syst. Sci. Data, 18, 507–533, https://doi.org/10.5194/essd-18-507-2026, https://doi.org/10.5194/essd-18-507-2026, 2026
Short summary
Short summary
Black carbon (BC), emitted from fires, industry, and fossil fuels, warms the climate and worsens air quality. Due to complex physical and optical properties, BC remains poorly constrained. We combined satellite observations with physical models to build a daily, high-resolution, decadal global dataset of BC mass, number, size, and mixing details. The data contains both known sources and new hotspots. These results can support climate models, satellite products, and pollution mitigation.
Fan Lu, Kai Qin, Jason Blake Cohen, Qin He, Pravash Tiwari, Wei Hu, Chang Ye, Yanan Shan, Qing Xu, Shuo Wang, and Qiansi Tu
Atmos. Chem. Phys., 25, 5837–5856, https://doi.org/10.5194/acp-25-5837-2025, https://doi.org/10.5194/acp-25-5837-2025, 2025
Short summary
Short summary
This work describes a field campaign and new fast emissions estimation approach to attribute methane from a large known and previously unknown coal mine in Shanxi, China. The emissions computed are shown to be larger than known oil and gas sources, indicating that methane from coal mines may play a larger role in the global methane budget. The results are found to be slightly larger than or similar to satellite observational campaigns over the same region.
Lingxiao Lu, Jason Blake Cohen, Kai Qin, Xiaolu Li, and Qin He
Atmos. Chem. Phys., 25, 2291–2309, https://doi.org/10.5194/acp-25-2291-2025, https://doi.org/10.5194/acp-25-2291-2025, 2025
Short summary
Short summary
This study applies an approach that assimilates NO2 vertical column densities from TROPOMI in a mass-conserving manner and inverts daily NOx emissions, presented over rapidly changing regions in China. Source attribution is quantified by the local thermodynamics of the combustion temperature (NOx/NO2). Emission results identify sources which do not exist in the a priori datasets, especially medium industrial sources located next to the Yangtze River.
Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen
Earth Syst. Sci. Data, 16, 5287–5310, https://doi.org/10.5194/essd-16-5287-2024, https://doi.org/10.5194/essd-16-5287-2024, 2024
Short summary
Short summary
Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.
Qiansi Tu, Frank Hase, Kai Qin, Jason Blake Cohen, Farahnaz Khosrawi, Xinrui Zou, Matthias Schneider, and Fan Lu
Atmos. Chem. Phys., 24, 4875–4894, https://doi.org/10.5194/acp-24-4875-2024, https://doi.org/10.5194/acp-24-4875-2024, 2024
Short summary
Short summary
Four-year satellite observations of XCH4 are used to derive CH4 emissions in three regions of China’s coal-rich Shanxi province. The wind-assigned anomalies for two opposite wind directions are calculated, and the estimated emission rates are comparable to the current bottom-up inventory but lower than the CAMS and EDGAR inventories. This research enhances the understanding of emissions in Shanxi and supports climate mitigation strategies by validating emission inventories.
Yuhang Zhang, Jintai Lin, Jhoon Kim, Hanlim Lee, Junsung Park, Hyunkee Hong, Michel Van Roozendael, Francois Hendrick, Ting Wang, Pucai Wang, Qin He, Kai Qin, Yongjoo Choi, Yugo Kanaya, Jin Xu, Pinhua Xie, Xin Tian, Sanbao Zhang, Shanshan Wang, Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Thomas Wagner, Robert Spurr, Lulu Chen, Hao Kong, and Mengyao Liu
Atmos. Meas. Tech., 16, 4643–4665, https://doi.org/10.5194/amt-16-4643-2023, https://doi.org/10.5194/amt-16-4643-2023, 2023
Short summary
Short summary
Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Xiaolu Li, Jason Blake Cohen, Kai Qin, Hong Geng, Xiaohui Wu, Liling Wu, Chengli Yang, Rui Zhang, and Liqin Zhang
Atmos. Chem. Phys., 23, 8001–8019, https://doi.org/10.5194/acp-23-8001-2023, https://doi.org/10.5194/acp-23-8001-2023, 2023
Short summary
Short summary
Remotely sensed NO2 and surface NOx are combined with a mathematical method to estimate daily NOx emissions. The results identify new sources and improve existing estimates. The estimation is driven by three flexible factors: thermodynamics of combustion, chemical loss, and atmospheric transport. The thermodynamic term separates power, iron, and cement from coking, boilers, and aluminum. This work finds three causes for the extremes: emissions, UV radiation, and transport.
Qiansi Tu, Frank Hase, Zihan Chen, Matthias Schneider, Omaira García, Farahnaz Khosrawi, Shuo Chen, Thomas Blumenstock, Fang Liu, Kai Qin, Jason Cohen, Qin He, Song Lin, Hongyan Jiang, and Dianjun Fang
Atmos. Meas. Tech., 16, 2237–2262, https://doi.org/10.5194/amt-16-2237-2023, https://doi.org/10.5194/amt-16-2237-2023, 2023
Short summary
Short summary
Four-year TROPOMI observations are used to derive tropospheric NO2 emissions in two mega(cities) with high anthropogenic activity. Wind-assigned anomalies are calculated, and the emission rates and spatial patterns are estimated based on a machine learning algorithm. The results are in reasonable agreement with previous studies and the inventory. Our method is quite robust and can be used as a simple method to estimate the emissions of NO2 as well as other gases in other regions.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Hongrui Gao, Qin He, Kai Qin, Jhoon Kim, Diego Loyola, Pravash Tiwari, Lingxiao Lu, and Jason B. Cohen
Earth Syst. Sci. Data, 18, 7883–7914, https://doi.org/10.5194/essd-18-7883-2026, https://doi.org/10.5194/essd-18-7883-2026, 2026
Short summary
Short summary
This study provides a continuous daytime-hourly tropospheric nitrogen dioxide dataset over four Asian pollution hotspots from 2019 to 2024, reconstructed using satellite observations, meteorological reanalysis, and physics-aware machine learning. The dataset fills observational gaps, captures fine-scale spatial and diurnal dynamics, and supports regional air-quality and health impact studies.
Ye Feng, Jason Blake Cohen, Xiaolu Li, Lingxiao Lu, Shuo Wang, Zhewen Liu, Lei Wang, Jian Liu, and Kai Qin
Atmos. Chem. Phys., 26, 15139–15159, https://doi.org/10.5194/acp-26-15139-2026, https://doi.org/10.5194/acp-26-15139-2026, 2026
Short summary
Short summary
Carbon monoxide pollution is difficult to track across Central Asia, where monitoring is limited. We used satellite observations and weather data to estimate emissions from 2019 to 2024 and assessed how uncertainty in the observations influenced the results. Average emissions fell by 37.9%, while the strength of major pollution hotspots declined by 40.8%. The study also identified emissions linked to persistent coal fires and changing industries, helping improve pollution records and monitoring.
Wei Hu, Jason Blake Cohen, Lingxiao Lu, Bo Zheng, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Jun Li, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2026-4468, https://doi.org/10.5194/egusphere-2026-4468, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study presents a perturbation experiment to investigate the impact of satellite data uncertainty which are not fully removed using a fast and physically-based divergence operator. The shape of the uncertainty distribution governs error propagation and thus effects the stability of derived fluxes. Coal-based emissions sources are reliably captured. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions.
Xinqi Wei, Qin He, and Kai Qin
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, 1477–1485, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1477-2026, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1477-2026, 2026
Huiru Zhong, Lu Shen, Fengwei Wan, Meng Qu, and Kai Qin
Atmos. Meas. Tech., 19, 4759–4778, https://doi.org/10.5194/amt-19-4759-2026, https://doi.org/10.5194/amt-19-4759-2026, 2026
Short summary
Short summary
Methane is a potent greenhouse gas, yet existing satellite and ground observations in China are too sparse to accurately quantify emissions, highlighting the need for an expanded ground network. Using Bayesian inversion and simulated annealing, we identify optimal locations for new sites. Results show that prioritizing stations in eastern and southwestern China would be most effective. Expanding the network with 50 stations could nearly double the current emission constraint capability.
Pravash Tiwari, Jason Blake Cohen, Hongrui Gao, Lingxiao Lu, Jun Wang, Oleg Dubovik, and Kai Qin
Atmos. Chem. Phys., 26, 9149–9180, https://doi.org/10.5194/acp-26-9149-2026, https://doi.org/10.5194/acp-26-9149-2026, 2026
Short summary
Short summary
Black carbon's climate impact is highly uncertain because its radiative effect depends on particle size, mixing state, and column loading. This study combines satellite data, physics-based simulations, and machine learning to estimate black carbon forcing across contrasting regions. The same black carbon amount can warm or cool the atmosphere depending on local aerosol properties. The machine learning framework provides a fast, transferable tool for regional climate assessment.
Luoyao Guan, Jason Blake Cohen, Shuo Wang, Pravash Tiwari, Zhewen Liu, Zhengqiang Li, and Kai Qin
Atmos. Chem. Phys., 26, 3107–3123, https://doi.org/10.5194/acp-26-3107-2026, https://doi.org/10.5194/acp-26-3107-2026, 2026
Short summary
Short summary
This study examines how black carbon particles from the coal industry influence regional climate by absorbing sunlight. Based on ground measurements and modeling, we find that conventional approaches, which oversimplify particle size and structure, which could leading a bias in terms of their warming effect. Our results highlight that more realistic particle characterizations are crucial for improving climate predictions in industrial regions.
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
Atmos. Chem. Phys., 26, 1931–1946, https://doi.org/10.5194/acp-26-1931-2026, https://doi.org/10.5194/acp-26-1931-2026, 2026
Short summary
Short summary
This study provides TROPOMI (TROPOspheric Monitoring Instrument) with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.
Zhewen Liu, Jason B. Cohen, Pravash Tiwari, Luoyao Guan, Shuo Wang, Zhengqiang Li, and Kai Qin
Earth Syst. Sci. Data, 18, 507–533, https://doi.org/10.5194/essd-18-507-2026, https://doi.org/10.5194/essd-18-507-2026, 2026
Short summary
Short summary
Black carbon (BC), emitted from fires, industry, and fossil fuels, warms the climate and worsens air quality. Due to complex physical and optical properties, BC remains poorly constrained. We combined satellite observations with physical models to build a daily, high-resolution, decadal global dataset of BC mass, number, size, and mixing details. The data contains both known sources and new hotspots. These results can support climate models, satellite products, and pollution mitigation.
Fan Lu, Kai Qin, Jason Blake Cohen, Qin He, Pravash Tiwari, Wei Hu, Chang Ye, Yanan Shan, Qing Xu, Shuo Wang, and Qiansi Tu
Atmos. Chem. Phys., 25, 5837–5856, https://doi.org/10.5194/acp-25-5837-2025, https://doi.org/10.5194/acp-25-5837-2025, 2025
Short summary
Short summary
This work describes a field campaign and new fast emissions estimation approach to attribute methane from a large known and previously unknown coal mine in Shanxi, China. The emissions computed are shown to be larger than known oil and gas sources, indicating that methane from coal mines may play a larger role in the global methane budget. The results are found to be slightly larger than or similar to satellite observational campaigns over the same region.
Lingxiao Lu, Jason Blake Cohen, Kai Qin, Xiaolu Li, and Qin He
Atmos. Chem. Phys., 25, 2291–2309, https://doi.org/10.5194/acp-25-2291-2025, https://doi.org/10.5194/acp-25-2291-2025, 2025
Short summary
Short summary
This study applies an approach that assimilates NO2 vertical column densities from TROPOMI in a mass-conserving manner and inverts daily NOx emissions, presented over rapidly changing regions in China. Source attribution is quantified by the local thermodynamics of the combustion temperature (NOx/NO2). Emission results identify sources which do not exist in the a priori datasets, especially medium industrial sources located next to the Yangtze River.
Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen
Earth Syst. Sci. Data, 16, 5287–5310, https://doi.org/10.5194/essd-16-5287-2024, https://doi.org/10.5194/essd-16-5287-2024, 2024
Short summary
Short summary
Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.
Qiansi Tu, Frank Hase, Kai Qin, Jason Blake Cohen, Farahnaz Khosrawi, Xinrui Zou, Matthias Schneider, and Fan Lu
Atmos. Chem. Phys., 24, 4875–4894, https://doi.org/10.5194/acp-24-4875-2024, https://doi.org/10.5194/acp-24-4875-2024, 2024
Short summary
Short summary
Four-year satellite observations of XCH4 are used to derive CH4 emissions in three regions of China’s coal-rich Shanxi province. The wind-assigned anomalies for two opposite wind directions are calculated, and the estimated emission rates are comparable to the current bottom-up inventory but lower than the CAMS and EDGAR inventories. This research enhances the understanding of emissions in Shanxi and supports climate mitigation strategies by validating emission inventories.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Yuhang Zhang, Jintai Lin, Jhoon Kim, Hanlim Lee, Junsung Park, Hyunkee Hong, Michel Van Roozendael, Francois Hendrick, Ting Wang, Pucai Wang, Qin He, Kai Qin, Yongjoo Choi, Yugo Kanaya, Jin Xu, Pinhua Xie, Xin Tian, Sanbao Zhang, Shanshan Wang, Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Thomas Wagner, Robert Spurr, Lulu Chen, Hao Kong, and Mengyao Liu
Atmos. Meas. Tech., 16, 4643–4665, https://doi.org/10.5194/amt-16-4643-2023, https://doi.org/10.5194/amt-16-4643-2023, 2023
Short summary
Short summary
Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Xiaolu Li, Jason Blake Cohen, Kai Qin, Hong Geng, Xiaohui Wu, Liling Wu, Chengli Yang, Rui Zhang, and Liqin Zhang
Atmos. Chem. Phys., 23, 8001–8019, https://doi.org/10.5194/acp-23-8001-2023, https://doi.org/10.5194/acp-23-8001-2023, 2023
Short summary
Short summary
Remotely sensed NO2 and surface NOx are combined with a mathematical method to estimate daily NOx emissions. The results identify new sources and improve existing estimates. The estimation is driven by three flexible factors: thermodynamics of combustion, chemical loss, and atmospheric transport. The thermodynamic term separates power, iron, and cement from coking, boilers, and aluminum. This work finds three causes for the extremes: emissions, UV radiation, and transport.
Qiansi Tu, Frank Hase, Zihan Chen, Matthias Schneider, Omaira García, Farahnaz Khosrawi, Shuo Chen, Thomas Blumenstock, Fang Liu, Kai Qin, Jason Cohen, Qin He, Song Lin, Hongyan Jiang, and Dianjun Fang
Atmos. Meas. Tech., 16, 2237–2262, https://doi.org/10.5194/amt-16-2237-2023, https://doi.org/10.5194/amt-16-2237-2023, 2023
Short summary
Short summary
Four-year TROPOMI observations are used to derive tropospheric NO2 emissions in two mega(cities) with high anthropogenic activity. Wind-assigned anomalies are calculated, and the emission rates and spatial patterns are estimated based on a machine learning algorithm. The results are in reasonable agreement with previous studies and the inventory. Our method is quite robust and can be used as a simple method to estimate the emissions of NO2 as well as other gases in other regions.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Cited articles
Alberto, M. C. R., Wassmann, R., Buresh, R. J., Quilty, J. R., Correa, T. Q., Sandro, J. M., and Centeno, C. A. R.: Measuring methane flux from irrigated rice fields by eddy covariance method using open-path gas analyzer, Field Crop. Res., 160, 12–21, https://doi.org/10.1016/j.fcr.2014.02.008, 2014.
Allen, D.: Attributing Atmospheric Methane to Anthropogenic Emission Sources, Accounts Chem. Res., 49, 1344–1350, https://doi.org/10.1021/acs.accounts.6b00081, 2016.
Allen, D. T., Torres, V. M., Thomas, J., Sullivan, D. W., Harrison, M., Hendler, A., Herndon, S. C., Kolb, C. E., Fraser, M. P., Hill, A. D., Lamb, B. K., Miskimins, J., Sawyer, R. F., and Seinfeld, J. H.: Measurements of methane emissions at natural gas production sites in the United States, P. Natl. Acad. Sci. USA, 110, 18023–18023, https://doi.org/10.1073/pnas.1304880110, 2013.
Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014.
Arad, V., Arad, S., Cosma, V. A., Suvar, M., and Sgem: Gazodinamic regime on geomechanics point of view for jiu valley coal beds, 14th International Multidisciplinary Scientific Geoconference (SGEM), Albena, Bulgaria, 17–26, Jun, 2014, 269–276, https://doi.org/10.5593/SGEM2014/B13/S3.036, 2014.
Baldwin, M. P., Gray, L. J., Dunkerton, T. J., Hamilton, K., Haynes, P. H., Randel, W. J., Holton, J. R., Alexander, M. J., Hirota, I., Horinouchi, T., Jones, D. B. A., Kinnersley, J. S., Marquardt, C., Sato, K., and Takahashi, M.: The quasi-biennial oscillation, Rev. Geophys., 39, 179–229, https://doi.org/10.1029/1999rg000073, 2001.
Blake, D. R., Mayer, E. W., Tyler, S. C., Makide, Y., Montague, D. C., and Rowland, F. S.: Global increase in atmospheric methane concentrations between 1978 and 1980, Geophys. Res. Lett., 9, 477–480, https://doi.org/10.1029/GL009i004p00477, 1982.
Bonan, G.: Turbulent Fluxes, in: Ecological Climatology, 3 ed., edited by: Bonan, G., Cambridge University Press, Cambridge, 209–217, https://doi.org/10.1017/cbo9781107339200.014, 2015.
Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J. H., and Klimont, Z.: A technology-based global inventory of black and organic carbon emissions from combustion, J. Geophys. Res.-Atmos, 109, 2003JD003697, https://doi.org/10.1029/2003jd003697, 2004.
Brandt, A. R., Heath, G. A., Kort, E. A., O'Sullivan, F., Petron, G., Jordaan, S. M., Tans, P., Wilcox, J., Gopstein, A. M., Arent, D., Wofsy, S., Brown, N. J., Bradley, R., Stucky, G. D., Eardley, D., and Harriss, R.: Methane Leaks from North American Natural Gas Systems, Science, 343, 733–735, https://doi.org/10.1126/science.1247045, 2014.
Buchwitz, M., de Beek, R., Burrows, J. P., Bovensmann, H., Warneke, T., Notholt, J., Meirink, J. F., Goede, A. P. H., Bergamaschi, P., Körner, S., Heimann, M., and Schulz, A.: Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models, Atmos. Chem. Phys., 5, 941–962, https://doi.org/10.5194/acp-5-941-2005, 2005.
Chen, D., Chen, A., Hu, X., Li, B., Li, X., Guo, L., Feng, R., Yang, Y., and Fang, X.: Substantial methane emissions from abandoned coal mines in China, Environ. Res., 214, 113944, https://doi.org/10.1016/j.envres.2022.113944, 2022.
Chen, G. J., Yang, S., Lv, C. F., Zhong, J. A., Wang, Z. D., Zhang, Z. N., Fang, X., Li, S. T., Yang, W., and Xue, L. H.: An improved method for estimating GHG emissions from onshore oil and gas exploration and development in China, Sci. Total Environ., 574, 707–715, https://doi.org/10.1016/j.scitotenv.2016.09.051, 2017.
Chen, Y.-H. and Prinn, R. G.: Estimation of atmospheric methane emissions between 1996 and 2001 using a three-dimensional global chemical transport model, J. Geophys. Res.-Atmos, 111, 2005JD006058, https://doi.org/10.1029/2005jd006058, 2006.
Christensen, T. R., Arora, V. K., Gauss, M., Hoglund-Isaksson, L., and Parmentier, F. W.: Tracing the climate signal: mitigation of anthropogenic methane emissions can outweigh a large Arctic natural emission increase, Sci. Rep., 9, 1146, https://doi.org/10.1038/s41598-018-37719-9, 2019.
Cohen, J. B. and Wang, C.: Estimating global black carbon emissions using a top-down Kalman Filter approach, J. Geophys. Res.-Atmos, 119, 307–323, https://doi.org/10.1002/2013jd019912, 2014.
Cohen, J. B., Prinn, R. G., and Wang, C.: The impact of detailed urban-scale processing on the composition, distribution, and radiative forcing of anthropogenic aerosols, Geophys. Res. Lett., 38, 2011GL047417, https://doi.org/10.1029/2011gl047417, 2011.
Cressot, C., Chevallier, F., Bousquet, P., Crevoisier, C., Dlugokencky, E. J., Fortems-Cheiney, A., Frankenberg, C., Parker, R., Pison, I., Scheepmaker, R. A., Montzka, S. A., Krummel, P. B., Steele, L. P., and Langenfelds, R. L.: On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements, Atmos. Chem. Phys., 14, 577–592, https://doi.org/10.5194/acp-14-577-2014, 2014.
Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., and Leip, A.: Food systems are responsible for a third of global anthropogenic GHG emissions, Nature Food, 2, 198–209, https://doi.org/10.1038/s43016-021-00225-9, 2021a. Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J. G. J., Grassi, G., Rossi, S., and Vignati, E.: GHG emissions of all world countries – 2021 Report, Office of the European Union, Luxembourg, [data set], https://doi.org/10.2760/173513 (last access: 5 January 2024), 2021b.
Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J. G. J., Grassi, G., Rossi, S., and Vignati, E.: GHG emissions of all world countries – 2021 Report, Office of the European Union, Luxembourg, https://doi.org/10.2760/173513, 2021.
Delwart, S., Bouzinac, C., Wursteisen, P., Berger, M., Drinkwater, M., Martin-Neira, M., and Kerr, Y. H.: SMOS validation and the COSMOS campaigns, IEEE T. Geosci. Remote, 46, 695–704, https://doi.org/10.1109/TGRS.2007.914811, 2008.
Deng, Z., Ciais, P., Tzompa-Sosa, Z. A., Saunois, M., Qiu, C., Tan, C., Sun, T., Ke, P., Cui, Y., Tanaka, K., Lin, X., Thompson, R. L., Tian, H., Yao, Y., Huang, Y., Lauerwald, R., Jain, A. K., Xu, X., Bastos, A., Sitch, S., Palmer, P. I., Lauvaux, T., d'Aspremont, A., Giron, C., Benoit, A., Poulter, B., Chang, J., Petrescu, A. M. R., Davis, S. J., Liu, Z., Grassi, G., Albergel, C., Tubiello, F. N., Perugini, L., Peters, W., and Chevallier, F.: Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions, Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, 2022.
Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K., Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E., Green, R. O., and Miller, C. E.: California's methane super-emitters, Nature, 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3, 2019.
Ehhalt, D. H.: The atmospheric cycle of methane, Tellus, 26, 58–70, https://doi.org/10.3402/tellusa.v26i1-2.9737, 1974.
European, C., Joint, R. C. O., J, Guizzardi, D., Schaaf, E., Solazzo, E., Crippa, M., Vignati, E., Banja, M., Muntean, M., Grassi, G., Monforti-Ferrario, F., and Rossi, S.: GHG emissions of all world: 2021 report, Publications Office of the European Union, LU, https://doi.org/10.2760/173513, 2021.
EPA: Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2021, U.S. Environmental Protection Agency, EPA 430-R-23-002, https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and- sinks-1990--2021, (last access: April 2023) 2023.
Felsenstein, J.: Confidence Limits on Phylogenies: An Approach Using the Bootstrap, Evolution, 39, 783–791, https://doi.org/10.1111/j.1558-5646.1985.tb00420.x, 1985.
Feng, S., Jiang, F., Wu, Z., Wang, H., Ju, W., and Wang, H.: CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017, J. Geophys. Res.-Atmos, 125, 2019JD031808, https://doi.org/10.1029/2019JD031808, 2020.
Fiehn, A., Kostinek, J., Eckl, M., Klausner, T., Gałkowski, M., Chen, J., Gerbig, C., Röckmann, T., Maazallahi, H., Schmidt, M., Korbeń, P., Neçki, J., Jagoda, P., Wildmann, N., Mallaun, C., Bun, R., Nickl, A.-L., Jöckel, P., Fix, A., and Roiger, A.: Estimating CH4, CO2 and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach, Atmos. Chem. Phys., 20, 12675–12695, https://doi.org/10.5194/acp-20-12675-2020, 2020.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: In Climate Change 2021: The Physical Science Basis, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.: Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021.
Frankenberg, C., Thorpe, A. K., Thompson, D. R., Hulley, G., Kort, E. A., Vance, N., Borchardt, J., Krings, T., Gerilowski, K., Sweeney, C., Conley, S., Bue, B. D., Aubrey, A. D., Hook, S., and Green, R. O.: Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region, P. Natl. Acad. Sci. USA, 113, 9734–9739, https://doi.org/10.1073/pnas.1605617113, 2016.
Gao, J., Guan, C., Zhang, B., and Li, K.: Decreasing methane emissions from China's coal mining with rebounded coal production, Environ. Res. Lett., 16, 124037, https://doi.org/10.1088/1748-9326/ac38d8, 2021.
Ge, H.-X., Zhang, H.-S., Zhang, H., Cai, X.-H., Song, Y., and Kang, L.: The characteristics of methane flux from an irrigated rice farm in East China measured using the eddy covariance method, Agr. Forest. Meteorol., 249, 228–238, https://doi.org/10.1016/j.agrformet.2017.11.010, 2018.
Gifford, F. A.: An Outline of Theories of Diffusion in the Lower Layers of the Atmosphere, in: Meteorology and Atomic Energy, 66–116, https://www.osti.gov/servlets/purl/4501607 (last access: 2 December 2021), 1968.
Goulden, M. L., Munger, J. W., Fan, S., Daube, B. C., and Wofsy, S. C.: Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy, Global Change Biol., 2, 169–182, https://doi.org/10.1111/j.1365-2486.1996.tb00070.x, 1996.
Hendel, J., Lukanko, L., Macuda, J., Kosakowski, P., and Loboziak, K.: Surface geochemical survey in the vicinity of decommissioned coal mine shafts, Sci. Total Environ., 779, 146385, https://doi.org/10.1016/j.scitotenv.2021.146385, 2021.
Hiller, R. V., Bretscher, D., DelSontro, T., Diem, T., Eugster, W., Henneberger, R., Hobi, S., Hodson, E., Imer, D., Kreuzer, M., Künzle, T., Merbold, L., Niklaus, P. A., Rihm, B., Schellenberger, A., Schroth, M. H., Schubert, C. J., Siegrist, H., Stieger, J., Buchmann, N., and Brunner, D.: Anthropogenic and natural methane fluxes in Switzerland synthesized within a spatially explicit inventory, Biogeosciences, 11, 1941–1959, https://doi.org/10.5194/bg-11-1941-2014, 2014.
Hmiel, B., Petrenko, V. V., Dyonisius, M. N., Buizert, C., Smith, A. M., Place, P. F., Harth, C., Beaudette, R., Hua, Q., Yang, B., Vimont, I., Michel, S. E., Severinghaus, J. P., Etheridge, D., Bromley, T., Schmitt, J., Fain, X., Weiss, R. F., and Dlugokencky, E.: Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions, Nature, 578, 409–412, https://doi.org/10.1038/s41586-020-1991-8, 2020.
Ho, Q. B., Vu, H. N. K., Nguyen, T. T., Nguyen, T. T. H., and Nguyen, T. T. T.: A combination of bottom-up and top-down approaches for calculating of air emission for developing countries: a case of Ho Chi Minh City, Vietnam, Air. Qual. Atmos. Hlth., 12, 1059–1072, https://doi.org/10.1007/s11869-019-00722-8, 2019.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Höglund-Isaksson, L.: Global anthropogenic methane emissions 2005–2030: technical mitigation potentials and costs, Atmos. Chem. Phys., 12, 9079–9096, https://doi.org/10.5194/acp-12-9079-2012, 2012.
Huang, M., Wang, T., Zhao, X., Xie, X., and Wang, D.: Estimation of atmospheric methane emissions and its spatial distribution in China during 2015, Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae, 39, 1371–1380, 2019.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, edited by: Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P., and Federici, S., IPCC, Switzerland, https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html (last access: June 2022), 2019.
Jaccard, M., Murphy, R., and Rivers, N.: Energy-environment policy modeling of endogenous technological change with personal vehicles: combining top-down and bottom-up methods, Ecol. Econ., 51, 31–46, https://doi.org/10.1016/j.ecolecon.2004.06.002, 2004.
Jackson, R. B., Saunois, M., Bousquet, P., Canadell, J. G., Poulter, B., Stavert, A. R., Bergamaschi, P., Niwa, Y., Segers, A., and Tsuruta, A.: Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources, Environ. Res. Lett., 15, 071002, https://doi.org/10.1088/1748-9326/ab9ed2, 2020.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019.
Ju, Y., Sun, Y., Sa, Z., Pan, J., Wang, J., Hou, Q., Li, Q., Yan, Z., and Liu, J.: A new approach to estimate fugitive methane emissions from coal mining in China, Sci. Total Environ., 543, 514–523, https://doi.org/10.1016/j.scitotenv.2015.11.024, 2016.
Kirchgessner, D. A., Piccot, S. D., and Masemore, S. S.: An Improved Inventory of Methane Emissions from Coal Mining in the United States, J. Air Waste Manage., 50, 1904–1919, https://doi.org/10.1080/10473289.2000.10464227, 2000.
Kostinek, J., Roiger, A., Eckl, M., Fiehn, A., Luther, A., Wildmann, N., Klausner, T., Fix, A., Knote, C., Stohl, A., and Butz, A.: Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling, Atmos. Chem. Phys., 21, 8791–8807, https://doi.org/10.5194/acp-21-8791-2021, 2021.
Krautwurst, S., Gerilowski, K., Borchardt, J., Wildmann, N., Gałkowski, M., Swolkień, J., Marshall, J., Fiehn, A., Roiger, A., Ruhtz, T., Gerbig, C., Necki, J., Burrows, J. P., Fix, A., and Bovensmann, H.: Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign, Atmos. Chem. Phys., 21, 17345–17371, https://doi.org/10.5194/acp-21-17345-2021, 2021.
Krings, T., Gerilowski, K., Buchwitz, M., Hartmann, J., Sachs, T., Erzinger, J., Burrows, J. P., and Bovensmann, H.: Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data, Atmos. Meas. Tech., 6, 151–166, https://doi.org/10.5194/amt-6-151-2013, 2013.
Li, X., Cohen, J. B., Qin, K., Geng, H., Wu, X., Wu, L., Yang, C., Zhang, R., and Zhang, L.: Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China, Atmos. Chem. Phys., 23, 8001–8019, https://doi.org/10.5194/acp-23-8001-2023, 2023.
Lin, C. Y., Cohen, J. B., Wang, S., Lan, R. Y., and Deng, W. Z.: A new perspective on the spatial, temporal, and vertical distribution of biomass burning: quantifying a significant increase in CO emissions, Environ. Res. Lett., 15, 104091, https://doi.org/10.1088/1748-9326/abaa7a, 2020.
Liu, G., Peng, S., Lin, X., Ciais, P., Li, X., Xi, Y., Lu, Z., Chang, J., Saunois, M., Wu, Y., Patra, P., Chandra, N., Zeng, H., and Piao, S.: Recent Slowdown of Anthropogenic Methane Emissions in China Driven by Stabilized Coal Production, Environ. Sci. Technol. Lett., 8, 739–746, https://doi.org/10.1021/acs.estlett.1c00463, 2021.
Long, Z., Zhang, Z. L., Liang, S., Chen, X. P., Ding, B. W. P., Wang, B., Chen, Y. B., Sun, Y. Q., Li, S. K., and Yang, T.: Spatially explicit carbon emissions at the county scale, Resour. Conserv. Recy, 173, 105706, https://doi.org/10.1016/j.resconrec.2021.105706, 2021.
Lu, F., Qin, K., Cohen, J. B., Tu, Q., Ye, C., Shan, Y., Tiwari, P., He, Q., Xu, Q., and Wang, S.: Quantifying and attributing methane emissions from coal mine aggregation areas using high-frequency ground-based observations, ESS Open Archive, December 10, https://doi.org/10.22541/essoar.170224563.30664612/v1, 2023.
Luther, A., Kostinek, J., Kleinschek, R., Defratyka, S., Stanisavljević, M., Forstmaier, A., Dandocsi, A., Scheidweiler, L., Dubravica, D., Wildmann, N., Hase, F., Frey, M. M., Chen, J., Dietrich, F., Nȩcki, J., Swolkień, J., Knote, C., Vardag, S. N., Roiger, A., and Butz, A.: Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network, Atmos. Chem. Phys., 22, 5859–5876, https://doi.org/10.5194/acp-22-5859-2022, 2022.
Ma, S., Worden, J. R., Bloom, A. A., Zhang, Y. Z., Poulter, B., Cusworth, D. H., Yin, Y., Pandey, S., Maasakkers, J. D., Lu, X., Shen, L., Sheng, J. X., Frankenberg, C., Miller, C. E., and Jacob, D. J.: Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions, Agu. Adv., 2, 2021AV000408, https://doi.org/10.1029/2021AV000408, 2021.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., and Fischer, M. L.: Gridded National Inventory of U.S. Methane Emissions, Environ. Sci. Technol., 50, 13123–13133, https://doi.org/10.1021/acs.est.6b02878, 2016.
Maasakkers, J. D., Varon, D. J., Elfarsdottir, A., McKeever, J., Jervis, D., Mahapatra, G., Pandey, S., Lorente, A., Borsdorff, T., Foorthuis, L. R., Schuit, B. J., Tol, P., van Kempen, T. A., van Hees, R., and Aben, I.: Using satellites to uncover large methane emissions from landfills, Sci. Adv., 8, eabn9683, https://doi.org/10.1126/sciadv.abn9683, 2022.
Marland, G.: Uncertainties in Accounting for CO2From Fossil Fuels, J. Ind. Ecol., 12, 136–139, https://doi.org/10.1111/j.1530-9290.2008.00014.x, 2008.
McDermitt, D., Burba, G., Xu, L., Anderson, T., Komissarov, A., Riensche, B., Schedlbauer, J., Starr, G., Zona, D., Oechel, W., Oberbauer, S., and Hastings, S.: A new low-power, open-path instrument for measuring methane flux by eddy covariance, Appl. Phys. B-Lasers O, 102, 391–405, https://doi.org/10.1007/s00340-010-4307-0, 2010.
McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020.
Mears, C. A. and Wentz, F. J.: The effect of diurnal correction on satellite-derived lower tropospheric temperature, Science, 309, 1548–1551, https://doi.org/10.1126/science.1114772, 2005.
Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E., Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L., Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S. A., Nehrkorn, T., and Sweeney, C.: Anthropogenic emissions of methane in the United States, P. Natl. Acad. Sci. USA, 110, 20018–20022, https://doi.org/10.1073/pnas.1314392110, 2013.
Nisbet, E. G., Fisher, R. E., Lowry, D., France, J. L., Allen, G., Bakkaloglu, S., Broderick, T. J., Cain, M., Coleman, M., Fernandez, J., Forster, G., Griffiths, P. T., Iverach, C. P., Kelly, B. F. J., Manning, M. R., Nisbet-Jones, P. B. R., Pyle, J. A., Townsend-Small, A., Al-Shalaan, A., Warwick, N., and Zazzeri, G.: Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement, Rev. Geophys., 58, 2019RG000675, https://doi.org/10.1029/2019RG000675, 2020.
Oberschelp, C., Pfister, S., Raptis, C., and Hellweg, S.: Global emission hotspots of coal power generation, Nat. Sustain., 2, 113–121, https://doi.org/10.1038/s41893-019-0221-6, 2019.
Ohara, T., Akimoto, H., Kurokawa, J., Horii, N., Yamaji, K., Yan, X., and Hayasaka, T.: An Asian emission inventory of anthropogenic emission sources for the period 1980–2020, Atmos. Chem. Phys., 7, 4419–4444, https://doi.org/10.5194/acp-7-4419-2007, 2007.
Oreggioni, G. D., Monforti Ferraio, F., Crippa, M., Muntean, M., Schaaf, E., Guizzardi, D., Solazzo, E., Duerr, M., Perry, M., and Vignati, E.: Climate change in a changing world: Socio-economic and technological transitions, regulatory frameworks and trends on global greenhouse gas emissions from EDGAR v.5.0, Global Environ. Chang., 70, 102350, https://doi.org/10.1016/j.gloenvcha.2021.102350, 2021.
Ou, Y., Roney, C., Alsalam, J., Calvin, K., Creason, J., Edmonds, J., Fawcett, A. A., Kyle, P., Narayan, K., O'Rourke, P., Patel, P., Ragnauth, S., Smith, S. J., and McJeon, H.: Deep mitigation of CO2 and non-CO2 greenhouse gases toward 1.5 °C and 2 °C futures, Nat. Commun., 12, 6245, https://doi.org/10.1038/s41467-021-26509-z, 2021.
Ouyang, Z. T., Jackson, R. B., McNicol, G., Fluet-Chouinard, E., Runkle, B. R. K., Papale, D., Knox, S. H., Cooley, S., Delwiche, K. B., Feron, S., Irvin, J. A., Malhotra, A., Muddasir, M., Sabbatini, S., Alberto, M. C. R., Cescatti, A., Chen, C. L., Dong, J. W., Fong, B. N., Guo, H. Q., Hao, L., Iwata, H., Jia, Q. Y., Ju, W. M., Kang, M., Li, H., Kim, J., Reba, M. L., Nayak, A. K., Roberti, D. R., Ryu, Y., Swain, C. K., Tsuang, B., Xiao, X. M., Yuan, W. P., Zhang, G. L., and Zhang, Y. G.: Paddy rice methane emissions across Monsoon Asia, Remote Sens. Environ., 284, 113335, https://doi.org/10.1016/j.rse.2022.113335, 2023.
Pandey, S., Gautam, R., Houweling, S., van der Gon, H. D., Sadavarte, P., Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., van Kempen, T., Hoogeveen, R., van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben, I.: Satellite observations reveal extreme methane leakage from a natural gas well blowout, P. Natl. Acad. Sci. USA, 116, 26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019.
Peltola, O., Hensen, A., Helfter, C., Belelli Marchesini, L., Bosveld, F. C., van den Bulk, W. C. M., Elbers, J. A., Haapanala, S., Holst, J., Laurila, T., Lindroth, A., Nemitz, E., Röckmann, T., Vermeulen, A. T., and Mammarella, I.: Evaluating the performance of commonly used gas analysers for methane eddy covariance flux measurements: the InGOS inter-comparison field experiment, Biogeosciences, 11, 3163–3186, https://doi.org/10.5194/bg-11-3163-2014, 2014.
Peng, S., Piao, S., Bousquet, P., Ciais, P., Li, B., Lin, X., Tao, S., Wang, Z., Zhang, Y., and Zhou, F.: Inventory of anthropogenic methane emissions in mainland China from 1980 to 2010, Atmos. Chem. Phys., 16, 14545–14562, https://doi.org/10.5194/acp-16-14545-2016, 2016.
Perez-Dominguez, I., Del Prado, A., Mittenzwei, K., Hristov, J., Frank, S., Tabeau, A., Witzke, P., Havlik, P., van Meijl, H., Lynch, J., Stehfest, E., Pardo, G., Barreiro-Hurle, J., Koopman, J. F. L., and Sanz-Sanchez, M. J.: Short- and long-term warming effects of methane may affect the cost-effectiveness of mitigation policies and benefits of low-meat diets, Nat. Food, 2, 970–980, https://doi.org/10.1038/s43016-021-00385-8, 2021.
Qin, K., Hu, W., He, Q., Lu, F., and Cohen, J. B.: Data for “Individual Coal Mine Methane Emissions Constrained by Eddy-Covariance Measurements: Low Bias and Missing Sources”, Figshare, [data set], https://doi.org/10.6084/m9.figshare.23265644, (last access: 5 January 2024), 2023.
Sadavarte, P., Pandey, S., Maasakkers, J. D., van der Gon, H. D., Houweling, S., and Aben, I.: A high-resolution gridded inventory of coal mine methane emissions for India and Australia, Elementa-Sci. Anthrop., 10, 00056, https://doi.org/10.1525/elementa.2021.00056, 2022.
Sadavarte, P., Pandey, S., Maasakkers, J. D., Lorente, A., Borsdorff, T., van der Gon, H. D., Houweling, S., and Aben, I.: Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations, Environ. Sci. Technol., 55, 16573–16580, https://doi.org/10.1021/acs.est.1c03976, 2021.
Sánchez-García, E., Gorroño, J., Irakulis-Loitxate, I., Varon, D. J., and Guanter, L.: Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite, Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, 2022.
Scarpelli, T. R. and Jacob, D. J.: Global Fuel Exploitation Inventory (GFEI) (V2), Harvard Dataverse [data set], https://doi.org/10.7910/DVN/HH4EUM, 2021.
Scarpelli, T. R., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J.-X., Rose, K., Romeo, L., Worden, J. R., and Janssens-Maenhout, G.: A global gridded (0.1°×0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change, Earth Syst. Sci. Data, 12, 563–575, https://doi.org/10.5194/essd-12-563-2020, 2020.
Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations, Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, 2022.
Schwietzke, S., Sherwood, O. A., Bruhwiler, L. M. P., Miller, J. B., Etiope, G., Dlugokencky, E. J., Michel, S. E., Arling, V. A., Vaughn, B. H., White, J. W. C., and Tans, P. P.: Upward revision of global fossil fuel methane emissions based on isotope database, Nature, 538, 88–91, https://doi.org/10.1038/nature19797, 2016.
Shan, C., Wang, W., Liu, C., Sun, Y., Hu, Q., Xu, X., Tian, Y., Zhang, H., Morino, I., Griffit, D. W. T., and Velazco, V. A.: Regional CO emission estimated from ground-based remote sensing at Hefei site, China, Atmos. Res., 222, 25–35, https://doi.org/10.1016/j.atmosres.2019.02.005, 2019.
Sheng, J.-X., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Zavala-Araiza, D., and Hamburg, S. P.: A high-resolution (0.1°×0.1°) inventory of methane emissions from Canadian and Mexican oil and gas systems, Atmos. Environ., 158, 211–215, https://doi.org/10.1016/j.atmosenv.2017.02.036, 2017.
Sheng, J., Song, S., Zhang, Y., Prinn, R. G., and Janssens-Maenhout, G.: Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends, Environ. Sci. Technol. Lett., 6, 473–478, https://doi.org/10.1021/acs.estlett.9b00294, 2019.
Silver, J. A.: Frequency-modulation spectroscopy for trace species detection: theory and comparison among experimental methods, Appl. Opt., 31, 707–717, https://doi.org/10.1364/AO.31.000707, 1992.
Singh, A. K., Singh, U., Panigrahi, D. C., and Singh, J.: Updated greenhouse gas inventory estimates for Indian underground coal mining based on the 2019 IPCC refinements, Science, 25, 104946, https://doi.org/10.1016/j.isci.2022.104946, 2022.
Song, W., Wang, H., Wang, G., Chen, L., Jin, Z., Zhuang, Q., and He, J. S.: Methane emissions from an alpine wetland on the Tibetan Plateau: Neglected but vital contribution of the nongrowing season, J. Geophys. Res.-Biogeo., 120, 1475–1490, https://doi.org/10.1002/2015jg003043, 2015.
State Administration of coal mine safety, state energy administration: Coal mine gas grade identification method, Coal Industry Press, Beijing, http://www.chinamine-safety.gov.cn/zfxxgk/fdzdgknr/tzgg/201805/W020180622503292134453.doc (last access: June 2022), 2018.
Stavert, A. R., Saunois, M., Canadell, J. G., Poulter, B., Jackson, R. B., Regnier, P., Lauerwald, R., Raymond, P. A., Allen, G. H., Patra, P. K., Bergamaschi, P., Bousquet, P., Chandra, N., Ciais, P., Gustafson, A., Ishizawa, M., Ito, A., Kleinen, T., Maksyutov, S., McNorton, J., Melton, J. R., Muller, J., Niwa, Y., Peng, S., Riley, W. J., Segers, A., Tian, H., Tsuruta, A., Yin, Y., Zhang, Z., Zheng, B., and Zhuang, Q.: Regional trends and drivers of the global methane budget, Global Change Biol., 28, 182–200, https://doi.org/10.1111/gcb.15901, 2022.
Swolkień, J., Fix, A., and Gałkowski, M.: Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission, Atmos. Chem. Phys., 22, 16031–16052, https://doi.org/10.5194/acp-22-16031-2022, 2022.
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E., Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049–7069, https://doi.org/10.5194/acp-15-7049-2015, 2015.
Varon, D. J., Jacob, D. J., Jervis, D., and McKeever, J.: Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations, Environ. Sci. Technol., 54, 10246–10253, https://doi.org/10.1021/acs.est.0c01213, 2020.
Vaughn, T. L., Bell, C. S., Pickering, C. K., Schwietzke, S., Heath, G. A., Petron, G., Zimmerle, D. J., Schnell, R. C., and Nummedal, D.: Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region, P. Natl. Acad. Sci. USA, 115, 11712–11717, https://doi.org/10.1073/pnas.1805687115, 2018.
Wang, K., Zhang, J., Cai, B., and Yu, S.: Emission factors of fugitive methane from underground coal mines in China: Estimation and uncertainty, Appl. Energ., 250, 273–282, https://doi.org/10.1016/j.apenergy.2019.05.024, 2019.
Wang, N., Zhu, T., Chen, S., and Luo, D. W.: Study on the Interprovincial Emission Factor of Chinese Coal Mine Methane, Appl. Mech. Mater., 295, 3354–3358, https://doi.org/10.4028/www.scientific.net/AMM.295-298.3354, 2013.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water vapour transfer, Q. J. Roy. Meteor. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980.
Wecht, K. J., Jacob, D. J., Wofsy, S. C., Kort, E. A., Worden, J. R., Kulawik, S. S., Henze, D. K., Kopacz, M., and Payne, V. H.: Validation of TES methane with HIPPO aircraft observations: implications for inverse modeling of methane sources, Atmos. Chem. Phys., 12, 1823–1832, https://doi.org/10.5194/acp-12-1823-2012, 2012.
William Irving, O. T.: Energy, in: Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories Intergovernmental Panel on Climate Change (IPCC), Switzerland, 129–144, 2000.
Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J. A., Roy, D. P., Storey, J. C., and Patt, F. S.: Achieving sub-pixel geolocation accuracy in support of MODIS land science, Remote Sens. Environ., 83, 31–49, https://doi.org/10.1016/S0034-4257(02)00085-8, 2002.
Xu, L., Burba, G., Schedlbauer, J. L., Zona, D., McDermitt, D. K., Anderson, T., Oberbauer, S. F., Oechel, W. C., Komissarov, A. V., and Riensche, B.: Eddy Covariance Measurements of Methane Flux at Remote Sites with New Low-Power Lightweight Fast Gas Analyzer, EGU General Assembly 2010, held 2–7 May, 2010 in Vienna, Austria, p. 3743, 2010.
Xu, L. K., Lin, X. M., Amen, J., Welding, K., and McDermitt, D.: Impact of changes in barometric pressure on landfill methane emission, Global Biogeochem. Cy., 28, 679–695, https://doi.org/10.1002/2013gb004571, 2014.
Yu, J. V., Hmiel, B., Lyon, D. R., Warren, J., Cusworth, D. H., Duren, R. M., Chen, Y. L., Murphy, E. C., and Brandt, A. R.: Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin, Environ. Sci. Technol. Lett., 9, 969–974, https://doi.org/10.1021/acs.estlett.2c00380, 2022.
Yuan, B., Nie, Z., and Di, X.: Life cycle inventories of fossil fuels in China (I): Energy sources consumption and direct pollutant emissions, Modern Chemical Industry, 26, 59–64, https://doi.org/10.16606/j.cnki.issn0253-4320.2006.03.017, 2006.
Zhang, D., Zang, J., Jiang, Y., and Wang, K.: A new borehole arrangement for draining the methane in mining induced fractured zone and the utilization of the drained methane in Yangquan Nanzhuang Coal Mine, 2nd International Symposium on Mine Safety Science and Engineering, Beijing, Peoples R China, February 2014, 299-303, https://doi.org/10.1201/b16606-57, 2014.
Zhang, L., Chen, Y., Zhao, Y., Henze, D. K., Zhu, L., Song, Y., Paulot, F., Liu, X., Pan, Y., Lin, Y., and Huang, B.: Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates, Atmos. Chem. Phys., 18, 339–355, https://doi.org/10.5194/acp-18-339-2018, 2018.
Zheng, S., Wang, Y., and Wang, Z.: Methane emissions to atmosphere from coal mine in China, Safety in Coal Mines, 36, 29–33, 2005.
Zhou, Z., Shi, F., Zhang, Y., Yu, Y., and Tang, S.: Influencing Factors of Gas Emission in Coal Mining Face, CSPS 2020, Lecture Notes in Electrical Engineering, vol 654, Springer, Singapore, 278–284, https://doi.org/10.1007/978-981-15-8411-4_35, 2021.
Short summary
We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground, overground, and abandoned mines. Mine-by-mine gas and flux data and 30 min observations from a flux tower located next to a mine shaft are integrated. The observed variability and bias correction are propagated over the emissions dataset, demonstrating that daily observations may not cover the range of variability. Comparisons show both an emissions magnitude and spatial mismatch with current inventories.
We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground,...
Altmetrics
Final-revised paper
Preprint