Articles | Volume 26, issue 20
https://doi.org/10.5194/acp-26-15139-2026
© Author(s) 2026. 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-26-15139-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2026
Research article |
| 28 Oct 2026
| 28 Oct 2026
Quantifying and attributing CO emissions over Central Asia using TROPOMI and explicit observational uncertainty
Ye Feng
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
Xiaolu Li
School of Geographic Sciences, Taiyuan Normal University, Jinzhong, 030619, China
Lingxiao Lu
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
Shuo Wang
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
Zhewen Liu
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
Lei Wang
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
College of Environment and Ecology, Taiyuan University of Technology, Taiyuan, 030024, China
School of Environment and Spatial Informatics, China University of Mining Technology, Xuzhou, 221008, China
Related authors
No articles found.
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.
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.
Yinbao Jin, Heng Huang, Jian Liu, Yiming Liu, Xiaoyang Chen, Yongqiang Chen, Licheng Li, and Qi Fan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-515, https://doi.org/10.5194/essd-2025-515, 2025
Revised manuscript not accepted
Short summary
Short summary
Fires in Southeast and East Asia release large amounts of smoke that harm air quality, weather, and climate. Existing datasets often miss night-time burning and how smoke rises in the atmosphere. We created an open dataset for 2023 that records fire emissions every hour in three dimensions at high resolution. By combining satellite data and machine learning, it improves understanding of when and where smoke is released and supports better forecasts and policy decisions.
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.
Kai Qin, Wei Hu, Qin He, Fan Lu, and Jason Blake Cohen
Atmos. Chem. Phys., 24, 3009–3028, https://doi.org/10.5194/acp-24-3009-2024, https://doi.org/10.5194/acp-24-3009-2024, 2024
Short summary
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.
Yinbao Jin, Yiming Liu, Xiao Lu, Xiaoyang Chen, Ao Shen, Haofan Wang, Yinping Cui, Yifei Xu, Siting Li, Jian Liu, Ming Zhang, Yingying Ma, and Qi Fan
Atmos. Chem. Phys., 24, 367–395, https://doi.org/10.5194/acp-24-367-2024, https://doi.org/10.5194/acp-24-367-2024, 2024
Short summary
Short summary
This study aims to address these issues by evaluating eight independent biomass burning (BB) emission inventories (GFED, FINN1.5, FINN2.5 MOS, FINN2.5 MOSVIS, GFAS, FEER, QFED, and IS4FIRES) using the WRF-Chem model and analyzing their impact on aerosol optical properties (AOPs) and direct radiative forcing (DRF) during wildfire events in peninsular Southeast Asia (PSEA) that occurred in March 2019.
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
Abduh, N., Muhibuddin, A., Zulkarnain, Z., Yusuf, R., and Buraerah, F.: Carbon monoxide gas pollution control model using reducing plants, J. Environ. Treat. Tech., 9, 428–34, https://doi.org/10.47277/JETT/9(2)434, 2021.
Adams, S., Boateng, E., and Acheampong, A. O.: Transport energy consumption and environmental quality: does urbanization matter?, Sci. Total Environ., 744, 140617, https://doi.org/10.1016/j.scitotenv.2020.140617, 2020.
Akhmedzhanov, A. Kh. and Karadanov, T. K.: Atmospheric Carbon Dioxide Dynamics over Kazakhstan Derived from Satellite Data, Russ. Meteorol. Hydrol., 45, 54–57, https://doi.org/10.3103/S1068373920010070, 2020.
Anderson, D. C., Duncan, B. N., Fiore, A. M., Baublitz, C. B., Follette-Cook, M. B., Nicely, J. M., and Wolfe, G. M.: Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers, Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, 2021.
Batey, J.: An overview of carbon monoxide generation and release by home appliances, BNL-52537; CONF-9704179, Brookhaven National Lab. (BNL), Upton, NY (United States), https://www.osti.gov/biblio/569154 (last access: 26 January 2025), 1997.
Beekmann, M. and Derognat, C.: Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, J. Geophys. Res. Atmos., 108, https://doi.org/10.1029/2003JD003391, 2003.
Bel, G. and Holst, M.: Evaluation of the impact of bus rapid transit on air pollution in Mexico City, Transp. Policy, 63, 209–220, https://doi.org/10.1016/j.tranpol.2018.01.001, 2018.
Borsdorff, T., Landgraf, J., and Sha, M. K.: TROPOMI Carbon Monoxide, in: Handbook of Air Quality and Climate Change, edited by: Akimoto, H. and Tanimoto, H., Springer Nature Singapore, Singapore, 261–270, https://doi.org/10.1007/978-981-15-2760-9_59, 2023.
Brenninkmeijer, C. A. M., Röckmann, T., Bräunlich, M., Jöckel, P., and Bergamaschi, P.: Review of progress in isotope studies of atmospheric carbon monoxide, Chemosphere-Glob. Change Sci., 1, 33–52, https://doi.org/10.1016/S1465-9972(99)00018-5, 1999.
Buchholz, R. R., Park, M., Worden, H. M., Tang, W., Edwards, D. P., Gaubert, B., Deeter, M. N., Sullivan, T., Ru, M., and Chin, M.: New seasonal pattern of pollution emerges from changing North American wildfires, Nat. Commun., 13, 2043, https://doi.org/10.1038/s41467-022-29623-8, 2022.
Cohen, J. B. and Prinn, R. G.: Development of a fast, urban chemistry metamodel for inclusion in global models, Atmos. Chem. Phys., 11, 7629–7656, https://doi.org/10.5194/acp-11-7629-2011, 2011.
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Sci. Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020.
Dai, M., Niu, Q., Wu, S., Lin, Y., Biswas, J. K., and Yang, C.: Hydroxyl radicals in ozone-based advanced oxidation of organic contaminants: A review, Environ. Chem. Lett., 22, 3059–3106, https://doi.org/10.1007/s10311-024-01772-w, 2024.
Dienhart, D., Crowley, J. N., Bourtsoukidis, E., Edtbauer, A., Eger, P. G., Ernle, L., Harder, H., Hottmann, B., Martinez, M., Parchatka, U., Paris, J.-D., Pfannerstill, E. Y., Rohloff, R., Schuladen, J., Stönner, C., Tadic, I., Tauer, S., Wang, N., Williams, J., Lelieveld, J., and Fischer, H.: Measurement report: Observation-based formaldehyde production rates and their relation to OH reactivity around the Arabian Peninsula, Atmos. Chem. Phys., 21, 17373–17388, https://doi.org/10.5194/acp-21-17373-2021, 2021.
Estrada, L. A., Varon, D. J., Sulprizio, M., Nesser, H., Chen, Z., Balasus, N., Hancock, S. E., He, M., East, J. D., Mooring, T. A., Oort Alonso, A., Maasakkers, J. D., Aben, I., Baray, S., Bowman, K. W., Worden, J. R., Cardoso-Saldaña, F. J., Reidy, E., and Jacob, D. J.: Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations, Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, 2025.
Fan, H., Wang, Y., Zhao, C., Yang, Y., Yang, X., Sun, Y., and Jiang, S.: The Role of Primary Emission and Transboundary Transport in the Air Quality Changes During and After the COVID-19 Lockdown in China, Geophys. Res. Lett., 48, e2020GL091065, https://doi.org/10.1029/2020GL091065, 2021.
Feng, Y.: Quantifying and Attributing CO Emissions Using TROPOMI-based CO retrievals and Explicit Observational Uncertainty over Rapidly Developing Central Asia, figshare [data set], https://doi.org/10.6084/m9.figshare.28462817, 2025.
Hanaoka, T. and Masui, T.: Exploring effective short-lived climate pollutant mitigation scenarios by considering synergies and trade-offs of combinations of air pollutant measures and low carbon measures towards the level of the 2 C target in Asia, Environ. Pollut., 261, 113650, https://doi.org/10.1016/j.envpol.2019.113650, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Holloway, T., Levy, H., and Kasibhatla, P.: Global distribution of carbon monoxide, J. Geophys. Res. Atmos., 105, 12123–12147, https://doi.org/10.1029/1999JD901173, 2000.
Hu, W., Qin, K., Lu, F., Li, D., Cohen, J. B.: Merging TROPOMI and eddy covariance observations to quantify 5 years of daily CH4 emissions over coal-mine dominated region, Int. J. Coal Sci. Technol., 11, 56, https://doi.org/10.1007/s40789-024-00700-1, 2024.
Inness, A., Aben, I., Ades, M., Borsdorff, T., Flemming, J., Jones, L., Landgraf, J., Langerock, B., Nedelec, P., Parrington, M., and Ribas, R.: Assimilation of S5P/TROPOMI carbon monoxide data with the global CAMS near-real-time system, Atmos. Chem. Phys., 22, 14355–14376, https://doi.org/10.5194/acp-22-14355-2022, 2022.
Jiang, Z., Jones, D. B. A., Worden, J., Worden, H. M., Henze, D. K., and Wang, Y. X.: Regional data assimilation of multi-spectral MOPITT observations of CO over North America, Atmos. Chem. Phys., 15, 6801–6814, https://doi.org/10.5194/acp-15-6801-2015, 2015.
Joshi, A., Pathak, M., Kuttippurath, J., and Patel, V.: Adoption of cleaner technologies and reduction in fire events in the hotspots lead to global decline in carbon monoxide, Chemosphere, 336, 139259, https://doi.org/10.1016/j.chemosphere.2023.139259, 2023.
Khalil, M. A. K. and Rasmussen, R. A.: The global cycle of carbon monoxide: Trends and mass balance, Chemosphere, 20, 227–242, https://doi.org/10.1016/0045-6535(90)90098-E, 1990.
Khalil, M. A. K. and Rasmussen, R. A.: Carbon monoxide in the Earth's atmosphere: Indications of a global increase, Nature, 332, 242–245, https://doi.org/10.1038/332242a0, 1988.
Khalil, M. A. K. and Rasmussen, R.: Carbon monoxide in the earth's atmosphere: increasing trend, Science, 224, 54–56, https://doi.org/10.1126/science.224.4644.54, 1984.
Kopacz, M., Jacob, D. J., Fisher, J. A., Logan, J. A., Zhang, L., Megretskaia, I. A., Yantosca, R. M., Singh, K., Henze, D. K., Burrows, J. P., Buchwitz, M., Khlystova, I., McMillan, W. W., Gille, J. C., Edwards, D. P., Eldering, A., Thouret, V., and Nedelec, P.: Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES), Atmos. Chem. Phys., 10, 855–876, https://doi.org/10.5194/acp-10-855-2010, 2010.
Koppmann, R., von Czapiewski, K., and Reid, J. S.: A review of biomass burning emissions, part I: gaseous emissions of carbon monoxide, methane, volatile organic compounds, and nitrogen containing compounds, Atmos. Chem. Phys. Discuss., 5, 10455–10516, https://doi.org/10.5194/acpd-5-10455-2005, 2005.
Landgraf, J., aan de Brugh, J., Scheepmaker, R., Borsdorff, T., Hu, H., Houweling, S., Butz, A., Aben, I., and Hasekamp, O.: Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements, Atmos. Meas. Tech., 9, 4955–4975, https://doi.org/10.5194/amt-9-4955-2016, 2016.
Landgraf, J., Borsdorff, T., Houweling, S., and Hasekamp, O. P.: Algorithm Theoretical Baseline Document for Sentinel-5 Precursor: Carbon Monoxide Total Column Retrieval, https://sentiwiki.copernicus.eu/__attachments/a_902399acf3f18 b3d1263d39b8a7f0c5ab8d981d352af2e86fe97e0d7a0cef839/ SRON-S5P-LEV2-RP-002 - Sentinel-5P TROPOMI ATBD Carbon Monoxide Total Column Retrieval 2024 - 2.5.0.pdf (last access: 22 October 2026), 2024.
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem. Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016.
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.
Li, X., Cohen, J. B., Tiwari, P., Wu, L., Wang, S., He, Q., Yang, H., and Qin, K.: Space-based inversion reveals underestimated carbon monoxide emissions over Shanxi, Commun. Earth Environ., 6, 1–14, https://doi.org/10.1038/s43247-025-02301-5, 2025.
Liang, L., Wang, Z., and Li, J.: The effect of urbanization on environmental pollution in rapidly developing urban agglomerations, J. Clean. Prod., 237, 117649, https://doi.org/10.1016/j.jclepro.2019.117649, 2019.
Lin, C., Cohen, J. B., Wang, S., Lan, R., and Deng, W.: A new perspective on the spatial, temporal, and vertical distribution of biomass burning: quantifying a significant increase in CO emissions, Environ. Res. Lett., 15, https://doi.org/10.1088/1748-9326/abaa7a, 2020.
Liu, Q., Gao, Y., Huang, W., Ling, Z., Wang, Z., and Wang, X.: Carbonyl compounds in the atmosphere: A review of abundance, source and their contributions to O3 and SOA formation, Atmos. Res., 274, 106184, https://doi.org/10.1016/j.atmosres.2022.106184, 2022.
Liu, Z., Cohen, J. B., Wang, S., Wang, X., Tiwari, P., and Qin, K.: Remotely sensed BC columns over rapidly changing Western China show significant decreases in mass and inconsistent changes in number, size, and mixing properties due to policy actions, Npj Clim Atmos Sci, 7, 124, https://doi.org/10.1038/s41612-024-00663-9, 2024.
Liu, Z., Cohen, J. B., Tiwari, P., Guan, L., Wang, S., Li, Z., and Qin, K.: A global black carbon dataset of column concentration and microphysical information derived from MISR multi-band observations and Mie scattering simulations, Earth Syst. Sci. Data, 18, 507–533, https://doi.org/10.5194/essd-18-507-2026, 2026.
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021.
Lu, L., Cohen, J. B., Qin, K., Tiwari, P., Hu, W., Gao, H., and Zheng, B.: Observational Uncertainty Causes Over Half of Top-Down Nox Emissions Over Northern China to Be Either Biased or Unreliable, https://doi.org/10.2139/ssrn.4984749, 12 October 2024, 2024.
Lu, L., Cohen, J. B., Qin, K., Li, X., and He, Q.: Identifying missing sources and reducing NOx emissions uncertainty over China using daily satellite data and a mass-conserving method, Atmos. Chem. Phys., 25, 2291–2309, https://doi.org/10.5194/acp-25-2291-2025, 2025a.
Lu, L., Cohen, J. B., Qin, K., Tiwari, P., Hu, W., Gao, H., and Zheng, B.: New Perspective on Using Observational Uncertainty to Improve Reliability of NOx Emissions Over Northern China, IEEE T. Geosci. Remote, 63, https://doi.org/10.1109/TGRS.2025.3620116, 2025b.
Lui, K. H., Ho, S. S. H., Louie, P. K. K., Chan, C. S., Lee, S. C., Hu, D., Chan, P. W., Lee, J. C. W., and Ho, K. F.: Seasonal behavior of carbonyls and source characterization of formaldehyde (HCHO) in ambient air, Atmos. Environ., 152, 51–60, https://doi.org/10.1016/j.atmosenv.2016.12.004, 2017.
Madrazo, J., Clappier, A., Belalcazar, L. C., Cuesta, O., Contreras, H., and Golay, F.: Screening differences between a local inventory and the Emissions Database for Global Atmospheric Research (EDGAR), Sci. Total Environ., 631–632, 934–941, https://doi.org/10.1016/j.scitotenv.2018.03.094, 2018.
Martínez-Alonso, S., Deeter, M., Worden, H., Borsdorff, T., Aben, I., Commane, R., Daube, B., Francis, G., George, M., Landgraf, J., Mao, D., McKain, K., and Wofsy, S.: 1.5 years of TROPOMI CO measurements: comparisons to MOPITT and ATom, Atmos. Meas. Tech., 13, 4841–4864, https://doi.org/10.5194/amt-13-4841-2020, 2020.
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.
Munsif, R., Zubair, M., Aziz, A., and Zafar, M. N.: Industrial air emission pollution: potential sources and sustainable mitigation, in: Environmental Emissions, IntechOpen, https://doi.org/10.5772/intechopen.93104, 2021.
National Energy Administration: Action Plan for Clean and Efficient Utilization of Coal (2015–2020), http://nyj.jcgov.gov.cn/mtqjgxly/zcwj/202310/t20231005_1863200.shtml (last access: 30 January 2025), 2015.
Olivier, J. G. J., Bloos, J. P. J., Berdowski, J. J. M., Visschedijk, A. J. H., and Bouwman, A. F.: A 1990 global emission inventory of anthropogenic sources of carbon monoxide on 1°×1° developed in the framework of EDGAR/GEIA, Chemosphere-Glob. Change Sci., 1, 1–17, https://doi.org/10.1016/S1465-9972(99)00019-7, 1999.
Patra, P. K., Krol, M. C., Montzka, S. A., Arnold, T., Atlas, E. L., Lintner, B. R., Stephens, B. B., Xiang, B., Elkins, J. W., Fraser, P. J., Ghosh, A., Hintsa, E. J., Hurst, D. F., Ishijima, K., Krummel, P. B., Miller, B. R., Miyazaki, K., Moore, F. L., Mühle, J., O'Doherty, S., Prinn, R. G., Steele, L. P., Takigawa, M., Wang, H. J., Weiss, R. F., Wofsy, S. C., and Young, D.: Observational evidence for interhemispheric hydroxyl-radical parity, Nature, 513, 219–223, https://doi.org/10.1038/nature13721, 2014.
Pirajno, F., Seltmann, R., and Yang, Y.: A review of mineral systems and associated tectonic settings of northern Xinjiang, NW China, Geosci. Front., 2, 157–185, https://doi.org/10.1016/j.gsf.2011.03.006, 2011.
Qin, K., Lu, L., Liu, J., He, Q., Shi, J., Deng, W., Wang, S., and Cohen, J. B.: Model-free daily inversion of NOx emissions using TROPOMI (MCMFE-NOx) and its uncertainty: Declining regulated emissions and growth of new sources, Remote Sens. Environ., 295, 113720, https://doi.org/10.1016/j.rse.2023.113720, 2023.
Rao, K. S.: Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure Appl. Geophys., 162, 1893–1917, https://doi.org/10.1007/s00024-005-2697-4, 2005.
Reilly, J. M., Edmonds, J. A., Gardner, R. H., and Brenkert, A. L.: Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model, Energy J., 8, 1–30, https://doi.org/10.5547/ISSN0195-6574-EJ-Vol8-No3-1, 1987.
Röckmann, T., Jöckel, P., Gros, V., Bräunlich, M., Possnert, G., and Brenninkmeijer, C. A. M.: Using 14C, 13C, 18O and 17O isotopic variations to provide insights into the high northern latitude surface CO inventory, Atmos. Chem. Phys., 2, 147–159, https://doi.org/10.5194/acp-2-147-2002, 2002.
Romahn, F., Pedergnana, M., Loyola, D., Apituley, A., Sneep, M., and Veefkind, J. P.: Sentinel-5 precursor/TROPOMI Level 2 Product User Manual Formaldehyde HCHO, https://sentiwiki.copernicus.eu/__attachments/a_3c840fd5391de
ffd212a14d47a98a8b9978ccff454a58304166cc10e75072f9e/
S5P-L2-DLR-PUM-400F%20-%20Sentinel-5P%20Level%20
2%20Product%20User%20Manual%20Formaldehyde%20
HCHO%202022%20-%202.4.pdf (last access: 12 October 2026), 2022.
Rupakheti, D., Yin, X., Rupakheti, M., Zhang, Q., Li, P., Rai, M., and Kang, S.: Spatio-temporal characteristics of air pollutants over Xinjiang, northwestern China, Environ. Pollut., 268, 115907, https://doi.org/10.1016/j.envpol.2020.115907, 2021.
Savenets, M., Dvoretska, I., Nadtochii, L., and Zhemera, N.: Comparison of TROPOMI NO, CO, HCHO, and SO data against ground-level measurements in close proximity to large anthropogenic emission sources in the example of Ukraine, Meteorol. Appl., 29, e2108, https://doi.org/10.1002/met.2108, 2022.
Schneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019.
Shen, L., Jacob, D. J., Gautam, R., Omara, M., Scarpelli, T. R., Lorente, A., Zavala-Araiza, D., Lu, X., Chen, Z., and Lin, J.: National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations, Nat. Commun., 14, 4948, https://doi.org/10.1038/s41467-023-40671-6, 2023.
Shepherd, R. H., King, M. D., Ward, A. D., Stuckey, E. J., Welbourn, R. J. L., Brough, N., Milsom, A., Pfrang, C., and Arnold, T.: The lifetimes and potential change in planetary albedo owing to the oxidation of thin surfactant organic films extracted from atmospheric aerosol by hydroxyl (OH) radicals at the air–water interface of particles, Atmos. Chem. Phys., 25, 2569–2588, https://doi.org/10.5194/acp-25-2569-2025, 2025.
Spivakovsky, C. M., Logan, J. A., Montzka, S. A., Balkanski, Y. J., Foreman-Fowler, M., Jones, D. B. A., Horowitz, L. W., Fusco, A. C., Brenninkmeijer, C. A. M., Prather, M. J., Wofsy, S. C., and McElroy, M. B.: Three-dimensional climatological distribution of tropospheric OH: Update and evaluation, J. Geophys. Res.-Atmos., 105, 8931–8980, https://doi.org/10.1029/1999jd901006, 2000.
State Council: Circular of the State Council on the Issuance of the Action Plan for Continuous Improvement of Air Quality, https://www.gov.cn/zhengce/zhengceku/202312/content_6919001.htm (last access: 22 October 2026), 2023.
Sun, J., He, Y., Ning, Y., Xue, Z., Wang, H., Zhang, Y., Ma, J., Chen, X., and Chai, F.: Pollution characteristics and sources of carbonyl compounds in a typical city of Fenwei Plain, Linfen, in summer, Environ. Pollut., 320, 120913, https://doi.org/10.1016/j.envpol.2022.120913, 2023.
Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W. D., Fuzzi, S., Gallardo, L., Kiendler-Scharr, A., and Klimont, Z.: Short-Lived Climate Forcers, in: Climate Change 2021 – The Physical Science Basis, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 817–922, https://doi.org/10.1017/9781009157896.008, 2021.
Tang, Z., Chen, J., and Jiang, Z.: Discrepancy in assimilated atmospheric CO over East Asia in 2015–2020 by assimilating satellite and surface CO measurements, Atmos. Chem. Phys., 22, 7815–7826, https://doi.org/10.5194/acp-22-7815-2022, 2022.
Tian, Y., Liu, C., Sun, Y., Borsdorff, T., Landgraf, J., Lu, X., Palm, M., and Notholt, J.: Satellite Observations Reveal a Large CO Emission Discrepancy From Industrial Point Sources Over China, Geophys. Res. Lett., 49, e2021GL097312, https://doi.org/10.1029/2021GL097312, 2022.
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012.
Wang, C., Wang, T., Wang, P., and Wang, W.: Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application, Remote Sens., 14, 214, https://doi.org/10.3390/rs14010214, 2022.
Wang, S., Cohen, J. B., Deng, W., Qin, K., and Guo, J.: Using a New Top-Down Constrained Emissions Inventory to Attribute the Previously Unknown Source of Extreme Aerosol Loadings Observed Annually in the Monsoon Asia Free Troposphere, Earths Future, 9, e2021EF002167, https://doi.org/10.1029/2021EF002167, 2021.
Wang, S., Cohen, J. B., Guan, L., Lu, L., Tiwari, P., and Qin, K.: Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions, Npj Clim. Atmos. Sci., 8, 87, https://doi.org/10.1038/s41612-025-00977-2, 2025.
Wang, S., Cohen, J. B., Qin, K., Guan, L., Tiwari, P., Feng, Y., Li, X., He, Q., Li, Z., Fan, C., and Wang, D.: Synergistic analysis and vertical transport attribution of carbon monoxide surface concentrations and satellite column loadings over China, Remote Sens. Environ., 344, 115562, https://doi.org/10.1016/j.rse.2026.115562, 2026.
Wei, C., Wang, M., Fu, Q., Dai, C., Huang, R., and Bao, Q.: Temporal characteristics of greenhouse gases (CO2 and CH4) in the megacity Shanghai, China: Association with air pollutants and meteorological conditions, Atmos. Res., 235, 104759, https://doi.org/10.1016/j.atmosres.2019.104759, 2020.
Worden, H. M., Deeter, M. N., Frankenberg, C., George, M., Nichitiu, F., Worden, J., Aben, I., Bowman, K. W., Clerbaux, C., Coheur, P. F., de Laat, A. T. J., Detweiler, R., Drummond, J. R., Edwards, D. P., Gille, J. C., Hurtmans, D., Luo, M., Martínez-Alonso, S., Massie, S., Pfister, G., and Warner, J. X.: Decadal record of satellite carbon monoxide observations, Atmos. Chem. Phys., 13, 837–850, https://doi.org/10.5194/acp-13-837-2013, 2013.
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, https://www.who.int/publications/i/item/9789240034228 (last access: 22 October 2026), 2021.
Yang, Y., Wang, Y., Huang, W., Yao, D., Zhao, S., Wang, Y., Ji, D., Zhang, R., and Wang, Y.: Parameterized atmospheric oxidation capacity and speciated OH reactivity over a suburban site in the North China Plain: A comparative study between summer and winter, Sci. Total Environ., 773, 145264, https://doi.org/10.1016/j.scitotenv.2021.145264, 2021.
Yin, Y., Chevallier, F., Ciais, P., Broquet, G., Fortems-Cheiney, A., Pison, I., and Saunois, M.: Decadal trends in global CO emissions as seen by MOPITT, Atmos. Chem. Phys., 15, 13433–13451, https://doi.org/10.5194/acp-15-13433-2015, 2015.
Zeng, Q. and Shen, L.: Experimental study on the oxidation kinetics of coal in typical coal mining areas of the Southern Junggar coalfield, Xinjiang, China, Int. J. Coal Sci. Technol., 9, 78, https://doi.org/10.1007/s40789-022-00542-9, 2022.
Zhang, F., Zhou, L. X., Novelli, P. C., Worthy, D. E. J., Zellweger, C., Klausen, J., Ernst, M., Steinbacher, M., Cai, Y. X., Xu, L., Fang, S. X., and Yao, B.: Evaluation of in situ measurements of atmospheric carbon monoxide at Mount Waliguan, China, Atmos. Chem. Phys., 11, 5195–5206, https://doi.org/10.5194/acp-11-5195-2011, 2011.
Zhang, L., Jacob, D. J., Bowman, K. W., Logan, J. A., Turquety, S., Hudman, R. C., Li, Q., Beer, R., Worden, H. M., Worden, J. R., Rinsland, C. P., Kulawik, S. S., Lampel, M. C., Shephard, M. W., Fisher, B. M., Eldering, A., and Avery, M. A.: Ozone-CO correlations determined by the TES satellite instrument in continental outflow regions, Geophys. Res. Lett., 33, https://doi.org/10.1029/2006GL026399, 2006.
Zhao, Y., Nielsen, C. P., McElroy, M. B., Zhang, L., and Zhang, J.: CO emissions in China: Uncertainties and implications of improved energy efficiency and emission control, Atmos. Environ., 49, 103–113, https://doi.org/10.1016/j.atmosenv.2011.12.015, 2012.
Zhao, Z., He, Q., Lu, Z., Zhao, Q., and Wang, J.: Analysis of Atmospheric CO2 and CO at Akedala Atmospheric Background Observation Station, a Regional Station in Northwestern China, Int. J. Environ. Res. Public. Health, 19, 6948, https://doi.org/10.3390/ijerph19116948, 2022.
Zheng, B., Chevallier, F., Ciais, P., Yin, Y., Deeter, M. N., Worden, H. M., Wang, Y., Zhang, Q., and He, K.: Rapid decline in carbon monoxide emissions and export from East Asia between years 2005 and 2016, Environ. Res. Lett., 13, 044007, https://doi.org/10.1088/1748-9326/aab2b3, 2018.
Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., Lei, Y., and He, K.: Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020, Earth Syst. Sci. Data, 13, 2895–2907, https://doi.org/10.5194/essd-13-2895-2021, 2021.
Zheng, B., Cohen, J. B., Lu, L., Hu, W., Tiwari, P., Lolli, S., Garzelli, A., Su, H., and Qin, K.: How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions, Atmos. Chem. Phys., 26, 1931–1946, https://doi.org/10.5194/acp-26-1931-2026, 2026.
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.
Carbon monoxide pollution is difficult to track across Central Asia, where monitoring is...
Altmetrics
Final-revised paper
Preprint