Articles | Volume 18, issue 10
https://doi.org/10.5194/acp-18-7095-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-18-7095-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Vertical distribution of aerosols over the Maritime Continent during El Niño
School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou,
China
Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou,
China
Daniel Hui Loong Ng
Tropical Marine Science Institute, National University of Singapore,
Singapore
Alan Wei Lun Lim
The Chinese University of Hong Kong, Hong Kong, China
Xin Rong Chua
Princeton University, Princeton, NJ, USA
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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
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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.
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There are always gaps in satellite records of air pollution because of factors such as clouds and operating conditions. We used a physics-aware deep learning method, producing a gapless 0.05° hourly dataset of tropospheric nitrogen dioxide over Asian pollution hotspots from 2019 to 2024. The dataset can capture sub-daily changes, revealing how traffic, industry, and clean-air policies shaped pollution, supporting health and environment studies.
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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.
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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
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5890, https://doi.org/10.5194/egusphere-2025-5890, 2026
Short summary
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Carbon monoxide emissions in Central Asia's coal-heavy regions, like Xinjiang Province and Kazakhstan, are poorly tracked. Using satellite data and a new approach, this study maps daily emissions (2019–2024) while addressing measurement errors. About 69 % of estimates were unreliable. Underground coal fires, often ignored, emit as much CO as power plants. Emissions peaked in 2019, dropped until 2022, then rose again, linking to policy changes and economic shifts.
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Short summary
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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.
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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.
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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
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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.
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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.
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.
Hongrui Gao, Qin He, Kai Qin, Jhoon Kim, Diego Loyola, Pravash Tiwari, Lingxiao Lu, and Jason Blake Cohen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-443, https://doi.org/10.5194/essd-2026-443, 2026
Revised manuscript under review for ESSD
Short summary
Short summary
There are always gaps in satellite records of air pollution because of factors such as clouds and operating conditions. We used a physics-aware deep learning method, producing a gapless 0.05° hourly dataset of tropospheric nitrogen dioxide over Asian pollution hotspots from 2019 to 2024. The dataset can capture sub-daily changes, revealing how traffic, industry, and clean-air policies shaped pollution, supporting health and environment studies.
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.
Ye Feng, Jason Blake Cohen, Xiaolu Li, Lingxiao Lu, Zhewen Liu, Lei Wang, Jian Liu, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-5890, https://doi.org/10.5194/egusphere-2025-5890, 2026
Short summary
Short summary
Carbon monoxide emissions in Central Asia's coal-heavy regions, like Xinjiang Province and Kazakhstan, are poorly tracked. Using satellite data and a new approach, this study maps daily emissions (2019–2024) while addressing measurement errors. About 69 % of estimates were unreliable. Underground coal fires, often ignored, emit as much CO as power plants. Emissions peaked in 2019, dropped until 2022, then rose again, linking to policy changes and economic shifts.
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.
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.
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.
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Short summary
Measured aerosol heights over the Maritime Continent are higher than previously thought, with 61 to 83 % of aerosols above the boundary layer. These aerosols should hence have a larger impact on the climate. The use of a plume rise model cannot match the measurements, unless the measured fire energy is increased by 0–60 %. Furthermore, the model is too spread, indicating the importance of including convection and aerosol–radiation interactions. Significant model improvements will be required.
Measured aerosol heights over the Maritime Continent are higher than previously thought, with 61...
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