Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5287-2024
https://doi.org/10.5194/essd-16-5287-2024
Data description paper
 | 
15 Nov 2024
Data description paper |  | 15 Nov 2024

The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2

Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen

Related authors

How can we trust TROPOMI based Methane Emissions Estimation: Calculating Emissions over Unidentified Source Regions
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1446,https://doi.org/10.5194/egusphere-2025-1446, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Identifying missing sources and reducing NOx emissions uncertainty over China using daily satellite data and a mass-conserving method
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
Surface Observation Constrained High Frequency Coal Mine Methane Emissions in Shanxi China Reveal More Emissions than Inventories, Consistency with Satellite Inversion
Fan Lu, Kai Qin, Jason Blake Cohen, Qin He, Pravash Tiwari, Wei Hu, Chang Ye, Yanan Shan, Qing Xu, Shuo Wang, and Qiansi Tu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1784,https://doi.org/10.5194/egusphere-2024-1784, 2024
Short summary
Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method
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
Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources
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

Related subject area

Domain: ESSD – Atmosphere | Subject: Energy and Emissions
A daily sunshine duration (SD) dataset in China from Himawari AHI imagery (2016–2023)
Zhanhao Zhang, Shibo Fang, and Jiahao Han
Earth Syst. Sci. Data, 17, 1427–1439, https://doi.org/10.5194/essd-17-1427-2025,https://doi.org/10.5194/essd-17-1427-2025, 2025
Short summary
Residential heating emissions for the Western Balkans
Christian Asker, Eef van Dongen, and Olivier Tasse
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-462,https://doi.org/10.5194/essd-2024-462, 2025
Revised manuscript accepted for ESSD
Short summary
Four-dimensional aircraft emission inventory dataset of Landing and takeoff cycle in China (2019–2023)
Jianlei Lang, Zekang Yang, Ying Zhou, Chaoyu Wen, and Xiaoqing Cheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-494,https://doi.org/10.5194/essd-2024-494, 2024
Revised manuscript accepted for ESSD
Short summary
In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes
Astrid Lampert, Rudolf Hankers, Thomas Feuerle, Thomas Rausch, Matthias Cremer, Maik Angermann, Mark Bitter, Jonas Füllgraf, Helmut Schulz, Ulf Bestmann, and Konrad B. Bärfuss
Earth Syst. Sci. Data, 16, 4777–4792, https://doi.org/10.5194/essd-16-4777-2024,https://doi.org/10.5194/essd-16-4777-2024, 2024
Short summary
Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020
Liu Yan, Qiang Zhang, Bo Zheng, and Kebin He
Earth Syst. Sci. Data, 16, 4497–4509, https://doi.org/10.5194/essd-16-4497-2024,https://doi.org/10.5194/essd-16-4497-2024, 2024
Short summary

Cited articles

Abdulmanov, R., Miftakhov, I., Ishbulatov, M., Galeev, E., and Shafeeva, E.: Comparison of the effectiveness of GIS-based interpolation methods for estimating the spatial distribution of agrochemical soil properties, Environ. Technol. Innov., 24, 101970, https://doi.org/10.1016/j.eti.2021.101970, 2021. 
Achite, M., Katipoğlu, Okan Mert, Javari, M., and Caloiero, T.: Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria, Theor. Appl. Climatol., 155, 1139–1166, https://doi.org/10.1007/s00704-023-04685-w, 2024. 
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., and Beckers, J.-M.: Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF, Ocean Sci., 5, 475–485, https://doi.org/10.5194/os-5-475-2009, 2009. 
Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens. Environ., 180, 137–145, https://doi.org/10.1016/j.rse.2016.02.044, 2016. 
Baek, K. and Kim, J.: Analysis of Characteristics of Satellite-derived Air Pollutant over Southeast Asia and Evaluation of Tropospheric Ozone using Statistical Methods, J. Korean Soc. Atmos. Environ., 27, 650–662, https://doi.org/10.5572/kosae.2011.27.6.650, 2011. 
Download
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.
Share
Altmetrics
Final-revised paper
Preprint