Articles | Volume 23, issue 14
https://doi.org/10.5194/acp-23-8001-2023
© Author(s) 2023. 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-23-8001-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China
Xiaolu Li
Institute of Environmental Science, Shanxi University, Taiyuan,
030006, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou, 221116, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou, 221116, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou, 221116, China
Hong Geng
Institute of Environmental Science, Shanxi University, Taiyuan,
030006, China
Xiaohui Wu
Shanxi Dadi Ecology and Environment Technology Research Institute
Ltd., Taiyuan, 030000, China
Liling Wu
School of Environment, Tsinghua University, Beijing, 10084, China
Chengli Yang
Shanxi Dadi Ecology and Environment Technology Research Institute
Ltd., Taiyuan, 030000, China
Rui Zhang
Shanxi Institute of Ecology and Environment Planning and Technology, Taiyuan, 030002, China
Liqin Zhang
Shanxi Institute of Ecology and Environment Monitoring and Emergency Response Center, Taiyuan, 030027, China
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Cited
10 citations as recorded by crossref.
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Identification of NO emissions and source characteristics by TROPOMI observations – A case study in north-central Henan, China H. Sheng et al. 10.1016/j.scitotenv.2024.172779
- New top-down estimation of daily mass and number column density of black carbon driven by OMI and AERONET observations J. Liu et al. 10.1016/j.rse.2024.114436
- 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 Z. Liu et al. 10.1038/s41612-024-00663-9
- Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. 10.5194/acp-24-3009-2024
- Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas F. Deng et al. 10.3390/rs16101785
- Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. 10.1016/j.ese.2024.100425
- Classifying and quantifying decadal changes in wet deposition over Southeast and East Asia using EANET, OMI, and GPCP S. Wang et al. 10.1016/j.atmosres.2024.107400
- Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning S. Shetty et al. 10.1016/j.rse.2024.114321
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. 10.5194/essd-16-5287-2024
10 citations as recorded by crossref.
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Identification of NO emissions and source characteristics by TROPOMI observations – A case study in north-central Henan, China H. Sheng et al. 10.1016/j.scitotenv.2024.172779
- New top-down estimation of daily mass and number column density of black carbon driven by OMI and AERONET observations J. Liu et al. 10.1016/j.rse.2024.114436
- 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 Z. Liu et al. 10.1038/s41612-024-00663-9
- Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. 10.5194/acp-24-3009-2024
- Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas F. Deng et al. 10.3390/rs16101785
- Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. 10.1016/j.ese.2024.100425
- Classifying and quantifying decadal changes in wet deposition over Southeast and East Asia using EANET, OMI, and GPCP S. Wang et al. 10.1016/j.atmosres.2024.107400
- Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning S. Shetty et al. 10.1016/j.rse.2024.114321
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. 10.5194/essd-16-5287-2024
Latest update: 23 Nov 2024
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.
Remotely sensed NO2 and surface NOx are combined with a mathematical method to estimate daily...
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