Y. Chen
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2 records found
1
Journal article
(2026)
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Y. Chen, Ronald J. van der A, Jieying Ding, Henk Eskes, Felipe Cifuentes, Pieternel Felicitas Levelt
India has high sulfur dioxide (SO2) emissions, primarily due to its extensive coal-fired power sector. SO2 column observations from Sentinel-5P Tropospheric Monitoring Instrument (TROPOMI) enables observation-based emission estimates using inversion techniques. Among inversion methods, the flux-divergence method is particularly sensitive to point source emissions and well-suited for estimating SO2 emissions in India. However, when applied to satellite observations, this method tends to spatially spread calculated emissions into neighboring grid cells around the source. This spreading effect weakens the emission signal at the exact source location, making precise quantification of emissions more difficult. In this paper, we design a sharpening algorithm to reverse the spreading and sharpen the emission signals while conserving total mass of the emissions. We apply the algorithm on gridded SO2 emissions at a high spatial resolution of 0.025° × 0.025° (≈ 2.5 km × 2.5 km) derived from TROPOMI observations that have a typical mean footprint size of 6.0 km × 6.0 km. After sharpening, the effective spatial resolution of the emissions matches the grid cell resolution. Emissions from point sources increase at their exact locations, while emissions in neighboring grid cells decrease. In the resulting SO2 emission inventory, about 80 % of coal-fired power plants with capacities above 100 MW are detected at their correct location, while the remaining 20 % fall below the detection threshold. The detected power plants account for 99 % of India's total coal-based power generation. We also identify twenty two previously unreported SO2 point sources, including coal-based thermal power plants, cement factories, crude oil production facilities, chemical fertilizers factory, and copper, steel, and aluminum industries. This sharpening algorithm improves emission detection and can also be extended to other pollutants emitted by point sources to enhance the accuracy of emission inventories.
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India has high sulfur dioxide (SO2) emissions, primarily due to its extensive coal-fired power sector. SO2 column observations from Sentinel-5P Tropospheric Monitoring Instrument (TROPOMI) enables observation-based emission estimates using inversion techniques. Among inversion methods, the flux-divergence method is particularly sensitive to point source emissions and well-suited for estimating SO2 emissions in India. However, when applied to satellite observations, this method tends to spatially spread calculated emissions into neighboring grid cells around the source. This spreading effect weakens the emission signal at the exact source location, making precise quantification of emissions more difficult. In this paper, we design a sharpening algorithm to reverse the spreading and sharpen the emission signals while conserving total mass of the emissions. We apply the algorithm on gridded SO2 emissions at a high spatial resolution of 0.025° × 0.025° (≈ 2.5 km × 2.5 km) derived from TROPOMI observations that have a typical mean footprint size of 6.0 km × 6.0 km. After sharpening, the effective spatial resolution of the emissions matches the grid cell resolution. Emissions from point sources increase at their exact locations, while emissions in neighboring grid cells decrease. In the resulting SO2 emission inventory, about 80 % of coal-fired power plants with capacities above 100 MW are detected at their correct location, while the remaining 20 % fall below the detection threshold. The detected power plants account for 99 % of India's total coal-based power generation. We also identify twenty two previously unreported SO2 point sources, including coal-based thermal power plants, cement factories, crude oil production facilities, chemical fertilizers factory, and copper, steel, and aluminum industries. This sharpening algorithm improves emission detection and can also be extended to other pollutants emitted by point sources to enhance the accuracy of emission inventories.
Journal article
(2025)
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Yutao Chen, Ronald J. van der A, Jieying Ding, Henk Eskes, Jason E. Williams, Nicolas Theys, Athanasios Tsikerdekis, Pieternel F. Levelt
The rapid development of the economy and the implementation of environmental policies adapted in India have led to fast changes of regional SO2 emissions. We present a monthly SO2 emission inventory for India covering December 2018 to November 2023 based on the Tropospheric Monitoring Instrument (TROPOMI) Level-2 COBRA SO2 dataset, using an improved flux-divergence method and estimated local SO2 lifetime, which includes both its chemical loss and dry deposition. We update the methodology to use the daily CAMS model output estimates of the hydroxyl-radical distribution as well as the measured dry deposition velocity to account for the variability in the tropospheric SO2 lifetime. It is the first effort to derive the local SO2 lifetime for application in the divergence method. The results show the application of the local SO2 lifetime improves the accuracy of SO2 emissions estimation when compared to calculations using a constant lifetime. Our improved flux-divergence method reduced the spreading of the point-source emissions compared to the standard flux-divergence method. Our derived averaged SO2 emissions covering the recent 5 years are about 5.2 Tg yr−1 with a monthly mean uncertainty of 40 %, which is lower than the bottom-up emissions of 11.0 Tg yr−1 from CAMS-GLOB-ANT v5.3. The total emissions from the 92 largest point-source emissions are estimated to be 2.9 Tg yr−1, lower than the estimation of 5.2 Tg yr−1 from the global SO2 catalog MSAQSO2LV4. We claim that the variability in the SO2 lifetime is important to account for in estimating top-down SO2 emissions.
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The rapid development of the economy and the implementation of environmental policies adapted in India have led to fast changes of regional SO2 emissions. We present a monthly SO2 emission inventory for India covering December 2018 to November 2023 based on the Tropospheric Monitoring Instrument (TROPOMI) Level-2 COBRA SO2 dataset, using an improved flux-divergence method and estimated local SO2 lifetime, which includes both its chemical loss and dry deposition. We update the methodology to use the daily CAMS model output estimates of the hydroxyl-radical distribution as well as the measured dry deposition velocity to account for the variability in the tropospheric SO2 lifetime. It is the first effort to derive the local SO2 lifetime for application in the divergence method. The results show the application of the local SO2 lifetime improves the accuracy of SO2 emissions estimation when compared to calculations using a constant lifetime. Our improved flux-divergence method reduced the spreading of the point-source emissions compared to the standard flux-divergence method. Our derived averaged SO2 emissions covering the recent 5 years are about 5.2 Tg yr−1 with a monthly mean uncertainty of 40 %, which is lower than the bottom-up emissions of 11.0 Tg yr−1 from CAMS-GLOB-ANT v5.3. The total emissions from the 92 largest point-source emissions are estimated to be 2.9 Tg yr−1, lower than the estimation of 5.2 Tg yr−1 from the global SO2 catalog MSAQSO2LV4. We claim that the variability in the SO2 lifetime is important to account for in estimating top-down SO2 emissions.