Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations
In this research, a new time-resolved emission inversion system was developed to investigate variations in SO<sub>2</sub> emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically opti...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/13/3/470 |
| _version_ | 1797472935175258112 |
|---|---|
| author | Jingyue Mo Sunling Gong Jianjun He Lei Zhang Huabing Ke Xingqin An |
| author_facet | Jingyue Mo Sunling Gong Jianjun He Lei Zhang Huabing Ke Xingqin An |
| author_sort | Jingyue Mo |
| collection | DOAJ |
| description | In this research, a new time-resolved emission inversion system was developed to investigate variations in SO<sub>2</sub> emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO<sub>2</sub> inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO<sub>2</sub> emission variations and the corresponding prediction improvement. The SO<sub>2</sub> emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO<sub>2</sub> emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO<sub>2</sub> emission inventory significantly improved the model SO<sub>2</sub> predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM<sub>2.5</sub> was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system. |
| first_indexed | 2024-03-09T20:08:09Z |
| format | Article |
| id | doaj.art-c5718a870b7248b8b079bc587028cf7e |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T20:08:09Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-c5718a870b7248b8b079bc587028cf7e2023-11-24T00:27:27ZengMDPI AGAtmosphere2073-44332022-03-0113347010.3390/atmos13030470Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based ObservationsJingyue Mo0Sunling Gong1Jianjun He2Lei Zhang3Huabing Ke4Xingqin An5Climate and Weather Disasters Collaborative Innovation Center, Nanjing University of Information Science &Technology, Nanjing 210044, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaClimate and Weather Disasters Collaborative Innovation Center, Nanjing University of Information Science &Technology, Nanjing 210044, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaIn this research, a new time-resolved emission inversion system was developed to investigate variations in SO<sub>2</sub> emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO<sub>2</sub> inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO<sub>2</sub> emission variations and the corresponding prediction improvement. The SO<sub>2</sub> emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO<sub>2</sub> emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO<sub>2</sub> emission inventory significantly improved the model SO<sub>2</sub> predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM<sub>2.5</sub> was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.https://www.mdpi.com/2073-4433/13/3/470lockdown effectsemission inversionSO<sub>2</sub> emission4DVar assimilation |
| spellingShingle | Jingyue Mo Sunling Gong Jianjun He Lei Zhang Huabing Ke Xingqin An Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations Atmosphere lockdown effects emission inversion SO<sub>2</sub> emission 4DVar assimilation |
| title | Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations |
| title_full | Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations |
| title_fullStr | Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations |
| title_full_unstemmed | Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations |
| title_short | Quantification of SO<sub>2</sub> Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations |
| title_sort | quantification of so sub 2 sub emission variations and the corresponding prediction improvements made by assimilating ground based observations |
| topic | lockdown effects emission inversion SO<sub>2</sub> emission 4DVar assimilation |
| url | https://www.mdpi.com/2073-4433/13/3/470 |
| work_keys_str_mv | AT jingyuemo quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations AT sunlinggong quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations AT jianjunhe quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations AT leizhang quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations AT huabingke quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations AT xingqinan quantificationofsosub2subemissionvariationsandthecorrespondingpredictionimprovementsmadebyassimilatinggroundbasedobservations |