{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:55:52Z","timestamp":1775667352710,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["41775123"],"award-info":[{"award-number":["41775123"]}]},{"name":"the National Natural Science Foundation of China","award":["42061134009"],"award-info":[{"award-number":["42061134009"]}]},{"name":"the National Natural Science Foundation of China","award":["41975167"],"award-info":[{"award-number":["41975167"]}]},{"name":"the National Natural Science Foundation of China","award":["41675004"],"award-info":[{"award-number":["41675004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Emission inventories are important for modeling studies and policy-making, but the traditional \u201cbottom-up\u201d emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a \u201ctop-down\u201d approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4\u201394.2% and 29.0\u201345.7%, respectively, and the correlation coefficients increased by 23.5\u201353.4%. This SO2 emission optimization methodology is computationally cost-effective.<\/jats:p>","DOI":"10.3390\/rs14010220","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation"],"prefix":"10.3390","volume":"14","author":[{"given":"Yiwen","family":"Hu","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Zengliang","family":"Zang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}]},{"given":"Xiaoyan","family":"Ma","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6241-1161","authenticated-orcid":false,"given":"Yanfei","family":"Liang","sequence":"additional","affiliation":[{"name":"No. 32145 Unit of PLA, Xinxiang 453000, China"}]},{"given":"Wei","family":"You","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Xiaobin","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Liqiong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Daichun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Zhendong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.atmosenv.2016.09.007","article-title":"Anthropogenic sulphur dioxide load over China as observed from different satellite sensors","volume":"145","author":"Koukouli","year":"2016","journal-title":"Atmos. 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