SO₂ Emission Source Optimization And Numerical Simulation Based On WRF-Chem Model And Variational Assimilation Metho

Emission inventories are essential for modeling studies and pollution control,but traditional emission inventories are usually updated after a few years by the statistics of“bottom-up”approach from the energy consumption in provinces,cities,and counties.However,the real emissions have varied significantly year by year,due to national pollution control policies and accidental special events.Data assimilation（DA）provides a“top-down”approach to optimize emissions by assimilating observation data.In this study,we developed two new“top-down”approach to optimize sulfur dioxide（SO₂）emission using the Weather Research and Forecasting model coupled with Chemistry（WRF-Chem）and variational method.The advance data assimilation systems are beneficial for the understanding the effectiveness and value of emission inventories and air quality model.In this study,we developed a three-dimension variational（3DVAR）Concentrations Covert to Emission（3DVAR-CCE）method to optimize and evaluate the SO₂emission inventory using WRF-Chem and a 3DVAR data assimilation system.The 3DVAR-CCE method was used to optimize and evaluate the emissions of October 2015,and Multi-resolution Emission Inventory for China（MEIC）based on 2010 year（MEIC＿2010）was set as the prior emission.Compared with the control experiment with MEIC 2010,the bias and root-mean-squared error of the optimized emission experiment decreased by 71.2%and 25.9%,and the correlation coefficients increased by 50.0%.For the Southern China,the bias and root-mean-square errors decreased by 76.4–94.2%and 29.0–45.7%,respectively,and the correlation coefficients increased by 23.5–53.4%.From the perspective of inter-annual variation,the optimized SO₂emissions in China from 2015 to 2018 were 1.39×10⁹,1.36×10⁹,1.31×10⁹and 1.16×10⁹kg,respectively,indicating the remarkable effect of China’s emission reduction policies in recent years.A four-dimensional variational assimilation（4DVAR）system based on the“top-down”approach was developed to optimize SO₂emissions by assimilating SO₂concentrations data from surface observational stations.The 4DVAR system was applied to obtain the SO₂emissions during the early period of Corona Virus Disease 2019（COVID-19）pandemic（from17 January to 7 February,2020）,and the same period in 2019 over China.The results showed that the average MEIC＿2016,2019,and 2020 emissions were 42.2×10⁶,40.1×10⁶,and36.4×10⁶kg d^-1.The 2020 emissions decreased by 9.2%in relation to the COVID-19lockdown compared with those in 2019.For Central China,where the lockdown measures were strict,the mean 2020 emission decreased by 21.0%compared with 2019 emissions.Three forecast experiments were conducted using the emissions of MEIC＿2016,2019,and2020 to demonstrate the effects of optimized emissions.The root-mean-square error in the experiments using 2019 and 2020 emissions decreased by 28.1%and 50.7%,and the correlation coefficient increased by 89.5%and 205.9%compared with the experiment using MEIC＿2016.For Central China,the average root-mean-square error in the experiments with2019 and 2020 emissions decreased by 48.8%and 77.0%,and the average correlation coefficient increased by 44.3%and 238.7%.We evaluated the volcanic SO₂emission during Tanga volcano eruption based on 4DVAR method and WRF-Chem model.The three kinds of hourly and vertical profile SO₂volcano emissions was obtained by assimilated TROPOspheric Monitoring Instrumen（TROPOMI）,GF-5,both TROPOMI and GF-5 SO₂column concentrations.The total amount of 3 kinds optimized emissions were 0.174 Tg,0.187 Tg and 0.172 Tg.Four set of forecast experiments were conducted based on the background emission and three optimized emissions.Compared with the control experiment,the root-mean-square error with the optimized emission experiments at 0100 UTC on January 16,2022 decreased by 71.09%,66.19%and 69.84%,and the correlation coefficients increased by 19.05%,9.52%and 17.46%,respectively.The results indicate that 4DVAR system and satellite observation data can reduce the uncertainty of volcanic emissions and further improve the understanding of volcanic eruption and its impact on global climate change.