Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China

Abstract. Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of “bottom-up” approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the “top-down” approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 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, and 36.4×106 kg d−1. The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite 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, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) 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 RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.

[1]  Yishu Zhang,et al.  Aloft Transport of Haze Aerosols to Xuzhou, Eastern China: Optical Properties, Sources, Type, and Components , 2022, Remote. Sens..

[2]  W. You,et al.  A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations , 2022, Geoscientific Model Development.

[3]  M. Xie,et al.  Land use and anthropogenic heat modulate ozone by meteorology: a perspective from the Yangtze River Delta region , 2022, Atmospheric Chemistry and Physics.

[4]  Wei You,et al.  Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation , 2022, Remote. Sens..

[5]  Qingcun Zeng,et al.  Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants III: Emission source inversion using a double correction iterative method , 2021, Science China. Earth Sciences.

[6]  Jianping Guo,et al.  Using a New Top‐Down Constrained Emissions Inventory to Attribute the Previously Unknown Source of Extreme Aerosol Loadings Observed Annually in the Monsoon Asia Free Troposphere , 2021, Earth's Future.

[7]  G. Geng,et al.  Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020 , 2021, Earth System Science Data.

[8]  T. Niu,et al.  Study on the variation of air pollutant concentration and its formation mechanism during the COVID-19 period in Wuhan , 2021, Atmospheric Environment.

[9]  T. Nakajima,et al.  Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem , 2020, Atmospheric Chemistry and Physics.

[10]  M. Abd Elaziz,et al.  Improved ANFIS model for Forecasting Wuhan City Air Quality and Analysis COVID-19 Lockdown Impacts on Air Quality. , 2020, Environmental research.

[11]  E. Saikawa,et al.  Drivers for the poor air quality conditions in North China Plain during the COVID-19 outbreak , 2020, Atmospheric Environment.

[12]  K. Bowman,et al.  Air Quality Response in China Linked to the 2019 Novel Coronavirus (COVID‐19) Lockdown , 2020, Geophysical research letters.

[13]  K. E. Knowland,et al.  Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone , 2020, Atmospheric Chemistry and Physics.

[14]  Yunsoo Choi,et al.  Impact of the COVID-19 outbreak on air pollution levels in East Asia , 2020, Science of The Total Environment.

[15]  Christopher J. Smith,et al.  Current and future global climate impacts resulting from COVID-19 , 2020, Nature Climate Change.

[16]  Mikalai Filonchyk,et al.  Impact Assessment of COVID-19 on Variations of SO2, NO2, CO and AOD over East China , 2020, Aerosol and Air Quality Research.

[17]  Jie Guang,et al.  The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China , 2020, Remote. Sens..

[18]  M. C. Ooi,et al.  Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation , 2020, Science of The Total Environment.

[19]  S. Davis,et al.  Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China , 2020, National science review.

[20]  Yuzhong Zhang,et al.  NOx Emission Reduction and Recovery during COVID-19 in East China , 2020, Atmosphere.

[21]  Qingcun Zeng,et al.  Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants II: Iterative optimization using a positive-negative discriminant , 2020, Science China Earth Sciences.

[22]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[23]  Hengmao Wang,et al.  CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017 , 2020, Journal of Geophysical Research: Atmospheres.

[24]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[25]  Hongliang Zhang,et al.  Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak , 2020, Resources, Conservation and Recycling.

[26]  Guoqing Li,et al.  High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015 , 2020, Environmental Research Letters.

[27]  F. Cao,et al.  Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing , 2019 .

[28]  Wei Wang,et al.  Hybrid Mass Balance/4D‐Var Joint Inversion of NOx and SO2 Emissions in East Asia , 2019, Journal of geophysical research. Atmospheres : JGR.

[29]  Dan Chen,et al.  The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system , 2019, Atmospheric Chemistry and Physics.

[30]  Jeffrey L. Anderson,et al.  Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China , 2019, Journal of Geophysical Research: Atmospheres.

[31]  H. Che,et al.  Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China , 2019, Atmospheric Environment.

[32]  Liangfu Chen,et al.  Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015 , 2019, Atmospheric Chemistry and Physics.

[33]  A. Ding,et al.  The impact of multi-species surface chemical observation assimilation on air quality forecasts in China , 2018, Atmospheric Chemistry and Physics.

[34]  Meng Li,et al.  Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions , 2018, Atmospheric Chemistry and Physics.

[35]  Qingcun Zeng,et al.  Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants Ӏ. Application of incomplete adjoint operator , 2018, Science China Earth Sciences.

[36]  Zhiquan Liu,et al.  Evaluating the Impact of Emissions Regulations on the Emissions Reduction During the 2015 China Victory Day Parade With an Ensemble Square Root Filter , 2018 .

[37]  E. Saikawa,et al.  Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China , 2017 .

[38]  Dan Chen,et al.  Improving PM 2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter , 2016 .

[39]  W. You,et al.  Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex , 2016 .

[40]  Nicolas Theys,et al.  Cleaning up the air: effectiveness of air quality policy for SO 2 and NO x emissions in China , 2016 .

[41]  Tao Song,et al.  Limitations of ozone data assimilation with adjustment of NO x emissions: mixed effects on NO 2 forecasts over Beijing and surrounding areas , 2015 .

[42]  N. Krotkov,et al.  Lifetimes and emissions of SO2 from point sources estimated from OMI , 2015 .

[43]  Chien Wang,et al.  Estimating global black carbon emissions using a top‐down Kalman Filter approach , 2014 .

[44]  Zifeng Wang,et al.  Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter , 2013 .

[45]  Henk Eskes,et al.  Global lightning NO x production estimated by an assimilation of multiple satellite data sets , 2013 .

[46]  Olivier Boucher,et al.  Atmospheric inversion of SO 2 and primary aerosol emissions for the year 2010 , 2013 .

[47]  Zhiquan Liu,et al.  Simultaneous three‐dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth , 2012 .

[48]  Zhijin Li,et al.  A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM 2.5 prediction , 2012 .

[49]  Olivier Boucher,et al.  Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model , 2012 .

[50]  Jianping Huang,et al.  Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model , 2012 .

[51]  H. Eskes,et al.  Global NO x emission estimates derived from an assimilation of OMI tropospheric NO 2 columns , 2011 .

[52]  Keywan Riahi,et al.  Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period , 2011 .

[53]  J. Qin,et al.  Improving the Noah Land Surface Model in Arid Regions with an Appropriate Parameterization of the Thermal Roughness Length , 2010 .

[54]  G. Carmichael,et al.  Asian emissions in 2006 for the NASA INTEX-B mission , 2009 .

[55]  Jerome D. Fast,et al.  Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) , 2008 .

[56]  Zhaoyan Liu,et al.  Adjoint inversion modeling of Asian dust emission using lidar observations , 2008 .

[57]  Yoram J. Kaufman,et al.  Retrieving global aerosol sources from satellites using inverse modeling , 2008 .

[58]  Hendrik Elbern,et al.  Emission rate and chemical state estimation by 4-dimensional variational inversion , 2007 .

[59]  Nobuo Sugimoto,et al.  Adjoint inverse modeling of dust emission and transport over East Asia , 2007 .

[60]  Itsushi Uno,et al.  Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation , 2006 .

[61]  J. Seinfeld,et al.  Development of the adjoint of GEOS-Chem , 2006 .

[62]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .

[63]  J. Müller,et al.  Grid‐based versus big region approach for inverting CO emissions using Measurement of Pollution in the Troposphere (MOPITT) data , 2006 .

[64]  Georg A. Grell,et al.  Fully coupled “online” chemistry within the WRF model , 2005 .

[65]  Adrian Sandu,et al.  Adjoint inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment , 2005 .

[66]  Trissevgeni Stavrakou,et al.  Inversion of CO and NO x emissions using the adjoint of the IMAGES model , 2004 .

[67]  G. Grell,et al.  A generalized approach to parameterizing convection combining ensemble and data assimilation techniques , 2002 .

[68]  Leonard K. Peters,et al.  A new lumped structure photochemical mechanism for large‐scale applications , 1999 .

[69]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[70]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[71]  G. Grell.  Prognostic evaluation of assumptions used by cumulus parameterizations , 1993 .

[72]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[73]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[74]  G. Zhuang,et al.  The air pollution caused by the burning of fireworks during the lantern festival in Beijing , 2007 .

[75]  Ionel M. Navon,et al.  Optimality of variational data assimilation and its relationship with the Kalman filter and smoother , 2001 .

[76]  M. Chou,et al.  Technical report series on global modeling and data assimilation. Volume 3: An efficient thermal infrared radiation parameterization for use in general circulation models , 1994 .