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. Ambient pollutants and emissions in China have changed significantly inrecent years due to strict control strategies implemented by the government.It is of great interest to evaluate the reduction of emissions and the airquality response using a data assimilation (DA) approach. In this study, weupdated the WRF-Chem/EnKF (Weather Research and Forecasting – WRF, modelcoupled with the chemistry/ensemble Kalman filter – Chem/EnKF) system to directlyanalyze SO2 emissions instead of using emission scaling factors, as inour previous study. Our purpose is to investigate whether the WRF-Chem/EnKFsystem is capable of detecting the emission deficiencies in thebottom-up emission inventory (2010-MEIC, Multi-resolution EmissionInventory for China), dynamically updating the spatial–temporal emissionchanges (2010 to 2015/2016) and, most importantly, locating the “new”(emerging) emission sources that are not considered in the a priori emission inventory. The 2010 January MEIC emission inventory was used as the a priori inventory (to generate background emission fields). The 2015 and 2016 Januaryemissions were obtained by assimilating the hourly surface SO2 concentration observations for January 2015 and 2016. The SO2 emissionchanges for northern, western, and southern China from 2010 to 2015 and from2015 to 2016 (for the month of January) from the EnSRF (ensemble square rootfilter) approach were investigated, and the emission control strategiesduring the corresponding period were discussed. The January 2010–2015differences showed inhomogeneous change patterns in different regions,including (1) significant emission reductions in southern China; (2) significant emission reductions in larger cities with a wide increase in thesurrounding suburban and rural regions in northern China, which may indicatemissing raw coal combustion for winter heating that was not taken intoaccount in the a priori emission inventory; and (3) significantly largeemission increases in western China due to the energy expansion strategy.The January 2015–2016 differences showed wide emission reductions from 2015to 2016, indicating stricter control strategies having been fully executednationwide. These derived emission changes coincided with the period of theenergy development national strategy in northwestern China and theregulations for the reduction of SO2 emissions, indicating that theupdated DA system was possibly capable of detecting emission deficiencies,dynamically updating the spatial–temporal emission changes (2010 to2015/2016), and locating newly added sources. Forecast experiments using the a priori and updated emissions were conducted.Comparisons showed improvements from using updated emissions. Theimprovements in southern China were much larger than those in northern andwestern China. For the Sichuan Basin, central China, the Yangtze River Delta,and the Pearl River Delta, the BIAS (bias, equal to the difference between the modeled valueand the observational value, representing the overall model tendency) decreased by 61.8 %–78.2 % (fordifferent regions), the RMSE decreased by 27.9 %–52.2 %, and CORR values (correlation coefficient, equal to the linear relationship between the modeled values and the observational values) increased by 12.5 %–47.1 %. The limitation ofthe study is that the analyzed emissions are still model-dependent, as theensembles are conducted using the WRF-Chem model; therefore, theperformances of the ensembles are model-dependent. Our study indicated thatthe WRF-Chem/EnSRF system is not only capable of improving the emissions andforecasts in the model but can also evaluate realistic emission changes.Thus, it is possible to apply the system for the evaluation of emission changes in the future.

atmospheric chemistry and physics

The 2015 and 2016 wintertime air pollution in China: SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emission changes derived from a WRF-Chem/EnKF coupled data assimilation system