Abstract
Abstract. Ambient pollutants and emissions in China have changed significantly in recent years due to strict control strategies implemented by the government. It is of great interest to evaluate the reduction of emissions and the air quality response using a data assimilation (DA) approach. In this study, we updated the WRF-Chem/EnKF (Weather Research and Forecasting – WRF, model coupled with the chemistry/ensemble Kalman filter – Chem/EnKF) system to directly analyze SO2 emissions instead of using emission scaling factors, as in our previous study. Our purpose is to investigate whether the WRF-Chem/EnKF system is capable of detecting the emission deficiencies in the bottom-up emission inventory (2010-MEIC, Multi-resolution Emission Inventory for China), dynamically updating the spatial–temporal emission changes (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 January emissions were obtained by assimilating the hourly surface SO2 concentration observations for January 2015 and 2016. The SO2 emission changes for northern, western, and southern China from 2010 to 2015 and from 2015 to 2016 (for the month of January) from the EnSRF (ensemble square root filter) approach were investigated, and the emission control strategies during the corresponding period were discussed. The January 2010–2015 differences 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 the surrounding suburban and rural regions in northern China, which may indicate missing raw coal combustion for winter heating that was not taken into account in the a priori emission inventory; and (3) significantly large emission increases in western China due to the energy expansion strategy. The January 2015–2016 differences showed wide emission reductions from 2015 to 2016, indicating stricter control strategies having been fully executed nationwide. These derived emission changes coincided with the period of the energy development national strategy in northwestern China and the regulations for the reduction of SO2 emissions, indicating that the updated DA system was possibly capable of detecting emission deficiencies, dynamically updating the spatial–temporal emission changes (2010 to 2015/2016), and locating newly added sources. Forecast experiments using the a priori and updated emissions were conducted. Comparisons showed improvements from using updated emissions. The improvements in southern China were much larger than those in northern and western 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 value and the observational value, representing the overall model tendency) decreased by 61.8 %–78.2 % (for different 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 of the study is that the analyzed emissions are still model-dependent, as the ensembles are conducted using the WRF-Chem model; therefore, the performances of the ensembles are model-dependent. Our study indicated that the WRF-Chem/EnSRF system is not only capable of improving the emissions and forecasts 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.
Highlights
China is one of the fastest growing countries in the world and produces a significant amount of air pollutant emissions
A series of strict control strategies has been implemented by the government since 2010, including both long-term pollution control strategies and temporary emergency measures activated under different air pollution alerts, which has led to large spatial–temporal changes in emissions
The assumption is that the GFS 6 h analysis data provide good meteorological initial condition (IC)/boundary condition (BC) values and that the model accurately simulated the meteorological conditions; the emissions were the major deficiency in the model
Summary
China is one of the fastest growing countries in the world and produces a significant amount of air pollutant emissions. A series of strict control strategies has been implemented by the government since 2010, including both long-term pollution control strategies and temporary emergency measures activated under different air pollution alerts, which has led to large spatial–temporal changes in emissions (factory mitigation from urban to rural regions, industries staggering peak production, and so on.). These spatial– temporal emission changes are difficult to reflect in a timely manner in both “bottom-up” emission inventories and air quality models, which creates large uncertainties. The forward approach using these models can neither accurately evaluate the spatial–temporal emission changes nor locate newly added sources that are missing from the bottom-up emission inventory
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