The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system
The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system
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2015年和2016年中国冬季空气污染：WRF-Chem/EnKF耦合数据同化系统得出的SO2排放变化
DOI：
10.5194/acp-19-8619-2019
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发表时间：
2019-07
影响因子：
6.3
通讯作者：
Chen Min
中科院分区：
文献类型：
--
作者：
Chen Dan;Liu Zhiquan;Ban Junmei;Chen Min
摘要。年，中国环境污染物和排放量发生了显著变化
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.
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影响因子：
5.1
作者：
A. Mizzi;A. Arellano;D. Edwards;Jeffrey L. Anderson;G. Pfister
通讯作者：
A. Mizzi;A. Arellano;D. Edwards;Jeffrey L. Anderson;G. Pfister
影响因子：
5
作者：
Wang, Xinfeng;Wang, Wenxing;Wang, Zhe
通讯作者：
Wang, Zhe
影响因子：
6.3
作者：
B. Zheng;Qiang Zhang;Yang Zhang;K. He;Kai Wang;G. Zheng;F. Duan;Yong-liang Ma;T. Kimoto
通讯作者：
B. Zheng;Qiang Zhang;Yang Zhang;K. He;Kai Wang;G. Zheng;F. Duan;Yong-liang Ma;T. Kimoto
影响因子：
5.2
作者：
K. Miyazaki;H. Eskes
通讯作者：
K. Miyazaki;H. Eskes
影响因子：
6.3
作者：
Zhao, Y.;Nielsen, C. P.;Hao, J.
通讯作者：
Hao, J.