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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-890
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Oct 2018

Research article | 19 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Retrospective analysis of 2015–2017 winter-time PM2.5 in China: response to emission regulations and the role of meteorology

Dan Chen1, Zhiquan Liu2, Junmei Ban2, Pusheng Zhao1, and Min Chen1 Dan Chen et al.
  • 1Institute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China
  • 2National Center for Atmospheric Research, Boulder, CO, 80301, USA

Abstract. To better characterize the anthropogenic emission relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from the GOCART aerosol scheme to MOSAIC-4BIN scheme. Three year (2015–2017) winter-time (January) surface PM2.5 observations from 1600+ sites were assimilated hourly using the updated 3DVAR system in the assimilation experiment CONC_DA. Parallel control experiment that did not employ DA (NO_DA) was also performed. Both experiments were verified against the surface PM2.5 observations, MODIS 550-nm AOD and also 550-nm AOD at 9 AERONET sites. In the NO_DA experiment using 2010_MEIC emissions, modeled PM2.5 are severely overestimated in Sichuan Basin (SB), Central China (CC), YRD (Yangzi River Delta), and PRD (Pearl River Delta) which indicated the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations in Northeastern China (NEC) and Xin Jiang (XJ) were also observed. The assimilation experiments significantly reduced the high biases of surface PM2.5 in SB, CC, YRD, and PRD, and also low biases in NEC. However the improvement of the low biases in XJ is relatively small due to the large difference between the observations and the model background in the DA process, likely indicating that the emissions in the model are seriously underestimated in this region. Assimilating surface PM2.5 also significantly changed the column AOD and resulted in closer agreement with MODIS data and observations at AERONET sites.

The observations and the reanalysis data from assimilation experiment were used to investigate the year-to-year changes. As the differences of the reanalysis data (CONC_DA) among years reflect combining effects of meteorology and emission and the differences of modeling result from control experiment (NO_DA, with same emissions) among years reflect the separate effect of meteorology, the important roles of emission and meteorology in driving the changes in the three years can be distinguished and analyzed quantitatively. The analysis indicated that meteorology played different roles in 2016 and 2017: the higher pressure system, lower temperature and higher PBLH in 2016 are favorable for pollution dispersion (compared with 2015) while the situation is almost the opposite in 2017 (compared with 2016) that leads to the increasing PM2.5 from 2016 to 2017 although emission control strategy were implemented in both years. There are still large uncertainties in this approach especially the inaccurate emission input in the model brings large biases in the analysis.

Dan Chen et al.
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Dan Chen et al.
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Short summary
To better characterize the anthropogenic emission relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from GOCART to MOSAIC-4BIN scheme. 2015–2017 winter-time (January) surface PM2.5 observations from 1600+ sites were assimilated hourly. The observations and the reanalysis data from assimilation experiment were used to investigate the year-to-year changes. The roles of emission and meteorology in driving the changes were also distinguished and quantitatively assessed.
To better characterize the anthropogenic emission relevant aerosol species, the GSI-WRF/Chem...
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