Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology

Abstract

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals during the KORUS-AQ period

“…It has been shown that the estimation of emission inventories as part of the DA procedure can help extend the impact of data assimilation in longer forecasts (Elbern et al, 2007;Kumar et al, 2019). Also, more sophisticated aerosol and chemical mechanisms might be able to improve air quality forecasting by reducing model deficiencies (Chen et al, 2019). A simultaneous assimilation of meteorological observations and measurements of individual chemical species as well as particulate matter would be certainly beneficial in both NWP and air quality forecasting.…”

Development and application of the WRFDA-Chem 3DVAR system: aiming to improve air quality forecast and diagnose model deficiencies

“…As in Chen et al (2019), the emission input is based on the Multi-resolution Emission Inventory for China (MEIC) (He 2012;Lei et al 2011;Li et al 2014;Zhang et al 2009), which has already been applied in many recent studies over China (Wang et al 2016;Wang et al 2013;Zheng et al 2015). The emission inventory has also been processed to match the model grid spacing (40.5 km) from an original grid spacing of 0.25º × 0.25º (Chen et al 2016).…”

“…Admittedly, the difference between the emission base year and our simulation year and the spatial-temporal allocations may arise uncertainties in our simulation, this emission is the only publicly available emission inventory when the study is conducted. Meanwhile, the inhomogeneous spatial changes and large uncertainties in seasonal allocations of the emissions made it difficult to simply scale the original emission inventory for our study period (Chen et al 2019).…”

“…To verify capability of the system, we use the data for the whole month of January 2017. As in Chen et al (2019), to perform statistical calculations, an observation dataset at 531 locations ( Fig. 1) is acquired by averaging all the original observations (1600+ sites) that fall into the same model grid.…”

“…In result, the MOSAIC scheme exhibits a better performance in representing the complex PM2.5 pollution over China (Chen et al 2016;Chen et al 2019). Therefore, to make the DA system suitable for different emphasis and applications, a flexible aerosol assimilation capability is built within the WRFDA system in this study, which will facilitate developments and applications for more chemistry schemes in the future.…”