A regional haze episode occurred in the Beijing, Tianjin and Hebei province (BTH) area in the North China Plain (NCP) from 16 to 19 January 2010. Data were collected and analyzed during the time frame of 14 through 23 January 2010 to include the haze event. The increase of secondary inorganic pollutants (SO₄²⁻, NO₃⁻, NH₄⁺) in PM_2.5 was observed simultaneously at four sites, especially in the plain area of the BTH, which could be identified as a common characteristic of pollution haze in east China. The sulfate and nitrate in PM_2.5 were mainly formed through the heterogeneous reaction process in the urban area. The organic matter (OM) increased more significantly at the Chengde (CD) site than the other three sites in the plain area. The secondary organic aerosols only existed during haze days at CD but in both haze and non-haze days at the other three sites, which suggested the greater regional impact of secondary formation process during the haze episode. The secondary formation of aerosol was one important formation mechanism of haze. The strong temperature inversion and descending air motions in the planetary boundary layer (PBL) allowed pollutants to accumulate in a shallow layer. The weak surface wind speed produced high pollutants concentration within source regions. The accumulation of pollutants was one main factor in the haze formation. The enhanced southwest wind in the last period of this episode transported pollutants to the downwind area and expanded the regional scope of the haze.

The MM5-SMOKE-CMAQ model system, which was developed by the United States Environmental Protection Agency (US EPA) as the MODELS-3 system, has been used for daily air quality forecasts in the Beijing Municipal Environmental Monitoring Center (Beijing MEMC), as a part of the Ensemble air quality Modeling forecast System for Beijing (EMS-Beijing) since the 2008 Olympic Games. According to the daily forecast results for the entire duration of 2010, the model shows good performance in the PM₁₀ forecast on most days but clearly underestimates PM₁₀ concentration during some air pollution episodes. A typical air pollution episode from 11–20 January 2010 was chosen, in which the observed air pollution index of particulate matter (PM₁₀-API) reached 180 while the forecast PM₁₀-API was about 100. In this study, three numerical methods are used for model improvement: first, by enhancing the inner domain with 3 km resolution grids, and expanding the coverage from only Beijing to an area including Beijing and its surrounding cities; second, by adding more regional point source emissions located at Baoding, Landfang and Tangshan, to the south and east of Beijing; third, by updating the area source emissions, including the regional area source emissions in Baoding and Tangshan and the local village/town-level area source emissions in Beijing. The last two methods are combined as the updated emissions method. According to the model sensitivity testing results by the CMAQ model, the updated emissions method and expanded model domain method can both improve the model performance separately. But the expanded model domain method has better ability to capture the peak values of PM₁₀ than the updated emissions method due to better reproduction of the pollution transport process in this episode. As a result, the hindcast results ("New(CMAQ)"), which are driven by the updated emissions in the expanded model domain, show a much better model performance in the national standard station-averaged PM₁₀-API. The daily hindcast PM₁₀-API reaches 180 and is much closer to the observed value, and has a high correlation coefficient of 0.93. The correlation coefficient of the PM₁₀-API in all Beijing MEMC stations between the hindcast and observation is 0.82, clearly higher than the forecast 0.54. The FAC2 increases from 56% in the forecast to 84% in the hindcast, and the NMSE decreases from 0.886 to 0.196. The hindcast also has better model performance in PM₁₀ hourly concentrations during the typical air pollution episode. The updated emissions method accompanied by a suitable domain in this study improved the model performance for the Beijing area significantly.

To facilitate the future inclusion of aerosol–radiation interactions in the regional operational numerical weather prediction (NWP) system RMAPS-ST (adapted from Weather Research and Forecasting, WRF) at the Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), the impacts of aerosol–radiation interactions on the forecast of surface radiation and meteorological parameters during a heavy pollution event (6–10 December 2015) over northern China were investigated. The aerosol information was simulated by RMAPS-Chem (adapted from the WRF model coupled with Chemistry, WRF-Chem) and then offline-coupled into the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme of WRF to enable the aerosol–radiation feedback in the forecast. To ensure the accuracy of the high-frequency (hourly) updated aerosol optical depth (AOD) field, the temporal and spatial variations of simulated AOD and aerosol extinction coefficient at 550 nm were evaluated against in situ and satellite observations. Comparisons with in situ and Moderate Resolution Imaging Spectroradiometer (MODIS), AErosol Robotic NETwork (AERONET), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite observations showed that the model could reproduce the spatial and vertical distribution as well as the temporal variation of the polluted episode. Further comparison of PM_2.5 with in situ observation showed WRF-Chem reasonably captured the PM_2.5 field in terms of spatial distribution and magnitude, with the correlation coefficients of 0.85, 0.89, 0.76, 0.92 and 0.77 in Beijing, Shijiazhuang, Tianjin, Hebei and Henan, respectively. Forecasts with and without the aerosol information were conducted further, and the differences of surface radiation, energy budget and meteorological parameters were evaluated against surface and sounding observations. The offline-coupling simulation (with aerosol–radiation interaction active) showed a remarkable decrease in downward shortwave (SW) radiation reaching the surface, thus helping to reduce the overestimated SW radiation during the daytime. The simulated surface radiation budget was also improved, with the biases of net surface radiation decreased by 85.3 %, 50.0 %, 35.4 % and 44.1 % during the daytime in Beijing, Tianjin, Taiyuan and Jinan respectively, accompanied by the reduction of sensible (16.1 W m⁻², 18.5 %) and latent (6.8 W m⁻², 13.4 %) heat fluxes emitted by the surface around noon. In addition, the cooling of 2 m temperature (∼0.40 ^∘C) and the decrease in horizontal wind speed near the surface (∼0.08 m s⁻¹) caused by the aerosol–radiation interaction over northern China helped to reduce the bias by ∼73.9 % and ∼7.8 % respectively, particularly during the daytime. Further comparisons indicated that the simulation-implemented AOD could better capture the vertical structure of atmospheric wind. Accompanied with the lower planetary boundary layer and the increased atmospheric stability, both U and V wind at 850 hPa showed convergences which were unfavorable for pollutant dispersion. Since RMPAS-ST provides meteorological initial conditions for RMAPS-Chem, the changes of meteorology introduced by aerosol–radiation interaction would routinely impact the simulations of pollutants. To verify the statistical significance of the results, we further conducted the 24 h forecasts for a longer period lasting 27 d (13 January–8 February 2017), with no AOD field (NoAero) and WRF-Chem-simulated hourly AOD fields (Aero) included, as well as a constant AOD value of 0.12 (ClimAero). The 1-month results were statistically significant and indicated that the mean RMSE of 2 m temperature (wind speed at 10 m) in Aero and ClimAero relative to NoAero was reduced by 4.0 % (1.9 %) and 1.2 % (1.6 %). More detailed evaluations and analysis will be addressed in a future article. These results demonstrated the influence of aerosol–radiation interactions on the improvement of predictive accuracy and the potential prospects to offline coupling of near-real-time aerosol information in regional RMAPS-ST NWP in northern China.

Different types of pollution boundary layer structures form via the coupling of different synoptic systems and local mesoscale circulation in the boundary layer; this coupling contributes toward the formation and continuation of haze pollution. In this study, we objectively classify the 32 heavy haze pollution events using integrated meteorological and environmental data and ERA-Interim analysis data based on the rotated empirical orthogonal function method. The thermodynamic and dynamic structures of the boundary layer for different pollution weather types are synthesized, and the corresponding three-dimensional boundary layer conceptual models for haze pollution are constructed. The results show that four weather types mainly influence haze pollution events in the Beijing area: (a) type 1 – southerly transport, (b) type 2 – easterly convergence, (c) type 3 – sinking compression, and (d) type 4 – local accumulation. The explained variances in the four pollution weather types are 43.69 % (type 1), 33.68 % (type 2), 16.51 % (type 3), and 3.92 % (type 4). In persistent haze pollution events, type 1 and type 2 surpass 80 % on the first and second days, while the other types are present alternately in later stages. The atmospheric structures of type 1, type 2, and type 3 have typical baroclinic characteristics at mid–high latitudes, indicating that the accumulation and transport of pollutants in the boundary layer are affected by coupled structures in synoptic-scale systems and local circulation. The atmospheric structure of type 4 has typical barotropic characteristics, indicating that the accumulation and transport of pollutants is primarily affected by local circulation. In type 1, southerly winds with a specific thickness and intensity prevail in the boundary layer, which is favorable for the accumulation of pollutants in plain areas along the Yan and Taihang Mountains, whereas haze pollution levels in other areas are relatively low. Due to the interaction between weak easterly winds and the western mountains, pollutants accumulate mainly in the plain areas along the Taihang Mountains in type 2. The atmospheric vertical structure is not conducive to upward pollutant diffusion. In type 3, the heights of the inversion and boundary layers are the lowest due to a weak sinking motion while relative humidity is the highest among the four types. The atmosphere has a small capacity for pollutant dispersion and is favorable to particulate matter hygroscopic growth; as a result, type 3 has the highest PM_2.5 concentration. In type 4, the boundary layer is the highest among the four types, the relative humidity is the lowest, and the PM_2.5 concentration is relatively lower under the influence of local mountain–plain winds. Different weather types will shape significantly different structures of the pollution boundary layer. The findings of this study allow us to understand the inherent difference among heavy pollution boundary layers; in addition, they reveal the formation mechanism of haze pollution from an integrated synoptic-scale and boundary layer structure perspective. We also provide scientific support for the scientific reduction of emissions and air quality prediction in the Beijing–Tianjin–Hebei region of China.