In this paper, the circulation background, the characteristics of meteorological elements configuration in the boundary layer and the stable meteorological conditions in two precipitation episodes (during February 19–21, 2015 and February 10–13, 2016, respectively) within the Beijing-Tianjin-Hebei region are compared and analyzed. Data from conventional meteorological observations, air quality monitoring, reanalysis and numerical models are used. The results show that before the two precipitation processes in 2015 and 2016, the circulations in the middle and high latitudes of Asia and Europe demonstrate “two troughs and one ridge”. Besides, the weather is stable and the pollutant concentration is relatively high. During the precipitation, the circulation is relatively stable for the episode in 2015, and no obvious change in the synoptic system is observed. However, during the episode in 2016, the formation of blocking high and the enhancement of the average ridge in western Asia cause obvious change in the circulation. The simulation results show that significant removal can be detected in both cases, and the PM_2.5 wet deposition fluxes are 647 g/ha and 486 g/ha, respectively, with the removal in 2015 slightly stronger than that in 2016. The removal and dissipation of pollutants is determined by the atmospheric diffusion conditions and the precipitation, especially in the former episode. In the case of February 2016, good diffusion conditions and the precipitation demonstrate obvious PM_2.5 removal effect. In the case of February 2015, the longstanding calm wind with high humidity and the physical quantity configuration in the lower mixed layer lead to the poor pollutant removal.

Haze is a majorly disastrous type of weather in China, especially central and eastern of China. The development of haze is mainly caused by highly concentrated fine particles (PM_2.5) on a regional scale. Here, we present the results from an autumn and winter study conducted from 2013 to 2020 in seven highly polluted areas (27 representative stations) in central and eastern China to analyze the growth mechanism of PM_2.5. At the same time, taking Beijing Station as an example, the characteristics of aerosol composition and particle size in the growth phase are analyzed. Taking into account the regional and inter-annual differences of fine particles (PM_2.5) distribution, the local average PM_2.5 growth value of the year is used as the boundary value for dividing slow, rapid, and explosive growth (only focuses on the hourly growth rate greater than 0). The average value of PM_2.5 in the autumn and winter of each regional representative station shows a decreasing trend as a whole, especially after 2017, whereby the decreasing trend was significant. The distribution value of +ΔPM_2.5 (PM_2.5 hourly growth rate) in the north of the Huai River is lower than that in the south of the Huai River, and both of the +ΔPM_2.5 after 2017 showed a significant decreasing trend. The average PM_2.5 threshold before the explosive growth is 70.8 µg m⁻³, and the threshold that is extremely prone to explosive growth is 156 µg m⁻³ to 277 µg m⁻³ in north of the Huai River. For the area south of the Huai River, the threshold for PM_2.5 explosive growth is relatively low, as a more stringent threshold also puts forward stricter requirements on atmospheric environmental governance. For example, in Beijing, the peak diameters gradually shift to larger sizes when the growth rate increases. The number concentration increasing mainly distributed in Aitken mode (AIM) and Accumulation mode (ACM) during explosive growth. Among the various components of submicron particulate matter (PM₁), organic aerosol (OA), especially primary OA (POA), have become one of the most critical components for the PM_2.5 explosive growth in Beijing. During the growth period, the contribution of secondary particulate matter (SPM) to the accumulated pollutants is significantly higher than that of primary particulate matter (PPM). However, the proportion of SPM gradually decreases when the growth rate increases. The contribution of the PPM can reach 48% in explosive growth. Compared to slow and rapid growth, explosive growth mainly occurs in the stable atmosphere of higher humidity, lower pressure, lower temperature, small winds, and low mixed layers.

In this paper, L-band sounding and surface observation data are used to calculate the boundary layer height (BLH) and evaluated CMA (China Metrological Administration Numerical Forecast System) and ERA5 in Xi’an for 2017–2021 using the Richardson (Ri) and Nozaki methods. For different PM_2.5 pollution levels, the correlation between the vertical profile of meteorological factors and BLH is explored. There is a certain negative correlation between BLH and PM_2.5 concentration. The BLH mean values of Nozaki, Ri, ERA5, and CMA from high to low are ~980 m, ~640 m, ~410 m, and ~240 m, respectively. The highest correlation is between ERA5 and CMA BLH with r² > 0.85 for all pollution processes, while it between other methods is significantly lower (r² < 0.58). The observational BLH is generally higher than the model results. Nozaki has a good adaptability on the light pollution, while Ri is more applicable to the stable boundary layer. In moderate and higher pollution, the ERA5 has a slightly better performance than CMA in BLH, while in light pollution there is a significant underestimation for both. Overall, the correlation between any two BLH methods gradually increases with increasing pollution level. In this study, there is about ~30% probability of polluted weather when BLH < 200 m and only 2000 m. It is difficult to simulate the neutral boundary layer and inversion processes for CMA and ERA5, but ERA5 has higher forecasting skills than CMA. This study can provide the data and theoretical support for the development of haze numerical forecast.

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study.