The extent of PM_2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which covers the lower flatlands in Hubei and Hunan provinces in central China, was found to be a high PM_2.5 pollution region, with annual mean PM_2.5 concentrations of 41–63 μg·m⁻³, which is larger than the values in YRD, SB, and PRD during 2014–2019, and high aerosol optical depth values (>0.8) averaged over 2000–2019 from the MODIS products. Heavy pollution events (HPEs) are frequently observed in the THB, with HPE-averaged concentrations of PM_2.5 reaching up to 183–191 μg·m⁻³, which exceeds their counterparts in YRD, SB, and PRD for 2014–2019, highlighting the THB as a center of heavy PM_2.5 pollution in central China. During 2014–2019, approximately 65.2% of the total regional HPEs over the THB were triggered by the regional transport of PM_2.5 over Central and Eastern China (CEC). This occurred in view of the co-existing HPEs in the NCP and the THB, with a lag of almost two days in the THB-PM_2.5 peak, which is governed by the strong northerlies of the East Asian monsoon (EAM) over CEC. Such PM_2.5 transport from upstream source regions in CEC contributes 60.3% of the surface PM_2.5 pollution over the THB receptor region. Hence, a key PM_2.5 receptor of the THB in regional pollutant transport alters the distribution patterns of PM_2.5 pollution over China, which is attributable to the climate change of EAMs. This study indicates a complex relationship between sources and receptors of atmospheric aerosols for air quality applications.

Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM_2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM_2.5 concentration was lower in CS with higher anthropogenic PM_2.5 emissions than in JM, and the correlation between wind speed and PM_2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM_2.5 and VWS between CS and JM might be due to the different influences of regional PM_2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM_2.5 transport co-determined the positive correlation between VWS and PM_2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM_2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM_2.5 transport to the PM_2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM_2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.

The impact of structural variations in the atmospheric boundary layer (ABL) during the regional transport of air pollutants on its local pollution changes deserves attention. Based on multi-source ABL detection and numerical simulation of air pollutants over the Twain-Hu Basin (THB) during 4–6 January 2019, the mechanism of the rapid growth of atmospheric pollutant concentrations in Xianning by the synergistic effect of regional transport and ABL evolution is explored, and the main conclusions are obtained as follows. The vertically stratified atmosphere is noticeable at nighttime, and the heavy humidity of near-surface fog within the stable boundary layer (SBL) promoted the generation and cumulative growth of secondary PM_2.5 components during the pollution formation stage. The horizontal transport characteristics of atmospheric pollutant concentration peak were observed in the residual layer (RL) of 500–600 m. At the pollution maintenance stage, the convective boundary layer (CBL) developed during the daytime, and northerly wind transported high-concentration pollutants from the north to the THB. Under the combined action of horizontal transport and turbulent mixing, the high-concentration atmospheric pollutants in the mixing layer (ML) from the ground to the 500 m height were mixed uniformly and maintained accumulation growth. The next day, the strong vertical turbulent mixing caused the downward transport of high-concentration pollutants in the RL during nighttime due to the development of the CBL again, resulting in a doubling of near-surface pollutant concentration in a short time. With the development of ABL turbulence, local pollution dissipated rapidly without the continuous input of pollutants from external regions. This study emphasizes the importance of multi-scale processes impact on pollution variation, that is, regional transport of atmospheric pollutants at the CBL development stage for the rapid growth of PM_2.5 concentration in the ML.

The influence of the underlying surface on the atmospheric environment over rivers and lakes is not fully understood. To improve our understanding, this study targeted the Twain-Hu Basin (THB) in central China, with a unique underlying surface comprising a dense “water network” over rivers and lakes. In this study, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) was used to simulate the impact of this dense “water network” on a wintertime heavy PM_2.5 pollution event in the THB. On this basis, the regulating effects of density and area of the lake groups, with centralized big lakes (CBLs) and discrete small lakes (DSLs), on PM_2.5 concentrations over the underlying surface of the dense “water network” in the THB were clarified, and the relative contributions of thermal factors and water vapor factors in the atmospheric boundary layer to the variation of PM_2.5 concentrations were evaluated. The results show that the underlying surface of dense “water networks” in the THB generally decreases the PM_2.5 concentrations, but the influences of different lake-group types are not uniform in spatial distribution. The CBLs can reduce the PM_2.5 concentrations over the lake and its surroundings by 4.90–17.68% during the day and night. The ability of DSLs in reducing PM_2.5 pollution is relatively weak, with the reversed contribution between −5.63% and 1.56%. Thermal factors and water vapor–related factors are the key meteorological drivers affecting the variation of PM_2.5 concentrations over the underlying surface of dense “water networks”. The warming and humidification effects of such underlying surfaces contribute positively and negatively to the “purification” of air pollution, respectively. The relative contributions of thermal factors and water vapor–related factors are 52.48% and 43.91% for CBLs and 65.96% and 27.31% for DSLs, respectively. The “purification” effect of the underlying surface with a dense “water network” in the THB on regional air pollution highlights the importance of environmental protection of inland rivers and lakes in regional environmental governance. In further studies on the atmospheric environment, long-term studies are necessary, including fine measurements in terms of meteorology and the environment and more comprehensive simulations under different scenarios.

The lack of long-term observational data on secondary PM_2.5 (SPM) has limited our comprehensive understanding of atmospheric environment change. This study develops an SPM estimation method, named Single-Tracer Approximate Envelope Algorithm (STAEA), to assess the long-term changes of SPM under different PM_2.5 levels and in all seasons in Wuhan, Central China, over the period of anthropogenic pollutant mitigation in 2015–2020. The results show that: (1) the average proportions of SPM in ambient PM_2.5 is 59.61% in a clean air environment, rising significantly to 71.60%, 73.73%, and 75.55%, respectively, in light, moderate, and heavy PM_2.5 pollution, indicating the dominant role of SPM in air quality deterioration; (2) there are increasing trends of interannual changes of SPM at the light and moderate pollution levels of 1.95 and 3.11 μg·m⁻³·a⁻¹ with extending SPM proportions in PM_2.5 pollution, raising a challenge for further improvement in ambient air quality with mitigating light and moderate PM_2.5 pollution; (3) the high SPM contributions ranging from 55.63% to 68.65% on a seasonal average and the large amplitude of seasonal SPM changes could dominate the seasonality of air quality; (4) the wintertime SPM contribution present a consistent increasing trend compared with the declining trends in spring, summer, and autumn, suggesting underlying mechanisms of SPM change for further deciphering the evolution of the atmospheric environment. Our results highlight the effects of air pollutant mitigation on long-term variations in SPM and its contributions with implications for atmospheric environment change.