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.

The Sichuan Basin (SCB) with complex mountain-basin topography, located immediately to the east of the Tibetan Plateau (TP) in southwest China, is identified as a region with severe PM2.5 pollution over China. To comprehensively understand the SCB terrain effect on the atmospheric environment change, we investigated the effect degree and meteorological mechanism of mountain-valley winds on wintertime PM2.5 in the western SCB, thermally driven mountain-valley winds between the mountains of eastern TP and the western edge of SCB based on near-surface observations of PM2.5 and the ERA5 reanalysis data of meteorology. The results show that the western SCB exhibits a significant diurnal change of mountain-valley winds, shifting between daytime easterly flows and nighttime westerly flows over the western edge of SCB. The frequency of the mountain-valley winds was accounted for 39% of the study’s duration, with the valley and mountain wind-controlling periods being from 2:00 pm to 5:00 pm and 01:00 am to 05:00 am in local time, respectively, and the remaining time being the transition period. Notably, a reduction of 4.5~20.7μg m-3 in near-surface PM2.5 concentrations averaged over the western edge of SCB, decreasing 6~74% in the PM2.5 pollution during the days of mountain-valley winds, which indicates that the mountain-valley winds could alleviate wintertime near-surface PM2.5 in improving air quality over the western SCB. The easterly wind speed was uplifted by 18% during the valley wind-controlling period, corresponding to a 22% reduction in the near-surface PM2.5 levels driven by the mountain-valley winds. The westerly wind speed increased by 50% at the mountain wind-controlling period with a 20% reduction in PM2.5, reflecting the attenuating effect of the favorable atmospheric diffusion conditions on PM2.5 pollution over the western SCB. The daytime noticeably enhanced easterly wind and the strong updraft flows over the eastern slope of TP promoted the dilution and diffusion of air pollutants over the western edge of SCB, and the nocturnal downhill flows along the slope in the westerly winds on the western edge of SCB brought clean TP air to the polluted SCB region in southwest China.

ERA5 100 m wind speed provides long-term and spatially continuous information for regional wind-resource assessment, but its grid-scale representation may introduce terrain- and wind-regime-dependent biases in complex terrain. This study evaluated ERA5 using 459,630 valid hourly observation–reanalysis pairs from 64 wind masts and developed a segmented residual-correction framework incorporating wind-regime, terrain, location, and temporal predictors. Three statistical correction methods and four tree-based models were compared under identical training and validation samples. The main analysis used a stratified random holdout, supplemented by chronological and tower-level spatial holdouts and repeated high-wind experiments. In the random validation subset, raw ERA5 yielded R = 0.669, R² = 0.307, RMSE = 2.261 m s⁻¹, MAE = 1.664 m s⁻¹, and ME = −0.904 m s⁻¹. Random forest achieved the best paired hourly performance, increasing R to 0.850 and reducing RMSE and MAE to 1.433 and 1.078 m s⁻¹, respectively. Its RMSE reductions remained positive but decreased to 14.15% and 14.47% under chronological and spatial holdouts. Terrain relief was strongly associated with tower-level raw ERA5 errors, although its controlled inclusion produced only a modest additional RMSE reduction of approximately 0.7%. Pooling the >9 m s⁻¹ training tail improved high-wind stability and outperformed a separately trained >12 m s⁻¹ model in 17 of 20 experiments. Random forest was preferable for paired hourly reconstruction, whereas quantile mapping more closely reproduced pooled theoretical wind-energy indicators. These results show that ERA5 bias correction should be selected according to the intended application and evaluated using complementary temporal, spatial, distributional, and high-wind diagnostics.