Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions.

With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.

Flood susceptibility mapping in the Yellow River Basin remains challenging due to uncertainties in sample selection and model generalization. This study develops a novel two-step coupling framework that integrates an information value (IV) model with machine learning (ML) to improve reliability. The IV model first identifies stable low-susceptibility zones to select robust non-flood samples, which are then combined with historical flood inventories to train ML models. The SHAP method is applied to quantify factor contributions and interpret outputs. The results show that the IV-RF model achieves the highest predictive performance, while a stacking ensemble further reduces uncertainty. Sensitivity analyses confirm that model outcomes remain stable across random data splits and repeated non-flood point selections. High-risk areas are primarily located in the Hetao Plain, the Weihe River Basin, and sections of the lower Yellow River Basin, where susceptibility is driven by drainage density, anthropogenic and urban–mining soils, a high topographic wetness index, and gentle slopes. This work provides a transferable methodology that enhances physically consistent sample selection and model interpretability for flood risk assessment.