As an international tourism and consumption center, Hainan has developed and increasingly service-oriented, and water intensive economy, especially the rapid demand growth from the tertiary-sector. However, previous studies have assessed tertiary-sector water demand in aggregate, with limited attention to tourism-specific economic linkages, supply-chain water-use intensity, and the drivers of tourism-related sectors. To address this gap, this study uses the input-output (IO) analysis with the IO tables of Hainan (year 2007, 2017) to examine inter-sectoral economic linkages and estimate water-use intensity from the consumption perspective. The Logarithmic Mean Divisia Index (LMDI) is employed to further quantify the drivers of tertiary-sector water consumption over 2008–2024. Our results show that the tertiary industry became increasingly important in Hainan’s economy growth, with its influence coefficient increased from 0.83 in 2007 to 1.039 in 2017. However, its water-use intensity in 2017 was about 5.86 times that of the secondary-industry. Over 2008–2024, tourism expansion was the biggest contributor to the net increase in tertiary-sector water use (145.30
The hilly and gully region of the Loess Plateau is characterized by complex topography and pronounced spatial heterogeneity in vegetation-cover change. Conventional raster-based analyses may not fully capture the influence of complex terrain from the perspective of geomorphic process units. In this study, 20 typical sample sites were selected in this region. Based on Landsat imagery from 2001 to 2024, a 30 m fractional vegetation cover dataset was generated on the Google Earth Engine platform, and a geomorphologically meaningful hillslope-type classification framework was established by integrating terrain relief, slope position, and aspect. Using the Theil–Sen slope estimator, Mann–Kendall test, and analysis of variance, we examined the spatiotemporal differentiation and trends of vegetation cover among hillslope types at annual and monthly scales. The results showed significant differences among hillslope types in multiyear mean vegetation cover and interannual stability. Variance decomposition showed that site background explained the largest proportion of vegetation-cover variation, while hillslope type remained significant after accounting for site differences. Shaded non-gully slopes in negative and positive terrain generally exhibited relatively high vegetation cover, whereas gully-bottom slopes had relatively low cover and pronounced variability. Monthly results were broadly consistent with annual patterns, and hillslope-type differences became more pronounced during the growing season. Gully-bottom slopes remained at relatively low levels, whereas the relative advantage of shaded slopes became more evident during vigorous growth. During 2001–2024, vegetation cover generally increased, but restoration intensity, stability, and degradation risk differed substantially among sample sites and hillslope types. Overall, shaded hillslope types showed stronger recovery signals, non-gully slopes in positive terrain exhibited better stability, and gully-bottom slopes showed stronger fluctuations and higher degradation risk. This study improves our understanding of the relationship between hillslope types and vegetation-cover differentiation and informs zoned ecological restoration and soil and water conservation management in the Loess Plateau.
This study presents an integrated framework combining a real probability-based approach with automated machine learning (AutoML) to quantitatively assess the landslide risk of the southern segment of the Beijing–Guangzhou Railway under heavy rainfall. A total of 6660 landslides triggered by a single rainfall event were analyzed, incorporating twelve key influencing factors, including accumulated rainfall, topography, and geological conditions. A sample database was constructed based on the real probability criterion, and three models, namely XGBoost, Random Forest (RF), and Extra Trees (ET), were efficiently optimized using the FLAML framework. Among them, XGBoost yielded the best performance, achieving the highest AUC and demonstrating the strongest spatial agreement between predicted high-risk zones and observed landslide occurrences. Feature importance analysis identified accumulated rainfall, strata, and elevation as the dominant contributing factors. The results reveal that the overall risk of the railway section during this event was primarily classified as extremely low or very low. Notably, areas experiencing intense precipitation still pose significant risks to railway operations due to potential rapid-onset landslides. This research provides a data-driven, scalable tool for quantitative disaster risk assessment and offers a foundation for the development of a dynamic early warning platform by incorporating real-time rainfall forecasts.
Different cover crop species (CCs) may exhibit variable responses to ultraviolet-B (UV-B) radiation and understanding its impact on CCs’ early growth, development, and physiologic response can inform more effective management strategies. The objective of the study was to quantify the effect of UV-B radiation (0, 5, 10, and 15 kJ·m−2·d−1) on the early growth, development, and adaptive responses of five CCs widely adopted by Mid-South United States growers. The study used five CCs: Mighty Mustard “Pacific Gold” (Brassica juncea; MM), crimson clover (Trifolium incarnatum; CrC), winter wheat (Triticum aestivum; WW), winter triticale (Triticum × Secale; WT), and cereal rye (Secale cereale; CR) in a sunlit, controlled-environmental chambers (SPAR) facility. UV-B radiation was delivered for 8 h daily using 40 W UV-B 313 lamps. The results indicated that mustard was the most sensitive to UV-B exposure, followed by the crimson clover, cereal rye, winter triticale, and winter wheat CC. Mustard’s leaf area, root length, and total dry weight decreased by 35
The issue of potentially toxic elements (PTEs) pollution in river sediments has a focal point of global concern in the environmental field. This study employed a receptor model and multiple indices to analyze the PTEs in river sediments from a dry-hot valley. The results showed that the average concentrations of PTEs within the river sediments followed the order: Pb > Zn > Cr > Cu > Ni > As > Cd > Hg, with elevated contents mainly observed in the middle and upper reaches. Three sources of PTEs were determined using the positive matrix factorization (PMF) model. Among these, mineral exploitation and transportation activities in the upper reaches mainly contribute to As, Cd, Hg, Pb, and Zn, accounting for the largest proportion (46.41%). This is followed by a natural source characterized by Cr and Ni, representing basalt weathering (27.40%), while agricultural activities (26.19%) in the middle and lower reaches mainly contribute to Hg and Cu. The outcomes of the geo-accumulation index (I-geo), potential ecological risk index (PERI), and environmental capacity based on the linear mass balance model suggest an overall slight to moderate pollution level (53.8%) and low ecological risk (43.6%), with high risks and pollution concentrated in the upper reaches. A total of 41.03% and 5.13% of the samples showed an unacceptable non-carcinogenic risk for children and adults. Cd and As were identified as the leading elements to the ecological and human health risks, respectively. While mining activities are the dominant contributor to both ecological and human health risks. Most areas in the upper reaches are environmental capacity overloaded zones, and the existing environmental capacity gradually increases from the upper reaches to the lower reaches. Moreover, the dynamic environmental capacity fluctuates and decreases over time, while the overloaded zones gradually recover. Overall, this study quantitatively identified three sources of PTEs in river sediments and conducted multi-dimensional evaluations of PTEs, confirming Cd and Pb pollution in the upstream area caused by mining activities.
