Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.

Alum sludge is an inevitable by-product from the water purification process, which had been applied as substrates in some constructed wetlands with good performance, especially for phosphorus (P) adsorption. The raw alum sludge is similar to a clay lump with an irregular shape, and there is a concern of it leaching into water. For better reuse, herein, some sludge was fired to produce alum sludge ceramsite (ASC) with a uniform spherical shape via a four-step process of kneading the sludge ball, air drying, preheating at 400 °C for 10 min, and firing at 600 °C for 5 min. Meanwhile, an air-dried alum sludge ball (adASB) was manufactured for comparison. The physicochemical properties and P adsorption ability of ceramsite were investigated subsequently. Through XRD and FT-IR tests, there was no obvious difference between ASC and adASB on the phase structure, but there was a certain amount of Al-OH group loss on the surface of ASC. The structure of ASC was still amorphous, similar to adASB, while ASC possessed more micropore structure and a bigger specific surface area than adASB. Adsorption experiments showed the P adsorption behaviors of ASC and adASB were much similar, and their adsorption kinetics were in accordance with the two-step adsorption kinetics rate equation and pseudo-second-order kinetics equation. The maximum adsorption capacities of ASC and adASB fitted by the Langmuir model were 1.66 mg/g and 1.89 mg/g, respectively. It should be pointed out that, compared with other adsorbents, the ASC produced in this study still had a greater ability to adsorb P. Therefore, ASC should have a great application potential for P removal in wastewater treatment in China.

This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized ground sampling and 1 m resolution multispectral imagery from China’s Gaofen-2 satellite. By integrating these technologies, the study aims to capitalize on the convenience and synchronized observation capabilities of UAV remote sensing, while leveraging the broad coverage of satellite remote sensing to overcome the limitations of each individual technique. Initially, a multispectral estimation model is developed for key water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), utilizing data from UAVs and coordinated ground samples. Subsequently, a comparison is made between the spectral band ratios (R/G and NIR/G) obtained from the UAV data and those from the Gaofen-2 satellite data, revealing a substantial similarity. Ultimately, this integrated methodology is successfully employed in monitoring water quality across a vast area, particularly along the midstream of the Hanjiang River in the Qinba Mountain region. The results underscore the feasibility, advantages, improved efficiency, and enhanced accuracy of this approach, making it particularly suitable for large-scale water quality monitoring in mountainous terrain. Furthermore, this method reduces the burden associated with traditional ground-based spectral acquisitions, paving the way for a more practical and cost-effective solution in monitoring vast water bodies.

Canopy–atmospheric water vapor output resistance (g_s) is a key parameter in researching forest canopy transpiration. It is important for quantifying the water vapor exchange in forest ecosystems. However, the method by which g_s is determined has been controversial, and it cannot precisely represent water vapor exchange. This study aimed to develop a model to quantify the water vapor resistance between the canopy and the atmosphere in Platycladus orientalis (P. orientalis) forests using sap flow and meteorological factors monitoring data. The resistance model was constructed using the relationship between canopy stomatal resistance (g_c) and aerodynamic resistance (g_a) from the mechanism perspective, and sap flow data and measurements of meteorological variables were used to model the stomatal and aerodynamic resistance of the canopy. The results indicate that the canopy-atmospheric water vapor output resistance was closer to the measured values and showed a unimodal curve in the diurnal scale, and this change could provide more accurate measurements of tree transpiration. At the same time, the canopy-atmospheric water vapor output resistance was strongly influenced by wind speed and PAR when 0.2 m/s < u < 0.4 m/s (R² = 0.871, p < 0.01). The stomatal and aerodynamic resistance were also both strongly influenced by wind speed, with the proposed model achieving a high degree of fit (R² = 0.949, p < 0.01), providing a new tool for analyzing forest transpiration. This research provides a new perspective and technical reference for clarifying the mechanism of forest canopy water output.

Sap flow is one of the most important physiological water transport processes of trees, and the characteristics of sap flow are greatly affected by the spatial and temporal distribution of water in the SPAC (soil–plant–atmosphere continuum). However, different precipitation characteristics have great influence on the water environment of forest trees, which causes considerable differences in sap flow. Therefore, researching the response of sap flow to precipitation type is the key to accurately determining plant transpiration in semi-arid areas. We used K-means clustering analysis to divide the rainfall during the study period into three rainfall types (the highest rainfall amount and intensity (types I), medium rainfall amount and intensity, with a long duration (types II); and the lowest rainfall amount and intensity (types III)) based on the rainfall amount and intensity in order to compare the differences in the response of sap flow trends and influencing factors of Pinus tabulaeformis and Robinia pseudoacacia under different rainfall types. The results showed that, under the daily scale average sap flow of P. tabulaeformis and R. pseudoacacia, rainfall type II decreased significantly relatively to rainfall types I and III (p < 0.05). In rainfall type II, The sap flow characteristics of R. pseudoacacia were positively correlated with solar radiation (p < 0.05), while those of P. tabulaeformis were positively correlated with temperature, solar radiation, and VPD (p < 0.01). The sap flow of P. tabulaeformis and R. pseudoacacia were significantly positively correlated with temperature, solar radiation, VPD, and soil moisture content (p < 0.01) and negatively correlated with relative humidity (p < 0.05) in rainfall type III. The hourly sap flow of P. tabulaeformis and R. pseudoacacia on rainfall days was higher than before the rainfall. Rainfall type I promoted the daily sap flow of both species, and the proportion of the sap flow in daytime was also higher. On rainy days, the sap flow rates of rainfall type I and III showed a “midday depression”. In type I rainfall events, the sap flow “midday depression” after rainfall occurred an hour ahead compared to the sap flow “midday depression” before rainfall. In type II rainfall events, the daytime sap flow rates of P. tabulaeformis and R. pseudoacacia were obviously inhibited, but the nighttime sap flow rate increased. In type III rainfall events, the sap flow before rainfall presented a unimodal curve versus time. The daily average sap flow of R. pseudoacacia was more susceptible to rainfall type II, while P. tabulaeformis was more susceptible to rainfall types I and III. The sap flow rate of R. pseudoacacia decreased on rainy days. The results show that the effects of different rainfall types on the sap flow trends of P. tabulaeformis and R. pseudoacacia were different. They revealed the responses of their sap flow trends to meteorological factors under different rainfall types, which provided basic data and theoretical support for further predicting the sap flow trends on rainy days, clarifying the effects of rainfall amount, rainfall duration, and rainfall intensity on sap flow trends and accurately estimating the transpiration water consumption of typical tree species in the sub-humid climate regions of China.

Functioning as a critical ecotone between terrestrial and aquatic ecosystems, riparian zones exhibit soil enzyme activities that serve as key biomarkers of their nutrient cycling processes. However, despite considerable focus on riparian soil properties, the dynamics and underlying drivers of these enzymatic activities are not yet fully characterized. To this end, soils were systematically sampled across varying widths and depths from three representative riparian zones to quantify the driving forces of physicochemical properties on enzyme activity dynamics. The results showed that the soil enzyme activity was highest in the forest riparian zone and lowest in the farmland riparian zone, with average enzyme activities of 37.95 (μmol·g⁻¹·h⁻¹) and 26.85 (μmol·g⁻¹·h⁻¹), respectively. The width of the riparian zone changes the spatial distribution of soil enzyme activity. The soil enzyme activity is higher in the land edge area far from the river (profile-1) and lower in the water edge area near the river (profile-4), with average enzyme activities of 47.4384 (μmol·g⁻¹·h⁻¹) and 17.0017 (μmol·g⁻¹·h⁻¹), respectively. Moreover, soil water content (SWC) has a strong impact on enzyme activity changes. The increase in soil depth reduces soil enzyme activity, with enzyme activity in the 0–20 cm soil layer being 1.5 times higher than in the 20–50 cm soil layer. Meanwhile, the primary factors influencing changes in soil enzyme activity have gradually shifted from total nitrogen (TN), nitrate nitrogen (NO₃-N), and soil organic carbon (SOC) to the sole control of SOC. Research has shown that human influence strongly interferes with soil enzyme activity in riparian zones. The width of the riparian zone and soil depth serve as key drivers of the spatial distribution of soil enzyme activity by modulating soil environmental factors. The patterns revealed in this study indicate that maintaining appropriate riparian zone width and reducing anthropogenic disturbances can enhance nutrient cycling dynamics at the micro-scale by increasing soil enzyme activity. This process is crucial for strengthening the riparian zone’s macro-level ecosystem services, particularly by effectively enhancing its capacity to sequester and transform nutrients like nitrogen and phosphorus from agricultural nonpoint sources, thereby safeguarding downstream water quality. Consequently, soil enzyme activity serves as a key indicator, providing essential scientific basis for assessing riparian health and guiding ecological restoration efforts.