Shengde Yu

Solar interfacial evaporation (SIE) provides a low-carbon route for seawater desalination, but its nonequilibrium phase-change interface may create an overlooked pathway for microplastic transfer from seawater to condensate. To address this water-quality risk, a low-cost and regenerable photothermal interface was constructed by modifying natural sea sand with polyaniline (PANI@SS). The PANI coating enhanced broadband solar absorption and promoted interfacial water activation, achieving an evaporation rate of 2.20 kg·m−2·h−1. Adsorption kinetics, thermodynamics, spectroscopic characterization, and molecular dynamics simulations showed that PANI converted the weak physical retention of pristine sea sand into stable interfacial anchoring. The strengthened retention counteracted phase-change-driven microplastic transport and confined particles at the evaporation interface. Reversible protonation/deprotonation of PANI enabled pH-regulated interfacial affinity, supporting facile regeneration and sustained antifouling performance. PANI@SS showed effective retention for seven representative microplastics with different structures and polarities, indicating broad applicability. In a 30-day outdoor desalination test using natural seawater collected from the Yellow, Yangtze, and Pearl River estuaries, cumulative freshwater production exceeded 220 kg·m−2, while the desalinated water met drinking-water guideline limits for major ions. Evaporation performance, microplastic retention, and material structure remained stable after repeated evaporation–regeneration cycles. This study reveals the competition between phase-change transport and interfacial retention as a key mechanism governing microplastic migration and product-water safety in SIE, and provides a practical design strategy for efficient, durable, and regenerable solar desalination.

Reservoirs are essential for global water management and energy regulation, but sedimentation threatens their longevity. This study investigates a 130 km section of the Yellow River between the Sanmenxia and Xiaolangdi dams, using deep learning to predict long‐term erosion and deposition patterns. From 2009 to 2023, we gathered water depth data from 56 sites (840 measurements) with unmanned survey boats and drone‐based LiDAR (Light Detection and Ranging), alongside flow and sediment records. After preprocessing, we evaluated three machine learning models: Convolutional Network for Multimodal Time Series (CNN‐MTS), Convolutional Transformer for Multimodal Time Series (CNN‐Transformer‐MTS), and Convolutional Bi‐LSTM for Multimodal Time Series (CNN‐BiLSTM‐MTS). The CNN‐BiLSTM‐MTS model excelled, achieving a mean absolute error (MAE) of 17.84 m, a coefficient of determination ( R ² ) of 0.9916, and reducing errors by up to 26% compared to alternatives. Key drivers of sediment dynamics included sediment load, maximum sediment concentration, and maximum flow. Data from 2009 to 2023 showed elevation shifts from −0.21 m near the dam to +1.158 m at the reservoir's tail. Predictions for 2024 to 2050 suggest varied riverbed changes, with the Guxian Reservoir's operation in 2036 expanding elevation ranges from −0.625 to 0.875 m. These findings highlight deep learning's potential for efficient sediment management in reservoirs and offer insights for sustainable hydraulic engineering. However, uncertainties persist in scaling the model, improving data resolution, and coordinating across regions.

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), permanganate index (COD_Mn), and 5-day biochemical oxygen demand (BOD₅)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH₃-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment.

The Yellow River basin, an area of extreme water scarcity, has faced significant challenges in water quality management due to rapid economic and social development since the 1980s. This study analyzes the water quality evolution over nearly 40 years, focusing on primary pollutants like chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), and permanganate index (COD_Mn). In the 1990s, sections of the river were severely polluted, with some areas failing to meet the lowest national standards. In 2000, 32% of the river water was classified as inferior Class V. However, enhanced water resource management and stricter pollutant regulations introduced after 2000 have significantly improved water quality. By 2010, water quality reached its nadir, with 16% of water classified as inferior Class V and 25% as Class IV–V. By 2020, water quality showed marked improvement, with a significant reduction in segments classified as inferior Class V and Class IV–V. Recent years have seen water quality stabilize, with COD meeting Class I standards and NH₃-N and COD_Mn meeting Class II standards based on national criteria. The study also highlights discrepancies in water quality between the mainstream and tributaries of the Yellow River. While the mainstream generally maintains good water quality, many tributaries remain severely polluted. In 2022, 85% of the water in tributaries was classified as Class I to III, 12.3% as Class IV to V, and only 2.7% as Class V. However, all water in the mainstream reached Class I–III, with 86% achieving Class II and 14% achieving Class I. A detailed analysis of the Huayuankou section over the past three decades shows a general decline in pollution indicators. Seasonal water quality fluctuations, correlated with flow rates and temperatures, were observed, often exhibiting normal distribution patterns. These findings underscore the effectiveness of sustained pollution control and the need for continuous, adaptive management strategies to improve and maintain water quality in the Yellow River basin.

The Three Gorges Reservoir (TGR), a landmark of human engineering, has significantly altered the hydrodynamics and ecology of its surrounding environment. Our research explores the hydrodynamic and ecological changes in the TGR, focusing on their implications for reservoir-induced water quality and water resource issues. We designed a 2D hydrodynamic and water quality model and implemented 15 operational scenarios with an advanced dynamic storage capacity method for the TGR during flood season, drawdown and impoundment periods. Our simulations well reproduced and predicted water levels, discharge rates, and thermal conditions of the TGR, providing critical insights. The dynamic storage capacity method significantly improved the precision of water level simulations. This approach achieved modeling errors below 0.2 m when compared to real measurements from seven stations. We performed a detailed analysis of the sensitive, sub-sensitive, and insensitive areas during three reservoir operation periods. The drawdown period showed the most extensive impact range (468 km river channel), while the impoundment period had the least impact range (76 km river channel). Furthermore, we quantified the delay of temperature waves during these periods, observing a maximum delay of approximately 120 km and a minimum delay of less than 10 km, which underscores the variability in hydrodynamic responses under different operational scenarios. Our findings reveal the complex sensitivities of the TGR to varied operational modes, aiding in the development of eutrophication and water resources control strategies. Our modeling application provides different operational scenarios and insights for ecological management strategies in large dam systems globally, informing future water resource management and policy-making, ensuring sustainable and effective management of large reservoir systems.