Search Results (164)

River water quality monitoring data are often non-stationary and nonlinear and may contain occasional abnormal values. To support anomaly early warning, this study proposes an LSTM–SOA–DA framework. Water quality monitoring data for six indicators, including pH, DO, COD_Mn, NH₃-N, TP, and TN, were collected from the Bahekou section in Xi’an at 4 h intervals from 2021 to 2023 and chronologically divided into training and testing sets at an 8:2 ratio. The Seagull Optimization Algorithm (SOA) was used to optimize the L2 regularization coefficient, initial learning rate, and number of hidden units of the Long Short-Term Memory (LSTM) network, establishing an LSTM-SOA forecasting model. Compared with traditional LSTM, BP neural network, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other optimization-based LSTM models, the proposed model achieved better RMSE and R² performance, indicating improved prediction accuracy. Based on the residuals between observed and predicted values, the DA method was then used to determine indicator-specific anomaly thresholds from the residual distributions. The model identified 193 abnormal points in the test set. After manual rechecking, the Precision, Recall, and F1-score reached 87.6%, 93.9%, and 90.64%, respectively. These results suggest that the LSTM–SOA–DA framework can effectively identify abnormal fluctuations in river water quality data and support timely water environment management. Full article

With the rapid urbanization of China, environmental risks posed by informal landfills, particularly those dominated by agricultural waste, are an urgent yet understudied concern. This study systematically monitored groundwater quality surrounding five typical informal agricultural waste landfills in a coastal Chinese city. Eight major pollutants were analyzed using pollution index evaluation, the health risk model and multivariate statistical methods. The results indicate one landfill as a high-priority concern, exhibiting a combined multi-index pollution pattern with an exceedance rate of 87.5%, where NO₃⁻-N, F⁻, COD_Mn, and total hardness are the dominant indicators. Another landfill showed high background levels and anthropogenic impacts. Total non-carcinogenic risk of all landfills is below 1 (negligible). Children face approximately twice the health risk of adults. The exposure risk through drinking water ingestion is three orders of magnitude higher than that from dermal contact, with NO₃⁻-N contributing >90% of the total risk. Groundwater deterioration is primarily affected by geological conditions and seawater intrusion (52.31%), followed by agricultural activities and soil characteristics. Given these findings, priority attention should be directed to nitrogen-driven landfill and multi-index composite pollution landfill, with reinforced source tracing and control of NO₃⁻-N, alongside long-term monitoring for regional groundwater protection. Full article

Dams are widely distributed in global water bodies and cause severe impacts on aquatic ecosystems. In this study, the Sizao River Basin was selected to explore the effects of dams on the macroinvertebrate community, including functional feeding groups (FFGs). Macroinvertebrate communities and environmental variables were monitored seasonally in April, August, October, and November of 2025. A total of 27 taxa were identified, including 3 phyla, 8 orders, and 15 families. Species richness, abundance, biomass, species diversity, and FFGs diversity in the gate-regulated section were generally lower than those in other river sections. Gatherer–collector dominated FFGs across the Sizao River Basin and accounted for most of the dominant species. An ecosystem assessment based on FFGs suggests that ecosystem attributes of macroinvertebrate communities were fragmented. The longitudinal spatial distribution of FFGs was roughly in line with the predications of the river continuum concept. Redundancy Analysis (RDA) indicated that the permanganate index (COD_Mn) and dissolved oxygen (DO) were major environmental variables affecting macroinvertebrate community structure, and DO and salinity (SAL) were major variables affecting FFGs. The explanatory power of RDA for FFGs was higher than that for macroinvertebrate community structure. These findings provide valuable insights into protecting aquatic ecosystems in gate-regulated water bodies. Full article

Dissolved organic matter (DOM) in algae-laden micro-polluted source water is highly complex, posing major challenges to drinking water treatment and risk control. However, the molecular fate of DOM and its associated toxicity consequences under different treatment processes remains insufficiently understood. In this study, a multi-scale characterization approach combined with toxicity prediction was used to systematically compare the effects of coagulation, ozonation, and adsorption on the molecular transformation and toxicity evolution of DOM. FT-ICR MS analysis assigned 1092 DOM molecular formulae in the raw water, while 741 and 800 assigned formulae remained after coagulation and adsorption, respectively. Both processes showed distinct molecular selectivity: saturated molecules were preferentially removed by both treatments, whereas coagulation showed a stronger preference for oxidized molecules. By comparison, ozonation achieved limited COD_Mn and DOC reduction but markedly reduced UV₂₅₄ and increased the number of assigned molecular formulae to 1500. The ozonated effluent was characterized by diverse transformation products, especially oxidized saturated small molecules, accompanied by enhanced bio-origin fluorescence signals and more prominent low-molecular-weight neutral and biopolymer fractions. In addition, ozonation increased the numbers of highly acute and highly chronic toxic molecules by 53.60% and 42.25%, respectively, whereas coagulation and adsorption reduced these high-risk molecules. These findings reveal the process-specific molecular transformation and toxicity evolution of DOM under three classical water treatment processes, providing a theoretical basis for process optimization and ecological risk control. Full article

Retrieving non-optically active water quality variables, such as total hardness (TH) and permanganate index (CODMn), from hyperspectral data remains challenging because these parameters are not directly linked to spectral reflectance. To improve their estimation from UAV hyperspectral imagery, a GA-MHSA-BPNN framework was developed by combining a genetic algorithm (GA), multi-head self-attention (MHSA), and a backpropagation neural network (BPNN). In this framework, MHSA was introduced to strengthen the representation of informative spectral features, while GA was applied to optimize the initial network parameters and thus enhance convergence stability. The proposed framework was evaluated against BPNN, GA-BPNN, MHSA-BPNN, and 1D-CNN models. Among the tested approaches, GA-MHSA-BPNN produced the most favorable results for both TH and CODMn, with R² values of 0.878 and 0.843, respectively. Additional experiments using different proportions of training samples showed that the model remained relatively stable when the training data were reduced to 70% and 50% of the original dataset. These results indicate that integrating GA and MHSA into a UAV hyperspectral retrieval framework can improve the estimation of non-optically active water quality variables and provide useful methodological support for efficient and refined monitoring of drinking water source areas. Full article

Shallow lakes in the Yangtze River Delta are characterized by fragile ecosystems, strong sediment–water interactions, and poor resistance to pollution shocks; they are prone to shift from macrophyte-dominated clear-water states to phytoplankton-dominated turbid states under intensive human disturbance. To improve the efficacy of aquatic ecological restoration, this study takes a typical shallow urban lake—Kuilei Lake in Kunshan—as the research object, and establishes a two-dimensional hydrodynamic and water quality model to simulate the temporal and spatial variations in flow fields, flow circulations, and water quality indicators (TP, NH₃-N, COD_Mn) throughout the year. The results are as follows: (1) The hydrodynamic regime of Kuilei Lake is dominated by wind-driven currents, with seasonal flow circulations regulating pollutant migration and the suitability for submerged macrophyte growth; (2) Intense circulations in summer (July–September) enhance sediment resuspension and endogenous nutrient release, which are unfavorable for submerged plant colonization; (3) April–June is the optimal window for ecological restoration, with a mean flow velocity of 2.0–2.5 cm/s, TP ≤ 0.06 mg/L, NH₃-N ≤ 0.20 mg/L, COD_Mn ≤ 3.0 mg/L, and water temperature of 15–25 °C, providing favorable thresholds for submerged macrophyte recovery. This study reveals the coupled hydrodynamic–water environmental thresholds for shallow lake restoration, and offers a scientific basis for flow field regulation and ecological reconstruction of shallow lakes in the Yangtze River Delta. Full article

This work focused on the Xinghua River, a typical urbanizing river, to investigate how different anthropogenic activities affect the composition, sources, and environmental impact of dissolved organic matter (DOM) during urbanization. Using fluorescence spectroscopy combined with multivariate statistics, we systematically explored DOM characteristics and their response to urbanization. A total of four fluorescent components were identified, including protein-like components C1 and C3, and humic-like components C2 and C4, with protein-like substances constituting the major fraction of DOM. Fluorescence indices indicated that DOM in the Xinghua River was primarily derived from autochthonous sources (FI > 1.9), with a low degree of humification reflecting the dominance of fresh organic matter input during urbanization. Spatial analysis revealed that from upstream to downstream, the source of DOM gradually shifted from autochthonous dominance to increased allochthonous input, accompanied by increasing trends in both protein-like and humic-like components, indicating an accumulative effect of anthropogenic activities along the river. 2D-COS further revealed that the transformation sequence of DOM components along the flow direction was C3 → C1 → C4 → C2, suggesting that tyrosine/tryptophan-like substances were the most sensitive to anthropogenic disturbance. Redundancy analysis identified total phosphorus (TP), total dissolved solids (TDS), and permanganate index (COD_Mn) as the key environmental factors influencing DOM distribution, highlighting the synergistic regulatory roles of nitrogen and phosphorus nutrients and organic pollution loads on DOM composition. This study not only elucidates the gradient effects of human activities on DOM in the Xinghua River but also provides a scientific basis for water management in urban rivers worldwide, particularly for zone-based control and source-oriented management. Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. Since 2000, the water quality of Lake Erhai has continuously deteriorated, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measures (IEPMs) were considered influencing factors for a comprehensive and systematic analysis of Lake Erhai’s water quality. The results indicate that rising air temperature may increase total phosphorus (TP) concentration, while rainfall may elevate both TP and total nitrogen (TN) levels. In contrast, higher wind speed may reduce chemical oxygen demand (COD_Mn), TP, and TN concentrations. Socio-economic development, meanwhile, may contribute to increased COD_Mn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. By identifying the key natural and anthropogenic drivers of water quality changes in Lake Erhai, this study provides a scientific basis for the development of targeted pollution control strategies and directly contributes to the protection of clean water sources. Moreover, its revelation of the coupled impacts of climate change and socio-economic activities enhances understanding of plateau lake ecosystem resilience. This insight is critical for ensuring regional ecological security and serves as a model for advancing sustainable development goals in similar lake systems worldwide. Full article

Nanofiltration (NF) is increasingly applied for advanced drinking water treatment, but achieving stable operation at high recovery rates remains challenging for surface waters with high scaling potential. This pilot study investigated the performance and optimization of a three-stage NF270 system (4:2:1 tapered array) for treating coagulated surface water in northern Jiangsu, China, aiming to identify sustainable operating conditions for high-recovery applications. The NF system was operated at recoveries of 80–90% with a feed flux of 20–23 LMH, and the effects of forward flushing frequency, acid dosing location, and concentrate recirculation on fouling behavior were evaluated. The NF270 membrane achieved consistent removal of organic matter (effluent chemical oxygen demand (COD_Mn) < 0.5 mg/L), hardness (40–60% rejection), and alkalinity (~20% rejection), meeting Jiangsu Province drinking water standards. However, operation at 90% recovery resulted in rapid third-stage fouling, with permeate flow declining by >60% within 2.5 h. Osmotic pressure analysis (local concentrate osmotic pressure: 3.8–4.2 bar; net driving pressure: 0.8–2.2 bar) confirmed physical scaling rather than hydraulic limitation as the dominant mechanism. Stage-wise concentration factor calculations (CF₁ = 1.6, CF₂ = 2.9, CF₃ = 4.4) revealed local Langelier Saturation Index (LSI) values of 1.8–2.2 in the third stage, identifying CaCO₃ supersaturation as the primary scaling cause. Reducing recovery to 85% and flux to 20 LMH with 2 h forward flushing extended stable operation. Acid addition effectively mitigated scaling, but dosing location was critical: first-stage addition (pH 8.1 → 7.6) reduced third-stage LSI to 0.7–0.9 and stabilized performance, whereas third-stage addition (pH 8.0 → 7.3) inadvertently promoted Al(OH)₃ precipitation from residual coagulant (feed Al: 0.07–0.11 mg/L). Concentrate recirculation (90% ratio) did not alleviate fouling. These findings demonstrate that for aluminum-rich coagulated surface waters, optimizing recovery, flushing frequency, and acid dosing location is essential for sustainable NF operation, and provide engineering guidance for full-scale applications. Full article

Baihua Reservoir, a typical large waterbody in the karst region of southwestern China and an essential drinking water source, is characterized by a high carbonate buffering capacity that profoundly shapes the structure and function of its phytoplankton community. This study systematically elucidates the multi-dimensional driving mechanisms underlying seasonal phytoplankton community assembly in karst reservoirs by integrating multiple analytical models—including the Neutral Community Model, β-diversity decomposition, co-occurrence network analysis, XGBoost-SHAP machine learning, and Partial Least Squares Path Modeling—based on monthly sampling at five sites from 2020 to 2024. The results revealed that: (1) Stochastic processes dominated community assembly across all four seasons, while deterministic processes played a crucial role in local species turnover. (2) The co-occurrence network structure showed significant seasonal dynamics, with the composition of keystone species adaptively shifting in response to changing environmental conditions. (3) The key environmental factors influencing the phytoplankton community exhibited clear seasonal patterns, primarily pH, NH₃-N, and COD_Mn in spring; water temperature, COD_Mn, and NH₃-N in summer; TN, TP, and pH in autumn; and pH, water temperature, and DO in winter. To support the sustainable management of karst reservoirs, we propose seasonally differentiated strategies derived from our phytoplankton community analysis: target COD_Mn reduction in spring and summer, focus on TN and TP load control in autumn, prioritize water column stability in winter, and maintain hydrological connectivity and pH monitoring year-round. This approach enhances phytoplankton community stability, safeguards drinking water safety, and provides a targeted management model for similar reservoir ecosystems globally. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

While understanding the drivers of river water quality is crucial, the dependence on ground observations hinders the accurate quantification of driver thresholds, as well as the scale-dependent effects of buffer zones. By transcending the limitations of ground observations, satellite remote sensing provides the spatially continuous data required to define effective buffer zones and determine the threshold intervals for natural and anthropogenic drivers, effectively promoting sustainable watershed management. Herein, we determined the total nitrogen (TN), total phosphorus (TP), permanganate index (COD_Mn), and turbidity in the Minjiang River of Fujian Province by synergizing Sentinel-2 imagery and in situ data (2021–2024). Subsequently, we further employed generalized additive models (GAMs) considering scale-dependent (50 m to 20 km) characteristics to screen and evaluate the natural–anthropogenic factors influencing the water quality indicators. The GAMs revealed that TN exhibited multiphasic responses to forest cover and water area, characterized by alternating positive and negative effects across their range. TP was found to be predominantly driven by agricultural and urban land use, showing clear scale–threshold effects. This study provides an integrated framework that moves beyond retrieval to quantitatively assess the impact of multi-scale natural–anthropogenic factors, offering actionable insights for precise watershed zoning and science-based management for the sustainable development of river systems. Full article

The precise identification of pollution sources constitutes a cornerstone for effective water environment management in mountainous watersheds. This study employed principal component analysis–absolute principal component scores–multiple linear regression (PCA-APCS-MLR) receptor modeling to analyze monthly water quality indicators across the Longxi River Basin. Results revealed comparable water quality between the main stream and its tributaries, with no statistically significant differences identified. Water quality exhibited a distinct spatial pattern, with superior conditions in the upstream and downstream segments compared to the middle reaches. Water quality parameters exhibited significant seasonal variations. During the wet period, the degradation of water quality was primarily driven by diffuse agricultural sources, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For Permanganate index (COD_Mn) exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For ammonium nitrogen (NH₃-N), wet season agricultural non-point sources (44.4%) and dry season rural domestic pollution (71.8%) were key contributors. Agricultural non-point sources were the dominant pollution source for total nitrogen (TN) in the wet season (84.2%). Effective water quality improvement in the Longxi River Basin hinges on targeted strategies—to mitigate diffuse agricultural sources through optimized fertilization, and to enhance the collection and treatment of rural domestic sewage. This study not only enhances the understanding of pollution source distribution and quantification in mountainous watersheds, but also serves as a vital reference for formulating targeted water environment management strategies. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article