[Objective] To investigate the variation patterns of pollutant fluxes in the Yangtze River mainstream before and after the operation of the Three Gorges Project, this study analyzes the annual variations in water quality, runoff, and pollutant fluxes at various control sections based on monitoring data from 2000 to 2023, and conducts a correlation analysis of the fluxes. [Methods] The interannual variation characteristics of pollutant concentration, runoff, and pollutant flux along the Yangtze River mainstream before and after the impoundment of the Three Gorges Project were analyzed. Meanwhile, the Spearman correlation analysis was used to evaluate the correlation between pollutant flux and runoff before and after the operation of the Three Gorges Project. [Results] From 2000 to 2023, the interannual variations in the concentrations of major pollutants including the permanganate index, ammonia nitrogen, and total phosphorus, along each section of the Yangtze River mainstream showed an overall downward trend. The year 2013 was an important turning point for the water quality change in the Yangtze River mainstream. During the “Twelfth Five-Year Plan” period, comprehensive and leapfrog progress was achieved in water pollution control across the Yangtze River Basin, leading to noticeable improvements in water quality. A higher consistency was observed between the variation trends of annual runoff and annual pollutant flux characteristic values at the sections upstream of the Three Gorges Dam, indicating a significant interception effect of the Three Gorges Project on pollutants in the Yangtze River mainstream. Among the main pollutant indicators in the Yangtze River mainstream, the permanganate index showed a highly significant correlation with runoff. The interception effect of the Three Gorges Dam amplified the influence of sediment on the total phosphorus concentration in the water body at the upstream sections, thereby reducing the impact of runoff and resulting in a non-significant correlation between total phosphorus and runoff at these upstream sections. The buffering effect of the Three Gorges Reservoir on the incoming flow from the upstream led to relatively small interannual variations in ammonia nitrogen concentration in the water body at the downstream sections, which further confirmed that the operation of the Three Gorges Project significantly impacted the variations in major pollutant fluxes in the Yangtze River mainstream. [Conclusion] The research findings can serve as a scientific basis for the management and protection of the water environment in the Yangtze River Basin.

[Objectives] Under the background of a new round of technological revolution and industrial transformation, boron is one of the important mineral sources required by global strategic emerging industries. China’s industrial development has a high demand for boron resources, but high-quality reserves are insufficient. As an important area of liquid boron reserves, the Qaidam Basin has great potential for boron resource development. The Nalenggele River watershed, located on the southern margin of this basin, is not only a typical area where hot springs, rivers, and salt lakes coexist and are hydrologically connected, but also a hotspot for studying the enrichment and mineralization of boron in the mountain-basin transition zone. [Methods] In this paper, the chemical characteristics of boron and the advantages and limitations of boron isotope tracing were systematically expounded. The boron enrichment features and hydrochemical consistency of regional geothermal water, river water, and salt lake brine water were also analyzed. Based on prior studies, the sources of high boron content in the rivers and salt lakes were discussed. [Results] 1) Boron isotopes were effective and sensitive tools for source identification,but their quantitative application in process assessment was inherently constrained by necessary preconditions due to its fractionation-prone property. 2) Geothermal waters,river waters,and salt lake waters in the Nalenggele River Basin were uniformly characterized by high boron concentrations,which was in contrast to other surface waters in the northern margin of the Eastern Kunlun Mountains. 3) Although multiple geochemical processes influenced the chemical composition of surface waters,geothermal water input constituted a dominant control on boron enrichment in the Nalenggele River Basin. [Conclusions] Based on current research on boron enrichment and mineralization in the study area,identifying the boron sources of riverine systems,quantifying weathering contributions during source-to-sink processes,and developing quantitative tracers are key research priorities.These results are expected to advance the understanding of surface boron cycling within the “hot springs-rivers-salt lakes” system in the Qinghai-Tibet Plateau.

[Objective] Dianchi Lake, an important freshwater lake in Southwest China, has experienced increasing water quality degradation and eutrophication in recent years due to urbanization and agricultural activities. Most existing studies primarily focus on interannual variations, with limited understanding of seasonal variation and spatial heterogeneity. This study aims to: (1) reveal the spatiotemporal distribution patterns of water quality in Dianchi Lake using the Water Quality Index (WQI) method; (2) evaluate eutrophication dynamics using a logarithmic power-function universal index; and (3) identify key driving factors to provide scientific support for targeted remediation strategies. [Methods] Using daily water quality data from 2021 to 2023 at ten nationally controlled monitoring stations in Dianchi Lake, the WQI—incorporating six indicators (TP, TN, COD_Mn, NH₃-N, DO, and turbidity)—was employed to classify water quality levels. Eutrophication Index (EI) calculated using the logarithmic power function model including Chl-a, TN, TP, and COD_Mn, was applied to evaluate eutrophication levels. Spatial patterns were depicted using Kriging interpolation in ArcGIS, and correlation analysis was conducted to identify the major influencing factors. [Results] 1) Spatiotemporal characteristics of WQI: (a) regarding temporal variations, the mean WQI was 65.03 (ranging from 31.33 to 82.67), with “moderate” water quality prevailing. Water quality was poorest in summer (only 16% rated “good”), primarily due to high temperatures accelerating organic decomposition, leading to decreased DO (8.40 mg/L) and increased COD_Mn (6.29 mg/L). Water quality was best in winter. (b) In terms of spatial variations, the average WQI in Caohai (68.96) was significantly higher than that in the Waihai (64.01), attributed to nutrient absorption by wetland vegetation. Severe pollution accumulation was observed in the central Waihai (e.g., Guanyinshan monitoring station) due to limited water exchange. 2) Dynamics of EI: (a) for seasonal patterns, eutrophication was most severe in spring, with an average EI of 55.166, and 16.8% of the area reached a “moderate eutrophication” level, due to runoff inputs during the peak agricultural fertilization season. Summer exhibited the greatest variation in EI (38.102-87.603), accompanied by frequent algal blooms. (b) In light of spatial differentiation, EI values in Caohai were generally higher than those in Waihai,particularly at Duanqiao and the center of Caohai, where direct urban sewage discharge was significant. In northern Waihai, areas such as Luojiaying exhibited higher eutrophication levels due to intensive human activities. 3) Key driving factors: (a) WQI was strongly positively correlated with DO (+0.492), and negatively correlated with NH₃-N (-0.485) and COD_Mn (-0.358), indicating that organic pollution primarily drove water quality variation. (b) EI was mainly influenced by TP (with a weight of 0.230) and Chl-a (0.326), suggesting that phosphorus control and algae management were crucial for mitigating eutrophication. [Conclusions] Dianchi Lake exhibits pronounced seasonal and spatial heterogeneity in both water quality and eutrophication. In summer, nonpoint source pollution should be strictly controlled, while in spring, agricultural fertilization should be limited. The ecological restoration experiences in Caohai could be extended to Waihai, and enhanced water circulation is needed in the deep-water central zone. This study innovatively integrates the WQI and EI models, establishing a replicable methodological framework for dynamic assessment of eutrophic lakes, and emphasizes the need for long-term monitoring data to refine management strategies.

[Objective] This study aims to propose a novel method for predicting effluent water quality in wastewater treatment plants, in order to enhance prediction accuracy and address the inadequate generalizability of existing models, thereby providing robust support for the operational optimization of wastewater treatment plants. [Methods] The proposed prediction framework primarily includes the following steps: First, the water quality sequence was decomposed into multiple subsequences with different characteristics using the variational mode decomposition (VMD) method. Subsequently, a comprehensive evaluation indicator (CEI) was introduced, based on which the deep learning algorithm with optimal prediction performance was selected for each decomposed subsequence. Four deep learning algorithms were involved in this study. Finally, the predicted values from each sub-model were aggregated to obtain the final effluent quality prediction. Taking the effluent chemical oxygen demand (COD) concentration of a wastewater treatment plant in Wuhan, Hubei Province as the research object, the proposed prediction framework was validated through a case study. The performance of the proposed framework was evaluated by comparing the prediction performance with that of single models. [Results] The effluent COD concentration data from a wastewater treatment plant in Wuhan were used for validation. The results showed that by decomposing the COD time series into different intrinsic mode functions (IMFs) using VMD, the complexity of the COD time series was effectively reduced. This provided simplified components for subsequent prediction, enabling the prediction model to better capture underlying patterns in the data and consequently improve prediction performance. Meanwhile, by introducing the CEI, four key evaluation indicators—mean absolute error (MAE), root mean square error (RMSE), standard deviation (STD), and mean absolute percentage error (MAPE)—were successfully integrated. This allowed for a comprehensive consideration of multi-dimensional error conditions when selecting the optimal prediction algorithm for each IMF subsequence, ensuring the comprehensiveness and accuracy of the selected algorithm. Finally, predictions were made for each different IMF based on the selected algorithm with optimal prediction performance. The results showed that this method effectively improved the overall model’s prediction accuracy, with the RMSE reaching 0.485. This confirmed that the proposed prediction framework achieved significant improvement in prediction performance compared to single models, providing strong support for accurate effluent water quality prediction in wastewater treatment plants. [Conclusions] The proposed water quality prediction framework based on VMD and multiple deep learning algorithms achieves high-precision prediction of effluent COD concentration in wastewater treatment plants by reasonably decomposing the water quality sequence and adaptively selecting prediction algorithms. The framework overcomes the limitations of existing single prediction models in handling complex nonlinear relationships, providing more accurate water quality predictions to support energy-saving and consumption-reduction decision-making in wastewater treatment plants. With significant practical value, it can be further extended in the future to predict other water quality indicators and be applied to wastewater treatment plants of different scales and types, thereby promoting intelligent operation and management in the wastewater treatment industry.

[Objective] The Hanzhong section of Hanjiang River is an important water source for the “South-to-North Water Diversion” and “Hanjiang-to-Weihe Diversion” projects. Its water quality directly affects the ecological environment and residents’ production and livelihoods along these water transfer systems. This study aims to analyze the current pollution status of heavy metals in Hanzhong section, explore their spatiotemporal distribution characteristics, and identify their primary sources. [Methods] Based on 240 sets of measured data of seven heavy metals (Hg, Pb, Cu, Zn, As, Cd, Cr) and four conventional monitoring indicators (pH, DO, NH₃-N, COD_Cr) collected from 20 sampling sites in Hanzhong section from January to December 2022, we analyzed the temporal differences of heavy metal pollution during wet season (July-September) and dry season (December-February) using an improved heavy metal pollution index (HPI). The Nemerow comprehensive pollution index was used to evaluate the current pollution status of heavy metals in the water bodies, with reference to the Class I surface water quality standard. Pearson correlation analysis and principal component analysis were jointly applied to investigate the correlations among heavy metals and between heavy metals and conventional indicators, and to identify their main pollution sources. [Results] The annual average concentrations of seven heavy metals in this river section followed the order: Zn>Cr>Cu>Pb>As>Cd>Hg, all within the Class II standard limits of GB 3838—2002. Spatially, high-concentration cross-sections mainly distributed around mining and metal smelting enterprises. Temporally, the HPI index during wet season was slightly higher than that during dry season, but neither exceeded the critical threshold. At some urban traffic arteries, due to traffic pollution and atmospheric deposition, the pollution index during dry season was relatively higher. The single-factor pollution index evaluation results indicated that Cu and Cr were primary pollution factors, followed by Hg and Zn. Correlation analysis results showed that Cd had highly significant positive correlations with Pb, Zn, and As. As was highly significantly positively correlated with Pb and Zn, and Zn was highly significantly positively correlated with Pb. Hg, Zn, Pb, Cu, As, and Cd had significant positive correlations with NH₃-N, suggesting that they had the same sources. Principal component analysis results revealed that the first principal component, including Pb, Zn, As, Cd, and Hg, was mainly affected by industrial sources, transportation, and domestic sewage discharge. The second principal component, including Cr, Hg, and Cu, was mainly affected by industrial and agricultural production activities. [Conclusion] The prevention and control of heavy metals in the water bodies of the Hanzhong section of the Hanjiang River should focus on Cu pollution, monitor the concentration changes of heavy metals such as Cr and Pb, optimize the layout of mining and smelting enterprises along the river, improve farmland soil to reduce the migration of heavy metals, and protect the water quality safety in this section of the river.

[Objective] Traditional dredging methods for reservoir sedimentation can cause resuspension of sediments, leading to the release of nutrients, and then affect the water environment. Proposing a reasonable and feasible ecological dredging scheme is important to prevent water environmental pollution caused by sediment dredging, which is a fundamental requirement for the sustainable exploitation of reservoir sediments. To address the issues of sedimentation in the Three Gorges Reservoir and secondary water pollution caused by traditional dredging methods, this study focuses on typical sediment-accumulated river sections and explores ecological dredging technologies suitable for deep-water and complex environments. [Methods] This study comparatively evaluated the characteristics, applicability, and environmental friendliness of existing reservoir dredging technologies. Based on the segmented sedimentation characteristics of the Three Gorges Reservoir, it proposed an environmentally friendly ecological dredging technology suitable for the reservoir. Typical sediment-deposited river sections in Fuling and Zhongxian were selected as representative areas. A two-dimensional hydrodynamic-suspended sediment mathematical model was established to simulate the dredging effects under different dredging equipment, and an ecological dredging technical scheme for these areas was developed. [Results] The results showed that ecological dredging technology outperforms traditional methods in precision control, pollution prevention, and resource utilization. The study recommended using pneumatic dredging for the near-dam section, pneumatic or jet dredging for the middle section, and pneumatic or eco-friendly cutter suction dredging for the upper section and fluctuating backwater area. Simulations indicated that pneumatic dredging had the least impact on suspended sediment concentration and diffusion. Based on the simulation results, comprehensive dredging recommendations were proposed, including equipment selection, construction timing, residual water treatment, and sediment resource utilization. [Conclusion] The ecological dredging scheme proposed in this paper can provide a reference for ensuring the storage capacity of the Three Gorges Reservoir and its long-term effective use, as well as sustaining comprehensive benefits. It shows strong potential for promotion and practical engineering application value.

[Objective] As the uppermost section of the lower Yangtze River, the water quality changes in the Anhui section have attracted significant attention. Investigating the spatiotemporal variation characteristics of water quality and the underlying influencing factors in this region over recent years can provide clearer guidance for future water environment management in the lower Yangtze River. [Methods] Based on daily water quality data from 29 national assessment sections in the Anhui section of the Yangtze River from 2021 to 2023, this study adopted the comprehensive water quality index method to evaluate water quality, and integrated principal component analysis, correlation analysis, and other methods to explore the spatiotemporal variation and influencing factors of water quality. [Results] (1) The daily data values of different water quality indicators at various monitoring sections in the Anhui section from 2021 to 2023 exhibited varying degrees of fluctuation. Additionally, the WQI showed a significant positive correlation with pH and dissolved oxygen (DO) (P<0.01), while it exhibited a significant negative correlation with permanganate index (COD_Mn), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) (P<0.01). (2) Overall, the water environment quality in the upstream basin in the Anhui section was superior to that of the downstream basin, with several downstream sections having WQI values above 80. The overall water quality in the Anhui section deteriorated progressively from the upstream to the downstream (flowing from the southwest to the northeast). (3) From 2021 to 2023, the overall water quality showed an upward trend, with annual average WQI values of 70.49, 72.24, and 72.49, respectively. Additionally, the quarterly trend within each year was characterized by an initial decline followed by an increase, with average WQI values for the first, second, third, and fourth quarters being 72.21, 71.74, 69.18, and 73.55, respectively. [Conclusion] (1) The overall average WQI value in the Anhui section of the Yangtze River is 71.65, indicating good water quality. COD_Mn, NH₃-N, and TN are identified as the primary indicators influencing the water environment quality of the region. (2) Spatially, the water quality of the upstream basin sections (except for XK section) is better than that of the downstream basin, which may be attributed to differences in the connectivity of upstream and downstream lake systems, the degree of mineral resource exploitation and development, and the scale of agriculture. (3) Temporally, influenced by meteorological factors such as temperature and precipitation, water quality exhibits a trend of first decreasing and then increasing across the four quarters, with better water quality in winter and spring compared to summer and autumn.

[Objectives] To enhance water quality prediction accuracy, this study aims to address the following challenges: (1) traditional prediction methods often rely on simple, elementary decomposition techniques, limiting their ability to extract meaningful data features. (2) Single models and basic optimization algorithms result in low prediction accuracy. (3) Most approaches fail to leverage the advantages of different networks to analyze components of varying complexity, leading to inefficient model utilization. (4) Few studies incorporate error correction after prediction. This study proposes a novel hybrid model for water quality prediction. [Methods] First, the original water quality sequence was decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Next, Fuzzy Dispersion Entropy (FuzzDE) categorized the components into high-, medium-, and low-complexity subsequences. Then, an Improved Mantis Search Algorithm (IMSA) optimized three distinct models: Bidirectional Long Short-Term Memory (BiLSTM) for high-complexity components, Least Squares Support Vector Regression (LSSVR) for medium-complexity components, and Extreme Learning Machine (ELM) for low-complexity components. The predictions were combined and reconstructed, and a BiLSTM-based error correction model further corrected the errors, yielding the final prediction results. [Results] The study introduced four key innovations to the original Mantis Search Algorithm (MSA): (1) combining Logistic-Tent chaotic mapping for population initialization, ensuring uniform and random distribution of initial solutions to enhance global search capability and convergence speed; (2) nonlinear acceleration factor, refining MSA’s core update formula to transition from global exploration to local exploitation, mitigating local optima entrapment; (3) elite-guided adaptive update strategy, addressing the excessive randomness in the position update strategy when Mantis attacks fail, improving late-stage search efficiency while preserving some randomness; (4) opposition-based learning, generating individuals opposite to the current individual to enhance global optimization. IMSA’s performance was validated using benchmark functions (Rosenbrock for unimodal, Michalewicz for multimodal), confirming improved global search and convergence precision. After determining the network hyperparameters, ablation experiments were conducted to analyze the contribution of each strategy to the network model, providing a clear understanding of how each strategy impacts prediction performance. Finally, the sequence of model usage was validated by using FuzzDE to calculate the complexity of each component, creating high-, medium-, and low-complexity subsequences. The learning capabilities of different networks for these subsequences were verified, with BiLSTM used to predict high-complexity components, LSSVR for medium-complexity components, and ELM for low-complexity components. [Conclusions] This study performed a simulation verification using dissolved oxygen (DO) concentrations from two sections of Youshui River (a tributary of the Yuanjiang River) and pH values from one station in the Xiangjiang River Basin. Missing values were addressed via linear interpolation. For outlier treatment, the study considered that outliers in the data might be caused by sudden pollution events and discontinuous non-point source pollution. Directly removing them could lead to information loss, so outliers were retained. After integrating decomposition, use of entropy, algorithm optimization, and error correction models, eleven comparative experiments were established to evaluate the effectiveness of each optimization method. The hybrid model’s effectiveness was validated using RMSE, R², and MAPE metrics. Ultimately, the R² reached over 90%, demonstrating that the prediction accuracy of the hybrid model outperformed other comparative models.

[Objectives] Polydisperse particles are widely present in natural environments. Accurately predicting the transport and deposition of polydisperse particles in saturated media under unfavorable conditions (where repulsive forces exist between particle and medium surfaces) is crucial. [Methods] A series of constant-flow one-dimensional sand column experiments with artificial recharge were conducted using polydisperse particles (0.375-18.863 μm) under different ionic strength conditions (1, 6, 20, and 200 mM). Deposition characteristics of polydisperse particles under different ionic strengths (unfavorable and favorable conditions) were investigated. By coupling the colloid attachment efficiency model under unfavorable conditions with the polydisperse particle deposition model, a deposition model for polydisperse particles under unfavorable conditions was established, and corresponding numerical simulations were performed. [Results] Both physical experiments and numerical simulations showed that under favorable conditions, the capture probability of polydisperse particles exhibited a non-monotonic V-shaped characteristic of first decreasing and then increasing with particle size due to uneven micro-force distribution. Compared with favorable conditions, the repulsive double-layer force between polydisperse particles and the medium surface under unfavorable conditions formed an energy barrier, leading to an 8.5%-67.6% reduction in capture probability. The smaller the particle size, the greater the reduction in particle capture probability. Under favorable conditions, the final deposition profile curve of particles exhibited a hyper-exponential distribution with “upper-steep, lower-gentle”. The total deposition amount increased with increasing ionic strength, with 16.7%-24.6% of the total deposition occurring in the upper part of the sand column. A decrease in ionic strength exacerbated the uneven “upper-steep, lower-gentle” distribution of the deposition profile. [Conclusions] The research results not only further reveal the transport and deposition characteristics of polydisperse particles under natural unfavorable conditions, but also provide a scientific basis for effective watershed water quality management.

[Objective] As an invasive fouling organism, Limnoperna fortunei causes increasingly severe damage to water conservancy projects. To develop new prevention and control methods, this study aims to explore the effects of different light conditions on the behaviors of Limnoperna fortunei. [Methods] The highly active Limnoperna fortunei of different shell lengths were collected from field, acclimated, and then placed in experimental porcelain dishes partially covered with shading plates to create shaded areas. At the beginning of the experiment, the Limnoperna fortunei were exposed to light, and the environmental conditions of different light intensities and different wavelengths of light were established. Four behavioral parameters—shell opening, adhesion, migration, and light avoidance—were used to characterize their responses to light stress, in order to investigate the effects of different light conditions on their behavioral characteristics. [Results] The results of shell opening and adhesion behaviors of Limnoperna fortunei under different light intensities showed that with increasing light intensity, shell opening frequency and material exchange decreased. Strong light inhibited the secretion of byssus and the adhesion behavior of Limnoperna fortunei to a certain extent. Different light colors varied in their effects on byssal adhesion, with white, purple, and red light showing the strongest effects. Specifically, red light at 40 000 lux had the most significant inhibitory effect on byssal adhesion. The results of migration and light avoidance behaviors of Limnoperna fortunei under different light intensities showed that as light intensity increased, migration distance and light avoidance tendency first increased and then decreased. Within the light intensity range of 10 000-20 000 lux, migration distance and light avoidance tendency peaked with increasing light intensity, but reached their minimum at 40 000 lux. The migration distance and light avoidance tendency of Limnoperna fortunei showed similar patterns under different wavelengths of light. Limnoperna fortunei with shell length of 5-10 mm were more affected by light stress and showed higher light avoidance tendency, while those measuring 10-30 mm exhibited greater light tolerance. [Conclusion] In the prevention and control of Limnoperna fortunei, strong light with a light intensity above 40 000 lux can be applied to create unfavorable conditions and reduce their activity. White, purple, and red light with a light intensity of more than 40 000 lux will reduce or affect their adhesion behavior, while light within the range of 10 000-20 000 lux is more effective for expelling Limnoperna fortunei. Although the practical application will be limited by available space, light attenuation, and other factors, light may serve as a simple, fast, effective, low-cost, and environmentally-friendly control method for Limnoperna fortunei.

[Objective] This study focuses on typical water bodies (lakes, rivers inside the polder area, and rivers outside the polder area) in the pilot zone of the Yangtze River Delta, aiming to: (1) analyze the spatiotemporal differentiation characteristics of water transparency (Secchi Depth,SD) in the three typical water bodies; (2) identify the key drivers of SD through correlation and regression models; (3) propose SD improvement thresholds based on the light compensation requirements of submerged vegetation restoration, providing scientific evidence for precise water quality management in plain river networks. [Methods] The study was based on field monitoring data from April to December 2023, focusing on water transparency and other water quality indicators in the Pilot Zone. The analysis combined trends in monthly variations of suspended solids (SS), total phosphorus (TP), chlorophyll-a (Chl-a), and turbidity to explore the spatiotemporal distribution and variation characteristics of SD. Correlation analysis and curve fitting models of SD with turbidity, SS, and TP were employed to quantify the driving mechanisms of SD. Based on the survival needs of local nearshore submerged vegetation, SD thresholds and strongly related water quality improvement targets were established. [Results] (1) River channels within embankments exhibited the highest average SD (68.75 cm), but fluctuated significantly due to rainfall disturbances. The seasonal difference in lake SD was significant (average 61.17 cm), with peak values in winter (80.14 cm) and minimum values in summer (48.00 cm). River channels outside embankments had the lowest SD (45.08 cm) due to strong hydrodynamic disturbances from navigation. Spatially, SD exceeded 70 cm in the southeast of Xitang Town and Lili Town, while SD was below 50 cm in Yuandang Lake, the northeast of Jinze Town, and Taipu River due to aquaculture pollution, construction runoff, and resuspension of bottom sediment. (2) SS concentration was the primary correlated indicator of SD (R=-0.65 to -0.79). The negative correlation between TP and SD was particularly significant in lake water (R=-0.71), and the C3 component (humic-like substances) of dissolved organic matter (DOM) shared a common origin with TP (R=0.64), indicating that TP in lake water mainly originated from soil erosion and surface runoff input. In river channels within embankments, there was no significant correlation between Chl-a and SD (R=-0.14), and Chl-a exhibited the lowest concentration (4.45 μg/L), attributed to the algae-suppressing effect of dense submerged plant growth. (3) SD in channels within embankments was significantly affected by heavy rainfall (73.90 mm in June), with a 20%-40% decrease, while SS (↑60 mg/L) and TP (↑0.12 mg/L) levels significantly increased. However, weak rainfall (≤46.93 mm) had a relatively insignificant effect on water transparency. (4) To maintain local submerged vegetation in the pilot zone, SD should be maintained above 59.14-83.06 cm (calculated based on light compensation depth), corresponding to the following thresholds for strongly correlated water quality indicators: turbidity (Turb) ≤7.46-16.14 NTU (for all water bodies), SS≤18.42-43.41 mg/L (for river channels outside embankments), and TP≤0.052-0.099 mg/L (for lakes). [Conclusion] Recommendations are proposed for future transparency enhancement projects in the pilot zone: for lakes, control land-based phosphorus input; for river channels outside embankments, enhance bottom sediment stabilization under navigation disturbances; for river channels within embankments, emphasize the algae-suppressing function of submerged vegetation, highlighting the synergistic effect of ecological restoration on transparency management.

[Objective] Surface runoff and soil erosion during rainfall events are the main drivers of phosphorus (P) loss in watersheds, particularly during the rainy season when a few key heavy rainfall events can dominate annual phosphorus load outputs. However, the characteristics of phosphorus loss, influencing factors, and lag effects during different intensities of typical rainfall events in the rainy season remain to be further explored. This study aims to understand the characteristics and influencing factors of phosphorus loss under various magnitudes of typical rainfall events and to analyze the phosphorus loss process during dominant rainfall events. [Methods] During the 2022 and 2023 rainy seasons, five rainfall events (E1-E5) of varying intensities were monitored at the outlet of the Fengyu River sub-watershed, a typical agricultural watershed in the Erhai Lake Basin, Yunnan Province. The rainfall types for events E1-E5 were moderate rain, light rain, moderate rain, heavy rain, and heavy rain. A total of 265 water samples were collected to determine three forms of phosphorus: total phosphorus (TP), total dissolved phosphorus (DP), and particulate phosphorus (PP). Redundancy analysis was used to explore the relationships between phosphorus concentrations/loads and rainfall-runoff characteristics, identifying the key factors influencing phosphorus output and its process. Lag analysis was applied to reveal the hydrological processes behind phosphorus loss. [Results] The results indicated that: (1) The trends of TP and PP concentrations were consistent with flow changes across different types of rainfall events. (2) Total rainfall, rainfall duration, peak flow, and maximum 30-minute rainfall intensity were positively correlated with phosphorus concentrations, while antecedent rainfall index was negatively correlated. (3) Particulate phosphorus dominated the phosphorus loss during rainfall events, accounting for 67%-93% of the total. (4) There was no uniform lag effect between TP, PP, and DP concentrations and discharge. TP and PP shared similar lag effects, indicating that phosphorus primarily originated from surface runoff. [Conclusion] Changes in phosphorus concentrations during rainfall events are influenced by rainfall magnitude, intensity, and antecedent soil conditions. Concentrations and loads of all phosphorus forms were strongly correlated with rainfall duration, total rainfall, peak flow, and 30-minute maximum intensity (I30). The relationship between nutrient concentration and discharge is jointly determined by total rainfall, rainfall duration, antecedent conditions, and hydrological regime. By identifying the characteristics of phosphorus output under various rainfall events during the rainy season, this study provides insights into phosphorus load contributions and supports the control of phosphorus pollution and eutrophication in watershed water bodies.

Biological activated carbon (BAC) filters that have been in service for an extended period (over 7 years) may encounter a decline or even a complete loss in the removal efficiency of organic compounds. To investigate the removal efficiency of organic compounds in long-serving BAC filters and propose targeted operational optimization strategies, we selected a 12-year-old BAC filter at the LC water treatment plant (WTP) in City Z as our research subject. We analyzed its operational performance by evaluating the total organic carbon (TOC) removal efficiency and explored optimization strategies from the aspects of ozone (O₃) dosage and backwashing methods. The results revealed that the LC WTP experienced significant fluctuations in the organic compound removal efficiency, with a high risk of ineffectiveness. Biodegradation is the primary mechanism for organic compound removal in long-serving BAC filters, accounting for approximately 67%, while adsorption only contributes 33%. When the specific O₃ dosage ranged from 0.36 to 0.52 mg O₃/mg TOC, the BAC filter at the LC WTP performed relatively well. Either too low or too high O₃ dosage was unfavorable for enhancing the performance of long-serving BAC filters. At the LC WTP, the TOC levels in the effluent from both the surface and upper layers of the BAC filter increased before backwashing. Backwashing with water containing 0.5 mg/L of effective chlorine can improve the performance of long-serving BAC filters.

Water turbidity is a crucial indicator of aquatic environment as it directly reflects water quality. Taking Liangzi Lake as the research object, this paper utilized Aqua MODIS remote sensing data from 2003 to 2021 on the Google Earth Engine (GEE) remote sensing cloud computing platform to invert the water turbidity of the Liangzi lake and analyze its spatiotemporal variation characteristics. Results indicate the following: (1) Liangzi Lake features higher water turbidity in winter and spring than in summer and autumn, with turbidity exceeding 30 NTU in winter and spring and below 20 NTU in summer and autumn.(2) The water turbidity of Liangzi Lake has gradually decreased in recent years. Before 2011, the average water turbidity was approximately 30 NTU, with significant spatial variation. Areas close to land had a higher turbidity of about 50 NTU compared to the average. After 2011, the average water turbidity gradually decreased to about 25 NTU, with areas close to land experiencing a notable decrease to approximately 30 NTU. (3) The water turbidity of Liangzi Lake is correlated with land cover types within the Liangzi Lake Protected Area, with the green area having the greatest impact with a correlation coefficient of -0.63. Larger green areas correspond to lower turbidity. Over the past ten years, the overall water turbidity of Liangzi Lake has decreased from 30 NTU to 25 NTU, a reduction of 16.67%, indicating significant achievements in the management and improvement of Liangzi Lake.

Accurately and efficiently predicting lake water quality is vital for water resource protection, ecological balance, and economic development. We propose a combined prediction model for total nitrogen (TN) concentration in lakes, integrating modal decomposition, multidimensional feature selection, Temporal Convolutional Network (TCN), self-attention mechanism, bidirectional long short-term memory (BiLSTM), and bidirectional Gate Recurrent Unit (BiGRU). First, we apply variational mode decomposition to break down the original TN sequence into intrinsic mode functions (IMFs) of different frequencies. This step effectively reduces the complexity and non-stationarity of the original sequence. Next, we use the random forest algorithm to select highly correlated features for each IMF. Then, we feed the filtered feature matrix into the TCN-BiLSTM hybrid network equipped with a self-attention mechanism for modeling. This network extracts key temporal information from the hidden data. Finally, to enhance the model’s prediction accuracy, we employ the BiGRU network to learn the detailed features of the residual sequence. We then fuse the residuals with the model’s prediction results to obtain the final prediction value. We conduct an experimental analysis using the water quality data from the Duchang Monitoring Station in Poyang Lake. The results demonstrate that, compared with other models, our model significantly improves the prediction accuracy of TN concentration. Specifically, its mean absolute error (MAE) is 0.03 mg/L, root mean square error (RMSE) is 0.049 mg/L, and coefficient of determination (R²) is 0.992.

Human activities, including reservoir construction, river diversion, embankment construction, and hydrological regulation in the upper reaches of the Yangtze River, have severely impacted fish habitats, causing environmental degradation and substantial habitat loss. To gain deeper insights into this issue, we selected habitat data over the past three years from four typical river sections at Zhutuo Station, which is in the middle part of the Yangtze River protection zone. We analyzed the hydrological features, water environment quality, flow velocity, water depth, and spatial distribution patterns of rare and endemic fish species to comprehensively characterize fish habitats. Based on the analysis results, we propose to establish comprehensive measures for the proliferation and release of rare and endemic fish species. Our research shows that 99% of the rare and endemic fishes aggregate in areas with flow velocities of 0.0-1.4 m/s, among which approximately 80% gather in slow-flow zones with velocities below 0.8 m/s. During the wintering period, the average water depth where fishes gather is 44.28 meters, with a minimum of 15 meters and a maximum of 57.47 meters. When the absolute water depth in the upper reaches of the Yangtze River ranges from 23 to 56 meters, it fails to provide suitable fish habitats. However, during the water-storage period, raising the water level can improve the living conditions of rare and endemic fish species while meeting requirements for flow velocity, water depth, and minimal interference. Studying the habitat characteristics of rare and endemic fish resources in the upper reaches of the Yangtze River enables us to better understand fish distribution and migration patterns, facilitating the rational planning and management of fishery resources. Simultaneously, researching proliferation measures can guide management actions such as artificial release, aiding in the restoration and enhancement of fish populations and ensuring the sustainable development of fisheries.

Core sediment samples collected from three distinct areas of Taojia Lake in Songzi County, Hubei Province, were tested for heavy metals including copper (Cu), zinc (Zn), chromium (Cr), nickel (Ni), lead (Pb), and arsenic (As), as well as grain size. The contamination status was assessed using the potential ecological risk index (RI), enrichment factor (EF), and geo-accumulation index (Igeo). Sources of these contaminants were traced through correlation analysis and positive matrix factorization (PMF) modeling. Results indicated that 1) Heavy metal contamination was most severe in the central area of the lake, followed by those in lakeshore and riparian zone in descending order. As depth increased, the contamination at lakeshore alleviated, but aggravated in the central area and the riparian zone. 2) Among the tested heavy metals, arsenic (As) and chromium (Cr) showed the most severe contamination, with As of light contamination and Cr mild-to-moderate contamination. Copper (Cu), zinc (Zn), nickel (Ni), and lead (Pb) exhibited less pollution. 3) Correlation analysis and PMF modeling revealed that heavy metals in the sediments primarily originated from mining activities (18.8%), natural sources (52.4%), and agricultural and industrial activities (28.8%). Specifically, Cu was mainly derived from mining activities, Pb from natural sources, and As from agricultural and industrial activities. Zinc (Zn), nickel (Ni), and Cr were influenced by all three sources. 4) Hydrodynamic conditions and human activities have led to spatial variations in heavy metal distribution in Taojia Lake. The strong hydrodynamics in the riparian zone have diluted heavy metal concentrations, resulting in lower heavy metal levels compared to the lakeshore and the central area. Changes in hydrodynamic conditions affected the vertical distribution of Pb and Ni. Variations in the distribution of Cu and Zn are primarily attributed to mining industrial activities, while annual increases in As levels were linked to agricultural activities.

The pollution characteristics of microplastics in the hydro-fluctuation belt (HFB) of Danjiangkou Reservoir, especially under different land use types, remain inadequately understood. This study aimed to investigate the current contamination levels of microplastics in soil samples from various land use types—open space, forested land, and cropland—within the HFB of Danjiangkou Reservoir. Our findings revealed average microplastic abundances in open space, forested land, and cropland at 985±333 n/kg, 798±143 n/kg, and 625 ± 175 n/kg, respectively. The dominant polymer types identified were polyethylene (PE), polypropylene (PP), and polystyrene (PS), accounting for 44.0%, 27.2%, and 11.1% of the total microplastics, respectively. Over 75% of the microplastics were fibrous in form, and around 39.5% of the particles exhibited sizes ranging from 50 to 500 μm. Blue was the most frequently observed color among the microplastics. Moreover, there were significant variations in microplastic abundances across different land use types in the HFB, with the abundance in open space being the largest, followed by forested land and cropland in descending order. These results offer valuable insights into the occurrence characteristics of microplastics in the HFB under diverse land use scenarios and serve as a reference for future research in this area.

To explore the treatment model for farmland drainage pollutants, this study conducted an experimental investigation from July to September 2023 on the interception and purification effects of ecological drainage ditches, constructed wetlands, and their combined system in the Hetao Irrigation District. The findings demonstrate that various treatment modes in ecological drainage ditches and constructed wetlands effectively remove nitrogen and phosphorus pollutants. Specifically, the reduction efficiency of total nitrogen (TN) and total phosphorus (TP) in ecological drainage ditches treated with skew stem Astragalus membranaceus, yellow sweet clover, alfalfa, bio-ball matrix, and bio-sheet matrix was 21.09% and 23.84%, 12.06% and 26.67%, 20.08% and 34.15%, 23.65% and 20.56%, and 19.92% and 25.83%, respectively. In the emergent and submerged aquatic plant areas, the reduction efficiency of TN and TP was 24.28% and 17.89%, and 26.85% and 10.21%, respectively. The ecological drainage ditches treated with alfalfa and bio-ball matrix showed superior performance in TP and TN removal, respectively. Similarly, emergent and submerged plant areas exhibited better TP and TN removal efficiencies. The combined system of ecological drainage ditch and constructed wetland achieved TN and TP removal rates of 37.55% and 11.47%, respectively, effectively realizing step-by-step interception, adsorption and purification of pollutants. This system significantly enhances the removal and purification of nitrogen and phosphorus pollutants, thus mitigating agricultural non-point source pollution.

As one of the regions with the most concentrated distribution of freshwater lakes in China, the middle and lower reaches of the Yangtze River has seen significant improvements in water ecological environment in the past decade. However, the trend of lake eutrophication has not been fundamentally contained; the water environment quality and eutrophication of some lakes continue to deteriorate. Current analyses of lake pollution are limited by inadequate samples, making it challenging to discern regional characteristics among lake groups. In this paper, the characteristics of nitrogen and phosphorus pollution in surface sediments of 23 major lakes in middle and lower Yangtze River were analyzed. Results reveal that the average total nitrogen (TN) concentration in river-connected lakes and half-connected lakes are the highest, with no significant differences observed, followed by suburban lakes and urban lakes in descending order. Conversely, the mean total phosphorus (TP) concentration features notable differences, with urban lakes ranking the highest, followed by suburban lakes, river-connected lakes, and half-connected lakes in descending order. Although improvements have been made in the eutrophication status of regional urban and suburban lakes, the comprehensive pollution degree classification suggests that most lakes in the area still face severe pollution challenges. Therefore, there is still an urgent need to strengthen efforts in controlling nitrogen and phosphorus eutrophication. The results provide an overall understanding of the eutrophication of lakes in the middle and lower reaches of the Yangtze River, and support the prevention and control of eutrophication of lakes in the Yangtze River basin.