[Objective] Hyperspectral remote sensing technology offers a new approach for non-contact, real-time sensing of water quality parameters. Existing research has shortcomings in developing dedicated sensing equipment and constructing multi-parameter collaborative inversion algorithms. This study aims to systematically explore the spectral response characteristics of five key water quality parameters—chemical oxygen demand (COD), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH₃-N)—based on a self-developed hyperspectral water sensing instrument, and to establish an adaptive hyperspectral sensing algorithm optimization framework for multiple water quality parameters. This framework aims to overcome the adaptability limitations of traditional single algorithms in complex water environments and provide technical support for achieving 24/7 continuous online water quality monitoring. [Methods] First, continuous spectral data and corresponding measured values of water quality parameters from a large number of water samples were collected using the self-developed hyperspectral water sensing instrument. Through spectral analysis, the absorption, reflectance characteristics, and differential patterns of COD, TSS, TP, TN, and NH₃-N within the visible to near-infrared spectral range were revealed, and the sensitive characteristic bands for each parameter were selected accordingly. On this basis, a multi-level, multi-type inversion model system was constructed. At the empirical model level, single-band regression, band ratio, and normalized difference index were employed to establish statistical relationships between spectral features and water quality parameters. At the machine learning level, BP neural network, random forest (RF), and XGBoost were introduced to fully exploit the nonlinear mapping relationships within the hyperspectral data. Furthermore, an adaptive algorithm optimization framework was proposed. By comparing the inversion accuracy and stability of various models across different water quality parameters and concentration ranges, this framework automatically matched the optimal inversion algorithm for each parameter, thereby achieving the optimal configuration for multi-parameter collaborative sensing. An independent dataset partitioning strategy was adopted for model training and validation, with the coefficient of determination (R²) and mean absolute percentage error (MAPE) as the core evaluation indicators to ensure the objectivity and reliability of the assessment results. [Results] The constructed adaptive algorithm optimization framework achieved excellent performance in the inversion of all five water quality parameters. In the training and validation datasets for the optimal algorithms, the R² values of the corresponding optimal models for each parameter ranged from 0.88 to 0.99, exhibiting extremely high fitting accuracy and generalization capability. From a practical accuracy perspective, 100% (COD), 91% (TSS), 90% (TP), 90% (TN), and 95% (NH₃-N) of total samples had a MAPE below 30%, fully meeting the practical requirements for water quality monitoring. Specifically, the inversion accuracy for the COD parameter was the highest, with all samples meeting the accuracy threshold, indicating that hyperspectral data possessed exceptional characterization capability for organic pollutant concentrations. NH₃-N followed closely, with 95% of samples meeting the accuracy requirement, reflecting significant spectral response characteristics of ammonia nitrogen in specific bands. The compliance rates for TSS, TP, and TN all exceeded 90%, verifying the universality and robustness of the framework in the inversion of different types of water quality parameters. Compared with traditional single-model methods, the adaptive optimization strategy significantly improved the overall accuracy and stability of multi-parameter collaborative inversion, effectively overcoming the adaptability deficiencies of single algorithms under complex water conditions. [Conclusion] This study proposes and validates an adaptive hyperspectral sensing algorithm optimization framework for multi-parameter water quality monitoring based on a self-developed hyperspectral water sensing instrument. The core innovations of this optimization framework lies in the following: first, it breaks through the conventional paradigm of “one algorithm fits all parameters” in traditional water quality hyperspectral inversion by implementing a multi-model competitive optimization mechanism, achieving automatic matching of the optimal algorithm for each parameter. Second, it integrates empirical models and machine learning models into a unified optimization system, balancing the model interpretability with nonlinear fitting capability. Third, the research findings demonstrate strong engineering application prospects and can directly support the construction of 24/7 continuous online water quality monitoring systems.

[Objective] The changes and specific diffusive fluxes between carbon sources and sinks in the Three Gorges Reservoir have long been a focal issue in monitoring and analyzing greenhouse gas changes within the reservoir. Few studies have focused on the differences and potential relationships in greenhouse gas carbon fluxes of the water level fluctuation zone and the water body. To clarify the spatial interaction of greenhouse gas carbon fluxes between the water level fluctuation zone and the water body in the Hubei section of the Three Gorges Reservoir, this study selects the Shennong Stream, an important tributary in this section, as the research object. [Methods] The Picarro G2301 greenhouse gas online analyzer was employed to monitor carbon dioxide and methane fluxes in June, July, August, and September 2024. Based on their spatial positions, ArcGIS, SPSS, and other statistical analysis tools were applied to analyze the variation characteristics and interaction relationships. [Results] (1) Carbon source and carbon sink of carbon dioxide and methane carbon fluxes vary between the water level fluctuation zone and the water body in the study area. The carbon dioxide fluxes in the water level fluctuation zone were approximately 400 mg/(m²·h), with a standard deviation of 228.73 and a coefficient of variation of 0.55, indicating an emission state. The methane carbon fluxes ranged from -0.04 mg/(m²·h) to 0.01 mg/(m²·h), with an average value of -0.01 mg/(m²·h), including absorption state and emission state. The carbon dioxide fluxes in the water body were in an absorption state, and the methane fluxes of water body were similar to those in the water level fluctuation zone, exhibiting an absorption state in some months and an emission state in others. The corresponding standard deviation and coefficient of variation were 0.16 and 1.60, respectively. (2) The diffusive fluxes of greenhouse gases in the water level fluctuation zone and the water body varied in different months. The carbon dioxide fluxes in the water level fluctuation zone peaked in July, exceeding 650 mg/(m²·h), and the methane fluxes in the water body peaked in June, exceeding 0.30 mg/(m²·h). (3) The carbon dioxide and methane fluxes in the water level fluctuation zone and the water body of the study area differed in both correlation direction and magnitude. Methane and carbon dioxide fluxes in the water level fluctuation zone were negatively correlated, with a correlation coefficient of -0.44. The pattern was similar in the water body, and the correlation between methane and carbon dioxide fluxes in the water body was also negative, with a correlation coefficient of -0.89. The carbon dioxide fluxes in the water level fluctuation zone were positively correlated with those in the water body, with a correlation coefficient of 0.45, whereas the methane fluxes in the water level fluctuation zone and water body were negatively correlated, with a correlation coefficient of -0.78. (4) Monthly temperature during the monitoring period may affect greenhouse gas carbon fluxes in the water level fluctuation zone and the water body in the Hubei section of the Three Gorges Reservoir. The carbon dioxide fluxes in the water level fluctuation zone were negatively correlated with temperature, with a correlation coefficient of -0.53, while the methane fluxes were positively correlated, with a correlation coefficient of 0.13. The carbon dioxide fluxes in the water body were positively correlated with temperature, with a correlation coefficient of 0.51, while the methane fluxes were negatively correlated with temperature, with a correlation coefficient of -0.65. These patterns required further verification through additional monitoring data. [Conclusion] As the transition zone between aquatic and terrestrial ecosystems, the water level fluctuation zone of the Three Gorges Reservoir area experienced a large migration of material and energy and a high degree of interconnection,leading to a complex relationship between the carbon cycles of water, soil,and vegetation.Consequently,the influence of spatial changes should be considered when exploring the dynamics of greenhouse gas carbon fluxes.

[Objective] This study investigates the synergistic and antagonistic effects of multiple factors on water self-purification capacity and examines self-purification efficiency of water under the combined influence of multiple factors, aiming to overcome the limitations of previous studies and provide new quantitative evidence for understanding water self-purification capacity in complex aquatic environments. [Methods] To investigate the interactions among multiple factors affecting water self-purification capacity, orthogonal experiments were conducted on water samples collected from the northwest side of a lake in Wuhan, which was affected by mixed pollution from domestic sewage and industrial wastewater and contained a microbial community. The orthogonal experiment results were analyzed using a combination of response surface methodology (RSM) and structural equation modeling (SEM) to reveal the nonlinear interaction mechanisms among typical influencing factors, including meteorological conditions, pollutant concentration background values, and biological activity. [Results] The results of orthogonal experiments showed that water self-purification efficiency (η), nitrification rate (r_N), and oxygen mass transfer efficiency (K_La) of water all varied nonlinearly across experimental groups, with maximum values observed at approximately temperature (T)=25 ℃, dissolved oxygen concentration (DO)=6 mg/L, flow rate (v)=0.1 m/s, microbial abundance (M)=5×10⁷ CFU/mL, and chemical oxygen demand (C)=100 mg/L. RSM analysis indicated that at the optimal parameter combination (T=24.8 ℃, DO=5.9 mg/L, v=0.27 m/s, C<100 mg/L, and M=4.7×10⁷ CFU/mL), η significantly increased to 83.24%, r_N exceeded 1 mg/(L·h), and K_La reached its maximum. In addition, in the binary factor interactions, the most significant interaction term was T×DO, with a synergistic contribution of 45.52%. SEM path analysis showed that v and T influenced the water self-purification process through both direct effect and indirect effect. Paths: v➝DO➝η; v➝DO➝Mi( microbial activity, including r_N and M)➝η; T➝DO➝η; T➝Mi➝η; T➝DO➝Mi➝η, where the direct effect of v on water self-purification process was 0.41, and the indirect effect was 0.25; and the direct effect of T on water self-purification process was 0.35, and the indirect effect was 0.25. The total effect of T and v on water self-purification process increased by 0.25 compared with the direct effect of each factor alone. [Conclusion] The results confirm that there is a synergistic amplification mechanism and significant threshold effects among different environmental influencing factors. Binary factor interactions not only show significant effects, but also factors originally negatively correlated with water self-purification capacity can significantly reduce their inhibitory influence on water self-purification efficiency after complex interactions with other factors. Additionally, factors such as flow rate and temperature affect the water self-purification process both directly and indirectly through their effects on other influencing factors. These findings provide a new quantitative basis for evaluating water self-purification ability and its controlling factors in complex aquatic environments.

[Objective] The time series of reservoir water quality indices,especially dissolved oxygen content, exhibit strong nonlinearity,high complexity,and uncertainty,which lead to insufficient accuracy of single prediction models.This study aims to construct a high-precision hybrid prediction model that integrates time series decomposition,intelligent optimization,and residual correction,thereby significantly improving the prediction accuracy of dissolved oxygen (DO) content and providing reliable support for water environment management and pollution early warning. [Methods] The core procedures of the proposed hybrid prediction model are as follows. 1) Data decomposition and reconstruction. Singular spectrum analysis (SSA) was applied to decompose the dissolved oxygen time series, and the series was reconstructed into trend components, periodic components, and residual components to reduce sequence complexity and highlight features at different frequencies. 2) An improved dung beetle optimizer (IDBO) which integrates piecewise chaotic mapping and opposition-based learning strategies was designed to enhance population diversity and initialization quality. The improved IDBO was used to optimize key hyperparameters of the GRU network, including the number of hidden layer neurons and the initial learning rate. 3) Component prediction and residual correction. The GRU model optimized by IDBO was used to predict the trend component and periodic components separately. A residual series prediction difference correction method (DCM) was proposed. The residual component was first predicted using GRU, and the difference sequence between the predicted values and the observed values was calculated. Variational mode decomposition (VMD) was then applied to the difference sequence to fully extract high-frequency detail information. Each decomposed component was predicted using GRU and aggregated to obtain the predicted difference values. Finally, the predicted differences were compensated into the initial residual prediction to obtain the corrected residual prediction results. 4) Model integration and validation. The prediction results of the three components were aggregated to obtain the final DO prediction values. Measured dissolved oxygen data from Daheiting Reservoir in Tangshan, Hebei Province were used for experiments. The dataset contained 2352 records with a sampling interval of four hours. Root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and the coefficient of determination (R²) were used as evaluation metrics. The proposed model was compared with GRU, SSA-GRU, SSA-DBO-GRU, SSA-IDBO-GRU, and models reported in the literature such as LSTM and PSO-GRU. [Results] The proposed SSA-IDBO-GRU-DCM hybrid model achieved the best performance among all comparative models. The prediction errors were significantly reduced, with an RMSE of 0.580 2 mg/L, an MAE of 0.329 2 mg/L, an MRE of 0.0269, and an R² of 0.918 8. Ablation experiments confirmed that the proposed IDBO improvement strategies effectively enhanced hyperparameter optimization accuracy. The residual difference correction method (DCM) significantly improved the prediction performance of the residual component and was the key factor contributing to the overall accuracy improvement. These results fully demonstrated the effectiveness and superiority of the “decomposition-optimization-correction” framework. [Conclusion] SSA effectively decouples the complex characteristics of water quality time series. IDBO efficiently and accurately optimizes GRU hyperparameters. The proposed VMD-GRU-based residual difference correction method (DCM) is the key innovation for improving overall prediction accuracy. The proposed model significantly improves the prediction accuracy of dissolved oxygen content and provides an efficient and reliable new approach for reservoir dissolved oxygen prediction. Future work can extend this framework to the prediction of other key water quality parameters such as ammonia nitrogen and total phosphorus, and further explore the integration of natural evolutionary strategies to improve computational efficiency and generalization ability.

[Objective] Microplastics are widely present in reservoirs across China, posing threats to water quality safety and the stability of reservoir ecological functions. Accurately assessing the current status of microplastic pollution in China’s reservoirs, analyzing their migration patterns and environmental behavior, and scientifically evaluating the associated ecological risks are essential prerequisites for implementing effective management and control measures. [Methods] This study systematically reviewed recent data on microplastic pollution in China’s reservoirs, summarized the impact of reservoir operation on microplastic transport behaviors, analyzed associated ecological and health risks, and proposed feasible prevention and control strategies based on current plastic restriction policies. [Results] 1) Research on microplastic pollution in China’s reservoirs primarily focused on the Yangtze River and its tributaries, followed by the Xiaolangdi Reservoir on the Yellow River, the Erdaozha Reservoir on the Haihe River, and the cascade reservoirs in the Shaying River Basin. 2) Field investigations revealed that the abundance of microplastics in the Jiayan Reservoir was relatively high, with water column microplastic abundance ranging from approximately 11 000 to 61 700 particles/m³ and sediment microplastic abundance ranging from 2 600 to 15 700 particles/kg. The Three Gorges Reservoir received considerable attention regarding its microplastic pollution status, with water column microplastic abundance ranging from 800 to 6 214 particles/m³ and sediment microplastic abundance ranging from 1 031 to 63 081 particles/kg. The Guanyinyan Reservoir on the Jinsha River and the cascade reservoirs in the middle and lower reaches of the Hanjiang River exhibited a relatively moderate level of microplastic abundance, while other reservoirs showed comparatively lower levels. 3) A diverse range of microplastic types was detected in reservoirs, predominantly smaller than 1 mm. Microplastic shapes included fibers, fragments, films, and microbeads. The primary polymer types identified were polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), polyamide (PA), and polyvinyl chloride (PVC). 4) In terms of microplastic origin, secondary microplastics constituted the majority in reservoirs, mainly derived from plastic waste associated with daily life, fishing and shipping, agricultural irrigation, and tourism activities. The primary sources of microplastics in reservoirs were upstream areas and tributary inflows. Additionally, rainfall and agricultural irrigation facilitated the transport of land-based microplastics into reservoir waters via surface runoff, while atmospheric deposition contributed to the settling of microplastics from the air into reservoir water bodies. [Conclusion] Reservoirs in China are generally polluted by microplastics, and diverse microplastic types pose potential threats to the ecological environment. Reservoir operations significantly affect the environmental behavior and transport of microplastics through dam interception and changes in hydrological and hydrodynamic conditions, indirectly influencing the ecological and environmental effects of microplastics. Currently, effective prevention and control measures for microplastic pollution in reservoirs are insufficient and face significant challenges. We recommend to strengthen the monitoring of microplastic pollution in reservoirs and to develop prevention, control, and removal technologies to alleviate microplastic pollution in reservoirs and ensure the health of reservoir ecosystems.

[Objective] This study focuses on the practical demand for advanced nitrogen and phosphorus removal from the tail water of urban wastewater treatment plants. Aiming at the problem of low carbon-to-nitrogen ratios in urban wastewater treatment that result in unsatisfactory nitrogen removal efficiency, this study investigates the effects of different combinations of emergent plants and composite substrates on nitrogen and phosphorus removal from tail water through experiments. The application potential of natural manganese sand as a substrate is evaluated, providing theoretical and practical support for the optimization of constructed wetland technology and the resource utilization of manganese ore. [Methods] Three emergent plants, Acorus calamus L., Iris wilsonii C. H. Wright, and Scirpus validus Vahl were selected. Three experimental groups were established based on pairwise plant combinations, including Gp1 (Iris wilsonii C. H. Wright + Scirpus validus Vahl), Gp2 (Iris wilsonii C. H. Wright + Acorus calamus L.), and Gp3 (Scirpus validus Vahl + Acorus calamus L.). A composite substrate without plants was used as the blank control group (BC). The composite substrate consisted of natural manganese sand, zeolite, and ceramsite mixed at a ratio of 1∶3∶1. Constructed wetland systems were built using transparent polyethylene cylindrical columns with a height of 500 mm and a diameter of 160 mm to simulate the tail water treatment process of wastewater treatment plants. The experiment lasted for 70 days. During the experimental period, water quality indicators including total nitrogen, total phosphorus, ammonia nitrogen, and nitrate nitrogen were measured regularly. High-throughput sequencing was used to analyze the structure of microbial communities in plant root zones. [Results] All three plant combinations effectively removed nitrogen and phosphorus pollutants from the tail water, and the Gp3 group (Scirpus validus Vahl + Acorus calamus L.) showed the best performance. The total phosphorus removal rate reached 93.2%, the total nitrogen removal rate was 77.7%, the ammonia nitrogen removal rate was 91.7%, and the nitrate nitrogen removal rate was 70.6%. Pollutant concentrations in all experimental groups decreased rapidly during the first 30 days and then tended to stabilize, while the purification performance of the blank control group was significantly poorer. Microbial community analysis showed that the Gp3 group exhibited the highest microbial diversity and richness, with a Shannon index of 7.24 and unique OTUs accounting for 69.15%. The dominant phyla were Proteobacteria and Bacteroidetes, which together accounted for nearly 80%. Among them, Gammaproteobacteria played a key role in the denitrification process, and its relative abundance in the Gp3 group reached 29.2%, which was the highest among the three groups. [Conclusion] The synergistic effects of plant combinations and root-associated microbial communities in constructed wetlands are the key to efficient nitrogen and phosphorus removal, and the combination of Scirpus validus Vahl and Acorus calamus L.performs particularly well in advanced tail water treatment. The composite substrate containing manganese sand shows potential application value, while the specific mechanisms by which it promotes plant growth and microbial community formation still require further investigation. This study confirms the optimal purification performance of the Scirpus validus Vahl and Acorus calamus L. combination and the potential application value of manganese sand as a substrate, providing a reference for plant selection and targeted microbial cultivation in constructed wetlands for wastewater treatment plant tail water.

[Objective] As the core water source of the middle route of the South-to-North Water Transfer Project, the spatiotemporal distribution of water temperature in Danjiangkou Reservoir directly affects the aquatic ecological processes and water quality evolution within the reservoir area. This study aims to clarify the spatiotemporal distribution patterns of water temperature in Danjiangkou Reservoir through prototype observations across the entire reservoir, to identify thermal stratification types, and to reveal the factors influencing differences in thermal structure between the Danjiang Reservoir and the Hanjiang Reservoir, thereby providing a scientific basis for reservoir water quality management, ecological protection, and so on. [Methods] In 2023, prototype observations of water temperature were carried out in Danjiangkou Reservoir during four typical hydrological periods, including the dry season (February), the normal flow season (June), and the flood season (September and October). A total of 16 observation sections were deployed throughout the entire reservoir, including one section in front of the dam, seven along the mainstream of the Hanjiang River, and eight along the mainstream of the Danjiang River. Based on the observation data, multiple discriminant methods such as water residence time (T_r), runoff-storage capacity ratio (α), reservoir width-depth ratio (R), and densimetric Froude number (Fr) were comprehensively applied to systematically analyze the thermal stratification structure of the reservoir and its spatiotemporal evolution characteristics, and to further explore the influence mechanisms of meteorological, hydrological, and topographic factors on thermal stratification. [Results] (1) The vertical thermal stratification structure of Danjiangkou Reservoir exhibited a significant seasonal variation pattern: during the dry season (February), the water body was well mixed with no obvious stratification. During the normal flow season (June), stable stratification formed, and increased air temperature and enhanced inflow strengthened vertical mixing of the water body, resulting in a double-thermocline type vertical water temperature structure in the Han Reservoir, with a surface-to-bottom temperature difference of approximately 20 ℃. During the flood seasons (September-October), the thermocline deepened, and thermal stratification was the most stable and exhibited greater thickness. (2) The reservoir’s surface water temperature was highly correlated with air temperature (R²=0.976), whereas variations in middle and bottom water temperatures exhibited a lag, regulated by heat transfer within the water body and hydrological processes. (3) The Dan Reservoir and the Han Reservoir exhibited significantly different hydrological and hydrothermal characteristics. The Han Reservoir had a shorter water residence time (257 days), a larger runoff-storage capacity ratio (α=1.47), and stronger hydrodynamic forcing, exhibiting characteristics of a riverine reservoir. Its thermocline was mainly controlled by inflow dynamics, with isotherms showing an inclined pattern. The Dan Reservoir had a longer water residence time (336 days), a smaller runoff-storage capacity ratio (α=1.04), and slower water flow conditions, showing characteristics of a lacustrine reservoir. Its thermocline was primarily controlled by solar radiation and meteorological conditions, with more horizontally distributed isotherms and a more stable and persistent stratification structure. (4) Operation of the intake in front of the dam had an impact on the local thermal structure, and clustering of isotherms was observed at depths near the intake. [Conclusion] Through systematic prototype observations, this study comprehensively reveals the complex spatiotemporal thermal stratification structure of Danjiangkou Reservoir, particularly confirming the existence of a double thermocline during the normal flow season and the essential differences in stratification mechanisms between the Dan Reservoir and the Han Reservoir. This study innovatively demonstrates that two thermal stratification modes, lacustrine (Danjiang Reservoir) and riverine (Hanjiang Reservoir), coexist within the Danjiangkou Reservoir, providing an important foundation for understanding migration and transformation of nitrogen and phosphorus, algal growth, and seasonal water quality changes in the reservoir area.

[Objective] Minimum ecological flow (e-flow) targets are increasingly used as enforceable constraints in basin management, but their spatiotemporal reliability and ecological representativeness remain insufficiently evaluated at large scales. This study aims to quantify e-flow compliance across the Yangtze River Basin (YRB), identify the drivers of noncompliance and spatial heterogeneity, examine how compliance aligns with river ecological environment conditions, and propose targeted recommendations for improving goal setting, monitoring, and assessment. [Methods] The daily e-flow compliance records were compiled for 114 key control cross-sections from the Yangtze River Water Resources Commission’s monthly monitoring bulletins between January 2023 and July 2024. Compliance was summarized as (i) whether each cross-section met the minimum target throughout the full study period, and (ii) the number of noncompliant days at each cross-section and by month. For ecological environment linkage, 40 e-flow cross-sections were matched with nearby national automated surface-water quality stations, and dissolved oxygen, permanganate index, total nitrogen, total phosphorus, and water-quality class were examined. Subsequently, a four-quadrant diagnostic framework was constructed using mean noncompliance days and the mean share of days classified as Class Ⅳ to Inferior Ⅴ. [Results] Throughout the study period, 71 of 114 cross-sections (62%) fully met the minimum e-flow targets, whereas 43 cross-sections (38%) experienced noncompliance ranging from 1 to 170 days (mean 25.8 d; median 5.0 d). Compliance exhibited a pronounced seasonal unimodal pattern, with lower performance in winter and spring and higher performance in summer and autumn. Specifically, 901 noncompliance days occurred from January to March and from November to December, compared to only 118 days from April to October. Notably, all cross-sections met targets in August. Interannual variability was substantial. From January to July, the number of noncompliance days decreased from 814 in 2023 to 335 in 2024, and the monthly average number of noncompliant cross-sections declined from 18.8 to 10.5. Spatially, heterogeneity was strong among secondary basins, and the left bank outperformed the right bank. Mechanistically, major contributors included seasonal unevenness in precipitation and runoff, differences in dam regulation, reduced mainstream-to-lake diversion affecting the Dongting system, intensive agricultural withdrawals in lake-dominated right-bank regions, and governance and measurement challenges in cross-province water allocation. For the 40 paired sites, pollutant indicators showed no significant monotonic relationship with e-flow compliance, and the total phosphorus could even appear lower in low-flow and noncompliant months due to particulate phosphorus dynamics. The quadrant analysis indicated that 60% of sites fell into high-match zones, but 16 sites showed notable mismatches, suggesting that e-flow compliance was a necessary but insufficient condition for good ecological environment status and that relying solely on flow as a warning indicator remained uncertain. [Conclusion] Large-scale e-flow compliance in the YRB is generally favorable but exhibits strong seasonal, interannual, and governance-linked spatial heterogeneity. To improve ecological relevance and management effectiveness, the following recommendations are proposed: (i) shifting from fixed single-value targets to higher time-resolution, multi-objective, and adaptively updated e-flow targets; (ii) optimizing monitoring networks to better cover ecological hotspots (e.g., key spawning habitats), implementing flexible temporal resolution, and conducting emergency monitoring during rapid ecological events; (iii) transitioning from flow-only, section-based assessment toward integrated basin-scale evaluation that couples flow, water quality, habitat, and biodiversity outcomes.

[Objective] Heavy metal pollution in sediments is a major factor contributing to the deterioration of lake water quality. To accurately assess the sources and potential ecological risks of heavy metals in lake sediments, it is necessary to conduct source analysis and risk assessment at the watershed scale. [Methods] Concentrations of Cr, Ni, Cu, Zn, As, Cd, and Pb in soil and sediments were measured at the small watershed scale in the western lake area of Taihu Lake, and a comprehensive ecological risk evaluation of heavy metals was performed using the enrichment factor (EF), geoaccumulation index (I_geo), and potential ecological risk index. [Results] The heavy metal contents in sediments were generally higher than those in soil. Moreover, the heavy metal contents in sediments from the northern lake area were higher than those in the central and southern lake areas. About 88.4% of the increase in heavy metal content in sediments came from exogenous inputs such as terrestrial rivers and runoff discharged into the lake. Additionally, the EF and I_geo demonstrated that the sediments experienced various degrees of heavy metal accumulation and pollution. The ecological risk values of Cr, Ni, Cu, and Zn were significantly higher than those of the corresponding elements in soil, indicating a relatively high potential ecological risk. Finally, the assessment using the potential ecological risk index showed that Cd exhibited a relatively high ecological risk value, and the sediments exhibited moderate to high comprehensive ecological risks. [Conclusion] Overall, the potential ecological risks of heavy metals in sediments from the western lake area of Taihu Lake are attributed to terrestrial inputs, including surface soil erosion. By coupling comparative analysis of exogenous and endogenous pollution levels of heavy metals at the small watershed scale of the lake, this study provides important management information and a scientific basis for heavy metal control in the Taihu Lake watershed.

[Objective] Identifying the key landscape pattern factors that affect water quality is of great significance for integrated watershed water quality management. [Methods] Based on the water quality data from January 2014 to August 2024, redundancy analysis (RDA) and Pearson correlation analysis were applied to investigate the relationship between landscape pattern indices and ammonium nitrogen concentration in the Gaoguan Reservoir Basin during the wet and non-flood periods. RDA was used to identify the key landscape pattern indices,and Pearson correlation analysis was used to quantify the intensity and significance of correlation coefficients between ammonium nitrogen concentration and landscape pattern indices. [Results] The results were as follows: (1) In the Gaoguan Reservoir Basin,the area of forest land was the largest,accounting for more than 84.9% of the whole area and significantly contributing to the improvement of water quality. (2) The absolute values of correlation coefficients between landscape pattern indices and ammonium nitrogen concentration in the non-flood period were always higher than those in the wet period,and the same trend was observed for landscape composition and land-use areas. (3) In the wet and non-flood periods,with the increase of landscape diversity,splitting degree,and degree of fragmentation,the risk of surface runoff carrying pollutants into water bodies increased and aggravated the ammonium nitrogen pollution. Largest Patch Index (LPI) presented a negative correlation with ammonia nitrogen content in wet period, but a positive correlation in level period. Conversely, Contagion (CONTAG) exhibited a opposite relationship with ammonia nitrogen content, and a significant positive correlation in the non-flood period; in other words,the better integrity of the landscape and the lower degree of fragmentation can reduce the output of ammonium nitrogen during the flood period. During the non-flood period,the closer the pollution-source landscape type was to the water body,the more severe the impact on water quality due to its proximity. [Conclusion] Overall,forest land exhibits a more pronounced effect on water quality improvement in the Gaoguan Reservoir watershed compared with other land-use types.During the non-flood period,landscape pattern indices demonstrate a more significant capacity to regulate and influence water quality relative to the wet period. However,in general,water quality in the reservoir is primarily governed by landscape integrity during the wet period,whereas during the non-flood period,it is mainly controlled by pollution-source patches located closer to the water body.The results will be of great significance for the integrated watershed management and water quality improvement of the Gaoguan Reservoir in the future.

[Objective] As a vital ecological barrier in Northwest China, the river ecosystem health of Zhangye City is of critical importance to regional ecological security. This study aims to provide a comprehensive understanding of the aquatic ecological health of rivers in Zhangye City across different seasons. [Methods] A survey of benthic macroinvertebrates was conducted in 2023 at 136 sampling sites along seven rivers in Zhangye City. The benthic macroinvertebrate-based index of biological integrity (B-IBI) system for rivers in Zhangye City was constructed by selecting 23 candidate biological parameters from five categories that increased in sensitivity with the level of disturbance. B-IBI values of each river were then calculated for different seasons to evaluate their ecological health. [Results] A total of 159 benthic species were identified across both the wet and dry seasons. These species belonged to 5 phyla, 8 classes, 17 orders, and 51 families. Arthropods were the dominant taxa in both seasons, particularly Baetis sp. and Orthocladius sp. The analysis revealed significant differences in benthic macroinvertebrate communities between the dry and wet seasons across rivers in Zhangye City (r=0.09,p<0.05). The B-IBI results indicated that the overall ecological health of the rivers was satisfactory, with minimal seasonal variation. The proportion of sites classified as “healthy” during the dry and wet seasons was 42.39% and 42.71%, respectively. [Conclusion] Despite the currently favorable state of aquatic ecological health in Zhangye City, ecological degradation remains a risk due to ongoing climate change and human activities. To enhance the stability and health of the river ecosystem, it is necessary to strengthen monitoring and management efforts, optimize water resource allocation, and conduct further research on the long-term adaptability of benthic macroinvertebrate communities to extreme weather events and human activities.

[Objective] Zoobenthos are the intermediate link in the food chain of river ecosystems, and promoting the restoration of healthy zoobenthos communities through the construction of suitable habitats is critical for river ecological restoration. Based on the living habits of zoobenthos, habitat modules suitable for the colonization of various zoobenthos species are designed and applied in experiments within the ecological restoration project of Fenghuang Creek to investigate the effectiveness of ecological restoration. [Methods] Prior to project implementation, zoobenthos samples were collected once from the river channel. One year after the completion of the project, when the river ecosystem was restored, zoobenthos samples within the modules were collected, with three sampling sites set up for this study. In addition, zoobenthos were collected from a river minimally affected by human activities to serve as a natural reference state. Collected zoobenthos samples were identified and counted, and ecological indicators were analyzed. A total of ten ecological indicators were analyzed to assess the restoration of zoobenthos communities. These indicators included: individual density and biomass density reflecting biomass characteristics; total number of taxa, number of sensitive taxa, and number of EPT taxa reflecting species richness; Shannon-Wiener diversity index, richness index, and evenness index reflecting community diversity; and biotic index (BI) and biological monitoring working party score (BMWP) reflecting environmental sensitivity. [Results] Among the biomass-related indicators, no significant change in individual density of zoobenthos was observed before and after the restoration project, and the values remained below 40% of those in the natural state. Biomass density, however, varied considerably among sampling sites after project implementation, with two sites exceeding twice and four times the values observed in the natural state, respectively. Regarding species richness-related indicators, the total number of taxa reached 60% of the natural state before project implementation. After the project, it increased slightly, ranging from 66.7% to 73.3% of the natural state, indicating a relatively small difference from the natural condition. Although the numbers of sensitive taxa and EPT taxa significantly increased after the project, they remained far below the natural state, with sensitive taxa at 30.0%-40.0% and EPT taxa at only 14.3% of the natural state. For species diversity-related indicators, slight increases were observed after project implementation compared with pre-project levels, and the gaps from the natural state were small, with some indicators even surpassing those of the natural state. Although indicators related to environmental sensitivity were improved after project implementation, they remained far below the natural state, with BI values at 41.5%-44.3% and BMWP scores at 55.7% of the natural state. [Conclusion] Following project implementation, the health of zoobenthos communities in the river shows a relatively pronounced restoration, but there remains a considerable gap compared with the natural state, mainly reflected in indicators closely associated with sensitive taxa—namely, the number of sensitive taxa, number of EPT taxa, BI value, and BMWP score. This can be attributed to the fact that the structure of zoobenthos communities is influenced not only by the river habitats but also by the types of surrounding terrestrial ecosystems. The reference site in this study is minimally affected by human activities, where the natural ecosystem is well preserved and suitable for the survival and reproduction of adult aquatic insects from sensitive taxa, resulting in a relatively rich assemblage of sensitive taxa. In contrast, the river under restoration is surrounded by villages and farmland, where terrestrial habitats and communities are relatively homogeneous, and the river ecosystem is frequently disturbed. Therefore, these conditions limit the development of sensitive zoobenthos taxa to a certain extent and make it difficult for community health to approach the natural state. Based on the analytical indicators used in this study, individual density, biomass density, Shannon-Wiener diversity index, richness index, and evenness index are relatively insensitive and fail to accurately reflect the differences before and after implementation or between post-implementation and natural state. In contrast, indicators related to sensitive taxa exhibit strong sensitivity and applicability in assessing zoobenthos community restoration and are recommended for use in similar studies or projects.

[Objective] The middle route of the South-to-North Water Diversion Project faces risks of sudden water pollution due to hazardous material transport over cross-canal bridges and inflow of external floodwaters during the flood season. To support the treatment of water pollution emergencies, a unified hydrodynamic and water quality model for the canal system, including structures such as control gates, offtakes, inverted siphons, and escape gates, was developed. [Methods] The St. Venant equations were solved to simulate hydrodynamic characteristics of the water flow. Based on a cross-sectional control volume, a one-dimensional water quality model was established, with the continuity equation representing the pollutant mass conservation equation. The model was developed using FORTRAN90, enabling online pre-simulation of sudden water pollution diffusion and multi-gate joint control operations. [Results and Conclusion] Simulation results show that closing the diversion and increasing the discharge of the water discharge gate can significantly improve the pollutant emission efficiency, while the closing speed of control gates determines the pollution control effectiveness. This study provides technical support for the prevention and control of sudden water pollution incidents in the middle route of the South-to-North Water Diversion Project.

[Objective] The Four Lakes (Changhu Lake, Sanhu Lake, Bailu Lake, and Honghu Lake) Basin, located in the hinterland of the Jianghan Plain in the middle reaches of the Yangtze River, is an essential agricultural production area and an ecologically sensitive wetland area in Hubei Province. To reveal the spatiotemporal distribution characteristics of nutrients and their pollution sources in the water bodies of the Four Lakes Basin after ecological restoration, this study systematically analyzes the variation patterns of water quality and the main driving factors based on measured monitoring data, aiming to provide a scientific basis for watershed water environment management and ecological restoration effectiveness assessment. [Methods] Based on field survey data from wet, normal, and dry seasons during 2022-2023, a total of 12 sampling sites were set up in the Four Lakes Basin, covering the upstream, midstream, and downstream areas, as well as mainstream and tributaries. Nine water quality indicators were measured, including water temperature (WT), dissolved oxygen (DO), turbidity (TUR), permanganate index (COD_Mn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD₅), ammonium nitrogen ( {\mathrm{NH}}_4^{+}-N), total phosphorus (TP), and total nitrogen (TN). The water quality was comprehensively evaluated using the water quality index (WQI) method. Correlation analysis and principal component analysis (PCA) were combined to identify the main pollution factors and sources. The characteristics of water quality evolution were systematically revealed from temporal, spatial, and pollution source aspects through data statistics, significance tests, and graphical visualization performed using software such as Excel, SPSS, and CANOCO. [Results] Water quality indicators in the Four Lakes Basin exhibited significant differences both temporally and spatially. Temporally, WT, {\mathrm{NH}}_4^{+}-N, TP, and COD_Mn were the highest during the flood season, while DO and TN showed opposite trends. TUR peaked during the normal season. Spatially, the concentrations of TUR, COD_Mn, COD, and BOD₅ in the downstream water bodies were significantly higher than those in the midstream and upstream. The {\mathrm{NH}}_4^{+}-N concentration was the highest in the midstream, while the TP and TN concentrations were the lowest in the upstream. Overall, the concentrations of various nutrient indicators demonstrated a pattern of “mainstream > tributaries”. The WQI values ranged from 15.61 to 32.88, indicating that the overall water quality in the Four Lakes Basin was at a “poor” to “very poor” level. Its temporal variation followed the order: normal season > dry season > wet season, and its spatial variation was characterized by upstream > midstream > downstream, and tributaries > mainstream. Correlation analysis showed that WT, DO, COD, {\mathrm{NH}}_4^{+}-N, TP, and TN were the main factors affecting WQI, among which N H_4^{+}-N, TP, and TN were significantly negatively correlated with WQI (P<0.05). PCA results indicated that pollutants during wet season were dominated by nutrients, primarily originating from external inputs such as agricultural fertilization, livestock and poultry farming, and domestic sewage. Pollution during normal season was mainly organic matter, largely from domestic sewage and industrial wastewater discharge. Pollution during dry season was influenced by both external input and internal release, with sediment resuspension and decomposition of plant and animal residues being important internal pollution sources. Overall, although the water quality in the Four Lakes Basin improved slightly after the implementation of ecological restoration, significant seasonal and regional pollution characteristics remained. [Conclusion] In summary, the water quality in the Four Lakes Basin exhibits significant temporal differences across the flood, normal, and dry seasons, while spatially, the upstream areas are superior to the midstream and downstream areas, and the tributaries are superior to the mainstream.. Although ecological restoration projects have been effective, the overall water quality of the basin remains at a moderate to severe pollution level. Agricultural fertilization, livestock and poultry breeding wastewater, domestic sewage, and industrial discharge are the main exogenous pollution sources, while the release of water body sediments and the decomposition of organic residues are the main endogenous pollution pathways. The innovation of this study lies in systematically revealing the spatiotemporal distribution pattern and pollution causes of nutrients in the Four Lakes Basin under the background of ecological restoration for the first time. This study also constructs a multi-level analytical framework of “WQI comprehensive evaluation-correlation analysis-PCA”, which can effectively identify key pollution factors and dominant sources, thereby providing scientific support for assessing the performance of watershed ecological restoration and implementing targeted management in the basin. The findings indicate that agricultural non-point source pollution control and sediment remediation should be further strengthened, and an integrated management system combining exogenous reduction and endogenous treatment should be established to promote the sustained improvement of the water environment and long-term restoration of ecological functions in the Four Lakes Basin.

[Objective] To investigate the impact of the completion and operation of the south-side drainage project of the Qingcaosha Reservoir on the water quality of the Changxing Island river network, this study quantitatively assesses the improvement effects of the project’s drainage on the river network’s water quality under different operating conditions. [Methods] Based on detailed fundamental data, a one-dimensional hydrodynamic and water quality model for Changxing Island was constructed, calibrated and validated using measured data. The improvement effects of the south-side drainage project on the river network water quality were quantified under different operating conditions; furthermore, a water resource scheduling approach for the Changxing Island river network was proposed and optimized under the planned scale conditions through multi-scheme comparison. [Results] The model simulation analysis showed that when the Changxing Island pump-gate system operated under routine scheduling, the project operation could increase the proportion of river length with a permanganate index of Class Ⅲ and above from 25% to 35%-40%, and increase the proportion of river length with total phosphorus of Class Ⅱ and above from 72% to 77%-82%. The west-to-east drainage scheme demonstrated a significantly better improvement effect on total phosphorus in the Changxing Island river network than the routine scheduling scheme. Under the maximum flow condition of the project, this scheme could increase the proportion of river length with total phosphorus of Class Ⅱ and above to 95%. [Conclusion] The completion and operation of the south-side drainage project of the Qingcaosha Reservoir can effectively improve the water environment quality of Changxing Island river network. To make full use of the high-quality water resources, the west-to-east drainage scheme demonstrates a significantly better improvement effect on total phosphorus in the Changxing Island river network than the routine scheduling scheme, delivering greater environmental benefits. The research findings can provide technical support for promoting water circulation and water resource scheduling in Changxing Island.

[Objective] This study examines the legacy effect in aquatic nitrogen pollution control, emphasizing the role of historically accumulated nitrogen. It reviews advanced methods for quantifying lag times and legacy loads, aiming to provide a scientific basis for more precise nitrogen management. [Methods] Based on a literature review, this study analyzed nitrogen fate and transport processes, focusing on biogeochemical and hydrological legacy nitrogen. It evaluated current quantification approaches and the limitations of hydrological models. [Results] The analysis indicated that historically accumulated nitrogen could remain in watershed soils and groundwater in various forms, constituting a persistent pollution source that prevented an immediate response to management measures. Although recent research made some progress in quantifying lag times and legacy loads, current hydrological models still exhibited significant shortcomings in accurately characterizing the spatial distribution of legacy nitrogen, which limited the predictive capabilities for the lagged nitrogen response. [Conclusion] The study concludes that, to overcome the limitations of current models and effectively address the challenge posed by lag time in nitrogen pollution management, future research should focus on establishing a source-pathway coupled model for nitrogen export. This model integrates precise source identification with advanced simulation of export pathways, thereby providing a critical tool for achieving precise nitrogen management and rapid water quality improvement with minimal investment.

[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.