Search Results (882)

The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R², RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication. Full article

Accurate estimation of Total Phosphorus, referred to as “Phosphorus, Total” (PPUT; µg/L) in the sourced monitoring data, is essential for understanding eutrophication dynamics and guiding water-quality management in inland lakes. However, lake-wide PPUT mapping at high resolution is challenging to achieve using conventional in-situ sampling, and nearshore gradients are often poorly resolved by medium- or low-resolution satellite sensors. This study exploits multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance, short-term meteorological descriptors (3 to 7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure), and in-situ Secchi depth (SSD) were used to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes that progressively extend from bands-only, to combinations with spectral indices and day-of-year (DOY), and finally to SSD-inclusive full-feature configurations. The inclusion of SSD led to a strong and systematic performance gain, with mean R² increasing from about 0.67 (SSD-free) to 0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on PPUT retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, a knowledge-distillation strategy was implemented in which an SSD-aware teacher transfers its learned representation to a student using only satellite and meteorological inputs. The optimal student model, based on a compact subset of 40 predictors, achieved R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L, retaining approximately 88% of the teacher’s explanatory power. Application of the student model to PlanetScope scenes from 2020 to 2025 produces meter-scale PPUT maps; a 26 July 2024 case study shows that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but coherent hotspots above 20 µg/L align with tributary mouths and narrow channels. The results demonstrate that combining commercial high-resolution imagery with physics-informed feature engineering and knowledge transfer enables scalable and operationally relevant monitoring of lake phosphorus dynamics. These high-resolution PPUT maps enable lake managers to identify nearshore nutrient hotspots, tributary plume structures. In doing so, the proposed framework supports targeted field sampling, early warning for eutrophication events, and more robust, lake-wide nutrient budgeting. Full article

Designing robust drinking water treatment schemes for eutrophic sources requires shifting from considering each treatment step separately to considering the full treatment process as a connected system. This study evaluated how treatment configuration and arrangement influence microbial community dynamics, organic carbon removal, and biological stability in a full-scale drinking water treatment plant. A Dutch treatment plant was monitored, operating two parallel lines: one conventional (coagulation, sedimentation, and rapid sand filtration) and one advanced (ion exchange, ceramic microfiltration, and advanced oxidation), both converging into granular activated carbon (GAC) filtration. Microbial and chemical water quality was assessed across treatment stages and seasons. This plant experiences periods of discoloration, taste, and odor issues, and an exceedance of Aeromonas counts in the distribution network. Advanced oxidation achieved a high bacterial cell inactivation (~90%); however, it significantly increased assimilable organic carbon (AOC) (300–900% increase), challenging biological stability. GAC filtration partially reduced AOC levels (from 70 μg Ac-C/L to 12 μg Ac-C/L) but also supported dense (10⁵ cells/mL) and diverse microbial communities (Shannon diversity index 5.83). Moreover, Gammaproteobacteria, which harbor opportunistic pathogens such as Aeromonas, persisted during the treatment. Archaea were highly sensitive to oxidative and physical stress, leading to reduced diversity downstream. Beta diversity analysis revealed that treatment configuration, rather than seasonality, governed the community composition. The findings highlight that treatment arrangement, oxidation, GAC operation, and organic and microbial loads critically influence biological stability. This study proposes integrated strategies to achieve resilient and biologically stable drinking water production when utilizing complex water sources such as eutrophic lakes. Full article

Thermal stability is a key factor in determining the phenomena of deep chlorophyll maxima (DCM) in stratified lakes, as it mediates the vertical balance between light and nutrients required by phytoplankton. While it is well established that lake stratification is sensitive to latitude gradients, the ways in which thermal stability modulates DCM characteristics (i.e., depth, thickness, and concentration) and nutrient–chlorophyll relationships across different latitude classifications remain unclear. In this study, data on thermocline depth, DCM feature, and water quality parameters were collected from 88 globally distributed stratified lakes. Our findings indicate that (1) higher-latitude lakes exhibit strong thermoclines, with light and nitrogen serving as the primary drivers of thermal stratification; (2) in high-latitude lakes, surface chlorophyll a concentrations are more tightly linked to total phosphorus than that at DCM depth in low-latitude lakes; and (3) structural equation modeling (SEM) results demonstrate that higher-latitude lakes form shallower and thinner DCM structures, where low light levels contribute to reduced peaks in algal biomass. These findings provide valuable insights for the management of stratified lakes facing the dual pressures of climate change and eutrophication. Full article

The aim of this study was to determine the factors that influence eutrophication. The factors causing eutrophication are widely known, but identifying the primary threat for a specific water body remains challenging. The study objects were the warm monomictic urban Dal Lake in Kashmir, India, and the artificial dam reservoir Dobczyce in Poland. Data analysis methods, including multiple regression and artificial neural networks (NNET and NeuralNet) from the R package [ver. 4.5.2], were used. Regarding Dal Lake, the factor most influencing the trophic change was total nitrogen. In contrast, for the Dobczyce dam reservoir, water temperature was the dominant factor. Although the regression method did not provide clear results, neural networks enabled the identification of the limiting factors; therefore, the proposed approach may be useful for determining the factors limiting the eutrophication process. The core novelty of this research lies in demonstrating the potential of artificial neural networks to identify key factors causing eutrophication, particularly under conditions of limited data. Full article

The Dal Lake ecosystem is a vital freshwater body situated in the heart of Srinagar, Kashmir, India. It is not only a natural asset but also a cornerstone of environmental health, economic vitality, cultural heritage, and urban sustainability. In the last few decades, the condition of the lake ecosystem and water quality has deteriorated significantly owing to the intensification of the eutrophication process. Effective integrated management of the lake is crucial for the long-term sustainable development of the region and the communities that rely on it for their livelihoods. The main reasons for eutrophication are the substantial quantity of anthropogenic pollution, especially nutrients, discharged from the catchment area of the lake and the overexploitation of the lake space and its biological resources. The research presented in this paper aimed to diagnose the state of the lake by analysing trends in eutrophication development and its long-term changes related to the catchment area and lake ecosystem relationships. The research period was 25 years, from 1997 to 2023. Land use and land cover data and water quality monitoring data, which are the basis for trophic state assessment, allowed us to analyze the long-term dynamics of eutrophication in the reservoir. For these purposes, GIS-generated thematic maps were created by using QGIS software version 3.44.1, and an appropriate methodology for quantifying eutrophication was chosen and adapted to the specifics of Dal Lake. The obtained results provide a foundation for a eutrophication management strategy that considers the specificity of the Dal Lake ecosystem and the impact of the catchment area. The outcomes highlighted the varied trophic conditions in different lake basins and the dominance of eutrophic conditions during the study period. The research highlights the complexity of the problem and underscores the need for a comprehensive lake management system. Full article

Water diversion projects are a crucial measure for addressing eutrophication in shallow lakes worldwide. However, the impacts of different water diversion operation schemes on lake hydrodynamics and water quality can vary significantly, necessitating targeted, refined simulation assessments. This study focuses on Chaohu Lake, one of China’s most eutrophic lakes, and uses a mesoscale meteorological model coupled with a three-dimensional hydrodynamic and water quality model to conduct detailed numerical simulations. The study evaluates the effects of three water diversion operation scenarios and three subsurface flow guide dam scenarios during the ecological water replenishment period in Chaohu Lake from September to November. The simulation results indicate that all three water diversion operation scenarios improve the hydrodynamic conditions of Chaohu Lake, but there are significant differences in their effects on pollutant reduction in the lake. The retention of chemical oxygen demand (COD) in the water ranges from −36,812.1 to 472.8 tons, total nitrogen (TN) retention ranges from −22,637.2 to 3 tons, total phosphorus (TP) retention ranges from −4974 to 10.7 tons, and chlorophyll-a (Chl-a) retention ranges from −310.8 to −3.3 tons. Among the three subsurface flow guide dam schemes, all can promote the outflow of pollutants from Chaohu Lake. The combined subsurface flow guide dam scheme is the most effective, enabling an approximately 7.4% increase in pollutant export. The study demonstrates that diverting Huaihe River water through Paihe into Chaohu Lake, along with adding a combined subsurface flow guide dam in the West Lake area, can significantly improve the hydrodynamics and water quality in the West Lake area. This research provides essential technical support for the future operation of the Yangtze-to-Huaihe River Water Diversion Project and the layout of subsurface flow guide dams in Chaohu Lake, offering valuable insights for the ecological management of other shallow lakes. Full article

Lake Batur, located within a volcanic caldera in Bali, Indonesia, is subjected to anthropogenic pressures related to agriculture, aquaculture, tourism, and religious activities, which may affect its water quality and ecology condition. This study investigates the physicochemical properties of lake water and diatom assemblages preserved in lake sediments to provide insight into environmental conditions in this volcanic alkaline ecosystem. Water quality parameters, including pH, temperature, electrical conductivity (EC), and total dissolved solids (TDS), were measured. Vertical profiles of temperature and conductivity revealed stable stratification, with minimal variation below 20 m water depth. Elevated nitrogen concentrations, including nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺), were observed, particularly in the southern basin, suggesting localized nutrient enrichment. Scanning electron microscopy (SEM) analysis of lake sediment samples identified ten diatom genera, including Ulnaria, Denticula, and Discostella, which are commonly associated with nutrient-enriched freshwater environments. Overall, the results indicate that Lake Batur exhibits conditions consistent with early-stage eutrophication in localized areas, highlighting the importance of continuous monitoring and targeted management strategies to protect the ecological integrity of this volcanic lake system. Full article

Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning. Full article

The ecological status of lakes based on ichthyofauna, as defined by the Water Framework Directive, is assessed using intercalibrated methods. However, the methods adopted (in Poland, the Lake Fish Index LFI-EN method, based on results of one-off fishing with multi-mesh gillnets) are labor-intensive and do not allow for frequent repeat testing. Therefore, the concept of a simple model describing changes in the relative number of single traces in the vertical profile (according to the TS target strength distribution) in a lake is presented, as well as an index (the sum of deviations from such a model), enabling quantification of the similarity of TS distributions in lakes with this model. Preliminary analyses were conducted on acoustic data collected in Lake Dejguny. This lake—the condition of which could be estimated based on historical data using the relationships between LFI and the degree of lake eutrophication (expressed by Carlson’s TSI)—was assessed as having a good status in 2006, whereas in 2021, (based on LFI-EN) it had a moderate status. The study tested the TS distribution model, calculated as the arithmetic mean of the relative number of single traces in 2 m-thick layers. It was also shown that the proposed indicator can effectively signal deterioration of ecological status—the sum of the absolute values of the TS distribution deviations in 2021 (moderate status) from the model was more than seven times greater than the sum of the deviations of the distributions from which the model was built (good status). The obtained results confirmed the hypothesis about the possibility of determining a characteristic distribution of single traces in the vertical profile when the lake was classified as being in good condition. Full article

Urbanization, expanding tourism, and infrastructure development are altering water quality in the Yucatán Peninsula (YP). This study evaluated temporal variations in water quality and trophic status using the Water Quality Index (WQI) and Trophic State Index (TSI) across ten inland water systems (IWS) monitored from 2012 to 2024. Spatial patterns from an additional 29 IWS sampled in 2024 were also analyzed. The Mann–Kendall test and Theil–Sen estimator revealed a significant decline in water quality (Z = −9.07, β = −2.62) and a sustained increase in eutrophication (Z = 4.00, β = 1.15). The NMDS separated two lake groups: one with high nutrients and total coliforms, and another with elevated TDS and conductivity. The PCA identified turbidity, nitrogen, chlorophyll-a, and total coliforms as variables exerting the strongest influence on water variability. The WQI indicated generally poor conditions except in Bacalar Centro and Xul-Ha, which showed fair quality. The highest TSI values occurred in inland systems, except for La Sabana, which exhibited hypereutrophic conditions linked to wastewater inputs. NT–PT ratio indicated nitrogen limitation in most lakes, likely driven by groundwater recharge and low surface runoff. Overall, results demonstrate a progressive decline in water quality and widespread eutrophication across the YP. Full article

Many lakes worldwide, including in China’s Yangtze River Basin, face eutrophication, which reduces phytoplankton diversity and increases bloom risk. Following severe pollution, these Chinese lakes have undergone substantial control and regulation. However, the efficacy of these measures is still unclear. Focusing on Lake Chaohu as a representative case, this study investigated the seasonal phytoplankton dynamics (2022–2023) under concurrent nutrient reduction and a fishing ban. The annual mean concentrations of total nitrogen, total phosphorus, and chlorophyll a were 1.57 mg/L, 0.184 mg/L, and 21.21 μg/L, respectively. The phytoplankton community was dominated by Cyanobacteria, which constituted approximately 75% of the total biomass. Co-occurrence network analysis revealed lower community stability during these warm, Cyanobacteria-dominated periods. Statistical analyses identified total phosphorus and temperature as key drivers, confirming bottom-up control via nutrient limitation as the fundamental mechanism. However, extreme heat events may have partly offset the benefits of nutrient reduction by promoting cyanobacterial dominance, which can decrease phytoplankton diversity. A recorded decrease in phytoplankton phosphorus use efficiency after the fishing ban suggests a potential strengthening of top-down control. These findings highlight that sustained nutrient load reduction is essential to reduce cyanobacterial bloom risk, while continued enforcement of the fishing ban may enhance the regulatory effect of top-down control on cyanobacterial blooms, thereby improving the stability and diversity of phytoplankton communities. Full article

To investigate the influence of land use landscape patterns on lake water quality in the basin, the land use and water quality data of the Yilong Lake Basin from 1993 to 2023 were analyzed with a geographic information system, remote sensing, and landscape ecology methods in this research. The results show that (1) the land use landscape pattern and water quality of the Yilong Lake Basin had significant changes: the lake surface area, farmland, and shrubland declined, with grassland showing the sharpest decrease and serving as the main source of conversion to other land types, while forest land expanded and built-up land increased by five times. The landscape pattern analysis showed that the aggregation degree of the core habitat in the basin increased and the landscape had decreased patch density and increased heterogeneity. Regarding water quality, the concentrations of total nitrogen (TN), total phosphorus (TP), and ammonium nitrogen (NH₄⁺-N); permanganate index (I_Mn); and biochemical oxygen demand over 5 days (BOD₅) decreased. Furthermore, the concentration of dissolved oxygen (DO) increased and the concentration of chlorophyll-a (Chl-a) fluctuated for a long time but did not decrease dramatically at the end of the period compared with the beginning. In general, the eutrophication degree of Yilong Lake slightly decreased. (2) The landscape configuration strongly shaped the water quality: the redundancy analysis (RDA) revealed that the edge density (ED), landscape shape index (LSI), largest patch index (LPI), and patch density (PD) were negatively associated with the eutrophication of Yilong Lake (TN, TP, NH₄⁺-N, Chl-a), whereas the contagion index (CONTAG) was positively associated; the Shannon’s diversity index (SHDI) was closely linked with TN and I_Mn but negatively with DO; and the patch cohesion index (COHESION) had a low interpretation power for water quality changes. In particular, larger and more cohesive ecological patches supported a higher DO, while an increased patch density was linked to an elevated I_Mn and reduced DO. These results indicate that the restoration of key ecological patches and enhanced landscape cohesion helped to improve the water quality, whereas increased patch density and landscape heterogeneity negatively affected it. (3) In the past 30 years, the ecological management and protection work on Yilong Lake, such as returning farmland to forests and lakes, wetland restoration, and sewage pipe network construction, achieved remarkable results that were reflected in the change in the relationship between land use landscape pattern and water quality in the basin. However, human activities still affected the dynamic evolution of water quality: the expansion of built-up land increased the patch density, the reduction in shrubland and grassland weakened natural filtration, and the rapid urbanization process introduced more pollution sources. Although the increase in forest land helped to improve the water quality, the effect was not fully developed. These findings provide a scientific basis for the management and ecological restoration of plateau lakes. Strengthening land use planning, controlling urban expansion, and maintaining ecological patches are essential for sustaining water quality and promoting the coordinated development of the ecology and economy in the Yilong Lake Basin. Full article

Lake eutrophication exhibits pronounced spatial heterogeneity at the watershed scale, yet a systematic and quantitative understanding of how landscape characteristics drive these variations remains limited. In this study, a long-term and internally consistent trophic state dataset for the Liangzi Lake Basin was constructed by integrating Landsat imagery from 1990 to 2022 with a semi-analytical water color inversion method. A multi-scale landscape feature system incorporating both land use composition and landscape pattern metrics was developed at the sub-basin level to elucidate the mechanisms by which landscape characteristics influence eutrophication dynamics. The XGBoost model was employed to characterize the nonlinear relationships between landscape attributes and trophic conditions, while the SHAP interpretability approach was applied to quantify the relative contribution of individual landscape components and their interaction pathways. The analytical framework demonstrates that landscape pattern attributes—such as fragmentation, diversity, and connectivity—play essential roles in shaping the spatial variability of eutrophication by modulating hydrological processes, nutrient transport, and ecological buffering capacity. By integrating remote sensing observations with interpretable machine learning, the study reveals the complexity and scale dependence of landscape–water interactions, providing a methodological foundation for advancing the understanding of eutrophication drivers. The findings offer theoretical guidance and practical references for optimizing watershed landscape planning, controlling non-point source pollution, and supporting ecological restoration efforts in lake basins. Full article

Urban lakes are essential for ecological balance and urban development. This study developed a comprehensive framework to evaluate the ecosystem health of urban lakes in China. Nineteen representative lakes from four lake zones were examined using three decades of remote-sensing data combined with hydrological, water-quality, and aquatic–biological investigations. An extended DPSIR model guided the selection of 52 indicators, and a hierarchical weighting scheme was used: the analytic hierarchy process determined criterion-level weights, while principal component analysis with Softmax normalization was used for indicator-level weights. The established index system was applied to Xuanwu Lake and Erhai Lake, and an obstacle-degree model was used to identify key ecological constraints from 2010 to 2020. Results showed that urban lakes in the Yunnan–Guizhou Plateau and Eastern Plain zones were mainly constrained by eutrophication and intensive urbanization, with state- and impact-related indicators contributing most to the health index. The framework captured the decline of Xuanwu Lake, driven by poor water exchange and external nutrient loading, and its subsequent improvement following governance interventions, as well as the post-2014 degradation of Erhai Lake driven by climate-induced hydrological stress and non-point source pollution, providing a practical tool for diagnosing constraints and supporting adaptive, region-specific lake management. Full article