Characterization and function of particulate organic matter: Evidence from lakes undergoing ecological restoration.

Particulate organic matter (POM) plays a crucial role in the organic composition of lakes; however, its characteristics remain poorly understood. This study aimed to characterize the structure and composition of POM in Lake Baiyangdian using many kinds of techniques and investigate the effects of different extracted forms of POM on water quality. The suspended particulate matter in the lake had complex compositions, with its components primarily derived from aquatic plants and their detritus. The organic matter content of the suspended particulate matter was relatively high (organic carbon content 27.29-145.94 g/kg) for the sum of three extractable states (water-extracted organic matter [WEOM], humic acid, and fulvic acid) and one stable bound state (humin). Spatial distribution analysis revealed that the POM content in the water increased from west to east, which was consistent with the water flow pattern influenced by the Baiyangdian water diversion project. Fluorescence spectroscopy analysis of the WEOM showed three prominent peaks with excitation/emission wavelengths similar to those of dissolved organic matter peaks. These peaks were potentially initial products of POM conversion into dissolved organic matter. Furthermore, the intensity of the WEOM fluorescence peak (total fluorescence peak intensity) was negatively correlated with the inorganic nitrogen concentration in water (p < 0.01), while the intensity of the HA fluorescence peak showed a positive correlation with the inorganic nitrogen concentration (p < 0.01). This suggested that exogenous organic matter inputs led to the diffusion of alkaline dissolved nitrogen from sediment into water, while degradation processes of aquatic plant debris contributed to the decrease in inorganic nitrogen concentrations in the water column. These findings enhance our understanding of POM characteristics in shallow lakes and the role of POM in shallow lake ecosystems.

Exploring the diversity of dissolved organic matter (DOM) properties and sources in different functional areas of a typical macrophyte - derived lake combined with optical spectroscopy and FT-ICR MS analysis.

Lake Baiyangdian is one of China's largest macrophyte - derived lakes, facing severe challenges related to water quality maintenance and eutrophication prevention. Dissolved organic matter (DOM) was a huge carbon pool and its abundance, property, and transformation played important roles in the biogeochemical cycle and energy flow in lake ecosystems. In this study, Lake Baiyangdian was divided into four distinct areas: Unartificial Area (UA), Village Area (VA), Tourism Area (TA), and Breeding Area (BA). We examined the diversity of DOM properties and sources across these functional areas. Our findings reveal that DOM in this lake is predominantly composed of protein - like substances, as determined by excitation - emission matrix and parallel factor analysis (EEM - PARAFAC). Notably, the exogenous tyrosine-like component C1 showed a stronger presence in VA and BA compared to UA and TA. Ultrahigh - resolution mass spectrometry (FT - ICR MS) unveiled a similar DOM molecular composition pattern across different functional areas due to the high relative abundances of lignan compounds, suggesting that macrophytes significantly influence the material structure of DOM. DOM properties exhibited specific associations with water quality indicators in various functional areas, as indicated by the Mantel test. The connections between DOM properties and NO3N and NH3N were more pronounced in VA and BA than in UA and TA. Our results underscore the viability of using DOM as an indicator for more precise and scientific water quality management.

Interannual succession of phytoplankton community in a canyon-shaped drinking water reservoir during the initial impoundment period: Taxonomic versus functional groups.

During the initial impoundment period of a canyon-shaped reservoir, the water body fluctuated violently regarding water level, hydrological condition, and thermal stratification. These variations may alter the structure of phytoplankton community, resulting in algal blooms and seriously threatening the ecological security of the reservoir. It is of great significance to understand the continuous changes of phytoplankton in the initial impoundment period for the protection of reservoir water quality. Therefore, a two-year in-situ monitoring study was conducted on water quality and phytoplankton in a representative canyon-shaped reservoir named Sanhekou and the interannual changes of phytoplankton community and its response to environmental changes during the initial impoundment period were discussed at taxonomic versus functional classification levels. The results showed that the total nitrogen and permanganate index levels were relatively high in the first year due to rapid water storage and heavy rainfall input, and the more stable hydrological conditions in the second year promoted the increase of algae density and the transformation of community, and the proportion of cyanobacteria increased significantly. The succession order of phytoplankton in the first year of the initial impoundment period was Chlorophyta-Bacillariophyta-Chlorophyta, or J/F/X1-P/MP/W1-A/X1/MP, respectively. And the succession order in the second year was Cyanobacteria/Chlorophyta-Bacillariophyta-Chlorophyta, or LM/G/P-P/A/X1-X1/J/G. Water temperature, relative water column stability, mixing depth, and pH were crucial factors affecting phytoplankton community succession. This study revealed the interannual succession law and driving factors of phytoplankton in the initial impoundment period and provided an important reference for the operation management and ecological protection of canyon-shaped reservoirs.

Removal of particulate matter and dissolved organic matter from sedimentation sludge water during pre-sedimentation process: Performances and mechanisms.

Sedimentation sludge water (SSW), a prominent constituent of wastewater from drinking water treatment plants, has received limited attention in terms of its treatment and utilization likely due to the perceived difficulties associated with managing SSW sludge. This study comprehensively evaluated the water quality of SSW by comparing it to a well-documented wastewater (filter backwash water (FBW)). Furthermore, it investigated the pollutant variations in the SSW during pre-sedimentation process, probed the underlying reaction mechanism, and explored the feasibility of employing a pilot-scale coagulation-sedimentation process for SSW treatment. The levels of most water quality parameters were generally comparable between SSW and FBW. During the pre-sedimentation of SSW, significant removal of turbidity, bacterial counts, and dissolved organic matter (DOM) was observed. The characterization of DOM components, molecular weight distributions, and optical properties revealed that the macromolecular proteinaceous biopolymers and humic acids were preferentially removed. The characterization of particulates indicated that high surface energy, zeta potential, and bridging/adsorption/sedimentation/coagulation capacities in aluminum residuals of SSW, underscoring its potential as a coagulant and promoting the generation and sedimentation of inorganic-organic complexes. The coagulation-sedimentation process could effectively remove pollutants from low-turbidity SSW ([turbidity]0 < 15 NTU). These findings provide valuable insights into the water quality dynamics of SSW during the pre-sedimentation process, facilitating the development of SSW quality management and enhancing its reuse rate.

Diversidad de ninfas de Leptohyphidae (Ephemeroptera) en el río Quenane-Quenanito, piedemonte llanero colombiano / Diversity of Leptohyphidae (Ephemeroptera) nymphs in the Quenane-Quenanito river, Colombian plain foothills

Resumen Introducción: Varias presiones antrópicas sufren los ecosistemas acuáticos del piedemonte llanero en Colombia. La respuesta a estresores ambientales aún se desconoce en organismos bioindicadores como Leptohyphidae. Objetivo: Determinar la diversidad de ninfas de Leptohyphidae del río Quenane-Quenanito, en dos periodos hidrológicos contrastantes y su relación con algunas variables fisicoquímicas. Métodos: En diciembre (2014) y febrero (2015) se recolectaron organismos con red Surber en seis estaciones a lo largo del río. Se analizó la diversidad alfa y beta y se aplicó análisis de redundancia y modelos lineales generalizados con el fin de establecer la relación entre los taxones y las variables ambientales. Resultados: Se identificaron 369 organismos pertenecientes a cuatro géneros (Amanahyphes, Traverhyphes, Tricorythopsis y Tricorythodes), dos especies y ocho morfoespecies. Se reporta por primera vez para el departamento del Meta Amanahyphes saguassu. Se registró la mayor diversidad de ninfas en la transición a la sequía y la mayor abundancia en sequía. La diversidad beta señaló que la configuración del ensamblaje cambia a nivel espacial y temporal. Conclusiones: Los organismos de Leptohyphidae prefieren hábitats de corrientes, particularmente en el periodo de sequía, donde hallan alimento (hojarasca, detritos) y refugio para establecerse exitosamente; actividades antrópicas como la urbanización afectan notablemente la diversidad. La alta diversidad registrada en este pequeño río de piedemonte llanero refleja la necesidad de incrementar este tipo de trabajos y esfuerzos de recolección de material de estudio en la región.

Abstract Introduction: Various anthropic pressures affect the aquatic ecosystems of the foothills of Colombia. The response to environmental stressors is still unknown in bioindicator organisms such as Leptohyphidae. Objective: To determine the diversity of Leptohyphidae nymphs of the Quenane-Quenanito river, in two contrasting hydrological periods and its relationship with some physicochemical variables. Methods: In December (2014) and February (2015), organisms were collected with a Surber net at six stations along the current. Alpha and beta diversity was analyzed and redundancy analysis and generalized linear model were applied to establish the relationship between taxa and environmental variables. Results: Were identified 369 organisms belonging to four genera (Amanahyphes, Traverhyphes, Tricorythopsis, and Tricorythodes), two species, and eight morphospecies. Amanahyphes saguassu is reported for the first time for the Meta department. High diversity of Leptohyphidae nymphs was recorded in the transition to drought season and greater abundance in drought. Beta diversity indicated that the configuration of the assemblage changes spatially and temporally. Conclusions: Leptohyphidae organisms prefer fast habitats, particularly in the dry period where they find food (leaf litter, detritus) and shelter to establish themselves successfully; anthropic activities such as urbanization notably affect diversity. The high diversity recorded in this small river in the foothills of the plains reflects the need to increase this type of works and collection efforts of study material in the region.

Integrated study of hydrochemistry, quality and risk to human health of groundwater in the upper reaches of the Wulong River Basin.

Groundwater, a vital source of water supply, is currently experiencing a pollution crisis that poses a significant risk to human health. To understand the hydrochemical formation mechanisms, quality and risk to human health of groundwater in the upper reaches of the Wulong River basin, 63 sets of groundwater samples were collected and analyzed. A combination of mathematical statistics, correlation analysis, Gibbs diagram, ion ratio, and cation exchange were comprehensively employed for hydrochemical analysis, and further water quality index (WQI) and human health risk assessment were conducted. The results indicate that groundwater is generally neutral to weakly alkaline. The dominant cations in the groundwater are Ca2+ and Mg2+, while the main anions are HCO3- and SO42-. The hydrochemical types of groundwater mainly include HCO3·SO4-Ca, HCO3-Ca and HCO3-Na. The diverse hydrochemical types are mainly due to the fractured and discontinuous nature of the aquifers. The hydrochemical characteristics are influenced by the dissolution of silicate and carbonate minerals, cation exchange processes, and anthropogenic pollution. The presence of NO3- in groundwater is primarily attributed to agricultural activities. The groundwater is mainly categorized as "Good" (36.6%) and "Poor" (60.8%). "Very poor" and "Excellent" categories are rare, accounting for only 1.2% and 1.4%, respectively, and no samples are classified as "Non-drinkable". The Ewi for NO3- is the highest, indicating severe contamination by anthropogenic NO3- pollution. Human health risk assessment reveals that water samples posing exposure risks account for 82.54% for children and 79.37% for adults. This study highlighted that anthropogenic nitrate pollution has deteriorated groundwater quality, posing risks to human health. It also suggests an urgent need to enhance research and protective measures for groundwater in similar regions, such as the Shandong Peninsula.

Exploring the scale effect of nonpoint source pollution risk on water quality in Lake Basins of Central Yunnan Plateau using the Minimum Cumulative Resistance model.

Nonpoint source (NPS) pollution has emerged as the predominant water environment issue confronting plateau lakes in central Yunnan. Quantitative analysis of the impact of NPS pollution on water quality constitutes the key to preventing and controlling water pollution. However, currently, there is a dearth of research on identifying NPS pollution risks and exploring their relationship with water quality based on the Minimum Cumulative Resistance (MCR) model in the plateau lake basins of central Yunnan. Particularly, studies on the spatial heterogeneity of the impact of NPS pollution on water quality from a multi-scale perspective are scarce. Therefore, this study focuses on three typical lake basins in the Central Yunnan Plateau-Fuxian Lake, Xingyun Lake, and Qilu Lake (the Three Lakes). Utilizing the MCR model to identify NPS pollution risks, the study analyzes seven different scales, including sub-basins, riparian buffer zones (100 m, 300 m, 500 m, 700 m, and 1,000 m) and lakeshore zones, to reveal the multi-scale effects of NPS pollution on water quality through correlation analysis. The results indicate that: (1) Over 60% of the areas in the Three Lakes Basin are at high or extremely high risk, mainly concentrated in flat terrain and around inflow rivers; (2) The area of NPS pollution from paddy field source landscape (PFSL) is greater than that from construction land source landscape (CLSL), and the high-risk areas of NPS pollution are also larger for PFSL compared to CLSL; (3) The mean resistance values of PFSL and CLSL show a significant negative correlation with monthly mean values of water quality indexes (NH3-N, TP, CODCr), with the 1,000 m riparian buffer zone scale showing the greatest correlation with most water quality indexes, especially NH3-N; (4) The correlation between the mean resistance value of CLSL and the monthly mean values of water quality indexes is significantly higher than that of PFSL, indicating a greater impact of CLSL on water quality compared to PFSL. In summary, PFSL and CLSL are the primary sources of NPS pollution in the Three Lakes Basins. The 1,000 m riparian buffer zone scale is the most sensitive to the impact of NPS pollution on water quality. This study provides scientific references for landscape pattern optimization and precise control of NPS pollution risks in the Central Yunnan Plateau lake basins and offers a new research perspective for exploring multi-scale effects of NPS pollution on water quality.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567 mg/L, with a median value of 4.3 mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Enhancing efficiency and quality control: The impact of Digital Twins in drinking water networks.

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations.

Performance of artificial intelligence model (LSTM model) for estimating and predicting water quality index for irrigation purposes in order to improve agricultural production.

The primary goal of this study is to predict the current and future water quality index for irrigation (WQII) of the western Mitidja alluvial aquifer in northern Algeria. The modified WQII was used to evaluate groundwater suitability for irrigation through geographic information system (GIS) techniques. Additionally, a long short-term memory (LSTM) model was employed to calculate the WQII and map future groundwater quality, considering factors like overexploitation, anthropogenic pollution, and climate change. Two scenarios were analyzed for the year 2030. Results from applying the modified WQII model to 2020 data showed that about 83% of the study area has medium to high groundwater suitability for irrigation. The LSTM model exhibited strong predictive accuracy with determination coefficients (R2) of 0.992 and 0.987, and root mean square error (RMSE) values of 0.061 and 0.084 for the training and testing phases, respectively. For the first 2030 scenario, the area with low and medium groundwater suitability is expected to increase by 4% and 7% compared to the 2020 map. Conversely, under the second scenario, groundwater quality is predicted to improve, with a decrease of 14% and 11% in the low and medium suitability areas. The combination of the modified WQII and LSTM model proves to be an effective tool for estimating and predicting water quality indices in similar regions globally, offering valuable insights for water resource management and decision-making processes.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

Modelling water quality parameters using model tree, random forest, and non-linear regression for Mula-Mutha River, Pune, India.

Evaluation of vital water-quality indicators, especially biological and chemical demand of oxygen (BOD and COD), is important for environmental factors, human health, and agricultural output. In the recent past, data-driven techniques (DDT) offer the ability to automate water quality assessment with more reliable and rapid evaluation. The present study thus aims to utilize various DDTs: random forest (RF), model tree (MT), and non-linear-regression (NLR) to predict vital water quality indicators such as BOD and COD for the three stretches of Mula-Mutha River, Pune, India. Since the river has three stretches: Mutha, Mula, and Mula-Mutha respectively, BOD-COD models have been developed separately for each using MT, RF, and NLR. Data analysis using a violin diagram is done to understand the data characteristics. Further, the models developed were developed using the appropriate input parameters for predicting BOD and COD. Error measures including coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the constructed models. The Taylor diagram, scatter plot, and hydrograph were also used for visual performance analysis. The findings suggest that the MT and RF techniques exhibit a stronger connection between the actual and anticipated levels of BOD and COD, with NLR following closely behind. Practical acceptance of these approaches is increased by RF in the form of trees, MT with an output in the form of a sequence of equations, and NLR with a single equation. These findings help us gain insight into DDT's water quality assessment model, which will also help future researchers and water quality professionals make decisions.

Environmental and societal consequences of winter ice loss from lakes.

Climate change is reducing winter ice cover on lakes; yet, the full societal and environmental consequences of this ice loss are poorly understood. The socioeconomic implications of declining ice include diminished access to ice-based cultural activities, safety concerns in traversing ice, changes in fisheries, increases in shoreline erosion, and declines in water storage. Longer ice-free seasons allow more time and capacity for water to warm, threatening water quality and biodiversity. Food webs likely will reorganize, with constrained availability of ice-associated and cold-water niches, and ice loss will affect the nature, magnitude, and timing of greenhouse gas emissions. Examining these rapidly emerging changes will generate more-complete models of lake dynamics, and transdisciplinary collaborations will facilitate translation to effective management and sustainability.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach.

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution. Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Statistical assessment for reusing permeate from dewatering water treatment sludge process: more sustainability in environmental sanitation.

Sludge from water treatment plants (WTPs) is usually processed by physicochemical clarification followed by thickening, which results in the production of an effluent from dewatering/drying sludge processes that can potentially impact the environment. This paper assessed the viability of employing sludge dewatering water from a water treatment sludge plant (WTSP) in São Paulo State, Brazil, for reuse purposes. Water quality variables were monitored in the effluent and receiving stream. The data were analyzed by paired samples Student t-test (parametric significance test), paired samples Wilcoxon signed rank test (non-parametric significance test), and principal component analysis (multivariate analysis). Despite the distribution of data, typically not Gaussian, both Student and Wilcoxon methods agreed in 9 out of 10 studied parameters regarding the influence of WTSP discharge on waterbody; only for total manganese the Wilcoxon approach provided better fit than Student. Principal component analysis helped to evince correlations among all variables. Results provided useful information for understanding the vocation of WTSP effluent for reuse. For direct non-potable reuse, recirculating the final effluent back to the WTP for two months saved 92,000 m3 of water, the volume of drinking water demanded by the city (n = 292,000 inhabitants) in approximately 30 h.

Evaluating water quality of rock glacier outflows in the Western Alps, Italy: a regional perspective.

Intact rock glaciers (RG) are considered valuable water storage because containing permafrost ice volumes. The hydrological relevance of RG is forecasted to increase with respect to glaciers under climate change scenarios, as well as RG's role as water resources in alpine basins for multiple uses. Besides the assessment of water amount stored in intact rock glaciers, the evaluation of water quality is of primary importance. Here, we present the results of a chemical survey performed on five outflows from intact RG in 2020-2023 in the Piedmont region, Western Alps, Italy, along a latitudinal gradient and in different geological settings. The survey aimed to assess the water quality of RG outflows based on chemical indicators (major ions, nutrients, trace metals). Sampling and analyses were performed according to standard methods for freshwater samples, paying specific attention to the analytical quality and consistency of the data. We considered seasonal and interannual variability of the main chemical variables and the possible effects of RG outflows on the chemistry of lakes and ponds located in proximity to the RG. All the investigated sites were characterized by low to moderate ion content, low nutrients, and trace metals close to or below the detection limit, indicating a good water quality status. Results suggested lithology as the main factor affecting the chemical composition of RG outflows. The results of this study indicate it is advisable to develop shared protocols and joint monitoring programs for data collecting at RG outflow sites all over the Alps, possibly integrating chemical and biological indicators, with the final aim of monitoring the water quality of these valuable resources and its temporal evolution under climate change.

Relationship between water quality and phytoplankton distribution of aquaculture areas in a tropical lagoon.

Aquaculture activities can affect water quality and phytoplankton composition. Our study estimated phytoplankton density and composition relating to aquaculture-impacted environmental factors. We analyzed water quality and phytoplankton at 35 sites in a tropical brackish lagoon, including inside aquaculture ponds (integrated farming of fish, shrimp, and crab), at wastewater discharge points, within 300 m of these points, and farther out in the lagoon. Measurements were taken after aquaculture activities started in March and again in July. In both periods, total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chl-a), and turbidity decreased from the aquaculture ponds to the farther lagoon areas. Principal component analysis showed that nutrients, turbidity, and Chl-a were critical factors in aquaculture ponds, while salinity, temperature, pH, dissolved oxygen (DO), and water depth influenced water quality outside the ponds. Phytoplankton density was higher in July than in March due to aquaculture characteristics. Redundancy analysis indicated that phytoplankton, typical of inorganic, turbid, shallow lakes, was present throughout, whereas marine phytoplankton characterized the open water area (OWA). Marine phytoplankton caused a higher Shannon-Wiener index in July compared to March for OWA. Phytoplankton in aquaculture ponds was dominated by Oscillatoria spp., while Thalassiosira spp. dominated outside the ponds. We also identified indicator genera for two connected lagoons. Although constant water exchange prevented identifying specific indicator phytoplankton groups for aquaculture, this revealed the impact of wastewater from aquaculture ponds on the natural environment in the lagoons. Research on phytoplankton communities is necessary for the sustainable development of aquaculture and environmental management in coastal lagoons.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Windthrow in riparian buffers affects the water quality of freshwater ecosystems in the eastern Canadian boreal forest.

Despite the wide application of riparian buffers in the managed boreal forest, their long-term effectiveness as freshwater protection tools remains unknown. Here, we evaluate windthrow incidence in riparian buffers in the eastern Canadian boreal forest and determine the effect of windthrow on the water quality index of the adjacent freshwater ecosystems. We studied 40 sites-20 riparian buffers, aged 10 to 20 years after harvesting and 20 control sites within intact riparian environments-distributed among clay and sandy (esker) soils and black spruce (Picea mariana) and jack pine (Pinus banksiana) stands. We observed more windthrow in the harvested stands (36%) relative to the control sites (16%), regardless of substrate and species. We determined that the most important factors explaining windthrow were exposition, harvesting, aquatic environment size, and stand characteristics. These factors drive wind exposure, speed, and force, which determine post-harvest windthrow risk. Furthermore, windthrow negatively affected the water quality index of the adjacent aquatic systems, i.e., greater windthrow decreased the protective effect of the riparian buffer. We recommend increasing the use of partial harvest near riparian environments and adapting riparian buffers to site conditions to ensure the long-term protection of adjacent freshwater ecosystems.