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A thorough understanding of the geographical and spatial attributes of nonpoint source pollution risk in watersheds is crucial for conducting nonpoint source pollutant studies and implementing effective scientific administration strategies. The inclusion of a water-related functioning zone was considered during the nonpoint source's pollution risks assessment procedure. Nevertheless, there has not been a thorough investigation into the potential risk of nonpoint sources of pollution to adequately safeguard the quality of water in watersheds having varying capacities to handle contaminants in the water. This research presents an innovative approach for assessing the risk of nonpoint sources contamination. This allows for a quantitative evaluation of the effect of discharges of pollution from a sub-catchment on the quality of water bodies nearby. The nonpoint source losses of nutrients process, as modeled by the Water and Soil Assessment Tool, had been used to assess the hazard of nonpoint source contamination in Le 'a River Watersheds. This assessment happened on both yearly and monthly scales. The findings indicated that the risk of nonpoint source contamination exhibits both seasonal and regional variations and is significantly impacted by the ability of the fluid ecosystem. Elevated nonpoint sources pollutants do not always equate to elevated pollutant dangers. On the other hand, a small amount of nutrients in the nonpoint sources does not indicate an insignificant degree of susceptibility to region risk. Furthermore, by utilizing a risk assessment method that considers the capacity of the water's environment, it is possible to identify variations in risk levels that may be overlooked when solely considering nonpoint sources contaminant losses, and fluid functioning zone. This approach allows for precise regulation of nonpoint sources of pollution administration. • Innovative method for assessing nonpoint source pollution risk in watersheds. • Quantitative evaluation of pollution discharges' impact on water quality. • Seasonal and regional variations affect nonpoint source contamination risks. • Risk assessment includes water environment capacity for accurate pollution control. [ABSTRACT FROM AUTHOR]

Trihalomethanes (THMs) and nitrate are widespread chemicals in drinking water. Chronic exposure has been associated with increased cancer risk despite inconclusive evidence, partly due to the challenges in long-term exposure assessment and potential exposure misclassification. We estimated concentrations of nitrate and THMs in drinking water using a public regulatory monitoring database (SISE-Eaux) for CONSTANCES, a French population-based prospective cohort. We obtained 26,322,366 measurements of drinking water parameters from 2000 to 2020. We excluded missing, implausible and duplicated measurements; we corrected or imputed missing geocodes of sampling locations; we calculated the annual median concentration of nitrate and THMs by surveillance area. To predict missing annual median concentrations, linear mixed models with random intercept using surveillance area as a clustering variable were developed for each region for nitrate and the four THM components (chloroform, chlorodibromomethane, bromodichloromethane and bromoform) separately. Concentrations in the nearest surveillance area from the household were merged per year among 75,462 participants with residential history geocoded for 2000–2020. Estimated concentrations resulting from this approach were compared with measured concentrations in 100 samples collected in Paris, Rennes and Saint-Brieuc in 2021. Median annual concentrations of total THMs and nitrate at study participants' homes for 2000–2020 were, respectively, 15.7 μg/l (IQR: 15.2) and 15.2 mg/l (IQR: 20.8). Among these, 35% were based on measurements for nitrate (16% for THMs), 44% (46%) were predicted using on linear mixed models, and 21% (38%) were based on distribution unit median values. Conditional R2 predictive models ranged from 0.71 to 0.91 (median: 0.85) for nitrate, and from 0.48 to 0.80 for THMs (median: 0.68). These concentrations will allow future association analyses with risk of breast and colorectal cancer. Our cleaning process introduced here could be adapted to other large drinking water monitoring data. • French drinking water parameters are routinely monitored and compiled in SISE-Eaux. • We estimated nitrate and THMs annual median concentrations over France (2000–2020). • The procedure of the database cleaning was described in detail. • We linked these estimates to participants' home address in the CONSTANCES cohort. • These exposure assessments will subsequently be used in epidemiological studies. [ABSTRACT FROM AUTHOR]

Colored dissolved organic matter (CDOM) is an indicator and optical proxy of terrestrial processes such as land use with allochthonous material fluxes, biogeochemical cycles, and water quality in coastal zones influenced by rivers. However, the role of land use changes on the spatial and temporal availability of CDOM has been poorly explored in Chile. Here, we studied two watersheds with similar climates and contrasting land use patterns in northern Patagonia considering the sampling of CDOM in their estuarine and adjacent coastal ocean. An empirical algorithm with the coefficients adjusted to our study areas to estimate CDOM was applied to Landsat 7 and 8 images to examine temporal variability of CDOM est from 2001 to 2011 and 2013–2020. Our results showed an increasing trend of CDOM est in both areas. Different trends in land use patterns between the two watersheds showed a significant correlation with CDOM est and contrasting associations with environmental variables. Higher humification was found in Yaldad in comparison with Colu. In both areas, allochthonous materials predominated, especially during austral spring according to the low values of the Fluorescence Index (FI). Our results highlight the potential of CDOM est to parameterize biogeochemical cycling models and to further understand the dynamics of CDOM in coastal ecosystems. [Display omitted] • CDOM can be used as an indicator of land use in coastal zone of Northern Patagonia. • Land use change had a high influence on the availability and quality of CDOM. • Inter annual time series of coastal CDOM showed an increasing trend in both basins. [ABSTRACT FROM AUTHOR]

Water temperature and dissolved oxygen are two important parameters which affect the ecosystem functions which estuaries provide. As such, we need to understand the long-term change of these parameters in English estuaries to better understand those areas which may be vulnerable in the present and future. Detecting these trends in monitoring data is difficult however, since temporally and spatially heterogeneous sampling has the potential to obscure any long-term component of change. In this study, we use a generalized additive model (GAM) approach to isolate the inter-annual change of water temperature and dissolved oxygen in spot sampled monitoring data across English estuaries. This technique can account for the potentially non-linear variability caused by temporal and spatial variations in sampling throughout the day and year. We find that water temperature in all modelled estuaries has warmed, with an average warming of 0.037 °C/yr between 1990 and 2022, in line with temperature change in rivers and the sea surface around England. For the same period, dissolved oxygen has increased for roughly half of modelled estuaries and decreased for the other half. The variation across English estuaries in these two conditions is best explained by the geographical location of an estuary. Many of the estuaries warming at the greatest rate are those where sea surface temperature increase has also been greatest. We suggest the observed distribution of changes in dissolved oxygen can be explained by three processes: (1) temperature driven change, (2) dissolved oxygen improvement (e.g., reduced nutrient inputs through wastewater management regulation) and (3) changes in the number of primary productivity-induced super saturation events. This study provides a comprehensive look at temperature and dissolved oxygen change across English estuaries over the past ∼30 years, which may provide information on the processes behind how these water quality parameters have changed and will continue to do so. • Temperature and dissolved oxygen change in English estuaries is determined. • Generalized additive models are used to remove seasonal and diurnal variations. • Extra value can be gained from monitoring data by using appropriate analysis. • All estuaries in England have warmed, at an average rate of 0.037 °C/yr. • Dissolved oxygen trends vary based on temperature, regulation or productivity. [ABSTRACT FROM AUTHOR]

Water column nutrient concentrations and autotrophy in oligotrophic ecosystems are sensitive to eutrophication and other long-term environmental changes and disturbances. Disturbance can be defined as an event or process that changes the structure and response of an ecosystem to other environmental drivers. The role disturbance plays in regulating ecosystem functions is challenging because the effect of the disturbance can vary in magnitude, duration, and extent spatially and temporally. We measured changes in total nitrogen (TN), dissolved inorganic nutrient (DIN), total phosphorus (TP), soluble reactive phosphorus (SRP), total organic carbon (TOC), and chlorophyll-a (Chl- a) concentrations throughout the Everglades, Florida Bay, and the Florida Keys. This region has been subjected to a variety of natural and anthropogenic disturbances including tropical storms, fires, eutrophication, and rapid increases in water levels from sea-level rise and freshwater restoration. We hypothesized that the rate of change in water quality would be greatest in the coastal ecotone where disturbance frequencies and marine P concentrations are highest, and in freshwater marshes closest to hydrologic changes from restoration. We applied trend analyses on multi-decadal data (1996–2019) collected from 461 locations distributed from inland freshwater Everglades (ridge and slough) to outer marine reefs along the Florida Keys, USA. Total Organic Carbon decreased throughout the study area and was the only parameter with a systematic trend throughout the study area. All other parameters had spatially heterogeneous patterns in long-term trends. Results indicate more variability across a large spatial and temporal extent associated with changes in biogeochemical indicators and water quality conditions. Chemical and biological changes in oligotrophic ecosystems are important indicators of environmental change, and our regional ridge-to-reef assessment revealed ecosystem-specific responses to both long-term environmental changes and disturbance legacies. • Disturbances regulate ecosystem functions and lead to shifts in ecosystem drivers such as nutrients. • Using a multi-decadal dataset of nutrient concentrations, we detected significant changes through space and time. • Total Organic Carbon decreased throughout the study area and was the only parameter with a regional systematic trend. • Changes in water column total organic carbon and chlorophyll- a over time suggest changes in aquatic primary production. [ABSTRACT FROM AUTHOR]

Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems. [Display omitted] • Machine learning algorithms were employed to predict P removal capacity of biochar. • A dataset involving 17 variables was compiled sourced from relevant literature. • The optimized CatBoost model demonstrated exceptional accuracy with an R2 of 0.9573. • Key factors were biochar dosage, initial P concentration, and C content in biochar. [ABSTRACT FROM AUTHOR]

Since the 1980s, fish farming (aquaculture) has been an important contributor to Scotland's economy, but there are concerns that nutrient-rich food waste and excreta from these farms are causing eutrophication. Water quality monitoring preceding the arrival and subsequent expansion of the industry is limited. Therefore, to better understand the impacts of in-lake fish farms on the quality of freshwater ecosystems, we examined the diatom records in sediment cores from seven freshwater lochs in Scotland over a timescale of c.100–200 years, spanning the period before and after installation of the fish cages at these sites. At three lochs (A, C, E) we observed marked diatom assemblage shifts indicative of eutrophication, coincident with arrival of the fish farms, at two lochs (B, G) there was evidence of enrichment over a longer timescale although with some further enrichment occurring with the advent of the fish farms, and at the other two lochs (D, F), diatom shifts were subtle and showed no sign of eutrophication. Thus, while marked ecological shifts are shown to occur with the arrival of fish farms in some sites, this is not always the case. The natural background conditions, the scale of operations, the siting of the fish cages in relation to location of inflows and outflows, the role of flushing rate and additional sources of nutrients are discussed as potential factors for the variable impacts observed across the seven lochs. Such factors should be considered when planning future installation and expansion of fish farms to ensure sustainable development of these resources. Our study provides an understanding of baseline conditions and long-term water quality trajectories in freshwater lakes with fish farms and demonstrates the value of paleolimnology in supporting management decisions with respect to fisheries. [Display omitted] • Diatom changes coincide with the arrival of fish farms at five lochs. • At two lochs, freshwater fish farms have not led to eutrophication. • The degree of impact is dependent on local conditions. • Paleolimnology is valuable for assessing ecological change caused by fish farms. • A long-term perspective can help to support management decisions. [ABSTRACT FROM AUTHOR]

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG. [Display omitted] • We present an open-source workflow for georectifying drone image data. • It is capable of accuracies of ±1 m but is sensitive to viewing distance. • The method is suitable for use over water surfaces where there are no fixed points. • Potential uses include mapping whitecap fraction, ocean glitter and ocean color. [ABSTRACT FROM AUTHOR]

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources. • Quantitative prediction of LUCC for the next decade using multi-temporal RS data. • PRM is optimal for reflecting relationship of LUCC and WQI. • Water quality is best under ecological protection scenario for the next decade. • The proposed method is generalizable and can be replicated in other watersheds [ABSTRACT FROM AUTHOR]

Ice, water, and sediment represent three interconnected habitats in lake ecosystems, and bacteria are crucial for maintaining ecosystem equilibrium and elemental cycling across these habitats. However, the differential characteristics and driving mechanisms of bacterial community structures in the ice, water, and sediments of seasonally frozen lakes remain unclear. In this study, high-throughput sequencing technology was used to analyze and compare the structure, function, network characteristics, and assembly mechanisms of bacterial communities in the ice, water, and sediment of Wuliangsuhai, a typical cold region in Inner Mongolia. The results showed that the bacterial communities in the ice and water phases had similar diversity and composition, with Proteobacteria , Bacteroidota , Actinobacteria , Campilobacterota , and Cyanobacteria as dominant phyla. The bacterial communities in sediments displayed significant differences from ice and water, with Chloroflexi , Proteobacteria , Firmicutes , Desulfobacterota , and Acidobacteriota being the dominant phyla. Notably, the bacterial communities in water exhibited higher spatial variability in their distribution than those in ice and sediment. This study also revealed that during the frozen period, the bacterial community species in the ice, water, and sediment media were dominated by cooperative relationships. Community assembly was primarily influenced by stochastic processes, with dispersal limitation and drift identified as the two most significant factors within this process. However, heterogeneous selection also played a significant role in the community composition. Furthermore, functions related to nitrogen, phosphorus, sulfur, carbon, and hydrogen cycling vary among bacterial communities in ice, water, and sediment. These findings elucidate the intrinsic mechanisms driving variability in bacterial community structure and changes in water quality across different media phases (ice, water, and sediment) in cold-zone lakes during the freezing period, offering new insights for water environmental protection and ecological restoration efforts in such environments. [Display omitted] • The bacterial structure in ice and water are similar, while sediments are significantly different from the two. • The bacterial distribution in ice and water showed higher spatial variability. • The bacterial communities in ice, water, and sediment are mainly cooperative. • Dispersal limitation and drift are the main driving mechanisms of bacterial assembly in frozen lakes. [ABSTRACT FROM AUTHOR]

Source and raw water quality may deteriorate due to rainfall and river flow events that occur in watersheds. The effects on raw water quality are normally detected in drinking water treatment plants (DWTPs) with a time-lag after these events in the watersheds. Early warning systems (EWSs) in DWTPs require models with high accuracy in order to anticipate changes in raw water quality parameters. Ensemble machine learning (EML) techniques have recently been used for water quality modeling to improve accuracy and decrease variance in the outcomes. We used three decision-tree-based EML models (random forest [RF], gradient boosting [GB], and eXtreme Gradient Boosting [XGB]) to predict two critical parameters for DWTPs, raw water Turbidity and UV absorbance (UV254), using rainfall and river flow time series as predictors. When modeling raw water turbidity, the three EML models (r RF − Tu 2 = 0.87 , r GB − Tu 2 = 0.80 and r XGB − Tu 2 = 0.81) showed very good performance metrics. For raw water UV254, the three models (r RF − UV 2 = 0.89 , r GB − UV 2 = 0.85 and r XGB − UV 2 = 0.88) again showed very good performance metrics. Results from this study suggest that EML approaches could be used in EWSs to anticipate changes in the quality parameters of raw water and enhance decision-making in DWTPs. [Display omitted] • Decision-tree-based ensemble machine learning models were tested to predict quality parameters of raw water supplying DWTP. • Random forest and eXtreme Gradient Boosting techniques yielded the highest metrics for predicting raw water quality. • Decision-tree-based ensemble machine learning techniques may be used in the modelling component early warning systems. [ABSTRACT FROM AUTHOR]

Rural areas lacking essential sewage treatment facilities and collection systems often experience eutrophication due to elevated nutrient loads. Understanding nitrogen (N) sources and transport mechanisms in rural catchments is crucial for improving water quality and mitigating downstream export loads, particularly during storm events. To further elucidate the sources, pathways, and transport mechanisms of N from a rural catchment with intensive agricultural activities during storm events, we conducted an analysis of 21 events through continuous sampling over two rainy seasons in a small rural catchment from the lower reaches of the Yangtze River. The results revealed that ammonia-N (NH 4 +-N) and nitrate-N (NO 3 −-N) exhibited distinct behaviors during rainstorm events, with NO 3 −-N accounting for the primary nitrogen loss, its load being approximately forty times greater than that of NH 4 +-N. Through examinations of the concentration-discharge (c-Q) relationships, the findings revealed that, particularly in prolonged rainstorms, NH 4 +-N exhibited source limited pattern (b = −0.13, P < 0.01), while NO 3 −-N displayed transport limited pattern (b = −0.21, P < 0.01). The figure-eight hysteresis pattern was prevalent for both NH 4 +-N and NO 3 −-N (38.1% and 52.0%, respectively), arising from intricate interactions among diverse sources and pathways. For NO 3 −-N, the hysteresis pattern shifted from clockwise under short-duration rainstorms to counter-clockwise under long-duration rainstorms, whereas hysteresis remained consistently clockwise for NH 4 +-N. The hysteresis analysis further suggests that the duration of rainstorms modifies hydrological connectivity, thereby influencing the transport processes of N. These insights provide valuable information for the development of targeted management strategies to reduce storm nutrient export in rural catchments. [Display omitted] • High-frequency data reveal distinct NH 4 +-N and NO 3 −-N transport in rural areas. • NH 4 +-N is source limited while NO 3 −-N is transport limited under prolonged storms. • Most events had Fig. 8 hysteresis highlighting diverse sources and pathways. • NO 3 −-N hysteresis shift from clockwise to counterclockwise with storm duration. [ABSTRACT FROM AUTHOR]

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments. [Display omitted] • Optimized K-means algorithm was used to categorize field measurement points. • Advanced variational AI models were applied to estimate DO and Chl-a indices. • Three hybrid-based methods were established to fuse the outputs of single models. • GA was utilized to assign the optimal weights of the ORNESS weighting method. • The bootstrapping stochastic method was used to evaluate the uncertainty of models. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality. [Display omitted] • Statistical methods are effective in modelling relationships between land use and water quality. • The results of reviewed studies have been remarkably consistent in several respects. • However, there remain several persistent knowledge gaps which require further research. • These relate to scale, landscape configuration, land use thresholds, and confounding variables. • There is a need for additional context-specific research, especially in developing countries. [ABSTRACT FROM AUTHOR]

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater. • Data was collected from two areas that were different from the river. • Physiochemical analysis for 296 samples has been performed. • Hydrogeochemical facies and dynamics have been investigated. • Groundwater appropriateness for various purposes has been evaluated. • ANFIS Model has been developed to predict WQI and IWQI. [ABSTRACT FROM AUTHOR]

Cyanobacteria are the dominating microorganisms in aquatic environments, posing significant risks to public health due to toxin production in drinking water reservoirs. Traditional water quality assessments for abundance of the toxigenic genera in water samples are both time-consuming and error-prone, highlighting the urgent need for a fast and accurate automated approach. This study addresses this gap by introducing a novel public dataset, TCB-DS (Toxigenic Cyanobacteria Dataset), comprising 2593 microscopic images of 10 toxigenic cyanobacterial genera and subsequently, an automated system to identify these genera which can be divided into two parts. Initially, a feature extractor Convolutional Neural Network (CNN) model was employed, with MobileNet emerging as the optimal choice after comparing it with various other popular architectures such as MobileNetV2, VGG, etc. Secondly, to perform classification algorithms on the extracted features of the first section, multiple approaches were tested and the experimental results indicate that a Fully Connected Neural Network (FCNN) had the optimal performance with weighted accuracy and f1-score of 94.79% and 94.91%, respectively. The highest macro accuracy and f1-score were 90.17% and 87.64% which were acquired using MobileNetV2 as the feature extractor and FCNN as the classifier. These results demonstrate that the proposed approach can be employed as an automated screening tool for identifying toxigenic Cyanobacteria with practical implications for water quality control replacing the traditional estimation given by the lab operator following microscopic observations. The dataset and code of this paper are publicly available at https://github.com/iman2693/CTCB. • Implementing ML for cyanobacteria classification. • Introduction of a dataset of 10 toxicogenic cyanobacterial genera. • Proposing the MobileNet for classification of Cyanobacteria. • Utilization of FCNN as the optimal classifier for cyanobacterial cell images. • Reaching to the accuracy and f1-score of 94.79% and 94.91%, respectively. [ABSTRACT FROM AUTHOR]

To study the distribution of trace elements in natural water of the Du River Source National Nature Reserve and to assess the water quality and health risks, Zhushan County in Hubei Province was selected as the study area. Element content in 361 natural water samples collected from Zhushan County were measured by ICP-MS, ICP-OES, and HG-AFS. The main anions and cations present in water samples from Zhushan County are Ca2+ and HCO 3 −. The water chemistry is predominantly influenced by the weathering of carbonate rocks. The water samples with high content of selenium (Se) (0∼82.9 μg/L, mean 4.6 μg/L) in natural water in Zhushan County are mainly distributed in the northern part of Zhushan. The strontium (Sr) content of 49.6% of the water samples (0.001–2.177 mg/L, mean 0.234 mg/L) reached the criteria of natural mineral water for drinking in China (Sr ≥ 0.2 mg/L), which is distributed throughout the county. The high content of metasilicic acid (H 2 SiO 3) (0.026–35.910 mg/L, mean 12.598 mg/L) and zinc (Zn) (0∼407.218 μg/L, mean 12.406 μg/L) are concentrated in northern Zhushan County. 99.7% water samples were freshwater and 98.9% meet the criteria of "good" water quality. All of the natural water samples have low health risk and low heavy metal pollution. 6.1% water samples meet the criteria of Se-type mineral water, while 45.4% meet the criteria of Sr-type mineral water, and 4.4% water samples meet the criteria of "low sodium, high Se, and high Sr" mineral water. Zhushan County has the potential for Se-type mineral water and Sr-type mineral water development. The findings of this study hold immense significance for the public health implications of drinking water in Du River Source, thereby offering valuable insights for effective water resources management. [Display omitted] • Natural water from the Du River Source meets drinking standards. • Light health risk of natural water from Du River Source. • Light heavy metal pollution of natural water from Du River Source. • The Du River Source has the potential to develop Se-rich and Sr-rich water. [ABSTRACT FROM AUTHOR]

Within the Huaihe River Basin, Guohe River, as its second-largest tributary, serves as a critical water supply source. Recent industrial and agricultural advancements have led to increased trace element contamination, adversely impacting the water quality within Guohe River Basin. Therefore, this study aimed to investigate the distribution characteristics, sources, water quality and risk assessment of trace elements in the surface water, groundwater, and sediments across the basin. The results showed that the spatial distribution of trace elements in the surface water and groundwater of Guohe River Basin was that most of the high concentrations appeared in Qiaocheng District of Bozhou City, the mean concentration of Fe in Guohe River sediments was the highest, the mean concentration of Sb was the lowest. The PMF source analysis results showed that the main source of trace elements in Guohe River Basin was natural geological processes, followed by human activities. The sodium adsorption ratio (SAR) indicated that the surface water samples of Guohe River in two seasons had high sodium and salinity hazards. The water quality index (WQI) showed that surface water and groundwater samples in the northwestern of Guohe River Basin had poor water quality. The results of the risk assessment showed that As and Mn posed great ecological risks to surface water and groundwater, respectively, and that F− was the pollutant with the most potential health risk hazard in the basin. The Geo-accumulation index (I geo) results showed that Cd, Se and As should be taken seriously as the main contaminants of the sediments in Guohe River Basin. Trace elements; Source analysis; Sodium adsorption ratio; Water quality index; Risk assessment; Geo-accumulation index. • The primary source of trace elements pollution in Guohe River Basin was natural geological processes. • The surface water samples were usually unsuitable for irrigation. • The quality of the surface water and groundwater samples in the northwest of the basin were poor. • F− was the pollutant with the most potential health risk hazard in the basin. • The river sediments were seriously contaminated by Cd, Se and As. [ABSTRACT FROM AUTHOR]

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters. For this purpose, Mat-SWMM was modified to equip it with the capacity to provide comprehensive water quality analysis outcomes. Additionally, this research also conducts a comparative examination of two distinct types of objective functions in the optimization. Rather than depending on previous literature, this study undertook a numerical campaign to ascertain an appropriate range for the relevant parameters within the case study, thereby ensuring the optimization algorithm's efficient functionality. This research also implements an integrated event calibration approach, i.e., a novel method that calibrates all rainfall events collectively, thus improving systemic interaction representation and model robustness. The findings indicate that employing this methodology significantly enhances the reliability of the outcomes, thereby establishing a more robust procedure. The first objective function (TSS instantaneous less squared difference function, OF 1), which is widely employed in the literature, was designed to minimize the difference between observed and predicted instantaneous Total Suspended Solids (TSS) concentrations. In contrast, the second function (mass and mass peak consistency function, OF 2) considers integral model outputs, i.e., the overall mass balance, the time of the peak mass flow rate, and its intensity. The analysis of the outputs revealed that both objective functions demonstrated sufficient performance. OF 1 provided slightly better performance in predicting the TSS concentrations, whereas OF 2 demonstrated superior ability in capturing global event characteristics. Notably, the optimal parameter set identified through OF 2 aligned with the physically plausible ranges traditionally recommended in technical manuals for urban catchments. In contrast, OF 1's optimal set necessitated an expansion in the acceptable parameter ranges. Finally, from a computational burden viewpoint, OF 1 demanded a significantly higher number of function evaluations, thus implying an escalating computational cost as the range expands. Conversely, OF 2 necessitated fewer evaluations to converge toward the optimal solution. • Enhanced the calibration of build-up/wash-off parameters through integrated event calibration. • Delineated the impact of parameter range size on model performance. • Scrutinized and compared two distinct objective functions. [ABSTRACT FROM AUTHOR]

Ongoing climate change and anthropogenic impacts will continue to worsen the problem of water crisis. In order to explore practical solutions for drought and climate change adaptation, this work uses a set of different methodologies to evaluate an operational fog collection project and discuss solutions for some emerging challenges. This study uses fog collection rate, Water Quality Index (WQI) and interviews with the community as indicators for the assessment. Results indicate that the project continues to deliver improved drinking water access to the community. Fog harvesting rate yielded an annual average from 1.6 to 6 L/m2/d. Interviews confirm the social commitment of the local community. However, challenging points emerge: (1) ongoing groundwater pollution due to the lack of sanitation; (2) if fog water is the only source, inconsistent fog yields may negatively impact water availability; (3) poor solid waste management. The findings represent a baseline for assessing and benchmarking other similar projects or project proposals in the Atlantic coast of North Africa. Therefore, the proposed methodology can also be used in other fog collection projects around the world. [Display omitted] • Sidi Ifni project, Morocco, is the largest fog collection project in the world. • A pilot multi-aspect assessment procedure is proposed. • The fog harvesting rate yielded an annual average of 1.6–6 L/m2/d. • Interviews confirm the technology appropriation by the local community. • Environmental pollution and fog yield variability represent the main challenge. [ABSTRACT FROM AUTHOR]

[Display omitted] • Establishing a health baseline to assist with investigations into mass bivalve die-offs. • Higher bacterial loads isolated from mortality samples. • Lower gene copy number of Endozoicomonas spp. detected in mortality samples. • Changes in infection prevalence and intensity can be more easily measured. Disease is a major threat to the economic, ecological and cultural services provided by wild bivalve populations. Over the past decade anecdotal reports on declining health of native bivalve populations around Aotearoa New Zealand have been supported by increasing observations of mass die-offs. Causes of declining health and mass die-offs of wild bivalves are not clear and could be due to a number of interactive and cumulative factors, including declining water quality, climate change, or disease. Pipi/kōkota (Paphies australis) within the Whangārei area (northern New Zealand) have suffered repeated die-offs and declining health since at least 2009. Baseline health data for wild native bivalve populations are scarce making it difficult to identify changes in pathogen infection prevalence and intensity and infer their importance to host health. This research aimed to examine and document the health of pipi in Whangārei with the objective of identifying factors that may contribute to their ill health and lack of population recovery. We sampled pipi from four sites within Whangārei, eight times across two years (total n = 640) to establish a health baseline using histopathology, general bacteriology, and qPCR for the intracellular bacteria Endozoicomonas spp. Three pipi mass die-offs occurred during the sampling window that were opportunistically sampled to compare against the health baseline established using healthy pipi. An increase in bacterial growth and a decrease in the abundance of Endozoicomonas spp. in mortality pipi was observed compared with the health baseline. Establishing a health baseline for pipi from Whangārei provided a benchmark to assess changes in a pipi population experiencing high mortality. Such data can help identify factors contributing to die-offs and to help inform what mitigation, if any, is possible in wild shellfish populations. [ABSTRACT FROM AUTHOR]

This study explores the application of a tyrosinase cantilever nanobiosensor for detecting 17β-estradiol and estrone in typical water systems. The physical-chemical parameters of water were evaluated within the Tigre River micro-basin in Erechim, RS, to determine water potability for urban populations. Water clarity, conductivity, and pH levels were essential markers, adhering to recognized standards for water quality and human consumption. The cantilever nanobiosensor demonstrated strong sensitivity and a broad linear range, with a limit of detection (<0.00051 ppb) surpassing other enzymatic biosensors and covering a range of 0.0001–100 ppb. The real water sample quality investigated in relation to contamination with 17β-estradiol and estrone by nanobiosensor showed values below the LOD for both compounds. Recovery studies demonstrated the reliability of the nanobiosensor. Selectivity tests indicated minimal interference from structurally similar substances. This study validates the nanobiosensor's potential for environmental monitoring and hormone detection, aligning with standard practices. [Display omitted] • Tyrosinase can be used in a cantilever nanobiosensor for hormone detection in water systems. • 17β-estradiol and estrone were detected using nanobiosensor in river water. • The nanobiosensor developed present practical application for environmental monitoring. [ABSTRACT FROM AUTHOR]

Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydraulic junction. Herein, we have sampled for a one-year and analyzed the water quality at the hydraulic junction of a dual-source DWDS. The results show that visible changes in drinking water quality, including turbidity, pH, UV 254 , DOC, residual chlorine, and trihalomethanes (TMHs), are observed at the sample point between 10 and 12 km to one drinking water plant. The average concentration of residual chlorine decreases from 0.74 ± 0.05 mg/L to 0.31 ± 0.11 mg/L during the water supplied from 0 to 10 km and then increases to 0.75 ± 0.05 mg/L at the end of 22 km. Whereas the THMs shows an opposite trend, the concentration reaches to a peak level at hydraulic junction area (10–12 km). According to parallel factor (PARAFAC) and high-performance size-exclusion chromatography (HPSEC) analysis, organic matters vary significantly during water distribution, and tryptophan-like substances and amino acids are closely related to the level of THMs. The hydraulic junction area is confirmed to be located at 10–12 km based on the water quality variation. Furthermore, data-driven models are established by machine learning (ML) with test R 2 higher than 0.8 for THMs prediction. And the SHAP analysis explains the model results and identifies the positive (water temperature and water supply distance) and negative (residual chlorine and pH) key factors influencing the THMs formation. This study conducts a deep understanding of water quality at the hydraulic junction areas and establishes predictive models for THMs formation in dual-sources DWDS. • The hydraulic junction area of dual-source DWDS was located. • Quantitative analysis of organic characterization is conducted by PARAFAC and HPSEC analysis. • Date-driven models for THMs prediction is established by ML. • The key factors that influencing THMs formation is identified by SHAP analysis. • The risks and advantages of dual-source DWDS are summarized. [ABSTRACT FROM AUTHOR]

Forest fertilization with municipal biosolids has been shown to increase tree growth and enhance forest soils. However, there are concerns that nitrogen from the biosolids could impact surface waters through movement from subsurface flow. Here we analyzed data on soil and surface water nitrogen from a working tree plantation that has used biosolids for over three decades to see if there was evidence of N movement through the soil to surface waters. GIS (Geographic Information System) was used to map application units over time and LiDAR (Light Detection and Ranging) was used to delineate watersheds. The program is located in King County Washington with biosolids provided by the King County Wastewater Treatment program. We assembled records to determine if there is any evidence of movement of NO 3 − through soils or any enrichment in surface waters. While soils show evidence of NO 3 − enrichment following biosolids application with cumulative loading rates up to 26 Mg ha−1, this is generally limited to the 'A' soil horizon and does not increase linearly with increased biosolids loading rates. There was no indication of increased surface water NO 3 − concentration relative to biosolids application rates, with a small trend of decreasing water NO 3 − over time. Surface water NO 3 − concentration was not correlated with the fraction of the watershed area that had been amended with biosolids, and there was no observable increase in surface water NO 3 − with increased frequency of biosolids applications to the tree plantations. These results suggest that the current biosolids program is sufficiently protective of ground and surface waters. These observations suggest that biosolids application can be conducted on a large scale with multiple benefits and no discernible impact to surface waters. [Display omitted] • Municipal biosolids enhance tree growth and improve soil quality on tree farms. • Nitrate leaching concerns can limit application frequency and rates. • Long-term data from commercial applications was used evaluate nitrogen movement. • Biosolids increased surface soil NO 3 - but no changes in water N were observed. [ABSTRACT FROM AUTHOR]

Drawing upon an extensive body of valuation literature focused on water quality, this paper performs a meta-analysis benefit transfer exercise aimed at quantifying willingness to pay (WTP) for an enhancement in drinking water quality for households that have been directly exposed to Perfluoroalkyl Substances (PFAS) over recent decades in Italy. The analysis compiles metadata of 72 WTP estimates extracted from 40 previous valuation studies conducted in advanced economies. The benefit transfer is realized estimating a meta regression model (MRM) which includes both study design and socio-economic explanatory variables, according to the Weak Structural Utility Theoretic approach. To determine the most suitable MRM specification, a comparative evaluation of various model configurations is developed exploiting the Least Absolute Shrinkage and Selection Operator (LASSO) selection criterion, and assessing their predictive performances in terms of transfer error and explanatory power. The mean transfer error (MTE) and the adjusted R-squared of the preferred MRM are in line with past published meta-analyses (0.665 and 0.607, respectively). The parameters estimated in the model align with both economic theory and intuition. The benefit transfer process results in an estimated annual WTP of € 250.80 per household for improved drinking water quality in the PFAS-affected area and an aggregated value of social benefits from PFAS decontamination of around € 12 million. • First meta-analysis focused on drinking water quality valuation in developed economies. • WTP for PFAS decontamination of drinking water via benefit transfer. • LASSO selection criteria applied for the selection of variables in the meta-regression model. • New dataset for drinking water quality valuation around the world. [ABSTRACT FROM AUTHOR]

Harvesting of plantation conifers on peatlands is carried out as part of restoration and forestry operations. In particular, in the UK and Ireland, conifer plantations on drained ombrotrophic blanket and raised bogs are increasingly being removed (by harvesting), along with blocking of drainage ditches to help raise water tables to reinitiate and restore bog vegetation and function. However, both tree harvesting and peatland restoration operations can have significant impacts on water quality at local and catchment scales. Previous research has suggested that leaching from leftover decomposing brash (tree tops and branches, including wood and needles) is the primary cause, while other work has suggested that release from rewetted peat also contributes to water quality changes. This research investigates the relative importance of peat rewetting, needles and branches on water quality using mesocosm experiments, to help elucidate the mechanisms behind water quality changes following restoration and harvesting operations. Peat and brash were collected from a drained afforested blanket bog in the Flow Country, Scotland. Short-term mesocosm experiments were conducted by incubating peat, peat + needles and peat + needles + branches with rainwater in quadruplicate. Brash from Sitka spruce (Picea sitchensis) and lodgepole pine (Pinus contorta) was investigated separately, while we also conducted experiments with fresh and aged (∼18 months) brash. Peat, needles and branches all significantly impacted water quality in the order of branches > needles > peat, while concentrations of DOC, PO 4 3−, NH 4 +, K and Mn were most impacted. Water quality impacts of spruce brash appeared generally greater than pine, while fresh brash had larger effects than aged brash. In our mesocosms, relative contributions to water quality changes were estimated by elemental yields. On average, peat contributed 25.4% (range 0.6–72.3%), while needles and brash contributed 19.7% (range 3.0–37.0%) and 54.9% (range 22.1–70.2%) to yields, respectively. We further estimate that 267 kg C ha−1 (255.8 kg as DOC, 10.7 kg as DIC), 27.4 kg K ha−1, 5.8 kg P ha−1 (as PO 4 3−) and 0.5 kg N ha−1 (as NH 4 +) could be released from brash, over nine days. [Display omitted] • Relative contributions of peat, needles and branches to water quality were assessed. • DOC, PO 4 3−, NH 4 +, K and Mn were collectively the variables most impacted. • Contributions to leaching were peat (25.4%), needles (19.7%) and branches (54.0%). • Removal of needles and branches could help mitigate water pollution. [ABSTRACT FROM AUTHOR]

The ecological health of freshwater rivers is deteriorating globally due to careless human activities, for instance, the emission of plastic garbage into the river. The current research was the first assessment of microplastics (MPs) pollution in water, sediment, and representative organisms (fish, crustacean, and bivalve) from the Surma River. Water, sediment, and organisms were sampled from six river sites (Site 1: Charkhai; Site 2: Golapganj; Site 3: Alampur; Site 4: Kazir Bazar; Site 5: Kanishail and Site 6: Lamakazi), and major water quality parameters were recorded during sampling. Thereafter, MPs in water, sediment, and organism samples were extracted, and then microscopically examined to categorize selected MPs types. The abundance of MPs, as well as size, and color distribution, were estimated. Polymer types were analyzed by ATR-FTIR, the color loss of MPs was recorded, the Pollution Load Index (PLI) was calculated, and the relationship between MPs and water quality parameters was analyzed. Sites 4 and 5 had comparatively poorer water quality than other sites. Microplastic fibers, fragments, and microbeads were consistently observed in water, sediment, and organisms. A substantial range of MPs in water, sediment, and organisms (37.33–686.67 items/L, 0.89–15.12 items/g, and 0.66–48.93 items/g, respectively) was recorded. There was a diverse color range, and MPs of <200 μm were prevalent in sampling areas. Six polymer types were identified by ATR-FTIR, namely Polyethylene (PE), Polyamide (PA), Polypropylene (PP), Cellulose acetate (CA), Polyethylene terephthalate (PET), and Polystyrene (PS), where PE (41%) was recognized as highly abundant. The highest PLI was documented in Site 4 followed by Site 5 both in water and sediment. Likewise, Sites 4 and 5 were substantially different from other study areas according to PCA. Overall, the pervasiveness of MPs was evident in the Surma River, which requires further attention and prompt actions. [Display omitted] • The Surma River's water, sediment, and organisms contain a large extent of MPs. • The water purity in the river sites might be degraded due to MPs. • ATR-FTIR detected six polymers including polyethylene as highly abundant. • The PLI ranked the Surma River as category I in the case of MP pollution. • The pervasiveness of MPs was evident in upstream water and its life. [ABSTRACT FROM AUTHOR]

Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrient sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies of nutrient source attribution have focused on large watersheds or counties at annual time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a generalizable Bayesian network model for phosphorus source attribution at the subwatershed scale (12-digit Hydrologic Unit Code). Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow information simulated by the SWAT model, and in-stream water quality measurements using Approximate Bayesian Computation to derive a posterior that attributes phosphorus contributions to subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach currently adopted by environmental agencies. Fertilizer contributes more soluble reactive phosphorus than manure, while manure contributes most of the unreactive phosphorus. While developed for the specific context of Maumee River Basin, our lightweight and generalizable model framework could be adapted to other regions and pollutants and could help inform targeted environmental regulation and enforcement. • Our model traces phosphorus sources by combining its excess, flow, and water quality. • Phosphorus release shows significant spatial variations and peaks in spring planting. • Fertilizer contributes more soluble reactive phosphorus than manure. • Manure contributes most of the unreactive phosphorus. • The lightweight and generalizable framework can improve targeted pollution management. [ABSTRACT FROM AUTHOR]

Excess nitrogen (N) discharged into streams and rivers degrades freshwater quality and threatens ecosystems worldwide. Land use patterns may influence riverine N export, yet the effect of location on N export and removal is not fully understood. We proposed a hybrid model to analyze N export and removal within the watersheds. The proposed model is satisfied for the riverine N modelling. The KGE and R2 are 0.75 and 0.72 in the calibration period which are 0.76 and 0.61 in the validation period. Human-impacted land use may modify the N yield in the watershed, and the net N export from built-up to the in-stream system was highest in the urbanized sub-watersheds (0.81), followed by the agricultural sub-watersheds (0.88), and forested sub-watersheds (0.96). Agricultural activities make a large contribution to the N exports in the watersheds, and the mean N input from the agricultural land use to in-stream were 2069–4353 kg km−2 yr−1. Besides, the excess inputs of N by overapplication of fertilizer and manure during the agricultural activities may increase legacy N in soil and groundwater. Biological processes for the riverine N removal may be controlled by the available substrate in the freshwater system, and temperature sensitivity of denitrification is highest in the flood seasons, especially for the human-impacted sub-watersheds. The riverine biological processes may be limited by other competitions. Our model results provide evidence that quantity and location of specific land use may control biogeochemistry within watersheds. We demonstrate the need to understand nutrient export and removal within watersheds by improving the representation of spatial patterns in existing watershed models, and we consider this study to be a new effort for the spatially explicit modeling to support land-use based N management in watersheds. [Display omitted] • A hybrid model was proposed to quantify the watershed nitrogen export and removal. • Human-impacted land use may modify the N yield in the watershed. • Agricultural activities contribute largely to the legacy N. • The riverine biological N uptake may be limited by competition with dissimilatory nitrate reduction to ammonium. [ABSTRACT FROM AUTHOR]

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (C TSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving C TSS using Landsat images. Subsequently, the long-term trends in C TSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating C TSS , with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the C TSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in C TSS. This study not only provides 36-year recorded datasets of C TSS in estuary water, but also offers new insights into the complex mechanisms that regulate C TSS spatiotemporal dynamics for water resource management. • Three novel algorithms allow remote-sensing monitoring of total suspended solids concentration (C TSS) in Pearl River Estuary (PRE). • 36-year Landsat observations unveiled significant spatiotemporal patterns and overall increasing trend of C TSS in PRE. • Driver analysis revealed that the cropland, industrial sewage, forests and grassland, build-up land can collectively explain 87.81% of the long-term changes in C TSS. • Our study has implications for future precise eutrophication control and efficient water resources management. [ABSTRACT FROM AUTHOR]

The food web is a cycle of matter and energy within river ecosystems. River environmental changes resulting from human activities are increasingly threatening the composition and diversity of global aquatic organisms and the multi-trophic networks. How multiple environmental factors influence food web patterns among multi-trophic microbial communities in rivers remains largely unknown. Using water quality evaluation and meta-omics techniques, we investigated the composition, structure and interaction characteristics, and drivers of food webs of microorganisms (archaea, bacteria, fungi, protists, metazoa, viridiplantae and viruses) at multiple trophic levels in different water quality environments (Classes II, III, and IV). First, water quality deterioration led to significant changes in the composition of the microbial community at multiple trophic levels, which were represented by the enrichment of Euryarchaeota in the archaeal community, the increase of r-strategists in the bacterial community, and the increase of the proportion of predators in the protist community. Second, deteriorating water quality resulted in a significant reduction in the dissimilarity of community structure (homogenization of community structure in Class III and IV waters). Of the symbiotic, parasitic, and predatory networks, the community networks in Class II water all showed the most stable symbiotic, parasitic, and predatory correlations (higher levels of modularity in the networks). In Class III and IV waters, nutrient inputs have led to increased reciprocal symbiosis and decreased competition between communities, which may have the risk of a positive feedback loop driving a system collapse. Finally, inputs of phosphorus and organic matter could be the main drivers of changes in the planktonic microbial food web in the Fen River. Overall, the results indicated the potential ecological risks of exogenous nutrient inputs, which were important for aquatic ecosystem conservation. [Display omitted] • Water quality deterioration drove shift in bacterial community to r-strategists. • Water quality degradation increased the proportion of heterotrophic protists. • Trophic transfer efficiency of bacteria, fungi, and metazoa was greatly affected. • Positive feedback loops and associated ecological destabilization risk increased. • TP and CODcr are the main factors driving the microbial food web in the Fen River. [ABSTRACT FROM AUTHOR]

The increasing concern over pesticide pollution in water bodies underscores the need for effective mitigation strategies to support the transition towards sustainable agriculture. This study assesses the effectiveness of landscape mitigation strategies, specifically vegetative buffer strips, in reducing glyphosate loads at the catchment scale under realistic conditions. Conducted over six years (2014–2019) in a small agricultural region in Belgium, our research involved the analysis of 732 water samples from two monitoring stations, differentiated by baseflow and event-driven sampling, and before (baseline) and after the implementation of mitigation measures. The results indicated a decline in both the number and intensity of point source losses over the years. Additionally, there was a general decrease in load intensity; however, the confluence of varying weather conditions (notably dry years during the mitigation period) and management practices (the introduction of buffer strips) posed challenges for a statistically robust evaluation of each contributing factor. A reduction of loads was measured when comparing mitigation with baseline, although this reduction is not statistically significant. Glyphosate loads during rainfall events correlated with a rainfall index and runoff ratio. Overall, focusing the mitigation strategy on runoff and erosion was a valid approach. Nevertheless, challenges remain, as evidenced by the continuous presence of glyphosate in baseflow conditions, highlighting the complex dynamics of pesticide transport. The study concludes that while progress has been made towards reducing pesticide pollution, the complexity of interacting factors necessitates further research. Future directions should focus on enhancing farmer engagement in mitigation programs and developing experiments with more intense data collection that help to assess underlying dynamics of pesticide pollution and the impact of mitigation strategies in more detail, contributing towards the goal of reducing pesticide pollution in water bodies. [Display omitted] • A 6-year monitoring study was conducted in an agricultural catchment in Belgium. • The study evaluated in-field vegetated strips for glyphosate mitigation. • A decrease in glyphosate point losses was observed. • Glyphosate event load reduction in the river was linked to weather and mitigation. [ABSTRACT FROM AUTHOR]

Managing surface water quality is a global challenge, and understanding spatial and temporal patterns of water quality is a key component to effective management. However, analysis of spatiotemporal patterns of impaired waters over broad areas is sparse due to disparate water quality data and variable water quality standards. Thus, here we leverage the Alabama 303(d) List of impaired waters to present a new perspective for investigating spatiotemporal water quality patterns. Every two years, each state in the United States is required to assess its surface water quality and compile a list of impaired waterbodies, meaning waters that do not meet water quality standards for their designated usage – referred to as the 303(d) List. The purpose of the 303(d) List is to identify impaired waters so that corrective action can be taken to reduce pollutant loads and, ultimately, improve water quality. Using GIS, a space time cube was created to analyze and visualize spatiotemporal patterns of the impaired rivers added to the Alabama 303(d) Lists from 1996 to 2022. For this analysis, the percentage of river length impaired out of the total river length, and number of times each impairment cause was listed, were summarized within Alabama sub-basins (Hydrologic Unit Code 8) (n = 51). Trend and hot spot analyses were conducted on the river impairment and causes. There was an up trend in river impairment for eight sub-basins across the state and a downtrend in one sub-basin. Over half of the sub-basins with an up trend in impairment also had an up trend in the number of times pathogens was listed as a cause of impairment. Additionally, coastal sub-basins were found to be a hot spot for river impairment. Interestingly, there was a down trend in the number of times nutrients, ammonia, and siltation were listed as a cause of impairment at the state and sub-basin scales of analysis. Altogether, these findings show the use of spatiotemporal pattern analysis of impaired waters and can indicate where, both spatially and by pollutant, management should prioritize water quality improvement efforts. [Display omitted] • Spatiotemporal analysis of impaired waters provides new insight on water quality. • Trends in river impairment change with the scale of analysis. • At the state scale, there is no trend in river impairment but trends in sub-basins. • Coastal sub-basins are hot spots of river impairment. • At the sub-basin scale, pathogens are the only impairment cause with an up trend. [ABSTRACT FROM AUTHOR]

One of the methods of increasing the availability of drinking water is to reduce water losses in existing water supply systems (WSS). The need to manage water losses in WSS is highlighted in the new Directive (EU) 2020/2184 of the European Parliament and of the Council of 16 December 2020 on the Quality of Water Intended for Human Consumption. It was indicated that the main cause of water losses is underinvestment in the maintenance and renovation of network infrastructure. The new legal provisions require a risk assessment to be carried out in the water supply system, taking into account the risk of leaks. The paper presents the concept of estimating the risk of water losses in the water supply network using the three-parameter risk assessment method and risk maps. The framework of the water balance proposed by International Water Association (IWA) were also presented, including the Infrastructure Leakage Index (ILI) for assessment of the water supply system Leakage Performance Category (LPC). The analysis was carried out for a water supply system used by 200,000 inhabitants. The LPC of the system was determined based on the ILI index. Then the water supply network pipes that could potentially be a source of leaks were identified. The analysis of the risk of water losses for the examined pipes allowed to determine which pipes should be first chosen to reduce the risk of water losses, i.e. active search for leaks. • A new method for assessing the risk of water leaks in the water supply network was proposed (three-parameters risk matrix). • The pipe: diameter, material, age, pressure and failure index was identified as a parameters influencing the risk of leakage. • The results of the analysis were presented on the risk maps. • The presented risk-based approach is consistent with the new legal provisions for EU Member States. [ABSTRACT FROM AUTHOR]

Water renewal and flushing in small, intermittently open or closed estuaries is receiving increasing attention particularly in light of the climate change induced alterations in run-off, wave and sediment transport conditions along coasts. The challenges of predicting the stratification-circulation state and the balance between tidal or freshwater flushing in response to the mouth dynamics of small, wave-dominated estuaries is the focus of the paper. Such predictions are required for determining estuary freshwater requirements or establishing an estuary's capacity to maintain sound water quality under pollutant discharges. Advances in simulating changes in stratification-circulation over long time scales are limited. Instead attention has focused on generating indices of stratification or water quality state using heuristic methods. In this paper, systems dynamics modelling is applied to simulate the non-linear response of the estuary to changes in river and marine water fluxes. The estuary is modelled as a basin with a specified water volume to water level relationship, connected to the sea by a channel with variable sill height, but fixed width. The direction and magnitude of the flow through the mouth determines whether the sill height erodes or accretes and hence the mouth dynamics (see Slinger, 2017). The tidal flux through the mouth co-determines the volumetric exchange of salt, influencing both the stratification state of the estuary and the degree of tidal or freshwater flushing. This is also influenced by run-off. The resulting dynamic balance is captured in two bulk indices, the Estuarine Richardson number and the bulk densimetric Froude number. Using measured data from the Great Brak Estuary, South Africa, the model is calibrated. Model simulations demonstrate the importance of tidal flushing and concomitant mouth breaching for water renewal as freshwater flushing declines under scenarios of increased water abstraction. Although the estuary remains partially mixed, there is increased average salinity and a more uniform the water column. Water releases and mouth breaching bring about a more natural stratification-circulation state, but these effects are short-lived. • Parametric simulation of water renewal in wave-dominated, intermittent estuaries. • A stratification index analogous to the Estuarine Richardson number is modelled. • A circulation index analogous to the bulk densimetric Froude number is simulated. • Stratification-circulation simulations indicate increased water column uniformity. • Mouth breaching & floods temporarily allow more natural stratification-circulation. [ABSTRACT FROM AUTHOR]

Since 2015, green tides have recurred annually in Jinmeng Bay (JMB), China. Reclamation projects have been considered an important anthropogenic perturbation in JMB for green tides. A coupled hydrodynamic and water quality model was used to simulate hydrodynamic and transport processes for different artificial structure scenarios. Numerical results prove that reclamation projects exert a significant impact on the water environment in JMB, and nutrient distributions are mainly influenced by Conch Island, which decreases pollutant dilution and diffusion in coastal waters. Moreover, the average residence time increases by about 35% after the construction of the two artificial islands. Areas with long residence time coincide with the major sources of the green tide blooms, which declares that the weak water exchange capacity affected by artificial islands is responsible for the water quality degradation. The increased nutrient levels and hydraulic residence time also suggest the high possibility of green algal blooms in JMB. • Reclamation projects effect on the water environment in Jinmeng Bay was assessed. • Residence time increases by 35% after the construction of artificial islands. • The artificial islands result in the deterioration of water quality in Jinmeng Bay. • Heavy river input combined with artificial islands is a key factor in green tides. [ABSTRACT FROM AUTHOR]

Understanding saltwater intrusion processes is crucial for both ecosystem health and water resource management in river estuaries. With the development of the global coastal economy, many estuaries worldwide have undergone shipping engineering constructions. Channel deepening in coastal areas adversely impacts the quality of freshwater required for ecological purposes, human consumption, and agricultural and industrial activities. Many studies have focused on saltwater intrusion processes in artificial canals capable of high and medium runoff characteristics; however, little is known about seawater intrusions in canals with low runoff and ship locks located far away from the estuary. This study focuses on the saltwater intrusion process in such a canal, using the Pinglu Canal of Qinjiang River Estuary in China as a prototype example. A three-dimensional (3D) numerical saltwater intrusion model using the standard k–ε turbulence closure was developed to simulate the saltwater intrusion process pre- and post-canal construction. The verification was conducted by comparing the simulated time series of the tidal level, velocity magnitude, and salinity with observations, and the impact of channel deepening on the saltwater intrusion in the Qinjiang River was analysed based on various numerical experiments. After channel deepening, mixing decreased following the decrease in flow velocity, the saltwater intrusion extent could reach the Youth Ship Lock after an increase of 5 km compared to the extent under natural conditions during high tide, and seawater remained in the channel during ebb tide, with freshwater discharge being Q < 20 m3/s. Furthermore, during the dry season, salinity in the approach channel downstream the Youth Ship Lock exhibited a trend of cumulative increase toward a stable value, and the correlation with the tide range cycle decreased from 0.62 under natural conditions to 0.09, whereas the correlation with the upstream inflow increased from 0.10 under natural conditions to 0.51. Results further indicated that the sensitivity of the saltwater intrusion to the flow rate was at a maximum, and its sensitivity to the tidal range was of minor significance after canal construction. Accordingly, a dimensionless function was constructed to quantify freshwater discharge from the Youth Hydro-hub to reduce the saltwater intrusion under variations in the tidal range and salinity during the low-water discharge period. This study can aid water resource management and suggest methods for mitigating saltwater intrusions in this type of artificial canal. • Salt intrusion process is investigated in an artificial canal with low runoff. • Saltwater intrusion extent can reach the ship lock far away from the estuary. • The sensitivity to water discharge is the strongest downstream the ship lock. • The sensitivity to tidal range decreased as increasing of the distance from estuary. • An empirical function is developed to quantify salinity reduction freshwater. [ABSTRACT FROM AUTHOR]

Dissolved organic matter (DOM) is important in determining the drinking water treatment and the supplied water quality. However, a comprehensive DOM study for the whole water supply system is lacking and the potential effects of secondary water supply are largely unknown. This was studied using dissolved organic carbon (DOC), absorption spectroscopy, and fluorescence excitation-emission matrices-parallel factor analysis (EEM-PARAFAC). Four fluorescent components were identified, including humic-like C1–C2, tryptophan-like C3, and tyrosine-like C4. In the drinking water treatment plants, the advanced treatment using ozone and biological activated carbon (O 3 -BAC) was more effective in removing DOC than the conventional process, with the removals of C1 and C3 improved by 17.7%–25.1% and 19.2%–27.0%. The absorption coefficient and C1–C4 correlated significantly with DOC in water treatments, suggesting that absorption and fluorescence could effectively track the changes in bulk DOM. DOM generally remained stable in each drinking water distribution system, suggesting the importance of the treated water quality in determining that of the corresponding network. The optical indices changed notably between distribution networks of different treatment plants, which enabled the identification of changing water sources. A comparison of DOM in the direct and secondary water supplies suggested limited impacts of secondary water supply, although the changes in organic carbon and absorption indices were detected in some locations. These results have implications for better understanding the changes of DOM in the whole water supply system to help ensure the supplied water quality. [Display omitted] • DOM was characterized throughout the whole city water supply system. • The advanced O 3 -BAC treatments improved the removals of DOM. • Humic and protein-like components showed selective removals by different treatments. • Optical indices can be used to detect water quality changes in water supply systems. • First study to examine the effects of secondary water supply on DOM. [ABSTRACT FROM AUTHOR]

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future. [Display omitted] • We developed a unique combination mode FL–SK for the first time. • The FL-SK mode could present pollution levels in natural water intuitively and accurately. • The evaluation of water bodies by FL-SK mainly relies on the fluorescence data of RDM-TPE. • Pattern discovery could enable the analysis of pollution levels in other ecosystems. [ABSTRACT FROM AUTHOR]

Nowadays, biomarkers are recognized as valuable tools to complement chemical and ecological assessments in biomonitoring programs. They provide insights into the effects of contaminant exposures on individuals and establish connections between environmental pressure and biological response at higher levels. In the last decade, strong improvements in the design of experimental protocols and the result interpretation facilitated the use of biomarker across wide geographical areas, including aquatic continua. Notably, the statistical establishment of reference values and thresholds enabled the discrimination of contamination effects in environmental conditions, allowed interspecies comparisons, and eliminated the need of a reference site. The aim of this work was to study freshwater-estuarine-coastal water continua by applying biomarker measurements in multi-species caged organisms. During two campaigns, eight sentinel species, encompassing fish, mollusks, and crustaceans, were deployed to cover 25 sites from rivers to the sea. As much as possible, a common methodology was employed for biomarker measurements (DNA damage and phagocytosis efficiency) and data interpretation based on guidelines established using reference values and induction/inhibition thresholds (establishment of three effect levels). The methodology was successfully implemented and allowed us to assess the environmental quality. Employing multiple species per site enhances confidence in observed trends. The results highlight the feasibility of integrating biomarker-based environmental monitoring programs across a continuum scale. Biomarker results align with Water Framework Directive indicators in cases of poor site quality. Additionally, when discrepancies arise between chemical and ecological statuses, biomarker findings offer a comprehensive perspective to elucidate the disparities. Presented as a pilot project, this work contributes to gain insights into current biomonitoring needs, providing new questions and perspectives. • A novel strategy to monitor aquatic continua using biomarkers is proposed. • A common analysis method was applied in multi-species caged organisms. • Biomarkers provide clear and easily useable assessment of the environmental quality. • Reference values and thresholds are recommended to interpret biomarker responses. • Biomarker-based monitoring on a wide scale complements the WFD evaluation. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H 2 S) is one of the sewer gases commonly found in wastewater collection systems. This anaerobic degradation product causes issues, ranging from odor nuisances and health hazards to pipe corrosion. Several studies have provided an understanding of H 2 S formation mechanism, including simulations of H 2 S emissions in sewers, especially in pressurized systems. However, the present models necessitate a large amount of data due to the complexity of the H 2 S processes and common routine-monitoring water quality parameters may not fit the requirements. This study aims to simulate the fate and transport of H 2 S in both air and water phases in combined sewers, with a realization of practicableness of the application. The study case is centered around a fresh market in Bangkok, where the sewers are commonly plagued with garbage-related issues. These challenges pose difficulties for site monitoring across various aspects, necessitating the application of unconventional methods. On-site hydrodynamics, wastewater quality, and H 2 S gas concentration data were monitored on hourly and daily bases. It was found that the sulfides in the combined sewerage were correlated with sewage quality, e.g., COD, sulfate (SO 4 2−), and pH concentrations in particular. The model results were in an acceptable range of accuracy (R2 = 0.63; NSE = 0.52; RMSE = 1.18) after being calibrated with the measured hydrogen sulfide gas concentration. The results lead to the conclusion that the simplified model is practical and remains effective even in sewers with untraditional conditions. This could hold promise as a fundamental tool in shaping effective H 2 S mitigation strategies. • Hydrogen sulfide notably persists in combined sewers despite stormwater dilution. • Tracer moment analysis effectively depicts hydrodynamics in obstructed sewers. • Thoughtful simplification enhances model implementability with minor trade-offs. • Original field monitoring of hydrogen sulfide enables insightful model calibration. • Resembling substrate attributes in domestic sewages suggest common fraction values. [ABSTRACT FROM AUTHOR]

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms—namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)—alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms—namely, random forest (RF) and support vector regression (SVR)—to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT–CNN–GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively. [Display omitted] • Developing singular DL algorithms, CNN and GRU, to estimate DO and Chl-a concentration. • Integrating the singular developed DL algorithms based on CNN-GRU. • Implementing the Wavelet Transform (WT) to enhance the results of the hybrid models. • Comparing the results of individual DL algorithms with that of the hybrid models. • WT improved the accuracy of water quality estimation for the training/testing phases. [ABSTRACT FROM AUTHOR]

Globally, agriculture is a significant pressure on water quality. While nutrient and sediment loss from agricultural land to water can be scientifically complex, mitigation measures primarily focus on reducing farm nutrient stocks or blocking loss pathways. The appropriateness of mitigation measures is dependent on the identification of specific context-related risks on individual farms. However, advisers also need to consider the likelihood of uptake of measures by farmers. Past research has looked at uptake of particular mitigation measures or a small range of measures. This research expands the literature with an analysis of uptake of a broad and diverse range of measures. Farm characteristics, farmer norms, knowledge required and costs (direct and indirect) associated with individual mitigation measures are investigated to identify factors that could influence greater adoption. Results show that alignment to farmer norms and lower specific costs were associated with high adoption rates. These results have implications for advisers in relation to the selection of measures most likely to be adopted by farmers, and also for policy-makers in relation to the need to incentivise the adoption of high-cost measures. • Understanding farmers' uptake of mitigation measures in bespoke plans by advisors is key for water quality improvements. • Implementation costs and alignment to farmer norms influence farmers adoption decisions. • Implementing improvement plans with consideration of these traits will focus resource use by advisors and policymakers. [ABSTRACT FROM AUTHOR]

Small watercourses are essential contributors to catchment water quality, but they continue to suffer degradation across Europe. A results-based agri-environment scheme, aimed at improving watercourse quality in Ireland, developed a rapid drainage assessment to identify point source risks. The assessment uses a scoring system based on visual indicators of nutrient and sediment risk, linking the outcomes to farmer payments. To understand how this novel drainage risk assessment relates to instream watercourse quality, we used three macroinvertebrate-based biotic indices (Q-value, Small Stream Impact Score and Proportion of Sediment Sensitive Invertebrates). Macroinvertebrate kick-sampling and physiochemical analysis were completed in May and July 2021 for 12 'At Risk' and 12 'Not at Risk' small watercourses as identified by the results-based scheme. Results show that the scheme's drainage risk assessment can identify point source risks but we found it does not directly reflect local instream quality as assessed by the biotic indices. Unexpectedly, the biotic indices showed watercourse degradation in 58% of the upstream (control) sampling points, indicating impacts not captured by the drainage risk assessment. Small watercourses displayed high heterogeneity, with significant species turnover between the sampling months. The Small Stream Impact Score was less influenced by temporal change than the Q-value index. There was a significant relationship between instream watercourse quality and sedimentation, as quantified by the Proportion of Sediment Sensitive Invertebrates. Including a measurement of instream sedimentation in the drainage assessments would improve the identification of risks and management. These results show that by linking farmer payments to the drainage risk assessments results-based payment schemes could positively contribute to improving catchment scale watercourse quality, but further work is required to capture wider sources of freshwater impacts. • Small watercourses are important for catchment water quality. • We tested links between rapid risk indicators and biotic indices. • Small watercourses are complex with high heterogeneity and species turnover. • Catchments had extensive levels of degradation linked to multiple stressors. • Biotic indices show significant link between watercourse quality and sedimentation. [ABSTRACT FROM AUTHOR]

Bottled drinking water (BDW) has emerged as the preferred and perceived healthier option for water consumption in the past two decades, even in developing nations. This study aims to assess the baseline levels of dissolved ions in BDW, compares them to drinking water standards, and proposes the optimal minimum ion concentrations. About 30 random BDW samples from densely populated cities in India were collected and analysed for pH, total dissolved solids (TDS), major cations and anions. The maximum and average concentrations of TDS and other dissolved major ions in BDW do not exceed the BIS, WHO, USEPA, and EU standards. Through adherence to food-related norms and human health requirements, this research establishes the minimum optimal concentration of major ions in BDW. In this study, the emphasis on importance of water quality shifts from pesticides and emerging contaminants to a focus on the necessary ion concentrations required in drinking water. • Critical analyses on people perception about BDW were illustrated. • Drinking water standards confirms the scant of minerals in BDW. • Potential health consequences of consuming BDW were represented. • Established the optimal requirements of minerals in BDW. • Importance of ensuring the essential ions in BDW were stressed. [ABSTRACT FROM AUTHOR]

Water scarcity has threatened the sustainability of human life, ecosystem evolution, and socio-economic development. However, previous studies have often lacked a comprehensive consideration of the impact of water quality and existing solutions, such as inter-basin water transfer and unconventional water resources, on water scarcity. In this paper, an improved approach was proposed to quantify water scarcity levels by comprehensively considering surface water quality and multiple solutions. China's water scarcity was first assessed at a high spatial resolution on a monthly basis over the 5-year period from 2014 to 2018. Then, the driving factors including water quality and solutions were identified by a geographic detector model. Finally, an in-depth investigation was conducted to unravel the effects of water quantity solutions (i.e., inter-basin water transfer and unconventional water use), and water quality solutions (i.e., improving surface water quality) on alleviating water scarcity. Based on monthly assessments considering water quality and multiple existing solutions, the results showed that over half of the national population (∼777 million) faced water scarcity for at least one month of the year. Agricultural water use and inadequate water quality were the main driving factors responsible for China's water scarcity. Over four-fifths of the national population (∼1.10 billion) could benefit from alleviated water scarcity through a combination of water quantity and quality solutions. However, the existing solutions considered were insufficient to completely resolve water scarcity in China, especially in Northern China, persisting as a challenging issue. The results obtained from this study provided a better understanding of China's water scarcity, which could contribute to guiding future efforts aimed at alleviating water scarcity and ensuring water security in China. • Water scarcity assessment incorporated water quality and multiple existing solutions. • China's water scarcity was assessed at a high spatial resolution on a monthly basis. • Over half of the national population faced water scarcity for at least one month of the year. • Agricultural water use and inadequate water quality were the main driving factors. • Over four-fifths of the national population could benefit from alleviated water scarcity by the combined water quantity and quality solutions. [ABSTRACT FROM AUTHOR]

The detachment of biofilm caused by changes in hydraulic conditions is an essential reason for the pollution of water in the drinking water distribution system (DWDS). In this research, the effect of flow fluctuation on bulk water quality was studied. The turbidity, iron concentration, manganese concentration, the total number of bacteria, biodegradable dissolved organic carbon (BDOC), bacterial community structure, and pathogenic genes in bacteria of bulk water were analyzed. The results indicate that the detachment of biofilm caused by fluctuant flow and reverse flow (especially instant reverse flow) can lead to the pollution of water. Throughout the entire experimental period, the turbidity under fluctuant flow velocity is 4.92%∼49.44% higher than that under other flow velocities. BDOC concentration is 5.68%∼53.99% higher than that under low and high flow velocities. The flow fluctuation increases bacterial regrowth potential (BRP) and reduces the biological stability of the bulk water. Low flow velocity is more conducive to the expression of pathogenic functional genes. In the short term, the water quality under low flow velocity is the best. Nevertheless, in a long-term operation (about seven days later), the water quality under high flow velocity is better than that under other flow velocities. This research brings new knowledge about the fluctuant hydraulic conditions on the bulk water quality within the DWDS and provides data support for stable drinking water distribution. [Display omitted] • Fluctuant flow velocities have the greatest impact on water quality deterioration. • The water quality is better under high flow velocity during the long-term operation. • The detachment of biofilm caused by hydraulic change increases the BRP. • Biofilm detachment reduces the biological stability of bulk water. [ABSTRACT FROM AUTHOR]

Dongping Lake is one of the most important regulation and storage lakes along the eastern route of the South-to-North Water Diversion Project in China, the water quality condition of which directly influences the safety of water diverting, because it serves as a Yangtze River water redistribution control point. However, the changes in algae, and in environmental factors affecting their community structures, before and after the water diversion project are rarely reported. In this study, the temporal variations of phytoplankton abundance were examined based on monthly samples collected at three stations from May 2010 to April 2022. The total abundance of algae greatly decreased after the water diversion project was implemented, with a relatively stable biodiversity and evenness before and after the water translocation. Multiple statistical methods were used together with the water quality indices (WQIs) and the nutrient status index (TSI M) to evaluate overall water condition and analyse relationships among environmental factors. The WQIs demonstrated a general "Good" water quality with a seasonal differentiation, and that water conditions during water transfer periods were better than during non-water transfer periods, which may be ascribed to the improved hydraulic conditions and purified water environment during water transfer periods. Redundancy analysis showed that water temperature, ammonia nitrogen, water transparency, and total phosphorus were the most important environmental factors, with relatively decreased contribution rates towards phytoplankton communities after the water translocation. Importantly, some dominant phytoplankton genera of Chlorophyta, Bacillariophyceae, and Cyanophyceae were similarly affected by water transparency, and nitrogen and phosphorus nutrients in summer after the water translocation. These research findings helped us gain a comprehensive understanding of the changing patterns of water quality and microalgae and their relationships before and after the water diversion project, providing a guidance for future lake management in regulating hydraulic conditions and improving water quality of Dongping Lake. [Display omitted] • Phytoplankton abundance in Dongping Lake significantly decreased after SNWDP. • Phytoplankton diversity and evenness remained stable before and after SNWDP. • WQI indicated better water quality during the water transfer period. • Environmental factors contributed less towards phytoplankton communities after SNWDP. • More attention to lake water management is recommended after SNWDP. [ABSTRACT FROM AUTHOR]

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change. • Lakes in taiga and tundra overall showed a decreased trend in browning from 2002 to 2021. • The browning trend of the study lakes varies significantly among ecoregions, temperature, precipitation, and lake area. • The results support the theory that climate change may cause increased DOC in lakes in pan-Arctic regions. [ABSTRACT FROM AUTHOR]