Drinking Water Lakes Research Articles

Prediction of Water Level in Lakes by RNN-Based Deep Learning Algorithms to Preserve Sustainability in Changing Climate and Relationship to Microcystin

In recent years, intensive water use combined with global climate change has increased fluctuations in freshwater lake levels, hydrological characteristics, water quality, and water ecosystem balance. To provide a sustainable management plan in the long term, deep learning models (DL) can provide fast and reliable predictions of lake water levels (LWLs) in challenging future scenarios. In this study, artificial neural networks (ANNs) and four recurrent neural network (RNN) algorithms were investigated to predict LWLs that were applied in time series such as one day, five days, ten days, twenty days, one month, two months, and four months ahead. The results show that the performance of the Long Short-Term Memory (LSTM) model with a prediction of 60 days is in the very good range and outperforms the benchmark, the Naïve Method, by 78% and the ANN at the significance level (p < 0.05) with an RMSE = 0.1762 compared to other DL algorithms. The RNN-based DL algorithms show better prediction performance, specifically, for long time horizons, 57.98% for 45 days, 78.55% for 60 days, and 58% for 120 days, and it is better to use a prediction period of at least 20 days with an 18.45% performance increase to take advantage of the gated RNN algorithms for predicting future water levels. Additionally, microcystin concentration was tightly correlated with temperature and was most elevated between 15 and 20 m water depths during the summer months. Evidence on LWL forecasting and microcystin concentrations in the context of climate change could help develop a sustainable water management plan and long-term policy for drinking water lakes.

Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake

Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluates the performance of multiple linear regression (MLR) and nineteen machine learning (ML) models, including algorithms based on regression, decision tree, and boosting. Models include linear regression (LR), least angle regression (LAR), Bayesian ridge chain (BR), ridge regression (Ridge), k-nearest neighbor regression (K-NN), extra tree regression (ET), and extreme gradient boosting (XGBoost). The research’s objective is to estimate the surface water quality of Al-Seine Lake in Lattakia governorate using the MLR and ML models. We used water quality data from the drinking water lake of Lattakia City, Syria, during years 2021–2022 to determine the water quality index (WQI). The predictive performance of both the MLR and ML models was evaluated using statistical methods such as the coefficient of determination (R2) and the root mean square error (RMSE) to estimate their efficiency. The results indicated that the MLR model and three of the ML models, namely linear regression (LR), least angle regression (LAR), and Bayesian ridge chain (BR), performed well in predicting the WQI. The MLR model had an R2 of 0.999 and an RMSE of 0.149, while the three ML models had an R2 of 1.0 and an RMSE of approximately 0.0. These results support using both MLR and ML models for predicting the WQI with very high accuracy, which will contribute to improving water quality management.

Pollution status, spatial distribution and ecological risk of heavy metals in sediments of a drinking water lake in South Eastern China

ABSTRACT This study investigates the pollution status, spatial distribution of heavy metals including Cr, Mn, Ni, Cu, Zn, As, Cd and Pb, in sediments from the Longhu Lake, and evaluates possible ecological risk of these metals. Average concentrations of Cu, Zn, Cd and Pb were 2.1, 3.5, 8.0 and 2.5 times higher than Grade I Chinese soil quality standard respectively. The geo-accumulation index indicates that the lake sediments are moderate to heavily contaminated by Zn, Cd and Pb, which may have higher toxic effects than other metals. The potential ecological risk factor (E i r) suggests low risk to aquatic organisms except for Cd which has a considerable risk to aquatic biota. Spearman correlation and principal component analysis suggest that Mn, Zn, Cd and Pb possibly having similar anthropogenic sources and behaviors in the lake. Overall, this study suggests that anthropogenic inputs are from local point and non-point sources, runoff and atmospheric deposition.

Determination of protection zones in drinking water basins: a case study from Turkey, Sapanca Lake Basin

To prepare specific protection and policies for drinking water basins is important for providing sustainable drinking water management. In this study, a decision support system composed of hydrogeological, hydrogeochemical, soil type and topological parameters was applied to define the protection zones. Littoral zone and slope parameters in topology, the grain size in soil media, hydraulic conductivity, aquifer type and well parameters in hydrogeology, hydrogeochemical and isotopic assessment were used to create protection zones in the decision support system based on the Geographic Information System. Identifying hydrological resource areas, and determination of residence time and recharge altitude in groundwater were done using hydrogeochemical parameters and isotopic tracers. The GIS platform was used to evaluate the maps generated from the parameters and to apply reclassification and overlapping methods. The proposed methodology was applied to the Sapanca Lake Basin in Turkey since the lake is one of the most crucial drinking water lakes in Turkey. According to the proposed methodology, green belt, wellhead, absolute, short-range groundwater protection, short and medium-range protection zones were defined in the basin. The absolute protection zones in the basin are distinctively the areas covered with karst structures which are dolines and swallow holes.

Variable responses of dissolved organic carbon to precipitation events in boreal drinking water lakes

In boreal regions, increased concentrations of dissolved organic carbon (DOC) have been linked to extreme wet years; however, less is known about the extent to which precipitation events are altering DOC concentration and quality. We assessed the effects of rain events on a suite of six lakes in Maine, U.S.A., to better understand how events alter DOC quantity and quality. DOC concentrations and DOC quality (measured as DOC-specific absorption coefficients (Specific Ultraviolet Absorbance (SUVA254 (also a*254), a*320, and a*380)) were quantified 24 h before, and at three time points (24–48 h, 5–7 days, and 3 weeks) after five different precipitation events. Our results revealed three types of responses across the lakes: (1) an initial spike in DOC concentrations of 30–133% and in the three quality metrics of 20–86% compared to pre-storm levels, followed by return to pre-storm concentrations; (2) a sustained increase in DOC concentrations (by 4–23%) and an increase in the three DOC quality metrics (by 1–43%) through the second post-storm sampling, with concentrations falling by the third post-storm sampling compared to pre-storm levels; and (3) no change during all sampling periods. Lake residence time was a key driver of changes in DOC concentration and DOC quality in response to storm events. Our research provides evidence that precipitation events contribute to short-term abrupt changes in DOC quantity and quality that are largely driven by key landscape and lake characteristics. These changes in DOC may have important implications for management of water utilities, including alteration or implementation of treatment strategies.

Historical accumulation and ecological risk assessment of heavy metals in sediments of a drinking water lake.

Heavy metal contamination in sediments is progressively being recognized as a challenging problem in large parts of the developing world, particularly in Asian countries. A drinking water lake in Yunnan-Guizhou plateau, China named Hongfeng Lake was selected as the research target. Forty surface sediment samples and 4 sediment cores were collected to reveal the accumulation of heavy metals in the sediments of the lake. The mean concentrations of Cr, Cu, Pb, Cd, As, and Hg in surface sediments were 81.67, 45.61, 29.78, 0.53, 22.71, and 0.25mg/kg, respectively, which exceeded the background levels of sediment 1.1~3.3 times. The calculation of geoaccumulation (Igeo) and potential ecological risk (PER) index analysis were preformed, and the results showed a considerable risk for Cd and Hg on the whole. Spatially, the northern part showed a higher risk than the southern part and tributaries of the lake, and a moderate risk in the overall sediment of the lake. The historical level of heavy metals in Hongfeng Lake was traced by vertical sediments study and it was dated back approximately 35years. The EF trends of a feature sampling site HF8 showed strong temporal variations, and peaked in the year 1995. After that, the EFs exhibited a declining trend, which reflects productive environmental protection and management by the local government. For the Hongfeng Lake, a typical lake with heavy metal-contaminated sediments, the in-situ remediation technique could be a suitable method for its remediation.

Inflow rate-driven changes in the composition and dynamics of chromophoric dissolved organic matter in a large drinking water lake

Drinking water lakes are threatened globally and therefore in need of protection. To date, few studies have been carried out to investigate how the composition and dynamics of chromophoric dissolved organic matter (CDOM) in drinking water lakes are influenced by inflow rate. Such CDOM can lead to unpleasant taste and odor of the water and produce undesirable disinfection byproducts during drinking water treatment. We studied the drinking water Lake Qiandao, China, and found that the concentrations of suspended particulate matter (SPM) in the lake increased significantly with inflow rate (p < 0.001). Similarly, close relationships between inflow rate and the CDOM absorption coefficient at 350 nm a(350) and with terrestrial humic-like fluorescence C3 and a negative relationship between inflow rate and the first principal component (PC1) scores, which, in turn, were negatively related to the concentrations and relative molecular size of CDOM (p < 0.001), i.e. the concentration and molecular size of CDOM entering the lake increased proportionately with inflow rate. Furthermore, stable isotopes (δD and δ18O) were depleted in the upstream river mouth relative to downstream remaining lake regions, substantiating that riverine CDOM entering the lake was probably driven by inflow rate. This was further underpinned by remarkably higher mean chlorophyll-a and in situ measured terrestrial CDOM fluorescence (365/480 nm) and apparent oxygen utilization (AOU), and notably lower mean PC1 and CDOM spectral slope (S275–295) recorded in the upstream river mouth than in the downstream main lake area. Strong negative correlations between inflow rate and a(250):a(365), S275–295, and the spectral slope ratio (SR) implied that CDOM input to the lake in rainy period was dominated by larger organic molecules with a more humic-like character. Rainy period, especially rainstorm events, therefore poses a risk to drinking water safety and requires higher removal efficiency of CDOM during drinking water treatment processes.

Dissolved organic matter fluorescence at wavelength 275/342 nm as a key indicator for detection of point-source contamination in a large Chinese drinking water lake.

Surface drinking water sources have been threatened globally and there have been few attempts to detect point-source contamination in these waters using chromophoric dissolved organic matter (CDOM) fluorescence. To determine the optimal wavelength derived from CDOM fluorescence as an indicator of point-source contamination in drinking waters, a combination of field campaigns in Lake Qiandao and a laboratory wastewater addition experiment was used. Parallel factor (PARAFAC) analysis identified six components, including three humic-like, two tryptophan-like, and one tyrosine-like component. All metrics showed strong correlation with wastewater addition (r(2) > 0.90, p < 0.0001). Both the field campaigns and the laboratory contamination experiment revealed that CDOM fluorescence at 275/342 nm was the most responsive wavelength to the point-source contamination in the lake. Our results suggest that pollutants in Lake Qiandao had the highest concentrations in the river mouths of upstream inflow tributaries and the single wavelength at 275/342 nm may be adapted for online or in situ fluorescence measurements as an early warning of contamination events. This study demonstrates the potential utility of CDOM fluorescence to monitor water quality in surface drinking water sources.

A hydrodynamics-based approach to evaluating the risk of waterborne pathogens entering drinking water intakes in a large, stratified lake

Pathogen contamination of drinking water lakes and reservoirs is a severe threat to human health worldwide. A major source of pathogens in surface sources of drinking waters is from body-contact recreation in the water body. However, dispersion pathways of human waterborne pathogens from recreational beaches, where body-contact recreation is known to occur to drinking water intakes, and the associated risk of pathogens entering the drinking water supply remain largely undocumented. A high spatial resolution, three-dimensional hydrodynamic and particle tracking modeling approach has been developed to analyze the risk and mechanisms presented by pathogen dispersion. The pathogen model represents the processes of particle release, transport and survival. Here survival is a function of both water temperature and cumulative exposure to ultraviolet (UV) radiation. Pathogen transport is simulated using a novel and computationally efficient technique of tracking particle trajectories backwards, from a drinking water intake toward their source areas. The model has been applied to a large, alpine lake – Lake Tahoe, CA–NV (USA). The dispersion model results reveal that for this particular lake (1) the risk of human waterborne pathogens to enter drinking water intakes is low, but significant; (2) this risk is strongly related to the depth of the thermocline in relation to the depth of the intake; (3) the risk increases with the seasonal deepening of the surface mixed layer; and (4) the risk increases at night when the surface mixed layer deepens through convective mixing and inactivation by UV radiation is eliminated. While these risk factors will quantitatively vary in different lakes, these same mechanisms will govern the process of transport of pathogens.

Integrating Principle Component Analysis and Canonical Correlation Analysis for Monitoring Water Quality in Storage Reservoir

Water quality monitoring is of great importance in managing drinking water lakes or reservoirs. However, developing such an effective monitoring system used in evaluating water quality is difficult, due to spatial and temporal variation in water quality that are usually hard to interpret. Principle component analysis (PCA) and canonical correlation analysis (CCA) techniques were used in this study to determine the principle water parameters, and explore the relationship between the physical and chemical water parameters, respectively in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. Twenty-eight water parameters including physical and chemical parameters were monitored each month from Jan 2001 to Dec 2010. The results showed that using PCA, six of the water quality parameters were found less important in explaining the monthly variation of the water quality and thus excluded from the further CCA, while applying CCA, the first six canonical correlations were 0.998, 0.988, 0.937, 0.778, 0.606 and 0.453, indicating that electro-conductivity and chloride compounds were the dominant (highly scored) variables of the physical parameters and chemical parameters, respectively. These results are helpful in understanding the main physical and chemical parameters involved in MSR, thus improving the water quality monitoring system in drinking water reservoirs.

Identification and characterization of hydrophobic microcystins in Canadian freshwater cyanobacteria

Hepatotoxic microcystins produced by cyanobacteria in freshwater lakes represent a significant health hazard to humans and agricultural livestock. Liquid chromatography (LC)-linked protein phosphatase (PPase) bioassay analysis of blooms of Microcystis aeruginosa produced in a Canadian drinking water lake identified several PPase inhibitors with significantly greater hydrophobicity than microcystin-LR, based on their retention time on C 18 reverse phase LC columns. Seven PPase inhibitors were purified to homogeneity by bioas ay-guided fractionation involving Sephadex LH-20 chromatography and two-step reverse phase at pH 6.5 and 2.0. One of the PPase inhibitors, isolated in a final yield of 1.5 μg/g lyophilized cyanobacteria, was identified as microcystin-LL by amino acid analysis and mass spectrometry. A further PPase inhibitor (20 ng/g cyanobacteria) was identified as microcystin-LL but with d-Ala replaced by an unknown amino acid. Four PPase inhibitors ( < 20 ng/g cyanobacteria) were characterized by amino acid analysis and identified as microcystin-LV, -LM, -LF and -LZ (where Z represents an unknown hydrophobic amino acid). A further microcystin was also identified ( < 10 ng/g cyanobacteria) in which arginine was apparently absent. The biological activity of the seven microcystins as inhibitors of the catalytic subunit of protein phosphatase-1 (PP-1c) was compared with microcystin-LR and motuporin (a hydrophobic analogue of nodularin). All of the compounds inhibited PP-1c with ic 50 values of 0.06–0.4 nM, consistent with their identification as microcystins. These findings further demonstrate the applicability of a sensitive PPase bioassay for the identification of variant microcystins in the natural environment.