Water Quality Management Strategies Research Articles

Distinct differences of vertical phytoplankton community structure in mainstream and a tributary bay of the Three Gorges Reservoir, China.

The vertical distribution of phytoplankton plays a crucial role in shaping the dynamics and structure of aquatic communities. In highly dynamic reservoir systems, water level fluctuations significantly affect the physiochemical conditions and the phytoplankton community. However, the specific effects on the vertical characteristics of phytoplankton between the mainstream and the tributary bay of the reservoir remain unstudied. This study investigated the vertical aspects of phytoplankton density, biomass, α and β diversity through monthly sampling over two years in the mainstream (Chang Jiang, CJ) and a tributary bay (Xiang Xi, XX) of the Three Gorges Reservoir in China. Phytoplankton density and biomass were significantly higher in XX, indicating an increased risk of algal blooms in the tributary. The phytoplankton community in CJ showed more stable species-environment relationships, a lower Shannon index and a higher evenness index, suggesting a relatively simple structure and a more uniform distribution of phytoplankton among different water layers. Conversely, XX showed greater differences between water layers (higher β diversity), with significant negative correlations with water level and positive correlations with DO difference, dissolved silica (DSi) difference, and stratification. Peak phytoplankton density and biomass, as well as high β diversity in XX, occurred during periods of decreased water levels with strong stratification in spring and summer. A structural equation model complemented by path analysis revealed that a decrease in water level could increase β diversity either directly through internal processes with extended residence time or indirectly by modifying stratification and the vertical distribution of DSi in XX. Therefore, a proposed water quality management strategy for XX was to increase the water level or reduce β diversity by implementing artificial mixing during stratification periods. Overall, this study lies in its comprehensive investigation of the vertical characteristics of the phytoplankton community in both the mainstream and the tributary bay of the Three Gorges Reservoir, elucidating the significant impact of water level fluctuations and providing insights for targeted water quality management strategies in the tributary bay to mitigate potential ecological impacts.

Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning

Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global concern. In this study, we introduce a novel interpretable machine learning (ML) workflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake. Utilizing seven ML methods and incorporating the covariance matrix adaptation evolution strategy (CMA-ES), we predict algal density across three distinct time periods, resulting in the construction of a total of 30 ML models. The CMA-ES-CatBoost model consistently demonstrates superior predictive accuracy and generalization capability across these periods. Through the collective validation of various interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effects of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predictive performance, striking a balance between accuracy and cost-effectiveness. These findings offer a scientific and economically efficient foundation for governmental agencies to formulate strategies for water quality management and sustainable development.

Recirculating aquaculture system as microbial community and water quality management strategy in the larviculture of Scylla paramamosain

The structure and function of the water microbial community can change dramatically between different rearing modes. Yet investigations into the relationships between microbial community and water quality remain obscure. We provide the first evidence that rearing modes alter bacterial community and water quality in the rearing water of the mud crab (Scylla paramamosain) larvae. The juveniles in the recirculating aquaculture system (RAS) had a higher viability than those in the water exchange system (WES). RAS had the significantly lower levels of total ammonia nitrogen (TAN), NH3, NO2−-N, total nitrogen (TN), total dissolved solids (TDS), and chemical oxygen demand than those of WES. The number of significantly different amplicon sequence variants between rearing modes increased as the larvae developed. NH3, TAN, TDS, NO2−-N, and TN were closely related to the late alterations in water bacterial community. Both the FAPROTAX tool and quantitative PCR analysis showed enhanced nitrogen cycling functional potential of water bacterial community of RAS. Random forest analysis identified the enriched water bacteria especially heterotrophic bacteria such as Phaeodactylibacter, Tenacibaculum, and Hydrogenophaga, which were vital in removing nitrogenous compounds via simultaneous nitrification and denitrification. Notably, RAS could save 18.5 m3 of seawater relative to WES in larviculture on the scale of 2.5 m3. Together, these data indicate that RAS could function as microbial community and water quality management strategy in the larviculture of crab.

APPLICATION OF IOT PROTOCOLS IN SURFACE WATER POLLUTION MONITORING SYSTEMS

The rapid pace of industrial growth and the rise in consumerism are increasingly contributing to the deterioration of water quality, posing significant risks to both ecological systems and human health. The urgent need for effective monitoring of water resources to mitigate pollution and ensure the sustainability of these vital ecosystems has never been more apparent. The integration of Internet of Things (IoT) technologies into surface water monitoring presents a transformative approach to addressing these challenges. By automating the collection and transmission of data on water quality, IoT technologies offer a leap forward in our ability to efficiently monitor and manage environmental health. This article explores the application of various data transmission protocols, including Wi-Fi, Zigbee, LoRa, NB-IoT, and BLE, in the context of IoT-enabled water resource monitoring systems. Considerable attention was paid to determining the physical conditions of operation and the limitations they impose on the functioning of water monitoring systems. Each protocol is examined for its potential advantages and limitations in terms of energy efficiency, transmission range, and reliability under the specific conditions encountered in surface water monitoring. Through a comparative analysis, this study not only highlights the distinctive features and suitability of each protocol but also proposes a comprehensive framework for selecting the most appropriate technology based on the specific requirements of water monitoring projects. The findings of this research underscore the critical role of IoT technologies in advancing environmental monitoring and offer valuable insights for the development of more effective and sustainable water quality management strategies.

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Water is one of the key elements involved in maintaining the quality of life. It is essential in the field of agriculture for food production and livestock farming. Water quality is continuously declining as a consequence of the growing trend of urbanization and industrialization, which can harm the environment and human health. In recent years, several researchers in the field of agriculture have shown advancements in techniques with the implementation of the Internet of Things (IoT) Artificial Intelligence (AI) machine learning (ML) and smart technologies. These advancements aim at improving water usage and enhancing the quality and quantity of different crops, with the ability to lower analysis costs, time and facilitate the achievement of management results. This paper outlines an overview of recent studies on water control and management using the IoT and ML models in the field of agriculture. Thus, new technologies for analyzing water quality metrics like pH, temperature, color, turbidity, Total Dissolved Solids (TDS), salinity, and nitrogen are described. It also describes few exposed contests used to draw related study suggestions in the future, such as the use of advanced intelligent tools and strategies for water quality assessment and management in the agriculture sector.

Aquaponics Fish Selection and Care

This article provides a thorough exploration of key factors crucial for the success of aquaponics systems, with a focus on fish health and welfare. It addresses critical considerations in selecting fish species, emphasizing factors such as water temperature requirements, growth rates, compatibility with plants, and market demand. The article further delves into maintaining optimal fish health, covering topics like water quality management, disease prevention, nutrition, and stress reduction. Additionally, it highlights the significance of proper stocking density and feeding practices for system performance, emphasizing their role in fish growth, nutrient cycling, and system sustainability. The article also provides insights into effective water quality management strategies, offering practical guidance for maintaining optimal conditions in aquaponics systems. Lastly, the article addresses breeding and fry rearing, essential components for sustaining fish populations, offering expert advice on selecting breeding pairs, providing suitable breeding conditions, and managing fry rearing. By incorporating these comprehensive insights, aquaponics practitioners can ensure the well-being of their fish population and optimize the overall performance and productivity of their systems.

Global scale identification of catchments phosphorus source shifts with urbanization: A phosphate oxygen isotope and Bayesian mixing model approach

Different scenarios of urban expansion can influence the dynamic characteristics of catchments in terms of phosphorus (P). It is important to identify the changes in P sources that occur during the process of urbanization to develop targeted policies for managing P in catchments. However, there is a knowledge gap in quantifying the variations of potential P sources associated with urbanization. By combining phosphate oxygen isotopes from global catchments with a Bayesian model and the urbanization process, we demonstrate that the characteristics of potential P sources (such as fertilizers, urban wastewater, faeces, and bedrock) change as urban areas expand. Our results indicate that using phosphate oxygen isotopes in conjunction with a Bayesian model provides direct evidence of the proportions of potential P sources. We classify catchment P loadings into three stages based on shifts in potential P sources during urban expansion. During the initial stage of urbanization (urban areas < 1.5 %), urban domestic and industrial wastewater are the main contributors to P loadings in catchments. In the mid-term acceleration stage (1.5 % ≤ urban areas < 3.5 %), efforts to improve wastewater treatment significantly reduce wastewater P input, but the increase in fertilizer P input offsets this reduction in sewage-derived P. In the high-level urbanization stage (urban areas ≥ 3.5 %), the proportions of the four potential P sources tend to stabilize. Remote areas bear the burden of excessive P loadings to meet the growing food demand and improved diets resulting from the increasing urban population. Our findings support the development of strategies for water quality management that better consider the driving forces of urbanization on catchment P loadings.

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna. Six ML models were adopted to predict contamination alarms from Daphnia behavioral parameters. The light gradient boosting machine model demonstrated the most significant improvement in predicting alarms from Daphnia behaviors. Compared with the traditional BEWS alarm index, the ML model enhanced the precision and recall by 29.50% and 43.41%, respectively. The speed distribution index and swimming speed were significant parameters for predicting water quality warnings. The nonlinear relationships between the monitored Daphnia behaviors and water physicochemical water quality parameters (i.e., flow rate, Chlorophyll-a concentration, water temperature, and conductivity) were identified by ML models for simulating Daphnia behavior based on the water contaminants. These findings suggest that ML models have the potential to establish a robust framework for advancing the predictive capabilities of BEWS, providing a promising avenue for real-time and accurate assessment of water quality. Thereby, it can contribute to more proactive and effective water quality management strategies.

Optimization of selective withdrawal strategy in a warm monomictic reservoir based on thermal stratification

Selective withdrawal from reservoirs is an effective strategy for balancing economic goals and environmental demands. Multilevel intake operation (MIO) is one of the main options for improving the thermal regime of deep reservoirs. In this study, a combination of measured data and a two-dimensional hydrodynamic model (CE-QUAL-W2) was utilized to analyze the impact of MIO processes on the thermal stability of a reservoir and the improvement effect of the withdrawal water temperature (WWT). The purpose of this study was to identify an optimal selective withdrawal strategy to meet the water temperature needs of fish downstream during their spawning and breeding activities. The results showed that the MIOs raised the withdrawal water intake position (8.3–11.2 m) for the WWT. Meanwhile, MIOs resulted in statistically significant changes in the water temperature structure of the reservoir (p < 0.01). The stratification stability of the water column weakened (6.8 %-34.5 %), and there were interannual differences. The WWT increased by 0.0 °C∼1.9 °C every ten days, and the time to reach the optimum temperature (18 °C) for fish spawning was 7 to 17 days earlier. There was a statistically significant positive correlation between the WWT improvement and the stability index (SI) (R = 0.7817, p < 0.01). The threshold values of the SI (above the water intake) for average WWT improvements of 0.5 and 1.0 °C were 90.3 and 221.9 kg/m2, respectively, and the recommended operating period was from early-April to May. Under MIOs, the weakening stratification and the increasing WWT during the warming period reduced risks to the water quality and ecological health in the reservoir and downstream rivers. This study provides a helpful reference for the implementation of selective withdrawal operations and the optimization of water quality management strategies in similar large, deep reservoirs.

A Management of Water Quality Parameters In Cultivating Vaname Shrimp (Litopenaeus vannamei) In Intensive Tambak PT. Aneka Tambak Oseana Nusantara , NTB

One of the causes of failure in vaname shrimp cultivation is poor management of water quality during the production period. Therefore, water quality management during the maintenance process is absolutely necessary. To study water quality management in growing white vaname shrimp (Litopenaeus vannamei) at PT. Aneka Pond Oceana Nusantara, NTB. Knowing the obstacles and solutions in managing water quality during practical activities at PT. Aneka Pond Oceana Nusantara, NTB. The technique for rearing vaname shrimp (Litopenaues vannamei) is as follows: preparing the container; the container is filled with water; fry selection; feeding interval; The results of water quality management strategies for vaname shrimp cultivation include: temperature (27 -28℃), morning brightness (30-118 cm), afternoon (28-108 cm). Morning pH (7.7- 8.2) afternoon (7.8-8.6), morning DO (5.87-6.18 ppm) evening (4.82-5.92 ppm), salinity (30- 33).

Assessment of Water Quality Parameters in a Rivers Using Qual2Kw: A Study in Bangkalan River

Increased human activity, shifts in land use and the increasingly diverse lifestyles of people in Bangkalan City have resulted in an increase in domestic waste and made the pollutant load in the Bangkalan River even greater over time. The Bangkalan River segmentation from the Tunjung Dam to the Bancaran River has indicated that the quality of river water has decreased. The purpose of this study was to determine the pollution level of the Bangkalan River, the condition of the Bangkalan River's water quality, and to obtain recommendations for treatment strategies for river water quality management in the future. This research approach uses descriptive, quantitative, and qualitative. The method used is calculating the Pollutant Index and Water Quality Index, simulates parameters in a scenario using QUAL2Kw modeling. In the results of this study it is known that the pollution level of the Bangkalan River is categorized as lightly polluted and the quality of the Bangkalan River is categorized as moderate. The results of the river water quality test in the simulation scenario with QUAL2Kw show that the water quality of the Bangkalan River is classified as class II water quality and is classified as medium water quality in the segment of the Tunjung Dam to the Bancaran River.

Factors controlling the variation in physicochemical parameters of springs in Melamchi area, Nepal

Understanding the factors controlling the spatial and seasonal variation of physicochemical parameters of groundwater are important for quick water quality assessment and for a better planning of the water quality management strategies. This study evaluated spring’s five physicochemical data (pH, EC, TDS, DO and temperature) for 75 springs in two seasons (pre and post monsoon). The aim of the research presented in this paper is to assess physicochemical parameters of spring water, understand their seasonal and spatial variation and the factors controlling the variation. Physicochemical parameters were correlated with each other to understand the processes and an attempt has been made to understand groundwater processes studying the physicochemical parameters. Groundwater is slightly acidic to slightly alkaline with pH ranging from 5.5 to 7.5 and is weakly to very weakly mineralized with EC range of 21.6–234.0 μS/cm in pre-monsoon and 10.5–189.8 μS/cm in post-monsoon. TDS ranged between 5.3–116.9 mg/l and 10.8–94.9 mg/l, DO between 3.1–9.4 mg/l and 2.4–7.3 mg/l and groundwater temperature between 18.4–27.9°C and 19.2–27.4°C in pre-monsoon and post monsoon season respectively. TDS and EC are noted to be higher in pre-monsoon whereas pH, DO and temperature are comparatively higher in post-monsoon. The south-east region of the study area shows minimum groundwater temperature. Minimum concentration of EC and TDS were observed in south-east and north-west part of the study area with values increasing towards the river valley. Groundwater temperature shows inverse relation with elevation, pH and DO while correlated with EC and TDS of groundwater in the region. The measured parameters were seasonally and spatially variable. The results indicate difference for measured physicochemical parameters mainly caused due to results generated by cumulative interactions of monsoonal rain, atmospheric temperature, rock-groundwater interaction, residence time of groundwater and seasonal influences. The change in parameters with respect to elevation and relation between different parameters shows shallow aquifer-related groundwater processes.

Prevalence and co-infections of pathogenic bacteria in Nile tilapia broodstock farms: implications for aquaculture management in Togo.

This study aimed to evaluate the prevalence of E. coli, Pseudomonas spp., and Enterococcus spp. in Nile tilapia broodstock farms in Togo and their association with fish diseases. In total, 60 samples were collected from two tilapia broodstock farms, including 40 broodstock tilapia and 20 water samples. Each farm had concrete tanks supplied with water from two drills. Of the 40 fish samples, 20 were healthy, and the other 20 were diseased. Microbiological analysis of samples was conducted using standardized methods. The study revealed significant contamination in all collected water samples, detecting multiple bacterial populations. Bacterial infections were more prevalent in symptomatic fish, with 85% exhibiting co-infections (df = 1, χ2 = 7.03, p = 0.008). Contamination levels varied among sample types, with higher counts observed in wastewater (E. coli: 0-300CFU/mL, Pseudomonas spp.: 3-300CFU/mL, Enterococcus spp.: 0-55 × 102CFU/mL) compared to drilling water (Pseudomonas spp.: 0-300CFU/mL, E. coli: 3-400CFU/mL, Enterococcus spp.: 0-14 × 102CFU/mL), and intestinal samples (E. coli: 0-120 × 102CFU/mL, Enterococcus spp.: 0-170 × 102CFU/mL, Pseudomonas spp.: 0-60 × 102CFU/mL) (df = 21, χ2 = 59.31, p < 0.000). Our findings emphasize the significant prevalence of pathogenic bacteria in Nile tilapia broodstock farms in Togo, highlighting the importance of water quality management and infection control strategies. Further studies are required to develop appropriate prevention strategies.

Performance of bacterial and mitochondrial qPCR source tracking methods: A European multi-center study

With the advent of molecular biology diagnostics, different quantitative PCR assays have been developed for use in Source Tracking (ST), with none of them showing 100% specificity and sensitivity. Most studies have been conducted at a regional level and mainly in fecal slurry rather than in animal wastewater. The use of a single molecular assay has most often proven to fall short in discriminating with precision the sources of fecal contamination. This work is a multicenter European ST study to compare bacterial and mitochondrial molecular assays and was set to evaluate the efficiency of nine previously described qPCR assays targeting human-, cow/ruminant-, pig-, and poultry-associated fecal contamination. The study was conducted in five European countries with seven fecal indicators and nine ST assays being evaluated in a total of 77 samples. Animal fecal slurry samples and human and non-human wastewater samples were analyzed. Fecal indicators measured by culture and qPCR were generally ubiquitous in the samples. The ST qPCR markers performed at high levels in terms of quantitative sensitivity and specificity demonstrating large geographical application. Sensitivity varied between 73% (PLBif) and 100% for the majority of the tested markers. On the other hand, specificity ranged from 53% (CWMit) and 97% (BacR). Animal-associated ST qPCR markers were generally detected in concentrations greater than those found for the respective human-associated qPCR markers, with mean concentration for the Bacteroides qPCR markers varying between 8.74 and 7.22 log10 GC/10 mL for the pig and human markers, respectively. Bacteroides spp. and mitochondrial DNA qPCR markers generally presented higher Spearman's rank coefficient in the pooled fecal samples tested, particularly the human fecal markers with a coefficient of 0.79. The evaluation of the performance of Bacteroides spp., mitochondrial DNA and Bifidobacterium spp. ST qPCR markers support advanced pollution monitoring of impaired aquatic environments, aiming to elaborate strategies for target-oriented water quality management.

Antimicrobial resistance and karst groundwater systems: A policy gap analysis for addressing water quality and contaminants of emerging concern

Aquifers are a significant drinking water source for the United States. Although vital to human well-being, development, and economies, these systems, specifically karst, either have minimal protections or are entirely overlooked in federal and state-level water quality policies and regulations. A lack of policies and regulations for the protection of karst groundwater resources is problematic, especially because these systems are characterized by distinctive landforms such as interconnected networks of underground rivers, sinkholes, springs and caves, and are extremely susceptible to contamination from polluted overland flows. This study considers policy approaches to improve water quality in groundwater, specifically karst systems, especially with regard to emerging contaminants of concern (CECs) and health threats, such as antimicrobial resistance (AMR). The study reviews and evaluates groundwater and karst groundwater quality and AMR policy gaps in federal water legislation (including the U.S. Clean Water Act and the Safe Drinking Water Act), and state-level programs in 44 states to provide recommendations for future policy development. Presently, there is limited federal and state-level protection for groundwater systems and minimal state-level for karst throughout the U.S. Also, there are neither federal nor state regulations for AMR mitigation in water resources. There is a need for policies and monitoring and regulatory programs that support water quality protection and management strategies for water resources with distinctive hydrology. Addressing water quality policy gaps for groundwater resources – especially karst aquifers – and prioritizing emerging contaminants in research, monitoring, and policymaking, are critical to safeguard the public against nascent public and environmental health threats.

Monitoring the spatial distribution of water quality of an urban stormwater pond using satellite images

Stormwater ponds play a critical role in managing urban runoff and mitigating the impact of pollutants on receiving water bodies. This study investigates the potential of remote sensing (RS) techniques in retrieving water level and key water quality parameters in urban stormwater wet ponds. Specifically, the focus is on water level, chlorophyll-a (Chl-a), total suspended solids (TSS), turbidity, and Secchi depth (SD). The results indicate that RS techniques can provide reliable estimates of water level, Chl-a, turbidity, SD, and TSS in urban stormwater wet ponds. Results revealed that Chl-a concentrations are higher near the inlets and within the forebay, with a significant decrease downstream of the forebay. TSS and total dissolved solids (TDS) concentrations follow a similar pattern to Chl-a, being higher near the inlet and within the forebay, but significantly lower downstream. Notably, the highest concentrations of TSS, TDS, and Chl-a are observed in July, likely due to heavy rainfall events during that period. Through principal component analysis (PCA) and correlation analysis, the study effectively captured the interrelationships among the water quality parameters in the stormwater pond. The spatial distribution of TSS and Chl-a shows a correlation. Furthermore, the study uncovers a relationship between water temperature and Chl-a concentration, with Chl-a decreasing as the temperature exceeds 20 °C and increasing when it drops below 20 °C. This relationship is influenced by sunlight availability, which affects Chl-a growth, as evidenced by variations in Chl-a concentration between the water column's bottom layers and surface. Overall, this research highlights the potential of utilizing RS techniques to retrieve water quality parameters, providing valuable information for water managers and decision-makers. The ability to generate maps of these parameters using reflected light measurements above stormwater wet ponds also contributes to effective water quality management strategies.

Modelling effects of nutrients and hypoxia on Lake Erie's central basin foodweb

Abstract Hypoxia (dissolved oxygen &amp;lt;2 mg l-1) has long been a prevalent feature of the central basin of Lake Erie. Studies of the sublethal impacts of hypoxia on fishes have focused on individual species feeding rates, behavior and spatial distributions over short time periods, but the long-term effects on the fish community and its foodweb are poorly known. Sublethal effects of hypoxia on fish include: interrupting their vertical migration, displacing them from bottom habitats either up into the water column or away from the hypoxic zones, altering predator-prey relationships by segregation or aggregation of predators and their prey, and increasing fishing mortality by concentrating fish at the edge of hypoxic zones. We used the Ecopath with Ecosim foodweb model to investigate the singular and combined effects of nutrient concentration and hypoxia on the foodweb structure in Lake Erie's central basin. Our model tracked predator-prey interactions and population biomass of 33 model groups. We balanced the model in Ecopath and calibrated it against biomass time series data from 1996 up to 2020. Model simulations were run with varied nutrients (from 20 to 220% of the previous nutrient loading target level) and hypoxia (none, average summer value from 1996 to 2017, historical high) as forcing variables on the foodweb. Model results suggested that nutrients had positive, non-linear effects on foodweb biomass, while hypoxia decreased biomass of benthos, benthivorous fishes, and some omnivores, but increased biomass of plankton and planktivorous fishes. Nutrient effects were greater than hypoxia effects on the foodweb. Results of the foodweb model analysis may inform water quality and fisheries management strategies for Lake Erie's central basin.

The combination of multiple linear regression and adaptive neuro-fuzzy inference system can accurately predict trihalomethane levels in tap water with fewer water quality parameters

Artificial Neural Network (ANN) models are accurate in predicting the levels of disinfection by-products (DBPs) in drinking water. However, these models are not yet practical due to the large number of parameters involved, which should take a significant amount of time and cost to detect. Developing accurate and reliable prediction models of DBPs with fewest parameters is essential in the management of drinking water safety. This study used the adaptive neuro-fuzzy inference system (ANFIS) and radial basis function artificial neural network (RBF-ANN) to predict the levels of trihalomethanes (THMs), the most abundant DBPs in drinking water. Two water quality parameters identified by multiple linear regression (MLR) models were used as model inputs, and the quality of the models was assessed based on criteria such as correlation coefficient (r), mean absolute relative error (MARE), and the percentage of predictions with absolute relative error less than 25% (NE<25%) and over than 40% (NE>40%), etc. The results showed that the ANFIS models had higher correlation coefficients (r = 0.853–0.898) and prediction accuracy (NE<25% = 91%–94%) compared to RBF-ANN models (r = 0.553–0.819; NE<25% = 77%–86%) and traditional MLR models (r = 0.389–0.619; NE<25% = 67%–77%). Conversely, the prediction error, as indicated by MARE and NE>40%, showed the opposite trend: ANFIS models (MARE = 8%–11%; NE>40% = 0–5%) < RBF-ANN models (MARE = 15%–18%; NE>40% = 5%–11%) < MLR models (MARE = 19%–21%; NE>40% = 11%–17%). The present study provided a novel approach for constructing high-quality prediction models of THMs in water supply systems using only two parameters. This method holds promise as a viable alternative for monitoring THMs concentrations in tap water, thereby contributing to the improvement of water quality management strategies.

Uncertainty assessment for three-dimensional hydrodynamic and fecal coliform modeling in the Danshuei River estuarine system: The influence of first-order parametric decay reaction

Modeling fecal contamination in water bodies is of importance for microbiological risk assessment and management. This study investigated the transport of fecal coliform (e.g., up to 2.1 × 106 CFU/100 ml at the Zhongshan Bridge due to the main point source from the Xinhai Bridge) in the Danshuei River estuarine system, Taiwan with the main focus on assessing model uncertainty due to three relevant parameters for the microbial decay process. First, a 3D hydrodynamic-fecal coliform model (i.e., SCHISM-FC) was developed and rigorously validated against the available data of water level, velocity, salinity, suspended sediment and fecal coliform measured in 2019. Subsequently, the variation ranges of decay reaction parameters were considered from several previous studies and properly determined using the Monte Carlo simulations. Our analysis showed that the constant ratio of solar radiation (α) as well as the settling velocity (vs) had the normally-distributed variations while the attachment fraction of fecal coliform bacteria (Fp) was best fitted by the Weibull distribution. The modeled fecal coliform concentrations near the upstream (or downstream) stations were less sensitive to those parameter variations (see the smallest width of confidence interval about 1660 CFU/100 ml at the Zhongzheng Bridge station) due to the dominant effects of inflow discharge (or tides). On the other hand, for the middle parts of Danshuei River where complicated hydrodynamic circulation and decay reaction occurred, the variations of parameters led to much larger uncertainty in modeled fecal coliform concentration (see a wider confidence interval about 117,000 CFU/100 ml at the Bailing Bridge station). Overall, more detailed information revealed in this study would be helpful while the environmental authority needs to develop a proper strategy for water quality assessment and management. Owing to the uncertain decay parameters, for instance, the modeled fecal coliform impacts at Bailing Bridge over the study period showed a 25 % difference between the lowest and highest concentrations at several moments. For the detection of pollution occurrence, the highest to lowest probabilities for a required fecal coliform concentration (e.g., 260,000 CFU/100 ml over the environmental regulation) at Bailing Bridge was possibly greater than three.

Geochemical Speciation, Ecological Risk and Assessment of Main Sources of Potentially Toxic Elements (PTEs) in Stream Sediments from Nile River in Egypt

Studying and understanding the complexity and interactions of different factors influencing stream sediment quality is necessary for the development of successful water quality management strategies. This study aims to evaluate the level of contamination by potentially toxic elements (PTEs) (As, Co, Cr, Cu, Mn, Ni, Pb, V, Zn) of the stream sediments of the Nile River. During the spring of 2019, river sediments were sampled at 23 sites along the Nile River. For each sample, one aliquot was digested in aqua regia and analyzed by ICP-MS for pseudo-total concentration, while for another aliquot, sequential extraction procedures were applied to determine chemical speciation. Compositional data analysis (CoDa) and k-means were applied to recognize the contribution of natural and anthropogenic sources, while pollution indices (EF, RAC) and sediment quality guidelines (SQGs) were applied to assess the ecological risk to biotic species. The results reveal that elements such as Cr, Mn, V and Fe, found in high concentrations in almost all samples (Cr up to 739 mg/kg, Mn up to 1942 mg/kg, V up to 507 mg/kg, Fe up to 98,519 mg/kg), have a natural origin, while the concentrations of Cu (up to 69 mg/kg), Ni (up to 88 mg/kg), Co (up to 42 mg/kg) and As (up to 9.8 mg/kg) are linked to both natural and anthropogenic processes. Sequential extraction shows that Mn, Co, Ni and, in some sites, Cu and Zn, are the most bioavailable elements. These elements present a high risk of toxicity, while the remaining elements imply a low-to-moderate risk.