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Escherichia coli has been widely used as a fecal indicator bacterium (FIB) for monitoring water quality in drinking water sources and recreational water. However, fecal contamination sources remain difficult to identify and mitigate, as millions of cases of infectious diseases are reported yearly due to swimming and bathing in recreational water. The objective of this study was to apply molecular techniques for microbial source tracking (MST) to identify sources of fecal contamination in a representative mixed land-use watershed located in the Appalachian Mountains of the United States of America (USA). Monthly samples were collected over one year at 11 sites, including the confluence of key first-order streams in the study watershed representing distinct land-use types and anticipated fecal sources. Results indicated that coupled monitoring of host-specific MST markers with the FIB E. coli effectively identified sources and quantified fecal contamination in the study watershed. Human-associated MST markers were abundant primarily at developed sites, suggesting septic or sewer failure is a key source of fecal input to the watershed. Across the dataset, samples positive for E. coli and human MST markers were associated with a higher pH than those samples from which each target was not detected, thereby suggesting that acid mine drainage in the watershed likely contributed to inactivation or loss of culturability in E. coli. In addition, this research provides the first evidence that the BacCan-UCD marker is present in fox feces and can influence MST results in areas where substantial wildlife activity is present. Identifying the sources of fecal contamination and better understanding the impact of in-stream physiochemistry throughout this study will help to develop sustainable and effective watershed management plans to control fecal contamination to protect drinking water sources and recreational water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In the developing world, the identification of clean, potable water continues to pose a pervasive challenge, and waterborne diseases due to fecal contamination of water supplies significantly threaten public health. The ability to efficiently monitor local water supplies is key to water safety, yet no low-cost, reliable method exists to detect contamination quickly. We developed an in vitro assay utilizing an odorant-binding protein (OBP), AgamOBP1, from the mosquito, Anopheles gambiae , to test for the presence of a characteristic metabolite, indole, from harmful coliform bacteria. We demonstrated that recombinantly expressed AgamOBP1 binds indole with high sensitivity. Our proof-of-concept assay is fluorescence-based and demonstrates the usefulness of insect OBPs as detector elements in novel biosensors that rapidly detect the presence of bacterial metabolic markers, and thus of coliform bacteria. We further demonstrated that rAgamOBP1 is suitable for use in portable, inexpensive "dipstick" biosensors that improve upon lateral flow technology since insect OBPs are robust, easily obtainable via recombinant expression, and resist detector "fouling." Moreover, due to their wide diversity and ligand selectivity, insect chemosensory proteins have other biosensor applications for various analytes. The techniques presented here therefore represent platform technologies applicable to various future devices.

qPCR has become increasingly popular for microbial water quality testing because it is faster, more specific, and more flexible than culture-based methods. However, qPCR method limitations such as quantification bias introduced by reliance on standards and susceptibility to PCR inhibitors are major obstacles for implementation in water testing. This is because water testing requires accurate quantification of rare targets and because environmental waters often contain PCR inhibitors. Digital PCR offers the opportunity to maintain qPCR's advantages over culture-based methods while ameliorating two of qPCR's major limitations: the necessity to run standard curves and high susceptibility to inhibition. Here we describe a complete method for simultaneous testing for a general microbial water quality indicator (Enterococcus spp.) and a human-associated fecal marker in environmental waters. The complete method includes water sampling and filtration to capture bacteria, DNA extraction from bacteria captured on the filter, and droplet digital PCR to quantify the genetic markers from bacteria indicative of general and human-associated fecal contamination.

In many karst regions in developing countries, the populations often suffer from poor microbial water quality and are frequently exposed to bacterial pathogens. The high variability of water quality requires rapid assays, but the conventional cultivation-based analysis of fecal indicator bacteria, such as Escherichia coli (E. coli), is very time-consuming. In this respect, the measurement of the enzymatic activity of E. coli could prove to be a valuable tool for water quality monitoring. A mobile automated prototype device was used for the investigation of β-d-glucuronidase (GLUC) activity at a remote karst spring, connected to a sinking surface stream, in Northern Vietnam. To assess the relationship between GLUC activity, discharge dynamics and contamination patterns, multiple hydrological, hydrochemical, physicochemical and microbiological parameters, including discharge, turbidity, particle-size distributions, and E. coli, were measured with high temporal resolution during ten days of on-site monitoring. A complex contamination pattern due to anthropogenic and agricultural activities led to high E. coli concentrations (270 to >24,200 MPN/100ml) and a GLUC activity between 3.1 and 102.2 mMFU/100ml. A strong daily fluctuation pattern of GLUC activity and particle concentrations within small size classes (<10μm) could be observed, as demonstrated by autocorrelations. A Spearman's rank correlation analysis resulted in correlation coefficients of rs=0.56 for E. coli and GLUC activity and rs=0.54 for GLUC activity and the concentration of 2-3μm particles. On an event scale, correlations were found to be higher (rs=0.69 and 0.87, respectively). GLUC activity and E. coli displayed a general contamination pattern, but with significant differences in detail, which may be explained by interferences of e. g. viable but non-culturable cells. Although further evaluations are recommended, GLUC activity is a promising, complementary parameter for on-site and near real-time water quality monitoring., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Water quality information is important for guiding water safety management and preventing water-related diseases. To assess the current status of regulated water quality monitoring in sub-Saharan Africa, we evaluated testing programs for fecal contamination in 72 institutions (water suppliers and public health agencies) across 10 countries. Data were collected through written surveys, in-person interviews, and analysis of microbial water quality testing levels. Though most institutions did not achieve the testing levels specified by applicable standards or World Health Organization (WHO) Guidelines, 85% of institutions had conducted some microbial water testing in the previous year. Institutions were more likely to meet testing targets if they were suppliers (as compared to surveillance agencies), served larger populations, operated in urban settings, and had higher water quality budgets (all p < 0.05). Our results indicate that smaller water providers and rural public health offices will require greater attention and additional resources to achieve regulatory compliance for water quality monitoring in sub-Saharan Africa. The cost-effectiveness of water quality monitoring should be improved by the application of risk-based water management approaches. Efforts to strengthen monitoring capacity should pay greater attention to program sustainability and institutional commitment to water safety.

Lake Guamuez is the second largest lake in Colombia and economically supports hundreds of families in the area. The main activities carried out in the region have focused on tourism, agriculture, livestock, and rainbow trout production; however, these activities have been associated with contamination of the lake. This research aimed to evaluate the water quality of Lake Guamuez using somatic coliphages (SCs) as bioindicators. For this purpose, periodic sampling was carried out for 6 months at nine strategic points of the lake. For the detection of SCs, the method described in 9211 D of the Standard Methods for the Examination of Water and Wastewater was used. The genomic variability and presence of virulence genes in the isolated SCs were determined. Water contamination in the lake is evident, and the SCs titer is greater in areas with a high flow of anthropogenic activities. An important degree of genetic diversity and a high prevalence of virulence genes could be observed among the SCs analyzed. The results when compared with guidelines and water quality standards from various countries showed concentrations of SCs higher than those allowed. The high prevalence of gastrointestinal diseases in the region suggests a link to water contamination. [ABSTRACT FROM AUTHOR]

This work aimed to determine the level of some toxic elements (As, Cr, Cd, and Pb) in the water of the Rupsha River, Bangladesh, concerning their potential dangers to human exposure. The elemental concentrations (mg/L) were determined using Atomic Absorption Spectrometer and found to decrease in the order of Cr (0.041) > Pb (0.029) > As (0.004) > Cd (0.002). The level of elements in this river water surpasses various international limits, making it unfit for human consumption. Furthermore, the metal pollution index and contamination index indicated that the water was also unsuitable for this purpose. The elements chosen were persuasive to discern the hazard quotient of non-carcinogenic risk. Moreover, total targeted hazard quotient (TTHQ) values were found for adults and children within acceptable limits (TTHQ <1). The value of carcinogenic risk did not surpass the range (10−6 to 10−4) of the threshold limit. Due to their high-water consumption per unit of body-weight and physiological development, children were found to be more sensitive than adults. Multivariate analyses demonstrated that human activities were the primary origin of toxic elements in river water. According to the findings, urban and industrial effluents should be treated before being released into rivers. Development along the river bank must be carefully controlled to safeguard the river environment. In the end, this will improve the quality of the water and lower the chance that people will be exposed to metals. [ABSTRACT FROM AUTHOR]

Water quality is a significant determinant of ecosystem health and human well-being particularly in cities undergoing rapid industrialization and population growth. This review highlights data from studies conducted between 2004 and 2024 to assess the water quality of surface and groundwater sources in Islamabad and Rawalpindi. The results indicate severe microbial contamination with high levels of total coliform, fecal coliform, and E. coli in both cities, with a potential risk of waterborne diseases. Heavy metal contamination exists, especially As and Pb, which is significantly higher in Rawalpindi due to the industries. Physicochemical parameters, such as turbidity and residual microbial contamination remain in treated water, implying inefficiency in the water treatment process. The contamination also shows seasonal fluctuation, increasing during monsoon due to sewage overflows and agricultural runoff. Both cities also have conventional water treatment infrastructure that causes widespread contamination in the distribution system. Public health impact is prevalent, and outbreaks of diseases from waterborne diseases including Hepatitis E reported both in urban and peri-urban areas quite frequently. A gap is critical in applying the Advanced Oxidation Process (AOP) and other emerging technologies for degrading persistent contaminants, most notably heavy metals and microbial pollutants in the AOPs. This review focuses on the urgency of upgrading infrastructure, strengthening the implementation of water quality standards, and using more advanced treatment technologies and offers recommendations to policymakers and stakeholders and technology solutions for tackling critical challenges in water quality management, and emphasizes the importance of continuous monitoring and the enforcement of regulations. [ABSTRACT FROM AUTHOR]

The Silway River has historically failed to meet safe fecal coliform levels due to improper waste disposal. The river mouth is located in General Santos City, the tuna capital of the Philippines and a leading producer of hogs, cattle, and poultry. The buildup of contaminants due to direct discharge of waste from chicken farms and existing water quality conditions has led to higher fecal matter in the Silway River. While there were technical reports in the early 2000s about poultry farming, this is the first study where fecal coliform from poultry farming was detected in the Silway River using highly sensitive protocols like qPCR. This study characterized the effect of flow velocity and physicochemical water quality parameters on chicken fecal contamination. Gene markers such as Ckmito and ND5-CD were used to detect and quantify poultry manure contamination through microbial source tracking (MST) and environmental DNA (eDNA) profiling. The results of this study showed the presence of chicken fecal bacteria in all stations along the Silway River. The results revealed that normal levels of water quality parameters such as temperature, pH, and high TSS concentrations create favorable conditions for chicken fecal coliforms to thrive. Multiple regression analysis showed that flow velocity and DO significantly affect chicken fecal contamination. A lower cycle threshold (Ct) value indicated higher concentration of the marker ND5-CD, which means higher fecal contamination. It was found that there was an inverse relationship between the Ct value and both velocity (R2 = 0.55, p = 0.01) and DO (R2 = 0.98, p = 0.2), suggesting that low flow velocity and low DO can lead to higher fecal contamination. Findings of fecal contamination could negatively impact water resources, the health of nearby residents, and surrounding farms and industries, as well as the health and growth of fish. [ABSTRACT FROM AUTHOR]

Groundwater is an essential drinking water source for humans. However, improper groundwater management leads to fecal contamination and waterborne diseases caused by viral pathogens. Therefore, this study aimed to investigate norovirus (NoV) contamination by conducting nationwide monitoring over five years (2019–2023). Groundwater samples were analyzed for water quality parameters, indicator microorganisms, NoV prevalence, and viral genotypes. Water quality was assessed for temperature, turbidity, and residual chlorine, whereas microorganisms were analyzed for total coliforms, Escherichia coli, and NoV genotypes. Of the 600 sites, 11 (1.8%) were NoV-positive, irrespective of season or location. Low residual chlorine levels (0.02–0.75 mg/L) were observed, possibly limiting viral inactivation. Total coliforms were detected in only three NoV-positive samples, and E. coli was absent. NoV genotypes were identified as GI.1 and GII.4, with GII.4 being the most frequently detected genotype. The present study demonstrated that periodic monitoring and expanded nationwide efforts are required for effective groundwater management and public health protection. [ABSTRACT FROM AUTHOR]

This study analysed the water quality of 156 samples collected from 13 different points within the Chief Albert Luthuli Local Municipality between January and December 2021, focusing on compliance with the South African National Standard 241 (SANS 241). Microbiological assessments revealed widespread contamination, with heterotrophic plate counts (HPCs) exceeding the recommended range in nearly all samples. The median HPC levels were particularly concerning, with the untreated source (RE000) exhibiting a median of 2555 CFU/mL. Total coliforms and Escherichia coli were present at alarming levels, indicating significant faecal contamination; the highest total coliform count, 2420 CFU/100 mL, was recorded at RE000. Physically, while all samples complied with electrical conductivity standards (0–70 µS/cm), turbidity levels showed variability, with nine samples exceeding the acceptable threshold of 1 NTU. The highest turbidity, 7.55 NTU, was recorded at the untreated source (RE000). Chemically, pH levels fell within safe ranges (6.0–9.0), with all samples being compliant with standards. However, only one sample (E001) met the free chlorine standard of 0.3–0.6 mg/L, suggesting inadequate disinfection practices. The untreated source was classified as 'Unsuitable for drinking' (WQI = 207.27). These findings highlight the critical need for enhanced monitoring and remediation strategies to improve drinking water safety in the region. [ABSTRACT FROM AUTHOR]

Access to safe drinking water is a fundamental human need for health and well-being implemented globally by the United Nations under Sustainable Development Goal (SDG) 6. Storing drinking water is common in rural areas of Ethiopia due to off-premises water sources and intermittent piped water supply. However, this practice can lead to further contamination during collection, transport, and storage, posing a risk to public health. The objective of this study was to identify the determinant factors of drinking water quality at the point of use in the rural setting of northwestern Ethiopia, South Gondar zone. A questionnaire survey was conducted, and water samples from 720 households were collected during the wet and dry seasons. The determinant factors were identified using the multivariable logistic regression model. About 39.2% of the surveyed households had basic water supply services, 41.9% were using unimproved sources, and 8.3% were using surface water. Only 9.4% were using basic sanitation services, and 57.2% were practicing open defecation. Safe water storage was practiced by 84.3% of households, while only 2% engaged in household water treatment. About 14% of dry and 8% of wet season samples from the storage were free from fecal coliform bacteria. Furthermore, 52.9% of dry and 62.2% of wet season samples fell under the high microbial health risk category. The season of the year, the water source type, storage washing methods, and the socioeconomic status of the household were identified as key predictors of household drinking water fecal contamination using the multivariable logistic regression model. It was observed that the drinking water in households had a high load of fecal contamination, posing health risks to consumers. To tackle these problems, our study recommends that stakeholders should enhance access to improved water sources, implement source-level water treatment, increase access to improved sanitation facilities, advocate for safe household water management practices, and endorse household water treatment methods. [ABSTRACT FROM AUTHOR]

For decades, the risk of exposure to infectious diseases in recreational beaches has been evaluated through the quantification of fecal indicator bacteria in water samples using culture methods. The analyses of sand samples have recently been developed as a complement to the monitoring of recreational waters in beach quality assessments. The growing use of molecular techniques for environmental monitoring allows for the rapid detection of pathogenic genes, thus providing more accurate information regarding the health risk of exposure to contaminated sand. The aim of this work was to determine the relationship between the fecal indicators abundance in water and sand and the presence of Shiga toxin-producer Escherichia coli (STEC) in sand by analyzing samples from touristic beaches using culture-dependent (fecal coliforms assay) and culture-independent (real-time PCR of stx1, stx2, and eae genes) techniques. We found a high concentration of coliform bacteria in water and sand in several beaches in eastern Uruguay, with different levels of sanitation networks and levels of urbanization. The presence of STEC virulence genes (mainly stx1) was confirmed in 8 out of 20 sand samples. The recreational use of sandy beaches may imply a risk to the health of its users, especially near streams and creek outflows, thus highlighting the need of monitoring sand bacteriological quality and pathogens using molecular tools. [ABSTRACT FROM AUTHOR]

This study assessed the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering eight different water sources (i.e., stream water from natural and anthropogenic impacted streams, domestic water, spring water, rainfall, groundwater, lake water and water from irrigation canals) were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international drinking and irrigation water quality requirements. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for E. coli and turbidity were within the permissible limits. A generalised problem of faecal contamination was found in the study area, including in domestic water, highlighting the need to identify sources of contamination and remediate them before distribution. The drinking water quality index (DWQI) classified 89% of the samples as unsuitable and 11% as excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. In contrast to the DWQI, the vast majority of the water samples (88%) can be used for irrigation without restrictions according to the irrigation water quality index (IWQI). The suitability of water for irrigation was also assessed using three indices, i.e., Kelley's Index, Soluble Sodium Percentage and Magnesium Ratio, which indicated potential problems with excess of sodium (about 30% of the samples) and magnesium (about 20%). Further studies combining suitability indices, soil characteristics and crop types are recommended to assess water quality for irrigation use. Article Highlights: The southern slopes of Mt. Kilimanjaro act as a water tower, providing drinking water and irrigation resources for surrounding communities. The drinking water quality was found to be compromised by a widespread presence of E. coli bacteria, which indicates faecal contamination. The irrigation water quality was generally good, although there were indications of sodium and magnesium excess in the central and eastern parts of the study area, respectively. [ABSTRACT FROM AUTHOR]

Prevalence of water borne diseases is a public health concern. The study assessed the quality of selected borehole waters in Jos North for their potability. The physicochemical parameters were assessed in addition to chlorides, phosphates, nitrates, and sulphates, total solids, total dissolved solids, total suspended solids, total hardness, using standard methods. Atomic absorption spectrophotometry used for elemental analysis of samples for manganese, lead, cadmium, copper and zinc. microbial analysis was carried out to test for presence of anaerobic bacteria, coliforms, Escherichia coli, Salmonella typhi, yeast and mould, Staphylococcus aureus using the plate count method. Water Quality Index by Weighted Arithmetic Index Method. Results ranged from 22.19-56.22 mg/L (chlorides), 0.00-0.07 mg/L (phosphates), 0.11-0.19 mS/cm (conductivity), 141.40-169.40 mg/L (alkalinity) and 86.70-160 mg/L (TSS). Kyan Primary school had Mn 0.632 mg/L against 0.500mg/L recommended by WHO. Microbial analysis revealed that Kyan primary school had 1 cfu per 100 mL water for E. coli, Salmonella typhi, yeast and mould. This indicated faecal contamination. The WQI revealed Village hostel, Kyan Village, Gadabiu had values of 6.54, 19.41, 10.97 are potable, Government College, Angwan Jarawa, Kyan Primary school had 28.90, 61.50, 93.60 are potable, poor, very poor, not potable. [ABSTRACT FROM AUTHOR]

Monitoring of stream water quality is a key element of water resource management worldwide, but methods that are commonly used in temperate habitats may not be appropriate in humid tropical systems. We assessed the influence of four land uses on microbial water quality in 21 streams in the Panama Canal Watershed over a one-year period, using a common culture-based fecal indicator test and 16S rDNA metabarcoding. Each stream was located within one of four land uses: mature forest, secondary forest, silvopasture, and traditional cattle pasture. Culturing detected total coliforms and Escherichia coli across all sites but found no significant differences in concentrations between land uses. However, 16S rDNA metabarcoding revealed variability in the abundance of coliforms across land uses and several genera that can cause false positives in culture-based tests. Our results indicate that culture-based fecal indicator bacteria tests targeting coliforms may be poor indicators of fecal contamination in Neotropical oligotrophic streams and suggest that tests targeting members of the Bacteroidales would provide a more reliable indication of fecal contamination. [ABSTRACT FROM AUTHOR]

This study investigates the impact of water faecal contamination on highly threatened European brook lamprey larvae (Lampetra planeri). Water samples and the midgut contents of lampreys collected from a small lowland river upstream (site 1) and downstream from a wastewater treatment plant (WWTP) discharge (site 2) were analysed to check how the faecal microbial load of the habitat is reflected in the intestines of larval lampreys. The counts of viable mesophiles, psychrophiles, Escherichia coli and faecal streptococci as bacterial indicators of microbial (including faecal) water contamination were estimated. Microbial composition and abundance in larval midgut contents depended on the numbers of various microorganisms in the water environment. At site 2, the water was heavily microbiologically contaminated throughout the year by sewage inflow from the WWTP, and the amounts of studied bacteria were also high in the midgut of lampreys inhabiting site 2 regardless of the season. At site 1, water quality was better, and the levels of tested microbial indicators were lower in the intestines of the lampreys living there. The numbers of bacteria dependent on water temperature were growing in warmer seasons both in water and in intestines. Sewage pollution negatively influenced the condition of lampreys in site 2, where they exhibited lower body condition than in site 1. [ABSTRACT FROM AUTHOR]

A four-week trial was conducted to compare the effects of a conventional flow-through system diet (FTS) and an experimental RAS diet (ERAS) on fish performance, water quality and general system implication in a replicated recirculation aquaculture system (RAS). Six identical RAS, each with a total system water volume of 1500 L and cylindrical rearing tanks of 1000 L were stocked with Atlantic salmon (Salmo salar) post-smolts with an average weight of 199.7 ± 28 g, to an average stocking density of 30 kg/m3 and reaching approximately 49 kg/m3 at the trial end. The ERAS diet were composed to inhabit typical RAS feed characteristics compared with the FTS diet, such as a higher fecal stability and reduced protein levels (−12%), but at the same time increased fat content (+8%) to secure similar gross energy levels (22–23 MJ kg−1) between the two diets. Water quality parameters were measured individually. The total accumulation of minerals and metals was analyzed in water from different parts of the system at the start and end of the trial period for both diets. No differences in growth, condition factor, feed conversion rate (FCR), or survival of fish fed the two dietary adaptations were observed. The system using the ERAS diet showed significantly higher pH (+1.2%) and alkalinity (+17%) and lower total ammonia nitrogen (TAN) (−18%) and NO2− (−46%) compared with the FTS diet. The count of drum filter activations was also significantly lower (−13%) with the ERAS diet. Compared with the FTS diet, the ERAS diet had a lower probability (−4%) of generating particles smaller than 50 μm, and that the RAS was also more effective in removing particles from the drum filter, prompting a lower daily activation of the filter of 22.1 ± 3.0 counts compared with 25.5 ± 3.5 for the FTS diet. Mineral analysis showed a significantly lower accumulation of total phosphorus (TP) (−90%) and dissolved phosphorus (DP) (−92%) in the RAS units using the ERAS diet compared with those using the FTS diet. Compared with a traditional flow-through diet, these results highlight the benefits of using an RAS-adapted diet that matches the energy requirement of flow-through diets regarding water quality, system performance, satisfactory growth, and condition. [ABSTRACT FROM AUTHOR]

Small water supply systems (SWSSs) are often more vulnerable to waterborne disease outbreaks. In Japan, many SWSSs operate without regulation under the Waterworks Law, yet there is limited investigation into microbial contamination and the associated health risks. In this study, the microbiological water quality of four SWSSs that utilize mountain streams as water sources and do not install water treatment facilities were monitored for over 2 years. In investigated SWSSs, the mean heterotrophic plate counts were below 350 CFU/mL, and the total bacterial loads (16S rDNA concentration) ranged from 4.71 to 5.35 log10 copies/mL. The results also showed the consistent presence of fecal indicator bacteria (FIB), i.e., Escherichia coli and Clostridium perfringens, suggesting the potential of fecal pollution. E. coli was then utilized as an indicator to assess the health risk posed by E. coli O157:H7 and Campylobacter jejuni. The results indicated that the estimated mean annual risk of infection and disability-adjusted life years (DALYs) exceeded acceptable levels in all SWSSs for the two reference pathogens. To ensure microbial water safety, implementing appropriate water treatment facilities with an estimated mean required reduction of 5–6 log10 was necessary. This study highlighted the potential microbial contamination and health risk level in SWSSs that utilize mountain streams as water sources, even though the water sources were almost not affected by human activities. Furthermore, this study would also be helpful in supporting risk-based water management to ensure a safe water supply in SWSSs. [ABSTRACT FROM AUTHOR]

Background: Irrigation with low-quality water without considering the acceptable levels negatively impacts not only humans, but also extends to the whole surrounding ecosystem. The current research is a comprehensive-integrated appraisal of the irrigation water quality gathered from El-Batts drain in Fayoum Governorate, from September 2021 to June 2022, together with the drain sediments and the soils adjacent to it. The characteristics (physical, chemical and biological), and the risk fate of some heavy metals (As, B, Cr, Cu, Zn, Cd, Pb, Ni, Fe and Mn) were measured. Moreover, the risks of the studied pollutants were assessed using many indices: irrigation water quality index (IWQI), contamination factor (Cf), degree of contamination (Cd), Salinity indices and Zn equivalent (ZE). Additionally, kinetic studies of the inorganic pollutants were evaluated to determine their availability and impact on the surrounding environment. Results: Data from IQWI showed that water in all five sites was assumed to be very bad and unsuitable for usage with a general average (14.62 and 25.35) in all four seasons. ZE exceeds the critical level of 250, which means there is a risk of soil contamination. Conclusions: The elevated levels of heavy metals, microbial enzymes, pathogenic fecal coliform and Salmonella reflect bad and direct risk of dealing with such water in irrigation and its effect on the soil ecosystem and the growing crops. Most of the tested parameters exceeded the acceptable levels of the Egyptian Governmental Law Guidelines, WHO and FAO, which may threaten aquatic life. Best managements practices and remediation techniques should be applied to minimize the hazards in such waters. [ABSTRACT FROM AUTHOR]

Climate change is altering rainfall patterns resulting in increasing variability and intensity of rainfall events worldwide. Increases to short duration, intense rainfall (i.e., convective rainfall), will lead to increases in sewage overflow and run‐off from agricultural land. Such events generate spikes in micro‐organisms from feces and manure, especially Escherichia coli and intestinal enterococci, that temporarily end up in bathing waters posing serious health risks to bathers. Forecasting of bathing water quality associated with convective rainfall presents a distinctive forecasting challenge due to high uncertainties associated with predicting the timing, location, and impact of such events. In this article, we review examples of bathing water quality forecasting practices, with a focus on the United Kingdom where convective rainfall in the summer bathing water season is a particular concern, and question whether the current approach is robust in a changing climate. We discuss potential upgrades in bathing water forecasting and identify the main challenges that must be addressed before an improved framework for bathing water forecasting can be achieved. Although developments in meteorological and hydrological short‐range forecasting capabilities are promising, convective rainfall forecasting has significant predictability limits. We suggest taking full advantage of short‐range forecasts to provide sub‐daily bathing water forecasts, focusing on targeted bathing water monitoring regimes to improve model accuracy with the ultimate goal of providing improved information and guidance for beach users. This article is categorized under:Science of Water > Water and Environmental ChangeHuman Water > Water GovernanceScience of Water > Water Quality [ABSTRACT FROM AUTHOR]

Access to safe drinking water is a critical public health challenge, especially in developing nations like India. Pathogenic microorganisms contaminating potable water pose serious health risks. This study aimed to conduct a comprehensive microbiological and physiochemical examination of various drinking water sources in the Bikaner district of Rajasthan. Water samples were collected from urban households, rural households, public taps, borewells, canals, rivers, ponds, and other sources across Bikaner. Standard microbiological and analytical chemistry techniques were employed to isolate, identify and characterize microorganisms present, as well as analyze key physiochemical parameters. The microbiological analysis revealed high levels of total coliforms, fecal coliforms, and pathogenic bacteria like Escherichia coli, Klebsiella, Pseudomonas, Citrobacter and Aeromonas across all water samples. Surface water sources like rivers and ponds showed the highest microbial contamination, exceeding WHO limits by over 1000-fold. Groundwater from borewells had comparatively lower but still unacceptable microbiological quality. Tap water also contained coliforms indicating inadequate treatment and post-supply contamination. In-depth comparative studies were conducted on the isolated bacterial strains to characterize their physiological, biochemical, genetic and pathogenic properties. Antibiotic resistance, virulence factors, biofilm formation and environmental persistence of these waterborne pathogens were investigated. Molecular microbiology techniques like PCR genotyping were used to identify species and strain types. The physiochemical analysis examined parameters like pH, turbidity, dissolved oxygen, chlorine residuals, toxic metals and other chemicals. Most sources violated drinking water standards for multiple parameters. Surface waters showed high turbidity, low dissolved oxygen and presence of heavy metals. Even groundwater sources had physicochemical quality issues due to geogenic and anthropogenic contamination. This multi- dimensional study provides comprehensive baseline data on the microbiological and chemical quality of drinking water available to communities in Bikaner region. The findings confirm widespread contamination by fecal bacteria, other pathogens and unsafe physiochemical traits across all sources tested. This information will guide efforts to mitigate public health risks through proper treatment interventions, water safety plans, and other preventive measures for ensuring access to potable water in this water-stressed area. [ABSTRACT FROM AUTHOR]

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA's superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management. [ABSTRACT FROM AUTHOR]

This study aimed at monitoring and management of the surface water and potentially pathogenic microbes of Lake Tonga (Algeria) with respect to. It characterized the main bacterial diversity patterns of Lake Tonga and predicted from water physicochemical parameters and water quality index (WQI) the distribution of bacterial species and the main indicator groups of faecal water contamination. Water samples were taken monthly at three sampling sites of different water depths. Several physicochemical parameters were measured; of which some were included in computing WQI to characterize the water quality of the lake. Counting, isolation and bacterial identification methods were used to characterize the existing aerobic heterotrophic bacteria. The composition of the microbial community of the waterbody of Lake Tonga included an abundant culturable bacterial flora belonging to several bacterial families and whose specific richness varied between water depths of the sites sampled. Species richness of the bacteria identified phenotypically varied between 7 and 11 per sample. The site with shallow water was the richest in bacterial species, compared to moderate and deep waters. The redundancy analysis showed the main physicochemical drivers of the microbial community composition. Our findings showed that high WQI scores indicated the water quality deterioration which triggered the increase in total load of faecal indicator bacterial groups. This study identified in Lake Tonga an important culturable aerobic bacterial flora whose specific richness and distribution varied spatially following the effects of water physicochemical parameters. Lake Tonga needs an integrated management plan to mitigate human disturbances declining water quality. [ABSTRACT FROM AUTHOR]

Monitoring freshwater quality in urban like Chennai is indispensable as the chief drinking water source for the city originates from freshwater lakes. This study portrays an economical, user‐friendly, economical method to detect water quality. A machine learning‐based portable water quality check test kit was developed along with a user interface. To validate the efficiency of the portable kit, this study focussed on the ten major freshwater lakes of Chennai City. Basic quality parameters of water, such as the potential of hydrogen (pH), level of ammonia, nitrite, nitrate, and total phosphate (T.P.), were considered when designing this mobile phone‐compatible user interface application. The experiment used and tested water samples from ten different lakes with the commercially available freshwater master test kit. The images of color developed during the test were captured, and corresponding Red, Green, Blue (RGB) values were calculated. These values were used in Igray, Iavg, and Euclidean distance algorithms to predict the pollution level of the samples. Based on the models, the Euclidean model fitted well with the calibration data with a correlation value of more than 0.98 for all the parameters. Percent accuracy values of the methods used in this study were compared, and the Euclidean model showed more than 98% accuracy for all parameters. The result of this study is alarming that all the ten freshwater lakes considered in this study were hypereutrophic. [ABSTRACT FROM AUTHOR]

In response to global urbanization and economic development, urban surface water pollution has become a universal challenge and particularly affects densely populated megacities, and Dhaka is no exception. The discharge of 98% of untreated domestic sewage and massive volumes of industrial wastewater from over 7000 industries escalate surface water crises. This study investigates microbial and fecal contamination with particular emphasis on Cryptosporidium in surface water, known for causing waterborne diseases, such as cryptosporidiosis. Findings reveal high Cryptosporidium oocyst concentrations and fecal contamination in various water bodies in Dhaka City. Among the investigated water bodies, the Buriganga River exhibits the highest Cryptosporidium oocyst concentration (46%), while the Balu River, Turag River, Shitalakkhya River, Dhanmondi Lake, Gulshan Lake, Banani Lake, Ramna Lake, and Crescent Lake also present high levels of oocyst concentrations ranging from 21–40%. This study also calculated infection risks and found that the infection risk of swimming is highest during the wet season and is (3.9 ± 2.2 (95% CI: 3.0–5.0)) × 10−2 per swimming event, whereas it is approximately (2.4 ± 1.9 (95% CI: 1.6–3.3)) × 10−2 during the dry season. Annual diving risks are approximately (1.2 ± 0.6 (95% CI: 0.9–1.4)) × 10−2, indicating considerably high risks. Most of the sampling sites generally show significantly higher risks than other study areas like the Mymensingh and Kushtia Districts. In light of these results, we strongly recommend immediate measures to address water quality issues and mitigate the risks associated with Cryptosporidium contamination in Dhaka's surface water. [ABSTRACT FROM AUTHOR]

Wastewater surveillance represents an alternative approach to regulating contamination and the early detection of infectious agents and outbreaks of diseases of public health importance. This study evaluated domestic wastewater effects on recreational waters in estuarine and seawater bodies in Guayas and Santa Elena provinces in Ecuador, South America. Fecal indicator bacteria (thermotolerant coliforms) served as key indicators for evaluation. Physical, chemical, and microbiological quality markers following the Ecuadorian environmental quality standard and the discharge of effluents to the water resource were analyzed. Samples were collected from 44 coastal sites and 2 oxidation lagoons during the dry and rainy seasons of 2020 and 2021, respectively. SARS-CoV-2 RNA was detected in samples with higher E. coli concentrations using reverse transcription quantitative PCR to detect the genes N and ORF1ab. All samples analyzed for SARS-CoV-2 showed Ct ˂ 40 for at least one gene. Four samples showed at least 20 genome copies of gene N per reaction. These were at an artisanal fishing port, an estuarine area (Palmar), a recreational bay, and an oxidation lagoon. A moderate correlation was found between SARS-CoV-2 RNA, thermotolerant coliform and E. coli (p-value ≤ 0.0037), and a strong and positive correlation between thermotolerant coliform and E. coli. (p-value ≤ 0.00001), highlighting the utility of these established parameters as a proxy of the virus. Significant differences were found in the concentrations of thermotolerant coliforms between seasons (p-value = 0.016) and sites (p-value = 0.005). The highest levels of coliforms were found in the dry season (63000 MPN/100 mL) in Anconcito and during the rainy season (14000 MPN/100 mL) at Esterillo in Playas County. It is recommended that the decentralized autonomous governments of the surveyed provinces in Ecuador implement urgent corrective actions and establish medium-term mechanisms to minimize a potential contamination route. Additional parameters must be included in the monitoring, such as Enterococcus and intestinal parasites, due to their public health implications. In the oxidation lagoons, maintenance actions must be carried out, including the dissolution of sediments, an increase in water retention times, and in situ treatment of the sludge, to improve the system's performance. [ABSTRACT FROM AUTHOR]

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash–Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error. [ABSTRACT FROM AUTHOR]

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

Estuarine water quality is declining worldwide due to increased tourism, coastal development, and a changing climate. Although well-established methods are in place to monitor water quality, municipalities struggle to use the data to prioritize infrastructure for monitoring and repair and to determine sources of contamination when they occur. The objective of this study was to assess water quality and prioritize sources of contamination within Town Creek Estuary (TCE), Beaufort, North Carolina, by combining culture, molecular, and geographic information systems (GIS) data into a novel contamination source ranking system. Water samples were collected from TCE at ten locations on eight sampling dates in Fall 2021 (n = 80). Microbiological water quality was assessed using US Environmental Protection Agency (U.S. EPA) approved culture-based methods for fecal indicator bacteria (FIB), including analysis of total coliforms (TC), Escherichia coli (EC), and Enterococcus spp. (ENT). The quantitative microbial source tracking (qMST) human-associated fecal marker, HF183, was quantified using droplet digital PCR (ddPCR). This information was combined with environmental data and GIS information detailing proximal sewer, septic, and stormwater infrastructure to determine potential sources of fecal contamination in the estuary. Results indicated FIB concentrations were significantly and positively correlated with precipitation and increased throughout the estuary following rainfall events (p < 0.01). Sampling sites with FIB concentrations above the U.S. EPA threshold also had the highest percentages of aged, less durable piping materials. Using a novel ranking system combining concentrations of FIB, HF183, and sewer infrastructure data at each site, we found that the two sites nearest the most aged sewage infrastructure and stormwater outflows were found to have the highest levels of measurable fecal contamination. This case study supports the inclusion of both traditional water quality measurements and local infrastructure data to support the current need for municipalities to identify, prioritize, and remediate failing infrastructure. [ABSTRACT FROM AUTHOR]

Wastewater discharge and runoff waters are significant sources of human and animal fecal microbes in surface waters. Human-derived fecal contamination of water is generally estimated to pose a greater risk to human health than animal fecal contamination, but animals may serve as reservoirs of zoonotic pathogens. In this study, quantitative microbial risk assessment (QMRA) tools were used to evaluate the hygienic impact of sewage effluents and runoff water from municipalities and animal farms on surface and bathing waters. The humanspecific microbial source tracking (MST) marker HF183 was used to evaluate the dilution of fecal pathogens originating from the sewage effluent discharge to the downstream watershed. As novel risk management options, the efficiency of UV-LED disinfection and wetland treatment as well as biochar filtration was tested on-site for the contamination sources. According to the dilution pattern of the MST marker HF183, microbes from wastewater were diluted (2.3-3.7 log10) in the receiving waters. The scenario-based QMRA revealed, that the health risks posed by exposure to human-specific norovirus GII and zoonotic Campylobacter jejuni during the bathing events were evaluated. The risk for gastroenteritis was found to be elevated during wastewater contamination events, where especially norovirus GII infection risk increased (1-15 cases per day among 50 bathers) compared with the business as usual (BAU) situation (1 case per day). The noted C. jejuni infection risk was associated with animal farm contamination (1 case per day, versus 0.2-0.6 cases during BAU). Tertiary treatment of wastewater with wetland treatment and UV-LED disinfection effectively reduced the waterborne gastroenteritis risks associated with bathing. Based on the experiences from this study, a QMRA-based approach for health risk evaluations at bathing sites can be useful and is recommended for bathing site risk assessments in the future. In case of low pathogen numbers at the exposure sites, the MST marker HF183 could be used as a pathogen dilution coefficient for the watershed under evaluation. The full-scale implementation of novel tertiary treatment options at wastewater treatment plants (WWTPs) as well as on-site runoff water treatment options should be considered for infection risk management at locations where scenario-based QMRA implies elevated infection risks. [ABSTRACT FROM AUTHOR]

Understanding the dynamic change in abundance of both fecal and opportunistic waterborne pathogens in urban surface water under different abiotic and biotic factors helps the prediction of microbiological water quality and protection of public health during recreational activities, such as swimming. However, a comprehensive understanding of the interaction among various factors on pathogen behavior in surface water is missing. In this study, the effect of salinity, light, and temperature and the presence of indigenous microbiota, on the decay/persistence of Escherichia coli and Pseudomonas aeruginosa in Rhine River water were tested during 7 days of incubation with varying salinity (0.4, 5.4, 9.4, and 15.4 ppt), with light under a light/dark regime (light/dark) and without light (dark), temperature (3, 12, and 20 °C), and presence/absence of indigenous microbiota. The results demonstrated that light, indigenous microbiota, and temperature significantly impacted the decay of E. coli. Moreover, a significant (p<0.01) four-factor interactive impact of these four environmental conditions on E. coli decay was observed. However, for P. aeruginosa, temperature and indigenous microbiota were two determinate factors on the decay or growth. A significant three-factor interactive impact between indigenous microbiota, temperature, and salinity (p<0.01); indigenous microbiota, light, and temperature (p<0.01); and light, temperature, and salinity (p<0.05) on the decay of P. aeruginosa was found. Due to these interactive effects, caution should be taken when predicting decay/persistence of E. coli and P. aeruginosa in surface water based on a single environmental condition. In addition, the different response of E. coli and P. aeruginosa to the environmental conditions highlights that E. coli monitoring alone underestimates health risks of surface water by non-fecal opportunistic pathogens, such as P. aeruginosa. Key points: Abiotic and biotic factors interactively affect decay of E. coli and P. aeruginosa E.coli and P.aeruginosa behave significantly different under the given conditions Only E. coli as an indicator underestimates the microbiological water quality [ABSTRACT FROM AUTHOR]

Inundation of coastal stormwater networks by tides is widespread due to sea‐level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies is poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (a) documented temporal variation in concentrations of Enterococcus spp. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a 2‐month period and more intensively during two perigean spring tide periods, (b) measured ENT concentrations in roadway floodwaters during tidal floods, and (c) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving waterway vary as a function of tidal stage and antecedent rainfall, but also site‐specific characteristics of the stormwater network that drains to the waterway. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceeded the threshold for safe public use of recreational waters. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR. Plain Language Summary: Communities along the US Atlantic Coast have begun to experience the effects of sea‐level rise as stormwater drainage pipes, originally designed when sea levels were lower, are now routinely inundated at high tide. We do not know whether frequent inundation of stormwater drainage pipes affects coastal water quality, specifically the fecal bacteria levels of coastal waters. In this study, we collected daily measurements of Enterococcus spp. (ENT), the bacteria used by regulators to assess the safety of marine waters for swimmers and recreators, from a tidal creek over a 2‐month period. We also collected water samples from roadway floodwaters and the adjacent tidal creek during the highest high tides when the stormwater pipes drained during the outgoing tide. After data collection, we developed statistical models to relate the amount of ENT in the tidal creek to tide, rainfall, and stormwater infrastructure. When comparing rainfall and tide, we observed that rainfall caused greater bacterial contamination in the tidal creek than high tide flooding. However, we observed high ENT concentrations in roadway floodwaters and in the creek during the outgoing tide, leading us to conclude that everyday flooding of underground stormwater pipes at high tide could create hazardous water quality. Key Points: Daily observations of Enterococcus spp. concentrations in a coastal waterway were similar during and outside of perigean spring tidesTidal inundation of stormwater networks occurred daily, but rainfall runoff produced the greatest bacterial contamination in the waterwayHigh Enterococcus spp. concentrations were observed in roadway floodwaters and the receiving waterway during perigean spring tides [ABSTRACT FROM AUTHOR]

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies. [ABSTRACT FROM AUTHOR]

Assessing river water quality is crucial for human and ecological needs because of the deterioration of the river and escalated water pollution under the threats of anthropogenic activities. In order to assess river water quality, the Damodar River water was evaluated from the perspectives of spatio-temporal dynamics of ecological (organic pollution index or OPI and eutrophication index or EI), bacteriological (coliform count and comprehensive bathing water quality index or CBWQI), and overall water quality assessments (water quality index or WQI and comprehensive pollution index or CPI). The OPI reveals that 44.66% of water samples have heavy organic pollution; however, EI depicts that almost all water samples of Damodar River have severe eutrophication, especially in the pre- and post-monsoon seasons. Moreover, the fecal coliform count and CBWQI indicate the unsuitability of river water for bathing. The overall WQI portrays that 21.56%, 33.59%, and 22.47% of water samples have heavy pollution in pre-monsoon, monsoon, and post-monsoon, respectively. Moreover, 73.39% of water samples have low CPI indicating slight comprehensive pollution. This study also reveals that the pollution level in the Damodar River downstream of the Durgapur barrage is high among the other stations. The major reasons behind the severe contamination of Damodar River water are urban-industrial and agricultural effluents mixing into the river that lead to higher concentrations of BOD, DO, fecal coliform, COD, fluoride TSS, and turbidity in the river water. Thus, this study carries appreciated information on policy recommendations for the different stakeholders of the Damodar River basin including regional planners, agri-engineers, and ecological river engineers for sustainable river management. [ABSTRACT FROM AUTHOR]

Anecdotal evidence and available literature indicated that contaminated water played a major role in spreading the prolonged cholera epidemic in Malawi from 2022 to 2023. This study assessed drinking water quality in 17 cholera-affected Malawi districts from February to April 2023. Six hundred and thirty-three records were analysed. The median counts/100 ml for thermotolerant coliform was 98 (interquartile range (IQR): 4-100) and that for Escherichia coli was 0 (IQR: 0-9). The drinking water in all (except one) districts was contaminated by thermotolerant coliform, while six districts had their drinking water sources contaminated by E. coli. The percentage of contaminated drinking water sources was significantly higher in shallow unprotected wells (80.0% for E. coli and 95.0% for thermotolerant coliform) and in households (55.8% for E. coli and 86.0% for thermotolerant coliform). Logistic regression showed that household water has three times more risk of being contaminated by E. coli and two and a half times more risk of being contaminated by thermotolerant coliform compared to other water sources. This study demonstrated widespread contamination of drinking water sources during a cholera epidemic in Malawi, which may be the plausible reason for the protracted nature of the epidemic. [ABSTRACT FROM AUTHOR]

Computer vision (CV) technologies have been applied extensively in turbid water assessment. However, the accuracies of CV to detect turbidity is limited by several factors such as inferior image quality and adoptation of traditional machine learning (MsL). Several past studies have shown that the accuracy of CV to detect turbidity can be enhanced by employing image pre-processing and artificial intelligence (AI). Therefore, this study proposes a combined CV and convolution neural network (CNN) approach for turbid water classification in accordance with national water quality standards. A total of 71 turbid water samples in the range of 0–100 nephelometry turbidity units (NTU) were prepared from a mixture of 1000 NTU stock Formazine solution and distilled water. Digital images of water samples were acquired with the Asus Zenfone Go smartphone and nephelometry principle. Synthetic minority oversampling technique (SMOTE) was employed for image pre-processing, synthesis, and augmentation. Classification models were developed with Keras CNN architecture and TensorFlow framework. Two national water standards referred to were Malaysia National Water Quality Standard Class I (NWQS-C1) and France Système D'évaluation de la Qualité des cours d'EAU bleu aptitude (SEQ-EAU-b). The proposed CV-CNN approach was successfully implemented with 94.34–98.42% accuracy. The accuracy of CNN was slightly higher when trained with color images (98.42%) than with grayscale images (94.34%). This study demonstrated that CV and CNN are excellent tools for water quality assessment, especially for classifying water turbidity. [ABSTRACT FROM AUTHOR]

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error. [ABSTRACT FROM AUTHOR]

Rivers in Africa have experienced dire pollution as a result of the poor management of wastewater effluent emanating from water resource recovery facilities (WRRFs). An integrated wastewater resource recovery model was developed and applied to identify ideal wastewater resource recovery technologies that can be used to recover valuable resources from a mixture of wastewater effluents in a case study in the Burgersfort WRRF in the Limpopo province, South Africa. This novel model incorporates the process of biological nutrient removal (BNR) with an extension of conventional methods of resource recovery applicable to wastewater. The assessment of results of effluent quality from 2016 to 2022 revealed that ammonia, chemical oxygen demand, total coliform, fecal coliform, and Escherichia coli levels were critically non-compliant with the permissible effluent guidelines, indicating a stable upward trend in terms of concentrations, and scored a very bad wastewater quality index rating. All variables assessed showed a significant loading, except for orthophosphates, and significant correlations were observed among the variables. The results of the integrated wastewater resource recovery model revealed a high probability of reclaiming recoverable resources such as nutrients, sludge, bioplastics, biofuel, metals, and water from wastewater, which have economic, environmental, and social benefits, thereby improving the effluent quality of a WRRF. [ABSTRACT FROM AUTHOR]

In this paper, several methods for the calculation of water quality evolution in drinking water distribution networks are analysed. The Lagrangian Time-Driven method has been implemented in the Epanet simulation software since version 2.0. In version 2.2, some improvements were implemented to deal with mass imbalances (Lagrangian Time-Driven improved method). However, it sometimes presents inaccuracies in calculations, especially when there are short-length pipes. To solve this problem, the implementation of the Lagrangian Event-Driven method is proposed, which provides more accurate quality results. In order to detail the differences and similarities of the results of the different methods and to determine under what conditions the results provided by Epanet are sufficiently adjusted, two practical examples have been carried out, one of them on a hydraulic model of a real network. [ABSTRACT FROM AUTHOR]

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple water uses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed that the area occupied by fish farms has increased in the reservoir from 2016 to 2022 and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir. [ABSTRACT FROM AUTHOR]

Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research. [ABSTRACT FROM AUTHOR]

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. [ABSTRACT FROM AUTHOR]

Discharge of untreated wastewater into water bodies pollutes the receiving waters. This study assessed the impact of abattoir and Kampala designers' effluent discharge on the water quality of the Yemoja River in Abeokuta, Nigeria. Twenty-seven water samples collected at three points, covering 180 m length, for 6 months were assessed for physicochemical parameters and metals and compared with the World Health Organization (WHO) and Standard Organization of Nigeria (SON) permissible standards. Most discharge point levels were found to be higher than their corresponding upstream and downstream values. Temperature, turbidity, magnesium, alkalinity, DO, TSS, phosphate, lead, BOD and potassium were found to be higher than normal levels for river water as prescribed by the WHO and SON while parameters like TDS, TS, calcium, chloride, nitrate, sulphate, iron and COD were lower than the standards. The total coliform values were higher than both national and international permissible limits, indicating contamination by human sewage or animal droppings. The water quality index indicated polluted water that is unfit for consumption. Findings from this research indicate that butchering and tie-and-dye activities have impacted river Yemoja water quality. Therefore, wastewater from the abattoir and textile industries be treated before discharge into water bodies. [ABSTRACT FROM AUTHOR]

The bull shark (Carcharhinus leucas) is an apex predator with a wide distribution in tropical and warm temperate waters. This species is known to utilise freshwater systems and estuaries as nursery grounds. During a wider bull shark tagging survey in the Rewa River, Fiji, in 2017 and 2018, 27 neonate bull sharks with red eye pupils were recorded. This opportunistic observation prompted an examination of water quality parameters in the Rewa River to collect preliminary data on the potential cause of pollution. Water samples were analysed for faecal indicator bacteria (Escherichia coli), ammonia, total nitrogen, and various metals. Results indicated high levels of E. coli contamination, as well as elevated ammonia and total nitrogen concentrations. However, due to the limited sample size and the likelihood of additional underlying causes, a definitive causal relationship could not be established. Hence, this finding stands as an opportunistic observation, detailed here for documentation and stimulation of discourse. Field observations of 27 neonate bull sharks with red eyes in the Rewa River, Fiji, raised concerns about water quality. Analysis indicated high levels of Escherichia coli contamination, ammonia, and total nitrogen. However, the definitive causal relationship is yet to be investigated. [ABSTRACT FROM AUTHOR]

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform metaanalyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quant ified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological difference s between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies. [ABSTRACT FROM AUTHOR]

The study aims to assess variations in spatio-temporal characteristics of water quality parameters from three tropical estuaries, namely Muri-Ganga, Saptamukhi, and Hooghly, in the western portion of the Indian Sundarbans. Reliable retrieval of near-surface concentration of water quality parameters such as Chlorophyll-a, SST & TSM from diverse aquatic ecosystems with broad ranges of tropical requirements has always remained a complex issue. In this study, application of Case 2 Regional Colour Correction (C2RCC) processor has been tested for its accuracy across different bio-optical regimes in both inland and coastal waters. Satellite images for the same period were also collected and analysed using the C2RCC processing sequence to retrieve parameters like the depth of water, surface reflectance, water temperature, inherent optical properties (IOPs), chlorophyll-a, salinity, total suspended matter (TSM), etc., using the SNAP software. In situ sampling from specific locations within these estuaries and water quality analysis were conducted for the period 2017–2019. The OLCI retrieved datasets were compared and corroborated with field survey datasets. It was observed that the highest amount of TSM was recorded at Diamond Harbour during the 2018 pre-monsoon season (301.40 mg/L field-based value and 308.54 mg/L estimated value). Similarly, chlorophyll-a had higher concentrations throughout the monsoon season (3.03 mg m−3, (field survey), and 2.96 mg m−3, (estimated) at Fraserganj and Sagar south points. A very good correlation was observed for all seasons for Chl-a (r = 0.829) and TSM (r = 0.924) between the OLCI data and in situ measurements. Higher correlation and significant 'r' values highlight the importance of having both field-based as well as remotely-sensed information in understanding any dynamic system in a sustained manner. Results also confirm that the water quality model using OLCI Chl-a and TSM products outperforms conventional techniques. The study demonstrates the efficacy of using Sentinel 3 OCLI data for shallow marine and estuarine remote sensing applications, especially for monitoring TSM and Chl-a concentrations. [ABSTRACT FROM AUTHOR]

Elevated total phosphorus (TP) leads to water eutrophication, affecting aquatic life and ecosystems. Effective control of phosphorus concentration in rivers is vital. Detecting TP and other nutrients is essential for evaluating river health. Establishing real-time TP prediction is key for studying eutrophication and blooms. In this study, we compared the prediction models combined with two different feature selection tools, Pearson and distance correlation coefficient, and evaluated them with four evaluation indicators. It was determined that the assessment outcomes of the distance correlation coefficient yielded heightened precision. Notably, the random forest validation contributed to an impressive 8.3% enhancement in the performance R2 of the model, and the utilization of BP neural network validation resulted in an improvement of 2.7% in the model's performance. A real-time TP prediction model using distance correlation coefficient was eventually created, which reaches an accuracy up to 82.6%. The final model was applied to field data, revealing elevated TP levels downstream of Dongjiang River in China due to high population density and numerous factories. Additionally, the results indicated that the distance correlation coefficient outperformed the Pearson correlation coefficient in predicting the total phosphorus in Dongjiang River. Our findings suggest that combining distance correlation coefficients with machine learning holds promise for more accurate water quality prediction models. These findings establish a foundation for enhancing the efficacy of the water quality prediction model. [ABSTRACT FROM AUTHOR]

With increasing ecological damage caused by regional development, many countries seek to promote sustainable development through spatial regulation. This study treated China's development of National Key Ecological Functional Zones (NKEFZ) as a quasi–natural experiment, applying a time–varying difference–in–differences model to data from 148 water quality monitoring cross–section points of major river systems in China from 2004 to 2018. The results revealed that NKEFZ improved the mean of comprehensive water quality (CWQ) and individual water quality indicators of dissolved oxygen and ammonia nitrogen content index by restricting urbanization and increasing government investment. The mean value of CWQ was improved by about 0.22 grades, and the optimization effect of spatial regulation on average water quality was more prominent in coastal cities, cities with high–pollution levels, and large–sized cities. Nevertheless, the impact of NKEFZ on water quality exhibited a "Matthew effect", in which NKEFZ were not effective in improving the bottom line standard of water quality in the pilot zones, as NKEFZ did not significantly improve the peak values of CWQ or individual pollutant indicator. In addition, an economic effect estimation indicated that NKEFZ significantly facilitated local economic growth and primary industry development and had a significantly negative impact on secondary industry growth and foreign investment. This study contributes new evidence for measuring the effect of the trans–regional spatial regulation, eliciting policy implications for improving ecological spatial regulation and incentive mechanisms, and promoting the sustainable development of the regional environment and economy. [ABSTRACT FROM AUTHOR]