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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]

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]

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]

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]

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]

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]

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]

Escherichia coli are routine indicators of fecal contamination in water quality assessments. Contrary to livestock and human activities, brushtail possums (Trichosurus vulpecula), common invasive marsupials in Aotearoa/New Zealand, have not been thoroughly studied as a source of fecal contamination in freshwater. To investigate their potential role, Escherichia spp. isolates (n = 420) were recovered from possum gut contents and feces and were compared to those from water, soil, sediment, and periphyton samples, and from birds and other introduced mammals collected within the Mākirikiri Reserve, Dannevirke. Isolates were characterized using E. coli-specific real-time PCR targeting the uidA gene, Sanger sequencing of a partial gnd PCR product to generate a gnd sequence type (gST), and for 101 isolates, whole genome sequencing. Escherichia populations from 106 animal and environmental sample enrichments were analyzed using gnd metabarcoding. The alpha diversity of Escherichia gSTs was significantly lower in possums and animals compared with aquatic environmental samples, and some gSTs were shared between sample types, e.g., gST535 (in 85% of samples) and gST258 (71%). Forty percent of isolates gnd-typed and 75% of reads obtained by metabarcoding had gSTs shared between possums, other animals, and the environment. Core-genome single nucleotide polymorphism (SNP) analysis showed limited variation between several animal and environmental isolates (<10 SNPs). Our data show at an unprecedented scale that Escherichia clones are shared between possums, other wildlife, water, and the wider environment. These findings support the potential role of possums as contributors to fecal contamination in Aotearoa/New Zealand freshwater. Our study deepens the current knowledge of Escherichia populations in under-sampled wildlife. It presents a successful application of high-resolution genomic methods for fecal source tracking, thereby broadening the analytical toolbox available to water quality managers. Phylogenetic analysis of isolates and profiling of Escherichia populations provided useful information on the source(s) of fecal contamination and suggest that comprehensive invasive species management strategies may assist in restoring not only ecosystem health but also water health where microbial water quality is compromised. [ABSTRACT FROM AUTHOR]

The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were utilized including 1—optimal artificial neural network (ONN), 2—adaptive neuro fuzzy inference system in which a hybrid algorithm was used for the training process (ANFIS) and 3—coupled evolutionary algorithm-adaptive neuro fuzzy inference system in which particle swarm optimization was utilized in the training process (EA-ANFIS). Three critical water quality parameters for cage fish farming were selected consisting of water temperature, dissolved oxygen (DO) and total dissolved solids (TDS). Moreover, two measurement indices, the Nash–Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE), were utilized to assess the predictive skills of the data driven models. Based on the results in the case study, EA-ANFIS is the best method to simulate water temperature and DO in the reservoir by the remote sensing technique. Furthermore, the ANFIS-based model is the best method to simulate TDS. According to the results in the case study, utilizing the spectral images might not be reliable to simulate DO concentration in the reservoirs. However, the images are robust to simulate water temperature as well as TDS concentration. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) is a critical factor in maintaining healthy aquatic ecosystems, including aquaculture ponds. Low DO levels can lead to hypoxia conditions, which are detrimental to fish health and productivity. To deal with this issue, we intend for a smart monitoring system that predicts hypoxia conditions due to low DO levels in aquaculture ponds. The proposed system collects water quality data using Internet of things (IoT) devices and segments it into different categories based on water quality parameters, with a particular focus on low DO levels. By detecting hypoxia conditions early, fish farmers can take corrective measures to prevent fish mortality and improve fish health. To achieve this, our proposed system uses a lightweight Spatially Shared Attention Long Short-Term Memory (SSA-LSTM) model that captures both temporal and spatial dependencies of DO content in water, enabling accurate prediction of hypoxia conditions. Our model outperforms traditional LSTM models and other existing state-of-the-art models, achieving 99.8% accuracy. The proposed system provides a reliable and efficient solution to monitor hypoxia conditions in aquaculture systems and help fish farmers make informed decisions for optimal fish health and productivity. [ABSTRACT FROM AUTHOR]

Mahseer fish Tor soro, an endemic species in Indonesia, is currently declining its population due to the overreliance on wild captures to meet consumption demands. Furthermore, the survival rate of fry is alarmingly low, necessitating the implementation of sustainable fry cultivation initiatives to bolster the demand for Mahseer fish. Environmentally friendly aquaculture practices, particularly those employing the zero-water discharge principle, offer a promising solution for establishing Mahseer fish nurseries. In cultivation methods, adding bottom substrates to resemble the natural habitat of Mahseer fish can enhance their physical performance and improve fish welfare. This study aimed to evaluate the effects of different substrates on the nursery of Mahseer fish, specifically utilizing (A) coral rock, (B) limestone, (C) crushed stone, and (D) no substrate (control) as treatments. The results demonstrated that the use of substrate media in the cultivation tanks positively influenced water quality, stress levels, and production performance of Mahseer fish. Among the treatments, the utilization of coral rock (A) as a substrate medium yielded the best results in terms of water quality, stress response, and production performance throughout the study compared to the other treatments. [ABSTRACT FROM AUTHOR]

Rivers are impacted by microbial faecal pollution from various sources. We report on a short-term faecal pollution event at the pre-alpine Austrian river Traisen caused by the large cultural event FM4 Frequency music festival, with around 200,000 visitors over 4 days. We observed a massive increase of the faecal indicator bacteria (FIB) intestinal enterococci during the event, while Escherichia coli concentrations were only slightly elevated. This increase poses a significant potential health threat to visitors and people recreating downstream of the festival area. A plausible explanation for the uncoupling of the two FIBs may have been a differential persistence caused by a combination of factors including water temperature, solar radiation, and the excessive presence of personal care products (PCPs) in the river water. However, a potential impact of PCPs on FIB assay performance cannot be ruled out. Our observations are relevant for other intensively used bathing sites; detailed investigations on persistence and assay performance of the FIB in response to different ingredients of PCPs are highly recommended. We conclude that for future festivals at this river or other festivals taking place under similar settings, a more effective management is necessary to reduce deterioration in water quality and minimise health risks. [ABSTRACT FROM AUTHOR]

This study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water temperature, dissolved oxygen, pH, electrical conductivity, secchi disk, alkalinity, total nitrogen and total phosphorus were measured. The actual values of zooplankton density and results obtained from the artificial neural networks were compared. Mean absolute percent error (MAPE) values were calculated with actual values and ANNs values. ANNs values were determined to be close to the real data. MAPE percentage value at the first station was determined as 1.143 for Rotifer, 0.118 for Cladocera, and 0.141 for Copepoda. The MAPE percentage value at the second station was determined as 0.941 for Rotifer, 0.377 for Cladocera, and 0.185 for Copepoda. The MAPE percentage value at the third station was determined as 0.342 for Rotifer, 0.557 for Cladocera, and 0.301 for Copepoda. In the present study, it has been seen that artificial neural networks with a learning feature are successful in predicting zooplankton densities in an aquatic environment. It can be concluded from the study that ANNs are a powerful tool for understanding their relationships with the environment [ABSTRACT FROM AUTHOR]

Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr). During the wet and dry seasons, 25% and 40% of the regions exhibited high CD, respectively. According to non-carcinogenic risk assessment, Cr presents the highest CDI and HQ in children and adults, followed by Mn, As and HI values, indicating elevated risk for children. The highest carcinogenic risk was for Cr in adults, while the lowest was for Cd in children. The sensitivity analysis found that heavy metal concentration and ingestion rate significantly impact both carcinogenic and non-carcinogenic risks. These findings provide critical insights for shaping policy and allocating resources towards effectively managing heavy metal contamination in residential drinking water. [ABSTRACT FROM AUTHOR]

With a focus on the impact of the Rustumiya sewage treatment facility, the physio-chemical and indicator bacterial contamination pollution of the Diyala River (Iraq) was examined. The Diyala River in Baghdad was sampled on a monthly basis from March 2022 to February 2023 for surface water and sediment. The findings demonstrated seasonal and geographical variation in physiochemical parameters (pH, EC, salinity, and TDS) and bacteriological markers (TC, FC, E. coli, and FS). The research procedures and methods showed that some physicochemical parameters (pH, EC, TDS) in sediment exceeded their standard limits concerning aquatic life protection in spring and summer. The level of bacteriological pollution in the water and sediments, evaluated by tracking of faecal indicator bacteria (E. coli and Enterococci), during the autumn and winter seasons had the largest concentration of bacterial contaminants. In this study, the main finding was the majority of the studied stations had water classified as not recommended or unacceptable and sediment classified as acceptable or conservatively accepted. The source of contamination, determined by the ratio value of faecal coliform bacteria to faecal Streptococcus, was from human origin in the majority of studied stations, and from mixed origin during the spring and summer. Bacterial indicators in this research exhibited both quantitative and qualitative fluctuation, suggesting their potential utility as a bio-indicator for contamination of water and sediment in the Diyala River. [ABSTRACT FROM AUTHOR]

Wastewater irrigation may reintroduce fecal pathogens to households and cause drinking water contamination. The aim of this study was to assess the effect of wastewater irrigation on the quality of drinking water among wastewater-irrigating urban farming households. Drinking water samples from 52 households of farming communities in 19 sampling sites were collected twice at PoS and PoC. The microbial quality of the water was assessed using E. coli counted by membrane filtration. Samples collected from exposed households at PoC were 100% positive for E. coli with mean E. coli at PoC increased 10× from the PoS and 3× higher compared to PoC in the unexposed households. The absence of hand washing water (ATE = 8.14), water storing (ATE = 7.75) and intermittent water supply (ATE = 6.25) were significant factors for the increased E. coli at PoC. Breaking the path of the pathogens from the farm to the house and from PoS to PoC needsintervention. [ABSTRACT FROM AUTHOR]

According to current research, machine learning algorithms have been proven to be effective in detecting both optical and non-optical parameters of water quality. The use of satellite remote sensing is a valuable method for monitoring long-term changes in the quality of lake water. In this study, Sentinel-2 MSI images and in situ data from the Dianshan Lake area from 2017 to 2023 were used. Four machine learning methods were tested, and optimal detection models were determined for each water quality parameter. It was ultimately determined that these models could be applied to long-term images to analyze the spatiotemporal variations and distribution patterns of water quality in Dianshan Lake. Based on the research findings, integrated learning algorithms, especially CatBoost, have achieved good results in the retrieval of all water quality parameters. Spatiotemporal analysis reveals that the overall distribution of water quality parameters is uneven, with significant spatial variations. Permanganate index (CODMn), Total Nitrogen (TN), and Total Phosphorus (TP) show relatively small interannual differences, generally exhibiting a decreasing trend in concentrations. In contrast, chlorophyll-a (Chl-a), dissolved oxygen (DO), and Secchi Disk Depth (SDD) exhibit significant interannual and inter-year differences. Chl-a reached its peak in 2020, followed by a decrease, while DO and SDD showed the opposite trend. Further analysis indicated that the distribution of water quality parameters is significantly influenced by climatic factors and human activities such as agricultural expansion. Overall, there has been an improvement in the water quality of Dianshan Lake. The study demonstrates the feasibility of accurately monitoring water quality even without measured spectral data, using machine learning methods and satellite reflectance data. The research results presented in this paper can provide new insights into water quality monitoring and water resource management in Dianshan Lake. [ABSTRACT FROM AUTHOR]

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies. [ABSTRACT FROM AUTHOR]

The potability of water, including underground sources, is constantly affected by human activities. To assess water quality and water security in rural and urban areas of southern Brazil, a quantitative, retrospective analysis of water samples collected monthly by the Brazilian health authorities (19,687 samples from 2013 to 2021) was performed. In rural areas, 5,979 water samples (77.54%) were found to be contaminated by coliform bacteria and 3,431 (44.50%) by Escherichia coli. In addition, 1,616 (20.95%) of the contaminated samples were significantly correlated with rainfall amount. In urban areas, 1,268 (10.95%) of the samples contained coliform bacteria and 293 (2.53%) of these samples contained E. coli, with the factor of rainfall associated with 1,081 samples (9.33%) with bacterial contamination. In terms of physicochemical parameters, turbidity exceeded the national standard (5 uT) in 448 (2.32%) samples and fluoride fell below the required level (0.8 mg/L) in 106 samples (0.54%). The presence of free residual chlorine (0.2–2.0 mg/L) was verified in 846 samples (14.38%) in rural areas and in 10,825 samples (56.13%) in urban areas. These results suggest a strong association between rainfall factors and physicochemical alterations, as well as the risk of greater microbial contamination of water for human consumption. [ABSTRACT FROM AUTHOR]

Aims: While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors. Methods and results: Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls). Conclusions: This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management. [ABSTRACT FROM AUTHOR]

Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr). During the wet and dry seasons, 25% and 40% of the regions exhibited high CD, respectively. According to non-carcinogenic risk assessment, Cr presents the highest CDI and HQ in children and adults, followed by Mn, As and HI values, indicating elevated risk for children. The highest carcinogenic risk was for Cr in adults, while the lowest was for Cd in children. The sensitivity analysis found that heavy metal concentration and ingestion rate significantly impact both carcinogenic and non-carcinogenic risks. These findings provide critical insights for shaping policy and allocating resources towards effectively managing heavy metal contamination in residential drinking water. [ABSTRACT FROM AUTHOR]

Lake Poso is a tectonic lake and the third largest of the lake in Indonesia, and is a potential area for endemic fish resources and tropical eels, disturbance to ecosystem balance in the form of habitat destruction and unfriendly fishing techniques will affect the existence of these resources. This study aimed to determine and evaluate the current condition of water quality, trophic status, and potential for fish production in Lake Poso. Observations were conducted in 2021 in five stations with random purposive methods sampling. The observed main parameters were water quality such as depth, transparency, temperature, conductivity, pH, dissolved oxygen, alkalinity, hardness, phosphate, nitrate, ammonia, and chlorophyll-a. The results showed that the characteristics of the water quality parameters and trophic status index, classified as waters were good for the life of fish and other organisms. The productivity rate of the lake waters seen from the value of the trophic status index was 46.18 which classified the waters at a mesotrophic level. The potential for fish production was low with numbers ranging from 2445-2605 tons/year and an average of 2545 tons/year. Finally, to maintain aquatic biological resources and sustainable fisheries in Lake Poso are needed (a) maintaining the value trophic state index in condition oligotrophic and mesotrophic, (b) protecting the habitat around the lake by maintaining the optimal vegetation limits, (c) maintaining the aquaculture activities so it no growth up and water condition keep stable and (d) maintaining the capture endemic fish species with traditional fishing gear methods. [ABSTRACT FROM AUTHOR]

The presence and level of faecal indicator bacteria are important factors in estimating the microbiological quality of surface water and the risk of human infection upon exposure to this water. Until 2014, ISO 9308-1:2000 was available and used to enumerate faecal indicator Escherichia coli in bathing water. In 2014, this ISO was technically revised and replaced by ISO 9308-1:2014. This ISO introduced a less selective method for enumeration of E. coli that allows non-specific growth from waters containing high levels of bacteria, such as surface waters. This implies that currently there is no suitable reference membrane filtration method for the compliance monitoring of official bathing sites for E. coli according to the European Bathing Water Directive. Here, the performance characteristics of three chromogenic culture media, namely Tryptone Bile X-glucuronide (TBX) agar, Chromogenic Coliform Agar (CCA), and CHROMagar E. coli/Coliform (ECC) were investigated at 44 °C for water with varying levels of bacteria according to ISO 13843:2017. Based on performance characteristics, colony counts, and practical usage, TBX appeared the most suitable culture medium for the enumeration of E. coli in bathing water and other waters with high levels of background bacteria, such as surface water in agricultural areas and wastewater discharge points. [ABSTRACT FROM AUTHOR]

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method. [ABSTRACT FROM AUTHOR]

This study investigated the bacteriological and physicochemical quality of seawater and sediment samples collected from four sampling sites in the Gulf of Annaba (Northeastern Algeria) over a one-year period. Culture-based techniques were used to quantify and assess Fecal Indicator Bacteria (FIB) and potentially pathogenic bacteria. Additionally, various physicochemical parameters including temperature, pH, salinity, dissolved oxygen, and suspended solids were measured. The results revealed seasonal variations in the physicochemical variables, reflecting the influence of environmental conditions in the research area. The highest concentrations of FIB were observed in samples obtained from Sidi Salem and Rezgui Rachid, indicating a possible association with sewage contamination. Furthermore, the sediments collected from all sites exhibited higher levels of FIB and potentially pathogenic bacteria compared to the seawater samples, particularly during the summer and fall seasons. [ABSTRACT FROM AUTHOR]

The impacts of nucleic acid-based methods - such as PCR and sequencing - to detect and analyze indicators, genetic markers or molecular signatures of microbial faecal pollution in health-related water quality research were assessed by rigorous literature analysis. A wide range of application areas and study designs has been identified since the first application more than 30 years ago (>1100 publications). Given the consistency of methods and assessment types, we suggest defining this emerging part of science as a new discipline: genetic faecal pollution diagnostics (GFPD) in health-related microbial water quality analysis. Undoubtedly, GFPD has already revolutionized faecal pollution detection (i.e. traditional or alternative general faecal indicator/marker analysis) and microbial source tracking (i.e. host-associated faecal indicator/marker analysis), the current core applications. GFPD is also expanding to many other research areas, including infection and health risk assessment, evaluation of microbial water treatment, and support of wastewater surveillance. In addition, storage of DNA extracts allows for biobanking, which opens up new perspectives. The tools of GFPD can be combined with cultivation-based standardized faecal indicator enumeration, pathogen detection, and various environmental data types, in an integrated data analysis approach. This comprehensive meta-analysis provides the scientific status quo of this field, including trend analyses and literature statistics, outlining identified application areas, and discusses the benefits and challenges of nucleic acid-based analysis in GFPD. [ABSTRACT FROM AUTHOR]

Sanitary indicators standardly used in water quality monitoring refer to fecal microorganisms or cyanobacterial toxins, but at present they do not include fungi, despite the fact that many fungi can pose potential health hazards. The aim of study was a quantitative and qualitative assessment of yeast in waters of urban artificial recreational reservoirs in relation to microbiological and sanitary cleanliness indicators and to demonstrate that yeast could be an indicator of anthropogenic pollution. In total, 18 species of yeast were isolated, including 13 classified as clinically relevant. Tested reservoirs exceeded the standards of sanitary indicators acceptable for bathing water. Since isolated yeasts are associated with living organisms, their presence in the water of urban recreational reservoirs may be a public health concern. Therefore, a mycological analysis should be incorporated into standard water quality assessment procedures. The results obtained are very promising, but further research is still needed. [ABSTRACT FROM AUTHOR]

Human sewage contamination of waterways is a major issue in the United States and throughout the world. Models were developed for estimation of two human-associated fecal-indicator and three general fecal-indicator bacteria (HIB and FIB) using in situ optical field-sensor data for estimating concentrations and loads of HIB and FIB and the extent of sewage contamination in the Menomonee River in Milwaukee, Wisconsin. Three commercially available optical sensor platforms were installed into an unfiltered custom-designed flow-through system along with a refrigerated automatic sampler at the Menomonee River sampling location. Ten-minute optical sensor measurements were made from November 2017 to December 2018 along with the collection of 153 flow-weighted discrete water samples (samples) for HIB, FIB, dissolved organic carbon (DOC), and optical properties of water. Of those 153 samples, 119 samples were from event-runoff periods, and 34 were collected during low-flow periods. Of the 119 event-runoff samples, 43 samples were from event-runoff combined sewer overflow (CSO) influenced periods (event-CSO periods). Models included optical sensor measurements as explanatory variables with a seasonal variable as an interaction term. In some cases, separate models for event-CSO periods and non CSO-periods generally improved model performance, as compared to using all the data combined for estimates of FIB and HIB. Therefore, the CSO and non-CSO models were used in final estimations for CSO and non-CSO time periods, respectively. Estimated continuous concentrations for all bacteria markers varied over six orders of magnitude during the study period. The greatest concentrations, loads, and proportion of sewage contamination occurred during event-runoff and event-CSO periods. Comparison to water quality standards and microbial risk assessment benchmarks indicated that estimated bacteria levels exceeded recreational water quality criteria between 34 and 96% of the entire monitoring period, highlighting the benefits of high-frequency monitoring compared to traditional grab sample collection. The application of optical sensors for estimation of HIB and FIB markers provided a thorough assessment of bacterial presence and human health risk in the Menomonee River. [ABSTRACT FROM AUTHOR]

In this paper, the development of a portable, multifunctional water quality monitoring system for aquaculture that is based on IoT technology is presented. The system integrates a main control module, sensor module, Human Machine Interface (HMI) module, Wi-Fi module and power module, and is equipped with system software based on Real Time Operating System (RTOS) for scheduling tasks. The main control module collects crucial water quality information, including water temperature (WT), dissolved oxygen (DO), ammonia nitrogen (NH3-N), and pH, through the sensor module and facilitates data interaction with the HMI module. The proposed aquaculture water quality evaluation model utilizes water quality parameters as input to assign a grade based on the evaluation result. These parameters are transmitted wirelessly to the OneNet cloud platform using Wi-Fi modules, enabling users to remotely monitor the water quality through a visual interface. The system structure has been meticulously designed to accommodate both portable and fixed-point remote monitoring applications. The experimental results demonstrate that the system is accurate, stable, and costeffective, providing a reliable and efficient solution for intelligent aquaculture in small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

Agriculture encompasses both plant growing (agronomy, horticulture, and forestry) and animal husbandry. Aquaculture is the aquatic embodiment of agriculture. Lagoons are the main sources of aquaculture in various countries around the world which especially yield shrimp, finfish, macrophytes, and mollusk. Agricultural effluents are highly affecting the lagoon water quality parameters in Sri Lanka since most of the agricultural farmlands in the country are on coastal landmasses. It leads to the increased risks of lagoon water contamination. Therefore, it is essential to identify the water quality parameters affected by anthropogenic agricultural activities and establish their threshold limits for ensuring the sustainability of lagoons. This study has identified the influential lagoon water quality parameters associated with agricultural effluents using bibliographic references, tested the existing values of the most influential parameters in five Sri Lankan lagoons, and briefly discussed their fate and transport. The lagoon water quality was classified into biological, physical, and chemical parameters and studied for their importance in enhancing the water quality. The overall experimental findings on temperature, turbidity, pH, salinity, DO, BOD, COD, phosphates, nitrates, ammonia content, and faecal coliforms in water specimens suggest that the selected Sri Lankan lagoons are heavily polluted because of the accumulation over the decades from the agricultural lands which worsens even more with this salinity from the seawater when the tide comes in. This paper concludes that there is a growing need for mechanisms that can be used to monitor and apply control measures to effectively manage lagoon water quality not only for the integrity of the lagoon itself but also for the dependent ecosystems both in and out of the lagoon. [ABSTRACT FROM AUTHOR]

Fecal indicator bacteria, Escherichia coli, for primary body contact recreation (PBCR) in Oklahoma waterbodies, is defined as the geometric mean of 10 samples from the recreation season, May 1 to September 30, with an impairment threshold of 126 colony forming units (cfu) per 100 mL. However, the water quality standards provide limited guidance on spatiotemporal and environmental factors that could influence samples collected and analyzed. In this study, two stream cross sections under baseflow conditions in a central Oklahoma urban perennial stream, Spring Creek, were densely sampled to investigate temporal and spatial variability of E. coli concentrations and water quality parameters across the stream channel. Water quality parameters (specific conductivity, temperature, dissolved oxygen, pH, turbidity, and total suspended solids (TSS)), stream discharge, and bacteria samples were collected simultaneously at equal intervals across the two cross sections in the morning and afternoon during one summer day with sunny, dry, and hot weather conditions. Results indicate a significant difference between time‐of‐day samples and water quality parameters and E. coli concentrations. Strong correlations between temperature, dissolved oxygen, and time versus E. coli concentrations were observed, while location, turbidity, and TSS were not significant or correlated to measured values. Furthermore, E. coli concentrations were highly variable spatially across each stream cross section, regardless of time of day or location. Results from this study provide an initial indication that stream water quality, spatial cross section sample location, and diurnal variations may be influencing factors on bacteria concentrations. [ABSTRACT FROM AUTHOR]

Kuwait Bay's southern shore is home to several facilities used for various purposes, impacting the beaches' ecosystems and coastal environment. Furthermore, the presence of numerous stormwater outfalls on Kuwait Bay's southern shores may be one of the sources of pollutants that are discharged from these outfalls. This research aims to evaluate the microbiological quality of seawater near 5 selected stormwater outfalls located on Kuwait Bay's recreational water by measuring the levels of microbial bacteria such as fecal coliform (FC), and Escherichia Coli. (E. Coli), and fecal streptococci (FS), and water quality parameters including temperature, pH, electrical conductivity (EC), and dissolved oxygen (DO). The analytical results were compared with Kuwait Environmental Public Authority (EPA) requirements for water discharge to Kuwait Bay to assess the suitability of the beaches for recreational activities. The field results revealed the presence of mildly alkaline seawater samples, a range of fresh to saline water types, and various DO levels from very low to acceptable levels. noteworthy, the microbial indicators were extremely high and far beyond EPA guidelines, demonstrating extensive microbiological pollution at these locations. The mean FC/FS ratios of C18 had the greatest mean FC/FS ratio (16.75), signifying human contamination. C3 and C5 indicated human waste in mixed pollution with averages of (3.45 and 2.34 respectively), whereas C8 and C10 showed human contamination with average values (of 5.98, and 4.21 respectively). It is suggested that the seawater quality close to wastewater outfalls be frequently evaluated and treated using efficient onsite treatment technologies to decrease the amount of polluted wastewater discharged directly to the beaches. [ABSTRACT FROM AUTHOR]

A synthesis of statistical inference and machine learning (ML) tools has been employed to establish a comprehensive insight of a coarse data. Water components' data for 16 central distributing locations of Lahore, the capital of second most populated province of Pakistan, has been analyzed to gauge current water stature of the city. Moreover, a classification of surplus-response variables through tolerance manipulation was incorporated to debrief dimension aspect of the data. By the same token, the influence of supererogatory variables' renouncement through identification of clustering movement of constituents is inquired. The approach of building a spectrum of colluding results through application of comparable methods has been experimented. To test the propriety of each statistical method prior to its execution on a huge data, a faction of ML schemes have been proposed. The supervised learning tools pca, factoran and clusterdata were implemented to establish an elemental character of water at elected locations. A location 'LAH-13' was highlighted for containing an out of normal range Total Dissolved Solids (TDS) concentration in the water. The classification of lower and higher variability parameters carried out by Sample Mean (XBAR) control identified a set of least correlated variables pH, As, Total Coliforms and E. Coli. The analysis provided four locations LAH-06, LAH-10, LAH-13 and LAH-14 for extreme concentration propensity. An execution of factoran demonstrated that specific tolerance of independent variability '0.005' could be employed to reduce dimension of a system without loss of fundamental data information. A higher value of cophenetic coefficient, c = 0.9582 provided the validation for an accurate cluster division of similar characteristics' variables. The current approach of mutually validating ML and SA (statistical analysis) schemes will assist in preparing the groundwork for state of the art analysis (SOTA) analysis. The advantage of our approach can be examined through the fact that the related SOTA will further refine the predictive precision between two comparable methods, unlike the SOTA analysis between two random ML methods. Conclusively, this study featured the locations LAH-03, LAH-06, LAH-12, LAH-13, LAH-14 and LAH-15 with compromised water quality in the region. [ABSTRACT FROM AUTHOR]

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as "confusers". For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters. [ABSTRACT FROM AUTHOR]

Favourable water quality is the basis of pond healthy aquaculture. In order to realize the automation of monitoring and sampling, an integrated unmanned water quality monitoring and sampling boat was designed and its performance test was carried out. Considering the requirements of water quality monitoring and sampling, the hull structure was designed, and the layout of the functional areas of water quality sensor, sampling system, power supply, control box, and water sample collection bottles in the cabin was designed considering the load bearing and stability. The autonomous cruise control system was developed based on the differential twin-propellers driven model and the integration of GPS and attitude sensor. The water sampling control system and water quality monitoring data platform were integrated with Internet of Things technology. The comprehensive test results showed that the trajectory of unmanned boat was accurate, the maximum deviation of monitoring point was 1.49 m, the minimum deviation was 0.39 m, and the average deviation was 1.003 m. The water quality monitoring and sampling function was stable, and the pH value, water temperature and dissolved oxygen of water could be returned in real time. Six 500 mL water sampling bottles could collect water samples of underwater 50 cm accurately, which is in line with national standards. The results provide a low-cost and reliable implementation scheme for the whole surface water quality monitoring of aquaculture. [ABSTRACT FROM AUTHOR]

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring. [ABSTRACT FROM AUTHOR]

In Dhaka megacity (Bangladesh), lakes located in the most exclusive areas are considered crucial urban water bodies. However, these water sources are confronting a heavy load of organic and inorganic pollutants by anthropogenic activities and are being polluted continuously. In this review , 50 relevant published documents on the lake water of Dhaka megacity to highlight the overall water quality status and the potential ecological and human health risks associated with contaminations were studied. The retrieved documents were organized according to the relevant information and examined thoroughly. After a careful review, it was found that most of the lake water in Dhaka city is severely contaminated with heavy metals such as Pb, Cd, Cu, Zn, and Mn with a higher concentration of Pb and Cd than the guideline values for drinking and industrial uses. The physicochemical properties and microbial contamination with an elevated level of fecal coliforms in lake water indicate very poor water quality in all lakes. The EC and TDS in most of the lake water exceeded the standard limits with the highest EC value (4920 µS/cm) for Gulshan Lake. The indices-based water quality assessment revealed considerable heavy metal contamination in the lake's water. Few lakes are found at a minimal level of ecological risks. The assessments of health risk indicate lower non-cancer risk. Overall, it can be inferred that the lake water in the Dhaka megacity does not meet the requirements for domestic, drinking, fishing, and industrial uses. This review study will be helpful for the policymakers and environmentalists to know about the status and sources of pollution in the lake water in the megacity which in turn assist to take necessary actions to mitigate the pollution level from the studied lakes of Dhaka city, Bangladesh. [ABSTRACT FROM AUTHOR]

It is critical to monitor water quality to keep water bodies ecologically healthy and facilitate the sustainable development of Kenyir Lake. Water quality differs temporally and spatially and is affected by several factors. Typically, water quality inspection systems are cost- and labour-intensive depending on water quality indicator count and sampling frequency. Optimising the frequency and location of water quality sampling is crucial. This study focused on collecting water samples from 22 locations in Kenyir Lake during different seasons (normal, dry, and wet). The study aimed to assess the spatial and temporal variations in the water quality of Kenyir Lake based on multivariate statistical methods. In this study, the following water quality parameters were selected for analysis: temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (NH3-N). In addition, a water quality index was also calculated. GIS software was used to assess water quality data, and various multivariate statistical methods like cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were employed. The outcome shows minor spatial differences concerning Kenyir Lake; however, the temporal variations were noteworthy during this study duration. Cluster analysis divided the locations into 3 clusters with TSS being key parameter affecting the spatial differences in water quality. Stepwise discriminant analysis based on three parameters, pH, temperature, and TSS, produced the associated classification matrix that correctly estimated 69.7% of the input. NH3-N and TSS were found to be the two critical aspects that affect water quality during dry, wet, or normal climatic conditions. [ABSTRACT FROM AUTHOR]

Classification of water quality is a valuable method for reporting quality status of a water body that can help to control pollution in monitored sites. In this study water quality of rivulet Holy Kali Bein in the state of Punjab, India was analysed by using water quality index (WQI) and multivariate statistical analysis on the bases of measured water quality parameters from five sampling sites (K1-K5) during the period 2017–2018. Discriminant analysis (DA) and WQI were applied to examine spatiotemporal changes and to spot possible pollution sources. These changes in water quality parameters were analysed by using stepwise DA method. The first spatial discriminate function (DF1) accounted for 96.9%, second discriminate function (DF2) accounted 2.1%, DF3 accounted for 0.8% and DF4 accounted only 0.2% of the total spatial variance. Similarly, the first temporal DF1 accounted for 80.1% and DF2 accounted for 19.9% of total temporal variance. The water quality index (WQI) score showed good water quality at sites K1 (30.67), K2 (43.98) and K3 (51.25). The sites K4 (64.50) and K5 (62.89) showed poor water quality status. The present investigation represents the first of its type undertaken on the rivulet Holy Kali Bein and shows that the water quality of the rivulet is good at origin and when we move towards its confluence point at Harike wetland (Ramsar site) water quality becomes poor due to input of municipal wastes and agricultural activities discharge at the bank of the rivulet. This observation is supported by the calculated WQI for different sites. The work is original in nature based on the primary data collected by the authors personally. [ABSTRACT FROM AUTHOR]

Abstract—The results of a bacterioplankton study (total abundance; saprophytic, hydrocarbon oxidizing, and phenol resistant bacteria; total microbial count; and coliform bacteria) in an urbanized tributary of Lake Onego in 2015 are presented. The lower reaches of the Lososinka River flow over the territory of the city of Petrozavodsk (Karelia) and receive 14 untreated storm runoff streams from the municipal storm sewers. Differences in the temporal abundance dynamics of bacteria, which are indicators of oil pollution, the sanitary condition, and saprobity, were found between the reference and urban parts of the river. These variations are not related to the hydrological and thermal regime of the river and reflect the impact of the pollutant discharge from the urban area on the formation of the bacterial community of the river. The study revealed a significant increase in all the studied indicators of the quantitative development of bacterioplankton in the urban part of the river as compared to the reference part. The number of heterotrophic bacteria of various ecological–trophic groups decreases in the lake water by a factor of 8–16 already at a distance of 500 m from the mouth of the river due to dilution. A significant decrease in the numbers of hydrocarbon oxidizing bacteria in the river water and an increase in sanitary-indicative microorganisms are shown to have occurred over four years. Within the city limits of Petrozavodsk, the Lososinka River has been assessed as polluted with petroleum products, as well as organic substances of fecal origin and unfit for recreation based on the level of the indicator bacteria. [ABSTRACT FROM AUTHOR]

Based on the data at 29 monitoring sites in the whole basin o£ the Yujiang River and the Xunjiang River in Guigang City during 2014-2021, the single-factor evaluation method and the modified Nemerow pollution index method were used to analyze temporal and spatial distribution characteristics of water quality in the Yujiang River and the Xunjiang River basins. The results showed that the Wusi River (the Yujiang River) and the Dahuang River (the Xunjiang River) had the best water quality, while water quality of the Duchong River (the Yujiang River) was the worst, and the proportion of inferior to class V reached 93%. The second was the Shepo River (the Xunjiang River), and the proportions of inferior to class V and below class IV were 21% and 64%. Comprehensive Nemerow pollution index was used to evaluate the water quality of the Yujiang River and the Xunjiang River during 2014 -- 2021. Five tributary sections with worse water quality were the Duchong River, Xiajiangkou, the Chencun River, the Liyu River, and the Dongtang River, and NH3-N and TP exceeded the standard seriously. Among them, water quality of the Duchong River was the worst and was slightly improved during 2018 -2021, but it still exceeded the standard seriously. The analysis showed that the main source of serious eutrophication in the basin was the increase of NH3-N and TP concentrations caused by agricultural non-point source emissions. [ABSTRACT FROM AUTHOR]