Water Quality Monitoring Programs Research Articles

Analysing the interlinkage between diarrhoea and drinking water faecal contamination in India

ABSTRACT Diarrhoea is one of the major waterborne diseases spread through the faecal–oral route causing over 10 million cases and over 1,000 deaths in India. This study critically evaluates the interlinkage between bacteriological water quality, i.e. faecal coliforms and diarrhoea cases for the three pre-pandemic years 2017, 2018 and 2019 based on multiple sources. With around 17% of households tap water connectivity as of August 2019, the majority of the Indian population depends on raw groundwater (GW) and surface water sources. For this, faecal coliform (FC) levels in surface and GW have been mapped at district levels using data from India's National Water Quality Monitoring Programme. Health Management Information System's data on diarrhoea have been used to understand the monthly and district-wise variation of diarrhoea. The trends of FC, diarrhoea inpatient cases, and diarrhoea inpatient rates have been discussed. The analysis showed issues associated with the reliability and usefulness of these datasets with 43% of total India districts with no reported FC values for the study period. This study reveals a clear gap in the interlinkage between diarrhoea and bacteriological water quality with the unavailability of granular water quality data as a major challenge.

Occurrence and risk assessment of pesticides and their transformation products related to olive groves in surface waters of the Guadalquivir river basin

Pesticides are considered one of the main sources of contamination of surface waters, especially in rural areas highly influenced by traditional agricultural practices. The objective of this work was to evaluate the impact caused by pesticides and their transformation products (TPs) related to olive groves in surface waters with strong agricultural pressure. 11 streams were monitored during four sampling campaigns over 2 years. A solid-phase extraction, followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) analysis was used in the quantitative target approach, with more than 70 validated compounds. Target method was combined with a suspect screening strategy involving more than 500 pesticides and TPs, using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) to identify additional pesticides and TPs out of the scope of analysis. A total of 43 different compounds were detected with the target method. The herbicide MCPA was present in all samples and at the highest concentration (1260 ng L−1), followed by the fungicide carbendazim (1110 ng L−1), and the herbicide chlorotoluron (706 ng L−1). The suspect screening strategy revealed the presence of 7 compounds out of the target analysis (1 pesticide and 6 TPs). 6 analytes were confirmed with the analytical standards. Semi-quantification results revealed that TPs exhibited higher concentrations than their corresponding parent compounds, indicating higher persistency. Some small streams showed a comparable number of pesticides and concentrations to the most polluted large river. The determined pesticide and TPs concentrations represented an estimated environmental hazard in almost all sampling sites under study. This work underscores the importance of including pesticide TPs and small streams impacted by extensive agricultural activities in water quality monitoring programs.

Bacteriological Evaluation of Surface and Groundwater used for Domestic Purposes in Ibadan

The right to safe and clean drinking water is a human right, unfortunately many communities rely on unsafe and contaminated water for drinking and domestic purposes. Through improper sewage and hospital waste disposal, pathogenic and antibiotic resistant bacteria have found their way to natural water sources used by humans. In this study we aim to profile pathogenic bacteria and resistant Escherichia coli (E. coli) from surface and ground water sources in three local government areas of Oyo state. 30 water samples were collected randomly from the selected local government areas. Bacteriological, biochemical and antibiotic-resistant analysis was carried out on the isolated bacteria from the water samples. E. coli (28.30%) and Kleb pneumoniae (23.77%) were the predominant bacteria isolates, while Shigella (16.22%) and Salmonella (16.22%) also had substantial percentages. Nineteen E. coli was isolated and identified, with high antibiotic resistance rates observed in nalidixic Acid (100%), augmentin (100%), ampiclox (94.74%) and third-generation cephalosporins. Conversely, moderate levels of resistance were observed in nitrofurantoin (55.55%), cefepime (57.89%), and second-generation fluoroquinolones. This study reveals substantial pathogenic bacteria and antibiotic resistant E. coli in water samples which endangers the health of communities’ dependent on these water sources and exposes the inhabitants to antibiotic-resistant organisms from contaminated water used for domestic purposes. It is crucial to put in place water treatment measures and quality monitoring programmes in order to guarantee that the populace has access to clean and safe drinking water.

Accounting for water quality—A framework for agricultural water use

Driven by increasing water demand, scarcity concerns, and climate change impacts, numerous countries prioritize solutions for enhanced water use efficiency. However, these solutions often focus primarily on managing water quantities to improve water productivity in agriculture, urban, and industrial sectors. Effective and sustainable water use, however, requires monitoring and management of both water quantity and quality. Traditionally, water quantity and water quality have been managed separately, often by different government agencies with different missions and limited interaction. Ensuring sufficient water quantity for agriculture and food production often takes precedence over managing water quality. Water accounting, as a tool for allocating and managing water quantity is now widely accepted and numerous examples of successful implementation exist worldwide. However, the concept of incorporating water quality into water accounting has not yet been widely promoted. Measuring both quantity and quality in the same water bodies is a fundamental principle of assessment of impacts on water quality through the determination of loads. The load is the amount of a given substance or pollutant for a given period of time. Using the key steps necessary for the development of a water quality monitoring and assessment programme, a framework has been developed that can be applied to water accounting projects using typical water accounting applications. Two examples of potential applications are used to consider the technical, institutional, and financial requirements. Implementing a framework for incorporating water quality monitoring and assessment into water accounting should contribute substantially to the need for more water quality data at global scale. Such data are required to facilitate achievement of Sustainable Development Goal 6 “Ensure availability and sustainable management of water and sanitation for all” through more efficient water resources management and greater awareness of water quality impacts in the agricultural water use sector.

Phytoplankton functional dynamics in relation to some physicochemical parameters in Lake Kuriftu (Oromia, Ethiopia)

The water quality of Lake Kuriftu is deteriorating, and monitoring programs are almost nonexistent in Ethiopia. Phytoplanktons are good indicators of environmental changes due to their fast response to a wide range of stressors. The phytoplankton functional groups (FGs) can be used as water quality indicators. This study aimed to assess the dynamics of phytoplankton functional groups (FGs) in relation to some physicochemical water quality parameters measured at three sites in Lake Kuriftu. Samples for the analysis of biological and physicochemical parameters were collected monthly, from December 2020 to April 2021. The results of the physicochemical parameters revealed that Lake Kuriftu is turbid (21.37 to 59.86 NTU), has a thin euphotic depth (ZSD0.304 to 0.548 m), and is eutrophic. Ninety-seven (97) phytoplankton taxa were identified and classified into 16 Reynolds Functional Groups (RFGs), of which six (J, MP, X1, Lo, S1, and F) were represented by more than six species. FGs S1, SN, Lo, and Lm, which often form Harmful Algal Blooms, contributed together about 33.54 mg L−1, or 87.84 %, of the total biomass recorded for Lake Kuriftu, demonstrating the water quality of the lake is deteriorating. Among the FGs, the most dominant (in terms of biomass) was S1, followed by J at all sites and in all sampling months. The results of redundancy analysis (RDA) revealed that the main driving factors of phytoplankton FG biomass and composition were TP, ZSD, TN, Salinity, and ammonia. Among these, TP and salinity impacted the FGs negatively, while TN and ammonia influenced them positively. Thus, the physicochemical parameters have a significant impact on the distribution of phytoplankton FGs, suggesting that the use of FGs for water quality monitoring programs is useful. To promote wise use and conservation of the lake ecosystem, more intensive study should be carried out on the biological and physicochemical aspects of the lake, as it is part of a tourist attraction area and a source of income for local inhabitants.

Linking Clusters of Micropollutants in Surface Water to Emission Sources, Environmental Conditions, and Substance Properties

Water quality monitoring programs yield a wealth of data. It is often unclear why a certain substance occurs in higher concentrations at a certain location or time. In this study, substances were considered in clusters with co-varying concentrations rather than in isolation. A total of 196 substance clusters at 19 monitoring sites in the rivers Rhine and Meuse were identified. A total of nine clusters were found repeatedly with a similar composition at different monitoring sites. Several environmental conditions and substance properties could be linked to clusters. In addition, overlap with reference substance lists was determined. These lists group multiple substances according to emission sources, substance types, or type of use. The reference substance lists revealed that Rhine and Meuse are similarly affected. The nine ‘repeating clusters’ were analyzed in more detail to identify drivers. For instance, a repeating cluster with herbicides was specifically linked to high temperatures and a high number of hours in the sun per day, e.g., summer conditions. A cluster containing polychlorinated biphenyls, identified as persistent and with a high tendency to bind organic matter, was linked to high river discharge and attributed to a potential release from sediment resuspension. Not all substances could be clustered, because their concentration did not structurally vary in the same way as other substances. The presented explorative cluster analyses, along with the obtained relations with substance properties, local environmental conditions, and reference substance lists, may facilitate the reconstruction of the processes that lead to the observed variation in concentrations. This knowledge can subsequently be used by water managers to improve water quality.

Water Quality and Dissolved Organic Carbon Content in Agricultural Streams: Northern Nile Delta Region, Egypt

Sustainable development goals (SDGs) 2 (zero hunger), 6 (clean water and sanitation), and 15 (life on land) are related to the human-water-soil nexus. Soil organic carbon and nutrients can be removed and transported to waterways through runoff and drainage. The main goals of this study are to quantify the water quality for irrigation and assess the dissolved organic carbon (DOC) contents in streams in the northern Nile Delta, Egypt. A 4-year water quality monitoring program is accomplished by collecting 35 irrigation and drainage water samples per year from the study area. The measured water quality parameters are as follows: salinity, pH, Na, SAR, Cl, and NO3–N. In addition, the DOC content is accessed. The salinity hazard ranged from moderate, for most irrigation samples, to high and very high for drainage samples. All collected water samples have low to medium sodium hazards. Results indicate that average DOC contents in irrigation canals are 2.32 and 2.93 mg L−1 for the summer and winter, respectively. The respective means of DOC concentration in drainage canals for the summer and winter seasons are around 3.96 and 5.09 mg L−1. This study revealed significant differences in EC, pH, Na, Cl, and SAR, as water quality parameters, between irrigation and drainage canals. Additionally, the studied agroecosystem has seasonal variability in DOC concentration in irrigation and drainage canals between summer and winter. Overall, reusing drainage water for irrigation in the study area requires the selection of suitable crops and site-specific management.

Can citizen science inform science? Evaluating the results of the Bellingen Riverwatch citizen science program and a complimentary government monitoring program

In 2015, the sudden decline in the only known population of Myuchelys georgesi (the Bellinger River snapping turtle) triggered a strong community response, and a link between turtle mortality and poor water quality in the Bellinger River was suggested. A multi-agency investigation later attributed the mortalities of M. georgesi to a novel virus (the Bellinger River virus) and not a direct effect of poor water quality. However, a lack of consistent water quality or river health data in the catchment limited the research of factors that may have heightened susceptibility to the virus or exacerbated its symptoms. Community consultation identified strong connections with the riverine environment and highlighted the cultural, social, economic, and environmental values of the Bellinger River catchment. In 2017 OzGREEN, a not-for-profit environmental education charity based in Bellingen, built upon their existing citizen science water quality monitoring program in collaboration with the Saving our Species (SoS) team in the New South Wales (NSW) Department of Planning and Environment (DPE), who provided funding and equipment and solicited the involvement of NSW Waterwatch, Western Sydney University and Taronga Zoo. Now known as Bellingen Riverwatch (Riverwatch), the program has become a long-term citizen science program that aims to assist the recovery of M. georgesi, now a critically endangered species, through the delivery of monthly water quality data covering the Bellinger River and its tributaries. SoS also engaged the DPE Estuaries and Catchments Team to commence the Bellinger River Health Program (BRHP), focusing on water quality and aquatic macroinvertebrates to assess river health, with the aim of providing scientifically rigorous data to support the management and recovery of M. georgesi. This case study compares and evaluates the Riverwatch citizen science and the BRHP professional science, examining methods and results to compare the accuracy of the citizen science data and assess its reliability for informing ongoing river management. The results demonstrate that Bellingen Riverwatch is a well-managed citizen science program and generally provides valid, accurate, and representative results that can be confidently used to enhance the spatial and temporal coverage of the professional science monitoring program.

Assessing the Performance of a Citizen Science Based Water Quality Monitoring Program for Nitrates Using Test Strips Implemented in the Medjerda Hydrosystem in Northern Tunisia

Recent technological progress in water management of hydrosystems has been made to deploy efficient and effective water quality monitoring systems (WQMS). Among these, a citizen science (CS)-based water quality monitoring (WQM) program using test strips is considered as a smart tool that may aid in the production of reliable, continuous, and comprehensive data on the water quality resources of hydrosystems over a broad range of spatial and temporal scales. In this case study, the objective is to evaluate the performance of a CS-based WQM for nitrates assessment using test water quality strips for the Medjerda watershed in Northern Tunisia. Overall, 137 samples were collected from 24 sampling sites and were analyzed by 33 participants. Citizens involved in the program were regrouped in five citizen types according to their socio-economic characteristics. Statistical tests, analysis of variance (ANOVA) and multiple correspondence analyses (MCA) were achieved to survey the goodness of fit of CS as a contribution to data collection in terms of the socio-economic profile of the participant. The results show that this tool could be reliable for obtaining the levels of nitrate in water samples. Water quality test trips can conveniently be used by citizens for WQM of nitrates when they are rigorously following the manufacturer’s instructions. Additional efforts in communication and training could help to improve the performance of this CS-WQM program for nitrate in the Medjerda watershed.

A Coherent Approach of Indexing Methods to Assess the Water Quality of River Bodies in Arrah, Bihar, India

Different river bodies are diverse sustaining systems that provide freshwater to rapidly growing needs at the domestic, agricultural and industrial levels of the people of India. The river bodies need periodical examination for conservation and rejuvenation because they are currently under stress due to pollution and climate change. Arrah is a district of Bihar, India and occupies an area of 2395 km2 for agriculture as the predominant activity. It is an attempt to assess the water quality of River Ganga, Sone and Gangi in this area by combining the overall pollution index (OPI), comprehensive pollution index (CPI) and water quality index (WQI) from November 2021 to October 2023 from 648 water samples and nine sampling sites to establish a relationship between any change in water quality and their sources. We assessed eleven water quality parameters following standard methods for winter, summer and monsoon seasons. Observations showed that dissolved oxygen, total alkalinity, fluoride and rarely biochemical oxygen demand exceeded Bureau of Indian Standards (BIS 2012) prescribed limits and indicated deterioration in the water quality of these river bodies. Two-way ANOVA suggested that the water quality parameters of these river bodies showed significant differences (p<0.05). We used Numerical equations to transform the concentration values into pollution indices. Values of OPI (1.70 to 2.40) and CPI (0.65 to 0.90) indicate acceptable/ slightly polluted/moderately polluted water in these rivers. Values of WQI of these river bodies ranged from 182.0-192.1, 220.1-242.2 and 190.7-238.9 during winter, summer and monsoon seasons, indicating poor/very-poor water quality. The observation shows that the causes of the declining water quality in these water bodies are anthropogenic activity, agricultural waste runoff and entry of untreated sewage. To reduce the time-consuming and expensive water quality monitoring and assessment programmes for these river bodies, the water quality of these water bodies felt the need to adopt proper management policies and conservation efforts.

Multidrug-resistant pathogens contaminate river water used in irrigation in disenfranchised communities

The contamination of fresh surface waters poses a significant burden on human health and prosperity, especially in marginalized communities with limited resources and inadequate infrastructure. Here, we performed in-depth genomic analyses of multidrug-resistant bacteria (MDR-B) isolated from Al-Oueik river water that is used for irrigation of agricultural fields in a disenfranchised area that also hosts a makeshift Syrian refugee camp. A composite freshwater sample was filtered. Faecal coliforms were counted and extended spectrum cephalosporins and/or ertapenem resistant bacteria were screened. Isolates were identified using MALDI-TOF-MS and analysed using whole-genome sequencing (WGS) to identify the resistome, sequence types, plasmid types, and virulence genes. Approximately 106 CFU/100 mL of faecal coliforms were detected in the water. Four drug-resistant Gram-negative bacteria were identified, namely Escherichia coli, Klebsiella pneumoniae, Enterobacter hormaechei, and Pseudomonas otitidis. Notably, the E. coli isolate harboured blaNDM-5 and a YRIN-inserted PBP3, representing an emerging public health challenge. The K. pneumoniae isolate carried blaSHV-187 as well as mutations in the gene encoding the OmpK37 porin. Enterobacter hormaechei and P. otitidis harboured blaACT-16 and blaPOM-1, respectively. This report provides comprehensive genomic analyses of MDR-B in irrigation water in Lebanon. Our results further support that irrigation water contaminated with faecal material can be a reservoir of important MDR-B, which can spread to adjacent agricultural fields and other water bodies, posing both public health and food safety issues. Therefore, there is an urgent need to implement effective water quality monitoring and management programs to control the proliferation of antibiotic-resistant pathogens in irrigation water in Lebanon.

Assessment of agricultural water protection strategies at a catchment scale: case of Finland

For decades, water quality monitoring programmes, agri-environment schemes and environmental permits for animal farms have targeted a decrease in agricultural loads. These have been supplemented with more recent policies related to nutrient recycling and a circular economy. However, eutrophication of surface water bodies remains a widespread problem. In this paper, we focus on the nutrient loading and recycling in the catchment of Eurajoki River, Finland. We statistically examine the generation of nutrient loading and its variation on a sub-catchment scale. We extend this examination to the factors behind the loading and estimate the impact of nutrient recycling on it. Our analysis reveals shortcomings in the availability and collection of data on the agricultural nutrient loadings and the parcel-specific data on the presence and application of manure and fertilisers. Nine sub-catchments were responsible for roughly half of the TP loading from the 46 sub-catchments studied. To reveal such hot spots and to better target agricultural water protection measures, we recommend short-term water quality sampling campaigns. Judging from our data, the rate of manure recycling is not high enough to reduce the regional P surplus to manageable levels. Therefore, we suggest facilitated collaboration between animal and crop farms to decrease the nutrient surplus. We also propose setting up a national database containing parcel-specific information, for example, on soil P content, manure and fertiliser application, agricultural practices and the presence of acid sulphate soils. We also suggest an independent soil fertility sampling to provide valid baseline soil P data for field parcels.

Monitoring Source Water Quality Adds Value at Loveland Water and Power

Key TakeawaysSource water quality monitoring programs benefit from sampling efforts that address data needs on different spatial and temporal scales.The cost of monitoring programs is more than offset by real savings, avoided costs, and increased support for decision makers.Loveland Water and Power has derived diverse and sometimes unexpected benefits from its monitoring program.

Comprehensive study on the potential environmental risk of temporal antibiotic usage through wastewater discharges

Antibiotic residues can reach aquatic ecosystems through urban wastewater discharges, posing an ecotoxicological risk for aquatic organisms and favoring the development of bacterial resistance. To assess the emission rate and hazardousness of these compounds, it is important to carry out periodic chemical monitoring campaigns that provide information regarding the actual performance of wastewater treatment plants (WWTPs) and the potential impact of the treated wastewater in the aquatic environment. In this study, 18 of the most widely consumed antibiotics in Spain were determined by liquid chromatography-tandem mass spectrometry in both influent (IWW) and effluent wastewater (EWW) samples collected over four seasons along 2021–2022. Eleven antibiotics were detected in EWW with azithromycin, ciprofloxacin and levofloxacin showing the highest concentration levels (around 2 μg L−1 of azithromycin and 0.4 μg L−1 of quinolone compounds). Data showed that only 4 out of the 11 compounds were removed by more than 50 % in the WWTP, with sulfamethoxazole standing out with an average removal efficiency >80 %. The risk that treated water could pose to the aquatic environment was also assessed, with 6 compounds indicating a potential environmental risk by exceeding established ecotoxicological and resistance thresholds. Based on the risk assessment, the WWTP removal efficiency required to reduce such risk for antibiotics was estimated. In addition, pooled wastewater samples were screened by LC coupled to high resolution mass spectrometry with ion mobility separation, searching for metabolites and transformation products of the antibiotics investigated to widen future research. Studies like this are crucial to map the impact of antibiotic pollution and to provide the basis for designing water quality and risk prevention monitoring programs.

Proposal for priority emerging pollutants in the Nakdong river, Korea: Application of EU watch list mechanisms

The Nakdong River, the longest in Korea, has received numerous pollutants from heavily industrialized and densely populated areas while being used as a drinking water source. A number of research have reported occurrences of emerging pollutants (EPs) in the river. The results requested efficient monitoring and systematic management strategies such as EU watch list under Water Framework Directive. The aim of this study is to propose a watch list through preliminary monitoring of the river and risk-based prioritization approach. As candidates for monitoring target, 632 substances were selected based on literature and database searches. Among them, 175 substances were subjected to target screening method whereas 457 were evaluated via suspect screening. A risk-based prioritization was applied to substances quantified through target screening based on concentrations, and a scoring-based prioritization was applied to substances tentatively identified through suspect screening. Sampling campaigns (n = 12) were conducted from October 2020 to September 2021, at 8 sampling sites along the river. As a result, 130 target substances were quantified above the LOQ. Among the 21 substances whose priority score was assigned through risk-based prioritization, telmisartan and iprobenfos were identified with very high environmental risk while candesartan, TBEP, imidacloprid, azithromycin and clotrimazole were classified with high or intermediate risk. As result of the scoring system for 39 tentatively identified substances, 6 substances (benzophenone, caprolactam, metolachlor oxanilic acid, heptaethylene glycol, octaethylene glycol and pentaethylene glycol), which were then confirmed with reference standards, showed a potential environmental risk. Those substances prioritized through target and suspect screening followed by scoring systems can be a subset for the watch list and potential targets for nationwide water quality monitoring program in the future.

Species versus generic identifications in water-quality monitoring and ecological studies of Trichoptera in North America: Is this question still unresolved after half a century?

Species-level identifications of the larval stages of caddisflies are available for only a limited number of taxa that are used currently in water-quality monitoring programs. This has been a long-lamented condition, but the proportion of species identifications available for aquatic forms that are used in these efforts has increased little over the past half-century. This is despite repeated and documented advantages that species-level identifications add to monitoring studies. Approaches to examining this question have ranged from anecdotal accounts of pollution tolerance among species within a genus to detailed analyses comparing information available through different hierarchical levels of taxonomy, from species to family. Justifications for not using species-level taxonomic resolution have ranged from financial considerations to suggestions that higher levels are equally as valid in showing trends as are species-level identifications. However, the evidence justifying the use of higher levels of taxonomy is lacking and more evidence favors the value of species-level identifications. Genetic techniques offer the promise of more larval-adult associations, more useful identification keys, and improved biological monitoring.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis.

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data.

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. 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). This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Development of a Machine Learning Model to Estimate the Biotic Ligand Model-Based Predicted No-Effect Concentrations for Copper in Freshwater.

The copper (Cu) biotic ligand model (BLM) has been used for ecological risk assessment by taking into account the bioavailability of Cu in freshwater. The Cu BLM requires data for many water chemistry variables, such as pH, major cations, and dissolved organic carbon, which can be difficult to obtain from water quality monitoring programs. To develop an optimized predicted no-effect concentration (PNEC) estimation model based on an available monitoring dataset, we proposed an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate for the major cations and alkalinity. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the Cu PNECs compared with the existing tools for the following four test datasets: Korean, United States, Swedish, and Belgian freshwaters. Consequently, it is expected that Cu BLM-based risk assessment can be applied to various monitoring datasets, and that the most applicable model among the three different types of DNN models could be selected according to data availability for a given monitoring database. Environ Toxicol Chem 2023;00:1-13. © 2023 SETAC.

Scientific substantiation to updating drinking water quality monitoring programs

Introduction. The results of systematic drinking water quality laboratory tests are the most informative sources. Recommendations on the managing of drinking water quality monitoring were developed in 2020. 
 The purpose of the study was to substantiate methodological approaches to updating drinking water quality monitoring programs.
 Materials and methods. The results of drinking water quality laboratory tests in the Murmansk region, conducted in 2021 in accordance with the monitoring program, were used. The results were analyzed for compliance with the recommendations of the drinking water quality monitoring program. An assessment of the compliance of the results of laboratory studies with hygienic standards and public health risk.
 Results. In Monchegorsk, the drinking water quality was studied at nine control points according to sanitary-chemical, microbiological, and parasitological indicators. The multiplicity of studies per year ranged from 1 to 12 studies. The quality of drinking water in Monchegorsk corresponded to hygienic standards, with the exception of single excesses in the content of chlorine residual free and colour.
 Limitations. When conducting the study, the results of the monitoring were used only for 2021, the results of drinking water quality production control were not taken into account.
 Conclusion. To carry out an objective drinking water quality assessment, it is necessary to update the existing programs of monitoring its quality in the settlements of the Murmansk region carried out within the framework of the social hygienic monitoring (SHM). The authors propose an algorithm for updating monitoring programs