Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River.

Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehensive monitoring system along the Vaigai River. The system was designed to collect real-time data on several crucial water quality parameters. The collected characteristics encompassed factors like water pollution levels, turbidity, pH readings, temperature, and total dissolved solids, offering a comprehensive view of river water quality. The monitoring system was methodically set up, with sensors strategically positioned at various locations along the river. This ensured that data collection would take place at regular intervals. The CNGRU-WQM model achieved a validation accuracy of 97.86%, surpassing the performance of other state-of-the-art approaches. Particularly noteworthy is the fact that the actual use of this system incorporates real-time warnings, which enable stakeholders to be instantly informed if water quality measurements surpass pre-set criteria. The study's contributions include its efficient river water quality monitoring system, which encompasses a variety of indicators, and its ability to significantly affect environmental conservation efforts by offering a helpful tool for informed decision-making and timely interventions.

Universal high-frequency monitoring methods of river water quality in China based on machine learning.

The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel "indirect" measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ±â€¯0.25 and 0.79 ±â€¯0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were large than pH and dissolved oxygen (DO), and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rate of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ±â€¯6.96 % and 92.33 ±â€¯6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method has high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

Spatiotemporal distribution, ecological risk assessment, and human health implications of currently used pesticide (CUP) residues in the surface water of Feni River, Bangladesh.

Spatiotemporal monitoring of pesticide residues in river water is urgently needed due to its negative environmental and human health consequences. The present study is to investigate the occurrence of multiclass pesticide residue in the surface water of the Feni River, Bangladesh, using an optimized salting-out assisted liquid-liquid microextraction (SALLME) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The optimized SALLME method was developed and validated following the SANTE/11312/2021 guidelines. A total of 42 water samples were collected and analyzed to understand the spatiotemporal distribution of azoxystrobin (AZ), buprofezin (BUP), carbofuran (CAR), pymetrozine (PYM), dimethoate (DMT), chlorantraniliprole (CLP), and difenoconazole (DFN). At four spike levels (n = 5) of 20, 40, 200, and 400 µg/L, the recovery percentages were satisfactory, ranging between 71.1 % and 107.0 % (RSD ≤13.8 %). The residues ranged from below the detection level (BDL) to 14.5 µg/L. The most frequently detected pesticide was DMT (100 %), followed by CLP (52.3809-57.1429), CAR (4.7619-14.2867), and PYM (4.7619-9.5238). However, AZ and BUP were below the detection limit in the analyzed samples of both seasons. Most pesticides and the highest concentrations were detected in March 2023, while the lowest concentrations were present in August 2023.Furthermore, ecological risk assessment based on the general-case scenario (RQm) and worst-case scenario (RQex) indicated a high (RQ > 1) risk to aquatic organisms, from the presence of PYM and CLP residue in river water. Human health risk via dietary exposure was estimated using the hazard quotient (HQ). Based on the detected residues, the HQ (<1) value indicated no significant health risk. This report provides the first record of pesticide residue occurrences scenario and their impact on the river environment of Bangladesh.

Non-targeted identification of tianeptine photodegradation products in water samples by UHPLC-QTOF MS/MS.

This study aims the characterization of several tianeptine transformation products in ultrapure water by simulated sunlight irradiation. Tianeptine was completely degraded after 106 h of exposition following pseudo-first-order kinetics (half-life time = 12.0 ± 2.4 h). Furthermore, an ultra-high-performance liquid chromatography coupled with a high-resolution quadrupole time-of-flight-mass spectrometry method was developed and fully validated taking into account different method performance parameters for the quantification of tianeptine in river water up to a concentration of 400 pg L-1. Following a non-targeted approach based on mass data-independent acquisition, eight different transformation products not previously reported in the literature were identified and accordingly elucidated, proposing a photodegradation mechanism based on the accurate tandem mass spectrometry information acquired. Irradiation experiments were replicated for a tianeptine solution prepared in a blank river water sample, resulting in the formation of the same transformation products and similar degradation kinetics. In addition, a toxicity assessment of the photoproducts was performed by in silico method, being generally all TPs of comparable toxicity to the precursor except for TP1, and showing a similar persistence in the environment except for TP2 and TP6, while TP4 was the only TP predicted as mutagenic. The developed method was applied for the analysis of four river water samples.

Chemical implication of the partition coefficient of 137Cs between the suspended and dissolved phases in natural water.

After the Fukushima Daiichi nuclear power plant accident, the terrestrial environment became severely contaminated with radiocesium. Consequently, the river and lake water in the Fukushima area exhibited high radiocesium levels, which declined subsequently. The partition coefficient of 137Cs between the suspended sediment (SS) and dissolved phases, Kd, was introduced to better understand the dynamic behavior of 137Cs in different systems. However, the Kd values in river water, ranging from 2 × 104 to 7 × 106 L kg-1, showed large spatiotemporal variability. Therefore, the factors controlling the 137Cs partition coefficient in natural water systems should be identified. Herein, we introduce a chemical model to explain the variability in 137Cs Kd in natural water systems. The chemical model includes the complexation of Cs+ with mineral and organic binding sites in SS, metal exchange reactions, and the presence of colloidal species. The application of the chemical model to natural water systems revealed that Cs+ is strongly associated with binding sites in SS, and a major chemical interaction between 137Cs and the binding sites in SS is the isotope exchange reaction between stable Cs and 137Cs, rather than metal exchange reactions with other metal ions such as potassium ions. To explain the effect of the SS concentration on Kd, the presence of colloidal 137Cs passing through a filter is significant as the dominant dissolved species of 137Cs in river water. These results suggest that a better understanding of stable Cs dissolved in natural water is important for discerning the geochemical and ecological behaviors of 137Cs in natural water.

Lithium Pollution and Its Associated Health Risks in the Largest Lithium Extraction Industrial Area in China.

Lithium (Li) is an important resource that drives sustainable mobility and renewable energy. Its demand is projected to continue to increase in the coming decades. However, the risk of Li pollution has also emerged as a global concern. Here, we investigated the pollution characteristics, sources, exposure levels, and associated health risks of Li in the Jinjiang River basin, the largest area for Li2CO3 production in China. Our results revealed the dominant role of Li extraction activities in the pollution of the river, with over 95% of dissolved Li in downstream river water being emitted from this source. Moreover, the Li concentration in aquatic plants (i.e., water hyacinth) and animals (i.e., fish) significantly increased from upstream to downstream areas, indicating a significant risk to local aquatic ecosystems. More importantly, our study found that local residents were suffering potential chronic noncarcinogenic health risks primarily from consuming contaminated water and vegetables. We also investigated the pollution characteristics of associated elements present in Li ores (e.g., Rb, Cs, Ni, and F-). By uncovering the remarkable impact of Li extraction activities on the Li content in ecosystems for the first time, our study emphasizes the importance of evaluating Li pollution from Li-related industrial activities, including mining, extraction, and recovery.

Development of an analytical method to quantify N-acyl-homoserine lactones in bacterial cultures, river water, and treated wastewater.

N-Acyl-homoserine lactones (AHL) play a major role in the communication of Gram-negative bacteria. They influence processes such as biofilm formation, swarming motility, and bioluminescence in the aquatic environment. A comprehensive analytical method was developed to elucidate the "chemical communication" in pure bacterial cultures as well as in the aquatic environment and engineered environments with biofilms. Due to the high diversity of AHLs and their low concentrations in water, a sensitive and selective LC-ESI-MS/MS method combined with solid-phase extraction was developed for 34 AHLs, optimized and validated to quantify AHLs in bacterial conditioned medium, river water, and treated wastewater. Furthermore, the developed method was optimized in terms of enrichment volume, internal standards, limits of detection, and limits of quantification in several matrices. An unanticipated variety of AHLs was detected in the culture media of Pseudomonas aeruginosa (in total 8 AHLs), Phaeobacter gallaeciensis (in total 6 AHLs), and Methylobacterium mesophilicum (in total 15 AHLs), which to our knowledge have not been described for these bacterial cultures so far. Furthermore, AHLs were detected in river water (in total 5 AHLs) and treated wastewater (in total 3 AHLs). Several detected AHLs were quantified (in total 24) using a standard addition method up to 7.3±1.0 µg/L 3-Oxo-C12-AHL (culture media of P. aeruginosa).

Integration of a Bismuth-Based Tris-Mononuclear Complex with 2D Functional Materials for Highly Efficient and Durable Aqueous Electrocatalytic Hydrogen Evolution.

The eminence of transitioning from traditional fossil fuel-based energy resources to renewable and sustainable energy sources is most evidently crucial. The potential of hydrogen as an alternative energy source has specifically focuses the electrocatalytic water splitting (EWS) as a promising technique for generating hydrogen. Development of efficient electrocatalysts to facilitate the EWS process while rationalizing the limitations of noble metal catalysts like platinum has become one of the daunting tasks. Consequently, porous functional materials such as metal complexes (MCs) and graphene oxide (GO) can act as potential catalysts for EWS. Therefore, a composite of GO and a mononuclear bismuth metal complex is synthesized through in situ facile synthesis, which is further utilized as an efficient electrocatalyst for the hydrogen evolution reaction (HER). Several potential electrocatalytic MC@GO composite (BMGO-3,5,7) materials were prepared with compositional variation of GO (3, 5, and 7 wt %). The experimental results demonstrate that the BMGO5 composite exhibits excellent HER activity with a low overpotential value of 105 mV at 10 mA cm-2 and a low Tafel slope of 44 mV dec-1 in 1 M KOH solution. Furthermore, a comprehensive investigation on the potentiality of the BMC-GO composite for hydrogen evolution from river water splitting was performed in order to address the issue of freshwater depletion. Inclusion of a mononuclear MC for facile synthesis of functional GO-based efficient electrocatalyst material is very scanty in the literature. This unique approach could assist future research endeavors toward designing efficient electrocatalysts for sustainable renewable energy generation. This is one of the first of its kind, where mononuclear MCs were utilized to develop GO-based functional composite materials for efficient electrocatalysis toward sustainable renewable energy generation.

Adsorption of antiretroviral drugs, abacavir, nevirapine, and efavirenz from river water and wastewater using exfoliated graphite: Isotherm and kinetic studies.

In this work, exfoliated graphite was used to adsorb antiretroviral drugs from river water and wastewater. The exfoliated graphite was prepared from natural graphite by intercalating it with the acids and exfoliating it at 800 °C. It was characterized using Fourier Transform Infrared Spectroscopy which showed phenolic, alcoholic, and carboxylic functional groups between 1000 cm-1 and 1700 cm-1. Energy-dispersive X-ray spectroscopy results showed carbon as the main element with splashes of oxygen. The Scanning Electron Microscopy images showed increased c-axis distance between graphene layers after intercalation, which further increased after the exfoliation. The exfoliation resulted in elongated distorted cylinders, which were confirmed by the lower density (0.0068 g/mL) of exfoliated graphite material compared to the natural graphite (0.54 g/mL). The X-ray diffraction pattern showed the characteristics of hexagonal phase graphitic structure by the diffraction plane (002) at 26.74°. Raman spectroscopy results showed the natural graphite, graphite intercalated, and exfoliated graphite contained the D, G, D', and G' peaks at about 1350 cm-1, 1570 cm-1, 2440 cm-1, and 2720 cm-1, respectively indicating that the material's crystallinity was not affected by the modification. The highest antiretroviral drugs removal (95-99%), from the water was achieved with a solution pH of 7, an adsorbent mass of 30 mg, and an adsorption time of 30 min. The kinetic model and adsorption isotherm studies showed that the experimental data fit well in pseudo-second-order kinetics and is well explained by Freundlich's adsorption isotherm. The maximum adsorption capacity of the exfoliated graphite for antiretroviral drugs ranges between 1.660 and 197.0, 1.660-232.5, and 1.650-237.7 mg/g for abacavir, nevirapine, and efavirenz, respectively. The obtained removal percentages were 100% in river water, 63-100% in influent and 70-100% in effluent wastewater unspiked samples.

Spatiotemporal variations in the occurrence of Campylobacter species in the Bloukrans and Swartkops rivers, Eastern Cape, South Africa.

An increase in the incidence of Campylobacter species in rivers raises concerns on the safety of river water for humans who get exposed to river water. This study examines the spatiotemporal dynamics of Campylobacter species in the Bloukrans and Swartkops rivers, analysing patterns of its occurrence in relation to meteorological conditions, physicochemical parameters, seasons, and sampling sites. Physico-chemical parameters and meteorological conditions were measured during water sampling from various sites along the rivers over a year, while Polymerase Chain Reaction (PCR) was utilised to detect Campylobacter genus-specific genes and selected antibiotic-resistant genes. Campylobacter was detected in 66.67% (Bloukrans River) and 58.33% (Swartkops River). In the Bloukrans River, multi-drug resistance genes cmeA (20%), cmeB (65%), cmeC (10%), were detected while and tetO was detected at 70%. In the Swartkops River, the corresponding prevalence were 28%, 66.67%, 28.56%, and 76%. The study indicates that sampling season did not significantly impact Campylobacter prevalence. However, variation in Campylobacter occurrence exists among different sites along the rivers, reflecting the influence of site proximity to potential contamination sources. The study suggests that Campylobacter infection may be endemic in South Africa, with rivers serving as potential sources of exposure to humans, thereby contributing to the epidemiology of campylobacteriosis.

Triple isotopes (δD, δ18O, δ17O) characteristic of river water and groundwater in an arid watershed from Qaidam Basin, Northwestern China: implications for hydrological cycle.

Combining traditional stable isotopes (δD and δ18O) and triple oxygen isotope (δ17O) is conducive to tracing hydrological cycle processes. The application of triple oxygen isotopes primarily focuses on precipitation, which is lacking in river water and groundwater. In this study, the spatial variations of δD, δ18O, δ17O, d-excess and 17O-excess of river water and groundwater in the Golmud River basin as well as the correlation between them were investigated to elucidate water origin and assess the evaporation influence on water bodies during flood season. Spatial changes in δD, δ18O and δ17O of river water exhibit a decrease-increase-stability pattern contrary to that observed for d-excess, 17O-excess has no distinct trend but is higher at both the source and downstream regions. The results show that river water and groundwater originate from precipitation in the mountainous area, and the meltwater in the source region also contribute to the river water with high d-excess and 17O-excess during flood season. The combination of d-excess and 17O-excess reveal that river water is also affected by evaporation and mixing of river water in tributaries. It was found that the river water is recharged in the mountains, undergoes evaporation in the upstream region and leaks into groundwater in the midstream region, which is recharged by the groundwater and evaporated again in the downstream region. This study could provide a more comprehensive understanding of the potential and value of triple oxygen isotopes in the hydrological cycle.

[Scale Effects of Landscape Pattern on Impacts of River Water Quality： A Meta-analysis].

The landscape pattern determines water pollution source and sink processes and plays an important role in regulating river water quality. Due to scale effects, studies on the relationship between landscape pattern and river water quality showed variance at different scales. However, there is still a lack of integrated study on the scale effect of landscape pattern and river water quality dynamics. This study collected 4 041 data from results of previous publications to address the characteristics of landscape pattern and river water quality dynamics at different scales and to identify the key temporal and spatial scales as well as landscape pattern indices for regulating river water quality. The results indicated that, compared to precipitation events, base flow periods, and interannual scales, the high-flow period was the key temporal scale for linking landscape pattern on river water quality. Compared to the watershed scale, the landscape pattern of buffer zones had a greater impact on river water quality. The high-flow period-buffer zone scale was the key spatiotemporal coupling scale for linking landscape pattern and river water quality. Compared to croplands, water bodies, grasslands, and the overall landscape of the watershed, the landscape pattern of forests and urban areas had a greater impact on river water quality. Fragmentation degree was the most important landscape pattern factor regulating river water quality. In river water quality management, it is important to focus on the landscape configuration of buffer zones, increase forest area, reduce patch density of forests and water bodies, and decrease the aggregation degree of urban areas.

Exploring correlations between microplastics, microorganisms, and water quality in an urban drinking water source.

The microplastic pollution in freshwater system is gradually becoming more severe, which has led to increasing attention on the distribution and potential harmful effects of microplastics. Moreover, microplastics may have an impact on river ecology and pose risks to ecosystems. Therefore, it is important to reveal this process. This study aimed to explore correlations between microplastics and free-living microorganisms in an urban drinking water source of Xiangjiang River by using multivariate statistical analysis. The results indicated that the abundance of microplastics (size 50 µm to 5 mm) in surface water and sediments ranged from 0.72 to 18.6 (mean ± SD: 7.32 ± 2.36) items L-1 and 26.3-302 (150 ± 75.6) items kg-1 dry weight (dw), respectively, suggesting potential microplastic pollution despite the protected status as a drinking water source. Higher microplastic abundances were observed in urban areas and the downstream of wastewater plants, with mostly granular shape, transparent and black color as well as 50-100 µm in size. The multivariate statistical analysis presented that the abundance of microplastics is not significantly correlated with water indicators, due to the complexity of the abundance data. The water indicators showed an obvious correlation with microplastics in colors of transparent and black, and smaller sizes of 50-100 µm. This is also true for microplastics and microorganisms in water and sediment. Proteobacteria was the main prokaryote in water and sediments, being positively correlated with 50-100 µm microplastics; while Chloroplastida was the dominated eukaryotes, presenting a weak correlation with smaller-size microplastics. Overall, when considering the properties of microplastics such as shape, color and size, the potential correlations with water indicators and microorganisms were more evident than abundance. This study provides new insights into the multivariate statistical analysis, explaining the potential correlations among microplastic properties, microorganisms and environmental factors in a river system.

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators.

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Novel biosensor for high-throughput detection of progesterone receptor-interacting endocrine disruptors.

Progesterone receptor (PR)-interacting compounds in the environment are associated with serious health hazards. However, methods for their detection in environmental samples are cumbersome. We report a sensitive activity-based biosensor for rapid and reliable screening of progesterone receptor (PR)-interacting endocrine disrupting chemicals (EDCs). The biosensor is a cell line which expresses nuclear mCherry-NF1 and a green fluorescent protein (GFP)-tagged chimera of glucocorticoid receptor (GR) N terminus fused to the ligand binding domain (LBD) of PR (GFP-GR-PR). As this LBD is shared by the PRA and PRB, the biosensor reports on the activation of both PR isoforms. This GFP-GR-PR chimera is cytoplasmic in the absence of hormone and translocates rapidly to the nucleus in response to PR agonists or antagonists in concentration- and time-dependent manner. In live cells, presence of nuclear NF1 label eliminates cell fixation and nuclear staining resulting in efficient screening. The assay can be used in screens for novel PR ligands and PR-interacting contaminants in environmental samples. A limited screen of river water samples indicated a widespread, low-level contamination with PR-interacting contaminants in all tested samples.

Evaluating acute gastroenteritis-causing pathogen reduction in wastewater and the applicability of river water for wastewater-based epidemiology in the Kathmandu Valley, Nepal.

Rapid urbanization and population growth without the implementation of proper waste management are capable of contaminating water sources, which can lead to acute gastroenteritis. This study examined the detection and reduction of five gastroenteritis-causing enteropathogens, Salmonella, Campylobacter coli, Campylobacter jejuni, Clostridium perfringens, and genogroup IV norovirus, and one respiratory pathogen, influenza A virus, in two municipal wastewater treatment plants (WWTP) using an oxidation ditch system (WWTP A; n = 20) and a stabilization pond system (WWTP B; n = 18) in the Kathmandu Valley, Nepal, collected between August 2017 and August 2019. All enteropathogens were detected in wastewater via quantitative PCR. The concentrations of the pathogens ranged from 5.7 to 7.9 log10 copies/L in WWTP A and from 4.9 to 8.1 log10 copies/L in WWTP B. The log10 reduction values of the pathogens ranged from 0.3 to 1.0 in WWTP A and from -0.1 to 0.2 in WWTP B. The association between the pathogen concentrations and the number of clinical cases in the corresponding week could not be evaluated; however, the consistent detection of pathogens in the wastewater despite low number of case reports suggested the use of wastewater-based epidemiology (WBE) for early warning of acute gastroenteritis (AGE) in the Kathmandu Valley. The pathogens were also detected in river water at approximately 7.0 log10 copies/L and exhibited no significant difference in concentration compared to wastewater, suggesting the applicability of river water for WBE of AGE. Insufficient treatment of all pathogens in the wastewater was observed, suggesting the need for full rehabilitation of the treatment plants. However, the influent may be utilized for early detection of AGE-causing pathogens in the city, whereas the river water may serve as an alternative in areas without connection to the WWTPs.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China.

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

Geostatistical appraisal of water quality, contamination, source distribution of potentially toxic elements (PTEs) in the lower stretches of Subarnarekha River (Odisha), India, and health risk assessment by Monte Carlo simulation approach.

In the present study, the status of water quality, environmental contamination in the lower stretch of Subarnarekha River with respect to potentially toxic elements (PTEs), its seasonal distribution, and ecotoxicological health impacts were investigated. For this purpose, a combination of indexing approaches and geospatial methods was used. The estimated water quality index (WQI) has shown that the river water falls under "moderate to very poor" category during the pre-monsoon and "moderate to poor" category in the post-monsoon season. The abundance of PTEs (Pb, Cu, Ni, Cd, Fe, and Cr) was on the higher side during the pre-monsoon in comparison with the post-monsoon season. The results of contamination index (Cd) and heavy metal evaluation index (HEI) explain that Subarnarekha River has low-to-moderate levels of contamination with PTEs in the majority of sampling sites. However, HPI indicated that the river water is moderate-to-highly contaminated with PTEs in both seasons. Principal component analysis (PCA) and cluster analysis (CA) reveal that anthropogenic sources are prime contributors to PTEs contamination in Subarnarekha River. The potential non-cancerous health concerns for child and adults due to Cr and Pb in some sampling stations along the river stretch have been observed. The carcinogenic risk (CR) has been established for Cr, Pb, and Cd in Subarnarekha River with Cr (> 10-4) as the most unsafe element. Monte Carlo simulation (MCS) indicates a high risk of cancer hazards due to Cr (values > 1E-04) in present as well as future for both child and adults.

Contamination of Bisphenol A, Nonylphenol, Octylphenol, and Estrone in Major Rivers of Mega Manila, Philippines.

Endocrine-disrupting compounds (EDCs) such as natural and synthetic hormones as well as phenolic industrial chemicals are considered contaminants of emerging concern in environmental waters. While EDCs carried through rivers may impact ecosystem health and productivity, these compounds are still not widely studied nor regulated. In the present study, we report the occurrence of EDCs in urban rivers in Mega Manila, namely, the Marikina, Pasig, Angat, and Pampanga Rivers that drain into Manila Bay. Endocrine-disrupting compounds may have reached these rivers through domestic wastewater and industrial effluents. Water samples from the rivers were extracted by solid-phase extraction before instrumental analysis using a liquid chromatograph coupled to a mass spectrometer. The analytical method exhibited good linear response (>99% in the concentration range of 1-50 µg/L) and low instrument detection limits (0.14-1.46 µg/L) for the hormones estrone (E1), estradiol, ethinylestradiol, progesterone, and testosterone, and the industrial chemicals bisphenol A, nonylphenol, and octylphenol. Of the hormones, E1 was detected up to 11 ng/L. Bisphenol A, nonylphenol, and octylphenol were measured up to 54, 1878, and 62 ng/L, respectively. Endocrine-disrupting compounds are not yet monitored in water bodies in the Philippines and there are no local guidelines yet on occurrence, pollution prevention, and mitigation. Environ Toxicol Chem 2024;43:259-266. © 2023 SETAC.

Antibiotic resistance determinants among carbapenemase producing bacteria isolated from wastewaters of Kathmandu, Nepal.

The emergence of carbapenem resistant bacteria (CRB) possesses a remarkable threat to the health of humans. CRB and carbapenem resistance genes (CRGs) have frequently been reported in clinical isolates from hospitals, however, their occurrence and distribution in wastewaters from various sources and river water have not been emphasized in Nepal. So, this study aimed to detect carbapenem resistant bacterial isolates and their resistance determinants in river water and different types of wastewaters. River water and both untreated and treated wastewater samples from hospitals, pharmaceutical industries, and municipal sewage were collected in summer and winter seasons. From 68 grab wastewater samples, CRB were detected only in 16 samples, which included eight hospital wastewater, and four each from untreated municipal sewage and river water. A total of 25 CRB isolates were detected with dominance of E. coli (44.0%) and K. pneumoniae (24.0%). The majority of the isolates harbored blaNDM-1 (76.0%), followed by blaOXA (36.0%) and blaKPC (20.0%) genes. Hospital wastewater majorly contributed to the presence of blaNDM-1, blaKPC, and blaOXA along with intI1 genes compared to river water and untreated municipal sewage, especially during the winter season. However, CRB were not detected in treated effluents of hospitals and municipal sewage, and both influents and effluents from pharmaceutical industries. The combined presence of each blaNDM-1 & blaOXA and blaKPC & blaOXA occurred in 16.0% of the bacterial isolates. The increased minimum inhibitory concentration (MIC) of meropenem was significantly associated with the presence of CRGs. The results of this study highlight the significance of carbapenem resistance in bacteria isolated from wastewater and river water, and underscore the necessity for efficient monitoring and control strategies to prevent the dispersion of carbapenem resistance in the environment and its potential consequences on human health.