Diversidad de ninfas de Leptohyphidae (Ephemeroptera) en el río Quenane-Quenanito, piedemonte llanero colombiano / Diversity of Leptohyphidae (Ephemeroptera) nymphs in the Quenane-Quenanito river, Colombian plain foothills

Resumen Introducción: Varias presiones antrópicas sufren los ecosistemas acuáticos del piedemonte llanero en Colombia. La respuesta a estresores ambientales aún se desconoce en organismos bioindicadores como Leptohyphidae. Objetivo: Determinar la diversidad de ninfas de Leptohyphidae del río Quenane-Quenanito, en dos periodos hidrológicos contrastantes y su relación con algunas variables fisicoquímicas. Métodos: En diciembre (2014) y febrero (2015) se recolectaron organismos con red Surber en seis estaciones a lo largo del río. Se analizó la diversidad alfa y beta y se aplicó análisis de redundancia y modelos lineales generalizados con el fin de establecer la relación entre los taxones y las variables ambientales. Resultados: Se identificaron 369 organismos pertenecientes a cuatro géneros (Amanahyphes, Traverhyphes, Tricorythopsis y Tricorythodes), dos especies y ocho morfoespecies. Se reporta por primera vez para el departamento del Meta Amanahyphes saguassu. Se registró la mayor diversidad de ninfas en la transición a la sequía y la mayor abundancia en sequía. La diversidad beta señaló que la configuración del ensamblaje cambia a nivel espacial y temporal. Conclusiones: Los organismos de Leptohyphidae prefieren hábitats de corrientes, particularmente en el periodo de sequía, donde hallan alimento (hojarasca, detritos) y refugio para establecerse exitosamente; actividades antrópicas como la urbanización afectan notablemente la diversidad. La alta diversidad registrada en este pequeño río de piedemonte llanero refleja la necesidad de incrementar este tipo de trabajos y esfuerzos de recolección de material de estudio en la región.

Abstract Introduction: Various anthropic pressures affect the aquatic ecosystems of the foothills of Colombia. The response to environmental stressors is still unknown in bioindicator organisms such as Leptohyphidae. Objective: To determine the diversity of Leptohyphidae nymphs of the Quenane-Quenanito river, in two contrasting hydrological periods and its relationship with some physicochemical variables. Methods: In December (2014) and February (2015), organisms were collected with a Surber net at six stations along the current. Alpha and beta diversity was analyzed and redundancy analysis and generalized linear model were applied to establish the relationship between taxa and environmental variables. Results: Were identified 369 organisms belonging to four genera (Amanahyphes, Traverhyphes, Tricorythopsis, and Tricorythodes), two species, and eight morphospecies. Amanahyphes saguassu is reported for the first time for the Meta department. High diversity of Leptohyphidae nymphs was recorded in the transition to drought season and greater abundance in drought. Beta diversity indicated that the configuration of the assemblage changes spatially and temporally. Conclusions: Leptohyphidae organisms prefer fast habitats, particularly in the dry period where they find food (leaf litter, detritus) and shelter to establish themselves successfully; anthropic activities such as urbanization notably affect diversity. The high diversity recorded in this small river in the foothills of the plains reflects the need to increase this type of works and collection efforts of study material in the region.

Groundwater geochemistry using modified integrated water quality index (IWQI) and health indices with special emphasis on nitrates and heavy metals in southern parts of Tirupati, South India.

Groundwater is particularly vulnerable to pollution in places with a high population density and extensive human usage of the land, especially in southern parts of Tirupati, India. To assess this, 60 bore-well samples were obtained and assessed for physical specifications, ion chemistry, and heavy metals during the pre- and post-monsoon seasons 2022. The current investigation employed a modified integrated water quality index (IWQI), conventional graphical and human health risk assessment (HHRA) of nitrates and heavy metals to know the groundwater chemistry and its detrimental health effects on humans. The major ions were analyzed using American public health association (APHA) standards, whereas heavy metals were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). Additionally, pH Redox Equilibrium and C (PHREEQC), a geochemical model written in C programming language was employed to determine the saturation indices of mineral facies and ArcGIS 10.3.1 was used for spatial distribution patterns of IWQI. Then, the HHRA of nitrates and heavy metals was performed using United States environmental protection agency (US EPA) guidelines. The noteworthy outcomes include elevated levels of Ca2+, Mg2+, Cl-, NO3-, Cu, Fe, Mn, and Pb, demonstrating rock-water interaction, silicate weathering, Ca-Mg-HCO3 followed by mixed water facies, dissolution/precipitation, reverse exchange, and anthropogenic contamination are the major controlling processes in groundwater of southern Tirupati, India. The modified IWQI reveals that most groundwater samples (38%) fall under the bad quality class, with (47%) in the poor quality class and only (15%) classified as medium quality class in pre- and post-monsoon seasons. Elevated IWQI were observed in all directions except in the east, which is suitable for drinking. Moreover, the major hazard quotient (HQ) and hazard index (HI) for nitrates (NO3-) and heavy metals like copper (Cu), iron (Fe), manganese (Mn), lead (Pb) and zinc (Zn) are above the critical value of 1, revealing potential risk to humans, especially infants, followed by children and adults, entailing the instantaneous implementation of proper remedial measures and stringent policies to reduce the risk associated with groundwater pollution in the southern parts of Tirupati.

Hydro-geochemistry and age-dependent health risk assessment of nitrate, nitrite, and fluoride in health facilities water: a multivariate analysis.

Groundwater from alluvial fan plains is the prevailing water source, especially for arid/semiarid regions, but its contamination poses substantial risks to water supply and public health. The recent study aims to assess the hydro-geochemistry, distribution, and potential health risks of NO3-, NO2-, and F- concentrations in the groundwater of previously unexplored health facilities in District Vehari, Punjab, Pakistan. In total, 75 groundwater samples were evaluated for NO3-, NO2-, and F- levels as well as pH, EC, TDS, CO32-, HCO3-, Cl-, Na+, Fe, K+, Ca2+, Mg2+, taste, odor, color, and turbidity. The Durav graph shows that the water type is Na-HCO3-Ca, with Na and HCO3 dominant, weak acids > strong acids, and alkaline ions > alkalis. Results revealed that drinking water samples (21.73% and 20%) taken from Tehsil Mailsi, and the Basic Health Unit (BHU) exceeded the WHO standard (1.5 mg/L) for F- concentration, respectively. Moreover, the mean chronic daily intake (CDI) of F- was 0.044, 0.018, and 0.02 mg/kg/day in children, men, and women, respectively. Similarly, the average CDI of NO3- was 0.113, 0.046, and 0.050 in children, men, and women, respectively, and the respective values of NO2- were 0.004, 0.001, and 0.001. The NO2- shows a significant range of hazard quotient (HQ) (0.0-1.172) in children. The range of HQ for F- was 0.0-3.114, 0.0-1.290, and 0.0-1.389 in children, men, and women, respectively. Additionally, the health risks analysis revealed an HQ > 1.0 for children in groundwater, indicating a potential carcinogenic risk from the F-. Pearson correlation and PCA analysis found a significant positive correlation (0.8) between NO3- and NO2- and a negative correlation (0.3) between F- and HCO3-. These findings highlight the need for groundwater treatment in healthcare facilities prior to water consumption. Enforcing international and national drinking water standards in healthcare units is vital to strengthening services and providing equitable access to safe drinking water. Legislative and efficient water management measures must be taken for the protection of public health.

Passive sampling of microbes in various water sources: A systematic review.

Traditional methods for monitoring pathogens in environmental waters have numerous drawbacks. Sampling approaches that are low-cost and time efficient that can capture temporal variation in microbial contamination are needed. Passive sampling of aquatic environments has shown promise as an alternative water monitoring technique for waterborne pathogens and microbial contaminants. The present systematic review aimed to compile and synthesize existing literature on the use of passive samplers for the monitoring of microbes in different water sources and identify research gaps. The review summarizes current knowledge on materials used for detection, deployment durations, analytical methods, quantification as well as benefits and limitations of passive sampling. This review found that electronegative nitrocellulose membrane filters are effective for both detection and quantification of viruses in wastewater, while gauze passive samplers have been effective for detecting bacterial targets in wastewater. There is a large knowledge gap in the use of passive samplers in a quantitative manner, especially for the back-calculation of water-column microbial concentrations or for correlation to outcomes of interest (e.g. prevalence rates). Further, there is very limited attention paid to the use of membrane filters for the monitoring of bacteria in any water source as well as a lack of studies utilizing passive sampling approaches for protozoa.

Microbial responses on upwelling signature across reversal wind pattern in tropical coastal environment off Mumbai, India.

Upwelling promotes marine productivity through water column mixing. The process disturbs the ecosystem, causing oxygen depletion and thermal variability. This study analyses effect of upwelling processes on microbial signature in coastal waters off Mumbai. The coastal environment with seasonal reversal winds was analysed using data during ten cruises. Coastal metocean processes are examined using water quality parameters and the Ekman approximation with wind stress. This analysis explains oxygen depletion and coastal upwelling, influenced by seasonal reversal wind pattern. The study connects hypoxia in the coastal water column to wind-induced upwelling. Concurrently, microbial structure is assessed through metrics such as Total Viable Count, Total Bacterial Count, Sulfate Reducing Bacteria (SRB), and denitrifiers. Notably, high levels of SRB are observed during hypoxia associated with coastal upwelling. This study investigates microbial level with combined result of physical processes and water quality parameters.

Ecosystem links: Anthropogenic activities, environmental variables, and macrophytes structure snail preferences in man-made waterbodies.

Freshwater snails act as obligate intermediate hosts for trematode parasites that cause trematodiases threatening public and veterinary health, and biodiversity conservation. While interest in snail control for trematodiases has re-emerged, their ecology remains poorly understood. We examined the relationship between ecosystem indicators - such as environmental variables, macroinvertebrates, macrophytes, and land use - and their correlation with snail abundance, diversity, and infection prevalence in 19 man-made ponds in eastern Zimbabwe. In total, 926 freshwater snails from 10 species were collected, with 547 individuals belonging to five schistosome-competent species: Bulinus tropicus, Bulinus truncatus, Bulinus globosus, Bulinus forskalii, and Biomphalaria pfeifferi. The remaining 379 snails comprised Radix natalensis, Gyraulus sp., and the exotic invasive species Melanoides tuberculata, Pseudosuccinea columella, and Physella acuta. Six cercarial types - mammalian schistosomes, avian schistosomes, longifurcate pharyngeates, echinostomes, amphistomes, and xiphidiocercariae - were isolated from 104 out of 926 snails (11.2 %). PCR revealed a significantly higher infection rate, with 70.2 % of snails testing positive for trematodes. Snail taxon diversity and infection rate significantly varied across land use types, with the lowest values observed in the commercial tobacco farm section, highlighting the potential adverse effects of agriculture on biodiversity. Ponds with extensive Lagarosiphon major (oxygen weed) coverage appeared to facilitate the presence and abundance of P. acuta and P. columella. Schistosome-competent snails such as B. truncatus and B. tropicus seemed to favor shallow water depths and more eutrophic sites characterized by high levels of nitrates, phytoplankton biomass, turbidity, and phycocyanin. These ponds were predominantly associated with the emergent macrophyte Cladium mariscus, revealing a potential association with important intermediate snail hosts. In conclusion, our study emphasizes the complex interplay among environmental factors, macrophyte composition, land use, and the abundance, diversity, and infection prevalence of freshwater snails, offering insights into potential strategies for targeted snail control and disease management in man-made waterbodies.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

Quantifying the relative importance of agricultural land use as a predictor of catchment nitrogen and phosphorus concentrations.

Agriculture is a major source of nitrogen (N) and phosphorus (P) in freshwater ecosystems, and different management strategies exist to reduce farmland nutrient losses and thus mitigate freshwater eutrophication. The importance of agricultural sources of N and P as drivers of water quality is known to vary spatially, but quantification of the relative importance of the nutrient sources shaping this variability remains challenging, especially with reference to inputs from waste water treatment works. Addressing this knowledge gap is key for targeting management strategies to where they are likely to have the greatest effect. To advance our understanding in this area, this study assesses the impact of population density as a driver of the relative importance of agricultural land use for predicting mean Total Oxidised Nitrogen (TON) and Reactive Phosphorus (RP) concentrations in rivers in England, using two different data-driven, statistical approaches: a generalised linear model and random forest. Our results show that agricultural N and P sources dominate in catchments with low population density, where stream water concentrations are lower and waste water treatment works are numerous, but smaller in terms of the population equivalent served. Agricultural N and P sources are not important predictors of N and P in catchments with high population density, where contributions from waste water treatment works dominate. These results require cautious interpretation, as model validation outcomes show that high TON and RP concentrations are consistently underpredicted. Altogether, our results suggest that the relative contribution of agricultural sources may be overestimated in densely populated catchments, relative to point sources from waste water treatment works, and that management strategies to reduce the contribution of agriculture to N and P in rivers may be better targeted towards catchments with lower population density, as this is where agricultural land use is the primary source of N and P.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Assessment of groundwater quality in the eastern part of tehran plain: Implications for human health.

Groundwater quality is a critical concern for human health, particularly in urban areas like the eastern part of Tehran Plain, where geological features and anthropogenic activities contribute to contamination risks. This study aimed to assess the quality of groundwater in this region, focusing on its implications for public health. The objectives of the study were to identify factors influencing hydrogeochemistry, evaluate environmental risk based on metal(loid)s using water quality indices, and conduct a health risk assessment. Groundwater samples were collected and analyzed for chemistry, water quality, heavy metal contamination, and associated health risks. The results indicated a relatively stable pH condition and a wide variation in the concentration of dissolved solids. The Water Quality Index (WQI) was employed to evaluate the overall water quality, revealing that approximately 50% of the samples fell into the poor and very poor quality categories, with two samples deemed unsuitable for drinking. Heavy metal contamination varied across different metals, with some indicating low levels while others showed moderate to very high levels. Priority pollutants such as mercury and arsenic were identified as having a greater potential impact on water quality deterioration. Exposure and health risk assessments indicated a negligible risk associated with aluminum exposure but high risks associated with arsenic, chromium, and mercury exposure. Carcinogenic risk assessments for arsenic, chromium, and mercury exceeded acceptable thresholds, emphasizing the urgent need for further investigation into contamination sources and strategies for mitigation. These findings highlight the importance of continuous monitoring and sustainable groundwater management practices, providing valuable insights for other regions facing similar challenges in groundwater quality and public health.

Interpretation of river water quality data is strongly controlled by measurement time and frequency.

Water quality monitoring at high temporal frequency provides a detailed picture of environmental stressors and ecosystem response, which is essential to protect and restore lake and river health. An effective monitoring network requires knowledge on optimal monitoring frequency and data variability. Here, high-frequency hydrochemical datasets (dissolved oxygen, pH, electrical conductivity, turbidity, water temperature, total reactive phosphorus, total phosphorus and nitrate) from six UK catchments were analysed to 1) understand the lowest measurement frequency needed to fully capture the variation in the datasets; and 2) investigate bias caused by sampling at different times of the day. The study found that reducing the measurement frequency increasingly changed the interpretation of the data by altering the calculated median and data range. From 45 individual parameter-catchment combinations (six to eight parameters in six catchments), four-hourly data captured most of the hourly range (>90 %) for 37 combinations, whilst 41 had limited impact on the median (<0.5 % change). Twelve-hourly and daily data captured >90 % of the range with limited impact on the median in approximately half of the combinations, whereas weekly and monthly data captured this in <6 combinations. Generally, reducing sampling frequency had most impact on the median for parameters showing strong diurnal cycles, whilst parameters showing rapid responses to extreme flow conditions had most impact on the range. Diurnal cycles resulted in year-round intra-daily variation in most of the parameters, apart from nutrient concentrations, where daily variation depended on both seasonal flow patterns and anthropogenic influences. To design an optimised monitoring programme, key catchment characteristics and required data resolution for the monitoring purpose should be considered. Ideally a pilot study with high-frequency monitoring, at least four-hourly, should be used to determine the minimum frequency regime needed to capture temporal behaviours in the intended focus water quality parameters by revealing their biogeochemical response patterns.

Windthrow in riparian buffers affects the water quality of freshwater ecosystems in the eastern Canadian boreal forest.

Despite the wide application of riparian buffers in the managed boreal forest, their long-term effectiveness as freshwater protection tools remains unknown. Here, we evaluate windthrow incidence in riparian buffers in the eastern Canadian boreal forest and determine the effect of windthrow on the water quality index of the adjacent freshwater ecosystems. We studied 40 sites-20 riparian buffers, aged 10 to 20 years after harvesting and 20 control sites within intact riparian environments-distributed among clay and sandy (esker) soils and black spruce (Picea mariana) and jack pine (Pinus banksiana) stands. We observed more windthrow in the harvested stands (36%) relative to the control sites (16%), regardless of substrate and species. We determined that the most important factors explaining windthrow were exposition, harvesting, aquatic environment size, and stand characteristics. These factors drive wind exposure, speed, and force, which determine post-harvest windthrow risk. Furthermore, windthrow negatively affected the water quality index of the adjacent aquatic systems, i.e., greater windthrow decreased the protective effect of the riparian buffer. We recommend increasing the use of partial harvest near riparian environments and adapting riparian buffers to site conditions to ensure the long-term protection of adjacent freshwater ecosystems.

Prediction of mine water quality by the Seq2Seq model based on attention mechanism.

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

Detection and Prediction of Toxic Aluminum Concentrations in High-Priority Salmon Rivers in Nova Scotia.

Elevated concentrations of toxic cationic aluminum (Ali) are symptomatic of terrestrial and freshwater acidification and are particularly toxic to salmonid fish species such as Atlantic salmon (Salmo salar). Speciated metal samples are rarely included in standard water monitoring protocols, and therefore the processes affecting Ali dynamics in freshwater remain poorly understood. Previous analysis of Ali concentrations in Nova Scotia (Canada) rivers found that the majority of study rivers had concentrations exceeding the threshold for aquatic health, but a wide-scale survey of Ali in Nova Scotia has not taken place since 2006 (Dennis, I. F., & Clair, T. A., 2012, Canadian Journal of Fisheries and Aquatic Sciences, 69(7), 1174-1183). The observed levels of dissolved aluminum in Atlantic salmon (Salmo salar) rivers of Atlantic Canada have potential serious and harmful effects for aquatic populations. We present the findings of the first large-scale assessment of the Ali status of Nova Scotia rivers in 17 years; we measured Ali concentrations and other water chemistry parameters at 150 sites throughout the Southern Uplands region of Nova Scotia from 2015 to 2022. We found that Ali concentrations exceeded toxic thresholds at least once during the study period at 80% of the study sites and that Ali concentrations increased during the study period at all four large-sample study sites. Modeling of relationships between Ali concentrations and other water chemistry parameters showed that the most important predictors of Ali are concentrations of the dissolved fractions of Al, iron, titanium, and calcium, as well as dissolved organic carbon and fluoride. We developed a fully Bayesian linear mixed model to predict Ali concentrations from a test data set within 15 µg/L. This model may be a valuable tool to predict Ali concentrations in rivers and to prioritize areas where Ali should be monitored. Environ Toxicol Chem 2024;00:1-12. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Data-driven water quality prediction for wastewater treatment plants.

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.

Diagnostic screening of private well water using gas chromatography with high resolution mass spectrometry to support well water management.

In the US, private well users are responsible for their own water quality testing, but local health officials are often uncertain what tests to recommend, particularly for regulated organic chemical contaminants. This study evaluated the utility of suspect and non-target screening (NTS) high resolution mass spectrometry (HRMS) as a tool to identify a wide range of organic chemicals of emerging concern in private well water and to inform well water management decisions. Qualitative NTS, which detects chemicals without providing concentrations, was used to analyze 25 private well water samples from Wake County, North Carolina. Using the NIST 20 mass spectral database (M1), NTS tentatively identified 106 unique organic chemicals across the 25 samples and an average of 11 organic chemicals per sample. At least one USEPA ToxCast chemical was identified in each private well water sample. Private well water users were interviewed prior to and after their sample's NTS results were reported back; four county groundwater managers were interviewed after aggregated results for all 25 water samples were reported back. All but one well user participant chose to participate in the reporting-back post-interview. The 24 private well users found NTS results useful and valued the contextualization of their results using NTS results for other well users and a local municipal water sample. Most private well users (67%) were surprised by their well water results, especially regarding the number of tentatively identified organic chemicals detected. All the groundwater managers believed the NTS results were useful and could help improve their testing recommendations to private well users. Even with qualitative limitations, NTS results can be an effective and valuable tool to inform the public and governance stakeholders in decisions around groundwater quality management.

Declined nutrients stability shaped by water residence times in lakes and reservoirs under climate change.

Water quality stability in lakes and reservoirs is essential for drinking water safety and ecosystem health, especially given the frequent occurrence of extreme climate events. However, the relationship between water quality stability and water residence time (WRT) has not been well elucidated. In this study, we explored the relationship based on nitrogen (N) and phosphorus (P) concentrations data in 11 lakes and 49 reservoirs in the Yangtze-Huaihe River basin from 2010 to 2022. Additionally, we examined the effects of hydrometeorological characteristics, the geomorphology of water bodies and catchments, and land use on the WRT, establishing a link between climate change and the stability of N and P in these water bodies. The results showed that a significant correlation between the stability of N and P in lakes and reservoirs and their WRT. The longer WRT tends to coincide with decreased stability and higher nutrient concentrations. Hydrometeorological factors are the primary factors on the WRT, with precipitation exerting the greatest effect, particularly under extreme drought. In recent years, extreme climatic events have intensified the fluctuations of WRT, resulting in a renewed increase in N and P concentrations and deterioration in stability. These findings highlight the importance of incorporating meteorological and hydrological factors alongside reinforcing ecological restoration into lake and reservoir management strategies, and providing a scientific basis for future efforts aimed at enhancing lake and reservoir water quality stability and safeguarding aquatic ecosystems.

Retrofitting agricultural detention systems can economically enhance nitrogen treatment with payment for services approach.

Despite their global presence and multiple services including water storage and treatment, limited field studies have focused on quantifying water quality effectiveness of agricultural stormwater detention areas (SDAs; farm ponds). Limited data available comes from unverified modeling studies. Economics is another neglected aspect of SDAs. An SDA in the Everglades basin of Florida was monitored to quantify its baseline nitrogen (N) treatment. Design flaws of underutilized surface storage in variably ponded areas and short-circuiting were rectified by retrofitting the SDA through compartmentalization, increase in outflow structure control elevation, and installing channel plugs. Post the retrofits, total N retentions doubled (7500 from 3700 kg), a similar trend was observed with respect to surface water volume indicating that water retention drives N retention. Targeting enhanced water retention through retrofits can achieve reductions in downstream nutrient flows. The cost of additional N treatment from retrofits was $12/kg, an order of magnitude less than the published costs for other detention systems including newly constructed wetlands ($180/kg) and tailwater recovery systems ($214/kg). A scale-up showed that retrofitting SDAs could reduce the N discharge to an estuary in the Everglades basin by over 50 %. A payoff to the farmers for SDA retrofits through a payment for environmental services program can achieve higher N treatment economically and facilitate large-scale adoption.

Priority screening of contaminants of emerging concern (CECs) in surface water: Comparing cell-based bioassays and exposure-activity ratios (EARs).

In this study, we compared a wide range of cell-based bioassays to the use of chemical analysis followed by exposure-activity ratio (EAR) and Toxicological Prioritization index (ToxPi) for prioritizing chemicals, sites, and hazard concerns in water samples. Surface water samples were collected from nine sites in three Central Pennsylvania streams and analyzed for a forty-six contaminants of emerging concern (CECs), including pesticides, personal care products, and pharmaceuticals. Cell-based reporter assays evaluated human and zebrafish molecular initiating events (MIEs) in endocrine and metabolic disruption, altered lipid metabolism, and oxidative stress. Bioassays showed that 12 out of 40 assays had at least one site with activity over the effect-based trigger (EBT) values. The receptors that exhibited the highest number of samples above the EBT that would be expected to cause toxicity were Aryl hydrocarbon receptor (AhR, human and zebrafish), Pregnane X Receptor (PXR), Estrogen Receptor-beta (ERB), and Androgen Receptor (AR). Characterizing the collection sites by their bioactivity aligned closely with the stream in which samples were collected. The sum of all EARs for each chemical indicated that the pharmaceutical Carbamazepine and the pesticides Carbaryl and Atrazine posed the greatest concern. However, predicted activity and site prioritization based on individual chemical analysis and calculated EAR were different than those measured by bioassay, indicating that biologically active chemicals are present in the samples that were not included in the targeted analytes. Taken together, these data show that chemical analysis and EAR analysis are beneficial for prioritization of chemicals, whereas mechanism-based bioassays are more inclusive of known as well as unknown chemical contaminants and thus of more use for overall water quality analysis and site prioritization.

Quality and health impact of groundwater in a coastal region: a case study from west coast of southern India.

Seawater intrusion seriously threatens the quality of coastal groundwater, affecting nearly 40% of the world's population in coastal areas. A study was conducted in the Kamini watershed situated in the Udupi district of Karnataka to assess the groundwater quality and extent of seawater intrusion. During the pre-monsoon period, 57 groundwater and 3 surface water samples were analyzed to understand the impact of seawater on the groundwater and surface water. The analysis revealed that the groundwater in the study area is slightly alkaline. The weighted overlay analysis map indicated that 11% of the study area is unsuitable for drinking water due to the influence of seawater. The Piper plot analysis revealed that the groundwater is predominantly CaMgCl facies. The hydrogeochemical facies evolution diagram (HFED) showed that 62% of the groundwater is affected by seawater. The HFED and Piper plots also indicate that the surface water is also affected by seawater. These results are also supported by various molar ratios such as Cl- vs. Cl⁻/HCO3⁻, Cl⁻ vs. Na⁺/Cl⁻, Cl- vs. SO42-/Cl-, and Cl⁻/HCO3- vs. Mg2+/Ca2+, suggesting that the majority of the water sample has been affected by seawater. The saturation indices indicated that mineral dissolution has significantly contributed to groundwater salinization. The correlation between sulfate concentration and calcite and dolomite dissolution suggested the influence of seawater intrusion in the coastal aquifer. The process of reverse ion exchange mainly influences the groundwater chemistry according to chloroalkali indices. The total hazard index (THI) values of nitrate and fluoride exceeded limits, posing health risks to adults and children. Studies suggest that with time and space, seawater intrusion is increasing in some pockets of the study area, especially along the west coast.