Spatial distribution and hydrogeochemical evaluations of groundwater and its suitability for drinking and irrigation purposes in kaligonj upazila of satkhira district of Bangladesh.

Groundwater is a significant water resource for drinking and irrigation in Satkhira district, Bangladesh. The depletion of groundwater resources and deterioration in its quality are the results of the confluence of factors such as industrialization, intensive irrigation, and rapid population growth. For this reason, this study focused on the evaluation of tubewell water of six unions of Kaligonj upazila in Satkhira district, which is situated in the coastal southwest part of Bangladesh. Major and trace elemental concentrations were assimilated into positive matrix factorization (PMF) to identify potential sources and their respective contributions. Principal component analysis (PCA) revealed that groundwater salinization and manmade activities were the primary causes of heavy metals in the coastal groundwater. Its average pH value was found to be 7.5, while Dissolved oxygen, Total dissolved solids, salinity, and conductivity, with values ranging from 1.18 to 7.38 mg/L, 0.5-4.88 g/L, 0.4-5%, and 0.95 to 8.56 mS/cm, respectively. The total hardness average value was 561.7 mg/L, classified into the very hard water categories, which is why 90% of the tubewell water samples were unfit for household purposes. All samples had an excessive level of arsenic present. The iron concentration of fifteen (15) samples crossed the standard limit according to WHO 2011 value. Around 63% of the samples were of the Na+-K+-Cl--SO42- type, and about 72% were sodium-potassium and alkali types. 98% of samples were covered in chloride and bicarbonate. The findings showed that 45.83% of the groundwater samples had negative Chloroalkaline index (CAIs), while 54.16% had positive. The permeability index (PI) was an average of 73%, and residual sodium carbonate (RSC) averaged 260.2 mg/L, and the findings clearly showed that 80% of the samples weren't appropriate for irrigation. According to the sodium adsorption ratio (SAR) value, 65% of the samples fell into the unsuitable category. These calculations indicated a high overall salinity hazard in the study area, which may be caused by the intrusion of sea water given that the study area is close to the coastal region. Findings compared to standards revealed that the majority of the samples were deemed unfit for drinking and irrigation purposes. Hence, additional attention must be paid to this area to ensure the availability of drinkable water and to preserve sustainable farming practices.

Hydrochemical characterization and assessment of health risks of trace elements in the Huai River Basin of China.

Basin water pollution is a global problem, especially in the densely populated areas. The Huai River Basin (abbreviated as HRB), including the Huai River system and the Yishu River system, is the sixth-largest and most densely populated watershed in China. However, there is a lack of comprehensive studies of river and well water throughout the Huai River basin, including hydrochemistry characterization and assessment of health risks. This study investigated water quality and pollution sources of river and well water in the HRB based on the hydrochemistry analysis and different water quality indices. The water body in the HRB showed weak alkalinity (pH = 8.4 ± 0.7) and had high TDS values (TDS = 339 ± 186 mg/L) with water types of HCO3-Ca-Mg and SO4-Cl-Ca-Mg in the Huai River system, and SO4-Cl-Ca-Mg in the Yishu River system. Atmospheric input and evaporation had less impact on hydrochemistry. Evaporite dissolution and carbonate weathering had a greater impact on hydrochemistry. Carbonate precipitation and cation exchange also influenced the dissolved solutes, especially Ca2+ and Na+. Samples had low to medium salinity hazards and sodium absorption ratios and were suitable for irrigation. For drinking purposes, samples were fresh water and have good or excellent according to the Water Quality Index (WQI). Land use types influenced water quality with the worst river water quality from cropland. Combining different assessment indices, the water quality of the Yishu River system performed better than the Huai River system. Absolute principal component analysis-multiple linear regression and the positive matrix factorization models identified the main pollutants as As, Ba, Cr, Ni, and Mn, with natural sources of As, Ba, and Ni and anthropogenic inputs of Cr, and Mn. Although the water quality of the HRB has improved in recent years, high potential risk from As, Cr, Mn, Ba, and Ni should be noted. This study provided vital information for basin hydrochemistry analysis and water quality assessment.

Occurrence of Pharmaceutical Micropollutants in Lake Nahuel Huapi, Argentine Patagonia.

Tourism is one of the most important activities for the economy of Nor Patagonia Argentina. In Bariloche City, located on the shores of Lake Nahuel Huapi, both the permanent and the temporary populations have increased significantly in recent decades, and this has not necessarily been accompanied by an improvement in sewage networks. Emerging micropollutants such as pharmaceutical compounds reach aquatic systems directly, in the absence of a domestic sewage network, or through effluents from wastewater treatment plants (WWTP), which do not efficiently remove these substances and represent a major threat to the environment. Therefore, the objective of our study was to monitor the presence of pharmaceutical compounds discharged both through wastewater effluents and diffusely from housing developments into Lake Nahuel Huapi. The results obtained demonstrate the presence of pharmaceuticals in Lake Nahuel Huapi with concentrations ranging from not detectable (ND) to 110.6 ng L-1 (caffeine). The highest pharmaceutical concentration recorded in WWTP influent corresponded to caffeine (41728 ng L-1), and the lowest concentration was paracetamol (18.8 ng L-1). The removal efficiency of pharmaceuticals in the WWTP was calculated, and ranged from 0% for carbamazepine to 66% for ciprofloxacin. This antibiotic showed the lowest % of attenuation (73%) in Lake Nahuel Huapi. These results on the occurrence of a wide variety of pharmaceuticals are the first generated in Patagonia, representing a regional baseline for this type of micropollutant and valuable information for the subsequent design of removal strategies for emerging pharmaceutical pollutants in surface water. Environ Toxicol Chem 2024;00:1-11. © 2024 SETAC.

Arsenic's shadowy influence: A systematic review of its carcinogenic role in gallbladder cancer.

INTRODUCTION: Gallbladder carcinoma (GBC) and cholangiocarcinoma are aggressive forms of cancer developed in the gallbladder and biliary tracts which are related to the liver. This systematic review aimed to highlight the significant association between gallbladder, biliary cancers, and arsenic exposure. METHODS: An extensive search was conducted in Embase, Cochrane, Scopus, PubMed, and Web of Science. We included studies that assessed arsenic levels in gallbladder cancer patients, without restrictions on age, sex, or language. Biological samples, such blood, bile, gallbladder tissue, gallstones, and hair were obtained, and arsenic levels were measured. Also, arsenic water and soil concentrations were collected. RESULTS: A total of 13 studies were included in our review. These studies included 2234 non-gallbladder carcinoma patients and 22 585 gallbladder carcinoma cases. The participant demographics showed a gender distribution of 862 males and 1845 females, with an age range of 20-75 years. The average body mass index (BMI) was 19.8 kg/m2 for nongallbladder carcinoma patients and 20.1 kg/m2 for gallbladder carcinoma cases. The selected studies examined arsenic concentrations across various biological samples, including blood, hair, gallstones, and bile. Blood arsenic levels ranged from 0.0002 to 0.3893 µg/g and were significantly associated with increased gallbladder carcinoma risk in several studies. Hair also demonstrated a significant correlation, with arsenic concentrations ranging from 0.0002 to 6.9801 µg/g. CONCLUSION: There is a strong link between arsenic exposure and gallbladder cancer or cholangiocarcinoma. Even chronic exposure to low-moderate amounts could lead to gallbladder carcinoma. These findings stress the need for more comprehensive and dedicated studies, to control arsenic water/soil levels and seek other preventive measures for this high mortality disease.

Selenium-mercury interactions and relationship to aquatic toxicity: A review.

A review of the literature pertaining to selenium-mercury (Se/Hg) interactions in aquatic species was performed to provide insight into the mechanisms allowing for the reported changes in bioaccumulation and toxicity that have been observed when the two elements occur at elevated concentrations. Selenium (Se) has been shown to protect against mercury (Hg) toxicity in all animal models evaluated (fish, birds, mammals, and plants). To explore the interaction between the two elements, data are presented on concentrations of both elements in wild-caught fish at numerous locations. The data show that most fish have Se/Hg ratios >1.0. The importance of this ratio has been reported, with suggestions that the protective interaction is due in large part to the formation of HgSe. Data show that when the Se/Hg molar ratio is <1.0 in the diet of fish and animals, Hg toxicity will be expressed, provided that the Hg concentration is sufficiently high. This toxicity is likely the result of Se deficiency leading to an excess of reactive oxygen species. Laboratory fish toxicity studies reviewed show that Se toxicity can be reduced or eliminated when Hg is added to the diet in moderate amounts. Field studies have shown reduced accumulation of Hg when Se concentrations are increased. When Hg in the diet is significantly elevated (usually >10 µg/g), toxicity is expressed regardless of the Se present. Likewise, amelioration of Se toxicity by Hg occurs over a limited range. Tissue thresholds for Se toxicity have been derived primarily from studies where fish eggs were extracted from wild fish and embryo deformities were observed; however, the amount of Hg in the fish or ovaries was not considered, which could lead to uncertainty in the toxicity threshold. It is recommended that both elements be measured and evaluated when performing risk assessments and setting water quality criteria. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.

Benthic macroinvertebrates as indicators of water quality: A case study of estuarine ecosystems along the coast of Ghana.

Increasing human activities in coastal areas of Ghana have led to the degradation of many surface waterbodies, with significant consequences for the ecosystems in the affected areas. Thus, this degradation extremely affects the health of ecosystems and disrupts the essential services they provide. The present study explored the use of benthic macroinvertebrates as an indicator of estuarine degradation along the coast of Ghana. Water and sediment samples were collected bimonthly from Ankobra, Kakum and Volta estuaries for physicochemical parameters, nutrients and benthic macroinvertebrates. The findings revealed the dominance of pollution-tolerant taxa such as Capitella sp., Nereis sp., Heteromastus sp., Tubifex sp., Cossura sp. and Chironomous sp. in Kakum Estuary while pollution-sensitive taxa such as Scoloplos sp., Euridice sp., Lumbriconereis sp. and Pachymelania sp. in the Volta Estuary. The species-environment interactions showed dissolved oxygen, temperature, salinity, orthophosphate, nitrates, ammonium, electrical conductivity, turbidity, and chemical oxygen demand as the most significant parameters that complement the use of benthic macroinvertebrates as indicators of environmental quality in the studied estuaries. There were correlations of some benthic macroinvertebrate taxa with environmental factors in the estuaries suggesting low, moderate and high levels of pollution in the Volta, Kakum and Ankobra estuaries, respectively. Nevertheless, the study finds Kakum Estuary to be the ecologically healthiest estuary than the Volta and Ankobra Estuaries. Therefore, the study has shown benthic macroinvertebrates as a key indicator of ecosystem health alterations, and it is recommended that they should be incorporated with other environmental data for pollution monitoring in Ghanaian coastal waters.

Influence of combined abiotic/biotic factors on decay of P. aeruginosa and E. coli in Rhine River water.

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

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

Exploring the contribution of nature-based solutions for environmental challenges in the Netherlands.

Nature-based solutions (NbS) offer a promising and sustainable approach to addressing multiple environmental challenges, including climate change, pollution, and biodiversity loss. Despite the potential of NbS, their actual effectiveness in solving these challenges remains uncertain. Therefore, this study evaluates the contribution of NbS implemented in a nature-inclusive scenario for six environmental challenges and associated policy targets in the Netherlands. Fifteen different NbS were applied in the scenario in urban, agricultural, aquatic, and protected nature areas, with measures like flower field margins, green roofs, groundwater level management, and river restoration. The spatially-explicit Natural Capital Model was used to quantify the effectiveness of all applied NbS at a national-scale. Results show NbS significantly contribute to simultaneously solving all six assessed environmental challenges. The most significant impact was seen in improving the quality of water bodies (+34 %), making agriculture more sustainable (+24 %), and protecting and restoring biodiversity (+22 %). The contribution of NbS to address the quality of the living environment (+13 %), climate change (+10 %), and the energy transition was less effective (+2 %). Furthermore, NbS can help to achieve sectoral policy targets at the global, EU, and national levels, including those related to the Birds Habitats Directives, carbon emission, and pesticide reduction targets. This study highlights the potential of NbS to effectively address multiple environmental challenges, although they do not provide a complete solution, and suggests that future research could focus on identifying even more effective ways to implement NbS, and to mainstream their use in policy and practice.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara.

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

Water quality monitoring and modeling for an urban storm drainage channel in Thane, India.

The absence of a sewer system and inadequate wastewater treatment plants results in a discharge of untreated wastewater to the urban drainage channels and pollutes receiving waters. Field visits were carried out to observe water quality parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) in an urban drainage system (Kolshet drain) in Thane City, Mumbai Metropolitan Region, India. Dye-tracing studies using rhodamine WT dye were used for computing the velocity, discharge, and dispersion coefficient of the drain. The data analysis shows that the BOD and COD values in the drain are higher than the permissible limits (30 mg L-1 for BOD and 250 mg L-1 for COD), which is not suitable for disposal to any receiving water body. Also, the DO was less than the permissible limit of a minimum of 3 mg L-1 (for the survival of aquatic life). It is seen that the higher BOD load significantly reduced the DO throughout the drain. The Water Quality Analysis Simulation Program (WASP 8.32, 2019) developed by the US Environmental Protection Agency (USEPA) has been used for the simulation of the DO and BOD in the drainage channel. The model simulates an appropriate estimate of the expected variation of DO and BOD at points of interest. The modeling for the Kolshet drain is expected to enable better estimates of the wastewater parameters and the pollution transport in the drain for planning purposes.

Longitudinal stream synoptic (LSS) monitoring to evaluate water quality in restored streams.

Impervious surface cover increases peak flows and degrades stream health, contributing to a variety of hydrologic, water quality, and ecological symptoms, collectively known as the urban stream syndrome. Strategies to combat the urban stream syndrome often employ engineering approaches to enhance stream-floodplain reconnection, dissipate erosive forces from urban runoff, and enhance contaminant retention, but it is not always clear how effective such practices are or how to monitor for their effectiveness. In this study, we explore applications of longitudinal stream synoptic (LSS) monitoring (an approach where multiple samples are collected along stream flowpaths across both space and time) to narrow this knowledge gap. Specifically, we investigate (1) whether LSS monitoring can be used to detect changes in water chemistry along longitudinal flowpaths in response to stream-floodplain reconnection and (2) what is the scale over which restoration efforts improve stream quality. We present results for four different classes of water quality constituents (carbon, nutrients, salt ions, and metals) across five watersheds with varying degrees of stream-floodplain reconnection. Our work suggests that LSS monitoring can be used to evaluate stream restoration strategies when implemented at meter to kilometer scales. As streams flow through restoration features, concentrations of nutrients, salts, and metals significantly decline (p < 0.05) or remain unchanged. This same pattern is not evident in unrestored streams, where salt ion concentrations (e.g., Na+, Ca2+, K+) significantly increase with increasing impervious cover. When used in concert with statistical approaches like principal component analysis, we find that LSS monitoring reveals changes in entire chemical mixtures (e.g., salts, metals, and nutrients), not just individual water quality constituents. These chemical mixtures are locally responsive to restoration projects, but can be obscured at the watershed scale and overwhelmed during storm events.

Evaluation and monitoring of the water quality of an Argentinian urban river applying multivariate statistics.

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

Climate and land use shape the water balance and water quality in selected European lakes.

This study provides insights into factors that influence the water balance of selected European lakes, mainly in Central Europe, and their implications for water quality. An analysis of isotopic, chemical and land use data using statistical and artificial intelligence models showed that climate, particularly air temperature and precipitation, played a key role in intensifying evaporation losses from the lakes. Water balance was also affected by catchment factors, notably groundwater table depth. The study shows that lakes at lower altitudes with shallow depths and catchments dominated by urban or crop cover were more sensitive to water balance changes. These lakes had higher evaporation-to-inflow ratios and increased concentrations of total nitrogen in the water. On the other hand, lakes at higher elevations with deeper depths and prevailing forest cover in the catchment were less sensitive to water balance changes. These lakes, which are often of glacial origin, were characterized by lower evaporation losses and thus better water quality in terms of total nitrogen concentrations. Understanding connections between water balance and water quality is crucial for effective lake management and the preservation of freshwater ecosystems.

Water quality prediction based on sparse dataset using enhanced machine learning.

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

[Hydrochemical Characteristics, Controlling Factors and Water Quality Evaluation of Shallow Groundwater in Tan-Lu Fault Zone （Anhui Section）].

To study the hydrochemical characteristics, controlling factors, and groundwater quality of the Tan-Lu fault zone (Anhui section), 86 groundwater samples were taken from the areas surrounding the Tan-Lu fault zone (Anhui section), which included the Jianghuai Wavy Plain, the Yanjiang Hill Plain, and the Dabie Mountains in western Anhui. Descriptive statistics, Piper diagram, Gibbs diagram, ion ratio analysis, saturation index, chloride-alkalinity index, and entropy weight water quality index (EWQI) were used to comprehensively study the hydrochemical characteristics and controlling factors of groundwater and to evaluate its quality. The results showed that the shallow groundwater in the Tan-Lu fault zone (Anhui section) was weakly alkaline, with dominant anions and cations of HCO3-, Ca2+ and Na+, respectively, and the hydrochemical types were mainly HCO3-Ca·Mg and HCO3-Na·Ca. The solute source of groundwater was mainly controlled by water-rock interactions, and the weathering of silicate and carbonate rocks jointly contributed to the formation of the chemical components of groundwater. Strong cation exchange adsorption was an important factor causing Na+ enrichment. The overall quality of groundwater in the study area was good but was polluted to a certain extent by human activities. Most of the groundwater in the Jianghuai Wavy Plain and Yanjiang Hill Plain was not suitable for direct drinking. The results of this research have important implications for the sustainable development and utilization of shallow groundwater resources and environmental protection in the Tan-Lu fault zone (Anhui section).

[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.

Assessing watercourse quality using results-based indicators in an agri-environment scheme.

Small watercourses are essential contributors to catchment water quality, but they continue to suffer degradation across Europe. A results-based agri-environment scheme, aimed at improving watercourse quality in Ireland, developed a rapid drainage assessment to identify point source risks. The assessment uses a scoring system based on visual indicators of nutrient and sediment risk, linking the outcomes to farmer payments. To understand how this novel drainage risk assessment relates to instream watercourse quality, we used three macroinvertebrate-based biotic indices (Q-value, Small Stream Impact Score and Proportion of Sediment Sensitive Invertebrates). Macroinvertebrate kick-sampling and physiochemical analysis were completed in May and July 2021 for 12 'At Risk' and 12 'Not at Risk' small watercourses as identified by the results-based scheme. Results show that the scheme's drainage risk assessment can identify point source risks but we found it does not directly reflect local instream quality as assessed by the biotic indices. Unexpectedly, the biotic indices showed watercourse degradation in 58% of the upstream (control) sampling points, indicating impacts not captured by the drainage risk assessment. Small watercourses displayed high heterogeneity, with significant species turnover between the sampling months. The Small Stream Impact Score was less influenced by temporal change than the Q-value index. There was a significant relationship between instream watercourse quality and sedimentation, as quantified by the Proportion of Sediment Sensitive Invertebrates. Including a measurement of instream sedimentation in the drainage assessments would improve the identification of risks and management. These results show that by linking farmer payments to the drainage risk assessments results-based payment schemes could positively contribute to improving catchment scale watercourse quality, but further work is required to capture wider sources of freshwater impacts.

Modeling the impacts of oil palm plantations on water quantity and quality in the Kais River Watershed of Indonesia.

Oil palm plantations can impact hydrological processes in many tropical watersheds. The rapid conversion of tropical rainforests for commercial operations in recent decades has been associated with water scarcity, flooding, and polluted rivers. However, this widespread and emerging issue is less studied and underreported due to limited data availability, modeling complexity, and the remote nature of these landscapes. Ecohydrologic modeling enables us to investigate changes in watershed conditions caused by large-scale land cover changes from plantations. This study examines the impact of oil plantations on water quantity and quality using the SWAT+ model in the Kais River Watershed, West Papua, Indonesia. The objective is to assess the hydrological changes concerning land cover conversion to oil palm plantations. Results show that establishing oil palm plantations increased surface runoff by 21 %, and sediment yields rose by 16.9 % compared to the baseline. There was also a significant increase of 78 % in mean annual total nitrogen and 144 % in total phosphorous after the plantations' establishment. The results show that forest conversion to oil palm plantations in the Kais River watershed is a primary driver of change in hydrological regimes, resulting in the deterioration of water quality. There is a need for conservation strategies to mitigate the impacts of significant landscape changes in watershed ecosystems.

Exposure Assessment of Pesticides in Surface Waters of Ontario, Canada Reveals Low Probability of Exceeding Acute Regulatory Thresholds.

The objectives of this study were to: (1) characterize the exposure of aquatic ecosystems in Southern Ontario, Canada to pesticides between 2002 and 2016 by constructing environmental exposure distributions (EEDs), including censored data; and (2) predict the probability of exceeding acute regulatory guidelines. Surface water samples were collected over a 15-year period by Environment and Climate Change Canada. The dataset contained 167 compounds, sampled across 114 sites, with a total of 2,213 samples. There were 67,920 total observations of which 55,058 were non-detects (81%), and 12,862 detects (19%). The most commonly detected compound was atrazine, with a maximum concentration of 18,600 ngL- 1 and ~ 4% chance of exceeding an acute guideline (1,000 ngL- 1) in rivers and streams. Using Southern Ontario as a case study, this study provides insight into the risk that pesticides pose to aquatic ecosystems and the utility of EEDs that include censored data for the purpose of risk assessment.