Arsenic Concentrations In Groundwater Research Articles

Hydrogeochemical processes of as enrichment and migration in the Surface’s critical zone of the Shiquan River basin in the western Qinghai–Tibet Plateau

The enrichment of As in the western Qinghai–Tibet Plateau and its surrounding basins has been widely reported. However, the spatial distribution of arsenic content in the Surface’s Critical Zone (SCZ) of the Shiquan River Basin in the western Qinghai–Tibet Plateau is poorly understood, as is the mechanisms through which water–rock interactions and surface water-groundwater interactions influence arsenic enrichment and migration. In this study, the contents of arsenic and other chemical components in surface water, groundwater, sediment, soil, and rock in the Shiquan River Basin were analyzed to investigate the hydrogeochemical process of arsenic migration. The results indicate that the concentration ratios of HCO3–/(Na++K+), Ca2+/(Na++K+), Na+/(Na++Ca2+) and δ18O in the surface water of the Shiquan River Basin vary along the runoff path. Carbonate and silicate weathering dominate the middle and upper reaches, while evaporite dissolution gradually increases in the lower reaches. Arsenic is enriched in rocks, soil, surface water, and groundwater in the SCZ, with slightly higher concentrations found in the surface water, sediment, and soil in the middle reaches than in the upper and lower reaches. This spatial distribution suggested that the hydrogeochemical processes along the runoff path controlled the distribution of As in the basin. Water–rock interactions and hot spring discharge of arsenic-bearing minerals contributed to its enrichment in the basin, while adsorption by riverbed sediment led to the gradual decay of dissolved arsenic. The underlying aquifer (alluvial-diluvial layer) recharged by surface water is the main source of high arsenic concentration in groundwater. The findings of this study can serve as a reference for studying arsenic in similar high-arsenic basins worldwide.

Geochemical Constraints on Arsenic and Rare Earth Elements in Groundwater of Ganges Delta, Nadia District, West Bengal

ABSTRACT Geochemical studies of groundwater from 302 tubewells and aquifer sediments of the Ganges delta plain of Quaternary age are conducted. The geochemical behaviour of Fe, Mn, SO42-, arsenic (As), REEs, and Eu parameters in groundwater indicates two contrasting environments: (i) a more oxic condition in the fluvial environment of the Jalangi River in the northern part, and (ii) a less oxidizing/reducing environment in the palaeo-lacustrine environment towards south of the study area. Arsenic concentration in groundwater is more in palaeo-lacustrine environment due to (i) reductive desorption from Fe-oxyhydroxide in the high pH reducing environment and (ii) mobilization by ion exchange with the help of fertilizer used in agricultural activities. Both these phenomena are attributed to the strong spatial correlation of arsenic (As) with pH, PO4, and SO4. However, the dissimilar nature of REE pattern in groundwater and aquifer sediment indicates that REE geochemistry of groundwater is being modified by the “reductive dissolution of Fe-oxyhydroxides” in sediment which releases REE into the groundwater. We conclude that desorption and “reductive dissolution of Fe-oxyhydroxide” controls release of As and REE into the groundwater in both oxic and reducing aquifer conditions in the Ganges delta.

Assessment on the Exposure of Air, Water and Noise Pollution, and Mental Stress on Preeclamptic Patients of Rajshahi, Bangladesh

Although preeclampsia claims the lives of 70,000 mothers and 500,000 newborns each year worldwide, its origin is still elusive and a number of risk factors such as environmental pollution are not yet addressed properly. In this study, exposures due to the extents of air, water and noise pollution as well as mental stress on preeclamptic patients have been investigated. Using a cross-sectional longitudinal design, 90 women hospitalized with preeclampsia in 7 hospitals of Rajshahi, Bangladesh were considered, of which Rajshahi Medical College Hospital is a tertiary referral hospital. The data were collected by interviewing the patients, physical examinations and merging the patients’ data with British Geological Survey’s groundwater data-sets (n=3,540). For statistical analyses, SPSS software was employed. It was found that most of the patients’ living rooms were within 15 feet from kitchen. Only 10% patients had good room ventilation, while the remaining 90% patients had either moderate or poor room ventilation. Combination of these facts reveals that the preeclamptic patients were subject to moderate CO2 exposure. Since 79% of the preeclamptic patients’ living rooms were below 50 ft from the nearest roads and 84% for 100 ft distance, they would experience noise pollution. Combination of traffic conditions and potential sources of noise pollution revealed that 60% of the preeclamptic patients experienced moderate to intense noise pollution. Groundwater arsenic, calcium, magnesium, iron and sodium concentrations in the patients’ drinking water were higher than WHO guideline values that should favour constipation and mild hypertension. While 70% patients were under high mental stress and 24% under very high mental stress, only 6% patients had moderate mental stress. It is concluded that air, water and noise pollution, and mental stress are potential risk factors of preeclampsia.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Coupled redox cycling of arsenic and sulfur regulates thioarsenate enrichment in groundwater

High‑arsenic groundwater is influenced by a combination of processes: reductive dissolution of iron minerals and formation of secondary minerals, metal complexation and redox reactions of organic matter (OM), and formation of more migratory thioarsenate, which together can lead to significant increases in arsenic concentration in groundwater. This study was conducted in a typical sulfur- and arsenic-rich groundwater site within the Datong Basin to explore the conditions of thioarsenate formation and its influence on arsenic enrichment in groundwater using HPLC-ICPMS, hydrogeochemical modeling, and fluorescence spectroscopy. The shallow aquifer exhibited a highly reducing environment, marked by elevated sulfide levels, low concentrations of Fe(II), and the highest proportion of thioarsenate. In the middle aquifer, an optimal ∑S/∑As led to the presence of significant quantities of thioarsenate. In contrast, the deep aquifer exhibited low sulfide and high Fe(II) concentration, with arsenic primarily originating from dissolved iron minerals. Redox fluctuations in the sediment driven by sulfur‑iron minerals generated reduced sulfur, thereby facilitating thioarsenate formation. OM played a crucial role as an electron donor for microbial activities, promoting iron and sulfate reduction processes and creating conditions conducive to thioarsenate formation in reduced and high‑sulfur environments. Understanding the process of thioarsenate formation and the influencing factors is of paramount importance for comprehending the migration and redistribution of arsenic in groundwater systems.

Machine learning prediction of health risk and spatial dependence of geogenic contaminated groundwater from the Hetao Basin, China

Geogenic contaminated groundwater (GCG), characterized by elevated arsenic, fluoride, and iodine levels, present a significant challenge to public health and government management. Conventional survey-based approaches of collecting groundwater samples, conducting physicochemical tests, and performing spatial interpolation to obtain regional groundwater chemical component maps are inefficient and costly. More importantly, it does not take into account the actual hydrogeological conditions or the characteristics of pollutant transport and enrichment. To address this issue, we utilized Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to analyze the likelihood of occurrence of arsenic, fluoride, and iodine as well as their spatial distribution in shallow groundwater from the Hetao Basin. Our study incorporated 20 indicators related to meteorology, soil physicochemical properties, and groundwater conditions, along with 1505 labeled samples consisting of groundwater arsenic, fluoride, and iodine concentrations and their corresponding coordinates. Subsequently, the study automatically analyzed the meteorological, soil physicochemical properties and groundwater conditions by constructing a machine learning model using the available data. In order to optimise and select the best prediction model, this paper presents a quantitative evaluation of the prediction performance of various machine learning models. The accuracy (AC), area under curve (AUC) and mean squared error (MSE) were calculated to predict the spatial distribution of CGC. Subsequently, the optimized model for predicting the spatial distribution of GCG was selected. The results showed that the XGBoost algorithm provided optimal predictions for groundwater with arsenic concentrations above 10 μg/L and fluoride concentrations exceeding 1.5 mg/L, whereas the RF model provided the best predictions for groundwater with arsenic concentrations surpassing 50 μg/L and iodine concentrations exceeding 100 μg/L. Subsequently, groundwater health risk zones were delineated based on an optimal prediction model, and demographic analysis was conducted in both the direct and potential groundwater risk zones. Model predictions indicated that hundreds of thousands of people in the Hetao Basin were facing a public health crisis caused by high concentrations of arsenic, fluoride and iodine in groundwater. These findings underscore the significant health challenge in the study area. Considering the agricultural development and increasing groundwater use in the area, our findings can guide local governments in managing the extent of groundwater development, establishing control zones, and enhancing protection measures for populations at risk from groundwater contamination.

Land-use interactions, Oil-Field infrastructure, and natural processes control hydrocarbon and arsenic concentrations in groundwater, Poso Creek Oil Field, California, USA

Like many hydrocarbon production areas in the U.S., the Poso Creek Oil Field in California includes and is adjacent to other land uses (agricultural and other developed lands) that affect the hydrology and geochemistry of the aquifer overlying and adjacent to oil development. We hypothesize that the distributions of hydrocarbons and arsenic in groundwater in such areas will be controlled by complex interactions between mixed land uses, oil-field infrastructure, and natural processes. In 2020–2021, samples of groundwater and surface water were collected and analyzed for a large suite of inorganic and organic chemicals and isotope and gas tracers to test this hypothesis. Those data are supplemented with ancillary data on historical geochemistry, hydrology, geology, and oil-field infrastructure. Hydrocarbons in groundwater (e.g., methane through pentane gases and benzene) are associated with natural processes (e.g., fault offsets or transition in sediment depositional environment) and oil-field infrastructure (e.g., fluid-migration pathways associated with uncemented annulus in oil wells or unlined pits). Arsenic concentrations >10 μg per liter (μg/L; maximum concentration 12.9 μg/L) are associated with natural processes in old, high-pH groundwater, and more recent recharge of water from natural and/or engineered recharge processes. Along the southwest margin of the oil field, pumping for drinking-water and irrigation supplies in combination with engineered groundwater recharge produce a depression in groundwater elevations where groundwater with elevated sulfate concentrations from agricultural areas and groundwater with hydrocarbons from the oil field mix to produce a zone of sulfate reduction that removes hydrocarbons and arsenic from groundwater but produces elevated sulfide (S2-) concentrations (maximum concentration 29 mg per liter, mg/L). In this study, multiple approaches were required to resolve the overlapping effects of land uses, oil-field infrastructure, and natural processes on the distributions of hydrocarbons and arsenic in groundwater. The combined use of geographic, historical, physical, chemical, isotopic, and other information to constrain processes could be a useful approach for studies in other hydrocarbon-production areas. This is particularly important where land uses affect aquifer hydrology to an extent that causes mixing of groundwaters with different chemical compositions.

Deciphering the spatial heterogeneity of groundwater arsenic in Quaternary aquifers of the Central Yangtze River Basin

Significant spatial variability of groundwater arsenic (As) concentrations in South/Southeast Asia is closely associated with sedimentogenesis and biogeochemical cycling processes. However, the role of fine-scale differences in biogeochemical processes under similar sedimentological environments in controlling the spatial heterogeneity of groundwater As concentrations is poorly understood. Within the central Yangtze Basin, dissolved organic matter (DOM) and microbial functional communities in the groundwater and solid-phase As-Fe speciation in Jianghan Plain (JHP) and Jiangbei Plain (JBP) were compared to reveal mechanisms related to the spatial heterogeneity of groundwater As concentration. The optical signatures of DOM showed that low molecular terrestrial fulvic-like with highly humified was predominant in the groundwater of JHP, while terrestrial humic-like and microbial humic-like with high molecular weight were predominant in the groundwater of JBP. The inorganic carbon isotope, microbial functional communities, and solid-phase As-Fe speciation suggest that the primary process controlling As accumulation in JHP groundwater system is the degradation of highly humified OM by methanogens, which drive the reductive dissolution of amorphous iron oxides. While in JBP groundwater systems, anaerobic methane-oxidizing microorganisms (AOM) coupled with fermentative bacteria, iron reduction bacteria (IRB), and sulfate reduction bacteria (SRB) utilize low molecular weight DOM degradation to drive biotic/abiotic reduction of Fe oxides, further facilitating the formation of carbonate associated Fe and crystalline Fe oxides, resulting in As release into groundwater. Different biogeochemical cycling processes determine the evolution of As-enriched aquifer systems, and the coupling of multiple processes involving organic matter transformation‑iron cycling‑sulfur cycling-methane cycling leads to heterogeneity in the spatial distribution of As concentrations in groundwater. These findings provide new perspectives to decipher the spatial variability of As concentrations in groundwater.

Geo-spatial epidemiology of gallbladder cancer in Bihar, India

Recently, a substantial increase in gallbladder cancer (GBC) cases has been reported in Bihar, India. The region's groundwater can naturally contain harmful concentrations of arsenic, which appears to be epidemiologically linked to the unusually high incidence. However, the root causes remain largely unexplored. Recent findings of uranium in the state's groundwater may also have associations. This study investigates the geo-spatial epidemiology of GBC in Bihar, India—with a focus on the correlation between environmental carcinogens, particularly arsenic and uranium in groundwater, and the incidence of GBC. Utilizing data from 8460 GBC patients' registration records over an 11-year period at a single health center, the research employs Semi-parametric Geographically Weighted Poisson Regression (S-GWPR) to account for non-stationarity associations and explores significant factors contributing to GBC prevalence at a subdistrict level. The S-GWPR model outperformed the standard Poisson regression model. The estimates suggest that arsenic and uranium concentrations in groundwater did not present significant associations; however, this could be due to the lower resolution of this data at the district level, necessitating higher resolution data for accurate estimates. Other socio-environmental factors included demonstrated significant regional heterogeneity in their association with GBC prevalence. Notably, each 1 % increase in the coverage of well- and canal-irrigated areas is associated with a maximum of 3.0 % and 5.2 % rise in the GBC incidence rate, respectively, likely attributable to carcinogen exposure from irrigation water. Moreover, distance to the health center and domestic electricity connections appear to influence the number of reported GBC cases. The latter suggests that access to electricity might have facilitated the use of groundwater pumps—increasing exposure to carcinogens. The results underscore the necessity for targeted health policies and interventions based on fine-resolution spatial analysis, as well as ongoing environmental monitoring and research to better understand the multifaceted risk factors contributing to GBC.

Functionalized activated carbon with whey protein amyloid fibrils for adsorption of arsenic from water

Globally, more than 200 million people are exposed to elevated concentrations of arsenic in groundwater, thus representing an important environmental and public health problem. Apart from inorganic species, water quality suffers also from the occurrence of organic pollutants discharged by the food sector like whey, a by-product of the dairy industry. The valorisation of these organic residues is relevant to reduce water pollution and to develop new materials for different applications. This research reports the synthesis and evaluation of a novel adsorptive activated carbon composite modified with whey protein amyloids for arsenic removal from water. The adsorbent was characterized with FT-IR spectroscopy, X-ray diffraction, N2 physisorption, pH at point of zero charge determination, and thermogravimetric analysis. Experimental adsorption kinetics and isotherms of arsenic removal were determined at optimal pH 5.0, best fitted with the PSO and Sips model, respectively. The corresponding maximum adsorption capacity for As5+ was 0.16 mmol g−1 through an endothermic process. Surface complexation was the predominant phenomenon in the adsorption mechanism as the As-O bond was formed via whey’s functional groups, hydroxyl, amine, and amide, with the latter having the strong affinity to arsenate as elucidated by the HSAB theory. This study highlights the potential of whey protein as a raw material to produce added-value products and its performance as a precursor of novel adsorbents for water purification, therefore minimizing their associated disposal cost and addressing relevant environmental concerns such as arsenic contamination.

Chemical Characteristics of Shallow Groundwater in the Yellow River Diversion Area of Henan Province and Identification of Main Control Pollution Sources

The northern plain of Henan in the lower reaches of the Yellow River is an area where the Yellow River is frequently diverted. The shallow groundwater quality in this area is poor， and many types of components have been found to be exceeding the limit value； however， the contribution of various environmental factors to water quality needs to be further quantified. In order to clarify the genesis of water quality of shallow groundwater in the study area， 330 groups of shallow groundwater samples were collected via a regional water quality survey. The evolution of shallow groundwater quality in the Yellow River diversion area of northern Henan was revealed using the principal component-absolute principal component score-multiple linear regression （PCA-APCS-MLR） model. The results showed that the components with a shallow groundwater excess rate greater than 10% in descending order were manganese， iron， total hardness， total dissolved solids， sodium， fluoride， arsenic， chloride ions， sulfate， and ammonium. In particular， the excess rate of manganese reached 76%. The four factors of dissolution enrichment， native origin of soil， redox conditions， and agricultural activities were identified as the main reasons for poor groundwater quality， which accounted for 71.24% of the cumulative interpretation rate of variance. In addition， the recharge from the surface water also influenced the groundwater quality. The effects of dissolution between the water and aquifer matrix and redox condition in the aquifer of the Yellow River dried-riverway like Xinxiang were significantly enhanced， resulting in the increasing concentration of iron， arsenic， total hardness， TDS， and other components in groundwater. Fluoride enrichment was caused by dissolution enrichment， the origin of the soil， and lateral replenishment of the Yellow River. Groundwater with high manganese concentration was widely affected by the soil matrix. Nitrate pollution of the groundwater was caused by the extensive use of chemical fertilizers in agricultural activities in individual areas.

Dynamics of Spatiotemporal Variation of Groundwater Arsenic in Central Rift Vally of Ethiopia: A Serial Cross-Sectional Study.

Arsenic is a well-known, highly poisonous metalloid that affects human health and ecosystems and is widely distributed in the environment. Nevertheless, data on the spatiotemporal distribution of arsenic in groundwater sources in Ethiopia are scarce. The principal aim of this study was to assess the extent of arsenic in groundwater sources and analyze the spatiotemporal variations in the central rift valley of Ethiopia. The study employed a serial cross-sectional study design and census sampling methods. The concentrations of arsenic in the groundwater samples were determined using inductively coupled plasma mass spectrometry (ICP-MS) at the Ethiopian Food and Drug Authority laboratory. Descriptive statistical analyses were performed using IBM SPSS version 29 software. Additionally, ArcGIS software was utilized to map the spatiotemporal distribution of arsenic. Furthermore, Minitab statistical software version 21.4 was employed to assess the correlation between spatiotemporal variations of arsenic concentrations in groundwater sources. The mean values of arsenic in the groundwater samples were 11.2 µg/L during the dry season and 10.7 µg/L during the rainy season. The study results showed that 18 wells (42.2%) and 22 wells (48.8%) had higher arsenic concentrations (>10 µg/L) during the dry and rainy seasons, respectively. Thus, arsenic levels in 42.2% and 48.8% of the samples exceeded the maximum threshold limit set by WHO, USEPA, and Ethiopian standards (10 µg/L), respectively, during the dry and rainy seasons. Furthermore, our analysis revealed a significant positive correlation between arsenic in groundwater and well depth (r = .75, P < .001), indicating a strong association between higher arsenic concentrations and deeper wells. Similarly, we observed a substantial positive correlation between arsenic concentration in groundwater and season (r = .9, P < .001), suggesting notable variations in arsenic levels between dry and rainy seasons. The majority of the groundwater sources in the studied area are unfit for human consumption because they contain high amounts of arsenic, which poses a significant risk to human health. Moreover, the arsenic concentration varied spatially and temporally. Therefore, special attention is needed to reduce arsenic exposure and associated health risks.

Assessing Acute and Chronic Risks of Human Exposure to Arsenic: A Cross-Sectional Study in Ethiopia Employing Body Biomarkers.

Arsenic, a widely recognized and highly toxic carcinogen, is regarded as one of the most hazardous metalloids globally. However, the precise assessment of acute and chronic human exposure to arsenic and its contributing factors remains unclear in Ethiopia. The primary goal of this study was to assess the levels of acute and chronic arsenic exposure, as well as the contributing factors, using urine and nail biomarkers. A community-based analytical cross-sectional study design was employed for this study. Agilent 7900 series inductively coupled plasma mass spectrometry was used to measure the concentrations of arsenic in urine and nail samples. We performed a multiple linear regression analysis to assess the relationships between multiple predictors and outcome variables. The concentration of arsenic in the urine samples ranged from undetectable (<0.01) to 126.13, with a mean and median concentration of 16.02 and 13.5 μg/L, respectively. However, the mean and median concentration of arsenic in the nails was 1.01, ranging from undetectable (<0.01 μg/g) to 2.54 μg/g. Furthermore, Pearson's correlation coefficient analysis showed a significant positive correlation between arsenic concentrations in urine and nail samples (r = 0.432, P < .001). Also, a positive correlation was observed between urinary (r = 0.21, P = .007) and nail (r = 0.14, P = .044) arsenic concentrations and the arsenic concentration in groundwater. Groundwater sources and smoking cigarettes were significantly associated with acute arsenic exposure. In contrast, groundwater sources, cigarette smoking, and the frequency of showers were significantly associated with chronic arsenic exposure. The study's findings unveiled the widespread occurrence of both acute and chronic arsenic exposure in the study area. Consequently, it is crucial to prioritize the residents in the study area and take further measures to prevent both acute and chronic arsenic exposure.

GIS based Groundwater Vulnerability Assessment of Lahore a Metropolitan using Modified DRASTIC Model

The metropolitans of the developing countries are experiencing the hazard of low quality drinking water. Lahore, the heart of Pakistan, has over 12 million population and is going to face the shortage of clean drinking water. The study was designed to create a groundwater vulnerability map of Lahore using modified DRASTIC model. The water table, land use/cover, hydraulic conductivity, vadose zone, soil and aquifer media layers of Lahore aquifer were analyzed using ‘geostatistical analyst’ and ‘spatial analyst’ extensions of ArcGIS software to compute the groundwater vulnerability. The Vulnerability index of modified DRASTIC model ranged from 64 – 144. The spatial variability of modified DRASTIC index were divided into 4 categories, namely, Low, Moderate, High and Very High groundwater vulnerable zones. The modified DRASTIC model revealed that 51.38% areas have low vulnerability, 36.77% areas have moderate vulnerability, 9.48% areas have high vulnerability and 2.37% areas have very high vulnerability to groundwater pollution. The ‘high’ and ‘very high’ vulnerability classes were found in the western parts of Lahore. The results of modified DRASTIC model were validated using the groundwater contamination data of arsenic in the study area. The groundwater arsenic concentrations map also showed higher values in the northwestern parts while in the eastern parts of the study area, the arsenic concentrations had a decreasing trend. In Ravi Town, the arsenic concentrations were positively correlated with modified DRASTIC vulnerability index and inversely correlated with water table depth (p &lt; 0.01). It is recommended that the arsenic removal plants should be installed at all the tubewells with higher arsenic levels (arsenic concentration &gt; 50 ppb) to provide safe drinking water to the citizens of Lahore. It is recommended that the modified DRASTIC model based on geospatial techniques can be applied on other metropolitans of the world for taking preventive measures against massive water pollution.

A case study on regional arsenic sources and its distribution in Mekong River groundwater

Arsenic contamination in the Mekong River is a well-known environmental issue yet to be resolved due to its transboundary nature which further limits its access and data collection. Other than that, the key mechanisms that controlling the arsenic release in Mekong sub-region groundwater was heterogeneously distributed and can be varied from region to region. The main purpose of this project is to identify the regional arsenic contamination levels in the hope of helping the government integrate regional groundwater arsenic risk reduction policy in their near future planning. Sampling was conducted during May and August 2022 in Cambodia and Laos, respectively. The findings revealed that the topography of certain areas in Cambodia has exposed significantly high concentrations of arsenic in groundwater compared to those in Laos. About 33% of the sampling sites in Cambodia had shown a high arsenic contamination (&gt; 10 µg/L, WHO guideline) with its concentration ranging from 47.7 ± 0.8 to 696.9 ± 5.6 ppb. The physicochemical properties revealed that the arsenic controlling mechanisms were totally different between both study area. More regional and site-specific arsenic contamination research related to climate change and arsenic hydrology at regional levels should be carried out to ensure the water safety plan in specific regions. Further, we believe that the findings of this study will be beneficial to policy and regional water safety plans for the Mekong River, especially in Cambodia.

Groundwater contamination & health risk assessment with special reference to As in different tehsils of Bhakkar district

The present study was conducted to evaluate the concentration of arsenic in groundwater in Bhakkar district, Western Punjab Province, Pakistan. To analyze hydrochemical characteristics, 68 groundwater samples from various water schemes in four tehsils were taken. While most of the samples' concentrations of major cations and anions were within the WHO's maximum allowed range, almost 44% had higher arsenic levels than what was considered acceptable for drinking water. Gibbs diagrams were used to show how groundwater chemistry is affected by the dominance of rocks and evaporation crystallization. An investigation of the Indus River's lateral distribution and upstream to downstream locations' arsenic concentrations showed variability. According to the Health Risk Assessment (HRA), samples from Bhakkar tehsil (73.3%), Darya Khan tehsil (60%), and Kalur Kot tehsil (13.3%) all surpassed the hazard quotient level of “1,” whereas all samples from Mankera tehsil were within acceptable bounds. The hazard quotient numbers displayed a range of 0.038–5.127, with an average of 1.149 – suggesting that a considerable portion of people consuming water laden with high levels of arsenic may suffer from non-cancerous health complications. Moreover, the carcinogenic risk values were as high as 2.31 × 10−3, implying a heightened danger of cancer over the long term due to arsenic-contaminated water. It was determined that Bhakkar tehsil had the highest quantity of arsenic among the tehsils in the Bhakkar district, making the groundwater unfit for drinking.

Elevated arsenic concentrations in groundwater of the Upper Indus Plain of Pakistan across a range of redox conditions

Groundwater of the Ravi River floodplain is particularly elevated in arsenic (As) on both sides of the Pakistan-India border. To understand this pattern, 14 sites were drilled to 12–30 m depth across floodplains and doabs of Pakistan after testing over 20,000 wells. Drill cuttings were collected at 1.5 m intervals, 132 of which were sand overlain by 77 intervals of clay and/or silt. Radiocarbon dating of clay indicates deposition of the aquifer sands tapped by wells 20–30 kyr ago. Most (85 %) of the sand samples were gray in color, indicating partial reduction to Fe(II) oxides, whereas most (92 %) of the clay and/or silt samples were orange. Associations between groundwater electrical conductivity, dissolved Fe, sulfate, and nitrate suggest that wells can be elevated (>10 μg/L) in As in the region due to either reductive dissolution of Fe oxides, evaporative concentration, or alkali desorption. In the Ravi floodplain, 47 % of 6445 wells tested contain >10 μg/L As compared to only 9 % of 14,165 tested wells in other floodplains and doabs. The As content of aquifer sands in the Ravi floodplain of Pakistan averages 4 ± 4 mg/kg (n = 66) and is higher than the average of 2 ± 2 mg/kg (n = 51) for aquifer sands outside the Ravi. Synchrotron spectroscopy and column-based speciation indicate predominance of As(V) over As(III) in both aquifer sands and groundwater. Whereas multiple processes may be responsible for elevated levels of As in groundwater across the region, spatial heterogeneity in groundwater As concentrations in the Ravi floodplain seems linked to variations in As concentrations in aquifer sands. Regulation by the solid phase may limit variations in groundwater As over time in response to natural and human-induced changes in hydrology. This means spatial heterogeneity could be taken advantage of to lower the exposure across the region with more testing and targeted drilling.

Large Scale Nationwide Screening of Bioavailable Tetracyclines and Arsenic Using Whole-cell Bioreporter from Pangasius and Tilapia Aquaculture System in Bangladesh

In this study, a large-scale screening of bioavailable tetracycline (TC) and arsenic (As) was carried out using Whole-Cell biosensor bacteria from the pond surface waters of selected pangasius and tilapia farms across Bangladesh. Bioavailability of Tetracyclines and Arsenic was detected using biosensor bacteria E. coli K12 pTetLux1 and E. coli DH5R (pJAMA-arsR), respectively. 62 samples were within the tetracycline detection limit; others were below the detection limit. The highest concentration 31.27&amp;micro;g/l was found in a pond of Jashore and the lowest concentration 2.57&amp;micro;g/l, was measured from Trishal, Mymensingh. Tetracyclines were detected in 19.57 % of all analyzed samples and varied from below the detection limit to a maximum of 31.27 &amp;mu;g/l. Khulna showed the maximum percentage of detected samples (90%), followed by Mymensingh (52%), Cumilla (27.5%), Jashore (14%), Bogura (14%), and Sathkhira (10%). Bioavailable As were detected in only 5.7% of all analyzed samples. Detectable as samples were higher in number among the collected samples at Madaripur and higher in percentage at Sathkhira (30%) followed by Madaripur (20%), Cumilla (5%), and Gopalgonj (3.2%). The pick level of Arsenic was found in Madaripur, which was 9.45 &amp;micro;g/l and the lowest figure was 2.35 &amp;micro;g/l, found in Cumilla. Though the concentrations of arsenic in groundwater (tube well water) were high in all studied regions, the concentrations of arsenic in pond water were low in every region. The concentration of as in the experimental ponds was neither very high nor in the danger limit. Use of shallow tube well water, fertilizers, drugs, pesticides, and herbicides possessed with as during fish culture might be the source of as contamination. However, extensive use of antibiotics results in the emergence and spread of antibiotic resistance in the aquatic environment.

Enrichment mechanism of arsenic in deep confined aquifers of Guide Basin under the influence of geothermal activities

High arsenic (As) concentrations in groundwater affected by geothermal activity have been reported worldwide, but the genesis mechanism is not adequately understood. To address this issue, 22 groundwater samples and 69 sediment samples were collected from the Guide basin in northwest China for hydrochemical and geochemical analyses and laboratory experiments. Results indicated that Fe/Mn oxide minerals were the main carriers of As, which was proved by a positive correlation between As and Fe or Mn in sediments. Analysis of hydrologic characteristics showed that direct contribution of As from geothermal water to confined groundwater was probably not significant in the Guide basin. Results of laboratory experiments indicated that As release from aquifer sediments was accelerated by increased temperature under different pH and ORP conditions. Raising temperature may stimulate the release of As by promoting dissolutions of As-bearing Fe (hydr)oxides, weathering of As-bearing silicates and As desorption from sediments into groundwater. The research results can not only increase the understanding of arsenic enrichment mechanism in groundwater under geothermal geological background, but also provide a scientific basis for other high-As groundwater with similar geological background in the world.

Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater—Making the Right Choice

The environmentally and economically sustainable provision of safe drinking water remains one of the most significant public health challenges facing society. It is particularly difficult in small municipalities that lack the human and financial resources to maintain complex and/or expensive water treatment systems, especially in regions where the water is naturally contaminated with toxic substances such as arsenic. This paper analyses two municipalities (Kanjiža and Senta) in northern Serbia with groundwater arsenic concentrations significantly higher than the 10 µg/L maximum allowable concentration. Two different water supply approaches are compared: (1) local supply, where each settlement has their own treatment plant using existing sources; (2) creation of a centralised network, which connects several settlements together with better quality water sources. The analysis considers techno-economical aspects and the quantity and quality of available water, as well as the yields investment and exploitation cost projections for both approaches. The results demonstrate that for small municipalities, a holistic approach to decision making is necessary to find the best solutions for drinking water supply, which in the case of the two municipalities studied is connection to a centralised system supplied from better quality water sources and the creation of connections beyond municipal administrative boundaries.