Concentrations In Groundwater Samples Research Articles

Terrigenic helium in brackish groundwaters of Kuwait, probable influences from hydrocarbon resources

The presence of helium in groundwater can shed light on its age, migration patterns, and potential recharge locations. The 4He, 3He/4He, 20Ne, and major ions concentrations in groundwater samples were analysed. He concentration in groundwaters of the Kuwait group aquifers ranged from 3.82 × 10−8 to 1.33x−6 cm3STP/g and that of Dammam formation ranged from 9.97 × 10−8 to 1.62 × 10−6 cm3STP/g. The distribution of He was inferred to be influenced by geological structures and it was found that He decreased with Cl concentration and δ18O with few exceptions. The δ18O signatures reflect mixing between aquifers. The higher He fluxes were recorded during the Last Glacial Maximum, but they rose steadily over time. Most of the north Kuwait samples, dating from 1650 to 348 yBP, had a tritiogenic component similar to the present atmosphere, as indicated by RA (Air normalized He ratios) values, which was inferred to be due to the decay of tritium derived from meteoric impact. Magnitude 2–4 earthquake epicenters occurred more frequently in areas with lower RA values. Lateral flow was predominant with insitu (He derived from rock matrix along the flow) and exogenous sources (vertical upward cross formational flow, atmospheric input and from hydrocarbon reservoirs). Groundwater samples near oil fields were found to contain more terrigenic He, suggesting its migration to shallow waters through fault planes. The presence of more He in brackish groundwater at a shallow depth may be indicative of an abundant source beneath. He excess (Heex) and air corrected He excess (ΔHe%) are indicators of non-neuclogenic and radiogenic He sources, such as the exsolution from adjoining hydrocarbon fields through structurally weaker planes.

Mapping of uranium in groundwater of Mysuru district, Karnataka, India and radiation dose to the population

Uranium in ground and surface water may pose significant health hazards, if they are ingested via drinking pathway because of its radiological and chemical toxicities. Higher uranium concentration in drinking water can lead to serious diseases like leukemia, kidney cancer, stomach cancer and renal damages etc. In the present investigation, uranium concentration in groundwater samples of Mysuru district has been studied using an LED fluorimeter. About 350 water samples have been collected covering all the areas of Mysuru district and the uranium concentration varied from 0.34 to 242.93 µg l−1. The age depended dose due to uranium in drinking water has been estimated to evaluate the health hazards. The annual ingestion dose varied from 0.51 to 95.76 µSv y−1. The radiological and chemical toxicity risk to the population due to uranium in water was estimated. The Excess Cancer Risk varied from 1.22 × 10−6 to 102.63 × 10−6. The mean value of Hazard Quotient 0.07 indicates that the populations are less prone to adverse health effects of uranium contamination in water.

Geospatial assessment and hydrogeochemical characterization of groundwater resources of Manipur Valley, India.

Spatio-temporal hydrogeochemical assessments of groundwater resources were carried out for the valley region of Manipur in India to investigate its quality status and suitability criteria for irrigational and potable uses. The assessment was carried out for 140 spatially distributed groundwater samples collected during 2018-2021 for pre- and post-monsoon seasons. To assess and comprehend the hydrogeochemical behavior of underlying aquifers and controlling factors for groundwater quality in the region, groundwater quality indices and statistical tools were used. Assessment of in situ parameters such as pH, TDS, salinity, and EC under field conditions reveal that the values exceed concentration in many locations. Most of the samples fall in the category of hard to very hard water. Moreover, observation of both positive and negative oxidation-reduction potential (ORP) and low dissolved oxygen (DO) concentration in groundwater samples indicates aquifers are of recent unstable geologic formations. Similarly, elevated concentrations of F-, Cl-, HCO3-, and Fe3+ greater than the prescribed standards of the World Health Organization in many samples indicate the unsuitability of the groundwater sources for potable uses. The geochemical interactions were found to be dominated and controlled by the rock-weathering geochemical process that contributes to HCO3--Ca2+-type water, followed by HCO3--Na+, Cl--Ca2+, and mixed-type water in both seasons. Spatio-temporal geospatial vulnerable groundwater zone mapping using interpolation techniques carried out in the ArcGIS platform identifies the aquifers based on the water quality and pollution indices. The study's significant findings can provide baseline information that can supplement the government's planning and management initiatives to deal with the current water security challenges in the region as groundwater uses are increasing due to various hydroclimatic phenomena in the state.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Use of Censored Multiple Regression to Interpret Temporal Environmental Data and Assess Remedy Progress.

Many methods to evaluate temporal trends in monitoring data focus on univariate techniques that account for changes in the response variable (e.g., concentration) by means of a single variable, namely time. When predictable site-specific factors, such as groundwater-surface water interactions, are associated with or may cause concentration changes, univariate methods may be insufficient for characterizing, estimating, and forecasting temporal trends. Multiple regression methods can incorporate additional explanatory variables, thereby minimizing the amount of unexplained variability that is relegated to the "error" term. However, the presence of sample results that are below laboratory reporting limits (i.e., censored) prohibits the direct application of the standard least-squares method for multiple regression. Maximum Likelihood Estimation (MLE) for multiple regression analysis can enhance temporal trend analysis in the presence of censored response data and improve characterizing, estimating and forecasting of temporal trends. Multiple regression using MLE (or censored multiple regression) was demonstrated at the U.S. Department of Energy Hanford Site where analyte concentrations in groundwater samples are negatively correlated with the stage of the nearby Columbia River. Incorporating a time-lagged stage variable in the regression analysis of these data provides more reliable estimates of future concentrations, reducing the uncertainty in evaluating the progress of remediation toward remedial action objectives. Censored multiple regression can identify significant changes over time; project when maxima and minima of interest are likely to occur; estimate average values and their confidence limits over time periods relevant to regulatory compliance; and thereby improve the management of remedial action monitoring programs. This article is protected by copyright. All rights reserved.

CrowdWaterSens: An uncertainty-aware crowdsensing approach to groundwater contamination estimation

Groundwater contamination poses serious threats to public health and environmental sustainability. In this paper, we explore smart groundwater contamination sensing, which aims to accurately estimate the nitrate concentration in groundwater via a crowdsensing approach. Existing solutions often require professional groundwater collection and high-quality measurement of groundwater properties, making the data collection process time-consuming and unscalable. In this work, we leverage the approximate nitrate concentration measured by crowd sensors (i.e., participants from well-dependent communities) to accurately estimate nitrate concentration in groundwater samples. Three critical challenges exist in developing the crowdsensing-based groundwater contamination estimation solution: (i) the spatial irregularity of the crowdsensing groundwater contamination data, (ii) the hidden temporal dependency of groundwater contamination in the anthropogenic context, and (iii) the uncertainty of crowdsensing nitrate measurements from crowd sensors. To address the above challenges, we develop CrowdWaterSens, an uncertainty-aware graph neural network framework that explicitly examines the uncertainty and spatial irregularity of the crowdsensing groundwater contamination data and its relevant anthropogenic context to accurately estimate groundwater nitrate concentration. We evaluate the CrowdWaterSens framework through two real-world case studies in well-dependent communities in Northern Indiana, United States. The evaluation results not only show the effectiveness of CrowdWaterSens in accurately estimating nitrate concentration, but also demonstrate the viability of crowdsensing for community-level groundwater quality monitoring.

Human Health Risk Assessment for Nitrate intake in Lucknow, India

Abstract: It is important to apply the methods to set the standard for drinking water quality and to conduct risk assessments since access to potable water resources exposes people to a variety of chemical pollutants, including nitrate. Drinking water with high nitrate levels can be harmful to health, especially for infants and pregnant women. The main objective of the present study was to determine the distribution levels of nitrate contamination in groundwater and its associated impact on human health risks in Lucknow India. For this 32 groundwater samples were collected randomly during April 2022. Nitrate concentration in groundwater samples ranged from 7 to 108 mg/L, with a mean value of 41.35 mg/L. The geographic information system (ArcGIS 10.7.1) was applied to mapping the nitrate concentration in groundwater resources of the studied area. About 43.75% of the groundwater samples are exceeding the permissible limits of nitrate (45 mg/l). Hence health risk assessment of nitrate has been carried out. Hazard quotient (HQ) values for male, female, children, and infants ranges from 0.14 to 2.07; 0.16 to 2.45; 0.17 to 2.63, and 0.16 to 2.53, respectively. The finding of data showed that HQ value was more than 1 in 53..125% of samples in groups of infants and children, 50% of samples for female, and 43.75% of samples in group of male.

Probabilistic potential health risk quantification, hydrochemistry, driving forces of groundwater nitrate and fluoride in a typical irrigation district, Northern China

Groundwater is the foremost water source of agricultural irrigation areas in northern China. However, the problem from excessive geogenic fluoride and anthropogenic nitrate in groundwater and its potential health risks are often neglected. This paper aims to identify the health threats, water quality and hydrochemistry in Weibei Plain, northern China. A total of 50 water samples collected in May 2021 were assessed by hydrogeochemical analysis, the entropy weight water quality index method and probabilistic human health risk assessment based on Monte Carlo stochastic simulation. The results showed that the hydrochemical type of surface water and groundwater was mainly SO4·Cl–Ca·Mg type. The rock weathering and cation exchanges were found to the important processes influencing hydrochemistry. The quality of surface water was higher than that of groundwater, with Class IV and Class V groundwater samples concentrated in the northwest of the study area. The F− concentration in groundwater samples generally exceeded the drinking water standard, which mainly came from mineral dissolution affected by excessive irrigation. Nitrate pollution in groundwater has regional characteristics, mainly from agricultural activities. Long−term use of groundwater might bring risks to residents' health, because more than 65% and 23% of groundwater samples exceeded the acceptable non−carcinogenic risk limits of F− and NO3− to children, respectively. Considering the uncertainty of model parameters, children will have a probability of more than 46.9% and 12.6% to face F− and NO3− risks in groundwater. Compared with NO3−, the high concentration of F− in groundwater posed a higher threat to human health, and children faced higher risks. Compared with the deterministic method, the stochastic simulation can more accurately reflect health risks. The findings of this study can help policymakers devise strategies to ensure a safe supply of domestic water.

Groundwater salinity and its effect on elevated concentrations of other contaminants in a coastal plain: a perspective for infrastructure protection

Salinization is a serious threat to coastal groundwater because it degrades water quality and results in trace metal dissolution. Meanwhile, groundwater quality is critical for human and development, as contaminated groundwater cause a negative effect on human`s health and infrastructure. Our investigation of a specific area, Indramayu, on West Java’s north coast revealed that most groundwater samples are brackish to saline in existence. TDS concentrations in groundwater samples were predominantly in the >1,000 to nearly 30,000 mg/L range. Concentration contours were disproportionately high in the north tip area. Numerous factors contribute to the high salinity of this area, including the interaction of saline water and sediment with low-salinity groundwater and evaporation. Additionally, high TDS levels were significantly correlated with Fe2+ and Mn2+ concentrations (p < 0.001). The area’s high salinity was thought to facilitate the dissolution of trace metals from minerals such as siderite and rhodochrosite (SI of the minerals > 1). Concentrations of Fe2+ and Mn2+ were very likely to damage the water piping system. In terms of water quality, less than 30% of samples were fit for human consumption. Finally, the government should take into account the groundwater environment when developing the area in order to protect human health and infrastructure.

Non-carcinogenic health risk assessment of nitrate and fluoride contamination in the groundwater of Noyyal basin, India

The study aims to assess the nitrate and fluoride concentration in groundwater and its adverse effects on human health. In 2019, 42 groundwater samples were collected from various bore wells within the western Noyyal basin, India. Sodium and chloride are the dominant cation and anion, respectively. The nitrate concentration in groundwater samples varies from 2 to 89 mg/L, of which 33.33% are above the permissible limit of 45 mg/L for drinking water. The fluoride concentration ranges from 0.2 to 2.4 mg/L, with 28.57% of the samples exceeding the safe value of 1.5 mg/L for drinking water. Correlation plots demonstrate that the potential of hydrogen (pH), electrical conductivity (EC), total dissolved solids (TDS), Na+ and HCO3− are positively correlated with F−, whereas Ca2+ is negatively correlated. Mixed Ca–Mg–Cl is the most common water type in the investigated region. The Gibbs diagram demonstrates that the interaction between rock and water impacts the groundwater chemistry. Using the method of the United States Environmental Production Agency (USEPA), this study assesses the non-carcinogenic health risk posed by nitrate and fluoride in different age groups (infants, children, and adults). The values of total hazard index (THI) vary from 0.59 to 10.07 (mean = 4.76) for infants, 0.36 to 6.23 (mean = 2.95) for children, and 0.19 to 3.32 (mean = 1.57) for adults. Furthermore, 97.62%, 92.86%, and 73.81% of the samples surpass the recommended limit (THI = 1) for infants, children, and adults, respectively. Thus, the health risk assessment (HRA) indicates that infants and children are more susceptible to non-carcinogenic health hazards than adults. The THI spatial variation map shows that central and southern regions of the study area have been identified as high health risk areas (THI >3.0) for all age groups.

3D printed opto-microfluidic autonomous analyzer for photometric applications

Nowadays, there is a trend towards developing and fabricating (bio) analytical chemistry systems adopting additive manufacturing techniques, also known as 3D printing. These developments focuses on manufacturing flow detection cell, mixing system and electrochemical electrodes, but not to automation, portability and real time analytical systems. A new 3D printed opto-microfluidic analyzer was developed to implement an autonomous and miniaturized analytical process with auto-calibration controlled by wireless communication. The device integrated an RGB photodetection system to select the operating wavelength in the visible range. The system was initially evaluated using solutions dyes based on methyl red, methylene blue and later to determinate fluoride concentrations in groundwater samples. The results were comparable with those obtained with a commercial instrument. The device allowed decreasing instrumentation size, and reagents and energy consumption, which achieved significant advantages for an in-situ and real-time in a portable analytical system. With the implementing of other nodes, a wireless sensors network could be achieved with an application for internet of things IoT to generate and process analytical data.

Temporal and spatial assessment of groundwater contamination with nitrate using nitrate pollution index (NPI), groundwater pollution index (GPI), and GIS (case study: Essaouira basin, Morocco).

Groundwater aquifers in Morocco's coastal regions are under serious threat as a result of climate change. This study was conducted to evaluate and map the quality of water resources, by evaluating the level of pollution of the groundwater in the Meskala-Ouazzi sub-basin, a coastal area of Essaouira based on the physico-chemical analysis of 58 samples using a geographic information system (GIS) technique, analytical analysis, nitrate pollution index (NPI), and groundwater pollution index (GPI). The diagram piper of the study area is dominated by Cl-Ca-Mg, Cl-Na, HCO3-Ca-Mg, and SO4-Ca types. The concentrations of nitrate ranged from 2 to 175mg/L. It was discovered that 22% of the groundwater samples had nitrate amounts greater than the World Health Organization's recommended maximum allowable level of 50mg/L. The NPI ranged between - 0.9 and 7.8. According to the classification of NPI, 44.8% of the total groundwater samples represent clean water, indicating that the groundwater in the study area is suitable for irrigation. GPI values ranging from 0.6 to 3.7, with an average of 1.7, identifies 37.9% of all groundwater samples as low polluted. The inverse distance weighting (IDW) approach was used to generate a spatial distribution map, which indicates that appropriate groundwater is present in the sub-upstream basin's part. Overall, the forte concentration in groundwater samples detected in western and central areas showed that the nitrate originated from large amounts of nitrogen fertilizer used by humans in agricultural activities during periods of irrigation. The low tritium (δ3H) content shows that the aquifer recharge is stale water and excessive use of fertilizers leads to groundwater pollution faster over time.

Comprehensive assessment of groundwater quality using heavy metal pollution indices and geospatial technique: a case study from Wanaparthy watershed of upper Krishna River basin, Telangana, India

The present study is to characterize groundwater quality using heavy metal pollution indices and geospatial variations. A total of 58 samples from hand pump/submersible bore wells were collected from the Wanaparthy watershed of the upper Krishna River basin according to the grid size (5×6 km2). The trace elements’ concentration in groundwater samples are found in the order of Zn (38.67%) > B (32.67%) > Ba (13.59%) > As (8.49%) > Hg (3.71%) > Cr (1.28%) > Ni (0.52%) > Cd (0.47%). Among these trace elements, arsenic (22.4%) and mercury (5.1%) were found above the permissible limits of WHO drinking water guideline values. A positive correlation between TH versus EC/TDS indicates the presence of trace elements due to chemical reaction (rock–water interaction). Arsenic correlation with EC/TDS/TH indicates artificial intervention. Drainage network analysis enumerates high concentration of parameters at near or joining to upper order of drainage system, which might be due to input of runoff water (interaction of variable rocks composition) and later stage infiltration to subsurface and reached to an aquifer. Heavy metal pollution index (HPI) showed 86.2% of samples are in the category of low class, whereas 12.07% of samples fall within medium class. According to metal index (MI) classification, 12.07% samples are in very pure, 24.14% samples are pure, while the remaining 63.79% samples are in the slightly to strongly affect category. This study suggested the main source of trace elements in groundwater might be from the dominant granitoid rocks because the area is mostly devoid of industrialization.

WITHDRAWN: Qualitative estimation for groundwater: A case study for Al-Mahaweel city in Iraq

Risk assessment for heavy metal concentrations in groundwater samples extracted from thirteen wells distributed over the study area in Al-Mahaweel city in Iraq was investigated intensively in this work. The potential risks for drinking water was evaluated by means of different indices, namely; Water Quality Index (WQI), Heavy metals Pollution Index (PHI), and Heavy metal Evaluation Index (HEI). The groundwater pollution with Lead (Pb), Nickel (Ni), Cadmium (Cd), Zink (Zn), and Iron (Fe) were analytically analyzed throughout the study. The results show that presence (expressed by concentration units) distributed within study area in the following order Cd < Pb < Fe < Zn < Ni. Based upon the WQI results, the groundwater from wells was considered unsuitable for drinking. From perspective of heavy metal pollution indices, HPI, and HEI, contamination level was declared as low level. On the other hand, irrigation water assessment was carried out and calculations of three factors, namely Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), and Soluble Sodium Percentage (SSP) were performed. The findings showed that, quality of irrigation water varies from “permissible” to “unsuitable”. These inferences probably resulted from human activates such as leached from landfills and percolation to groundwater from septic tanks. In addition to the alkali nature of agricultural soil in study area. In particular, presence of these elements in silicate rock to form minerals and advanced are transported into the mineral part of soils.

Analyzing the major ions and trace elements of groundwater wells in Kuala Langat, Selangor

The analysis of trace elements and major ions on groundwater wells in Kuala Langat become the aim of this paper for the purposes to investigate the quality of the groundwater to be considered as a suitable alternative water source for domestic purposes. The groundwater sampling was conducted in thirteen stations of groundwater wells. The groundwater samples were taken from the groundwater wells which scatter in agricultural areas in Kuala Langat, Selangor. The major ions parameters have analyzed in the groundwater samples were calcium, magnesium, potassium, bicarbonate, chloride, and sulfate (Ca2+, Mg2+, K+, Na+, HCO3 -, Cl-, SO4 2-). Meanwhile, the trace element parameters were aluminum, iron, manganese, strontium, zinc, and copper (Al, Fe, Mn, Sr, Zn, and Cu). Principal Component Analysis (PCA) was conducted to determine the influence of major ions and trace elements concentration in groundwater. Chloride, potassium, magnesium, strontium, calcium, and bicarbonate (Cl-, Na+, K+, Mg2+, Sr2+, Ca2+, and HCO3 -) were principal parameters in the first component of PCA analysis. The concentration of trace elements shows iron is the high concentration in groundwater samples. Hence, the concentration of iron in current sampling shows exceed the recommended level for raw water of the Ministry of Health. The influences from seawater intrusion and Sodium Adsorption Ratio (SAR) in groundwater also have been discussed.

Use of Aluminium Metal Embedded ThujaOccidentalis Leaves Carbon (AMETLC) for Fluoride Removal from Water: Equilibrium and Kinetic Studies

In this research work, Aluminum metal embedded Thuja Occidentalis leaves carbon (AMETLC) is used an adsorbent for the removal of fluoride from water. On batch technique, under key factors like pH, the dosage of adsorbent, variation of fluoride concentration and contact time, 92% of fluoride removal is observed at room temperature under optimum experimental conditions with an adsorbent particle size of &lt;75 µm. The impact of existing negative ions on percentage removal of fluoride is studied, and it is noticed in the direction of PO43- &gt; HCO3-&gt; SO42- &gt; NO3- &gt; Cl-. The adsorbent is characterized by using FTIR, SEM, EDX, and XRD techniques. Experimental data reveal that the Langmuir isotherm model(maximum adsorption capacity obtained is 0.625 mg g-1) provided the best correlation (R2 = 0.988) and is well fit followed by pseudo-second-order kinetic model represents mono-layered, and chemisorptions are the rate-determining step for fluoride adsorption. Thermodynamic studies reveals better adsorption is attained at lower temperatures. The standard free energy (ΔH0) is observed as negative and the negative value of standard free energy (ΔH0) represents that the adsorption process is exothermic. Moreover, desorption study reveals that fluoride leaching takes place at a pH 13.0. In addition, succinct cost estimation was done for the AMETLC which offered one of the best alternatives for the removal of fluoride. The application of AMETLC is helpful to decrease the fluoride concentration in groundwater samples to meet the permissible limit according to BIS-2012 standards.

Natural radium isotopes in waters determined with gamma-ray spectrometry using a HPGe detector

This paper describes a methodology developed to measure the activity concentration of the four natural radium isotopes 226Ra, 228Ra, 224Ra and 223Ra in water samples, using γ-ray analysis performed with a hyperpure germanium detector (HPGe). The detector efficiency was properly determined using a set of thorium standards exhibiting concentrations between 10 and 10,000 μg/g. The developed analytical protocol was applied for evaluating the radium activity concentration in groundwater samples providing from different aquifer systems in three Brazilian states. The results were compared with the guideline reference values for 226Ra and 228Ra as proposed by the Brazilian and WHO legislations.

Groundwater chemistry and Sr isotope ratios shed light on connectivity and water-rock interactions in the coastal aquifer of the Caribbean coast, Mexico

Radiogenic strontium isotopic ratios (87Sr/86Sr) and solute concentrations in groundwater samples from the coastal area of the eastern Yucatán Peninsula, Quintana Roo, Mexico were measured to assess connectivity and water-rock interactions within different groundwater systems in the region. The average 87Sr/86Sr ratios vary from south to north with values in Xcalak around 0.70889, Sian Ka'an around 0.70847 and at Cancun and Puerto Morelos around 0.70880. The values show 3 clusters suggesting 3 distinct groundwater systems, Cancun-Puerto Morelos in the north, Tulum-Sian Ka'an in the center and Xcalak in the South. Water-rock interactions, while unique within each aquifer system, encompass processes observed throughout the Yucatan karst system including dissolution of evaporites, precipitation of carbonate, sulfate reduction and mixing with seawater. This study highlights the use of groundwater chemistry of understanding groundwater connectivity and the importance of potential distal anthropogenic impacts on groundwater quality and related effect on coastal population and ecosystems.

RADON CONCENTRATION IN SOIL AND GROUNDWATER OF WEST COASTAL AREA, BANGLADESH.

On-site radon concentration has been measured in soil gas and ground water using AlphaGUARD PQ2000 PRO (Saphymo, Germany) radon monitor at the west coastal area of Bangladesh. The measured radon concentration in ground water samples is in the range of 1.41±0.29 to 3.2±0.59Bq/l with the mean value of 2.33±0.50Bq/l, which lies within the safe limit recommended by UNSCEAR (2008). The total annual effective dose estimated due to radon concentration in ground water ranges from 3.85 to 8.74μSv/y with the mean value of 6.37μSv/y, which is lower than the safe limit set by WHO (2004) and EU (1998). In soil samples, radon concentration has been measured at three different depths (0, 20 and 40cm) in each location. The highest and the lowest concentrations are 4790±51 and 10±04Bq/m3 at 40 and 0cm (surface) depth, respectively, which lie within the natural background levels.

Probabilistic human health risk assessment associated with fluoride and arsenic co-occurrence in drinking water from the metropolitan area of San Luis Potosí, Mexico.

A major public health concern in Mexico is the natural contamination of groundwater with fluoride and arsenic. Therefore, this work aimed to evaluate the magnitude of human health risk after determining fluoride and arsenic concentrations in groundwater samples (n = 50) from the Metropolitan area of the city of San Luis Potosi, Mexico. Fluoride levels in water were determined via a potentiometric method using an ion-selective electrode. Arsenic concentrations in water samples were determined with an Atomic Absorption technique. Subsequently, a probabilistic health risk assessment was developed (Monte Carlo Analysis). Fluoride levels in water ranged from 0.20 to 3.50mg/L. For arsenic, the mean level found in the assessed water samples was 15.5 ± 5.50μg/L (range: 2.50-30.0μg/L). In addition, when the probabilistic health risk assessment was completed, a mean HI (cumulative hazardous index) of higher than 1 was detected, indicating a high NCR (non-carcinogenic risk) for children and adults. According to the results found in this study, exposure protection campaigns are imperative in the Metropolitan area of the city of San Luis Potosí, Mexico, to successfully diminish exposure to arsenic and fluoride and, as a consequence, decrease the NCR in the population living in that region of Mexico.