Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi Province, Thailand.

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

Soil Microbiomes and their Arsenic Functional Genes in Chronically High-Arsenic Contaminated Soils.

Microbial arsenic transformations play essential roles in controlling pollution and ameliorating risk. This study combined high-throughput sequencing and PCR-based approaches targeting both the 16 S rRNA and arsenic functional genes to investigate the temporal and spatial dynamics of the soil microbiomes impacted by high arsenic contamination (9.13 to 911.88 mg/kg) and to investigate the diversity and abundance of arsenic functional genes in soils influenced by an arsenic gradient. The results showed that the soil microbiomes were relatively consistent and mainly composed of Actinobacteria (uncultured Gaiellales and an unknown_67 - 14 bacterium), Proteobacteria, Firmicutes (particularly, Bacillus), Chloroflexi, and Acidobacteria (unknown_Subgroup_6). Although a range of arsenic functional genes (e.g., arsM, arsC, arrA, and aioA) were identified by shotgun metagenomics, only the arsM gene was detected by the PCR-based method. The relative abundance of the arsM gene accounted for 0.20%-1.57% of the total microbial abundance. Combining all analyses, arsenic methylation mediated by the arsM gene was proposed to be a key process involved in the arsenic biogeochemical cycle and mitigation of arsenic toxicity. This study advances our knowledge about arsenic mechanisms over the long-term in highly contaminated soils.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach.

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model's performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy.

Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation-reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.

Urinary arsenic and health risk of the residents association in contaminated-groundwater area of the urbanized coastal aquifer, Thailand.

Determining of arsenic (As) exposure was conducted in 110 residents which divided into two groups using the WHO guidelines for As in drinking water of 10 µg/L. Moreover, questionnaires with face-to-face interviews were used to make a health risk assessment and to determine the associated factors. The median of As in urine was 61.33 µg/L (5.38-600.86 µg/L), accounting for 68.18% of participants who exposed to the contaminated groundwater had obviously high urinary As levels, exceeded the normal value of 50 µg/L of As, as set by the National Health and Nutrition Examination Survey (NHANES). The major factor affecting As in urine was the As contaminated groundwater. Pearson's chi-squared test showed that the urinary As level was influenced on the different groups of As level in groundwater (p-value <0.001). Multiple linear regression confirmed that the actual risk factors of As in urine were the As level in groundwater and the oral exposure route but not the dermal contact. Meanwhile binary logistic regression revealed that all socio-demographic factors were not influenced. Approximately 45.45% of the area had the HI above the risk level of 1, mostly via groundwater drinking pathway. The estimated total cancer risk values, 5.11 × 10-6 to 2.08 × 10-3, were higher than the safe level of 10-6. For long-term exposure, the As concentration and exposure duration were the most variables influencing health risk level. This finding suggests that chronic As exposure should be monitored and also the groundwater should be improved to provide the safe drinking water for the residents.

Acid mine drainage potential of waste rocks in a gold mine (Thailand): application of a weathering cell test and multivariate statistical analysis.

In the process of gold mining, large amounts of broken waste rocks are produced and left at the surface under atmospheric conditions, which may generate acid mine drainage (AMD). This study aimed to predict the AMD generation potential and determine the concentrations of potentially toxic metals at three dump sites for a gold mine in Thailand. The AMD generation potentials of waste rock samples collected from the oxide, transition and sulfide dump sites was determined using the weathering cell test. The kinetic test had a 7-d cycle and was run for ~ 21 cycles; the effluent pH, conductivity, redox potential and levels of sulfate, and major and trace metals (i.e., As, Co, Cu, Fe, Mn, Pb and Zn) present in each cycle were measured. Some samples generated significant amounts of AMD, especially the massive sulfide samples from the transition and sulfide dump sites. The effluent water pH in the oxide and sulfide dump sites was neutral to slightly alkaline (pH ~ 6-9), while it was acidic to neutral (pH ~ 3-7) in the transition dump site. The transition dump site samples generated significantly higher acidity and sulfate levels than those from the oxide and sulfide dump sites. Furthermore, some waste rock samples, including the massive sulfide from the transition dump site, released relatively high amounts of heavy metals; in addition, sulfate reached levels (9.48 mg kg-1 of waste rock) high enough to pose a risk to ecosystems. The long-term acid generation suggested that some waste rock samples from sulfide dump site and transition dump site will continue to generate acid for long periods. Based on data from the weathering cell test and multivariate statistical analysis, the transition dump site potentially generates a lower pH leachate than other waste rock dumps.

Spatial distribution and health risk assessment of As and Pb contamination in the groundwater of Rayong Province, Thailand.

This study investigates the presence of arsenic (As) and lead (Pb) in groundwater and their spatial distribution in Ban Khai District, Rayong Province, Thailand. Forty groundwater samples were collected at different locations in the dry and wet seasons during March and August of 2019, respectively. The hydrochemical facies illustrate that the major groundwater types in both seasons mainly consisted of Ca-Na-HCO3, Ca-HCO3-Cl and Na-HCO3 types. The concentration of As ranged from <0.300 to 183.00 µg/L, accounting for 22% (18 of 80 samples), exceeding the WHO guidelines of 10 µg/L. The spatial distribution of As was distinctly predominant as a hot spot in some areas during the wet season. The wells may have been contaminated from human activity and thus constituted a point source in the adjacent area. For Pb, its concentration in all the wells were not exceeded 10 µg/L of the WHO guidelines, appearing as a background concentration in this area. Most of the wells were shown to be in an oxidation state, supporting AsV mobility. Moreover, the area also had a nearly neutral pH that promoted AsV desorption, while the presence of undissolved Pb in the aquifers tended to increase. Furthermore, chemical applications to agricultural processes could release the As composition into the groundwater. The health risk resulting from oral consumption was at a higher risk level than dermal contact. The non-carcinogenic risk affecting the adult population exceeded the threshold level by approximately 27.5% of the wells, while for the children group, the risk level was within the limit. Total cancer risk (TCR) of adult residents exceeded the acceptable risk level (1 × 10-6) in all wells, causing carcinogenic health effects. Therefore, health surveillance is important in monitoring the toxic effects on the local residents who use groundwater from these contaminated wells. Furthermore, a sanitation service and an alternative treatment of the water supply will be needed, especially in wells with high As levels.

Related health risk assessment of exposure to arsenic and some heavy metals in gold mines in Banmauk Township, Myanmar.

Exposure to heavy metals in mining activities is a health issue among miners. This study was carried out at three small-scale gold mining sites situated in Banmauk Township, Myanmar and aims to assess the occupational health risks of small-scale gold miners who are exposed to arsenic (As), cadmium (Cd), mercury (Hg) and lead (Pb) in the soil through the dermal route. Soil samples were analyzed through atomic absorption spectroscopy (AAS). The concentrations of the heavy metals in soils found As, ranged 1.04 mg/kg to 22.17 mg/kg, 0.13 mg/kg to 3.07 mg/kg for Cd, 0.15 mg/kg to 77.44 mg/kg for Hg, and 7.67 mg/kg to 210.00 mg/kg for Pb. In this study, 79% of the participants did not use any form of personal protective equipment (PPE) while working in gold mining processes. Regarding noncancer risk assessment, the results found all hazard quotient were lower than acceptable level (HQ < 1). In addition, all hazard index (HI) was lover than 1, the highest HI was found as 5.66 × 10-1 in the amalgamation process. On the other hand, the result found cancer risk ranged from 8.02 × 10-8 to 1.75 × 10-6, and the estimated cancer risks for 9 years ranged from 4.78 × 10-7 to 1.04 × 10-5. Therefore, the cancer risks of the miners were greater than the United State Environmental Protection Agency (U.S. EPA) acceptable cancer risk level, 1 × 10-6, and the miners may be at risk of developing carcinogenic diseases. The suggestion is to educate miners about the health risks of heavy metals and to encourage the use of proper PPE all the time while working in gold mine.

Human biomarkers associated with low concentrations of arsenic (As) and lead (Pb) in groundwater in agricultural areas of Thailand.

Human biomarkers were used to evaluate the lead (Pb) and arsenic (As) exposure of local people who lived in an agricultural area with intense agrochemical usage and who consumed groundwater. Although the heavy metals/metalloids in the groundwater were at low concentrations, they could cause adverse effects due to a high daily water intake rate over the long term. Biomarkers (hair, fingernails and urine) were collected from 100 subjects along with the local shallow groundwater and tap water, which is the treated deep groundwater, and investigated for the concentrations of As and Pb. Shallow groundwater had an average pH of 5.21 ± 1.90, ranging from 3.77 to 8.34, with average concentrations of As and Pb of 1.311 µg/L and 6.882 µg/L, respectively. Tap water had an average pH of 5.24 ± 1.63, ranging from 3.86 to 8.89, with the average concentrations of As and Pb of 0.77 µg/L and 0.004 µg/L, respectively. The levels of both As and Pb in the hair, fingernails and urine of shallow groundwater-consuming residents were greater than those in the hair, fingernails and urine of tap water-consuming residents. Interestingly, the As level in urine showed a linear relationship with the As concentration in groundwater (R2 = 0.91). The average water consumption rate was approximately two-fold higher than the standard; thus, its consumption posed a health risk even at the low As and Pb levels in the groundwater. The hazard index (HI) ranged from 0.01 to 16.34 (average of 1.20 ± 2.50), which was higher than the acceptable level. Finally, the concomitant factors for As and Pb in the urine, hair and nails from both binary logistic regression and odds ratio (OR) analysis indicated that groundwater consumption was the major concomitant risk factor. This study suggested that direct consumption of this groundwater should be avoided and that the groundwater should be treated, especially before consumption. In conclusion, urine is suggested to be a biomarker of daily exposure to As and Pb, while for long-term exposure to these metals, fingernails are suggested as a better biomarker than hair.

Arsenic speciation, the abundance of arsenite-oxidizing bacteria and microbial community structures in groundwater, surface water, and soil from a gold mine.

The arsenic speciation, the abundance of arsenite-oxidizing bacteria, and microbial community structures in the groundwater, surface water, and soil from a gold mining area were explored using the PHREEQC model, cloning-ddPCR of the aioA gene, and high-throughput sequencing of the 16S rRNA gene, respectively. The analysis of the aioA gene showed that arsenite-oxidizing bacteria retrieved from groundwater, surface water, and soil were associated with Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. In groundwaters from the mining area, there were relatively high ratios of aioA/total 16S rRNA gene copies and the dominance of As5+, which suggested the presence and activity of arsenite-oxidizing bacteria. Metagenomic analysis revealed that the majority of the soil and surface water microbiomes were Proteobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi, whereas the groundwater microbiomes were dominated exclusively by Betaproteobacteria and Alphaproteobacteria. Geochemical factors influencing the microbial structure in the groundwater were As, residence time, and groundwater flowrate, while those showing a positive correlation to the microbial structure in the surface water were TOC, ORP, and DO. This study provides insights into the groundwater, surface water, and soil microbiomes from a gold mine and expands the current understanding of the diversity and abundance of arsenite-oxidizing bacteria, playing a vital role in global As cycling.

Effects of Arsenic and Iron on the Community and Abundance of Arsenite-Oxidizing Bacteria in an Arsenic-Affected Groundwater Aquifer.

Arsenic (As) contamination of groundwater aquifers is a global environmental problem, especially in South and Southeast Asian regions, and poses a risk to human health. Arsenite-oxidizing bacteria that transform As(III) to less toxic As(V) can be potentially used as a groundwater As remediation strategy. This study aimed to examine the community and abundance of arsenite-oxidizing bacteria in groundwater with various As concentrations from Rayong Province, Thailand using PCR-cloning-sequencing and quantitative PCR (qPCR) of catalytic subunit of arsenite oxidase gene (aioA). Key factors influencing their community and abundance were also identified. The results demonstrated that arsenite-oxidizing bacteria retrieved from groundwater were phylogenetically related to Betaproteobacteria and Alphaproteobacteria. The aioA gene abundances ranged from 8.6 × 101 to 1.1 × 104 copies per ng of genomic DNA, accounting for 0.16-1.37% of the total 16S rRNA bacterial gene copies. Although the abundance of arsenite-oxidizing bacteria in groundwater was low, groundwater with As(III) dominance likely promoted their abundance which possibly played an important role in chemolithoautotrophic oxidation of As(III) to As(V). Fe and As(III) were the major environmental factors influencing the community and abundance of arsenite-oxidizing bacteria. The knowledge gained from this study can be used to further contribute to the development of bioremediation strategies for As removal from groundwater resources.

Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes.

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and aioA genes. Although Proteobacteria, Cyanobacteria, Actinobacteria, Patescibacteria, Bacteroidetes, and Epsilonbacteraeota were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of Burkholderiaceae and Gallionellaceae potentially controlled arsenic immobilization in the aquifers. Analysis of the aioA gene suggested that arsenite-oxidizing bacteria phylogenetically associated with Alpha-, Beta-, and Gamma proteobacteria were present at low abundance (0.85 to 37.13%) and were more prevalent in shallow aquifers and surface water. The concentrations of dissolved oxygen and total phosphorus significantly governed the microbiomes analyzed in this study, while the combination of NO3 --N concentration and oxidation-reduction potential significantly influenced the diversity and abundance of arsenite-oxidizing bacteria in the aquifers. The knowledge of microbial community structures and functions in relation to deep and shallow aquifers is required for further development of sustainable aquifer management.

Mechanisms of arsenic contamination associated with hydrochemical characteristics in coastal alluvial aquifers using multivariate statistical technique and hydrogeochemical modeling: a case study in Rayong province, eastern Thailand.

The rapid development of Rayong Province has resulted in increased demands on groundwater usage. This has potentially induced the release of contaminants such as arsenic (As), among others (i.e., NO3-, PO43-) from various land use types-especially in intensive agricultural areas and heavy industrial areas, including landfill sites. The objectives of this research are to investigate the As speciation and groundwater chemistry occurring due to different hydrogeological settings and the influence of human activities and to explain the mechanism of As release in the coastal alluvial aquifers in Rayong Province using multivariate statistical techniques and hydrogeochemical modeling (PHREEQC). Six major water facies, mainly consisting of Ca-Na-HCO3-Cl and Ca-Na-Cl, were included in the hydrochemical analysis. Arsenic levels were inversely correlated with NO3-, SO42-, DO, and ORP, confirming the reducing environment in the groundwater system. The results from the PHREEQC model show that most wells were strongly under-supersaturated with respect to arsenorite, scorodite, and arsenic pentoxide. Arsenic (As) is probably derived from the dissolution of Fe oxide and hydroxide (i.e., Fe(OH)3, goethite, maghemite, and magnetite). The multivariate statistical techniques revealed that the As species mainly consisted of As(III), governed by the reducing environment, while As(V) may be desorbed from Fe oxide and hydroxide as the pH increases. Anthropogenic inputs and intensive pumping may enhance the reducing environment, facilitating the release of As(III) into the groundwater. The knowledge gained from this study helps to better understand the mechanisms of As contamination in coastal groundwater aquifers, which is useful for groundwater management, including the optimum pumping rate and long-term monitoring of groundwater quality.

Influences of pH on transport of arsenate (As5+) through different reactive media using column experiments and transport modeling.

This research aims to evaluate the effects of pH, including both acidic and neutral conditions to simulate an acid mine environment, on the sorption and transport of As(V) in contaminated groundwater through different reactive materials by using column experiments and mathematical modeling. Six saturated columns were set up to evaluate the migration and removal efficiency of As(V) with three different materials acting as permeable reactive barrier (PRB) media under different pH conditions (pH 4 and pH 7). The reactive materials consisted of pure sand (control column), iron oxide-coated sand (IOCS) and a combination of IOCS and zero-valent iron-coated sand (ZVICS) (ZVICS + IOCS). According to the column experiments, the descending order of removal capacity (mg As/g) for ZVICS + IOCS, IOCS and sand was 0.452 > 0.062 > 0.0027 mg As/g at pH 4 and 0.117 > 0.0077 > 0.0022 mg As/g, respectively, at pH 7. The column experiments showed that the removal and retardation factor (RF) of As(V) generally increased with decreasing pH. The SEM images and the corresponding EDX spectra of acid-washed natural sand, IOCS and ZVICS + IOCS from the columns showed that the peak of As was detectable on the reactive materials. The mechanism of As(V) sorption onto sand at pH 4 and pH 7 corresponded to the uniform (equilibrium) solute transport model, whereas the IOCS and ZVICS + IOCS columns corresponded to the two-site model (TSM) with the Freundlich isotherm. The fraction of instantaneous sites (f) for As(V) sorption onto IOCS and ZVICS + IOCS appeared to decrease with increasing pH, especially for ZVICS + IOCS, which indicates that nonequilibrium sorption/desorption mainly dominated during As(V) migration.

Metagenomic insights into microbial diversity in a groundwater basin impacted by a variety of anthropogenic activities.

Microbial communities in groundwater are diverse and each may respond differently to environmental change. The goal of this study was to investigate the diversity, abundance, and dynamics of microbial communities in impacted groundwater and correlate them to the corresponding land use and groundwater geochemistry, using an Illumina MiSeq platform targeting the V3 and V4 regions of the 16S rRNA gene. The resulting MiSeq sequencing revealed the co-occurrence patterns of both abundant and rare microbial taxa within an impacted groundwater basin. Proteobacteria were the most common groundwater-associated bacterial phylum, mainly composed of the classes Gammaproteobacteria, Betaproteobacteria, Alphaproteobacteria, and Deltaproteobacteria. The phyla detected at less abundances were the Firmicutes, Bacteroidetes, Planctomycetes, Actinobacteria, OD1, and Nitrospirae. The members of detected groundwater microorganisms involved in natural biogeochemical processes such as nitrification, anammox, methane oxidation, sulfate reduction, and arsenic transformation. Some of the detected microorganisms were able to perform anaerobic degradation of organic pollutants. The resulting PCA indicates that major land usage within the sampling area seemed to be significantly linked to the groundwater microbial distributions. The distinct microbial pattern was observed in the groundwater collected from a landfill area. This study suggests that the combinations of anthropogenic and natural effects possibly led to a unique pattern of microbial diversity across different locations at the impacted groundwater basin.

Facilitated transport of cadmium with montmorillonite KSF colloids under different pH conditions in water-saturated sand columns: Experiment and transport modeling.

This study investigated the impact of pH on the migration of cadmium(II) ions (Cd2+) in relation to montmorillonite KSF colloids through a water-saturated sand column (WSSC). The sorption isotherms of Cd2+ on colloids and sand at pH values of 3, 6, and 8 were characterized by batch experiments. Cd2+ sorption by colloids and sand fit well with the Freundlich model. In the column experiments, increasing the pH increased the retardation factors and KF values of Cd2+ both with and without the presence of the colloids. The amount of Cd2+ sorbed onto the montmorillonite KSF colloids in the column effluent increased from 0.29 to 0.97 mg as the pH increased. The colloid increased Cd2+ mobility and acted as a carrier at a high solution pH. The increasing level of Cd2+ sorbed on colloids as the pH increased resulted in a long tailing of the breakthrough curve (BTC) of the total Cd, indicating that the total Cd was controlled by rate-limited reactions. These findings indicate that when the solution pH was greater than the point of zero charge (PZC) of the colloids (pH > 6), the system tended to follow a nonequilibrium two-site (TSM) model rather than an equilibrium (CDeq) model. This implies that the PZC of the colloids in the groundwater system is the main factor in predicting facilitated Cd2+ transport.

Using hair and fingernails in binary logistic regression for bio-monitoring of heavy metals/metalloid in groundwater in intensively agricultural areas, Thailand.

In this study, the hair and fingernails of the local people in an intensively cultivated agricultural area in Ubon Ratchathani province, Thailand, were used as biomarkers of exposure to arsenic (As) and heavy metals. The study area has shallow acidic groundwater that is contaminated with As and heavy metals. The local people often consume this shallow groundwater; thus, they are exposed to As and heavy metals. Hair and fingernail samples were collected to characterize the differences between shallow groundwater drinking (SGWD) and tap water drinking (TWD) residents. The concentrations of As and the heavy metals Cd, Pb and Hg were significantly higher in the hair samples from the SGWD group than those from the TWD group, especially for As (0.020-0.571 vs. 0.024-0.359µg/g) and Cd (0.009-0.575 vs. 0.013-0.230µg/g). Similarly, the concentrations of As and the heavy metals in the fingernail samples collected from the SGWD group were larger than those of the TWD group, especially for As (0.039-2.440µg/g vs. 0.049-0.806µg/g). The χ2 statistic and binary logistic regression were used to find the associated factors and assess the associated probabilities. The regression results show that the factors associated with the concentrations of As and the heavy metals in the hair samples were drinking water source, rate of water consumption, gender, bathing water source, education, smoking and underlying disease, whereas the factors associated with the concentrations of these species in the fingernail samples were drinking water source, gender, occupation, work hours per day, alcohol consumption, and the use of pesticides.

Using urine as a biomarker in human exposure risk associated with arsenic and other heavy metals contaminating drinking groundwater in intensively agricultural areas of Thailand.

Urine used as a biomarker was collected and compared between two groups of participants: (1) a groundwater-drinking group and (2) a non-groundwater-drinking group in intensively agricultural areas in Ubon Ratchathani province, Thailand. The statistical relationship with the metal concentration in shallow groundwater wells was established with urine data. According to the groundwater data, the health risk assessment results for four metals appeared to be higher for participants who drank groundwater than for the other group. The carcinogenic risk and non-carcinogenic risk of arsenic (As) were found in 25.86 and 31.03% of participants, respectively. For lead (Pb), 13.79% of the participants had a non-carcinogenic risk. Moreover, 30 of the 58 participants in the groundwater-drinking group had As urine higher than the standard, and 26, 2 and 9 of the 58 participants had above-standard levels for cadmium (Cd), Pb and mercury (Hg) in urine, respectively. Both the risk assessment and biomarker level of groundwater-drinking participants were higher than in the other group. The results showed an average drinking rate of approximately 4.21 ± 2.73 L/day, which is twice as high as the standard. Interestingly, the As levels in the groundwater correlated with those in the urine of the groundwater-drinking participants, but not in the non-groundwater-drinking participants, as well as with the As-related cancer and non-carcinogenic risks. The hazard index (HI) of the 100 participants ranged from 0.00 to 25.86, with an average of 1.51 ± 3.63 higher than the acceptable level, revealing that 28 people appeared to have non-carcinogenic risk levels (24 and 4 people for groundwater-drinking participants and non-groundwater-drinking participants, respectively). Finally, the associated factors of heavy metals in urine were the drinking water source, body weight, smoking, sex and use of personal protective equipment.

Effects of kaolinite colloids on Cd²âº transport through saturated sand under varying ionic strength conditions: Column experiments and modeling approaches.

Column experiments were performed under various ionic strengths (0.0-0.9 mM) using 10 mg L(-1) of Cd(2+) without kaolinite colloids and 10 mg L(-1) Cd(2+) mixed with 100 mg L(-1) kaolinite colloids. The nonequilibrium two-site model (TSM) described the behavior of both Cd(2+) transport and Cd(2+) co-transported with kaolinite colloids better than the equilibrium model (CD(eq)) (R(2)=0.978-0.996). The results showed that an increase in ionic strength negatively impacted the retardation factors (R) of both Cd(2+) and Cd(2+) mixed with kaolinite colloids. The presence of kaolinite colloids increased the retardation factors of Cd(2+) from 7.23 to 7.89, 6.76 to 6.61 and 3.79 to 6.99 for ionic strengths of 0.225, 0.45 and 0.9 mM, respectively. On the other hand, the presence of kaolinite colloids decreased the retardation factor of Cd(2+) from 8.13 to 7.83 for ionic strength of 0.0 mM. The fraction of instantaneous sorption sites (f) parameters, kinetic constant for sorption sites (α) and Freundlich constant (K(f)) were estimated from HYDRUS-1D of TSM for Cd(2+) transport. The fraction of instantaneous sorption sites was found to increase for an increase in ionic strength. K(f) values of Cd(2+) transport without kaolinite colloids for 0.0, 0.225 and 0.45 mM were found to be higher than those of Cd(2+) transport with kaolinite colloids, except for ionic strength of 0.9 mM. Hence, the presence of kaolinite colloids probably retarded the mobility of Cd(2+) in porous media for higher ionic strengths. Furthermore, retardation factors and K(f) values of both Cd(2+) transport and Cd(2+) co-transport were shown to decrease when ionic strength increased. Interestingly, according to TSM, the fraction of instantaneous sorption sites tends to increase for an increase in ionic strength, which imply that the mechanism of Cd(2+) sorption onto quartz sand can be better described using equilibrium sorption rather than nonequilibrium sorption for an increase in ionic strength.

Heavy metal contamination and human health risk assessment in drinking water from shallow groundwater wells in an agricultural area in Ubon Ratchathani province, Thailand.

Most local people in the agricultural areas of Hua-ruea sub-district, Ubon Ratchathani province (Thailand), generally consume shallow groundwater from farm wells. This study aimed to assess the health risk related to heavy metal contamination in that groundwater. Samples were randomly collected from 12 wells twice in each of the rainy and the dry seasons and were analyzed by inductive coupled plasma spectrometry-mass spectrometry (ICP-MS). The concentration of detected metals in each well and the overall mean were below the acceptable groundwater standard limits for As, Cd, Cr, Cu, Hg, Ni and Zn, but Pb levels were higher in four wells with an overall average Pb concentration of 16.66 ± 18.52 µg/l. Exposure questionnaires, completed by face-to-face interviews with 100 local people who drink groundwater from farm wells, were used to evaluate the hazard quotients (HQs) and hazard indices (HIs). The HQs for non-carcinogenic risk for As, Cu, Zn and Pb, with a range of 0.004-2.901, 0.053-54.818, 0.003-6.399 and 0.007-26.80, respectively, and the HI values (range from 0.10 to 88.21) exceeded acceptable limits in 58 % of the wells. The HI results were higher than one for groundwater wells located in intensively cultivated chili fields. The highest cancer risk found was 2.6 × 10(-6) for As in well no. 11. This study suggested that people living in warmer climates are more susceptible to and at greater risk of groundwater contamination because of their increased daily drinking water intake. This may lead to an increased number of cases of non-carcinogenic and carcinogenic health defects among local people exposed to heavy metals by drinking the groundwater.