Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning.

Chronic exposure to elevated geogenic arsenic (As) and fluoride (F-) concentrations in groundwater poses a significant global health risk. In regions around the world where regular groundwater quality assessments are limited, the presence of harmful levels of As and F- in shallow groundwater extracted from specific wells remains uncertain. This study utilized an enhanced stacking ensemble learning model to predict the distributions of As and F- in shallow groundwater based on 4,393 available datasets of observed concentrations and forty relevant environmental factors. The enhanced model was obtained by fusing well-suited Extreme Gradient Boosting, Random Forest, and Support Vector Machine as the base learners and a structurally simple Linear Discriminant Analysis as the meta-learner. The model precisely captured the patchy distributions of groundwater As and F- with an AUC value of 0.836 and 0.853, respectively. The findings revealed that 9.0% of the study area was characterized by a high As risk in shallow groundwater, while 21.2% was at high F- risk identified as having a high risk of fluoride contamination. About 0.2% of the study area shows elevated levels of both of them. The affected populations are estimated at approximately 7.61 million, 34.1 million, and 0.2 million, respectively. Furthermore, sedimentary environment exerted the greatest influence on distribution of groundwater As, with human activities and climate following closely behind at 29.5%, 28.1%, and 21.9%, respectively. Likewise, sedimentary environment was the primary factor affecting groundwater F- distribution, followed by hydrogeology and soil physicochemical properties, contributing 27.8%, 24.0%, and 23.3%, respectively. This study contributed to the identification of health risks associated with shallow groundwater As and F-, and provided insights into evaluating health risks in regions with limited samples.

Multiple pathways of vanadate reduction and denitrification mediated by denitrifying bacterium Acidovorax sp. strain BoFeN1.

Contamination of aquifers by a combination of vanadate [V(V)] and nitrate (NO3-) is widespread nowadays. Although bioremediation of V(V)- and nitrate-contaminated environments is possible, only a limited number of functional species have been identified to date. The present study demonstrates the effectiveness of V(V) reduction and denitrification by a denitrifying bacterium Acidovorax sp. strain BoFeN1. The V(V) removal efficiency was 76.5 ± 5.41 % during 120 h incubation, with complete removal of NO3- within 48 h. Inhibitor experiments confirmed the involvement of electron transport substances and denitrifying enzymes in the bioreduction of V(V) and NO3-. Cyt c and riboflavin were important for extracellular V(V) reduction, with quinone and EPS more significant for NO3- removal. Intracellular reductive compounds including glutathione and NADH directly reduce V(V) and NO3-. Reverse transcription quantitative PCR confirmed the important roles of nirK and napA genes in regulating V(V) reduction and denitrification. Bioaugmentation by strain BoFeN1 increased V(V) and NO3- removal efficiency by 55.3 % ± 2.78 % and 42.1 % ± 1.04 % for samples from a contaminated aquifer. This study proposes new microbial resources for the bioremediation of V(V) and NO3-contaminated aquifers, and contributes to our understanding of coupled vanadium, nitrogen, and carbon biogeochemical processes.

[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.

Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling.

The Hetao region is one of the regions with the most serious problem of the greatest measured arsenic concentrations in China. The enrichment of arsenic in groundwater may poses a great risk to the health of local residents. A comprehensive understanding of the groundwater quality, spatial distribution characteristics and hazard of the high arsenic in groundwater is indispensable for the sustainable utilization of groundwater resources and resident health. This study selected six environmental factors, climate, human activity, sedimentary environment, hydrogeology, soil, and others, as the independent input variables to the model, compared three machine learning algorithms (support vector machine, extreme gradient boosting, and random forest), and mapped unsafe arsenic to estimate the population that may be exposed to unhealthy conditions in the Hetao region. The results show that nearly half the number of the 605 sampling wells for arsenic exceeded the WHO provisional guide value for drinking water, the water chemistry of groundwater are mainly Na-HCO3-Cl or Na-Mg-HCO3-Cl type water, and the groundwater with excessive arsenic concentration is mainly concentrated in the ancient stream channel influence zone and the Yellow River crevasse splay. The results of factor importance explanation revealed that the sedimentary environment was the key factor affecting the primary high arsenic groundwater concentration, followed by climate and human activities. The random forest algorithm produced the probability distribution of high arsenic groundwater that is consistent with the observed results. The estimated area of groundwater with excessive arsenic reached 38.81 %. An estimated 940,000 people could be exposed to high arsenic in groundwater.

Remediation of aniline-contaminated aquifer by combining in-well Rhizobium borbori and circulated groundwater electrolysis.

Aniline has become a common groundwater contaminant due to its wide use as a raw material in agriculture and pharmaceutical products. The current technologies for in situ remediation of aniline in groundwater are limited by the strains deficient in bacterial species, limited oxygen supply, excessive waste gas load and cost. Accordingly, we conducted a laboratory sand tank experiment to remediate groundwater contaminated with aniline by combining circulated groundwater electrolysis and in-well Rhizobium borbori, which was isolated from activated sludge. The results of the experiment indicated that the optimum concentration of aniline for Rhizobium borbori is about 5 mg/L, beyond which the maximum cell density and the highest specific growth rate decreases as the aniline concentration increases. The optimized duration for immobilizing the Rhizobium borbori into the bioreactor is 4-5 days. Though the Rhizobium borbori was strongly inhibited by the high-concentration of aniline, the immobilized bioreactor in the 350 mg/L aniline solution successfully formed biofilm. The aniline volatilization had limited influence on the observation of bioremediation performance, and the combination of circulated groundwater and in-well Rhizobium borbori supplied a steady dose of oxygen to the bioreactor efficiently degrading the entire region between the injection and extraction well. In addition, a numerical model for the sand tank remediation experiment was used to estimate the yield coefficient of oxygen to be 0.484 g/g, which indicates the presence of ammonia nitrogen as by-products; accordingly, a smaller wellbore size as well a higher circulation flow rate and intensity of current are recommended to improve the water quality. Despite the positive outcomes and potential of the newly developed technology to degrade subsurface aniline, parallel experiments should be conducted to estimate the environmental risk of the by-products and explore the controlling mechanisms of each component in this comprehensive system.

Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence.

Henan Province's plain area is the granary of China, yet its regional aquifer is being polluted by industrial wastewater, agricultural pesticide, fertilizer and domestic wastewater. In order to safeguard the security of food and drinking water, and in response to the problem of low prediction accuracy caused by the lack of samples and unevenly distributed groundwater monitoring data, we propose a new way to predict the aquifer vulnerability in large areas by rich small-scale data, so as to identify the pollution risks and to address the issue of sample shortage. In small regions with abundant nitrate data, we employed a Random Forest model to screen key impact indicators, using them as features and nitrate-N concentration as the target variable. Consequently, we established six machine learning prediction models, and then selected the best bagging model (R2 = 0.86) to predict the vulnerability of aquifers in larger regions lacking nitrate data. The predicted results showed that highly vulnerable areas accounted for 20 %, which were mainly affected by aquifer thickness (65.91 %). High nitrate-N concentration implies serious aquifer contamination. Therefore, a long series of groundwater nitrate-N concentration monitoring data in a large scale, the trend and slope of nitrate-N concentration showed a significant correlation with the model prediction results (Spearman's correlation coefficients are 0.75 and 0.58). This study can help identify the risk of aquifer contamination, solve the problem of sample shortage in large areas, thus contributing to the security of food and drinking water.

Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models.

The relative importance of groundwater geochemicals and sediment characteristics in predicting groundwater arsenic distributions was rarely documented. To figure this out, we established a random forest machine-learning model to predict groundwater arsenic distributions in the Hetao Basin, China, by using 22 variables of climate, topographic features, soil properties, sediment characteristics, groundwater geochemicals, and hydraulic gradients of 492 groundwater samples. The established model precisely captured the patchy distributions of groundwater arsenic concentrations in the basin with an AUC value of 0.84. Results suggest that Fe(II) was the most prominent variable in predicting groundwater arsenic concentrations, which supported that the enrichment of arsenic in groundwater was caused by the reductive dissolution of Fe(III) oxides. The high relative importance of SO42- indicated that sulfate reduction was also conducive to groundwater arsenic enrichment in inland basins. Nevertheless, parameters of climate variables, sediment characteristics, and soil properties showed secondly important roles in predicting groundwater arsenic concentrations. The other two models, which excluded parameters of groundwater geochemicals and/or sediment characteristics, showed much worse predictions than the model considering all variables. This highlights the importance of variables of groundwater geochemicals and sediment characteristics in improving the precision and accuracy of predicting results. Future studies should probe a method constructing the random forest predicting model with high precision based on the limited number of groundwater samples and sediment samples.

Identification of arsenic spatial distribution by hydrogeochemical processes represented by different ion ratios in the Hohhot Basin, China.

The Hohhot Basin, a typical inland basin of the Yellow River Basin in China, has high concentrations of arsenic (As) in its shallow groundwater, while the factors dominating the distribution of high arsenic levels remain to be further identified. An analysis of the ratio of hydrogeochemical compositions can help to reveal the spatial characteristics of the shallow groundwater environmental conditions and the distribution of high-arsenic water (As >10 µg/L). In this study, a total of 170 samples of shallow groundwater in the Hohhot Basin were collected and water samples with As >10 µg/L accounted for 29.4% of the total. Based on the slope changes of the cumulative frequency curves of (HCO3- + CO32-)/SO42-, Ca2+/(HCO3- + CO32-), Ca2+/Mg2+, and Na+/Ca2+, the groundwater in the study area can be categorized into six different zones according to the environmental characteristics including redox condition, water recharge intensity, and cation exchange level. The result shows that the groundwater in the front of the piedmont alluvial plain and platform is in a weak reducing condition with high lateral recharge intensity, fast runoff, and weak cation exchange. In the Dahei River alluvial plain, which serves as the groundwater discharge zone, the groundwater runoff is sluggish with poor lateral recharge, sufficient exchange between cations in the groundwater and the aquifer matrix, and enhanced reducibility. The degree of oxidation increased in the groundwater near the Hasuhai Lake and the drainage canal, which adverse to the arsenic enrichment. High-arsenic groundwater is mainly distributed in aquifers of (HCO3- + CO32-)/SO42 > 10, Na+/Ca2+ > 13, and Ca2+/(HCO3- + CO32-) < 0.1, which represent the strong reducing condition, low surface water recharge intensity, and strong cation exchange condition. Reductive dissolution of iron oxide, strong evaporation and concentration process, and competition from phosphate in aquifers jointly lead to the release of arsenic into groundwater.

Enrichment of High Arsenic Groundwater Controlled by Hydrogeochemical and Physical Processes in the Hetao Basin, China.

Based on 447 samples collected from a shallow aquifer (depths from 0 to 150 m) in the Hetao Basin, Northern China, an integrated hydrogeochemical approach was used in this study to conceptualize the enrichment of high arsenic groundwater in the Hetao Basin. An unconventional method of distinguishing hydrogeochemical and physical processes from a dataset was tested by investigating the cumulative frequency distribution of ionic ratios expressed on a probability scale. By applying cumulative frequency distribution curves to characterize the distribution of ionic ratios throughout the Hetao Basin, hydrogeochemical indicators were obtained that distinguish the series of hydrogeochemical processes that govern groundwater composition. All hydrogeochemical processes can basically be classified as recharge intensity of groundwater, evaporation concentration intensity, and reductive degree controlling the spatial distribution of arsenic. By considering the three processes, we found that the concentration of arsenic was more than 10 µg/L when the (HCO3-+CO32-)/SO42- ratio was over 4.1 (strong reductive area). As the evaporation concentration intensity increased, the median value of arsenic increased from 10.74 to 382.7 µg/L in the median reductive area and rapidly increased from 89.11 to 461.45 µg/L in the strong reductive area. As the river recharge intensity increased (with the intensity index increasing from 0 to 5), the median value of arsenic dropped from 40.2 to 6.8 µg/L in the median reductive area and decreased more markedly from 219.85 to 23.73 µg/L in the strong reductive area. The results provide a new insight into the mechanism of As enrichment in groundwater.

Contrasting behaviors of groundwater arsenic and fluoride in the lower reaches of the Yellow River basin, China: Geochemical and modeling evidences.

Genesis of the contrasting distributions of high arsenic (>10 µg/L) and fluoride (>1 mg/L) groundwater and their negative correlations remain poorly understood. We investigated spatial distributions of groundwater arsenic and fluoride concentrations in the lower reaches of the Yellow River basin, Henan Province, China, using bivariate statistical analyses and geochemical simulations. Results suggest that high arsenic and fluoride groundwater showed contrasting distributions with few overlapped area. Groundwater arsenic concentrations were significantly negatively correlated with oxidation-reduction potential (ORP) values and positively with NH4+ and Fe(II) concentrations, while the opposites were true for groundwater fluoride concentrations. These may suggest that high arsenic groundwater is related to stronger organic matter degradation and Fe(III) oxide reduction, while groundwater fluoride enrichment occurs with less extent of organic matter degradation. Geochemical calculations supported that groundwater fluoride enrichment was governed by extent of fluorite dissolution, which was constrained by varied saturation indices of fluorite in groundwater. However, groundwater arsenic mobility may be explained by different solubility of Fe(III) oxides. Higher Fe(III) oxide solubility corresponding to goethite and lepidocrocite was related to higher arsenic concentrations, while hematite was too low in solubility to produce high arsenic groundwater. The study presented both geochemical and modeling evidences for the contrasting behaviors of groundwater arsenic and fluoride concentrations in anoxic aquifers.

Enrichment mechanisms for the co-occurrence of arsenic-fluoride-iodine in the groundwater in different sedimentary environments of the Hetao Basin, China.

Abnormal levels of co-occurring arsenic (As), fluoride (F-) and iodine (I) in groundwater at the Hetao Basin are geochemically unique. The abnormal distribution of As, F- and I is obviously related to the sedimentary environment. It is necessary to study the enrichment mechanisms for the co-occurrence of As, F- and I in groundwater under the influence of the sedimentary environment in Hetao Basin. In this study, 499 groundwater samples were collected. Sedimentary environments, hydrogeochemical process, isotopes were analyzed to elucidate their enrichment mechanisms. The environment of groundwater is weakly alkaline. The hydrochemical types of groundwater are mainly Na-Cl-HCO3. The distribution of isotope δ18O demonstrates that irrigation from the Yellow River is the main recharge source. The main drainage channel is the discharge area in the Hetao basin. Based on the clay-sand ratio (R), the number of clay layers (N) and terrain slope (S), Hetao Basin was divided into four sedimentary environmental zones. The distribution of As (0-916.70 µg/L), F- (0.05-8.60 mg/L) and I (0.01-3.00 mg/L) was featured by a clear zonation of the sedimentary environment. The high As and high I groundwater were mainly distributed in the paleochannel zone of the Yellow River, with exceedance rates of 80.28% and 52.58%, and the median values of 73.91 µg/L and 0.11 mg/L, respectively. In the reducing environment, the release of As initially adsorbed on iron hydrogen and iron oxide, the reductive of iron hydroxide itself, rock weathering and evaporation are the key factors affecting the enrichment of As in groundwater. In this area, large amounts of aquatic organisms and plankton deposited in the sediment and channel filling deposits abundant with organic matter is the premise of high-I groundwater. The reduction of iodate and nitrate directly leads to the high concentration of I in groundwater. The high F- groundwater was mainly distributed in the piedmont alluvial-pluvial fan and the north margin of Ordos Plateau, with exceedance rates of 58.62% and 43.30%, and the median values of 1.10 mg/L and 0.86 mg/L, respectively. High F- groundwater in the two zones is affected by the abundant biotite and hornblende in Langshan Mountain and Ordos Plateau. Under evaporation, the precipitation rate of CaF2 and pH plays key roles in the enrichment of F- in groundwater. In the Hetao Basin, sedimentary environment is the main controlling factor for the co-mobilization of As, F- and I in groundwater.

Changes of groundwater arsenic risk in different seasons in Hetao Basin based on machine learning model.

Arsenic pollution of shallow groundwater is serious in Hetao Basin. At present, there are few studies on the seasonal variation and mechanism of high As groundwater. In order to master the risk difference and influence mechanism of high As groundwater in different seasons, we collected 506 shallow groundwater samples in the Hetao Basin, and used climatic factors, topographic factors, and others (influence of irrigation channels, vegetation index) that are closely distributed with As in groundwater to establish a high-precision random forest model of high As groundwater in the Hetao Basin in summer. We used climate factors as dynamic predictors to predict the distribution of high As risks in winter and established human health risk zones in the Hetao Basin. The results show that from winter to summer, the probability of high As in high risk areas further increases with the influence of factors such as temperature increase, rainfall increase, and enhanced evapotranspiration, while the probability of high As in low risk areas is the opposite and shows a downward trend. The areas with increased probability of high human health risks and stable areas are mainly distributed along the drainage canals and concentrated in the middle of the basin. From winter to summer, as the local residents' demand for groundwater increases, the probability of high As has increased and stabilized in high risk areas. The number of threatened populations reached 246,000 and 108,000, respectively. Therefore, we need to focus on them. The results of this research explored the changing trend and mechanism of high As groundwater risks under the influence of climate, further enriching the regional high As groundwater research system, and can also be provided as a reference for similar research in other regions.

Remote sensing of wetland evolution in predicting shallow groundwater arsenic distribution in two typical inland basins.

A large number of studies have shown that the existence of wetlands may influence arsenic concentrations in adjacent shallow groundwater. However, little is known about the linkage between wetland evolution and arsenic enrichment in shallow groundwater. This study investigated wetland evolutions from 1973 to 2015 in two arid-semiarid inland basins along the Yellow River catchment (i.e., the Yinchuan Basin and the Hetao Basin) based on remote sensing data, and their association with arsenic distributions based on arsenic concentrations of 244 and 570 shallow groundwater samples, respectively. The long-term Landsat images reveal that the covering area of wetlands exhibited increasing trends in both the Yinchuan Basin and the Hetao Basin. Wetlands in the Yinchuan Basin and the Yellow River water-irrigation area in the Hetao Basin varied with precipitation before 2000, but exhibited increasing trends because of wetland restoration policies since 2000. Wetlands in groundwater-irrigation area in the Hetao Basin decreased due to increasing exploitation of shallow groundwater. Wetlands with long existence time were mainly distributed along the Yellow River and drainage channels and in large lakes in the northern Yinchuan Basin and the Hetao Basin, where high­arsenic (>10 µg/L) groundwater occurred. The probability of high­arsenic groundwater distribution increased with the proportion of wetland existence time to the entire studied period (42 years), which can be best explained by a BiDoseResp growth curve. Longer existence of wetlands may cause greater probability of high­arsenic groundwater. This was likely related to long-term introduction of biodegradable organic matter into shallow aquifers and thereafter enhancement of arsenic mobility and/or arsenic being released beneath wetlands and transported into shallow aquifers under continuing wetland water recharge. We therefore suggest that mapping wetland evolutions could probably serve as a good indicator for predicting high arsenic groundwater distributions in shallow aquifers.

Increases in groundwater arsenic concentrations and risk under decadal groundwater withdrawal in the lower reaches of the Yellow River basin, Henan Province, China.

The spatiotemporal variability in groundwater arsenic concentrations following extensive groundwater extractions over decades was rarely studied on a large scale. To fill this gap, variations in groundwater arsenic concentrations in the North Henan Plain in China from 2010 to 2020 were investigated. The possibility of high-arsenic groundwater (>10 µg/L) was higher than 40% in aquifers within a distance of 100 m from paleochannels. This may be due to the fact that deposits in paleochannels were rich in organic matter and suitable for arsenic enrichment. Following groundwater withdrawal over ten years from 2010 to 2020, nearly half of groundwater samples (44%) were elevated in groundwater arsenic concentrations, and the proportion of high arsenic groundwater increased from 24% in 2010 to 26% in 2020. These may be related to enhanced Fe(III) oxide reduction under decadal groundwater withdrawal. However, around 56% groundwater samples were decreases in arsenic concentrations because of increased NO3- levels in these samples in 2020. Furthermore, extensive groundwater withdrawal decreased groundwater tables averagely by 4.6 m from 2010 to 2020, which induced the intrusion of high-arsenic groundwater from shallow aquifers into deeper ones. More importantly, the long-term groundwater pumping has perturbed groundwater flow dynamics and redistributed high-arsenic groundwater in the plain, leading to 18% more areas and 33.8% more residents being potentially at risk. This study suggests that the threat of groundwater overexploitation may be much more severe than previously expected.

Controls of paleochannels on groundwater arsenic distribution in shallow aquifers of alluvial plain in the Hetao Basin, China.

Less is known about controls of sedimentary structures in groundwater As distributions in sedimentary aquifers, and quantitative description of relationship between sedimentary environment and high As groundwater (according to WHO, As>10µg/L) is a challenging issue. Three hundred and eighty-two hydrogeological borehole loggings (well depths of 50-300m) were collected and four hundred and ninety nine groundwater samples were taken to investigate controls of paleochannels on groundwater arsenic distribution in shallow aquifers of alluvial plain in the Hetao Basin. Results showed that the swing zone, formed by bursting, diversion and swing of ancient Yellow River course since the Late Pleistocene, has an obviously corresponding relationship with spatial variability of groundwater As in the Hetao Basin. "Swing Intensity Index" (S), which is firstly defined as the sum of clay-sand ratio (R) and the number of clay layers (N), can be used as the sedimentary facies symbol to establish the new recognition method for hosting high As groundwater. There is a positive correlation between the swing intensity index (S) of paleochannels and groundwater As concentrations. The swing zones of paleochannels with high S values represent hydrogeochemical characteristics of the strong reducing environment, serious evaporation, strong cation exchange, and the low infiltration recharge of surface water, which lead to enrichment of groundwater As in the shallow aquifers.

Contrasting distributions of groundwater arsenic and uranium in the western Hetao basin, Inner Mongolia: Implication for origins and fate controls.

Although As concentrations have been investigated in shallow groundwater from the Hetao basin, China, less is known about U and As distributions in deep groundwater, which would help to better understand their origins and fate controls. Two hundred and ninety-nine groundwater samples, 122 sediment samples, and 14 rock samples were taken from the northwest portion of the Hetao basin, and analyzed for geochemical parameters. Results showed contrasting distributions of groundwater U and As, with high U and low As concentrations in the alluvial fans along the basin margins, and low U and high As concentrations downgradient in the flat plain. The probable sources of both As and U in groundwater were ultimately traced to the bedrocks in the local mountains (the Langshan Mountains). Chemical weathering of U-bearing rocks (schist, phyllite, and carbonate veins) released and mobilized U as UO2(CO3)2(2-) and UO2(CO3)3(4-) species in the alluvial fans under oxic conditions and suboxic conditions where reductions of Mn and NO3(-) were favorable (OSO), resulting in high groundwater U concentrations. Conversely, the recent weathering of As-bearing rocks (schist, phyllite, and sulfides) led to the formation of As-bearing Fe(III) (hydr)oxides in sediments, resulting in low groundwater As concentrations. Arsenic mobilization and U immobilization occurred in suboxic conditions where reduction of Fe(III) oxides was favorable and reducing conditions (SOR). Reduction of As-bearing Fe(III) (hydr)oxides, which were formed during palaeo-weathering and transported and deposited as Quaternary aquifer sediments, was believed to release As into groundwater. Reduction of U(VI) to U(IV) would lead to the formation of uraninite, and therefore remove U from groundwater. We conclude that the contrasting distributions of groundwater As and U present a challenge to ensuring safe drinking water in analogous areas, especially with high background values of U and As.