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

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.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.

Mechanistic insight into lead immobilization on bone-derived carbon/hydroxyapatite composite at low and high initial lead concentration

The contamination of heavy metal lead has a serious impact on the natural environment and organisms. Among various materials for lead removal, animal bone derived hydroxyapatite has received extensive attention. However, there are different opinions among researchers regarding the mechanism of lead removal by hydroxyapatite, possibly due to varying initial lead concentrations used in different studies and lack of accuracy in the study of lead removal mechanisms. In present work, we synthesized a carbon-containing hydroxyapatite (CHAP) through pyrolysis of bovine bone with excellent lead removal efficiency, and further investigated the lead removal mechanism of CHAP under high and low initial lead concentrations by combining XRD Rietveld refinement, FTIR, XPS, HRTEM etc. methods. The results showed that under low initial Pb2+ concentration condition, the main mechanism of lead removal by CHAP was chemical precipitation (94.1 %), with small contributions of lead complexation with carbon functional groups and cation-π interactions on the amorphous carbon in CHAP, and surface adsorption on the precipitates. Under high initial Pb2+ concentration condition, chemical precipitation remained the main mechanism (74.68 %), but the contributions of the other three mechanisms increased, and ion exchange appeared in the later stage of the removal process. This study provides new insights on the lead immobilization mechanism by CHAP at different initial Pb2+ concentrations in water.

Prioritization of control factors for heavy metals in groundwater based on a source-oriented health risk assessment model

Heavy metals (HMs) in groundwater seriously threaten ecological safety and human health. To facilitate the effective management of groundwater contamination, priority control factors of HMs in groundwater need to be categorized. A total of 86 groundwater samples were collected from the Huangpi district of Wuhan city, China, during the dry and wet seasons. To determine priority control factors, a source-oriented health risk assessment model was applied to compare the pollution sources and health risks of seven HMs (Cu, Pb, Zn, Cr, Ni, As, and Fe). The results showed that the groundwater had higher As and Fe contents. The sources of HM pollution during the wet period were mainly industrial and agricultural activities and natural sources. During the dry period, origins were more complex due to the addition of domestic discharges, such as sewage wastewater. Industrial activities (74.10% during the wet period), agricultural activities (53.84% during the dry period), and As were identified as the priority control factors for groundwater HMs. The results provide valuable insights for policymakers to coordinate targeted management of HM pollution in groundwater and reduce the cost of HM pollution mitigation.

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.

Controlling factors and health risks of groundwater chemistry in a typical alpine watershed based on machine learning methods

Groundwater is a key water resource in alpine watersheds, but its quality is deteriorating due to human activities. The Golmud River watershed is a representative alpine watershed in Northwest China, and it was chosen to explore groundwater chemistry, associated controlling factors, source contributions, and potential health risks. The analysis includes the use of a self-organizing map (SOM), positive matrix factorization (PMF), ionic ratios, and a Monte Carlo simulation. The content of total dissolved solids in phreatic water was higher in the dry season and increased from the mountainous zone to the fine-soil plain-overflowing zone. Additionally, the water type varied from HCO3- to Cl- types whereas confined groundwater was chemically stable and of a HCO3- type. The SOM results showed a visual correlation between the ions in groundwater. The combination of SOM, PMF, and ionic ratios identified water-rock action as a dominant factor of groundwater chemistry. It was also found that Clusters I and III were mainly influenced by silicate weathering (a total contribution of 38.4 %), whereas evaporation was dominant in Cluster VI (a contribution of 32.5 %). Anthropogenic pollution was mainly associated with clusters V and IV and was related to industrial and agricultural activities during the snowmelt and wet seasons, and fluorine deposition formed by residential coal heating during the dry season (contributions of 1.4 % and 23.8 % in Clusters V and IV, respectively). The sudden increases in B3+ and Li+ in Cluster II were due to inputs from small tributaries (a contribution of 3.9 %). The probabilistic health risk assessment showed that fluoride posed a greater non-carcinogenic risk to human health than Sr2+, B3+, and NO3-, and its potential threat to children was more significant during the dry season than in other seasons. It is necessary for local governments to establish urgent fluoride emission control policies within the Golmud River watershed.

Accumulation pattern and risk assessment of potentially toxic elements in selected wastewater-irrigated soils and plants in Vehari, Pakistan

There are scarce data about the accumulation pattern and risk assessment of potentially toxic elements (PTEs) in soil and associated potential ecological risks, especially in less-developed countries. This study aims to assess the pollution levels and potential ecological risks of PTEs (As, Cr, Cd, Cu, Ni, Mn, Pb and Zn) in wastewater-irrigated arable soils and different edible-grown plants in selected areas of Vehari, Pakistan. The results revealed that the values of PTEs in soil samples were higher than their respective limit values by 20% for As, 87% for Cd, 15% for Cu, 2% for Cr, 83% for Mn, 98% for Fe, and 7% for Zn. The values of soil risk indices such as the potential ecological risk (PERI >380 for all samples), pollution load index (PLI >4 for 94% of studied samples), and degree of contamination (Dc > 24 for all samples) showed severe soil contamination in the study area. Some vegetables exhibited a high metal accumulation index (e.g., 8.1 for onion), signifying potential associated health hazards. Thus, long-term wastewater irrigation has led to severe soil contamination, which can pose potential ecological risks via PTE accumulation in crops, particularly Cd. Therefore, to ensure food safety, frequent wastewater irrigation practices need to be minimized and managed in the study area.

Spatial distribution, source apportionment and health risk assessment of inorganic pollutants of surface water and groundwater in the southern margin of Junggar Basin, Xinjiang, China

Surface water (SW) and groundwater (GW) are crucial water supply sources in the southern margin of Junggar Basin in Xinjiang. The sources of toxic components in SW and GW and their negative effects on human health are of great concern. A total of 40 SW and 596 GW samples were collected at the oasis belt to analyze distribution, sources and potential health risks of inorganic pollutants in SW and GW. Results revealed that SW quality was severely affected by Hg, 30.0% of which had Hg concentration greater than the national drinking water standard. High Hg SW was mainly distributed near Manas County and Urumqi City. GW quality was mostly affected by SO₄^2-, 24.7% of which had SO₄^2- concentration greater than the national drinking water standard. High SO₄^2- GW primarily occurred in the northwest and middle of the study area. Source apportionment of inorganic pollutants identified geological background, municipal wastewater disposal, water-rock interaction, geological environment, geological structure and industrial emission were the prominent potential sources of inorganic pollution in SW, with contribution rates of 1.2%, 10.0%, 43.6%, 35.1%, 6.3% and 3.8%, respectively. Five potential pollution sources in GW (including geological background, municipal wastewater disposal, water-rock interaction, geological environment and aquifer burial depth) were identified, with contribution rates of 0.7%, 9.6%, 77.6%, 11.1% and 1.0%, respectively. Probabilistic health risk assessment showed that Cl^- and As in SW and GW were the main inorganic pollutants threatening human health. Non-carcinogenic risks for adults and children were negligible, while carcinogenic risks cannot be negligible. Furthermore, the contribution of potential pollution sources to health risks was quantified using positive matrix factorization coupling with health risk assessment model. Based on which, we offered the suggestion that water quality improvement in contaminated areas should be implemented in combination with pollution monitoring systems.

Pollution Characteristics, Source Apportionment, and Health Risk Assessment of Potentially Toxic Elements (PTEs) in Road Dust Samples in Jiayuguan, Hexi Corridor, China

The sources of potentially toxic elements (PTEs) in road dust are complex and potentially harmful to humans, especially in industrial cities. Jiayuguan is the largest steel-producing city in Northwest China, and this study was the first to conduct a related study on PTEs in road dust in this city, including the pollution characteristics, source apportionment, and health risk assessment of PTEs in road dust. The results showed that the highest concentration of PTEs in the local road dust samples were Mn, Ba, Zn, and Cr. The enrichment factor (EF) of Se was the highest, and it was "Very high enrichment" in areas other than the background area, indicating that the local Se was more affected by human activities. The geoaccumulation index (I_geo) of Se was also the highest, and the pollution level was 5 in all areas except the background area, indicating that the local Se was more polluted and related to coal combustion. The sources of PTEs in local road dust samples mainly included geogenic-industrial sources, coal combustion, traffic sources, and oil combustion. For the non-carcinogenic risk, the hazard index (HI) of each element of children was higher than that of adults, and the sum of the HI of each element was greater than 1, indicating that there was a non-carcinogenic risk under the combined influence of multiple elements, which was especially obvious in industrial areas. For the carcinogenic risk, the cancer risk (CR) of Cr at a certain point in the industrial area exceeded 10^-4, which was a carcinogenic risk, and the Cr in this area may be related to the topsoil of the local abandoned chromate plant.

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Groundwater is a significant component of water resources, but drinking groundwater with excessive heavy metals (HMs) is harmful to human health. Currently, quantitative source apportionment and probabilistic health risk assessment of HMs in groundwater are relatively limited. In this study, 60 groundwater samples containing seven HMs were collected from Hainan Island and analyzed by the coupled absolute principal component scores/multiple linear regression (APCS/MLR), the health risk assessment (HRA) and the Monte Carlo simulation (MCS) to quantify the pollution sources of HMs and the health risks. The results show that the high-pollution-value areas of HMs are mainly located in the industry-oriented western region, but the pollution level by HMs in the groundwater in the study area is generally low. The main sources of HMs in the groundwater are found to be the mixed sources of agricultural activities and traffic emissions (39.16%), industrial activities (25.57%) and natural sources (35.27%). Although the non-carcinogenic risks for adults and children are negligible, the carcinogenic risks are at a high level. Through analyzing the relationship between HMs, pollution sources, and health risks, natural sources contribute the most to the health risks, and Cr is determined as the priority control HM. This study emphasizes the importance of quantitative evaluation of the HM pollution sources and probabilistic health risk assessment, which provides an essential basis for water pollution prevention and control in Hainan Island.

Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution

Rice crops are especially vulnerable to arsenic exposure compared to other cereal crops because flooding growing conditions facilitates its uptake. Besides, there are still many unknown questions about arsenic’s mode of action in rice. Here, we apply two untargeted approaches using liquid chromatography coupled to mass spectrometry (LC-MS) to unravel the effects on rice lipidome and metabolome in the early stages of growth. The exposure is evaluated through two different treatments, watering with arsenic-contaminated water and soil containing arsenic. The combination of regions of interest (ROI) and multivariate curve resolution (MCR) strategies in the ROIMCR data analyses workflow is proposed and complemented with other multivariate analyses such as partial least square discriminant analysis (PLS-DA) for the identification of potential markers of arsenic exposure and toxicity effects. The results of this study showed that rice metabolome (and lipidome) in root tissues seemed to be more affected by the watering and soil treatment. In contrast, aerial tissues alterations were accentuated by the arsenic dose, rather than with the watering and soil treatment itself. Up to a hundred lipids and 40 metabolites were significantly altered due to arsenic exposure. Major metabolic alterations were found in glycerophospholipids, glycerolipids, and amino acid-related pathways.

Distribution, Genesis, and Human Health Risks of Groundwater Heavy Metals Impacted by the Typical Setting of Songnen Plain of NE China

Heavy metals pollution in groundwater and the resulting health risks have always been an environmental research hotspot. However, the available information regarding this topic and associated methods is still limited. This study collected 98 groundwater samples from a typical agricultural area of Songnen Plain in different seasons. The pollution status and sources of ten heavy metals (As, Ba, Cd, Co, Cr (VI), Cu, Fe, Mn, Ni, Pb, and Zn) were then analyzed and compared. In addition, the human health risks assessment (HHRA) model was used to calculate human health risks caused by heavy metals in groundwater. The results revealed that heavy metals were mainly distributed in the northwest of the study area and along the upper reaches of the Lalin river and that the concentrations of heavy metals were higher during the wet season than the dry season. Industrial and agricultural activities and natural leaching are the main sources, and each kind of heavy metal may have different sources. Fe and Mn are the primary pollutants, mainly caused by the native environment and agricultural activities. The exceeding standard rates are 71.74% and 61.54%, respectively based on the Class III of Quality Standard for Groundwater of China (GB/T 14848-2017). The maximum exceeding multiple are 91.45 and 32.05, respectively. The health risks of heavy metals borne by different groups of people were as follows: child > elder > young > adult. Carcinogenic heavy metals contribute to the main risks, and the largest risks sources are Cr and As. Therefore, the government should appropriately restrict the use of pesticides and fertilizers, strictly manage the discharge of enterprises, and control man-made heavy metals from the source. In addition, centralized water supply and treatment facilities shall be established to prevent the harm of native heavy metals.