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

Spatio-temporal prediction of groundwater vulnerability based on CNN-LSTM model with self-attention mechanism: A case study in Hetao Plain, northern China

Located in northern China, the Hetao Plain is an important agro-economic zone and population centre. The deterioration of local groundwater quality has had a serious impact on human health and economic development. Nowadays, the groundwater vulnerability assessment (GVA) has become an essential task to identify the current status and development trend of groundwater quality. In this study, the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism. The study firstly builds the CNN-LSTM model with self-attention (SA) mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that the CNN-LSTM model outperforms these models, demonstrating its significance in groundwater vulnerability assessment. It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years. This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities. Moreover, the overall groundwater vulnerability risk in the entire region has increased, evident from both the notably high value and standard deviation. This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities. The model can be optimized for diverse applications across regional environmental assessment, pollution prediction, and risk statistics. This study holds particular significance for ecological protection and groundwater resource management.

Seasonal variations in groundwater chemistry and quality and associated health risks from domestic wells and crucial constraints in the Pearl River Delta

Groundwater quality is strongly compromised by polluted surface water recharge in rapidly developing urban regions. However, gaps still remain in the understanding of the critical contaminants controlling water quality and the health risks associated with groundwater consumption, particularly considering seasonal and climate changes in rainfall. This work focused on changes in groundwater quality and critical contaminants in domestic wells in the fast-developing Pearl River Delta (PRD) from the wet season to the dry season. The stable isotope δD and δ18O values indicated that groundwater was largely impacted by precipitation and has experienced strong evaporation. The groundwater generally exhibited oxidizing and slightly alkaline properties and was predominantly of the Ca-HCO3 type. Owing to the dominant water type of Ca-HCO3 and the high concentrations of Ca, concerns related to hard water arose, particularly during the wet season, which promotes the need for water softening before groundwater use. Although the heavy metal pollution index (HPI) and water quality index (WQI) indicated excellent or good water quality, 34% and 47% of the groundwater samples presented elevated concentrations of arsenic and nitrate, respectively, compared with the WHO recommended levels, and the contamination level was elevated during the dry season. To our knowledge, this study is the first to report the fluoride concentrations in the PRD groundwater, with median values below 0.5 mg L-1, underscoring the need for dietary fluoride supplementation. Health risk assessment confirmed the presence of both noncarcinogenic risks from arsenic and nitrate and cancer risk from arsenic in local populations resulting from groundwater consumption in the PRD region. This research emphasizes the importance of critical contaminants that constrain groundwater quality from different seasons with large variations in rainfall. Our work highlights the urgent need for the construction of adequate sanitation systems and for the control of agricultural nonpoint source pollution in rapidly urbanizing areas to safeguard both surface water and groundwater resources.

Arsenic toxicity exacerbates China's groundwater and health crisis

Arsenic (As) contamination is considered a major threat to groundwater quality and human health. The uneven distribution of arsenic contributes to regional variations, creating discrimination related to arsenic enrichment and carcinogenic risk. Here, we have analyzed 2,737 groundwater samples across China, which spans a broad range of geo-environments, climates and land use types. We find that regional inequality of groundwater arsenic concentration is caused by ontology environment. By mapping the groundwater arsenic distribution across China and conducting a global meta-analysis, the spatial response of arsenic concentration to different cancer risks was revealed, and neglected As(V) should be given attention. A random forest analysis identified chemical properties (including oxidation-reduction potential, pH, total manganese ion, total iron ion, total dissolved solids, and sulfate ion) as the most influential drivers, contributing 56% to the model's explanatory power, followed by geographical factors at 28%, climatic factors at 10%, and human activities at 6%. Additionally, reducing the proportion of groundwater supply with high arsenic concentration in drinking water in regions without water treatment may help lower the potential carcinogenic risk. This study emphasizes the potential health risk associated with high arsenic groundwater, making it particularly important to roll out efficient water purification technologies given the natural enrichment of arsenic, especially rural regions.

Spatial distribution and hydrogeochemical processes of high iodine groundwater in the Hetao Basin, China

To understand the genesis and spatial distribution of high iodine groundwater in the Hetao Basin, 540 groundwater samples were analyzed for the chemistry and isotope. Total iodine concentrations in groundwater range from 1.32 to 2897 μg/L, with a mean value of 159.2 μg/L. The groundwater environment was mainly characterized by the weakly alkaline and reducing conditions, with the iodide as the main species of groundwater iodine. High iodine groundwater (I > 100 μg/L) was mainly distributed in shallow aquifers (< 30 m) of Hangjinhouqi near the Langshan Mountain and the discharge areas along the main drainage channels. The δ18O and δ2H values ranged from -12.09 ‰ to -3.99 ‰ and - 91.58 ‰ to -52.80 ‰, respectively, and the correlation between groundwater iodine and isotopes indicates the dominant role of evapotranspiration in the enrichment of iodine in the shallow groundwater with depth <30 m. It was further evidenced by the correlation between groundwater iodine and Cl/Br molar ratio, and significant contributions of climate factors identified from the random forest and XGBoost. Moreover, irrigation practices contribute to high iodine levels, with surface water used for irrigation containing up to 537.8 μg/L of iodine, which can be introduced into shallow aquifer directly. The iodine in irrigation water can be retained in the soil or shallow sediment, and later leach into groundwater under favorable conditions.

Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review

Climate change is a major concern for a range of environmental issues including water resources especially groundwater. Recent studies have reported significant impact of various climatic factors such as change in temperature, precipitation, evapotranspiration, etc. on different groundwater variables. For this, a range of tools and techniques are widely used in the literature including advanced machine learning (ML) and artificial intelligence (AI) approaches. To the best of the authors' knowledge, this review is one of the novel studies that offers an in-depth exploration of ML/AI models for evaluating climate change impact on groundwater variables. The study primarily focuses on the efficacy of various ML/AI models in forecasting critical groundwater parameters such as levels, discharge, storage, and quality under various climatic pressures like temperature and precipitation that influence these variables. A total of 65 research papers were selected for review from the year 2017-2023, providing an up-to-date exploration of the advancements in ML/AI methods for assessing the impact of climate change on various groundwater variables. It should be noted that the ML/AI model performance depends on the data attributes like data types, geospatial resolution, temporal scale etc. Moreover, depending on the research aim and objectives of the different studies along with the data availability, various sets of historical/observation data have been used in the reviewed studies Therefore, the reviewed studies considered these attributes for evaluating different ML/AI models. The results of the study highlight the exceptional ability of neural networks, random forest (RF), decision tree (DT), support vector machines (SVM) to perform exceptionally accurate in predicting water resource changes and identifying key determinants of groundwater level fluctuations. Additionally, the review emphasizes on the enhanced accuracy achieved through hybrid and ensemble ML approaches. In terms of Irish context, the study reveals significant climate change risks posing threats to groundwater quantity and quality along with limited research conducted in this avenue. Therefore, the findings of this review can be helpful for understanding the interplay between climate change and groundwater variables along with the details of the various tools and techniques including ML/AI approaches for assessing the impacts of climate changes on groundwater.

Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 μg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations.

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

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

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.

Understanding spatial heterogeneity of groundwater arsenic concentrations at a field scale: Taking the Datong Basin as an example to explore the significance of hydrogeological factors

The spatial heterogeneity of arsenic (As) concentration exceeding the 10 μg/L WHO limit at the field scale poses significant challenges for groundwater utilization, but it remains poorly understood. To address this knowledge gap, the Daying site was selected as a representative case (As concentration ranged from 1.55 to 2237 μg/L within a 250 × 150 m field), and a total of 28 groundwater samples were collected and analyzed for hydrochemistry, As speciation, and stable hydrogen and oxygen isotope. Principal component analysis was employed to identify the primary factors controlling groundwater hydrochemistry. Results indicate that the spatial heterogeneity of groundwater As concentration is primarily attributed to vertical recharge and competitive adsorption. Low vertical recharge introduces reductive substances, such as dissolved organic matter, which enhances the reductive environment and facilitates microbial-induced reduction and mobilization of As. Conversely, areas with high vertical recharge introduce oxidizing agents like SO42- and DO, which act as preferred electron acceptors over Fe(III), thus inhibiting the reductive dissolution of Fe(III) oxides and the mobilization of As. PCA and hydrochemistry jointly indicate that spatial variability of P and its competitive adsorption with As are important factors leading to spatial heterogeneity of groundwater As concentration. However, the impacts of pH, Si, HCO3-, and F- on As adsorption are insignificant. Specifically, low vertical recharge can increase the proportion of As(III) and promote P release through organic matter mineralization. This process further leads to the desorption of As, indicating a synergistic effect between low vertical recharge and competitive adsorption. This field-scale spatial heterogeneity underscores the critical role of hydrogeological conditions. Sites with close hydraulic connections to surface water often exhibit low As concentrations in groundwater. Therefore, when establishing wells in areas with widespread high-As groundwater, selecting sites with open hydrogeological conditions can prove beneficial.

Monthly variations of groundwater arsenic risk under future climate scenarios in 2081-2100

The seasonal variations of shallow groundwater arsenic have been widely documented. To gain insight into the monthly variations and mechanisms behind high groundwater arsenic and arsenic exposure risk in different climate scenarios, the monthly probability of high groundwater arsenic in Hetao Basin was simulated through random forest model. The model was based on arsenic concentrations obtained from 566 groundwater sample sites, and the variables considered included soil properties, climate, topography, and landform parameters. The results revealed that spatial patterns of high groundwater arsenic showed some fluctuations among months under different future climate scenarios. The probability of high total arsenic and trivalent arsenic was found to be elevated at the start of the rainy season, only to rapidly decrease with increasing precipitation and temperature. The probability then increased again after the rainy season. The areas with an increased probability of high total arsenic and trivalent arsenic and arsenic exposure risk under SSP126 were typically found in the high-arsenic areas of 2019, while those with decreased probabilities were observed in low-arsenic areas. Under SSP585, which involves a significant increase in precipitation and temperature, the probability of high total arsenic and trivalent arsenic and arsenic exposure risk was widely reduced. However, the probability of high total arsenic and trivalent arsenic and arsenic exposure risk was mainly observed in low-arsenic areas from SSP126 to SSP585. In conclusion, the consumption of groundwater for human and livestock drinking remains a threat to human health due to high arsenic exposure under future climate scenarios.

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.

The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater.