Statistical optimization of arsenic removal from synthetic water by electrocoagulation system and its application with real arsenic-polluted groundwater

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.

Evaluation of the dual-process approach for in-situ groundwater arsenic removal

While the worldwide distribution of geogenic arsenic (As)-affected groundwater is highly overlapped with the areas with abundant groundwater, utilization of As-contained groundwater is an inevitable compromise in those areas where surface water is not enough for irrigation. Since the occurrence of As in groundwater is often accompanied by high iron (Fe) contents, the facilitation of As and Fe precipitation without adding additional oxidizers and adsorbents is considered an environmental-friendly approach to removing As in groundwater. In the present study, the oxidation/filtration dual-process with sprinkling height of 25 cm and 120 kg filter media efficiently increased the dissolved oxygen (DO) concentration (0.36-1.52 mg/L) and oxidation-reduction potential (ORP) (24-63 mV), which facilitated the formation of Fe oxides and As co-precipitation. The correlation of As removal efficiencies with their respective flow rates indicated that a decrease in groundwater Fe and an increase of Fe in sands and gravels filters as the flow rate increased evidenced the rapid oxidation of Fe to form the Fe hydroxides. In a 40-hour continuous aeration/filtration operation, As and Fe concentrations in groundwater were reduced by 79.5% and 64.88% within 40 hrs, respectively. The ease of filter replacement and cost-effectiveness in operation can be the major attractions and innovations for future field practices.

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

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

Spatial pattern of groundwater arsenic contamination in Patna, Saran, and Vaishali districts of Gangetic plains of Bihar, India

Groundwater is an essential source of drinking as well as irrigation water. It has recently become a significant challenge to maintain good and safe drinking water for all living beings. The continuous supply of arsenic detected in groundwater poses a severe health problem and has adverse effects on humans and the ecosystem. Researchers also identified arsenic contamination globally across various regions. However, a few studies also identified that the groundwater of Patna, Saran, and Vaishali districts of Bihar is intoxicated by arsenic. To assess the toxic level of arsenic in groundwater, samples from various GPS-based pointed locations were collected from the study area using a GARMIN GPS device. The total concentration of arsenic in drinking water (mostly traces of arsenic, level of μg L-1 or less) can be detected only by sophisticated analytical techniques such as ICP-MS, GF-AAS, and HG-AAS. The standard procedures were followed to determine quality attributes in groundwater. Arsenic contamination persists in most areas and exceeds the permissible limits prescribed by the World Health Organization (WHO), negatively impacting the health of more than 10 million people in the state. The 90.47% and 85.71% groundwater samples of the study area exceeded the permissible limit of the WHO (0.01 mg L-1) and Bureau of Indian Standards (BIS (0.05 mg L-1), respectively. The analyzed data was obtained, and variability was noticed in total arsenic concentrations ranging from 0.002 to 7.801 mg L-1, with a mean value of 0.87 mg L-1. Similarly, the water quality attribute like total dissolved solids were identified in 14.28% of samples, which crossed 201 to 1026 mg L-1, with a mean value of 375.33 mg L-1.

Removal of arsenic in groundwater from western Anatolia, Turkey using an electrocoagulation reactor with different types of iron anodes

Electrocoagulation (EC) is a significantly efficient method for As removal from waters and received considerable attention recently. In this study, the natural groundwater (GW) samples containing As concentrations of GW-1: 538.8 μg L⁻¹, GW-2: 1132.1 μg L⁻¹, and GW-3: 52, 000 μg L⁻¹ were obtained from different provinces and treated by EC process using different iron anodes (plate, ball, and scrap). To achieve drinking water As standard (10 μg L⁻¹), the operational time, applied current, and As removal optimization for all anode types were studied. At applied current of 0.025 A, the As removal efficiency, EC time, and operating cost were >99.9%, 180 min and 0.406 $ m⁻³ for ball anodes, >99.9%, 100 min and 0.0813 $ m⁻³ for plate anodes, >99.9%, 80 min and 0.0815 $ m⁻³ for scrap anodes for GW-3, respectively. It was observed that as the As concentration in the GW increased, the EC time and operating cost increased. Overall, it was concluded that Fe scrap anodes are more advantageous than other types of anodes in terms of operating cost in EC reactor for As removal.

Tangential effluent inlet in a cylindrical electrocoagulation reactor containing curved electrodes, and its use in crude oil in water treatment

A continuous electrocoagulation reactor, with curved electrodes, polarity switch, and cylindrical geometry, was used for emulsified crude oil in water separation. Apparatus novelty consists of an inlet arranged to promote a circular flow regime. The effects of flow rate (2 and 6 mL.s^-1), electrical current (2 and 4 A), and distance between electrodes (1.5 and 2.5 cm) were investigated using a full factorial design and statistical analysis. Using 6 mL.s^-1 flow rate, 2 A electric current and 2.5 cm electrode distance; 86% oil removal was obtained at a pH < 9.0. For this configuration, the system will process 21.6 L of oily emulsion while consuming 6.92 Wh. Oil removal increased with flow rate, a novel characteristic created by the unusual geometry of the system.