Groundwater Arsenic-Attributable Cardiovascular Disease (CVD) Mortality Risks in India

A comprehensive review of human health risks of arsenic and fluoride contamination of groundwater in the South Asia region

The present study found that ∼80 million people in India, ∼60 million people in Pakistan, ∼70 million people in Bangladesh, and ∼3 million people in Nepal are exposed to arsenic groundwater contamination above 10 μg/L, while Sri Lanka remains moderately affected. In the case of fluoride contamination, ∼120 million in India, >2 million in Pakistan, and ∼0.5 million in Sri Lanka are exposed to the risk of fluoride above 1.5 mg/L, while Bangladesh and Nepal are mildly affected. The hazard quotient (HQ) for arsenic varied from 0 to 822 in India, 0 to 33 in Pakistan, 0 to 1,051 in Bangladesh, 0 to 582 in Nepal, and 0 to 89 in Sri Lanka. The cancer risk of arsenic varied from 0 to 1.64 × 1-1 in India, 0 to 1.07 × 10-1 in Pakistan, 0 to 2.10 × 10-1 in Bangladesh, 0 to 1.16 × 10-1 in Nepal, and 0 to 1.78 × 10-2 in Sri Lanka. In the case of fluoride, the HQ ranged from 0 to 21 in India, 0 to 33 in Pakistan, 0 to 18 in Bangladesh, 0 to 10 in Nepal, and 0 to 10 in Sri Lanka. Arsenic and fluoride have adverse effects on animals, resulting in chemical poisoning and skeletal fluorosis. Adsorption and membrane filtration have demonstrated outstanding treatment outcomes.

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.

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.

Sustainable ceramic membrane for decontamination of water: A cost-effective approach

A sustainable ceramic membrane embedded with silver has been developed using quartz, kaolin and calcium carbonate. All the chemicals involved in this process were commonly available, non-toxic and cheap. The process was very simple, convenient and does not involve any wastage of water. Decoration of silver particles onto the porous ceramic membrane with the help of APTES as a connecting molecule leads to the formation of a durable material having strong antibacterial capacity. The fabricated membrane holds wide pore morphology with pore size of 4.4 μm and average porosity of 19.5% with an estimated cost of fabrication of about 60 dollar/m². The membrane was found capable in reducing the TDS, BOD and COD of water samples that confirms that it is efficient for water treatment applications.

Assessment of Arsenic in Hair of the Inhabitants of East Croatia—Relationship to Arsenic Concentrations in Drinking Water

The problem of elevated arsenic concentrations in water and environment is an increasing public health concern. The aim of the study was to assess the arsenic content in human hair in selected areas of eastern Croatia and to compare them with measured values after installation of a new water supply system. The hair samples were taken in the areas of wider Osijek and Vinkovci area and analyzed using the ICP–MS method. These data were also compared with data for Vinkovci previously published in 2004. Depending on the investigated area, the median concentrations ranged from 0.02 to 0.9 µg g−1, whereby this last value exceeded the upper range of the reference value (0.319 µg g−1). The arsenic concentrations from the Našice, Osijek and Vinkovci areas were within or slightly above the maximum allowed reference range. The highest median values in hair samples were detected in Čepin, with arsenic-contaminated potable water, while in areas where the water source was changed, the values were significantly lower. The results add to the conclusion that there has been significant reduction in hair arsenic concentrations in the population that was given access to clean, uncontaminated water from other regional sources.