Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment

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.

Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches

Deterioration of groundwater quality is a long-term incident which leads unending vulnerability of groundwater. The present work was carried out in Murshidabad District, West Bengal, India to assess groundwater vulnerability due to elevated arsenic (As) and other heavy metal contamination in this area. The geographic distribution of arsenic and other heavy metals including physicochemical parameters of groundwater (in both pre-monsoon and post-monsoon season) and different physical factors were performed. GIS-machine learning model such as support vector machine (SVM), random forest (RF) and support vector regression (SVR) were used for this study. Results revealed that, the concentration of groundwater arsenic compasses from 0.093 to 0.448 mg/L in pre-monsoon and 0.078 to 0.539 mg/L in post-monsoon throughout the district; which indicate that all water samples of the Murshidabad District exceed the WHO's permissible limit (0.01 mg/L). The GIS-machine learning model outcomes states the values of area under the curve (AUC) of SVR, RF and SVM are 0.923, 0.901 and 0.897 (training datasets) and 0.910, 0.899 and 0.891 (validation datasets), respectively. Hence, "support vector regression" model is best fitted to predict the arsenic vulnerable zones of Murshidabad District. Then again, groundwater flow paths and arsenic transport was assessed by three dimensions underlying transport model (MODPATH). The particles discharging trends clearly revealed that the Holocene age aquifers are major contributor of As than Pleistocene age aquifers and this may be the main cause of As vulnerability of both northeast and southwest parts of Murshidabad District. Therefore, special attention should be paid on the predicted vulnerable areas for the safeguard of the public health. Moreover, this study can help to make a proper framework towards sustainable groundwater management.

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

Shallow Groundwater Quality Assessment and Its Suitability Analysis for Drinking and Irrigation Purposes

For shallow groundwater, hydrogeochemical processes and quality assessment must be addressed because shallow groundwater is freely available in many parts of the globe. Due to recent anthropogenic activities and environmental changes in Sakrand, Sindh, Pakistan, the groundwater is extremely vulnerable. To provide safe drinking and agricultural water, hydrogeochemical analysis is required. Ninety-five groundwater samples were analyzed using agricultural and drinking indices to determine the hydrogeochemical parameters using multivariate analysis such as Pearson correlations, principal component cluster analysis, as well as Piper diagrams and Gibbs plot for drinking and agricultural indices. An abundance of ions was observed through the statistical summary; however, cations and anions were recorded in the orders Na+ > Ca2+ > Mg2+ > K+ and HCO3− > Cl− > SO42− > NO3− > F−. The hydrogeochemical process used to quantify the major reactions occurring in the groundwater system showed rock dominance; the Piper diagrams evaluated the water type. A mixed pattern of calcium, magnesium, and chloride ions (Ca2+−Mg2+−Cl− type) was observed. Additionally, the ion exchange method showed an excess of bicarbonate ions due to carbonic acid weathering. The water quality index (WQI) resulted 32.6% of groundwater being unsuitable for human consumption; however, the United States Salinity Laboratory (USSL) diagram showed 60% of samples were unsuitable for irrigation due to high salinity and the Wilcox diagram depicted 5% of samples lying in the unsuitable region. Most of the water samples were suitable for drinking; only a few samples were unsafe for drinking purposes for children due to the high hazard index.

Health effect and risk assessment of the populations exposed to different arsenic levels in drinking water and foodstuffs from four villages in arsenic endemic Gaighata block, West Bengal, India

Health exposure and perception of risk assessment have been evaluated on the populations exposed to different arsenic levels in drinking water (615, 301, 48, 20 µg/l), rice grain (792, 487, 588, 569 µg/kg) and vegetables (283, 187, 238, 300 µg/kg) from four villages in arsenic endemic Gaighata block, West Bengal. Dietary arsenic intake rates for the studied populations from extremely highly, highly, moderately, and mild arsenic-exposed areas were 56.03, 28.73, 11.30, and 9.13 μg/kg bw/day, respectively. Acute and chronic effects of arsenic toxicity were observed in ascending order from mild to extremely highly exposed populations. Statistical interpretation using 'ANOVA' proves a significant relationship between drinking water and biomarkers, whereas "two-tailed paired t test" justifies that the consumption of arsenic-contaminated dietary intakes is the considerable pathway of health risk exposure. According to the risk thermometer (SAMOE), drinking water belongs to risk class 5 (extremely highly and highly exposed area) and 4 (moderately and mild exposed area) category, whereas rice grain and vegetables belong to risk class 5 and 4, respectively, for all the differently exposed populations. The carcinogenic (ILCR) and non-carcinogenic risks (HQ) through dietary intakes for adults were much higher than the recommended threshold level, compared to the children. Supplementation of arsenic-safe drinking water and nutritional food is strictly recommended to overcome the severe arsenic crisis.

Effect of Phosphorus Application on Arsenic Species Accumulation and Co-Deposition of Polyphenols in Rice Grain: Phyto and Food Safety Evaluation

The present study was aimed at exploring the effect of soil application of different concentrations of orthophosphate (P) (0, 10, 20, 30, and 40 mg kg-1) on rice agronomic and yield parameters, arsenic (As) species accumulation, and polyphenol levels in the grain of rice grown under As spiked soil (10 mg kg-1). The contents of As species (As(V), As (III), MMA and DMA) and polyphenols in rice grain samples were estimated using LC-ICP-MS and LC-MS/MS, respectively. P treatments significantly reduced the toxic effects of As on agronomic parameters such as root weight and length, shoot and spike length, straw, and grain yield. Among the treatments studied, only the treatment of 30 mg kg-1 P helps to decrease the elevated levels of As (V), As (III), and DMA in rice grains due to As application. The study revealed that 30 mg kg-1 was the optimal P application amount to minimize AS accumulation in rice grains and As-linked toxicity on agronomic parameters and chlorophyll biosynthesis. Furthermore, the levels of trans-ferulic acid, chlorogenic acid, caffeic acid, and apigenin-7-glucoside increased in response to accumulation of As in the rice grain. In conclusion, the precise use of phosphorus may help to mitigate arsenic linked phytotoxicity and enhance the food safety aspect of rice grain.

The combined exposure of microplastics and toxic contaminants in the floodplains of north India: A review

Microplastics in aquatic ecosystem are an emerging environmental threat, primarily aggregating into sediments and living biota besides providing active transportation to toxic pollutants. Recent studies have revealed that a microplastic surface cannot be considered as "inert" and therefore the rate and stage of degradation of microplastic will determine its capability in adsorbing and transporting the solute to longer distances. Our concern is driven by the fact that there has been an absence of widescale research in India despite a country with one of the longest networks of rivers and a 7500 km long active coastline. Anthropogenic pollutants are expected to increase and the situation will further worsen when more persistent organic pollutants (POCs) and geogenic contaminants will find its sink via monsoon runoff. Studies on aquatic species including COD, daphnia magna and zebrafish suggest strong links of bio-accumulation, suspecting to a more serious situation for the coastal India where there is an almost three times increase in the density of the microplastics as the monsoon progresses. Evidences also suggests that microplastics can adsorb known carcinogens as well as endocrine disrupting chemicals leaving our aquatic life exposed to higher mortality. Our review is a first ever scientific attempt in compiling these evidences through researches done in this field to understand the risk that the major floodplains of North India are currently facing. We have adapted the theories and inferences of the available research to predict and postulate a probable mechanism that could explain the severity of the situation in India.

Compost-mediated arsenic phytoremediation, health risk assessment and economic feasibility using Zea mays L. in contrasting textured soils

Maize (Zea mays L.) is considered as a potential energy-yielding crop which may respond to compost application for arsenic (As) phytoremediation depending on soil type and compost application levels in soil. Here, we explored compost-mediated As phytoremediation potential of maize in the two different textured soils (sandy loam soil and clay loam soil) at varying As (0-120 mg kg^-1) and compost (0-2.5%) levels under glasshouse conditions. Results revealed that in the absence of compost maize plants grown at different soil As levels (0-120 mg kg^-1) accumulated 1.20-1.71 times more As from sandy loam soil than that of clay loam soil. The compost addition in soil at all levels, with 120 mg kg^-1As enhanced As accumulation in maize plants in the clay loam soil by 13%, while it reduced As phyto-uptake by 27% in sandy loam soil. This may be due to an increase in phosphate-extractable (bioavailable) soil As content from 2.7 to 3.8 mg kg^-1 in clay loam soil. The estimated daily intake (EDI) of As (0.03-0.15 μg g^-1 of body weight day^-1) was above the US EPA's standard value. Arsenic phytoremediation potential of the maize plants was found to be economical for sandy loam soil with 1% compost level and for clay loam soil at 2.5% compost level, suggesting soil type specific dose dependence of compost for As phytoremediation programs. Novelty statement: To our knowledge, the role of compost in economic feasibility of energy crops at contaminated soils in general, and in the growing of maize at As-contaminated soil in particular, has not been addressed, so far. Moreover, it is the first time to evaluate environmental and health risk of compost-mediated As phytoremediation in different soil types.This study provided new insights of economic evaluation and risk assessment in the phytoremediation and mechanisms of compost in biomass production of energy crop at different As concentration. These aspects in phytoremediation studies are imperative to understand for developing safe, cost-effective and soil specific remediation strategies.

Acid mine drainage formation and arsenic mobility under strongly acidic conditions: Importance of soluble phases, iron oxyhydroxides/oxides and nature of oxidation layer on pyrite

Acid mine drainage (AMD) formation and toxic arsenic (As) pollution are serious environmental problems encountered worldwide. In this study, we investigated the crucial roles played by common secondary mineral phases formed during the natural weathering of pyrite-bearing wastes-soluble salts (melanterite, FeSO₄·7H₂O) and metal oxides (hematite, Fe₂O₃)-on AMD formation and As mobility under acidic conditions (pH 1.5-4) prevalent in historic tailings storage facilities, pyrite-bearing rock dumps and AMD-contaminated soils and sediments. Our results using a pyrite-rich natural geological material containing arsenopyrite (FeAsS) showed that melanterite and hematite both directly-by supplying H⁺ and/or oxidants (Fe³⁺)-and indirectly-via changes in the nature of oxidation layer formed on pyrite-influenced pyrite oxidation dynamics. Based on SEM-EDS, DRIFT spectroscopy and XPS results, the oxidation layer on pyrite was mainly composed of ferric arsenate and K-Jarosite when melanterite was abundant with/without hematite but changed to Fe-oxyhydroxide/oxide and scorodite when melanterite was low and hematite was present. This study also observed the formation of a mechanically 'strong' coating on pyrite that suppressed the mineral's oxidation. Finally, As mobility under acidic conditions was limited by its precipitation as ferric arsenate, scorodite, or a Fe/Al arsenate phase, including its strong adsorption to Fe-oxyhydroxides/oxides.

Safeguarding the fragile rice–wheat ecosystem of the Indo-Gangetic Plains through bio-priming and bioaugmentation interventions

Managing agrochemicals for crop production always remains a classic challenge for us to maintain the doctrine of sustainability. Intensively cultivated rice–wheat production system without using the organics (organic amendments, manures, biofertilizers) has a tremendous impact on soil characteristics (physical, chemical, and biological), environmental quality (water, air), input use efficiency, ecosystem biodiversity, and nutritional security. Consequently, crop productivity is found to be either decreasing or stagnating. Rice–wheat cropping system is the major agroecosystem in India feeding millions of people, which is widely practiced in the Indo-Gangetic Plains (IGP). Microorganisms as key players in the soil system can restore the degraded ecosystems using a variety of mechanisms. Here, we propose how delivery systems (i.e., the introduction of microbes in seed, soil, and crop through bio-priming and/or bioaugmentation) can help us in eradicating food scarcity and maintaining sustainability without compromising the ecosystem services. Both bio-priming and bioaugmentation are efficient techniques to utilize bio-agents judiciously for successful crop production by enhancing phytohormones, nutrition status, and stress tolerance levels in plants (including mitigating of abiotic stresses and biocontrol of pests/pathogens). However, there are some differences in application methods, and the latter one also includes the aspects of bioremediation or soil detoxification. Overall, we have highlighted different perspectives on applying biological solutions in the IGP to sustain the dominant (rice–wheat) cropping sequence.

One-dimensional graphene for efficient aqueous heavy metal adsorption: Rapid removal of arsenic and mercury ions by graphene oxide nanoribbons (GONRs)

There is a knowledge gap for the application of one-dimensional graphene in the adsorption process. Our hypothesis was based on the fact that graphene oxide nanoribbons (GONRs) as one-dimensional graphene with more desired edges and specific surface area than other carbonaceous nanomaterials have more oxygen containing functional groups (active sites) on their edges and basal planes and therefore are more capable in adsorption of pollutants. In this regard, we synthesized GONRs by unzipping of multi-walled carbon nanotubes (MWCNTs) and investigated the adsorption behavior of GONRs by ultrasonic-assisted adsorptive removal of As(V) and Hg(II) ions from aqueous solution. The obtained results showed that As(V) ions are more favorably adsorbed onto the GONRs than Hg(II) ions and with increasing initial As(V) and Hg(II) ions concentration to 300 ppm, the equilibrium adsorption uptake of the synthesized GONRs increases to 155.61 and 33.02 mg/g for As(V) and Hg(II) ions, respectively through a rapid separation process in just 12 min. Also, three kinetic models and Freundlich and Langmuir adsorption isotherms were applied to evaluate the obtained experimental results. Our findings highlight the potential application of GONRs as one-dimensional graphene adsorbent with more desired edges than MWCNTs and graphene oxide (GO) and high adsorption capacity for selective removal of heavy metals.

Transport of arsenolipids to the milk of a nursing mother after consuming salmon fish

OBJECTIVE

We address two questions relevant to infants' exposure to potentially toxic arsenolipids, namely, are the arsenolipids naturally present in fish transported intact to a mother's milk, and what is the efficiency of this transport.

METHODS

We investigated the transport of arsenolipids and other arsenic species present in fish to mother's milk by analyzing the milk of a single nursing mother at 15 sampling times over a 3-day period after she had consumed a meal of salmon. Total arsenic values were obtained by elemental mass spectrometry, and arsenic species were measured by HPLC coupled to both elemental and molecular mass spectrometry.

RESULTS

Total arsenic increased from background levels (0.1 μg As kg-1) to a peak value of 1.72 μg As kg-1 eight hours after the fish meal. The pattern for arsenolipids was similar to that of total arsenic, increasing from undetectable background levels (< 0.01 μg As kg-1) to a peak after eight hours of 0.45 μg As kg-1. Most of the remaining total arsenic in the milk was accounted for by arsenobetaine. The major arsenolipids in the salmon were arsenic hydrocarbons (AsHCs; 55 % of total arsenolipids), and these compounds were also the dominant arsenolipids in the milk where they contributed over 90 % of the total arsenolipids.

CONCLUSIONS

Our study has shown that ca 2-3 % of arsenic hydrocarbons, natural constituents of fish, can be directly transferred unchanged to the milk of a nursing mother. In view of the potential neurotoxicity of AsHCs, the effects of these compounds on the brain developmental stage of infants need to be investigated.