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

Chattopadhyay, Arghya; Singh, Anand Prakash; Singh, Satish Kumar; Barman, Arijit; Patra, Abhik; Mondal, Bhabani Prasad; Banerjee, Koushik

doi:10.1016/j.chemosphere.2019.124623

Cited by 35 publications

(6 citation statements)

References 48 publications

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“…Closely related to PCA, factor analysis (FA) can be applied in a variety of ways to polarize the loadings of the original variables and obtain new factors, called varifactors (VFs), that more effectively explain the potential information contained in the original variables (Zheng et al, 2015;Chattopadhyay et al, 2020).…”

Section: Pca-apcs-mlr Modelmentioning

confidence: 99%

Water quality assessment and source apportionment of Huangpu River water pollution in Shanghai City, eastern China using APCS-MLR

Yue-ya¹

2023

Preprint

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As a result of increasing anthropogenic disturbance, the degradation of the surface water environment has become a key concern for water resource management. Controlling possible pollution sources is necessary for protecting water resources. In this study, water quality data from online monitoring national control stations were analyzed in terms of pH, water temperature (WT), electrical conductivity (EC), turbidity (NTU), dissolved oxygen (DO), and concentrations of permanganate index (CODMn), ammonium nitrogen (NH3+–N), total nitrogen (TN), total phosphorus (TP). Principal component analysis/factor analysis (PCA/FA) were employed to qualitatively figure out the potential sources of river water pollution of Huangpu River in Shanghai City, eastern China. An absolute principal component score-multiple linear regression (APCS-MLR) receptor model was used to analyze each source's contribution to the variables affecting water quality quantitatively. The results showed that all observed water quality indices met the quality criteria specified in the Chinese surface water standards, except for TN. Five sources of river water pollution were identified, and their contribution ratios in a descending order were as follows: the meteorological process (26%) > agricultural activities (14%) > industrial sewage (10%) > natural environmental sources (4%) = domestic sewage (4%). Therefore, recommendations for enhancing the quality of surface water resources in this area involve decreasing agricultural pollution and improving the sewage system.

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Section: Pca-apcs-mlr Modelmentioning

confidence: 99%

Water quality assessment and source apportionment of Huangpu River water pollution in Shanghai City, eastern China using APCS-MLR

Yue-ya¹

2023

Preprint

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“…According to reports, the metamorphic and granitic Himalayan parent rocks that the Ganga, Brahmaputra, and Meghna rivers flow through are the primary source of the sulfide minerals found in the fluvial silt [91]. It was highlighted that the low correlation of arsenic with bicarbonate (HCO 3 − ) suggests that competitive adsorption by bicarbonate may not be the primary mechanism for the mobilization of arsenic in the Ganga and Ghagra River basins.…”

Section: Oxidation Of Arsenic-rich Sulfide Mineralsmentioning

confidence: 99%

“…The Mn(IV)-oxyhydroxides in solid form are believed to have a significant impact on redox buffering. It rapidly oxidizes As(III) to As(V) and adheres As(V) to effectively remove arsenic from water [91]. Several parts of the Bengal basin have been shown to contain high manganese (Mn) concentrations despite having relatively low iron and arsenic contents, respectively.…”

Section: Redox Conditionsmentioning

confidence: 99%

Arsenic Contamination in Indian Groundwater: From Origin to Mitigation Approaches for a Sustainable Future

Marghade,

Mehta,

Shelare

et al. 2023

Water

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The presence of arsenic in Indian groundwater poses a significant threat to both the ecosystem and public health. This review paper comprehensively addresses the topic, encompassing the underlying causes and potential solutions. Health consequences examines the serious health risks of drinking water contaminated with arsenic. Arsenic’s complex geochemical processes of mobilization, transport, and distribution in groundwater are investigated. Mathematical models, geographical analysis, and data-driven modeling are discussed in the context of Indian groundwater. A comprehensive assessment of removal methodologies and the various factors influencing the mobility of arsenic is addressed. It was documented that community water purifiers and plants have successfully eliminated approximately 90% of arsenic, and the implementation of rainwater collection systems has also enhanced the overall quality of water. This review aims to address existing knowledge gaps and assess various strategies aimed at ensuring a more secure and sustainable water supply for the regions in question. The ultimate goal is to enhance the overall well-being of the population and protect the integrity of local ecosystems.

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“…Arsenic has been recognized as one of the most hazardous mettaloid being part of earth crust and leads to water resources contamination (Irem et al, 2017). Drinking water contamination by arsenic is a crucial issue due to its toxic nature causing cancer or other chronic diseases (Chattopadhyay et al, 2020). Arsenic can be found in natural water as arsenite (As(III)) and arsenate (As(V)).…”

Section: Introductionmentioning

confidence: 99%

Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

Karaman¹

2020

European Journal of Science and Technology

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The main threats from heavy metals specifically arsenic-contaminated drinking water have been emerging as an environmental and social crucial issue. Herein, the arsenic (V) (As(V)) biosorption performance of waste orange peel (OP) driven-graphene-like porous carbon (GPC) was investigated experimentally and an artificial neural network (ANN) approach was used to model the biosorption process. The initial pH (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, and 10.0), initial As(V) concentration (25.0, 50.0, 100.0, 250.0, 500.0 and 750.0 mg.L -1 ), biosorbent dosage (1.0, 2.0, 3.0, 4.0 and 5.0 g.L -1 ), and contact time (0-120.0 min) were investigated to optimize the biosorption process. The as-synthesized GPC biosorbents with a high specific surface area (985 m 2 .g -1 ) and pore volume (1.04 cm 3 .g -1 ) offered superior removal efficiency as 88.2% (equilibrium uptake capacity of 46.5 mg.g -1 ) at initial pH 6.0, initial As(V) concentration 100 mg.L -1 , and biosorbent dosage 2.0 g.L -1 . A three-layer ANN model was developed to forecast the Ar(V) biosorption performance of GPCs. Several experimental data points were considered as test data to validate the ANN model. The ANN model was performed with the Levengberg-Marquardt algorithm (LMA), linear transfer function (purelin) at the output layer, and a tangent sigmoid transfer function (tansig) in the hidden layer with 12 neurons. The values of coefficient of determination and mean squared error were calculated to be 0.9858 and 0.0014, respectively. The results revealed that the experimental data were in accordance with ANN-driven data as well as reveling the high accuracy of the ANN approach in estimating the target variable. The developed ANN model is useful for the optimization of process conditions for pilot-scale utilization of As(V) biosorption process by GPC.

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

Cited by 35 publications

References 48 publications

Water quality assessment and source apportionment of Huangpu River water pollution in Shanghai City, eastern China using APCS-MLR

Water quality assessment and source apportionment of Huangpu River water pollution in Shanghai City, eastern China using APCS-MLR

Arsenic Contamination in Indian Groundwater: From Origin to Mitigation Approaches for a Sustainable Future

Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

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