Source apportionment, chemometric pattern recognition and health risk assessment of groundwater from southwestern Punjab, India

Kumar, Rajiv; Mittal, Sunil; Sahoo, Prafulla Kumar; Sahoo, Sunil Kumar

doi:10.1007/s10653-020-00518-1

Cited by 69 publications

(14 citation statements)

References 63 publications

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“…In contrast, the western coastal and northeast areas have mostly forest coverings and less agricultural area (Figure ), which is reflected in the predicted low risks in these areas. Previous studies reported the occurrences of elevated groundwater nitrate in Rajsthan, central Telangana, the basin of lower Bhavani river in Tamil Nadu, Hoshangabad district in Madhya Pradesh, western Gujarat, the Godavari river basin in Maharashtra, Kurukshetra district in Haryana, Anantpur district of Andhra Pradesh, and southern Punjab that confirms the predicted distribution of high-risk areas in this study. Major cities like Bengaluru, Hyderabad, and Jaipur are also observed to be at high risk (Figure a).…”

Section: Resultssupporting

confidence: 91%

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

et al. 2022

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Elevated groundwater nitrate poses risk to the ecosystem and human health, and delineating the extent of elevated groundwater nitrate risk is essential for effective groundwater management and public health safety. Here, using machine learning models (Random Forest, Boosted Regression Tree, and Logistic Regression) on a large, in situ dataset, we have predicted the first nationwide extent of groundwater nitrate contamination risk (concentration >45 mg/L) across India. We also aimed to delineate the intrinsic (e.g., climate, geomorphic, hydrogeologic) and extraneous (e.g., anthropogenic input) predictors for identifying groundwater pollution risk. Of these models, Random Forest performed best and was considered to develop the final prediction map of groundwater nitrate at 1 km2 resolution. Climate variables like precipitation and aridity, and anthropogenic influence, e.g., fertilizer application and population density, were identified as the most important variables for predicting groundwater nitrate risk. Dry arid and semiarid regions in the west, south, and central parts of the country contained the majority of high-risk areas. Predictions suggested that about 37% of India’s areal extent and 380 million people were exposed to elevated nitrate. The prediction model performed satisfactorily over the validation dataset that indicates the prediction ability of the model at the local scale. The study aims to provide an effective approach for identifying elevated groundwater nitrate risk and aid in the development of awareness and strategies to uphold public health safety.

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Section: Resultssupporting

confidence: 91%

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

et al. 2022

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“…High loading of EC with these ions indicates influence of both anthropogenic and natural activities on groundwater quality. The significant positive loading of anions including Cl − , NO 3 − and SO 4 2− indicates influence of anthropogenic activities [60]. The study area is a part of alluvial floodplain with extensive agriculture activities, discharge of fertilizers along with irrigation return flow might contributed NO 3 − and Cl − in groundwater; however, leaching of sewage and human waste might be responsible for high SO 4…”

Section: Multivariate Statisticsmentioning

confidence: 91%

Integration of multivariate statistics and water quality indices to evaluate groundwater quality and its suitability in middle Gangetic floodplain, Bihar

2021

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This study is conducted along the middle Gangetic floodplain, to investigate the hydrogeochemical characteristics and suitability of groundwater for irrigation and human consumptions. Altogether 65 groundwater samples were collected and analyzed for major ions and water quality parameters. pH of all the samples except 1 is found > 7, which suggests alkaline aquifer condition. Groundwater samples predominately belong to Ca-Mg-HCO3 water type followed by Na-HCO3, Mg-HCO3 and Mg-SO4 water types. Hierarchical cluster analysis (HCA) combines groundwater into two distinct groups, Group 1 is found as less mineralized as the average EC value is found 625.3 μS/cm, while it is found 1375 μS/cm for Group 2. The results of correlation analysis and PCA suggest influence of natural and anthropogenic activities on groundwater. PCA extracts four major PCs which describes 71.7% of total variance. PC1 indicates influence of both lithogenic and anthropogenic activities on groundwater quality. PC2 and PC3 infer natural factors, and PC4 suggests influence of anthropogenic activities on groundwater. Exceeding concentration of F−, Fe and Mn above WHO guidelines are found as major public health concern. WQI of all except 4 groundwater samples suggests excellent to good water quality; however, 23% of the samples are not suitable based on WPI values. Irrigation indices suggest that groundwater is mostly suitable for irrigation; however, 10.7%, 12.3% and 3% samples for RSBC, MAR and KR, respectively, exceed the recommended limits and are unsuitable for irrigation. A proper management strategy and quality assurance is recommended before groundwater consumption and use in the study area.

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“…Multiple techniques, for instance, Gibbs plot, saturation index and ion exchange index, etc., are used to study the various hydrogeochemical processes, such as precipitation, rock-water interaction, and evaporation (Gibbs 1970;Feth and Gibbs 1971) To establish the relationship between water composition and aquifer lithological characteristics, Gibbs draws a graph between TDS and cation ratio [Na + /(Na + + Ca 2+ )] and anions ratio [Cl -/(Cl -+ HCO3 -)] and explains the mechanism that controls the groundwater chemistry. Through the Gibbs plot's study, the various research groups (Thakur et al 2016;Kaur et al 2017;Kumar et al 2021) suggested that the groundwater chemistry of shallow and deep aquifers is mainly controlled in the Malwa region by the rock water interaction. In the southwestern part, along with rock-water interactions, the evaporation processes also played an important role (Pant et al 2020c).…”

Section: Physicochemical Parameters Of Groundwater Of Malwa Regionmentioning

confidence: 99%

Critical review on uranium and arsenic content and their chemical mobilization in the groundwater: A case study of the Malwa region Punjab, India

Kumar

Saha

Kumar

et al. 2022

Preprint

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Groundwater played a pivotal role in the social and economic development of the Malwa Region. With the advent of the green revolution, water for irrigation and domestic use led to the development of groundwater resources. Slowly, the green revolution changed into a greed revolution, and the exploitation of groundwater resources converted into their overexploitation. Groundwater's overexploitation not only led to groundwater depletion but also led to a change in the chemistry of the Malwa region's aquifers. Researchers from academia and institutions worked and published their findings of the uranium and arsenic contaminations in the Malwa region of Punjab. In this article, we are the first to bring all the dispersed data to one commonplace. By studying the physicochemical parameters of groundwater of all districts of the Malwa region and their correlation, this paper is going to highlight the various chemical reactions occurring in the Malwa region's aquifer and how they impact groundwater chemistry. For understanding, we devised a hypothetical model to understand the complex interplay of this region's natural dynamics of groundwater aquifers. Finally, we tried to describe how the various chemical changes in the groundwater aquifer can be the reason for the mobilization of arsenic and uranium by making schematic chemical flow-charts of their mobilization. This article aims to highlight the importance of using a multidisciplinary and interdepartmental approach to comprehending the complex problem of groundwater management.

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Source apportionment, chemometric pattern recognition and health risk assessment of groundwater from southwestern Punjab, India

Cited by 69 publications

References 63 publications

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

Integration of multivariate statistics and water quality indices to evaluate groundwater quality and its suitability in middle Gangetic floodplain, Bihar

Critical review on uranium and arsenic content and their chemical mobilization in the groundwater: A case study of the Malwa region Punjab, India

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