Evaluation of hydrogeochemical processes and groundwater quality in the Jhansi district of Bundelkhand region, India

Singh, Abhay Kumar; Raj, Beenu; Tiwari, Ashwani Kumar; Mahato, Mukesh Kumar

doi:10.1007/s12665-012-2209-7

Cited by 109 publications

(51 citation statements)

References 22 publications

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“…However, the geostatistics, a rapidly evolving branch of applied statistics and mathematics that offers a collection of tools, has been utilized extensively to illustrate the spatial variability in many fields. Several studies have been carried out to investigate the spatial variability of groundwater quality (Adhikary et al 2012;Narany et al 2015;Mahato et al 2016;Singh et al 2013). In recent year, the geostatistics and GIS techniques are very common practices to assess the groundwater, surface, and mine water quality and spatial variability (Khashei-Siuki and Sarbazi 2015;Tiwari et al 2015Tiwari et al , 2016a.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

et al. 2017

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The analyses of 180 groundwater samples of AlKharj, Saudi Arabia, recorded that most groundwaters are unsuitable for drinking uses due to high salinity; however, they can be used for irrigation with some restriction. The electric conductivity of studied groundwater ranged between 1.05 and 10.15 dS m -1 with an average of 3.0 dS m -1 . Nitrate was also found in high concentration in some groundwater. Piper diagrams revealed that the majority of water samples are magnesium-calcium/sulfate-chloride water type. The Gibbs's diagram revealed that the chemical weathering of rock-forming minerals and evaporation are influencing the groundwater chemistry. A kriging method was used for predicting spatial distribution of salinity (EC dS m -1 ) and NO 3 -(mg L -1 ) in Al-Kharj's groundwater using data of 180 different locations. After normalization of data, variogram was drawn, for selecting suitable model for fitness on experimental variogram, less residual sum of squares value was used. Then cross-validation and root mean square error were used to select the best method for interpolation. The kriging method was found suitable methods for groundwater interpolation and management using either GS? or ArcGIS.

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Section: Introductionmentioning

confidence: 99%

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

et al. 2017

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“…Water pollution is a major concern in mining operation area [12,13,16,17,[19][20][21][22]. Water pollution in mining areas are getting affected mainly due to overburden dumps(OBD), surface impoundments, industrial effluents, acid mine drainage, tailing ponds [23], discharge of pumped out mine water which contain heavy metal [24][25][26][27], discharge of leachate from mine waste, surface runoff from OBD [16,28], disposal of solid waste generated by mining operations, waste rock, sub-grade ore and mineralized reject which in result cause disturbance of water level of surface and ground water quality and environmental pollution [29,30].…”

Section: Introductionmentioning

confidence: 99%

“…Study shows that there is health risk associated with those metal contaminanted water [34]. Those metal contaminated water pose direct threat to human health when that water is used as drinking purpose [31,35] or pose indirect threat to human health when that water is used in irrigation purpose [23,[35][36][37]. River and streams located near to mining activities get contaminated continuously from point as well as non-point source.…”

Section: Introductionmentioning

confidence: 99%

“…Access to drinking water in India has increased over the past few decades with the tremendous adverse impact of unsafe water for health [23].Scarcity of clean and potable drinking water has emerged in recent years as one of the most serious developmental issues in many parts of India like West Bengal, Jharkhand, Orissa, Western Uttar Pradesh, Andhra Pradesh, Rajasthan and Punjab [38]. The 30% of urban and 90% of the rural Indian population still depends completely on untreated surface or groundwater resources [39].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Water Quality of Local Streams at Gandhamardan Iron Mines, Suakati, Keonjhar District of Odisha, India

Harichandan¹,

Patra²,

Sethy³

2017

J Pollut Eff Cont

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The present study focused on assessing the water quality by using water quality index (WQI) of six streams located surrounding iron ore mine of Suakati area of Keonjhar District of Odisha, which includes the physicochemical analysis of fourteen parameters: pH, colour, total dissolved solids (TDS), dissolve oxygen (DO), biochemical oxygen demand (BOD), total hardness (TH), chloride (Cl -), nitrate (NO 3 -), oil & grease (O&G), sulphate (SO 4 2-), iron (Fe), manganese (Mn), fluoride (F -) and arsenic(As). The WQI of these streams ranges from 4.47 to 244 indicating bad water quality of few streams. The high value of WQI has been found to be mainly from the higher values of TDS, TH, NO 3 -, O&G, Mn, SO 4 2-, As, Fe and F -in different stream water. Correlation analysis shows a strongest value of positive Correlation coefficient (r) exist between O&G and WQI (r=0.979) at SW1, between as and WQI (r=0.985) at SW2, between O&G and WQI (r=0.99) at SW3, between O&G and WQI (r = 0.995) at SW4, between as and WQI (r=0.948) at SW5 and between TDS and WQI (r=0.753) at SW6. The analysis reveals average WQI score for water sample from locations SW1 (126.4), SW2 (222.6), SW3 (105.2) and SW6 (156.6) are in higher sides and quality is very poor. The study shows the quality of water is fit to use in irrigation but not fit for domestic purpose. It also proofs that anthropogenic activity like mining activity is putting deleterious impact on the surrounding water bodies.

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“…On the other hand, in urban areas, where reservoirbased water supply is generally the source, nowadays groundwater is also playing an important role. Access to drinking water in India has increased over the past few decades with the tremendous adverse impact of unsafe water for health (Singh et al 2013a). Scarcity of clean and potable drinking water has emerged in recent years as one of the most serious developmental issues in many parts of West Bengal, Jharkhand, Orissa, Western Uttar Pradesh, Andhra Pradesh, Rajasthan and Punjab .…”

Section: Introductionmentioning

confidence: 99%

A GIS based DRASTIC model for assessing groundwater vulnerability of Katri Watershed, Dhanbad, India

Ghosh

Tiwari

Das

2015

Model. Earth Syst. Environ.

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Groundwater vulnerability assessment could be defined as the degree of assimilation capacity of the area to the contaminant from surrounding surface above the aquifer. It has become an important element for sustainable natural resource management and proper landuse planning. This study aims at estimating shallow aquifer vulnerability by applying the GIS based DRASTIC model in Katri watershed, Dhanbad, Jharkhand. The probable groundwater quality decline due to various anthropogenic activities within the Katri watershed has necessitated this study using a combination of DRASTIC and GIS method as an effective method for groundwater pollution risk assessment. This model is based on the seven hydrogeological data layers that provide the input to the modelling. It corresponds to the initials of seven layers i.e. Depth of water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity. The mapping of the DRASTIC index allows us to delineate zones with various degrees of pollution vulnerability. The study reveals that about 16.91 % of the watershed area is exposed to high-risk, 30.69 % exposed to medium-risk, and 52.4 % exposed to low risk. The south eastern and south western parts of the watershed are dominated by high vulnerability classes while the south, north western and lower middle portions are characterized by moderate vulnerability classes. The elevated northern, north eastern, and middle part of the study area displayed low aquifer vulnerability.

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Evaluation of hydrogeochemical processes and groundwater quality in the Jhansi district of Bundelkhand region, India

Cited by 109 publications

References 22 publications

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

Evaluation of Water Quality of Local Streams at Gandhamardan Iron Mines, Suakati, Keonjhar District of Odisha, India

A GIS based DRASTIC model for assessing groundwater vulnerability of Katri Watershed, Dhanbad, India

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