Spatial-Temporal Monitoring of Groundwater Using Multivariate Statistical Techniques in Bareilly District of Uttar Pradesh, India

Singh, Sudhir Kumar; Singh, Chander Kumar; Kumar, Kewat Sanjay; Gupta, Ramvtar; Mukherjee, Saumitra

doi:10.2478/v10098-009-0005-1

Cited by 74 publications

(41 citation statements)

References 28 publications

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“…CA groups the objects into the classes on the basis of similarities within a class and dissimilarities between different classes. The results of CA help in interpreting the data and indicate patterns (Singh et al 2009;Singh et al 2013a, b;Gupta et al 2014;Singh et al 2015). The heavy metals of sediments data sets were used for hierarchical agglomerative CA and were performed on the normalized data set by means of Ward's linkage method using squared Euclidean distances as a measure of similarity.…”

Section: Statistical Analysis and Cluster Analysis (Ca)mentioning

confidence: 99%

Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India

et al. 2017

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The present study includes a systematic analysis of sediment contamination by heavy metals of the River Ghaghara flowing through the Uttar Pradesh and Bihar in Indian Territory. To estimate the geochemical environment of the river, seven heavy metals, namely Co, Cu, Cr, Ni, Cd, Zn, and Pb were examined from the freshly deposited river bed sediment. All the sediment samples were collected on a seasonal basis for the assessment of fluctuation in 2014-2015 and after preparation samples were analyzed using standard procedure. Result showed that heavy metal concentration ranged between 11.37 and 18.42 mg/kg for Co, 2.76 and 11.74 mg/kg for Cu, 61.25 and 87.68 mg/kg for Cr, 15.29 and 25.59 mg/kg for Ni, 0.21 and 0.28 mg/kg for Cd, 13.26 and 17.59 mg/kg for Zn, 10.71 and 14.26 mg/ kg for Pb in different season. Metal contamination factor indicates the anthropogenic input in the river sediment was in the range of (0.62-0.97) for Co, (0.04-0.26) for Cu, (0.68-0.97) for Cr, (0.22-0.38) for Ni, (0.70-0.93) for Cd, (0.14-0.19) for Zn, and (0.54-0.71) for Pb. The highest contamination degree of the sediment was noticed as 4.01 at Ayodhya and lowest as 3.16 at Katerniaghat. Geo-accumulation index was noted between (0 and 1) which showed that sediment was uncontaminated to moderately contaminated and may have adverse affects on freshwater ecology of the river. Pollution load index (PLI) was found highest at Chhapra which was 0.45 and lowest at Katerniaghat which was 0.35 and it indicates that the river sediment has a low level of contamination. Significant high correlation was observed between Co, Cu, and Zn, it suggests same source of contamination input is mainly due to human settlement and agriculture activity. Positive correlation between Zn, Co, Cu, Cr, and Ni indicated a natural origin of these elements in the river sediment. Cluster analysis suggests grouping of similar polluted sites. The strong similarity between Co, Zn, Pb, Ni, Cu, and Cd showed relationship of these metals come from the same origin, which is possibly from natural and anthropogenic input which was also confirmed by correlation analysis. Using the various pollution indicators it was found that the river bed sediment is less contaminated by toxic metals during the study but the sediment quality may degrade in the near future due to increasing anthropogenic inputs in the river basin, hence proper management strategies are required to control the direct dumping of wastewater in the river.

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Section: Statistical Analysis and Cluster Analysis (Ca)mentioning

confidence: 99%

Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India

et al. 2017

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“…Monitoring of different sites requires money and manpower; this could be reduced by clustering techniques. The application of hierarchical classification approach is well known for the interpretation of data and provides a valuable tool for reliable and effective monitoring and management (Singh et al 2009). …”

Section: Introductionmentioning

confidence: 99%

A study of the effectiveness of sewage treatment plants in Delhi region

et al. 2012

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This is a conventional kind of monitoring study. The objective of the study was to assess and monitor the physicochemical parameters in wastewater at inlet and outlet of sewage treatment plant (STP) and also to study the effectiveness of the STPs. The average concentration of parameters at inlet sampling site pH, electrical conductivity, total dissolved solids, are 7.16, 2,169 lS/cm, 766.06 mg/l, and major ions bicarbonate, nitrate, sulphate, phosphate, chloride, sodium, potassium, magnesium and calcium values 515. 88, 4.28, 82.85, 15.17, 7.01, 23.08, 29.34, 4.14 and 84.31 mg/l. While the average concentration of these parameters, after treatment shows following values 7.47, 2,161.43 (lS/cm), 695.81, 436.52, 1.25, 99.22, 12.69, 6.83, 23.18, 29.07, 4.40 and 82.65 mg/l, respectively. Further, to check the Na % and sodium absorption ratio at inlet and outlet which 27.89 %, 0.67 and 28.19 %, 0.68, respectively, for the suitability of the wastewater. Finally, the agglomerative hierarchical clustering techniques were used to study the similarity in the sewage treatment plants. The result suggests that there is considerable improvement in the wastewater quality after treatment except at the Pappankalan and Coronation Pillar, Timarpur.

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“…The application of multivariate statistical techniques is very useful for classification, modeling and interpretation of large data sets which allow the reduction in dimensionality of the large data sets (Singh et al 2009. FA/PCA techniques are applied for multivariate analysis of data sets of lake water quality.…”

Section: Multivariate Statistical Methodsmentioning

confidence: 99%

“…Principal component analysis (PCA) and factor analysis (FA) offer a valuable tool for consistent, reliable, effective management of water resources Singh et al 2009Singh et al , 2013dSingh et al , 2015. Many authors in past have used multivariate statistical techniques to characterize and evaluate surface and groundwater quality and have found it interesting for studying the variations caused by geogenic and anthropogenic factors (Shrestha and Kazama 2007;Singh et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Appraisal of urban lake water quality through numerical index, multivariate statistics and earth observation data sets

Singh

Gautam

2015

Int. J. Environ. Sci. Technol.

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The earth observation data sets were employed to study the land use/land cover change in study area from year 2000-2010. Vegetation, built-up area and agriculture classes had shown maximum changes. The lake water samples were analyzed, and further, Water Quality Index (WQI) was computed to categorize the lake water. The average value of WQI is 64.52, 52.23 and 42.45 in premonsoon, monsoon and post-monsoon seasons, respectively. Generally, pre-monsoon samples have higher number of polluted samples. Moreover, we applied the multivariate statistical techniques for handling large and complex data sets in order to get better information about the lake water quality. Factor analysis and principal component analysis are applied to understand the latent structure of the data sets, and we have identified a total of four factors in pre-monsoon, three factors in monsoon and three factors in post-monsoon season, which are responsible for the whole data structure. These factors have explained that 90.908, 89.078 and 85.456 % of the cumulative percentage variance of the pre-monsoon, monsoon and post-monsoon data sets. Overall analysis reveals that the agricultural runoff, waste disposal, leaching and irrigation with wastewater, land transformation in the surrounding areas are the main causes of lake water pollution followed by some degree of pollution from geogenic sources such as rock weathering. Hence, there is an urgent need of proper attention and management of resources.

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Spatial-Temporal Monitoring of Groundwater Using Multivariate Statistical Techniques in Bareilly District of Uttar Pradesh, India

Cited by 74 publications

References 28 publications

Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India

Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India

A study of the effectiveness of sewage treatment plants in Delhi region

Appraisal of urban lake water quality through numerical index, multivariate statistics and earth observation data sets

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