Multivariate statistical analyzing of chemical parameters of groundwater in Vojvodina

Mrazovac, Sanja; Vojinovic-Miloradov, Mirjana; Matić, Ivan; Marić, Nenad

doi:10.1016/j.chemer.2012.11.002

Cited by 31 publications

(8 citation statements)

References 11 publications

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“…Processes, such as cationic exchange and dissolution of mica and Na-feldspars, cause domination of Na + in the deeper groundwaters [17]. Groundwaters from IIa and partially IIIa aquifers belong to a non-dominant type (cation triangle), with characteristics shifting to mixing zone (central diamond).…”

Section: Resultsmentioning

confidence: 99%

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Assessing Groundwater Quality for Multiuse and Geochemical Evolution in the South Banat Area of Serbia, Pannonian Basin

Majkic-Dursun¹,

Oros²,

Oparušić³

et al. 2019

Pol. J. Environ. Stud.

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Groundwater constitutes the largest reservoir of freshwater in the world (excluding glaciers and ice caps); thus it is major source of potable, agricultural and industrial water. According to the International Groundwater Resources Assessment Centre (IGRAC) [1], groundwater provides almost 50% of all drinking water worldwide, about 40% of water for irrigated agriculture and about 30% of water supply required for industry. Similar to the majority European countries, the contribution of groundwater to the water supply in Serbia is around 70%, of which more than 50% comes from alluvial aquifers [2]. These valuable resources are under increasing pressure due to different human activities (including population-and wealth growth) and climate change [1]. The water supply in the northern part of Serbia, Province of Vojvodina, depends only on groundwater resources [3]. Municipal and industrial water supplies in Vojvodina rely entirely on groundwater abstractions from the three types of aquifers: alluvial aquifers, the

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Section: Resultsmentioning

confidence: 99%

“…4d). Ratio Na/Cl versus electrical conductivity could be used for identifying the evaporation process [17]. Although plotted dots show that the evaporation process could explain the increase of EC for relatively constant Na/Cl ratios in the shallow aquifer, it cannot be used for deeper ones (Fig.…”

Section: Resultsmentioning

confidence: 99%

Assessing Groundwater Quality for Multiuse and Geochemical Evolution in the South Banat Area of Serbia, Pannonian Basin

Majkic-Dursun¹,

Oros²,

Oparušić³

et al. 2019

Pol. J. Environ. Stud.

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“…What is used as the basis for clustering is the similarity of the analyzed scores. Data regarding the size of the similarity can be analyzed with cluster analysis to determine who belongs to which cluster (Mrazovac et al, 2013). The cluster analysis steps formulate the problem, choose the distance's size, select the clustering procedure, determine the number of clusters, and interpret the cluster profile (the clusters formed).…”

Section: Methodsmentioning

confidence: 99%

Hydrogeological Cluster Analysis With Average Linkage and Ward Method in the Southern Slope of Merapi Mountain

Riswandi¹,

Sukiyah²,

Tania³

2022

Proceedings of the 5th International Conference of Geological Engineering Faculty

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This study aims to see the cluster analysis process using the average linkage method and the Ward method, and comparing thus results in analysis to clustering several related variables deciding to use the data of depth and hydro chemical character of groundwater. Processing cluster analysis with the average linkage method is pairing objects that combine into one cluster. Then, calculating the two proximity of the object to another variable, the next merging occurs in the most similar clusters than other variables, forming the second cluster. The second combination is to calculate using the average linkage method formula, forming a new distance matrix. The cluster analysis steps with the Ward method starts by the close look at N clusters, which have one respondent for each cluster (all variables consider cluster). The first cluster is formed by selecting two of these N groups, which, when combined, have the smallest value of Error Sum of Squares (SSE). N-1 clusters then consider again to determine which of these two clusters can minimize heterogeneity. Thus, N clusters are systematically reduced by N-1, become N-2, and so on until they become one cluster. The results of clustering the two methods compared with the criteria for standard deviation within groups (SW) and standard deviation between groups (SB). The best method has a smaller SW and SB ratio. The results showed that the average linkage method and the Ward method have an SB and SW ratio value. This result shows that the average linkage method has better performance than the Ward method.

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“…Cluster analysis involves a progression of multivariate strategies which are utilized to find right groups of information or stations. In clustering, the objects are grouped with the end goal that comparative articles fall into a similar class (Danielsson et al 1999;Mrazovac et al 2013). The hierarchical cluster analysis (CA) was applied utilizing Ward's strategy (linkage between groups), Euclidian separation as a similarity measure and synthesised in dendrograms.…”

Section: Cluster Analysismentioning

confidence: 99%

Assessment of groundwater quality, toxicity and health risk in an industrial area using multivariate statistical methods

2019

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Background: This study investigates the common and anthropogenic activities that impact the science of groundwater in and around an industrial zone and exhibits the utilization of multivariate statistical methods for groundwater quality, toxicity and health risk associated with contaminated industrial sites for proficient administration of water assets. A total of 120 groundwater samples were collected during summer and winter season, and analyzed for their twenty physicochemical constituents including seven toxic heavy metals (pH, EC, total dissolved solids (TDS), F, K, Na, Ca, Mg, Cl, CO 3 , HCO 3 , NO 3 , SO 4 , As, Cd, Cr, Cu, Ni, Pb and Zn). Data obtained was treated using principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (HCA), Correlation coefficient and health risk analysis to find the common pollution source. Results: The results for mean abundance during two seasons for cations and anions were 7 and 6.9 for pH; 1875 and 1527 for TDS; 3 and 3.3 (µs/cm) for EC; 655 and 569 for Ca 2+ ; 59 and 56 for Mg 2+ ; 340 and 211 for Na + ; 5 and 4 mg/L for K + ; 148 and 126 for CO 3 2− 301 and 228 for HCO 3 − ; 289 and 223 for Cl − 0.5 and 0.85 for F − ; 99 and 86 for SO 4 2− 28 and 23 mg/L for NO 3 −. While for heavy metals 18 and 4 for As; 2 and 0.4 for Cd; 29 and 5 for Cr; 17 and 4 for Cu; 25 and 6 for Ni; 82 and 3 for Pb; 953 and 989 µg/L for Zn, respectively. FA identified six dominant factors for each during summer and winter seasons that explained 70.43% and 71.06% of the variance in the dataset. Health risk assessment of chronic daily intake (CDI) and hazard quotient (HQ) during both seasons were in the order Ca > Na > HCO 3 > Cl > CO 3 > SO 4 > Mg > NO 3 > K > F and was as well computed. Conclusion: The significant reasons for water quality degrading in the study area were associated with various natural and anthropogenic sources and their unsystematic apportionment, show that proper land uses, industrial planning, design some remedial techniques and implementation of existing laws to have active groundwater resource management.

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Multivariate statistical analyzing of chemical parameters of groundwater in Vojvodina

Cited by 31 publications

References 11 publications

Assessing Groundwater Quality for Multiuse and Geochemical Evolution in the South Banat Area of Serbia, Pannonian Basin

Assessing Groundwater Quality for Multiuse and Geochemical Evolution in the South Banat Area of Serbia, Pannonian Basin

Hydrogeological Cluster Analysis With Average Linkage and Ward Method in the Southern Slope of Merapi Mountain

Assessment of groundwater quality, toxicity and health risk in an industrial area using multivariate statistical methods

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