Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine, Dinajpur, Bangladesh
A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix ( r ), cluster analysis, principal component/factor analyses,...
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| Veröffentlicht in: | Environmental earth sciences 2016-12, Vol.75 (24), p.1, Article 1501 |
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| description | A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (
r
), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO
3
type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO
3
, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl
−
and SO
4
2−
ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K
+
, Fe
total
, SO
4
2
, As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this resea |
| doi_str_mv | 10.1007/s12665-016-6309-7 |
| format | Article |
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r
), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO
3
type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO
3
, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl
−
and SO
4
2−
ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K
+
, Fe
total
, SO
4
2
, As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-016-6309-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Anthropogenic factors ; Biogeosciences ; Chemical analysis ; Earth and Environmental Science ; Earth Sciences ; Environmental Science and Engineering ; Geochemistry ; Geology ; Granite ; Groundwater mining ; Hydrology/Water Resources ; Ions ; Original Article ; Pollution sources ; Qualitative analysis ; Statistical analysis ; Statistical methods ; Terrestrial Pollution ; Variance analysis ; Water analysis ; Water quality control ; Water treatment ; Water treatment plants</subject><ispartof>Environmental earth sciences, 2016-12, Vol.75 (24), p.1, Article 1501</ispartof><rights>Springer-Verlag Berlin Heidelberg 2016</rights><rights>Environmental Earth Sciences is a copyright of Springer, 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a339t-6c83feb9f3ade1b81e41c319883c13d7c2c5efaa1e1fe49c9a6dcb4faea110503</citedby><cites>FETCH-LOGICAL-a339t-6c83feb9f3ade1b81e41c319883c13d7c2c5efaa1e1fe49c9a6dcb4faea110503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12665-016-6309-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-016-6309-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Howladar, M. Farhad</creatorcontrib><creatorcontrib>Mustafizur Rahman, Md</creatorcontrib><title>Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine, Dinajpur, Bangladesh</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (
r
), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO
3
type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO
3
, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl
−
and SO
4
2−
ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K
+
, Fe
total
, SO
4
2
, As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.</description><subject>Anthropogenic factors</subject><subject>Biogeosciences</subject><subject>Chemical analysis</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Granite</subject><subject>Groundwater mining</subject><subject>Hydrology/Water Resources</subject><subject>Ions</subject><subject>Original Article</subject><subject>Pollution sources</subject><subject>Qualitative analysis</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Terrestrial Pollution</subject><subject>Variance analysis</subject><subject>Water analysis</subject><subject>Water quality control</subject><subject>Water treatment</subject><subject>Water treatment plants</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kcFuEzEURUeISlSlH9CdJbYd8IsTj80OQmmRWrFp19aL5znjaMYOtocqfBcfiEMQYoM3z5bPubZ0m-YK-FvgvHuXYSHlquUgWym4brsXzTkoWU8LrV_-3Sv-qrnMecfrEiA0l-fNz_WACW2h5H9g8TGw6NgcekrbFOtkZQ6BRvaMFWHDoU_RDjR5iyPLh1xoYlipOVNmZajKdmDTPBb_HZOvDsulxubyW5ioDLHP7xkyi_l4N_cH5lKc2AP2_YD7-hd2mzD4aj74QNfskw-428_pmn3EsB2xpzy8bs4cjpku_8yL5unzzeP6rr3_evtl_eG-RSF0aaVVwtFGO1Et2CigJVgBWilhQfSdXdgVOUQgcLTUVqPs7WbpkBCAr7i4aN6ccvcpfpspF7OLcwr1SQNKKSm07laVghNlU8w5kTP75CdMBwPcHPsxp35M7ccc-zFddRYnJ1c2bCn9k_xf6RfKLpi5</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Howladar, M. Farhad</creator><creator>Mustafizur Rahman, Md</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20161201</creationdate><title>Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine, Dinajpur, Bangladesh</title><author>Howladar, M. Farhad ; Mustafizur Rahman, Md</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a339t-6c83feb9f3ade1b81e41c319883c13d7c2c5efaa1e1fe49c9a6dcb4faea110503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Anthropogenic factors</topic><topic>Biogeosciences</topic><topic>Chemical analysis</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Granite</topic><topic>Groundwater mining</topic><topic>Hydrology/Water Resources</topic><topic>Ions</topic><topic>Original Article</topic><topic>Pollution sources</topic><topic>Qualitative analysis</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Terrestrial Pollution</topic><topic>Variance analysis</topic><topic>Water analysis</topic><topic>Water quality control</topic><topic>Water treatment</topic><topic>Water treatment plants</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Howladar, M. 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Farhad</au><au>Mustafizur Rahman, Md</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine, Dinajpur, Bangladesh</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2016-12-01</date><risdate>2016</risdate><volume>75</volume><issue>24</issue><spage>1</spage><pages>1-</pages><artnum>1501</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (
r
), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO
3
type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO
3
, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl
−
and SO
4
2−
ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K
+
, Fe
total
, SO
4
2
, As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-016-6309-7</doi></addata></record> |
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| subjects | Anthropogenic factors Biogeosciences Chemical analysis Earth and Environmental Science Earth Sciences Environmental Science and Engineering Geochemistry Geology Granite Groundwater mining Hydrology/Water Resources Ions Original Article Pollution sources Qualitative analysis Statistical analysis Statistical methods Terrestrial Pollution Variance analysis Water analysis Water quality control Water treatment Water treatment plants |
| title | Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine, Dinajpur, Bangladesh |
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