Analysis and assessment of heavy metal contamination in the vicinity of Lake Atamanskoe (Rostov region, Russia) using multivariate statistical methods
Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference...
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| Veröffentlicht in: | Environmental geochemistry and health 2022-02, Vol.44 (2), p.511-526 |
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| creator | Linnik, Vitaly G. Saveliev, Anatoly A. Bauer, Tatiana V. Minkina, Tatiana M. Mandzhieva, Saglara S. |
| description | Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference tree (CIT), were used to identify patterns and potential sources of heavy metals (Co, Ni, Cu, Cr, Pb, MnO, and Zn). The investigation was carried out on 81 sample points, using 20 testing parameters. A strong positive correlation found among Ni, Cu, Zn, and HCA results has confirmed the common origin of the elements from waste discharge. Hierarchical CA divided the 81 test sites into 5 classes based on the soil quality and HMs contamination similarity. Regression trees for Cr, Pb, Zn, and Cu were verified by the splitting factor including HMs content and soil chemistry factors. The CIT has revealed that the elements (Cr, Pb, Zn, and Cu) concentration values are split at the first level by some other metal, indicating common anthropogenic impact resulting from industrial waste discharges. The factors at the next hierarchical level of splitting, in addition to the HMs, include compounds belonging to soil chemistry variables (SiO
2
, Al
2
O
3
, and K
2
O). The CIT nonlinear regression model is in good agreement with the data:
R
2
values for log-transformed concentrations of Cr, Pb, Zn, and Cu are equal to 0.775; 0.774; 0.775; 0.804, respectively. |
| doi_str_mv | 10.1007/s10653-021-00853-x |
| format | Article |
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2
, Al
2
O
3
, and K
2
O). The CIT nonlinear regression model is in good agreement with the data:
R
2
values for log-transformed concentrations of Cr, Pb, Zn, and Cu are equal to 0.775; 0.774; 0.775; 0.804, respectively.</description><identifier>ISSN: 0269-4042</identifier><identifier>EISSN: 1573-2983</identifier><identifier>DOI: 10.1007/s10653-021-00853-x</identifier><identifier>PMID: 33609207</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Aluminum oxide ; Anthropogenic factors ; Chemistry ; China ; Chromium ; Cluster analysis ; Contamination ; Copper ; Correlation analysis ; Discharge ; Earth and Environmental Science ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental Monitoring - methods ; Geochemistry ; Heavy metals ; Human influences ; Industrial wastes ; Lakes ; Lead ; Loam soils ; Machine learning ; Metals ; Metals, Heavy - analysis ; Multivariate analysis ; Multivariate statistical analysis ; Nickel ; Original Paper ; Pollutants ; Pollution ; Public Health ; Regression analysis ; Regression models ; Risk Assessment ; Silica ; Silicon dioxide ; Silicon Dioxide - analysis ; Soil ; Soil - chemistry ; Soil contamination ; Soil Pollutants - analysis ; Soil pollution ; Soil quality ; Soil Science & Conservation ; Soils ; Splitting ; Statistical analysis ; Statistical methods ; Statistics ; Terrestrial Pollution ; Zinc</subject><ispartof>Environmental geochemistry and health, 2022-02, Vol.44 (2), p.511-526</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-b33a0135cb7e7497b3a9acd9bc968b8943bcc63568d2c5c830c42efd8fbb27463</citedby><cites>FETCH-LOGICAL-c375t-b33a0135cb7e7497b3a9acd9bc968b8943bcc63568d2c5c830c42efd8fbb27463</cites><orcidid>0000-0002-6270-7744 ; 0000-0001-6000-2209 ; 0000-0002-1667-3811 ; 0000-0003-3022-0883 ; 0000-0002-6751-8686</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10653-021-00853-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10653-021-00853-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33609207$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Linnik, Vitaly G.</creatorcontrib><creatorcontrib>Saveliev, Anatoly A.</creatorcontrib><creatorcontrib>Bauer, Tatiana V.</creatorcontrib><creatorcontrib>Minkina, Tatiana M.</creatorcontrib><creatorcontrib>Mandzhieva, Saglara S.</creatorcontrib><title>Analysis and assessment of heavy metal contamination in the vicinity of Lake Atamanskoe (Rostov region, Russia) using multivariate statistical methods</title><title>Environmental geochemistry and health</title><addtitle>Environ Geochem Health</addtitle><addtitle>Environ Geochem Health</addtitle><description>Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference tree (CIT), were used to identify patterns and potential sources of heavy metals (Co, Ni, Cu, Cr, Pb, MnO, and Zn). The investigation was carried out on 81 sample points, using 20 testing parameters. A strong positive correlation found among Ni, Cu, Zn, and HCA results has confirmed the common origin of the elements from waste discharge. Hierarchical CA divided the 81 test sites into 5 classes based on the soil quality and HMs contamination similarity. Regression trees for Cr, Pb, Zn, and Cu were verified by the splitting factor including HMs content and soil chemistry factors. The CIT has revealed that the elements (Cr, Pb, Zn, and Cu) concentration values are split at the first level by some other metal, indicating common anthropogenic impact resulting from industrial waste discharges. The factors at the next hierarchical level of splitting, in addition to the HMs, include compounds belonging to soil chemistry variables (SiO
2
, Al
2
O
3
, and K
2
O). The CIT nonlinear regression model is in good agreement with the data:
R
2
values for log-transformed concentrations of Cr, Pb, Zn, and Cu are equal to 0.775; 0.774; 0.775; 0.804, respectively.</description><subject>Aluminum oxide</subject><subject>Anthropogenic factors</subject><subject>Chemistry</subject><subject>China</subject><subject>Chromium</subject><subject>Cluster analysis</subject><subject>Contamination</subject><subject>Copper</subject><subject>Correlation analysis</subject><subject>Discharge</subject><subject>Earth and Environmental Science</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental Monitoring - methods</subject><subject>Geochemistry</subject><subject>Heavy metals</subject><subject>Human influences</subject><subject>Industrial wastes</subject><subject>Lakes</subject><subject>Lead</subject><subject>Loam soils</subject><subject>Machine learning</subject><subject>Metals</subject><subject>Metals, Heavy - analysis</subject><subject>Multivariate analysis</subject><subject>Multivariate statistical analysis</subject><subject>Nickel</subject><subject>Original Paper</subject><subject>Pollutants</subject><subject>Pollution</subject><subject>Public Health</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Risk Assessment</subject><subject>Silica</subject><subject>Silicon dioxide</subject><subject>Silicon Dioxide - analysis</subject><subject>Soil</subject><subject>Soil - chemistry</subject><subject>Soil contamination</subject><subject>Soil Pollutants - analysis</subject><subject>Soil pollution</subject><subject>Soil quality</subject><subject>Soil Science & Conservation</subject><subject>Soils</subject><subject>Splitting</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Terrestrial 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and assessment of heavy metal contamination in the vicinity of Lake Atamanskoe (Rostov region, Russia) using multivariate statistical methods</title><author>Linnik, Vitaly G. ; Saveliev, Anatoly A. ; Bauer, Tatiana V. ; Minkina, Tatiana M. ; Mandzhieva, Saglara S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-b33a0135cb7e7497b3a9acd9bc968b8943bcc63568d2c5c830c42efd8fbb27463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aluminum oxide</topic><topic>Anthropogenic factors</topic><topic>Chemistry</topic><topic>China</topic><topic>Chromium</topic><topic>Cluster analysis</topic><topic>Contamination</topic><topic>Copper</topic><topic>Correlation analysis</topic><topic>Discharge</topic><topic>Earth and Environmental Science</topic><topic>Environment</topic><topic>Environmental Chemistry</topic><topic>Environmental Health</topic><topic>Environmental Monitoring - 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Health</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>44</volume><issue>2</issue><spage>511</spage><epage>526</epage><pages>511-526</pages><issn>0269-4042</issn><eissn>1573-2983</eissn><abstract>Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference tree (CIT), were used to identify patterns and potential sources of heavy metals (Co, Ni, Cu, Cr, Pb, MnO, and Zn). The investigation was carried out on 81 sample points, using 20 testing parameters. A strong positive correlation found among Ni, Cu, Zn, and HCA results has confirmed the common origin of the elements from waste discharge. Hierarchical CA divided the 81 test sites into 5 classes based on the soil quality and HMs contamination similarity. Regression trees for Cr, Pb, Zn, and Cu were verified by the splitting factor including HMs content and soil chemistry factors. The CIT has revealed that the elements (Cr, Pb, Zn, and Cu) concentration values are split at the first level by some other metal, indicating common anthropogenic impact resulting from industrial waste discharges. The factors at the next hierarchical level of splitting, in addition to the HMs, include compounds belonging to soil chemistry variables (SiO
2
, Al
2
O
3
, and K
2
O). The CIT nonlinear regression model is in good agreement with the data:
R
2
values for log-transformed concentrations of Cr, Pb, Zn, and Cu are equal to 0.775; 0.774; 0.775; 0.804, respectively.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>33609207</pmid><doi>10.1007/s10653-021-00853-x</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-6270-7744</orcidid><orcidid>https://orcid.org/0000-0001-6000-2209</orcidid><orcidid>https://orcid.org/0000-0002-1667-3811</orcidid><orcidid>https://orcid.org/0000-0003-3022-0883</orcidid><orcidid>https://orcid.org/0000-0002-6751-8686</orcidid></addata></record> |
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| subjects | Aluminum oxide Anthropogenic factors Chemistry China Chromium Cluster analysis Contamination Copper Correlation analysis Discharge Earth and Environmental Science Environment Environmental Chemistry Environmental Health Environmental Monitoring - methods Geochemistry Heavy metals Human influences Industrial wastes Lakes Lead Loam soils Machine learning Metals Metals, Heavy - analysis Multivariate analysis Multivariate statistical analysis Nickel Original Paper Pollutants Pollution Public Health Regression analysis Regression models Risk Assessment Silica Silicon dioxide Silicon Dioxide - analysis Soil Soil - chemistry Soil contamination Soil Pollutants - analysis Soil pollution Soil quality Soil Science & Conservation Soils Splitting Statistical analysis Statistical methods Statistics Terrestrial Pollution Zinc |
| title | Analysis and assessment of heavy metal contamination in the vicinity of Lake Atamanskoe (Rostov region, Russia) using multivariate statistical methods |
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