Assessment of Soil Heavy Metal Pollution with Principal Component Analysis and Geoaccumulation Index
TL;DR: In this article, the authors applied two methods, Principal Component Analysis (PCA) and Geoaccumulation Index (I geo), to assess heavy metals contamination levels in the area around copper mine tailing, and compared the results with Hakanson potential ecological risk index techniques (RI).
Abstract: The assessment of pollution levels of heavy metals soil contamination is significant to human health and environmental management. The purpose of this article is to apply two methods, which are Principal component analysis (PCA) and Geoaccumulation index ( I geo ), to assess heavy metals contamination levels in the area around copper mine tailing, and to compare the results with Hakanson potential ecological risk index techniques (RI). The rank of soil Cd pollution levels, which is assessed using I geo , is consistent with the one by RI, while the PCA assessments result of comprehensive contamination level in soil discrepancy with RI and I geo . PCA concerned with the distribution of all elements in soil, while I geo and RI are mainly determined by the elements with high concentration or big Toxic Response Factor value. The combined application of PCA and I geo can effectively identify the comprehensive and single pollution levels of elements in soil, thus important to the extent determination of heavy metals pollution in soil.
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TL;DR: A comprehensive method for pollution index choice is presented, in order to best interpret pollution in different soils (farmland, forest and urban), and an evaluation of various geochemical backgrounds used in heavy metal soil pollution assessments is included.
Abstract: The paper provides a complex, critical assessment of heavy metal soil pollution using different indices. Pollution indices are widely considered a useful tool for the comprehensive evaluation of the degree of contamination. Moreover, they can have a great importance in the assessment of soil quality and the prediction of future ecosystem sustainability, especially in the case of farmlands. Eighteen indices previously described by several authors (Igeo, PI, EF, Cf, PIsum, PINemerow, PLI, PIave, PIVector, PIN, MEC, CSI, MERMQ, Cdeg, RI, mCd and ExF) as well as the newly published Biogeochemical Index (BGI) were compared. The content, as determined by other authors, of the most widely investigated heavy metals (Cd, Pb and Zn) in farmland, forest and urban soils was used as a database for the calculation of all of the presented indices, and this shows, based on statistical methods, the similarities and differences between them. The indices were initially divided into two groups: individual and complex. In order to achieve a more precise classification, our study attempted to further split indices based on their purpose and method of calculation. The strengths and weaknesses of each index were assessed; in addition, a comprehensive method for pollution index choice is presented, in order to best interpret pollution in different soils (farmland, forest and urban). This critical review also contains an evaluation of various geochemical backgrounds (GBs) used in heavy metal soil pollution assessments. The authors propose a comprehensive method in order to assess soil quality, based on the application of local and reference GB.
441 citations
Cites background or methods from "Assessment of Soil Heavy Metal Poll..."
...In the case of comparisons of the content of heavy metals to the limiting values given in the literature, it is possible to only approximately determine the probability of contamination and this does not provide holistic information on the state of soil quality (Caeiro et al. 2005; Jiang et al. 2014; Nannoni and Protano 2016; Zhiyuan et al. 2011)....
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...In general, the interpretation of PCA is based on gathering all the similarities in one quarter: the closer the distance between components, the more the similarities that can be found between them (Gąsiorek et al. 2017; Zhiyuan et al. 2011)....
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...In general, the interpretation of PCA is based on gathering all the similarities in one quarter: the closer the distance between components, the more the similarities that can be found between them (Gąsiorek et al. 2017; Zhiyuan et al. 2011)....
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...Ward’s hierarchical cluster analysis (HCA), as well as principal component analysis (PCA), is helpful to standardize pollution indices to allow better comparison between them (Wang et al. 2015; Qingjie et al. 2008; Wold et al. 1987; Zhiyuan et al. 2011)....
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...Further, PCA is useful for the comparison of patterns between studied pollution indices and the establishing of possible similarities (Chen et al. 2015; Varol 2011; Zhiyuan et al. 2011)....
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TL;DR: The results indicate that the lifetime cancer risk associated with As and Cr(VI) exposure is significant at selected restaurants, which is of concern for restaurant workers.
200 citations
Cites methods from "Assessment of Soil Heavy Metal Poll..."
...PCA is a multivariate statistical analysis method that is frequently used to simplify large, complex data sets and identify correlated variables (i.e., possible common sources) (Zhiyuan et al., 2011)....
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TL;DR: Alternative methods for the assessment of the degree of heavy metal contamination in urban soils using selected pollution indices using different local and reference geochemical backgrounds are presented.
167 citations
TL;DR: The marine coastline environment was found to be enriched with Cd and Zn in comparison to other metals, and the high uptake of metals in green algae and brown algae suggested that these algae may be used as potential biomonitors for heavy metal pollution.
156 citations
TL;DR: In this paper, a study was designed to probe the levels of heavy metals (Cd, Pb, Cr, Mn, Cu, Ni, Zn and Fe) for different environmental matrices (ground water, wastewater, sediment, soil, dust and leachates).
96 citations
References
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Abstract: Chapter 1 The Biosphere Chapter 2 The Anthroposphere Introduction Air Pollution Water Pollution Soil Plants Chapter 3 Soils and Soil Processes Introduction Weathering Processes Pedogenic Processes Chapter 4 Soil Constituents Introduction Trace Elements Minerals Organic Matter Organisms in Soils Chapter 5 Trace Elements in Plants Introduction Absorption Translocation Availability Essentiality and Deficiency Toxicity and Tolerance Speciation Interaction Chapter 6 Elements of Group 1 (Previously Group Ia) Introduction Lithium Rubidium Cesium Chapter 7 Elements of Group 2 (Previously Group IIa) Beryllium Strontium Barium Radium Chapter 8 Elements of Group 3 (Previously Group IIIb) Scandium Yttrium Lanthanides Actinides Chapter 9 Elements of Group 4 (Previously Group IVb) Titanium Zirconium Hafnium Chapter 10 Elements of Group 5 (Previously Group Vb) Vanadium Niobium Tantalum Chapter 11 Elements of Group 6 (Previously Group VIb) Chromium Molybdenum Tungsten Chapter 12 Elements of Group 7 (Previously Group VIIb) Manganese Technetium Rhenium Chapter 13 Elements of Group 8 (Previously Part of Group VIII) Iron Ruthenium Osmium Chapter 14 Elements of Group 9 (Previously Part of Group VIII) Cobalt Rhodium Iridium Chapter 15 Elements of Group 10 (Previously Part of Group VIII) Nickel Palladium Platinum Chapter 16 Elements of Group 11 (Previously Group Ib) Copper Silver Gold Chapter 17 Trace Elements of Group 12 (Previously of Group IIb) Zinc Cadmium Mercury Chapter 18 Elements of Group 13 (Previously Group IIIa) Boron Aluminum Gallium Indium Thallium Chapter 19 Elements of Group I4 (Previously Group IVa) Silicon Germanium Tin Lead Chapter 20 Elements of Group 15 (Previously Group Va) Arsenic Antimony Bismuth Chapter 21 Elements of Group 16 (Previously Group VIa) Selenium Tellurium Polonium Chapter 22 Elements of Group 17 (Previously Group VIIa) Fluorine Chlorine Bromine Iodine
9,739 citations
"Assessment of Soil Heavy Metal Poll..." refers background in this paper
...Previous researchers have pointed that mining activity was a vital source of heavy metals in the agricultural soils near mining areas [4]....
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TL;DR: In this article, a sedimentological risk index for toxic substances in limnic systems should at least, account for the following four requirements: the following: the quality of the sediment, the water quality, the sediment quality, and the sediment diversity.
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TL;DR: In this article, the authors describe and display multivariate data, characterizing and displaying Multivariate Data, Characterizing and Displaying Multivariate data and characterising and displaying multivariate Data.
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TL;DR: In this article, applied multivariate data analysis was used to analyze the performance of a multivariate dataset in the context of data mining and analysis in the field of applied multi-dimensional data analysis.
Abstract: Applied multivariate data analysis , Applied multivariate data analysis , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
1,604 citations