Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy)
This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was e...
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| Language: | English |
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MDPI AG
2025-01-01
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| author | Lucia Rita Pacifico Annalise Guarino Antonio Iannone Stefano Albanese |
| author_facet | Lucia Rita Pacifico Annalise Guarino Antonio Iannone Stefano Albanese |
| author_sort | Lucia Rita Pacifico |
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| description | This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements. The first PCA identified groups of highly correlated variables, which were then reduced to 20 representative elements for a second PCA. The three most significant principal components (PC1, PC2, and PC3) explained approximately 65% of the total variability. PC1 (accounting for 29.97% of variability) revealed an anticorrelation between Ti, La, and Sc with Au, Hg, and Ag, with positive scores primarily located in the inland Apennine areas. PC2 (21.8%) was dominated by Na, K, and Cu, with positive scores corresponding to volcanic deposits, aligning with the dispersion patterns of historical Vesuvian eruption products. PC3 (11%) was associated with Ca and S, with higher scores found in the alluvial plains and inland areas. These results demonstrate the efficacy of CoDA in minimizing spurious correlations and uncovering latent relationships between elements, thereby enhancing the interpretation of natural and anthropogenic processes influencing soil variability in the region. |
| format | Article |
| id | doaj-art-e1c4ec15fea6440b9a7ff6081435575a |
| institution | Kabale University |
| issn | 2076-3263 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj-art-e1c4ec15fea6440b9a7ff6081435575a2025-01-24T13:34:11ZengMDPI AGGeosciences2076-32632025-01-011512010.3390/geosciences15010020Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy)Lucia Rita Pacifico0Annalise Guarino1Antonio Iannone2Stefano Albanese3Department of Earth Sciences, Environment and Resources (DiSTAR), University of Naples Federico II, 80126 Naples, ItalyDepartment of Earth Sciences, Environment and Resources (DiSTAR), University of Naples Federico II, 80126 Naples, ItalyDepartment of Earth Sciences, Environment and Resources (DiSTAR), University of Naples Federico II, 80126 Naples, ItalyDepartment of Earth Sciences, Environment and Resources (DiSTAR), University of Naples Federico II, 80126 Naples, ItalyThis study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements. The first PCA identified groups of highly correlated variables, which were then reduced to 20 representative elements for a second PCA. The three most significant principal components (PC1, PC2, and PC3) explained approximately 65% of the total variability. PC1 (accounting for 29.97% of variability) revealed an anticorrelation between Ti, La, and Sc with Au, Hg, and Ag, with positive scores primarily located in the inland Apennine areas. PC2 (21.8%) was dominated by Na, K, and Cu, with positive scores corresponding to volcanic deposits, aligning with the dispersion patterns of historical Vesuvian eruption products. PC3 (11%) was associated with Ca and S, with higher scores found in the alluvial plains and inland areas. These results demonstrate the efficacy of CoDA in minimizing spurious correlations and uncovering latent relationships between elements, thereby enhancing the interpretation of natural and anthropogenic processes influencing soil variability in the region.https://www.mdpi.com/2076-3263/15/1/20environmental geochemistrycompositional data analysisgeochemical mappingmultivariate statistics |
| spellingShingle | Lucia Rita Pacifico Annalise Guarino Antonio Iannone Stefano Albanese Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) Geosciences environmental geochemistry compositional data analysis geochemical mapping multivariate statistics |
| title | Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) |
| title_full | Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) |
| title_fullStr | Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) |
| title_full_unstemmed | Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) |
| title_short | Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy) |
| title_sort | accounting for the compositional nature of geochemical data to improve the interpretation of their univariate and multivariate spatial patterns a case study from the campania region italy |
| topic | environmental geochemistry compositional data analysis geochemical mapping multivariate statistics |
| url | https://www.mdpi.com/2076-3263/15/1/20 |
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