| Autor: |
Nedyalkova Miroslava, Simeonov Vasil |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Open Chemistry, Vol 17, Iss 1, Pp 711-721 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2391-5420 |
| DOI: |
10.1515/chem-2019-0082 |
| Popis: |
In this study, an interpretation and modeling of the soil quality by monitoring data using an intelligent data analysis is presented. On an annual average, values of 12 soil surface chemical parameters as input variables were determined at 35 sampling sites as objects of the study in the region of Burgas, Bulgaria are used as input data set. Cluster analysis (hierarchical and non hierarchical methods abbreviated as HCA and K-means, respectively) and the principal components analysis (PCA) are used as chemometric tools for data interpretation, classification and modeling. Additionally, principal components regression analysis (APCS approach) is introduced to determine the contribution of each identified by PCA latent factor to the total concentration of the chemical parameters. The formation of different patterns of similarity between the variables or the objects of the study by cluster analysis is interpreted with respect to the risk of pollution or spatial conditions. The input data set structure is analyzed by PCA in order to determine the most significant factors responsible for the data structure. Four major patterns of similarity between the chemical parameters measured are found to define soil quality in the region related to industrial and agricultural activity in the region since the objects are separated into two patterns corresponding to each geographical location of the sampling sites. Analogous results were obtained by the use of PCA where the level of explanation of the data set structure is quantitatively assessed by the total explained variance of the system. The apportionment model indicated that the contribution of latent factors (sources of pollution) to the total chemical concentration of the species tested – pH, soil nutrition components, total and organic carbon content and toxic metals. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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