Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination-A review

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 (I_geo, PI, EF, C_f, PI_sum, PI_Nemerow, PLI, PI_ave, PI_Vector, PIN, MEC, CSI, MERMQ, C_deg, 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.

Keywords: Different soil uses; Geochemical background; Heavy metals; Pollution indices.

Fig. 1

Principal component analysis (PCA) biplot for the individual indices

Fig. 2

Principal component analysis (PCA) biplot for the complex indices

Fig. 3

Ward’s hierarchical cluster analysis of the studied individual pollution indices based on different land uses

Fig. 4

Ward’s hierarchical cluster analysis of the studied complex pollution indices based on different land use

Fig. 5

Ward’s hierarchical cluster analysis of the studied complex pollution indices based on farmland soils

Fig. 6

Ward’s hierarchical cluster analysis of the studied complex pollution indices based on forest soils

Fig. 7

Principal component analysis (PCA) biplot for the individual indices computed for forest soils

Fig. 8

Ward’s hierarchical cluster analysis of the studied complex pollution indices based on urban soils