Fig. 6

Correlation matrix and Principal component analysis (PCA) of the contaminants. (A) The heatmap plot represents the visualization of the correlation matrix with three different significance levels (0.001, 0.01, 0.05 marked by ***, **, and * respectively). Insignificant correlations are left blank in the lower part of the plot. Note that DEP and DPB were removed from the correlation analysis since both contaminants were not detected in many sampling sites. (B) The correlation plot of variables illustrates the degree to which different variables are represented on the first two principal components and their interrelationships. Positively correlated variables are clustered together, while negatively correlated variables are positioned in opposite quadrants of the plot origin. The distance of variables from the origin reflects their quality on the factor map, with those farther away being more accurately depicted. Variables are color-coded based on their cos2 values, indicating the quality of their representation. A high cos2 value suggests a strong representation of the variable on the principal component, with the variable positioned close to the circumference of the correlation circle. Conversely, a low cos2 value indicates that the variable is not well represented by the principal components.