Principal Component Analysis to Discriminate and Locate Natural and Anthropogenic Sources of Contamination Within a Strongly Anthropized Region: A Technical Workflow

Abstract

Environmental risks often stem from contamination driven by chemical stressors introduced from multiple sources, either geogenic or anthopogenic. Differentiating between anthropogenic chemical anomalies and those inherent to the environment is crucial. This distinction is essential for defining feasible remediation objectives. This study applied univariate and multivariate statistical techniques to analyse geochemical data from over 7000 topsoil samples in Campania (Southern Italy), over an area of approximately 13,600 km2. A key step in the methodology was applying Normal Score Transformation (NST), which stabilized the variance of the dataset, pulling the extreme outliers back to normal ranges, making it more suitable for multivariate analysis. Principal Component Analysis (PCA) was performed, and four components were selected; the spatialization of their scores revealed four primary independent sources controlling geochemical variability across the region. Specifically, two distinct volcanic districts were identified, plus a siliciclastic and an anthropogenic component. The integration of RGB composite maps further refined this differentiation, emphasising the coexistence or the predominance of one component over the other. The methodological approach demonstrated here provides valuable insights for environmental risk assessment and remediation planning in geochemically complex and anthropized regions.