The Anthropigene: a new approach in environmental geochemistry to discriminate anomalies from natural background

Abstract

Assessing the geochemical background is critical for addressing soil contamination, particularly in regions with complex interactions between the natural geological context and anthropic activities. Traditional methods for distinguishing geochemical backgrounds from anthropogenic anomalies often struggle to account for overlapping signals in such areas, leading to limitations in accurately identifying contamination sources. This study introduces the “anthropigene” method, an adaptation of the “geochemical gene” method initially developed for mining applications in the environmental context. By classifying geochemical indicators (“genes”) associated with urban and agricultural contamination, the anthropigene provides a robust framework for distinguishing anthropogenic anomalies from natural geochemical signals. Applied to approximately 3000 topsoil samples from the Campania region in Italy, the method allowed the determination of multivariate geochemical patterns linked to urban and agricultural sources of contamination. Samples considered contaminated were eliminated from the original dataset, and the remaining data were used to assess geochemical backgrounds.

Results showed that the background values determined through the proposed approach significantly differed from those generated by applying Italian guidelines; they are also generally more conservative if used as a reference for a tier-one human health risk assessment and environmental restoration. Using the proposed method could have favorable practical implications for unveiling the presence of large-scale diffuse contamination processes that could be easily mistaken for natural enrichments due to their spatial extension.

The method certainly has wide margins for improvement, and future studies will focus on identifying specific indicators of anthropic processes not considered in this paper and improving the techniques for estimating background values at a regional scale.