Suitability of magnetic proxies to reflect complex anthropogenic spatial and historical soil heavy metal pollution in the southeast Nile delta

Abstract

Magnetic proxy methods are effective tools for detecting anthropogenic heavy metal pollution of the environment. In this study we investigated the viability of using this method in an area of a complex setting where natural geogenic input, interfering land-use, and multiple different industries affect the magnetic signal. For this purpose, we took surface (N = 70) and core (N = 18) samples from an ~80 km2 area in the south-eastern Nile delta that was partly flooded before construction of a major dam, with overlapping agricultural, residential (urbanization and land reclamation), and multiple industrial activities. Using ICP-MS we characterized the spatial pollution pattern and found enrichments in seven potentially toxic heavy metals; Cu, Zn, Pb, As, Sb, Cd, and Hg, located near industrial hotspots, with highly varying pollution levels, high concentrations in the upper soil and clear depletion with depth. Magnetic susceptibility (ϰ) was measured in-situ at 170 sites, and on all samples. Thermomagnetic runs reveal that magnetite and Ti-rich titanomagnetite control the magnetic signal. Despite industrial activities are predominantly located in more sandy areas, and ϰ is found to be strongly related to spatial lithological variation, the magnetic results reasonably outline the industrial areas and show elevated ϰ levels around drains where pollutants are discharged and redistributed by irrigation. In most of these locations, ϰ decreases with depth in parallel with the pollution level, and there is a moderate correlation of ϰ with the pollution load index for the topsoil values of the core samples normalized to their bottom values. Despite the area's complexity, the spatial ϰ pattern matches reasonably well with the chemistry data of cores located in the vicinity of the main industrial spots. Therefore, also for this complex setting, time-efficient ϰ mapping provides a helpful tool as a qualitative approach for detecting key features of the spatial distribution of pollutants, which will be useful for supporting a better-targeted chemical sampling.