Robust geostatistical prediction of trace elements across France

National-scale soil monitoring networks are required to identify where the soil quality is threatened and evaluate the effectiveness of any remediation efforts. In particular, trace elements (TEs) should be monitored to assess human exposure to potentially toxic elements and to ensure that crops take up sufficient quantities of elements that have essential functions in the human body. However the spatial variation of TEs is often highly complex since hot-spots at point sources are superimposed upon more regular patterns produced by diffuse pollution and natural processes. Geostatistical methods used to analyse national-scale soil monitoring networks must be general enough to accommodate such behaviour but simple enough to be computed in a reasonable time for large datasets. We show how a simplified version of a recently developed robust geostatistical algorithm to map the underlying variation of six TEs (Cr, Cu, Pb, Ni, Th and Zn) across France using observations from the French National Soil Monitoring Network (Réseau de Mesures de la Qualité des Sols). Cross-validation results suggest that these TEs cannot be modelled by non-robust methods but that the simplified robust approach is sufficient. Differences between the maps of different elements and the abundance of TEs from different parent materials are evident. Large concentrations of Cr, Ni and Zn occur in soils on Jurassic rocks whereas Pb and Th concentrations are large in soils on crystalline rocks. Volcanic parent material leads to large concentrations of Cr, Cu, Ni and Zn but small concentrations of Pb and Th. Diffuse pollution of certain elements (mainly Pb, and to a lesser extent Zn) is evident in industrial regions in the north and the north-east of France and close to Paris. The pattern of outlying values is indicative of local anthropogenic processes such as industrial pollution in the north of France and close to Paris, and application of Cu on vineyards and of geological anomalies such as large concentrations of some TEs in the south of the Massif Central Mountains. Future phases of the RMQS will describe the spatial and temporal trends of the concentrations of these TEs.

Research highlights
► We map underlying trace element concentration in topsoil across France.
► The data include extreme values because of small-scale anomalous processes.
► Robust geostatistical methods are required to model the underlying variation.
► Cross-validation results suggest that the method of moments is suitable to fit this model.
► Soil experts identify possible natural and anthropogenic sources of variation.