Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. In this manuscript, we applied a machine learning model on 21,682 soil samples from the Land Use and Coverage Area frame Survey (LUCAS) topsoil database of 2009/2012 to assess the spatial distribution in Europe of topsoil Zn concentrations measured by aqua regia extraction, and to identify the influence of natural drivers and anthropogenic sources on topsoil Zn concentrations. As a result, a map was produced showing topsoil Zn concentrations in Europe at a resolution of 250 m. The mean predicted Zn concentration in Europe was 41 mg kg-1, with a root mean squared error of around 40 mg kg-1 calculated for independent soil samples. We identified clay content as the most important factor explaining the overall distribution of soil Zn in Europe, with lower Zn concentrations in coarser soils. Next to texture, low Zn concentrations were found in soils with low pH (e.g. Podzols), as well as in soils with pH above 8 (i.e., Calcisols). The presence of deposits and mining activities mainly explained the occurrence of relatively high Zn concentrations above 167 mg kg-1 (the one percentile highest concentrations) within 10 km from these sites. In addition, the relatively higher Zn levels found in grasslands in regions with high livestock density may point to manure as a significant source of Zn in these soils. The map developed in this study can be used as a reference to assess the eco-toxicological risks associated with soil Zn concentrations in Europe and areas with Zn deficiency. In addition, it can provide a baseline for future policies in the context of pollution, soil health, human health, and crop nutrition.
Keywords: LUCAS; Micronutrients; Quantile regression forest; Soil contamination; Zinc.
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