Contamination of soils by potentially toxic elements (PTEs) such as cadmium, arsenic and cobalt can negatively impact human health, agricultural production, and the environment. Researchers have now developed a machine learning approach to map the areas where 11 potentially toxic chemicals exceed safety guidelines in Czech farmland. The model accurately predicts hazardous areas and identifies important contamination.
The researchers produce an accurate, high-resolution map to predict the extent to which topsoil PTE concentrations exceed existing Czech safety alert thresholds. The model used existing data on concentrations of eleven PTEs found in the top 20 cm of farmland soils (obtained from a national soil monitoring database in the country), and included:
- Environmental variables that predispose soils to accumulate PTEs (such as temperature, rainfall, soil type, land structure and features, and land cover)
- Variables that affect natural sources of PTEs (such as the physical characteristics of rocks – basaltic rocks are markedly enriched in copper, cobalt, and vanadium, for example, whilst cretaceous ‘marlstones’ are rich in cadmium)
- Variables that affect man-made sources of PTEs (such as the level of particulate matter air pollution)
The researchers made their data accessible via a publicly available Czech knowledge-based platform for complex assessment of soil pollution called “SoilPAss” (Soil Pollution Assessment), on which users can use the map to view PTE probability and concentration predictions, alongside measurements of uncertainty.
Compared to previous continental assessments of soil in Europe (FOREGS, GEMAS, and LUCAS), this approach presents an analysis based on high-density data, enabling more detailed predictions about soil contamination by PTEs.
Reference:
Skála, J. Žížala, D. & Minařík, R. (2025) Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland, Journal of Environmental Management, Volume 380,125035, doi.org/10.1016/j.jenvman.2025.125035.
