High‐resolution mapping of soil pollution by Cu and Ni at a polar industrial barren area using proximal and remote sensing

• Published in: Land Degradation & Development
• Main Authors: Dvornikov, Yury, Slukovskaya, Marina, Yaroslavtsev, Alexey, Meshalkina, Joulia, Ryazanov, Alexey, Sarzhanov, Dmitrii, Vasenev, Vyacheslav
• Other Authors: Russian Science Foundation, Russian Foundation for Basic Research
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: kola peninsula
• Online Access: http://dx.doi.org/10.1002/ldr.4261; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ldr.4261; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ldr.4261

Abstract Industrial pollution by potentially toxic elements (PTE) remains a key environmental threat, resulting in soil and ecosystem degradation. Remediation of the industrial barrens is challenging in polar regions, where plant growth is hampered by severe climatic conditions. High‐resolution mapping of soil pollution is needed to support soil remediation and management projects. The distribution of nickel (Ni) and copper (Cu) was analyzed in the topsoil within the industrial barren around the Ni and Cu smelter in Kola Peninsula, Russia using a field‐portable XRF analyzer. Bulk Cu and Ni contents were measured at 84 observation points within the area of two hectares planned for remediation. The PTE content varied between 0.2 and 9.0 g kg −1 for Cu and between 0.2 and 21 g kg −1 for Ni. The area was surveyed with unmanned aerial vehicles and differential global navigation satellite systems to obtain a high‐accuracy digital terrain model for exploring the factors behind the spatial variability. Field observations were interpolated by regression kriging with different input resolution of auxiliary data (0.5–1.0–1.5–2.0 m) and different regression models (gradient boosting machines and multiple linear regression). Model performance and validation showed that 1.0–1.5 m resolution of auxiliary data were the best for projecting Cu and Ni topsoil contents within the study site. The soil type and topographic wetness index were the most important variables explaining Cu and Ni content variability.