Investigation of spatially varying relationships between cadmium accumulation and potential controlling factors in the topsoil of island of Ireland based on spatial machine learning approaches

Xu, Haofan, Wang, Hailong, Croot, Peter, Liu, Juan, Li, Yunfan, Beiyuan, Jingzi,et al (2025). Investigation of spatially varying relationships between cadmium accumulation and potential controlling factors in the topsoil of island of Ireland based on spatial machine learning approaches. Environmental Research, 275, 121466. https://doi.org/10.1016/j.envres.2025.121466

Abstract

Background Cadmium (Cd) contamination in soils is a pressing environmental issue due to its toxicity and persistence. Given the diverse geological formations and intensive agricultural activities in Ireland, understanding the distribution and sources of soil Cd is particularly important. Methods This study used multiple GIS-based and spatial machine learning (SML) techniques to investigate the spatial distribution and controlling factors of Cd in 16,783 topsoil samples across the island of Ireland. Three analytical methods were applied: hot spot analysis to detect clusters of high and low Cd concentrations, Geographically Weighted Pearson Correlation Coefficients (GWPCC) to explore how Cd relationships with other soil properties vary across space, and Random Forest (RF) to rank the contributing factors in Cd accumulation. Results Hot spot analysis revealed strong spatial overlap between Cd concentrations and key geochemical variables including CIA, Fe, P, pH, SOC, and Zn. GWPCC further highlighted their spatially varying relationships, with significantly strong positive correlations between Cd and pH, Zn, and P in the central midlands. The local correlation coefficients obtained from the GWPCC ranged from negative to the highest values of 0.80, 0.92 and 0.86, respectively, which were significantly higher than the results of traditional Pearson correlation coefficients. These patterns were associated with impure limestones, Zn mineralization, and phosphate fertilizer inputs. Furthermore, the RF model ranked Zn (39.4 %) and P (17.6 %) as the most influential factors, with their importance increasing in limestone-dominated areas (50.9 % and 27.4 %), which emphasized the external contributions from local Zn mineralization and phosphate fertilizers in addition to natural accumulation. Conclusion This study demonstrated the effectiveness of integrating SML techniques with geochemical analysis for identifying Cd sources in the topsoil of Ireland, highlighting the roles of lithology and agricultural activities in Cd accumulation. The results provided valuable insights for contamination management and environmental policy development in Ireland and elsewhere.

Funder

National Natural Science Foundation of China
National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation

Publisher

Elsevier

Rights

CC BY

cb

Collections

Geography (Scholarly Articles)