Geochemical fingerprinting of a radon anomaly: high-resolution PCA–ANOVA case study, Castleisland, SW Ireland

Radon represents a global health risk, and so accurate delineation of radon-prone areas is a prerequisite for evidence-based radon mitigation and public health protection. National probabilistic radon models of Ireland, based on 1:1 M bedrock geological maps that group the Clare Shales with limestones achieve ~74% accuracy. Despite the high accuracy of Ireland's national radon map, some discrepancies still exist. Using geochemical and geostatistical methods, we investigated a significant and persistent radon anomaly over the Clare Shale Formation at Castleisland, Co. Kerry, Ireland. Fifty-six topsoil samples collected from the A-horizon within a 6 km² grid, with samples spaced every 250–500 m apart were included in the analysis and compared to co-located soil-gas radon measurements. Following centred and isometric log-ratio transformations of ICP-MS/OES data for 37 elements, soils above the Clare Shales exhibited a median U concentration of 4 mg kg^–1 (range 1.4–37 mg kg^–1). Pearson correlations between log₁₀ soil-gas radon and individual elements peaked at r = 0.57 for Sr (R² = 0.32), with similarly strong associations for V (r = 0.54), Ag (r = 0.52), P (r = 0.47), Au (r = 0.46), U (r = 0.45) and Tl (r = 0.43) (all p < 0.001). One-way ANOVA indicates radon-class categories explain a median 43.8% of variance in 14 trace elements, while bedrock geology explains 27.8%. Shared tracers (U, V, P, Ag, Sb) underscore overlapping lithological and radiogenic controls. Principal Components 1–3 capture 62.9% of total variance (PC1 = 31.8%, PC2 = 17.2%, PC3 = 13.9%). PC1 is defined by strong positive loadings on Sc, Mg, Fe, Al, Th, Ni and Co and negative loadings on Sr, Ag, U, P and V, neatly contrasting shale-derived, high-radon soils from carbonate terrains. This study demonstrates that local-scale litho-geochemical proxies, resolved at 250 m and calibrated against a 1:100 k geological framework, can effectively delineate elevated radon sources. This approach offers a systematic means to investigate map anomalies, refine national radon models, enhance spatial accuracy, and support evidence-based radon risk assessment and public health protection initiatives.