Multivariate Statistical Analysis [MSA] can be employed alongside conventional graphical techniques to very effectively differentiate water chemistry samples into distinct clusters. This approach can identify chemical signatures associated with low or even trace concentration parameters, which are often masked by major ion chemistry. This project aims to build on both low-blank analytical capabilities and reconnaissance datasets for a suite of metals in Irish groundwaters through analysis of high-quality mine datasets. Work is presently focussed on the recently closed Lisheen mine, County Tipperary. An expansive
hydrochemistry dataset, collected underground during mining operations, has been refined to isolate samples representative of the surrounding carbonate aquifer (e.g. fissure inflows and rock face seepages). The presence of dissolved metals in the samples, including As, Pb and Ni, permit preliminary analyses of spatial variations in metal occurrences while further groundwater sampling at selected boreholes across the mine site is underway to compile a high-quality hydrochemistry dataset for MSA, and also to account for seasonality. Interrelationships uncovered between the metal analytes can be interpreted in the broader lithostratigraphic and structural context of the host bedrock in the vicinity of the mine, allowing the potential identification of geogenic metal sources and groundwater flow paths within these aquifers.