An integrated geophysical and geochemical approach to characterizing acid mine drainage in a headwater mountain stream in Colorado, USA

This study integrates geochemical sampling, fluid electrical conductivity (sigmaf) logging, electromagnetic induction (EMI), and electrical resistivity imaging (ERI) to investigate the impact of acid mine drainage (AMD) from the Minnesota Mine, an inactive gold and silver mine, on Lion Creek, a headwater mountain stream near Empire, Colorado. The study goal is to assist in evaluating remediation options by characterizing seasonal variation in AMD contamination levels and identifying the controls of point and diffuse sources of AMD entering Lion Creek. The pH and log (sigma f) of the water in Lion Creek inversely correlate, indicating that the low pH characteristic of AMD-impacted water correlates to high sigma f values that can serve as a target for the geophysical methods. ERI surveys were run along the east bank of Lion Creek along a reach where acidic water seeps out of the steep stream bank and into the creek. These ERI surveys identified two areas where diffuse contamination is likely entering the stream: (1) the subsurface extent of two seepage faces visible on the surface and (2) runoff leaching through a tailings pile on the east bank of Lion Creek. sigmaf in the stream was lowest when the stream level was highest in the early summer and then increased throughout the summer as the stream level decreased, indicating that the concentration of dissolved solids in the stream is largely controlled by dilution due to snowmelt. Total dissolved solids (TDS) were calculated using an empirical relationship between sigma f and TDS, and TDS load (TDS concentration times stream discharge) was calculated. TDS load is greatest in the early summer and displays a large diel signal. This work will be helpful in informing remediation efforts because the identification of diffuse sources of AMD allows for more targeted remediation options and knowledge of seasonal variation is necessary in predicting the costs and outcome of different remediation scenarios.