PREDICTED EFFECTS OF PRE-MINE WEATHERING AND ROCK TYPE ON TDS RELEASE FROM APPALACHIAN COAL MINE SPOILS

Appalachian coal mines have been implicated as major stressors to biota in headwater streams due to discharge of total dissolved solids (TDS). Large volumes of blasted sedimentary rock spoils are placed into highwall backfills and head-of-valley fills during the mining and reclamation process. While the industry has made great advancements since the 1970’s in utilizing acid-base accounting procedures to limit low pH and high metal discharge waters, bulk TDS release in excess of 500 mg/L is common and is typically dominated by SO₄ and alkali cations. Unweathered sedimentary rock overburden materials can contain carbonates, feldspars, micas and pyrite which react rapidly following mining disturbance to produce TDS. Our primary objective was to evaluate a column leaching approach for TDS prediction. Over 45 typical non-acid forming ground spoils (< 1.25 cm) were leached (unsaturated) with 2.5 cm of simulated precipitation (pH 4.6) for 20 weeks (2X per week) in lab columns (7.5 cm x 40 cm). Leachates were analyzed for pH, electrical conductance (EC), Ca, SO_4, and other parameters. Initial leachate EC was moderate (< 1000 uS/cm) for most samples, although some ranged to >3000. Leachate EC decreased rapidly within several pore volumes and most samples achieved a steady-state with relatively low EC levels (< 500 uS/cm) within 20 leaching cycles. Rock strata that have undergone significant pre-mining oxidation and leaching generate much lower EC when compared to local unweathered materials from the same strata. Finer textured siltstones and shales typically generate higher leachate EC than sandstones, but total pyritic-S content is a better predictor of both initial and long-term TDS release than rock type. Ongoing research conducted by the Appalachian Research Initiative for Environmental Science (ARIES) is focused on (1) developing and validating new quick static tests to predict both peak and long-term TDS production and (2) determining appropriate scaling factors to relate both column and static test data to predict field conditions.