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Journal of Environmental Quality Abstract - Surface Water Quality

Acidity Decay of Above-Drainage Underground Mines in West Virginia


This article in JEQ

  1. Vol. 39 No. 3, p. 1043-1050
    unlockOPEN ACCESS
    Received: June 17, 2009

    * Corresponding author(s): jskousen@wvu.edu
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  1. B. Macka,
  2. L. M. McDonaldb and
  3. J. Skousen *b
  1. a Water Research Institute
    b Division of Plant and Soil Sciences; West Virginia Univ., Morgantown, WV, 26506. Scientific article number 3067, West Virginia Agriculture and Forestry Experiment Station, Morgantown, WV. Assigned to Associate Editor Robert Darmody


Acidity of water from abandoned underground mines decreases over time, and the rate of decrease can help formulate remediation approaches and treatment system designs. The objective of this study was to determine an overall acidity decay rate for above-drainage underground mines in northern West Virginia from a large data set of mines that were closed 50 to 70 yr ago. Water quality data were obtained from 30 Upper Freeport and 7 Pittsburgh coal seam mines in 1968, 1980, 2000, and 2006, and acidity decay curves were calculated. The mean decay constant, k, for Upper Freeport mines was 2.73 × 10−2 yr−1, with a 95% confidence interval of ± 0.0052, whereas the k value for Pittsburgh mines was not significantly different at 4.26 × 10−2 yr−1 ± 0.017. Acidity from the T&T mine, which was closed 12 yr ago, showed a k value of 11.25 × 10−2 yr−1 This higher decay rate was likely due to initial flushing of accumulated metal salts on reaction surfaces in the mine, rapid changes in mine hydrology after closure, and treatment. Although each site showed a specific decay rate (varying from 0.04 × 10−2 yr−1 to 13.1 × 10−2 yr−1), the decay constants of 2.7 × 10−2 yr−1 to 4.3 × 10−2 yr−1 are useful for predicting water quality trends and overall improvements across a wide spectrum of abandoned underground mines. We found first-order decay models improve long-term prediction of acidity declines from above-drainage mines compared with linear or percent annual decrease models. These predictions can help to select water treatment plans and evaluate costs for these treatments over time.

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