Factors affecting alkalinity generation by successive alkalinity-producing systems: regression analysis.
Use of successive alkalinity-producing systems (SAPS) for treatment of acidic mine drainage (AMD) has grown in recent years. However, inconsistent performance has hampered widespread acceptance of this technology. This research was conducted to determine the influence of system design and influent AMD chemistry on net alkalinity generation by SAPS. Monthly observations were obtained from eight SAPS cells in southern West Virginia and southwestern Virginia. Analysis of these data revealed strong, positive correlations between net alkalinity generation and three variables: the natural log of limestone residence time, influent dissolved Fe concentration, and influent non-Mn acidity. A statistical model was constructed to describe SAPS performance. Subsequent analysis of data obtained from five systems in western Pennsylvania (calibration data set) was used to reevaluate the model form, and the statistical model was adjusted using the combined data sets. Limestone residence time exhibited a strong, positive logarithmic correlation with net alkalinity generation, indicating net alkalinity generation occurs most rapidly within the first few hours of AMD-limestone contact and additional residence time yields diminishing gains in treatment. Influent Fe and non-Mn acidity concentrations both show strong positive linear relationships with net alkalinity generation, reflecting the increased solubility of limestone under acidic conditions. These relationships were present in the original and the calibration data sets, separately, and in the statistical model derived from the combined data set. In the combined data set, these three factors accounted for 68% of the variability in SAPS systems performance.
Jage
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Regional Land Trust Representative, New Jersey Conservation Foundation, Far Hills 07931, USA.
Zipper
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Noble
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D002138:Calibration; D006863:Hydrogen-Ion Concentration; D007501:Iron / Q000737:chemistry; D008906:Mining; D015233:Models, Statistical; D010084:Oxidation-Reduction; D012037:Refuse Disposal; D012044:Regression Analysis; D012989:Soil Pollutants; D014876:Water Pollution / Q000517:prevention & control*
D012989:Soil Pollutants; D007501:Iron