Prediction of extractable metals in retention pond sediments at surface coal mines

Fifty-two grab samples of bottom sediment in settling ponds were obtained at 17 surface coal mines in the eastern and midwestern U.S. A series of laboratory extraction procedures were designed to simulate a wide range of possible natural conditions. The three types of laboratory extraction procedures were (1) a low-pH buffered extract; (2) a series of low-pH, near-neutral-pH, and high-pH nonbuffered extracts; and (3) a DTPA extract. For the transition metals examined (Fe, Mn, Ni, Zn, Co, Cu, Cr, Fe, Al), higher percentages were extracted by the low-pH buffered extract than by the low-pH nonbuffered extract and the DTPA extract. Within the nonbuffered series, higher percentages of individual metals were extracted at lower pH levels. There was generally a consistent order of “extractability” for all the extracts performed. At the mines using a chemical treatment to neutralize acid mine drainage, Mn was the most mobile and Fe and Al the least mobile of the metals considered; at the mines not using a chemical treatment, Ni, Zn, and Co were among the most mobile and Fe, Al, and Cr the least mobile of the metals studied. Two stepwise regression procedures (maximum R² improvement and backward elimination) were used to suggest a ranking of independent variables that influence extractable metals. Statistically significant independent variables differed for the various metals. In general, the total amount of metal present was most important in determining metal extractability in the buffered extract at the mines using chemical treatment, and variables related to the natural acidity or alkalinity of the sediment and element interrelationships were important in the other extracts. A detailed examination of regression equations for the buffered extract suggests that it is possible to predict extractable metals using simple regression models based on the total amount of metals present, metals interrelationships, and sediment acidity or alkalinity.