GUIDE FOR
PREDICTING WATER CHEMISTRY FROM WASTE ROCK PILES
Mine Environment Neutral Drainage at CANMET-MMSL |
MEND Project 1.27.1a
July 1996
EXECUTIVE
SUMMARY
The Mine Environment
Neutral Drainage (MEND) Program is developing tools for prediction
of waste rock dump leachate quality. The first objective of this
study was to evaluate a recently proposed empirical approach for
predicting concentrations of metals in waste rock dump leachate
primarily using pH (Morin and Hutt 1993). The method has previously
been successfully applied at two mines. The second objective was
to investigate refinements to the approach.
Five waste
rock piles were selected for the study. Vangorda Plateau (Yukon
Territory) and Sullivan (south eastern British Columbia) mines are
volcanogenic massive deposits. The Cinola project, Queen Charlotte
Islands, British Columbia was a previous MEND study of small test
waste rock piles at a proposed sediment-hosted epithermal gold deposit
mine. Mine Doyon is a gold vein deposit located between Val D'Or
and Rouyn, Quebec. Eskay Creek is a stratiform and stratabound gold
and silver deposit located in northwestern British Columbia. Usefulness
of the data sets was limited by missing data, variable detection
limits and lack of associated flow information (where applicable).
The first step
involved examination of histograms for each variable and calculation
of regression equations for pH and conductivity against all other
parameters. The study confirmed the utility of the empirical approach.
Element concentrations were generally negatively correlated with
pH but positively correlated with conductivity. Geochemical evaluation
of the trends using the equilibrium solution speciation model MINTEQA2
was not useful. However, evaluation of regression equations for
sulphate and element concentrations showed good correspondence with
predicted geochemical behaviour, consistency with site mineralogy
and strong similarities between sites suggesting common mineralogical
controls.
The major problems
encountered with the empirical models were outliers and excessive
positive skewness, variable detection limits, non-normality of residuals,
departures from linearity and sub-populations. Several refined data
screening methods were evaluated to address these problems, however,
the effect on estimates of regression parameter is minimal. Alternatives
to least squares regression and separation of data according to
sub-populations can be considered.
The second
step involved investigation of several multivariate techniques:
multiple regression, Principal Components Analysis (PCA) and Cluster
Analysis. Due to the excellent inter-correlation of many parameters,
multiple regression does not increase the predictive power of bivariate
regressions. PCA and Cluster Analysis have no predictive power but
are useful as initial data screening tools to restrict the number
of bivariate regressions required to model leachate chemistry.
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