For many years regulatory agencies have been exploring
methodologies toevaluate the harmful effects of anthropogenic
activity on the environment. Often times legislation is built
on a comparison of available technology, observed
environmental effects and requirements for industrial growth.
In recent years, goals have been set by most agencies for
sustainable industrial development, but often without reliable
long term environmental impact information.
In this report, a model is presented that is intended to
predict trace metal speciation in the environment. It is felt
that it may be a useful tool in helping to regulate the
release of acid mine drainage (AMD). AMD has been the subject
of much review and research in recent years (e.g., Nolan,
1987; Pain 1987; Morin and Cherry, 1988; and Morin et al.,
1988).
Regulatory agencies have historically been monitoring total
metal levels for permitting criteria and they have attempted
to correlate these levels with observed environmental
biological effects. Our own activity with the B.C. Government
has established subcellular techniques that can establish the
effect that a given effluent may have on, for example, the
metal metabolism of fish.
In this report a number of concepts have been used that may
not be familiar to all readers. To aid in the understanding
of the contents of this report, definitions of some terms used
are provided.
Free Metal Ion
This refers to the fraction of the disclosed metal
concentration that is in the free cation of hydrate form,
e.g. t
cu2+ + I?’ * CUL
Free Metal Ion Ligand Metal Ligand Complex
Hydrate forms such as CU(=~~ may also be regarded as “free
metal ions” from the point of view of this report as their
dissociation is–very–raps on interaction with biological
membranes. dther compleies such as CuCO,, for example, would
not be included in this term.
Ion Speciation Model
This term refers to the typeof model used in this report. In
the model, specific ionic species are evaluated and the
interactions of each catonic spec&es with each anionic species
is considered. Through an intmive process of balancing,
each reaction is determined w!:!:& the parameters of known
redox and thermodynamic process,
Biologicallv Available
This term is used to describe the cumulative fraction of a
specific element species that are able to be taken up by
biological membranes. This term is somewhat subjective as
several mechanisms of metal uptake can occur both in the
dissolved phase (both active and passive) and in some cases by
pinocytosis (partial uptake) (George et al., 1976, 1977).
Equilibrium Constant
Consider the reaction
cu + L + CUL.
At the equilibrium, the formation constant or the rate of
formation of CuL is equal to the rate of dissociation of CuL
to its constituents. The equilibrium constant for the above
reaction may be defined as
Single Ion Activity Coefficient
Single ion actitity coefficients are constants that greatly
simplify calculations. They are not measurable individually;
only ratios or Egaducts of ionic activity coefficients are
measurable. These theoretical expressions are usually based
on the Debye-Hincke1 Limiting Law (see Stumm and Morgan,
1970). Other eqirical relationships can also be used. For
a more detailed explanation, see Stump and Morgan, 1970.
It has been clear since the 1970’s (Sunda and Guillard, 1976)
that the free metal ion is the predominant metal species that
is biologically available. For some biological systems,
specific pathways of metal membrane transfer and partitioning
are well established (Florence, et al., 1984).
In this proposal we have used an ion speciation model to
calculate, from the total concentration of parameters usually
collected by the government agenciesl the environmental
concentration ranges 0% free metals.
The use of such procedures as a predictive tool is assessed in
several stages:
- first, the model has been adapted to best fit with
existing Tsolam River data - second, the model is amended with data to allow for the
organic bonding observed at this location to be evaluated - third, the model predictions for this site are compared
with determined metal species - fourth, the accuracy of prediction is assessed by
comparing model data and determined species.
In an attempt to evaluate the effectiveness of the model, the
major parameters affecting speciation and its potential
usefulness to regulators are discussed.
A number of mathematical models have been developed to
determine the chemical speciation of anions and cations in the
environment. Some of these have been based on equilibrium
constant data and some using kinetic effects.
The MINE 1.2 model is an equilibrium model based on a specific
ion interaction. The methodology for the model was first
introduced by Francois Morel of M.I.T. in 1979. The principle
of this model is that a single ion activity coefficient can be
determined by considering specific ion interactions. That is
the interaction of each anion with each and every cation.
In our model, this process is repeated for each cation and
each anion.
In order to allow for kinetic effects that’may impact the
observed free metal ion activities, the stability constants of
some ligands have been amended to allow for the observed
effects. These new values have been termed ‘*apparent
stability constants”.
An example of such effects would be the release of lead from
particles and colloids. These reactions often cannot be
described just in terms of the the.zz:r>dynamic equilibrium. The
release of lead from many natural. surfaces is slow and thus
the apparent “stability constar%Zt’P is different from the
theoretically predicted value. Such variations can be
accounted for by using an expertiwkally derived “stability
constant” number which is referrad to as the “conditional
stability constant”. These “canstants” are operationally
defined but can be used to improvs “;he ability of the model to
fit observed data.
The operatian of MINE 1.2 is fully described in the manual
(Mine 1.2, ZRR 1992). Several alternate calculations are
available, and are described in tha manual.
Options now exist for both input and output data to be
selected. The output data has been reorganized in an attempt
to clarify the data obtained.
An example of output data is shown in Appendix 1.
The output data tabulates input concentrations and calculated equilibrium values.
The percentage contribution to the dissolved phase is also calculated together with the
concentration of each species. The partitioning between the
particulate and dissolved phases are based on redox
eguilibriux’phase considerations. The output has been divided
into three categories, each of which may be selected in the
program using an on-off toggle.
In addition, the format of the output has been improved to
more clearly show the results of the model predictions.
