-------
flexible enough to describe a number of physical situations. One of the
simplest such models was presented by Lapidus and Amundson (1952). They
solved the following set of partial differential equations:
^=k,c-k2n (2)
The second equation describes the kinetics of the reaction of trace con-
taminant with the soil. It assumes that there are first-order rate terms for
both forward and backward reactions. These are effective reaction rates
rather than mechanistic descriptions of the reactions taking place. Some of
the more important modifications that might be anticipated in the model fol-
low. First, the reaction terms are probably second-order overall, and depend
on the availability of finite (but possibly numerous) reaction sites. Sec-
ondly, there may exist more than one kind of site. One result of this would
be the dependence of the leaching studies on the input concentration used.
Thirdly, different trace metals may be simultaneously competing for reaction
sites. Also of interest may be the effect of solution pH on reaction rate.
It should also be emphasized that all modeling and experimental work was
conducted using saturated soil and steady flow rates. The most likely effect
of unsaturated conditions will be to increase the ability of the soil to
attenuate trace contaminants.
In most cases taking into account a more realistic description of the
reaction rate results in equations whose solution must be obtained through
expensive finite difference of finite element techniques. The distinct
advantage of the Lapidus-Amundson solution is that it can be presented in
relatively compact or closed form. The solution is:
f- = evz/2D[F(t) + k2 / F(t) dt] (3)
o o
where
F(t) = e"k2t / Io[2A1k2x(t-x)'/a]z/2^rEJx3exp-(z2/4Dx+xd)dx (4)
d = V2/4D + k^a - k£ (5)
A computer program was written to present the above solution. A listing
of the program and some sample data cards are available from the author. The
program output includes a plot of concentration as a function of time or
distance. Figures 23 and 24 are examples of the input data points from a
column experiment and the output curve generated using equation (3). The
curves in these figures are not merely a least squares fit to the input data
but are calculated with the solution to equations (1) and (2) using values of
D, ki, and k2 estimated from the input data. (It should be noted that the
concentration at any point in the column is the solution concentration and
not the amount reacted.) To use the model, three empirical parameters D, Ki,
and k2 must be known. In addition, the flow rate and porosity of the soil
91
-------
1.0
.9
.8
.7
.6
^°-5
Y .4
I -3
Wagram l.s,
As
r2 = 0.85
Ava si.c.l
Cd
r2 = 0.79
Kalkaska s.
Ni
r2 = 0.92
10 20 30 40 50 0
TIME - days
10 20 30 40 50
Figure 23. Computer rate curves and experimental points for the migration of
As, Cd, Zn, and Ni through Wagram l.s., Ava si.c.l., and Kalkaska s.,
respectively.
92
-------
Anthony s.l
As
r2 = 0.74
.£.
.1
0
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
Anthony s.l
V
r = 0.88
10
Figure 24.
I
Anthony s.l,
Cd
r2 = 0.79
Anthony s.l
Ni
r = 0.92
20 30 40 50 0 10 20
TIME - days
30 40
50
Computer rate curves and experimental points for the migration of
As, Cd, Ni, and V through Anthony sandy loam.
93
-------
must be available. The next section will describe how these parameters may
be estimated from the experimental data.
The problem of the amount reacted at any point in the column is also of
interest, particularly in describing the results of extractions of trace
metals from column segments. While Lapidus and Amundson (1952) do not pre-
sent a solution for this aspect of the problem, the solution is readily ob-
tained by straightforward methods of ordinary differential equations. The
result is:
n = e"k2t / ek2t k] c dt (6)
o
where c is the solution previously given. At this time no further work has
been done with this portion of the model.
Parameter Estimation
One of the major problems in using the model described above is that of
obtaining meaningful values for the parameters. The problem is complicated
because the parameters appear in a nonlinear form in the equation. Also of
concern is the fact that although the model is deterministic, the data to be
analyzed is stochastic.
A general approach to parameter estimation has been reviewed by Bard and
Lapidus (1968). Using the procedures outlined therein a general parameter
estimation program was written so as to allow the estimation of the two rate
constants and the dispersion coefficient. Analytic derivatives were calcu-
lated and used in estimating the second derivatives. The criteria function
used was the sum of squares, although likelihood estimators may be of more
value for model discrimination. At this time no attempt was made to estimate
parameters for any models other than the one presented here.
A list of all parameters estimated to date are given in Table 34. The
incompleteness of the table is the result of two factors. First, time and
money were limited. The average 1975 cost to estimate a single set of param-
eters is of the order of $125-150. The cost will vary with the number of
data points analyzed. Until its utility is justified, those samples which
were analyzed are those whose migration rate is the greatest. The second
limitation is that for some soils the output concentration never rose above
one-tenth the input concentration. For these columns only bounds can be
estimated for the parameters from the effluent data. It may be possible to
estimate the parameters for these cases from the extraction data, although
this has not been done at this time.
For most of the columns analyzed, the dispersion coefficient remained
nearly constant, in the vicinity of 9.5 cm2/day. One way to increase the
efficiency of the program may be to assume a uniform value of ID, and estimate
only the rate constants. This procedure is relatively easy to do within the
context of the program as it is written.
94
-------
TABLE 34. PARAMETERS ESTIMATED
Element
Arsenic
Cadmium
Beryllium
Lead
Mercury
Nickel
Vanadium
Zinc
Soil
Anthony
Kalkaska
Wag ram
Anthony
Ava
Davidson
Wag ram
Anthony
Nicholson
Wag ram
Anthony
Kalkaska
Wagram
Anthony
Ava
Wagram
D
(cm2/day)
9.9244
9.9305
9.9185
9.9067
9.9214
9.8293
9.9750
9.8136
9.878
9.9316
10.540
9.8660
9.869
10.001
9.8909
9.9207
K!
(I/day)
0.3916
0.3572
0.6748
0.5611
0.2940
2.2983
1.2386
4.2171
0.3749
0.2972
2.1257
1.4866
0.5029
1.4621
0.6705
0.3980
K2
(I/day)
0.09893
0.06337
0.2457
0.1025
0.1173
0.2812
0.3044
1.4402
0.1048
0.05206
0.5018
0.4050
1.9177
0.4044
0.1504
0.1945
9
0.33
0.43
0.36
0.34
0.49
0.46
0.36
0.33
0.44
0.36
0.33
0.43
0.36
0.33
0.49
0.36
V
(cm/ day)
8.58
7.04
8.39
8.64
5.22
8.45
9.47
10.8
12.1
9.24
13.3
7.78
13.4
13.2
6.68
13.1
95
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Extrapolation of Results
While a knowledge of the parameters allows us to predict the
concentration at any time or place, this may not be the most effective
presentation of the results. What we would prefer to know is how long can
we apply leachate before a given fraction of the original concentration will
appear in the groundwater. Or how long will a material used as a liner react
with the trace contaminant before allowing it to pass through, These types
of questions can be answered if instead of using the model to predict the
concentration, we fix the concentration and look at how far it has moved in
the soil profile. To do this, we should like to be able to solve the model
equation in terms of x (distance traveled) and then plot x y_s_ t. Unfortun-
ately, this cannot be done in a straightforward manner. The values of x must
be obtained by an interpolation and plots made of x vs t for different con-
centrations and flow rates. Examples of these curves are given in Figures
25 » 26 » 27 » and 28- In each case the median flow rate chosen was the
same as the experimental conditions under which the column was run. The
concentrations which are expressed as ratios to the input concentration were
arbitrarily chosen. The actual value desired would depend on the input con-
centration and the water quality standard set for that particular element.
Figures 25 and 26 represent a fixed concentration level and varying flow
rate. Figures 27 and 28 are based on the same data, presented as a fixed
flow rate and varying concentration. Similar curves using other concentra-
tion levels or flow rates can be generated using the computer program given.
The predictions made, using the information presented here, should be
interpreted only as an estimate which can be strongly modified by unsaturated
conditions, clogging and changes in the structure of the soil. Of these,
unsaturated conditions may be used to enhance the ability of the soil to
attenuate the trace metal. The intermittent application (or rotation of
application among several leach fields) may provide a cheap means of increas-
ing the efficiency of the soil in treating wastestreams.
The accuracy of any projection depends on how well the differential
equations actually describe the physical situation. Without any validation
of the model either for longer times or different physical regimen, any pre-
dictions must be used with caution. A further discussion of validation is
presented in a later section. At this point the model must be viewed as
tentative and its accompanying predictions must be viewed as examples of how
the model could be applied after it is developed more completely.
Example--
An illustration of how Figure 23 can be used now follows. Assume a
wastestream of 0.25 ppm As is ponded on a Wagram l.s. (or similar soil). If
the water quality standards are 0.05 ppm, then we are interested in the move-
ment of As at a relative concentration of .05/.25 or 0.2 (this number is
dimensionless). We may be interested in either how long it will take to
reach groundwater or how far it will move in a given amount of time. First,
let's assume it is 60 cm to groundwater, and 3.7 cm of wastestream or leach-
ate from a waste can be applied per day. Then the velocity is 3.7 cm/day
divided by the porosity (see Table 34) or 3.7 cm/day/0.36 =8.3 cm/day. Using
96
-------
100
80
60
40
20
100
E
o
I
UJ
o
en
Q
80
60
40
20
80
60
40
20
Anthony s.
Vanadium
C/Co = 0.3
Wagram I. s.
Arsenic
C/Co = 0.2
Velocities (V)
are in cm/day
Ava si. c.l.
Zinc
C/Co = 0.2
Wagram I. s.
Arsenic
C/Co = 0.8
Ava si. c. I.
Zinc
C/Co = 0.8
Anthony s.
Vanadium
C/Co = 0.5
10 20 30 40 50 10
TIME-days
20
30
40
50
Figure ^
Plot of distance traveled v_s_ time for V in Anthony sJ., As in Wagram
l.s. and Zn in Ava si. c.l., where C/C0 is fixed and flow rate (V) varies.
97
-------
80
60
40
20
100
80
E
o
I 60
LU
O
Davidson c
Beryllium
C/Co = 0.5
CO
Q
40
20
Velocities (V)
are in cm/day
100
80
60
40
20
Nicholson
Mercury
C/Co =
10
20 30
Anthony s. I
Nickel
C/Co = 0.5
40 50
Davidson c.
Beryllium
C/Co = 0.3
10
Nicholson si.c.
Mercury
C/Co = 0.2
I
I
20
30 40
50
TIME-days
Figure 26. Plot of distance traveled V£ time for Be in Davidson c., Ni in Anthony s.l
and Hg in Nicholson si.c., where C/C0 is fixed and flow rate (V) varies.
98
-------
100
80
60
40
20
100
80
E
o
I 60
UJ
O
40
CO
Q
20
100
80
60
40
20
Nicholson si. c.
Mercury
V=I5.I cm/day
C/CO=0.\-/;
C/Co = 0.3
C/Co=0.l
Anthony s.l.
Nickel
V=I7.3 cm/day
C/Co =
= 0.3
C/Co = 0.5
Anthony s.l.
Vanadium
V=|7.2 cm/day
10
20
Nicholson si.c.
Mercury
v = 9.i cm/day
Anthony s. I.
Nickel
V=9.3 cm/day
C/Co = O.
Anthony s.l.
Vanadium
V=9.2 cm/day
C/Co = O.
C/Co=0.3
C/Co = 0.5
30
20
30
40 50
Figure 27.
40 10
TIME-days
Plot of distance traveled vs_ time for Hg in Nicholson si. c., and
Ni and V in Anthony s.l., where flow rate (V)' is fixed and con-
centration (C/C0) varies.
99
-------
100
80
60
Davidson c.
Beryllium
V=ll.4 cm/day
40
20
C/Co = 0.3
C/Co=0.
C/Co=0.5
100
80
E
o
i 60
LU
O
2
CO
Q
40
20
80
60
40
20
Ava si. c.l.
Zinc
V = 9.6 cm/day
C/Co = 0.2
C/Co=0.8
Kalkaska s.
Nickel
V= 10.7 cm/day
10
20
30
40
50
Davidson c.
Beryllium
V=5.4 cm/day
= 0.1
) = 0.5
Kalkaska s.
Nickel
V = 4.7 cm/day
C/Co=0.3
Ava si. c. I.
Zinc
v=3.6 cm/day
10
20
30
40 50
Figure 28.
TIME-days
Plot of distance traveled ys_ time for Be in Davidson c., Ni in
Kalkaska s., and Zn in Ava si. c. 1., where flow rate (V) is
fixed and concentration (C/C0) varies.
100
-------
the middle line of Figure 25, we see that it will take about 36 days to reach
this depth. Application would have to be terminated prior to 36 days because
continued leaching with only rainwater would move the 0.2 relative concentra-
tion some additional distance down into the soil.
The second question can be exemplified by seeing how far this level will
move after a fixed time period, say 15 days. Again, using the already calcu-
lated value for the velocity of 8.3 cm/day, this concentration should have
moved about 25 cm.
Alternative Uses of the Parameters
One of the problems in using correlations to analyze data is that of
choosing soil or solution characteristics to measure. If the mechanism of
attenuation is uncertain, then at best we must pick (perhaps randomly) soil
properties and hope a significant correlation will appear. One alternative
is to use pattern recognition techniques, another is to utilize the parame-
ters from a mathematical model. If the model is at all representative of the
true system, then the parameters of the model should relate strongly to basic
soil physical or chemical properties. We might expect that such correlations
would be stronger than those obtained otherwise. If this is true, then we
would have an even stronger basis for generalizing our results on trace metal
movement to previously untested soil types. Because of the incomplete esti-
mation of parameters, no conclusive statements about the efficiency of this
approach can be made at this time.
Validation of the Model
The fact that a particular model under a limited set of experimental
conditions can be made to fit available data does not imply that the model
is an accurate representation of the physical and chemical system. The best
test of any model is to test it under conditions dissimilar to those under
which it was calibrated (i.e., the parameters were estimated). There are
several variables that could be manipulated to provide this critical test of
the model. Because data collected suggests that attenuation is sensitive to
the flow rate (or solution flux) this was chosen as the appropriate independ-
ent variable. If, in future work, the parameters estimated at one flux are
comparable to those at a different flux, then we will have substantial evi-
dence that our model was the appropriate one. If the parameters are incon-
sistent, they may provide some clue as to the best step to take in upgrading
the model. Only when we have confidence that the model accurately describes
the soil-leachate system will we be assured that any projections made with
the model can be reliably used.
SPECIAL STUDIES WITH MERCURY AND CYANIDE
Mercury and cyanide attenuation in soils could not be evaluated by the
same methods as the other trace metals because of their individual peculiari-
ties to form insoluble or volatile compounds with leachate constituents under
conditions of the general procedures adopted for the other trace ions. Eval-
uation of toxic volatile substances also required fabriacation of different
equipment. Special studies, independent of the trace elements therefore were
101
-------
initiated to better understand the mobility of Hg and CN through soil when
present with municipal solid wastes deposited on land. Research on both Hg
and CN only got underway as the contract period was being terminated. This
lack of time and financial support permitted the development of only a small
amount of data. In fact, the CN program is represented primarily by a liter-
ature search and methodology development. Perhaps the most perplexing prob-
lem was the finding that CN could not quantitatively be recovered from the
soil once it had made contact. Nevertheless, the efforts described here rep-
resent necessary time expenditure, useful to establish a point of departure
for further studies aimed at understanding Hg and CN migration through soil.
Mercury Reactions in Soil
Mercury is regularly being introduced into our environment both by natural
(Wiklander, 1969) and by industrial sources (Joensuu, 1971). The disposal of
wastes containing mercury has come under both governmental and scientific con-
cern.
Previous studies indicate that mercury under field conditions moves very
slowly if at all through soils (Poelstra et al., 1973). Furthermore, re-
searchers studying soils to which mercury fungicides have been added also
report that mercury mobility is minimal (Kimura and Miller, 1964). In con-
trast, water soluble organic compounds formed by leaf fall of several tree
species in soil leachate was found to decrease the adsorption of mercury by
soils. These leaf compounds in an extracting solution also increased the
removal of mercury from the soil (Makhonina, 1969). This suggests that
mercury may be mobile or immobile in soils, depending on the characteristics
of the leaching solution.
The objective of this research was to examine the mobility of mercury in
different solution matrices. Mercury was leached through 4 soils in the
following solution matrices: municipal landfill leachate, deionized water,
and a 0.25 mM solution of sodium EDTA in deionized water.
The four soils, Anthony s.l., Davidson c., Fanno c., and Chalmers si.c.
1., were packed in a 5 x 10 cm PVC pipe to a density of 1.8, 1.4, 1.4, and
1.4 g/cm, respectively. The solutions were: (a) municipal landfill leachates
spiked with HgCl2 to give 75 ppm Hg, (b) deionized water with HgCl2 to give
90 ppm Hg, (c) deionized water with 0.25 mM Na2 EDTA plus HgCl2 to give 90
ppm Hg. These high concentrations were used so that differences in mobility
of Hg through the soil would be readily apparent and so that analysis would be
less difficult. These concentrations also minimize the effect of Hg loss by
vaporization (Newton and Ellis, 1974) and compensate for the possible extran-
eous adsorption by container material (Poelstra et al., 1973). All solutions
were adjusted to pH 5 with HC1. The solution of Na2 EDTA was found, with a
Technicon autoanalyzer, to contain approximately 250 ppm COD which was close'
to the 230 ppm COD measured in the municipal landfill leachate. The methods
and procedures for solution displacement and collection used for Hg studies
were similar to those previously described but modified by strategic place-
ment of traps to evaluate losses by volatilization.
The results in Figure 29 illustrate the adsorption of Hg from the three
102
-------
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ro
o
en x
C7>
00
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CXJ
I
cj
O
Z
Z
2
o
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o
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&H
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103
-------
solutions by the Fanno soil. Data for the soils, Davidson and Chalmers, were
very similar to the Fanno soil in spite of the differences in their proper-
ties. This indicates that the leachate composition had a more significant
effect on Hg mobility than did soil properties.
Table 35 shows that Hg in the landfill leachate is more mobile than Hg
in deionized water. This is in agreement with Makhonina (1969) who found
that organic compounds in the leachate reduce the ability of soils to adsorb
mercury. Therefore Hg mobility through soils would be expected to be in-
creased in the presence of landfill leachate. Table 35 also shows that only
in the Anthony soil was Hg in the EDTA solution more mobile than in water
alone. This suggests that EDTA chelation is not the only factor in mercury
movement. It is pertinent to note that there are several other kinds of
compounds and complexes that Hg forms with organic matter (Symposium, 1966),
and any or all of these could play a part in Hg mobility.
The landfill leachate was kept under anaerobic conditions, pH 5, and had
about 230 ppm COD; Hg+2 may have been reduced to the mercurous ion (Hem,
1970) in this solution. The monovalent ion could be more mobile than the
divalent mercuric ion due to the effect on the double layer (King, 1959).
It is evident from this study that soils can adsorb Hg from water more
effectively than from a landfill leachate. Formation of mercurous ions in
the presence of organic matter supports these findings.
Cyanide Reactions in Soil
Cyanide appears in nature and industry in many chemical and biological
combinations and forms: these require some discussion as a basis for under-
standing the work reported in this section. In industrial wastes, "cyanide"
refers to all CN groups in the cyanide compounds present that can be deter-
mined as the cyanide ion, CN~, by the methods used (Taras, 1971). The cyan-
ides are conveniently classified into (a) simple, and (b) complex groups.
The simple forms occur as:
A(CN)x (1)
where A = an alkali (Na, K, NH ) or a metal, x = the valence of A and the
number of CN groups, and CN = is present as CN~.
The complex forms are many and varied but the alkali-metallic cyanides
have the formula:
AyM(CM)Y (2)
A
where A = the alkali present y times, M = a heavy metal (Fe , Fe , Cd, Cu,
Ni, Ag, Zn and others), and x = the number of CN~ groups and is equal to the
valence of A taken y times plus that of the heavy metal.
The anion radicals in the complex cyanides appear as M(CN)X.
104
-------
C/0
1— 1
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105
-------
When the simple cyanides come in contact with acids, HCN forms. The
metal cyanides vary widely in their decomposition to HCN in acid. As a
matter of convenience they may be grouped further, on a basis of rate of
decomposition, as
Readily decomposable - metallic forms of Ag, Au, Cd, Cu, Ni, Pt, and Zn.
Slowly decomposable - Fe, Co.
The stability of alkali-metallic cyanides also varies in aqueous solu-
tion alone. Many remain rather stable in water. Because of the toxicity of
CN~, the formation of the more stable cyanides has been a significant factor
in the activity of biological systems.
Aqueous Wastestreams—
Studies involving cyanides and aqueous wastestreams although only indir-
ectly related to cyanide attenuation in soils, can provide some clues to
chemical and biological reactions which may be expected to occur in soils.
Reactions in most waste waters (landfill leachates, sewage waters, polluted
streams, and waste waters from a wide variety of industrial sources) are more
readily definable in aqueous than soil media. Moreover, toxic limits of CN
to biological systems appear to be more readily identified in the absence of
the highly variable sorption and buffering capacities of soil materials.
The identification and evaluation of CN~ in sewage, leachate, waste and pol-
luted waters appears, from our studies at Arizona to be much more quantitative
than for soils. A number of procedures have been reviewed by Taras (1971),
certain of which are offered as quantitative with the exception of cobalti-
cyanide. Sulfides, heavy-metal ions, fatty acids, oxidizing agents, and
other interfering substances which often respond to removal by distillation,
however, can seriously influence the quantitative evaluation of CN. These
may be expected to be some of the same substances which influence the migra-
tion rate of CN in soils.
Virtually no organic compound is left immune to degradation by the high-
ly versatile microbial population. Cyanide is no exception despite the fact
that it is highly toxic to biological systems as CN~, (Taras, 1971, Ludzack
et al., 1951, and Dodge and Reams, 1949). Simple alkali cyanides and many
alkali-metallic cyanides, which form CN~ in aqueous solution may decompose
slowly to CN", resulting in varying degrees of toxicity. The level of tox-
icity of the more stable cyanides depends on the metal present and the pro-
portion of CN groups converted to simpler alkali cyanides.
The threshold limit of CN" toxicity on biological activity of aqueous
systems also varies widely with such environmental factors as water quality,
temperature, type and size of the organism. Thus, definite effects cannot be
established except in terms of the nature of the effects. For example,
Lockett and Griffiths (1947) report that 5.0 ppm of CN in sewage treated by
the activated process had a marked depression effect on the purification pro-
cess, whereas Ludzack et al. (1951) found inhibition effects as low as 0.3
ppm under certain other conditions. In concentrations of 6% CN, all waters
studied were purified up to 50% or more of the control within 10 days of
incubation.
106
-------
Of particular interest because of its toxicity to the cytochrome system
is the utilization of cyanide by specific microorganisms. Ware and Painter
(1955) isolated an aerobic autotrophic actinomycete from sewage which is cap-
able of growing on silica gel containing only KCN as a source of carbon and
nitrogen. This organism can utilize concentrations of CN up to 15 mgm/100 ml
but grows more favorably at 4 mgm/100 ml concentrations. The rate of utili-
zation in colony culture approached a maximum of 0.5 mgm CN/day. Presumably
the general reaction proceeds as follows:
2KCN + 4H20 + 02 + 2KOH + 2NH3 + 2C02 (3)
Other examples of specific microbial assimilation and transformation of CN in
synthetic media (not soil) are those of Reynolds (1924), Strobel (1964), and
Allen and Strobel (1966). The active organisms all are fungi. Howe and Howe
(1966) have patented a process for biological degradation of CN using the
biological masses of the activated sludge system. They claim to have suc-
cessfully degraded or detoxified more than 570,000 Ibs of CN~ in a period of
a year. The system does not require any specific organism and may be written
as:
Microbial masses + CN" /+r pn NH T Vitamin BIZ in the biomass (4)
( LO,^u4,i^M3; arid degrac|ation of CN
Soils—
Cyanide finds its way into soils primarily through the activity of man,
although it is produced by some fungi (Bach, 1956); at least one bacterium
(Michaels and Copre, 1965); and many higher members of the plant kingdom
(Robinson, 1963). Cyanide also is utilized as an energy source and/or source
of nitrogen by plants and microorganisms (Goldschmidt et al., 1963, Allen and
Strobel, 1966, and Ware and Painter, 1955). In fact, cyanide and related
compounds as cyanamid, dicyanodiamid, and guanidine nitrate have long been
regarded by the agriculturalists as potential nitrogen fertilizers. As early
as 1918 Cowie (1919a) of the Rothamsted Experiment Station, Harpenden,
England, recognized that cyanamid can serve as a valuable fertilizer because
it forms ammonia readily in soils. Nitrate-nitrogen then accumulates
through the usual microbial-ammonia-oxidation channel (Cowie, 1919, McCool,
1945b, and Fuller, et al., 1950a, 1950b).
Fuller, et al. (1950), using a calcareous soil and Volk (1950), using an
acid soil found that cyanamid was inhibitory to ammonia nitrification in soil
at high concentrations. In the calcareous soil, cyanimid was readily con-
verted to nitrate when applied at rates of 100 ppm N. At 200-ppm-N rates,
only about half of the nitrogen was converted to ammonium-nitrogen during
the year, and only small amounts of nitrate-nitrogen were detected. The pH
at the high rate was near 8.0 and above. At pH values of 7 or below the
reaction may be expected to be:
4CaCN2 + 9H20 + 2Ca(OH)2 + (CaOH)2 CN + 3CO(NH2)2 (5)
See Fuller, et al. (1950). The urea may then hydrolyze to yield NH3 + C02.
107
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At pH values between 7 and 8 both reactions may be expected to occur
with the formation of urea exceeding that of dicyanodiamid.
Anaerobic soil conditions presumably cause cyanamide to decompose yield-
ing nitrogen gas.
Pi cyanodiamid--applied to a calcareous soil was found by Fuller et al.
(1950) to yield only small amounts of ammonium-nitrogen over a year's time.
Nitrates do not form in appreciable quantities and were depressed even below
that of untreated soil. Cowie (1919a) also claimed dicyanodiamid gave no
evidence of nitrification in soil over periods of several months. Dicyano-
diamid inhibits oxidation of ammonia, although it may not be toxic to organ-
isms other than the nitrifiers.
Cyanide--(CN") added to soil in modest amounts (up to 200 ppm NaCN)
appears to be readily transformed and/or degraded depending on the oxidation/
reduction conditions. In fact, McCool (1945b) suggests it is only slightly
less effective as an N-fertilizer for tobacco, corn, and mustard than sodium
nitrate when applied to nitrogen-deficient acid soils. Cyanide as KCN15 was
shown by Strobe! (1964) to yield C02 and NH3 in the presence of non-sterile
soils. He further suggests that cyanide is fixed by various soil organisms
in several ways, all of which give rise to some organic nitrile. The nitriles
yield ammonia plus the corresponding organic acid as a result of nitrilase
activity. Many microorganisms of the soil can utilize ammonia and fix the N
in the form of living cells. Strobe!'s (1967) experiments with doubly
labeled CN (C1JtN15) showed that the N of the cyanide was retained more firmly
than the C. Mobility of CN~ through soil according to McCool (1945a) also
appeared to be slower than that of nitrate from sodium nitrate sources.
Despite the fair amount of information on cyanide reactions in natural
and waste systems, a number of critical gaps exist which need filling before
predictions concerning the fate of CN" in the wide variety of habitats in
soil can be made with confidence. Some of the most obvious deficiencies in
information are in the area of anaerobic reactions. Since many of the CN
wastestreams, waste ponds, and leachates end in an anoxic or anaerobic habi-
tat, the initial research program obtained data in this area.
Objectives--
The objectives of this research program were:
1. To study the rate of degradation of simple and complex cyanide under
saturated and unsaturated (60% of field water-holding capacity) soil condi-
tions.
2. To evaluate the influence of different organic energy sources (as
glucose, straw, sawdust, manure) on the rate of degradation or transformation
of CN.
3. To study the mobility of CN, in water and municipal landfill leach-
ate, through soils with distinctly different chemical and physical proper-
ties to statistically relate soil parameters to observed attenuation and
degradation.
108
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Procedures—
Cyanide movement—Three sets of three different soils were packed in a
pvc column 5 cmin diameter and 10 cm long to a specified bulk density.
Each set was connected to one of three constant head devices, each one con-
taining a different cyanide solution. Solution #1 contains potassium cyanide
in water; solution #2 contains potassium cyanide in a landfill leachate, and
solution #3 contains a complex form of cyanide in deionized water alone,
Table .
TABLE 36. CHARACTERISTIC OF CYANIDE SOLUTION
Concentration ofType of ion
Cyanide solution pH of solution cyanide in solution present
ppm
KCN in deionized
water 10.0 97 CN°
K3Fe(CN)6 in
deionized water 8.5 98 Fe(CN)g
KCN in landfill
leachate 7.0 80 Unknown
The flow of the solution is regulated to provide approximately one pore
volume of displacement each day. The displacements were continued until 20
pore-space volumes were collected. The effluent was collected each day in a
125 ml bottle containing 5 ml of dilute sodium hydroxide. The sodium hydrox-
ide (cone. 1 Nj is necessary to keep the cyanide that comes through with the
solution from volatilizing. The solution collected was analyzed for total
cyanide by the Liebig distillation method according to Taras (1971). See
Figure 30. The distillation of the sample is required in order to convert
all the complex cyanide in the sample into simple cyanide ions which can be
easily analyzed by the Liebig titration method using silver nitrate and
Rhodanin indicator if the concentration is above 1 ppm. When the concentra-
tion is below 1 ppm, the Liebig colorimetric method using pyridine-pyrazalene
was used.
Cyanide degradatip n—One soil and one concentration of cyanide was used
for the study of the degradation of cyanide. A total of 80, 250 ml
Erlenmeyer flasks, evenly divided between saturated and unsaturated soil
moisture condition were used. To each flask, 20 g of soil was added. The
samples were broken down into four sets, each set contained 20 flasks, ten
for each moisture tension. Set #1 received 2 g of sucrose. Set #2 received
2 g of straw. Set #3 received 2 g of manure and nothing was added to check
109
-------
S-
to
Q.
QL
(O
O
(/)
*p—
-o
0)
O
ro
a>
110
-------
set #4. To the unsaturated soil samples enough solution of 1 ppm cyanide was
added to bring the soil moisture level to 60% of field capacity. The soil
sample under saturated conditions was completely saturated with the 1 ppm
cyanide solution. The treated soils were thoroughly mixed to provide a
homogeneous medium.
Incubation continued for periods of 1, 2, 3, 5, 10, 15, 20, 30, 45, and
60 days. After each incubation period was completed, 50 ml of KC1 was added
to each sample and its pH brought to 8 or slightly above. The samples then
were shaken for a half an hour followed by filtration through a Buchner
funnel fitted with #40 Whatman paper. The filtrate for each sample was
collected and analyzed for total cyanide by the modified Liebig method in the
same manner as the samples were analyzed for the movement study.
Results—
The relative mobility of the three cyanide solutions is best illustrated
in Mohave (Ca) clay loam and Kalkaska sand (Figure 31). All data are plotted
as pore volume versus C/Cmax, where C/Cmax is the ratio of effluent concen-
tration to influent concentration. KCN and K3Fe(CN)6 in deionized water
were both found to be very mobile in soils, while KCN in landfill leachate
was the least mobile of the three solutions.
The effect of soil type on the movement of the three cyanide solutions
is illustrated in Figure 32, showing the amount of KCN in deionized water
that was leached through four soils, Mohave (Ca), Ava, Nicholson, and
Molokai. The figure indicates KCN leached most rapidly in the soil having
the highest pH and free CaC03 (Mohave (Ca) clay loam). The negative charges
on the clay surface of Mohave (Ca) tends to repel the CN9, causing it to be
leached out more rapidly than in acid soils. The CN9 was retained most by
soils having a high concentration of Mn and hydrous oxides of Fe (Nicholson
silty clay and Molokai clay). Korte et al. (1975) found similar results
working with the anion forms of As, Cr, Se, and V. This conclusion is fur-
ther supported by data from Berg and Thomas (1959). They found Cl9, which
is similar to CN° in its adsorption behavior, attenuated in soils having a
high percentage of kaoljn clay and iron and aluminum oxides. Schofield
(1939) also reports that soils high in these oxides have a high anion
exchange capacity. Kamprath (1956) found good retention of S0i»2~ by an
acidic soil high in oxides and kaolin, whereas the 3-layer minerals appeared
to have poor retention for S0i*2~. The acidic soil (Ava silty clay loam) in
this study proved, on the contrary, to be a poor attenuator of CN6. Texture
seems to have little measurable effect on the attenuation of KCN. Free
iron oxide and CaCOa seem to have a greater influence on the movement of KCN
in water than either soil pH or texture.
Figure 32 illustrates the movement of K3Fe(CN)6 in deionized water
through four soils (Mohave (Ca) clay loam, Ava silty clay loam, Nicholson
silty clay loam and Kalkaska sandy loam). The ferricyanide ion also migrated
most rapidly through soils having a high pH and in the presence of free
CaCOs (Mohave (Ca) clay loam) for the same reason as KCN in water. Ferri -
cyanide moved slowest in soils having a low pH (Ava silty clay loam and
Kalkaska sandy loam). A low pH would indicate the clay surface to have a
high percentage of positive exchange sites which would attract the Fe(CN)63~
111
-------
1.0
.8
.6
.4
.2
x
<
5
o
o 1.0
.8
.6
.4
.2
Kalkaska sand
-• KCN in water
-• K3Fe(CN)6 in water
-* KCN in leachate
I i i i i
Mohave c. I.
• KCN in water
• KjFetCNlgin water
• KCN in leachate
j i i i
4 6 8 10
PORE VOLUME
12
14
Figure 31. Relative mobility of three cyanide solutions (water and
leachate) through Kalkaska sand and Mohave (Ca) clay
loam.
112
-------
1.0
.8
.6
.4
.2
KCN in water
•—MohaveCa c.l.
*—Ava si. c.l.
•—Nicholson c:
•— Molokai c.
j
KCN in Leachate
-*—Ava
Kalkaska
-•—Nicholson
-•—Molokai
i i i i
K3Fe(CN)6 in water
Mohaveca
•—— Nicholson
Kalkaska
*— Ava
i ii i
PORE VOLUME
Figure 32. Effect of soil type on the mobility of KCN in water and leachate
and KoFe(CN)g in water.
113
-------
ion and retain it. Texture seems to play a more important role in this case.
The high clay content soil (Ava silty clay loam) retained more of the Fe(CN63"
than the sandier soil of similar pH (Kalkaska sandy loam). Although iron-
oxide seemed to have some affinity for Fe(CN)e3", its presence was not as
effective as soil pH in governing the movement of this form of cyanide.
Figure 32 portrays KCN in landfill leachates migrating through four
soils (Ava silty clay loam, Kalkaska sandy loam, Nicholson silty clay and
Molokai clay). This solution moved most rapidly through soils with low pH
(Ava silty clay loam and Kalkaska sandy loam). Cyanide was retained most by
soils having a high concentration of iron-oxide. Cyanide in leachate seemed
to behave similarly to KCN in deionized water.
Of the three solutions, KCN in leachate was found to be attenuated the
best. This can be partly explained by the precipitation of Prussian blue
when KCN was added to the leachate (Robine, Lenglen and LeClere, 1906). This
blue precipitate was found permeating the top 4 cm of the soil columns. The
accumulation indicates that Prussian blue may be quite immobile in soils.
The cyanide that came through the soil probably was the CN^ that did not
react with the Fe in solution to form Prussian blue.
The anaerobic state of the soil columns inhibited any microbial degrada-
tion of cyanide. Microorganisms responsible for degrading cyanide under
anaerobic conditions are very sensitive to high cyanide concentration.
Coburn (1949) found 2 ppm in the wastestream to be the limit for effective
anaerobic degradation of cyanide. This concentration is much less than that
passed through the soil columns. The incubation studies, therefore, were
negative with respect to cyanide degradation, i.e. degradation did not occur
in the water-saturated, anaerobic soil samples. Nitrate, nitrite, and
ammonium were not found as a result of possible CN" transformation.
Conclusion-- _ Q
Cyanide as Fe(CN)63~ and CN in water were found to be very mobile in
soils. Cyanide as KCN in natural landfill leachate was found to be less
mobile. Soil properties such as low pH, percent free-iron oxide and kaolin,
chlorite and gibbsite type clay (high positive charges), tended to increase
the mobility of the cyanide forms. Cyanide could possibly contaminate the
groundwater if proper treatments are not used. Such treatment would include,
(a) selection of disposal sites where fine textured (clayey) soils predomi-
nate and are high in natural hydrous oxides of iron, (b) arable soils free
from waterlogging, (c) soils that could be treated with organic residue such
as straw to promote rapid conversion of CN" to less harmful oxides of nitro-
gen as N02~, nitrites, and N03~, nitrates. Obviously inclusion of wastes
possessing strong mineral acids with CN" should never take place. Acids re-
lease free CN gas to the atmosphere.
114
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APPENDIX A
SUPPLEMENTARY SOIL CLASSIFICATION INFORMATION
This appendix contains field descriptions of the soils used in this
study and additional details on soil classification systems that will be
helpful in relating this report to other reports on waste disposal, site
selection, and soils research.
Table Al describes the characteristics, under field conditions, of the
soils used in this study. Also included are the location and depth from
which the soils were collected and their classification in both the present
(1960, 1968) and the old (1938) USDA soil classification systems.
There are three systems under which soils are most likely to have been
classified in the United States: The Unified Soil Classification System, the
old (1938) U.S. Department of Agriculture System, and the present (1960,
1968) U.S. Department of Agriculture System.
The Unified Soil Classification System (USCS) serves engineering uses of
soils and the criteria for soil types in the system are based on the grain
(particle) size and response to physical manipulation at various water con-
tents. Major divisions, soil type symbols, and type descriptions are shown
in Table A2. This is an abbreviated description of the system and does not
include complete information on the use of the manipulation tests (liquid
limit and plasticity index) in the classification.
The U.S. Department of Agriculture System (USDA) serves agricultural
and other land management uses and the criteria for classification in the
system are based on both chemical and physical properties of the soil. The
USDA system in general use between 1938 and 1960 was based on soil genesis—
how soils formed or were thought to have formed. The present USDA compre-
hensive soil classification system is based on quantitatively measurable
properties of soils as they exist in the field. Although the present USDA
system is incomplete and is being continually refined, it is generally
accepted by U.S. soil scientists and its nomenclature is used in most of
the current literature. The present USDA system is described in Table A3
and the approximate equivalents in the 1938 USDA system are listed in Table
A4.
The part of the USDA classification which may be compared most directly
with the soil types in the USCS system is soil texutre (distribution of
grain or particle size) and associated modifiers such as gravelly, mucky,
diatomaceous, and micaceous. The size ranges for the USDA and the USCS
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particle designations (e.g. sand, gravel) are listed in Table A5. Although
a correlation of the USCS and USDA systems is presented in this Appendix, the
two systems are not exactly comparable. In the USDA system, the soil texture
designation (e.g. sandy loam, silty clay) is based only on the amounts of
sand-, silt-, and clay-sized particles in the soil. The diagram used to make
this textural classification of soils in the USDA system is shown in Figure
Al. Texture is only one element used in classification of soils in the USDA
system (See Table A3). In the USCS, the soil type is determined on the basis
of both the amounts of certain sizes of particles in the soil and on the
response of the soil to physical manipulation at various water contents.
A correlation (U.S. Soil Conservation Service, 1971) of the USCS and
USDA systems on the basis of texture is presented in Tables A6 and A7. It
should be emphasized that this correlation is valid only for USDA soil
texture and USCS soil type. It would not be feasible to correlate USCS soil
type with other parts of the USDA system because texture is a high level
(major) criterion in the USCS while texture is a low level (minor) criterion
in the USDA system. A soil of a given texture can be classified into only a
limited number of the 15 USCS soil types while in the USDA system, soils of
the same texture may be found in many of the 10 Orders and 43 Suborders
because of differences in their chemical properties or the climatic areas in
which they are located.
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