ENVIRONMENTAL ENGINEERING SCIENCE
Volume 29, Number 7, 2012
ª Mary Ann Liebert, Inc.
DOI: 10.1089/ees.2011.0211
Kinetics of Permanganate Consumption
by Natural Oxidant Demand in Aquifer Solids
Ki Young Cha,1,* Michelle Crimi,2 Michael A. Urynowicz,3 and Robert C. Borden1
1Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina.
2Institute for a Sustainable Environment, Clarkson University, Potsdam, New York.
3Department of Civil and Architectural Engineering, University of Wyoming, Laramie, Wyoming.
Received: April 26, 2011 Accepted in revised form: August 15, 2011
Abstract
Effectiveness of permanganate (MnO4 ) injection for in situ chemical oxidation is often controlled by the natural
oxidant demand (NOD) of the aquifer solids. In this work, a simple procedure was developed and applied to
generate a database of NOD kinetic parameters for six different models for 50 different aquifer materials.
Representing oxidant consumption as an initial instantaneous reaction with a portion of the total NOD and as a
second order reaction between MnO4 and the remainder of the NOD provided a good match with experimental
results from batch studies, without imposing an unnecessary computational burden. Wide variations in NOD
parameters were observed including total NOD, fraction fast/instantaneous, and second order rate coefficients.
Approximately 80% of the samples had a total NOD between 0.002 and 0.158 mmol/g with a median value of
0.028 mmol/g. Most of the NOD present was slow reacting, so MnO4 could persist for weeks to months once
the fast reacting fraction is depleted. Total NOD was not correlated with fraction fast/instantaneous or the
reaction rate coefficients, thus indicating that NOD reactivity is independent of the total amount of NOD. Results
from 48-h NOD measurements were also shown to be poor predictors of total NOD and should not be used to
estimate long-term MnO4 consumption.
Key words: in situ chemical oxidation; ISCO; kinetics; models; natural oxidant demand; NOD; permanganate
Introduction acteristics. When NOD is high, then contaminant treatment
efficiency may be less than desired due to nonproductive
In situ chemical oxidation (ISCO) using permanganate consumption of MnO
4 (Siegrist et al., 2001; Urynowicz, 2008).
(MnO4 ) can be very effective for treatment of chlorinated The reaction between MnO
4 and NOD will depend on
ethenes including perchloroethene, trichloroethene, and some MnO4 concentration and reaction time, as some fraction of
aromatic hydrocarbons (naphthalene, phenanthrene, pyrene, the NOD may react slowly. Mumford et al. (2005) found that
and phenols) (Yan and Schwartz, 1999; Siegrist et al., 2001). NOD exerted during short-term column experiments was
When MnO4 is injected into the subsurface, MnO
4 accepts much lower than that in long-term batch incubations. Even
three electrons and is subsequently reduced to MnO2 solids after 21 weeks incubation, the NOD in the soil was not com-
(Siegrist et al., 2001). A portion of the electrons consumed are pletely oxidized and MnO4 continued to be depleted. Several
from oxidation of the target contaminant, and a portion are investigators (Yan and Schwartz, 1999; Siegrist et al., 2001;
from the aquifer material including natural organic matter Mumford et al., 2005; Urynowicz et al., 2008) have reported
(NOM), reduced iron, manganese, and sulfur minerals (Yan that the measured NOD varies with the MnO4 concentration
and Schwartz, 1999; Siegrist et al., 2001; Mumford et al., 2005; and reaction time. Higher concentrations of MnO4 com-
Hønning et al., 2007; Urynowicz, 2008; Urynowicz et al., 2008). monly result in higher measured values of NOD (Siegrist,
The amount of MnO4 that reacts with nontarget chemicals is 2001; Hønning et al., 2007; Urynowicz et al., 2008). To reflect
often referred to as natural oxidant demand (NOD) and can these observations, MnO4 consumption is commonly mod-
vary from < 1% to over 99% of the total MnO4 demand, de- eled as a second order reaction between MnO
4 and NOD
pending on the contaminant concentration and aquifer char- (Zhang and Schwartz, 2000; Urynowicz et al., 2008; Xu and
Thomson, 2009).
More recent work has shown that NOD is composed of
*Corresponding author: Department of Civil, Construction, and
several components or fractions with varying reactivity
Environmental Engineering, North Carolina State University, Cam-
pus Box 7908, Raleigh, NC 27695. Phone: 919-515-7207; Fax: 919-515- (Mumford et al., 2005; Hønning et al., 2007; Urynowicz et al.,
7908; E-mail: kycha@ncsu.edu 2008; Xu and Thomson, 2009). Ideally, NOD would be
646
KINETICS OF MNO4 BY NOD 647
represented with a continuum of reaction rates where the less radation, where the slow NOD reaction rate varied from 0.014
reactive fraction becomes progressively more important as the to 0.72 L/mmol-day with a median value of 0.077 L/mmol-
more reactive NOD fraction is depleted. However, studies by day, in batch experiments.
Urynowicz et al. (2008) and Xu and Thomson (2009) suggest
that MnO4 consumption by NOD can be reasonably well Objective and Approach
described by assuming that NOD is composed of disparate
fast and slow fractions. In batch experiments conducted by The performance of ISCO systems can potentially be im-
Urynowicz et al. (2008), the fast NOD appeared to be con- proved by using numerical models to simulate ISCO with
sumed within about 48 h, followed by a slower depletion of MnO4 under realistic conditions using spatially heteroge-
MnO4 at rates of 0.024 to 0.13 day
- 1, depending on MnO4 neous, three-dimensional flow, transport, and chemical reac-
dose. Xu and Thomson (2009) found that a fraction of NOD tion models. However, simulation of ISCO with MnO4
was depleted in a few hours followed by much slower deg- requires constitutive relationships that accurately represent
Table 1. Natural Oxidant Demand and MnO -4 Kinetic Relationships for All Six Models
Model Equation Description
d(MnO
1 4
) ¼ k0 Zero-order loss of MnO4
dt
d(MnO )
2 4 ¼ k1M (MnO ) First-order loss of MnO4
dt 4
d(NOD)
3 ¼ k1N (NOD) First-order loss of NOD
dt
d(MnO4 )¼ qBk1N(NOD)=ndt
d(MnO
4 4
)¼ qBk2(NOD)(MnO4 )=n Second-order loss of MnOdt 4and NOD
d(NOD)¼ k (NOD)(MnO2 )
dt 4
d(MnO4 )5 ¼ qBk2F(NODF)(MnO4 )=n Second-order loss of MnO4 with fastdt
and slow NOD qBk2S(NODS)(MnO4 )=n
d(NODF)¼ k2F(NOD F)(MnO4 )dt
d(NODS)¼ k 2S(NODS)(MnO )
dt 4
6 When concentration of MnO4 > (NODI)qB=n Second-order loss of MnO
with
MnO ¼MnO
4
4 4 qB(NODI)=n and NODI ¼ 0 instantaneous and slow NOD
otherwise
MnO4 ¼ 0 and NODI ¼NODI (MnO4 )n=qB
Then
d(MnO4 )¼ qBk2S(NODS)(MnOdt 4 )=n
d(NODS)¼ k2S(NODS)(MnO4 )dt
t, Time (day).
N, Porosity (liter of water per liter of reactor).
qB, Aquifer solids bulk density (kg dry soil per liter of reactor).
MnO 4 , MnO4 concentration (mmol/L).
NOD, Total NOD concentration (mmol/kg).
NODF, Fast NOD concentration (mmol/kg).
NODS, Slow NOD concentration (mmol/kg).
NODI, Instantaneous NOD concentration (mmol/kg).
k0, Zero-order MnO
4 consumption rate (mmol/L-day).
k1M, First-order MnO
4 consumption rate (day
- 1).
k1N, First-order NOD consumption rate (day
- 1).
k2, Second-order NOD consumption rate (L/mmol-day).
k2F, Second-order Fast NOD consumption rate (L/mmol-day).
k2S, Second-order Slow NOD consumption rate (L/mmol-day).
NOD, natural oxidant demand.
648 CHA ET AL.
the kinetics of MnO4 consumption by NOD for a range of aquifer solid. The Solver function in MS Excel was used to
conditions. Ideally, these kinetic models could be applied search for the parameter set that minimized the RMSE using a
without imposing an excessive computational burden, using quasi-Newton search method. From this procedure, a single
parameters estimated from standard NOD test protocols. set of NOD parameters were generated for each aquifer solid
In the work presented here, six different kinetic models were that minimized the RMSE in predicted MnO4 concentration
fit to data from batch NOD tests on 50 different aquifer samples for all experimental conditions. Additional details on the
with varying MnO4 concentrations and/or aquifer solid to
water ratios following ASTM D 7262-07 (2007). The six models
evaluated are summarized in Table 1. Subroutines were de-
veloped within MS Excel for each model and applied to auto-
matically estimate model parameters for each aquifer sample.
Results of this work were used to identify the models that best
fit the experimental results and to generate a database of pa-
rameters which can be used in future modeling studies.
The models examined in this work included simple zero-
and first-order single parameter models (1, 2, and 3) and
second-order models incorporating the effects of oxidant
concentration and NOD concentration (4, 5, and 6). For
Models 5 and 6, NOD was assumed to be composed of slowly
reacting and fast/instantaneously reacting components. This
work aimed at developing relatively simple models that could
accurately represent reaction kinetics without imposing an
excessive computational burden and, therefore, only terms
that could be estimated from the standard ASTM test protocol
were used in the simulations.
NOD measurement and parameter estimation protocol
A total of 50 samples of aquifer solids were analyzed from
twelve different facilities located throughout the United States
(Supplementary Table S1), where ISCO with MnO4 was be-
ing considered a possible remedial alternative. NOD was
measured following ASTM D 7262-07 (2007) by reacting air-
dried aquifer solids in glass reactor bottles with analytical
grade KMnO4 (Carus Corporation, Peru, IL) and deionized
water. However, the reaction period was extended to better
characterize the longer-term consumption of MnO4 by
slowly reacting NOD. Samples were stored at room temper-
ature and mixed once per day by inverting the bottles. MnO4
concentrations were monitored for up to 41 days by mea-
suring absorbance at 525 nm (Crimi and Siegrist, 2005). Ty-
pically, NOD tests were run with low (200–600 mg/L),
medium (1000–3000 mg/L), and high (4500–10,000 mg/L)
KMnO4 concentrations. The aquifer solid to water ratio was
varied, thus resulting in KMnO4 doses ranging from 5.8 to 61
mmol/kg. The experimental conditions for each sample are
listed in Supplementary Table S2. Shorter test periods and/or
higher doses were used when MnO4 was rapidly consumed.
For each of the six kinetic models, an MS Excel Visual Basic
for Application subroutine was developed by using a fourth-
order Runge-Kutta solution of the ordinary differential
equations. The computation time step was set to 5% of the
experimental time step to minimize computational errors. For
each aquifer solid, a single value for each NOD parameter was
assumed, and the model was used to estimate the change in
MnO4 concentration with time for every experimental
treatment. The model error was defined as the difference be-
FIG. 1. Comparison of simulated and observed MnO
tween simulated and measured MnO4 concentration. The
4
concentrations for aquifer sample YT-YS2 with (a) 30.1
global root-mean-square error (RMSE) was calculated by mmol/L initial MnO4 concentration and 0.38 g solids per
using the error at every time point for every experimental mL water; (b) 24.5 mmol/L MnO4 and 0.38 g solids/mL;
treatment (varying aquifer solid:water ratios and varying and (c) 5.9 mmol/L MnO4 and 0.95 g solids/mL. MnO
4 ,
aqueous MnO4 concentrations) and replicates for each permanganate.
KINETICS OF MNO4 BY NOD 649
FIG. 2. Simulated versus observed DMnO4 for all treatments of YT-YS2: (a) Model 1, (b) Model 2, (c) Model 3, (d) Model 4,
(e) Model 5, and (f) Model 6. Solid line represents a perfect match between simulated and observed DMnO4 .
650 CHA ET AL.
numerical solution techniques are provided in Borden et al.
(2010).
Model evaluation
Each of the six models were fit to the NOD experimental
results for each of the 50 aquifer solids. Figure 1 shows typical
results for a single aquifer solid (YT-YS2). Aquifer sample YT-
YS2 was tested in duplicate (Experiment A and Experiment B)
with three experimental treatments (MnO4 ¼ 30:1, 24.5, or 5.9
mM; solid:water ratio= 0.38 or 0.95 g aquifer solids per mL
water) over 22 days, thereby resulting in a total of 78 con-
centration measurements. Over the 22-day experimental pe-
riod, MnO4 concentration declined by up to 9 mM as the
oxidant was consumed by NOD. The difference in MnO4
consumption between the three different treatments is pre-
sumably due to differences in initial MnO4 concentration and
aquifer solids to water ratio.
The lines shown in Fig. 1 are simulation results generated
by using the single set of coefficients for each model. The
model coefficients were determined by (a) calculating the er-
ror between simulated and observed concentration; (b) cal-
culating the RMSE for the entire YT-YS2 data set; and (c)
searching for the model coefficients that minimized the
RMSE. Models 1 and 2 do not follow the overall trends in the
data and provide a poor match to the experimental results for
all three treatments. Model 3 provides a reasonably good fit to
the two higher concentration experiments (Fig. 1a, b), but
significantly underestimates the MnO4 concentrations dur-
ing much of the low concentration experiment (Fig. 1c). In
contrast, Models 4, 5, and 6 provide a reasonably good match
with the general trends in MnO4 concentration versus time
for all three treatments.
Data from Fig. 1 are replotted in Fig. 2 to better illustrate
FIG. 3. Cumulative frequency distributions of (a) mean
the performance of the different models. For every experi-
absolute error (MAE) and (b) root-mean-square error
mental measurement, the change in MnO4 concentration
(DMnO
(RMSE) for Models 1–6 using NOD data from 50 aquifer
4 ) was computed as the difference between the ini- solids. NOD, natural oxidant demand.
tial concentration and the measured concentration and
compared with the simulated DMnO4 for each model. Fig-
ure 2 shows a comparison of simulated and experimental Model 4 (2 parameters) provide relatively good fits (low MAE
DMnO for all 78 measurements made with the YT-YS2 and RMSE) to the experimental data for more than 50% of the4
aquifer solids. Ideally, all the data would plot along the solid aquifer samples. However, for the worst 10%–20% of the
45-degree line, thus indicating a perfect match between samples (highest MAE and RMSE), the performance of
simulated and observed values. For Models 1 and 2 (Fig. 2a, Models 5 and 6 is substantially better than that of Model 4.
b), the data points show clear trends above and below the 1:1 To provide an overall estimate of model performance, the
line, thus indicating a very poor match with the experi- data on model fit to all 50 aquifer solids were pooled together
mental results. For Model 3 (Fig. 2c), the data generally plot (2894 MnO4 measurements) and used to compute the global
along the 1:1 line, thus indicating a relatively good match to
the data. However, there are areas where groups of points
are clustered above or below the 1:1 line, thus indicating Table 2. Mean Absolute Error
some underlying trend that is not captured by the model. In and Root-Mean-Square Error for Models 1–6
Using Pooled Data from 50 Aquifer Solids
contrast, the data points for Models 4, 5, and 6 plot along the
45-degree line with no obvious trends in DMnO4 . Model 5 # of MAE RMSE
with four calibration parameters provides the lowest mean Model parameters (mmol/L) (mmol/L)
absolute error (MAE) and RMSE, indicating that it provided
the best fit to the YT-YS2 experimental results. However, 1 1 1.31 2.40
Model 6, with three calibration parameters, provided a rel- 2 1 1.36 2.38
atively good fit to the experimental data with both MAE and 3 2 0.81 1.46
< 4 2 0.70 1.22RMSE 5% of the maximum DMnO4 . 5 4 0.46 0.80
Figure 3 shows cumulative frequency distributions for the 6 3 0.58 0.98
MAE and RMSE for each model for all 50 aquifer solid sam-
ples. Model 5 (4 parameters), Model 6 (3 parameters), and MAE, mean absolute error; RMSE, root-mean-square error.
KINETICS OF MNO4 BY NOD 651
FIG. 4. Cumulative frequency distributions for Models 5 and 6: kinetic parameters generated using NOD data from 50
aquifer solids.
MAE and RMSE (Table 2). Consistent with the previous dis- from the 50 aquifer solids, Model 5 is superior to Model 6.
cussion, Model 5 provided the best fit to the data (lowest MAE However for many individual aquifer solid samples, the dif-
and RMSE), followed by Model 6, then Model 4, then Model 3. ference is not significant.
Models 1 and 2 provided a poor fit to the experimental results.
A Kruskal–Wallis nonparametric test was conducted to
NOD parameter estimates
evaluate whether the performance of Model 6 was signifi-
cantly different from that of Model 5 based on the distribution Parameter estimates and error statistics for Models 1 to 6
of absolute error (Ott and Longnecker, 2001). Using the for the 50 aquifer solids are provided in Supplementary
pooled data from all 50 samples, the difference in the per- Tables S3 and S4. For Models 5 and 6, we report total NOD
formance of Model 5 and 6 was significant at the 0.001 level. (NODT) and the fraction fast or instantaneous (f) to simplify
However, when applied to individual aquifer solids, there comparison between the different models and to be consis-
was no significant difference between Models 5 and 6 for 39 of tent with previous studies. NODS =NODT (1 - f) and NODF
the 50 samples (78%). This indicates that for the total data set and NODI =NODT · f. In most cases, parameter values were
652 CHA ET AL.
Table 3. Statistical Characteristics of Kinetic Parameter Distributions for Models 1–6
Generated Using Natural Oxidant Demand Data from 50 Aquifer Solids
Percentile
Model Parameter Unit 10% 25% 50% 75% 90%
1 k0 mmol/L-day 0.055 0.074 0.521 1.170 2.746
2 k1M 1/day 0.010 0.015 0.035 0.077 0.290
3 NODT mmol/g 0.001 0.002 0.027 0.069 0.158
k1N 1/day 0.083 0.131 0.275 0.592 2.306
4 NODT mmol/g 0.001 0.002 0.027 0.069 0.158
k2 L/mmol-day 0.009 0.014 0.030 0.062 0.202
5 NODT mmol/g 0.002 0.002 0.037 0.114 0.172
Fraction instantaneous – 0.087 0.181 0.248 0.424 0.553
k2F L/mmol-day 0.053 0.143 0.944 6.329 8.245
K2S L/mmol-day 0.001 0.002 0.006 0.017 0.028
6 NODT mmol/g 0.002 0.002 0.028 0.070 0.158
Fraction instantaneous – 0.028 0.046 0.126 0.208 0.361
k2S L/mmol-day 0.003 0.008 0.018 0.036 0.395
not normally or log normally distributed at the 0.05 level measurements. Once injected, the MnO4 concentration will
based on the Shapiro–Wilk test (Shapiro and Wilk, 1965). To vary in time and space due to advection, dispersion, and
provide a better representation of the parameter distributions, consumption by the contaminant and NOD. Previous studies
cumulative frequency plots for each of the Model 5 and 6 pa- have shown that MnO4 consumption varies with time, that
rameters are shown in Fig. 4. The 10, 25, 50, 75, and 90 percentile higher MnO4 concentrations often result in greater demand,
values for every model parameter are summarized in Table 3. and that NOD may be composed of several components or
Previous studies have reported large variations in the total fractions with varying reactivity (Siegrist, 2001; Mumford et
amount of NOD. However, there is much less information on al., 2005; Hønning et al., 2007; Urynowicz et al., 2008; Xu and
the fraction of fast/instantaneous NOD and reaction rate co- Thomson, 2009). Given this previous work, there is no simple
efficients. In the 50 samples examined in this study, large way to estimate the required injection concentration and
variations in each of the parameters were observed, thus re- volume from a few simple measurements. To reduce reme-
flecting the wide variations in both the amount and reactivity diation system costs and improve performance, a simple,
of NOD in natural aquifer solids. Total NOD varied from easy-to-implement approach is needed for estimating MnO4
0.0013 to 0.95 mmol/g (equivalent to 0.2 to 150 g KMnO4/kg), distribution and longevity.
similar to previously reported ranges (Siegrist et al., 2001; In this work, a simple procedure was developed and
Mumford et al., 2005; Hønning et al., 2007; Urynowicz, 2008; applied to estimate NOD kinetic parameters for six dif-
Xu and Thomson, 2009). The fraction fast/instantaneous ferent models for 50 different samples of aquifer material.
varied from 1% to 67% with a median of 13%. The search Consistent with previous work (Urynowicz, 2008; Xu and
procedure had a constraint that fraction fast/instantaneous
‡ 1%. However, this constraint only influenced one out of the
50 samples. For Model 5, fast reaction rates varied from 0.02 to
over 10 L/mmol-day, whereas the slow reaction rates were
typically 1 to 2 orders of magnitude lower. Model 6 reaction
rates were typically intermediate between the Model 5 fast
and slow rates.
Some practitioners estimate the aquifer NOD based on the
amount of MnO4 consumed over 48 h as a screening test for
potential ISCO sites (ASTM D 7262-07). Figure 5 shows the
ratio of the best fit value of total NOD for Models 5 and 6 to the
48-h value. This ratio varied widely, thus indicating that there
is a very poor correlation between the best fit values of Total
NOD to the 48-h NOD. This indicates that 48-h test results
should not be used to estimate the amount of MnO4 that may
be consumed during extended incubation periods and should
not be used to simulate MnO4 consumption in transport
models.
Discussion
FIG. 5. Cumulative frequency distributions showing ratio
Some remediation practitioners estimate the amount of of best fit total NOD to 48-h NOD for Models 5 and 6 using
MnO4 required for ISCO based on a few short-term NOD NOD data from 50 aquifer solids.
KINETICS OF MNO4 BY NOD 653
Thomson, 2009), simulation of MnO4 consumption as a Acknowledgment
second-order reaction with separate fast and slow NOD
Financial support for K.Y. Cha was provided by the En-
fractions provided the best fit to the experimental data. For
vironmental Security Technology Certification Program
the 50 samples examined in this study, the median half life
(ESTCP) under project ER-0625.
(T½) for NODF was 0.02 days (range = 10 - 4 to 2 days), and
median T½ for NODS was 9.4 days (range = 10 - 2 to 84 Author Disclosure Statement
days). These results indicate that (a) the fast NOD fraction
will be rapidly consumed; and (b) once the fast fraction is No competing financial interests exist.
depleted, remaining MnO4 may persist for weeks to References
months, diffusing into lower permeability zones where
contaminants may reside. However, accurate simulation of ASTM D 7262-07. (2007). Standard Test Method for Estimating the
MnO4 consumption by the fast fraction (Model 5) will
Permanganate Natural Oxidant Demand of Soil and Aquifer Solids.
require very short time steps with a proportionate increase West Conshohocken, PA: ASTM International, 04.09.
in computer run times. In contrast, if the fast fraction is Borden, R.C., Cha, K.Y., Simpkin, T., and Lieberman, M.T.
(2010). Technical Report—Design Tool for Planning Permanganate
assumed to react instantaneously (Model 6), then the
Injection Systems. Environmental Science and Technology
computational time step will be controlled by NODS (me- Certification Program, Washington, DC.
dian T½ for NODS = 0.3 day, range 0.01 to 64 days), re- Crimi, M.L., and Siegrist, R.L. (2005). Factors affecting effec-
sulting in an order of magnitude reduction in run times. tiveness and efficiency of DNAPL destruction using potas-
Given the good performance of Model 6 in representing sium permanganate and catalyzed hydrogen peroxide. J.
NOD kinetics in batch studies and the long residence time
Environ. Eng. ASCE. 131, 1724.of MnO4 in aquifers, use of Model 6 should allow accurate Hønning, J., Broholm, M.M., and Bjerg, P.L. (2007). Quantifica-
simulation of ISCO without imposing an unnecessary tion of potassium permanganate consumption and PCE oxi-
computational burden. dation in subsurface materials. J. Contam. Hydrol. 90, 221.
Wide variations in NOD parameters were observed in- Mumford, K.G., Thomson, N.R., and Allen-King, R.M. (2005).
cluding total NOD, fraction fast/instantaneous, and second- Bench-scale investigation of permanganate natural oxidant
order rate coefficients. In about one third of 50 samples, total demand kinetics. Environ. Sci. Technol. 39, 2835.
NOD was greater than 0.06 mmol/g (10 g KMnO4/kg soil) Ott, R.L., and Longnecker, M. (2001). Introduction to Statistical Meth-
and, therefore, ISCO with MnO may not be practical due to ods and Data Analysis, 5th edition. Belmont, CA: Duxbury Press.4
the large amounts of oxidant required. The fraction fast/in- Shapiro, S.S., and Wilk, M.B. (1965). An analysis of variance test
stantaneous (f) typically varied between 0.05 and 0.2, thus for normality (Complete Samples). Biometrika 52, 591.
indicating most of the NOD exhibited behavior characteristic Siegrist, R.L., Urynowicz, M.A., West, O.R., Crimi, M., and
of the slow fraction. In general, these parameters were not Lowe, K.S. (2001). Principles and Practices of In Situ Chemical
normally or log normally distributed. Total NOD was not Oxidation Using Permanganate. Columbus, OH: Battelle Press.
correlated (r2 < 0.2) with the fraction of fast/instantaneous Urynowicz, M.A. (2008). In situ chemical oxidation with per-
NOD or the reaction rate coefficients for both Models 5 and 6, manganate: assessing the competitive interactions between
target and nontarget compounds. Soil Sediment Contam. 17, 53.
thus indicating that the total amount of NOD is not a good
Urynowicz, M.A., Balu, B., and Udayasankar, U. (2008). Kinetics
predictor of reactivity.
of natural oxidant demand by permanganate in aquifer solids.
Given the large variations observed and poor correlation
J. Contam. Hydrol. 96, 187.
between these parameters, the kinetic model parameters Xu, X., and Thomson, N.R. (2009). A long-term bench-scale in-
should be measured for each site and/or aquifer material to
vestigation of permanganate consumption by aquifer materi-be treated with MnO4 . The batch NOD tests should be run for
als. J. Contam. Hydrol. 110, 73.the range of MnO4 concentrations and durations expected to Yan, Y.E., and Schwartz, F.W. (1999). Oxidative degradation and
occur in the field. NOD kinetic parameters can be estimated kinetics of chlorinated ethylenes by potassium permanganate.
by following the procedures just described using the CDISCO J. Contam. Hydrol. 37, 343.
(Conceptual Design of ISCO) tool (Borden et al., 2010; Zhang, H., and Schwartz, F.W. (2000). Simulating the in situ
download at http://serdp-estcp.org) and then used to design oxidative treatment of chlorinated ethylenes by potassium
ISCO systems using MnO4 . permanganate. Water Resour. Res. 36, 3031.