www.epa.gov/aaa
United States
Environmental Protection
Agency
A Guide for Assessing
Biodegradation and Source
Identification of Organic Ground
Water Contaminants using
Compound Specific Isotope
Analysis (CSIA)
Office of Research and Development
National Risk Management Research Laboratory, Ada, Oklahoma 74820
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A Guide for Assessing
Biodegradation and Source
Identification of Organic Ground
Water Contaminants using
Compound Specific Isotope
Analysis (CSIA)
Daniel Hunkeler
University of Neuchatel, Center of Hydrogeology,
Neuchatel, Switzerland
Rainer U. Meckenstock
Institute of Groundwater Ecology, Neuherberg, Germany
Barbara Sherwood Lollar
University of Toronto, Ontario, Canada
Torsten C. Schmidt
University of Duisburg-Essen, Duisburg, Germany
John T. Wilson
National Risk Management Research Laboratory, U.S.
Environmental Protection Agency, Ada, Oklahoma, USA
Office of Research and Development
National Risk Management Research Laboratory, Ada, Oklahoma 74820
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Notice
The International Atomic Energy Agency, and the U.S. Environmental Protection Agency through
its Office of Research and Development, funded and managed the development of this Guide. It
has been subjected to U.S. Environmental Protection Agency peer and administrative review and
has been approved for publication as an EPA document. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
All research projects making conclusions or recommendations based on environmental data
and funded by the U.S. Environmental Protection Agency are required to participate in the Agency
Quality Assurance Program. This project did not involve the collection and use of environmental
data and, as such, did not require a Quality Assurance Plan.
Front Cover photos:
#1 an isotope ratio mass spectrometer
#2 purging a well to provide a sample of ground water for analysis
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Foreword
The U.S. Environmental Protection Agency is charged by Congress with protecting the Nation's land, air, and
water resources. Under a mandate of national environmental laws, the Agency strives to formulate and implement
actions leading to a compatible balance between human activities and the ability of natural systems to support
and nurture life. To meet this mandate, EPA's research program is providing data and technical support for
solving environmental problems today and building a science knowledge base necessary to manage our ecological
resources wisely, understand how pollutants affect our health, and prevent or reduce environmental risks in the
future.
The National Risk Management Research Laboratory is the Agency's center for investigation of technological
and management approaches for preventing and reducing risks from pollution that threatens human health and
the environment. The focus of the Laboratory's research program is on methods and their cost-effectiveness for
prevention and control of pollution to air, land, water, and subsurface resources; protection of water quality in
public water systems; remediation of contaminated sites, sediments and ground water; prevention and control
of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and private
sector partners to foster technologies that reduce the cost of compliance and to anticipate emerging problems.
NRMRL's research provides solutions to environmental problems by: developing and promoting technologies
that protect and improve the environment; advancing scientific and engineering information to support regulatory
and policy decisions; and providing the technical support and information transfer to ensure implementation of
environmental regulations and strategies at the national, state, and community levels.
As part of the U.S. EPA Quality System for Environmental Data and Technology, U.S. EPA requires that a
Quality Assurance Project Plan (QAPP) be developed and approved before environmental samples are collected
and analyzed. Compound Specific Isotope Analysis (CSIA) has only recently been applied to understand the
degradation of organic compounds, or to identify the sources of ground water contamination at hazardous waste
sites. As a result, there is little information available that can be used to develop, review, or approve a QAPP
for the application of CSIA at a hazardous waste site. This Guide provides general recommendations on good
practice for sampling ground water for CSIA, and quality assurance recommendations for measurement of isotope
ratios. The Guide also provides recommendations for data evaluation and interpretation to use CSIA to document
degradation of organic contaminants, or to associate plumes of contaminants in ground water with their sources.
Robert W. Puls, Acting Director
Ground Water and Ecosystems Restoration Division
National Risk Management Research Laboratory
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Contents
Notice ii
Foreword iii
Contents v
Acronyms ix
Acknowledgements x
Executive Summary xi
1.0 Introduction 1
2.0 Data Quality Issues 4
2.1. CSIA Principles 4
2.2. Nomenclature and International Standards 5
2.3. Laboratory Working Standards 5
2.3.1. CO2 Reference Gas 5
2.3.2. Compound Specific Working Standards 5
2.4. Method Testing, Quality Assurance and Quality Control 6
2.4.1 Reproducibility 7
2.4.2. Total Uncertainty and the Critical Role of Linearity 7
2.4.3. Establishing Concentration Thresholds or "Detection limits" 8
2.4.4. Application of These Principles to Other CSIA Measurements 9
2.4.5. Extraction Methods for CSIA 9
2.5. Avoiding Some Pitfalls in CSIA Measurements 15
2.6. Recommended Routine for Daily Laboratory Quality Assurance/Quality Control (QA/QC)
for Carbon Isotope Analysis 15
3.0 Collection, Preservation and Storage of Samples 16
3.1. Collection of Ground Water from Monitoring Wells 16
3.2. Need to Replicate Samples 17
3.3. Requirement for Sample Preservation 18
3.4. Performance of HC1 and TSP as Preservatives During Storage of Samples 19
3.5. Avoid Isotopically Labelled Surrogate Compounds and Internal Standards 20
4.0 Interpretation of Stable Isotope Data from Field Sites 21
4.1. Prerequisites for Application of Isotope Data to Demonstrate and Quantify Biodegradation. ... 21
4.1.1. Does Biodegradation of the Compound Produce Isotope Fractionation? 22
4.1.2. Is the Observed Extent of Fractionation Significant? 22
4.1.3. Is the Observed Fractionation Reproducibly and Accurately Correlated to a
Distinct Process? 23
4.1.4. Do Non-Degradative Processes Influence the Observed Isotope Fractionation? 24
4.1.5. Do Abiotic Degradation Processes Occur and Produce Isotope Effects for the
Compound of Interest? 25
4.1.6. Is the Rayleigh Equation an Appropriate Model to Describe the Data Set? 25
4.2. Recommended Steps for the Quantification of Biodegradation Based on CSIA 26
4.2.1. Site Characterization 26
4.2.2. Evaluate Field Data for the Fit to the Rayleigh Model 27
4.2.3. Determination of the Primary Isotope Signature (513C or 52H ) 27
J * ° v source source7
4.2.3.1. Value of 513C or 52H Based on Literature 27
source source
4.2.3.2. Values of 513C or 52H Based on Most Negative Value at the Site 27
source source °
4.2.3.3. Values of 513C or 52H Based on Point to Point or Time to
source source
Time Comparisons 27
4.2.3.4. Selection of an Appropriate Enrichment Factor 28
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4.2.3.5. Estimating an Enrichment Factor when none is Available 29
4.2.3.6. Concurrent Application of CSIA Analysis for Different Elements
(Two-Dimensional Analysis) 30
4.3. Conversion of Calculated Extent of Biodegradation (1-f) to Biodegradation Rates 32
4.4. Using Estimates of Rates of Biodegradation to Predict Plume Behaviour 32
4.5. Effect of Heterogeneity in Biodegradation in the Aquifer on Stable Isotope Ratios 36
4.6. Recommended Practices to Minimize the Confounding Effects of Heterogeneity 37
5.0 Strategies for Field Investigations 38
5.1. Design of Stable Isotope Fractionation Studies 38
5.2. Temporal Design 38
5.3. Spatial Sampling Design 38
6.0 Use of Stable Isotopes for Source Differentiation 41
6.1. Variability of Isotope Ratios of Different Sources 41
6.2. Contaminated Sites Scenarios 41
6.3. Evaluating the Relevance of Biodegradation 42
6.4. Designing a Sampling Strategy to Distinguish Sources 43
6.5. Data Evaluation to Distinguish Sources 45
6.6. A Case Study of Source Differentiation 45
7.0 Derivation of Equations to Describe Isotope Fractionation 47
7.1. Expressing and Quantifying Isotope Fractionation 47
7.2. The Rayleigh Equation 47
7.3. Quantification of Isotope Fractionation in Laboratory Studies 49
7.4. Equations to Evaluate Field Isotope Data 49
8.0 Stable Isotope Enrichment Factors 51
9.0 Recommendations for the Application of CSIA 58
10.0 References 59
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Figures
Figure 2.1. Schematic of the GC-IRMS and general procedure used in compound specific
isotope analysis of carbon 4
Figure 2.2. Example of a chromatogram obtained in GC-IRMS 6
Figure 2.3. An illustration the difference between precision (reproducibility) and accuracy of
several data points, using the bull's eye of a target as the goal for high accuracy and
good precision 7
Figure 2.4. Typical linearity test of a laboratory working standard run by CSIA over a wide
range of different peak sizes (or signal sizes) by varying the amount of analyte
introduced 8
Figure 2.5. Example of the evaluation of method detection limits (MDLs) in CSIA 9
Figure 3.1. Effect of the extent of purging and vertical heterogeneity on concentrations of
contaminants sampled by a monitoring well 17
Figure 4.1. Degradation of (A) TCE and (B) benzene by enrichment cultures 23
Figure 4.2. Testing field data on CSIA and concentrations of contaminants for fit to the Rayleigh
equation 26
Figure 4.3. Relative influence of different values for 513C (Panel A) and different values
•—' source v '
for the isotopic enrichment factor e (Panel B) on the calculated extent of toluene
biodegradation 29
Figure 4.4. Concurrent analysis of 513C in MTBE and 52H in MTBE in ground water to associate
natural biodegradation of MTBE in ground water with an anaerobic process, which
allows the selection of an appropriate value for the enrichment factor (e) to be used to
estimate the extent of biodegradation of MTBE 31
Figure 4.5. Concentration of MTBE in selected monitoring wells at a gasoline spill site in
Dana Point, California, USA in 2004 33
Figure 4.6. Hypothetical illustration of a heterogeneous plume, where a monitoring well that
produces ground water from some flow paths where biodegradation of an organic
contaminant is rapid and extensive (upper part of the saturated zone), and other flow
paths where biodegradation of the organic contaminant is absent 36
Figure 4.7. Theoretical experiment of the effect of heterogeneity in biodegradation on the stable
isotope ratio for carbon in residual MTBE in water produced from a monitoring well,
when MTBE does not degrade in certain portions of the aquifer as depicted in Figure 4.6. . 37
Figure 5.1. Development of a spatial and temporal sampling design for CSIA surveys to
evaluate MNA 39
Figure 6.1. Minimum, maximum and mean carbon (A) and chlorine (B) isotope ratio of chlorinated
hydrocarbons from different manufacturers and production batches measured to date 42
Figure 6.2. Flow chart for the design and evaluation of a source identification strategy based on
stable isotope analysis 44
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Figure 6.3. Concentrations and carbon isotope ratios of PCE in two transects downgradient of
unidentified PCE sources 46
Figure 7.1. Simulated evolution of carbon isotope ratios of reactant (TCE) and degradation
product (c/s-DCE) according to the Rayleigh equation 49
Tables
Table 2.1. Extraction or sample preparation techniques used in CSIA for volatile ground
water pollutants 11
Table 3.1. Compounds that are adequately preserved in ground water with Hydrochloric Acid to
pH <2 or with 1% Trisodium Phosphate 20
Table 3.2. Compounds that are adequately preserved in ground water with Hydrochloric Acid to
pH <2, but are not adequately preserved with 1% Trisodium Phosphate 20
Table 4.1. Ranges of carbon isotope enrichment factors for microbial reductive dechlorination of
chlorinated ethenes published in the literature to date 23
Table 4.2. Rates of natural biodegradation of MTBE in ground water moving along a flow path to
monitoring wells 34
Table 6.1. Recommended sampling strategies for the use of CSIA to evaluate the origin of ground
water contamination 43
Table 8.1. Isotope enrichment factors (e) for aerobic and anaerobic degradation of selected ground
water pollutants 51
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Acronyms
Bio-Sep - trade name for adsorptive particles used as substratum for microbial growth
BTEX - benzene, toluene, ethylbenzene, xylene
CSIA - compound specific isotope analysis
DCA - dichloroethylene
DCE - dichlorethene
513C - delta 13C [%o], carbon stable isotope ratio
DIG - dissolved inorganic carbon
DOC - dissolved organic carbon
DNAPL - dense non aqueous phase liquid
EA-IRMS - elemental analyser - isotope ratio mass spectrometer
ETBE - ethyl tertiary butyl ether
GC-IRMS - gas chromatograph - isotope ratio mass spectrometer
GC/MS - gas chromatography / mass spectrometry
H, C, O, N, Cl - hydrogen, carbon, oxygen, nitrogen, chlorine
IRMS - isotope ratio mass spectrometry
IUPAC - International Union of Pure and Applied Chemistry
LNAPL - light non aqueous phase liquid
MDL - method detection limits
MNA - monitored natural attenuation
MTBE - methyl tertiary butyl ether
NA - natural attenuation
NAPL - non aqueous phase liquid
PAH - polycyclic aromatic hydrocarbons
PCE - perchloroethylene
P&T - purge and trap
QA/QC - Quality Assurance/Quality Control
SIP - stable isotope probing
SPME - solid phase micro extraction
TAME - tertiary amyl methyl ether
TEA - tertiary butyl alcohol
TCE - trichloroethylene
TSP - trisodium phosphate dodecahydrate
U.S. EPA- United States Environmental Protection Agency
VC - vinyl chloride
VOA - volatile organic analyses
VOC - volatile organic compound
V-PDB - Vienna - PeeDee Belemnite reference standard
V-SMOW - Vienna - Standard Mean Ocean Water reference standard
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Acknowledgements
Peer reviews were provided by Paul Philp (Department of Geology and
Geophysics, University of Oklahoma, Norman, Oklahoma), Hans Hermann Richnow
(Department Isotope Biogeochemistry, Helmholtz Centre for Environmental Research,
Leipzig, Germany), Ned Black (United States Environmental Protection Agency,
Region 9, SanFrancisco, California), PatrickMcLoughlin (Microseeps Inc., Pittsburgh,
Pennsylvania), and Robert Pirkle (Microseeps Inc., Pittsburgh, Pennsylvania).
Pat Bush (an Information Coordinator with the Senior Environmental Employee
Program, a grantee with U.S. EPA at the R.S. Kerr Environmental Research Center,
Ada, Oklahoma) is acknowledged for her technical editing to provide consistency in
formatting and grammar. Martha Williams (a Publication Editor for SRA, a contactor
to U.S. EPA at the R.S. Kerr Environmental Research Center in Ada, Oklahoma)
assisted with final editing and formatting for publication.
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Executive Summary
Managing the risk associated with hazardous organic compounds in ground water at hazardous waste
sites often requires detailed knowledge of the extent of degradation of the organic contaminants at the
site. An evaluation of the contribution of natural biodegradation or abiotic transformation processes in
ground water is usually crucial to the selection of Monitored Natural Attenuation (MNA) as a remedy for a
site. Documentation that the organic contaminant is actually being degraded is important for performance
monitoring of MNA, performance monitoring of active in situ bioremediation, and performance monitoring
of many other active remedial technologies.
The traditional approach of monitoring a reduction in the concentrations of contaminants at sites often
does not offer compelling documentation that the contaminants are actually being degraded. When data
on concentrations are the only data available, it is difficult or impossible to exclude the possibility that the
reduction in contaminant concentrations are caused by some other process such as dilution or dispersion,
or that the monitoring wells failed to adequately sample the plume of contaminated ground water. Stable
isotope analyses can provide unequivocal documentation that biodegradation or abiotic transformation
processes actually destroyed the contaminant.
When organic contaminants are degraded in the environment, the ratio of stable isotopes will often change,
and the extent of degradation can be recognized and predicted from the change in the ratio of stable
isotopes. Recent advances in analytical chemistry make it possible to perform Compound Specific Isotope
Analysis (CSIA) on dissolved organic contaminants such as chlorinated solvents, aromatic petroleum
hydrocarbons, and fuel oxygenates, at concentrations in water that are near their regulatory standards.
At many hazardous waste sites, progress toward cleanup of contamination in ground water depends on
successful identification of the true source of the contamination. Often, the ratio of stable isotopes in
materials in commerce will vary, depending on the isotope ratio in the feed stock used for synthesis of
the material, and on the particular chemical process used to manufacture the material. Different spills
of the same material may have different isotopic "signatures" that can be used to associate a plume of
contamination in ground water with a particular spill.
Because CSIA is a new approach, there are no widely accepted standards for accuracy, precision
and sensitivity, and no established approaches to document accuracy, precision, sensitivity and
representativeness. This Guide provides general recommendations on good practice for sampling ground
water for CSIA, and quality assurance recommendations for measurement of isotope ratios. The Guide also
provides recommendations for data evaluation and interpretation to use CSIA to document degradation of
organic contaminants, or to associate plumes of contaminants in ground water with their sources.
This Guide is intended for managers of hazardous waste sites who must design sampling plans that will
include CSIA and specify data quality objectives for CSIA analyses, for analytical chemists who must carry
out the analyses, and for staff of regulatory agencies who must review and approve the sampling plans and
data quality objectives, and who must review the data provided from the analyses.
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1.0
Introduction
The atoms of a particular element must have the
same number of protons and electrons, but they
can have different numbers of neutrons. When
atoms differ only in the number of neutrons, they
are referred to as isotopes of each other. If a par-
ticular isotope is not radioactive, it is called a
stable isotope. Because they differ in the number
of neutrons, isotopes differ in mass, and they can
be separated using a mass spectrometer. In recent
years mass spectrometers have been joined to gas
chromatographs to allow separation of individual
organic compounds in a mixture, followed by
combustion of each separate organic compound
to carbon dioxide, and then determination of the
ratio of isotopes in the carbon dioxide with a mass
spectrometer. Even more recently, new techniques
of sample preparation, such as purge and trap or
solid phase micro-extraction, have made it pos-
sible to obtain adequate material for analyses from
water with low concentrations of organic contami-
nants. For the first time, it is possible to perform
Compound Specific Isotope Analysis (CSIA) on
dissolved organic contaminants such as chlorinated
solvents, aromatic petroleum hydrocarbons, and
fuel oxygenates, at concentrations in water that are
near their regulatory standards.
Biodegradation can come about through natural
biological processes, or through active in situ biore-
mediation. When organic contaminants are degrad-
ed in the environment, the ratio of stable isotopes
will often change, and the extent of degradation
can be recognized and predicted from the change in
the ratio of stable isotopes; CSIA has great prom-
ise to improve our understanding of the behavior
of organic contaminants at hazardous waste sites.
Better understanding can lead to better decisions
on the remedies that are selected. CSIA can also be
used to monitor the progress of natural attenuation
or active biological remediation, and identify rem-
edies that are not performing as expected.
The U.S. Environmental Protection Agency
requires that data quality objectives be developed
for the methods and procedures that are used to
characterize hazardous waste sites. The U.S. EPA
also requires that the data that are used to make
decisions must meet predetermined goals for data
quality, including the accuracy, precision, and
sensitivity of the measurement, and the extent
to which the sample submitted for analysis are
representative of the environmental medium being
sampled. Other regulatory agencies world-wide
have similar expectations. Because CSIA is a new
approach in environmental investigations, there are
no widely accepted standards for accuracy, preci-
sion and sensitivity, and no established approaches
to document accuracy, precision, sensitivity and
representativeness.
This Guide is intended for managers of hazardous
waste sites who must design sampling plans that
will include CSIA and specify data quality objec-
tives for CSIA analyses, for analytical chemists
who must carry out the analyses, and for staff of
regulatory agencies who must review and approve
the sampling plans and data quality objectives, and
who must review the data provided from the analy-
ses. This Guide provides recommendations and
suggestions to site managers, chemists and regula-
tors. The recommendations and suggestions in this
Guide are not legal guidance, and the site manag-
ers, chemists, and regulators may negotiate among
themselves to develop objectives and approaches
that are most appropriate for their site.
This Section describes the benefits and value of
data provided by CSIA, and contrasts the informa-
tion provided by CSIA to information provided by
long-term monitoring of concentrations of contami-
nants, or information provided from techniques
where specific stable isotopes are added to environ-
mental samples.
Site investigations of soil and ground water con-
tamination are carried out at industrial installations,
at sites with leaking underground storage tanks,
or at sites with accidental spills (Wiedemeier, et
al., 1999). The goal of these investigations may
include an evaluation of the responsibility for a
release (environmental forensics) as well as an
evaluation of the necessity for remedial actions.
Investigations to evaluate the responsibility for a
release consider the timing of a release, the exact
location of the source or sources, and the associa-
tion of pollution in ground water with a particular
source (Morrison, 2000). Although CSIA is an
established approach in other areas of forensics
such as the authenticity and purity of food stuffs
and the control of doping in athletics (Aguilera
et al., 2002; Asche, 2003; Rossmann, 2001) the
application of CSIA in environmental forensics is
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a recent development (Asche, 2003; Schmidt et al.,
2004; Slater, 2003). CSIAhas been used success-
fully at a variety of sites to distinguish between
contaminant releases which occurred at differ-
ent times and places at complex spill sites. This
knowledge can be used to identify the parties that
were responsible for the contamination (Hunkeler
et al., 2004; Stark et al., 2003; Walker, et al., 2005)
and CSIA has been accepted as one line of evidence
in litigation.
To determine the need for active remediation, it is
useful to have a good knowledge of the behavior of
the contaminants in soil and ground water, includ-
ing the extent of biodegradation and abiotic trans-
formation. This is especially important for passive
remedies such as Monitored Natural Attenuation
that use naturally occurring processes to attenuate
concentrations of contaminants (Wiedemeier et al.,
1999).
Although natural attenuation has been the focus of
many remediation investigations due to its expected
economic benefits, it is often difficult to unequivo-
cally prove that a contaminant is being transformed
in ground water and that the extent of attenuation is
sufficient to protect receptors that are down gradi-
ent of the source. The standard approach that is
usually taken to characterize degradation in the
field is to monitor the concentrations of the con-
taminant at selected wells and use mass balance
calculations to estimate the extent of degradation.
This approach has many shortcomings, and the
shortcomings are particularly severe for common
ground water pollutants that degrade slowly. The
conventional approach requires a dense network of
monitoring wells, monitoring that extends for long
periods of time, and a rather homogeneous aquifer
with well-understood hydrogeology. These require-
ments are rarely met at real sites, and even when
they are, the evidence of degradation is only pro-
vided indirectly through a calculation of the miss-
ing mass of the contaminant after accounting for all
the other processes that might reduce the concentra-
tion of the contaminant. These shortcomings have
been nicely illustrated in a study of the natural bio-
degradation of methyl tertiary butyl ether in ground
water at the Borden site in Canada (Schirmer and
Barker, 1998).
New and different approaches will be required to
gain wider acceptance of natural attenuation by
regulatory authorities and by the public. If biodeg-
radation or abiotic transformation produces a mea-
surable change in the ratio of stable isotopes in the
contaminant, CSIA may provide direct evidence of
the degradation of the contaminant in ground water
at the site (Hunkeler et al., 1999; Meckenstock et
al., 1999; Sherwood Lollar et al., 1999). Over the
past decade there have been numerous successful
applications of CSIA that have demonstrated its
potential to recognize and even quantify processes
at field scale.
CSIA offers a new kind of information that has
great economic value to site managers. The tradi-
tional approach for monitoring of concentrations of
contaminants at sites often does not offer adequate
information about the processes that are responsible
for removal of the contaminants. Stable isotope
analyses can provide an in-depth understanding of
biodegradation or abiotic transformation processes
in contaminated aquifers. This better understand-
ing can improve the conceptual model of the site,
which can lead to a more effective remedial strate-
gy. The traditional approach of monitoring concen-
trations of contaminants can be very costly in the
long run. The inclusion of CSIA in the monitoring
plan can reduce overall costs by making it possible
to reduce the amount of traditional monitoring.
Prior to the development of CSIA, isotope
techniques relied on changes in the carbon iso-
tope ratios of CO2 or DIG (dissolved inorganic
carbon) to evaluate the degradation of organic
contaminants (Hunkeler et al., 1999). Although
this earlier approach can be helpful, it was often
difficult to resolve the signal of the carbon that was
added to the pool of CO2 or DIG by degradation
of the contaminant from the influence of the many
other carbon sources and sinks in the subsurface.
Furthermore, the sensitivity of the comparison was
dependent on the difference in the composition of
carbon isotopes in the CO2 produced by biodegra-
dation of the contaminants compared to the isotope
composition of the background CO2. In addition,
the sensitivity of the older technique was often
limited by the slow rate of CO2 production from
degradation of the contaminant relative to the large
pool of DIG in ground water (Dempster et al., 1997
and references therein). In contrast to the earlier
techniques, CSIA provides a direct measurement of
the isotope ratio in the individual organic contami-
nants. Interpretations of CSIA data are much less
problematic.
There are several new techniques to study biodegra-
dation in ground water that involve the addition of
contaminants that are artificially labeled with a car-
bon isotope (usually 13C-label). Examples include
stable isotope probing (SIP) and Bio-Sep® beads
amended with 13C-labeled substrates. These tech-
niques work in much the same way as radiocarbon
labeling; the 13C-label is used to track the transfer
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of carbon from the substrate to its metabolites, or to
the DIC pool, and its subsequent incorporation into
the microbial biomass (Geyer et al., 2005; Stelzer
et al., 2006). The disappearance of the label from
the substrate pool is convincing evidence that the
targeted compound is indeed degrading, and the
identification of 13C-label in microbial biomass is
definitive proof that the compound was biologically
degraded. There is an important caveat with these
new techniques. Once a substrate with an isotope
label has been added to a field site or to micro-
cosms, the natural abundance of isotopes has been
disturbed to an unpredictable extent, and a funda-
mental assumption in the CSIA approach is no lon-
ger valid. It is important to choose one approach or
the other; they can not be used together.
To date, CSIA is most frequently applied to car-
bon isotopes, and CSIA for carbon isotopes can be
considered to be a mature technique applicable on
a routine basis for compounds containing less than
ten carbon atoms. With current technology, the
heaviest compounds that can be analysed for shifts
in the ratio of stable carbon isotopes contain twelve
to thirteen carbon atoms. In larger molecules, the
isotope shifts are in the range of the experimental
error of the isotope analysis (Morasch et al., 2004).
Although very promising, isotopic analysis of the
other elements currently amenable to CSIA (hydro-
gen, oxygen, nitrogen and chlorine), has not been
carried out to the same extent as CSIA for carbon
isotopes; however, the other elements may become
widely used (Berg et al., 2007; Hofstetter et al.,
2008; Holmstrand et al., 2006; Sessions, 2006).
This guide is focused on biodegradation of organic
contaminants in ground water because biodegrada-
tion represents the majority of applications to date.
Nevertheless, the general principles of CSIA also
apply to abiotic transformation reactions. They
can be applied to natural materials or to engineered
systems such as permeable reactive barriers. This
guide can be used for a wide range of applications
where reactive processes in ground water produce a
change in the ratio of stable isotopes.
Currently, CSIA is in transition from a research tool
to an applied method that is well integrated into
comprehensive plans for management of contami-
nates sites. For this reason, the authors felt that it
was timely to provide general guidance on good
practice for sampling, for measurement, for data
evaluation and for interpretation in CSIA based on
our experience in research and consulting.
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2.0
Data Quality Issues
Section 2 has primary application for analysts that
will analyze samples for CSIA. It explains and
defines the delta notation (513C and 52H) that is used
to report the stable isotope ratio in a sample. This
section explains the nature and source of the refer-
ence standards for stable isotope ratios of carbon
and hydrogen in organic compounds, provides
recommendations on the preparation of laboratory
working standards, and the use of working stand-
ards to document accuracy, precision, and sensi-
tivity of CSIA. It also explains the relationship
between the linear range of the continuous flow
isotope ratio mass spectrometer and the uncertainty
of the determination of 513C, identifies a threshold
in signal strength below which the uncertainty of
the determination of 513C is not stable, and recom-
mends that the threshold be used as an operational
method detection limit for determination of 513C.
Section 2 provides recommendations for the fre-
quency of analysis of CO2 working standards,
compound specific working standards, and sample
replicates. Finally this section reviews the sensitiv-
ity provided by various methods for preparation of
the samples for analysis, and the effect of different
Compound
1 2
Combustion
methods that can be used to prepare the sample on
the value of 513C that is determined by the isotope
ratio mass spectrometer.
2.1. CSIA Principles
Compound specific isotope analysis (CSIA)
involves a three step process, using a set of instru-
mentation typically referred to as GC-IRMS (gas
chromatograph isotope ratio mass spectrometer):
(1) separation of individual carbon-bearing com-
pounds on a gas chromatograph, (2) quantitative
conversion of each compound to CO2 in a high
temperature combustion oven, and (3) removal of
H2O produced in combustion and introduction of
the CO2 derived from each compound into the mass
spectrometer for isotopic analysis (Figure 2.1).
After ionization of CO2, the mass spectrometer
separates ions with different mass-to-charge ratios
in space, allowing the simultaneous measurement
of the ions with fixed Faraday cups. The high
precision required in CSIA at the natural abundance
level of stable isotopes can be achieved only with
this simultaneous ion measurement.
1 2
Mass separation
A
GC separation
C02 C02
Back flush
Interface
Isotope Ratio Mass
Spectrometer
Open split
Ion source
Faraday
cups
Figure 2.1. Schematic of the GC-IRMS and general procedure used in compound specific isotope analysis
of carbon. The lower figure shows a schematic view of the instrumentation and the upper figure
the respective output from the different steps.
-------
2.2. Nomenclature and International
Standards
Stable isotope analysis of carbon or hydrogen
involves measurement of the relative abundance
of the two stable isotopes of carbon (13C and 12C)
or hydrogen (2H and JH). In order to ensure inter-
laboratory comparability and accuracy, these ratios
are expressed relative to an international standard
(typically V-PDB for carbon and V-SMOW for
hydrogen). Measured values are reported as 513C
and 52H respectively. These terms are denned in
Equations 2.1 and 2.2 as follows:
xlOOO 2.7
3C/12C - 13C/12C
/sample V /standard
3/-I/12 /~i\
c/ CL
2H/'H) -(2H/'H)
2H/1H)
1 standard
xlOOO
2.2
Since the resulting 5 values are very small (for 513C
typically < 0.05), they are generally multiplied
for convenience by 1000 and reported as parts per
thousand or "per mill", indicated by the symbol %o.
Sometimes, the standard is explicitly indicated after
the %o symbol, e.g. for carbon isotopes the values
are reported as %o V-PDB. If no information is
given, it can be assumed that the values are report-
ed relative to the usual standard material.
For decades the International Atomic Energy
Agency (IAEA) in Vienna, in conjunction with the
National Bureau of Standards in the United States,
has administered and overseen the storage and
distribution of the key international stable isotope
standards. Analysis and reporting of the other stable
isotope systems (O, N, Cl, etc.) follow an analo-
gous approach (Clark and Fritz, 1997).
In the common delta notion, the deviation of the
stable isotope value of the sample from the standard
will be either negative or positive. A negative value
means that the sample is depleted in its 13C-content
relative to the 13C/12C content of the standard
whereas a positive sign implies an enriched
13C-content.
According to the IUPAC definition, compounds
that only differ in their isotope composition (such
as 12CO2 and 13CO2) are called isotopologues. The
term "isotopomer" is used for isomers having the
same number of each isotopic atom but differing in
their positions. As an example, C1212C-13CHC1 and
C1213C-12CHC1 are the two isotopomers of TCE with
respect to carbon.
In the following sections we will focus on data
quality issues of carbon isotope analysis since car-
bon is by far the most frequently measured element
inCSIAtodate.
2.3. Laboratory Working Standards
The following sections are intended primarily
for laboratory staff that will actually analyze the
samples, and for staff that will prepare or review
Quality Assurance Project Plans.
2.3.1. CO2 Reference Gas
Since the international standard materials are made
available to each laboratory in limited amounts,
they are not used for daily operations and measure-
ments. For daily operations and standardization,
each laboratory obtains pure CO2 reference gas and
cross-calibrates it against the international standard
materials to develop in-house working standards.
To obtain maximum accuracy, this cross-calibration
should be done by the conventional dual inlet
approach; alternatively, the isotope composition
of working standards can be determined by an
elemental analyser - isotope ratio mass spectrom-
eter (EA-IRMS). Once the laboratory's working
CO2 standard is characterized (1) it should be used
daily to calibrate the isotope ratio mass spectrom-
eter, (2) the CO2 working standard should be cross-
checked against the international standard materials
every few months to ensure continued accuracy,
and (3) aliquots of the CO2 working standard
should be stored in glass ampoules so it can be
available on a long term basis for calibration checks
and quality control, and for inter-laboratory com-
parisons. See Coplen et al., (2006) and Qi et al.,
(2003).
2.3.2. Compound specific Working
Standards
The CO2 working standard is included in individual
sample sets to act as an internal standard. Since
organic compounds may behave differently in this
analytical system than pure CO2 (due to differ-
ences in chromatographic separation, combustion
efficiency, peak shape, etc.), it is important for each
laboratory to also characterize compound specific
working standards for the target compounds that
they typically analyze (Figure 2.2).
Isotopic characterization of the working standards
should be done off-line using the sealed quartz
tube combustion technique and conventional dual
inlet mass spectrometry to ensure maximum preci-
sion and accuracy with respect to the international
-------
5
in 1.5-1
a
1.4-
1.3-
1.2-
1.1-
1.0-
.E 4000-
S-
g 3500-
0)
- 3000-
2500-
2000-
1500-
1000-
500-
0-
N
E »
It
= ?
I. 1
£ Eo
o os
^ oc
" 5P
: £ =
»-^ 0)
n
o
o
c<
o
SS c * •"
C2 O
%>:2 £
= 0. >. *= -
£f -i I
•5
PI
500
1000 1500
Time in Seconds
2000
Figure 2.2. Example of a chromatogram obtained in GC-IRMS. Upper panel: Isotope ratio trace with the
typical isotope swings due to the partial separation of isotopologues prior to on-line combus-
tion caused by the inverse isotope effect in gas chromatography (modified after Jochmann et
al. 2006). Lower panel: Gas chromatograph. Note that the CO2 working standard produces the
flat-top peaks at the start of the chromatogram.
standard (V-PDB) and the laboratory CO2 work-
ing standard. Typical precision for this approach
is ±0.15 %0 (Clark and Fritz, 1997). There is an
increasing trend to measure the isotopic composi-
tion of organic working standards by an elemen-
tal analyser - isotope ratio mass spectrometer
(EA-IRMS) which is appropriate as long as the
careful procedures outlined in Qi et al. (2003) are
followed.
The following recommendations for archiving and
storage of compound specific working standards
are for compounds that are liquid at room tempera-
ture and pressure. Purchase pure product for each
compound to be analyzed. Ensure highest purity as
even small amounts of contaminants can affect the
ability to accurately characterize the compound of
interest. At least two dozen sealed glass ampoules
should be set aside as ARCHIVED standards for
use when needed to cross-check the isotopic value
in the future, or for inter-laboratory comparisons.
Set up a second set of thirty to forty sealed glass
ampoules to be used as WORKING standards for
daily standardization, controls on experiments, and
for correcting problems with the performance of the
instrument. It is advisable to test the procedure used
to seal the ampoule to insure that the ampoules are
flame-sealed quickly. If significant amounts of
compound are lost through volatilization, this might
change the isotopic ratio of the standard.
2.4. Method Testing, Quality Assurance
and Quality Control
Conventional off-line preparation techniques and
dual inlet isotope ratio mass spectrometry (IRMS)
provide optimized analytical conditions to obtain
maximum precision. In contrast, continuous flow
IRMS provides for rapid analysis of complex
mixtures of organic compounds, and for dissolved
organic compounds in environmental samples.
Continuous flow IRMS requires a sample size that
is approximately four to five orders of magnitude
smaller than the sample needed for off-line prepara-
tion techniques. Continuous flow IRMS however
produces an inevitable loss of precision. The loss
-------
of precision is related to a wide variety of factors
which are beyond the scope of this Guide but which
include a higher source pressure for the helium car-
rier gas, higher background concentrations of water,
and the need to tune the source for optimum linear-
ity rather than optimum sensitivity. In the follow-
ing sections, we provide an approach to determine
reasonable values for reproducibility, accuracy and
the detection limit for compound specific work-
ing standards that are analyzed by continuous flow
mass spectrometry, and provide attainable goals for
reproducibility, accuracy and detection limits for
CSIA data (for more detail see Sherwood Lollar et
al., 2007; Jochmann et al, 2006).
2.4.1. Reproducibility
Reproducibility (or precision) refers to the ability
to obtain the same value when the same sample or
standard is analyzed repeatedly (Figure 2.3). In
compound specific isotope analysis (CSIA), if a
sample is run in duplicate or triplicate under con-
stant operating conditions, the standard deviation of
the mean of the replicate measurements is typically
<0.1 to 0.3%o for 513C values. While reproducibility
is necessary to minimize uncertainty, it is not a suf-
ficient expression of the total degree of uncertainty
(error) in a measurement. As illustrated in Panel C
of Figure 2.3, a measurement can be highly repro-
ducible (precise) but nevertheless be inaccurate.
a) Good Precision,
High Accuracy
b) Poor Precision,
High Accuracy
c) Good Precision,
Low Accuracy
d) Poor Precision,
Low Accuracy
Figure 2.3. An illustration the difference between
precision (reproducibility) and accu-
racy of several data points, using the
bull's eye of a target as the goal for
high accuracy and good precision.
2.4.2. Total Uncertainty and the Critical
Role of Linearity
When a suite of real samples are analyzed by CSIA,
all the operating conditions are not held constant
from one run to the next as described above in the
definition of analytical precision. In fact, one of
the key advantages of continuous flow CSIA is that
it permits, in the same analytical run, the measure-
ment of the 513C values for several compounds that
are present in the sample mixture at different con-
centrations. However, specific sample preparation
parameters, such as split ratios, are often adjusted
to bring the concentration of all of the analytes into
the linear range of the instrument. In contrast to
conventional dual inlet systems where the stand-
ard peak size must be carefully balanced to match
the sample peak size, in continuous flow CSIA the
standards can be input at one peak size (typically
1 to 2 Volts) and then used to characterize sample
peaks that are either above or below that size. The
linear range, the range over which accurate meas-
urements are possible, varies from instrument to
instrument. The linear range depends on the mass
spectrometer itself as well as the chromatograph
and combustion system.
As used in analytical chemistry, the term linear-
ity usually refers to a linear increase of the signal
with increasing amount of analyte. As applied to
isotope analysis, linearity indicates that (within
an acceptable range) the obtained isotope ratio is
independent of the amount of compound injected.
The following sections provide further details on
how to establish the linearity of the CSIA analytical
system and the implications that linearity has for
documenting the uncertainty and detection limits
associated with isotope ratios (see also Sherwood
Lollar, et al., 2007).
Figure 2.4 shows the results of atypical linearity
test. Multiple analyses of a laboratory working
standard for TCE were run under identical opera-
tional parameters including constant concentration
of TCE, and constant chromatographic conditions,
combustion temperature, flow rate, and split setting.
However, a wide range of different peak sizes (or
signal sizes) were obtained by varying the amount
of sample introduced. When this type of test is car-
ried out, for any given measurement, the 513C value
obtained is typically within ±0.5%o of the value
for the laboratory working standard obtained by
off-line preparation techniques and dual inlet mass
spectrometry. Based on these results a sample that
is run under similar conditions should also have a
total uncertainty of approximately ±0.5%o.
As illustrated in Figure 2.4, there is a threshold in
the size of the signal below which the variation for
-------
replicate values of the working standard increases
significantly. The values of 513C that are measured
below this threshold can be more enriched than the
standard value, or less enriched than the standard
value, and whether they are more or less than the
standard value varies with time under constant
operating conditions - hence no corrections should
be attempted for values of 513C that are measured
below the threshold. See also Jochmann, et al.
(2006) and Sherwood Lollar, et al. (2007).
-27
-28
-29
= 31
«5 -Ol
-32
-33
2345678
Signal Size (Volts)
Figure 2.4. Typical linearity test of a laboratory
working standard run by CSIA over a
wide range of different peak sizes (or
signal sizes) by varying the amount
of analyte introduced. Modified after
Sherwood Lollar et al. (2007). The
solid line is the mean of all the repli-
cate analyses of the working standard.
The dotted lines are ± one standard
deviation of the mean.
The largest effects on the value of 513C are typi-
cally attributable to the effects of sample size on
linearity or to a change in a major parameter such
as purposely changing the split setting. The effects
of these changes vary somewhat from compound
to compound. Therefore it is highly recommended
to document the effect of changes in these para-
meters on the measured value of 513C for each
compound specific working standard. The total
uncertainty varies from compound to compound.
Maintenance of a control chart to monitor this
variation is an important part of good practice for
Quality Assurance/Quality Control (QA/QC). In
any laboratory inter-comparison, values measured
from one laboratory to another should agree within
±0.5%o (or other level of uncertainty specific to a
particular compound) if each laboratory has prop-
erly calibrated their working standard to V-PDB.
Sherwood Lollar et al. (2007) and references there-
in demonstrate that a total uncertainty of ±0.5%o is
typical for many hydrocarbon contaminants investi-
gated to date, including alkanes, certain chlorinated
ethenes, certain chlorinated ethanes and aromatic
hydrocarbons. However, at the current state of
practice for CSIA, an uncertainty of ±0.5%o can not
be routinely attained for analysis of carbon isotopes
in every volatile organic compound that might be
of regulatory interest. It is the responsibility of the
analyst to provide documentation of the upper limit
on uncertainty that is associated with a particular
compound of interest when analyzed following a
particular protocol for CSIA. It is the responsibil-
ity of the user of the data to determine whether the
achieved upper limit on uncertainty is acceptable
for their particular application.
2.4.3. Establishing Concentration
Thresholds or "Detection limits"
Mass spectrometry can produce a 513C value for
very small signals. However, as indicated above,
at signal sizes below a certain threshold both the
accuracy and reproducibility of 513C measurements
deteriorate. We recommend that the operational
detection limit be defined as that concentration of
the compound in the water sample below which the
accuracy and reproducibility of the value for 513C
deteriorate beyond acceptable limits. The crite-
rion for "acceptable limits" depends on the use of
the data, and is dependent on the methods and the
instruments used. As mentioned above, for many
compounds of interest, most laboratories can attain
a standard deviation of the mean of triplicate sam-
ples of ±0.5%o for CSIA of carbon. Jochmann et al.
(2006) compared the variation in 513C in triplicate
analyses over a range of concentrations. They com-
pared the data to identify the concentrations that
met two criteria: the mean value of triplicate mea-
surements at a particular concentration was within
±0.5%o of the mean of all analyses over the range
of concentrations, and the standard deviation of
the triplicate analyses at a particular concentration
was less than ±0.5%o, as is illustrated in Figure 2.5.
They defined the method detection limit as the low-
est concentration that satisfied both of the criteria.
The minimum quantity of sample necessary to keep
the uncertainty in the determination of the isotopic
ratio within acceptable limits will vary from com-
pound to compound and may also depend on the
technique used to prepare the samples for analysis.
For any particular technique to prepare the sample,
the minimum quantity will be associated with a
minimum concentration necessary to keep uncer-
tainty within acceptable limits. This minimum
-------
concentration becomes the effective detection limit
for determination of isotopic ratios. It therefore
should be established separately for each compound
and each injection/preconcentration technique
using compound specific working standards. In the
example shown in Figure 2.5, the MDL for benzene
is 0.2 ug/L, which corresponds to a peak height of
250 mV.
As discussed above, it is not generally possible
to "correct" for values run at smaller signal sizes.
Ideally, improving detection limits for CSIA relies
on increasing the efficiency of sample preparation
and preconcentration steps to provide higher sig-
nal peaks (see below), and not by trying to "cor-
rect" values or simply to report values close to the
threshold at which the accuracy of the determina-
tion will be compromised.
2.4.4. Application of These Principles to
Other CSIA Measurements
For hydrogen isotopes, the principles for establish-
ing reproducibility, total uncertainty and detection
limit are the same as for carbon isotopes. However,
there are few formal studies on the uncertainty
associated with analysis of 52H in environmen-
tal investigations, and we will not make specific
performance recommendations at this time. As a
rule of thumb the total uncertainty for hydrogen is
usually at least an order of magnitude greater than
for carbon; total uncertainty for 5 2H is typically
± 5%0 versus ± 0.5%0 for 513C (Sherwood Lollar et
al., 2007). For an example of this kind of method
development for hydrogen isotope analysis see
Gray et al. (2002). Similar principles will be appli-
cable to H, N, Cl, S, and O using continuous-flow
compound specific methods. See Sessions (2006)
for an extensive review of analytical methods. As
with any new method, there may be other important
operational parameters in addition to those that
affect carbon and hydrogen CSIA measurements
and careful work is needed.
2.4.5. Extraction Methods for CSIA
Based on the requirements specified by the vari-
ous manufacturers of isotope ratio mass spectrom-
eters, it is necessary to inject approximately 1 nmol
carbon or 8 nmol hydrogen on column to have
adequate mass to provide an accurate and precise
measurement of the isotope ratio. These criteria
assume that the GC-IRMS instrument is tuned to
maximum linearity, and that the chromatographic
resolution (R.) is greater than 1.5, which provides
narrow peaks with good peak separation.
Benzene
Amplitude of mass 44 (mV)
10000
8000 -
6000 t
4000
L
2000
n ^
Method Detection Limit (0.2 ng/L)
1 -J£FJ. . . ill \A .
& /
'/. •
?5"
n 8isc
^> Amplitude of mass 44
0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
r -23.0
-24.0
-25.0
-26.0
- -27.0
-28.0
-29.0
-30.0
•3-1 n
8.0
O
n
«S
Concentration in water (|ig/L)
Figure 2.5. Example of the evaluation of method detection limits (MDLs) in CSIA. The squares represent
the 513C values in %o and the diamonds show the amplitude of mass 44 in mV. Error bars
indicate the standard deviation of triplicate measurements. The horizontal broken line
represents the iteratively calculated mean value after the methods of Jochmann et al. (2006)
and Sherwood Lollar et al. (2007). The solid lines around the mean value represent the
standard deviation on the mean of triplicate measurements. Figure modified after Jochmann et
al. (2006).
-------
These criteria can be used to estimate the mass of
individual compounds that must be delivered on
column:
Inmol
m. = M, for carbon 2.3
and
x
%nmol
mi = M. for hydrogen 2.4
x
where nij is the required mass in ng, x is the num-
ber of carbon or hydrogen atoms respectively in the
compound, and Mj is the molecular weight of the
compound in g/mole.
For methyl tertiary butyl ether, for example, this
yields a minimum mass of 18 ng. Using a dimen-
sionless air-water partition constant Kaw of 0.12 at
50 °C (Arp and Schmidt, 2004) for a typical head-
space extraction (10 mL sample and 10 mL head-
space, 1 mL gas injection, 50 °C) this is equivalent
to a concentration in the water sample of 170 (ig/L.
For hydrogen, at least 8 nmol are required and the
same calculation as above yields 59 ng or 550 (ig/L.
These are calculated minimum numbers under
optimum conditions and, as Table 2.1 shows, are
often not achievable. Unfortunately, environmen-
tal concentrations of interest are frequently below
these levels, especially at contaminated sites out-
side the plume core or if substantial degradation has
occurred. Efficient extraction or preconcentration
techniques must be integrated with GC-IRMS in
order to fully exploit the potential of the method
for a wide range of samples, in particular for ele-
ments other than carbon. To meet this need, over
the past few years several studies have worked to
lower the detection limits for CSIA by the use of
sorptive extraction techniques such as solid-phase
microextraction (SPME) or purge and trap (P&T).
Note that almost all these studies have focused on
compounds that are relatively water soluble and
volatile, such as the BTEX compounds, MTBE,
and chlorinated ethylenes. These compounds are
among the most common industrial ground water
pollutants.
The concentration thresholds or effective detection
limits are constrained by the physical limits of the
gas chromatograph isotope ratio mass spectrometer
system, as well as by the technique used to prepare
the sample. Ideally, the extraction or preconcenta-
tion technique will be free of isotope fractionation
effects and will be adequate to concentrate enough
material from each compound of interest to deter-
mine the isotopic ratio at concentrations that are
relevant to plumes of contaminated ground water.
Table 2.1 summarizes the effective detection limit
that has been reported for a variety of techniques
that are used for sample preparation prior to CSIA
of the common organic contaminants in ground
water. As a first approximation, the sensitivity is
correlated with the molar concentration of carbon
(or any other element of interest) of the compound
in the sample. Limits of detection should be deter-
mined based on the lower range of linearity of the
instrument (see Section 2.4.3). However, Table 2.1
provides the concentration corresponding to the
typical operational limits of detection, based on the
criteria of 1 nmol carbon and 8 nmol hydrogen on
column and our experience with the technique.
Detection limits for nitrogen or oxygen isotope
analysis are provided by the manufacturer of the
instrument. However, there are only a few methods
available for extraction and preparation of samples,
or protocols available for CSIA, that are appli-
cable to isotopes of nitrogen, chlorine, and oxygen
in ground water contaminants (Berg et al., 2007;
Hartenbach et al., 2006; Holmstrand et al., 2006;
Penning and Conrad, 2007). Studies for hydrogen
isotope analysis of ground water contaminants
are also still relatively limited (Kuder et al., 2005;
Mancini et al., 2002; also Table 2.1).
A prerequisite for the selection of any extraction
or preconcentration technique used to prepare
samples for CSIA is adequate sensitivity. A further
prerequisite is a negligible change in the value of
513C or 52H during the extraction or enrichment
process, or at least a highly reproducible change.
Before an extraction or preconcentration technique
is implemented on a routine basis, it is mandatory
to thoroughly evaluate the technique for changes
in the value of 513C or 52H during sample prepara-
tion, rather than relying on data reported by oth-
ers. The change in the value of 513C or 52H may
vary depending on analytical conditions such as
the split ratio and extraction time. Each compound
that will be analyzed should be tested using work-
ing standards with a known isotopic composition
(see Section 2.3). The evaluation should cover the
typical range of operating conditions. The standard
deviation of replicate analyses should typically
be smaller than ±0.5%o for carbon, otherwise the
method is not suited for typical applications.
A number of extraction methods have been
shown to provide accurate isotope ratios, while
other methods change the value of 513C or 52H
(Table 2.1). There are some general trends.
Typically headspace and direct immersion SPME
are not accompanied by a substantial changes in the
value of 513C or 52H (Dayan et al., 1999; Slater et
-------
al., 1999; Bias and Freeman, 1997; Hunkeler and
Aravena, 2000b; Zwank et al., 2003). If changes in
the value of 513C or 52H are observed with SPME,
the analyte tends to be depleted in 13C compared
to pure phase standards, i.e., the lighter compound
partitions more strongly into the fiber which is
then subsequently analyzed. This resembles
the same inverse isotope effect that is observed
in gas chromatography. Although this effect is
often quite small, for carbon tetrachloride, Zwank
et al. (2003) found high deviation using direct
immersion SPME, which could not be explained.
Furthermore, Hunkeler et al. (200la) found a
significant 13C-depletion of tertiary butyl alco-
hol extracted by SPME. Sometimes significant
inverse isotope effects are seen during headspace
equilibration with an aqueous sample. An enrich-
ment of 13C in the gas phase of up to 1.46%o, has
been observed (Hunkeler and Aravena, 2000b), thus
Hunkeler et al. (2005) applied corrections in sub-
sequent work in order to allow for a comparison of
isotope data generated with different methods.
There is no consistent pattern in the changes in the
value of 513C or 52H. Compounds other than ter-
tiary butyl alcohol behave differently (Slater et al.,
1999), which emphasizes the need for testing each
individual compound during method validation.
Dynamic extraction methods such as purge and trap
and dynamic headspace extraction aim for a quanti-
tative (100%) extraction of the sample with subse-
quent trapping and thermo-desorption of the analyte
into the GC column. These dynamic extraction
methods are more appropriate for isotope analysis
at very low concentrations. In the various studies
conducted to date that used an adequate purge time,
no significant change in the value of 513C or 52H
has been reported. Zwank et al. (2003) have shown
for a number of volatile organic compounds that
sample preparation does not compromise the analy-
sis unless the extraction efficiencies drop below
approximately 40%.
Table 2.1. Extraction or sample preparation techniques used in CSIA for volatile ground water pollutants.
Adapted and updated from Schmidt et al. (2004).
Compound
Methyl
Tertiary
Butyl Ether
Injection/
preparation
technique
liquid
injection3
headspace
injection
headspace
SPME
headspace
SPME
direct
immersion
SPME
Change in the
value of 513C
or 52H during
analysis
OCb <0.3%o;
SLC~1%0
n.s.c.e
C: -0.9%o
H: -17%o
(both with resp.
to HS injection)
Significant but
small change
(-0.67±0.21%0)
Reproducible
change
(<0.5%o),
but presence
ofBTEX
concentrations
>3 mg/L caused
2%o deviation.
Definition of the
Detection Limit
Amplitude > 0.5 V
Amplitude > 0.5 V
Amplitude > 0.5 V
Amplitude > 0.5 V
Amplitude > 0.75 V
Amplitude > 0.5 V
Operational
Detection limit
[US/L]
513C
24000
5000
4000
(TAME:
6000)
350
11
16
52H
-
50000
-
1000
-
-
Reference
(Zwank et
al., 2003)
(Gray et al.,
2002)
(Somsamak
et al., 2005)
(Gray et al.,
2002)
(Hunkeler et
al.,2001a)
(Zwank et
al., 2003)
-------
Compound
Benzene
Toluene
Chlorinated
Methanes
Injection/
preparation
technique
P&T
P&T
P&T
P&T
liquid
injection3
headspace
injection
direct
immersion
SPME
P&T
P&T
liquid
injection3
headspace
injection
headspace
injection
direct
immersion
SPME
direct
immersion
SPME
P&T
P&T
liquid
injection3
Change in the
value of 513C
or 52H during
analysis
Small shift of
513C values
(+0.33%o)
n.r.d
n.s.c.e
n.r.d
n.s.c.e
n.s.c.e
n.s.c.e
n.s.c.e
n.r.d
OCbn.s.c.e
SLC~-1%0
n.s.c.e
n.s.c.e
n.s.c.e
n.s.c.e
n.s.c.e
n.s.c.e
CHCl3,~-1.5%o
CC14, OCb
-3.31±0.34%0
Definition of the
Detection Limit
n.r.d
n.r.d
Amplitude > 0.5 V
< 0.5%o precision
Amplitude > 0.5 V
Amplitude > 0.5 V
Amplitude > 0.5 V
Amplitude > 0.5 V
Moving mean
within ± 0.5%o
interval and a <
0.5%o
Amplitude > 0.5 V
Amplitude > 2 V
Amplitude > 0.2 V
Peak area equiv. to
50pmolCO2atthe
ion source
(ca. 0.7 Vs)
Amplitude > 0.5 V
Amplitude > 0.5 V
Moving mean with-
in ± 0.5 %o interval
and a < 0.5%o
Amplitude > 0.5 V
Operational
Detection limit
[^g/L]
513C
15
5
0.63
2.5
19000
500
22
0.30
0.20
9500
-
100
45
9
0.25
0.07
170000
to
220000
52H
-
-
-
20
-
-
-
-
-
-
2000
-
-
-
-
-
-
Reference
(Smallwood
etal, 2001)
(Kolhatkar
et al, 2002)
(Zwank et
al., 2003)
(Kuder et
al., 2005)
(Zwank et
al., 2003)
(Mancini et
al., 2003)
(Zwank et
al., 2003)
(Zwank et
al., 2003)
(Jochmann
et al., 2006)
(Zwank et
al., 2003)
(Ward et
al., 2000)
(Slater et
al., 1999)
(Bias and
Freeman,
1997)
(Zwank et
al., 2003)
(Zwank et
al 7003^
(Jochmann
et al., 2006)
(Zwank et
al., 2003)
-------
Compound
Chlorinated
Ethenes
Injection/
preparation
technique
direct
immersion
SPME
direct
immersion
SPME
headspace
injection
P&T
P&T
liquid
injection3
headspace
injection
direct
immersion
SPME
headspace
injection
direct
immersion
SPME
P&T
P&T
Change in the
value of 513C
or 52H during
analysis
CHC13,
-1.8±0.28%0
CC14,
-7.3±0.22%0
n.s.c.e
-0.09 to 0.40 %0
1.03 to 1.29%0
CHC13 and
CC14, n.s.c.e
CHC13, ~-1.5%o
CCl4andDCM,
n.s.c.e
Small but
significant
change observed
for TCE and
cis-DCE
TCE, n.s.c.e
n.s.c.e
-0.37to+0.06%0
0.21 to 0.69 %0
Small (~l%o)
but significant
change observed
for cis-DCE
only
n.s.c.e
Small (~0.7%o)
but significant
change observed
for cis-DCE
only
Definition of the
Detection Limit
Amplitude > 0.5 V
1.5 nmol C on
column
1.5 nmol C on
column
Amplitude > 0.5 V
Moving mean
within ± 0.5%o
interval and
a<0.5%0
Amplitude > 0.5 V
Amplitude > 0.2 V
1.5 nmol C on
column
1.5 nmol C on
column
Amplitude > 0.5 V
Not given
Amplitude > 0.5 V
Operational
Detection limit
[ug/L]
513C
170 to
280
360 to
2200
800 to
3300
<5.0
18 to 27
7 1000 to
84000
400
130 to
290
170 to
1000
66 to
130
5
1.1 to
3.6
52H
-
-
-
-
-
-
-
-
-
-
-
-
Reference
(Zwank et
al., 2003)
(Hunkeler
and
Aravena,
2000b)
(Hunkeler
and
Aravena,
2000b)
(Zwank et
al., 2003)
(Jochmann
et al., 2006)
(Zwank et
al., 2003)
(Slater et
al., 1999)
(Hunkeler
and
Aravena,
2000b)
(Hunkeler
and
Aravena,
2000b)
(Zwank et
al., 2003)
(Song et al.,
2002)
(Zwank et
al., 2003)
-------
Compound
Misc.
Compounds
Methyl-
cyclohexane
Alkylated
Benzenes
Hexanol
Tertiary
Butyl
Alcohol
Tertiary
Butyl
Alcohol
Bromoform,
Ethylene
Dibromide
Nitro-
aromatic
compounds
Anilines
Injection/
preparation
technique
P&T
dynamic
headspace
extraction
direct
immersion
SPME
P&T
direct
immersion
SPME
direct
immersion
SPME
P&T
P&T
direct
immersion
SPME
direct
immersion
SPME
Change in the
value of 513C
or 52H during
analysis
n.s.c.e
n.s.c.e
< 0.5 %0
n.s.c.e
< 0.5 %0
Significant
change
(-1.18±0.12%o)
n.r.d
n.r.d
Significant
change for some
compounds (up
to-1.3%o)
Significant
change for some
compounds (up
to l.l%o)
Definition of the
Detection Limit
Moving mean
within ± 0.5%o
interval and
a<0.5%0
Amplitude > 0.2 V
Peak area equiv.
to 50 pmol CO2
at the ion source
(ca. 0.7 Vs)
Moving mean with-
in ± 0.5 %o interval
and s < 0.5%o
Peak area equiv.
to 50 pmol CO2
at the ion source
(ca. 0.7 Vs)
Amplitude > 0.75 V
< 0.5%o precision
Moving mean with-
in ± 0.5 %o interval
and s < 0.5%o
Amplitude > 0.5 V
(equiv. to ca.
0.8 nmol C on
column)
Amplitude > 0.5 V
(equiv. to ca.
0.8 nmol C on
column)
Operational
Detection limit
[^g/L]
513C
0.8 to
5.1
10 to 38
24
0.07 to
0.35
4200
360
25
14,3.9
73 to
780
320 to
1600
52H
-
-
-
-
-
-
-
-
-
-
Reference
(Jochmann
et al, 2006)
(Morrill et
al., 2004)
(Bias and
Freeman,
1997)
(Jochmann
et al., 2006)
(Bias and
Freeman,
1997)
(Hunkeler
et al.,
200 la)
(Kuder et
al., 2005)
(Jochmann
et al., 2006)
(Berg et al.,
2007)
(Berg et al.,
2007)
a Analyte dissolved in solvent.
b On column injection.
0 Splitless injection.
d Not reported in reference.
e No significant change (<0.5%o) observed.
-------
2.5. Avoiding Some Pitfalls in CSIA
Measurements
Sessions (2006) gives an excellent overview of
requirements for successful isotope analysis.
Blessing et al. (2008) recently discussed potential
pitfalls in CSIA of environmental samples. Some
of their recommendations are summarized here.
Many analysts nowadays are accustomed to the
selectivity provided by mass spectrometric detec-
tors in quantitative analysis. However, the continu-
ous flow GC-IRMS is non-selective. In the case of
carbon, all of the compounds are converted to CO2
before analysis of the isotopic ratio. The system
"sees" all the carbon (or other element) eluted from
the column. Therefore, it is of the utmost impor-
tance to remove coeluting non-target compounds
as completely as possible or to modify separation
methods to allow a baseline separation.
Samples should be screened by GC/MS or GC/FID
prior to CSIA measurements to avoid overloading
of the GC-IRMS system with non-target analytes.
If the interfering compounds are sufficiently sepa-
rated from the target analytes, they can be elimi-
nated by switching a valve installed between the
GC column and the combustion oven. The valve
diverts the flow of carrier gas with the interfering
compounds away from the combustion oven.
Peak integration should be closely monitored
and adjusted manually if necessary. This is a
much more delicate task than in quantitative
analysis because shifting the peak delimiters can
significantly change the calculated isotope values
due to the partial chromatographic separation of
isotopologues. Isotope swings can serve as good
indicators of peak quality.
A correction can be applied with care to account
for material that bleeds from the GC column. Use
of CO2 standards within the sample run is helpful
to provide the "ground-truth" for such corrections.
Data can be automatically corrected using various
algorithms that are available for this purpose in
commercial instruments. However, at the time of
this writing (Spring 2008) a thorough comparison
of the various methods has not been conducted
(Sessions, 2006).
2.6. Recommended Routine for Daily
Laboratory Quality Assurance/
Quality Control (QA/QC) for Carbon
Isotope Analysis
Test the linearity and sensitivity of the instrument
with the CO2 working standard. Then test the lin-
earity of the instrument with the compound specific
working standards over a typical range of operating
conditions that will be used for the day's samples.
Operating conditions include the range of concen-
trations, split or flow settings, and the technique
used to prepare the samples, such as a headspace
sampler, SPME or purge and trap. Values of 513C
for each standard typically should remain within
±0.5%o (1 o) of the previously determined isotopic
working standard value to ensure both accuracy and
reproducibility. Plotting these values on a con-
trol chart will allow for continuous monitoring of
QA/QC over the long term.
Analyze samples under the same conditions as
above, ensuring baseline separation for the target
compounds. Requirements for excellent chromatog-
raphy are even more stringent than for concentra-
tion analysis.
At a minimum, the CO2 working standard should
be analyzed at the beginning of each sample run.
At least every fifth sample should be a replicate. At
least every tenth sample should be the compound
specific working standard.
All samples should stay within the previously
established range of acceptable linearity and above
the established threshold limit. If a sample falls
outside the acceptable range, the concentrations of
the analytes should be adjusted, if possible, to bring
the sample within the established range, and the
sample analyzed a second time. Follow the specifi-
cations provided by the manufacturer of the instru-
ment for the upper limit of the range of linearity.
-------
3,0
Collection, Preservation
Storage of Samples
This section provides specific recommendations
for collection, preservation and storage of ground
water samples that are intended for CSIA analysis.
The section has application to the development
of Quality Assurance Project Plans for site
characterization, and is intended for contractors that
sample ground water, and for site managers and
regulators that develop and approve sampling plans.
3.1. Collection of Ground Water from
Monitoring Wells
Procedures and conditions that would compromise
a sample intended for analysis of concentrations
of a particular organic contaminant will also com-
promise the sample for CSIA analysis. Procedures
and conditions that sustain and maintain the suit-
ability of a sample for analysis of concentrations of
a particular organic contaminant will also sustain
and maintain the suitability of the sample for CSIA
analysis. As a general rule, established good prac-
tice for acquisition of samples for analysis of con-
centrations of organic contaminants can be accepted
as good practice for samples intended for CSIA
analysis. In particular, criteria for methods of pres-
ervation, for methods for shipping samples from the
field site to the laboratory, and criteria for holding
times and holding temperatures to store samples
that have been established for analysis of concen-
trations of organic contaminants are also applicable
to CSIA analyses.
U.S. EPA has provided recommendations and
guidance on collecting ground water samples
(Barcelona et al., 1985; Yeskis and Zavala, 2002)
and a comprehensive description of good practice
for ground water sampling is available in Nielsen
(2006).
Ground water can be collected from monitoring
wells by a variety of devices including bailers,
above ground peristaltic pumps with plastic sam-
pling tubes inserted into the well, and down-hole
pumps (Nielsen, 2006). Any of the devices can
produce an adequate sample if they are used appro-
priately. To minimize the loss of volatile analytes,
the device should be used in a manner that mini-
mizes the exposure of the ground water sample to
the atmosphere during sampling.
and
If a well is not purged before sampling, the water
pumped from the well may or may not be repre-
sentative of the ground water in the aquifer being
sampled (Nielsen, 2006). Volatile compounds can
be lost to the headspace above the water column
in the well. Oxygen from the air above the water
in the well can diffuse into the water and support
aerobic biodegradation of organic compounds in the
water in the well that might not occur in the ground
water in anoxic aquifers.
There are two general approaches to purge the
water from the well before it is sampled for chemi-
cal analysis. In the first approach, the volume of
water contained in the casing of the well is calcu-
lated from the depth of the water column in the
well and the diameter of the well, then two or three
casing volumes of water are purged from the well
before the well water is sampled. In the second
approach, field instruments are used to continuously
monitor sensitive parameters such as temperature,
redox potential, and concentrations of dissolved
oxygen in the water purged from the well. The
samples are taken after the sensitive parameters
become stable (Yeskis and Zavala, 2002).
Depending on the vertical interval screened by
a monitoring well, on the vertical distribution of
hydraulic conductivity, and on the vertical extent of
concentrations of contaminants in ground water, the
concentrations of contaminants in well water may
change over time as a well is purged. Figure 3.1
presents a common scenario where this situation
might be expected. Ground water contamination is
present in an aquifer in the flood plain of a major
river. The land surface is comprised of silts and
clays of the flood plain, while sands and gravels
from old meanders of the river occur at depth. The
water table occurs in the silts and clays, and the
monitoring well is screened in the silts and clays.
Contaminated ground water moves through the
layers of sand and gravel because they have higher
hydraulic conductivity.
When the monitoring well is pumped to a mod-
est extent, it will produce water from the silts and
clays. This water often is recent recharge water
from precipitation, and is free of contamination.
When the well is purged more extensively, con-
taminated water is drawn in from the deeper, more
-------
Figure 3.1 Effect of the extent of purging and vertical heterogeneity on concentrations of contaminants
sampled by a monitoring well.
hydraulically conductive intervals. The actual
physical relationship between water bearing units
that are sampled after modest purging and the units
sampled after extensive purging will vary from
one site to another. However, all aquifers are to
some extent heterogeneous, and similar effects can
be expected at most sites. The concentrations of
contaminants can go up or down as a well is purged
more extensively. Aquifer heterogeneity is dis-
cussed in more detail in Section 4.5.
To select appropriate wells for concentration and
isotope analysis, it is necessary to know the rela-
tionship between the screen of the monitoring well
and the various units in the aquifer that can yield
ground water to the monitoring well. If a well has
a short screen that is installed near the center of a
transmissive sand and gravel unit, the geochemi-
cal parameters will likely stabilize when the well
is purged, and the concentrations of contaminants
in the well water will be representative of the
aquifer unit that is being sampled. If a well has
a long screen that extends between layers of sand
or gravel and layers of silt or clay, the well will
sample different aquifer units that may have differ-
ent concentrations of contaminants and different
values for the ratio of stable isotopes in the con-
taminants. The water from the well is a composite
of the water from the different units, and the rela-
tive contribution of the different aquifer units may
vary over time as the well is purged. When this is
the case, there may be a problem in interpretation
of both concentrations and stable isotope ratios in
organic contaminants and one should consider the
heterogeneities.
When water from a monitoring well comes from
several different aquifer units, the ground water
often is not in geochemical equilibrium. The
water may have measurable concentrations of iron
(II) or sulfide, or a low redox potential, but also
have oxygen or nitrate concentrations greater than
1 mg/L. Oxygen or nitrate should not occur along
with reduced species of iron or sulphur in the same
ground water. If they occur together, this is strong
evidence that ground waters from aerobic and
anaerobic geochemical environments have been
mixed together in the monitoring well.
It is difficult to fully avoid contamination of a
ground water sample with oxygen from the atmo-
sphere. The simultaneous presence of oxygen and
reduced species of iron and sulphur may be an arti-
fact of sampling. However, this is not the case with
nitrate. The simultaneous presence of nitrate and
reduced species of iron or sulphur is an unequivo-
cal indication that the water produced from the well
came from different geochemical environments in
the aquifer.
3.2. Need to Replicate Samples
Once the monitoring well has been adequately
purged, the sample can be taken into appropriate
containers such as small glass vials marketed for
Volatile Organic Analyses. These VOA vials have
a volume of 40 ml, and are sealed with Teflon®-
faced silicone rubber septa secured in place with
-------
screw caps. They are appropriate for water samples
that will be prepared for analysis by purge and trap
or by headspace extraction.
It is good practice to collect several replicate VOA
vials from each monitoring well. This is necessary
for the regular measurement of sample replicates
as discussed in Section 2.6. The dynamic range
of an isotope ratio mass spectrometer is relatively
narrow. In order to determine the appropriate con-
centration for determination of the stable isotope
ratio, as recommended earlier, many laboratories
will first determine the concentrations of analytes
using conventional analytical techniques. If an
adequate number of replicate samples are collected,
a replicate VOA vial can be opened as needed for
each separate analysis, for laboratory duplicates,
and to provide a spare sample in case there is an
instrument failure and an analysis must be repeated.
Any replicates that are not needed for an analysis
can be discarded once the necessary data have been
collected. In the authors' experience, a minimum
of four replicate VOA samples should be acquired
from each well sampled. The replicates should be
packaged separately. If the samples are shipped
to the laboratory for analysis, and the samples are
particularly critical, half the replicates should be
shipped in one container and half in another.
If the samples are prepared by liquid extraction
with pentane or cyclohexane (e.g. samples for
analysis of BTEX or PAH), the water can be col-
lected in a larger container to avoid the need to
handle small volumes of volatile solvents. A 1-liter
glass bottle is convenient. Again samples should
be taken in replicate. At least two replicate samples
should be acquired from each well.
3.3. Requirement for Sample
Preservation
It is the practice in some laboratories to forgo the
use of chemical preservatives, and rely on cooling
of the sample at 4°C or 10°C to prevent biodeg-
radation of analytes. This practice is not recom-
mended. The ambient temperature of ground water
at many sites in the temperate parts of the Earth is
only a few degrees warmer than that in refrigera-
tors. The micro-organisms in these aquifers are
already acclimated to the lower temperatures. As
an example, Bradley and Chapelle (1995) reported
that micro-organisms in sediment from a contami-
nated aquifer at Adak, Alaska metabolized toluene
under aerobic conditions with a first order degrada-
tion rate near 11% per day at 5°C. Bradley et al.
(2005) documented anaerobic reductive dechlorina-
tion of trichloroethylene, c/'s-dichloroethylene, and
vinyl chloride at 4°C in aquifer sediments and river
sediments from Alaska. It is prudent to chemically
preserve the samples.
The most widely used preservative is the addition
of a solution of 36% hydrochloric acid diluted 1:1
in water to produce a pH < 2 in the sample. For
most ground water samples, only three to five drops
of the 1:1 dilution are necessary to preserve a 40 ml
sample. Purge and trap methods that are approved
by U.S. EPA specify that samples be preserved
by addition of hydrochloric acid to obtain a pH
< 2 (U.S. EPA, 1984; U.S. EPA, 1990; U.S. EPA,
1996).
Hydrochloric acid was generally considered a
safe "Universal Preservative" until O'Reilly et al.
(2001) reported that methyl tertiary butyl ether
(MTBE) was hydrolyzed at pH near 2. Following
the recommendation of Kovacs and Kampbell
(1999), McLoughlin, et al. (2004) proposed using
trisodium phosphate dodecahydrate (TSP) at a con-
centration of 1% as a preservative for ethers such as
MTBE. This concentration of TSP will buffer the
water sample to a pH near 10.5. Other alternatives
for preservatives include sodium hydroxide at a
concentration of 0.1%, sodium azide, and mercury
salts. Sodium hydroxide is a useful preservative;
however, it may hydrolyze chloroethanes (Jeffers,
et al., 1989; Pagan, et al., 1998).
If sodium azide or mercury salts are used as pre-
servative, the preserved water sample becomes
a hazardous waste when the analysis is com-
pleted. These compounds are not recommended.
Hydrochloric acid, trisodium phosphate, and sodi-
um hydroxide act by maintaining the pH in a range
that is not tolerated by most micro-organisms.
When the analysis is completed, it is a simple mat-
ter to neutralize the preservative before the samples
are disposed.
Kovacs and Kampbell (1999) demonstrated that
several volatile organic compounds sorbed to the
Teflon-faced septum used to seal a conventional
VOA vial. The sorption of longer chain aliphatic
compounds such as 2,3-dimethylpentane,
2,4-dimethylhexane, and 2,2,5-trimethylhexane
was substantial. From 20% to 30% of the
material originally present would sorb within
21 days storage at 4 C. To prevent sorption to
the Teflon-faced septum, they covered the septum
with lead foil (3M® Company, Lead Foil Tape
420). The foil was cut into circles with a device
used to bore holes in rubber stoppers, and then
the circles were attached to the Teflon-face of the
septum. To prevent dissolution of the lead foil
by hydrochloric acid used as preservative, they
preserved the samples with 1% trisodium phosphate
dodecahydrate (TSP).
-------
3.4. Performance of HCI and TSP as
Preservatives During Storage of
Samples
Preservatives prevent biodegradation or abiotic
transformation of analytes. If a preservative func-
tions as intended, the concentrations of the analyte
of concern will not change during storage. Science
can not be used to prove a negative. Experimental
trials with preservatives can not be used to prove
that a preservative is universally effective. One
sample of ground water may have active micro-
organisms, or have reactive chemical species such
as Fe+2 or HS"1, while another does not. There is
no way to know whether concentrations of the ana-
lytes of concern were stable in a trial because the
preservative was effective, or because the samples
of water submitted to the evaluation of the preser-
vative did not contain active micro-organisms or
reactive chemicals.
Most evaluations of preservatives have been con-
ducted with drinking water. Tap water would not
be expected to contain organisms that degrade
organic contaminants, or contain reactive chemi-
cals other than chlorine and dissolved oxygen.
In particular, tap water would not be expected
to have organisms that are capable of anaerobic
biodegradation of organic contaminants. To evalu-
ate the performance of hydrochloric acid to pH
<2 or l%trisodium phosphate as preservatives
for contaminated ground water, common ground
water contaminants were spiked into ground water
acquired from monitoring wells at hazardous
waste sites. The ground water used in the trial
was collected from monitoring wells at a gasoline
spill site, an industrial landfill, and a municipal
solid waste landfill. Three replicate water samples
were analyzed immediately, and three replicate
water samples were stored at 4°C for 28 days,
and then analyzed. The difference in the average
concentrations of the contaminants was evaluated
statistically.
For most of the contaminants, the variance in
the analytical data made it possible to detect
any removal of the contaminant that was greater
than 20% of the initial concentration at 95%
confidence. If the removal was less than 20% of
the initial concentration this was considered as
evidence of adequate preservation. It has to be
noted that the study may not be representative
for all hydrochemical conditions. Depending
on specific conditions (e.g. presence of reactive
species Fe2+, HS1"), sample alterations may occur
even if preservatives are added. Table 3.1 and
3.2 provides a summary of the performance of
hydrochloric acid and trisodium phosphate in
conserving organic contaminant samples.
If biodegradation in the samples is stopped by the
addition of preservatives and appropriate measures
are taken to prevent analyte losses by evaporation
or decay, then isotope values can be stable over
time periods of 1 to 3 months and even longer in
some cases. This was shown for BTEX compounds
after a holding time of 4 weeks (Hammer et al.,
1998) and PCE after a holding time of 4 months
(Blessing et al., 2008). However, systematic stud-
ies of holding times in CSIA under varying storage
conditions have not been published to date.
The stability of the compounds in samples col-
lected for CSIA should be carefully evaluated. In
the absence of other information, adopt contain-
ers, methods of shipping, conditions for storage,
and holding times for samples that are intended
for CSIA of contaminants in ground water that are
the same as the containers, methods of shipping,
conditions for storage, and holding times that are
acceptable to the regulatory authority for the analy-
sis of concentrations of the contaminants.
-------
Table 3.1. Compounds that are adequately pre-
served in ground water with Hydrochloric Acid to
pH <2 or with 1% Trisodium Phosphate. Less than
20% of the initial concentration should be lost from
ground water stored in at 4°C for 28 days in 40-ml
VOA vials with a Teflon-faced silicone rubber
septum.
Hydrocarbons,
alcohols, aldehydes
and ethers
benzene
toluene
ethyl benzene
ra+p-xylene
o-xylene
1,2,3-trimethylbenzene
1 ,2,4-trimethylbenzene
1,3,5 -trimethylbenzene
nitrobenzene
naphthalene
methyl tertiary butyl
ether
ethyl tertiary butyl ether
tertiary amyl methyl
ether
di-isopropyl ether
tertiary butyl alcohol
tertiary amyl alcohol
acetone
isopropyl alcohol
2-butanone
4-methyl-2-pentanone
1,4-dioxane
Halogenated
compounds
methylene chloride
chloroform
carbon tetrachloride
bromoform
dibromochloromethane
1 , 1 -dichloroethane
1,2-dichloroethane
tetrachloroethene
trichloroethene
trans- 1 ,2-dichloroethene
cis- 1 ,2-dichloroethene
1,1-dichloroethene
vinyl chloride
chlorobenzene
1 ,2-dichlorobenzene
1 ,3 -dichlorobenzene
1 ,4-dichlorobenzene
Table 3.2. Compounds that are adequately pre-
served in ground water with Hydrochloric Acid to
pH <2, but are not adequately preserved with 1%
Trisodium Phosphate. More than 20% of the initial
concentration might be lost from ground water
stored in at 4°C for 28 days in 40-ml VOA vials
with a Teflon-faced silicone rubber septum.
Hydrocarbons,
alcohols,
aldehydes and
ethers
Halogenated compounds
Adequately preserved with Hydrochloric Acid,
not adequately preserved with Trisodium
Phosphate
dibromofluoromethane
l,2-dibromo-3-chloropropane
1 ,2-dibromoethane
1,1,1 -trichloroethane
1 , 1 ,2-trichloroethane
1,1,1 ,2-tetrachloroethane
1 , 1 ,2,2-tetrachloroethane
Not adequately preserved with either
Hydrochloric Acid or Trisodium Phosphate
styrene
chloromethane
bromomethane
3.5. Avoid Isotopically Labelled
Surrogate Compounds and Internal
Standards
In the analysis of contaminant concentrations,
surrogate compounds and or internal standards
are typically introduced to the samples during the
sample preparation process to allow corrections for
losses of analytes and for variability in the response
of the instrument which might be caused by factors
such as slight differences in the injection volume
or in the flow rate of the carrier gas. It is essential
that there is base line separation of the peaks of
the added surrogate or internal standard and the
peaks of any target analyte. This in particular
precludes the use of isotopically labelled surrogate
compounds and or internal standards, which are
common in GC/MS analysis. If such isotopically
labelled surrogate compounds are used for analysis
of contaminant concentrations, additional samples
must be provided for CSIA that did not receive
the isotopically labelled surrogates or internal
standards.
-------
4.0
Interpretation of Stable Isotope Data from
Field Sites
This section is intended for contractors and consult-
ants that will evaluate data on stable isotope ratios,
and produce a report for the site manager and the
regulatory staff. It is also intended for regulators
who will review the report. This section presents a
simple equation (the Rayleigh equation) that may
be used to predict the extent of biodegradation of
an organic compound from changes in the value of
the stable isotopic ratio (513C or 52H). This section
discusses conditions that are necessary to apply
the Rayleigh equation to predict biodegradation of
an organic contaminant in ground water samples
from field sites. It discusses the different assump-
tions that are necessary to calculate the extent of
biodegradation, and evaluates situations where
the various assumptions are most appropriate. It
compares rates of biodegradation extracted from
concentration data from monitoring wells to rates
of biodegradation extracted from CSIA analyses.
The section illustrates the use of CSIA analyses to
estimate field-scale rates of biodegradation when it
is impossible or misleading to extract the rates from
data on attenuation of concentrations. It discusses
the effect of heterogeneity of flow and of the rate
of biodegradation on stable isotopic ratios, and it
provides recommendations to minimize the con-
founding effect of heterogeneity on the estimate of
biodegradation.
4.1. Prerequisites for Application of
Isotope Data to Demonstrate and
Quantify Biodegradation
Sherwood Lollar et al. (1999) suggested four crite-
ria that must be met to apply CSIA to provide evi-
dence for biodegradation in the field. These original
criteria, with two additional criteria, form the basis
for the recommendations below and hence will be
discussed in some detail.
In the course of many biochemical and abiotic
reactions, molecules containing the lighter isotopes
exclusively (i.e. 12C) tend to react more rapidly
compared to molecules containing the heavy stable
isotope (i.e. 13C). As the reaction proceeds, the
ratio of stable isotopes in the material that remains
behind, in the material that has not gone through
the reaction, will therefore change. The more the
reaction proceeds the more pronounced the isotope
shift in the ratio of 13C to 12C will be. This change
in the ratio of stable isotopes is called stable isotope
fractionation and can be expressed as the stable
isotope fractionation factor alpha (a) as described
in Equation 4.1:
4.1
where R is the stable isotope ratio (13C/12C) of
the compound, and the subscripts a and b may
represent a compound at time zero (to) and at a
later point (t) in a reaction; or a compound in a
source zone, versus a down gradient well. For many
organic contaminants, stable isotope fractionation
during biotic and abiotic degradation can also
often be quantitatively described by the Rayleigh
equation (Equation 4.2)
R = R0/(a"1) 4.2
where R is the stable isotope ratio (13C/12C) of the
compound at time t, RO is the initial isotope value of
the compound and/is the ratio (C/C0) of the con-
centrations of the compound at time t and zero.
As discussed in Section 2, the stable isotope ratio
is reported in the delta notation, where the ratio is
normalized to the ratio in a standard.
Equation 4.2 can be rearranged to produce
Equation 4.3 (Section 7 for details)
(° ^groundwater ° (-'source )'£)
— t
4.3
where 513C
'groundwater
is the measure of the isotope
ratio in the organic contaminant in the sample
of ground water, 513Csource is the isotopic ratio in
the un-fractionated organic contaminant before
biodegradation has occurred, and epsilon (e) is
the stable isotope enrichment factor as defined in
Equation 4.4.
e = (oc-l)*1000 4.4
The larger the fractionation during the reaction,
the more negative is the corresponding value of
epsilon. Throughout this Guide we will use the
-------
stable isotope enrichment factor (s) to make all the
data easily comparable.
The next few sections discuss in detail the criteria
that must be met to apply CSIA to provide evidence
for biodegradation in the field.
4.1.1. Does Biodegradation of the
Compound Produce Isotope
Fractionation?
For CSIA to be useful, laboratory studies must have
demonstrated that significant fractionation does
occur during biodegradation (see Table 8.1 for a
compendium of information on enrichment factors
during biodegradation). While this basic principle
has been established for a wide range of organic
contaminants (including chlorinated ethylenes and
ethanes, aromatic hydrocarbons such as the BTEX
compounds, lower molecular weight alkanes,
MTBE, TEA, and some PAHs), it is not true for all
compounds under all circumstances. For example,
high molecular weight petroleum hydrocarbons
tend to be isotopically conservative because any
fractionation due to biodegradation is generally
"diluted" by the large number of non-reactive
carbon atoms. Similarly, for some compounds
under specific conditions (i.e. aerobic toluene
biodegradation) significant carbon isotope fraction-
ation is observed only if the degradation pathway
proceeds by reactions that attack the methyl group
rather than reactions that attack the benzene ring
(Moraschetal, 2002).
4.1.2. Is the Observed Extent of
Fractionation Significant?
To be significant, the extent of fractionation must
be greater than the total analytical uncertainty. In
addition, the observed difference in the values of
513C must exceed the spatial and temporal variabil-
ity introduced by different sources of contamina-
tion at the site, by the mixing of ground water flow
lines, and by what are typically the minor effects
of processes such as sorption or volatilization. As
demonstrated in Section 2.4, the total analytical
uncertainty for 513C analyses is typically ± 0.5%o.
As a result, the observed fractionation must be at a
minimum > 1%0. To ensure reliable interpretation,
we recommend that fractionation on the order of
2%o be used as a criterion for positive identification
of degradation in order to minimize the possibil-
ity of an erroneous interpretation. Provided that
other causes for the differences in the stable isotope
values can be excluded, there is a qualitative indi-
cation of biodegradation or transformation along a
flow path in ground water when the values of 513C
in the compounds of interest in the down gradient
wells are enriched (less negative) by 2%o compared
to values of 513C in the up gradient well.
It is important to appreciate that this criterion of
2%o will be met at very different levels of biodeg-
radation, depending on the extent of fractionation
during degradation of a given compound. For
example, due to the large enrichment factors (s)
associated with reductive dechlorination of TCE,
observed fractionation exceeds 2%o at a very early
stage of biodegradation, when < 20% is degraded
or > 80% is still remaining (Panel A of Figure 4.1).
In contrast, for petroleum hydrocarbons such as
benzene and toluene, the important but more subtle
carbon isotope effects observed during degradation
are such that significant fractionation > 2%o is only
discernable when biodegradation has proceeded
more extensively and almost 60% of the original
contaminant mass has been degraded, as illus-
trated in Panel B of Figure 4.1 (Ahad et al., 2000;
Mancini et al., 2003; Meckenstock et al., 1999;
Morasch et al., 2004). Several studies suggest that
for compounds with small enrichment factors for
carbon, such as the aromatic hydrocarbons, the
larger enrichment factors (s) associated with hydro-
gen isotope fractionation may make coupling of
CSIA for carbon and hydrogen the best approach to
identify biodegradation (Fischer et al., 2008; Gray
et al., 2002; Mancini et al., 2003; Mancini et al.,
2008a).
Once biodegradation is documented in a qualitative
fashion, the next step is an evaluation of whether
isotopic variation can be used to quantitatively
calculate the extent of biodegradation and to derive
biodegradation rates based on the CSIA data.
-------
-10
-15
^
O -20
CO
-25
-30
-35
0.0 0.2 0.4 0.6 0.8
Fraction of TCE Remaining
1.0
-18
-20
-22 \
-24
-26 \
-28
0.0 0.2 0.4 0.6 0.8
Fraction of Benzene Remaining
1.0
Figure 4.1. Degradation of (A) TCE and (B)
benzene by enrichment cultures. The
stable carbon isotope ratios in the
substrate that remains after biodegrada-
tion are plotted against the fraction of
the original concentration remaining.
Data for TCE degradation are after
Sherwood Lollar et al. (1999) and for
benzene after Mancini et al. (2003).
Dotted lines represent ± 0.5 %o around
the 513C0 value of TCE and of benzene,
respectively. The vertical solid red
arrow represents the extent of fraction-
ation necessary to recognize biodegra-
dation in field data (2%o).
4.1.3. Is the Observed Fractionation
Reproducibly and Accurately
Correlated to a Distinct Process?
If fractionation is to be used to predict degrada-
tion, the isotopic enrichment factor for a particular
contaminant that is degraded by a particular process
or pathway must be reproducible from one study
to the next. The results of extensive research have
shown this criterion to be necessary for valid inter-
pretation of data on 513C.
Published information on laboratory-derived,
compound specific enrichment factors that were
determined for biodegradation processes under
various redox conditions is available from recent
review articles (Eisner et al., 2005; Mancini et
al., 2003; Meckenstock et al., 2004; Morrill et al.,
2006; Schmidt et al., 2004). Many values are sum-
marized in Table 8.1. Enrichment factors are also
available on the internet at www.isodetect.de. and
this website will provide updated information over
time. The web page is available in either German
or English. For the English Language website;
select the link for the English Website from the
menu, follow the link to isotope enrichment, and
follow the link to table Isofrac. In the experiments
listed in Table 8.1, either single strains or mixed
bacterial cultures degraded the compounds as the
sole carbon source using a single electron accep-
tor (e.g. oxygen, nitrogen, sulfate). For the same
compound and the same biochemical pathway of
degradation, the agreement among the enrichment
factors determined by the different studies is quite
good, reflecting the fact that, to first approximation,
the main controlling influence on fractionation is
the reaction mechanism (e.g. bond breakage).
For many of the compounds in Table 8.1, differ-
ent laboratories and different studies report a range
of enrichment factors for the same biodegradation
process. Table 4.1 below summarizes data from
Table 8.1 to compare the total range of values pub-
lished to date for reductive dechlorination of chlori-
nated ethenes. Carbon isotope fractionation during
reductive dechlorination of chlorinated ethenes
is perhaps the most extensively studied system to
date, with the values in Table 4.1 reflecting experi-
ments done by a large number of different groups
worldwide with a wide range of different microbial
consortia and microcosm conditions.
Table 4.1. Ranges of carbon isotope enrichment
factors for microbial reductive dechlorination of
chlorinated ethenes published in the literature to
date (Bloom et al., 2000; Cichocka et al., 2007;
Hunkeler et al., 2002; Lee et al., 2007;
Slater et al., 2001). See also Table 8.1.
Compound
TCE
cis-DCE
VC
Range of a
values
0.9975 to 0.9771
0.9859 to 0.9789
0.9785 to 0.9689
Range of z
(%»)
-2.5 to -22.9
-14.1 to -21.1
-21.5 to -31.1
While variation in the range of published enrich-
ment factors for a given degradation reaction
are very important from the point of view of
-------
understanding the details of the reaction mecha-
nism, the variation in published values does not
necessarily introduce a large uncertainty into the
estimate of the fraction remaining after degradation
(/) as calculated using Equation 4.2 or 4.3.
For instance, the total analytical uncertainty in
measured 513C values is typically ±0.5%o for carbon
CSIA for many of the hydrocarbon contaminants
investigated to date. Total uncertainty in (/), the
fraction of contaminant remaining, is at a mini-
mum the analytical uncertainty associated with
typical VOC concentration analyses. While under
optimized performance, VOC concentrations can
be determined to a precision of ±5%; typically,
commercial VOC analyses are ±20 to 30%. In the
estimate of (/) using Equation 4.2, uncertainty in
the second or third decimal place in the exponent
(ot-1) does not contribute as much uncertainty as
does the uncertainty in the direct calculation of (/)
caused by uncertainty in the analysis of VOC con-
centrations. This can be shown by calculating the
propagation of errors for the individual parameters
in the Rayleigh equation (Griebler et al, 2004b).
More examples and discussion are provided in
Section 4.2.3.4a.
Data published to date suggests that the rate of bio-
degradation does not seem to significantly impact
the observed enrichment factor e (Mancini et al.,
2006; Morasch et al., 2001). The dominant con-
trolling parameter on fractionation is the reaction
mechanism. As is predicted from theoretical princi-
ples of isotope fractionation, degradation pathways
or reaction mechanisms can have characteristic sta-
ble isotope enrichment factors based on the bonds
that are broken. Variations between the stable
isotope enrichment factors for one pathway com-
pared to another are one of the most important fac-
tors influencing stable isotope fractionation during
biodegradation. This principle is a well-established
foundation of stable isotope geochemistry, having
been demonstrated for microbial methanogenesis
via different pathways in a landmark paper in 1985
(Whiticar and Faber, 1985) and elucidated for
photosynthesis by C3 versus C4 metabolic path-
ways more than twenty years ago (O'Leary, 1981).
It follows that conditions that control the dominant
degradation pathway can control the characteristic
fractionation pattern, and the value of the isotopic
enrichment factor. For compounds that degrade
under different reaction mechanisms under aero-
bic versus anaerobic conditions, the characteristic
isotopic fractionation observed varies with redox
conditions. This has been quite extensively studied
for MTBE (Hunkeler et al., 200la; Kolhatkar et al.,
2002; Kuder et al., 2002; Kuder et al., 2005; Resell
et al., 2007; Zwank et al., 2005), benzene and tolu-
ene (Ahad et al., 2000; Fischer et al., 2007; Fischer
et al., 2008; Hunkeler et al., 200Ib; Mancini et al.,
2003; Mancini et al., 2008a; Meckenstock et al.,
2004; Morasch et al., 2001; Morasch et al., 2002;
Morasch et al., 2004) and is recently being eluci-
dated for the chlorinated ethenes (Chartrand et al.,
2005; Chu et al., 2004). Even under similar redox
conditions, if different microbial populations use
different degradation pathways, each can result in
a reproducible and distinct value for the isotopic
enrichment factor, as has been shown for aerobic
biodegradation of 1,2-dichlorethane (Hirschorn
et al., 2004), aerobic biodegradation of toluene
(Morasch et al., 2002), and aerobic biodegradation
of MTBE (Resell et al., 2007).
In most aerobic degradation pathways, the first step
is usually an activation of the molecules by an oxy-
genase reaction to introduce hydroxyl, epoxide or
other reactive oxygen-containing groups. For some
compounds, there are several types of oxygenase
reactions, and the extent of isotope fractionation
can depend on the particular oxygenase reaction
that is responsible for biodegradation. In the case
of aromatic hydrocarbons this may range from
undetectable fractionation of stable isotopes of
carbon for reactions that are carried out by dioxy-
genase enzymes that attack the 7i-electron system of
the aromatic ring to strong fractionation caused by
reactions carried out by monooxygenase enzymes
that attack the ring or methyl groups. Practical rec-
ommendations for assessing the uncertainty intro-
duced by the range of available fractionation factors
are discussed in detail in Section 4.4.
4.1.4. Do Non-Degradative Processes
Influence the Observed Isotope
Fractionation?
In order to use CSIA to understand the degradation
of contaminants, the isotope fractionation dur-
ing degradation must be readily discernable from
isotope effects associated with other subsurface
processes that do not destroy the contaminant, such
as volatilization, dissolution and sorption. Isotope
fractionation during volatilization (Harrington et al.,
1999; Ward et al., 2000); dissolution (Dempster et
al., 1997; Hunkeler et al., 2004; Slater et al., 1999;
Ward et al., 2000); diffusion (Hunkeler et al., 2004;
Bouchard et al., 2008) and sorption (Harrington
et al., 1999; Kopinke et al., 2005; Meckenstock et
al., 1999; Schuth et al., 2003; Slater et al., 2000)
is typically small or is indiscernible outside of the
analytical uncertainty typical for CSIA (±0.5%o
for carbon isotopes; ± 5%o for hydrogen). During
sorption of contaminants to carbonaceous material,
-------
a hydrogen isotope shift of only 8%o was observed
after 95% of the contaminant was sorbed (Schuth
et al., 2003). Significant hydrogen isotopic effects
were only observed in laboratory experiments
where aromatic hydrocarbons underwent near
complete vaporization or sorption, in excess of 95%
removal (Schuth et al., 2003; Wang and Huang,
2003). Hence, Wang and Huang (2003) noted that
large isotopic shifts might be relevant to processes
such as air sparging and to studies in the unsatu-
rated zone, but large isotopic shifts are not likely
to be significant in most natural systems where
extensive mass loss due to volatilization or sorption
is unusual. In a recent study documenting carbon
isotope fractionation due to diffusion, Bouchard et
al. (2008) demonstrated that even in the unsaturated
zone where diffusive effects on isotope composi-
tion might be expected to be most pronounced com-
pared to the saturated zone, diffusive effects were
only observable if measured within a few days of
the spill, and where measurements could be done at
a very fine spatial scale.
4.1.5. Do Abiotic Degradation Processes
Occur and Produce Isotope
Effects for the Compound of
Interest?
The relative importance of biodegradation versus
processes of abiotic degradation at the site must be
considered. In the past few years, the principles of
Rayleigh controlled isotope fractionation of organic
contaminants in ground water have been shown to
apply to abiotic degradation as well as biodegrada-
tion (Bill et al., 2001; Eisner et al., 2007a; Eisner
et al., 2008; Hofstetter et al., 2008; Slater et al.,
2002; VanStone et al., 2004; VanStone et al., 2008;
Zwank, et al., 2005). Zero valent iron is widely
used in active remediation of ground water con-
tamination. While much research is still underway
to understand the precise reaction mechanisms
associated with degradation of chlorinated eth-
enes on zero valent iron, CSIA indicates that the
mechanisms are similar to the familiar mechanisms
associated with biodegradation, and that different
abiotic degradation mechanisms are associated with
different characteristic patterns of fractionation.
Traditionally, rates of natural abiotic degradation
in ground water were thought to be insignificant
unless they were enhanced through abiotic reme-
diation schemes such as the addition of zero valent
iron. This view is changing. There have been
several recent studies of the role of abiotic reac-
tions with minerals and the role of microbially-
mediated abiotic reactions at field sites (Bradley
and Chapelle, 1997; Butler and Hayes, 1999;
Cervini-Silva et al., 2001; Ferrey et al., 2004; Lee
and Batchelor, 2002; McCormick and Adrians,
2004).
The possibility of abiotic degradation introduces
the challenge of distinguishing between the effects
of abiotic and biotic isotopic fractionation in any
system where both types of degradation may be sig-
nificant. Liang et al. (2007) noted that the isotope
fractionation during abiotic degradation of PCE
and TCE by FeS was much greater than the frac-
tionation during anaerobic biodegradation of PCE
and TCE. Reduced iron sulfides such as FeS can
be an important component of aquifer sediments
at hazardous waste sites. Liang et al. (2007) warn
that the use of an enrichment factor appropriate for
biodegradation instead of the factor appropriate for
the abiotic mechanism may overestimate the true
extent of degradation at field scale. A similar pat-
tern of smaller biological enrichment factors com-
pared to abiotically-mediated degradation has been
identified for MTBE and 1,1,1-TCA (Eisner et al.,
2007a; 2007b) and PCE (Lee et al., 2007; Nijenhuis
et al., 2005; Slater et al., 2001; Slater et al., 2003),
suggesting that additional rate-limiting factors
in biochemical reactions require more in depth
research. VanStone et al., (2008) and Eisner et al.,
(2008) discuss the potential of using CSIA to distin-
guish between abiotic and biodegradation processes
where both types of processes may be important.
4.1.6. Is the Rayleigh Equation an
Appropriate Model to Describe the
Data Set?
For compounds that are intermediates in degrada-
tion pathways, such as the products of reductive
dechlorination of chlorinated ethylenes, a straight-
forward application of the Rayleigh equation
(Equation 4.2) is not strictly possible. The isotope
ratio in the intermediate compound will change due
to the combined effects of isotopic fractionation
during its production from the parent compound
and isotopic fractionation due to its own continu-
ing degradation. There is one important exception.
The Rayleigh equation can be used when complete
transformation of the parent compound occurs prior
to further degradation of the intermediate com-
pound (Morrill et al., 2005).
When production and degradation of the interme-
diate compound occurs simultaneously, a more
complex isotope evolution occurs that can be
evaluated using multistep reactive transport models
(van Breukelen et al., 2005; Morrill et al., 2006).
Quantitative information on biodegradation can be
obtained by fitting an analytical model (Beranger
et al., 2005) or numerical model (van Breukelen,
-------
et al., 2005) that describes the isotope evolution
during sequential processes to the measured isotope
data. Van Breukelen et al. (2005) used a simple
one dimensional model to provide insight in the
rates of transformation of parent and intermediate
compounds. The simulation of different degrada-
tion scenarios such as various degrees of degrada-
tion or different relative rates of biodegradation for
different steps in a multi-step process can also be
very useful as a benchmark for a semi-quantitative
interpretation of isotope data.
For certain chlorinated solvents the situation is even
more complex because the degradation pathways of
different compounds can converge and produce the
same daughter products (Kirtland et al., 2003). For
example, trichloroethylene (TCE) can be produced
from biological reductive dechlorination of tetra-
chlorethylene (PCE), or through an abiotic reaction
from 1,1,2,2-tetrachlorethane. If several potential
parent compounds are present in ground water at
the same time, it is difficult to interpret the behav-
iour of the compound from CSIA. Due to these
complexities, the conceptual model for biodegrada-
tion at a site should distinguish those compounds
that are only present as parent compounds from
those compounds which might be present both as
parent and daughter compounds. See for example
Hirschorn et al., (2007). Section 4.2 covers the
appropriateness of a Rayleigh model in more detail
with respect to field data.
4.2. Recommended Steps for the
Quantification of Biodegradation
Based on CSIA
4.2.1. Site Characterization
Use of CSIA is no silver bullet and will be most
useful and cost effective when applied within the
context of the hydrological, geological, geochemi-
cal and microbiological parameters at the site. The
factors that affect contaminant transport and degra-
dation over time as well as space must be identified
and evaluated. This includes the important geohy-
drological parameters (ground water flow direction,
hydraulic conductivity, hydraulic gradient) and
geochemical conditions (concentrations of oxygen,
nitrate or sulfate within the plume). Ultimately,
a conceptual site model can be developed that
will reveal practicable remediation goals that are
capable of protecting existing or potential recep-
tors from contamination. Iterative generation and
interpretation of field data from a general survey
is necessary to identify the major compartments of
the plume (the source, the fringe, the center line,
and the mixing zones) as well as the most relevant
processes that contribute to natural attenuation.
4.2.2. Evaluate Field Data for the Fit to
the Rayleigh Model
The Rayleigh model (Equation 4.2 and 4.2) predicts
that a plot of 513C or 52H on the logarithm of the
concentration remaining should be a straight line.
If field data are plotted as described above, and the
data follow a straight line, then a single process for
biodegradation or abiotic transformation likely con-
trols the concentrations at field scale, as illustrated
in Figure 4.2. This is called a Rayleigh correlation.
Dilution, dispersion, sorption, volatilization, and
mixing between contaminant sources with different
values of 513C or 52H will cause the data to fall off
of the straight line.
Given the importance of dilution and dispersion at
field scale, it might intuitively seem likely that no
set of realistic field data would show a Rayleigh
correlation. However, case studies and evolving
field experience have in fact shown that a signifi-
cant number of sites do have field data that fit the
Rayleigh model (Abe and Hunkeler, 2006; Griebler
et al., 2004b; Kolhatkar et al., 2002; Morrill et al.,
2005). The existence of such a correlation indicates
that biodegradation or abiotic transformation is the
significant process that controls changes in concen-
trations of contaminants.
8
3
-2
-7
P-12
10 -17
-22-I
-27
-32
-37
Mixing with Another
Contaminant Source
Dilution,
Dispersion,
Sorption,
Volatilization
-\
Range Expected for MTBE in Gasoline
234567
Natural Logarithm of Cone. MTBE (ng/L)
Figure 4.2. Testing field data on CSIA and concen-
trations of contaminants for fit to the
Rayleigh equation. Deviations from a
straight regression line in the plot of
513C on the natural logarithm of con-
centration can indicate that processes
other than degradation control the con-
centrations of contaminants. Example
data plotted for Table 1 of Kolhatkar
et al. (2002). The dotted lines bound
the values of 513C that are expected for
MTBE that was blended into gasoline.
-------
The first recommendation for using CSIA to
quantify biodegradation is to plot the 513C of the
compound against the natural logarithm of the
concentration of the compound to determine if
these parameters show a Rayleigh correlation as
illustrated in Figure 4.2. This "test" is simply the
first step in determining if the Rayleigh controlled
fractionation inherent in Equation 4.2 is an appro-
priate model for the site. There is no need to take
the location of the respective wells into account on
this level when performing the Rayleigh analysis
because the location does not influence the calcula-
tion. However, data points that drastically fall of the
straight regression line can be identified and might
be evaluated further for other processes that influ-
ence the compound apart from biodegradation, such
as dilution in the monitoring well, dispersion along
the flow path, or volatilization. A strong correlation
to the Rayleigh model adds considerable confidence
to the application of CSIA data to understand the
behaviour of a contaminant at a site.
Variations in the length and elevation of the
screened interval of monitoring wells can cause a
well to produce ground water that is either domi-
nated by the plume of contamination, or cause the
well to produce water that has a small contribution
from the plume and a major contribution from clean
ground water above or below the plume. Details
of well construction can have a strong effect on the
concentration of the organic compound in water
produced by the well. A poor correlation to the
Rayleigh model may be due to these incidental
perturbations in the concentration that are created
by the monitoring wells. As a result, a poor cor-
relation does not automatically disqualify a site for
the application of CSIA to understand the transfor-
mation processes.
4.2.3. Determination of the Primary
Isotope Signature (813Csource or
The primary isotopic signature is the isotopic ratio
of the organic contaminant of concern before it is
fractionated by biodegradation processes or abiotic
transformations. The ideal approach would be to
measure the isotopic signature of the primary con-
taminant that was spilled at the site. However, this
is rarely feasible. Nor is measurement of 513Csource
or 52Hsource for the most recent spill necessarily rel-
evant at many sites where there has been a history
of multiple spills or leakage.
There are three basic approaches to determination
of 513Csource or 52Hsource. One approach compares
values of 513C or 52H for contaminants in ground
water to values of 513C or 52H reported in the
literature. The second and third approaches are
entirely site specific. They compare 513C or 52H for
contaminants in different samples of ground water
to determine the extent of degradation between
points in space (between different wells) or points
in time (temporal variation within a single well) at
a specific site.
4.2.3.1. Value of 513Csource or 52Hsource
Based on Literature.
In the routine case where samples of the actual
spilled material are neither available nor relevant,
the approach is to make an assumption for 513Csource
or 52Hsource based on published values in the lit-
erature for undegraded pure product. This is not
unreasonable for petroleum hydrocarbons, or for
anthropogenic compounds such as chlorinated
ethenes produced from petroleum hydrocarbon
feedstocks, because the range of 513C for petroleum
hydrocarbons is well characterized and relatively
well constrained. As degradation proceeds, a point
is reached where the value of 513C or 52H may be
more positive (more enriched in 13C or 2H) than
any reported value from commercially available
products. When the value of 513C or 52H in the
field is more positive than the range in the pure sub-
stance, degradation at the site is evident (compare
Figure 4.2).
4.2.3.2. Values of 513Csource or 52Hsource
Based on Most Negative Value
at the Site
Because biodegradation induces a shift of the
residual compound to less negative values of 513C
or 52H, the most negative values measured for the
organic contaminant in ground water at the site
can be the best estimate of the original values of
513Csource or 52Hsource. While this approach can work
well for compounds for which the fractionation due
to biodegradation is large (tens of %o) relative to
the variation in assumed 513Csource, the approach is
not recommended for compounds such as benzene
and toluene for which the error in the assumption
of 513Csource will be large with respect to a relatively
small changes in 513C caused by biodegradation.
4.2.3.3. Values of 513Csource or 52Hsource
Based on Point to Point or Time
to Time Comparisons
Quantifying the relative amount of biodegradation
between wells, or in a given well over time, is com-
pelling since it involves fewer assumptions than the
literature-based approach. It does, however, require
a good hydrogeological and geological understand-
ing of the site. In this approach, one can select
wells for 513Csource or 52Hsource that sample the known
-------
source zone. As an example, the wells might be
screened across an interval with non aqueous phase
liquids (NAPL) that act as the source of ground
water contaminant. Wells in the source area would
be expected to produce water with the highest con-
centrations of contaminants. Since biodegradation
produces more enriched (less negative) 513C values,
such wells may be assumed to represent the least
degraded material at the site.
It is important to note that this approach will pro-
vide a conservative estimate of the extent of bio-
degradation. If undegraded compound is in fact
being added to the plume through mixing, desorp-
tion, or continued dissolution of NAPL, the addi-
tion of this more isotopically negative 513C material
will minimize the observed fractionation effects
produced by biodegradation (Abe and Hunkeler,
2006, Morrill et al., 2005). While continued dis-
solution of NAPL close to the source zone may
result in a complete suppression of the fractionation
signal of biodegradation, the calculation can at least
provide a conservative upper boundary on C/Co.
The true fraction remaining may be less than the
estimate.
The most thorough approach would be to calcu-
late the extent of biodegradation using all three
approaches for determining 513Csource. If the three
estimates agree, the extent of biodegradation is well
constrained. In several case studies this was indeed
the situation because the source well 513C values
were not only the most negative 513C values at the
site, but they were within the published range for
undegraded pure product (Sherwood Lollar et al.,
2001).
4.2.3.4. Selection of an Appropriate
Enrichment Factor
This Guide assumes that the isotope enrichment
factors derived from laboratory microcosm studies
are applicable to the field. In contaminant hydrol-
ogy, the removal of organic contaminants in tradi-
tional laboratory microcosm studies is commonly
used to predict the removal in field scale plumes.
The assumptions made in extrapolating isotope
enrichment factors to the field are equivalent to the
assumptions made in extrapolating data on con-
taminant degradation from laboratory microcosm
studies to predict the behaviour of a plume at field
scale.
As discussed in Section 4.1.3, there are two
important sources of uncertainty in extrapolation
of enrichment factors. The value of the enrich-
ment factor is sensitive to the biodegradation
pathway (and hence to parameters such as redox
conditions and microbial populations) and to the
reproducibility of fractionation factors under any
given set of conditions for any given biodegrada-
tion reaction pathway. The selection and evalua-
tion of enrichment factors from the literature is a
two step process. First, use site specific data on
geochemical parameters to determine the most
probable pathway for metabolism (or abiotic trans-
formation) of the contaminant at field scale. Then
search the literature (Table 8.1) for published
enrichment factors for the compound of interest
under the relevant redox conditions.
The variation in published enrichment factors for
a given set of conditions is a measure of the repro-
ducibility of the enrichment factor. One option to
deal with the variation in published enrichment
factors is to select the largest enrichment factor in
the literature to estimate the extent of biodegrada-
tion at field scale. In this case, the "largest" enrich-
ment factor is the most negative factor, the factor
with the largest absolute value. For a given change
in the value of 513C or 52H, the largest enrichment
factor will predict the largest value for the fraction
remaining after biodegradation and will predict the
smallest extent of biodegradation. As a result, the
largest value for the enrichment factors will provide
the most conservative estimate of the extent of bio-
degradation. When the difference in values of 513C
between the source and the down gradient monitor-
ing wells is small (2%oto 5%o), the value selected
for the enrichment factor can have a stronger influ-
ence on the extent of biodegradation predicted from
Equation 4.3.
A second option to deal with variation in the
published values of the enrichment factors is to
calculate a lower boundary on the extent of bio-
degradation using the highest published enrich-
ment factor, an upper boundary using the lowest
published enrichment factor, and a best estimate
of bioremediation using the mean of all the enrich-
ment factors, then compare the predictions of the
extent of bioremediation. When this approach was
applied to data from studies of bioremediation of
TCE and cis-DCE at spill sites at Dover Air Force
Base in Dover, Delaware, USA and at Kelly Air
Force Base in San Antonio, Texas, USA, the differ-
ence between the upper and lower boundaries on
the extent of biodegradation was small (Morrill et
al., 2005, Sherwood Lollar et al., 2001).
A third option is to calculate the mean and the
standard deviation of the enrichment factors, and
then use statistical techniques to estimate propaga-
tion of error to determine the effect of the variation
in published values for the enrichment factor on the
estimate of the extent of biodegradation.
-------
Reactions with large fractionation factors (e more
than an absolute value of 3%o) allow a more sensi-
tive quantification of biodegradation, while reac-
tions with small fractionation factors (e smaller
than an absolute value of l%o) require a large
degree of biodegradation (>90%) before a signifi-
cant isotopic difference between source and moni-
toring wells can be resolved (Ahad et al., 2000). As
a general principle, as the difference between 513C
and 513Csource becomes larger, the uncertainty in the
calculation of the extent of biodegradation becomes
smaller.
Figure 4.3 compares the relative effect of the value
of the isotopic enrichment factor, and the value of
513Csource, on the predicted extent of biodegrada-
tion. When the value of 513C is close to the value of
513Csource, the estimate of the extent of biodegrada-
tion is more sensitive to the value of 513C. When
the value of 513C is further away from the value of
513Csource, the estimate of the extent of biodegrada-
tion is more sensitive to the value of the enrichment
factor e.
One may be tempted to use fractionation data from
a contaminated field site to determine implicit iso-
tope enrichment factors. Although some scientific
studies have practiced this approach (Steinbach et
al., 2004), it cannot be recommended as a general
procedure. The complexity of hydrogeological
and microbial processes in the field will give only
a crude estimate of the enrichment factor com-
pared to well-controlled laboratory experiments
and will certainly introduce additional uncertainty.
Therefore, it is advisable to take appropriate labora-
tory-derived enrichment factors from the literature.
100
5 90
1-80
E 60-
D)
j 50-
il 40-
% 30-
uj -10-
n .
/ / — Methanogenic
/ I Conditions
/ '
-29.0 -28.5 -28.0 -27.5 -27.0 -26.5 -26.0
813C(%»)
-29.0 -28.5 -28.0 -27.5 -27.0 -26.5 -26.0
813C(%«)
Figure 4.3. Relative influence of different values
for 513Csource (Panel A) and different
values for the isotopic enrichment fac-
tor e (Panel B) on the calculated extent
of toluene biodegradation. The extent
of biodegradation is expressed in per-
cent of the material originally present,
calculated as B = (I-/), where /is the
fraction remaining as calculated from
Equation 4.3. The dashed lines are es-
timates of the extent of biodegradation
from 513C for biodegradation of toluene
under methanogenic conditions where
s = -0.5%o with two different values for
513Csource. The solid line is an estimate
of the extent of biodegradation under
sulfate-reducing conditions where
4.2.3.5. Estimating an Enrichment Factor
when none is Available.
Although the literature on isotope enrichment fac-
tors is expanding rapidly, there may be occasions
when an isotopic enrichment factor for a particular
compound is not available in the literature. The
following material describes an approach that may
be used to estimate an isotopic enrichment factor
from the data available for similar compounds.
Stable isotopic fractionation occurs at a distinct
chemical bond within a molecule, where the
enzymatic reaction takes place. A heavy isotope at
an adjacent position might still affect the reaction
but to a much lower extent (referred to as a second-
ary isotope effect) and can usually be neglected.
Heavy atoms further distant from the reactive posi-
tion have no influence on isotope fractionation. As
a first approximation, only the atom in the reactive
position of the molecule undergoes isotope frac-
tionation. However, in CSIAthe isotopic composi-
tion of all of the atoms of a respective element in
the molecule is measured (e.g. all carbon atoms).
-------
The stable isotope effect is therefore "diluted" by
the number of atoms at non-reactive positions of a
compound. One can distinguish between the intrin-
sic isotope enrichment factor fa) which considers
only the isotope shifts at the reactive position and
the overall isotope enrichment factor (e) which
determines the isotope fractionation of the entire
molecule. Details of this approach can be found in
Eisner et al. (2005) and Morasch et al. (2004). The
relation of (Sj) and (e) follows Equation 4.5, where
(n) is the total number of atoms of a particular ele-
ment in the molecule.
= e,/n
4.5
From the stable isotope enrichment factors and the
intrinsic factors published for anaerobic or aerobic
degradation of mineral oil constituents and chlori-
nated solvents it is apparent that CSIA can be suc-
cessfully applied to recognize isotope fractionation
in compounds with no more than twelve to thirteen
carbon atoms. For larger molecules, the expected
isotope shifts will be so strongly diluted that they
fall into the range of the experimental error of the
isotope analysis (Morasch et al., 2004).
Expressing fractionation as the intrinsic enrichment
factor (BJ) reveals that the same biochemical reac-
tions produce similar intrinsic enrichment factors
for different compounds. Anaerobic degradation of
BTEX compounds and methylnaphthalene provide
a good example. The primary enzyme reaction in
the anaerobic degradation pathways of methylated
aromatic hydrocarbons (toluene, xylene, methyl-
naphthalene) is always a fumarate addition to the
methyl group by glycyl radical enzymes. The
intrinsic carbon isotope enrichment factors have
been shown to be similar (Morasch et al., 2004).
If there is no published value for the isotope enrich-
ment factor (e) for a compound, but the biochemi-
cal reaction of the primary degradation step is
known, it should be possible to use literature values
for the intrinsic enrichment factors fa) of similar
compounds to estimate an isotope enrichment fac-
tor (e) for the compound. Such estimates have been
shown to be in the same range of accuracy as those
obtained from laboratory experiments with the
respective compounds (Meckenstock et al., 2004;
Morasch et al., 2004; Zwank et al., 2005).
As an example, a representative carbon isotope
enrichment factor for toluene which can be taken
from the literature is -1.7%o. As toluene contains 7
carbon atoms the intrinsic enrichment factor s1 for
the reactive carbon position is -1.7%o * 7 = -11.9%o
(Table 8.1). Imagine that we require an enrichment
factor for xylene. Because the initial reaction of
the degradation pathway of xylene is similar to
toluene degradation we will make an assumption
that the intrinsic enrichment factor Sj for xylene
is the same as for toluene (s; = -11.9%o). For the
overall enrichment factor e we divide the esti-
mate of Sj by 8 (xylene contains 8 carbon atoms)
to produce an estimate for the enrichment factor
of-11.9%o / 8 = - 1.5%0. This estimate is exactly
equivalent to the only value that is available in the
literature for a pure culture study of the anaerobic
biodegradation of xylene (Morasch et al., 2004;
Table 8.1). However, Table 8.1 also reveals that the
variation of fractionation factors determined for
anaerobic xylene degradation is quite large.
4.2.3.6. Concurrent Application of CSIA
Analysis for Different Elements
(Two-Dimensional Analysis).
For some contaminants, such as MTBE and ben-
zene, there is a fundamental difference in the
enzymatic mechanism for biodegradation under
aerobic and anaerobic conditions, and the differ-
ence in enzymatic mechanism is reflected in a
large difference in the values of the enrichment
factors (e) under aerobic or anaerobic conditions.
As discussed in Section 4.1.5 and Section 4.2.3.4,
depending on the compound, different values of e
may have an effect on the predicted extent of bio-
degradation, and the implications should be investi-
gated as in the example in Figure 4.3.
If field measurements of 513C are to be used to
estimate the extent of degradation, it is necessary
to know the mechanism of degradation to be able
to select the correct value of e. Frequently it is
not possible from conventional site characteriza-
tion data to unequivocally associate biodegradation
with either the aerobic mechanism or the anaerobic
mechanism. However, it may be possible to iden-
tify the mechanism of degradation from the con-
current enrichment of both carbon and hydrogen
isotopes. Kuder et al. (2005) compared the enrich-
ment of carbon and hydrogen during biodegrada-
tion of MTBE in anaerobic microcosms and in field
samples from gasoline spill sites in the USA. In a
plot of 52H for MTBE against 513C for MTBE, the
data from the field sites had the same distribution
as the distribution of the data from the anaerobic
microcosm study (Figure 4.4).
Zwank et al. (2005) made the same comparisons
of 52H against 513C for MTBE contamination in
ground water at a former industrial landfill in South
America, and established that MTBE degraded
under anaerobic conditions at that site as well.
Zwank et al. (2005) applied the term "two-dimen-
sional analysis" to describe the concurrent CSIA
-------
for both carbon and hydrogen, and offered the
approach as a useful tool to distinguish the pathway
of biodegradation of MTBE in ground water at field
scale.
The dotted line in Figure 4.4 projects the values
of 52H and 513C for MTBE that would be expected
from values of sc of -2.4%o and SH of -30%o. These
values are the extremes in the range reported in
Gray et al. (2002) for aerobic biodegradation
of MTBE by strain PM1 or mixed cultures that
resembled PM1 in their behavior. These organisms
degrade MTBE by oxidation of the methyl group
with an oxygenase enzyme. Because oxygenase
enzymes act by extracting a proton from the methyl
group, there is a very strong enrichment of deute-
rium in the residual MTBE. To provide the most
conservative estimate in Figure 4.4, the projections
of 52H and 513C expected in MTBE in ground water
start from the most positive values of 52H and 513C
determined in MTBE in gasoline as reported in
Kuderetal. (2005).
The actual distribution of 52H against 513C for
MTBE at field scale was very different than the
distribution that would be expected from aerobic
biodegradation of MTBE. The actual distribution
of 52H corresponds to eH of -11.5%o. Zwank et
al. (2005) reported an estimate of SH of -15.6%o at
the site in South America. The actual distribution
of 513C corresponds to a value of sc in the range
-8.9%oto-10.2%0.
The first step in anaerobic biodegradation of MTBE
is hydrolysis of the ether bond (Kuder et al., 2005;
Zwank et al., 2005). In the hydrolysis reaction,
there is strong enrichment of 13C in the carbon
atoms involved in the ether bond. Because the
hydrogen atoms are not directly involved, there is
much less fractionation of hydrogen.
Resell et al. (2007) compared the distribution of
52H against 513C for MTBE during aerobic deg-
radation by two cultures that metabolized MTBE
through a different pathway that involves attack
on the ether bond. The value of eH was -0.2%o for
strain L108 and +5%o for strain IFP2001. In these
organisms, the values of SH are much lower than
is the case for organisms like PM1. Enrichment
of 52H and 513C during aerobic biodegradation
by these organisms is projected as the solid line
in Figure 4.4. The values used in the projection
were sc of -1.48%o and SH of -0.2%o. There was
considerable overlap of the field data of Kuder et
al. (2005) and plausible values of 52H and 513C that
would be expected from aerobic biodegradation of
MTBE by organisms similar to strains LI08 and
IFP2001. As a consequence, Resell et al. (2007)
warn against uncritical comparison of 52H and 513C
-Expected from aerobic biodegradation
al
?
2
Sb
50'
30'
10'
-10'
-30'
-50'
-70'
-90'
-110'
/ V\a oxidation of the methyl group
/
/
/
/ x
'
/ x
x Field Data
x
' x nAnaerobic Microcosms
' x #*
DJ^ *6< X
j^£^XxXx
Sp^S — -—•» Expected from aerobic biodegradation
x via cleavage of ether bond
x
••—Known range of 813C and 82H in MTBE in gasoline
-40.0-30.0-20.0-10.0 0.0 10.0 20.0 30.0 40.0 50.0 60.0
813C MTBE %o
Figure 4.4. Concurrent analysis of 513C in MTBE and 52H in MTBE in ground water to associate natural
biodegradation of MTBE in ground water with an anaerobic process, which allows the selec-
tion of an appropriate value for the enrichment factor (e) to be used to estimate the extent of
biodegradation of MTBE.
-------
in MTBE in the field to infer the primary pathway
for biodegradation.
The length of the solid line in Figure 2.4 is the
range of values of 52H and 513C that would be
expected in MTBE when MTBE is degraded from
an initial high concentration of 100,000 |o,g/Lto
1 ng/L. Only 5% of gasoline spill sites in the
USA have initial concentrations of MTBE above
100,000 |og/L, and 1 ng/L is the lower limit for
determination of 52H and 513C in MTBE in water
samples. The solid line in Figure 4.4 represents
the plausible range of 52H and 513C that would be
expected during aerobic biodegradation of MTBE
by organisms similar to strains LI08 and IFP2001.
By examination of Figure 4.4, and allowing for
uncertainty in the estimation of 52H of 10%o and
513C of 0.5%o, the two dimensional approach pro-
posed by Zwank et al. (2005) can be used to distin-
guish anaerobic biodegradation of MTBE whenever
the value of 52H in MTBE in the field sample is
more positive than -67%o and 513C is more positive
than -9%0.
Similar success has been reported recently for
determining benzene biodegradation pathways
(Fischer et al., 2007; Fischer et al., 2008; Mancini
et al., 2008a). The approach will find wider use,
and have more validity, as more data are available
on the concurrent enrichment of 52H and 513C in
organic contaminants by different microorganisms
under different geochemical conditions.
4.3. Conversion of Calculated Extent
of Biodegradation (1-f) to
Biodegradation Rates
At many hazardous waste sites, mathematical
models are used to predict the transport of contami-
nants in ground water from source areas to potential
receptors such as drinking water wells. These
models are calibrated using estimates of the rate of
biodegradation of the contaminant in ground water.
Most commonly the rates of biodegradation are
extracted from field monitoring data. These con-
ventional approaches compare changes in concen-
tration of the contaminant with travel time along a
flow path in an aquifer.
One valuable application of CSIA is an independ-
ent evaluation of the rates of biodegradation of
contaminants. Section 7.3 derives equations that
can be used to calculate the rates of biodegrada-
tion or abiotic transformation at field scale from an
estimate of the fraction remaining after biodegrada-
tion (C/Co) and from some assumptions about flow
paths and ground water flow rates for the site. This
approach combines the uncertainty in the estimates
of the hydrogeological parameters with any uncer-
tainties in the estimate of the extent of biodegrada-
tion based on CSIA and Equation 4.3. Nonetheless,
several recent case studies have shown good agree-
ment between biodegradation rates extracted from
isotope studies and rates derived by conventional
approaches that are based on changes in concentra-
tions in monitoring wells along a flow path in the
aquifer (van Breukelen et al., 2005; Fischer et al.,
2006; Hirschorn et al., 2007; Morrill et al., 2005).
A key point to emphasize is that CSIA typically
provides a more conservative estimate of the degra-
dation rate compared to the conventional approach
(Abe and Hunkeler, 2006; Chartrand et al., 2005;
Morrill etal, 2005).
4.4. Using Estimates of Rates of
Biodegradation to Predict Plume
Behaviour
In the conventional approach, the extent of removal
along a flow path is estimated by dividing the
concentration of contaminant in a down gradient
well (Ct) by the concentration in an up gradient
well (C0). Often at field scale, monitoring wells
are screened vertically across plumes, and produce
samples of the contaminated plume that are diluted
with clean water from above or below the plume.
Occasionally a well will only sample the top or
bottom of a plume. In this case the apparent attenu-
ation of concentrations of contaminants has a strong
component of dilution, and data on concentrations
cannot be used in the conventional approaches to
estimate the extent of removal.
Fischer et al. (2006) provided an approach for
solving this problem by taking the concentration
that is actually measured in the down gradient
well Ct and the measured values of 513C in the two
wells, to calculate a theoretical value for C0 using
the Rayleigh equation. The difference between the
calculated theoretical value of C0 and the measured
value of Ct provides an estimate of the amount of
compound that was degraded that is independent of
dilution or other non destructive processes that can
lead to a reduction of the contaminant concentra-
tion (Fischer et al., 2006). Because the estimate of
the extent of biodegradation provided by CSIA is
independent of the concentration of the contaminant
in the ground water sample, the extent of biodeg-
radation from the CSIA analyses and the estimated
travel time from the source of contaminant to a well
can be used to estimate the rate of biodegradation
along the flow path.
The behaviour of contaminants in most plumes
is heterogeneous, with extensive biodegradation
-------
in some regions and little or no biodegradation in
others. When a plume is heterogeneous, it is best to
consider the behaviour of the contaminant in each
flow path, instead of trying to predict the average
behaviour of the entire plume. The approach will be
illustrated with data from a plume of MTBE from
a gasoline spill at a site in Dana Point, California,
USA (Figure 4.5). Additional details of this case
study are described in section 6 of an EPA report
(Wilson etal., 2005a).
The direction of ground water flow for separate
rounds of sampling is presented as flow arrows in
Figure 4.5. The length of each arrow is proportional
to the distance ground water would move in one
year under the hydraulic gradient during that par-
ticular round of sampling. The length was calculated
by multiplying the hydraulic gradient by the aver-
age hydraulic conductivity (11 meters per day), then
dividing by an estimate of porosity (0.25).
After the spill of gasoline was discovered, the
leaking underground storage tanks and most of the
surrounding fill material were excavated. However,
residual gasoline in the aquifer acts as a continuing
source of MTBE in ground water. The highest con-
centrations of MTBE are immediately down gradi-
ent of the underground storage tanks (Figure 4.5).
A second source is associated with the distribution
lines to the south-eastern dispenser island.
Table 4.2 compares the concentrations of MTBE in
selected monitoring wells to the fraction of MTBE
remaining as predicted from Equation 4.3 using
the 513C of MTBE in the ground water in each well
and a value of -27.4%0 for the 513C that would be
expected for MTBE in gasoline. This value is the
most positive 513C value that has been published for
MTBE in gasoline (O'Sullivan et al., 2003). To be
conservative, the most negative enrichment factor
available in the literature was used in the calcula-
tions (s = -14.6%o; Somsamak et al., 2006). This
approach provided the most conservative estimate
MW-11
334
N
/
10 meters
^ MW-7
\» 114M9/L
MW-10
<0.5 ug/L
MW-3
MW-6
\ «174|jg/L • 612|jg/L
MW-12
3.6 ug/L
•
TPHg>100mg/kg
TPHg>1,OOOmg/kg
MW-9«
<0.5 pg/L
Underground
Storage Tanks
Dispenser
Islands
Figure 4.5. Concentration of MTBE in selected monitoring wells at a gasoline spill site in Dana Point, Cal-
ifornia, USA in 2004. The cluster of arrows is a flow rose indicating the direction and distance
ground water would move in one year based on the elevation of the water table in monitoring
wells on particular sampling dates. The dashed arrows indicate possible flow paths between
wells. Concentrations are MTBE in ground water. TPHg is the Total Petroleum Hydrocarbons
in the range of constituents of gasoline.
-------
of the fraction of MTBE remaining compared to the
MTBE that was originally present in the gasoline
spilled to the aquifer.
Table 4.2 reveals that the most conservative
approach to calculate C/Co must have underesti-
mated the true extent of biodegradation at this site.
Well MW-11 had a value of 513C for MTBE that
was even more negative than the value assumed for
MTBE in the gasoline that was spilled. The true
value of 513C for MTBE in the gasoline that was
spilled may have been even more negative than
-28.9%o. This most conservative approach was
taken because this study was conducted as part of
a risk evaluation, and the rates extracted from the
CSIA analyses were the only rates available. If the
purpose of the study were to validate other rates of
biodegradation that were extracted from conven-
tional approaches, it would have been appropriate
to use estimates of 513Csource that were more likely
to be representative of the true 513Csource.
The most contaminated well at the site (MW-14
in Figure 4.5) is located in an area that had
9,000 mg/kg of Total Petroleum Hydrocarbons in
the range of constituents of gasoline (TPHg). Wells
MW-3 and MW-8 are further down gradient of the
source of MTBE associated with the underground
storage tanks (Figure 4.5). The 513C of MTBE in
wells MW-3 and MW-8 is much more enriched in
13C than MTBE in gasoline with values of +8.5%o
and +38.0%o respectively. The fraction remain-
ing corresponds to 91% and 99% biodegradation
of MTBE. The attenuation in concentration of
MTBE in wells MW-3 and MW-8 compared to well
MW-14 can be attributed to biodegradation.
Well MW-6 appears to be cross gradient to the
source of MTBE associated with the under-
ground storage tanks (compare the flow arrows in
Figure 4.5). However, well MW-6 is directly down
gradient of the secondary source associated with
the dispenser islands. The behaviour of MTBE
in well MW-6 is very similar to wells MW-3 and
MW-8. The 513C of MTBE (-1.6%o) is highly
enriched relative to MTBE in gasoline, and the
predicted fraction remaining corresponds to 83%
biodegradation of MTBE.
Wells MW-7 and MW-11 are even further down
gradient of the source of MTBE. The concentra-
tions of MTBE are low, and it would be tempting to
attribute the low concentrations to biodegradation.
However, the 513C of MTBE in these wells is even
more depleted in 13C (-27.3%o, -28.9%o) than the
513C in MW-14, the most contaminated well. The
513C of MTBE in these wells falls near or within
the range of 513C expected for MTBE in gasoline.
Hence, there is no evidence from the 513C of MTBE
that biodegradation contributed to attenuation of
MTBE in these two down gradient wells.
Because the isotope fractionation provides a direct
estimate of the fraction of contaminant remaining
after biodegradation, the rate constant for bio-
degradation can be calculated from the removal
Table 4.2. Rates of natural biodegradation of MTBE in ground water moving along a flow path to monitor-
ing wells. The rates were calculated from the estimated seepage velocity of ground water and the fraction of
MTBE remaining after biodegradation.
Well
MW-14
MW-3
MW-8
MW-7
MW11
MW-6
MTBE
(Hi/L)
28,800
174
21
114
334
612
513C
MTBE
(%»)
-21.6
8.5
38.0
-27.3
-28.9
-1.6
Fraction
MTBE
Remaining
(C/C0)
0.67
0.085
0.0113
0.995
1.11
0.171
Distance from
MW-14
(meters)
0
9.6
11.7
23.0
44.1
Distance from
Dispenser Island
(meters)
31.1
Rate of
Degradation
with Distance
(per meter)
0.26
0.38
0.00021
0
0.057
Rate of
Degradation
with Time
(per year)
9.4
14.1
0.0077
0
2.1
-------
of contaminant along the flow path in the aquifer,
the distance between wells, and an estimate of the
interstitial seepage velocity. If biodegradation fol-
lows a pseudo first order rate law, the rate of attenu-
ation can be expressed directly as a first order rate
of attenuation with distance, or the rate of attenua-
tion with distance can be multiplied by an estimate
of the seepage velocity of ground water to calculate
a rate of attenuation with time of travel. The rate
of attenuation with distance is calculated follow-
ing Equation 4.6. Attenuation with time follows
Equation 4.7.
with distance
= -\n(f)/d
4.6
4.7
In Equation 4.6 and 4.5, X is the rate of natural
biodegradation,/is the fraction of contaminant
remaining predicted from Equation 4.3, d is the
distance along the flow path between the up gradi-
ent well and the down gradient well, and v is the
ground water seepage velocity.
The average hydraulic conductivity at the site in
Dana Point, California is 11 meters per day. The
average hydraulic gradient over eight rounds of
sampling was 0.0023 meter per meter. Assuming
the effective porosity is 0.25, the average ground
water seepage velocity should be near 37 meters
per year. Table 4.2 presents the rates of biodeg-
radation of MTBE along flow paths between the
most contaminated well (MW-14), and down gradi-
ent wells MW-3, MW-7, MW-8, and MW-11, and
between the secondary source at the pump island
and down gradient well MW-6. In wells MW-3 and
MW-8, the first order rate of degradation is rapid,
on the order of 0.3 per meter of travel, or 10 per
year of residence time. In well MW-6, the rate of
biodegradation is about ten fold slower. In well
MW-7, the rate of biodegradation was one thousand
fold slower, and in well MW-11 biodegradation was
not detected at all.
The field rates estimated for wells MW-3, MW-6
and MW-8 are in good agreement with laboratory
rates reported in the literature. The rate of anaero-
bic biodegradation of MTBE in a microcosm study
constructed with material from a gasoline spill in
Parsippany, New Jersey, varied from 11 ± 2.3 per
year to 12 ± 2.9 per year (Wilson et al, 2005b).
The rate of anaerobic MTBE biodegradation in
a microcosms study constructed with core mate-
rial from a JP-4 jet fuel spill in Elizabeth City,
North Carolina, was 3.02 ± 0.52 per year and
3.5 ± 0.65 per year (Wilson et al., 2000).
The distance travelled before the concentration of
contaminant reaches a particular goal (further) can be
calculated by rearranging Equation 4.7 to produce
Equation 4.8. In Equation 4.8, F is the ratio of the
goal to the existing concentration in the monitoring
well.
^further ~~ ~~
distance
g
If .F is calculated by dividing the U.S. EPA advisory
limit of 20 ug/L by the concentration of MTBE
remaining in monitoring wells MW-3, MW-6, or
MW-8 (Table 4.2), and if the first order rates of bio-
degradation also apply to the flow path that is down
gradient of the monitoring wells, then the plume
would move 8.4 meters past MW-3, and 60 meters
past MW-6. Biodegradation had essentially already
brought the concentration of MTBE to the limit in
well MW-8.
In contrast, the first order rate of biodegradation in
well MW-7 (Table 4.2) is much slower. At a rate
of 0.00021 per meter, starting at a concentration of
114 ug/L, the MTBE plume would be expected to
move 8,300 meters further down gradient before it
reached the advisory limit of 20 ug/L.
In well MW-11, biodegradation of MTBE could
not be established based on the 513C for MTBE in
the ground water. The concentration of dissolved
oxygen in water from well MW-1 was 0.65 mg/L,
the concentration of Iron(II) was 0.2 mg/L, and
the concentration of methane was 0.018 mg/L.
Conditions were not favourable for aerobic biodeg-
radation. The only processes that can be reasonably
expected to attenuate MTBE further down gradient
of MW-11 are dilution and dispersion. It would
appear that while the biodegradation of MTBE
in the core of the plume was rapid and extensive,
MTBE in the periphery of the plume was not
degraded.
As a consequence of the spatial heterogeneity in
the rate of biodegradation, the extent of the plume
would be seriously underestimated if a single rate
constant for biodegradation was applied to the
maximum concentration of MTBE in the source
area. On the other hand, the maximum extent of
the plume was seriously overestimated if biodegra-
dation was ignored. At this point in the evolution
of risk evaluation, a conservative course of action
is to recognize that plumes are heterogeneous. An
independent estimate of the extent of MTBE con-
tamination further down gradient should be made
for each well used in the risk evaluation, based on
the concentration of MTBE in each well, and the
rate of biodegradation in the flow path leading to
each well.
-------
4.5. Effect of Heterogeneity in
Biodegradation in the Aquifer on
Stable Isotope Ratios
The rate and extent of biodegradation may be het-
erogeneously distributed in an aquifer. As ground
water moves away from a source of contamination,
the organic contaminants are removed in flow paths
where biodegradation is rapid and extensive, and
persist in flow paths where biodegradation is weak
or absent. This effect can confuse the interpretation
of a shift in the isotopic ratio in the residual organic
contaminant. As the contaminant is degraded in
the flow paths where biodegradation is rapid and
extensive, the residual contaminant is fractionated.
However, the concentration of the contaminant
that is fractionated is reduced much faster than the
concentration of contaminant that is not fraction-
ated. With time and distance away from the source
area, the total mass of contaminant that is contrib-
uted by the flow paths that degrade the contami-
nant will decline compared to the flow paths that
do not degrade the contaminant. Eventually, the
contribution of the fractionated contaminant to the
total concentration of contaminant is negligible.
Even though a large proportion of the total mass of
contaminant has been removed in the aquifer, the
stable isotope ratio of the residual material closely
resembles the ratio in the material that was released
from the source. An analysis of stable isotope
ratios in contaminants in water from a monitoring
well that blended the flow paths would suggest that
the contaminant had not fractionated, and had not
been biologically degraded. This situation is illus-
trated in Figure 4.6.
Figure 4.7 presents a thought experiment that
illustrates the effect. In the thought experiment,
the isotope enrichment factor for anaerobic bio-
degradation of MTBE in an aquifer is -12%o, and
MTBE in various proportions of the ground water
is not degraded. Initially, the 513C in the total mass
of MTBE increases as biodegradation progresses
in the aquifer. Eventually, the total mass of 13C in
MTBE in the regions where MTBE is degrading
becomes less than the total mass of 13C in MTBE in
the regions where MTBE is not degrading. From
that point forward, the 513C in the total mass of
MTBE decreases as biodegradation proceeds in
the aquifer. Eventually the 513C in residual MTBE
returns to the initial 513C, even though a small frac-
tion of the original mass of MTBE remains. If a
shift in the stable isotope ratio was the only crite-
rion to estimate biodegradation, the contribution of
biodegradation could be seriously underestimated
or missed altogether.
Figure 4.6. Hypothetical illustration of a hetero-
geneous plume, where a monitoring
well that produces ground water from
some flow paths where biodegradation
of an organic contaminant is rapid and
extensive (upper part of the saturated
zone), and other flow paths where bio-
degradation of the organic contaminant
is absent.
-------
70
60
50
40
LJ 30
H 20
O 0
fo -10
-20
-30
-40
-+- no heterogeniety
-*- no degradation in 0.1 % of ground water
-A- no degradation in 1 % of ground water
-» no degradation in 10% of ground water
\
\
\
1 0.1 0.01 0.001
Fraction of MTBE remaining after anaerobic biodegradation,
corresponding with distance along the flow path in Figure 4.6
Figure 4.7. Theoretical experiment of the effect
of heterogeneity in biodegradation
on the stable isotope ratio for carbon
in residual MTBE in water produced
from a monitoring well, when MTBE
does not degrade in certain portions of
the aquifer as depicted in Figure 4.6.
The Y-axis shows the calculated values
for 513C of MTBE that is a mixture of
MTBE from a flow path with biodeg-
radation and MTBE from a flow path
with no biodegradation.
The scenario described above is an extreme case
where we have either 100% biodegradation or 0%
in different sections of the aquifer. However, there
have been recent publications that tried to assess
the problem of heterogeneity in a mathematical
model, and an in situ tracer test where biodeg-
radation was monitored by several methods and
the estimate based on stable isotope fractionation
was verified in the field (Abe and Hunkeler, 2006;
Fischer et al., 2006). Both studies concluded that
the influence of spatial heterogeneities in a gravel
sediment aquifer was not significant for the cal-
culation of biodegradation. For other sites with
more complex heterogeneity, these potential effects
should be considered; biodegradation might be sig-
nificantly underestimated. In any case, situations
that protect a portion of the contaminant from frac-
tionation, such as unreactive flow paths or sorption
to organic matter, will cause an underestimate of
the extent of biodegradation (Kopinke et al., 2005).
4.6. Recommended Practices to
Minimize the Confounding Effects
of Heterogeneity
Water samples for determination of stable isotope
ratios should be acquired from wells with short
screen intervals, or from temporary push wells or
from cluster wells with small screens. Whenever
possible, the depth interval of the well screen
should be compared to the lithology of the aquifer,
and only wells that are screened across a single unit
in the aquifer should be sampled. Often, the very
top layer of an anoxic contaminated aquifer will be
oxic. This can result from diffusion of oxygen into
the ground water from the capillary fringe, or from
recharge of aerobic uncontaminated ground water
from surface precipitation. Avoid sampling wells
that are screened across the water table.
Wells should be purged to the minimum extent nec-
essary to bring geochemical parameters to stability.
If the geochemical parameters do not stabilize after
three casing volumes have been purged, purg-
ing should stop at that point and the ground water
should be sampled. If the well water is not in geo-
chemical equilibrium, there is reasonable chance
that the well will blend organic contaminants that
have been fractionated to different extents.
Use geochemical parameters to recognize the
"footprint" of a contaminant plume when the con-
taminant of interest has been extensively degraded
and may not be present at high concentrations in
the ground water. As an example, the "footprint" of
a plume from a fuel spill often has high concentra-
tions of methane, alkalinity and iron(II), and low
concentrations of soluble electron acceptors such
as sulfate, nitrate, or oxygen. The "footprints"
are expressed in aquifers in both horizontal view
(two dimensional space) and with depth. Select
locations and depth intervals for CSIA where the
geochemical parameters indicate that they are in
the "footprint" of the plume, even though they may
have lower concentrations of the contaminant of
concern.
-------
5.0
Strategies for Field Investigations
Sampling and analysis by CSIA can be expensive,
which produces a financial incentive to minimize
the number of samples analyzed by CSIA. As a
result, there is risk that too few samples will be
acquired and analyzed to adequately describe the
behaviour of the contaminants at the site. This
section discusses important considerations in the
design of a sampling strategy that will allow an
adequate characterization of the degradation of
organic contaminants in ground water at a par-
ticular site. This section is primarily intended for
consultants who will devise sampling strategies and
consultants and regulatory staff who will review
reports provided by others on the degradation of
contaminants in ground water.
5.1. Design of Stable Isotope
Fractionation Studies
According to the standard of the U.S. EPA (1999)
at least one of three lines of evidence should be
provided to demonstrate natural attenuation in
contaminated aquifers: first, field data should show
a reasonable decrease of contaminant concentration
or mass; second, hydrogeological and geochemical
data should indirectly reveal the type and the rate
of attenuation; and third, microcosm studies in field
or laboratory should demonstrate the occurrence of
substantial attenuation processes at the site. Isotope
fractionation is a tool that can contribute to all
three of the lines of evidence for monitored natural
attenuation. It can demonstrate contaminant mass
loss due to biodegradation as part of the first line
of evidence. It can provide information for direct
calculation of biodegradation rates for use in the
second line of evidence. Finally, isotope fractiona-
tion can provide direct unequivocal evidence of
biodegradation in the aquifer.
5.2. Temporal Design
The design of a stable isotope study always depends
on a site conceptual model that is unique to every
site. This section can only depict the general frame
work for the design of CSIA studies.
Before a major investment is made in CSIA, it is
prudent to get an indication of the utility of CSIA
to understand the behavior of contaminants at the
site. First, a snapshot of stable isotope fractionation
should be made during a sampling event that is rou-
tinely performed for measurement of contaminant
concentrations. Such a preliminary study would
concentrate on the monitoring of four to six wells
with CSIA. Of course, this would provide a limited
data set which is not adequate to interpret the bio-
degradation potential on the site in a serious way.
Nevertheless, a preinvestigation might provide suf-
ficient evidence to justify a detailed and extensive
stable isotope survey.
In order to provide reliable data for interpretation
of biodegradation on the site, a comprehensive
survey of CSIA across the entire plume is recom-
mended, which usually requires monitoring of
twelve to twenty wells depending on the size and
complexity of the site. A second sampling event
after two to three months is particularly necessary
in highly variable plumes to insure reproducibility
of the data from the CSIA. At least the important
wells that have been identified in the first sampling
event should be sampled a second time after two to
three months. A complete investigation will require
approximately four to seven months. Once the
contribution of biodegradation has been established
using CSIA, the long-term behaviour and stability
of fractionation within the plume should be evalu-
ated in a final isotope survey conducted one to three
years after the first survey (Figure 5.1).
Before CSIA of the samples, the concentrations of
the contaminants should be determined by conven-
tional methods such as GC/MS. This information is
necessary to select the appropriate concentrations of
the samples that will bring the analytes within the
linear range of the isotope ratio mass spectrometer,
and to ensure that the intended method of sample
preparation (such as purge and trap, or SPME) pro-
vides adequate sensitivity.
5.3. Spatial Sampling Design
The information needed for the design of a sam-
pling strategy includes the location and extent of
the source, the direction of the ground water flow,
and the extent of the plume. The sampling pat-
tern should cover each of the compartments of the
plume (the source, the plume center line, and the
fringes) with an adequate number of monitoring
wells. As a general rule, isotope data from twelve
to twenty wells would be appropriate for a reason-
able and detailed evaluation of biodegradation at a
typical site. These numbers depend on the extent
-------
Lines of evidence for support of MNA
• chemistry or isotope data show reasonable decrease of contaminant mass
• hydrologic and geochemical data indirectly demonstrate the type and rate of attenuation
• microcosms in field or laboratory prove occurrence and efficiency of attenuation
Relevant factors for a valid sampling design in isotope surveys of contaminated sites
• spatial plume structure (particularly source and extension)
• groundwater flow lines (i.e. center line and multiple flow lines)
• hydrological plume variation (e.g.from fluctuating infiltration)
• redox conditions (e.g. presence of oxygen, and electron donors or acceptors)
• groundwater flow velocity (e.g. between pairs of wells)
• remedial strategies (e.g. in situ biodegradation)
• parallel data from samples (e.g. contaminant concentrations and redox conditions)
Spatial Sampling Frame
• spot-checking 4-6 wells
• main investigation 12-20 wells
- upgradient source 1 -2 wells
- source zone 3-5 wells
- center flow line 4-5 wells
- plume area 4-8 wells
- vertical dimension 1 -4 levels
• long-term control 6-15 wells
Temporal Sampling Frame
• spot-checking 1-3 months
• main investigation 4-6 months
- primary sampling duration 1 day
- repetition 2-3 months later
• long-term control 1-3 years later
Figure 5.1. Development of a spatial and temporal sampling design for CSIA surveys to evaluate MNA.
The number of wells are offered as an example for an optimal study of contamination in a
single aquifer. The design of a real survey should be adapted to the specific conditions at the
site.
and the complexity of the plume; multiple sources
require more samples. The authors recommend tak-
ing samples for chemical and isotope analysis from
every well and storing aliquots for isotope analysis
as recommended in Section 3.3 above. Analyze a
water sample from each of the wells for the con-
centration of the contaminants, and then use the
information on concentrations to select the subset
of wells that will be subjected to CSIA.
As discussed in Section 6.1, the value of 513C or
52H in the feed stock used to manufacture an indus-
trial chemical may vary depending on the source
of the feed stock, and as a consequence, the value
of 513C or 52H in the industrial chemical can vary
from one batch to another. The carbon and hydro-
gen in the feed stock may be fractionated during the
manufacturing processes, and the values of 513C or
52H in the industrial chemical can vary if different
-------
processes were used to manufacture the chemical.
The value of 513C or 52H for an industrial chemical
may vary from one batch to another, or from one
manufacturer to another, and different releases of a
an industrial chemical may have different values of
513C or 52H.
If there is more than one source of contamination
at a site, each of the sources must be identified and
delineated, because the contaminants in each of the
different releases may have started with different
values of 513C or 52H. If there are multiple sources,
it is possible that 513C or 52H from a well in one
source will be compared to the 513C or 52H plume
produced by a second source, and the difference in
513C or 52H might give the false impression of bio-
degradation and lead to misinterpretations. In order
to be able to make a reliable quantification of bio-
degradation processes, it is necessary to determine
the values of 513C or 52H of the contaminants in
three to five wells in each of the the source zones.
Often, it will not be possible to assess the source
zone directly because it might be buried below
buildings, roads, or other infrastructure. In this
case, it will be sufficient to take the first monitoring
well that is accessible down gradient of the source
area and use the concentrations and values of 513C
or 52H as the initial values (primary signature) for
the interpretation of data from wells located further
down gradient. If an analysis of the data on con-
centrations indicates that the ground water upgradi-
ent of the source is already slightly contaminated,
isotope measurements from an upgradient well
should be performed as a control for the interpreta-
tion of isotope values in fringe areas of the main
plume (one to two wells). The clearest picture for
isotope fractionation analysis can generally be
derived from isotope data at the center flow line of
plumes where there is a better understanding of the
geohydrology of the plume. The center flow line
should be sampled in at least four to five wells and
more if possible.
A comprehensive analysis should consider the
entire extent of the plume, realizing that the dis-
tribution of contamination at the plume fringe is
often insufficiently defined. It should be taken into
account that some of the monitoring wells may not
be hydrologically connected. At least four to eight
wells should be sampled in the central parts of the
plume in addition to the four to eight center line
wells. Several wells should also be sampled in the
fringes of the plume. It is important to sample the
downgradient margin of the plume because this
portion of the plume is most important for the pre-
diction of future migration of the contaminant.
If multi-level wells, or multi-level well clusters are
available to provide vertical resolution in the dis-
tribution of contaminants and electron acceptors,
the wells can be sampled to evaluate any vertical
differences in the extent of biodegradation in the
plume.
In common practice, the number of monitoring
wells that are available for a CSIA survey of a
site will be often fewer than the twelve to twenty
wells that we recommend. It may be necessary to
acquire water samples from temporary push wells
to adequately delineate and characterize the plume.
The fewer the number of wells that are analysed in
the study, the higher the risk of misinterpreations.
In such cases, the comprehensive interpretation of
many different lines of evidence becomes more
important. As a result, we can not offer a general
design for CSIA studies which is applicable to sites
with only a few monitoring wells.
-------
6,0
Use of Stable Isotopes for Source
Differentiation
In industrial and urban areas, multiple sources of
the same contaminant frequently occur. This is
particularly true for releases of chlorinated solvents
and petroleum hydrocarbons. This section discuss-
es the application of isotope data to identify dif-
ferent sources of the same contaminant and to link
sources to contaminated ground water down gradi-
ent of the source. This section is primarily intended
for consultants who will devise sampling strategies
to associate particular contaminants in ground water
plumes with particular sources, and consultants and
regulatory staff who will review reports provided
by others on the source of contaminants in ground
water.
6.1. Variability of Isotope Ratios of
Different Sources
The isotopic composition of synthetic organic
compounds depends on the isotope ratio of the
source materials and on isotope fractionation during
production of the compounds. For example, chlo-
rinated methanes sold in commerce generally have
more negative values of 513C compared to chlori-
nated ethanes and ethylenes because they are pro-
duced from methane in natural gas. The methane in
natural gas is formed when heat and pressure deep
in the earth pyrolyze native organic matter in sedi-
ments. Because of the strong fractionation during
pyrolysis, the methane is depleted in 13C (Whiticar,
1999;WhiticarandFaber, 1985).
The values of 513C and 537C1 for a particular chlo-
rinated compound in commerce can vary from one
manufacturer to another and also between different
production batches produced by the same manufac-
turer (Beneteau et al., 1999; Jendrzejewski et al.,
2001; Shouakar-Stash et al., 2003; van Warmerdam
et al., 1995). Data on the variation in the isotopes
of carbon and chlorine in chlorinated solvents and
chlorinated production chemicals are summarized
in Figure 6.1. Even larger variations have been
reported for isotopes of hydrogen in trichloroeth-
ylene and 1,1,1-trichloroethane (Shouakar-Stash
et al., 2003). Similarly, Smallwood et al. (2001)
observed differences in carbon and hydrogen
isotope ratios for a range of different natural
hydrocarbons in gasoline as well as methyl tertiary
butyl ether (MTBE). The variation in isotope ratios
of compounds in gasoline reflects variations in the
origin of the crude oil.
All the studies discussed above were concerned
with variations between commercial products. Any
variation in the source and isotopic composition of
the material that is spilled adds additional complex-
ity at field scale. A contiguous source of ground
water contamination can be heterogeneous with
respect to its isotopic composition if it is the result
of several different spill events overtime, and if the
compound that was spilled had different sources
with different isotopic compositions. It is also pos-
sible that different spill events in different locations
can have the same isotopic composition. To dis-
tinguish these possibilities, it is helpful to perform
CSIA for several elements at the same time, such
as carbon, hydrogen, and chlorine, and in cases of
multi-component mixtures, several compounds.
This is especially true for compounds of larger
molecular mass as they undergo smaller shifts in
stable isotope ratios during biodegradation because
the reactive atom is "diluted" with greater numbers
of other atoms (see section 4.2.3.5). Larger mol-
ecules of multi-component spills such as mineral
oil products can therefore be used as conservative
tracers when isotope fingerprinting is applied.
6.2. Contaminated Sites Scenarios
Isotope analysis is especially useful when there are
multiple sources of the same ground water con-
taminants. Table 6.1 summarizes several common
scenarios that may be encountered and outlines
questions to be addressed, and potential sampling
strategies. The actual strategy can vary depend-
ing on the complexity of the site and the available
information about source location and transport
mechanisms. While it may frequently be pos-
sible to clearly locate and sample source zones for
LNAPLs (Light Non-Aqueous Phase Liquids),
this is often not the case for DNAPLs (Dense
Non-Aqueous Phase Liquids) where source zones
are often inferred from high concentrations of the
contaminant in ground water.
-------
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Figure 6.1. Minimum, maximum and mean carbon (A) and chlorine (B) isotope ratio of chlorinated hydro-
carbons from different manufacturers and production batches measured to date. The number in
parentheses following each compound name indicates the number of samples analyzed for that
compound. PCE is tetrachloroethylene, TCE is trichloroethylene, DCE is dichloroethylene,
1,1,1-TCAis 1,1,1-trichloroethane, 1,2-DCAis 1,2-dichloroethane, CT is carbon tetrachloride
(tetrachloromethane), CF is chloroform (trichloromethane) and DCM is dichloromethane. Data
compiled from (Beneteau et al., 1999; Holt et al., 1997; Hunkeler and Aravena, 2000c; Jendr-
zejewski et al., 1997; Jendrzejewski et al., 2001; Shouakar-Stash et al., 2003; van Warmerdam
et al., 1995; Zwank et al., 2003).
6.3. Evaluating the Relevance of
Biodegradation
Generally, there is little significant isotopic frac-
tionation caused by transport and partitioning
processes (See Section 4 for a discussion of the
exceptions). As a consequence, transport and
partitioning processes will not mask the variation
in stable isotope ratios that are associated with
different sources. Because the differences in iso-
tope ratios of different sources are commonly on
the order of several %o, the change in the isotope
ratio due to biotic or abiotic degradation can rap-
idly become more important than variations due
to different sources. Before carrying out a source
differentiation study using CSIA, complementary
data such as the concentrations of daughter prod-
ucts and the redox conditions should be evaluated
to determine whether degradation processes can be
expected to cause changes in the isotope ratios, or
whether the ratios of the compounds of interest are
conservative.
For highly chlorinated hydrocarbons such as PCE
and TCE, little biodegradation occurs as long
as oxygen is present and the redox potential is
elevated. The relevance of biodegradation can
be evaluated by characterizing redox conditions.
The absence of degradation products such as cis-
DCE and VC can serve as an additional indicator
for conservative behaviour. For less chlorinated
hydrocarbons and for petroleum hydrocarbons,
biodegradation should be expected under a range of
redox conditions.
If the isotope fractionation factor and the degree
of biodegradation are known, it should theoreti-
cally be possible to make a correction for isotope
fractionation due to biodegradation. However, in
most cases the extent of biodegradation cannot be
estimated independently (which is the reason why
isotopes are used to assess biodegradation) and
any such correction becomes very uncertain. In
the case of reductive dechlorination of chlorinated
ethenes, the original isotope ratio of carbon in
the parent compound can be estimated based on
a mass balance of carbon in parent and daughter
compounds assuming that carbon is conserved dur-
ing degradation. However, the uncertainty of the
calculated value is larger than is the case when only
the parent compound is present. In the future, with
increased possibilities for dual isotope measure-
ment, it may be possible to distinguish shifts due to
different sources from shifts due to biodegradation
because shifts due to biodegradation follow a sys-
tematic trend. Such an approach has been used to
track the sources of nitrate in ground water (Widory
et al., 2005), and of benzene at a contaminated site
with multiple source zones (Mancini et al., 2008b).
-------
6.4. Designing a Sampling Strategy to
Distinguish Sources
Recommended sampling strategies for differ-
ent scenarios are summarized in Table 6.1 and
Figure 6.2. The design of the sampling strategy
should be based on a conceptual model of the site
that summarizes the actual or potential location of
the sources and the pathways of contaminant migra-
tion. If possible, for each presumed source and
associated plume segment, at least three samples
should be taken to evaluate within-source vari-
ability and to facilitate data evaluation. Take into
account that different sources may coincidentally
have the same isotopic composition. Therefore, it
is advisable to carry out preliminary sampling to
evaluate differences in isotope ratios between dif-
ferent sources or plume segments before carrying
out more extensive sampling to delineate different
plumes.
Table 6.1. Recommended sampling strategies for the use of CSIA to evaluate the origin of ground water
contamination
Scenario
Question
Sampling
Plume and several known
source zones
What is contribution of
the different sources to
the plume (s)?
Source characterization
Take NAPL samples of
different sources;
if not available, take three
ground water samples in
high concentration zone
close to each source.
Plume characterization
(only if sources have dif-
ferent isotopic composi-
,' \
tion)
Take at least three sam-
ples in each of the plume
segments presumably
linked to the each of the
sources.
Plume and one known
source zone
Are there additional
sources contributing
to the plume?
Source
characterization
Take NAPL sample of
source; if not availa-
ble take ground water
samples in high con-
centration zone close
to the source.
Plume
characterization
Take at least three
samples in each plume
or plume segment.
Up gradient and
down gradient
11 j-°
pollution
Does the site con-
tribute to down
gradient contami-
nation?
Take at least
three ground
water samples
up gradient and
down gradient of
the location of the
potential source.
plume but
no identified
source zone
Is plume
linked to one
or several
sources?
Take at least
three ground
water samples
in each plume
or plume
segment.
-------
Development of a conceptual model based on:
-geological and hydrogeological data
-hydrological data such as water table elevations
-historical data on concentrations of contaminants
-results of initial sampling events providing information on:
-potential location of sources and their relation to plumes
-occurrence of degradation or transformation products
Sample at least
three locations in
each presumed
plume or discrete
flowpath for
CSIA, then carry
out statistical tests
to evaluate
similarities
between samples.
No
Degradation
occurs and
likely leads to
isotope
fractionation
Methods only
applicable under
special conditions
(see text)
RESULT
There are no significant differences
in the isotope ratio between
different locations.
The samples partition into distinct
groups each having a different
isotope ratio, and the groups
correspond to different plumes or
discrete flow paths.
The samples partition into distinct
groups that are in conflict with
the conceptual model.
Most of the samples are
significantly different from
each other.
CONCLUSIONS AND RECOMMENDATIONS
Not possible to distinguish between a single source
and multiple sources with the same isotope ratio.
Consider CSIA for additional elements.
The different plumes or discrete flow paths most
likely originate from different sources.
Revise conceptual model. Examine all the data on
water table elevations todetermine if there are major
variations in the direction of ground water flow.
Consider the possibility of undiscovered sources.
Contaminant degradation may occur.
Evaluate spatial characteristics of trends. Are trends
consistent in the direction of ground water flow?
There may be a large number of spill events.
Figure 6.2. Flow chart for the design and evaluation of a source identification strategy based on stable
isotope analysis.
-------
6.5. Data Evaluation to Distinguish
Sources
The stable isotope data evaluation can involve dif-
ferent levels of complexity:
If only one or a few compounds were analyzed, as
is typically the case for chlorinated hydrocarbons,
and if the different sources or plume segments can
be clearly distinguished, calculate the mean and
standard deviation for each source and/or plume
segment. Compare the means using a Student's
t-test based on the calculated standard deviations.
If the site conceptual model is not sufficiently
detailed to group the samples into different sources
or plumes, an analysis of variance (ANOVA) can
be carried out to determine whether there are sig-
nificant differences between any of the samples.
If the ANOVA indicates that there are significant
differences between samples, then the Newman-
Keuls test or other equivalent test that makes pair-
wise comparisons can be used to identify whether
any particular sample is different from any other
particular sample. Details on the calculations are
available in many introductory statistics textbooks.
In the case of petroleum hydrocarbons, isotope data
are often available for a number of compounds in
the same water sample. One possibility for compar-
ing these data for pairs of samples is to carry out
a student t-test for each of the compounds, as was
done by Smallwood et al. (2002). However, for
a series of /-tests the probability of a type I error
increases (falsely rejecting the hypothesis that
isotope values are the same), and samples may be
determined to be different by the test when in real-
ity there is no difference. To circumvent this prob-
lem, reduce the number of variables using Principle
Component Analysis and then test the transformed
variables for similarity (Boyd et al., 2006).
Once the similarities and differences are evaluated,
the data should be compared with the conceptual
model of the site. Different situations can be envis-
aged. There may be no significant differences in
the isotope ratios for different sources and plume
segments. In this case it is not possible to distin-
guish between a single source and multiple sources.
The isotope data are consistent with the hypothesis
that there is only one source but do not demonstrate
it. Consider using CSIA for other elements such as
hydrogen or chlorine.
The samples may partition into different groups,
with each group having different isotope ratios
which correspond to different sources and plume
segments. In this case, the isotope data provide
strong evidence that the different plume segments
originate from different sources.
The samples may partition into different groups
that are in conflict with the conceptual model.
Reconsider the conceptual model with respect to
the potential for additional sources or spill events,
additional contaminant migration pathways, or the
possibility of reactive processes that change iso-
tope ratios in some zones. Revisit the assumptions
behind the interpretation of the isotopic data, such
as conservative behaviour and lack of fractionation
due to biodegradation.
Most of the samples may be significantly different
from each other, making it difficult to exclude a
wide variety of possible interpretations. There may
be a substantial number of different spill events
or the compounds may be affected by reactive
processes. If reactive processes occur, the isotope
ratios may show a trend with distance from the
source.
6.6. A Case Study of Source
Differentiation
A PCE plume was characterized in a sandy aqui-
fer of a small town (Angus) in Ontario, Canada
(Hunkeler et al., 2004). The plume was very wide
(60 m) close to the presumed source area, and there
were several discrete zones with high concentra-
tions of PCE, which raised the question of whether
there were one or several sources for the plume
(Figure 6.3). The carbon isotope ratio of PCE was
determined for a large number of samples from
multilevel samplers in two transects at two different
distances down gradient from the presumed source
area. In the up gradient transect, three different
plume cores with significantly different isotope
ratios can be distinguished (contained within
intervals A, B, and C in Transect 1 as presented
in Figure 6.3). This observation together with the
considerable width of the plume indicates that the
plume likely originates from several spatially sepa-
rated sources. The relatively large width of inter-
val C may be due to lateral migration of DNAPL
in the source zone. Two of the three plume cores
can still be identified at the down gradient transect
(Transect 2 in Figure 6.3), while on either side of
the plume core, there are zones with more enriched
values for 13C compared to corresponding loca-
tions in the up gradient transect, the enriched values
indicate biodegradation of PCE in the flow path
between the two transects. The trend towards more
negative values in the low concentration fringes
observed in some multilevel samples may be due
to a small (1-2%0) diffusive effect (Hunkeler et al.,
2004).
-------
Transect 1: 40 to 50 m down gradient of source
186
184
00
N3
o
'+-•
£0
j> 180 -
HI
178
176
A B
-32.9 ±0.5%. -27.510.5%.
-25.5 ± 0.5 %o
9 - -
^•-32.6
n n
a\
a\
fa
n-28.5
--
n-25.7\
-26.6 fa
-26.4
-26.9
XTpZM
Ji
/ 1 to100|jg/LPCE
100to1,OOOMg/LPCE
1,000 to 10,000 Mg/L PCE
0 10 15 20 25 30 35 40 45
Distance perpendicular to flow (m)
Transect 2: 220 m down gradient of source
A B? C
184 -I -28.8%o -31.05%. -26.2%o -24.9 ± 0.3%. -22.4%.
50
182 -
jf
.1 180 •
1
A 7 n -22^7
o -26.4 "^~~~~\, _*^x n IA B OT^O.^
— ~—— ______ D Oi25.3 ^~i—~3^ 1 /n
~~ — ~~~— n -24 »x D -24.6.x /^
|jg/L PCE ^ — ~~~— — — — -^
000 |jg/L PCE
1 0,000 [jg/L PCE
10 15 20 25 30 35 40
Distance perpendicular to flow (m)
45
50
Figure 6.3. Concentrations and carbon isotope ratios of PCE in two transects downgradient of unidenti-
fied PCE sources. All values are given in %o relative to the V-PDB standard. Filled squares
are depths sampled for determination of both concentration and 513C. Open squares are depths
sampled for concentration only. The figure is modified after Hunkeler et al. (2004).
-------
7.0
Derivation of Equations to
Describe Isotope Fractionation
This section is intended for contractors and consul-
tants that will work up data on stable isotope ratios,
and produce a report for the site manager and the
regulatory staff. It is also intended for regulators
who will review the report. This section derives the
equations that are used to calculate the extent of
biodegradation from the change in the stable iso-
tope ratio, and the rate of biodegradation from the
extent of biodegradation.
7.1. Expressing and Quantifying Isotope
Fractionation
In a kinetic reaction, isotope fractionation occurs
due to slight differences in the reaction rates of
molecules with a heavy and light isotope, respec-
tively, at the reactive site of the molecule. The
magnitude of isotope fractionation is usually
expressed by the isotope fractionation factor
(a) that quantifies the difference in isotope ratio
between the product that is formed at a given time
(IP - instantaneous product), and the reactant (R).
For a general reaction: R —» P
The isotope fractionation factor is given by:
Rro
7.1
with
dH
and
where
OCPR = the isotope fractionation factor,
RR =the isotope ratio of the reactant
RIP = the isotope ratio of the product
(instantaneous basis)
5'ER = the isotope ratio in %o of reactant
S'Ejp = the isotope ratio in %o of product
(instantaneous basis)
HR = the amount of the heavy isotope in
reactant
dHP = the instantaneous rate of production of
the heavy isotope in the product
LR =the amount of the light isotope in reactant
dLP = the instantaneous rate of production of
the light isotope in the product.
Rearranging Equation 2.1, where Rs is the isotope
ratio of the standard:
8%* 1000 =
and
then
Rff _ 8%,+1000
RD 8''£„+! 000
7.2
Often isotope fractionation is expressed on a %o
scale, using the isotope enrichment factor (s) which
is defined as:
For reactions of any order, the isotope fractionation
factor corresponds to the ratio of the rate constant
for reaction of molecules with a heavy isotope (KH)
anywhere in the molecule compared to the rate con-
stant for reaction of molecules with light isotopes
only (KL) (Bigeleisen and Wolfsberg, 1959).
«„=£•
A constant fractionation factor throughout the deg-
radation process is also expected for Monod kinet-
ics (Simon and Palm, 1966).
7.2. The Rayleigh Equation
For evaluating laboratory and field data, an equa-
tion is required that describes the changes in the
isotope ratio as the reaction progresses. Such an
equation can be derived starting from Equation 7.1,
the definition of the fractionation factor:
dHpldLp
HRILR
7.5
-------
For mass balance reasons
dHp = -dHR
dLp = -dLR
7.6
Combining equation 7.5 and equation 7.6 and
rearranging leads to
dH
= oc
7.7
PR
Integration of equation 7.7 from HROto HR and L^0
to LR, where HRO=the initial amount of heavy
isotope and L^0= the initial amount of light isotope
yields:
.
In
rr
—
H
.
= ocDD-ln-
R,0
L,
'R,0
or
H
R,0
L
'R,0
Dividing both sides by LR/LR 0 yields
'R,0
where RR and RQ are the isotope ratios at a given
time t and at time zero, respectively.
The fraction of substrate that has not reacted (/) at
time t is given by:
f_ Ct _ HR+LR
r' H
7.9
From equation 7.9, if the amount of heavy isotope
(HR and HRO) is small compared to the amount of
the light isotope (LR and L^Q), as is typical for stud-
ies at the natural abundance of isotopes, (LR/ L^0)
can be approximated by (/), and Equation 7.8
transforms to
f=t=
HR+LR =
7.10
Equation 7.10 is usually denoted as the Rayleigh
equation and describes the evolution of the isotope
ratio of the reactant as a function of the progress of
the reaction (Clark and Fritz, 1997; Mariotti et al.,
1981).
Sometimes, the isotope fractionation factor is
expressed as the inverse of the ratio given in
Equation 7.1. If this is the case, the value of a will
be larger than 1.0, and accordingly the exponent in
Equation 7.10 corresponds to (l/ot)-l.
Equation 7.10 is often expressed using the delta
notation for isotope ratios and the isotope enrich-
ment factor instead of the isotope fractionation
factor:
1000.ln8'£/1000+1=e...ln/ 7.11
8!E0/iooo+:
-PR
where 5'E0 and 5'E are the initial isotope ratio of
the compound and the isotope ratio at a moment in
time, respectively.
Because ln(l+u) corresponds approximately to u
when u is small compared to 1, as can be shown
using a Taylor series expansion, Equation 7.11 can
be further simplified to:
8'E = 8'E + £ • In f ~7 72
An equation for the accumulated product can be
derived from equation 7.12 using an isotope mass
balance equation that links the isotope ratio of reac-
tant and accumulated product:
-f + (\-f)-RB
7.13
where
RB = the isotope ratio of the accumulated product
RR = the isotope ratio of the reactant at the time
of measurement
R0 = the initial isotope ratio of the reactant
Inserting Equation 7.10 into Equation 7.13 followed
by rearrangement leads to:
1 _ f<*PR
RB -
I-/
7.74
The corresponding approximate equation for the
accumulated product can be derived by combined
an isotope mass balance equation in 5-notation
analogous to Equation 7.13 with Equation 7.12:
/'In/
I-/
7.75
Following Equation 7.15, the value of S'Ejp for
the instantaneous product is always offset by SPR
compared to the 5'ER of the reactant in a Rayleigh
controlled system, while the 5'EP of the accumu-
lated product approaches the initial isotope ratio of
the parent compound (S'Eg) as the reaction proceeds
(Figure 7.1).
-------
-10
TCE
Instantaneous c/s-DCE
Average c/s-DCE
-30
-40
0.8 0.6 0.4
Fraction Remaining (/)
0.2
0.1
Figure 7.1. Simulated evolution of carbon isotope
ratios of reactant (TCE) and degrada-
tion product (c/s-DCE) according to
the Rayleigh equation. The isotope
evolution of the product that is formed
at a certain moment in time (instan-
taneous product) as well as the aver-
age isotope ratio of the accumulation
product is shown. The average cis-
DCE deviates from the instantaneous
product to more depleted values as it is
a mixture of product that accumulates
from the start of the reaction. As the
instantaneous product becomes heavi-
er, the accumulated product becomes
heavier. The enrichment factor s stays
constant overtime. An isotope enrich-
ment factor of -8.5%o was assumed in
the simulation.
7.3. Quantification of Isotope
Fractionation in Laboratory Studies
Laboratory data should be evaluated using the full
Rayleigh equation (Equation 7.10) unless the exper-
iments were carried out with labelled compounds.
In this case, it is necessary to use an equation
derived from Equation 7.8 without simplifications.
When the uncertainty of the measurement is in the
same range as the uncertainty of the isotope ratio of
the reference gas, the following linearized form of
Equation 7.10 is recommended to quantify otPR:
, R S'E/ 1000 + 1
In — =
8'E/1000 + 1
^ , ,
-ll-ln/
'
7'16
The linear regression should be carried out with-
out forcing the regression line through the origin.
Laboratory experiments are often carried out in
replicates, which raises the question of how the
data should be combined to obtain a representa-
tive fractionation factor. The non-weighted average
fractionation factor should only be used if the
replicates consist of a similar number of observa-
tions spread over a similar range in / Otherwise,
Scott et al. (2004) propose a method based on linear
regression with dummy variable and a method
based on a Pitman estimator.
7.4. Equations to Evaluate Field Isotope
Data
Under certain conditions (see Section 3), the degree
of biodegradation or the first order rate constant
for biodegradation can be quantified for the zone
between the source and a monitoring point, or
between two monitoring points along a flow path.
By rearrangement of equation 7.16, the following
equation is obtained to quantify the fraction remain-
ingtf):
7.77
where
5'E = the isotope ratio at the downgradient
monitoring point, and
5'E0 = the isotope ratio at the source or
upgradient monitoring point.
The amount of biodegradation or abiotic trans-
formation (in percent of the material originally
present) is given by:
B = (!-/)• 100 7.78
The change in the isotope ratio from the source
area to a monitoring well or from well to well can
be used for two purposes. The CSIA data can be
used to test the hypothesis that the concentration
decrease is predominantly due to biodegradation
or abiotic transformation, and the data can be used
to extrapolate the removal that would be expected
further along the flow path. The expected concen-
tration at a down gradient monitoring point can be
calculated using:
C =C
exp o
f
7.19
where
Cexp = the expected concentration at the down
gradient monitoring point
C0 = the concentration at the source or the up
gradient monitoring point along a flow path.
-------
If the reductions in concentrations are due to a
particular process that has a characteristic value
for SPR , then the value of (/) as obtained from
Equation 7.17, should substitute into Equation 7.19
to predict a value for Cexp that is in good agreement
with the measured concentration in the down gradi-
ent monitoring well. If the values are not in good
agreement, then it is possible that other processes
such as dilution in the well or dispersion along the
flow path has a stronger influence on the measured
concentration. If is also possible that there is an
error in the conceptual model, that some other pro-
cess is responsible for destruction of the compound,
and the assumed value of SPR is in error.
The CSIA data can also be used to extrapolate con-
taminant degradation further down the flow path.
The first order rate constant for contaminant remov-
al can be estimated by combining Equation 7.10
with the equation describing first-order degradation
of a substance:
C
f = — = exp(-A,, • T) 7.20
C0
where:
T = the average travel time of the compounds of
interest between source and monitoring point or
between two monitoring points along a flow line.
For retarded compounds, the travel time is given
by T=RT-TW where RT is the retardation factor and
Tw is the average travel time of water,
A,^= the first order rate constant for reduction in
concentration due to biodegradation or abiotic
transformation.
Solving Equation 7.20 for \ and then substituting
Equation 7.17 for/ produces Equation 7.21.
Equation 7.20 can be solved for the travel time
required along the flow path (Trequired) to attain any
desired concentration (Crequired) at the field-scale rate
of removal At.
(C \
T — 1~ required , ^ 7 OO
1 required ~ ~ lH ~^, - ' A( ' 'll
Multiplication of (Trequired) by the contaminant
velocity (Vseepage) yields an equation for the distance
along the flow path from the source or the up gradi-
ent monitoring well that is required to reduce the
contaminant concentration to the desired concentra-
tion (Lreqmred).
L,
C
required
—
7 23
*£-\J
'required
where
"VSeepage = the actual seepage velocity of the
contaminant in ground water along the flow path.
The seepage velocity is usually estimated by divid-
ing the Darcy velocity by the effective porosity,
and then dividing by the retardation factor for the
contaminant.
If the value ofCrequiredis a Maximum Contaminant
Level (MCL), a clean up goal, or other regulatory
standard, a value for Lrequiredcan be used to estimate
a perimeter beyond which the concentrations of a
contaminant should no longer be of regulatory con-
cern. The value ofLrequired can be calculated without
knowledge of the ground water flow velocity. The
calculation of Lrequiredis conservative because it does
not include reductions in concentrations caused by
dilution or dispersion.
1
•In
1000 , 8''£ + 1000
In
-PR
T
8''£•„+1000
T
7.21
The calculated rate constant represents the rate of
removal from biodegradation or abiotic transforma-
tion. The rate of removal is distinct from the bulk
attenuation rate k that is calculated by plotting the
natural logarithm of the concentrations against the
time of travel along the flow path (Newell et al.,
2002). The bulk attenuation rate also includes the
effect of dilution through dispersion on the concen-
tration in addition to the effect of removal.
-------
8.0
Stable Isotope Enrichment Factors
This section summarizes the isotope enrichment factors that are available in the literature at the time this
section was written. However, the literature on isotope fractionation is growing rapidly, and this section is
only offered as a point of departure. The reader should perform a literature search to update the information
needed for a particular application.
Table 8.1. Isotope enrichment factors (s) for aerobic and anaerobic biodegradation of selected ground water
pollutants. Intrinsic enrichment factors (&1) for carbon isotope fractionation have been calculated following
Morasch et al. (2004), where s^s *n. Values of&1 are provided in the third column to illustrate the isotope
effect at the atom where the reaction takes place.
Compound
13C/12C
fractionation
Intrinsic
13C/12C
fractionation
Fraction-
ation of
other
elements
Conditions
Bacteria
Reference
BTEX Compounds
Benzene
Benzene
Benzene
Benzene
Benzene
Ethylbenzene
Ethylbenzene
Toluene
Toluene
s = -1.46
s = -3.53
e = -2.4
e = -2.0
s = -3.6
s = -1.9
s = -2.1
e = -2.2
e = -3.7
Not significant
e = -3.3
8, = -8.8
8, = -21.2
8, = -14.4
8, = -12
8, = -21.6
8; = -11.4
8; = -12.6
8; = -17.6
s, = -30
8; = -23.1
WH:
s = -12
WH:
8 = -ll.
2H/1H:
s = -29
s = -35
WH:
s = -79
2H/1H:
s = -60
Oxic
Oxic
Nitrate-
reducing
Sulfate-
reducing
Methanogenic
Nitrate-
reducing
Sulfate-
reducing
Oxic
Oxic
Acinetobacter sp.
Burkholderia sp.
Enrichment culture
Enrichment culture
Enrichment culture
Strain EBN1
Enrichment culture
Microcosms
Pseudomonas
putida strain mt-2
(Hunkeler et
al.,2001b)
(Hunkeler et
al.,2001b)
(Mancini et
al., 2003)
(Mancini et
al., 2003)
(Mancini et
al., 2002)
(Mancini et
al., 2003)
(Meckenstock
et al., 2004)
(Wilkes et al.,
2000)
(Sherwood
Lollar et al.,
1999)
(Morasch et
al., 2002)
-------
Compound
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
ra-Xylene
w-Xylene
/>-Xylene
o-Xylene
13C/12C
fractionation
s = -1.7
(high iron)
s = -2.5
(low iron)
s = -l.l
s = -0.4
s = -1.7
s = -1.8
s = -0.8
s = -1.5
s = -2.2
s = -1.7
e = -0.5
s = -1.7
s = -1.8
s = -2.3
s = -1.5
Intrinsic
13C/12C
fractionation
8i = -11.9
8, = -17.5
Ei = -7.7
8, = -2.8
8i = -11.9
s^-12.6
s^-5.6
s^-10.5
s^-15.4
8i = -11.9
Ei = -3.5
8; = -13.6
8; = -14.4
8; = -18.4
8; = -12
Fraction-
ation of
other
elements
WH:
s = -77
(high iron)
s = -159
(low iron)
WH:
s = -728
WH:
s = -198
WH:
s = -12
s = -65
Conditions
Oxic
Oxic
Oxic
Nitrate-
reducing
Fe(III)-
reducing
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Methanogenic
Methanogenic
Oxic
Sulfate-
reducing
Oxic
Sulfate-
reducing
Bacteria
Pseudomonas
putida strain mt-2
Ralstonia pickettii
strain PKO1
Pseudomonas
putida strain F 1
Thauera aromatica
Geobacter
metallireducens
Enrichment culture
Column
experiment
Desulfobacterium
cetonicum
Strain TRM1
strain TRM1
Desulfobacterium
cetonicum
Enrichment culture
Consortium
Pseudomonas
putida strain mt-2
Strain OX39
Pseudomonas
putida strain mt-2
Strain OX39
Reference
(Mancini et
al, 2006)
(Morasch et
al., 2002)
(Morasch et
al., 2002)
(Meckenstock
etal., 1999)
(Meckenstock
etal., 1999)
(Ahad et al.,
2000)
(Meckenstock
etal., 1999)
(Morasch et
al., 2001)
(Meckenstock
etal., 1999)
(Morasch et
al., 2001)
(Morasch,et
al., 2001)
(Ahad et al.,
2000)
(Ward et al.,
2000)
(Morasch et
al., 2002)
(Morasch et
al., 2004)
(Morasch et
al., 2002)
(Morasch et
al., 2004)
-------
Compound
o-Xylene
o-Xylene
ra-Cresol
p-Cresol
13C/12C
fractionation
s = -l.l
e = -3.2
e = -3.9
8 = -1.6
Intrinsic
13C/12C
fractionation
8, = -8.8
8, = -25. 6
e, = -27.3
8i = -11.2
Fraction-
ation of
other
elements
Conditions
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Sulfate-
reducing
Bacteria
Column
experiment
Enrichment culture
Desulfobacterium
cetonicum
Desulfobacterium
cetonicum
Reference
(Richnow et
al., 2003)
(Wilkes et al.,
2000)
(Morasch,et
al., 2004)
(Morasch et
al., 2004)
Polyaromatic Hydrocarbons
Naphthalene
Naphthalene
2-Methyl-
naphthalene
2-Methyl-
phenanthrene
Fluoranthene
8 = -0.1
8 = -l.l
e = -0.9
No
enrichment
No
enrichment
8; = -!.!
S--11
s, = -9.9
Oxic
Sulfate-
reducing
Sulfate-
reducing
Oxic
Oxic
Pseudomonas
putida strain
NCIB9816
Enrichment culture
N47
Enrichment culture
N47
Sphingomonas sp.
strain 2MPII
Sphingomonas
paucimobilis
(Morasch et
al., 2002)
(Griebler et
al., 2004b)
(Griebler et
al., 2004b)
(Mazeas and
Budzinski,
2002)
(Hammer et
al., 1998)
Chlorinated Hydrocarbons
PCE
PCE
PCE
PCE
PCE
PCE
TCE
e = -5.2
Enrichment
estimated
2%o
s = -5 .2 to
-8.8
e = -0.42 to
-1.7
e = -0.46 to
-3.2
e = -18.2
8, = -10.4
s; = -10.4 to
-17.6
s, = -0.84 to
-3.4
s, = -0.92 to
-6.4
8, = -36.4
37Q/35Q
8 = -10
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Oxic
Strain T,
consortium N,
consortium F
Consortium
(butyric acid)
Microcosm
experiment
Desulfitobacterium
sp. PCE-S
Sulfurospirillum
multivorans
Sulfurospirillum
halorespirans
Burkholderia
cepacia strain G4
(Numata et
al., 2002)
(Slater et al.,
2001)
(Hunkeler et
al., 1999)
(Nijenhuis et
al., 2005)
(Nijenhuis et
al., 2005)
(Cichocka et
al., 2007)
(Bill et al.,
2001)
-------
Compound
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
TCE
cis-DCE
cis-DCE
13C/12C
fractionation
s = -l.l
s = -10.9 to
-12.2
s = -13.2 to
-18.7
s = -18.7 to
-22.9
s = -16.4
s = -3.3
s = -9.6
s = -6.6;
s = -2.5
s = -7.1
s = -13.8
Enrichment
estimated
4%o
No enrichment
s = -21.1
Intrinsic
13C/12C
fractionation
s, = -2.2
s; = -21. 8 to
-24.4
s, = -26.4 to
-37.4
s, = -37.4 to
-45.8
8, = -32.8
8, = -6.6
8, = -19.2
8, = -13.2
8; = -5
8, = -14.2
s, = -27.6
s, = -42.2
Fraction-
ation of
other
elements
^Cl/^Cl
s = -5. 5
^Cl/^Cl
s = -5. 6
37C1/35C1
s = -5.7
37C1/35C1
8 = -30
Conditions
Oxic,
cometabolic
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic, deha-
logenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Methanogenic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Sulfate-
reducing,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Oxic,
cometabolic
Anoxic,
dehalogenating
Bacteria
Methylosinus
trichosporium
OB3b
Desulfitobacterium
sp. PCE-S
Sulfurospirillum
multivorans
Sulfurospirillum
halorespirans
Sulfurospirillum
multivorans
Dehalobacter
restrictus strain
PER-K23
Dehalococcoides
ethenogenes 195
Enrichment culture
Mixed facultative
anaerobic culture
Consortium
(MeOH)
Microcosm
experiment
Strain T
Consortium N
Consortium F,
nitrate reducing
Strain T,
consortium N,
consortium F
Methylosinus
trichosporium
OB3b
Dehalococcoides
ethenogenes 195
Reference
(Chu et al.,
2004)
(Cichocka et
al., 2007)
(Cichocka et
al., 2007)
(Cichocka et
al., 2007)
(Lee et al.,
2007)
(Lee et al.,
2007)
(Lee et al.,
2007)
(Bloom et al.,
2000)
(Sherwood
Lollar et al.,
1999)
(Slater et al.,
2001)
(Hunkeler et
al., 1999)
(Numata et
al., 2002)
(Numata et
al., 2002)
(Numata et
al., 2002)
(Numata et
al., 2002)
(Chu et al.,
2004)
(Lee et al.,
2007)
-------
Compound
cis-DCE
cis-DCE
cis-DCE
cis-DCE
cis-DCE
trans-DCE
trans-DCE
trans-DCE
trans-DCE
1,1 -DCE
1,1 -DCE
1,1 -DCE
VC
VC
VC
VC
VC
VC
VC
VC
13C/12C
fractionation
e = -16.9
8 = -14.1
8 = -16.1
e = -19.9
s = -20.4
Enrichment
estimated
12%0
e = -3.5
e = -6.7
e = -21.4
e = -30.3
e = -7.3
s = -5.8
e = -8.4
e = -5.7
e = -3.2
e = -4.8
s = -4.5
e = -5.5
e = -8.2
8 = -7.1
8 = -7.1
Intrinsic
13C/12C
fractionation
8, = -33. 8
8, = -28.2
8; = -32.2
8; = -39.8
8; = -40.8
6i = -7
8; = -13.4
8; = -42.8
8; = -60.6
8; = -14.6
8i = -11.2
8; = -16.8
8i = -11.4
s, = -6.4
s, = -9.6
s, = -9.0
Ei = -ll
8, = -16.4
8, = -14.2
8; = -14.2
Fraction-
ation of
other
elements
Conditions
Anoxic,
dehalogenating
Methanogenic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Oxic,
cometabolic
Oxic,
cometabolic
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Oxic,
metabolic
Oxic,
cometabolic
Oxic,
cometabolic
Oxic,
cometabolic
Oxic,
cometabolic
Oxic
Oxic
Oxic
Bacteria
Dehalococcoides
sp. Strain B AVI
Enrichment culture
Microcosms
Consortium
(MeOH)
Microcosm
experiment
Methylomonas
methanica
Methylosinus
trichosporium
OB3b
Dehalococcoides
sp. Strain B AVI
Microcosms
Microcosms
Dehalococcoides
ethenogenes 195
Dehalococcoides
sp. Strain B AVI
Mycobacterium
aurum LI
Methylosinus
trichosporium
OB3b
Mycobacterium
vaccae JOBS
Enrichment culture
Travis
Enrichment culture
Alameda
Mycobacterium sp.
JS60
Mycobacterium sp.
JS61
Mycobacterium sp.
JS617
Reference
(Lee et al.,
2007)
(Bloom et al.,
2000)
(Hunkeler et
al., 2002)
(Slater etal.,
2001)
(Hunkeler et
al., 1999)
(Brungard et
al., 2003)
(Brungard et
al., 2003)
(Lee et al.,
2007)
(Hunkeler et
al., 2002)
(Hunkeler et
al., 2002)
(Lee et al.,
2007)
(Lee et al.,
2007)
(Chu et al.,
2004)
(Chu et al.,
2004)
(Chu et al.,
2004)
(Chu et al.,
2004)
(Chu et al.,
2004)
(Chartrand et
al., 2005)
(Chartrand et
al., 2005)
(Chartrand et
al., 2005)
-------
Compound
VC
VC
VC
VC
VC
VC
Dichloro-
methane
Dichloro-
methane
Dichloro-
methane
1,2-Dichloro-
ethane
1,2-Dichloro-
ethane
1,2-Dichloro-
ethane
1,2-Dichloro-
ethane
1,2-Dichloro-
ethane
1,1,2-Trichloro-
ethane
Chlorobenzene
Chlorobenzene
Chlorobenzene
Chlorobenzene
1,2,4-Trichloro-
benzene
13C/12C
fractionation
e = -7.6
s = -24.0
e = -21.5
s = -22.4
8 = -31.1
s estimated
-26
s =
-41 to -66
s =
-46 to -61
s = -32
s = -27
e = -32.1
s = -32.3
e = -32.1
e = -3.0
s = -2.0
s = -0.4
e = -0.3
s = -0.2
8 = -0.1
Not significant
Intrinsic
13C/12C
fractionation
8, = -15.2
s, = -48
Ei = -43
8, = -44.8
s, = -62.2
Ei =
-41 to -66
Ei =
-45 to -61
s, = -64
Ei = -54
s, = -64.2
s, = -64.6
s, = -64.2
s, = -6.0
8, = -4
s = -2.4
8 = -1.8
8 = -1.2
s = -0.6
Fraction-
ation of
other
elements
37Q/35Q
e = -3.8
Conditions
Oxic
Anoxic,
dehalogenating
Methanogenic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Oxic
Oxic
Denitrifying
Oxic
Anoxic,
dehalogenating
Oxic
Oxic
Oxic
Anoxic,
dehalogenating
Oxic
Oxic
Oxic
Oxic
Oxic
Bacteria
Nocardioides sp.
JS614
Dehalococcoides
sp. Strain B AVI
Enrichment culture
Consortium
(MeOH)
Microcosms
Microcosm
experiment
Biodegradation
Various
methylotrophic
bacteria
Various
methylotrophic
bacteria
Xanthobacter
autotrophicus
Microcosms
Xanthobacter
autotrophicus GJ10
Ancylobacter
aquaticus AD20
Pseudomonas sp.
StrainDCAl
Microcosms
Ralstonia sp.
R. erythropolis
P. veronii
A. facilis
Pseudomonas sp.
strain P5 1
Reference
(Chartrand et
al., 2005)
(Lee et al.,
2007)
(Bloom et al.,
2000)
(Slater etal.,
2001)
(Hunkeler et
al., 2002)
(Hunkeler et
al., 1999)
(Holt et al.,
1997)
(Nikolausz et
al., 2006)
(Nikolausz et
al., 2006)
(Hunkeler
and Aravena,
2000a)
(Hunkeler et
al., 2002)
(Hirschorn et
al., 2004)
(Hirschorn et
al., 2004)
(Hirschorn et
al., 2004)
(Hunkeler et
al., 2002)
(Kaschl et al.,
2005)
(Kaschl et al.,
2005)
(Kaschl et al.,
2005)
(Kaschl et al.,
2005)
(Griebler et
al., 2004a)
-------
Compound
1,2,4-Trichloro-
benzene
1,2,3-Trichloro-
benzene
13C/12C
fractionation
e = -3.2
e = -3.5
Intrinsic
13C/12C
fractionation
8, = -19.2
8; = -21
Fraction-
ation of
other
elements
Conditions
Anoxic,
dehalogenating
Anoxic,
dehalogenating
Bacteria
Dehalococcoides
sp. strain CBDB1
Dehalococcoides
sp. strain CBDB1
Reference
(Griebler et
al., 2004a)
(Griebler et
al., 2004a)
Fuel Oxygenates
MTBE
MTBE
MTBE
MTBE
MTBE
MTBE
MTBE
MTBE
MTBE
MTBE
ETBE
ETBE
ETBE
TAME
TEA
8 = -2
e =-2.4
8 = -1.5
8 = -1.8
e = -1.52
8 = -l.
s = -0.48
s = -0.28
e = -2.4
Estimated
s = -9.2
e = -14.2
s = -4.2
8 = -13
e = -15.6
e = -14.4
e = -0.68
e = -0.8
e = -0.8
e = -13.7
s = -4.2
8; = -10
8; = -12
Ei = -7.5
Ei = -9
s, = -7.6
Ei = -5
Ei = -2.4
8, = -1.4
8i = -11.8
8, = -45. 8
8, = -70.8
8; = -20.8
e, = -65
s, = -78
s, = -78
8, = -4.1
s, = -4.6
s, = -4.4
e; = -68.5
8, = -16.8
2H/1H
s = -36
2H/1H
s=-66
s=-29
2H/1H
no enrich-
ment
2H/1H
no enrich-
ment
2H/1H
s = -42
2H/1H
8 = -16
2H/1H
s = -14
2H/1H
8 = -ll
2H/1H
8 = -ll
Oxic
Oxic
Oxic
Oxic
Oxic
Oxic
Anoxic
Anoxic
Methanogenic
Methanogenic
and sulfate-
reducing
Oxic
Oxic
Oxic
Methanogenic
Oxic
Strain PM1
Enrichment culture
Microcosm
experiments
Strain LI 08
Strain IFP2001
(resting cells)
Strain R8
Microcosms
Enrichment culture
Microcosm
experiments
Enrichment
cultures
Strain LI 08
Strain LI 08
(resting cells)
Strain IFP2001
(resting cells)
Microcosm
experiments
Microcosm
experiments
(Gray et al.,
2002)
(Gray et al.,
2002)
(Hunkeler et
al.,2001a)
(Resell et al.,
2007)
(Resell et al.,
2007)
(Resell et al.,
2007)
(Kolhatkar et
al., 2002)
(Kuder et al.,
2005)
(Somsamak et
al., 2005)
(Somsamak et
al., 2006)
(Resell et al.,
2007)
(Resell et al.,
2007)
(Resell et al.,
2007)
(Somsamak et
al., 2005)
(Hunkeler et
al.,2001a)
-------
9.0
Recommendations for the
Application of CSIA
Compound Specific Isotope Analysis (CSIA)
provides another dimension of information on
pollutants in the environment to supplement
knowledge of their chemical identity and their
concentration. CSIA has matured into a technique
that can be used on a routine basis, in particular for
carbon isotope analysis, for a wide range of organic
contaminants relevant to hydrogeology and envi-
ronmental geochemistry. Modern instrumentation
can provide valid determinations of isotope ratios
at low concentrations of contaminants that are near
their regulatory standards or clean-up goals.
Application of CSIA at a contaminated site should
start with a clear idea of the information that is
sought from stable isotope analysis. Basically,
there are three distinct goals: (i) source character-
ization or differentiation; (ii) qualitative proof of
biodegradation or abiotic transformation; or, (iii)
quantification of biodegradation or abiotic trans-
formation processes. Advantages and limitations
of the use of CSIA for these purposes have been
discussed in detail throughout this guideline. If the
specific interest is quantification of degradation, the
first step is to consult the literature (summarized
in Table 8.1) to determine whether an appropriate
isotopic enrichment factor is available. Using the
enrichment factor, the second step is to estimate
whether the observed changes in concentration of
the contaminant at the field site are sufficient to
produce a measurable change in the isotope ratio.
If these two prerequisites are met there is a good
chance that it will be possible to put a conservative
boundary on the extent of biodegradation or abiotic
transformation at the field site.
On a per sample basis, the cost of an individual
isotope analysis is substantially higher than the cost
of a VOC analysis to identify the chemical, and
determine its concentration. This is due to the price
of the equipment required to perform CSIA, the
costs of consumables, the level of training and ex-
perience required for the analytical chemist, and the
number of standards and sample duplicates that are
needed to ensure reliable data as discussed in sec-
tion 2.4. However, a simple comparison of direct
costs for analyses is not very meaningful. Instead,
consider the total cost of a site investigation. If the
additional information from CSIA leads to a robust
conceptual model for a site, it can lead to savings
in conventional monitoring. If there is greater faith
in the site conceptual model, adequate monitoring
can be attained with fewer rounds of sampling and
analysis, and fewer monitoring wells.
Furthermore, CSIA can guide decisions on selec-
tion and implementation of remediation strategies,
and can be used to monitor the performance of
remedial technology in an early stage of imple-
mentation. Thus, the huge waste in resources that
are associated with the selection of an unsuitable
remediation strategy might be avoided.
Compound specific isotope analysis can be applied
on a routine basis when precautions are taken to
ensure high quality data and the appropriate in-
terpretation of the data. However, CSIA cannot
replace a proper hydrological and geochemical
characterization or measurements of contaminant
concentrations. Multiple lines of evidence will
continue to be necessary to come to a meaningful
assessment of the risks associate with the contami-
nants and the selection of an appropriate remedy.
It is our hope that this Guide will be a useful in-
troduction for beginners in environmental isotope
analysis. We expect that CSIA will have a grow-
ing role in investigations at hazardous waste sites.
The growth in the application of CSIA is driven
by continued improvements in analytical methods,
by more widespread availability of the instruments
used in CSIA, by an increasing number of publica-
tions showing the broad applicability of CSIA to
a variety of contaminants, and by an increasing
appreciation for the unique information provided by
CSIA. We have only "scratched the surface" of the
potential of CSIA to provide a better understanding
of the source, distribution, and behavior of organic
compounds at contaminated field sites.
-------
10.0
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