United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-00/023
July 2000
US EPA Office of Research and Development
An International
Workshop on Life Cycle
Impact Assessment
Sophistication
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EPA/600/R-00/023
July 2000
An International Workshop on Life Cycle
Impact Assessment Sophistication
Co-Organized by:
Jane C. Bare
U.S. Environmental Protection Agency (EPA)
Helias A. Udo de Haes
Centre of Environmental Science (CML)
David W. Pennington
U.S. EPA Oak Ridge Institute for Science and Education (ORISE) Fellow
in co-operation with United Nations Environment Programme (UNEP)
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
Printed on Recycled Paper
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Notice
The views expressed in these Proceedings are those of the individual authors and do not neces-
sarily reflect the views and policies of the U.S. Environmental Protection Agency (EPA). Scientists in
EPA's Office of Research and Development have prepared the EPA sections, and those sections
have been reviewed in accordance with EPA's peer and administrative review policies and approved
for presentation and publication.
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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 sci-
ence knowledge base necessary to manage our ecological resources wisely, understand how pollut-
ants 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 reducing risks from threats to human health and the
environment. The focus of the Laboratory's research program is on methods for the prevention and
control of pollution to air, land, water and subsurface resources; protection of water quality in public
water systems; remediation of contaminated sites and ground water; and prevention and control of
indoor air pollution. The goal of this research effort is to catalyze development and implementation of
innovative, cost-effective environmental technologies; develop scientific and engineering information
needed by EPA to support regulatory and policy decisions; and provide technical support and informa-
tion transfer to ensure effective implementation of environmental regulations and strategies.
This publication has been produced as part of the Laboratory's strategic long-term research plan.
It is published and made available by EPA's Office of Research and Development to assist the user
community and to link researchers with their clients.
E.Timothy Oppelt, Director
National Risk Management Research Laboratory
in
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Preface
This is the report of the second workshop on a topic in the field of Life Cycle Assessment organized
under the auspices of UNER The first one was held in June 1998 in San Francisco under the title
Towards a Global Use of LCA". An important characteristic of'that workshop was the strong input
from experts of developing countries.
The second workshop was in Brussels in November 1998, and focused on the sophistication of Life
Cycle Impact Assessment. Since its coming out in the early seventies, the focus of LCA has been on
the Inventory Analysis. Life Cycle Impact Assessment, or LCIA, was for a long time regarded "to be in
an early stage of development".This changed with two SETAC working groups, both in North America
and Europe, and with the unifying guidance of the ISO process. In the coming ISO standard on LCIA
only the framework of LCIA will be defined. This still allows for very diverging types of analysis that all
can claim to be ISO compatible. In itself there is nothing wrong with that, but a second SETAC-Europe
working group on LCIAhas begun the process of providing more guidance in this area.
An important issue in this field regards the present topic, the appropriate level of sophistication of
LCIA. As will be clear from the present report, this involves quite a number of issues A major point
concerns the extension of the characterization modeling to include also the fate of the substances
and not only their effects. Another issue concerns a possible differentiation in space and time. Studies
can include impact models that use data just at world level and do not specify time periods; in
contrast, more recent options involve spatial differentiation of impacts and distinguish between differ-
ent time periods. A further point concerns the type of modeling. Traditionally LCIA uses linear model-
ing, like in the Inventory Analysis; but more sophisticated possibilities arise which take background
levels of substances into account and make use of nonlinear dose-response functions An important
question here is whether there are real science based thresholds, or whether these thresholds are
always of a political origin. A further question relates to the role and practicality of including uncer-
tainty analysis. Sensitivity analysis is increasingly included in LCA studies; but this is not yet the
case for uncertainty analysis. Finally, there is the question of how to apply these different options for
sophistication of LCIA; which applications can afford to keep it simple, and for which applications is a
more detailed analysis needed?
Like with the first workshop in this series, the partners in the process were the US EPA and Centre
of Environmental Science (CML). We can expect that it will not be the last one. An issue that could
only be touched upon indirectly during the second workshop concerns the choice of the category
indicator in the environmental mechanism of an impact category. Should it be chosen at midpoint
level, as is done traditionally in many categories, or should the modeling proceed up to the level of the
category endpoints? We expect that this will be a topic for a third workshop in this series to be
organized in May 2000 in Brighton.
IV
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Table of Contents
Pre-Workshbp Summary
Summary of Technical Issues Discussed
.VI
..1
Agenda 9
Remarks by Co-Chairs
Life Cycle Impact Assessment Sophistication (Jane Bare, U.S. EPA, USA)
LCIA Sophistication Issues Overview (David Pennington, ORISE Fellow, U.S. EPA, USA)
Bio of Helias Udo de Haes, CML.The Netherlands
Determination of Sophistication
ISO Standards: Life Cycle Assessment & Comparative Assertions 17
(J.Willie Owens, Procter & Gamble, USA)
Impact Assessment for Ecodesign 25
(MarkGoedkoop, Pre Consultants, The Netherlands)
Application Dependency of LCIA 31
(Henrik Wenzel, Technical University of Denmark)
Impact Categories & Uncertainty Analysis within HumanToxicity & Ecotoxicity
Types of Uncertainty in the Human Toxicity Potential 35
(Edgar Hertwich, University of California, Berkeley, USA)
The Different Levels of Uncertainty Assessment in LCI A; The Case of Carcinogenic
Effects 39
(Patrick Hofstetter, Environmental Sciences: Natural and Social Science Interface, ETH Zurich)
Human Toxicity and Ecotoxicity - Modeling versus Scoring 46
(Olivier Jolliet, EPFL, Switzerland)
Priority Assessment of Toxic Substances in LCAA Probabilistic Approach 54
(MarkHuijbregts, IVAM, Netherlands)
Acidification, Eutrophication, andTropospheric Ozone
Implications of Inventory Structure for Life Cycle Impact Assessment and Uncertainty
Analysis 61
(Gregory A. Norris, Sylvatica, Maine, USA)
Levels of Sophistication in Life Cycle Impact Assessment of Acidification 67
(Jose Potting and Michael Hauschild, Inst. for Product Development, Technical U. of Denmark)
Eutrophication Aquatic and Terrestrial State of the Art ..77
(Goran Finnveden, fms and Stockholm University)
Appendix 83
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Pre-Workshop Summary
Jane Bare, Helias Udo de Haes, and David Pennington
In meetings and journals worldwide, practitioners have
debated the utility of conducting Life Cycle Assessments
(LCAs).The debate has often hinged on the appropriate level
of sophistication. While some have advocated abandoning
LCA altogether, since it is not achievable in its most so-
phisticated form, others have supported the concept of con-
ducting LCAs while making the limitations and uncertain-
ties transparent.
This proposed workshop is needed to address the appro-
priate level of sophistication in Life Cycle Impact Assess-
ment (LCIA).The workshop will be formulated to help evalu-
ate available approaches in terms of framework, scope,
impact assessment methodologies and uncertainty analy-
sis. Additionally this workshop will serve to educate poten-
tial practitioners in developing countries about issues con-
fronting LCIA, and to include developing countries in small
working group discussions. A number of prominent practi-
tioners and researchers will be invited to present a critical
review of the associated factors, including the current limi-
tations of available impact methodologies and a compari-
son of the alternatives in the context of uncertainty. Each
set of presentations will be followed by a discussion ses-
sion to encourage an international discourse with a view to
improving the understanding of these crucial issues. Dis-
cussions will be focused around small working groups of
LCA practitioners and experts including representatives from
industry, government, and academia.
Introduction
A UNEP Workshop titled Towards Global Use of LCA was
held on June 12 -13,1998 in San Francisco. The purpose
of this workshop was to develop recommendations and an
action plan that would lead toward greater use of LCA in the
context of sustainable development. Fifty LCA users and
experts from 26 different countries attended the workshop.
At the end of the workshop, each of the participants was
asked what actions could lead to greater development and
use of LCA for sustainable development decision making.
One of the many ideas discussed included providing a fo-
rum for an International discussion of the appropriate levels
of sophistication within LCIA.
Practitioners of LCA are faced with the task of trying to
determine the appropriate level of sophistication to provide
a sufficiently comprehensive approach to assist in environ-
mental decision making. The impact assessment stage of
LCA, termed LCIA, helps decision-makers to interpret in-
ventory data in the context of a number of impact catego-
ries.
In an ideal world, an LCIA would be based on high quality
data. All impact categories and all steps in each cause-
effect chain would be considered using state-of-the-art meth-
ods, which include fate and account for spatial variation. In
this ideal world, decisions could be made based on these
environmental assessments with a high level of certainty.
In the real world, practitioners have had to deal with the
questions of inaccuracy and uncertainty in LCA and LCIA
in particular. Hence, many simplifications are made within
the LCIA stage. Simplifications may include: reduction in
spatial discrimination (or ignoring spatial variation altogether),
ignoring fate, ignoring background levels of pollutants, as-
suming linear dose-response curves, and/or eliminating an
impact category altogether because the appropriate assess-
ment methodologies do not exist.
While ideally an impact assessment should be sophisti-
cated in all dimensions, obviously, high levels of sophisti-
cation require exhaustive time, data, and resources, or can-
not be reached at all. Hence, the scope of the assessment
needs to be defined, possibly iteratively, to provide the ap-
propriate level of sophistication. Defining this scope appro-
priately is key to effective environmental decision making.
Conducting an uncertainty analysis can be one approach
to determining the appropriate level of sophistication. Un-
fortunately, many of the studies conducted in the past were
deterministic and therefore, may have misled commission-
ers into believing that decisions could be based on the stud-
ies, when in reality, the compared options were essentially
non-differentiable.This workshop will explore the sophisti-
cation of LCIAs including the discussion of uncertainty analy-
sis involved.
Factors Involved in the Determination of
Sophistication
The important issue of deciding the appropriate level of
sophistication is not typically addressed implicitly in LCA.
Often, the determination of sophistication is based on con-
VI
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siderations which may, or may not, be appropriate, but which
may include practical reasons for limiting sophistication (e.g.,
the level of funding). A discussion of the most appropriate
ways of determining sophistication will include:
project objective
an uncertainty and/or sensitivity analysis
the inventory data and availability of accompanying pa-
rameters
depth of knowledge and comprehension in each impact
category
' perceived value placed on the specific impact catego-
ries
the anticipated level of impact
the quality and availability of modeling data
available supporting software
the level of funding
Hope for the Future
There have been a number of advances made in LCIA in
recent times.
The framework for LCIA is becoming standardized, en-
hancing the comparability and avoiding unnecessary
variation between studies.
The fate of substances is increasingly taken into ac-
count, in particular using multimedia modeling as a basis
for characterization.
The results of different characterization procedures for
the same category are compared amongst each other
and they show convergence.
Better distinctions are being made between scientific
information and value choices.
These developments are leading to advances in the prac-
tice of LCIA, but the major limitations and uncertainties can-
not be expected to go away in the near future. Instead, practi-
tioners must learn how to best address the concerns and
limitations of the methodologies. Among the questions that
remain, the following list will be tentatively posed within small
group discussions:
Determination of Sophistication
Methods for
What are the most common methods by which the level of
sophistication is determined?
Which methods are considered more acceptable? Why?
What are the barriers to using the acceptable methods?
What can be done to overcome these barriers?
Various Uses of LCIA
Is LCIA application dependent? Why or why not?
What are the expectations in sophistication for the vari-
ous LCA uses (e.g., government, industry/public communi-
cation, and internal use)?
When should LCIAs be as detailed as possible, because
every increase of detail increases accuracy and removes
uncertainty? And when is it better to limit the scope of LCA
to addressing questions on a macroscopic scale, leaving
spatial and threshold consideration to other analytical tools?
How do practitioners determine the value of the trade-offs
necessary when sophistication and comprehensiveness are
"at odds" (e.g., choosing a modeling approach may limit the
number of pollutants that can be characterized vs. a scor-
ing approach which may limit the sophistication of the mod-
eling)?
Uncertainty Analysis
What case studies are available using uncertainty analy-
ses within LCIA? And what are the major findings to date
(levels of uncertainty discovered)? When is the uncertainty
determined to be unacceptable?
Specific Impact Categories & Uncertainty
Analysis within LCIA
Human Toxicity and Ecotoxicity
In human toxicity and ecotoxicity, when is spatial and/or
temporal differentiation necessary? If necessary, what spa-
tial and/or temporal details are recommended (e.g., indoor/
outdoor, and height of emission point)?
In ecotoxicity what is the best approach to addressing
multiple species? If suggested, what are the recommended
representative species?
In human toxicity and ecotoxicity, what are the greatest
barriers to conducting uncertainty analysis?
What are the recommendations for research and/or de-
velopment in these impact categories?
Eutrophication, Acidification, and
Tropospheric Ozone Formation
In each category, when is spatial and temporal differen-
tiation necessary? If so, what spatial and temporal scales
are recommended (e.g., regionally, or differentiating terres-
trial, sweet water and marine)?
In each category, what are the greatest barriers to con-
ducting uncertainty analysis? What are the recommenda-
tions for research and/or development in these impact cat-
egories?
vn
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Summary of Technical Issues Discussed
Jane C. Barei, David W. Penningtonz, and Helias A. Udo de Haes3
Abstract
On November 29 - 30,1998 in Brussels, an international
workshop was held to discuss Life Cycle Impact Assess-
ment (LCIA) Sophistication. Approximately 50 LCA experts
attended the workshop from North America, Europe, and
Asia. Prominent practitioners and researchers were invited
to present a critical review of the associated factors, includ-
ing the current limitations of available impact assessment
methodologies and a comparison of the alternatives in the
context of uncertainty. Each set of presentations, organized
into three sessions, was followed by a discussion session
to encourage international discourse with a view to improv-
ing the understanding of these crucial issues. The discus-
sions were focused around small working groups of LCA
practitioners and researchers, selected to include a balance
of representatives from industry, government and academia.
This workshop provided the first opportunity for Interna-
tional experts to address the issues related to LCIA So-
phistication in an open format. Among the topics addressed
were: 1) the inclusion or exclusion of backgrounds and thresh-
olds in LCIA, 2) the necessity and practicality regarding the
sophistication of the uncertainty analysis, 3) the implica-
tions of allowing impact categories to be assessed at "mid-
point" vs. at "endpoint" level, 4) the difficulty of assessing
and capturing the comprehensiveness of the environmental
health impact category, 5) the implications of cultural/philo-
sophical views, 6) the meaning of terms like science-based
and environmental relevance in the coming ISO LCIA stan-
dard, 7) the dichotomy of striving for consistency while al-
lowing the incorporation of state-of-the-art research, 8) the
role of various types of uncertainty analysis, and 9) the role
of supporting environmental analyses (e.g., risk assess-
ments). Many of these topics addressed the need for in-
creased sophistication in LCIA, but recognized the conflict
1 Corresponding author: U.S. Environmental Protection Agency, National Risk Man-
agement Research Laboratory, Cincinnati, Ohio 45268, USA. Email:
bare.jane@epa.gov
2 ORISE Research Fellow, U.S. Environmental Protection Agency, National Risk
Management Research Laboratory, Cincinnati, Ohio 45268, USA.
3 Centre of Environmental Science (CML), Leiden University, P.O. Box 9518, NL-
2300 RA Leiden, Netherlands.
this might have in terms of the comprehensiveness and
holistic character of LCA, and LCIA in particular.
Introduction
A UNEP Workshop titled 'Towards Global Use of LCA"
was held on June 12-13,1998 in San Francisco. The pur-
pose of the San Francisco workshop was to develop rec-
ommendations and an action plan that would lead towards
a greater use of LCA in the context of sustainable develop-
ment. At the end of the San Francisco workshop, each of
the participants was asked what actions could lead to greater
development and use of LCA in sustainable development
decision making. One of the many ideas suggested was to
provide a forum for an International discussion of the appro-
priate practice of LCIA. LCIA Sophistication was taken up
as subject of the workshop held in Brussels, which was
attended by approximately 50 LCA practitioners and experts
from various countries.
Practitioners of LCA are faced with the task of trying to
determine the appropriate level of sophistication in order to
provide a sufficiently comprehensive and detailed approach
to assist in environmental decision making. Sophistication
has many dimensions and dependent upon the impact cat-
egory, may simulate the fate and exposure, effect and tem-
poral and spatial dimensions of the impact. (Udo De Haes,
1999a, Owens, et al., 1997, Udo de Haes, 1996, Fava, et
al., 1993). In the context of the Brussels workshop, sophis-
tication was considered to be the ability of the model to
accurately reflect the potential impact of the stressors, or
in language more consistent with recent ISO publications,
the ability to reflect the environmental mechanism with sci-
entific validity. (ISO, 1999).
The impact assessment phase of LCA, termed LCIA,
helps decision-makers interpret inventory data in the con-
text of a number of impact categories and to bring them into
a more surveyable format. Ideally, an LCIA would be based
on high quality data. All impact categories and processes
in the environmental mechanism of each of these catego-
ries would be considered using state-of-the-art techniques,
which would fully account for spatial and temporal variation.
In such an Ideal World, decisions would be made based on
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these assessments with a high level of confidence and cer-
tainty. However, real world practitioners have to deal with
limitations (e.g., budget, and poor quality data) and simplifi-
cations are made. Some modifications may include: 1) re-
duction in spatial and temporal discrimination (or ignoring
these dimensions altogether), 2) ignoring fate, 3) assuming
linear dose-response curves and/or 4) eliminating an im-
pact category because appropriate data or assessment
methodologies do not exist.
While ideally an impact assessment should be sophisti-
cated in all dimensions, this high level of sophistication re-
quires exhaustive time, data, and resources and generally
cannot be reached due to limitations in methodology and
data available. Hence, the scope of the assessment needs
to be defined, possibly iteratively, to provide the appropriate
level of sophistication, including the required level of detail
and accuracy, together with an uncertainty analysis practi-
cal for individual studies, and the specification of value
choices within the framework of the LCA. Appropriate defi-
nition of this scope, including sophistication, uncertainty
analysis, and comprehensiveness is the key to effective
environmental decision making.
Many practitioners in the past have attempted to evalu-
ate impacts to support broad LCA-based decisions, but have
oversimplified the impact assessment step. Unfortunately,
limitations in simulation sophistication lead to a reduced
ability of the study to answer the questions at hand with a
high degree of certainty. In the absence of accompanying
uncertainty analysis, and validation (which addresses model
uncertainty), many LCAs are conducted at such a low level
of simulation sophistication that they are ineffectual in dif-
ferentiating the very options they are trying to evaluate.
(Coulon, 1997; Potting, 1997, Udo de Haes, 1996). Work-
shop participants also discussed the dichotomy of sophis-
tication and comprehensiveness. As an example, very sim-
plistic methods such as relying solely on toxicity data may
allow a larger chemical database set than a more sophisti-
cated approach which would require additional chemical and
physical properties to determine the relative human health
potentials.
More recently, researchers are recognizing the many types
of uncertainty involved in environmental decision making.
Two types of uncertainty discussed at this workshop were
model uncertainty and data uncertainty. Data uncertainty
may be estimated by the propagation of uncertainty and
variability of the input parameters. Model uncertainty can
only be characterized by comparison of the model predic-
tion with the actual response of the system being addressed.
As data uncertainty is relatively easy to characterize,
whereas model uncertainty is difficult, especially in a field
like LCIA, the presentation of data uncertainty alone may
not appropriately be used to compare two methodologies.
For example, a simplistic approach utilizing only persistency,
bioaccumulation, and toxicity data may appear to be more
certain when compared in terms of data uncertainty to a
more complex multimedia/human exposure approach, but
the unaddressed model uncertainty may significantly over-
shadow the data uncertainty.
The specification of value choices has a bearing on the
level of sophistication and has been the subject of many
recent papers. (Owens, 1998, Finnveden, 1997, Volkwein,
1996a, Volkwein, 1996b, Powell, 1996, and Grahl, 1996).
Some practitioners are uncomfortable with the subjectivity
of the Valuation Process, but fail to recognize the role of
subjectivity in other phases of the LCA framework. All LCAs
are conducted under the influence of subjective decisions.
In fact, subjective decisions, value choices, or scientific or
engineering judgements are made throughout the LCA pro-
cess.Thus, the selection, aggregation, or disaggregation of
impact categories and the determination of the methodolo-
gies to quantify the potential impacts are all influenced by
value choices. The Brussels workshop was chosen to ex-
plicitly address the incorporation of value choices within
the LCA process.
Unfortunately, the important issues of deciding the ap-
propriate level of sophistication often remain unaddressed
in LCIA. The determination of the level of sophistication is
often not based on sound and explicit considerations, but
on practical reasons (e.g. the level of funding, level of in-
house knowledge). The workshop was therefore formulated
to allow a more explicit discussion of the many factors out-
lined above that can influence the choice of the level of
sophistication of a study, including:
The project objective
The perceived value placed on the specific impact cat-
egories
The availability of inventory data and accompanying
parameters
The depth of knowledge and comprehension in each
impact category
The quality and availability of modeling data
The uncertainty and sensitivity analyses
The level of validations
The available supporting software
The level of funding
This paper provides a summary of the results of this work-
shop, including discussion on many of the above topics. An
attempt is made to provide short reviews of the presenta-
tions and discussions. However, in documenting the work-
shop it was not possible to capture the full detail of the
many points raised.
Workshop Logistics
On the 29th and 30th of November, 1998 in Brussels,
Belgium an international workshop was held to discuss Life
Cycle Impact Assessment (LCIA) Sophistication. Approxi-
mately 50 LCA experts attended the workshop, coming from
Europe, Asia and the USA. Several prominent practitioners
and researchers were invited to present a critical review of
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the associated factors, including the current limitations of
available impact methodologies and a comparison of the
alternatives in the context of uncertainty. Each set of pre-
sentations, organized into three sessions, was followed by
a discussion session to encourage international discourse
with the aim to improve the understanding of these crucial
issues. The discussions were focused around small work-
ing groups of LCA practitioners and researchers, deliber-
ately selected to include a balance of representatives from
industry, government and academia. Each group was given
the charge to address the questions that most interested
them, as opposed to assigning specific groups with spe-
cific questions.
Introductory Session
Jane Bare of the U.S. Environmental Protection Agency
(EPA) opened the workshop noting that many of the partici-
pants had been involved in previous meetings as LCIA ex-
perts, sometimes even discussing related issues in the
development of ISO 14000 series and SETAC Working
Groups on LCA and LCIA. Requirements are being devel-
oped under IS014042 to specify a high level of sophistica-
tion for Comparative Assertions, including language con-
cerning the scientific validity, environmental relevance, and
the role of value choices. Within SETAC-Europe efforts are
on going to develop a document related to the selection of
the "state-of-the-art" impact assessment methodologies.
Bare asked that participants consider the present workshop
as a more open format than either of these settings to allow
a completely uninhibited technical exchange. She stressed
that Life Cycle Impact Assessment can be effective in sup-
porting environmental decision making, but only if the data
and methods are sufficiently scientifically defensible. Sci-
entifically defensible was defined as being dependent upon
the level of sophistication, the level of certainty (including
both data and model certainty), the level of comprehensive-
ness, and data availability. The participants were challenged
to address several additional questions throughout the two
days of discussions including: What is "scientifically defen-
sible"? In the sphere of determining whether impact assess-
ment is based on sound science, where does one draw the
line between sound science and modeling assumptions?
Garrette Clark from the United Nations Environment
Programme (UNEP) then provided a short history of UNEP's
involvement in the area of LCA, which includes providing
technical assistance to developing countries and the devel-
opment of an associated guidance document for LCA (UNEP,
1996). She stated that LCA is considered by UNEP to be an
important tool for achieving cleaner production and consump-
tion. She also summarized findings from the recent LCA
workshop in San Francisco in June 1998 (UNEP, 1998).
David Pennington discussed two extremes of LCIA so-
phistication. One extreme he called the "Contribution or
Burden" approach, which is comparable to what has been
historically used in LCIA (reflecting the Precautionary Prin-
ciple and the combinatory potential to cause impacts).The
other extreme, the "Consecutive Risk Assessment" ap-
proach, he noted as being particularly recommendable for
use in areas with high stakes, such as comparative asser-
tions, but as often limited to the assessment of chemicals
in isolation. He introduced the question concerning the need
for spatial differentiation and asked when site-specific dif-
ferentiation was appropriate. He also pointed out that the
category indicators are chosen at different points in the
environmental mechanism (or cause-effect chain), and
stated that the U.S. EPA has been using the term of "mid-
point" to address indicators that stop short of expected ef-
fects on the final "endpoint" of the environmental mecha-
nism. He presented acidification as an example of a cat-
egory with the indicator at "midpoint" level and human health
as a possible example of a category with the indicator at
"endpoint" level. He concluded by asking about the different
levels of sophistication. What is possible? What is required?
When to use the various levels of sophistication?
Session One: Overview
Willie Owens of Procter and Gamble spoke about com-
parative assertions (i.e., public comparisons between prod-
uct systems) and the requirements for LCIA under ISO
14042. He stated that ISO 14042 requires a sufficiently
comprehensive set of category indicators, a comparison
conducted indicator by indicator (i.e. no weighting) and that
LCIAs should not be the sole basis for comparative asser-
tions. Current language in IS014042 states that subjective
scores, such as weighting across categories, shall not be
used for comparative assertions; that category indicators
be scientifically defensible and environmentally relevant and
that sensitivity and uncertainty analyses shall be conducted.
Mark Goedkoop of Pre Consultants discussed LCIA for
ecodesign. He pointed out that the point of conducting an
LCA is typically to determine whether A is better than B. He
then presented three problems with LCA and ecodesign: 1)
LCA studies are too time consuming, 2) LCA studies are
hard to interpret, and 3) Designers never become experts,
but remain dependent upon experts. His proposed solution
for these problems was to calculate predefined single scores
for the most commonly used materials and processes, and
to incorporate uncertainty into the modeling. He also dis-
cussed the sometimes hidden role of societal values in
characterization modeling, even for internationally agreed
models. As an example, he presented the three classes of
carcinogens (proven, probable and possible) and pointed
out that the practitioner must make a decision about whether
to include one, two, or all three classes. He proposed that a
single truth does not exist and that modeling is dependent
upon the chosen perspective. He then introduced three dif-
ferent views of the world based on values: egalitarian, hier-
archical and individualist. (A topic discussed later in more
detail by Patrick Hofstetter.) He pointed out that if A is not
better than B in all three cases then the result is dependent
upon the perspective.
Henrik Wenzel of the Technical University of Denmark
discussed the application dependency of LCIA. He men-
tioned several applications including life cycle management,
strategic planning, product development, process design,
green procurement and public purchasing, and marketing.
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In addition, he discussed three main variables governing
application dependency: the environmental consequence of
the decision (including spatial and temporal scale), the so-
cioeconomic consequence and the decision context. He dis-
cussed the application dependency of uncertainty, trans-
parency, documentation and the inclusion of temporal and
spatial resolution. He stated that the need for sophistication
of LCIA is largest in decisions with the highest requirements
for certainty. He also stated that the decision-maker might
impact the choice of normalization and weighting. (Wenzel,
1998)
Helias Udo de Haes wrapped up this first session by pro-
viding a summary of some of the key points covered and
challenging the participants to address the questions pro-
vided during the small group discussions. Workshop par-
ticipants were asked to address the following questions and
to provide additional questions to aid discussion.
Determination of Sophistication and Uncertainty Analysis
1. What are the most common methods by which the
level of sophistication is determined?
2. Which methods are considered more acceptable? Why?
3. What are the barriers to using the acceptable meth-
ods? What can be done to overcome these barriers?
4.To what extent should LCIA be application dependent?
5. What are the expectations regarding the level of so-
phistication for the various LCA applications (e.g., by
government, by industry, for public communication, and
for internal use)?
6. When should LCIAs be as detailed as possible, aiming
at the maximum level of accuracy? And when is it
better to limit the scope of LCA to addressing ques-
tions on a macroscopic scale, leaving spatial and
threshold considerations to other analytical tools?
7. How do practitioners deal with the trade-offs neces-
sary when sophistication and comprehensiveness are
"at odds" (e.g., choosing a detailed modeling approach
that may limit the comprehensiveness vs. a scoring
approach that may limit the sophistication)?
8. What case studies are available using uncertainty
analyses within LCIA? And what are the major find-
ings to date (levels of uncertainty discovered)? When
is the uncertainty determined to be unacceptable?
Questions Added at Workshop
9. What is scientifically and technically valid, as included
in the requirements of IS014042?
10. If LCIA is an iterative process, what drives the deci-
sion on the level of sophistication (e.g., uncertainty
analysis, relevance, and existence of trade-offs)?
11. Define uncertainty in the context of LCIA. What pa-
rameters must be analyzed?
12. How do we incorporate background levels into LCIA?
Should we define working points (as in Mark
Goedkoop's presentation)? Should this be done for
individual chemicals or combined?
13. What is the best currently available method to repre-
sent the combined effect of chemicals without double
counting, or inappropriately allocating?
14. How do we incorporate (or should we incorporate) the
differing philosophical views in characterization?
First Session Discussion Summary
An aggregation of the resultant views is presented below:
Determination of Sophistication-Many different groups
commented on the appropriate level of impact assessment
sophistication. One group commented that some sound
decisions may be/have been made on the basis of LCA
studies, which did not have very sophisticated LCIAs, but
these tended to be more obvious cases.They recommended
using the most sophisticated impact assessment models
that provide information closest to the endpoint. Another
group commented that sophistication is dependent upon a
number of things including: inventory data availability, the
availability of characterization models and data to support
these models, objective, the application dependency, the
decision maker's sphere of influence and the impact cat-
egory. A third group stated that the choice of sophistication
depends upon an iterative process, where the iterations may
be dependent upon uncertainty, the environmental relevance
of the results and the minimum level of certainty required to
support a decision. Several participants commented that
sophistication is often limited by budget, inventory data
availability, ease of use of impact assessment methods
and in-house knowledge.
These participants stressed the practical side of LCA and
recognized the difficulty in data collection and the structur-
ing of public databases to support more sophisticated analy-
ses.
Application Dependency-There was a general belief that
LCIA sophistication is application dependent, according to
the type of application and not the individual user. For ex-
ample, screening level LCA studies may not require the rig-
orous use of sophisticated impact assessment techniques
but final comparative assertions may require much more
rigor, particularly if the benefits are not apparent. LCIA stud-
ies should be performed based on the type of question or
decision at hand and the purposes that the LCIA may be
serving.
Validating the Results of LCIAs- There was agreement
that one cannot validate the results of a single LCIA study,
because of the lack of temporal and spatial specification
associated with the inventory data, and an inability to accu-
rately model complex interactions in the environment, in-
-------
eluding the combinatory effects of chemical mixtures. How-
ever, input data can be quality checked, and elements in
the models can be compared with models developed in the
context of other applications such as environmental risk
assessment. It was also noted that validation might not be
as important in the context of LCIA since models simply
reflect a relative comparison as opposed to an absolute
assessment.
Backgrounds and Thresholds - Practitioners have tried to
incorporate background levels in LCA studies in the past
but there was a lot of discussion that this practice may or
may not be appropriate. One of the questions at hand is
whether emissions do occur in above or below threshold
situations. Another issue concerned the fear that defining
backgrounds and thresholds will lead to treating many envi-
ronments as infinite sinks (e.g., for acidic chemicals) when
in reality nature's ability to absorb the impact may be ex-
ceeded at some future time. The distinction was also made
that thresholds may be less strict, because of the presence
of very sensitive species or human individuals.Thresholds
may also not be protective enough in many environments in
which the combined effects of chemicals may cause ef-
fects at a level much lower than the threshold effect. Fi-
nally, practitioners were cautioned not to use LCIA to the
exclusion of recognizing the problem of hot spots surround-
ing facilities. (See the following point for more information
on mixtures). On the other hand, some participants believed
that thresholds might be valuable indicators of relative po-
tency for many chemicals and that thresholds had been
derived with statistically sound methods. Further clarifica-
tion of the decision-making context may be necessary to
determine the value of thresholds and backgrounds in par-
ticular applications of LCIA.
Mixtures - One of the basic limitations of the current state-
of-the-science of LCIA of human and ecotoxicity is the in-
ability to effectively deal with potential combinatory effects
of chemical mixtures. Toxicologists operate under the as-
sumption that chemicals acting on the same organ can be
considered to have an additive effect, but often LCIA im-
pact categories are much broader than a focus on target
organs. Therefore, the same assumptions used in risk as-
sessment are not applicable to LCIA. This is especially an
issue when practitioners try to incorporate threshold levels
for individual chemicals into LCIA. Because mixtures are
not well characterized in LCIA, effects may be occurring at
much lower levels than the accepted threshold levels of the
'individual chemicals. Practitioners often try to compensate
for these and other model deficiencies by adopting the Pre-
cautionary Principle.
Data Gaps-There was a concern that data gaps can be
significant. Particularly in human and ecotoxicity, availabil-
ity and quality of both inventory and chemical data to sup-
port the modeling of a large number of chemicals can be
frustrating. These impact categories are a good example of
where less sophisticated screening techniques may, with
an appropriate degree of caution prove useful.
Uncertainty Analysis - LCIA still faces great challenges
before fully addressing uncertainty analysis. Some of these
challenges include the lack of awareness, lack of associ-
ated methodology, and the perceived difficulty of present-
ing the results to decision-makers. Specifically, practitio-
ners need better knowledge of uncertainties in existing
methods within the different impact categories and of the
potential for improvement, if any, by using methods with
greater sophistication. Many participants acknowledged a
need for a better understanding of the uncertainty involved
in each of the impact assessment methodologies for each
of the impact categories, noting that uncertainty is associ-
ated with the models as well as the input data. The potential
trade-off in available models between increased sophistica-
tion (i.e., detail) and reduced comprehensiveness (e.g.,
number of stressors simulated) was again noted.
Unnecessary Rigor? - There was a belief that the ISO
standard on LCIA, specifically for the comparative asser-
tions to be disclosed to the public, is too demanding in the
areas of scientific validity and certainty. Examples were given
of some other modeling arenas that face the same chal-
lenges (e.g., economic modeling, risk assessment stud-
ies). In these fields large uncertainties are accepted, ex-
pected and (sometimes) clearly documented.There was also
a concern that the rigor expected of the impact categories
without a working international acceptance (e.g., human
toxicity) exceeds the rigor and certainty requirements com-
pared with the impact categories that benefit from having
international consensus (e.g., global warming potentials).
Model uncertainty vs. data uncertainty - Some partici-
pants commented that the current disparity in levels of un-
certainty analysis may have led to the false impression that
the more sophisticated models have increased uncertainty
when compared to less sophisticated techniques.Typically
this is not the case. Usually, with a more sophisticated model
the model uncertainty has decreased and the ability to model
data certainty quantitatively has increased. Deceptively
(since model uncertainty is not typically characterized) the
increased characterization of data certainty may have
seemed to increase total uncertainty. (Additional details on
uncertainty analysis may be found in Edgar Hertwich's pre-
sentations.)
Standardization- While it was recognized that the level
of sophistication might depend upon the type of application
and the availability of data, there was a belief that consis-
tency of approach or methodology may be an important
priority to allow comparability between studies. Some par-
ticipants pointed out that certain studies may only require
Life Cycle Thinking and therefore, should not be subject to
the standardized methodologies. Others addressed the idea
of approach hierarchies that differentiate between screen-
ing and more intensive techniques but noted that the ap-
proaches could be consistent within these tiers. It was simi-
larly noted that there could be a trade-off between sophisti-
cation and comprehensiveness, while one approach pro-
vides a more complete picture but with low level of detail,
another may provide a higher level of detail but at the ex-
pense of comprehensiveness. It was further noted that there
is continual development of methods and standardization
should not discourage further research efforts.
-------
More Focused Research - More energy needs to be ex-
pended to ensure that LCA research is focused on areas
that will have the greatest impact. Research needs to be
conducted in deriving better methodologies for more relevant
indicators. Specifically, land use, habitat alteration, and
environmental toxicity were mentioned as examples of im-
pact categories requiring much more research.
Session Two: Human Health and Ecotoxicity
Edgar Hertwich of the University of California, Berkeley
opened the session on Human and Ecotoxicity with his pre-
sentation: "A Framework for the Uncertainty Analysis of the
Human Toxicity Potential". He presented the purpose of
uncertainty analysis: "to develop confidence in an analyti-
cal result, as an input to formal decision analysis techniques
and as a tool to refine impact assessment methods." He
noted that uncertainty analysis includes: parameter uncer-
tainty, model uncertainty, decision rule uncertainty and vari-
ability. He then presented various examples of each of these
as they might pertain to modeling for human toxicity impact
assessment in LCIA. Finally, he pointed out that simply
conducting a sensitivity analysis can often provide valu-
able insights about the significance of the multiple uncer-
tainties involved in the decision and can help refine impact
assessment techniques. (Hertwich, et al., 1993; Hertwich,
1999)
Patrick Hofstetter of the Swiss Federal Institute of Tech-
nology in Zurich addressed the question of "What is sci-
ence?" in the presentation: 'The Different Levels of Uncer-
tainty Assessment in LCIA: The Case of Carcinogenic Ef-
fects." He stated that the development of models is depen-
dent on the perspective of the modeler. Three perspectives
were described: hierarchist, individualist and egalitarian. An
individualist optimizes the spending of resources based upon
the known or certain types of harm that can be modeled
(e.g., only choosing to include IARC Group 1 Carcinogenics
in an analysis). A hierarchist could be closest to the operat-
ing positions typically held by government and international
organizations and would include Group 1 and Group 2 Car-
cinogens. Egalitarians tend to take a more risk aversive
and preventive standpoint and thus would include Groups
1,2, and 3 in a carcinogenic analysis. Similarly, these dif-
ferent perspectives would derive different discount rates for
use within an assessment in terms of the Disability Ad-
justed Life Years (DALY). An illustration showed the combi-
nation of the assumptions of all three cultural perspectives
in an eco-index probability graph. Finally, he concluded that
LCIA could be made simple to use and yet robust by incor-
porating the values associated with various perspectives
and allowing an analysis of the related technical, method-
ological and epistemological uncertainties. (Hofstetter, 1998)
Olivier Jolliet of the Swiss Federal Institute of Technol-
ogy in Lausanne discussed "Human Toxicity and Ecotoxicity
Modeling vs. Scoring." He opened by saying 'Tell me your
results and I will tell you who paid you!" Then he called for
the identification of best available practice regarding impact
assessment methods to reduce the ability to provide LCAs
that support such malpractice. He also proposed that this
process should try to meet the ISO 14042 requirements to
be "scientifically and technically valid" and "environmentally
relevant." After comparing different human toxicity model-
ing efforts, he pointed out parameters and model character-
istics that are important in human and ecotoxicity model-
ing, including exposure and fate uncertainties, that can be
responsible for significant uncertainty and which open op-
tions for reduction of modeling uncertainty by proper em-
pirical or experimental validation. He concluded by saying
that modeling comparisons should be made based on model
characteristics and consistent data.
Mark Huijbregts of the University of Amsterdam presented
a paper on "Priority Assessment of Toxic Substances in
LCA: A Probabilistic Approach." Citing previous publications
(e.g., Guinee, et al., 1996 and Hertwich, et al., 1998), he
suggested that the following specific improvements are
needed: a review of default values with the possibility of
using more realistic values, an inclusion of all relevant envi-
ronmental compartments and inclusion of a Monte Carlo
type of uncertainty analysis. He presented a probabilistic
simulation of weighted human, aquatic and terrestrial Risk
Characterization Ratios (RCRs) for 1,4-dichlorobenzene and
2,3,7,8-TCDD and demonstrated that only a few substance-
specific parameters are responsible for the uncertainty in
results. Finally, Huijbregts concluded that variability is not
of significance if it is identical for all options being com-
pared and asked that researchers continue to explore the
issue of when data uncertainty/variability cancel in relative
comparison applications.
Second Session Discussion Summary
Workshop participants were asked to address the follow-
ing questions and to provide additional questions to aid dis-
cussion.
1. In human toxicity and ecotoxicity, when is spatial and/
or temporal differentiation necessary? If necessary,
what spatial and/or temporal details are recommended
(e.g. indoor/outdoor, height of emission point)?
2. With respect to ecotoxicity what is the best approach
to addressing multiple species? If suggested, what
are recommended representative species?
3. With respect to human toxicity and ecotoxicity, what
are the greatest barriers to conducting uncertainty
analysis?
4. What are recommendations for research and devel-
opment in these impact categories?
An aggregation of the groups'views is presented below:
Standardization - Again the question of standardization
was discussed. Specifically, if the practitioner or study com-
missioner can have such a strong influence on the final
results of the study, then perhaps some standardization
would be useful to provide comparability between studies.
However, what perspective or aggregation of perspectives
should be represented in a standardized approach? Should
-------
central tendency assumptions or worst-case assumptions
be used? Some participants stated that additional time was
needed to ferment an opinion in this area. Others contended
that "allowing" for too many methods and approaches could
undermine the credibility of LCIA. However, many believed
that now is the time to capture the state-of-the art in a docu-
ment, while still allowing room for advances in the future.
Several participants expressed interest in being involved in
the current SETAC-Europe Working Group on Life Cycle
Impact Assessment. (Udo de Haes, et al, 1999a and Udo
deHaes, etal, 1999b).
Midpoint vs. Endpoint Level- In further discussion of the
concepts of midpoints vs. endpoints, many participants dis-
cussed the advantages of making all impact assessment
models as close as possible to the final endpoints of the
environmental mechanism of the impact categories (e.g.,
quantifying fish kills and trees lost as opposed to the acidi-
fication potential of the substances). One benefit of this
approach would be to allow more common endpoints for the
valuation process, perhaps even opening the door to allow-
ing more economic valuation of endpoints. Others pointed
out that this might be unnecessary in a relative comparison
context. They stated that extending the models to the end-
points will narrow down the comprehensiveness of the im-
pacts considered, and will include many more assumptions
and value judgements into the assessment. This may sub-
sequently increase the uncertainty of the results and re-
duce credibility by further mixing "science and value judge-
ments."
Ecotoxicity- There was a strong call for research in this
area.There was a recognized need to extrapolate ecotoxicity
in a manner similar to human toxicity with representative
species but also a realization that representative species
may vary within different areas. However, there was also
some discussion that LCA is a very macroscopic tool and,
can not be expected to accurately model local issues. Per-
haps, ecotoxicity is so specific to the locality affected that
an attempt should not even be made to include it as an
impact category. The most widely held view on this topic
seemed to be that ecotoxicity should continue to be included,
for the sake of providing a more holistic picture, and that
the potential for more site-specific approaches should be
considered further.
Potentially Affected Fraction of Species (PAFs) - Mark
Goedkoop gave an impromptu presentation on PAFs. He
stated that PAFs are different from PNECs in that they take
the background level of the substances into account and
thus enable nonlinear modeling of impact on the species
composition. Many in principle liked the idea of PAFs and
combined PAFs that represent the combined effect of chemi-
cals. However, there were concerns related to the possibil-
ity of identifying PAFs, due to the limited availability of dose
response curves and of background concentration data for
so many chemicals. A discussion of Eco-lndicator 98's re-
lationship to PAFs was held. (Goedkoop, 1998)
Borrowing from Risk Assessment - Concern was voiced
that LCIA for human toxicity is often based on typical risk
assessment practice (e.g., the use of toxicological bench-
marks). Caution was particularly high in the context of de-
terministic safety factors used in the toxicity component of
the characterization factors, many of which compensate for
low test species numbers. As this reduces the equity and
comparability of chemicals, participants suggested that LCIA
must be careful when adopting deterministic risk assess-
ment perspectives.
Research into Increasing Sophistication and the Role of
Other Assessment Techniques - One group asked for in-
creasing temporal modeling, real ground concentration mea-
surement, incorporation of population density into simula-
tions and better representation of food webs. In this group,
there was a concern that the current direction of research in
multimedia modeling would not address these areas. How-
ever this must be viewed in the context of the aims which
are to be met by LCA as opposed to the types of analytical
tools. Thus, another group stressed that perhaps practitio-
ners are too concerned with detail. Perhaps the focus should
remain on macro differentiation of substances in terms of
their persistent, bioaccumulative, and toxic (PBT) proper-
ties. This could be subsequently complimented (if required)
by local scale analysis using other tools, and would help to
include a larger set of chemicals at a sufficient level of
differentiation.
Session Three: Acidification, Eutrophication
and Inventory
Greg Norris of Sylvatica, North Berwick, Maine, USA,
presented a "Value-of-lnformation Approach." [He pointed
out that uncertainty analysis allows some additional infor-
mation (e.g., confidence intervals associated with data un-
certainty) within the decision-making framework.] Norris
stated that the level of sophistication should be partially
dependent upon the inventory data and its uncertainty, upon
the appropriate models and upon decisions about weight-
ing. He suggested using Input/Output-based upstream LCI
databases to answer many of the common questions that
practitioners face, such as "How many sites, with how much
geographic dispersion, contribute significantly to inventory
totals?" And "What are the expected shapes of these distri-
butions?" He also cautioned participants against trying to
draw conclusions about the preferability of more detailed
LCIA, based on a Probability Density Function (PDF) or
Cumulative Density Function (CDF) diagram, pointing out
that further simulations may be required. Finally, he dis-
cussed the difference between analyzing uncertainty in
weighting and in characterization modeling and the need to
treat these issues jointly in the determination of the level of
sophistication and decision support.
Jose Potting of the Technical University of Denmark pre-
sented "Levels of Sophistication in Life Cycle Impact As-
sessment of Acidification." Potting presented a case study
comparing alternative locations for copper production and
demonstrated the potential need for site-specific simula-
tions, including emission dispersion and deposition patterns,
background depositions on receiving ecosystems, and the
sensitivity of receiving ecosystems. [She used the Regional
-------
Air pollution information System (RAINS) model (from
IIASA) with calculations based on Critical Loads provided
by the National Institute of Public Health and the Environ-
ment (RIVM) in the Netherlands and transfer-matrices from
EMEP MSC-Watthe Norwegian Meteorological Institute.]
She announced that easy-to-use acidification factors had
been established for 44 European regions and suggested
that utilizing this site dependent approach for acidification"
resulted in a significant reduction in uncertainty.
G6ran Finnveden of Stockholm University presented two
topics - "EutrophicationAquatic and Terrestrial - State of
the Art," and "Thresholds/No Effect Levels/Critical Loads."
Finnveden discussed the site dependency of eutrophica-
tion in three models, developed since 1993. He presented
additional topics for discussion and research related to
eutrophication. In his second presentation, Finnveden pro-
posed that thresholds may, at the macrolevel, have no sci-
entific basis and in fact may just be "acceptable" levels of
risk and thus constitute value choices. Acidification and
human toxicity were used as examples of impact catego-
ries that should not ignore "below threshold values." In line
with this, he proposed that threshold values should not ex-
ist in LCIA for any impact category.
The third session was concluded with the large group
documenting some of the earlier topics and discussing the
value of conducting future similar workshops. An on-site
workshop summary was presented by two of the co-chairs.
Conclusions
In meetings and journals world wide, practitioners have
debated the utility of conducting Life Cycle Assessment
studies. The debate has often hinged on the appropriate
level of sophistication. While some have advocated aban-
doning LCA altogether, since it is not achievable in its most
sophisticated form, others have supported the concept of
conducting LCA studies at a more holistic level, while mak-
ing the limitations and uncertainties transparent. This work-
shop discussed many of the issues of dealing with the ap-
propriate level of sophistication in the Life Cycle Impact
Assessment phase of an LCA study.
A number of prominent practitioners and researchers pre-
sented a critical review of the associated factors, including
the current limitations of available impact methodologies
and a comparison of alternatives in the context of model
and data uncertainty. On the one hand the workshop ad-
dressed the various factors which are connected with an
increase of sophistication in LCIA. Examples include: a)
the need for better fate and effect models, b) the role of
spatial and temporal differentiation, c) the identification of
background levels and thresholds, and d) the need to specify
value-laden aspects such as connected with different cul-
tural perspectives. On the other hand, the holistic and com-
parative character of LCA was stressed. In this context,
many questioned whether LCA should aim to conduct so-
phisticated site specific risk assessments, particularly when
this high level of detail may give a false impression of great
confidence, especially when it is not presented with a strin-
gent uncertainty analysis. Moreover, it was recognized that
thresholds reflect value choices about what is regarded
acceptable, rather than science based parameters. And fi-
nally, increasing level of detail can increase model certainty,
but, in some cases, may reduce the comprehensiveness.
Workshop speakers and participants discussed the way
that philosophical views may affect not only the valuation
process, but also the impact assessment phase by includ-
ing assumptions that include values based on the differing
perspectives. This further complicates the question of what
is "science-based" and what are "reasonable" modeling as-
sumptions. Arguments were raised both for and against striv-
ing for consistency at this time in the effort to standardize
some of the methods and assumptions to allow compara-
bility between studies.
There was much discussion about the decision-making
framework and the role of other environmental analyses,
such as risk assessment. From the sophisticated uncer-
tainty analyses presented it was obvious that great advances
are being made, but there are many very basic principles
that still lack consensus (e.g., the use of threshold values
and background concentrations). As in risk assessment,
there is great attention to being true to the science, but in
the interest of practicality, a great need for simplifying as-
sumptions.
There was consensus that the workshop was very valu-
able and that this exchange should be continued through e-
mail discussions and periodic workshops (next target work-
shop in Brighton, U.K. in May 2000). Several topics were
mentioned for future workshops, including: LCIA at strate-
gic levels of decision making (including sustainable devel-
opment decision support), community planning using LCIA-
type indicators, the role of value choices in characteriza-
tion modeling, and the state-of-the-science for characteriz-
ing ecotoxicity in LCIA.
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Step Within LCA. Part II: A Formalized Method of
Prioritization by Expert Panels. International Journal of
LCA, Vol. 1, No. 4, pp. 182 - 192.
Wenzel, H., (1998): Application Dependency of LCA Meth-
odology: Key Variables and Their Mode of Influencing
the Method. International Journal of LCA, Vol. 3, No. 5,
pp. 281-287.
Workshop Agenda
Sunday, Nov. 29
15:00 -15:45 Introduction of Workshop by Co-Chairs
15:45 - 21:30 Determination of Sophistication
15-45 -16-15 ISO Standards: LCA & Comparative As-
sertions (Willie Owens, P&G, USA)
16:15 -16:45 Impact Assessment for Ecodesign (Mark
Goedkoop, Pre, Netherlands)
16:45 -17:15 Application Dependency of LCI A (Henrik
Wenzel, Tech U of Denmark)
17:15-19:00 Dinner
19:00 - 20:30 Small Group Discussion of Determina-
tion of Sophistication
Questions Provided
20:30 - 20:45 Coffee Break
20:45 - 21:30 Small Group Presentations
Monday, Nov. 30
08:00 -17:30 Specific Impact Categories & Uncer-
tainty Analysis within LC1A
08:00 -12:00 Human Toxicity & Ecotoxicity
08:00 - 08:30 Types of Uncertainty in the Human Tox-
icity Potential
(Edgar Hertwich, UC Berkeley, USA)
-------
08:30-09:00
09:00-09:30
09:30-10:00
10:00-11:15
The Different Levels of Uncertainty As-
sessment in LCIA;The Case of Carcino-
genic Effects (Patrick Hofstetter, Swiss
Federal Institute of Technology)
Human Toxicity and Ecotoxicity - Model-
ing vs. Scoring (Olivier Jolliet, EPFL,
Switzerland)
Priority Assessment of Toxic Substances
in LCA: A Probabilistic Approach (Mark
Huijbregts, IVAM, Netherlands)
Small Group Discussion of Human Health
and Ecotoxicity
Questions Provided
11:15-12:00 Small Group Presentations
12:00-13:30 Lunch
13:30 -16:30 Acidification, Eutrophication, andTro-
pospheric Ozone
13:30-14:00 Implications of Inventory Structure for
LCIA and Uncertainty Analysis (Greg
Norris, Sylvatica)
14:00 -14:30 Levels of Sophistication in LCIA of Acidi-
fication (Jose Potting, Tech U of Den-
mark)
14:30 -15:00 Eutrophication - Aquatic and Terrestrial-
State of the Art (Goran Finnveden, fms
and Stockholm University)
15:00-15:15 Coffee Break
15:15-16:45 Large Group Discussion
16:45-17:30 Workshop Revelations and Lessons
Learned
Closing Remarks by Co-Chairs
Bare's Biography
Bare is the Impact Assessment and Measurement (I AM)
Team Leader within the Sustainable Technology Division of
the National Risk Management Research Laboratory. The
role of the I AM Team is to influence environmental deci-
sion making in Sustainable Development, Life Cycle Im-
pact Assessment, and Pollution Prevention through the re-
search, development, and application of comprehensive
impact assessment and progress measurement. Bare has
been active in ISO 14042 (Life Cycle Impact Assessment)
providing written and verbal comments to the U.S. position
and representing the U.S. position on several issues. She is
also the U.S. EPA representative and an active member of
the American Institute of Chemical Engineers (AICHE) Cen-
terfor Waste Reduction Technologies (CWRT) Sustainabil-
ity Metrics Working Group which is designing a framework
for Sustainable Development decision making within large
U.S. chemical companies, including Monsanto, DuPont,
Rohm and Haas, Eastman Kodak, and others. She has given
numerous U.S. and International presentations related to
Life Cycle Impact Assessment and Sustainable Develop-
ment. Bare and other members of the I AM team conduct
research into improved impact assessment methodology
development and impact assessment application to sup-
port environmental decisions. One tool - TRACI - Tool for
the Reduction and Assessment of Chemical and other en-
vironmental Impacts will provide improved impact assess-
ment methodologies for five of seven major chemical im-
pact categories. Bare is a chemical engineer with 15 years
of experience within the U.S. EPA.
10
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Bare's Overheads
Life Cycle Impact Assessment
Sophistication
UNEP
CML
Ceadeof Eavironmentil Science
Organized by:
Jane C. Bare, U.S. EPA ORD
Helias A. Udo de Haes, CML
David W. Pennington, U.S. EPA ORISE Fellow
in co-operation with UNEP
Life Cycle Impact Assessment
can be effective in
environmental decision making,
but
only If the and
support a
defensible
point
Scientifically Defensible
Decision Point
Dependent Upon:
Level of Sophistication
Level of Uncertainty Analysis
Level of Comprehension
Data Availability
Data Quality
How is LCIA Sophistication
Determined?
To Be Addressed
project objective or application
state-of-art for the impact category
uncertainty and/or sensitivity analysis
quality and availability of modeling data
How is LCIA Sophistication
Determined?
Recognized but Not Addressed
perceived value of the impact category
anticipated level of impact
inventory data available ,
supporting software available
awareness of available models
the level of funding
Impact Category Trends
Simulations are becoming more
sophisticated as technology advances.
Researchers are increasingly aware of
developments in other countries and are
testing and comparing methodologies.
More discussions are addressing the
limitations of LCIA and the difference
between scientific and non-scientific
model simplifications.
11
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Your Challenge
To discuss:
current LCIA sophistication, including
temporal and spatial resolution,
major findings to date concerning the
scientific validity of current LCIAs, and
the research needs in this area.
Sunday Afternoon, Nov. 29
15:00-15:45
15:45-21:30
15:45-16:15
16:15-16:45
16:45-17:15
17:15-17:30
17:30-19:00
19:00-20:30
20:30 - 20:45
20:45-21:30
Introduction of Workshop by UNEP & Co-Chairs
Determination of Sophistication
Industrial Use: Comparative Assertions
(Willie Owens, P&G)
Internal Use Only (Mark Goedkoop, Pre Consultants)
Application Dependency of LCIA -
(Henrik Wenzel, Tech U of Denmark)
Summary Remarks by Co-Chair
Dinner
Small Group - Determination of Sophistication
Questions Provided
Coffee Break
Small Group Presentations
Monday Morning, Nov. 30
08:00-17:30
08:00-12:00
08:00-08:30
08:30-09:00
09:00-09:30
09:30-10:00
10:00-11:15
11:15-12:00
12:00-13:30
Specific Impact Categories & Uncertainty
Analysis within LCIA
Human Toxicity & Ecotoxicity
Discussion of Uncertainty Types & Human Health
Example (Edgar Hertwich, UC Berkeley, USA)
Structured Approach with Quantitative Uncertainty
Assessment (Patrick Hofstetter, Swiss Fed. Inst. of
Tech.)
Human Toxicity and Ecotoxicity Modelling vs. Scoring
(Olivier Jolliet, EPFL, Switzerland)
Uncertainty in Toxicity (Mark Huijbregts, IVAM,
Netherlands)
Small Group - Human Health and Ecotoxicity
Questions Provided
Small Group Presentations
Lunch
Monday Afternoon, Nov. 30
13:30-16:30
13:30-14:00
14:00-14:30
4:30 -15:00
5:00-15:15
5:15-16:00
6:00-16:45
6:45-17:30
Acidification, Eutrophication, and Tropo. Ozone
Smog Formation Characterization Analysis (Greg
Norn's, Sylvatica)
Levels of Soph. Of Acidification (Jose Potting, Tech U
of Denmark)
Eutrophication (Goran Finnveden, fms and Stockholm
University)
Coffee Break
Small Group - Three Impact Categories
Questions Provided
Small Group Presentations
Workshop Revelations and Lessons Learned
Closing Remarks by Co-Chairs
Determination of Sophistication
Methods for
1. What are the most common methods by which the level of sophistication is
determined?
Which methods are considered more acceptable? Why?
2. What are trie barriers to using the acceptable methods? What can be done
to overcome these barriers?
Various Uses o( LCIA
Human Toxicity and Ecotoxicity
ncertalntv Analysis
3. Is LCIA application dependent? Why or why not?
4, What are the expectations in sophistication for the various LCA uses (e.g.,
government, Industry/public communication, internal use)?
5. Whan should LCIAs be as detailed as possible, because every increase of
detail increases accuracy and removes uncertainty? And when is it better to
limit the scope of LCA to addressing questions on a macroscopic scale, leaving
spatial and threshold consideration to other analytical tools?
6. How do practitioners determine the value of the trade-offs necessary when
sophistication and comprehensiveness are "at odds" (e.g., choosing a modeling
approach may limit the number of pollutants that can be characterized vs. a
scoring approach which may limit the sophistication of the modeling)?
What case studies are available using uncertainty analysis within LCIA? And
hat are the major findings to date (levels of uncertainty discovered)? When is
e uncertainty determined to be unacceptable?
uman Toxicitv and Ecotoxieity
In human toxicity and ecotoxicity, when is spatial and/or temporal
fferentiation necessary? If necessary, what spatial and/or temporal details
e recommended (e.g., indoor/outdoor, height of emission point)?
In ecotoxicity what is the best approach to addressing multiple species? If
ggested, what are the recommended representative species?
In human toxicity and ecotoxicity, what are the greatest barriers to
nducting uncertainty analysis?
What are the recommendations for research and/or development in these
pact categories?
12
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Eutrophication, Acidification, and
Trope-spheric Ozone Formation
Uncertainty Analysis
1. What case studies are available using uncertainty analysis within LCIA? And
what are the major findings to date (levels of uncertainty discovered)? When is
the uncertainty determined to be unacceptable?
Eutrophication. Acidification, and Trooospheric Ozone Formation
2. In each category, when is spatial and temporal differentiation necessary?
so, what spatial and temporal scales are recommended (e.g., regionally, or
differentiating terrestrial, sweet water and marine)?
3. In each category, what are the greatest barriers to conducting uncertainty
analysis?
4. What are the recommendations for research and/or development in these
impact categories?
13
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Pennington's Biography
Pennington began his career at the University of Surrey in
England, where he obtained an Honors Degree in Chemical
Engineering in 1990. Aftertwo years with the consultant com-
pany Advanced Mechanics and Engineering (AME),
Pennington accepted a scholarship at the Hong Kong Univer-
sity of Science & Technology to perform research in the De-
partment of Chemical Engineering under the supervision of
department head, Professor P.L. Yue. Completing development
of a software tool and the associated Ph.D. thesis "A Pollution
Prevention Tool for Continuous Chemical Processes (P2TCP)"
in 1997, he then accepted a Post Doctoral Research Fellow-
ship at the Systems Analysis Branch of the US EPA National
Risk Management Research Laboratory (NRMRL) in Cincin-
nati, Ohio. There, Pennington has continued his research and
development activities in the areas of environmental impact
comparison and screening. A key interest in his R&D activi-
ties has been the simplification and use of multimedia mod-
els in the application of predicting persistence, relative im-
pact potentials and long-range transport capabilities of chemi-
cals. He is currently a participant in three key activities: the
US EPA Persistent Bioaccumulative and Toxic Pollutants Ini-
tiative (PBTI) working group; Jane Bare's development of Pol-
lution Prevention Progress (P2P) and the Tool for Reduction
and Assessment of Chemical and other environmental Im-
pacts (TRACI); and the SETAC Europe LCA working groups
on chemical fate and exposure, human toxicological impacts
and ecological toxicological impacts.
.14
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Pennington's Overheads
LOiA Sophistication
Issues Overview
Dr. David W. Pennington
ORISE Research Fellow
US EPA Systems Analysis Branch
Cincinnati
TwoExtrensliutwhatisappriale?
"Contributions or Burden" Approach
Typical LCIA Approach
Not actual impacts associated with specific emissions
More than one chemical and one emission
"Consecutive Risk Assessments" Approach
Comparative assertions (?) and LCSEA
Actual impacts associated with a specific emissions
Thresholds ?
^ฃ<2aK&*t
Subjectivity!
What level of subjectivity acceptable ?
Need to distinguish science vs. non-science based?
Disability Adjusted life Years
Average human inhalation rate in models
Sophistication of Model
Screening: PBT Scoring Approaches
Scientifically combined with release quantity ?
Qualitative ranking (high, medium, low) ?
More chemicals ?
Continuous scales: Potentials
Potentials modify release quantities
Ability to be comprehensive (state-of-the-art) ?
All pathways, species, interaction of chemicals...? /j
Spatial Differentiation
Global
Human & Eco?
Regional/Remote
Human & Eco?
What boundaries?
Advection rates?
What Species?
Other Fate Parameters ?
Which Models?
Acute Impacts?
Local
Human & Eco?
What is local?
InLCA?
Acute Impacts?
Which Models?
Midpoints vs Endpoints
Midpoints: Acidification Potential - # of H+ ions
Simplicity & reflective of "contributions" LCIA approach
Lower uncertainty ?
ซ How to measure it?
Lack of spatial and temporal variation-Problem? How?
Endpoints: Human Health Potentials - fate, exposure and effects
Complex but not time intensive?
Uncertainty and variation quantifiable ?
High?
Can consider spatial and temporal variation-Do we need to?
15
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ISO Standards: Life Cycle Assessment & Comparative Assertions
J.Willie Owens,
Procter & Gamble
Cincinnati, OH, USA
Summary
ISO Standards: Life Cycle Assessment &
Comparative Assertions
IS014040 defines a comparative assertion as an "envi-
ronmental claim regarding the superiority or equivalence of
one product versus a competing product which performs
the same function." The IS014020 series establishes sev-
eral principles for any environmental claim, including com-
parative assertions: information will be accurate, verifiable,
relevant, and non-deceptive; scientific methods will used to
generate the results; the process is open and participatory;
the information is transparent and available to all (e.g., pur-
chasers, interested parties, etc.); any claim is based on
measurable differences including consideration of variations
and uncertainty; and clear explanatory statements justify
and qualify the claim. The 14040 LCA standards and draft
standards meet these principles.
JS014040 and 14041 provide a detailed framework cov-
ering the LCA study goal definition and scope; functional
unit and system boundaries; data inclusion and exclusion;
data quality; data collection, handling, and calculations; al-
locations; and reporting.There are also specific requirements
when the comparative assertion is disclosed to the public:
data quality parameters that consider time-related, geo-
graphical, and technological variations; data precision, com-
pleteness and representativeness for different processes;
consistency and reproducibility of the methods; data
sources; and uncertainty. Public comparative assertions also
require equivalence between compared systems for func-
tional unit, methodological considerations, performance, sys-
tem boundaries, data quality, allocation procedures, deci-
sion rules on inputs and outputs, and the impact assess-
ment phase. Together, these requirements establish a fair
comparison in the inventory phase that is technically sound
transparent, and non-deceptive.
ISO 14040 requires that public comparative assertions
be based on a full LCA, including an impact assessment
phase. The mandatory portions of impact assessment in
FDIS14042 are Figure 1 .The concept of an LCIA category
indicator and its relationship to the impact category and the
characterization model is shown in Figure 2.
ISO 14042 requires categories, indicators, and models
used be: consistent with study goal and scope, sources for
models, etc., be referenced; selections be transparently jus-
tified and explained; a comprehensive set of environmental
issues within the goal and scope be covered; and describe
environmental mechanism and model so that the LCI re-
sults are related to the indicator, and the appropriateness of
model is understandable. A scientific approach in the im-
pact category model is taken by basing the model on a
defined environmental mechanism, i.e., a system of physi-
cal, chemical, and biological processes linking category LCI
results to category indicators and to category endpoints. A
highly simplified example for an acidification environmental
mechanism is shown in Figure 3.
ISO 14042 requires for public comparative assertions: a
sufficiently comprehensive set of indicators, a comparison
conducted indicator by indicator, that LCIAs not be sole
basis for comparative assertions as additional information
may be necessary to overcome limitations in clause 8 of
14042), that subjective scores such as weighting shall not
be used; that category indicators used shall be both 1) sci-
entifically and technically valid, and 2) environmentally rel-
evant, and sensitivity and uncertainty analyses of results
shall be conducted.
Thus, each of the mandatory steps used to derive an
indicator must be scientifically and technically valid: group-
ing into impact categories (Classification), converting LCI
results (1st Characterization step), and aggregating con-
verted LCI results (2nd step). This may present difficulties
for several current practices such as aggregation of differ-
ent effects, aggregation of a similar effect from different
places and times, and the use of subjective scores. For
example, an ILSI expert panel to toxicologists states that
"It is inherently impossible to make a purely scientific com-
parison of qualitatively or quantitatively different toxicity
impacts. This type of aggregation is equivalent to compar-
ing the impacts of global warming and acid rain. Such com-
parisons cannot be done scientifically."
For environmental relevance, IS014042 establishes key
criteria to meet: the indicator will reflect the actual conse-
16
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quences of the system operation on the category
endpoint(s), at least qualitatively. In addition, the category
model must incorporate environmental data or information,
including: environmental condition of the category
endpoint(s); intensity of environmental changes; spatial and
temporal aspects such as duration, residence time, persis-
tence, timing, etc.; reversibility; and uncertainty.
ISO also established a critical review of any study used
for public comparative assertions so that methods used will
be consistent with ISO, all methods are scientifically and
technically valid, the data are appropriate and reasonable,
study interpretations reflect the limitations, and that the study
report is transparent and consistent. The panel members
should be familiar with ISO and have scientific/technical
expertise to address the impact categories covered.There
are also extensive reporting requirements for the conduct
of a study to ensure transparency, the critical review panel
report must included in study report, and the report must be
made available to all upon request.
As a result, the ISO 14040 series clearly complies with
the 14020 series principles for environmental claims made
in the opening paragraph.
Thought Piece: Comments on Science
First, science is the exploration of the physical world and
the body of knowledge that results from that exploration.
This knowledge is gained via a number of processes in-
cluding basic research, applied research, and applications
such as environmental monitoring.
Second, this body of knowledge is both gained and as-
sessed by applying the scientific method. The tenets or
characteristics of the scientific method are:
The method begins with defined and framed observa-
tions or questions.
A testable hypothesis is generated to explain the spe-
cific observation or address the specific question. The
hypothesis is then used to design physical experiments.
This includes clear descriptions of assumptions to be
used and variables to be tested.
The experiments include positive and negative controls.
In many cases, statistical tests of the results and the
variables are also included.
The experimental observations are based on results that
are physically measurable and obtained using validated
measurement techniques.
The hypothesis, methods and results are transparently
described so that
1. Both the hypothesis and the experiment can be un-
derstood, reproduced and the results verified.
2. The hypothesis can be tested in other experiments
with different designs and measures.
An interpretation consistent with the experiment is of-
fered, including care to distinguish the statistical sup-
port, to note alternative interpretations where they ex-
ist, and to carefully qualify additional hypotheses or
speculations that are not directly supported by the re-
sults.
Independent researchers can then explore the hypoth-
esis and findings where new experiments may support
or challenge the results and interpretation.The ultimate
outcome is acceptance or rejection of the hypothesis.
Third, the last point has major implications. Science rarely
offers absolute proof, and cannot prove a negative or null
hypothesis. The body of knowledge generated by the scien-
tific method is subject to assessment with new experimen-
tal evidence, and even periods of controversy when mat-
ters are disputed. In fact, certain findings can be highly
contentious when established models and thinking are chal-
lenged by new experiments.The acceptance of these new
experiments then awaits testing, verification, and under-
standing. For example, a strong debate continues among
evolutionary scientists between continuous evolutionary
change and another model termed punctuated equilibrium.
In another case, proposals for a synergistic effect among
so-called endocrine disrupters could not be reproduced ei-
ther in other laboratories or the original laboratory, so the
authors retracted their findings. Thus, science and its body
of knowledge is not dogma. Science operates by construct-
ing and assessing the weight of evidence.
Fourth, scientific weight of the evidence is often a gradual
accumulation of data and knowledge. For example, the
empirical observation of PCB body residues in Baltic fauna
in the mid-1960s led to the reproduction of this observation
in other areas and species as well as with other compounds
such as DDT. As a result, we now have detailed body of
knowledge about:
- the physical and biological properties underlying the fate
and transport of substances,
- the degradation and persistence of substances,
- their many mechanisms of toxicity,
- the processes of bioaccumulation and biomagnification,
and
- the means to measure and model these processes as
well as how to validate the models.
Owens' Biography
Owens is a Principal Scientist at Procter & Gamble and
has responsibility for LCA development. He has a B.S. in
Biology (summa cum laude, Christian Brothers College,
Memphis, TN) and Ph.D. in Molecular Biology (US National
Science Foundation Fellow, Washington University, St. Louis,
MO). His prior experience contributing to his LCA background
includes product development, manufacturing, and consumer
testing within P&G, such as upstream product development
17
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for both disposable medical products and industrial cellu-
lose products, pilot plant experience in modified cellulose
fiber production, in-plant process development experience
at pulp mills, pilot scale production and consumer testing of
consumer paper products, service as a human toxicologist
for pulp and paper products, and environmental assessment
studies at pulp and paper mills. The largest environmental
study involved the design and the execution of the
multidisciplinary and multi-year effort in Alberta, Canada.
He has participated in the conduct of several life-cycle stud-
ies within P&G. He is a member of SETAC's LCA Advisory
Board, chair of the SETAC LCA Work Group on Impact
Assessment (North America), and is a member of the U.S.
delegation chair for the IS014000 TC 207 LCIA Work Group.
18
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Owen's Overheads
ISO Standards:
Life Cycle Assessment
Comparative Assertions
J. William Owens
Procter & Gamble
Cincinnati, OH, USA
UNEP LCIA Workshop
Nov. 29-30, 1998
Brussels
Definition of
Comparative Assertions
74040 - Definition 3.2
environmental claim
regarding the superiority or equivalence
of one product versus a competing product
which performs the same function
ISO 14000 Standards
Normative Terminology
shall - requirement:
critical review ฅ/ou!d note whether or not
study was in compliance
should- recommendation;
critical review would note whether or not
recommendation was followed
may- suggests.conslderatton
IS014020 Series
Declarations & Labels
Necessary elements for claims
ป Accurate, verifiable, relevant, and non-deceptive
ป Scientific method used where results are
are accurate and reproducible
Open, participatory process
* Information available to purchasers, interested
parties, etc. (transparent)
IS014020 Series
Declarations & Claims
Other elements:
Based on measurable difference;
analysing data ranges and variability
Explanatory statements where needed
Relevant to the geographic area
/ Life cycle assessment standards are
consistent with these requirements
IS014040 and 14041
Coverage
Brief Overview of Areas
definition and scope
Functional unit and system boundaries
Data inclusion and exclusion for inventory
Data quality
collection, handling, and calculations
Energy data handling and reporting
Allocations
Reporting
Accurate, technically sound, verifiable, & transparent
19
-------
Goal Definition & Scope
Clearly define application & audience as well as:
- the functions of system or systems
the functional unit;
- the product system to be studied
- the product system boundaries;
allocation procedures;
- categories, methodology, and interpretation of impact
assessment (also see Appendix A of 14042);
- data requirements;
- assumptions;
limitations;
Initial data quality requirements
type of critical review, if any;
type and format of study report.
Data Quality
Comparative assertions
All the data quality parameters must be addressed
and included in the study
- time-related
- geographical
- technology
- precision, completeness and representativeness
-of data
- consistency and reprodycibiilty of the methods
- data sources and their representativeness
- uncertainty
Equivalence of Comparisons
Comparative Assertions -14040
Equivalence necessary to make valid comparisons:
- same functional unit;
- equivalent methodological considerations:
performance,
system boundaries,
data quality,
allocation procedures,
decision rules on inputs and outputs and
Impact assessment.
v' Non-deceptive & based on measurable difference
Life Cycle Impact Assessment
4 phases of LCA LCI LCA
- goal and scope definition + *
- inventory analysis + +
- impact assessment *
- interpretation + 4-
LCIA phase necessary for comparative assertions
Discussion for
Mandatory Elements Only
Category Indicator
Selection of impact categories,
category indictors and models
Assignment of LCI results [Classification] 1
Example
HCS, etc.
Aefdiffctttion
Acidifying Emissions
ate.)
Calculation of category indicator results
[Characterisation]
Category indicator results (LCIA profile)
Life cycle Inventory results
LCI results assigned to
mpact category
Proton S0fe.sss (H>)
20
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Goal Definition & Scope
Clearly define application & audience as well as:
-the functions of system or systems
- the functional unit;
- the product system to be studied
- the product system boundaries;
- allocation procedures;
- categories, methodology, and interpretation of impact
assessment (also see Appendix A of 14042);
- data requirements;
- assumptions;
- limitations;
- initial data quality requirements
type of erif icai review, if any ;
- type and format of study report.
Data Quality
Comparative assertions
All the data quality parameters must be addressed
and included in the study
- time-related
- geographical
- technology
- precision, completeness and representativeness
- consistency reproducibility of the methods
sources and their representativeness
- uncertainty
Equivalence of Comparisons
Comparative Assertions -14040
Equivalence necessary to make" valid comparisons:
same functional unit:
equivalent methodological considerations:
performance,
system boundaries,
data quality,
allocation procedures,
decision rutes on inputs and outputs and
impact assessment.
/ Non-deceptive & based on measurable difference
Life Cycle Impact Assessment
4 phases of LCA
- goal and scope definition
- inventory analysis
- impact assessment
- interpretation
LCI
4-
LCA
+
+
LCIA phase necessary for comparative assertions
Discussion for
Mandatory Elements Only
Selection of impact categories,
category indictors and models
Category Indicator
Assignment of LCI results [Classification]
Example
kg SO,, HCl, etc.
Acidiftcallsn
Acidifying Emissions
(NO, $0ป etc.)
Calculation of category indicator results
v
[Characterisation]
Category Indicator results (LCIA profile)
Life cycle inventory results
tCI results assigned to
impact category
Proton Rstesse (tf)
21
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Algebraic Formula of Calculations
The conversion of the inventory result is
a multiplication step - in its simplest form:
CIR = CF MR
whore
CIR - converted inventory result
CF - characterisation factor
IR - inventory result
or algebraically:
y = a*x
For LCI Result Conversion
When seen as Clfl = CF * IR:
y = a*x
Converted
Inventory
Result
Zero intercept
No threshold
slope (a) is Characterisation Factor
vป
Linear response
Inventory Result
(IR) is x-axis
AGGREGATION
Is Aggregation of Converted LCI results Valid?
E?
Do the LCI results truly act and combine together?
Are the LCI results interchangeable across
spatial and temporal scales?
or
Are the LCI results independent in the environment
where action in one place or
time has no connection to another?
HUMAN TOXICITY
Current Issues
issue Toxicologists LCA
Thresholds Exist Absent
Dose-Response Case-Specific Linear
Endpoint Additivity Common Mode / Universal
Common Organ
Exposure* Case & Circumstance Simultaneous
Specific & Aggregated
* Assumes common place and time in aggregation
HUMAN TOXICITY
I LSI Panel on Aggregation
"It is inherently impossible to make & purely
scientific comparison of qualitatively or
quantitatively different toxicity impacts.
This type of aggregation is equivalent to
comparing the impacts of global warming
and acid rain. Such a comparisons cannot
be done scientifically."
Burke, Doull, McKone, Paustenbach, Scheuplein,
Udo de Haes, and Young
Criteria for Environmental Relevance
indicator reflects the consequences on the category
ertdpoint(s), at qualitatively.
Incorporate environmental data or information in the
category model, including:
- environmental condition of the category endpoint(s)
- intensity of the-projected impacts,
- spatial extent of projected impacts,
- temporal aspects, duration, residence time,
persistence, timing, etc,, of projected impacts,
- reversibility of projected impacts, and
- uncertainty of projected Impacts
22
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Critical Review
Summary of requirements
- methods consistent with ISO
- methods are scientifically and technically valid
- data are appropriate and reasonable
- interpretations reflect the limitations
- report is transparent and consistent
Panel requirements - 14040 & 14042
- famiiiar with ISO and scientific/technical expertise
- may include other parties effected by study
- pane! report included In study report
- expertise for impact categories covered
Conclusions
The 14040 LCA standards
Procedures technics! & scientific valid so that
analysis is credible to all parties
Results will be environmentally relevant
Procedures, assumptions, and decisions transparent
Independent review of the study and its conclusions
Flexibility for different applications and for
current and future methods development
Open and equal treatment to industry, NGOs,
government, consultants, universities, etc.
23
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Impact Assessment for Ecodesign
Mark Goedkoop, PRe Consultants,The Netherlands,
Email: Goedkoop@pre.nl
Introduction
The problem with LCA and ecodesign can be summa-
rized as:
LCAs are too time consuming in product development
processes.
The results of LCAs are hard to interpret for a designer;
often ten different scores for different environmental
impact categories are presented as "the" answer.
Our solution: Develop a set of predefined scores for the
most commonly used materials and processes. The set
should include indicators for common materials, production
processes, transport, energy consumption and different dis-
posal options per material. With this set anyone can per-
form its own LCA in a matter of minutes on the back of an
envelope.
A crucial factor in this solution is the reliability of the
indicators. The first effort to develop this set of indicators
resulted in the Eco-indicator 95 LCIA methodology, which
now in use by many companies. Currently a follow-up project
is undertaken for the Dutch government resulting in the Eco-
indicator 99 methodology (Goedkoop 98).
Calculating standard indicators
Standard indicators are calculated with the LCA method-
ology. This means we have to deal with the three different
aspects of any LCA: inventory, impact assessment and
valuation issues. In more abstract terms we have described
these three different issues as "spheres":
Technosphere, describing the technical system, or the
inventory phase.
Ecosphere, describing the environmental cause and ef-
fect mechanisms, resulting in a number of damage cat-
egories or endpoints.
Valuesphere, describing the societal values in assess-
ing the seriousness of the damages, and describing
some of the value choices that are unavoidable in the
modeling of cause and effect mechanisms.
Each sphere has its own typical characteristics and prob-
lems.
Technosphere
Ecosphere
Valuesphere
Subject
of the
modeling:
Verification
Main
problems:
values
Uncertainty
Concrete technical Models complex Societal prefer-
systems cause and effect ences and
chains values
Possible in many
cases
Boundaries
Allocations
Difficult or
impossible
Limited under-
standing of
mechanisms
and data
problems
A single truth
does not exist
How do we
measure
in society?
How do we
deal with
incompatible
views
low (far less then High (sometimes High, depending
an order of magni- several orders of on procedure
tude) magnitude for information
gathering
The problems in technosphere are relatively well understood
in LCA. The problems in ecosphere can in principle be solved
as our scientific understanding progresses, although consid-
erable research efforts will still be needed. The problems in
value sphere are fundamental, as they deal with incompatible
views of different people. These problems cannot really be
solved, but they can be managed if they are well understood.
The nature of the Valuesphere problems can be illustrated
with two examples.
Example 1: What level of scientific proof is required?
For only a few substances the carcinogenicity is sufficiently
proven. Many substances are generally suspected to be car-
cinogenic, but scientific proof is incomplete. Some people will
maintain that all substances under suspicion must be included,
while others would demand sufficient scientific proof if a sub-
stance were to be included. Who will decide?
Example 2: What timeframe do we adopt?
24
-------
In fate models an important parameter is the time per-
spective. If we take a long time perspective, substances
like heavy metals become very dominant in the analysis. In
fact the choice of the time perspective determines the char-
acterization values to a great extent. Some people will ar-
gue an indefinite time perspective must be used, while oth-
ers would argue it is nonsense to look at damages that
would occur after 100 years. Who will choose the time per-
spective, and thus decide on the relative importance of heavy
metals?
In a system with standard indicators such questions be-
come very important, as, unlike in a full LCA, the stake-
holders can not make such decisions on a case by case
basis. In the Eco-indicator 99 methodology the solution is
to choose three different perspectives.This means we pro-
duce three different versions of this methodology, each with
its own consistent set of value choices. (See also [Hofstetter
1998] and [Thompson 1990] on Cultural Theory).
Egalitarian: Precautionary principle: include as much
as possible, use a long time perspective. (Typical atti-
tude of environmental groups)
Hierarchical: Include facts that are backed up by sci-
entific and political bodies. (Typical attitude of scien-
tists).
Individualist: Only proven cause effect relations, short
time perspective; future damages are far less impor-
tant then present damages, (typical attitude of entre-
preneurs)
The users of the indicator system cannot make value
choices on a case by case basis, but they can choose
between three sets of value choices. This underlines the
obvious fact that a single truth is not available in LCA, even
when indicators are used.
Conclusions
It is much more efficient to calculate a large set of reli-
able single scores for commonly used materials and
processes. With these any designer can make their own
LCA without the help of other experts.
The issue of value choices becomes very important in
a system with predefined indicators, as the user can-
not influence them, while the results they get partially
depends on such choices.
By separating three different perspectives we give the
user the opportunity to choose a perspective he can
adhere to, and we show that a single truth does not
exist.
References
Goedkoop, M.J., Hofstetter, P., Muller-Wenk, R., Spriensma,
R.The Eco-indicator 98 Explained, InternationalJour-
nal on LCA, December 98, ECOMED Publishers,
Munchen
Hofstetter, P. (1998): Perspectives in Life Cycle Impact
Assessment; A Structured Approach to Combine Mod-
els of theTechnosphere, Ecosphere and Valuesphere.,
Kluwers Academic Publishers, 1998, Info: www.wkap.nl/
book.htm/07923-8377-X.
Thompson M,, Ellis R., Wildavsky A.; Cultural Theory,
Westview Print Boulder 1990
Goedkoop's Biography
Goedkoop is a Consultant and Scientist, born in 1954.
After obtaining an Electronic Engineering degree at Alkmaar
Polytechnic and an Industrial Design Engineering degree at
Delft University of Technology in 1983, he established an
industrial design engineering consultancy, called Evegoed.
In 1990 he shifted the attention to Ecodesign and Life Cycle
Assessment and renamed the consultancy as PRe
Consultancy, (seewww.pre.nl)
PRe was in 1990 the first Dutch design consultancy
exclusively devoted to Eco-design and LCA. We ex-
ecuted one of the first LCA in the Netherlands.
From the start we worked on the development of LCA-
based tools for Eco-design, like SimaPro LCA software
(introduced in 1990) and the development of Eco-indi-
cator 95 methodology. SimaPro is by far the most widely
used LCA software world-wide.
Development of ECO-it, a low cost, simple tool for de-
signers, based on Eco-indicators.
The most important projects run by Goedkoop are
Active participation in seven Eco-design projects un-
der the two National Dutch Ecodesign projects, as well
as participation in several other Ecodesign-type projects.
Project leader for the Eco-indicator 95 Methodology un-
der commission from The Dutch NOH project.
Project leader for the follow up project, the Eco-indicator
97/98 under commission of the Dutch ministry of Environ-
ment, in collaboration with several companies and well known
LCA experts in the Netherlands and Switzerland.
25
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Goedkoop's Overheads
Impact assessment for Ecodesign
Mark Goedkoop
PR6 Consultants B.V. Plotterweg 12 3821BB Amersfoort The Netherlands
E-mail: goedkoop@pre.nl web site: www.pre.nl
The problem with LCA and ecodesign
LCA's are too time consuming in product
development processes.
The results of LCA's are hard to interpret for a
designer; often ten different scores for different
environmental impact categories are presented
as "the" answer.
Designers will neve become LCA experts.
They may not stay dependent on experts
Stages in the design process
Example: LCA of a coffee machine
Stase
Product idea generation
Definition of
requirements
Idea generation
Concept development
Detailed design
LCA tool
Trends, experience,
policy, etc
LCA of reference, calc.
of new indicators
Eco-indicators
Eco-indicators
LCA for verification
disposal of I disposal in
filters + coffee I municipal
in org. waste I waste
Result of coffee machine analysis
7.3 fcg 1kg 0.1kg 0.3kg 0.4kg
paper I |polystyrenc| | aluminium| | steel | I glass I"
; injection ' extrusion
I. moulding f
11 r~
:onmng
375 kWh
electricity
Our solution: Pre-defined single
scores
1 Simplification of the methodology is not the
solution.
- The results will not be reliable
- Speed improvements are not sufficient
Pre-defined Single scores are the solution...
Important requirement: LCAs based on
single scores should predict results of later
full LCAs
26
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Calculating single scores
In order to make predefined scores extremely
careful modelling is needed.
The model should contain all three spheres *):
- Techno-sphere,
- Eco-sphere and
- Value-sphere
Different value systems can now be made
explicit.
) Sec also: Hofstetter, P. (1998): Perspectives in Life Cycle Impact Assessment; A
Structured Approach to Combine Models of the Technosphere, Ecosphere and
Valuesphere., Kluwers Academic Publishers, 1998
The three Spheres
Modelling Technosphere
Typical characteristics Techno-sphere
Description of concrete technical systems
Uncertainties can be low (far less then an
order of magnitude)
Verification is possible in many cases
Main problems:
- Boundaries.
- Allocations
Modelling Eco-sphere
Typical characteristics Eco-sphere
Models complex cause and effect chains
Usually high uncertainties (sometimes
several orders of magnitude)
Verification is difficult or impossible
Main problems:
- Limited scientific understanding of mechanisms
- Limited availability of data
- Different perspectives are possible
27
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Modelling Value-sphere
Typical characteristics of Value-sphere
Models aspects natural science cannot take
into account: "seriousness"
Uses social science
A single truth does not exist
Main problems:
- How do we measure values in society
How do we deal with incompatible views
The three Spheres
Values in Eco-sphere
Example: There are three classes of carcinogenic
substances:
- Carcinogenic effect is proven
- Carcinogenic effect is probable
- Carcinogenic effect is possible
Do we include all classes?
Who will decide this?
Cultural theory as management
system of subjectivity
Three types of perspectives:
- Egalitarian: Future generations just as
important as present. In case of uncertainty take
the worst case.
- Individualist: Limit time scale, In case of
uncertainty take proven facts only.
Hierarchist: Base analysis on what is agreed
upon in policies and science.
Three different ecosystem models are needed!
Three instead of one single score
We will develop three alternative sets of
single scores.
Each set represents a cultural perspective.
If A is better then B in all perspectives, then
the result is robust.
If A is not better then B, the result is
dependent on perspective
28
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Compatibility with other
approaches
Based on damage approach
Themes are made explicit as intermediate results
Characterisation not always based on
equivalency, but damages
If ISO compatibility is required, final weighting
may not be used in comparative assertions.
Eco-indicator society should promote proper
application
Overview Eco-indicator 98
Conclusions
Single scores need very careful modelling, as
they need to predict the outcomes of full
LCAs
By separating three different perspectives we
acknowledge the fact that different views are
.possible, and that a single truth does not exist
(unlike most full LCAs)
Modelling three spheres is essential
This method can also be used in full LCA
29
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Application Dependency of LCIA
HenrikWenzel,Tech U of Denmark
The reason to perform an LCA is essentially to use it in
support of a decision. A decision gives rise to a change
somewhere in society compared to a scenario in which this
decision was not taken. The key requirement for the LCA in
any application is, therefore, that it shall reflect the environ-
mental change caused by the decision. It is found that the
need to differentiate LCA methodology for the use in differ-
ent applications is born by a few key characteristics of the
decision to be supported.
The first key characteristic is the environmental conse-
quence of the decision, i.e. the nature and extent of the
environmental change caused by the decision. When mod-
eling the environmental change, its extent in time and space
will differ between decision types, thus giving rise to differ-
ent requirements, primarily for the scoping and inventory
phases of the LCA. Furthermore, some decisions will imply
trade-offs between different impact categories, while others
will not, thus causing different requirements for the impact
assessment. The second key characteristic is the social
and economic consequence oi the decision, the magnitude
of which will influence the need for certainty, transparency
and documentation. The third characteristic is the context
in which the decision is taken, including the decision-maker
and interested parties, implicitly influencing the impact as-
sessment and weighting.
Wenzel's Biography
Wenzel has expertise in environmental Life Cycle Assess-
ment of products (LCA) and environmental design of prod-
ucts. Research and development of methods and tools.
Special experience with electronics and electro-mechani-
cal products, textiles, packaging, furniture and energy sys-
tems.
Wenzel has experience in Cleaner Production: Environ-
mental auditing, identification of options, research and tech-
nology development, implementation and dissemination.
Special experience within water based industry including
textile industry, paper industry, industrial laundries. Exper-
tise in reclamation and reuse of water and waterborne en-
ergy and chemicals. Development of methods and proce-
dures.
He also has expertise in environmental impact assess-
ment of industrial effluents, and industrial wastewater treat-
ment and control. Special experience with chemical indus-
try, pharmaceutical industry and pulp industry. Development
of methods.
30
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Wenzil's Overheads
Application Dependency of LCIA
Henrik Wenzel
Institute for Product Development
Technical University of Denmark, Building 424
DK2800Lyngby
Denmark
e-mail: wenzel@ipt.dtu.dk
LCA Applications
- some of the most useful
Companies
Life Cycle Management X
Strategic Planning X
Product Development X
Purchase X
Process Design/Selection X
Marketing
- active claims X
- passive information X
Authorities
X
X
X
X
Goal Definition
The proof of the pudding lies in the eating
Goal and Scope Definition
The decision
maker
In plain language: you know It's good if it's used
More philosophically:
the identity of a thing is borne by its being used for its purpose
Scope Definition
No decision / no env. consequence
- who cares about what method was used?
Decision / env. consequences?
- defined scope and inventory data should
reflect the caused environmental changes
= XA-BACO2
Application dependency of LCA
- hypothesis
Three main variables govern application dependency:
1. The environmental consequence of the decision
- time scale
- spatial scale
2. The economic/social consequence of the decision
3. The decision context
- the decision maker
- interested parties
- implicit assessment criteria
31
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The Environmental Consequence of the Decision
- differentiation of LCA applications
\
x \ \ \
\ \ \
. ^ ^ Prtwuctioit (ethnology isscunicnl
X \ \ \
. '- \ \ \
w \ \ ' "
\ \ X.
\
\
NMdroriKespcdficarinfbnmUiin
Figure 4. Examples of LCA applications which differ in the time scale of the
decision and in their need for site specific information
The Economic/Social Consequence of the Decision
- differentiation of LCA application
Need for certainty, transparency and documentation
Economic or social consequence
Figure 5. Examples of LCA applications with different need for certainty,
transparency and documentation caused by difference in economic or social
consequence of the decision to be supported
LCA Applications in 3 Governing Dimensions
Need for certainty, transparency
& documentation
Societal
lion plans
Time scale
Simplification of LCA Methods
Consensus: simplification is about delimitation and focusing. An
iterative procedure using all phases of LCA in increasing level of detail.
Matrix LCA
- qualitative or
semiquantative
-passjy
' Site speciality
Screening LCA
- quantitative
- readily available data
Full LCA
- quantitative
- new data
jnventory/
Figure 3. Three iterations of LCA
Extent of Work Required at Different LCA Levels
- rough average time estimates
LCA Simplification and Iterative Procedure
- the reasons to continue with next iteration
Extent of Work (days)
100
10
Semi-quantitative
Matrix LCA
Quantitative LCA
- readily ,
available data /
7
Is there a trade-off?
Can you conclude without impact
assessment?
Are data readily available?
Can you conclude without new data
inventory?
Matrix LCA Screening LCA Full LCA
32
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LCA Simplification and Iterative Procedure
- continue as far as needed
SumE-quuuititative
Matrix LCA
The three iterations of the LCA supports different decisions and conclusions
The LCA application = each single conclusion/decision
The Decision Context
1. Implicit assessment criteria
2. Implicit weighting criteria
- e.g. national priorities in LCA for national strategies
and action plans
3. Interpretable interpretation support
- e.g. Dutch normalisation and weighting for a Dutch
decision maker?
Application Dependency of LCIA
I. LCIA only needed when LCA is applied on decisions
involving a trade-off
2. There is a need for sophistication of site dependency
in LCIA when decisions involving trade-offs cannot be
supported without it
3. The need for site dependency in LCIA is largest for
applications of LCA to trade-off decisions for which the
consequences are limited to few specific sites
4. The need for sophistication of LCIA is largest in trade-off
decisions with highest requirements for certainty
5. The decision context may imply certain weighting criteria
6. The background and knowledge of the decision maker
may influence the choice of normalisation/weighting
33
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Types of Uncertainty in the Human Toxicity Potential
Edgar Hertwich
University of California
Berkeley, CA, USA
A framework for uncertainty analysis, which was origi-
nally developed to risk assessment (Finkel 1990), is ap-
plied to the exposure modeling component of the Human
Toxicity Potential (HTP) (Hertwich 1999). The HTP is a
weighting factor, similar to that goal warming potential, that
is used to multiply emissions in a life cycle inventory to
obtain a single metric representing the human health haz-
ard (Hertwich et al. 1998).The HTP presents evaluations of
hazard based on the toxic potency of a substance and the
potential dose in a so-called unit world.The toxic potency is
expressed by cancer potency factors q1 * or the inverse of
the allowable daily intake or reference dose. The exposure
is calculated using CalTOX (McKone 1993; Maddalena et
al. 1995), a risk assessment model that integrates a multi-
media environmental fate model with a multiple pathway
exposure model. We distinguish between carcinogens and
non-cancer effects that are expressed in benzene and tolu-
ene equivalents, respectively. For the same chemical, the
HTP will depend on the compartment of the release. HTP
comes significantly closer to an actual risk assessment
than alternative approaches without requiring site-specific
input data (Hertwich et al. 1998). HTP values were calcu-
lated for approximately 300 chemicals representing > 80%
of the air emissions reported by U.S. manufacturing facili-
ties in theTRI. HTP values for air and surface water emis-
sions can be downloaded from http://www.scorecard.org/
env-releases/def/tep_gen.html
The framework for uncertainty analysis distinguishes
among the uncertainty and variability in input parameters,
model uncertainty, and the decision rule uncertainty.
1. Parameter uncertainty: measurement errors (resolu-
tion of instrumentation), random errors (sampling er-
rors-representativeness of entire population) and sys-
tematic errors (biases introduced through experimental
design or instruments; e.g., the "healthy worker effect"
according to which epidemiological studies are not rep-
resentative for the general population because workers
in general are of better health than the average citizen.)
Parameter uncertainty captures the possible errors as-
sociated with the inputs to mathematical models. These
uncertainties can also be due to estimation methods
and guesses.
2.
The CalTOX model uses three different sets of param-
eters: chemical-specific parameters (decay rates, vola-
tility), landscape parameters (biomass inventory, pre-
cipitation rate), and exposure parameters (diets, breath-
ing rates). For the purpose of the present assessment,
we assume that chemical-specific data is uncertain,
whereas landscape and exposure parameters are vari-
able.
The uncertainty associated with chemical specific in-
put parameters was investigated using Monte Carlo
analysis (Hertwich et al. in press). First, chemicals were
grouped according to the most important exposure route.
For each group, a representative chemical was selected.
For each of these chemicals, Monte Carlo simulations
were conducted and the importance of the input param-
eters was evaluated using rank correlation.The analy-
sis indicates that the uncertainty varies between 1/2
and 3 orders of magnitude, but is commonly around
one order of magnitude.This is little compared to a varia-
tion of potential dose among chemicals of 7 orders of
magnitude and indicates that there is a significant gain
in information from modeling the potential dose. The
most important input parameters were reaction half lives,
as a sensitivity analysis indicates. The results show
that the uncertainty due to the exposure modeling can
be as large as the uncertainty in the toxic potencies,
which has been evaluated by others (Gaylor et al. 1993;
Bairdetal. 1996).
Variability expresses the known natural variation of spe-
cific parameters, such as diets, proximity to emission
sources, temperatures and weather patterns, annual or
diurnal variation of emissions rates, transformation rates,
and transport. Variability differs from parameter uncer-
tainty because it is not related to how precisely we can
measure a specific quantity but it is due to natural varia-
tions of a parameter.
The influence of variability in landscape and exposure
parameters was investigated together with the param-
eter uncertainty using Monte Carlo analysis, as de-
scribed above (Hertwich et al. 1998). For most chemi-
34
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cals, the variation in landscape and exposure param-
eters is insignificant compared to the uncertainty in
chemical specific parameters. In some cases differ-
ences in exposure parameters have a significant ef-
fect. What about uncertainty in the exposure param-
eters?
3. True model uncertainty captures the uncertainty that
is introduced through making models, the idealization
of reality. Model uncertainty includes the uncertainties
introduced by surrogates (the uses of rodents as a model
for toxic effects in humans); through the exclusion of
specific mechanisms; through the inability of models
to capture abnormal conditions which may have a large
impact on the outcome; and through incorrect model
form (errors in the model formulation, or insufficient
knowledge about the true nature of the investigated
model processes). An example for model uncertainty is
that the shape of the dose-response curve in cancer
risk assessment is usually not known, and the selec-
tion of any specific model has a large impact on the
predicted cancer risk at ambient environmental expo-
sures (low doses) from the extrapolation of rodent test
results at high doses (Chiu et al. 1999).
Two examples of model uncertainty were investigated:
1) Two alternative model formulations for the air-plant
interactions in the multimedia model CalTOX were com-
pared. For half of the chemicals, the concentration in
the plant compartment differed significantly. Significant
differences in human exposure were observed only for
20% of the chemicals, and the differences were always
below a factor of 5.
2) The effect of the steady state assumption for wet
deposition was investigated. For 10% of the chemicals,
the continuous rain led to a significantly lower air con-
centration than a situation without rain. This led to dif-
ferences in exposure of up to 2 orders of magnitude.
The steady state assumption for wet deposition needs
to be questioned for chemicals with low Henry's Law
constants. While the rainfall rate has little difference on
the steady-state results, the total absence of rain does.
This problem was addressed by combining the model
results with and without rain.
4. Decision rule uncertainty arises whenever there is
ambiguity or controversy about how to quantify or com-
pare social objectives. Examples include choosing an
appropriate measure to describe risk, choosing a sum-
mary statistic to characterize uncertain risk, choosing
parameters that define acceptable risk (such a one in
million chance) or the monetary value of risk (the value
of a life saved or a disease avoided), choosing a sum-
mary statistics, choosing a social welfare function, and
choosing a method for trading off immediate versus
delayed consequences.
In LCA a decision rule specifies how impacts should be
assessed (selection of assessment method, choice of
a specific endpoint, etc.). Decision rule uncertainty
arises when there is ambiguity about the goals of the
analysis. For example, HTPs are based on the product
of toxic potency and potential dose. Potential dose in
models like CalTOX can be assessed using different
specifications of removal of the pollutant. In a closed
system, no advection is allowed and the entire life cycle
of the pollutant is tracked by the model. In an open
system, advection is allowed and the assessment is
closer to a region-specific assessment. This difference
in boundary conditions can have a large influence on
the HTP values, especially for persistent pollutants such
asTCDD.
References
Baird, S. J., J. T. Cohen, J. D. Graham, A. I. Shlyakheter and
J. S. Evans (1996). "Noncancer Risk Assessment: A
Probabilistic Alternative to Current Practice." Hum. Ecol.
Risk Assess. 2(1): 79-102.
Chiu, W., D. Hassenzahl and D. Kammen (1999). "A compari-
son of regulatory implications of traditional and exact two-
stage dose-response models." Risk Anal. 19(1): 15-22.
Finkel, A. M. (1990). Confronting Uncertainty in Risk Manage-
ment-A Guide for Decision-Makers. Washington, Re-
sources for the Future.
Gaylor, D. W., J. J. Chen and D. M. Sheehan (1993). "Uncer-
tainty in Cancer Risk Estimates." Risk Anal. 13(2): 149-
154.
Hertwich, E. G. (1999). Toxic Equivalency: Accounting for
Human Health in Life-Cycle Impact Assessment. Ph.D.
thesis, Energy and Resources Group, University of Cali-
fornia, Berkeley. 237 p.
Hertwich, E. G., T. E. McKone and W. S. Pease (in press).
"Parameter uncertainty and variability in evaluative fate
and exposure models." Risk Anal.
Hertwich, E. G., W. S. Pease andT. E. McKone (1998). "Evalu-
ating toxic impact assessment methods: What works
best?" Environ. Sci.Technol. 32(5): A138-A144.
Maddalena, R. L, T. E. McKone, D. W. Layton and D. P. H.
Hsieh (1995). "Comparison Of Multi-Media Transport and
Transformation Models - Regional Fugacity Model Vs
CalTOX." Chemosphere 30(5): 869-889.
McKone, T. (1993). CalTOX, A Multimedia Total Exposure
Model for Hazardous-Waste Sites. Livermore, CA,
Lawrence Livermore National Laboratory http://
www.cwo.com/~herd1 /caltox.htm
Hertwich's Biography
Hertwich is a Ph.D. candidate in the Energy and Resources
Group (ERG) of the University of California, Berkeley. He
holds a BA in physics from Princeton University and an MS
in Energy and Resources. His work on the application of
35
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multimedia, multiple pathway fete and exposure models lead
to the development of toxic equivalency potentials for can-
cer and noncancer effects. This weighting system is being
used by the Environmental Defense Fund's Scorecard project
[www.scorecard.org] to weight toxic emissions from all major
US manufacturing facilities.
Hertwich's Overheads
A Framework for the Uncertainty
Analysis of the Human Toxicity
Potential
Edgar G. Hertwich
Thomas E. McKone
William S. Pease
University of California, Berkeley
http://greenmfg.me.berkeley.edu/~edgar
http://www.scorecard.org
Finkel's Framework for Uncertainty
Analysis
Parameter uncertainty: random, systematic, and
measurement errors
Model uncertainty: due to simplifications , the use
of surrogates, errors in model form etc.
Decision rule uncertainty: ambiguity or
controversy about social objectives
Variability: natural variation of
input parameters
The Purpose of
Uncertainty Analysis
Uncertainly analyiis it a tool for recognizing how prudent or imprudent proposed
actions may be fn trie face of incomplete knowledge, and for modifying control
and research decisions accordingly. (Finkel, p. 39)
Uncertainty Analysis is required :
to develop confidence in an analytical result
as an input to formal decision analysis techniques,
and
as a tool to refine impact
assessment methods.
Decision Rule Uncertainty
In order to take any actions using the outputs of a risk assessment, including the
decision not to take action, one must be prepared to make a series of potentially
controversial value judgements.
(Finkel 1990, p. 16)
r
I"
Decision: do we allow advection of the pollutant out
of the control volume?
Model Uncertainty
Case: Plant Compartment
Model Uncertainty
Case: Continuous Rain?
Alternative models for the interaction between plant
and air, and their effect on human exposure, as well
as on the concentration in the plants.
1 E-1 4
>*>
,Mj ',/ s v- * ^ V
1 E-4 -1-^-
1 E-2 1 E-1
Tres_air [da for 'no rain'
36
-------
Model Uncertainty
Case: Continuous Rain?
IE-12 1E-10 1E-8 1E-6 1E-4 1E-2 1E+0
Parameter Uncertainty
Chemical Specific Parameters
1 E-2
jg 1 E-4 .
j> 1 E-S *
1 E-7
Alremlssic
* 1 1
'1
".
T* *
Iff
i = s
s
ns
ฃ
1
0
1
=
it
f 1
*.
Surface water emission:
1 1 i I H
s 1
' *',*ป Mean
' SOXiie - _ ,,
L j'h
r* * lOSIIe
V'
--SSIIfr
fill!
' ^ * ฃ
Variability
Landscape-Specific Parameters
Total Adult Exposure
US vs. California
y = 09457x 18618 ' !,.>
Ra = 09845 -ffli^
- Jป>T
J-Jg.
calculated exposure
resulting Item air
eraisslonj of 1 fnoi/fl
Into closed systems
based on contiguous
U.S.andCaltrotnla
Landscape data from
PATRIOT, I
-12 -10
Exposure, California
Log units
Error bars indicate chemical-specific parameter uncertainty
Conclusions
Finkel's framework is useful for organizing
uncertainty
Uncertainty Analysis is exploratory and
never complete l
Some uncertainties are significant, others
are negligible
This type of analysis provides useful insight
into the power and limitations of HIP
Uncertainty analysis can help to develop
and refine analytical decision-support tools.
37
-------
The Different Levels of Uncertainty Assessment in LCIA;
The Case of Carcinogenic Effects
Patrick Hofstetter, Env. Sciences:
Natural and Social Science Interface, ETH Zurich
Life Cycle Impact Assessment (LCIA) aims to assess
the changes in the environment which are judged to be dis-
advantageous, i.e., damages to safeguard subjects (repre-
sentatives of the environment). The environmental system
as such can be best described as overcomplex (Berg et al.
1994). The identification of disadvantageous changes and
of safeguard/subjects are inherently value-laden steps. So-
phistication in LCIA methods means therefore not just to be
more detailed or comprehensive in modeling but as well to
pay attention to the different levels of uncertainties by the
use of appropriate methods.
Van Asselt ef al. (1996) identified a similar situation in
integrated assessment and chose to use a typology of un-
certainties based on Funtowicz et al. (1989) (see typology
in the annex). In LCIA we can distinguish three levels of
uncertainties which ask for sophisticated methods to deal
with them:
1. The environmental system is overcomplex, only some
causal relationships between environmental interven-
tions and damages to safeguard systems can be es-
tablished. Lack of understanding and disagreement
among experts lead to epistemological uncertainties.
2. Many steps in the assessment need value-laden judge-
ments, e.g., the perception of the ecosphere or the time
preference are dependent on the individual world-view.
Subjectivityis therefore a further source of uncertainty.
3. The quantitative modeling of the ecosphere is partly
based on approximations using data with inherent vari-
ability and statistical variation. This leads to technical
and some methodological uncertainties.
Suggestions to deal with these three levels of uncertainty
will be given here. However, the focus will be on level 3
using the example of carcinogenic effects in humans. A
detailed description of these steps including data for respi-
ratory and carcinogenic effects is available in Hofstetter
(1998). A more concise description of the damage assess-
ment approach was published in Goedkoop et al. (1998).
1) Epistemological uncertainty
It is suggested to model the overcomplex ecosphere by
three submodels. The known damage is modeled by the
available knowledge on causal relationships between envi-
ronmental interventions and damages to safeguard subjects.
This includes fate, exposure, effect, and damage analyses
(impact-pathway analysis) and requires that safeguard sub-
ject and damaging changes to safeguard subjects are de-
fined beforehand. The unknown damage is a proxy for all
interactions in the ecosphere we do not know (yet). For the
time being it is suggested to use the ratio between the po-
tential to accumulate and the present geogenic plus anthro-
pogenic flows as characterization factors to construct the
proxy indicator. The third submodel addresses the manage-
ability of damages that is operationalized by three indica-
tors: Ease of damage reduction, excess of long-term policy
targets, success of regulation.
These three submodels permit inclusion not only of a vast
amount of information on causation but also of precaution-
ary principles and dynamic aspects.
2) Subjectivity
It is suggested to model and manage the subjectivity rather
than to hide or separate it.Therefore, Cultural Theory (Th-
ompson et al. 1990) has been chosen to model society.
Three of the five archetypes described by Cultural Theory
have been chosen for a sophisticated scenario generation
because of their active role in decision-making: Individual-
ists, hierarchists, and egalitarians. The aim was to model
all value-laden sub-steps compatible with these cultural
perspectives, i.e., to come up with three instead of one
LCIA, each one being based on a consistent set of value
judgements.
In the case of the three submodels for the ecosphere this
means that individualists will place most importance on the
submodel known damages while egalitarians will follow a
more precautionary approach and judge the submodel on
unknown damages to be the most important.
Tumors at different cancer sites give rise to individual
histories of disease and lead - to some extent - to prema-
38
-------
ture death at different ages. The years life lost (YLL) per
cancer site and the average duration and severity of dis-
eases (leading to the years lived disabled (YLD)) can be
aggregated to disability adjusted life years (DALYs) (Murray
et al. 1996).To do so a number of value choices are neces-
sary on equity between races, age, and social status, on
time discounting, and on the appropriate procedures to es-
timate the disability weights. This being a typical example
where value choices are needed. World-view dependent
judgements on these factors are summarized in the appen-
dix.
3) Technical and some methodological uncertainties
The damage assessment of substances causing carci-
nogenic effects in human beings is an illustration of how to
deal with methodological and technical uncertainties in quan-
titative modeling (see appendices). First of all it is assumed
that point estimates have to be supplemented by informa-
tion on the probable distribution. For reasons of simplicity it
is assumed that all parameters are lognormally distributed
and that the 95% confidence interval is described by the
squared geometric standard deviation o2. The tables and
figures in the appendix indicate the impact pathways for
damages to human health and illustrate how the uncertain-
ties have been dealt with.
Most of the fate factors (F) have been modeled using a
multi-media steady-state model.The accuracy of the model
outcome depends on the quality of the input parameters,
the suitability of the model for the substance to be mod-
eled, and by the ways substances are distributed and parti-
tioned. Using these factors, uncertainty classes for the sub-
stances at hand have been defined and geometric standard
deviations estimated. In the case of heavy metals dry and
wet deposition velocities from literature were used to make
more accurate estimates for the air to air fate factors, which
leads to much smaller geometric standard deviations than
with the fugacity model. The fate factors for heavy metals
and some other substances that do not conform to the limi-
tations of fugacity models should be further improved by
using other models for intake by drinking water and food.
The exposure of human beings is dependent on many
factors and shows high variability. One of the more decisive
factors is the population density. Assuming that an emis-
sion occurs in Europe and an average wind speed of 5 to 10
m/s, one can assume that substances with high atmospheric
residence times will be distributed over a large area includ-
ing less densely populated areas in Asia and the sea. Tak-
ing into account the geographical situation, the population
densities in the concerned countries and a maximum popu-
lation density at the emission source results in the graph
included in the appendix. A substance-specific population
density has been attributed to each substance.This proce-
dure significantly reduces the uncertainty in the exposure
assessment.
Effect data for carcinogenic substances is available from
epidemiological studies, bioassays and other toxicology
studies. The International Agency for Research on Cancer
(IARC) groups the substances according to the strength of
evidence for their causing of cancer in human beings (Group
1: highest evidence). The selection of the lARC-Groups to
be included in the analysis can not be based on scientific
reasoning and has nothing to do with the quality of the dose-
response factors. Cultural Theory is used here to select the
groups that fit best with each of the three cultural perspec-
tives, i.e., it is treated like a question of value choices (level
2). In addition to this epidemiological uncertainty there is
the uncertainty due to variability and approximation. The
extrapolation to low doses and the transferability from ani-
mals to humans are major uncertainty factors. Thanks to
some epidemiological studies (mostly for inhalation) for
substances subsumed under Group 1 and 2, one can as-
sume that the geometric standard deviations is lower for
substances with higher evidence and for the inhalation.
The following tables show the aggregation of all the
substeps of the fate, exposure, effect and damage analy-
ses for some carcinogenic substances of lARC-Group 1. It
can be seen that the geometric standard deviations are
especially large for the heavy metals which poorly fit with
the fate model used. The figure with error bars in the appen-
dix represents the results of the damage assessment for a
set of 55 carcinogenic substances that are emitted to air,
including all the exposure routes. The 95% confidence in-
terval spans 1.5 to 5 orders of magnitude. However, the
highest and the lowest mean for a substance show a differ-
ence of eleven orders of magnitude, i.e., the uncertainty is
reduced from eleven to at least five and in many cases to
two or three orders of magnitude.
An additional reduction of uncertainty is possible thanks
to the three distinct LCIA models for each of the three cul-
tural perspectives. In the case of a one-score eco-index for
a specified product system, the probability distribution for
just one cultural perspective will be much narrower than the
value-independent probability distribution for all combina-
tions of value choices.
The three levels of uncertainty discussed each asks for a
different tool to deal with it. The solutions presented here to
deal with these uncertainties will enhance the sophistica-
tion of LCIA, on the one hand, and make LCIA results more
valuable as decision support, on the other hand.This com-
prehensive modeling approach may well lead to a robust
LCA that is simple to use in practice.
References
Berg M., M. Scheringer, Problems in Environmental Risk As-
sessment and the Need for Proxy Measures, Fresenius
Environmental Bulletin 3 (8): 487-92 (1994)
Funtowicz S.O., J. Ravetz, in: Les Experts sont Formels:
Controverse Scientifiques et Decisions Politiques dans
le Domaine de I'Environnement, Arc et Sanas, France
1989
Goedkoop M., P. Hofstetter, R. Miiller-Wenk, R. Spriensma,
The Eco-Indicator 98 Explained, Int. J. LCA 3 (6) 352-
360(1998)
39
-------
Hofstetter P., Perspectives in Life Cycle Impact Assess-
ment; A Structured Approach to Combine Models of
theTechnosphere, Ecosphere, and Valuesphere, Kluwer
Academic Publishers, Boston 1998
Murray Ch.J.L, Lopez A.D. (Eds.), The Global Burden of
Disease, Volume I of Global Burden of Disease and
Injury Series, WHO / Harvard School of Public Health /
World Bank, Harvard University Press, Boston 1996
Thompson M., Ellis R., Wildavsky A., Cultural Theory,
Westview Print Boulder 1990
van Asselt M.B.A., A.H.W. Beusen, H.B.M. Hilderink, Un-
certainty in integrated assessment: A social scientific
perspective, Environmental Modeling and Assessment
1(1996)71-90
Hofstetter's Biography
Hofstetter, 1989, graduate degree in mechanical engineer-
ing ETH Zurich; 1989 LCA of light bulbs including an evalu-
ation of methods for LCIA; since 1989 own consultancy "Biiro
fur Analyse & Okologie" in the field of industrial ecology,
processes for waste treatment, LCA, and eco-design. 1990-
1994 research for the report and database "Okoinventare
fur Energiesysteme" (Life Cycle Inventories for Energy Sys-
tems), at the Energy Systems Laboratory, ETH Zurich. Since
1994 member of the LCA-Steering Committee of SETAC-
Europe; 3. - 6. 1994 research stay at the Centruum voor
Milieukunde (CML), University of Leiden; since 1994 research
on Life Cycle Impact Assessment. 1998 Ph.D. thesis on
"Perspectives in Life Cycle Impact Assessment; A Struc-
tured Approach to Combine Models of the Technosphere,
Ecosphere, and Valuesphere" at the Chair of Environmental
Sciences: Natural and Social Science Interface, ETH Zurich.
Since September 1999 he is an ORISE research fellow at
U.S. EPA, Cincinnati and visiting scientist at Harvard School
of Public Health, Boston.
40
-------
Hofstetter's Overheads
EidgetiSssische
Technische Hochschale
ZSrich
Ecole polylechniquefederale de Zurich
Polilecnico federate di Zurigo
Swiss Federal Institute ofTechnology Zurich
An International Workshop on
Life Cycle Impact Assessment Sophistication
Brussels 29-30 November 1998
Co-organised by UNEP, U.S. EPA, and CMLLeiden
THE DIFFERENT LEVELS OF UNCERTAINTY ASSESSMENT IN LCIA:
THE CASE OF CARCINOGENIC EFFECTS
Patrick Hofste tier,
Environmental Sciences: Natural and Social Science Interface (UNS)
Swiss Federal Institute of Technology (ETH Zurich)
ho6tetter@uns.umnw.ethz.cli
Multifactor interaction network in carcinogenesis based on some of the major
relationships identified (dashed double lines that break an arrow symbolise
inhibition or blockage of the path) (Arcos et al 1995)
How to deal with main problems in LCIA.?
Environmental system and its causal relationships are overcomplex
=> Assessment has to reflect epistemological uncertainty
(known damage, unknown damage, manageability)
Perception of the ecosphere and its behaviour is dependant on the
perspective
=> Assessmentshould cope with this shaped perception
(subjectivity)
(different models for different perspectives, Cultural Theory)
Modelling is hampered by technical and methodological uncertainties
=> Use quantitative uncertainty assessment to cope with
technical uncertainties and use more than one method to
reduce methodological uncertainty
(Monte Carlo Simulation, combination of descriptive AND
prescriptive approaches)
The modelling framework for the ecosphere (the arrows - the third modelling
level of'manageability1 - symbolise the dynamic aspects of a damage
reduction towards an acceptable damage).
HtncUlft modcUftfie ci
Technosphere
Inventory Analysis
A\\\\\\\\l "
I Fate and Exposure
ฅ Analyses
Exposure
I Effect Analysis
Effects on Single Endpoints
Damage to Safeguard
Subjects
Models*^
Single compartment
Steady state
' Steady state
Dynamic
Multi-media
Empirically derived fate factors
jf Toxicological tests, laboratory experiments
C jf Ecological studies
^ Epidemiologica!studies ^-Cohortstudies
^- Cross-sectional studies
> Revealed preferences
hdlvldmilht'i modcUriht ci
'i ปoikl offtirf ojpher
Fig. 4.11: The relevance of the three submodels for the ecosphere judged
from the viewpoint of the three active perspectives. The question mark
indicates that fatalists need not to be considered here because they are no
active decision makers.
General set-up of the impact pathway analysis within the ecosphere. The
upperarrows for each level of analysis give examples for the cause-oriented
modelling and the lower arrows specify the descriptive modelling
41
-------
Empirical data to reduce uncertainty of fate factors for heavy metals
wtocity\fr votocilyVp "
(FfeeynalDSS) (Pacynaefa/.
19S5)
* rn'o/irf cnVs m'a/m" nftlnf
(1) 100(((1).36S-24. (2) 100/((2)-36 ' 10tt/(((2)40.2)-
36001 5-24- 36001 365-21-3SO01
WMI* ttl-0.4 32E.6-OE-8 0.2 16E-6 8E-6 4
ซ*! 04-2 8E-8-1.6E-8 0.5 6E-6 4.5 E-6 4
Cปซ0 is BB a
Calculation of the fate factors F *>,* from the deposition velocity. The
wet deposition velocity is calculated by the ratio of the assumed dilution
height of 800 m with the residence time due to wet deposition (Tซซ = 5
days) resulting at about 0.2 cm/s.
mat
ace
o .
Substance-specific population density
~\^
6M1 Q001 001 01 l '
AtmMptmto mUvMM Ura bl air [ylln]
Population density as a function of the atmospheric residence time of substances emitted
in Western Europe according to Equation (6.12).
Variability and approximation of unit risk factors
IARC-Group O2 for inhalation Og for drinking water and oral intake
1
2A
21!
3
2 4
3 6
S 10
10 20
Estimated geometric standard deviations Qognormal
distribution) for the unit risk factors given in
Appendix (6.7).
Main questions: Extrapolation to low doses
Transferability from animals to
humans
Epidemiological studies are important for validation
Uncertainty classes for different substance classes
CMteria Substances concerned 222
our off af
(for emission tซ air) P'air. F'air P'uir.
More than 95% of the total Methyl chloride, 1.1.1.2-Telrachtoroethane, 1.2- 248
intake is inhaled, i.ev. the Dichloroethane. Chloroform. Dicbtoromelhane, 1.2-
substance shows only minor Dibromoethune, Hexachloroelhune, 1,1,2-TridiIoroethane,
partitioning Perchloroelhylene, Brornodichloromelhane. Ethybne oxide.
The properties are well known Benzene- B^yl-chloride. Trichlorcthyfcne, Vinyl chloride.
Acrylonitrile, Epichlorhydrin, Diesel soot particles, 1,1
The substance is well suited Dichtorethylene. Carbon- telmchloride, Styrene. DCEE,
for the chosen model BCME Acetaldehyde, 1,3- Butadiene
Tlie substance parnt^ns Trifluralin. Hexachlorobenzene. alpha-HCH, Dichbrvos. 3 f, 12
The properties arc well known DEHP' ^^^^^^ l^-HCH. R>rmaldehyde.
Aklrm. gammu-HCH. l,4-Dbxane,PCBs.
The subsume K well suiled Benzfajanlhracene. BaP. Dieldrm. 3-MKthyk;hloanthrene,
for the chosen model TCDD, Dibenz[aJi]anUiracene
The properlks are uncertain or Arsenic. Cadmium. Nickel. Nickel-ref.-dust. Nickel- 20 4() ffl)
"nt- w sulKiilfide, airomium(VI). Propylene-oxide. 1 .1.2.2- (ง)
The substance Is not well Telrachloroelhme, Benzotrichloride. Hexachlorobutadiene.
suiled for the chosen model 2.4.fi-TrichJorphenol. PenUu;h!orphenol
IARC - Groups of evidence: Epistemological and methodological
uncertainties
Cultural Attitude and Argumentation Choice
Perspective
Individualist The individualist prefers an adaptive management style which means that resources Group 1
are spent fur pniven harm tu human health at Uie moment of decision. The decision
will immediately be adjusted if new evidence emerges so as to realise an optimal
resource allocation. Risks which have not yet resulted in evident harm are accepted.
Such an attitude makes i( possible to consider Group I substances only because of the
limited evidence for all other substances.
Hierarchist The hierarchic I's management style is to control situations. He is therefore closest to Groups
the authoritative bodies in government and in international organisations. The 1, 2A,
responsibility for the group and towards the leader of the group demands finding a ,23*
balance between evidence and probability. WHO (1987, 1996a) gives in its guidelines
risk estimates fora small selection of Group 1. 2A and 2B substances. Substances
from Group 3 are not included because this could lead to an unjustified cut-back of
needs already expressed by society.
Egalitarian The risk avcrsive and preventive management style asks for an attentive altitude Groups
towards risk management. Egalitarians are well aware that many substances in the 1-3
inclusive Group 3 mighlln the coming decades be proven to be potent human
carcinogens. The responsibility towards the others in the group (and coming
generations) leads to the inclusion ofall risk factors. Egalitarians may be ready to
reduce the satisfaction of their needs in order to spend more resources on risk
prevention than individualists or hierarchists would accept (however this is here not
the important point).
Variability and approximation of the damage analysis by
Disability Adjusted Life Years (DALYs)
Cancersiles YLI,m(0,0) Average Average YLDm(0,0) DALYsm(0,0>
disability duration of CTS
weights Din disease Lm
(Murray el (Murray el
al. 1996a) ui I996b)
Lu"g 15.8 0.146 2 0.29 161 15
Skin melanoma 3.2 0.045 4.4 0.20 3.4 1 5
Nasal, nose, nasopharynx 12.1 0.096 4.3 0.41 12,5 2
Uver 15.8 0.239 1.77 0.42 1&2 1 5
Leukaemia 16.0 0.06 3.8 0.45 165 15
AH HHffl "ft 01ซ
-------
Discounting and age-weighting in the
DALYs-concept per cultural perspective
Individualist Egalitarian Hierarchist
Discount rate derived from Cultural Theory 5%
Discount rate due to LC A sensu stricto restrictions 0%
0%
2%
0%
Age-weighting derived from Cultural Theory
yes
Multiplying Lognormal Distributions
ซ?= eP ซg = geometric mean, i.e., expectation value
ag=ea ag = geometric standard deviation
The product of n independent lognormally distributed random variables Xj j = l...n
with parameters (/Jgj, agj)j = l-n is lognormally distributed with
expectation value
fig tot = IT(j-j__n tig} and
geometric standard deviation
Composition of aU elements in the Impact-Pathway Analysis
(Uncertainties in quantities)
Compilation of the geometric standard deviations of aU elements in the Impact-Pathway
Analysis
Substance-name
vlARCX
Arsenic, inorganic
Benzene
Bis(chloromelhyl)etl>er BCME
Chromium(VI)
Nickel
Nickel-refinery-dust
Nickel-subsulfide
Vinyldilorid
3roup*l
eg2 a?
DALYs air. DALYs air,
inhalalion drinking water
cV erg2 ag2 cfg2
DALYs DALYs water, DALYs water, DALYs
air.food inhalation drinking water waler.food
as2 eg2 og2 of
-------
Conclusions
Dealing with overcomplex and ill-defined systems asks for:
- tools dealing with technical, methodological, and epistemological
uncertain lies
- not aiming for world-models but sufficient information for ranking of
alternatives, i.c., most important impact pathways are mostly sufficient
Present knowledge on causal relationships between interventions and
damages shall be compiled and harmonised (and supported by proxy
indicators)
This tremendous work will allow fora feedback to science policy and
priorisation in the research of the concerned disciplines
The turn-around via increased sophistication will allow for simple to use
and robust LCIA
Quantitative uncertainty assessment allows to identify non-significant
differences between product alternatives (important finding)
44
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Human Toxicity and Ecotoxicity - Modeling Versus Scoring
O. Jolliet
Swiss Federal Institute of Technology Lausanne, Institute of Soil and Water Management
Ecosystem management, CH-1015 Lausanne EPFL
olivier.jolliet@epfl.ch, http://dgrwww.epfl.ch/GECOS/DD
Introduction
In order to avoid the present possibility to choose an im-
pact assessment method in order to obtain a desired result,
there is an urgent need for criteria to select consistent im-
pact assessment methods. The present paper aims at the
following objectives:
- to define criteria for consistent effect, fate and expo-
sure analysis,
- to identify key parameters for the fate assessment and
to analyze different fate models on the basis of these
parameters,
- to discuss the possibility of overall experimental checks
and finally,
- to conclude on modeling versus scoring approaches.
Framework for Fate and Exposure in
Toxicity: Consistency Requirements
The first SETAC-Europe working group on Life Cycle Im-
pact Assessment (Jolliet et al., 1996) developed a frame-
work for human and ecotoxicity where characterization fac-
tors are expressed as the product of an effect factor times
a fate factor (fig. 3).This framework also provided a consis-
tency principle: the Life Cycle Impact assessment method
performs a full fate and exposure analysis if the fate factor
F effectively relates the inventory emission to the chosen
toxicity effect factor (fig. 4). For instance, if a Reference
Dose is used as the toxicity reference, then the emission
should be related to the amount of pollutant effectively in-
haled or ingested.
Identification of Key Parameters for
Exposure Efficiency
Jolliet and Crettaz (1999) introduced the concept of hu-
man exposure efficiency in LCIA: retaining a dose as toxic-
ity reference, the consistency principle means that the
fate factor for human toxicity can be described as the over-
all fraction of an emission which is taken in (inhaled or
ingested) by all human beings over the lifetime of a
substance (fig. 3):
r~ I""*
G ;
mass of substance i emitted in media m and
taken in by mankind through pathway n
mass of substance i emitted in media n
Evans et al. (1997) used the same concept for risk as-
sessment, calling the ratio of total human intake to total
emissions the "exposure efficiency" (e).
Jolliet and Crettaz (1999) also developed an analytical
and modular approach to calculate exposure efficiency (fig.
5). The new model is developed on the basis of a modular
approach which offers an alternative way of simplification
than the traditional Mackay I, II and III classification. A sum-
mary of this calculation is provided in the appendix. The
approach allowed characterization of the fate and exposure
in each media using the five following key parameters (bold-
faced):
a) in a first step, fate and exposure is calculated for each
media, assuming that the concentration in other media is
null. Concentration increases are proportional to resident
times divided by the height of dilution. To obtain expo-
sure, these factors are then multiplied by the total amount
of air, water and food ingested annually by humans and
by bio-concentration factors for food exposure.
b) In a second step, the different compartments are con-
nected on the basis of inter-media transfer factors (frac-
tion of the emitted substance in medium m that reaches
medium n)
c) It becomes possible to take into account the exposure
to daughter degradation product of a substance, introduc-
ing an inter-substance transfer factor, the mass of daugh-
ter degradation product j per unit mass of substance i ini-
tially emitted
Analysis of Different Fate Modeling +
Scoring
a) No fate analysis
Most of the methods applied in life cycle assessment
until recently did not consider the "fate behavior" of pollut-
45
-------
ants, i.e., they assumed that all substances have the same
fate properties ("critical volume", CML 1992, Ecolndicator
95, etc.).This is clearly not a valid assumption, since routes
of exposure and residence times vary widely (up to factors
of 10,000).
b) Partial fate analysis: scoring
The ED/Pmethod (Hauschild and Wenzel, 1997) performs
a "partial fate" analysis. Fate is incorporated through a bio-
degradability factor based on OECD biodegradability tests,
varying from 0.2 for biodegradable substances to unity for
persistent substances.This allows coverage of a large num-
ber of substance, but does not satisfy the consistency re-
quirements of fig. 4. Ranking methods based on Persis-
tence-Bioaccumulation-Toxicity information could be inter-
esting to screen and select pollutants; however such coef-
ficients cannot usually be directly multiplied by the mass of
the environmental interventions, as required for LCA (Udo
de Haes et at. 1999). It is suggested that this information
can be used to extrapolate the key parameters described in
fig. 5 and appendix 1. Figure 8 presents how half-life in soil
could be extrapolated based on OECD biodegradability tests.
c) Full fate and exposure analysis using the concept of
exposure efficiency
So far, determination of fate coefficients fortoxicity char-
acterization in LCIA has mostly been based on the prin-
ciple of fugacity modeling (Guineeetal., 1996; Hertwich et
al., 1997; Goedkoop et al., 1998). Especially Mackay type
three models have been used in several calculations of char-
acterization factors for human and ecotoxicity (fig. 9 to 11).
The applied models are rather comprehensive in the de-
scription of the exposure pathways, but have the drawback
of complexity linked to the integrated partitioning between
the different compartments. Indeed, a bias may be more
easily hidden in a more sophisticated approach. The num-
ber of parameters and equations to be solved simultaneously
make them very difficult to check against measured data or
even to check their order of magnitude (Berding et al., 1999).
Based on this concept of exposure efficiency, the com-
parison between two fugacity multi-media models is pre-
sented: errors and default data can be quickly identified and
corrected. Large variations of up to seven orders of magni-
tude are observed on the overall exposure efficiency. The
highest exposure efficiencies are observed for substances
that are ingested through the food pathway, the exposure by
inhalation being a factor smaller for these substances.
Residence times and exposure efficiencies calculated by
the EUSES and CALTOX models were compared (Jolliet et
al, 1998, fig. 10 and 11). On the one hand, figures 12 and 13
show that the use of default data for risk assessment can-
not be used in LCIA, as best estimates and not safe values
are required. On the other hand, once errors or default data
are corrected, there is a good agreement in the exposure
efficiencies calculated by the two models, with a variation
of up to 8 orders of magnitude (fig. 13). Interestingly, the
highest exposures are observed for exposure by food in-
gestion.
d) an intermediary approach
The modular approach described by figure 5 and appen-
dix 1 (Jolliet and Crettaz, 1999) offers an alternative to fugac-
ity modeling (fig. 14 to 16). Each compartment is modeled
in an independent module and then connected to the other
compartments by inter-media transfer coefficients. This
enables each module to be validated against measurements
or even to shortcut some parts using empirical data. Be-
cause of its modular character, this approach also enables
to take into account the exposure to daughter degradation
product of a substance (appendix 1). Hauschild and Jolliet
(1999) have recently developed the air module that predicts
the atmospheric residence time and the fraction of the emis-
sion deposited (fig. 17). Checked against a number of mea-
surements, there was a good correlation between predicted
and measured lifetimes in air (fig. 18: R2=0.85).The inter-
mittent character of rain plays an important role and a simple
model for intermittent wet deposition has been developed
for steady state modeling (fig. 19).
Effect Data for Human- and Ecotoxicity
For the analysis of effect coefficients, Hofstetter (1998)
recently proposed a first approach to determine Years of
Life Lost or Years of Life Disabled for carcinogenics and a
few other pollutants for which epidemiological data are avail-
able. Crettaz et al. (1999) proposes to use a slope based on
ED10, enabling a similar approach for carcinogenics and
non carcinogenic substances and avoiding the use of con-
servative estimates such as q1* for carcinogenics (fig. 20
and 21). Extrapolation procedure are being developed and
checked for substances with little information. Most of the
conclusion for human toxicity remains valid and can be
adapted to ecotoxicological damages, considering that hu-
man toxicity aims at the protection of the individual whereas
ecotoxicology aims at the species protection.
Need for Sophistication
Spatial differentiation can be introduced as modifiers of
the effect and fate factors (fig. 23 and 24), as a function of
geographical or background information. Rabl and Spadaro
(1998) provides a good example of exposure variations with
population density around the emission source or with stack
height. Differentiation is to be introduced only if significant
changes (e.g. more than a factor 5) are observed. If a fully
site-specific approach is necessary, an environmental im-
pact assessment is preferred to LCIA.
Conclusion and Consistency Criteria
- Variations on exposure efficiency and fate are very large,
up to 106 (6 orders of magnitude)
- Consistency between effect and fate is a must! The
choice of the level of the effect factor depends on un-
certainties.
- Exposure efficiency and overall residence times are
useful concepts as an intermediary between modeling
and scoring.
46
-------
- Reasonable consistency between results of different
models is observed, once errors are identified. Model
comparison needs to be performed in a systematic
manner with checks against measurements.
References
Berding, V.; S. Schwartz; M. Matthies (1999). Visualization
of the Complexity of BUSES. Environmental Science
and Pollution Research 6:37-43.
Crettaz, P., Brand, K., Rhomberg, L. and Jolliet, O., 1999.
Human health effects of carcinogenic compounds; an
application for LCIA. AlChE Presentation Record, Oc-
tober 99.
Evans, J.S., Thompson, K.M. and Hattis D., 1997. Expo-
sure efficiency. Journal of the air and waste manage-
ment association.
Goedkoop, M.J., P. Hofstetter, R. Muller-Wenk and R.
Spriensma, 1998:The Eco-lndicator 98 Explained. Int.
J. LCA 3 (6): 352-360.
Guinee, J., Heijungs, R., van Oers, L., van de Meent, D.,
Vermeire, T. and Rikken, M., 1996. LCA impact assess-
ment of toxic releases. Generic modelling of fate, expo-
sure, and effect on ecosystems and human beings with
data for about 100 chemicals. RIVM report, pp. 70.
Haushild, M.andWenzel, H., 1997. Environmental assess-
ment of products - Scientific background, Thompson
science, Chapman & Hall, New York, pp. 525.
Hauschild, M. and Jolliet, O., 1999. Determination of atmo-
spheric residence times and transfer factors from air to
soil and surface water. Prepared for Chemosphere.
Hertwich, E., McKone, T. and Pease W., 1997. Hazard and
risk-based approaches to comparing toxic emissions.
IEEE international symposium on Electronics and the
Environment, pp. 261-266.
Hofstetter, P., 1998: Perspectives in life cycle impact as-
sessment. A structured approach to combine models
of the technosphere, ecosphere and valuesphere.
Kluwer, Boston, Dordrecht, London.
Jolliet, O., 1996, editor. Impact assessment of human and
eco-toxicity in Life Cycle Assessment, in "Towards a
methodology for Life Cycle Impact Assessment" (Udo
de Haes ed.). SETAC, pp. 49-61.
Jolliet, O. and Crettaz, P., 1997: Calculation of fate and ex-
posure coefficients for the life cycle toxicity assess-
ment of air emissions. International Journal of LCA 2
(2): 104-110.
Jolliet, O., Crettaz, P., Hofstetter, P., Hertwich, E. and
Spriensma, R., 1998. Comparison and check of mod-
els to include fate and exposure in LCIA. Abstracts of
the SETAC-Europe annual conference, Bordeaux.
Jolliet, O. and Crettaz, P., 1999:Tox Mod: Modular Fate and
Modelling of exposure efficiency for the characteriza-
tion of human toxicity in Life Cycle Assessment. Int.
Journal of Risk Analysis, submitted.
Rabl, A. and Spadaro, J.V., 1998 Estimates of real damage
from air pollution: site dependence and simple impact
indices for LCA. Abstracts of the SETAC-Europe con-
ference Bordeaux, May 98.
Udo de Haes, H.A., O. Jolliet, G. Finnveden, M. Hauschild,
W. Krewitt and R. Mueller-Wenk, 1999: Best available
practice regarding impact categories and category indi-
cators in Life Cycle Impact Assessment. Report of sec-
ond working group on life cycle impact assessment of
SETAC-Europe.
Appendix
A Modular Approach to Calculate Fate and
Exposure Efficiency
Jolliet and Crettaz (1999) also developed and presented
in detail an analytical and modular approach to calculate
exposure efficiency (fig. 5).The new model is developed on
the basis of a modular approach that offers an alternative
way of simplification than the traditional Mackay I ,ll and III
classification:
a) in a first step, exposure is calculated for each media,
assuming that the concentration in other media is null.
Three issues are successively addressed:
a1) From emission to concentration: assuming steady
state, the increase in concentration can be calcu-
lated as the initial emission flow per unit area multi-
plied by the ratio of two key parameters: the overall
residence time (T/", yr) divided by the volume of
dilution per unit area (l/m, m3/m2) of the medium
that contains the pollutant, per unit area:
,-_*?_.,ซ [kg/m3] (D
This ratio corresponds to the intuitive deduction that the
longer the residence time, the higher the concentration in-
crease. The overall residence time is a combination of the
different removal processes: both the degradation of the
substance and its removal by transfer to another medium
on the one hand, and its advection on the other hand.
a2) From concentration to direct intake: Direct intake is
directly proportional to the time integrated concen-
tration increase in air (resp. water and soil) multi-
plied by the overall volume of air inhaled (resp. water
or soil ingested) annually by humans per unit area.
H (2)
Where P is the population density [pers/m2] and ^'"the
yearly inta&e of air, water or soil particles per person and
47
-------
per year [m3/pers-yr]. Depending on local characteristics
(e.g. air emission in high or low populated area), exposure
varies typically by a factor of 10 to 30 (Rabl and Spadaro,
1998). This is relatively restricted compared to the total
variation in exposure efficiencies of up to 8 orders of mag-
nitude between different substances (fig. 13). It could there-
fore be addressed through generic spatial differentiation (e.g.
scenarios for emissions in high, mean and low populated
areas).
a3) Bioconcentration and indirect intake through the diet
For all media, it is possible that an increase in the
concentration in the media will lead to an increased
concentration in food products (plants, fishes), that
eventually will be ingested by humans. The resulting
exposure efficiency through the diet is very similar to
that for direct intake: only here, the daily intake of the
surrounding medium is replaced by the food intake
and multiplied by the bioconcentration factor:
Tm Bf*E>mfo(*d
^=^^^PP2/0ฐrf " ฎ
Where BCFimlo
-------
Jolliet's Overheads
Human toxicity and eeotoxiclty
Modelling vs scoring
mi mil i mam mom mm mm mm urn $
1. Introduction
2. Framework for fate and exposure in toxicity
3. Analysis of different fate modelling + scoring
4. Enabling experimental shortcuts and check
+ robust extrapolations
5. Conclusion and consistency criteria
Prof O.Jolliet (olivier.jolliet@epfl.ch)
EPFL-Swiss Federal Institute of Technology-Lausanne
Institute of Soil and Water Management
IATE-HYDRAM, CH-1015 Lausanne
Tel. +41 21 693 7011, Fax +41 21 693 70 84
Challenge
Tell me your results
I will tell who paid you !
Urgent need for recognised and consistent
impact assessment methods
+ address ISO 14042 requirements
(scientifically valid + environmentally relevant)
--> needs for a general framework for toxicity
assessment.
No an !
2.Framework for toxicity characterisation
(SETAC working group on toxicity assessment 1996)
Fate factors required for a full
fate and exposure analysis
M
E: factor
Toxicity measure
F: fate factor
links emission-effect
neglected until 1994!
Human
toxicity:
RFD,, [kg/pers-an]
Reference dose
8,, [-] "exposure efficiency"
= fraction of the emission
taken in by mankind 01M.
Effect
coefficient
Effect
variable
Fate
factor
required
Concentration
to
concentration
increase
ADI, RfD
Dose
Emission to
intake
dose
Acceptable
deposition rate
Deposition
rate
Emission to
deposition
Fate: analysis of existing methods
Modelling versus scoring
Mo No only emissions, E=1, F=1
effect: critical volumes or analog
(Tellus 92, BUWAL, CML 92,): F = T,/ V = 1
Compares an emission with a concentration limit!
Pollutants with short lifetimes are overestimated
I EDIP method (Haushild, Denmark),
F=Biodegradibility factor equal to 0, 0.5 or 1;
Problem of consistency
- Consistency between fate and effect is a priority
- Important to have coefficients multiplicative with
mass
- Simple ranking is not sufficient: in LCA it matters
if it is 5 times or 10'000 times higher
- Use Persistence - Bioaccumulation - Toxicity
informations to extrapolate key parameters in a
consistent fate & effect assessment
49
-------
Full fate: Modelised approach
fugacity modelling
Inter-media Complexity Accuracy
1 Box model No low low
Mackay 3 yes high ???
CML-RIVM, 1996:USES / Hertwich, 1997:CALTOX
- inter-media transport included
- beware default data,
- little check against measured data
- height of dilution fixed to 1000 m3/m2
Fate modelisation: intermediary level
Challenge: Simple and robust+accurate approach !
idea: introduce a double level
- Detailed modeiisation, description
of physico-chemichal phenoma,
laboratory experiments (>150 parametres)
- intermediary modelling based on a limited number
of key parameters, according to physics&chemistry,
check at larger scale
key parameters
direct exposure
key parameters
food exposure
Human Toxicity Potentials (HTP)
Method comparison
Method
Critical Surface-Time
CST air
CST soil-food
CST air-soil-food
CST 95
CML96VUSES "original
CML96/USES revised
Pollutant
Pb"
Pb"'
Pb"-'-'
NOx"
'NOx"
NOx"
Volume of
dilution
[m3/m2)
3500
0.2
(11% depos.)
890
1000
1000
Residence
time
[day]
11
200 to 2000 yr
1.1
180
1
exposure
efficiency
[-] .
6.8E-7
0.024 to 024
0.0028 to 0.028
2.5E-7
4.0E-5
Z2E-7
Hum.Tox.P.
HTP
[kgpb "/kg,]
1
MOO to 200 00
230 to 2M
0.002
inter-media transfers
Full fate: empirical approach
Critical surface-time
(Jolllot and Crettaz, 1994 and 1997):
Modelling atmospheric fate:
a modular approach to fate modelling
"'ffWWilKIIBWIVWrffffBl'M BHHRtffM MKMM 90SSM JMffll WKS $&& ^ ^ VS J
F, =
Ii
V,
Possible shortcut:
C : measured
5 M, : estimated
> overall response of the environment
At regional, continental or world level
ป%dop
orti'o
into
(Wx
prccipi
-2 10
)
1 y~
WBideppg
1
into precipitation
": '. -
.w
1C
v
llhiJ
ceptori
(plant*)
50
-------
Intermittent wet deposition:
variations of concentration in air
'wet
Residence times:
check with measured values
predicted residence time of pollutant in air as a
function of measured residence time
tmeas
Mboo.oo
Comparison and check of models to
include fate and exposure in LCI A
Fugacity fate and exposure model
CALTOX (UC Berkeley): risk assessment
Dr O.Jolliet, P. Crettaz
EPFL-Swiss Federal
Institute of Technology
Lausanne (CH)
P.Hofstetter E. Hertwich R.Spriensma
ETHZ-Swiss Federal University of Pre consultant
Institute of Technology California Amersfoort (NL)
Zurich (CH)'- UC-Berkeley (USA)
SETAC-Europe conference in Bordeaux, May 98
Comparison between CALTOX and EUSES
- Residence time in air and water
- Transfer factor between air and water
- Overall human exposure efficiency
Sediment
> 150 parameters,
Based on partition coefficient at equilibrium,
EUSES (CALTOX)
residence half-lives in air
Exposure efficiency for air emissions
Models comparison UC-Berkeley/ NL
loco).
S ,
i Phenol(default)
- t *T
:ฃ < jtf
ฃ aoioi ooioi oil om 'njjK^T
s ^^^
1 ^^-^
o 4^--^ ,
is _j Heavy metals *'
g \jtjt omnm
CALTOX Residence h
* -ff*^
ป3ซ"*r* ป
>^^*'ป
* 10 100 1000 10000
*^-1
tetrachloride: d'1
alfliteinalr(d)
On the basis of the EPFL intermediary model
NL
Pb air-soil-food
5*0.00000010.000001 0.00001 0.0001 O.OOT\ 0.01
t
tn
CALTOX exposure efficiency (-)
UC Berkeley (USA)
51
-------
Ecotoxicity characterisation
(SETAC working group on toxlclty assessment:! 996)
s,=
,
E: effect factor
Toxlcity measure
Eco
toxicity:
RFC,,[kg/pers-an]
Reference concentration
F: fate factor
links emission-effect
neglected until 1994!
S'=^5C'A
At ^' * M
NECj '
5C, , [-] "integrated
concentration increase"
Toxicity characterisation
(SETAC working group on toxicity assessment:!996)
"i mmm mmm mam mm mm BB n m iปi
Spatial differentiation
Toxicity score: S, = Ej Fr M,
IE; effect factor!
effect information
background" information
geographical Information ;
In case of large variations:
F and E can be spatially differentiated as a
function of geographical or background
information (water volume, indoor, etc.)
Spadaro, 1998: exsposure indices
Population density: 0.5 rural 1 avg 6 urban
Stack height: 15 (Om) 1 (10m) 0.6 (200m)
For a fully site-specific approach: EIA !
Conclusions
IXII
- Variations on exposure efficiency/fate are very
large, up to 10s (6 orders of magnitude)
- Consistency between effect and fate is a must!
The choice of the level of the effect factor
depends on uncertainties.
- Exposure efficiency and overall residence times
are useful concepts as an intemediary between
modelling and scoring
- Reasonable consistency between results of
different models, once errors are identified.
Models comparison needs to be performed
systematic., with checks against measurements^
52
-------
Priority Assessment of Toxic Substances in LCA
- A Probabilistic Approach -
M. Huijbregtsi and L. Reijnders
Interfacuity Dept. of Environmental Sciences
University of Amsterdam
Amsterdam,The Netherlands
U.Thissen and A. Ragas
Dept. of Environmental Studies
University of Nijmegen
Nijmegen,The Netherlands
T. Jager and D. van de Meent
National Institute of Public Health and the Environment,
Bilthoven,The Netherlands
'Address correspondence to: Drs. M.A.J. Huijbregts, Interfaculty Department of Environmental Science, Faculty of Environmental Science, University of Amsterdam, Nieuwe
Prinsengracht 130, NL-1018 VZ, Amsterdam, The Netherlands, tel. ++31 525 62 63, e-mail: m.huijbregts@frw.uva.nl
1. Model development
In the LCA impact assessment, toxicity potentials are
used for a weighted aggregation of the potential impacts of
a large number of toxic pollutants into one toxicity score.
Up to now, toxicity potentials were calculated with multi-
media fate models, using unrealistic system settings to meet
LCA requirements. Some of these unrealistic system set-
tings can be overcome by modelling a larger part of the
world, i.e. by using the nested multi-media fate and expo-
sure model USES 2.0. This model has four scales, which
are a local, a regional, a continental and a global scale (Fig-
ure 1).The latter consists of three parts, reflecting arctic,
moderate and tropical geographical zones of the Northern
Hemisphere.
Using this model, toxicity potentials were calculated for
the impact categories human toxicity, aquatic ecotoxicity,
terrestrial ecotoxicity and sediment ecotoxicity after initial
emission to the compartments air, fresh water, seawater,
industrial soil and agricultural soil, respectively. Because
the model was originally developed for risk assessment
Figure 1: Schematic representation of the scales in USES 2.0.
53
-------
purposes, a number of LCA-specific model changes were
implemented in USES 2.0. The most important model
changes are:
(1) Discarding the local and regional scales. In current
life-cycle inventories, emissions are summed up per pollut-
ant type regardless of the spatial context of these emis-
sions. This results in an inventory outcome lacking any re-
trievable relation with a particular region. As a consequence,
the local and regional scales in USES 2.0 are inappropriate
for the computation of toxicity potentials;
(2) Excluding 'economic' processes. In LCA there is a
strict distinction between economic processes, which should
be taken into account in the inventory analysis, and the
impact assessment in which the potential impact of these
emissions is estimated. Therefore, economic processes in
USES 2.0, such as waste water treatment and emission
estimates, are switched off in the model calculations;
(3) Standard emissions at the continental scale.The built-
in emission estimation procedures of USES 2.0 were not
used, but standard emissions to the different environmen-
tal compartments (air, fresh water, seawater, industrial soil
and agricultural soil, respectively) were implemented at the
continental scale. When using continental standard emis-
sions in the computation of toxicity potentials, it is implic-
itly assumed that the accumulated emissions related to the
complete life-cycle of a product system, i.e. the outcome of
a life-cycle inventory, (i) all take place in Western Europe
and (ii) are uniformly distributed over the initial emission
compartment;
(4) Aggregation of risk characterization ratios (RCRs) at
the continental and global scales. RCRs, also known as
PEC/NEC-ratios, are calculated by dividing the estimated
exposure level with a no-effect parameter for that particular
substance. RCRs form the basis of the calculation of toxic-
ity potentials. USES 2.0 gives the possibility to compute
RCRs on a local and regional scale. However, these scales
are not taken into account in the computation of toxicity
potentials. Consequently, RCRs are computed on the conti-
nental scale, and the three global scales for every impact
category. But one is still left with the problem that several
RCRs are computed for the same impact category. Compu-
tation of one toxicity potential per initial emission compart-
ment for the four impact categories can only take place
after aggregating the RCRs of the different environmental
scales.This aggregation was performed with weighting fac-
tors resulting in a weighted RCR for each toxicity category.
The RCRs of the aquatic compartments were aggregated
on the basis of the compartment's volume, and the RCRs
of the sediment and terrestrial compartments were both
aggregated on the basis of the compartment's mass. For
humans, the human population present at a certain scale
has been used as a weighting factor. Thus, per impact cat-
egory the weighted RCR of a substance after a standard
emission to a certain compartment is calculated by
Weighted RCR = S
(PECxW)
NEC
(1)
in which W is the compartment-specific weighting potential;
(5) Introduction of a reference substance and reference
compartments. It is chosen to use a reference substance
in the calculation of toxicity potentials which follows the
established use of carbon dioxide (CO2), ethylene (C2H4),
and chlorofluorocarbon (CFC11) for evaluating global warm-
ing, photochemical ozone formation and stratospheric ozone
depletion, respectively. 1,4-Dichlorobenzene (1,4-DCB) is
taken as a reference substance in the calculation of toxicity
potentials. Furthermore, a reference emission compartment
per toxic impact category is chosen: the air compartment
for human toxicity, the fresh water compartment for aquatic
ecotoxicity and sediment ecotoxicity, and the industrial soil
compartment for terrestrial ecotoxicity. Toxicity potentials
(TP) are calculated by dividing the aggregated RCRs of a
substance after emission to a certain compartment with
the aggregated RCRs of the reference substance after emis-
sion to the reference compartment:
TPx=
WeightedRCRx
Weighted RCR ref
(2)
resulting in 20 toxicity potentials per substance (see Table
1).
2. Probabilistic Approach
Apart from the possibility to calculate toxicity potentials
in a deterministic way, it is also possible to follow a probabi-
listic approach in which the operational uncertainty in the
toxicity potentials is quantified by specifying the uncertain-
ties in the input parameters and performing Latin Hypercube
sampling. To save time and money in collecting data for
Table 1. Type of Toxicity Potentials per Substance
Emission compartment
Impact category Air
Aquatic ecosystems AETPoir
Terrestrial ecosystem TETPปir
Sediment ecosystem SETP^
Humans HTP..,
Fresh
Water
AETP|W
TETP,,
SETP|W
HTR,
Sea
Water
AETPsaa
TETPaea
SETPSBป
HTP
Industrial
Soil
AETp,3
TETPfe
SETPr3
HTP.
Agricultural
Soil
AETPป
TETPM
S^P.3
HTP
54
-------
defining density distributions, only the most important pa-
rameters may be considered in detail. Those parameters
can be identified by a preliminary uncertainty importance
analysis. In the preliminary analysis, the uncertainty distri-
butions of all parameters are estimated only roughly, with
the aim to determine what input parameters contribute most
to the uncertainty of the output parameters.The parameters
that appeared to contribute insignificantly to the output un-
certainty can be replaced by their most likely values. From
our preliminary analysis, it appeared that in general uncer-
tainty in substance-specific input parameters from the ref-
erence substance and environmental characteristics will not
contribute significantly to the uncertainty of LCA toxicity
potentials. Consequently, only substance-specific input pa-
rameters of the substances under study and human char-
acteristics should be specified in more detail. This can be
done in a second more detailed uncertainty analysis, re-
sulting in frequency plots of the output parameters repre-
senting the operational uncertainty of the toxicity poten-
tials.
To simplify the implementation of the uncertain toxicity
potentials in LCA case studies, the sampled distributions
may be fitted to standard uncertainty distributions, such as
Lognormal and Gamma distributions. The uncertainty in the
toxicity potentials may be represented by an uncertainty
factor k which is defined such that 95% of the values of a
stochastic variable (X) is within a factor k from the median
M(X) of a skewed distribution:
V.
=0.95
(3)
As these uncertainty distributions are to be used in LCA
case studies, correlations between the toxicity potentials
should also be known. The toxicity potentials of one sub-
stance may be correlated, because they all stem from the
same uncertain substance-specific parameters.
3. Discussion
Although both method and input data were improved in
comparison to former research, still a number of further
improvements should be addressed. First, model improve-
ments in USES 2.0 itself are needed. For instance, the
groundwater compartment is not implemented in the model
as a full compartment. LCA-specific changes in the model
structure may also have an effect on the validity of the model
results. One item for improvement relates to the geographi-
cal choice. An important assumption in the calculation of
toxicity potentials is that all life cycle emissions take place
in Western Europe. Of course, this is a simplification of
reality because emissions in many product life cycles take
place partly outside Western Europe. Another problem of
using Western Europe as the initial emission compartment
is that, in this way, all emissions are assumed to be homo-
geneously distributed over Western Europe, not allowing
computation of specific toxicity potentials per West Euro-
pean country. Finally, the aggregation of RCRs related to
aquatic and terrestrial toxicity take place on the basis of
volumes and weights of the related compartments, respec-
tively. Other weighting methods, for instance, on the basis
of species density per compartment may result in other
toxicity potentials for aquatic, terrestrial and sediment eco-
systems.
Huijbregts' Biography
Huijbregts studied Environmental Science and Health
Science at the University of Nijmegen from 1990 to 1996. In
1996, he started his Ph.D. research at the Interfaculty De-
partment of Environmental Sciences from the University of
Amsterdam.The main goal of this research is to perform an
uncertainty analysis for the Life Cycle Assessment of low-
energy options applied in Dutch one-family buildings. Re-
cent publications are: Huijbregts, M.A.J., 1998. Application
of uncertainty and variability in LCA. Part I: A general frame-
work for the analysis of uncertainty and variability in life
cycle assessment. Int. J. LCA 3 (5): 273-280, and Part II:
Dealing with parameter uncertainty and uncertainty due to
choices in life cycle assessments. Int. J. LCA 3 (6): 343-
351
55
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Huijbregts' Overheads
Priority Assessment of Toxic Substances in LCA
A Probabilistic Approach
M. Huijbregts & L Reijnders (IDES-UvA)
U. Thisssn & A. Ragas (Dep. Env. Studies, Nijmegen)
T. Jager & D. van de Meent (RIVM-ECO)
Brussels, 30 November 1998
1. Introduction
Why Toxicity Potentials in LCA?
Weighted summation of toxic emissions
. Toxic Impact categories
(1) Humans
(2) Aquatic ecosystems
(3) Terrestrial ecosystems
How are current Toxicity Potentials calculated?
Fate & Effects are taken into account
(Guinea et a/., 1996; Hertwich ef a/., 1998)
Use of Reference Substance
Improvements needed in current approach?
Avoiding the artificial change of system parameters
Taking into account all relevant compartments
Usage of realistic values instead of defaults
Goal
Improvement of model structure
Show effect of data uncertainty and variability
2. Methods
Uniform System for the Evaluation of Substances
2.0
Global nested multi-media fate model (Simplebox 2.0)
Human exposure model
Risk Characterisation Ratios
56
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Adaptations needed for LCA purposes
Regional and local scale are turned off
Economic processes are turned off
Standard emission at continental scale
Weighted summation of RCRs per impact category
Use of reference substance and compartment
Calculation of Weighted Risk Characterisation Ratios
Terrestrial PEC/PNEC
Western Europe
,- Natural soil
-Agricultural soil
-Industrial soil
Moderate zone
Arctic zone
Tropic zone
Human PDI/HLV
Western Europe
Moderate zone
Arctic zone
Trope zone
Probabilistic simulation
Sampling method: Latin Hypercube
- Substance-specific input data
- Human characteristics
- No system parameters
Importance analysis: Spearman rank correlation
Covariance matrix: Spearman rank correlation
Separate treatment of Reference Substance
Weighted Aquatic RCR after emission to fresh water
2,3,7,8-TCDD
Proba
.229
.171
.114
.057
.000
bllity fce^
1
1
IlliB,1iA,w.i-.
ency
1143
857
571
285
O.OE+0 6.3E-3 1 ,3E-2 1 .9E-2 2.5E-2
Median = 1E-3
Mean = 2.4E-2
= 2.3E+3
1,4-Dichlorobenzene
Proba
.118
.088
.059
.029
.000
Weighted Aquatic RCR after emission to fresh water
biiity FreclL
1
1
IL^...,
ency
588
441
294
147
0
O.OEtO 2.3E-7 4.5E-7 6.8E-7 9.0E-7
Weighted Terrestrial RCR after emission to indust soil
2,3,7,8-TCDD
Probability
.033f
Frequency
O.OE+0 1.3E+1 2.5E+1 3.8E+1 S.OE+1
Median = 4E-8
Mean ='
= 4.9E+1
Median = 7.0
Mean = 13.7
k = 1
57
-------
Weighted Terrestrial RCR after emission to indust soil
1,4-Dichlorobenzene
Probability Frequency
(
i'
i!
i "ป
*
! .072
035
000
t
IBKiUbu*...... .
, , ...'. -.-^-, , j_L
0.0&0 3.8E-3 7.5E-3 1.2E-2 1.5E-
717
537
358
179
0
2
Mean = 2.7E-2
k=1.7E+6
Weighted Human RCR after emission to air
1.4-Dichlorobenzene
Prol
.138
,103
.069
.034
.000
wbilily Frequency
L
t
S^Hi&'iM.ซ4...... j . t,
r ' ' ~TJ" ^
O.OE+0 2.5E-5 5.0&5 7.5E-5 1.0E-
688
516
344
172
0
4
Mean = 1.5E-4
= 7.8E+3
Covarlance matrix
A
AIR T
H
A
FW T
H
SEA A
T
H
A
AGR T
H
A
IND T
H
AIR
ATM
1
1
1
1.0
0.7
0.9
1.0
0.7
0.9
0.9
1.0
0.9
0.9
1.0
0.8
FW
A T H
1
1
1
1.0
1.0
1.0
0.9 0.8
0.8
0.9 0.8
0.9
SEA
ATM
1
1
1
0.9
0.8
0.9
0.9
AGR
A T H
1
1
1
1.0
1.0
0.8
Weighted Human RCR after emission to air
2,3,7,8-TCDD
Potability R-equency
.171
.128
.035
.043
000
L
IIIIISl.l.u,,,i.,. ...
lUUU > ' '' ' 4 '
O.C&0 6.3E& 1.3E+7 1.9E+7 25E+
854
640
427
213
0
7
Mean = 1.2E+7
1.3E+3
Uncertainty importance
Aquatic ecotoxicity
Fresh water emission
Parameter Importance
Koc 63%
AFIab-field 24 %
Kdeg_sed 6 %
Kdeg_water 3 %
VP 3 %
TOTAL: 99 %
Terrestrial ecotoxicity
Industrial soil emission
Parameter Importance
Kdeg_soil 75 %
AFIab-field 24 %
TOTAL: 99 %
Human toxicity
Air emission
Parameter Importance
AFJnter 72%
BAF_meat 12 %
Kdeg_soil 5 %
VP 4 o/0
AFJntra 3%
TOTAL: 96 %
Discussion & Conclusions
Validity of results
Model structure is more in line with LCA
Uncertainties in model structure are still important
Standard emission at West European scale is not fully
in line with LCA
Simple weighting factorsare used within impact
categories
Defining input uncertainties is uncertain itself
58
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Significance of results
Human variability and system parameter uncertainty
are not of significant importance
Only a few substance-specific parameters are
responsible for uncertainty in Toxicity Potentials
Correlations between Toxicity Potentials are large for
the same substance and impact category
Care must be taken with implementation of Toxicity
Potential uncertainties in LCA case studies
- Reference substance
- Correlations
- Overlap in product comparisons
59
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Implications of Inventory Structure for
Life Cycle Impact Assessment and Uncertainty Analysis
Gregory A. Morris
Sylvatica, Maine, USA.
Site-specificity in characterization within Life Cycle Im-
pact Assessment (LCIA) is a highly active area of current
research and development.This level of activity may largely
be in response to two factors: (1) past criticisms of the
simplicity of generic or "global" methods for characteriza-
tion analysis, and (2) empirical information which indicates
that location can contribute to multiple orders of magnitude
differences among appropriate characterization factors, for
emissions emanating from a given site.
However, the emissions within an LCA inventory ema-
nate from many sites, not one. The important question is
how many. If only a few sites contribute the bulk of emis-
sions for most products' life cycles, then site-specificity is
essential and feasible. If on the other hand, scores of sites
contribute significantly to inventory emissions totals for most
products, then site-specificity is less feasible and much
less necessary. Thus, the need for location-based charac-
terization factors, and the uncertainty reduction benefits
which result from applying locational information in LCIA,
depend critically upon an unexamined issue: the structure
of the emissions inventory.
By "structure" we mean a few related attributes of the
inventory emissions. First, of importance is the minimum
number of sites whose emissions must be taken into ac-
count in order for an analysis (such as an LCIA) to address
high percentages (e.g., 80% or 90% or 95%) of the total life
cycle emissions. More generally, the shape of the ranked
cumulative distribution of emissions is important and of in-
terest.
The above factors influence the spaf/a/distribution of the
emissions, which, together with the regional variability in
appropriate characterization factors, ultimately determines
the uncertainty reductions which accrue from site- or re-
gion-specific LCIA. How geographically dispersed are the
life cycle emissions? On the one hand, we want to be able
to address this question for specific life cycle inventories.
At the same time, it would be highly valuable to gain some
insight about the answers to these questions about inven-
tory structure for life cycle inventories in general, to help
guide the development of LCIA in general.
This presentation summarized results of empirical inves-
tigations into the issues outlined above. First, a method
and database were presented for addressing the dependence
of terrestrial (non-oceanic) deposition of sulfur and nitrogen
upon the geographic location of SO2 and NOx emissions
sources. A modified characterization factor that accounts
for predicted terrestrial deposition was introduced. Next, the
influence of the number of emissions sites in an inventory
upon the expected inventory characterization error was simu-
lated and presented, under the simplifying assumption of
equal emissions from all inventory sites, and random geo-
graphic distribution of sites. Following this discussion, an
input/output LCA system for the USA was briefly described,
as a means for characterizing aspects of the inventory struc-
ture in general, across products. Finally, the presentation
summarized the implications of the concepts for desired/
required levels of specificity in life cycle impact assess-
ment, and for uncertainty analysis and decision making in
LCA.
Morris' Biography
Norris has been active in Life Cycle Assessment for 5
years. He founded Sylvatica, an LCA-related consultancy
located in Maine, USA. He conducts research in LCA and
Industrial Ecology-related topics for several US Federal
Agencies, and applies LCA on behalf of private corpora-
tions and non-profit research institutes. Norris is adjunct
professor with the Complex Systems Research Center at
the University of New Hampshire, and director of US Op-
erations for the international Athena Institute, which devel-
ops LCA databases and decision support software for build-
ing designers and architects. He has degrees in Engineer-
ing and Natural Resources, from MIT, Purdue University,
and the University of New Hampshire.
60
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Morris' Overheads
Specificity in LCIA:
Towards a Value-of-lnformation Approach
Gregory A. Norris
Sylvatioa/ Univ. of New Hampshire / Harvard Univ.
USA
Our Conference Organizers are to be
Thanked and Congratulated for Posing
the Question:
"What levels of sophistication are
needed, desirable, and useful
in Life Cycle Impact Assessment?"
To answer this question:
B Requires a quantitative assessment of the
uncertainty reduction payoff in results
achieved by increasing sophistication
: requires LCA with uncertainty analysis as a
function of modeling/methods choices
what matters is clarity of decision support as a
function of choices - reliability or confidence in
conclusion that "A < B"
m If done well will also point to places in
analysis where uncertainty reductions yield
biggest gains in clarity of decision support
A main message
LCI
Char.
Valuation >*> Decision
Most guidance to our search for
appropriate levels of sophistication must
come from context of full analysis
Can't make much headway when LCIA
is isolated from LCI and Valuation
Specif really...
"you've got to look at the whole system... "
m not this claim again!
ซ"Prove it."
"And Be Specific."
ซ"With real examples, real data."
FOR ONE THING:
m Clearly, site-specific factors may dramatically
increase precision when characterizing site-
specific emissions.
B But do they matter for the LCIA results as a
whole?
m Will they increase the clarity of decision
support?
a Will they appreciably narrow confidence
intervals on [A-B]?
61
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Specifically...
Suggest that at least these factors matter
when searching for the desired level of soph.:
i The structure of the inventory system
2 Uncertainty in the LCI results
3 The appropriate modeling, and our ability to
model, regionally of inventory emissions
4 Decisions about valuation
Examples drawn from: Regionalized CFs
taking account of SOx and NOx transport,
deposition, and concentration effects in USA
Example Starting Point
Tracking and Assessment Framework
B US DOE Integrated Assessment, 1996
Valuation of impacts of reducing sulfur
and nitrogen pollution emissions
m Human respiratory, visibility, and limited
ecological effects (aquatic toxicity in a
few example lakes --> recreation)
Source/Receptor Matrices
Basis: Lagrangian ASTRAP model, validated
against Eulerian RADM model
Translates: seasonal emissions of NOx and
SO2 per US (source) state (KT/season)
Into:
Seasonal wet & dry dep/ (KT S, N per (receiving)
US state, 10 Canadian prov., northern Mexico;
Delta avg. seasonal ambient concentrations of
nitrogen and sulfur species (mg/m3)
Also characterizes climate variability
(...to model of uncertainty* resulting from
using generic characterization factors
when emissions occur at
randomly selected (i.e., unknown) sites)
* Expected characterization prediction error
The Multiplicity Question(s)
How many sites,
with how much geographic dispersion,
contribute significantly to inventory totals?
What are the expected shapes of these
distributions?
We would like to know these answers
for LCI's in general - that is,
for products' inventories in general.
The Multiplicity Questions)
CHALLENGE 1:
The results will be strongly influenced by
the boundaries of the LCI - need very
comprehensive LCIs.
CHALLENGE 2:
There are a lot of products.
62
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US Input/Output-Based Upstream LCI:
500 sectors consuming goods and services from other
sectors, emitting pollution, and consuming resources
US Input/Output-Based Upstream LCI:
ซ Consumed inputs are measured in $:
include materials, fuels, intermediate
products, equipment, services
m Allocation among products on $ basis
n Pollution emissions in kg/$ of output
ซInput/output data result from census
m Annual sectoral emissions inventories
Elements of the
Input/Output View of Multiplicity
No boundary truncation
s Multiple product outputs, modeled as 1
* not all inputs may be used for given product
.- many inputs used for more than one product
** Each input flow modeled as 1 flow
*< net result: likely to be undercounting sites
*ป Model each input as single-supplier
Can re-do, supressing inputs of services, etc.
(to model of tierwise emissions)
a
What does the distribution of
emissions within each tier look like?
(to model of site-by-site pollution
within each tier -
next time!)
Two final, very brief, topics
Correlation and making distinctions
A "Value Space" heuristic
63
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Correlation, Canceling, and
Confidence about Differences
Cannot draw conclusions by comparing
PDFs or CDFs! Below could imply...
B
...either of the following:
A-B
0
A-B
Must Simulate the Difference!
Requires simulated model to contain
information on correlations and/or their
structural causes.
Value of Information Analysis
How might we apply uncertainty in LCA
decision making
How do we treat valuation and
uncertainty jointly for decision support
Which uncertainties matter most
What level of sophistication is needed
The "Value Space" as a heuristic for VOI
and distinguishing uncertainties from
decisions/values
V2/V1
0.1 1 10 V3/V1
95% CI's for S's lower bound
The "Value Space" as a heuristic for VOI
and distinguishing uncertainties from
decisions/values
Example with higher uncertainty
V2/V1
90%
V2/V1
r2 o
0.1 1 10
Confidence isoclines for S>0
V3/V1
0.1 1 10
95% CI's for S's lower bound
V3/V1
64
-------
Example with higher uncertainty
"Confidence isoclines" for S>0
V2/V1
50% 80%
0.1 1 10
V3/V1
Example with higher uncertainty
"Confidence isoclines" for S<0
V2/V1
50%
0.1 1 10
V3/V1
65
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Levels of Sophistication in
Life Cycle Impact Assessment of Acidification
Jose Potting and Michael Hauschild
Institute for Product Development,
Building 424,Technical University of Denmark
DK-2800l_yngby
Denmark
Telephone: +45-45-254677
Fax:+45-45-935556
Email: JP@IPT.DTU.DK
introduction
The lack of spatial differentiation in current life cycle im-
pact assessment affects the relevance of the assessed
impact. A framework has been developed for how to arrive
at factors that relate the region of emission to the acidifying
impact on its deposition areas. Next, such factors are es-
tablished for 44 European regions with help of the RAINS
model.
RAINS is an integrated assessment model that combines
information on regional emission levels with information on
long range atmospheric transport in order to estimate pat-
terns of deposition for comparison with critical loads1 for
acidification2.
Application of the acidification factors in life cycle impact
assessment is straightforward. The only additional data re-
quired, the geographical site or region where an emission
takes place, is in general already provided by current life
cycle inventory analysis, or by the scoping of the product
system. The use of the acidification factors add resolving
power of a factor thousand difference between highest and
As a matter of fact, RAINS works with critical load functions, giving all combina-
tions ol sulphur and nitrogen deposition above which ecosystems are at risk to be
damaged (see Posch et al. 1995). For the sake of clarity, the term "critical load" is
used in this paper in stead of the correct term "critical load function".
"RAINS contains projections of the actual emissions for 44 regions covering all of
Europe, including the European part of the form Soviet Union. The model first calcu-
lates atmospheric dispersion and depositions from the emissions of all European
over the 612 grid elements (150* 150km) that together form the European grid.
Next, Ihe total deposition in each grid element is compared with the critical loads
for all ecosystems within that grid element (hence that several grid elements distin-
guish more than several thousand ecosystems; Posch et al. 1995). In this way, the
area ol unprotected ecosystem is determined per grid element (i.e., the area of
ecosystems where critical loads are exceeded). Finally, the model accumulates
over all grids resulting in the total area of unprotected ecosystems over the full
European grid. These features of RAINS are used to calculate acidification factors
that quantity the chance in area of unprotected ecosystems caused by the change
in the emission of one region (while background emissions and resulting deposi-
lions from other regions remain the same). See Potting et al. (1998"'b) for further
clarification.
lowest rating, while the combined uncertainties in RAINS
are cancelled out to a large extent in the acidification fac-
tors due to the large area of ecosystems they cover. The
information gained by the use of these factors thus com-
pensates fully for the additional uncertainties they introduce.
The framework presented is suitable to establish similar
factors for eutrophication and tropospheric ozone creation.
RAINS also covers these impact categories.
An extensive description of the developed framework, and
the achieved acidification factors are given in Potting et al.
(1998a'b). Since these publications cover also quite well the
presentation at the workshop, the presentation is not fur-
ther summarized here. Rather, some main discussion points
in relation to site-characterization are raised and commented
upon here.
Different Levels of Sophistication
Due to lack of time, it was impossible to elaborate in the
presentation on the different levels of sophistication allowed
by the acidification factors of Potting et al. (1998a-b). Since
these publications do not discuss this issue either, it is
addressed in more detail here.
The level of sophistication in impact assessment can, as
mentioned a few times during the workshop, be understood
in two ways (see also Potting et al. 1997b):
1. The extent to which relevant parameters in the causal-
ity chain are taken into account in the characterization
factors (i.e. whether the characterization factors are
based on no, some or full fate and exposure model-
ing).
2. The extent to which spatial (and temporal) variation is
allowed in each parameter of the modeling underlying
the characterization factors
The acidification factors from Potting et al. (1998a'b) are
sophisticated in both senses. They cover all the relevant
66
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parameters in the causality chain, and they allow a high
degree of spatial variation. Next Sections will discuss other
types of acidification factors with a similar level of sophisti-
cation. This Section will go into the limitations in spatial
differentiation in the factors from Potting et al. (1998a'b).
As already mentioned, application of the acidification fac-
tors from Potting et al. (1998a'b) in life cycle impact assess-
ment is quite straightforward. Each emission is multiplied
with the acidification factor for the relevant substance and
region. Next the product from all emissions times acidifica-
tion factors are summed-up to arrive at the total acidifying
impact from the analyzed product.
Application of the acidification factors from Potting et al.
(19983ib) requires data additional to current impact assess-
ment: The geographical site or region where an emission
takes place. The requirement of this additional data is often
put forward as an objection against spatial differentiation.
However, the geographical site or region where an emission
takes place is in general already provided by current life
cycle inventory analysis (since this data is needed to cal-
culate the interventions from transport). Nevertheless, this
spatial differentiation may not always be possible or de-
sired. In that case, the acidification factors from Potting et
al. can be used also to establish a site- or region-generic
default per substance. Such default can for example be
based on the European average acidification factor. Though
these defaults avoid spatial differentiation, they do cover all
relevant parameters in the causality chain (in contrast with
acidification factors based on the potential of substances
to release hydrogen ions). An additional advantage is that
at least the uncertainties posed by refraining from spatial
differentiation are known.
The acidification factors of Potting et al. (1998a>b) do ac-
count quite accurately for regional dispersion, but local dis-
persion is modeled rather poorly in RAINS3. There are mod-
els available that estimate regional dispersion similar as
RAINS, but account in a more sophisticated way for local
dispersion. Better modeling of local dispersion probably
contributes little to the accuracy of the acidification factors,
however, since the first few kilometers only have a small
share in the total impact from long-lived substances as sul-
fur and nitrogen. In addition, such additional local modeling
would increase considerably the data demands from inven-
tory analyses. Nevertheless some life cycle assessments
do allow (and also desire) such high level of spatial differen-
tiation as discussed in Potting et al. (1999). In those cases,
the more sophisticated modeling of local dispersion can be
used in addition4.
It would be interesting to investigate what additional infor-
mation different degrees of spatial differentiation provide,
and for which application they are relevant (see also the
presentation of Norris elsewhere in these proceedings).
Basis for Spatial Characterization
The RAINS model also contains the data to establish
impact factors for regional ozone formation and for terres-
trial eutrophication from atmospheric depositions. A similar
framework as for the acidification factors can be followed
for these impact categories. Results will be published in
Potting and Hauschild (forthcoming). These three impact
categories have some general features in common that
actually provide the basis for spatial differentiation.
As we emphasized in Potting et al. (1998a'b), Potting and
Hauschild (1997a'b), and several presentations, many present
environmental problems are characterized by the fact that
the exposure of any receptor is the result of the long dis-
tance transport of emissions from very many sources. The
background exposure of one single receptor results thus
from multiple sources (far more than hundred thousand5).
The other way around, one single source contributes to ex-
posure of very many ecosystems (also far more than hun-
dred thousand6). This means that a single source (thus in-
herently also an emission per functional unit) contributes
only very little, in most cases marginally to the background
exposure of any receptor. This means also that the time
behavior of the emission from a single source (i.e. whether
it is a flux or a pulse) is hardly important. The temporal
variation of the very small contribution from our single source
emission will namely be cancelled out anyway against the
very high background exposure from all sources together.
These are the characteristics that provide the possibility
to establish site-dependent impact factors that with consid-
erable accuracy estimate the given impact over the whole
European grid posed by a single source located in a given
region. To establish these impact factors, one needs an
assessment model like RAINS that integrates information
on regional emission levels with atmospheric transfer-ma-
trices and threshold evaluation.
Uncertainties in Threshold Information
A hot item at the workshop was the use of threshold infor-
mation. Finnveden showed in his presentation (see else-
3The dispersion estimates in RAINS are based on trajectory modelling. A trajectory
model assumes emissions to disperse homogeneously within an air parcel and next
follows this air parcel on its way of atmosphere motion. While the assumption of
homogenous dispersion not affects the accuracy in the medium to long range, it
does lead to an underestimate of concentrations within the first few kilometres from
a source. That is because the course of the concentrations from an emission will
rapidly increase and decrease again within these first kilometres, after which it
continues to decrease more gradually. More accuracy in the first kilometres can be
obtained by Gaussian plume modelling. There are models available that integrate
Gaussian plume modelling with trajectory modelling.
Hence that critical loads are available on rather detailed scale (see previous foot-
note), which facilitates evaluation of deposition patterns with high spatial resolu-
tion.
5This follows logically from the breakdown of total deposition in one grid element
into the separate contributions from each region (RAINS is able to provide this
information). The emission of each region (usually similar with countries) is on its
turn the total from all small and big sources individually in that region.
6Relative long-lived substances as sulphur and nitrogen distribute over several
thousand kilometres. Depositions from a particular source occur thus basically
over the whole European grid. The number of ecosystems distinguished over the
full European grid amounts to almost 700,000 in Posch et al. (1997).
67
-------
where in these proceedings) that uncertainty in threshold
information could be:
A factor two due to parameter uncertainty (see his ex-
ample about critical loads for acidification), and
Up to a factor million due to differences in the chosen
endpoint and/or way of effect modeling (see his example
about no-effect-levels for human toxicity).
These large uncertainties raised during the workshop the
justified question whether characterization factors in life
cycle assessment should be based on threshold informa-
tion. We like to add to a few additional elements to this
discussion.
The impact assessment phase emerged from the wish to
aggregate the large amount of data from inventory analysis
to a manageable amount of impact data. For most impact
categories, initially rather simple modeling was used to es-
tablish characterization factors. These characterization fac-
tors were limited to equivalency assessment on the basis
of intrinsic substance characteristics like the potential to
release hydrogen ions (acidification assessment) or (no-)
effect-levels (toxicity assessment)7. No assessment of
threshold exceedance (i.e. PEC/PNEC >1)8 was performed
since the available data did not allowsuch evaluation.Thresh-
old information, usually in the form of a no-effect-level, was
used in toxicity assessment only to express the emission
of a given substance in the equivalent emission of a refer-
ence substance, or as a dilution volume of the receiving
environment. The basis of equivalency was taken in the
toxicity potential of each substance9. The impact from an
emission quantity equal to the no-effect-level was put on
one, and the impact from any deviating quantity as the ratio
of the emission quantity divided by the no-effect-level. This
provided the possibility to aggregate substances with dif-
ferent toxic effects9.
Present toxicity factors cover now also fate and expo-
sure modeling, but aggregation of the calculated exposure
increases from different substances is still often based on
no-effect-levels10 ( A PEC/PNEC; see elsewhere in these
proceedings the presentation of Hertwich, Jolliet and
Huijbregts).
Threshold Information in Life Cycle
Assessment
Though evaluation of threshold exceedance was initially
not performed due to lack of data, it has meanwhile turned
No-effect-levels are based on experiments on test-species under laboratory condi-
tions and therefore say something about the intrinsic potential of a substance to
cause toxic effect (rather than something about the sensitivity of a species in real-
life for this toxic substance).
PEC la the acronym of Predicted Environmental Concentration. PNEC is the acro-
nym of Predicted No Effect Concentration.
*The underlying assumption is that the toxicity impact from a quantity at the no-
elfect-tevel of one substance has the same importance as the toxicity impact from
a quantity at the no-effect-level of another substance. To put it more clearly: If the
quantities of both substances are at their no-effect-level, the impacts from a neuro-
toxic substance and an irritating substance are regarded as equally important. The
adding together o( completely different effects is one of the more serious problems
In LCA, but not further addressed here.
for many practitioners into a principle in itself that is justi-
fied by the reasoning that "less pollution is better". The thresh-
old discussion is topical for already quite some years now.
Unfortunately, the discussion seems to get stuck in a con-
troversy about "less is better" versus "only above thresh-
old".
As mentioned in the previous section, the question was
raised at the workshop whether characterization factors in
life cycle assessment should make use of threshold infor-
mation because of their uncertainty. Reluctance was ex-
pressed with regard to acidification and eutrophication in
particular. Refraining from the use of threshold information
in toxicity assessment was on the other hand not a point of
discussion10. This is surprising, since the presentation of
Finnveden illustrated that uncertainties in threshold infor-
mation about toxicity of substances are far larger than for
acidification and eutrophication.
Present toxicity factors use threshold information to fa-
cilitate aggregation of exposure increases of different sub-
stances (see also previous section). The use of threshold
information inherently requires making a threshold evalua-
tion. As the next section will show, this applies as well for
"less is better" methods!
We like to stress here that the uncertainties in toxic no-
effect-levels are as a matter of fact far more problematic
than the uncertainties in the threshold information of other
impact categories11. On the other hand, the modeling of fate
and exposure are sources for considerable uncertainty as
well in toxicity factors particularly. It would thus not solve
any problem to exclude threshold information as basis for
characterization factors in life cycle assessment.
We fully agree with Finnveden that the large uncertainties
in threshold information (together with other uncertainties)
are of major concern. However, it seems far more relevant
to discuss how to deal with those uncertainties, than sim-
ply to abandon the use of uncertain information from life
cycle assessment. Such discussion does also justice to
the valuable points brought forward by Finnveden.
As we will discuss in the next sections, the issue of how
to deal with uncertainties in threshold information is quite
another one than the issue of how to perform threshold evalu-
ation.
'"Threshold information is still needed to aggregate different substances. If it were
no longer acceptable to base toxicity factors on no-effect-levels because of their
uncertainty, we would almost be back to square one. There would be little possibili-
ties left than to start again adding together toxic substances on mass basis, or to
report them as separate impact categories.
"The critical loads for acidification for instance are based on the capability of
target-systems to cope with system pressure, but not on the effect of the individual
substance as such (as is the case for toxic no:effect-levels). The capability of
target-systems to cope with acidifying substances depends mainly on the charac-
teristics of the exposed target system to neutralize and/or remove acidifying sub-
stances and the sensitivity of the target species for acidifying exposure. As a
result, the uncertainties in critical loads are mainly due to uncertainties in target
characteristics, but hardly resulting from uncertainties in substance characteris-
tics. Since critical loads are based on system pressure, also uncertainties posed
by differences in the chosen endpoint and/or way of modelling effect are hardly at
stake (in contrast with toxic no-effect-levels).
68
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"Less is Better" Also Performs Threshold
Evaluation
We would like to elaborate a little bit further on the use of
threshold information as basis for characterization factors.
Although up to now implicit, also "less is better" methods
do perform a threshold evaluation by basing their character-
ization factors on threshold information. We like to illustrate
and clarify with an example about acidification. We will use
this example to illustrate other forms of threshold evalua-
tion as well.
Let's assume that we have three ecosystems receiving
similar quantities of acidifying substance from our functional
unit12. Let's further assume that these ecosystems have a
priori equal tolerance for acidifying loading (i.e. similar criti-
cal loads), but that they are now at different levels of back-
ground loading:
A Scottish ecosystem with a background load at that
critical load
A Scandinavian ecosystem with a background load far
below that critical load
A German ecosystem with a background load far above
that critical load
We want to prioritize for which ecosystem we like to re-
duce the full acidifying load (background load + marginal
increase from our functional unit).
All ecosystems receive an equal contribution from our
functional unit and have also similar critical loads. "Less is
better" acidification factors consistent with the existing ones
for toxicity can easily be established from the increase in
the acidifying load divided by critical load for the given eco-
systems ( A PAL/CL)13. Since all ecosystems have the same
critical load, this would result in a similar acidification factor
for each of the above ecosystem. The background concen-
tration does not play a role here.
The practical implication is that the ecosystem exposed
around the critical load is regarded equally important as the
ecosystem exposed far above the critical load (and thus
difficult to rescue), and as equally important as the ecosys-
tem exposed far below the critical load (and thus hardly in
danger). Whether these ecosystems are exposed far below
or at or far above their critical loads, they are all character-
ized as being equally vulnerable in such a "less is better"
approach. That might be or might not be a justified choice,
but it is at least important to realize that such choice is
made by following a "less is better" approach!
12We would like to recall here the important feature of many present environmental
problems that a single source (thus also an emission/f.u.) contributes only very
little, in most cases marginally to the background exposure of any receptor.
""PAL!' stands for predicted acidifying load (copied from predicted environmental
concentration in toxicity assessment).
"Only Around Threshold" An Alternative?
We make another choice than the above "less is better"
approach. We would like to rescue that nice ecosystem in
Scotland that is still unspoiled but about to be damaged.
We therefore would like to prioritize the Scottish ecosys-
tem. We would thus like to reduce the acidifying load on the
Scottish ecosystem rather than the load on that Scandina-
vian ecosystem or the load on that German ecosystem.
One source (i.e. one process) contributes to very many
ecosystems (far more than hundred thousand).Those eco-
systems may differ in their acidifying background loads (as
in the above example), but they can also differ in sensitivity
(i.e. the moors in Scotland, and the rather insensitive eco-
systems/soils in South Europe). In Potting et al. (1998a'b),
we based our characterization factors on the slope of the
sigmoid dose-effect curve that is defined by the critical loads
of all ecosystems to which one source contributes. We es-
tablished the slope of this dose-effect curve in the working
point that is determined by the operative background loads
and thus accounted for both differences in ecosystem sen-
sitivity and differences in background load!
The slope of the sigmoid dose-effect curve used by Pot-
ting et al. (1998a'b) is one way to account for both differ-
ences in ecosystem sensitivity and differences in back-
ground load. The characterization factors based on this slope
tell us what area of ecosystem becomes unprotected as a
result of an emission.The practical implication of using the
change in unprotected ecosystem area is, as we have al-
ways emphasized explicitly, that ecosystems with deposi-
tions far above or far below their critical loads are not ex-
pressed in the characterization factor (it might inspire one
to name "our" approach as "only around threshold"). That
may be or may not be regarded as a disadvantage. How-
ever, several other possibilities are available to deal with
both differences in ecosystem sensitivity and differences
in background load.
Other Ways of Threshold Evaluation
In Potting et al. (1998a'b), we propose to transfer "our" risk-
based dose-effect curve into a damage-based curve to over-
come this problem. A critical load value can tell if there is a
risk of ecosystem damage (i.e. the acidifying load is at or
above critical load), but it doesn't tell whether this risk actu-
ally results in damage or how large this damage will be.
Nevertheless, one expects the damage to become asymp-
totically larger with increasing exceeding of critical load
values. This can be used to establish acidification factors
in which all ecosystems exposed above ecosystem are re-
flected, but in which ecosystems exposed around critical
load have higher importance than ecosystems exposed far
above critical load. The damage oriented eco-indicator of
Goedkoop follows basically a quite similar approach, al-
though his acidification factors are based on a different end-
point and lacks for the time being fate and exposure model-
ing (see elsewhere in these proceedings).
Lindfors et al. (1998) established acidification factors
based on models very similar to those used by Potting et
69
-------
al. (1998a'b).Their acidification factors are calculated as
the share of an emission that deposits on ecosystems
with already exceeded critical loads multiplied with the
potential of the given substance to release hydrogen ions.
These characterization factors tell us what change an
emission evokes in the exceedance of already exceeded
critical loads (A PAL/CLI PAL>CL). Huijbregts (1998)
proposes acidification factors almost similar to the ones
of Lindfors etal. (1998).
A disadvantage of the acidification factors of Lindfors et
al. and Huijbregts is that they do not account for the extent
to which the critical loads of ecosystems are exceeded (i.e.
differences in background loads above threshold do not play
a role). Practical implication of their acidification factors is
that ecosystems exposed around critical load are regarded
equally important as ecosystems exposed far above criti-
cal load. Another, more sophisticated way to account for
background load is presaged by Pleijel et al. (1997). The
characterization factors of Lindfors and Huijbregts can eas-
ily be adjusted by multiplying the change in exceedance
with a severity factor given by the existing background load
divided by the critical load for the given ecosystem (Posch
1998). Such acidification factors do, similar as the damage
based ones from Potting et al. (1998a'b), account for both
differences in ecosystem sensitivity and differences in back-
ground loads above threshold.
Huijbregts (1998) also proposes another way to calculate
acidification factors. These factors are basically similar to
those mentioned in the previous section about "Less is bet-
ter". He wants to calculate the relative contribution of a source
to exceedance of the critical load by calculating the share
of emission that deposits on ecosystems with already ex-
ceeded critical loads divided by the critical load of the given
substance. Such acidification factors do account for differ-
ences in ecosystem sensitivity, but not for differences in
background loads above threshold (see above).
"Less Is Better" Versus "Only Above
Threshold" Too Simple
The threshold discussion has been topical for already quite
some years now but seems to get stuck in a controversy
about "less is better" or "only above threshold" principle.
As shown in the previous sections, the "only above thresh-
old" principle covers very different ways of dealing with
threshold. An important objection from the "less is better"
advocates towards the "only above threshold" and the "only
around threshold" principles, is that ecosystems exposed
below their critical loads are not accounted for in the char-
acterization factors. That might be a justified criticism, but
it is the question whether it is a good alternative to consider
all ecosystems as equally vulnerable by disregarding their
background loads. However, most of the characterization
factors discussed above can be adjusted easily to cover
also ecosystems exposed below their critical load. It would
therefore be interesting to calculate each of the above-dis-
cussed acidification factors with the same models (for in-
stance with the RAINS model), but to establish each of
these factors for a below, and above, and below & above
threshold situation (Table 1).
Within each principal framework of the acidification fac-
tor, the "below & above threshold factors" should logically
be a summation of the "below threshold factor" with the
"above threshold factor". Furthermore, we expect the spa-
tial differentiated "below & above threshold factors" to show
the same, but smoothed trend as the similar "above thresh-
old factor". However, it would be interesting to see whether
the acidification factors based on different principal frame-
works also show the same trends.
The results of above comparisons, completed with a thor-
ough and fair evaluation of the implications of each type of
Table 1.
Name
acidification factors
Excoodance of CL
Severity of exceedance
Exceedance relative to CL
Principal
framework
AF=PAL
AF=ฃAPAL*PAL/CL
AF=SAPAL/CL
AF = "principal
PAUCL
X
X
X
X
X
X
framework"
PAL>CL
X
X
X
X
X
X
Authors
Huijbregts 1998, Lindfors et al. 1998
Present document
Huijbregts 1998
Pleijel etal. 1997
Present document, Posch
Present document, Posch
Huijbregts 1998
Present document
Huijbregts 1998
1998
1998
Unprotected area
Ecosystem damage
AF=SA
(forPAL+APAL)/CL=1)
AF=2D(PAL+ APAL)
X
X
X
(PAL=CL)
X
X
Potting etal. 1998
Potting etal. 1998
Present document
Goedkoop in present proceedings
CL stands for Critical Load, PAL stands for Predicted Acidifying Load, A stands for area of ecosystem, D stands for Damage
70
-------
acidification factor may give fresh input to the threshold
discussion. It will for sure provide a sound basis to make an
explicit choice for either one or another type of acidification
factor.
Closing Remark
It might be clear that the whole discussion above is ex-
emplified with acidification but is valid as well for the other
impact categories!
Acknowledgment
We like to thank Goran Finnveden for the stimulating dis-
cussions in relation to this presentation.
References
Huijbregts, M. Operational uncertainties in life-cycle impact
assessment of acidification and eutrophication; a pro-
posal. Amsterdam (the Netherlands), Interdisciplinary
dept. of Environmental Science (IVAM) at the Univer-
sity of Amsterdam, not published.
ILSI. Human health impact assessment in life-cycle assess-
ment: analysis by an expert panel. Washington DC (United
States of America), International Life Science Institute,
1996.
Lindfors, LG., M. Alkemark, C. Oscarsson and C. Spannar. A
manual for the calculation of ecoprofiles intended for third
party certified environmental product performance decla-
rations. Stockholm (Sweden), Swedish Environmental
Institute (IVL), 1998.
Pleijel, K., J. Altenstedt, H. Pleijel, G. Lovblad, P. Grennfelt, L.
Zetterberg, J. Fejes and L-G. Lindfors. A tentative meth-
odology for the calculation of global and regional impact
indicators in type-Ill ecolabels used in Swedish case stud-
ies. Stockholm (Sweden), Swedish Environmental Insti-
tute (IVL), not published.
Posch, M., P.A.M. de Smet, J.P. Hettelingh, R.J. Downing.
Calculation and mapping of critical thresholds in Europe:
Status report 1995. Bilthoven (the Netherlands), Co-ordi-
nation Center of Effects located at the National Institute
of Public Health and the Environment, 1995.
Posch, M., J.P. Hettelingh, P.A.M. de Smet, R.J. Downing.
Calculation and mapping of critical thresholds in Europe:
Status report 1995. Bilthoven (the Netherlands), Co-ordi-
nation Center of Effects located at the National Institute
of Public Health and the Environment, 1997.
Posch, M. Personal communication from M. Posch. Groningen
(the Netherlands), 1998.
Potting, J., M. Hauschild, H. Wenzel. "Less is better" and "only
above threshold";Two incompatible paradigms for human
toxicity in life-cycle assessment? Journal of Life Cycle
Assessment, Vol. 4 (1999), Issue 1, pp 16-24.
Potting, J. and M. Hauschild. Predicted environmental im-
pact and expected occurrence of actual environmental
impact. Part 1: The linear nature of environmental im-
pact from emissions in life-cycle assessment. Interna-
tional Journal of Life Cycle Assessment, Vol.2 (1997a),
Issue 3, pp 171-177.
Potting, J. and M. Hauschild. Predicted environmental im-
pact and expected occurrence of actual environmental
impact. Part 2: Spatial differentiation in life-cycle as-
sessment by site-dependent assessment of environ-
mental impact from emissions. International Journal
of Life Cycle Assessment, Vol. 2 (1997&), Issue 4, pp
209-216.
Potting, J. and M. Hauschild. Spatial differentiation in life-
cycle impact assessment; technical backgrounds (pro-
visional title). Copenhagen (Denmark), Danish Environ-
mental Protection Agency, forthcoming.
Potting, J., W. Schopp, K. Blok and M. Hauschild. Com-
parison of the acidifying impact from emissions with
different regional origin in life-cycle assessment. Jour-
nal of Hazardous Materials, Vol. 1998a, Issue 61, pp
155-162.
Potting, J., W. Schopp, K. Blok and M. Hauschild. Site-
dependent life-cycle impact assessment of acidifica-
tion. Journal of Industrial Ecology, Vol. 2 (1998b), Is-
sue 2, pp 63-87.
Potting's Biography
In 1991, Potting started her research in the field of life-
cycle assessment and energy analysis within the Dept. of
Science, Technology and Society at Utrecht University. Her
research was case study oriented, but she made also sev-
eral outstanding methodological contributions. This resulted
in an invitation by the Life-Cycle Centre at the Technical
University in Denmark to elaborate her thoughts on spatial
differentiation in life-cycle assessment. In 1996, she started
as a guest researcher at the Technical University of Den-
mark. She was offered here in 1997, on return from a three
month fellowship at the International Institute of Applied
System Analysis in Laxenburg in Austria, a permanent po-
sition at the Institute of Product Development that is affili-
ated to the Technical University of Denmark. Since 1996,
the research activities from Potting are focussed fully on
method development for spatial differentiation in life-cycle
assessment. During all her appointments, she actively par-
ticipated in several international work groups (NNI/ISO,
SETAC, LCANET a/o.). At present, she is chair of the sci-
entific task group on acidification, eutrophication and nutri-
ent enrichment (WIA2/STG-AENE) established under the
European branch of SETAC. In all her university appoint-
ments, Potting has been actively involved in educational
matters. She initiated and elaborated several MSc-courses
and she tutored in several subjects.
71
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Polling's Overheads
Levels of sophistication in life
cycle impact assessment of
acidification
November 1998
Jose Potting (EPU/DTU)
Wolfgang Schopp (EASA)
Komelis Blok (NWenS-UU)
Michael Hauschild (IPU/DTU)
Outline
Problem setting
Methods and means
Results
Levels of sophistication
Conclusions
An example:
Location alternatives for copper
production
in
Albania & Belgium & Finland
Assumptions
Similar production technologies
with
Similar emission quantities per kton copper
Inventory analysis
Acidifying emissions per kg copper
SO2 * 100 gram
NOX ซ 10 gram
Assessment of "hazard potential"
(per kton copper)
SO2 ~ 100 * 1ซ 100 gram SO2-equivalents
NOX ~ 10 * 0.7 ~ 7 gram SO2-equivalents
total * 100 + 7 ซ107 gram SO2-
equivalents
Poor accordance between
Hazard potential and actual impact
Due to disregard of:
Emission dispersion and deposition patterns
Background depositions on receiving ecosystems
Sensitivity of receiving ecosystems
72
-------
Traditional impact assessment tools
(like gaussian plume modelling)
High accuracy to predict acidification local
to the source (r < 50 km)
Unable to predict acidification over large
distances (r >1000 km)
RAINS
Regional Air pollution INformation System
Emission inventories
Atmospheric dispersion and deposition
Critical loads
(Optimization of emission reduction)
Emissions of SO
Albania Belgium Finland
11
35
2
35
13
4
0
100
120
0
26
27
32
10
5
0
100
317
3
35
24
30
6
3
0
100
Sector
% Fuel conversion
% Industrial combustion
% Industrial processes
% Power plants/district heating
% Domestic, agriculture
% Transportation
% Otherwise
% Total
260 kton Total
SOa depositions from
Finland
Transfer-matrices from EMEP
MSC-W at the Norwegian
Meteorological Institute
SO2 depositions from
All 44 regions
Transfer-matrices from EMEP
MSC-W at tiie Norwegian
Meteorological Institute
o
o
i
Deposition of SO2 on grid x=20, Y=26 of Finland
Region
Austria
Baltic sea
Belarus
Belgium
Bohemia
Bulgaria
Croatia
Czech republik
Denmark
Estonia
Rnland
France
Germany-exBRD
Germany-exDDR
Hungaria
Ireland
Italy
Latvia
Contribution
0.01%
1.24%
3.13%
0.38%
0.11%
0.14%
0.04%
3.81%
0.97%
8.6 %
25.95 %
0.67 %
13.4%
1.64%
1.21 %
0.02%
0.14%
1.52%
Region
Lithuania
North sea
Norway
Poland
Romania
Russia - Kalingrad
Russia - Kola, Karelia
Russia - Remaining areas
Russia - St. Petrusburg
Slovakia
Slovenia
Sweden
The Netherlands
Ukraine
United Kindom
Yugoslavia
Natural emissions
Non-attributable sources
Total
Contribution
1.77%
0.24%
0.23%
9.68%
0.53%
2.84%
3.69%
0.16%
3.15%
0.61 %
0.01 %
1 .77 %
0.24%
2.41%
3.89 %
0.13%
0.36%
5.33%
100.00%
0 500 1000 1500 2000 2500
Critical Loads for actual acidity in eq/ha.yr
73
-------
r
Calculations based on:
Transfer-matrices from EMEP
MSC-W at the Norwegian
Meteorological Institute
Critical loads provided by
CCEatthe National Institute
for Effects of the Public Health
and the Environment in The
Netherlands
Area (in %) of unprotected
ecosystem (depositions
exceed critical loads)
Logical deductions
Total emissions from a region contribute little to the
total depositions on receiving grid elements
The full emission from a separate source contributes
little to the total emissions from a region
The full emission from a separate source contributes
marginally to the total depositions on receiving grid
elements
Mathematical framework for the acidification factors
m
( UES(E(ref)) - ZUESsJ((l-5)E(ref))) / 5Es4(ref) =
fi
Example for an SO2 emission from Finland
( 81671-81278 )/26 =
394/26 =
15 ha/ton
0.15 mVgram
Site-dependent assessment of the acidifying impact
(per kton copper)
n z
E Z Emission; * Acidification Factor;
' '
SO2 NOX
Albania 100*0.00 + 10*0.00 = Om2
Belgium 100*0.01 + 10*0.01 = 1.4m2
Finland 100*0.15 + 10*0.02 = 15.2m2
Acidification factors (in ha/ton)
Region
Albania
Austria
Batons
Belgium
BosniaHerzogovina
Bulgaria
Croatia
CRZF
Denmark
Estonia
Finland
France
Gomanynow
Gomany old
Grooco
Be
S02
0.02
1.31
4.6S
1.28
0.15
0.07
0.30
1.91
5.56
12.43
15.14
0.79
2.17
1.94
0.01
NO.
0.00
0.42
4.54
0.82
0.04
0.02
0.12
0.69
2.02
1.54
2.42
0.47
0.90
1.42
0.00
NHj
0.01
3.44
5.72
1.10
0.06
0.05
0.17
1.26
5.28
3.92
13.40
0.74
1.89
3.31
0.01
Interim-conclusions
Acidification factors have been established
for 44 European regions
The application of these factors is very simple
The resolving power is large, while the
uncertainties are small
Acidification factors show reasonable good
stability for changes in the reference situation
74
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Recent developments/
forthcoming work
Hogan et al. (Int. J. of LCA 1996)
Tolle et al. ant. J. of LCA 1997)
Seppalla (FBI 1997)
Potting et al. (JIE 1998)
Norris (proposal; US-EPA 1998)
Rabl et al. (1998)
Pleijel et al. (proposal; not published)
Huijbregts (proposal; not published)
Levels of sophistication
Detail of assessment?
Uncertainties?
Marginal or average impacts?
Threshold evaluation?
Anticipated indicators/endpoints?
Type of modelling?
Final conclusions
A considerable level of spatial detail in LCA is
possible with limited additional data
requirement from inventory analysis
Lack of spatial differentiation results in large
uncertainties in the assessed impact
The level of sophistication in impact
assessment may differ depending on the type
of application
All recent developments make use of (similar)
integrated assessment models
75
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Eutrophication - Aquatic and Terrestrial
State-of-the-Art
Goran Finnveden
fms (Env. Strategies Research Group)
National Defence Research Establishment (FOA)
Jose Potting
Institute of Product Development
Technical University of Denmark
Abstract
State of the art and research needs for the impact cat-
egory eutrophication are discussed. Eutrophication is a dif-
ficult impact category because it includes emissions to both
air and water, both subject to different environmental mecha-
nisms, and impacts in different types of terrestrial and
aquatic ecosystems.The possible fate processes are com-
plex and include transportation between different ecosys-
tems. In some recent approaches modeling of transporta-
tion of air emissions has been included. However, in gen-
eral the characterization methods used lack fate modeling
and this is a limitation. Also the definition of the impact
indicator may need further research.The inclusion of other
nutrients than those typically considered should also be
investigated.
On "Thresholds'VNo Effect Levels"/
"Critical Loads" etc.
Based on two example, human toxicity and acidification,
it is suggested that "thresholds" are always based on a choice
of what is "acceptable", or "significant" or something similar
that in turn is based on value judgements. It is also sug-
gested that the determination of "thresholds" is based on
models that often are highly uncertain. It is thus clear that
LCA is not the only science where there is an uncertainty
concerning the appropriate methods and where different
methods can give significantly different results. It is also
suggested that since the determination of "thresholds" ulti-
mately is based on values, the "only above thresholds" ap-
proach should be avoided if we want to avoid values in the
characterization. It is also important to realize that impacts
can occur below "thresholds".
References
Finnveden, G. and Potting, J. (1999): Eutrophication as an
Impact Category - State of the Art and Research Needs.
Int. J. LCA. In press.
Finnveden, G.and Potting, J. (1999): On the nature of "thresh-
olds" and some implications. In preparation. Provisional
title.
Finnveden's Biography
Finnveden has a M.Sc in Chemical Engineering and PhD
in Natural Resources Management. Currently working at fms
(Environmental Strategies Research Group), which is a co-
operation between the Department of Systems Ecology at
the Stockholm University and the National Defence Research
Establishment (FOA) in Stockholm, Sweden. Has been
working with Life Cycle Assessment since 1990, both on
case studies and development of methodology, e.g. the
development of the Nordic Guidelines on Life Cycle Assess-
ment. Active in international working groups, e.g. the "Inter-
national Forum on Life Cycle Assessment and Integrated
Solid Waste Management" and the SETAC-Europe working
group on Life Cycle Impact Assessment where he currently
is chairing the task group on normalization and weighting.
76
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Finnveden's Overheads
Eutrophication -
Aquatic and Terrestrial -
State of the Art
Goran Finnveden
fms and Stockholm University
Jose Potting
Institute of Product Development
(affiliated to the Technical University of Denmark)
Eutrophication is a difficult category
Emissions to air and water
Impacts in different types of terrestrial and aquatic systems
Fate processes are site dependent
Impacts are site dependent
- different background loads
- different nutrients may be limiting
- different sensitivities of ecosystems
Inventory parameters normally
assigned to "Eutrophication"/
"Oxygen depletion"
N-emissions to air
Macro-nutrients (N and P) to water
Organic material to water
Other nutrients which locally could be
limiting are normally not considered
Limiting nutrients
Terrestrial systems, often N
(at least for terrestrial ecosystems in Europe and North
America)
Aquatic systems
- freshwater often P-limited
seawater often N-limited
- coastal and brackish water, P or N or both
...but...
The picture of "limiting nutrient" is often
simplified because
- the limiting nutrient may change over the year
- the limiting nutrient may change over the years
the balance between nutrients is often important
- nutrients may be transported
Therefore, the contribution from an emission to
eutrophication is always(?) larger than 0
Examples of possible fate of N
emitted to air
It can be deposited on surface water
It can be deposited on (natural) soil or vegetation
- it can be leached out to surface water
the fraction being leached out depends on site specific aspects
(Varies between approx 5-80 % in Europe).
- it can be taken up by growing vegetation, contributing
to terrestrial eutrophication
after degradation it can be taken up again, leached out,
exported with harvested timber, immobilised in the soil or
denitrified
77
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...fate...
It can reach strictly P-limited waters
it can be lost from the system (e.g. by denitrification)
- it can be transported to other waters
It can reach waters where N contributes to
eutrophication
after degradation it can be taken up again, transported
to other waters, exported with fish, or denitrified
Three methods were suggested in
1992-93
Method 1 (Jensen et al, 1992)
No aggregation at all
Four subcategories
- emissions of N to air
- emissions of N to water
- emissions of P to water
- emissions of BOD
...three methods...
Method II (Heijungs et al, 1992)
Complete aggregation into one category
Definition of the effect: Biomass production.
All emissions of N (air and water), P and organic
material is assumed to contribute once and to the
same impact.
Weighting factors based on Redfield ratio:
C:N:P= 106:16:1
...three methods...
Method III (Finnveden et al, 1992 and Samuelsson, 1993).
Scenario based approach
Terrestrial and aquatic systems are separate
Terrrestrial systems: N-emissions to air.
Aquatic systems:
- Definition of the effect: Oxygen consumption
- Weighting factors based on Refield ratio
- Emissions can only contribute once, but sometimes
not at all
...three methods...
Method in - continued
Four different scenarios for aquatic systems:
P-limited: Aggregation of P and org. mat.
- N-Iimited: Aggregation of N to water and org. mat.
- N-limited+- air: Aggregaton of N to water and air and
org. mat.
- Max: Aggregation of P and N to water and air and org.
mat. (Identical to Heijungs et al, 1992)
Developments 1993-1996
Demands for site dependent factors
Nice student work from ETH suggesting site dependent
factors reflecting sensitivity and limiting nutrient.
Method IV (Hauschild and Wenzel, 1996).
- Similar to methods II and III
- Organic material excluded
Either N and P separate or combined
Method V (Tolle, 1997)
Regional scaling factor (1-9) based on present loadings
78
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SETAC-Europe working group
onLOA(1996)
Asking for adaption of earlier approaches:
- Several subcategories: terrestrial systems, smaller
aquatic systems, larger aquatic systems?
-Other nutrients?
Sensitivity of receiving environments
- Transportation
For emissions to air, similar models as for acidification
For water emissions: ?
Recent work by Seppala
Two impact categories for aquatic ecosystems:
Oxygen depletion: BOD
Aquatic eutrophication
- Definition of the effect: Increased production based on
Redfield ratio
- For water emissions of N and P, site specific transport
and effect factors were determined by experts.
For air emissions of N, transport factors were based on
EMEP-data (in this case, 6-7% of emitted N are
deposited in N-sensitive areas)
Recent work by lindf ors et al and
Pleijel et al
Aquatic oxygen depletion for aquatic ecosystems
- Definition of the effect: oxygen depletion
- Weighting factors based on Redfield ratio
- Site specific assessment in which emissions are only
considered for receiving environment where pollutants
have an effect as determined by expert judgement
Recent work by Potting et al
So far, only emissions of N to air for terrestrial
ecosystems
- (research on water emissions is ongoing)
Transport from EMEP-models
Definition of the effect: A marginal approach:
The area of ecosystem that becomes unprotected as a
result of that emission
Discussion issues - effects
Distinction between terrestrial and aquatic systems?
Definition of the effect
Aquatic systems: Oxygen consumption/biomass
production?
* 7
- Terrestrial systems:
* Area "unprotected" which receives emission?
* Amount emitted into "unprotected" area?
*9
Only above thresholds, or both below and above, perhaps
in separate subcategories?
Marginal or average approach?
Research issues - fate and target modelling
Emissions to air
Integrated assessment models?
"Background" concentrations
Target sensitivity
Leaching of nutrients?
N deposited on land can be leached out and reach N-
sensitive area (possibly after transport)
Emissions to water?
Research project? Study some site specific cases in
order to get an understanding of the variability.
Role for expert judgements?
79
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Research issues - spatial info
Spatial differentiation?
How do we make optimal use of spatial
information?
Can we develop spatially differentiated and
not differentiated characterisation factors
which are compatible?
Requirements towards inventory
On "thresholds'/no effect
levels'Vcritical loads" etc
Goran Finnveden
fms and Stockholm University
with contributions from
Jose Potting
Technical University of Denmark
Suggestion
"Thresholds" are always based on a choice of
what is "acceptable" or "significant" or similar.
The choice of an "acceptable" impact is based on
values.
Determination of "thresholds" are based on
models which are uncertain
Two examples:
acidification and human toxicity
Acidification
Critical loads often defined as
"A quantitative estimate of an exposure to one or
more pollutants below which significant harmful
effects on specified sensitive elements of the
environment do not occur according to present
knowledge".
(Nilsson and Grennfelt, 1988)
Acidification....
Results from "Lake Gardsjon", Sweden,
Andersson et al (1998):
Comparison of 8 more or less "official"
methods for calculating CL:
CL for S varies between 1-20 kg S/ha yr
CL for N varies between 8-15 kg N/ha yr
Human toxicity
Dose-response curves for individuals may
or may not have a threshold
Dose-response curves for populations do
not have a threshold.
"Thresholds" are always based on a choice
of a "acceptable" risk, such as 1:106
80
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Human toxicity...
Differences in model-derived estimates of virtually safe doses
at 10-6 risk level, relative to the one-hit model
(Casarett and Doull's Toxicology, based on other sources)
Carcino- One-hit
gen
Linearized Weibull
multistage
Multihit
Vinyl 1
chloride
Aflatoxin 1
1
20
1000
2*10~8
8000
Conclusions/discussion
LCA is not the only science where there is
an uncertainty concerning the appropriate
methods and where different methods can
give significantly different results
Conclusions/Discussion...
If we want to avoid values in the characterisation, we
should perhaps avoid the use of "only above thresholds"
Impacts can occur below "thresholds"
- especially if the threshold is for a single substance while it
contributes to a cocktail of pollutants for which no threshold has
been defined
Suggestion: if thresholds are used, include both above and
below thresholds in the impact assessment, but make a
distinction
We can use thresholds as a basis for characterisation
factors and use the science behind to define the effect
Strive for a good balance between the additional
information gained and the new uncertainties introduced
81
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Appendix
List of Participants
Guntra Aistars
University of Michigan
Dana Building 430 E. University
Ann Arbor, Ml 48109-1115
tele:734-764-1412
fax:734-647-5841
email: guntra@umich.edu
Jane Anderson
BRE
Watford, Herts, WD2 7JR United Kingdom
fax:44-1923-664084
email: andersonj@bre.co.uk
Jane Bare
U.S. EPA (MS-466)
Cincinnati, OH 45268
tel: 513-569-7513
fax:513-569-7111
email: bare.jane@epa.gov
Angeline de Beaufort-Langeveld
FEFCO-GO-KI
Groeterweg13
6071 ND Swalmen
tel. 0475-501 014
fax. 0475-504 478
e-mail: beulang@wxs.nl
Mark Buridard
International Iron & Steel
Rue Colonnel Boury 120
1140 Brussels Belgium
email: lcaletter@iisi.be
Paul Butterworth
International Iron & Steel Institute
email: butterworth@IISI.be
Andreas Ciroth
Technische Universitat Berlin
Institut fQrTechnischen Umweltschutz
Abfallvermeidung und Sekundarrohstoffwirtschaft
Sekt. KF 6
Strasse des 17. Juni 135
D-10623 Berlin
tel.: 49-30-314 79564
fax:49-30-31421720
email: ciroth@itu302.ut.tu-berlin.de
Garrette Clark
UNEP
Tour Mirabeau -39-43,
Qua! Andre" Citroen
75739 Paris Cedex 15 France
Email: aarrette.clark@unep.fr
Pierre Crettaz
Ecole Polytechnique Federale de Lausanne
DGR-IATE-HYDRAM
CH-1015 Lausanne Switerland
tel.:41-21-6933729
fax:41-21-6933739
email: pierre.crettaz @ epf l.ch
Mary Ann Curran
U.S. EPA (MS-466)
Cincinnati, OH 45268
tel: 513-569-7782
fax:513-569-7111
email: curran.maryann@epamail.epa.gov
Suzy Edwards
BRE
Watford, Herts, WD27JR United Kingdom
fax:44-1923-664084
email: edwardss@bre.co.uk
Tomas Ekvall
Chalmers Industriteknik
Chalmers Teknikpark
S-41288G6teborg Sweden
tel. 46-31-772 4336
fax. 46-31 -827 421
e-mail: tomas.ekvall@cit.chalmers.se
Goran Finnveden
Fmw and Department of Systems Ecology
Box2142
SE-10314 Stockholm Sweden
tel.: 46-8-402 3827
fax:46-8-4023801
email: finnveden ฉfms.ecology.su.se
Marina Franke
Proctor & Gamble
Global Technical Policy/Environmental Management
Postfach 5825, Industriestrafe 30-34
D-65733 Eschborn, Tanus
tele:(06196) 89-4723
fax:(06196)89-6648
email: f ran ke. m @ pg .com
Jean-Paul Fretiere
European Product Responsibility Manager
3M France
Boulevard de I'Oise
95006 Cergy Pontoise Cedex
tele:33/130318107
fax:33/130318192
email: ifretiere@mmm.com
82
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Bernard Gonzales
3M
U.S.A.
Email: bagonzales@ mmm.com
MarkGoedkoop
PRe Consultants B.V.
Plotterweg12
NL-3821 BBAmersfoort Netherlands
tel.: 31-33-455 5022
fax:31-33-4555024
email: goedkoop@pre.nl
Jeroen Guinee
Centre of Environmental Science
P.O. Box 9518
NL-2300 RA Leiden Netherlands
tel.: 31-71-527 7477
fax:31-71-5277434
email: guinea @ rulcml.leidenuniv.nl
Michael Hauschild
Department of Manufacturing Engineering
Technical University of Denmark
Building 424
DK-2800 Lyngby Denmark
tel.: 45-45-254 664
fax:45-45-935556
email: mic@ipt.dtu.dk
David Heath
ICI
email: david heath@ici.co.uk
Edgar Hertwich
University of California, Berkeley
310 Barrows Hall #3050
Berkeley, CA 94720-3050
tele:510-642-8853
fax:510-642-5815
email: hertwich @ socrates.berkelev.edu
Patrick Hofstetter
Umweltnatur- und Umweltsozialwissenschaften (UNS)
ETH-Zentrum HAD F1
CH-8092 Zuerich Switzerland
tel: 41-1-632 49 78
fax:41-1-6321029
email: hofstetterฎ uns.umnw.ethz.ch
Maarten ten Houten
TNO-lnstitute of Industrial Technology
P.O. Box 5073
NL-2600 GB Delft Netherlands
tel.: 31-15-260 8828
fax:31-15-2608756
email: m.tenhouten@ind.tno.nl
Mark Huijbregts
IVAM, University of Amsterdam
Nieuwe Prinsengracht 130
NL-1018 VZ Amsterdam Netherlands
tel.: 31-20-525 6263
fax:31-20-5256272
email: m.huijbregts@frw.uva.nl
Norihiro Itsubo
Japan Environmental Management Association for Industry
Hirokohji Bldg. Ueno 1 -17-6, Taitoh-ku
Tokyo Japan 110-8535
tel.: 81-3-3832 0515
fax:81-3-38322774
email: itsubo @ jemai.or.jp
Olivier Jolliet
Institute of Soil and Water Management
EPFL-IATE-HYDRAM
CH-1015 Lausanne Switzerland
tel..: 41-21-693 7011
fax: 41-21-693 7084
email: olivier.jollietฎ epfl.ch
Henry King
Unilever
email: henry.king@unilever.com
Walter Klopffer
C.A.U.GmbH
DaimlerstraBe 23
D-63303 Dreieich Germany
tel.: 49-6103-983 28
fax:49-6103-98310
email: C.A.U.@t-online.de
Helena Malkki
VTT Chemical Technology
P.O. Box 14031
FIN-02044VTTEspoo Finland
tel.: 358-9-4566442
fax:358-9-4567043
email: Helena.Malkki@vtt.fi
Lloreng Mila i Canals
Universitat Autonomade Barcelona
Unitat de Qufmica Ffsica, Edifici Cn,
Bellaterra
Barcelona 08193 Spain
tel.: 34-3-581 21 64
fax:34-3-5812920
email: llorenc@klingon.uab.es
Karl-Michaesl Nigge
Rheinweg 27
D-53113 Bonn Germane
tel. + fax: 49-228-233 763
email: Karl-Michael.Niaae@dir.de
83
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Leo De Nocker
VITO
Boeratang 200
B-2400 Mol Belgium
32-14-334885
fax:32-14-321 185
email: denockel@vito.be
Gregory Morris
Sylvatica
147 Bauneg Beg Hill Rd.
Suite 200
North Berwick, ME 03906
Tel: 207-676-7640
Fax:207-676-7647
email: norris @ svlvatica.com
Stig Irving Olsen
Institute for Product Development
Technical University of Denmark
Building 424
DK-2800 Lyngby Denmark
tel.: 45-45-254 611
fax:45-45-935556
email: sio@ipt.dtu.dk
Willie Owens
Procters Gamble
5299 Spring Grove Ave.
Cincinnati, Ohio 4521 4
Tel: 51 3-627-81 83
Fax:513-627-8198
email: owens.jw@pg.com
David Pennington
ORISE Post Doctoral Researcher
U.S. EPA (MS-466)
Cincinnati, OH 45268
tel: 51 3-569-761 8
fax:513-569-7111
email: pennington.david@epamail.epa.gov
Jose" Potting
Technical University of Denmark
c/o Bo Pedersen Weidema
IPU, Life Cycle Engineering Group
Building 40311
DK-2800 Lyngby Denmark
tele:4545254677
fax:4545935556
email: j
Steven Rolander
SAIC
11251 Roger Bacon Dr.
Reston,VA20190
Tel: 703-31 8-4605
Fax:703-736-0826
email: rolanders@saic.com
En/van Saouter
Procter & Gamble, Environmental Science Department
lOOTemselaan
B-1853 Strombeek Bever Belgium
tel.: 32-2-4562491
fax:32-2-4562845
email: saouter.e@pg.com
Guido Sonnemann
Chemical Engineering Department
Universitat Rovira i Virgili
Carretera de Salou s/n
E-43006 Tarragona Spain
tel.: 34-977-559 618
fax:34-977-559667
email: gsonnema@etseq.urv.es
Bengt Steen
CPM
Chalmers University of Technology
SE-412 96 Gothenburg Sweden
tel.: 46-31-772 2177
fax:46-31-7722172
email: bengts@vsect.chalmers.se
DuaneTolle
Battelle Columbus Division
Columbus, OH 43201
Tel.:614-424-7591
Fax:614-424-3404
email: tolled@battelle.org
Arnold Tukker
TNO-STB
P.O. Box 541
NL-7300AMApeldoorn Netherlands
tel.: 31-55-5493907
fax:31-55-5421458
email: tukker@stb.tno.nl
Helias A. Udo de Haes
Centre of Environmental Science
P.O. Box 9518
NL-2300 RA Leiden Netherlands
Tel.: 31-71-527 7488
Fax:31-71-5275587
email: udodehaes@rulcml.leidenuniv.nl
Bo Weidema
Technical University of Denmark
Inst. for Product Development
Building 424
2800 Lyngby Denmark
email: bow@ipt.dtu.dk
84
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Keith Weitz
Research Triangle Institute
3040 Cornwallis Rd.
RO. Box 12194
RTR NC 27709
Tel: 919-541-6973
Fax:919-541-7155
email: kaw@rti.org
HenrikWenzel
IPU-DTU
Technical University Denmark
Building 424
D-2800 Denmark Denmark
tel.: 45-45-254 663
fax:45-45-935556
email: hwc@ipt.dtu.dk
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85
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