Front page for deliverables
Project no.
003956
Project acronym
NOMIRACLE
Project title
Novel Methods for Integrated Risk Assessment of
Cumulative Stressors in Europe
Instrument
IP
Thematic Priority
1.1.6.3, ‘Global Change and Ecosystems’
Topic VII.1.1.a, ‘Development of risk assessment
methodologies’
Deliverable reference number and title:
D.4.1.4 Report describing a method for the quantification of impacts on
aquatic freshwater ecosystems resulting from different stressors (e.g.,
toxic substances, eutrophication, etc).
Due date of deliverable: 30 April 2006
Start date of project: 1 November 2004
Actual submission date: 14 June 2006
Duration: 5 years
Organisation name of lead contractor for this deliverable: EPFL
Revision [draft, 1, 2, …]: Draft 2
1
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
Dissemination Level
PU
PP
RE
CO
Public
Restricted to other programme participants (including the Commission Services)
Restricted to a group specified by the consortium (including the Commission Services)
Confidential, only for members of the consortium (including the Commission Services)
X
Authors and their organisation:
Dr Jérôme Payet (EPFL)
Deliverable no:
D.4.1.4
Nature: R
Status: Draft 2
Dissemination
level: PU
Date of delivery:
Date of publishing:
Reviewed by (name and period):
Ad Ragas, 2006
2
Table of Contents
Table of Contents........................................................................................................................................ 1
Summary report ......................................................................................................................................... 2
1General introduction ...................................................................................................................... 3
2 – Comparative Assessment for toxics .................................................................................................... 1
2-1- Building Dose-Effect relation for toxics ............................................................................................ 1
2-1-1 Context ............................................................................................................................................ 1
2-1-2-Ecotoxicological measures ............................................................................................................. 2
2-1-3-Statistical principles for calculating the HC50s .............................................................................. 2
2-1-4- Calculation of Effect Factor .......................................................................................................... 4
2-1-5-Ecological realism .......................................................................................................................... 5
2-2- Field validation of the dose effect relation for toxic ......................................................................... 6
2-2-1- Materials and Methods .................................................................................................................. 6
Pesticide monitoring ............................................................................................................................. 6
Biodiversity indicator ........................................................................................................................... 6
The HC50EC50 for the Comparative Risk Assessment of pesticides ..................................................... 6
2-2-2- Results ........................................................................................................................................... 7
2-2-3- Discussion ................................................................................................................................... 10
2-3- Conclusion ...................................................................................................................................... 11
3 – Quantifying effects of eutrophication in freshwater ecosystems .................................................... 13
3-1- context............................................................................................................................................. 13
3-2- Databases and methods ................................................................................................................... 13
3-3- Building Dose-Effect relations for freshwater ecosystems ............................................................. 14
3-4-Field validation of dose-effect relation for eutrophication ............................................................... 17
3-5-Discussion and conclusion ............................................................................................................... 20
4 - Dose Effect relation for Acidification ................................................................................................ 22
4-1-Introduction...................................................................................................................................... 22
4- 2- Exposure assessment ...................................................................................................................... 24
4-3- Effect modeling............................................................................................................................... 26
4-4- Conclusions..................................................................................................................................... 27
5- General Conclusions ............................................................................................................................ 28
Summary report
The task of the EPFL was to define a framework for the development of Comparative
Risk Assessment method for freshwater ecosystem. On the basis of our experience in
Life cycle impact assessment on ecosystems, we have first defined the key issues related
to the development of such a method: We have explored the appropriate scale,
considering three scale sorts, the spatial scale, the time scale and the biological
organization level. As a second step, we have explored currently used endpoint
measures for existing multiple stressor studies, and proposed the most relevant endpoint
as a trade of between methodological requirements and data availability. In this context,
the endpoint express the level of modeling where results of impact from all stressors can
be added together, typically the endpoint is the level where we can link the stressor and
the change in biodiversity.
On the basis of a literature review of existing multistress risk assessment studies and
ecological handbooks, we have selected 5 priority stressors that are likely to dominate
the impacts on ecosystems: Eutrophying substances, acidifying substances, pesticides,
biocides and other organic substances. For these stressors, the availability of relevant
risk assessment methods and their level of compatibility with an Comparative risk
assessment approach have been analyzed. Dose-effect relations have then been
developed based on an “damage-oriented” approaches, enabling an integration of the
different risks. Furthermore in order to ensure the usefulness and the reliability of the
method, we compared calculated results to observed biodiversity indexes within two
different case study in order to validate the dose-effect relationship.
We can foresee several areas of application in relation with current European
regulations. Providing a comparative assessment of stressors and substances, this
method is able to support the prioritization of stressors and substances, the identification
of the most hazardous substances, or a first tier assessment for the remediation of
freshwater ecosystem. Furthermore, these developments are also suited for LCA
methodology:, the new development of this project will provide a useful basis for the
development of characterization factors used in damage oriented LCIA methods.
In terms of potential use, it could be interesting to apply this method as a decision
support tool for different European directive, such as the Water Framework Directive,
as a first screening assessment tool for evaluating water bodies; it could also be
promising for REACH or for the Biocide directive, for quantifying the potential
environmental gain in substituting substances by others less hazardous. It can also be
interesting to perform chemical ranking on the basis of average response of species.
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1- General introduction
The EPFL work within NoMiracle aims to identify the anthropogenic stressors
producing impacts on aquatic ecosystems, to select the dominant stressors, and to
provide a framework and a methodology to integrate the different stressors in order to
quantify their relative impact on freshwater ecosystems.
Ecological Risk Assessment (EcoRA) considers the potential effects of single substance
and single stressor in the ecosystems. Nevertheless, most of the freshwater ecosystems
are exposed at the same time to a large number of stressors, causing impacts on
numerous species present in the ecosystems.
1-1 Context
During the years 60s and 70s, the adverse effect of human activities on ecosystems
where more and more highlighted (Carson 1962; Sládecek 1973). In order to limit
environmental burden of anthropogenic activities, regulations were set up restricting the
emissions of numerous pollutants. The common point of these regulations was to ensure
the integrity of the ecosystems, identifying threshold of no-effect per substance or
stressor that should not be exceeded. These regulations were justified by the Paracelsius
paradigma, Sola dosa fecit venenum, assuming that no effect is expected from stressor at
very low levels or concentrations in the ecosystems. This stressor-oriented strategy
enables substantial improvement of media quality when only a small number of
stressors drive most of the impacts. Nevertheless, in spite of these limitations, the
constant increase in the number of toxics and point source of emissions lead to an
increase of the overall burden on ecosystem. With respect to regulation thresholds, most
of ecosystems are now exposed to several stressors and several hundreds of substances
at the same time. In those conditions, effects on organisms can occur even at low level
of exposure. This is a limitation of the current regulation for Ecological Risk
Assessment (EcoRA). More recently, multiple stressors approaches were explored.
These methods can be stressor-oriented or effect-oriented integrating at the same time
potential effects of several stressors and pollutants in the environment. The effectoriented methods are promising, considering the change of a potential harmful effect
due to an increase or decrease of a stressor in the ecosystem on a given existing level of
effect in the ecosystem itself.
Considering potential impacts of different stressors, Comparative Risk Assessment is a
powerful decision support tool for defining environmental strategies. This is of interest
for several applications both at regional and continental scale for environmental priority
setting, substance regulation, and Life Cycle Assessment studies, but also at local and
regional scale for ecosystem management, or pollution remediation. In terms of
regulation, Comparative Risk Assessment methods provide a useful basis for supporting
several regulation tools such as the Water Framework Directive (for prioritizing aquatic
ecosystems improvements), the REACH or the Biocide directive (for identifying the
most hazardous substance on a comparative basis).
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At this time, the current level of science development limits the use of Comparative
Risk Assessment methods. Current method for risk assessment of toxics does not fit
with the requirements of comparative assessment (e.g. the need of unbiased assessment
for comparative assessment), and for the other stressors, dose-effect relations are not
available. This is a strong limitation for Comparative Risk Assessment quantitative
tools that cannot be applied for comparing the relative importance of stressors in the
environment.
1-2 State of the art for multiple stressor methods
Several approaches can be used for the calculation of substances threshold for
regulations. The most common is the PNEC approach (Predicted No Effect
Concentration).
The PNEC-based method (Predicted No Effect Concentration) aims at defining the noeffect for toxics (US-EPA 1984; EU-Commission 1994; EU-Commission 1996). The
threshold definition is based on the assumption that if the most sensitive species is not
affected at a given toxic concentration, all ecosystem species and therefore all functions
of the ecosystems can be regarded as protected. Nevertheless, this method is sometimes
regarded as too conservative, and alternatives have been proposed, like the introduction
of a threshold value based on a statistical extrapolation from ecotoxicity data, i.e. the
HC5 method (Van Straalen and Denneman 1989). This new method for calculating
PNECs aims at assessing the level at which 95% of the species are regarded as
protected, defining a concentration of chemical which should affect 5% of the species
present in the ecosystem. The concentration of pollutant that aims at protecting 95% of
the species of the ecosystem is commonly called HC5 (also referred to as PNEC0.05).
While the PNEC using the most sensitive species can be based on all kinds of
ecotoxicological endpoints (acute or chronic, ECx or NOEC) using different
extrapolation factors, the HC5 for regulatory purposes is currently preferably based on
chronic NOECs. Nevertheless, the uncertainty on the HC5 value can influence the
safety threshold. Some authors (Chèvre 2000) therefore propose to use the 95% CI on
the HC5 in order to increase the degree of certainty about the protection offered to the
ecosystem.
On the one hand, the original PNECs thresholds, calculated applying safety
extrapolation factors on the most sensitive species are regarded as too conservative, and
the trend for ecotoxicology is to develop best estimate values that typically increase the
authorized emission threshold. But on the other hand, ecologists observed a constant
decrease in the ecosystems quality in spite of the application of protective thresholds
and therefore question the ecological realism of threshold calculation methods. From an
ecological point of view, substance occurs in the ecosystem in a huge mix of other
stressors (including other substances) and their behavior is likely to be completely
different compared to laboratory predictions. Therefore, the need of a convergence
between ecotoxicology and ecology have been identified (Van Straalen 2003), and some
methods have been proposed in US for applying multiple stressor methods at the local
and regional scale (Pastorok et al. 2003; Weigel 2003; Yuan and Norton 2004; Brooks
and Novotny 2005; Morris et al. 2006). A large review of existing multiple stressor
assessment led us to the definition of three categories of methods ; (i) Interpretative
methods mainly illustrated with the Weight of Evidence (WOE) methods (Culp et al.
1999; Lowell et al. 2000; Adams 2003). These methods typically focuses on one aspect
of the ecosystem functioning or structure and looks for a qualitative link with a given
stressor or a given group of stressor. These approaches are limited by the lack of a
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consensus framework for their application, and the importance of expert judgment in
their applications that lead to many different possible interpretations of the RA results
(Weed 2005); (ii) « Stressor-oriented » methods represent methods mainly derived from
current ecological risk assessment framework without important modification. The
“toxic response” approach (Brooks and Novotny 2005) or the Czech Method (Soldan
2003) quantify the relative risk associated with different substances in watersheds using
available tools such as the HC5 or the msPAF (De Zwart and Posthuma 2005); (iii)
« Effect-oriented » methods are regarded as retrospective for application in ecosystems,
linking an observed change in biodiversity for example with the presence of a stressor,
but models enables their use in a prospective way for the prediction of effect of multiple
stressors on ecosystems. The main methods of this category are the Relative Risk Model
(Landis 2005), and the Canadian Cumulative Effect Assessment (CEA) method (Dube
and Munkittrick 2001; Dube 2003). Among these three groups of methods, “Effectoriented” methods looks more relevant for assessing impact on ecosystems, putting the
emphasis on the state of the ecosystem more than the properties of stressors assessed
separately. Nevertheless, their application requires to develop new concepts and tools
for each stressor considered since requirements for impact quantification is definitely
different from “stressor oriented” methods.
1-3 The Scale issue in Comparative Risk Assessment
A key issue in ecology is to define the scale of the assessment. Three scales must be
considered in order to ensure the coherence of the analysis as presented in Figure 1-1:
the temporal scale, the spatial scale and the organization level among ecosystems and
species.
Temporal scale
Millenium
Century
Decade
Year
Spatial scale
Day
local
Indiv.
Organisation
level
Regional
Continental
Species
Biocenosis
Figure 1-1 : Presentation of different scales for impact assessment on ecosystems
Multiple stressor studies have explored different spatial scales. Assessments for local
scale (the ecosystem scale) concern primarily well documented studies that aim to
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identify the main causes for a given environmental problem (e.g. a decrease in fish
production in a lake).
The time scale of effects for multiple stressor assessment can vary from a few hours for
acute exposure to more than 1000 year for persistent chemicals or for radio-nuclides.
Furthermore the endpoints addressed are most of the time related to biodiversity and
this is definitely dependent on the time scale retained.
The biological organization level addressed is coherent with the time and space scale
retained and the purpose of the stud: Since most of the data are based on laboratory
testing, effect modeling is often required for converting laboratory data in a biodiversity
related endpoint assessment.
1-4 Biodiversity endpoint
The unit used for expressing the potential impact on ecosystem should be compatible
with other stressors, enabling the integration of the assessment of different impact
category (e.g. toxics, eutrophication, acidification) in a final score expressing the sum of
all impacts on the considered ecosystem, and enabling the calculation of the relative
weight of each stressor on the overall burden.
1-5 Report structure
This report aims to identify the current limits of Comparative Risk Assessment methods
for aquatic ecosystems and to propose a methodology for a multiple stressor approach.
This will require to (i) Define a consistent framework for Comparative Risk Assessment
(Scale, Endpoint, Etc); (ii) Identify and prioritize stressors that have to be considered;
(iii) Develop dose effect relations for the selected stressors; and (iv) Perform as far as
possible a field evaluation of the dose-effect relations proposed for the Comparative
Risk Assessment method.
In chapter 2, the report presents a method for assessing impact of toxics (valid for
pesticides, biocides and organic non-pesticides) in a multiple stressor assessment. This
method is validated with a case study assessing expected impact of pesticides on
streams.
In chapter 3, a method for assessing impact of eutrophying substances is presented, a
case study with this new method assessing change in biodiversity linked with a change
in concentration of eutrophying substances for two big rivers is presented.
In chapter 4, a method enabling the relative impact of acidifying substances in a
multiple stressor approach is presented.
A general conclusion is proposed in chapter 5.
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2 – Comparative Assessment for toxics
2-1- Building Dose-Effect relation for toxics
2-1-1 Context
The use of the HC50 appears to be a good basis for comparative assessment purpose but
this indicator can be calculated in different manners. Examples of application of the
HC50 for multiple stressor assessment are mainly from Life Cycle Assessment. In
EcoIndicator (Goedkoop et al. 2000), the HC50 is used as a basis for the assessment of
the multiple substance impact on ecosystem. In that case, the HC50 is based on the
geometric mean of NOECs ecotoxicity data (No Observed Effect Concentration). No
limits are provided for the calculation of the HC50 in terms of minimum data
requirement (HC50 can eventually be based on one NOEC only, and generic
conservative extrapolation factors are provided for assessing chronic endpoint on the
basis of acute data). Therefore the HC50 was transformed in an effect factor by
modeling a typical dose-effect relationship considering mixture of chemicals in the
media on a concentration additive basis, and estimating the effect proportional at the
slope at the tangent to the curve for a given level of species affected in the ecosystem
(considering 24% of affected species in aquatic ecosystems). It was the first time that an
“Effect oriented” approach was preferred to a “Stressor based” approach typically used
in Ecological Risk Assessment (EcoRA) strengthening the ecological realism of the
multiple stressor assessment. At the same time the method was impaired by the use of a
dose-effect relation disconnected from the actual response of the ecosystems.
Observation in the field indicates very different effects can occur from the same change
in concentration of stressor, this is not revealed with tangential measure point on a
smoothed dose effect curve. An estimate of the impact for at a working point is
therefore limited by a very large uncertainty, that is not the case for an average estimate
of the impact. An alternative is proposed calculating HC50s on the basis of the median
of at least 5 chronic EC50s. The frequency of non log-normal distribution of ecotoxicity data was argued for the selection of the median as the most relevant statistical
estimator and a method for assessing the confidence interval on the median was
proposed. No extrapolation factor was provided for the calculation of chronic indicator
on the basis of acute toxicity data. Effect factors were derived from the indicator
assuming a simple linear dose-effect relation form the median down to 0, and
surprisingly the median appears to be a stable indicator in spite of its breakdown point
property. Nevertheless, the minimum requirement of 5 chronic EC50s per substance and
the lack of acute-to-chronic extrapolation factors were reducing drastically the number
of substances covered, and the environmental relevance of a confidence interval based
on bootstrap was disputable, since it looks sometimes underestimated. A review of
methods were analyzing the relative influence of the dose-effect relationship and the
mixture models comparing concentration addition and response addition in a multiple
species assessment (Pennington et al. 2004). It was concluded that the HC50s based on
the EC50s with a mixture model based on concentration additive response was more
relevant for comparative assessment.
This article intend to propose a method enabling the derivation of reliable effect factors
for Comparative risk assessment of multiple stressors, on the basis of aquatic toxicity
data for large scale assessment (regional, continental). Different alternatives are
explored, especially concerning the selection input ecotoxicity data; the choice of the
statistical estimator, its uncertainty, and its applicability to small dataset; the feasibility
of an extrapolation procedure for using acute toxicity database for assessing chronic
endpoint in a comparative way. Also, the issue of the best model assumption for the
derivation of effect factors on the basis of the HC50, and the associating environmental
relevance is discussed, in line with the review paper that highlight the need of this
analysis.
2-1-2-Ecotoxicological measures
Numerous tests results can be used for ecological risk assessment, the most common
being the EC50, the NOEC (No Observed Effect Concentration) and the LOEC (Lowest
Observed Effect Concentration). Several criticisms of the NOEC pointed out that the
result is strongly dependent on the experimental design (Laskowski 1995; OECD 1998).
Depending on whether the number of concentrations tested is high or low, the NOEC
value - the highest concentration at which no effects are observed - may vary. The same
remark can be applied to the LOEC (Lowest Observed Effect Concentration) - the
lowest concentration at which effect occurs. EC5s and EC10s are less dependent on
environmental design than the NOEC, but these effect levels cannot generally be
distinguished from the test control, and these measurements are therefore below the
level of observable effect in many cases (Isnard et al. 2001). Consequently, these data
are mostly estimated via extrapolation and not confirmed experimentally.
Basing the effect indicator on acute or chronic EC50 data has a number of advantages in
a comparative approach: For most ecotoxicological studies, EC50 modeling result in an
interpolation of the EC50 level among concentrations tested. Consequently, the doseeffect ratio presents minimum variability at the 50% or mean effects level or close to
that level of effect (Forbes and Forbes 1993; Riviere 1998).
In terms of data availability the EC50 value is the most frequently reported
ecotoxicological endpoint for vertebrates, invertebrates and plants. Considering the
biggest existing databases available (EU-Commission 2000; US-EPA 2001)
representing more than 115,000 tests results in acute toxicity testing and nearly 18,000
tests concerning chronic toxicity, 90% and 50% are EC50s data respectively for acute
and chronic toxicity data. Furthermore, in Comparative Risk Assessment, it is relevant
to explicitly link an impact like ecotoxicity to the damage it causes to exposed
ecosystems, to enable integration with other stressors. The link with different damage
can be established through measuring the reduction in biodiversity (e.g. quantification
of disappeared species) for example but this link is more likely to be established on the
basis of the quantification of an effect, for example linking an ECx value and a
probability of disappearance (Tanaka and Nakanishi 2000). On the contrary, it is more
difficult to relate reduction in biodiversity to an endpoint based on a no-effect level like
the NOEC.
2-1-3-Statistical principles for calculating the HC50s
Selection of the statistical estimator:
For comparative assessment, a stable and robust indicator is required. As stable, we
mean that the indicator should not vary considerably with the inclusion of new data. For
example, an indicator based on the most sensitive species can vary by several orders of
magnitude, depending on whether a very sensitive species is included in the database,
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giving unstable indicator. In terms of robustness, the indicator must be least sensitive to
deviation from statistical assumptions like assumption on the type of distribution
(Normal, Logistique, triangular).
The HC50EC50 can be calculated on the basis of the mean, the median and the geometric
mean of EC50 test results. The mean of the EC50 is not relevant in this framework since
most of the data are log-normally distributed and the mean is therefore strongly
influenced by the highest EC50s. The use of the median has been explored (Payet and
Jolliet 2004), but this is a breakdown point estimator sensitive to multi-modal
distributions. Since EC50s generally fit a log-normal distribution, the geometric mean
appears the most appropriate statistical estimator of the HC50EC50. Furthermore, even in
the case of multi-modal distribution, this estimator is the most robust, being close to the
mode of the distribution. Furthermore, compared to the median, the use of the geometric
mean also facilitate the use of extrapolation procedures for estimating a chronic average
response of species on the basis of acute toxicity data.
Calculation of uncertainties and confidence intervals
Beyond the choice of the most relevant indicator, the framework of comparative risk
assessment requires to quantify its uncertainty. Two methods have been explored for the
assessment of a Confidence Interval on the HC50EC50. A non-parametric estimate of the
Confidence Interval (CI) around the geometric median using bootstrapping has first
been explored (Payet and Jolliet 2004), and an alternative based on the geometric mean
and a CI based on Student have been tested. Both methods quantify the 95% CI of the
mean (or median), and in both cases, the size of the CI decreases when the number of
data increases. Nevertheless, there is no distribution assumption for the CI based on a
non-parametric estimate while those based on Student is subject to the assumption of a
log-normal distribution of EC50s. Secondly, the asymmetric non-parametric CI is
strongly influenced by the very sensitive species, while the Student-based CI is less
sensitive to outliers due to its symmetric nature. Furthermore, the Student-based CI can
be calculated with three EC50s only, while the non-parametric CI based on bootstrap
needs at least 5 data (unless we make an assumption concerning tail distribution). On
these considerations, the CI based on Student seems relevant for small samples or when
EC50 data are log-normally distributed. It should also be noticed that the confidence
interval based on student decreases with increasing number of EC50s used for the
calculation of the HC50. This property of the CI calculation provides the opportunity to
reduce the CI by inclusion of new data, improving thus the quality of the assessment.
Nevertheless, for large EC50s dataset, the CI around the geometric mean is small,
indicating a good confidence on the median value but at the same time sensitive phyla
can be exclude from the confidence interval. In that situation, the average sensitivity of
the most sensitive phyla can sometimes be regarded as the lower limit of the Confidence
Interval.
Management of small datasets
In terms of data availability, the trade-off between low data requirements and a reliable
indicator is an important issue. A method for comparative risk assessment needs a large
coverage of existing substances but at the same there is a limited amount of data
available and a great variability between data for both acute and chronic effects. The
minimum data requirement needed for the calculation of stable HC50s has been
explored.
Two components have to be considered in the definition of the minimum dataset. The
minimum number of EC50s required, but also a minimum representation of biodiversity
to ensure that the variability in the species response corresponds to the biological
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variability. Indeed, the statistical estimator must represent an average response of
species. The phyla level was selected as the most relevant taxonomic level for ensuring
a good representation of the biological variability. In order to check the reliability of
HC50EC50 based on small dataset (1, 2 or 3 to 5 phyla), we have compared in HC50EC50
values based on 1 to 5 phyla from small European and US databases (Mayer and
Ellersieck 1986; EU-Commission 2000; ECETOC 2002) with a reference HC50EC50s
based on a large database (maximum number of phyla available) (US-EPA 2004).
Result of the comparison indicates a correlation between HC50EC50s based on small
dataset and the reference HC50EC50s (Pvalue<0.05 in all cases). Nevertheless, R2 of
0.56, 0,86 and 0.95 for 1, 2 and 3-5 phyla respectively indicates that the spread of points
can be quite large even if the correlation at 95% is effective. We have therefore
compared, for each substance, the CI interval of the HC50 for 1, 2 and 3 to 5 phyla with
the reference HC50. HC50 based on 1 or 2 phyla appear to be a bad predictor of the
reference HC50, this value being sometimes excluded from the confidence interval,
while confidence interval of HC50s based on 3 phyla or more always include the
reference HC50.
Extrapolation procedures
In spite of the possibility to work with small datasets, we are often confronted to the
lack of sufficient chronic data. In that case, the calculation of chronic HC50EC50s can be
based on acute data extrapolating a chronic HC50EC50 on the basis of acute toxicity data.
In ecological risk assessment, this sort of extrapolation is typically based on
conservative extrapolation factors, while comparative assessment need best-estimate
extrapolation factors in order to avoid bias in chronic HC50s. We have therefore
developed a set of best-estimate extrapolation factors (Payet 2004), giving acute-chronic
ratios (ACR) for three groups of substances, i.e. organics non-pesticides, inorganics,
and pesticides organics. For each substance group, three values are provided, the ACR
for the lower bound and the upper bound of the HC50s’ confidence limit and for the
HC50. For Organics, inorganics and pesticides, ACR are respectively 1.9, 2.8 and 2.2
for the HC50; 4.2, 7.4 and 6.1 for their lower bound; and 0.8, 1.1 and 0.8 for their upper
bound. These ACR are valid for most of the substances excepted for substances from
the family of the carbamates and organotins.
Furthermore, in some cases, NOEC or LOEC are available while EC50s are missing. In
that case, best estimate extrapolation factors are also provided for estimating EC50 on
the basis of LOECs or NOECs (Payet 2004). For extrapolating from a NOEC to an
EC50, factor is 3.3 for acute exposure and 4.8 for chronic; for extrapolating from a
LOEC to an EC50, the factor is 2.1 both for acute and chronic exposure.
2-1-4- Calculation of Effect Factor
After calculating the HC50 (Hazardous concentration affecting 50% of the species over
a given threshold) as the indicator of potential harmful effects of a substance, the Effect
Factor (EF) is derived expressing the intensity of the effect of a given substance on the
freshwater media. The effect factor can be view as a relation between the change in
concentration of toxics in the media and an expected change in biodiversity. This
relationship is based on an average linear model (HC50EC50 down to 0), which is the
most common model when no assumption can be made concerning curve shape (Udo de
Haes et al. 2003; Pennington et al. 2004). The following equation is used for calculation
of the Effect Factor based on the acute or chronic HC50EC50 :
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EF 
PAF
0.5

C
HC 50 EC 50
(Eq 2-1)
Where EF is the change in the Potentially Affected Fraction of species that experiences
an Increase in stress for a change in contaminant concentration [m3 kg-1], C is the
Exposure concentration [kg m-3], HC50EC50 is the geometric mean of the hazardous
concentration affecting 50% of the species tested above their EC50, and PAF is the
Potentially Affected Fraction of species due to exposure to the chemical for which an
EF is derived.
The Effect Factor, which is defined by the slope of the HC50 down to 0 has the
following properties: (i) it increases with greater toxicity (lower HC50EC50); (ii) it can be
interpreted as the change in PAF due to a unit increase in concentration.
2-1-5-Ecological realism
Comparative Risk (or Impact) Assessment method differs fundamentally from current
Ecological Risk Assessment for substance regulation. Even the bases of the methods are
different. On the one hand Comparative Risk Assessment method intend to calculate a
best-estimate of the potential impact associated to a given stressor that is added to the
overall burden on the ecosystems, thus considered as an Effect-based method. On the
other hand, Ecological Risk Assessment method for chemical regulation intends to
define threshold of effects considering each stressor separately as a stressor-based
method. This fundamental difference will conditioned the ecological realism of the
Comparative Risk Assessment method. Indeed, stressor never occurs alone in
ecosystems, and aquatic ecosystems can be viewed as a complex mixture of substances
and stressors where each increase or decrease of stressor can be considered as a
marginal change in the overall burden on the ecosystem. Nevertheless, for large scale
(regional, continental) risk assessment, the media conditions and the structure of the
biodiversity per ecosystems can vary greatly, and therefore detailed dose-effect curves
cannot be modeled. In such a variable situation, the best estimate of all the possible
marginal slope of effect is the average linear slope of effect from the HC50 down to 0.
The second important issue concerning ecological realism is the coherence between an
indicator based on laboratory species and the wildlife present in aquatic ecosystem. The
representative of the HC50 regarding wildlife is questionable but at the same time, most
of the variability of the toxic response is driven by the substances and not the species
(Vaal et al. 1997). Therefore, the minimum requirement of three species from three
different phyla ensures a minimum biological variability of organisms’ response to
ensure that the HC50s has a minimum representative of the species response.
The question related to the effect modeling of multiples substances has also been
covered in this approach. Practically, the calculation rule of the HC50 based on the
average estimate of the toxicity and the sum of the potential toxicity of each substance
to express the overall PAF corresponds to a concentration additive mixture model under
the assumption of no-interaction between substances. This model gives better results
that response additive modeling for complex mixture that is the case in the field studies.
Nevertheless, such consideration remains quite theoretical and the most efficient way to
ensure ecological realism is to make a validation of the effect model on the basis of
monitoring data of toxics impacts on species. Thus, the comparison between the
predicted change in biodiversity using effect factors derived from HC50s and the
observed change in biodiversity considering a given endpoint is the best way to validate
and to calibrate the proposed effect factors.
5/42
2-2- Field validation of the dose effect relation for toxic
2-2-1- Materials and Methods
Chemo-physical properties and pesticides loads data of four Swiss rivers are from the
SESA (Service des eaux, sols et assainissement), an organization of the Department of
the Environmental Safety of the Vaud Canton. Boiron de Morges, Morges, Orbe and
Talent are the Swiss streams studied; all of these are located in the Vaud region, around
the Geneva lake, at an average height of 500 meters. These are small streams with a
maximum wide of 5 meters.
Data have been analyzed with the Statistica 6.0 software. Specifically, these data allows
to test the quality of the relationship established between concentration and damage
factor for phosphates and pesticides, based on community structure parameters, such as
sensitive taxa and IBGN..
Pesticide monitoring
Atrazine, Caffeine, Carbendanzime, Carbofuran, Diuron, Isoproturon, Linuron,
Metamitron, Metlachlor, Simazine and Therbuthylazine, the most common-used
pesticides in agriculture, were analyzed. Most of them are herbicides, except
Carbendazime, which is an insecticide, and Carbofuran, that is a fungicide. Caffeine is
also presented in this list even if it is no currently used as a pesticide. Nevertheless, this
substance is potentially toxic and often observed in streams. In all the investigated
rivers, measurements have been collected for all seasons from 1998 to 2003. Sample
stations were located from upstream to downstream, to have a complete view of the
river gradients.
Biodiversity indicator
Macroinvertebrate community structure was measured by the biotic index IBGN. The
IBGN is a standardized procedure (AFNOR, December 1992). It is found on the Global
Biological Index, which constitutes a synthetic information of the attitude of a river to
develop its benthos community, and it permits to classify objectively the water quality
of natural and modified hydrological systems. This index is based on the reduction of
taxonomic richness of macro invertebrates community characteristic of clean water and
on the progressive decrease in specific sensitive species (AFNOR 1992). The index
value is calculated on a matrix with the nine sensitive species on the one hand and the
14 classes of taxonomic richness on the other hand to have finally as result the value
associated to a specific river. The value scale goes from 1 to 20, with the highest quality
at the maximum value of the scale. The selected sensitive species are the ones which
have showed a good correlation with water quality, taking account pollution parameters,
especially organic enrichment (GREBE 1992).
Macroinvertebrate samples were collected from spring to autumn seasons, during 1998
to 2003, coinciding with the use of chemicals and pesticides.
The HC50EC50 for the Comparative Risk Assessment of pesticides
The HC50EC50 is a method enabling the comparison of impacts of toxic substances
(Payet 2004), implemented in the IMPACT 2002 model for LCIA (Jolliet et al. 2003). It
intends to provide a quantitative measure of toxicological impact on aquatic ecosystem.
6/42
The HC50EC50 is the geometric mean of the EC50 (Effect Concentration affecting 50%
of tested individuals), that is specifically developed for the comparative assessment of
aquatic toxicity of substances.The objective is to calculate the Effect Factor (EF), which
quantifies the environmental impact due to the mass of a substance presents in the fresh
water. Equation for the calculation of the Effect factors for toxics is presented in
paragraph 2-1-4. The PAF, expressed in percentage, can be calculated and used as a
potential impact for a specific substance at a specific concentration, as a basis for an
Comparative Risk Assessment.
The EC50 of pesticides, used to calculate the HC50, originates from laboratory testing
and expresses the direct effect of the substance’s toxicity on, at least, three different
taxa. The reference concentration is considered as the concentration in which the
community does not have to bear any damage or stress, and obviously the one of
pesticides in all the rivers is zero.
2-2-2- Results
In Table 2-1 the HC50 values of the eleven considered pesticides are listed.
Carbendazime and Terbuthylazine, respectively with an HC50 value of 2,63E-02 mg/L
and 5,63E-02 mg/L, appear to be the most toxic substances in aquatic ecosystem.
Table 2-1. HC50 of 11 different pesticides.
Atrazine
Caffeine
Carbendazime
Carbofuran
Diuron
Isoproturon
Linuron
Metamitron
Metolachlor
Simazine
Terbuthylazine
HC50 mg/l
3,06E-01
3,44E+01
2,63E-02
1,74E-01
3,25E-01
1,67E-01
1,34E-01
1,88E+01
3,98E-01
3,44E-01
5,63E-02
Comparing the expected impact (multiplying the measured concentration in streams
with the Effect Factor value) with the observed biodiversity of benthic
macroinvertabrates on the basis of the IBGN, we observed a relation between the
predicted value and the observed biodiversity concerning the impact assessment of
pesticides (r=-0,58, p<0.005, n=64). Observing the distribution of PAF vs IBGN, as the
pesticides concentration and so the correspondent damage factor increased, the
biodiversity tended to decrease, showing that the aquatic community of these rivers
were sustaining a pression due to pesticides Figure 2-1.
7/42
Vaud rivers
16
PAF:IBGN: r2 = 0,3407; r = -0,5837, p = 0,0000004; y = 9,95615443 - 645,371876*x
14
12
IBGN
10
8
6
4
2
-0,002
0,000
0,002
0,004
0,006
0,008
0,010
0,012
PAF
Figure 2-1. Relationship between IBGN with total PAF of pesticides in the Vaud rivers.
The correlation between the IBGN and the total fraction of affected species presents a
R2 of 0.34.
8/42
7,00E-03
Bois Billens
6,00E-03
Tolochenaz
5,00E-03
P 4,00E-03
AF
3,00E-03
Fontaine-du-Chasseur
2,00E-03
PAFLinuron
PAFIsoproturon
PAFDiuron
PAFCarbofuran
PAFCarbendazime
PAFCafféine
PAF Atrazine
Mo
uli
n
de
Vill
ars
1,00E-03
0,00E+00
PAFTerbuthylazine
PAFsimazine
PAFMetolachlor
PAFMetamitron
19 19 20 20 20 20 19 19 20 20 20 20 19 19 20 20 20 20
98 99 00 01 02 03 98 99 00 01 02 03 98 99 00 01 02 03
year
Figure 2-2: PAF of pesticides in Boiron de Morges.
Figure 2-2 shows a spatial trend, with increasing concentrations when moving from
upstream to downstream stations. In fact environmental conditions at any point in a
stream are continuously influenced by condition at the upstream points, so in the last
stations an increasing amount of substances is observed coming from upstream sites and
due to crop fields and waste water outputs of cities.
According to the previous results on pesticides in water and in an attempt to assess the
relative influence of each pesticides on biodiversity in Vaud streams system, a more
detailed analysis is performed. This analysis indicates that only 4 herbicides, i.e.:
Atrazine, Isoproturon, Simazine and Terbuthylazine drive most of the impact. During
some years the sum of concentrations of these only four pesticides represented the 90%
of the total amount of chemicals in water.
9/42
100
90
partial concentration (%)
80
70
60
Partial [Terbut]
50
Partial [Simaz]
40
Partial [Isopr]
Partial [Atraz]
30
20
10
2000
2000
2000
1999
1999
1999
2000
1999
2003
2002
2001
2003
2002
2001
2000
2003
2002
2001
2000
1999
0
years
Figure 2-3. Partial concentration of the four pesticides (Atrazine, Isoproturon, Simazine
and Terbuthylazine) in Vaud rivers.
Then among these selected pesticides, a stepwise multiple regression analysis was also
carried out. IBGN was resulted to be significant related to concentration of Atrazine and
Isoproturon, explaining 35% of the variation of the biotic index.
2-2-3- Discussion
Biotic indexes based on macro-invertebrates, as IBGN, are nowadays very often used as
a tool to assess rivers quality (Brodersen et al. 1998; Roy et al. 2003; Ndaruga et al.
2004; Maloney and Feminella 2005): This group of animals constitutes an important
component of the biotic community in lotic system , is widely dispersed and of has a
very short reproductive time. In addition the structure and the diversity of
macroinvertebrate community reflect changes in rivers caused by natural and
anthropogenic events (Hauer and Lamberti 1996).
We found that IBGN responded to the damage rate caused by herbicides. This is
surprising since IBGN covers numerous species of macroinvertabrates but does not
considered algae or macrophytes, and we can observed an impact of herbicides on
macroinvertabrates even at low concentration of substances. This can be due to indirect
effect but in all cases, it suggests that substances do not only act on organisms directly
sensitive to their expected mode of action (in our case photosynthetic inhibitors).
The second surprising observation is that even if we can observe a linear relation
between the predicted PAF and the observed changes in biodiversity, highlighting the
environnemental relevance of the model, the amplitude of this change is much higher
than expected. Indeed, IBGN shift for 14 (good river) to 4 (bad river) on a scale for 0 to
20 (Change of 50% in term of disappeared species). At the same time, the PAF shift
from 0 to 0.012, indicating a change of 1.2% of increase of the affected species. In spite
of the linear relation, this change indicates clearly an underestimation of the prediction
of affected species. Assuming that the observed shift n IBGN represent a change of 50%
10/42
of the biodiversity, we can consider that the HC50 calculation underestimate the impact
on species by 1 or 2 orders of magnitude. This could be explain by for causes: (i) it is
possible that additional substances are present in the streams but not measured, leading
to an underestimation of the PAF. In this case, the considered pesticides would
constitute indicators of the overall contamination to which they are correlated; (ii) It is
possible that IBGN is based on unlinear relationship with biodiversity, indicating a big
shift in the note for the early small changes in biodiversity structure; (iii) it is also
possible that unexpected synergies between substances occurs in the media between
substances that are present at the same time in the stream. This assumption looks
relevant since synergism between atrazine and organophosphate have been reported in
the literature, explaining increase in toxicity of atrazine up to three orders of magnitude
(PapeLindstrom and Lydy 1997; DeLorenzo and Serrano 2003; Anderson and Zhu
2004). Therefore, the current HC50 approach based on the assumption of a
concentration additive mixture model under the assumption of no interaction between
chemicals would not be adapted; (iv) synergies could also occur between species
disappearing, meaning that a few additional disappeared species could indeed lead to
large changes in biodiversity due to species interdependency.
The third observation that can be noticed is related to the quality of the relation between
the predicted and the observed biodiversity. We can observe a large spread of points in
the left part of Figure 2-1 while points are more gathered on the right part of the figure.
This is a typical distribution of multiple stressor situation, and in that case, it is
suggested not to use the slope of the linear regression as the slope of effect but to use
the slope of the Maximum Species Richness Line (MSRL) (Brooks and Novotny 2005;
Novotny et al. 2005) that would suggest a stronger effect of pesticides on biodiversity.
Based on the observations, atrazine and terbuthylazine imposed the highest pression on
aquatic ecosystem. It is well known that the s-triazine compounds, which comprises
Atrazine and Terbuthylazine, are usually termed recalcitrant, and especially the first
one, due to its asymmetric substituent groups, is particularly resistant to biodegradation
(Varghaa et al. 2005). These two chemicals are furthermore herbicides which affects the
photosynthetic electronic transport, inhibiting the algal growth in aquatic environment
(Eullaffroy and Vernet 2003), the primary level of the food web. In addition Atrazine
even at low exposure concentrations (5µg l-1) affected significantly aquatic organisms
(Steinbergi et al. 1995).
2-3- Conclusion
The method presented here for calculating the HC50s enables the assessment of
numerous substances with an indicator based on several species, and providing a
Confidence Interval on the Effect Factor. The method avoids most of the bias due to a
conservative approach.
The key features of the HC50 calculation presented in this article are the following: (i)
Chronic EC50s are preferred for calculating HC50s, but if only NOEC or LOECs are
available, EC50s can be extrapolated using best estimate factors; (ii) HC50s are based
on the geometric mean of the species response with a confidence interval based on
Student; (iii) Input data can be reduced down to a minimum of 3 phyla, and if chronic
data can not be found, best estimate ratio can be used for assessing chronic HC50s using
acute toxicity data; (iv) a linear average model have been retained for deriving effect
11/42
factors on the basis of the HC50s, providing a useful assessment basis in an “Effectoriented” method, and enabling the expression of the results in terms of change in
biodiversity for a given change in concentration of toxics in the media.
All the key features listed above for the calculation of HC50s trend to strengthen
theimpact assessment of toxics in multiple stressor assessment. Nevertheless, some
limitations remain especially concerning (i) A distinction between freshwater and
marine ecosystems is necessary for a better estimate of the impact; (ii) An improvement
of the consideration of the bio-availability of metals, as currently, HC50s for metals and
metalloids are based on an average estimate of metal toxicity disregarding
environmental parameters; (iii) current method is developed for regional or continental
scale assessment, in the future, effect factors should be also adapted to the
environmental conditions of local ecosystems, providing opportunities of applying the
method to ecosystems management issue.
In spite of these limitations, the method presented here provides a basis for the
derivation of dose-effect relation for toxics in an effect-based approach for Comparative
risk (or impact) assessment. At this time, dose-effect relations have been calculated for
nearly than 1500 substances, and are expressed as Effect Factors calculated on the basis
of the HC50EC50s. Furthermore, similar indicator is used for Life Cycle Assessment
enabling synergies between Comparative Risk Assessment and Life Cycle Assessment.
Furthermore, this new method for assessing potential impacts on aquatic ecosystems
enables substance ranking and substance prioritization for evaluation at large scale.
12/42
3 – Quantifying effects of eutrophication in freshwater
ecosystems
3-1- context
Eutrophication is a major stressor affecting freshwater ecosystem biodiversity, but its
relative importance compared to other stressors cannot be easily estimated. Instead of
considering all stressors separately, a screening method that assesses the relative
biodiversity impairment due eutrophication among other stressors is necessary.
The purpose of this section is to develop a freshwater damage model that enables
impact assessment of eutrophying substances in terms of fraction of damaged
biodiversity, thereby enabling a comparison between this stressor and other methods of
assessing the impacts of toxic substances.
The impact of eutrophication depends on the substances considered, their
concentration in water, and their potential adverse effects on biodiversity. Phosphorus
and nitrogen are the basic nutrients that regulate plant growth. Nevertheless, phosphorus
is considered to be a limiting nutrient in freshwater ecosystems while nitrogen mainly
plays that role in coastal and marine waters (Barroin 2003; Norris 2003; Paerl et al.
2004; Seppälä et al. 2004). As N concentration in freshwater is relatively more abundant
than P and it is more available due to atmospheric fixation by algae, only P limits plant
growth (Barroin 2003). Organic matter also plays a role in eutrophication, especially in
rivers and streams (Spellman 1996). The dynamics of different types of freshwater
ecosystems cover a large diversity of ecological conditions but can be restricted to lotic
(rivers) and lentic (lakes) ecosystems. Lotic ecosystems are characterized by
continuously running water, and consequently low residence times of water and
substances and an abundant reoxygenation. Lentic systems have a higher residence time
for both water and pollutants, and are more exposed to other processes such as
sedimentation or release of substances from benthic sediments than lotic ecosystems.
Several other factors influence these processes including temperature, turbulence or
bioturbation, dissolved oxygen concentration, calcium concentration and sediment
concentrations of aluminium, manganese, particulate organic carbon, and iron (Melack
1995). Models have been developed to estimate the magnitude of these processes in a
simplified way (Vollenweider 1975).
3-2- Databases and methods
The responses of living species to eutrophication depends on different key parameters
related to the receptor medium (freshwater ecosystem) and the sensitivity of the
organisms present. The background nutrient level is a key aspect of the media
sensitivity. Three sorts of freshwater ecosystems can be distinguished on the basis of
their eutrophication level: oligotrophic, mesotrophic and eutrophic; in ascending order
depending on the magnitude of their phosphate concentration (Tachet 2000). A second
distinction is based on the load of organic matter. Four levels are identified on the basis
of the saproby level: oligosaproby, andmesosaproby and eusaproby (Sládecek
1973). Discharge is the other key environmental parameter since it determines the
residence time of substances in freshwater ecosystems. Lotic and lentic ecosystems are
13/42
separated in the database, mainly because the relative importance of eutrophying
substances depends on the residence time of water. Compared to organic matter that is
readily biodegradable, the persistence of phosphorus tends to give it a higher relative
impact in lentic than in lotic water. The species sensitivity changes between
eutrophication levels, and different freshwater ecosystems have different original
species composition. Calculation of potential effects as the Fraction of Affected Species
(PAF) and as Fraction of Disappeared species (PDF) for a change in concentration of
eutrophying substances is based on the two ecological qualitative databases presented
above (Sládecek 1973; Tachet 2000) and propose specific profiles for each studied taxa
depending on ecological variables. Tachet’s database covers 22 variables concerning
organisms (such as age, feeding method) and media (type of water, pH, etc.) using
levels of affinity (the probability of occurrence of a species in a given media) for
eutrophication levels for the 472 different species studied. The 4 (occasionally 6) levels
of affinity can be translated into a frequency of apparition. The database is composed of
the aquatic macroinvertebrate diversity found in French freshwaters. Sládecek’s (réf.
biblio) database is based on the same principle but only provides biodiversity measures
for saprobity using all freshwater taxa existing in Czech.
Considering the different media conditions and the corresponding species sensitivity,
different dose-effect relationships express the link between a change in pollutants
concentration and the corresponding effect on ecosystems . We calculate effect factors
following Equation 1. The effect factor expresses the intensity of effect for a given
change in concentration of the pollutant in the aquatic media.
PAF
C
PDF

C
EFPAF 
Eq. 3-1
EFPDF
Eq. 3-2
Where PAF (no unit) is the change in fraction of affected species or PDF the
change in fraction of disappeared species; C (kg.m-3) is the change in pollutant
concentration and EF (Effect Factor) in m3.kg-1 corresponds to the number of cubic
meters of water that is polluted with 1 kg of emission.
Effect factors assume a linear relationship between the change in concentration of
substances in freshwater, and the change in effect (the fraction of species affected);
therefore, the intensity of effect is directly related to the change in concentration. The
unit of Effect Factors, i.e. m3.kg-1 expresses the number of cubic meters of water
necessary in order to dilute 1 kg of eutrophying substance considering that all species
are affected. As is the case with to toxic substances, this linear relationship is only
expected to be valid for a small change in concentration that affets between 20 to 50%
of the species present in the freshwater ecosystems. This percentages of affected species
corresponds to typical values found in freshwater ecosystems.
3-3- Building Dose-Effect relations for freshwater ecosystems
The selection of eutrophying substances is based on their potential effect on
freshwater ecosystems. Phosphorus is considered to be a limiting nutrient in freshwater
ecosystems while nitrogen is not (Barroin 2003). Organic matter degradation and the
associated oxygen depletion can also produce eutrophication. Aerobic decomposition of
14/42
organic matter by bacteria can reduce dissolved oxygen concentration in water and have
harmful effects on the aquatic fauna (Spellman 1996). The amount of substances in
water that can lead to a decrease in dissolved oxygen are expressed as the Biological
Oxygen Demand (BOD) or Chemical Oxygen Demand (COD), corresponding to the
amount of oxygen that is necessary to degrade these organic matter or chemical
substances. While related in terms of consequences, indicators for phosphorus
concentration and increases in COD/BOD reflect different cause–effect relationships
than indicators for nutrient enrichment (Pennington et al. 2004) and hence,
eutrophication and saprobity are considered separately and are related to differences of
concentration of phosphate and measured BOD.
We distinguished between oligotrophic and mesotrophic ecosystems (with a low or a
high load of nutrients) among the three levels proposed by Tachet (réf. biblio).
Similarly, two background levels of saprobity are used for the model: oligosaprobic and
β-mesosaprobic, among the 4 levels proposed by Tachet based on Sladecek’s work (réf.
biblio). Each level of eutrophication and saprobity is defined with a median
concentration and a range of variability calculated as follows.
Table 3-1: Median and range values for three eutrophication categories (µg.L-1 of
totalP).
Ecosystem
River
Oligotrophic
Cmedian
Cmin-Cmax
50
0-100
Mesotrophic
Cmedian
Cmin-Cmax
175
100-250
Eutrophic
Cmedian
625
Cmin-Cmax
250-1000
Lake
8
27.7
67.5
35-100
4-10
10-35
As presented in Table 3-1 for lakes, OECD’s Cooperative Program on Eutrophication
(Vollenweider 1982) proposes total phosphorus concentration for each eutrophication
state. Other classifications have been proposed (SEPA 1994) but limits between
categories are rather similar. These values are not valid for rivers and streams because
ecosystems dynamics must also be taken into account. As explained by different authors
(Dodds et al. 1998; Capblancq 2002), there is not an existing criterion that characterizes
the eutrophication level in running waters. One of the reasons is that production is rarely
limited by nutrients and hydraulic parameters (such as the flow and residence time of
water) play an important role in these ecosystems (Capblancq 2002). As a result,
concentration boundaries will be higher than in lakes, and boundaries proposed by
Dodds and colleagues (Dodds et al. 1998) are not used in this work. Table 3-1 presents
the classification proposed for the calculation of the dose-effect relationships in running
waters. It is based on values concerning the Rhone watershed (Rankin 1999). The range
of variability of DBO concentration for saprobity for rivers and lakes is taken from
values proposed by Sládecek (Sládecek 1973) and is presented in Table 3-2.
Table 3-2: Median and range values for saprobic categories (DOB5 in mg.L-1 of O2).
Ecosystem
River and lake
Oligosaprobic
β-mesosaprobic
-mesosaprobic
Cmedian
Cmin-Cmax
Cmedian
Cmin-Cmax
Cmedian
Cmin-Cmax
1.75
1-2.5
3.75
2.5-5
7.5
5-10
15/42
Since different species are present in lakes and rivers, different fractions are
calculated for each kind of ecosystem on the basis of the affinity of each species to
different kind of ecosystem (river, lake, temporary waters, etc.). In order to calculate the
potential effect of eutrophying substances, we studied the difference of affinity of each
species present in one level when passing from that level to the consecutive more
polluted level. Using each category, the total of species present in each level was
calculated and then compared to the consecutive more polluted level. Table 3-3 presents
the results distinguishing 3 group of species; (i) species that disappear when going
through the next level, this group is use for calculating the PDF; (ii) other species
affected if the probability of occurrence of the species is reduced, included those which
disappear (as logically, disappeared species are affected, the total PAF results in the
addition of disappeared species and affected species, hence PAF will always be higher
than PDF); (iii) species that remain unaffected or increase their affinity with a higher
level, and species that are only present in the more polluted level, not accounted for in
the calculation as we consider that species are not interchangeable.
Table 3-3: Number of species per media type and fraction of affected and disappeared
species based on Tachets’ (réf. biblio) database. (NSp: Total number of species; NSpa
and NSpd: Number of species affected or disappeared passing from one level to the next
highest level).
Eutrophication
level
Oligotrophic
Mesotrophic
NSp
NSpa
NSpd
NSp
NSpa
NSpd
River
290
139
75
319
217
121
Lake
227
83
41
297
177
86
Saprobic level Oligosaprobic
 mesosaprobic
NSp
NSpa
NSpd
NSp
NSpa
NSpd
River
287
103
49
319
254
107
Lake
234
61
25
305
217
72
Practically, the calculation of the Effect Factor based on the fraction of affected
species for mesotrophic level is described in Equation 2.
NSpai  NSpdi
NSpi
EFPAF,i 
Eq 3
Cmedian i 1  Cmedian i
EFPDF,i
NSpdi
NSpi

Cmedian i 1  Cmedian i
Eq 4
Where EFPAF,i is the Effect factor at the eutrophying level i in (m3.kg-1); NSpai and
NSpdi are respectively the number of species affected or disappeared when passing
from the eutrophying level i to the higher level i+1; NSpi expresses the total number of
species considered at the eutrophying level i; [Cmedian]i+1-[Cmedian]i expresses the change
16/42
in average concentration of eutrophying substances in the ecosystem when passing from
the eutrophying level i to the level i+1, Cmedian refers to the values presented in Table 31 for eutrophying levels and Table 3-2 for saprobity level.
One major difference between the effect part of this does-effect relationship when
compared to those of toxic substances is that (NSpa+NSpd)/NSp, and NSpd/NSp
express respectively the fraction of species actually affected or disappeared from the
ecosystem based on field observations, whereas with toxic substances is based on
ecotoxicological test.
For the development of dose-effect relationships, all substances containing
phosphorus and organic matter are considered. Substances containing phosphorus are
expressed as PO4 equivalent (depending of the fraction of phosphorus per molecular
weight). As values presented in Table 3-1 are expressed in total phosphorus, the
relationship to convert total phosphorus into phosphates is based on molar masses.
Organic matter is expressed in terms of DBO. Potential influence of chemicals in
eutrophication process is also considered through the DCO. Table 3-4 presents Effect
Factors for different media conditions, highest values indicating highest potential
impacts.
Table 3-4. Effect factors per phosphate and BOD for 4 types of ecosystem expressed in
(m3.kg-1)
River
Lake
Oligotrophic
Mesotrophic
Oligotrophic
Mesotrophic
EFPAF
EFPDF
EFPAF
EFPDF
EFPAF
EFPDF
EFPAF
EFPDF
PO4
in
water
1.3E+03 6.8E+02 4.9E+02 2.8E+02 6.1E+03 3.0E+03 4.9E+03 2.4E+03
BOD in
water
1.8E+02 8.5E+01 2.1E+02 8.9E+01 1.3E+02 5.3E+01 1.9E+02 6.3E+01
3-4-Field validation of dose-effect relation for eutrophication
While the first part of the article has described how Effect Factors are calculated, this
section aims at applying parameters found in a “real world” example in order to ensure
their validity.
Actual field data used for the comparison are from Agence de l'Eau RhôneMéditerranée et Corse (http://sierm.eaurmc.fr/telechargement/index.php). This database
has gathered water quality data from about 400 locations in the Rhone watershed for
nearly 6 years. Two sorts of data have been used from this database. (i) Physicochemical data indicating the concentration in phosphate in water and the Biological
Oxygen Demand (BOD) as an indicator of the fraction of nutrients present at the
measuring point. (ii) The most sensitive taxa observed at the point of measure have been
extracted from the database, and are used as an indicator of biodiversity. The most
sensitive taxa is one of the two components (with the species richness) of the biotic
index IBGN (Standardized Global Biotic Index) (AFNOR 1992). For the IBGN, taxa
are ranked in terms of sensitivity and a value between 1 and 9 is attributed to each taxon
on the basis of its sensitivity, from the least sensitive to the most sensitive. It should
also be noted that there are restrictions concerning the use of the IBGN as an indicator
for quantifying biodiversity. The indicator is usually restricted to freshwater streams
with a limited width (theoretically no more than 10 meters); however, in certain
hydraulic conditions this indicator can be valid even for large rivers (Verneaux et al.
17/42
1982; Cellot 1987; AFNOR 1992). Bearing this in mind, we use a prediction based on
effect factors related to river for the comparison. Therefore the validation only applies
for effect factors related to rivers corresponding to a mesotrophic eutrophying level. In
order to ensure the applicability of the comparison, we used only data where the
biological and the physico-chemical sampling were carried out with three or less days of
difference between biological and chemical sampling for each location, corresponding
to a total of 223 samples for the regression concerning phosphates and 212 for the
regression concerning BOD.
Scatter plots presented in Figure 2-1 for phosphate and Figure 2-2 for BOD are
typical of multiple stressor assessments. This model is similar to a concept originally
proposed by Fausch and colleagues (Fausch et al. 1984), the Maximum Species
Richness (MSR). MSR recognizes the fact that under certain single stressor conditions
there is a limited maximum number of species within taxa that will be found in the
stretch of the river. However, because the number of species is a response to multiple
stressors, the actual number of species can be anywhere between zero and the
maximum. The actual number is then related to the effects of the other risks and also
includes a random component (Novotny et al. 2005). It also indicates that below a
certain magnitude of the stressor the effect is minimal and above a threshold of effect
species richness and compositions are adversely affected (Novotny et al. 2005).
10
9
Most sensitive taxa
8
7
6
5
4
3
2
1
0
0
0.5
1
1.5
2
2.5
3
3.5
Phosphates (mg*l-1 PO4)
Figure 3-1: Presence of sensitive taxa for different PO4 concentrations for 400 points
of measure, black line represents the MSRL (Maximum Species Richness Line).
On the basis of the MSR concept, Figure 3-1 can be divided in 2 areas. Most of the
points are concentrated around low concentrations of eutrophying substances. For high
concentrations of eutrophying substances, in spite of the limited number of points, only
insensitive species can be observed. In this relationship, the sensitivity of the taxa found
is inversely proportional to the concentration of phosphate and BOD.
Sensitive taxa and resistant taxa have been observed on the first part of Figure 3-1,
covering concentration of phosphate from 0 to 0.25 mg.L-1. This indicates that several
other stressors can also be present limiting the biodiversity in spite of the low
18/42
concentration of eutrophying substances. The concentration of 0.21 mg.L-1 is the
highest concentration at which we can observe the most sensitive taxa. We can therefore
consider this value to be a threshold of effect from which increase in phosphate
concentration can result in adverse effects on biodiversity. This observation is coherent
with former studies mentioning a possible threshold of effect close to this phosphate
concentration (Rankin 1999). On the second part of the graph (greater than 0.25 mg.L1), we can observe a decrease in biodiversity, with values of most sensitive taxa from 9
at 0.25 mg.L-1 of phosphate to 2 at 3.5 mg.L-1. This indicates an increase in the relative
influence of the stressor on the biodiversity for that range of concentration compared to
other stressors.
Only points located in the top of the graph are carrying information related to the
stressor. The relevant line indicating a dose effect relationship is called the Maximum
Species Richness Line (MSRL) and corresponds to the line that presents the best-fit
with the points in the top part of the figure. The MSRL indicates that very sensitive taxa
(values from 6 to 9) are not expected to be present for high phosphate concentration
such as 2 mg.L-1. At the same time, the presence of points below the MSRL also
indicates that a decrease in phosphate concentration will not systematically result in an
increase in biodiversity. In that case another stressor can be revealed, maintaining a high
environmental pressure.
In the range of effect of the increase of phosphate the MSRL presents a slope of effect
of 28% of decrease in biodiversity per milligram increase in PO4 concentration. This
observed value must be compared to the effect factor calculated in the first part of the
article (see Table 3-4) that indicates a fraction of disappeared species of 0.28 per unit
increase in phosphate concentration. These two values are very close to each other and
confirm the realism of the effect factors proposed in this article. Nevertheless, the
validity of this observation is limited to the range of effect of phosphate over the
threshold of effect or to phosphate concentrations higher than the threshold of effect.
We based the comparison on the PDF value instead of the PAF because observed
biodiversity considers taxa that are present or absent (disappeared) and not taxa that are
affected.
10
9
Most sensitive taxa
8
7
6
5
4
3
2
1
0
0
2
4
6
8
10
12
14
BOD(mg*l-1 of O2)
Figure 3-2: Presence of sensitive taxa for different BOD concentrations for 400 points
of measure, black line represents the MSRL (Maximum Species Richness Line).
19/42
Similar observations can be made on Figure 3-2 about the DBO. The two crossed
points with a value of 9 for the most sensitive taxa that are distinct from the others are
outliers probably due to the difference between the time of stressor occurrence and the
change in biodiversity. Nevertheless, we observe a possible threshold of effect for
concentration of DBO of 3 mg.L-1 of O2, and a strong maximum effect with
biodiversity of one (only very resistant taxas remain) for DBO of 9 mg.L-1 of O2.
As for phosphate, we compare the MSRL above the effect threshold to the effect
factor calculated for DBO in the first part of the work. For DBO, ranking from 3 to 9
mg.L-1 of O2, MSRL presents an observable decrease of biodiversity of 9.5% per
increasing unit of DBO, that should be compared to a PDF of 0.089 per increase mg.L-1
of DBO predicted with the proposed effect factors (ref. to part of article where this is
mentioned). Even if the effect factors seem to underestimate the impact on biodiversity,
the slight (or small) difference between the observation and the prediction highlights the
ecological realism of the effect factor calculated in the article.
3-5-Discussion and conclusion
Beyond the calculation of effect factors for eutrophying substances such as PO4 and
DBO, several points can be discussed. It concerns especially the differences in
sensitivity between media, regarding phosphate and DBO for media parameters (such as
background concentration or hydrodynamics). It also concerns points related to the
biodiversity indicator, such as the limit of its representativity regarding actual
freshwater ecosystems. These points are discussed below.
Several authors have discussed the impact assessment for BOD and phosphate at the
same time. As what is done here for eutrophication, two sorts of effects are typically
considered (Seppälä 1998), oxygen depletion (measured by BOD) and eutrophication
(measured by limiting substances). Both factors are in general treated similarly, but
some authors remark that an increases in BOD does not necessarily result in increased
biomass, as this is primarily associated with changes in nitrogen and phosphorus levels
(Pennington et al. 2004). Also Potting and Hauschild (Potting and Hauschild 2004)
recommend not using BOD to avoid a double accounting, since organic material often
contains nitrogen or phosphorus. Nevertheless, phosphate sampling in the field typically
does not include phosphate in organic matter. Therefore we suggest assessing the
biodiversity decrease due to phosphate and organic matter separately.
Different freshwater ecosystems can present varying sensitivity to eutrophication.
Classifications found (Vollenweider 1982; SEPA 1994) for eutrophication levels in
lakes show the great vulnerability to changes in phosphorus concentrations existing in
these ecosystems. Changes in concentration of some micrograms per litre can alter the
balance of the ecosystem. In general, rivers and streams, where hydrodynamics are
different, can stand higher concentrations of phosphates, up to 0.3 mg.L-1 without
suffering changes in specific composition, whereas lakes are considered hypereutrophic
for phosphate concentrations equal to or greater thatn 0.1 mg.L-1 . This is most likely
associated to the mix of water. In streams and rivers, water mixing and the large
interface between the water's surface and the air lead to relatively high concentration of
dissolved oxygen. In lotic ecosystems, the small interface between air and water,
combined with a large volume of water and low current lead to a limited dissolved
oxygen concentration that is strongly influenced by the biological activity in the lake,
and therefore much more influenced by phosphate concentration than streams or rivers.
For saprobity, as communities are different in lentic and lotic environments, effects are
20/42
also different. Nevertheless, differences are not as great as for eutrophication in lakes.
Organic matter degradation dominates eutrophication processes in streams, but remains
marginal in lakes, where the effect of phosphorus dominates largely.
The determination of boundaries between categories is also disputable. As the
database used does not give boundaries for different eutrophication levels, these are
determined based on different references. In lakes, boundaries have been fixed by
different authors (Vollenweider 1982; SEPA 1994), but these values are not valid for
rivers and streams as explained by different authors (Dodds et al. 1998; Capblancq
2002). The determination of values for rivers is based on different references (Rankin
1999) and on calibrations based on actual data but the uncertainty associated is higher
than in the case of lentic ecosystems. Values to differentiate saprobic levels are given by
Sládecek (Sládecek 1973; Sládecek 1975) and are equal for all kinds of ecosystems.
Hence, the associated uncertainty is lower, as they were already fixed and they have not
been determined.
Another key issue is the validity of the most sensitive taxa as a biodiversity indicator.
The database used to calculate effect factors was built by studying the affinity profiles
of benthic macroinvertebrates to different characteristics in French freshwater
ecosystems. The use of freshwater quality indicators based on macroinvertebrate fauna
is mainly based on (i) the large spectrum of responses of the different taxa to the
environmental changes, and (ii) the fact that benthic macroinvertebrates represent the
major part of the freshwater biodiversity (algae, plants, and chordates represent only a
very small part). Nevertheless, this is disputable since macroinvertebrates are directly
dependent on plants and algae growth, and a substantial change in biomass growth can
alter the ecosystem biodiversity. This is even more problematic in the case of
eutrophication since an increase in biomass (that is the first effect of eutrophying
substances) could also be viewed as a possible increase in biodiversity. We have
therefore performed a sensitivity analysis on that point by comparing our results with
Sladecek’s database, which is not restricted to benthic macroinvertebrate taxa (Sládecek
1973). We find similar results in terms of PAF and PDF; therefore, possible error due
the taxa selection seems limited.
Ecosystem recovery has not been considered in the effect factors calculated in this
work. The model assumes that if a decrease of phosphorus occurs, it will lead to an
increase of biodiversity in terms of quality (increase in sensitive species). However,
ecosystems’ recovery is, more complicated than the elimination of taxa, as substitution
of generalist species occurs more gradually than sensitive species in a recolonization
process.
21/42
4 - Dose Effect relation for Acidification
4-1-Introduction
Aquatic acidification is the increasing of the acidity (hydrogen ion concentration) in
water bodies. The load of substances causing acidification (SO2, NOx basically) can be
buffered and tolerated by the ecosystem. If the buffer capacity is exceeded, this load can
lead to direct and indirect impacts on ecosystems as Figure 4-1 shows. Main sources of
acidifying substances are acid rain and, in a lower degree, ammonium produced by
livestock. Acid rain results from gases emissions from the burning of fossil fuels in
different human activities (mainly industry and road traffic). In spite of a drop due to
new environmental regulations, acidification must be still considered as one of main
environmental problems in Europe.
Fig. 4-1: Acidification chain of potential impacts (Norris 2003)
The criteria established to evaluate existing acidification methods are quite similar to
those to evaluate eutrophication. Affected ecosystems, transport, spatial differentiation,
multistressor comparison and effect-dose response operate in the same way for this
damage category. As explained in the eutrophication case, dynamics in ecosystems must
be considered because of their influence in transport processes. The spatial
differentiation attained by the model is important because it affects to results’ accuracy.
The importance of reaching the endpoint level to allow a multistressor comparison
indicated for eutrophication is also valid in this case. Substances covered do not
represent a criterion for acidification because all methods consider all substances
causing acidification. On the other hand, it is important to consider emissions in all kind
of media (air and water basically) and this will be, as for eutrophication, a criterion.
Main models for the characterization of acidification assessment are summarized in
Table 4-1.
22/42
Table 4-1: Existing methods for assessing acidification in a multiple stressor assessment for Life Cycle Impact Assessment
Fate
No
Type of
ecosystem
Freshwater
Effect
Midpoint
Spatial differentiation
Site generic
DoseMultistres.
effect
compatibility relation
No
No
Midpoint
Site generic
No
No
Site generic
Yes
No
Yes
No
Yes: vasc.
plants
No
Name
CML
Year Reference
1992 (Heijungs 1992)
Covered
substances
S, N, HCl
EDIP97
1997 (Wenzel et al.
1997)
S, N, HF,
HCl
No
Freshwater
EPS2000
1999 (Steen 1999)
S, HF, HCl,
NH3
S, N
No
SMART
Site generic Endpoint: sps.
extinction
Terrestrial
Endpoint: PDF
N, S
RAINS
Terrestrial
Midpoint
Site dependant: per
ecosystem
Site dependant : source
S, N, HCl,
HF
S, N
IMPACT
Freshwater
Midpoint
Site generic : continental
No
No
TRACI
Terrestrial
Midpoint
Site specific : source
No
No
S, N, HF,
HCl
S, N
RAINS
Terrestrial
Midpoint
No
No
EMEP
Terrestrial
Midpoint
Site dependant: source
and sensibility
Site specific : source
No
No
Ecoindicator 2001 (Goedkoop and
99
Spriensma 2001)
Huijbregts
2001 (Huijbregts
2001)
IMPACT
2003 (Jolliet 2003)
2002+
TRACI
2003 (Norris 2003)
EDIP 2003
2004 (Potting 1998)
2005 (Hettelingh
2005)
23/42
For models used in LCIA methods covering both acidification and eutrophication (CML,
EDIP97, IMPACT, TRACI, EPS2000, RAINS and Ecoindicator99), the evaluation is relatively
similar and characteristics are summarized in table 4-1. Hetteling’s method (Hettelingh 2005)
calculates country dependant characterization factors for acidification in Europe using EMEP as
air transportation model. It does not consider transportation and transformations in water
compartment. Ecoindicator99 treats acidification and eutrophication in a same impact category.
Hence, the evaluation of this method is the same already seen for eutrophication. All models but
IMPACT2002+ treat acidification without making difference between terrestrial and aquatic
ecosystems. As in eutrophication, just some models develop an endpoint approach, enabling a
multistressor comparison. EPS2000 calculates the extinction of species with no spatial
differentiation. In contrast, models where the transport is calculated regionally (RAINS, TRACI,
EMEP) do not achieve the endpoint. As in eutrophication, none of the methods is site-specific,
so specificities of each kind of aquatic ecosystems are not taken into account.
As we can see in Table 4-1, 3 models over the 10 reviewed concern aquatic ecosystems and none
of them are quantifying an impact at the endpoint level. The only model that works at endpoint
modeling for acidification is Eco-Indicator 99, but it concerns terrestrial ecosystems and focuses
on dose effect relation for plants. From this observations there is a need in terms of method to
model a dose effect relation for acidifying substances for water bodies, and to identify where the
use of such effect factors can be relevant in the European area.
4- 2- Exposure assessment
As already mentioned in CML technical background (Guinée 2001), several methods have been
proposed to deal with local differences in sensitivity to acidification:
 Neglecting emissions in non sensitive areas.
 Weighting emissions according to local sensitivity
 Assessing a maximum and minimum scenario
 Modelling regional sensitivity and fate.
In this case, a weighting according to total sensitivity area in Europe is proposed. The Total
sensitive affected area in Europe is presented in Table 4-2. This percentage is used in calculation
to represent the percentage of an average European surface which would be affected by
acidification.
24/42
Table 4-2: Country areas. Total, affected and % affected. From (Potting 1998)
Country
total area
affected area
%unprotected
Albania
2881
0
Austria
8373
941
Belarus
20706
53
Belgium
3054
117
Bosnia-Herzegovina
5151
0
Bulgaria
11102
0
Croatia
5640
1
Czech Republic
7904
613
Denmark
4217
38
Estonia
4549
10
Finland
33449
1210
France
54783
82
German
35642
2528
Greece
12582
0
Hungary
9297
44
Ireland
6900
4
Italy
30174
184
Latvia
6441
0
Lithuania
6498
12
Luxembourg
260
7
Macedonia
2537
0
Moldova
2917
0
Netherlands
3610
121
Norway
31752
3535
Poland
31119
1928
Portugal
8884
0
Romania
23713
66
Russia
373489
4094
Slovakia
4836
83
Slovenia
2029
47
Spain
49525
24
Sweden
44469
1233
Switzerland
4126
105
Ukraine
57977
104
Utd. Kingdom
23103
2110
Yugoslavia
10215
0
TOTAL
943904
19294
0,00
0,11
0,00
0,04
0,00
0,00
0,00
0,08
0,01
0,00
0,04
0,00
0,07
0,00
0,00
0,00
0,01
0,00
0,00
0,03
0,00
0,00
0,03
0,11
0,06
0,00
0,00
0,01
0,02
0,02
0,00
0,03
0,03
0,00
0,09
0,00
0,02
25/42
4-3- Effect modeling
Acidification effect factors are calculated in an analogous way to eutrophication factors: pH
variable from Tachet’s database are used to calculate the PAF (fraction of affected species), the
PDF (disappeared fraction of species) and PnAF (fraction of non-affected species). These three
categories have been calculated for several background levels between a pH of 6 until a pH of 4.
Slopes of the dose-effect are calculated using equation 4-5 and 4-6 below. To calculate the
difference of concentration in terms of mol of hydrogen, a maximum and a minimum pH are
determined for each category. Effect Factors are calculated are showed in table 4-4. As
mentioned before, these factors are only valid if ecosystems’ buffer capacity is overpassed.
Table : 4-3 Number of species per media type and fraction of affected and disappeared species
based on Tachets’ database for acidification. (NSp: Total number of species; NSpa and NSpd:
Number of species affected or disappeared passing from one level to the next highest level).
Rivers
Lake
pH
NSp
NSpa
NSpd
Total sps initial NSpa NSpd
125
60
41
141 58
38
4
221
149
96
235 148
94
4.5
283
169
62
286 156
51
5
327
115
44
318 101
32
5.5
375
200
48
353 184
35
6
On the basis of the table 4-3, the number of moles of H+ is calculated for each pH level and the
change in pH level form the range n to n+1 is calculated and used in the following equations:
EFPAF,i
EFPDF,i
NSpa n  NSpd n
NSp n

H  n 1  H  n
NSpd n
NSp n

H n 1  H n
Eq 5
Eq 6
Where EFPAF,i is the Effect factor for affected species at the acidity level i in (m3.kg-1) and
EFPDF,i is the Effect factor for disappeared species; NSpai and NSpdi are respectively the number
of species affected or disappeared when passing from the acidity level n to the higher level n+1;
NSpi expresses the total number of species considered at the acidity level n; [H+]n+1-[H+]n
expresses the change in average concentration of eutrophying substances in the ecosystem when
passing from the eutrophying level n to the level n+1.
26/42
Using the formula above, Effect Factors ar calculated presented in Table 4-4 enabling a direct
use for quantifying the changes in biodiversity linked with a change in pH.
Table 4-4: Acidification effect factors for different pH background. (PAF or PDF*m3/molH+).
River
Lake
pH
EF(PAF) EF (PDF) EF(PAF) EF (PDF)
pH <4
4.85E-02 3.31E-02 4.16E-02 2.72E-02
pH [4,5-4] 7.49E+00 4.83E+00 7.00E+00 4.44E+00
pH [5-4,5] 2.10E+01 7.70E+00 1.92E+01 6.27E+00
pH [5.5-5] 3.91E+01 1.50E+01 3.53E+01 1.12E+01
pH [6-5,5] 1.87E+02 4.50E+01 1.83E+02 3.48E+01
4-4- Conclusions
Using the effect factors in Table 4-4, it is possible to assess a change in pH concentration in a
non-buffered water body in terms of fraction of affected, or disappeared species, in both cases an
indicator of change in biodiversity. In terms impact modeling, it is interesting to interpret at the
same level acidification and other impact categories affecting ecosystems.
Nevertheless, as a difference with eutrophication and toxics, this model is here developed but not
confirmed with a field validation, and such a step would improve the reliability of the
assessment. Unfortunately, it was not possible to manage such a validation for acidification since
no database have been found enabling the link between a measured pH and a Biodiversity
measure like a biotic index. This step is therefore planned as a perspective of improvement of the
model.
27/42
5- General Conclusions
The Nomiracle project intends to develop Novel Methods for Integrated Risk Assessment of
Cumulative Stressors in Europe. The task of the EPFL was to define a framework for the
development of Comparative Risk Assessment method for freshwater ecosystem. On the basis of
our experience in Life cycle impact assessment on ecosystems, we have first defined the key
issues related to the development of such a method. First we have explored the scale issue,
considering three sorts of scale, the spatial scale, the time scale and the biological organization
level. As a second step, we have explored currently used endpoint measures for existing multiple
stressor studies, and proposed the most relevant endpoint as a trade off between methodological
requirements and data availability. Afterwards, we have identified the relevant stressors to
integrate in priority in the method, which are toxics, eutrophication and acidification. On that
basis, dose-effect relations have been developed for the selected stressors in order to build an
Integrated Risk Assessment method expressing results in terms of fraction of affected species or
fraction of disappeared species.
Finally, a novel method has been developed for integrating the three prioritized stressors put in a
one-assessment endpoint. This method is based on an “effect-oriented” basis and is radically
different from current methods for ecological risk assessment. Furthermore in order to ensure the
usefulness and the reliability of the method, we have applied it in two different case study in
order to validate the dose-effect relationship that have been developed for impact assessment of
toxics and impact assessment of eutrophication.
After applying the method, we can foresee some areas of applications mainly in four fields: (i)
priority setting of stressors for reducing overall burden on ecosystem; (ii) Ecosystem
management in order to define remediation strategies; (iii) the comparative risk assessment of
substances for the selection of substances presenting the lowest risk on environment; and (iv)
Life Cycle Assessment for assessing toxic stress in endpoints methods.
In terms of potential use, it could be interesting to apply this method as a decision support tool
for different European directives, such as the Water Framework Directive, as a first screening
assessment tool for evaluating water bodies; it could also be promising for some regulation to
quantify the potential environmental gain in substituting substances by others less hazardous. It
can be also interesting to perform chemical ranking on the basis of average response of species.
The comparability between the different PAF must be pointed out in the conclusion. All the PAF
and PDF considered are based on similar calculations and concerns similar ecosystems. There is
a difference in terms of species composition but it is not expected to affect strongly the results
since a minimum level of biological variability is covered with each methods. Therefore we can
consider methods for the three stressors provide comparable results. Practically we mentioned a
discrepancy between the PAF toxic and the observation in section 2, indicating a possible
underestimation of the impact with the result. Hidden substances or interaction between species
can explain such a discrepancy but we should bear in mind this point when performing a
comparison using the method for toxics, and eventually perform sensitivity studies considering
this result.
28/42
Nevertheless, even if the methods used for comparative assessment provides a good basis for
building a multiple stressor approach, only three stressors can be currently integrated in the
assessment and further developments are needed for integrating new stressors such as endocrine
disruptors, metals, DNA transfer from GMOs, and pharmaceuticals which can be integrated in
the methods.
29/42
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