Setting and Using Environmental
Standards
Highlights of SETAC workshop
Faringdon, October 2006
Paul Whitehouse
Chemicals Science
Environment Agency
SETAC workshop
An opportunity to ‘take stock’
• of technical developments
• wider aspects e.g. role of stakeholders
in regulatory decision-making
Scope
• chemicals 
• environmental receptors 
• human health ()
• microbes, radionuclides 
SETAC workshop - Working Groups
Aquatic
effects
assessment
Terrestrial
effects
assessment
Socio-economic
issues
Implementation
Selected highlights
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Types of standard
A process for delivering standards
Effects assessment - data and extrapolation
Implementing standards
Incorporating an economic dimension
Types of standard
Statutory threshold must not be
exceeded
always numerical, usually with
accompanying conditions e.g.
duration, confidence of failure,
return period
•
‘standard’
‘limit value’
Failure can have serious
implications (legal, financial)
• Costs and benefits important
Threshold that prompts
action - not usually
statutory
An aspiration - not
statutory
‘benchmark’
‘trigger value’
• Implicationsvalue’
of failure less serious
‘screening
Usually, but not
always numerical
• Conservativism is appropriate
‘goal’
‘guideline’
A process for developing standards
What is the standard intended to achieve?
To what extent should economic factors
influence the outcome?
Who will be affected?
PROBLEM FORMULATION
Consistency across
DEVELOP• SPECIFICATION
How should the standard be expressed?
Methodology
- constraints?
regulatory
regimes
Who needs to be involved?
• Need for transparency - report
assumptions, decisions,Are
uncertainties
the data adequate?
DERIVE• STANDARD
Involve stakeholders
• Value of peer review
IMPLEMENT STANDARD
Account for uncertainties
Incorporate socio-economic factors
Where is to be applied?
How confident do we need to be before
we take action?
What will we do in the event of failure?
Effects assessment - overview
Step 1
Data gathering
Step 2
Data selection
Step 3
Data extrapolation
Step 4
Predicted no effect
concentration
determination
Data
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Quality assessment of data important e.g. Klimitsch codes
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acceptable - supporting - unacceptable
Relevance
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demographic endpoints (survival, reproduction, development)
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magnitude of effect (LOEC)
Reliability
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test conditions stated
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QA regime e.g. GLP
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dose-response, taking account of limit of solubility
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measured exposures
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NOECS are bounded (I.e. there is an effect conc)
Data not generated to standard guidelines are acceptable
How to use field and
mesocosm data in deriving
thresholds?
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Sometimes, we have substantial quantities of field data or
data from mesocosm studies
But …
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can’t always eliminate other stressors
goals of study may not always be consistent with those of
standard (e.g. ‘soil fertility’ vs ‘protection of ecoreceptors’)
Use as critical data to derive a standard or to corroborate
one based on lab data (i.e. adjust AF)?
Support for use as ‘driving’ data as long as study goals are
consistent with those identified at Problem Formulation and
other quality criteria are met
What level of protection?
A more conservative
than B
Screening values might
be type A and
Threshold can
protect
mandatory
standards
against
effects’
more
like‘no
type
B or
‘Warning’ and ‘Action’
limits
May be useful as a way
of setting bounds
within which economic
or policy factors can
operate
‘Horses for courses’
Protection
level
• Selection of data: magnitude of effect
e.g. EC10 vs EC50
• Extrapolation: assessment factor or percentile
at risk (e.g. HC5 vs HC10)
• Burden of proof before we will take action
Minimum
protection
level
NATURE
RESERVE
RESIDENTIAL
WITH GARDEN
Soil use (decreasing sensitivity)
INDUSTRIAL
SITE
Extrapolation methods
- workshop view on reliability
Reliability
High
Medium
Low
Derivation method
Model ecosystem data with small assessment
factor
SSDs based on chronic NOECs (or ECx) with
minimum data set or greater, plus small or no
assessment factor
Medium assessment factor applied to lowest
chronic NOEC from data set of 5 or more
species
SSDs based on acute data, with large
assessment factor
Lowest acute LC(EC)50 data for 5 or more
species, with large assessment factor
Small acute dataset with very large assessment
factor;
Small chronic dataset with large assessment
factor
Dealing with background
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Principle of allowing for background accepted for naturally
occurring metals and some organics e.g. PAHs
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Assumes adaptation to background and hence need to manage
only the anthropogenic fraction
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‘Added Risk’ currently the only feasible approach:
Threshold = Background + Maximum Permissible Addition*
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Can we reliably estimate a background?
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should it include anthropogenic inputs (mining from 2000
years ago)?
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distribution of backgrounds may have large variance - where
to set the background?
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What scale? Site-specific? Geotype? National?
* PNEC derived from ecotoxicity testing
Dealing with background
threshold =
background?
express as
‘total risk’
F
?
express as
‘added risk’
F
Background conc
The ‘Added Risk’ approach
Env conc < threshold?
Y
NFW
N
Determine
background
Env conc < threshold
+ background?
N
Take action
Y
NFW
(Bio)availability
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Metals can exist in different chemical forms - largely influenced
by prevailing environmental conditions
Only a small proportion of total metal may be in a form that can
be taken up or exert biological effects
Availability can now be predicted for a number of metals (e.g.
‘WHAM’, BLMs)
Accounting for speciation and availability can remove much of
the scatter in conc-effect relationships
[Total]
[Dissolved]
[Speciation-based]
implementation costs increasing
risk of false +/- increasing
Implementing a standard
numeric value - only one part of a
standard, especially if measurement
required to determine pass/fail
statistical confidence
with which failure must
be demonstrated before
action taken (“burden
of proof”)
design risk - how often is it acceptable
to fail? e.g. “1 in 20 years”
How often the limit may be
exceeded (e.g. 5% of the
time) - express standard as
mean or percentile
period of time over which this
statistic applies e.g. a year
“Burden of proof”
threshold
F
Do we give benefit of doubt to the
environment … or polluter … or
face value?
If we give benefit of doubt to the
‘polluter’ then we require a higher
level of confidence before taking
action - effectively raising the
standard (or increase sampling
frequency)
Depends on seriousness of failure?
Concentration (or dose)
Standards
- social and economic aspects
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Openness and consultation are now important ways of working
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Regulators are required to address costs
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Social and economic aspects of standards are dealt with through
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Regulatory Impact Assessments
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derogations because of disproportionate cost
non-implementation of standards
Costs
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of monitoring (regulators, industry)
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of compliance e.g. limiting emissions so that
the standard can be met
Socio-economic analysis when significant risk of
failure, investment implications, risks to certain sectors
MCDA - options appraisal
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Multi Criteria Decision Analysis is a technique for ‘balancing’
conflicting risks
Formal approach that involves identifying criteria against which
we will make a decision, measuring preferences and finding an
option that provides the best overall balance for a standard
Recently used to assess options for sheep dip chemicals, taking
account of concerns about environmental protection, animal
welfare, farmer livelihoods etc
Can include a ‘do nothing’ option
Robust scientific analysis is a key element - but other elements
will also influence the standard
Participative and transparent - opportunity to involve stakeholders
Key points
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Stepwise process with clear roles for policy, science and
stakeholders
Technical groups largely ‘consolidated’ conventional practice
Methods emerging for dealing with backgrounds and
bioavailability - ‘research to regulation’
Flexibility recognised in standards for different purposes - in
the way standards are set and the way they are used
5 requirements of an ‘ideal’ standard
Recognise socio-economic realities - robust scientific analysis
could be just one of a number of inputs to standard setting