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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 - PowerPoint PPT Presentation
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Setting and Using Environmental Standards
Highlights of SETAC workshopFaringdon, October 2006
Paul Whitehouse
Chemicals Science
Environment Agency
SETAC workshopAn 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
Implementation
Socio-economic issues
Terrestrial effects assessment
Selected highlights
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
Threshold that prompts action - not usually statutory
An aspiration - not statutory
‘benchmark’‘trigger value’
‘screening value’
‘standard’‘limit value’
‘goal’‘guideline’
Usually, but not always numerical
always numerical, usually with accompanying conditions e.g. duration, confidence of failure, return period
• Failure can have serious implications (legal, financial)
• Costs and benefits important
• Implications of failure less serious
• Conservativism is appropriate
A process for developing standards
PROBLEM FORMULATION
DEVELOP SPECIFICATION
DERIVE STANDARD
IMPLEMENT STANDARD
What is the standard intended to achieve?To what extent should economic factors influence the outcome?Who will be affected?
How should the standard be expressed?Methodology - constraints?Who needs to be involved?
Are the data adequate?Account for uncertaintiesIncorporate 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?
• Consistency across regulatory regimes
• Need for transparency - report
assumptions, decisions, uncertainties
• Involve stakeholders
• Value of peer review
Effects assessment - overview
Step 4Predicted no effect
concentration determination
Step 4Predicted no effect
concentration determination
Data extrapolation
Step 1 Data gathering Step 1 Data gathering
Step 2 Data selection
Step 3
Data Quality assessment of data important e.g. Klimitsch codes
acceptable - supporting - unacceptable Relevance
demographic endpoints (survival, reproduction, development) magnitude of effect (LOEC)
Reliability test conditions stated QA regime e.g. GLP dose-response, taking account of limit of solubility measured exposures 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? Sometimes, we have substantial quantities of field data or data from
mesocosm studies
But …
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?
Threshold can protect against ‘no effects’ or
A more conservative than B
Screening values might be type A and mandatory standards more like type B
‘Warning’ and ‘Action’ limits
May be useful as a way of setting bounds within which economic or policy factors can operate
‘Horses for courses’
Soil use (decreasing sensitivity)
NATURERESERVE
INDUSTRIALSITE
RESIDENTIALWITH GARDEN
Protectionlevel
Minimumprotectionlevel
• Selection of data: magnitude of effecte.g. EC10 vs EC50
• Extrapolation: assessment factor or percentileat risk (e.g. HC5 vs HC10)
• Burden of proof before we will take action
Extrapolation methods - workshop view on reliability
Reliability Derivation methodModel ecosystem data with small assessmentfactorSSDs based on chronic NOECs (or ECx) withminimum data set or greater, plus small or noassessment factor
High
Medium assessment factor applied to lowestchronic NOEC from data set of 5 or morespeciesSSDs based on acute data, with largeassessment factor
Medium
Lowest acute LC(EC)50 data for 5 or morespecies, with large assessment factor
Low Small acute dataset with very large assessmentfactor;Small chronic dataset with large assessmentfactor
Dealing with background Principle of allowing for background accepted for naturally
occurring metals and some organics e.g. PAHs Assumes adaptation to background and hence need to manage
only the anthropogenic fraction ‘Added Risk’ currently the only feasible approach:
Threshold = Background + Maximum Permissible Addition* Can we reliably estimate a background?
should it include anthropogenic inputs (mining from 2000 years ago)?
distribution of backgrounds may have large variance - where to set the background?
What scale? Site-specific? Geotype? National?
* PNEC derived from ecotoxicity testing
Dealing with background
F
F
Background conc
express as ‘total risk’
? express as ‘added risk’
threshold = background?
The ‘Added Risk’ approach
Env conc < threshold? NFW
Determine background
NFWEnv conc < threshold + background?
Take action
Y
Y
N
N
(Bio)availability
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
design risk - how often is it acceptable to fail? e.g. “1 in 20 years”
period of time over which this statistic applies e.g. a year
How often the limit may be exceeded (e.g. 5% of the time) - express standard as mean or percentile
statistical confidence with which failure must be demonstrated before action taken (“burden of proof”)
“Burden of proof”
threshold
Concentration (or dose)
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?
Standards - social and economic aspects
Costs of monitoring (regulators, industry) 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
Openness and consultation are now important ways of working Regulators are required to address costs Social and economic aspects of standards are dealt with through
Regulatory Impact Assessments derogations because of disproportionate cost non-implementation of standards
MCDA - options appraisal 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
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