Download ERMS Activity 5.8 Validation

ERMS Activity 5.8 Validation
(input from PROOF)
• Two main issues:
– How can we control monitor
estimated environment risks?
– Check the validity
of the currently applied risk values
• The work…
• Next steps…
The work…
•
Theoretical work
– to establish the conceptual basis for integration of risk assessment and
biomarker based monitoring
•
Laboratory studies
– conducted field relevant exposures to examine relationships between
biomarkers, fitness effects, and predicted ecological risks
•
Field studies
– Participated in field relevant exposures to validate biomarkers to oil
industry discharges in relation laboratory exposures
•
Applied Statistical extrapolation methods (SSDs)
– to link biomarker response levels to ecological risk levels
– to validate fitness response levels to existing risk curves
•
Integrate biomarker distributions in ERMS models
– application of the findings to extend the risk models
Key terms
• Fitness
– Refers to the ability of the organism to successfully grow
and reproduce and maintain the population of which it
forms part
• Biomarkers
Ecosystem
– Refers to any biological response in a living organisms that
results from the exposure to a pollutant chemicals
Level of
Biological
organization
Individuals
NB !
Cells
Molecules
–
–
Fitness data are currently used for
Environmental Risk Assessment
Population
Population
Fitness
Population
Population
Uptake of
chemicals
Biomarkers
Fitness is difficult to measure in the field
–
Fitness and Biomarkers often differ
in time and level of biological organization
–
Biomarkers can be Early warning of Ecosystem health
Biochemical
responses
Early
effects
time
Late
effects
A key conceptual element:
How is Biomarkers linked with Ecosystem health
• What characterizes a healthy ecosystem?
– The constituent animals, plants and microbes must,
on the whole, be healthy
• How do we characterize ecosystem health?
– Biomarkers measure exposure to pollutants and give an assessment of
the health status of individual animals
– By measuring the health status of a range of species representing
different phylogenies and feeding types, we can use a weight of
evidence approach to envisage the ecological concequences of
pollutant exposures
» Depledge & Galloway, Front Ecol Environ 2005; 3(5): 251-258.
Link between environmental risk and monitoring
…enables predictions of ecosystem health
which can be control monitored
Animal Fitness
Environmental Risk
Risk probability or
Environmental
impact factors
Laboratory
testing
Prediction
(prognosis)
Field
measurement
(diagnosis)
Biomarker
signals
Survival and
reproductive
capacity
Under
Construction
Approach:
Compare (statistically)
biomarker-response-distributions and fitness-SSDs
Species Sensitivity Distributions for fitness
and biomarker responses
1
11
1
Alkaline
unwinding
DNA strand
breakreponse
Combinedstability
risk
curve
Lysosomal
response
Lysosomal
stability
Lysosomal
stability
response
Alkaline
unwinding
reponse
Combined
risk
curve
Combined
curve
Combined
riskrisk
curve
0.9
0.9
Fitness
0.8
Potetially affected fraction of species
0.8
0.8
0.8
(=risk curve)
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
Above threshold level
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
5%
0
0 1
0
1
1
Limit line
Andel
potensielt
påvirkete
arter
Potetially
affected
fraction
species
Potetially
Potetially
affected
affected
fraction
fraction
of
ofof
species
species
0.9
0.9
Under threshold level
0
1
10
10
10
Oil
10
Threshold
level
100
100 100
1000
1000 1000
100
1000
Concentration
THC
Concentration
Concentration
THC
THC
Concentration
THC
concentration
(mg total
hydrokarbons pr. liter)
10000
10000 10000
10000
Construct Biomarker Sensitivity Distributions (BSDs) !
•
Statistical evaluation of biomarker response levels versus risk levels
• Comparison of
• Species Sensitivity Distributions applied in risk assessment
(based on literature data)
• and Biomarker response distributions
(based on our experimental data)
1.
2.
3.
4.
Development of SSDs for different oil component groups
Construction of risk curve for the different exposures
Construction of one average risk curve for all exposures
Construction of BSDs for different types of biomarkers:
DNA damage, oxidative stress, lysosomal stability and
PAH metabolites
5. Comparison of biomarker response levels and risk levels
From SSD based on EC50 to risk curve
1
0,9
0,8
Chronic SSD
0,7
SSD based on EC50 values
PAF
0,6
Devide by 100
0,5
0,4
0,3
0,2
corresponding PAF
0,1
exposure concentration
0
1
10
100
1000
Concentration
10000
100000
3. Construction of one average risk curve for all exposures
Risk curve for Goliath and Statfjord exposures
1
Potentially affected fraction of species
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
10
100
Concentration
THC
Concentration
THC
1000
10000
4. Construction of BSDs
• Results from different experiments are combined in BSDs
• The sensitivity of the species tested (Cod, Shrimp, Sheepshead minnow, Sea
urchin, Mussel & Scallop) represents the sensitivity of all species
• Four oil types and one produced water exposure
• Lowest Observable Effect Concentrations (LOECs) in biomarkers are applied
• BSDs indicates the variation in lowest exposure levels where the specific
biomarkers come to expression for different species
• Biomarkers which respond at low levels but do not respond at higher
concentrations are not suited for this. At levels higher than the LOEC the
biomarkers must still respond.
BSD: DNA damage related biomarkers
1 1
Shrimp- -Goliat
Goliat
Shrimp
0.90.9
Potentially affected fraction of species
Potentially affected fraction of species
Sheepshead -- North
Sheepshead
North Sea
Sea oil
oil
0.80.8
Cod
Cod -- Goliat
Goliat
0.70.7
Cod - Statfjord B
Cod - Statfjord B
0.6
0.6
Cod - North Sea oil
0.5
0.5
Cod - North Sea oil
Scallop - Goliat
0.4
0.4
Scallop - Goliat
0.3
0.3
Shrimp - Statfjord
0.2
0.2
Sea urchin - Statfjord
Sea urchin - Statfjord
0.1
0.1
DNA
damage
DNA
damage
Combined risk curve
Shrimp - Statfjord
Mussel - Statfjord
Mussel - Statfjord
5% level
0
1
0
1
10
100
10
100 THC
Concentration
Concentration THC
Concentration
THC
1000
1000
10000
10000
BSD: Oxidative stress related biomarkers
1
1
Cod - Statfjord
0.9
Cod - Statfjord
Potentially affected fraction of species
Potentially affected fraction of species
0.9
0.8
Shrimp - Goliat
0.8
Shrimp - Goliat
0.7
0.7
Mussel Statfjord
Mussel Statfjord
0.6
0.6
Cod - Goliat
Cod - Goliat
0.5
0.5
0.4
0.4
Sea
Sea urchin
urchin -- Statfjord
Statfjord
Oxidative stress
Oxidative risk
stress
Combined
curve
0.3
0.3
Shrimp
Shrimp -- Statfjord
Statfjord
0.2
0.2
0.1
0.1
Scallop--Goliat
Goliat
Scallop
5% level
00
1
10
Concentration
100 THC
Concentration THC
Concentration THC
1000
10000
BSD: General toxicity related biomarkers
(Lysosomal stability)
1
1
Sea urchin - Statfjord B
0.9
0.9
Potentially affected fraction of species
Potentially affected fraction of species
Sea urchin - Statfjord B
0.8
0.8
0.7
0.7
Shrimp - Statfjord B
Shrimp - Statfjord B
0.60.6
0.50.5
0.40.4
Scallop
- Goliat
Scallop
- Goliat
0.30.3
0.2
0.2
Lysosomalstability
stability
response
Lysosomal
response
Combined risk curve
Shrimp - Goliat
Shrimp - Goliat
0.1
0.1
5% level
0
0
1
1
10
10
100
100
Concentration THC
Concentration THC
Concentration THC
1000
1000
10000
10000
BSD: Exposure related biomarkers (PAH metabolites)
1
1
0.9
Cod - Goliat
Potentiallyaffected
affectedfraction
fraction
species
Potentially
ofof
species
Cod - Goliat
0.9
0.8
0.8
Sheepshead - North Sea A
0.7
0.7
Sheepshead - North Sea A
0.6
0.6
Cod - Statfjord B
Cod - Statfjord B
0.5
0.5
0.4
0.4
0.3
Sheepshead- -North
NorthSea
SeaBB
Sheepshead
PAH metabolites
Combined risk curve
PAH metabolites
0.2
0.2
Cod - North Sea A
Cod - North Sea A
0.1
0.1
0
00.1
0.1
5% level
1
10
1
100
10
Sum PAH concentration (water)
Sum PAH concentration (water)
100
5. “A Biomarker Bridge”:
Comparison of biomarker response levels and risk levels
1
0.9
0.8
Ecological risk (PAF)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Fraction of biota with observed DNA damage
(shrimp/mussel/sea urchin/cod/carp)
0.8
0.9
1
Conclusions – part 1
• How can we control monitor
estimated environment risks?
– A bridge that links environmental risk and biomarkers has been
constructed
– It allows to express environmental risk with biomarker values
– It makes it possible to control measure accepted risik with
biomarker measurements in the field
– There is a need for biomarker- and fitness- measurements for
produced water for more animal species to build a sufficiently
robust BSD (approx. 15 species) which can be used to
characterize ecosystem health
– Biomarker data from Svan field study and preliminary results
from PROOF Drilling Discharges project indicate that the
approach will be applicable also for drilling discharges
(effects in the water column)
Validation by comparing to
laboratory experiments
•
•
•
•
Produced water – low dose
Log term (chronic) exposures
Vulnerable life stages
Fitness LOECs
From SSD based on EC50 to risk curve
1
0,9
0,8
Chronic SSD
0,7
SSD based on EC50 values
PAF
0,6
Devide by 100
0,5
0,4
0,3
0,2
corresponding PAF
0,1
exposure concentration
0
1
10
100
1000
Concentration
10000
100000
Risk curve, BSDs and LOEC curve for Goliath and Statfjord exposures
1
Sheepshead - North sea A (6 wks)
Potentially affected fraction of species
0.9
0.8
Sheepshead - North sea B (5 wks)
0.7
0.6
Shrimp - Statfjord B (3 months)
0.5
0.4
Shrimp - Goliat (3 months)
0.3
Oxidative stress
DNA damage
Lysosomal stability response
LOECs
0.2
Mussel - Statfjord B (3 months)
0.1
0
1
10
100
Concentration THC
1000
10000
Fitness LOECs plotted in the normal distribution of the SSD
Normal distribustion of species sensitivity
• Normal distribution
0.07
species sensitivity
Experimental LOECs
0.06
probability
0.05
0.04
0.03
0.02
0.01
0
1
10
100
concentration THC (mg/l)
1000
10000
Conclusions - part 2
• Checking the validity of existing ERA values
– A validation of the presently used SSD for Risk
assessment has been done based on relevant laboratory
experiments
– The LOEC values obtained from these fitness studies
were lower than the present SSD
Next steps
• Biomarkers integrated in ERA
– BSDs should be fully developed for PW discharges
– BSDs should be considered developed for drilling discharges
– The concept should be taken into account in next revision of
monitoring programmes related to oil and gas discharges
• The Validation results with chronic exposures
and vulnerable life stags should be
– taken into account in the evaluation of application factors for
ERA related to oil and gas discharges
• Thank you!