european perspective on metals´ bioavailability research and implementation of the biotic ligand...
TRANSCRIPT
European perspective on metals´ bioavailability research and
implementationof the Biotic Ligand Model (BLM) into
regulatory frameworks
Karel De Schamphelaere
Bioavailability of metals seminar – 18 October [email protected]
BLM IN THE REAL WORLD
Karel De Schamphelaere
Bioavailability of metals seminar – 18 October [email protected]
Scientific EQS approach for metals
EQS = HC5 based on Species Sensitivity Distribution (SSD)
Metals (Cu, Zn, Ni, Cd) very data rich
NOEC/EC10 available for 19-32 species
Potential pitfall:
NOEC/EC10 obtained in test media with widely varying chemistry
(= very different bioavailability)
Generic/uncorrected SSD does not represent ‘intrinsic sensitivity’
alone but rather a mix of ‘intrinsic sensitivity’ + bioavailability
effects
Need models to perform bioavailability normalization of
NOEC/EC10 to site/region specific water chemistry before SSD
and HC5 estimation
e.g., Biotic Ligand models (BLM)
MeOH+
MeCO3
Me-DOC
pH
[Me] on ‘biotic ligand’
Toxic effect
Water Organism
H+
pH
Me2
+
Ca2+
Na+
Mg2
+
‘biotic ligand’ e.g. gill, cell
surface
Speciation
(WHAM)
Intrinsic sensitivityCompetition
(log K’s)
Log KCaBL
Log KMgBL
Log KNaBL
Log KHBL
Log KHBL
Overview of available models
Standard test organisms
Cu Zn Ni Cd
Algae X X X -
Daphnia X X X X
Fish X X X X
Ceriodaphnia - - X -
Cu, Zn, Ni: BLM models or similar taking into account the effects
of DOC, pH, hardness (Ca+Mg), Na, alkalinity
Cd: Bioavailability correction based on hardness-toxicity relation
for 3 species and 7 datapoints (applied to all species)
HC5 (µg Cd/L) = 0.09 x (Hardness/50)0.7409
BLM’s are validated in field waters
Factor 10 to 30 variability of toxicity > 90% of prediction errors < factor 2
10
100
1000
10000
10 100 1000 10000
observed EC50 (µg/L)
pre
dic
ted
EC
50 (
µg
/L)
Daphnia - acute - Cu
Daphnia - chronic -Cu
Daphnia -acute -Zn
Daphnia - chronic -Zn
Daphnia - acute - Ni
Field cladocerans -acute - CuRainbow trout -chronic - Zn
What is normalization with BLM?
Principle = NOECalg with algae-BLM, NOECinvertebrate with Daphnia-
BLM, NOECfish/vertebrate with fish-BLM
=Refinement compared to hardness-Cd toxicity correction
NOECspecies A (µg/L)
Test waterX
(pHx, DOCx, Cax)
Site waterY
(pHY, DOCY, Cay)
BLM
NOECspecies A
[Me-BL]NOECspecies A (µg/L)
For site water Y
BLM
Intrinsic sensitivity
SSD and HC5 Plot normalized NOEC’s
according to increasing probability
pH 6.9 - DOC 6.3 mg/L - Hardness 106 mg CaCO3/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000 10000
NOEC (µg Zn/L)
Cu
mu
lati
ve
Pro
ba
bili
tyAlgae
Invertebrates
Fish
SSD and HC5 Plot normalized NOEC’s
according to increasing probability
Fit statistical distribution (SSD)
pH 6.9 - DOC 6.3 mg/L - Hardness 106 mg CaCO3/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000 10000
NOEC (µg Zn/L)
Cu
mu
lati
ve
Pro
ba
bili
tyAlgae
Invertebrates
Fish
SSD and HC5 Plot normalized NOEC’s
according to increasing probability
Fit statistical distribution (SSD)
Calculate HC5(50%)
pH 6.9 - DOC 6.3 mg/L - Hardness 106 mg CaCO3/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000 10000
NOEC (µg Zn/L)
Cu
mu
lati
ve
Pro
ba
bili
tyAlgae
Invertebrates
Fish
HC5 = 25 µg Zn/L
SSD and HC5 Plot normalized NOEC’s
according to increasing probability
Fit statistical distribution (SSD)
Calculate HC5(50%)
pH 6.9 - DOC 6.3 mg/L - Hardness 106 mg CaCO3/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000 10000
NOEC (µg Zn/L)
Cu
mu
lati
ve
Pro
ba
bili
tyAlgae
Invertebrates
Fish
HC5 = 25 µg Zn/L
pH 8.0 - DOC 23 mg/L - hardness 326 mg CaCO3/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000 10000
NOEC (µg Zn/L)
Cu
mu
lati
ve
Pro
ba
bili
ty
Algae
Invertebrates
Fish
HC5=
168 µg Zn/L
HC5 increases substantially with increasing pH, DOC and hardness
Bioavailability matters!
REAL WORLD ISSUES
Real world issues
Limited number of BLM’s (for standard
species)
Extrapolation to other species? (“non-BLM
species”)
Lab to field extrapolation?
Species vs. communities?
Conservatism?
Models have boundaries
What to do outside boundaries? Extrapolate
BLM’s?
How to implement in regulation?
Consequences + practicalities
ISSUE 1
Limited number of BLM’s
Extrapolation to other (non-BLM) species?
A few examples
Same effect of pH on chronic toxiity of Cu2+ for 4 species of algae (slope ~ 1.4)…
…and 3 different endpoints (growth, biomass, P-uptake)
Extrapolatable!
Natural waters?
BLM Cu algae
4
5
6
7
8
9
10
11
12
4 5 6 7 8 9
pH
Ec
x p
Cu
P. subcapitata (NOEbC)
S. quadricauda (EpC50)
C. reinhartii (ErC10)
C. reinahrdtii (ErC50)
y = 1.371x - 1.769R2 = 0.930
y = 1.301x - 0.626R2 = 0.958
5
6
7
8
9
10
11
5 6 7 8 9
pH
ErC
10p
Cu
P. subcapitata
C. vulgaris
De Schamphelaere & Janssen (2006) ES&T 40, 4514-4522
Typically: factor 10 to 30 variability in toxicity > 90% of prediction errors < factor 2
10
100
1000
10000
10 100 1000 10000
observed EC50 (µg Cu/ L)
pred
icte
d EC50 (
µg C
u/L)
P. subcapitataChlorella sp.C. reinhardtiiP. subcapitata (NOEC, fi eld)
BLM Cu algae in natural waters
0 10 20 30 40 50 60 70 80
C. reinhardtii
C. vulgaris
P. subcapitata
D. magna
D. pulex
H. azteca
O. kisuch
O. mykiss
P. fluviatilis
P. notatus
P. promelas
S. fontinalis
Factor verschil tussen hoogste en laagste NOEC
Opgelost Cu
Biobeschikbaar Cu
From Cu VRAR report (2007) Supports extrapolation of BLM’s across species
Reduction of variability in NOEC data from literature
Fish
-BLM
Dap
hn
ia
BLM
Alg
a-B
LM
A single BLM can be used to effects of pH, hardness, and DOC on acute and chronic Ni toxicity to rainbow trout and fathead minnow
Extrapolation possible!
Ni-BLM fish
Deleebeeck et al. (2007) Ecotoxicology and Environmental Safety 67: 1–13
Very similar pH slope for Zn among two algae species Can be extrapolated!
Zn BLM algae
De Schamphelaere et al. (2005) Environ Toxicol Chem 24:1190-1197
y = - 0.7542x - 1.294
R2 = 0.9621
y = - 0.8197x - 0.5775
R2 = 0.9448
- 8.0
- 7.0
- 6.0
- 5.0
- 4.0
5.5 6.0 6.5 7.0 7.5 8.0 8.5
pH
log(
EC10 a
s Zn2
+)
Chlorella sp. P. subcapitata
Wilde et al. (2006) Arch. Environ. Contam. Toxicol. 51: 174–185
Much evidence that Cu-BLM’s for all trophic levels can be accurately extrapolated (see also additional evidence in Cu-VRAR documents)
Clear evidence that Ni-BLM for fish may be extrapolated to non-BLM fish
Results of a comprehensive “spot-check” study indicate that BLM’s for other trophic levels may also be extrapolated (this issue is still under discussion at TC-NES)
Clear evidence that Zn-BLM for algae may be extrapolated to other algae
Although there is no toxicity-based evidence for invertebrate and fish Zn-BLM’s, extrapolation may possibly be justified on the basis of:
Very similar mode of action (disruption of Ca-balance) Ca is most important protective cation BLM-constants (log K’s) of fish and Daphnia are very similar
Clear need for toxicity-based research to test applicability of extrapolation
Extrapolation: conclusions & outlook
ISSUE 2
LAB TO FIELD EXTRAPOLATION
Three high quality mesocosm studies
Estimate HC5 based on NOEC values for the species within the mesocosm experiment = observed HC5
Estimate HC5 based on SSD with single-species literature toxicity data normalized to mesocosm chemistry (pH, DOC, Ca, …) = predicted HC5
Compare observed vs. predicted HC5
Example 1: Cu mesocosm data
From Cu VRAR (2007) – arrow reflects uncertainty due to non-equilibrium
HC5(observed) from 3.4 to 19.6 µg/L Good agreement between observed and predicted HC5 SSD+BLM methodology for Cu seems appropriate for accurate
protection in the field
Example 1: Cu mesocosm dataPrediction of mesocosm sensitivity
1
10
100
1 10 100
Observed HC5-50 mesocosm
Pre
dic
ted
HC
5-5
0
me
so
co
sm
Roussel, 2006
Schaefers, 2001- all
Hedke, 1984
Roussel, 2006 correct at inflow"
Conducted for the UK Environment Agency
Research consortium of Centre for Ecology and Hydrology (UK), UGent (B), Univ. Antwerp (B), Univ. Wageningen (NL)
Monitoring of full chemistry, invertebrate and diatom community composition, metal bioaccumulation in invertebrates, Toxicity Identification Evaluation for reference and metal contaminated streams (n=35)
Aims: To investigate if water chemistry and bioavailability should be
taken into account when looking at ecological, community-level effects in the field
To investigate if current and proposed EQS methodologies are adequate for protecting field communities
Example 2: UK EQS project
Chemical analyses(dissolved metals, DOC,
pH, major ions, alkalinity, etc.)
UK EQS project - concept
Physical site characterization
(width, depth, stream velocity, etc.)
RIVPACS MODEL
Expected No. of TAXA present in stream
Ecological analyses(invertebrates,
diatoms)
Observed No. of TAXA present in stream
Predicted HC5 and % affected species
BLM+SSD
Observed/Expected No. of TAXA
Agreement?
Conservatism?
Chemistry clearly influenced how metals affect community composition
Both speciation and competition effects seemed important
The importance of metal mixtures in the field could not be dismissed
Regression analysis suggested that ecological effects in non-acidic sites (pH>6) could best be explained in terms of contamination by Zn and/or Al and/or Cu and/or a mixture of these elements, although Cd could not be excluded either due to its correlation with Zn
Under these circumstances: predictive capacity of Zn-BLM + SSD approach for effects observed in the field?
UK EQS project – Main Results
Ecological effects are significantly correlated to exceedence of HC5(Zn)
7 sites correctly classified as non-impacted, 12 sites correctly classified as impacted, 6 false-negatives, 4 false-positives
Mean (Zn/HC5) vs. field effectsPreliminary calculations – do not quote
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0.01 0.10 1.00 10.00 100.00 1000.00
geomean(Zn/HC5)
Ob
serv
ed /
Exp
ecte
d N
o.
of
Tax
a
r2=0.18p=0.02
≥ 0.79RIVPACS
Class A quality
Interpretation
In general: ecological effects in the field can be related to exceedence of thresholds (HC5) based on laboratory-based ecotoxicity data, normalized for bioavailability
False-positives can be due to: Over-conservative HC5 Tolerance acquisition of local communities
False-negatives can be due to: Under-conservative HC5 Temporary exceedence of HC5 (in two out of six cases) Toxicity contribution from other metals, including Al (mixture
effects)
In order to understand better the ecological effects of metal contamination in the field: mixture toxicity needs to be understood
Preliminary approach for metal mixtures toxicity in the field
Assume that organisms consist of a set of binding sites relevant for accumulation and toxicity with which all metals and competing cations react (cf. BLM) in a similar way as humic acid reacts with all metals and cations
Then: the total amount of all metal calculated with WHAM VI to be bound to HA (mol/g) could potentially be related to accumulation and effects
The Toxicity Binding Model (TBM)
Two examples: Metal accumulation in bryophytes Toxicity to P. subcapitata in the field samples
Metal accumulation in bryophytes
Metals in bryophytes agrees fairly well with WHAM VI calculated metal binding to HA proof of principle that mixture-BLM is possible
Metal toxicity to algae in field samples
Ftox = [metal bound to HA]/[metal’s specific toxicity] TBM approach is also promising for predicting metal mixture toxicity
Main conclusions UK EQS project
Chemistry (both speciation and competition) seemed to be important for ecological effects of metals in the field
As shown for Zn, ecological effects in the field can be related to exceedence of thresholds (HC5) based on laboratory-based ecotoxicity data, if normalized for bioavailability
Metal mixtures in the field are a reality
The TBM shows that BLM-like approaches might be valuable for taking mixture effects into account
Final report expected soon (end 2007) Further information: UK Environment Agency (Paul Whitehouse)
ISSUE 3
MODELS HAVE BOUNDARIES
Boundaries within which bioavailability models for three trophic levels have been developed and/or
validatedCu Zn Ni Cd
pH range 6 – 8.5 6 - 8 5.9 – 8.2
Hardness range (mg/L)
10 - 360
- 25 – 320* > 40
Ca range (mg/L) - 5 - 160
-* Before Ni research with soft waters (lower hardness boundary was reduced to 6 mg CaCO3/L based on Ni-SOFT research (see further)
Cu toxicity to cladocerans in acidic waters
Ni toxicity to cladocerans in soft waters
Cd toxicity to Daphnia longispina in soft waters
Three examples
Collected field waters and their inhabiting field cladocerans (water fleas) populations
Toxicity test results in standard medium with these species were used to calibrate Cu-BLM Daphnia to sensitivity of field-species
Predicted toxicity in natural waters with varying composition was compared with observed toxicity in natural waters
Cu toxicity to field cladocerans
For normal sites (pH > 5.5): 27/28 LC50’s accurately predicted
=further evidence in support of extrapolation
Cu toxicity to field cladocera
1
10
100
1000
10000
1 10 100 1000 10000
Observed 48-h EC50 (µg Cu L-1)
Pre
dict
ed 4
8-h
EC
50 (µ
g C
u L
-1)
Bossuyt et al. (2004) Environ Sci Technol 38: 5030-5037
Cu toxicity to field cladocera
1
10
100
1000
10000
1 10 100 1000 10000
Observed 48-h EC50 (µg Cu L-1)
Pre
dict
ed 4
8-h
EC
50 (µ
g C
u L
-1)
For acidic sites (pH < 5.5): general overestimation of toxicity
Further research requiredBossuyt et al. (2004) Environ Sci Technol 38: 5030-5037
For normal sites (pH > 5.5): 27/28 LC50’s accurately predicted
=further evidence in support of extrapolation
Ni SOFT project
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zone A
hardness ( mg/L Ca CO3)# <2.5# 2.5 - 10# 10 - 25# 25 - 100# >100
900 0 900 1800 Kilometers
N
EW
S
Selected region for sampling: Both soft and hard close to each other Low anthropogenic input (N,P) Same climate Calcareous deposits for ‘hard’ region
Collect cladocerans and algae from soft (H~6) and hard water (H~42) and test for chronic Ni toxicity in soft, moderately hard and hard water
Deleebeeck et al. (2007) Aquat. Toxicol 84:223-235
Ni SOFT project – hypotheses
Cladocerans originating from soft water would be inherently more sensitive to Ni than those originating from hard water
Cladocerans from soft water would be more protected against Ni toxicity by hardness than those from hard water
Ni SOFT project – design
Chronic toxicity testing (reproduction, 10d to 21d)
Species from soft water tested in Soft (S, hardness 6 mg/L) and Moderately Hard (MH, hardness 16 mg/L) water
Species from hard water tested in Moderately Hard (MH) and Hard water (H, hardness 42 mg/L)
Allows comparison of species sensitivity (comparison of EC50 in MH water)
hardness effect (comparison of KCaBL and KMgBL
estimated for soft and hard water species)
Chronic Ni toxicity to cladocerans
Chronic EC50 (µg Ni/L)
Species from soft moderate
hard
Peracantha truncata soft 15.3 47.2
Ceriodaphnia quadrangula
soft 4.4 23.4
Simocephalus serrulatus soft 7.67 54.2
Ceriodaphnia quadrangula
hard 11.3 36.2
Ceriodaphnia pulchella hard 16.2 31.2
Simocephalus vetulus hard 11.2 28.9
Daphnia longispina hard 58.6 125 No significant difference in intrinsic sensitivity No significant difference in protective hardness effect Ni-BLM can be extrapolated down to hardness 6 mg/L
Cd SOFT project
Hardness correction equation proposed in Cd RAR was only derived for hardness > 40 mg CaCO3/L
Can equation be extrapolated to hardness as low as 5 mg CaCO3/L?
Chronic toxicity testing (reproduction, 21d) with D. longispina
In two Swedish soft waters with manipulated hardness
RAR hardness slope (0.7409 – dashed line) cannot be extrapolated to hardness < 40 mg CaCO3/L
Hardness effect at hardness <50 is much lower (slope=0.1562=n.s.)
Cd SOFT project
0 10 20 30 40 50 60
Hardness, mg CaCO 3 /l
0
2
4
6
8
10
12
14
21-d
ay E
C50
, ug
dis
solv
ed C
d/l
Conclusion extrapolation outside model boundaries
Based on the given examples, any type of outcome may be expected from extrapolation outside model boundaries (accurate, overconservative, underconservative)
Thus, extrapolation outside model boundaries will usually not be recommended without additional investigation for the specific local or regional abiotic conditions
IMPLEMENTATION
Demonstration project in NL
NL issue Cu and Zn were considered nation-wide problematic substances
Yellow, orange and red dots are sites where [Me] > EQS
Baseline EQS not corrected for bioavailability (1.5 µg Cu/L, 9.4 µg Zn/L)
Additional metal removal step from WWTP was planned nationally
Large investments required while local water agencies wanted to invest
mainly in ‘more important’ problems (eutrofication, habitat restoration)
Thus: how large are true ecological risks if bioavailability is considered?
Cu
Zn
Monitoring campaignJune 2006 – January 2007
Total and dissolved metal (Cu, Zn, Ni)
TOC, DOC, pH, Ca, Mg, Na, K, Cl, SO4, alkalinity
River basins # Sites # Samples / site
Total # Samples
Regge & Dinkel 8 6 48
Dommel 6 5-6 33
HHSK 6 7 42
Velt &Vecht 6 2-5 27
Hunze & Aa’s 5 3-4 18
Vallei en Eem 8 6 48
Total 31 2-7 216
Chemistry summary(percentiles)
10% 50% 90%
pH 6.90 7.48 8.05
Hardness (mg CaCO3/L) 106 189 326
Ca (mg/L) 33 63 100
DOC (mg/L) 6.3 12 23
Metal measurements
min 10% 50% 90% max
Dissolved Cu (µg/L) < 0,7
1 2 4,2 8,3
Dissolved Ni (µg/L) < 1 2 3,8 9,8 33
Dissolved Zn (µg/L) < 4 6 9 30 170
Despite careful discussions with and protocol transfer to people
from local water agencies some difficulties noted
Some laboratories acidified samples before filtration
In many samples dissolved metal > total metal (varies among
agencies)
Cu (7-34%), Ni (2-50%), Zn (5-21%)
HC5 vs. baseline EQS
min 5% 10% 50% max EQS*
HC5-Cu (µg/L)
1.3 11 19 37 133 1.5
HC5-Ni (µg/L) 9 16 21 34 100 5.1
HC5-Zn (µg/L) 11 25 30 63 206 9.4
HC5’s varied 10-fold (Cu, Ni) to 20-fold (Zn)
HC5 is in most samples much higher than the baseline EQS
* This is the baseline EQS, not corrected for bioavailability
HC5 vs. DOC
Possibilities to develop simple equations (avoiding the use of complex BLM calculations + SSD fittings)
EQS of metals without DOC measurement are worthless
pH second most important
Best fit
y = 4.2056x + 15.563
R2 = 0.7527
0
50
100
150
200
250
0 10 20 30 40 50
DOC (mg/L)
HC
5 (µ
g Z
n/L
)Best fit
y = 1.787x + 12.632
R2 = 0.8689
0
20
40
60
80
100
120
0 10 20 30 40 50
DOC (mg/L)
HC
5 (µ
g N
i/L
)
Best fit
y = 2.4503x + 8.411
R2 = 0.6336
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50
DOC (mg/L)
HC
5 (µ
g C
u/L
)
Compliance with HC5 vs. baseline EQS
without bioav. correction
with bioav. correction
n>EQS %>EQS n>HC5 %>HC5
Cu 116 58 % 2 1.0 %
Ni 51 26 % 0 0.0 %
Zn 63 32 % 9 4.5 %
Non-compliance with baseline EQS for 26-58% of samples
Non-compliance with bioavailability-corrected HC5 for 0-4.5% of
samples
Conclusion - implementation
Analysis of dissolved metal concentrations is not as easy to
implement as many people tend to believe
A very different view about the “nation-wide metal problem” was
obtained in NL when bioavailability is considered; the
recommendation was to extend the analysis to all WFD
monitoring stations of NL
Bioavailability corrections might provide a more accurate picture
of true ecological risks, thus avoiding “useless” investment of
money that could be used for more important issues (e.g.,
eutrofication)
Complex BLM + SSD calculation may be simplified without very
much loss of accuracy…
General conclusions Validated bioavailability models are now available for Cu, Zn, Ni, (and
Cd)
Extrapolation of models across species:
OK for Cu
Available information for Ni under discussion at TCNES
Some supportive information available for Zn, but more research
recommended
Extrapolation from lab to field:
OK for Cu (mesocosms)
Supportive information for Zn (UK project), mesocosm studies under
investigation
No data available for Ni
Mixtures are a reality (mixture BLM seems possible – more research
required)
Extrapolation outside model boundaries:
Variable outcomes; hence extrapolation not recommended without extra
research
Implementation in legal frameworks:
Provides more accurate picture of true ecological risk – avoids wrong
investments
Training will be required (analytical issues, BLM+SSD calculations)