Risk assessment of environmental multichemical … Assessment of... · Risk assessment of...

Spanish Council for Scientific Research

Institute of Environmental Assessment and Water

Risk assessment of environmental multichemical exposure: Tentative relationships with ecotoxicity and ecosystem variables

Antoni Ginebreda1, Aleksandra Jelić1, Mira Petrović2, Miren López de Alda1, Damià Barceló1,3, Marianne Köck1, Marta Ricart3,4, Helena Guasch4, Rikke Brix1, Anita Geiszinger4, Julio C.López-Doval5, Isabel Muñoz5, Cristina Postigo1, Anna M. Romaní4, Marta Villagrasa3, Sergi Sabater3,4, Maria H. Conceição6

1 Institute of Environmental Assessment and Water Research, Barcelona, Spain2 Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.3 Catalan Institute for Water Research, Girona, Spain4 University of Girona, Girona, Spain5 University of Barcelona, Barcelona, Spain6 Universidade de Brasília, Brasilia, Brazil

ADVANCED COURSE ON ANALYSIS, FATE AND RISKS OF ORGANIC CONTAMINANTS IN RIVER

BASINS UNDER WATER SCARCITY .

7-8 february 2011, Valencia, Spain



Outline

1. Introduction2. Models for multichemical exposure ecotoxicology 3. Method development (synergistic effects and compound

prioritisation)4. Preliminary field results (Case Studies)5. Conclusions



1. Introduction



RISK ASSESSMENT

Definition:Procedures aiming to identify hazards and to quantify the associated risk (in our case, related to contaminants) concerning:

• Human health

• Ecosystems

Introduction



RISK ASSESSMENT

Introduction



RISK ASSESSMENT

HAZARD IDENTIFICATION

EXPOSURE ASSESSMENT

EFFECT ASSESSMENT

RISK CHARACTERISATION

Risk = Expossure × Adverse Effects Risk = Expossure × Adverse Effects

Introduction



Introduction

• Use of chemicals by our technological society can be estimated in ~100,000 compounds, most of them organics [Schwarzenbach et al., 1994] and this number is continuously growing.

• Depending on their properties and extent of use these chemicals can potentially reach the environment, being their environmental and health effects unpredictable in long term.

• A simultaneous and huge progress on the analytical capabilitieshas taken place, mostly associated to the development of multiresidue analytical methods based on chromatographic techniques (GC-MS and LC-MS), capable to identify and quantify many of these compounds at trace levels of ng/l or pg/l

Given these facts....



...Three questions arise:

1) Is there any relationship between chemical pollution exposure and ecosystem impairment ?

2) Exposure to multiple chemicals may result on any synergic effect (“cocktail effect”) ?

3) What to analyze ? (prioritization of target compounds)“not all measurable compounds are worth to be measured”(this point is particularly relevant when routine monitoring control has to be

implemented)

Introduction



The basic environmental risk assessment approach:

Multivariate AnalysisANOVA.....

CHEMICAL EXPOSURE

ci : concentrations

ECOLOGICAL

STATUSEcosystem variables:biofilm, macroinvertabrates ....

ECOTOXICOLOGY

Ecotoxicity variables:EC50i, PNECi , NOECi ....

)(.. HQfVE =

Introduction

ecotoxicologicalmulticomponent models

Ex.: HQ



2. Models for multichemical exposure ecotoxicology



Environmental exposure to multiple chemicals:

Ecotoxicological assessment depending on Toxic Mode of Action

• Independent action or response addition model (IA):• Concentration addition model (CA):

Models for multichemical exposure ecotoxicology



Concentration Addition model (CA):

100050 j

j

ECPNEC =

j

ijij PNEC

chq =

∑=j

iji hqHQHQ: hazard quotient of site i (also called TU’s ‘TOXIC UNITS’)

hqij : hazard quotient of compound j at site i

cij : concentration of compound j at site i

PNECj : Predicted No Effect Concentration of compound j


• All components are assumed to share the same action mechanisms

(Loewe and Muinschnek, 1926)



Independent Action (IA) :


• All components are assumed to act by dissimilar mechanisms

• Response (i.e., effects) addition

(Bliss, 1939)

)()()()( BAPBPAPBAP ∩−+=∪

Toxic mode of action is calculated analogously to probability calculus lawsFor two compounds A and B, their joint response is:

For a mixture of n components:

E(ci) : Effect caused by component iE(mixture): Effect caused by the

mixture of n components[ ]∏=

−−=n

iicEmixtureE

1

)(11)(



Environmental exposure to multiple chemicals:

Ecotoxicological assessment depending on Toxic Mode of Action

• Independent action or response addition model (IA):• Concentration addition model (CA):

•Even though IA and CA models are conceptually very different, results are no so much.


•IA predicts lower mixture toxicity than CA.

•When compared to experimental values, IA tends to underestimate whereas CA tend to overestimate toxicity

•CA (expressed as HQ) is often preferred due to its simplicity.



3. Method development (synergistic effects and compound prioritisation)



The basic environmental risk assessment approach:

Multivariate AnalysisANOVA.....

CHEMICAL EXPOSURE

ci : concentrations

ECOLOGICAL

STATUSEcosystem variables:biofilm, macroinvertabrates ....

ECOTOXICOLOGY

Ecotoxicity variables:EC50i, PNECi , NOECi ....

)(.. HQfVE =ecotoxicologicalmulticomponent models

Ex.: HQ



Ecological effects caused by exposure to multiple chemicalsSome published examples:

ECOTOXICOLOGYCHEMICAL EXPOSURE

ECOLOGICAL

STATUS

SPEAR (species at risk index) ≈ log (HQ pesticides, daphnia)[Liess and von der Ohe. Environmental Toxicology and Chemistry 2005; 24: 954-965 ]

[Schäfer et al. Science of Total Environment 2007; 382: 272-285]

Macroinvertebrate Biodiversity (Shannon Index) ≈ log (HQ pharmaceuticals, daphnia)[Ginebreda et al. Environ. Intern. 2010. 36, 153–162]

Multispecies potentially affected fraction (msPAF) ≈ f(HQ, SSD)[De Zwart and Posthuma, Environmental Toxicology and Chemistry. 2005. 24: 2665–2676]

CHEMICAL EXPOSURE

ECOLOGICAL

STATUS

Pharmaceuticals vs. community structures of macroinvertebrates and diatoms[Muñoz et al. Environmental Toxicology and Chemistry 2009; 28: 2706 - 2714 ]

Pesticides vs. Biological communities (macroinvertebrates, diatoms, biofilm metrics)[Ricart et al. J. Hydrology. 2010. 383, 52 – 61]

Introduction & objectives



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Method development

hq

Environmental sample characterized by its HQ:For a given analytical profile characterizing a site sample, HQ is

readily computed from concentrations and PNEC’s:

∑=

=++++=n

kkn hqhqhqhqhqHQ

1321 ....

Is it possible to get additional information from HQ?



Identification of relevant compounds (prioritization):For a given analytical profile characterizing a site

a) Compute hq’s for all identified compounds b) Normalize hq’s to %c) Rank all compounds by decreasing hqd) Calculate h index (Hirsch) and others alikee) Identify H set of compounds (those comprised within h index)

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Rank

hqk

(%)

Rank k

hqk

(%)

H set

Hirsch index h = 5 Example of a Pareto distribution

Applicable to any additive property such as concentrations, hq’s etc.

Method development



Example of a Pareto distributionVilfredo Pareto (1848-1923) italian economist who stated in 1906 the so called "80:20 rule" (Pareto Principle)

“20 % of people own 80% of wealth”

“20 % of causes account for 80% of failures”

“Few compounds are the responsible for most of the risk”

Method development

h index allows identifying the most relevant compounds (H set)



Complexity embedded within HQ:Assuming valid the CA modelGiven a certain value of HQ, it may be obtained from different compound distributions

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HQ = 10 + 0 + 0 + 0 = 10 HQ = 2,5 + 2,5 + 2,5 + 2,5 = 10

HQ = 4 + 3 + 2 + 1 = 10 HQ = 7 + 2 + 1 + 0 = 10

All the above patterns are the same?

hq

hq hq

hq

Method development



Fitting a potential law (Zipf law) to a Pareto distribution:

α−⋅= khqhqk 0

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Rank

Xk

(%)

WWTP1(Infl)Calculated (Zipf law; all compounds)

Rank k

hqk

(%)

h index

Zipf law: hqk = 41.32 · k -1.928

H set

Method development



y = -1.9278x + 4.539R2 = 0.9197

-4

-3

-2

-1

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0 0.5 1 1.5 2 2.5 3 3.5 4

Ln k

Ln

hqk

Fitting a potential law (Zipf law) to a Pareto distribution:

log – log plot:

0logloglog hqkhqk +⋅−= α

Method development



Breaking down the HQ structure (under the Zipf law):

∑=

=++++=n

kkn hqhqhqhqhqHQ

1321 ....

∑∑∑=

−

=

−

=

⋅=⋅==n

k

n

k

n

kk khqkhqhqHQ

10

10

1

αα

HQ = hq0 ·ξ(n, α) “Complexity”

“Intensity”

h index

α exp.

ξ(n, α)

….

Method development

ξ(n, α)



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HQ = 10 + 0 + 0 + 0 = 10 HQ = 2,5 + 2,5 + 2,5 + 2,5 = 10

HQ = 4 + 3 + 2 + 1 = 10 HQ = 7 + 2 + 1 + 0 = 10

All the patterns are the same? NO

• Equal HQ

• Different power equation → different INTENSITY and COMPLEXITY

hq

hq hq

hqα = ∞ α = 0

MAX intensity

MIN complexity

MIN intensity

MAX complexity

Method development



4. Preliminary field results (Case Studies)



Preliminary Results

Llobregat River Basin:

[Muñoz et al. Env. Tox. Chem. 2009. 28, 2706 – 2714][Ricart et al. J. Hydrology. 2010. 383, 52 – 61][Ginebreda et al. Environ. Intern. 2010. 36, 153 –162]

Ecosystem variables:Biofilm metrics:Chl-a, EPS, Ymax , Yeff , F1/F3Macroinvertebrate biodiversity:

Shannon-Wiener Index

Chemical variables: Polar pesticides in waterHerbicides (20):- Atrazine, Simazine, Cyanazine, Desethylathrazine, Terbutylazine,

Deisopropylatrazine, Diuron, Isoproturon, Linuron, Chlortoluron,Mecoprop, 2,4-D, Bentazone, MCPA, Molinate, Propanil, Alachlor, Metolachlor

Insecticides (4):- Diazinon, Dimethoate, Fenitrothion, Malathion

MODELKEY (Project 511237-2 GOCE)

Ecotoxicity: HQ vs. algae and daphnia



Compound prioritization based on h (Hirsch) indexes:

HQ (pesticides, algae)

Isoproturon, Linuron, Diuron, Terbutylazine

22 → 4

HQ (pesticides, daphnia)

Diazinon, Fenitrothion, Linuron

22 → 3

Point # h index H-compounds % of HQ explainedA1 4 Isoproturon, Linuron, Diuron, Terbutylazine 96.7A2 2 Diuron, Linuron 98.5A3 2 Diuron, Linuron 98.1LL1 3 Diuron, Terbuthylazine, Linuron 96.9LL2 3 Diuron, Terbuthylazine, Linuron 98.5LL3 3 Diuron, Terbuthylazine, Linuron 98.5LL4 1 Diuron 97.0

Point # h index H-compounds % of HQ explainedA1 2 Fenitrothion, Linuron 99.0A2 1 Diazinon 99.8A3 1 Diazinon 99.3LL1 3 Diazinon, Fenitrothion, Linuron 99.7LL2 2 Diazinon, Fenitrothion 99.0LL3 1 Diazinon 98.6LL4 1 Diazinon 99.1

In the example studied:

• H compounds account for most of the risk

• H set is efficient in the prioritization of compounds

• H sets based on different species ecotoxicity reflect well its specific sensibility

Preliminary Results



Preliminary Results

Some Correlations between ecosystem variables and HQ(pesticides):

y = -0,0661x + 2,0764R = -0,933

0,00

0,50

1,00

1,50

2,00

2,50

0,0 5,0 10,0 15,0 20,0 25,0

HQ (algae)

Shan

non

Bio

dive

rsity

Inde

x (m

acro

inve

rteb

rate

s)

y = -0,0087x + 0,6733R = -0,660

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,0 5,0 10,0 15,0 20,0 25,0

HQ (algae)

F1/F

3

Ecosystem Variable HQ (ecotoxicity sp.) R

Shannon Diversity algae -0.933

F1/F3 algae -0.660

Yeff algae 0.401

Ymax algae 0.449

Shannon Diversity daphnia -0.747

F1/F3 daphnia -0.886



Pesticides (algae)Contribution of "Intensity" and "Complexity"

0,1

1,0

10,0

100,0

Haza

rd Q

uotie

nt

hq 0complexityHQ

hq 0 0,151 15,574 4,373 0,605 1,080 5,068 20,015

complexity 2,810 1,362 1,171 1,847 1,465 1,166 1,031

HQ 0,424 21,208 5,121 1,117 1,582 5,909 20,644

A1 A2 A3 LL1 LL2 LL3 LL4

Pesticides (daphnia)Contribution of "Intensity" and "Complexity"

0,0

0,1

1,0

10,0

100,0

1000,0

Haz

ard

Quo

tient

hq 0complexityHQ

hq 0 0,026 310,125 22,375 0,280 1,251 9,378 16,973

complexity 1,496 1,002 1,007 1,271 1,059 1,014 1,009

HQ 0,040 310,884 22,539 0,356 1,325 9,512 17,123

A1 A2 A3 LL1 LL2 LL3 LL4

HQ = hq0 · complexityComplexity effects on HQ (Zipf law):

In the studied cases:1. ‘intensity’ seems to have in general greater

weight than ‘complexity’ in the resulting HQ

2. ‘Complexity’ values show low differencesamong the different points

Preliminary Results



4. Conclusions



Conclusions (1)

1) Is there any relationship between chemical pollution exposure and ecosystem impairment ?

Ecosystem variables may be correlated to ecotoxicological multicomponent exposure models such as CONCENTRATION ADDITION (CA), expressed as hazard quotients (HQ)

1) What to analyze ? (prioritization of target compounds)

Under the assumption of CA model, compounds may be ranked in descending order according to its normalized hazard quotient (hq). On the so obtained (Pareto type) distribution, appropriate indexes, such as h (Hirsch) well known in other scientific domains can be applied in order to identify and prioritize relevant compounds for the scenario under study.



Conclusions (2)

3) Exposure to multiple chemicals may result on any synergistic effect (mixture effects) ?

Rank lists can be numerically represented according to a potential law equation (Zipf law), which allows:

(a) To break down HQ in two parts, corresponding respectively to the effects of intensity and complexity of the mixture

(b) Zipf exponents can serve also as a measure of complexity

4) Preliminary illustrative examples of the above concepts have been shown for the Llobregat River case study (pesticides in water vs. biofilm metrics or macroinvertebrates diversity).

More work is needed on the interpretation of results

We are far from solving the question, but….



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