Exposure Science Community of Practice Seminar, September ... · Exposure Science Community of...

Exposure Science Community of Practice Seminar, September 8,2009 1

Exposure Science Research at the JRC Institute for Health and Consumer

Protection

Dimosthenis A. SARIGIANNIS, PhD

Scientific Coordinator

European Commission - Joint Research Centre, Institute for Health and Consumer Protection, 21027 Ispra (VA)


Risk assessment of chemicals

Policy needs for health and safety data:

Consumer Policy and REACH: need for data on chemical safety of consumer products and on aggregate exposure

Env & Health Action Plan: Address mixture effects/Indoor air

Food safety: safety of chemicals in FCM/foodstuff

Methodological Problems linked to:

Complexity of exposure pathways

Cocktail (beyond additive) effect of mixtures

Dose extrapolation

Integrated use of exposure data (incl. human epidemiological and biomonitoring data)


JRC involvement

International collaborative research projects: HEIMTSA (Integrated Health Impact Assessment Toolbox)2-FUN (Health Risk Assessment for Future Scenarios)HENVINET (Health and Environment Network)CAIR4HEALTH (Air quality and Health)HEREPLUS (Health Risk from Environmental Pollution Levels in Urban Settings)GENESIS (Generic EU Sustainable Information Space for Environment)In-house projects:Human Exposure Data Centre (with EEA) Biology based dose-response modelingToxicogenomics for mixture toxicity assessment and exposure/effect biomarker identification


Challenges for Exposure Science

• Plethora of analytical/monitoring data• 30-100,000 chemicals in the marketIn the European Union:• REACH introduces exposure-based waiving of toxicity

testing• REACH uses exposure surrogates: market volume p.a.• Very ambitious time plan for evaluating risk of 30,000

chemicals• E&H action plan: poses the problem of chemical

mixtures and of susceptible population groups


Challenges for Exposure Science

• Adequate support of green chemistry towards the Sustainable Development goals

• Increased public awareness of risks of chemicals

• Need to set priorities for efficient risk assessment of chemicals

• The most plausible avenue/greatest challenge is to link all available data, incl.:- environmental- human biomonitoring- “sentinels”- surrogates


Exposure assessment

Full chain assessment• Sources-emissions• Media concentrations• Personal exposure• Internal dose• Biology Based Dose Response

Methodological tools exploitation• Measurements data

• environmental parameters• concentrations• personal exposure• biomonitoring

• Toxicity testing• animal data• gene expression and other omics

• Epidemiological data• Clinical data

How we can optimize exposure assessment of chemicals?A Holistic Approach is needed, regarding:

How can we connect all these elements?

A single-word answer: the Exposome


Toward Exposure Biology, through modelling and data assimilation

cell organ organism

“Systems Biology” Approach

“Physiome” Approach

Physiologically Based Pharmacokinetic (PBPK) Models


Exposure Biology

Characterizationof Exposure

Factors

Aggregate and Cumulative

Exposure Models

•Biologically EffectiveDose

- Early BiologicalEffects

Dose–Effect ModelsBiomarkers of• exposure• effects

- Life Styles- Polymorphisms

IndividualResponse

Biomarkers ofindividualSusceptibility

Individual Profiles

Population Studies MolecularDosimetry

Assessment ofRisk Factors

ProbabilisticExposure

Expressomics

Integrated Multi-layer computational Approach


Tool Development


T n nn

C f C= ⋅∑

( ) E - kCVCCC Q dtdCV a +−+= 0

ijijijijijjiij

i BindingAbsorpEMetabCVCAQdt

dCV Prlim)( −+−−−=

Full chain exposure assessmentPlatform components

MCMC simulation

in all stages( )

2

1

2

2, exp2

y y

z yy y

q yC x y dyuπσ σ

−

−

−=

∫ )exp(1)( 32 cybyayyP ++−=


Full chain approach-Platform User Interface


Generic PBTK model

ijijijijijjiij

i BindingAbsorpEMetabCVCAQdt

dCV Prlim)( −+−−−=General formula describing ADME:

Absorption

Distribution

Metabolism

Elimination

Tissue characteristics that affect the internal concentrations are:•Blood flow • Perfusion• Protein binding• Metabolic and elimination activity


Biomarkers andSystems Toxicology

Models

Gene Identification

Validation by Quantitative PCR Statistical Evaluation

Tissues RNAMice, Rats, Humans

Whole Genome Discovery Systems

(32.000 genes)

Experimental Design Environment and HealthSignature of chemicals in productsImplementation of Risk Assessment

BIOINFORMATICS

Integrated approachwith

Proteomics/Metabonomics

Genes ModulationGenes Classification

Genes Pathway

Expressomics for the Exposome


Addressing Variability and Uncertainty

Human Biomonitoring

Early diagnosis of cardiovascular diseaseassociated with exposure to chemicals throughanalysis of metabolites can be used for easy,non-invasive monitoring of health effectindicators

Biomarkers of exposure and effects

The difference in susceptibility to chemicalexposure between males and females wasdemonstrated by analysis of the wholegenome in cell lines and tissues exposed tomixtures of chemicals

By identifying the difference in geneexpression we can have early warning aboutanticipated health effects

MaleFemale


Chemical Mixtures – Cumulative Exposure


Chemical mixtures: molecular fingerprinting


0

1000

2000

3000

4000

5000

IAM PAHs IAM+PAHs

N. o

f m

odul

ated

gen

es

Common genes Specific genes

0

2000

4000

6000

8000

10000

IAM PAHs IAM+PAHsN

. of

mod

ulat

ed g

enes

HaCaT -FC>1.6 A549 FC >1.6


Ha-CAT IAM+ PAHs

Ha-CaT IAM

A549 IAM

A549 PAHs

Ha-CAT PAHs

A549 IAM+ PAHs

Comparative Cluster Analysis between Ha-CaTand A549 exposed to Air Mixtures


IAM: red Aromatics: greenAldehydes: orangeTerpens: blue

Yellow: components in more than one treatment

p53 Pathway: differential modulation of gene expressionin A549 cells by Indoor Air Mix and components


Oxidative stress pathway

MKK3/6

eEF2K

MEF-2

c-jun

MKP5

= Formaldheyde

= Indoor Air Mix

= Aromatics

= Terpenes


Environmental exposure: in-/outdoor/personal


BTEX “in vitro” experiments

Mixture A:

•20% Benzene•40% Toluene• 10% Ethylbenzene•30% Xylene

Mixture B:

•10% Benzene•60% Toluene• 10% Ethylbenzene•20% Xylene

• Cells: A549

• Time of exposure: 4h and 24 h

• Doses: 10ng/L, 100ng/L, 10ug/L

• Tissue: Mouse Lung after i.tr.

• Time of exposure: 4h and 24 h

• Dose: 10 ug/L (=250ng/Kg)

BTEX “in vivo” experiments


Number of modulated genes by differentmixtures (A and B) in A549, 4h and 24 h, 2FC


0 50 100 150 200 250 300

Cell surface receptomediated signaling

Electron transport

mRNA transcription

Protein biosynthesis

Protein metabolism

Signal transduction

Mix A

10ug/l100ng/l10ng/l

0 50 100 150 200 250 300

Mix B

Biological processes (pvalue ≤0.01), BTEXin A549 cells, Mix A and Mix B, 4h


0 20 40 60 80 100

Mix B

0 20 40 60 80 100

Cell proliferation and differentiation

Cytokine and chemokine mediated

signaling

Hematopoiesis

mRNA transcription

mRNA transcription regulation

Protein biosynthesis

Protein metabolism

Protein modification

Mix A

10ug/l100ng/l10ng/l

Biological processes (pvalue ≤0.01), BTEXin A549 cells, Mix A and Mix B, 24h


Apoptosis Signaling Pathway –BTEX, Mouse Lung (i.t.)

Mix A

Mix B

Common

4h

24h


Linking with the physiome


Benzene metabolism

O

epoxidehydrolase

OH

OH

O

O

O

CYP2E1

OH

OH

CYP2E1

O

O

H O

O H

OH O

O OH

OH

OH

OH

CYP2E1

OH

Non enzymatic rearrangement

OH

OH

O

CYP2E1OH

OH

dihydrodioldehydrogenese

OH

SCH2CH

COOH

NHCOCH3

Benzene oxide

Phenol

Hydroquinone


PBPK Model for Benzene (with Metabolism)


Lyapunov exponent: system stability

If λ > 1the system is chaotic and unstable

λ measures the sensitivity of the system to its initial conditions

If λ < 1the system is attracted to a stable point or a stable periodic trajectory (limit cycle). This is a non conservative condition. The absolute value of λ is a metric of system sensitivity

If λ = 1 the system is stable, conservative and at steady state

λBTEX< 1Unstable system


Biological system dynamics:emergence of limit cycles

0

0,00005

0,0001

0,00015

0,0002

0,00025

0 0,000002 0,000004 0,000006 0,000008 0,00001 0,000012

Bon

e m

arro

w c

once

ntra

tion

(m

g/kg

)

Venous blood concentration (mg/l)

Through phase space analysis unstable attractors like limit cycles are identified.The system is inherently meta-stable


Increment in maximum bone marrow concentration of benzene for exposition to different mixtures of BTEX


N O

NN

C

CC

C

CC

H

H

H

H

H

H

C

O

H H

Health endpoint-triggered exposure assessment

4-(N-nitrosomethylamino)-1-(3-pyridyl)-1-butanone (NNK)

Benzene

Formaldehyde

Lung cancer

Leukemia

Nasopharyngeal cancer


Benzene, NNK, Formaldehyde metabolism – DNA adducts formation

O

epoxidehydrolaseOH

OH

O

O

OCYP2E1

OH

OH

CYP2E1

O

O

H O

O H

OH O

O OH

OH

OH

OH

CYP2E1

OH

Non enzymatic rearrangement

OH

OH

O

CYP2E1 OH

OH

Dihydrodioldehydrogenese

OH

SCH2CH

COOH

NHCOCH3

Benzene oxide

Phenol

Hydroquinone

N

O

N

CH3

N O

N

N

CH3

N OOGluc

N

N

CH3

N OOGluc

N∗

N

CH3

N OOH

Gluc

N∗

N

CH3

N OOH

Gluc

N

N N

N

O

NH

Benzene oxide DNA adduct

NH

NN

N

NH2

N

O

O

dR

NNK DNA adductNH

NN

N∗

NH2

N

O

O

dR

NNK DNA adduct

Gluc – (S) NNAL Gluc – (R) NNAL

N

N

CH3

N OOH

(S) NNAL

N

N

CH3

N OOH

(R) NNAL

Benzene NNK

C

OH

H H

NH

NNH

N

O

NH

C

HH

O

Formaldehyde

Formaldehyde DNA adducts


Constituent Unit Range SS/MS ratioa

Ammonia mg/cig. 4.0–6.6 147

1-Aminonaphthalene ng/cig. 165.8–273.9 7.10

2-Aminonaphthalene ng/cig. 113.5–171.6 8.83

3-Aminobiphenyl ng/cig. 28.0–42.2 10.83

4-Aminobiphenyl ng/cig. 20.8–31.8 5.41

Benzo[a ]pyrene ng/cig. 51.8–94.5 3.22

Formaldehyde µg/cig. 540.4–967.5 14.78

Acetaldehyde µg/cig. 1683.7–2586.8 1.31

Acetone µg/cig. 811.3–1204.8 1.52

Acrolein µg/cig. 342.1–522.7 2.53

Benzene µg/cig 71-134 0.8

Propionaldehyde µg/cig. 151.8–267.6 1.06

Crotonaldehyde µg/cig. 62.2–121.8 1.95

Butyraldehyde µg/cig. 138.0–244.9 2.68

Hydrogen cyanide mg/cig. 0.19–0.35 0.77

Mercury

Nickel

Chromium

ng/cig.

ng/cig.

ng/cig.

5.2–13.7

ND–NQ

ND–ND

1.09

Cadmium ng/cig. 122–265 1.47

Arsenic

Selenium

ng/cig.

ng/cig.

3.5–26.5

ND–ND

1.51

Lead ng/cig. 2.7–6.6 0.09

Nitric oxide mg/cig. 1.0–1.6 2.79

Carbon monoxide mg/cig. 31.5–54.1 1.87

‘Tar’ mg/cig. 10.5–34.4 0.91

Nicotine mg/cig. 1.9–5.3 2.31

Catechol µg/cig. 64.5–107.0 0.85

Hydroquinone µg/cig. 49.8–134.1 0.94

Resorcinol µg/cig. ND–5.1

m e t a -Cresol + para -Cresolb µg/cig. 40.9–113.2 4.36

ortho -Cresol µg/cig. 12.4–45.9 4.15c

NNN ng/cig. 69.8–115.2 0.43

NNK ng/cig. 50.7–95.7 0.40

NAT ng/cig. 38.4–73.4 0.26

NAB ng/cig. 11.9–17.8 0.55

1,3-Butadiene µg/cig. 81.3–134.7 1.30

Average values of major smoke constituents in thesidestream smoke of 12 commercial cigarette brandsassayed in the 1999 Massachusetts BenchmarkStudy using Massa-chusetts smoking parameters(IARC, 2004)

• Smoking emissions (IARC)

• Smoking prevalence-population exposed to ETS (WHO)

•Time activity patterns• Volumes of residences• Indoor/outdoor air exchange rate

Data necessary for the EU-27 scale estimation

(EXPOLIS study)

0

10

20

30

40

50

AT BE BG CY CH DK EE FI FR D GR HU IE IT LT LV LU MT NL PL PT RO SK SI ES SE UK

Smok

ing

prev

alen

ce (

%)


Hierarchical population exposure model

Hierarchical population model used in Bayesian analysis (Bois et al, 1996).

Circles represent distributionsand squares/rectangles represent known entities.

μ: prior mean distribution Σ2: prior variance distribution θ: study level distributions for each of the parameters based on randomly selected values for the mean and variance from the population distributions μ and Σ2


EU-27 cancer risk estimationsIndividual risk-Expected lifetime cases


Aggregate exposure


0,0

20,0

40,0

60,0

80,0

100,0

120,0

AT BE CY DK FI FR D GR HU IT NL SE UK

Form

alde

hyde

indo

or c

onc.

(μg

/m3 )

Max

Average

Min

Formaldehyde exposure


Formaldehyde exposure

0

20000

40000

60000

80000

100000

120000

140000

4,E-07 9,E-06 2,E-05 3,E-05 3,E-05 4,E-05 5,E-05 6,E-05

Am

ount

of

popu

lati

on (

/106 )

DPX concentration (mM)

MCMC simulation (Exposure+Biological parameters)

MCMC simulation (Biological parameters only)

Limit of observed biologicalresponses

0.1% of the exposed population

0.01% of the exposed population

0% of the exposed population


Catalytic passenger vehicles61.17%Non catalytic

passenger vehicles12.10%

Diesel passenger vehicles 8.22%

Light trucks6.19%

Heavy trucks 1.78%

Buses 1.61% Motorcycles, scooters8.92%

Aggregate exposure: the Benzene study

EuroI 59%

EuroII 26%

EuroIII 12%

EuroIV 3%

0

5

10

15

20

Summer Winter

Ben

zene

exp

osur

e (μ

g/m

3 ) 75%

max

min

25%

0

1

2

3

4

5

6

7

8

9

5 20 35 50 65 80

Ben

zene

em

issi

on r

ate

per

vehi

cle

(g/k

m)

Speed (km/h)

CATALYTIC

NON CATALYTIC

DIESEL PASSENGER VEHICLES

LIGHT DUTY VEHICLES

HEAVY DUTY VEHICLES

MOTORCYCLES


Activities time fraction

Size: ExposureColor: ETS presenceX axis: Fraction of time spend OutdoorY axis: Fraction of time spend indoorZ axis: Fraction of time spend driving

Unstandardized Regression Coefficient

Standardized Regression Coefficient

Significance (>0.05)

Summer Winter Summer Winter Summer Winter

Constant 3.1 2.3 0.02 0.67

Walking/Outdoor -0.06 0.09 0.03 0.03 0.80 0.54

Driving 0.53 0.62 0.46 0.53 0.00 0.00

Ind.Loc. Zone 1 0.20 0.06 0.33 0.16 0.00 0.07

Ind. Loc. Zone 2 -0.04 -0.01 0.08 0.02 0.19 0.47

ETS presence 3.41 4.41 0.17 0.29 0.02 0.02


0

10

20

30

40

5 15 25 35 45 55 65

Per

cent

age

of o

bser

vati

ons

(%)

Exposure (μg/m3)

DrivingWalkingIndoor

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

Driving Walking/ Outdoor

Indoor

Ben

zene

con

cent

rati

ons

(μg/

m3 )

Active sampling measurements


( )

( )

( )

= + ⋅ − ⋅ − ⋅ − ⋅ + ⋅ +

+ ⋅ − ⋅ − ⋅ − ⋅ + ⋅ + − ⋅ − ⋅ − ⋅ + ⋅

11.1 4.3 4 2.5 0.6 1.3

36.5 11.9 14.8 3.72 0.001 2.5

22.2 6.2 1.1 5.8 2

IB

DB

WB

T Sm Lz Tz Ws UT

T Wc Lz Tz Ws UTT Lz Tz Ws UT

E Sm: ETS presence (fraction 0 till 1)

Wc: Closed (1) or open (0) windows during driving

Lz: Location zone (1-3)

Tz: Time zone (1-4)

Ws: Wind speed (m/sec)

UB: Average urban benzene conc (μg/m3)

0

5

10

15

20

25

0 5 10 15 20 25

Pre

dict

ed (μg

/m3 )

Observed (μg/m3)

Summer

Winter

Linear (Summer)

Linear (Winter)

Regression exposure modelling


- Considering the observed exposure levels, no acute effects from exposure to benzene are expected-The interest is focused on the prolonged chronic exposure which is responsible for leukemia- The estimated risk due to benzene exposure in the area under study is calculated considering:

•Benzene exposure levels

•Benzene internal concentration

•Biologically effective dose of benzene metabolites in target tissue (bone marrow)

•Dose response relationship

•Susceptibility of the population considering that the enzymes (CYP2E1, quinonereductase NQO1, and myeloperoxidase) related to benzene metabolism are polymorphic

Leukemia risk estimation


Advantages of Biology Based Exposure assessment – mechanistic approaches

oBenzene exposure during the day is not constant. Internal dose variation is exposure-dependentbut not linearly linked to encountered microenvironment concentrations. Inhaled benzene and theproduced metabolites are dynamically and continuously calculated through time (not just steadystate estimations)

0

0,2

0,4

0,6

0,8

1

1,2

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10 12 14 16 18 20 22 24

Ben

zene

met

abol

ites

con

cent

rati

on

(μg/

l)

Ben

zene

con

cent

rati

on (μ

g/m

3 )

Time

Ambient air

Home

Workplace

Exposure

Benzene metabolites


Advantages of Biology Based Exposure Assessment – mechanistic approaches

o Dose response relation takes into account the internal dose at the target tissue, which is the realexposure metric

o Biology-based dose response is more representative for low exposure levels, sinceepidemiological approaches are based on extrapolations obtained by incidences that occurred atexposure levels 4-5 orders higher

o Traffic emissions and health endpoints are linked within a “continuous” mathematical frameallowing the exploration of alternative scenarios and the explicit incorporation of uncertaintyand variability in the final risk estimates

o Capturing both toxicokinetics, toxicodynamics and exposure dynamics allowed us toincorporate mechanistic knowledge on exposure assessment and thus improve on the validity andrelevance of the dose-response relationship

o Multiple pathways (air, water, food, consumer products) and routes of exposure (inhalation,oral) for the same pollutant can be incorporated into the PBTK/D model and provide a realisticaggregate exposure assessment


0

1000

2000

3000

4000

5000

6000

7000

2,00E-05 7,00E-05 1,20E-04 1,70E-04 2,20E-04 2,70E-04

Am

ount

of

popu

lati

on (

N/1

05 )

Leukemia probability

Non smokers Smokers

Leukemia risk estimation

Distributions are necessary in order to describe:

•Variability in exposure• Variability in enzymatic activity


0%

20%

40%

60%

80%

100%

Max Min Mean Max Min Mean

Act

ivit

ies

cont

ribu

tion

to

the

over

all l

euke

mia

ris

k

Smoking

ETS

Indorr loc. zone 3

Indoor loc. Zone 2

Indoor loc. Zone 1

Driving

Walking/outdoor

Risk attributable to the activities


Traffic fleet composition under the “what if ” scenarios

0%

20%

40%

60%

80%

Gasol. Dies. Mop. Gasol. Dies. Mop. Gasol. Dies. Mop. Gasol. Dies. Mop. Gasol. Dies. Mop. Gasol. Dies. Mop. H2 Mop.

Current situation 1st Scenario 2nd Scenario 3rd Scenario 4th Scenario 5th Scenario 6th Scenario

Fle

et c

ompo

siti

on

H2 powered Hybrid Euro 4 Euro 3 Euro 2 Euro 1 Conventional

46%

69%

47%

64%

77%

92%

20%

40%

60%

80%

100%

1st Scenario 2nd Scenario 3rd Scenario 4th Scenario 5th Scenario 6th Scenario

Ben

zene

em

issi

on

redu

ctio

n


Risk mitigation

0

1000

2000

3000

4000

5000

6000

7000

4,50E-06 2,95E-05 5,45E-05 7,95E-05

Am

ount

of

popu

lati

on (N

/105 )

Current situation

Scenario 1&3

Scenario 2

Scenario 4

Scenario 5

Scerario 6

0

1000

2000

3000

4000

5000

6000

1,85E-04 2,15E-04 2,45E-04 2,75E-04

Probability of cancer


1,0E-06

5,1E-05

1,0E-04

1,5E-04

2,0E-04

2,5E-04

a b c d e c+e a b c d e c+e

Non smokers Smokers

Leu

kem

ia li

feti

me

risk

pro

babi

lity

MaxMinMean

a- current situationb- theoretical scenarioc- expected scenariod – optimal scenarioe – smoking ban

Aggregate risk mitigation


0

1

2

3

4

5

6

0

200

400

600

800

1000

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00

Ben

zene

con

cent

rati

on (μg

/m3 )

Tra

ffic

flo

w (

veh/

h) -

Am

ount

of

fuel

tra

ded

(l/h

)

Traffic Flow

Amount of fuel

Background concentration

0204060

N

NE

E

SE

S

SW

W

NW

Measurements:- benzene in 16 points around the gasoline station- benzene urban background concentration- benzene in a later point of the adjacent road (to optimize CALINE 4)- traffic parameters of the adjacent road- meteorological observations (wind, temp, humidity, cloudiness)- fuel traded rate

Modelling of the contribution of the adjacent street with CALINE4 model

= − −point_ _gas.station point_ _ measured background point_ _ streetC i C i C C i

Gasoline station effect


0

20

40

60

80

100

0 10 20 30 40

Ben

zene

con

cent

rati

ons

(μg/

m3 )

Distance from fuel pumps (m)

Urban station

Rural station

Fit urban

Fit rural

⋅=

⋅

0.713 0.0298

B 0.95 2.55F TCD W



0,0

10,0

20,0

30,0

40,0

50,0

60,0

0 1 2 3 4 5 6 7 8 9 10

Ave

rage

wee

kly

benz

ene

expo

sure

(μg

/m3 )

Exposed subjects

Summer

Winter

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90 100

Per

cent

age

of o

bser

vati

ons

(%)

Exposure (μg/m3)

Refuelling

Miscellaneous activities

Office employees

Passive sampling

Active sampling

Gasoline station employee exposure


0%

20%

40%

60%

80%

100%

Car refuelling employees

Employees of miscellaneous

activities

Cashiers

37,4 31,0 21,9

17,018,2

20,1

22,4 24,127,8

14,2 16,014,0

9,0 10,7 16,2

Rel

ativ

e Im

port

ance Background

concentration

Traffic flow

Temperature

Wind speed

Amount of gasoline

AMOUNT OF FUEL

EXPOSURE

WIND SPEED

TEMPERATURE

TRAFFIC FLOW

URBAN BACKGROUND

INPUT HIDDEN OUTPUT

Exposure modelling by ANNs



0

1000

2000

3000

4000

5000

6000

7000

2,00E-05 4,40E-05 6,80E-05 9,20E-05 1,16E-04 1,40E-04

Am

ount

of

popu

lati

on (N

/105 )

Probability of cancer

General Population

Gasoline Station Employees

People living in the vicinity (North West side)

People working in the vicinity (North East side)


Health impact assessment of policies: the case of Arsenic


Policy health impact assessment

Sector Specific policies consideredLarge combustion plants Baseline 2010: IPPC Directive – BREF on large combustion plants

Large Combustion Plants Dir (2001)Baseline 2020: Emerging techniquesMFTR 2020: Kyoto Protocol – Council Decision 2002/358/ECDirective 2001/77/EC – IGCC & supercritical polyvalent processes

Iron / Steel production Baseline 2010: IPPC Directive – BREF on iron/steel productionBaseline 2020 – emerging techniques in sintering, catalytic oxidationMFTR 2020 – new iron-making techniques: direct reduction/smelting reduction

Cement industry Baseline 2010: IPPC Directive – BREF on cement and lime manufacturingBaseline 2020: FGD techniques, activated C filters for HM reductionMFTR 2010 = Baseline 2020MFTR 2020 = all plants with HM reduction technologies

Petrol Baseline 2010: Directives 98/70/EC and 2003/17/EC- Ban in use of leaded petrol- 5 mg Pb/l in unleaded petrol- high % of passenger vehicles comply with Euro 2000 and 2005 norms- high % of HDV comply with Euro III normBaseline 2020: significant % of LPG cars and lot of HDV comply with Euro IV and VMFTR 2010 = Baseline 2020 + increase of % of LPG carsMFTR 2020: increase of share of electric/FC cars


Integrated risk assessment based on BED


Modeling framework

• Stuttgart Emission Tool (SET) for country-specific emissions, by activity sectors

• MSCE-HM for transboundary transport across Europe• WATSON for soil, water concentration and food-

relevant exposure• XtraFood for food contamination through plant uptake• JRC BBDR platform and ISE for internal dosimetry and

risk assessment• VSL and contingent valuation functions for monetary

cost assessment • Quantification/reduction of uncertainty with MCMC


Spatial distribution of anthropogenic air emissions of arsenic in Europe for the year 2000 [kg/km2/y].


Spatial distribution of anthropogenic air emissions of arsenic in Europe (a) for the BAU scenario and (b) for the MFTR scenario projection of the year 2020 [t/y].


Spatial distribution of concentrations in European top-soils including adjacent territories [mg/kg] (a) and mean annual concentration in ambient air (b) for arsenic for the year 2000.


Spatial distribution of arsenic annual wet (a) and dry (b) deposition over Europe in 2000.


Contaminant flows in the food chain


Human exposure routes via contaminated food


As PBPK/PD model


•In utero exposure•Newborn exposure•Childhood exposure•Adulthood exposure

Lifetime exposureincluding:


Mother-fetus model for 2-generation effects


In utero exposure to dioxins

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0 5 10 15 20 25 30 35

Con

cent

rati

on o

f T

CD

D in

ven

ous

bloo

d of

mot

her

(mM

ol/l

)

Time (days)

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0 5 10 15 20 25 30 35

Con

cent

rati

on o

f T

CD

D in

pla

cent

a (m

Mol

/l)

Time (days)

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0 5 10 15 20 25 30 35

Con

cent

rati

on o

f T

CD

D in

art

eria

l bl

ood

of f

etus

(m

Mol

/l)

Time (days)

0

0,001

0,002

0,003

0,004

0,005

0,006

0,007

0 5 10 15 20 25 30 35

Con

cent

rati

on o

f T

CD

D in

mot

her

veno

us b

lood

(m

Mol

/l)

Time (days)


The case of Bisphenol A (BPA)


0,0000

0,0001

0,0010

0,0100

0,1000

1,0000

Newborn 1 month 3 months 6 months 9 months 12 months 18 months 24 months

Pla

sma

conc

entr

atio

n (μ

g/l)

Max

Mean

Min

Dose: 0.25-0.4 μg/kg BW/day

Dose: 0.25-11 μg/kg BW/day

Dose: 2-13 μg/kg BW/day

EFSA Tolerable Daily Intake (50 μg/kg BW/day)

Bisphenol A (BPA)


0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0 5760 11520 17280 23040

BPA

blo

od c

once

ntra

tion

(μg

/l)

Time (h)

Gestation period (9 months) Breast feeding (till 3rd

month)

Bottle feeding from 6th to 9th

month (7.5 μg/kg BW/d)

Bottle feeding from 9th to 18th month (13 μg/kg BW/d)

Bottle feeding from 18th to 24th

month (5.3 μg/kg BW/d)

Bisphenol A (BPA)


Conclusions (1/2)

Benefits to public health – improved risk assessmentExpressomics allowed identification of gene expression profiles

characterising exposure tochemicals alone and in co-exposure toother substances

Gene expression profiles can be used as biomarkers of exposure to taking into account risk modifiers such as:

diet genderagetime length of exposure

Whole genome micro-arrays allow reviewing all gene associations modulating physiological response and identifying end points specific to the most significant associations

Bioinformatic data analysis holds great potential for building plausible mechanistic hypothesis on mechanism of action andexposure biomarker discovery


Conclusions (2/2)

Towards the exposome:The exposome approach can be implemented coupling:

macro-/micro-environmental modeling passive/active personal monitoringhuman biomonitoringexpression biomarkersphysiologiy-based biokinetic modelingsystems biology modeling

A tiered approach should be developed to use exposure information for toxicity prioritization:

Tier 1 exposure surrogatessentinels of exposure

Tier 2Full chain exposure assessment


Thank you for your attention


Acknowledgments

• JRCA GottiS KarakitsiosI LiakosF CamilleriE MarafanteG CiminoRealeB CasattiA ColottaR Brustio

D KotziasJ Barrero-MorenoS TirendiO GeissA Katsogiannis

• IERP FantkeB TirruchitampalamU Kummer• MSC-EastO Travnikov• VITOJ BierkensR Torfs

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