Agricultural Operator Exposure BUNDESINSTITUT …€¦ · BUNDESINSTITUT FÜR RISIKOBEWERTUNG...

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BUNDESINSTITUT FÜR RISIKOBEWERTUNG Agricultural Operator Exposure Model (AOEM) Claudia Großkopf

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    Agricultural Operator Exposure Model (AOEM)

    Claudia Grokopf

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 2

    Introduction

    Current situation: different models used in risk assessment for PPPs

    mainly based on data for outdated equipment and practices

    plant protection product

    exposure

    Scope: use of new exposure data

    statistical approach, fully transparent

    main outdoor application scenarios

    applicable for authorisation

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 3

    Quality criteria for exposure studies

    compliance with OECD Series No. 9

    trained operators working in accordance with Good Agricultural Practice

    data recording and observations according to current scientific knowledge

    compliance with GLP

    consistent field recovery

    suitable data form

    whole body dosimetry for dermal exposure

    appropriate inhalation fraction samplers for inhalation exposure

    representative application methods and application techniques

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 4

    Database

    grapevine

    cereals

    olives

    citrus

    pome

    potatoes / sugarbeets

    fallow land / stubble field

    UK

    FranceBelgium

    NL

    Italy

    Portugal

    Greece

    Spain

    Switzerland

    Germany

    884051761496010859147344280all

    -134601460---9044HCHH

    88-960-19--4849LCHH

    -1336612-55555410979HCTM

    -1384163-3449397108LCTM

    knapsacktankSC/SLEC/EWWPWGnoyesAM/L

    EquipmentFormulationCabinReplicates

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 5

    LCHH (knapsack)

    area (ha)

    num

    ber

    of tr

    ials

    0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

    02

    46

    810

    12

    LCTM

    area (ha)

    num

    ber

    of tr

    ials

    0 50 100 150

    020

    40

    60

    80

    HCHH (tank)

    area (ha)

    num

    ber

    of tr

    ials

    0 1 2 3 4 5 6 7

    010

    20

    30

    40

    50

    HCTM

    area (ha)

    num

    ber

    of tr

    ials

    5 10 15 20

    010

    20

    30

    Database

    1 ha 50 ha

    10 ha4 ha

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 6

    Types of dosimeters

    Head dosimeter (hat/cap, hood, face/neck wipe)

    Outer body dosimeter(work clothes, coverall)

    Inner hand exposure (absorbent gloves, hand rinse/wash)

    Outer Hand exposure((absorbent) gloves)

    Inner body dosimeter (long underwear)

    Personal air sampler

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 7

    909090909090323232444432HCHH

    246247246276320278169170169278255201all

    393939204839404040494940LCHH

    717272929783404141776652HCTM

    464645748566575756108

    96

    77LCTM

    HeadBodyouterBody nnerGloveHandInhalat.HeadBody outerBodyinnerGloveHandInhalat.

    ApplicationMixing/Loading

    Exposure data

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 8

    Exposure variables

    Potential body exposure: sum of deposits on outer and inner body dosimeters

    Actual body exposure: deposits on inner body dosimeters (below one layer of clothing)

    Potential hand exposure: sum of deposits on protective gloves and hands

    Inhalation exposure:amount collected by air sampling corrected for respiratory rate

    Head exposure:deposit on the head dosimeter corrected for the whole head

    Actual/protected hand exposure: deposits on protected hands (inner gloves, hand rinse/wash)

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 9

    Model

    log-linear model: log X = log A + [Fi] 0 < 1

    X = A ci

    A

    exp

    osu

    re

    log A

    log e

    xposu

    re

    log X = log A

    log X = log A + F1 > 1

    0 < < 1

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 10

    P

    R2

    Analysis

    n

    AIC P

    R2

    Analysis

    n

    AIC

    P

    R2

    Analysis

    n

    AIC P

    R2

    Analysis

    n

    AIC

    AnalysisAnalysis

    AnalysisAnalysis

    Analysis Analysis AnalysisAnalysis

    Statistical analysis

    formulation type

    total amount a.s. used per day

    number of containers handled

    number of M/L tasks

    concentration of a.s.

    equipment (induction hopper)

    duration of M/L

    formulation type

    total amount a.s. used per day

    concentration of a.s. in spray solution

    equipment (cabin)

    size of area treated

    spray droplet size

    cleaning

    duration of cleaning

    M/L task Application task

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 11

    Induction hopper

    yes no

    23

    45

    6

    log

    to

    tal

    ha

    nd

    s e

    xp

    os

    ure

    yes no

    2.5

    3.5

    4.5

    5.5

    log

    to

    tal

    bo

    dy

    ex

    po

    su

    re

    yes no

    -2-1

    01

    23

    4

    log

    he

    ad

    ex

    po

    su

    re

    yes no

    -2-1

    01

    23

    4

    log

    in

    ha

    lati

    on

    ex

    po

    su

    re

    no impact

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 12

    Model

    Method: Quantile Regression

    0.2 0.5 1.0 2.0 5.0 10.0 50.0 200.0

    100

    1000

    10000

    100000

    1000000

    potential hands

    TA

    measure

    d v

    alu

    e

    LSQR

    WGliquidWP

    0.2 0.5 1.0 2.0 5.0 10.0 50.0 200.0

    0.0

    11.0

    0100.0

    010000.0

    0

    protected hands

    TA

    measure

    d v

    alu

    e

    LSQR

    WGliquidWP

    Potential Hands tank M/L Protected Hands tank M/L

    log e

    xposu

    re (

    measu

    red v

    alu

    e)

    log e

    xposu

    re (

    measu

    red v

    alu

    e)

    log TA (kg a.s./day) log TA (kg a.s./day)

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 13

    Validation

    Robustness (Cross validation)

    3.0 3.5 4.0 4.5 5.0 5.5

    2.5

    3.0

    3.5

    4.0

    4.5

    5.0

    5.5

    Predicted (fit to all data)

    observ

    ed

    ML tank, total body exposureQuantile regression, 75.percentileFold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Fold 1Fold 2

    Fold 3Fold 4

    Fold 5Fold 6

    Fold 7Fold 8

    Fold 9Fold 10

    Prediction (MLA data)

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 14

    Validation

    Prediction (MLA data)

    -2

    0

    2

    4

    6HCTM

    inner bodyLCTM

    inner body

    HCTMtotal body

    -2

    0

    2

    4

    6LCTM

    total body

    -2

    0

    2

    4

    6HCTM

    protected handLCTM

    protected hand

    HCTMtotal hand

    1 2 3 4 5

    -2

    0

    2

    4

    6LCTM

    total hand Robustness (Cross validation)

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 15

    Model

    Mixing/loading tank

    potential hands log DM(H) = 0.77 log TA + 0.57 [liquid] + 1.27 [WP] 0.29 [glove wash] + 3.12

    protected hands log DM(Hp) = 0.65 log TA + 0.32 [liquid] + 1.74 [WP] + 1.22

    total body log DM(B) = 0.70 log TA + 0.46 [liquid] + 1.83 [WP] + 3.09

    protected body log DM(Bp) = 0.89 log TA + 0.11 [liquid] + 1.76 [WP] + 1.27

    head log DM(C) = log TA + 0.90 [liquid] + 1.28 [WP] + 1.79 [no face shield] - 0.98

    inhalation log IM = 0.30 log TA - 1.00 [liquid] + 1.76 [WP] + 1.57

    Mixing/loading -knapsack

    potential hands

    75th percentile (above 1.5 kg a.s. linear extrapolation)

    protected hands

    total body

    protected body

    head

    inhalation

    Downward spraying vehicle mounted

    potential hands log DA(H) = log TA + 0.37 [normal droplets] - 1.04 [normal equipment] + 2.84

    protected hands log DA(Hp) = 0.54 log TA + 1.11 [normal droplets] + 0.29 [normal equipment] - 0.23

    total body log DA(B) = log TA + 0.81 [normal droplets] - 1.43 [normal equipment] + 2.54

    protected body log DA(Bp) = log TA + 0.70 [normal droplets] - 1.09 [normal equipment] + 0.74

    head log DA(C) = log TA + 0.88 [normal droplets] - 0.53 [normal equipment] + 0.24

    inhalation log IA = 0.50 log TA + 0.01 [normal droplets] - 0.71 [normal equipment] + 0.72

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 16

    Upward spraying vehicle mounted

    potential hands log DA(H) = 0.89 log TA + 0.28 [no cabin] + 3.12

    protected hands log DA(Hp) = log TA - 1.55

    total body log DA(B) = log TA + 0.48 [no cabin] + 3.47

    protected body log DA(Bp) = log TA + 0.23 [no cabin] + 1.83

    head log DA(C) = log TA + 1.89 [no cabin] + 1.17

    inhalation log IA = 0.57 log TA + 0.82 [no cabin] + 0.99

    Downward spraying hand-held

    potential hands

    75th percentile (above 1.5 kg a.s. linear extrapolation)

    protected hands

    total body

    protected body

    head

    inhalation

    Upward spraying hand-held

    potential hands log DA(H) = 0.84 log TA - 0.83 [normal culture] + 4.26

    protected hands log (DA(Hp) = log TA - 0.88 [normal culture] + 2.26

    total body log DA(B) = 0.16 log TA - 1.29 [normal culture] + 6.08

    protected body log DA(Bp) = - 1.64 [normal culture] + 4.65

    head log DA(C) = 0.32 log TA - 1.09 [normal culture] + 3.27

    inhalation log IA = 0.83 log TA - 0.26 [normal culture] + 2.17

    Model

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 17

    0.150.03LCHH

    0.070.01Knapsack

    0.040.11HCTM

    0.050.11LCTM

    0.040.05Tank WP

    0.020.02Tank liquid

    0.040.03Tank WG

    0.030.02HCHH

    0.040.03all

    A

    0.040.02all

    ML

    75th percentile75th percentile

    Work clothesProtective gloves

    Protection factor

    Actual hand Actual body

    Mixing/Loading: Face shield

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 18

    AOEM in risk assessment

    Comparison with German model and UK POEM

    Example: 300 g a.s./L; 5 L container (45 or 63 mm); 1 kg a.s./ha; 100 % dermal absorption; AOEL: 1 mg/kg bw

    German modelcoverall

    UK POEMlong shirt + trousers

    AOEM tankworkwear

    AOEM knapsackworkwear

    FCTM400 L/ha

    liquid 130 343 183

    WG 116 653 59

    WP 232 1324 910

    HCTM400 L/ha

    liquid 160 556 103

    WG 155 650 67

    WP 201 852 314

    LCHH200 L/ha15 L tank

    liquid 921 71 33

    WG 1137 53 33

    WP 1139 176 33

    HCHH200 L/ha2.5 L tank

    liquid 351 1019 41 23

    WG 88 905 24 23

    WP 131 907 146 23

    117 125

    111 125

    155 125

  • Claudia Grokopf, 2014-06-18, European Conference on Safe Use of PPP Page 19

    Conclusions

    new model for typical outdoor scenarios

    exposure factors selected by statistical analysis

    log linear model (Quantile Regression)

    validation

    tiered approach possible

    updating of the model if new data become available

    http://www.bfr.bund.de/cm/350/joint-development-of-a-new-agricultural-operator-exposure-model.pdf

    http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s00003-013-0836-x

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    Thank you for your attention

    Claudia Grokopf

    Federal Institute for Risk Assessment

    Max-Dohrn-Str. 8-10 10589 Berlin, GERMANY

    Tel. +49 30 - 184 12 - 3371 Fax +49 30 - 184 12 - 47 41

    [email protected] www.bfr.bund.de