Exposure Assessment: What are we all about? 2003 Weselowski Award Presentation

Mike Lebowitz• The scientific discipline of EA needs & deserves

growth - conceptually & in technology.• We need to educate – insure that EA is used

appropriately in bio-medical environmental sciences and hazard assessment, in risk assessment, and in policy making

• We need to learn how best to utilize good EA in policy making.

Interactions between

Host, Agent &

Environment

Environment

HostAgent

Biological Physical

Social

Exposure Assessment & the Environmental Health Paradigm

Sexton (1992)

Sources (s) • PropertiesAmount Released/UsedLocation/Setting

Concentrations AirWaterSoil/DustFoodSurfaces

Human ExposuresRouteMagnitudeDurationFrequency

Internal DoseAbsorbed DoseTarget DoseBiomarkers

Health Effect (s)CancerNon Cancero Damage/Diseaseo Signs/Symptoms

Exposure Assessment

Effects Assessment

Steps in Exposure estimation

1.1. Environmental Sources & Pathways in Multiple Environmental Sources & Pathways in Multiple MediaMedia

2.2. Concentration Measurements (Monitoring) & Concentration Measurements (Monitoring) & Exposure Info Exposure Info

3.3. Population Distributions of Concentrations in Population Distributions of Concentrations in MediaMedia Measurements of Time-Activity & Other Measurements of Time-Activity & Other Exposure FactorsExposure Factors

4.4. Statistical Modeling of Exposure Factors on Statistical Modeling of Exposure Factors on Exposures Exposure Assessment ModelingExposures Exposure Assessment Modeling

Lioy & Pellizzari, 1995

Lioy & Pellizzari, 1995

WHO - Biomarkers and Risk Assessment: Concepts and Principles

(ICPS, EHC 155, 1993)(ICPS, EHC 155, 1993)

WHO - Biomarkers and Risk Assessment: Concepts and Principles

(ICPS, (ICPS, EHC 155, 1993)EHC 155, 1993)

Exposure & Health Hazard Assessments

Exposure - Dose Estimates

Health Hazards Assessments

Risk Assessments

& Proposed Risk Management

Policies & Logistics:

Avoidance, Prevention, & Intervention Programs

Lioy & Pellizzari, 1995

Percentile

98877665544231205.88BDLBDL

Val

ue (

ug/m

^3)

140

120

100

80

60

40

20

0 DL

Air – PM10-Outdoor

Percentile

928068554330188BDL

Val

ue (

ug/m

^3)

300

250

200

150

100

50

0

GL

DL

Air – PM10-Indoor

Comparison of Indoor Air Arsenic

Mann-Whitney U Test: p<.001

Normalized T-Test: p<.001

Percentile

1009080706050BDL

Va

lue

(n

g/m

3̂)

30

25

20

15

10

5

0

STUDY

Border

NHEXAS

Pb, As, and Cd ComparisonsAmong Border Counties

Analyte Media Lead Arsenic Cadmium Fooda NS NS NS Airb Indoor p<.001 p<.001 p<.001 Outdoor p=.034 p<.001 p<.001 Soilb p<.001 p<.001 p<.001 Floor Dustc NS NS NS a NS: no significant differences between counties (Pb, p=.08; As, p=.92; Cd, p=.53) b significant differences between counties using Kruskal-Wallis Test c NS: no significant differences between counties (Pb, p=.25; As, p=.39; Cd, p=.57)

Mean Percent Total Ingestion of Metals from Different Sources

Bottled Water% Solid

Food% Liquid

Food% 2ndary %

Treated/ % Tap Water

Total Ingn(mg/da

Arsenic 53.6 19.5 4.9 3.1 19 37.2

Barium 76.2 14.4 2.6 0.4 6.4 684.3Cadmium 87.8 8.7 3.1 0.2 0.2 13.6Chromium 63.8 23.5 6.7 1.5 4.6 71.7Copper 77.3 15.1 4 0.4 3.2 943.7Lead 39 19.9 36.9 1.5 2.7 18.6Manganese 85.1 13.5 1.3 0.02 0.1 2694.6Nickel 76.8 16.9 2.2 1.1 2.9 146.3Selenium 87.6 10.8 0.3 0.2 1.1 75.7Vanadium 43.3 24.9 11.7 2.3 17.8 28.2Zinc 83.9 14.6 1 0.3 0.3 10,084.00

Hispanic Ethnicity & Food Contamination

Specific food types associated with elevated metal concentrations

• Hispanicity itself was a significant predictor of lead and cadmium when included in the models (p<.001); retained significance even after adjusting for food types

• Significant differences in arsenic and lead; primarily driven by region & outliers• Food from Border Hispanics contains significantly

more lead than the food from Hispanics living elsewhere in the State

• Most outliers occur in Hispanics vs. non-Hispanics• Significant differences in cadmium - Higher levels among

non-Hispanics and in AZ v.s. Border

Indoor / Outdoor Air VOC Ratio at the 90th Percentile

VOC Non-Border Community

Border Community

Benzene 3.54 / 1 2.31 / 1 Formaldehyde 1.79 / 1 1.60 / 1

Toluene 4.57 / 1 4.20 / 1 TCE BDL BDL

1,3-butadiene BDL BDL

Percentile

968778696153BDLBDLBDLBDLBDLBDL

Val

ue

(u

g/L

)

40

30

20

10

0

Drinking Water – VOC - chloroform

Percentile

988878695949BDLBDLBDLBDLBDL

Val

ue

(u

g/L

)

100

50

10

5

1

.5

.1

.05

.01

.005

Tap Water – VOC - chloroform

Percentile

9889807163544536271910BDL

Val

ue (

ug/L

)

20

18

16

14

12

10

8

6

4

2

0

Percentile

988979695949403020BDLBDL

Val

ue (

ug/L

)

10

5

1

.5

.1

.05

.01

.005

.001

Drinking Water – VOC - toluene

Blood

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

25% 50% 75% 90% max

ng

/mL

Tap Water – VOC - toluene

Benzene Indoor Air Concentrations

Fre

qu

en

cy

log-indoor air benzene-.430783 4.50203

0

71

Benzene Blood ConcentrationsF

req

ue

ncy

log-blood concentration benzene-5.116 -.088831

0

25

Benzene: Blood vs. Indoor Air Concentrations

log-b

lood

conce

ntr

atio

n b

en

zene

log-indoor air benzene-.430783 4.25845

-5.116

-.088831

Benzene: Regression of Blood vs. Indoor Air Concentrations

• All • Intercept = -2.602 • Coef. = 0.024• SE = 0.010• p = 0.019• R2 = 0.041

• Smoking Adjusted• Intercept = -4.130• Expos. = 0.132• SE = 0.061• Smoking = 1.317• SE = 0.189• p < 0.001• R2 = 0.305

VOCs: Blood

25% 50% 75% 90% Max N

Benzene* 0.046 0.075 0.120 0.375 0.915 149

Toluene*0.075 0.135 0.288 0.980 3.300 149

Chloroform* 0.005 0.010 0.022 0.038 0.155 122

PCE* 0.012 0.016 0.026 0.068 0.575 150

p-Dichlorobenz.0.038 0.068 0.205 0.798 51.000150

- Age, ethnicity, interaction significant*NS: Age, gender, ethnicity

Percentile

988878685848BDLBDLBDLBDL

Val

ue (

ug/m

^3)

50

10

5

1

.5

.1

.05

.01

.005

.001

GL

DL

Air-Pesticide-Chlorpyrifos (indoor)

Percentile

98BDLBDLBDLBDLBDLBDLBDLBDLBDLBDL

Val

ue (

ug/m

^3)

50

10

5

1

.5

.1

.05

.01

.005

.001

DL

GL

Air-Pesticide-Chlorpyrifos (outdoor)

Percentile

968777675848402920101

Val

ue (

ug/g

cre

atin

ine)

60

40

20

108

6

4

2

1

Urine-Pesticide- 356TCPY

  50th Percentile90th Percentile

Floor Dust 0.1 μg/g 3.2 μg/g

Sill Wipe < 0.1 μg/m2 0.3 μg/m2

Dermal 0.0 μg/sample 0.2 μg/sample

Foundation Soil 0.0 μg/g 0.2 μg/g

Yard Soil 0.0 μg/g < 0.1 μg/g

Food/Beverage 0.0 μg/g 2.0 μg/kg

Air Indoors 0.0 μg/m3 0.1 μg/m3

Air Outdoors 0.0 μg/m3 < 0.1 μg/m3

Exposure Assessment Models

• Geographic, Temporal & Sub Population Variability & Assessment of Errors and Uncertainties

• Identification of High Exposures

• Identification of Potential Problems– Areas, Sources, & Populations

(For Policy, Prevention, Avoidance, Mitigation & Other Interventions, and Communications)

Proportion of time spent in different locations by age group

< 3< 3 3-53-5 6-17 18-65 >65

In-HomeIn-Home 79.679.6 79.179.1 63.263.2 62.962.9 77.777.7

In-WorkIn-Work -------- -------- 1010 11.511.5 1.41.4

In-OtherIn-Other 7.67.6 6.66.6 7.17.1 6.66.6 4.94.9

Out-HomeOut-Home 5.55.5 7.17.1 6.56.5 4.54.5 7.57.5

Out-Work Out-Work -------- -------- 2.42.4 3.23.2 0.70.7

Out-OtherOut-Other 2.92.9 2.82.8 5.65.6 3.73.7 3.33.3

TransitTransit 4.34.3 4.44.4 5.25.2 7.77.7 4.54.5

Inhalation Exposure Model

j,ij

j,ii tCE

E= [CDsApsTps(1 DOps)] + [CSs(SpsSAps SOps)M]

Total Dermal Exposure Model

Ingestion Exposure Model

i

iLS L

iLiSiS

iT BW

WCWC

E

,,,,

,

106

Metals Exposure -Dose Model

  25%25% 50%50% 75%75% 90%90%

Cadmium 18.86 23.44 28.87 37.87

Chromium 47.30 66.98 99.37 168.34

Nickel 91.35 135.10 181.23 282.62

Lead 25.0 30.91 39.96 65.95

Estimates of Total Daily Intake Doses (g/day) of Selected Metals (N=176)

Analyte Smoker Non- smoker Sig. Blood Pb (ug/dL) 2.4 1.5 .001 (2t) Blood Cd (ug/L) 1.02 .55 .002 (2t) Urine Cd (ug/L) .7 .50 .013 (2t)

Smoker > Non-smoker

Smoker vs. Non Smoker

Smoker vs. Non SmokerPb

Analyte Smoker Non-smoker Sig. PM10 Indoors (ug/m3) 53.53 25.27 .000 (2t) PM10 Outdoors(ug/m3) 31.14 20.55 .000 (2t) Tap Water (ug/L) .4 .38 .863 (2t) Bottled Water (ug/L) .07 .25 .718 (2t) Food (Solid) (ug/Kg) 11.9 8.9 .194 (2t) Food Bev. (ug/Kg) 1.93 1.90 .535 (2t) Floor Dust (ug/g) 78.1 63.4 .397 (2t) Surface Dust (ug/ m3) 90.5% BDL (13 Samples>DL) 13 Samples > DL (4 smoking HH, 9 non-smokng)

Smoker vs. Non SmokerCd

Analyte Smoker Non-smoker Sig. PM10 Indoors (ug/m3) 53.53 25.27 .000 (2t) PM10 Outdoors(ug/m3) 31.14 20.55 .000 (2t) Tap Water (ug/L) .04 .71 .113 (2t) Bottled Water (ug/L) .013 .023 .668 (2t) Food (Solid) (ug/Kg) 15.3 17.25 .101 (2t) Food Bev. (ug/Kg) .52 .56 .716 (2t) Floor Dust (ug/g) 63.71 64.17 .946 (2t) Surface Dust (ug/ m3) 11.3 14.2 .368 (2t)

Contributions to Metal Exposures

•PM10, air and dust Cd concentrations are significantly greater (p < 0.05) in homes with smokers when compared to non-smoking Households.

•Individuals who smoke have significantly greater Pb & Cd in blood and Cd in Urine (p < 0.05).

•Indoor PM10 correlated with blood Pb:

•rs .385 (p=.001) all Households

•rs .610 (p=.001) Smoking Households

•Food, Beverage and Water are the primary contributors to metal exposures.

Lead Blood ConcentrationsF

ractio

n

log-blood concentration Pb-1.20397 2.89037

0

.136646

Lead Total Daily DoseF

ractio

n

log-total daily dose Pb2.35758 4.67571

0

.142045

Pb-Blood vs. Total Daily Doselo

g-b

loo

d c

on

ce

ntr

atio

n P

b

log-total daily dose Pb2.35758 4.67571

-1.20397

2.89037

Cadmium Urine ConcentrationsF

requency

log-biomarker concentration Cd-2.99573 1.72277

0

22

Cadmium Total Daily DoseF

ractio

n

log-total daily dose Cd, ug2.11048 4.24839

0

.142045

Cadmium Blood by Total Daily Dose Conc.

log

-blo

od

co

nce

ntr

atio

n C

d

log-total daily dose Cd2.11048 4.24839

-1.89712

1.06471

Bio-marker Distributions for Select Metals (g/dL)

*Hispanics and non-Hispanics significantly different; # Significantly different by age; see regression results for Ni & Pb; ** No significant differences The two significant regressions of biomarkers on total daily dose estimates were for Ni (involving age and

hispanicity as well); and Pb (involving sex and hispanicity,after which neither age nor the total daily dose were significant).

  25% 50% 75% 90% Max

Cadmium-Urine* 0.200 0.500 0.900 1.500 5.600

Cadmium-blood** 0.150 0.600 1.000 1.500 2.900

Chromium# 0.200 0.400 0.800 1.400 18.400

Nickel 2.800 4.200 6.400 8.100 21.100

Lead 1.100 1.700 2.600 4.000 18.000

Lead Dose Distribution by Media

Contribution to Lead Dose for an Adult Male at the 90th Percentile of exposure for each Media

Weighted and un-weighted mean metals intake for Hispanic and Non-Hispanic Whites, AZ NHEXAS

    Weighted Exposures Unweighted Exposures

    Hispanic Non-Hispanic Hispanic Non-Hispanic

  N = 1284049 3652221 53 126

Inhalation          

  As 0.032 0.036 0.049 0.043**

  Mn 0.475 0.399* 0.427 0.379*

Ingestion          

  As 24.71 47.299 18.405 45.122

  Mn 1805.621 2166.714 2324.044 2850.442*

Total          

  As 24.743 47.335 18.455 45.165

  Mn 1806.096 2167.114 2324.471 2850.821*

Descriptive Statistics for Drinking Water Chemical Residue Concentrations in Tap and Non-tap Water

  Concentration (ug/L) - Arizona Concentration (ug/L) - Border

Chemical Tap Non-Tap Tap Non-Tap

Residue Median 90th% Median 90th% Median 90th%* Median 90th%

Arsenic 4.76 16 0.14 4.23 5.09 10.7 0.23 4.67

Chromium 1.09 11.3 0.28 1.74 1 4.34 0.36 4.08

Lead** 0.39 1.59 0.05 0.94 0.28 0.6 0.17 0.95

Nickel** 0.32 9.62 3.9 5.89 2.27 5.62 0.33 4.27

1,2-DCE 0.29 1.94 0.3 2.28 0.29 0.77 0.08 0.72

1,3-Butadiene - - - - 0.048 0.11 0.031 0.11

DCM 0.074 0.57 0.067 0.64 - - - -

Chloroform** 0.03 2.04 0.05 2 0.11 1.19 0.15 0.87

Toluene 0.22 4.51 0.57 6.78 0.49 5.71 0.5 2.67

*Sample size is not adequate for distribution fitting, percentile value is calculated from empirical cumulative distribution** Median tap water concentrations of Arizona and Border are significantly different with Mann Whitney test

Uncertainty Analysis of Probabilistic ADE Estimates

  ADE (ng/kg/day) - Arizona

Chemical Median ADE 90th Percentile ADE

Residue Mean SD Range Mean SD Range

Arsenic 22.2 2 19.7-24.5 172.3 11.1 159-187

Chromium 18.1 1.1 16.7-19.3 148.6 12.5 121-147

Lead 6.4 0.3 6.1-6.7 26.8 1.6 24.8-29.0

Nickel 15.3 1.3 13.6-17.1 138.5 11.6 123-152

1,2-DCE 7.6 0.4 7.1-8.0 34.8 2.2 31.7-37.5

1,3-Butadiene - - - - - -

DCM 0.6 0.1 0.53-0.65 7.6 0.8 6.5-8.6

Chloroform 1.2 0.1 1.0-1.3 36.2 5 30-43

Toluene 4 0.4 3.6-4.5 65.8 7.3 58-77

Uncertainty was estimated in a two-step process using the bootstrap technique. 1- selects randomly a set of input to the model variable values, and estimates an exposure value. This step is repeated 1,000 times to formulate one exposure distribution. 2 - the process is repeated 200 times, resulting in 200 exposure distribution curves.

Uncertainty Analysis of Probabilistic ADE Estimates  ADE (ng/kg/day) - Border

Chemical Median ADE 90th Percentile ADE

Residue Mean SD Range Mean SD Range

Arsenic 19.65 1.76 17.4-21.6 186 14 168-205

Chromium 19.41 1.15 17.9-20.8 143 12 129-157

Lead 7.49 0.61 6.8-8.4 104 12 89-118

Nickel 25.22 1.63 23.3-27.3 177 14 159-194

1,2-DCE 3.54 0.21 3.3-3.8 17 1 15.9-18.4

1,3-Butadiene 0.37 0.02 0.35-0.39 1.4 0.1 1.3-1.5

DCM - - - - - -

Chloroform 4.9 0.29 4.6-5.3 32 3 29-35

Toluene 6.97 0.39 6.5-7.5 39.7 2.7 36-43

Uncertainty was estimated in a two-step process using the bootstrap technique. 1- selects randomly a set of input to the model variable values, and estimates an exposure value. This step is repeated 1,000 times to formulate one exposure distribution. 2 - the process is repeated 200 times, resulting in 200 exposure distribution curves.

Relationship of Diseases to VOC Indoor Air Concentrations

VOCs* Odds Ratio 95% CI #Cases

Toluene

gastrititis (9.1%) 2.15 1.20-3.88 16

Formaldehyde gastrititis 2.64 1.15-6.09 16

* No diseases were significant for benzene. Adjusted for hispanicity, age, and sex.

Other significant diseases include indigestion (9.1%), colitis (3.4%), intestinal /bowl trouble (7.4%) and other liver trouble (2.9%)

Relationship of Diseases to VOC Biomarkers

VOCs* Odds Ratio 95% CI#Cases

Benzene

kidney disease (3.4-5.7%) 2.20 1.07-4.56 10

PCE Indigestion (9.1%) 2.83

* No diseases were significant for chloroform, toluene, p-dichlorobenzene. Adjusted for hispanicity, age, and sex.

Relationship of Diseases to Metal Daily Dose

Metals* Odds Ratio 95% CI #Cases

Cadmium liver trouble 10.83 1.04-112.83 5

Lead yellow jaundice 9.74 2.09-45.26 8

* No diseases were directly related with chromium, arsenic, and nickel. Adjusted for all significant covariates; hispanicity, age, sex, individual and household smoking

Relationship of Diseases to Metal Biomarkers

Metals* Prev. Odds Ratio 95% CI #

Cadmium

Cd urine-stomach 7.4% 2.60 1.17-5.79 167

Cd-blood-hepatitis 6.9% 4.02 1.48-10.94 161

Nickel (urine)

jaundice 4.6% 4.71 1.11-19.96 165

Lead (blood)

bowl trouble 7.4% 3.47 1.27-9.47 160

* No diseases were directly related with chromium and arsenic. Adjusted for all significant covariates; hispanicity, age, sex, individual and household smoking

Other significant diseases include colitis (3.4%) and bowl trouble

Other significant diseases included fatty liver (2.2%)

PM10 Concentration

NHEXAS P5

n 163 271

Mean (µ g/m3) 40.7 34.8

Median (µ g/m3) 31.3 27.3

Range (µ g/m3) BDL - 211.7 BDL-179.4

1717179179.

43

BDL 2 (1.2%) 6 (2.2%)

Ln(Detection Limit) = 1.7 µ g/m3

Prediction Model

Variable P Intercept 2.8 < 0.01

(Cigarettes/day smoked by each HH member) ½ 0.1 < 0.01 Monsoon -0.4 < 0.01 People 0.1 < 0.01 Winter 0.3 < 0.01 Remodeled (yes/no) 0.2 < 0.01 (# Smokers)½ 0.2 < 0.01 Pet (yes/no) 0.2 0.02 Fall 0.2 0.03 Vacuumed (yes/no) -0.2 0.03 Adj. R2 = 0.376 N = 244

A

Association of Prevalent Respiratory Disease with Measured PM10

  # Cases

Adj odds ratio*

95% ConfInterval p-value

Asthma 125 1.5 0.83, 2.72 0.183Chronic

Bronchitis 89 1.45 1.00, 2.11 0.051

Emphysema 7 2.37 0.70, 8.07 0.168

Bronchiectasis 32 1.5 0.83, 2.72 0.183

* Adjusted for age, gender, smoking    

Association of Incident Cardiovascular Disease with Measured PM10

Odds 95% Conf #cases Ratio Interval p-value

HighBlood Pressure 10 1.32 0.60, 2.90 0.493

Other HeartProblems 5 4.60 1.30, 16.31 0.018

Some Conclusions• We have come a long way and have a long way

to go – Scientifically & Politically – We need to be proactive

• We need better objectives, study designs, screening instruments, & collaborations with social scientists

• We should learn about policy-making and work with politicians to insure use of our science & should participate more out there

• We should not be too insecure or too secure