Pb NAAQS Human Health Risk Assessment – Overview of Design and ImplementationNovember 12th, 2008

Dr. Zachary Pekara and Dr. Jee-Young Kimb

a - Office of Air Quality Planning and Standards (OAQPS), USEPA

b – National Center for Environmental Assessment (NCEA), USEPA

2

Overview of presentation

Background – the role of risk assessment in the National Ambient Air Quality Standards (NAAQS)

Key attributes of Pb from a risk assessment standpoint Case study approach Air-quality scenarios Sensitive populations, sentinel health endpoint and blood Pb

metric Types of exposure and risk metrics modeled Conceptual framework for the Pb NAAQS risk assessment More detailed overview of indoor dust modeling step Blood Pb results Concentration-response function(s) for IQ loss Key IQ loss (risk) results Areas for refinement of risk assessment approach ADDITIONAL SLIDES

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Background on NAAQS Process: Statutory Considerations and Role of Administrator

NAAQS includes a primary standard (human health focus) and secondary standard (welfare and ecosystem)

Primary standard (for public health protection) – judged by the Administrator to protect public health with an adequate margin of safety Includes consideration for sensitive subpopulations

Administrator considers risk and evidence-based information (provided by staff) along with peer-review and public comments in making decision regarding appropriate NAAQS

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Background on NAAQS Process: Risk Assessment and Evidence-Based Analysis Risk assessment – application of more complex step-wise

analysis of exposure and resulting risk for residential populations associated with selected case studies

Mechanistic and empirical modeling elements:• Exposure modeling framework• Health impact (risk) modeling framework

Estimate distribution of exposure and risk for populations within specific study areas (e.g., area surrounding smelter facility)

Evidence-based analysis – use data obtained directly from the literature (empirical) to estimate risk estimates using simple analysis framework

For Pb, have air-to-blood ratio to estimate exposure and simple CR function slope to translate that into IQ loss

• IQ loss = Pb-air * AB ratio * IQ loss slope Generate simple estimate of risk (no characterization of risk

distribution across population)

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Background on NAAQS Process: Indicator, Level, Averaging Time and Form

Indicator: chemical species or mixture that is to be measured (Pb NAAQS is TSP)

Level: amount of Pb that can be in ambient air Averaging time: period over which air measurements are

averaged to arrive at a level to compare to the level Form: air quality statistics (e.g., max, or second max) that is

to be compared with the level (works with averaging time) EXAMPLE: Current NAAQS: 0.15 µg/m3 max rolling 3 month

average• Level: 0.15 ug/m3• Averaging time: rolling 3 month average• Form: maximum

Risk Assessment informs: level and to a certain extent averaging time

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Key Attributes of Pb-Related Risk with Implication for the NAAQS Review – Multi-pathway and persistent nature of Pb

Pb in ambient air

deposition penetrates

indoors

deposition to indoor dust

ingestion of outdoor soil

ingestion of indoor dust

outdoor soil

Food (crops)

Drinking water

dietary and drinking water ingestion

inhalation

Pb paint Auto

Pb

Re-entrainment

Simplified representation

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Key Attributes of Pb-Related Risk with Implication for the NAAQS Review – Air-related and background pathways

Pb in ambient air

deposition penetrates

indoors

deposition to indoor dust

ingestion of outdoor soil

ingestion of indoor dust

outdoor soil

Food (crops)

Drinking water

dietary and drinking water ingestion

inhalation

Air-related (policy-relevant) Non-air related (background)

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Key Attributes of Pb-Related Risk with Implication for the NAAQS Review – Non-linearity of Exposure and Risk Modeling

Non-linearity in Pb exposure modeling and IQ concentration-response requires consideration of total Pb exposure (not just air-related) in order to representatively “place” a modeled child on the CR function curve

IQ loss

Blood Pb level (ug/dL)1.0 10

1pt

6pts

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Design Aspects:Case study approach

General urban case

study

Location-specific urban case study

Primary Pb smelter case study

One single exposure zone (uniform ambient air Pb level

and demographics)

Each US Census block is a separate exposure zone (varying ambient air Pb levels and

demographics across study area)

2km radius study area

Small neighborhood with ambient air levels

at standard

Larger urban area with varying ambient air Pb levels and

demographics

2km radius residential area surrounding large Pb smelter with varying ambient air Pb

levels and demographics

Pb smelter facility

5-20 km

Comparatively small area

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Air quality scenarios evaluated

Current conditions scenario PREVIOUS – 1978 NAAQS scenario (urban case

studies hypothetically assumed to have ambient air Pb levels just meeting current NAAQS) Assume proportional rollup for location specific

urban case studies based on TSP monitor data Alternate (lower) standard levels

0.5, 0.2, 0.05, and 0.02 ug/m3 Varying averaging times (max monthly and max

quarterly)

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Sensitive populations, sentinel health endpoint and blood Pb metric selected for risk modeling Neurological effects in children (0-7 yrs of age): developing

nervous system in children most sensitive and effects shown to occur at lower blood Pb levels

Evidence for neurological effects is well supported by epi and tox studies

Available epi studies support derivation of CR functions for IQ loss

Epi studies investigating neurological effects have focused on number of blood Pb metrics (concurrent, lifetime average, peak, and early childhood).

All 4 metrics have been correlated with IQ, but the concurrent and lifetime average have been shown to have the strongest association (in the Lanphear 2005 pooled analysis)

Concurrent (strongest association of the 4) emphasized in presenting final results

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Types of Exposure and Risk Metrics: population-weighted distributions and population incidence

Exposure: Population-weighted distributions of

blood Pb levels

Risk (Pb-related IQ loss): Population-weighted distributions of total

IQ loss

Population incidence estimates• Number of children with total Pb related

IQ loss greater than 1 IQ point, 5 IQ points, 7 IQ points, etc.

Blood Pb levels (ug.dL)

% o

f pop 50th % 95th %

Points of IQ loss

% o

f pop 50th % 95th %

Points of IQ loss

% o

f pop

1,350 kids with > 4 IQ points lost

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Conceptual framework for risk assessment - 1

STEP 1:Multi-pathway blood Pb modeling

Blood Pb levels (ug.dL)

Single central tendency blood Pb level for entire

study area

STEP 2: Application of geometric standard

deviation (GSD)

STEP 3: Application of IQ loss functions

Single population distribution of blood Pb

levels for entire study area

Blood Pb levels (ug.dL)

% o

f pop

Points of IQ loss

% o

f pop

Single population distribution of IQ loss for

entire study area

Location-specific urban case study

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Conceptual framework for risk assessment - 2

Ambient air Pb levels

MODELindoor dust Pb levels

Soil Pb levelsBackground Pb

levels (diet and

drinking water)MODEL blood Pb levels (IEUBK) –

central-tendency levels for EACH exposure zone

• multi-pathway intake modeling• biokinetic BLL modeling

Inter-individual variability in residential blood Pb

levels (GSD) MODEL Population-

distribution of blood Pb levels for ENTIRE

study area

MODEL Population-

distribution of IQ points lost for entire

study area

CR functions relating blood Pb levels and

IQ loss

Estimate policy-relevant IQ loss for population

percentiles of interest

Demographic data for exposure zones

Exposure Analysis (central-tendency level)

Risk Characterization(IQ loss)

Exposure Analysis (population distribution)

Blood Pb levels (ug.dL)

% o

f pop

Blood Pb levels (ug.dL)

% o

f pop

Points of IQ loss

% o

f pop

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Modeling Approach: Characterizing indoor dust Pb levels - 1

General urban and location-specific urban case studies Primary Pb smelter case study

• Hybrid model: mechanistic-empirical model

• SUB-MODEL 1: Mechanistic compartmental model to predict indoor Pb loadings given ambient air Pb levels (recent-air contribution). Considers: air exchange rates, deposition velocity, cleaning rates and efficiency. Dynamic mass-balance model which is solved for steady-state.

• Background (non-air) component of indoor dust Pb loading estimated by subtracting air-related estimate from total residential Pb loading estimate. Total estimate of indoor dust Pb levels obtained from HUD dataset (median of US residential range).

• SUB-MODELS 2 and 3: Empirical-based log-log regression equations used to (critical non-linearity):

a) Convert wipe equivalent loadings (from mechanistic model) to vacuum loadings and then

b) Convert vacuum loadings to concentrations

• Log-log regression model based on site-specific data from the remediation zone. Data include:

• Indoor dust Pb concentrations from 17 houses in remediation zone (units of analysis). Temporally-averaged values were used for each house.

• Annual-average Pb concentrations from US Census block centroids located within 200m of each house

• Road dust measurements within 300m of each house

• Post-remediation yard soil Pb levels for each house

• Model selected relates natural log of ambient air Pb to natural log of indoor dust Pb (this model had better predictive power compared with models which included soil or road dust variables).

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Modeling Approach: Characterizing indoor dust Pb levels - 2

Presentation of indoor dust Pb models used in Pb NAAQS risk assessment

0.0500.0

1000.01500.02000.02500.03000.03500.04000.04500.0

0 1 2 3

Ambient air Pb levels (ug/m3)

Ind

oo

r d

us

t P

b (

pp

m)

Hybrid (urban) model

Primary Pb smelterregression model

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Modeling Approach: Estimating blood Pb levels (IEUBK modeling)

IEUBK blood Pb model

Media Pb concentrations (air, soil, indoor dust, diet, drinking

water)(single value across all 7 years)

Ingestion and inhalation rates

(7 values – differentiated by child age)

Concurrent BLL estimate(7th year estimate)

Lifetime average BLL estimate(average of 6th month to 7th year)

Combined with Geometric Standard Deviation (GSD) characterizing inter-individual blood Pb level variability in

population

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Modeling Approach: Blood Pb results (and performance evaluation)

0

2

4

6

8

10

12

14

median 75th 90th 95thPopulation percentile

Blo

od

Pb

leve

ls (

ug

/dL

)

NHANES-IV (interpolated 1999-2002, 7yr old)

General urban case study (meancurrent conditions - higher)

Primary PB smelter (smaller 1.5kmstudy area - current NAAQS)

Location-specif ic case study(Chicago)

Location-specif ic case study(Cleveland)

Location-specif ic case study(LA)

Comparison – Modeled Concurrent BLLs for Case Studies Compared to NHANES-IV Data(modeled results are for current conditions)

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Modeling Approach: Specification of CR Functions for IQ Loss – 1

Lanphear et al. (2005) – An international pooled analysis from seven prospective cohorts

Development of regression model involved multistep process• First examined fit of linear model then considered quadratic and cubic terms to

examine non-linearity • Restrictive cubic spline function indicated that log-linear model provided a good fit to

the data Ten potential confounders considered

• Final model adjusted for site, HOME score, birth weight, maternal IQ, and maternal education

• Addition of child’s sex, tobacco and alcohol exposure during pregnancy, maternal age at delivery, marital status, and birth order did not alter effect estimate

Four measures of BLL examined• Concurrent, peak, early childhood, and lifetime average all highly correlated, but

concurrent BLL exhibited strongest relationship with IQ Stability of model evaluated

• Results of random-effects model were similar to fixed-effects model• Identical log-linear models that were fit with each model omitting data from one of

the sites indicated that the pooled analysis did not depend on data from any single cohort

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Modeling Approach: Specification of CR Functions for IQ Loss – 2 Relationship between Blood Pb and Children’s IQ in Lanphear et al. (2005)

Log-linear model (95% CI shaded) for concurrent blood lead concentration adjusted for HOME score, maternal education, maternal IQ, and birth weight. The mean IQ (95% CI) for the intervals <5, 5-10, 10-15, 15-20, and >20 µg/dL are shown. (Lanphear et al., 2005)

Log-linear model for concurrent blood lead concentration along with linear models for concurrent blood lead levels among children with peak blood lead levels above and below 10 µg/dL. (Lanphear et al., 2005)

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Modeling Approach: Specification of CR Functions for IQ Loss - 3

Plot of four CR functions specified for the risk assessment (based on Lanphear et al., (2005) pooled analysis results)

0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9 10

Concurrent blood Pb (ug/dL)

IQ lo

ss

log-linear with cutpoint

dual linear - stratifiedat 10 ug/dL, peakdual linear - stratifiedat 7.5 ug/dL, peaklog-linear with low-exposure linearization

Stratified at 7.5 peak BLL

Stratified at 10 peak BLL

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Modeling Approach: Risk Estimation – Prediction of IQ Loss

Blood Pb levels (ug.dL)

% o

f pop

Points of IQ loss

% o

f pop

Results of exposure modeling Results of risk modeling

four CR functions relating blood Pb levels to IQ loss

Population percentile

TOTAL IQ loss

Blood Pb Level

(concurrent:

ug/dL)

Pathway contribution (based on fraction of total UPTAKE)

DietDrinking

Water InhalationIndoor dust

(air)

Indoor dust

(other)Outdoor soil/dust

50th% -4.5 1.918% 10% 0.5% 28% 6% 28%

95th% -7.7 6.5

General urban case study (current conditions, LLL CR function)

Background Recent Air Past Air

LLL function

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Modeling Approach: Risk Estimation – Risk Results

Points IQ Loss

Air Quality Scenario RECENT AIR: Inhalation + indoor dust ingestion (air)

RECENT + PAST AIR: Inhalation + indoor

dust ingestion (total) + soil TOTAL Pb Exposure

Current NAAQS (1.5 µg/m3, max quarterly) 3.5 4.8 5.8 Alternative NAAQS (0.5 µ g/m3, max monthly) 1.9 3.6 4.8 Alternative NAAQS (0.2 µ g/m3, max quarterly) 1.5 3.4 4.6 Current conditions - mean (0.14 µ g/m3 max quarterly) 1.3 3.2 4.5 Alternative NAAQS (0.2 µ g/m3, max monthly) 1.2 3.2 4.4 Alternative NAAQS (0.05 µ g/m3, max monthly) 0.5 2.8 4.1 Alternative NAAQS (0.02 µ g/m3, max monthly) 0.3 2.6 4.0

Median population percentile risk (IQ loss) results (LLL CR function) General Urban Case Study

Air-related (policy-relevant) risk

LOW BOUND HIGH BOUND

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Areas for Potential Refinement of the Pb NAAQS Risk Assessment Approach

Exposure modeling: Further refine indoor dust modeling (provide coverage for foot

tracking mechanism that links ambient air to indoor dust Pb) Develop probabilistic approach for modeling inter-individual

variability in multi-pathway exposure to Pb (with emphasis on ambient-air related pathways) – alternate to GSD approach

Refine ability to pathway-apportion exposure (and risk) particularly for higher population percentiles

Enhance ability to relate shorter-term changes in Pb exposure to blood Pb levels (enhance shorter-term blood Pb modeling)

Refine our ability to model the impact of ambient air-Pb changes on adult blood Pb levels

Risk modeling: Further refine our understanding of low-exposure (low-blood Pb)

IQ loss with the goal of enhancing our CR functions Refine our ability to model other low-exposure related health endpoints

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ADDITIONAL SLIDES

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Policy-relevant apportionment of risk estimates (policy-relevant versus background)

“Recent air”

Background sources

Total risk = recent air pathways + past air pathways + background pathways

• The risk assessment simulates attainment of alternate NAAQS by reducing recent air exposures.

• In fact, attaining alternate NAAQS could also involve reduction of past air exposures (e.g., historically emitted and deposited lead).

Policy-relevant sources

Ambient air

• newly emitted lead• resuspension of historically emitted and deposited lead

• historically emitted and deposited lead• paint

• Diet• Drinking water

paint

Indoor dust“Past air”

Indoor dust

Outdoor soil

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Conceptual framework for risk assessment - Extra

Blood Pb levels (ug.dL)

% o

f pop

Points of IQ loss

% o

f pop

Points of IQ loss

% o

f pop

Points of IQ loss

% o

f pop

Population-weighted

aggregation

900 children

100children

Census block #n

Census block #1

Location-specific urban case study

Blood Pb levels (ug.dL)

% o

f pop

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Modeling Approach: Characterizing ambient air Pb levels, inhalation exposure air concentrations, and background (diet and drinking water) concentrations

Media category General urban case studyLocation-specific urban

case study Primary Pb smelter case study

Ambient air Pb levels

single ambient air Pb level assumed across entire study area (mean values from urban areas with > 1 million people).

US Census block groups within study areas assigned to nearest TSP monitor (point source and non-point source monitors handled differently). 6 to 11 exposure zones depending on location.

ISC-PRIME dispersion modeling (NAAQS attainment scenario) used to estimate centroid levels for US census blocks and block groups. 22 US Census block groups and 115 blocks.

Inhalation exposure air concentrations

National Air Toxics Assessment (NATA) – derived ratios of modeled Pb air exposure levels to ambient air Pb levels. The average ratio for the overall NATA analysis was used.

NATA-derived ratios estimated for set of relevant US Census tracts

Outdoor soil Pb levels

Arithmetic mean from HUD data set intended to characterize residential soil Pb levels across houses constructed between 1940 and 1998.

Site-specific post-remediation soil Pb measurement data (for subarea)

Dietary Pb levels Based on (a) Pb food residue data from US FDA Total Diet Study (2001) and (b) food consumption data from NHANES III (CDC, 1997)

Drinking water Pb levels

Geometric mean of values reported in studies of US and Canadian populations (residential water).

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Modeling Approach: Specification of CR Functions for IQ Loss - Extra

Log-linear function• n = 1,333• Median concurrent BLL 9.7 μg/dL• β = -2.70 (95% CI: -3.74, -1.66)• Estimate IQ point decrement: 3.9 points for BLL 2.4 to 10 μg/dL; 1.9 for

BLL 10 to 20 μg/dL Dual linear stratified at peak BLL 10 μg/dL

• n = 244• GM concurrent BLL 4.3 μg/dL• β = -0.80 (95% CI: -1.74, 0.14) for <10 μg/dL

β = -0.13 (95% CI: -0.23, -0.03) for ≥10 μg/dL Dual linear stratified at peak BLL 7.5 μg/dL

• n = 103• GM concurrent BLL 3.2 μg/dL• β = -2.94 (95% CI: -5.16, -0.71) for <7.5 μg/dL

β = -0.16 (95% CI: -0.23, -0.08) for ≥7.5 μg/dL