Approach for Estimating Exposures

and Incremental Health Effects from

Lead due to Renovation, Repair, and

Painting Activities in Public and

Commercial Buildings

  

   July 28, 2014

  

EPA Office of Pollution Prevention and Toxics

1200 Pennsylvania Avenue, N.W.

Washington, DC 20460

   Assistance and support provided by ICF International under Contract number EP‐C‐14‐001

    
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Peer Review Draft iii July 28, 2014

Contents

1. Executive Summary ....................................................................................................................... 1

2. Scope of Analysis and Overview of the Approach........................................................................... 3

2.1. Scope ............................................................................................................................................3

2.2. Conceptual Models....................................................................................................................... 4

2.3. Models Chosen ............................................................................................................................. 7

2.4. Variables and Renovation Activities Covered............................................................................... 8

2.5. Time Series of Exposures and Population Activity Patterns.........................................................9

2.6. Exposure Metrics Selected for the Analysis ...............................................................................11

2.7. Health Effects and Age Groups Covered in the Analysis............................................................12

2.8. Incorporating Variability............................................................................................................. 12

2.9. Monte Carlo Methods ................................................................................................................ 13

3. Estimating the Renovation‐Related Media Concentrations during Exterior

Renovations................................................................................................................................. 14

3.1. Renovation Characteristics......................................................................................................... 15

3.1.1. Characterizing the Renovated Building .........................................................................15

3.1.2. Characterizing the Renovation Jobs..............................................................................16

3.2. Lead Emission Rates during the Renovation Job........................................................................16

3.2.2. Aerosolized Particles ..................................................................................................... 17

3.2.3. Particulate Debris.......................................................................................................... 19

3.3. Control Options .......................................................................................................................... 20

3.4. Fate and Transport Modeling ..................................................................................................... 22

3.4.1. Receptor Buildings......................................................................................................... 23

3.4.2. Meteorological Conditions............................................................................................ 25

3.4.3. Obstruction Adjustment................................................................................................ 27

3.5. Air Concentrations...................................................................................................................... 28

3.6. Outdoor Hard Surface and Soil Concentrations .........................................................................29

3.7. Indoor Dust Concentrations during the Renovation ..................................................................32

3.7.1. Indoor Dust Loadings..................................................................................................... 32

3.7.2. Indoor Dust Concentrations .......................................................................................... 35

4. Estimating the Renovation‐Related Media Concentrations during Interior

Renovations................................................................................................................................. 35

4.1. Renovation Characteristics......................................................................................................... 36

4.1.1. Characterizing the Renovated Building .........................................................................36

4.1.2. Characterizing the Renovation Job................................................................................40

4.2. Lead Released During Renovation.............................................................................................. 41

4.2.1. Dust Study Processing ................................................................................................... 43

4.2.2. Other Data Sources Considered ....................................................................................44

4.2.3. Dust Time Series............................................................................................................ 46

4.2.4. Location of the Exposed Individual................................................................................48

4.3. Work Practice/Control Options.................................................................................................. 51

4.4. Contributions from Exterior Renovations and Track‐in to Renovated Building .........................52
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5. Selection and Update of the Leggett Model ................................................................................. 53

6. Estimating Health Effects in Children and Adults.......................................................................... 54

6.1. Selection of Health Effects.......................................................................................................... 55

6.2. Selection of Lead Model Metric ................................................................................................. 56

6.3. Concentration‐Response Characterization for IQ in Children ....................................................56

6.4. Concentration‐Response Characterization for Low Birth Weight..............................................60

6.5. Concentration‐Response Characterization for Kidney Function ................................................63

6.6. Concentration‐Response Characterization for Cardiovascular Disease Mortality .....................66

6.7. Summary of Health Effect Concentration‐Response Functions Used in the

Approach........................................................................................................................... 71

7. Implementing the Monte Carlo Approach.................................................................................... 75

7.1. Developing Exposure Scenarios.................................................................................................. 76

7.1.1. Exteriors......................................................................................................................... 76

7.1.2. Interiors ......................................................................................................................... 79

7.2. Estimating Time‐Dependent Background Intakes......................................................................84

7.3. Building Time Series of Exposure and Blood Lead......................................................................85

7.3.1. Selection of Ages at Renovation and at Blood Lead Measurement..............................85

7.3.2. Age‐specific Activity Patterns and Exposure Factors.....................................................88

7.4. Incorporating Population‐Level Biokinetic Differences..............................................................89

7.5. Metrics Saved for Incremental Media Concentrations, Blood Lead and Health

Effect Changes .................................................................................................................. 90

7.6. Testing the Full Monte Carlo Models ......................................................................................... 93

7.6.1. Accuracy.........................................................................................................................93

7.6.2. Convergence.................................................................................................................. 94

8. Results of the Monte Carlo Analysis............................................................................................. 95

8.1. Pre‐Screening to determine which Exterior and Interior Scenarios require

Monte Carlo ...................................................................................................................... 95

8.1.1. Exteriors......................................................................................................................... 95

8.1.2. Interiors ......................................................................................................................... 96

8.2. Summary of Results for Exterior Monte Carlo Scenarios...........................................................97

8.2.1. Trends across Scenario Variables..................................................................................97

8.2.2. Maximum Hourly Dust................................................................................................... 99

8.2.3. Renovation‐Related Blood Lead and Bone Lead .........................................................100

8.2.4. Health Effects................................................................................................................... 3

8.3. Sensitivity Analysis for an Exterior Scenario................................................................................. 7

8.3.1. Categorical Sensitivity Analysis........................................................................................ 7

8.3.2. Numerical Sensitivity Analysis....................................................................................... 11

8.4. Summary of Results for Interior Monte Carlo Scenarios............................................................14

8.4.1. Trends Across Scenario Variables..................................................................................15

8.4.2. Maximum Hourly Dust................................................................................................... 17

8.4.3. Renovation‐Related Blood Lead and Bone Lead ...........................................................19

8.4.4. Health Effects................................................................................................................. 25

8.5. Sensitivity Analysis for an Interior Scenario ...............................................................................28

8.5.1. Categorical Sensitivity Analysis......................................................................................28

8.5.2. Numerical Sensitivity Analysis....................................................................................... 30

8.5.3. Sensitivity to Individual’s Location in Time and Space ..................................................33
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9. Uncertainties in the Model Approach .......................................................................................... 36

9.1. Input Variables............................................................................................................................ 37

9.1.1. The Dust Study and Estimation of the Emission Rates and Fraction Emitted ...............37

9.1.2. Cleaning Frequency and Cleaning Efficiency .................................................................38

9.1.3. Effect of Building Obstructions......................................................................................38

9.1.4. Frequency and Duration of Renovation Activities.........................................................38

9.1.5. Time Spent and Location of the Exposed Individual .....................................................39

9.2. Modeling Approaches................................................................................................................. 39

9.2.1. Application of AERMOD................................................................................................. 39

9.2.2. Loading to Concentration Conversion Equation............................................................40

9.2.3. Application of Adult Health Effect Equations and the Time Since Renovation.............40

9.2.4. Lookup‐Table Approach................................................................................................. 41

9.2.5. Correlated Variables...................................................................................................... 42

9.2.6. Point‐Estimate Variables and the Population Distribution ...........................................42

9.2.7. Convergence of Upper Percentiles................................................................................42

10. References................................................................................................................................... 43

Figures

Figure 2‐1. Conceptual Model for the Interior Analysis................................................................................ 6

Figure 2‐2. Conceptual Model for the Exterior Analysis............................................................................... 6

Figure 2‐3. Illustration of Renovation and Post‐Renovation Exposure Phases........................................... 10

Figure 2‐4. Schematic of Renovation Effects on Blood and Bone Lead (adapted from Hu

et al., 1998) ............................................................................................................................... 11

Figure 3‐1. Illustration of Exposure Mechanisms for Exterior Renovations............................................... 15

Figure 3‐2. Illustration of Emission from Renovation Activity .................................................................... 16

Figure 3‐3. Illustration of Control Options for Renovation Activity............................................................ 20

Figure 3‐4. Illustration of Transport and Deposition .................................................................................. 22

Figure 3‐5. Summary schematic of example renovated building and locations of receptor

buildings. ................................................................................................................................... 24

Figure 3‐6. NOAA Climate Regions.............................................................................................................. 26

Figure 3‐7. Illustration of Air Concentration............................................................................................... 28

Figure 3‐8. Illustration of Soil Lead Estimation ........................................................................................... 29

Figure 3‐9. Illustration of Dust Loading Estimation .................................................................................... 32

Figure 4‐1. Example Building Layout with Different Renovation and Occupant Locations........................ 37

Figure 4‐2. Representative Building Layouts Used in the Approach........................................................... 39

Figure 4‐3. Dust Study Processing Steps..................................................................................................... 43

Figure 6‐1. Renovation‐Related Blood Lead Values and Associated Health Effect

Magnitudes ............................................................................................................................... 75

Figure 7‐1. Illustration of the Lookup Table Method to Link Specific Scenarios with the

Answers in the Lookup Table .................................................................................................... 83

Figure 7‐2. Illustration of Blood Lead Measurement Times (dashed lines) for Two

Different Age‐at‐renovation Values (0.6 and 4.8 years old) in the 0‐ to 10‐

year‐old Age Group ................................................................................................................... 87

Figure 7‐3. Diagram of Concurrent and Lifetime Average Blood Lead Metrics in the

Approach ................................................................................................................................... 88

Figure 8‐1. Maximum Hourly Dust Distribution for Exteriors................................................................... 100
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Figure 8‐2. Renovation‐Related Blood Lead Distribution for Exteriors .................................................... 101

Figure 8‐3. Renovation‐related Blood Lead Distributions for All Exterior Scenarios.................................... 2

Figure 8‐4. Renovation‐related Bone Lead Distributions for Exteriors......................................................... 3

Figure 8‐5. Renovation‐related IQ Change Distributions for Exteriors......................................................... 4

Figure 8‐6. Hazard Ratio (A), Risk Ratio (B), and Birth Weight Reduction (C) Distributions

for Exteriors................................................................................................................................. 6

Figure 8‐7. Categorical Scenario Variable Sensitivity Analysis Trends, Exteriors.......................................... 9

Figure 8‐8. Categorical Monte Carlo Variable Sensitivity Analysis Trends for Exteriors............................. 11

Figure 8‐9. Numerical Variable Elasticities and Sensitivity Scores (Where Available),

Exterior Analysis........................................................................................................................ 14

Figure 8‐11. Renovation‐related Blood Lead Distribution for Interiors...................................................... 19

Figure 8‐12. Renovation‐related Blood Lead Distributions for All Interior Scenarios ................................ 24

Figure 8‐13. Renovation‐related Bone Lead Distributions for Interiors..................................................... 25

Figure 8‐14. Renovation‐related IQ Change Distributions for Interiors ..................................................... 26

Figure 8‐15. Hazard Ratio (A), Risk Ratio (B), and Birth Weight Reduction (C)

Distributions for Interiors.......................................................................................................... 27

Figure 8‐16. Categorical Scenario Variable Sensitivity Analysis Trends, Interiors...................................... 29

Figure 8‐17. Monte Carlo Categorical Variable Sensitivity Analysis Trends, Interiors................................ 30

Figure 8‐18. Numerical Variable Elasticities and Sensitivity Scores (Where Available),

Interior Analysis......................................................................................................................... 33

Figure 8‐19. Location Sensitivity Analysis Results....................................................................................... 36

Tables

Table 2‐1. Scenario Variables in the Approach............................................................................................. 8

Table 2‐2. Renovation activities considered in the Approach. ..................................................................... 9

Table 2‐3. Health Effects Included in the Approach ................................................................................... 12

Table 3‐1. Population distribution by climate region. ................................................................................ 26

Table 4‐1. CBECS Buildings and Mapped Assessment Categories.............................................................. 38

Table 4‐2. Other Data Sources Considered for Interior Dust Loading Estimation...................................... 46

Table 4‐3. Locations of Individuals and Timing of Exposure....................................................................... 51

Table 4‐4. Control Option Combinations Considered in the Approach ...................................................... 52

Table 4‐5. Low and High Dust and Soil Levels from Track‐in to Same P&CB.............................................. 53

Table 6‐1. Log‐linear Blood‐lead IQ Coefficients and Confidence Intervals Derived by

Crump et al. (2013) Based on Original and Corrected Data from Lanphear et

al. (2005) ................................................................................................................................... 58

Table 6‐2. Predicted Birth Weight Reductions Based on Fractional Polynomial Model1 ........................... 61

Table 6‐3. Fully‐Adjusted (Model 3) Odds Ratios for Reduced eGFR (Navas‐Acien et al.,

2009) ......................................................................................................................................... 64

Table 6‐4. Odds Ratios for Reduced eGFR Associated with an Increase in Blood Lead

Concentration from 1.1 to 2.4 g/dL ........................................................................................ 64

Table 6‐5. Hazard Ratios and 95% Confidence Intervals of All‐Cause, Cardiovascular

Disease, Myocardial Infarction, and Stroke Mortality Associated with Tertile

of Lead....................................................................................................................................... 67

Table 6‐6. Summary of Adjusted Hazard Ratios for a 3.4‐Fold Change in Blood Lead ............................... 69

Table 6‐7. Summary of Concentration‐Response Functions for Approach Health Effects......................... 72
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Table 6‐8. Post‐Renovation Blood Lead Values and Associated Health Effect Magnitudes

in Approach ............................................................................................................................... 74

Table 7‐1. Scenario‐specific inputs. ............................................................................................................ 77

Table 7‐2. Sampled Variables for Exterior Analysis .................................................................................... 79

Table 7‐3. Separation of Scenario Variables Into Categories for Lookup Table Approach......................... 82

Table 7‐4. Sampled Variables for Interior Analysis..................................................................................... 84

Table 7‐5. Blood Lead Measurement Times Relative to the Renovation ................................................... 86

Table 7‐6. Model Metrics Saved from the Simulations .............................................................................. 92

Table 8‐1. Summary of Trends in Exterior Analysis .................................................................................... 99

Table 8‐2. Percentage of Exterior Scenarios with 95th Percentile Renovation‐Related

Blood Lead Change Levels Exceeding Specific Values*........................................................... 102

Table 8‐3. Percentage of Exterior Scenarios with Either 1% or 10% of Iteration Blood

Lead Changes Exceeding Specific Values* .............................................................................. 103

Table 8‐4. Categorical Scenario Variables Included in Exterior Sensitivity Analysis..................................... 8

Table 8‐5. Categorical Monte Carlo Variables Included in Exterior Sensitivity Analysis............................. 10

Table 8‐6. Numerical Variables Included in Exterior Sensitivity Analysis................................................... 11

Table 8‐7. Elasticities for the Numerical Variables in the Exterior Sensitivity Analysis.............................. 13

Table 8‐8. Summary of Interior Trends Analysis......................................................................................... 17

Table 8‐9. Percentage of Interior Scenarios with 95th Percentile Renovation‐Related

Blood Lead Change Levels Exceeding Specific Values*............................................................. 22

Table 8‐10. Percentage of Interior Scenarios with Either 1% or 10% of Iteration Blood

Lead Changes Exceeding Specific Values* ................................................................................ 23

Table 8‐11. Scenario Categorical Variables Included in Interior Sensitivity Analysis ................................. 28

Table 8‐12. Monte Carlo Categorical Variables Included in Interior Sensitivity Analysis........................... 30

Table 8‐13. Numerical Variables Included in Interior Sensitivity Analysis.................................................. 31

Table 8‐14. Elasticities for the Numerical Variables in the Interior Sensitivity Analysis............................. 32

Table 8‐15. Sensitivity Analysis Cases That Explore Effects of Location and Exposure

Timing........................................................................................................................................ 35
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Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

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Acronyms and Abbreviations

Acronym / Abbreviation Stands For

AERMOD Atmospheric dispersion modeling system developed by the American

Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA)

Regulatory Model Improvement Committee (AERMIC)  

BWR Birth Weight Reduction

CBECS Commercial Building Energy Consumption Survey

CDC Centers for Disease Control and Prevention

CHAD Consolidated Human Activity Database

CV Coefficient of Variation

CVDM Cardiovascular Disease Mortality

DOE Department of Energy

EPA U.S. Environmental Protection Agency

FSIQ Full Scale Intelligence Quotient

GFR Glomerular Filtration Rate

GSD Geometric Standard Deviation

HOME Home Observation Measurement for the Environment

HR Hazard Ratios

ICD International Code of Diseases

IEUBK Integrated Exposure Uptake Biokinetic Model for lead

ISA U.S. EPA Integrated Science Assessment for Lead

LCL Lower Confidence Limit

LRRP U.S. EPA Lead‐based paint Renovation, Repair, and Painting Rule for residences

NHANES National Health and Nutrition Examination Survey

NTP National Toxicology Program

ORs Odds Ratios

P&CBs public and commercial buildings

PbB blood lead

RECS Residential Energy Consumption Survey

SAB Science Advisory Board

TSCA Toxic Substances Control Act

UCL Upper Confidence Limit

XRF X‐Ray Fluorescence
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Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

Peer Review Draft 1 July 28, 2014

1. Executive Summary  

The U.S. Environmental Protection Agency (EPA) is currently in the process of determining whether  

lead‐based paint hazards are created by renovation, repair, and painting activities in public and

commercial buildings (P&CBs), as required under section 402(c)(3) of the Toxic Substances Control Act

(TSCA). If the EPA determines that any renovation activities in P&CBs create lead‐based paint hazards,

TSCA directs EPA to address those hazards through regulation.  

EPA recently released a document for public comment, “Framework for Identifying and Evaluating Lead‐

Based Paint Hazards from Renovation, Repair, and Painting Activities in Public and Commercial

Buildings” (79 FR 31072; FRL9910‐44). This Framework document described, in general terms, how EPA

could identify and evaluate hazards in P&CBs.  

The current document, “The Approach for Estimating Exposures and Incremental Health Effects due to

Lead During Renovation, Repair and Painting Activities in Public and Commercial Buildings” (referred to

hereafter as the “Approach”) describes in detail the methods and Approach EPA is considering to

estimate potential environmental concentrations, lead body burdens, and incremental health effects

related to exposure to lead from renovations in P&CBs.  

The Approach describes how EPA is modeling the potential overall magnitude and distribution of

estimated renovation‐related health effects due to lead exposure from a P&CB renovation, taking into

account background lead levels and health effects when no such renovation exposure occurs. Based on

information developed through the Approach, renovation‐related health effects will be estimated as the

difference between total health effects (background plus renovation‐related) and background.

Exposures from renovation activities that disturb lead‐based paint are connected to subsequent health

effects in children and adults through modeling. Separate Monte Carlo‐based models were constructed

for the analysis of exterior and interior renovations of P&CBs.  

In constructing the Monte Carlo‐based modeling Approach, a key distinction is made between three

kinds of input variables: scenario, sampled, and fixed variables. Scenario variables were treated

discretely, and when they are combined determine the overall number of exposure scenarios. Sampled

variables were varied within the Monte Carlo iterations to capture population‐wide variations. EPA did

not have enough information to create a distribution for fixed variables, and they were set to central

tendency values. The interior analysis modeled approximately 300,000 scenarios using 3,000 Monte

Carlo iterations, and the exterior analysis modeled approximately 30,000 scenarios, using 10,000 Monte

Carlo iterations. This resulted in approximately 1.2 billion total modeling iterations, which are believed

to adequately describe the range and variability of scenarios considered in this analysis.   

The exterior renovation of a P&CB can generate aerosolized lead dust, which is transported through the

air, and particulate debris, which is not transported through the air. The ambient air concentrations and

lead deposition rates were estimated using the AERMOD dispersion model. Penetration of airborne
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

Peer Review Draft 2 July 28, 2014

aerosolized lead dust through the outer shells of buildings is a greater source of exposure than

deposition and track‐in of lead dust from exterior soils and hard surfaces. The exception to this occurs

when high‐emitting exterior activities result in elevated soil concentrations at close receptor buildings

where many people are expected to enter each day.

The interior renovation of a P&CB can generate aerosolized lead dust and particulate debris within the

vicinity of the disturbed lead‐based paint. Dust loadings are estimated within the work area, the work

room, and adjacent rooms. Dust loadings were estimated through close review and refinement of

variables from the Dust Study and were found to increase with the intensity of the job and with smaller

room sizes. All variables that impact loadings were grouped and a representative range of loadings for

all combinations of possible renovation activities was derived. The range of loadings can be updated

with additional information related to renovation activities (frequency, duration, intensity), should such

information later become available. For the Approach, EPA used a Geographic Information Systems‐

based methodology to estimate the number of different types of P&CBs and the distances between

different categories of receptor buildings and renovated P&CBs.  

An important component of the Approach is how blood and bone lead were modeled. The renovation

scenarios modeled for the Approach result in short‐term exposures, which are best reflected in blood

lead levels. The renovation causes a short‐term spike in blood lead.  Over a relatively short period of

time, blood lead concentrations return to background levels. In contrast, bone is a long‐term storage

site for lead in the body. Both blood and bone lead were modeled using the Leggett model, which EPA

updated in coordination with one of the original model developers. Key model parameters were scaled

for children, bone transfer rates were evaluated and updated, and the model results were calibrated

against measured data sources for both children and adults. The updated Leggett model performed well

when evaluated using the National Health and Nutrition Examination Survey (NHANES) data for children

and occupational lead smelter data for adults, indicating good agreement with both these measured

data sources and IEUBK model estimates.  

The Approach estimated blood lead in two ways. A lifetime average blood lead value was estimated for

children and adults by allowing the lifetime variable to vary with the exposure scenario, as defined by

the age of an exposed individual at the start of the renovation activity and the time for the individual’s

blood lead to return to the background level. Concurrent blood lead levels were also saved from the

Leggett model at nine different post‐renovation time points (1 month, 5 months, 10 months, 15 months,

20 months, 25 months, 30 months, 40 months, and 50 months).  

Concentration‐response (C‐R) functions based on studies reported in the lead health effects literature

were applied to blood lead estimates assuming no renovation (“background”), and from corresponding

scenarios including a renovation, to estimate the incremental renovation‐related health effects. There is

model parameter uncertainty in the estimated health effects that occur at any given blood lead value, as

indicated by the lower and upper confidence intervals reported for the function, and the Approach

incorporated this model parameter uncertainty in the estimation of the effects.  
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

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Overall trends across different scenarios matched the direction of trends in the related input variables,

with conditions describing higher exposures resulting in higher incremental health effects. For the

common metric of blood lead, children elicit a higher response than adults given the same exposure

scenario. Blood lead levels are highest at 1 month post‐renovation and lowest at 50 months. This trend

reverses for bone‐lead. Lifetime blood lead estimates are higher in children than adults, and generally

fall between 1‐month and 50‐month estimates. In the Monte Carlo model, some effect estimates fall

below levels that can be quantified with accuracy.  

The results presented in the Approach encompass all combinations of scenarios that were modeled. The

overall magnitude and distribution of results for lead dust loadings, blood lead, and renovation‐related

health effect changes are presented to help provide context for the Approach. Further discussion of

what the Approach currently presents and how information from the Approach could be used in future

EPA activities is provided in Section 2.1  

2. Scope of Analysis and Overview of the Approach

2.1. Scope  

The results described in the Approach will be used for EPA’s assessment of the potential range of health

effects that could result from P&CB renovations. The results presented here are not intended to, and do

not, represent a distribution of exposure or health impacts that mimics exposure across the U.S.

population, but instead, represents hypothetical individuals experiencing hypothetical exposure

scenarios. EPA is using this initial analysis to understand what types of renovation scenarios should be

considered as EPA works to determine whether P&CB renovations create lead‐based paint hazards.  

The Approach presents a broad range of possible exposure scenarios, including those which may be very

high and unlikely and others that might be very low.  It is important to note is that each scenario

predicts health impacts to a hypothetical individual under a specific set of conditions that define an

exposure scenario. While one iteration within an exposure scenario predicts health impacts for a

hypothetical individual, the thousands of iterations within an exposure scenario represent a range of

exposures to thousands of hypothetical individuals that are possible within that scenario. Any single

scenario distribution incorporates the variability across the sampled variables included in the Approach.  

The results presented in the Approach, in combination with other additional information, will be

evaluated in a subsequent step to inform EPA in determining whether or not hazards are created from

the renovation of P&CBs.  EPA plans to consider other additional information including but not limited

to: the likelihood that a hypothetical scenario occurs, the baseline cleaning and work practices used in

the P&CB renovation industry, the frequency of different kinds of renovation activities occurring within

P&CBs in the United States, the number, types, and locations of individuals that may be exposed in a

given scenario, and the magnitude of health effects that EPA considers to be adverse (and therefore a

hazard). Given the additional work necessary, the summary of hypothetical results presented in the
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

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Approach should not be interpreted as an assessment of whether or not hazards are created from the

renovation of P&CBs.  

Once the frequency of occurrence of each scenario has been determined, EPA can apply a weighted

average of the individual scenario results in order to create an estimated population‐level distribution of

blood lead changes and resulting estimated health effects for the U.S. population. However, in this

Approach document, a broad range of results for hypothetical scenarios are shown. It is therefore

critical to understand that the percentage of scenarios that share a specific outcome is not equivalent to

the percentage of the U.S. population expected to experience this specific characteristic.

2.2. Conceptual Models  

As a first step in developing the Approach for estimating exposure to lead from renovation of P&CBs, a

conceptual model was designed. Conceptual models are diagrams that show the steps needed to

connect sources of exposure, fate and transport, uptake, and ultimately health effects.

Figures 2‐1 and 2‐2 display the conceptual models for both the interior and exterior renovation analyses

and an overview of the types of input variables that were used in the Approach. The modeling Approach

connects the emitted lead dust from the renovation to the subsequent health effects in children and

adults. This linkage is accomplished by estimating the renovation‐related air, soil, and dust

concentration time series, lead intake from both renovation and background sources, blood lead levels

from both the renovation and background sources (estimated at specific post‐renovation times and as a

lifetime average), adjusted blood lead levels based on biokinetic differences across the population, and

the incremental health effect changes due to renovation activities.

In general, the conceptual model uses building parameters and renovation parameters to estimate the

air, soil, and dust concentrations associated with the renovation. The Approach modeled different types

of P&CBs to capture a range of building occupancy patterns, uses, sizes, room configurations, air

exchange rates, and cleaning methods for buildings found in the United States.  

For the Approach, activity patterns were incorporated to determine how much of the time the

hypothetical children and adults are in contact with the renovation‐derived lead as opposed to

background levels in other locations. A blood lead model was used to estimate the blood and bone lead

values associated with the exposure concentrations. Concentration‐response functions linked the blood

lead estimates with estimates of the associated health effects in children and adults. Modeled bone lead

values were saved for potential future use if concentration‐response functions for health effects based

on bone lead concentrations are developed.

EPA estimated renovation‐related dust loadings, soil concentrations, air concentrations, blood lead,

bone lead, and incremental health effect changes for both interior and exterior modeling. Here,

“renovation‐related” refers to the total post‐renovation blood lead and health effect minus any blood
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

Peer Review Draft 5 July 28, 2014

lead or health effect that would have occurred at background lead exposure levels. Renovation‐related

metrics can be applied to media concentrations, blood lead, or health effects. The renovation‐related

values were estimated by finding the total post‐renovation blood lead and subtracting the background

blood lead the person would have had in the absence of a renovation. Another metric considered is the

time for environmental media and blood lead to return to background (or pre‐renovation) levels.   

The conceptual models in Figures 2‐1 and 2‐2 also provide an overview of the types of input variables

that are needed for the Approach.  

   
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

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Figure 2‐1. Conceptual Model for the Interior Analysis

Figure 2‐2. Conceptual Model for the Exterior Analysis
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

Painting Activities in Public and Commercial Buildings

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2.3. Models Chosen  

The models for estimating the media concentrations and blood lead levels were chosen with three

specific goals:

1. To use peer‐reviewed models wherever possible,

2. To avoid models that require more inputs than can be determined realistically using existing data,

and

3. To remain consistent, when possible, with the 2008 lead‐based paint renovation, repair, and

painting (LRRP) analysis for residences (US EPA 2008), while updating any models that have been

improved since then.

For the exterior analysis, EPA used AERMOD (US EPA 2009a) to model the dispersion of lead‐containing

dust downwind of the renovated building. AERMOD is a Gaussian dispersion model that incorporates

meteorological and land‐use characteristics. The Approach incorporated adjustments for obstructions to

the transport using the AERMOD Plume Rise Model Enhancements (PRIME) model. This model is

described in Appendix G.  

To estimate the indoor dust loadings resulting from the airborne renovation‐derived lead at nearby

receptor buildings, a mass‐balance indoor dust model was used. The model incorporated air exchange

between the indoor and outdoor environments, indoor deposition, indoor resuspension, cleaning

frequency and efficiency, and track‐in of dust from outdoor soil and hard surfaces. This model is

described in Appendix J.  

A Monte Carlo model was constructed for the Approach separately for exteriors and interiors using the

Python programming language (Python 2012). The model consisted of several modules that perform

variable sampling and calculations based on input files specifying the distributions of parameter values,

such as the AERMOD air concentration files, indoor dust and outdoor soil concentration files, and the

Leggett model uptake tables. Each lookup and calculation step was designed as a separate function in

the code. These modules provided the overall model with data about how environmental

concentrations vary over time, and how hypothetical individuals of different ages come into contact

with these environmental concentrations based on age‐driven activity patterns and exposure factors.

After verifying the calculations being performed, test cases were run in the model and trends across

model scenarios were examined for the purposes of quality assurance.  

EPA used a recently updated version of the 1993 Leggett biokinetic blood lead model (Leggett 1993) for

estimating both childhood and adult changes in blood lead. The Leggett model enables incorporation of

short‐term (less than 1 month) changes in exposure levels and modeling of both children and adult

blood lead levels. The tissue volumes and key age‐dependent pharmacokinetic parameters used in the

model were recently updated to better calibrate the model for the age groups that were modeled. This

model is described in Section 5 and Appendix M.  
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2.4. Variables and Renovation Activities Covered

Scenario variables in the Approach were variables that had a predetermined set of possible values. A

complete list of the scenario variables (Table 2‐1) and the renovation activities (Table 2‐2) incorporated

into the Approach are provided below. The selection of renovation activities was driven by the needs of

the economic analysis and the available data sources, and they cover both paint removal and other

renovation activities.

Table 2‐1. Scenario Variables in the Approach

Variable

Scenario Variables

Exterior Interior

Renovation activities  

Control options  

Renovated building use type  

Vintage of renovated building  

Size of renovated building  (Use Configuration)

Configuration of renovated building (Use Size) 

Size of renovated and adjacent rooms N/A 

Size of job area in the room N/A 

Location of person within building (in workspace or

adjacent) or distance of person from the renovated

building (receptor distance)

 

Whether receptor building is carpeted Not Scenario (Sampled) 

Age range (children, young adults, older adults)  

Health effects  

Each renovation activity was modeled at various intensities. The intensity of an activity varied based on

the job size and number of paint layers removed. Job size was related to the type of building where the

renovation occurred.  

Renovation activities can occur individually or in various combinations. Large renovation jobs consist of

several combined renovation activities. EPA assumed that combined renovation activities were

performed concurrently rather than sequentially and chose to model a person’s most recent exposure

(either individual or combined renovation activities). This was done due to lack of available information

on frequency and sequence of multiple renovation activities and to avoid additional complexity

introduced if these assumptions were not made, in terms of the number of modeling runs required.  
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Table 2‐2. Renovation activities considered in the Approach.

Activity type Exterior Interior

Paint Removal Activities

Power Sanding (with and

without HEPA)

 

Torching 

Heat Gun  

Needle Gun (with and

without HEPA)

   

Dry Scrape  

Other Activities

Cabinet Removal 

Cutouts 

Remove Window, No Saw 

Remove Window, Saw 

Exterior Trim Replacement 

Exterior Door Replacement 

Demolition  

2.5. Time Series of Exposures and Population Activity Patterns

Renovation activities generally occur over a short‐term period and vary depending on the type of

renovation activity (individual or combined) and the size of the job. The dust‐generating phase is quicker

than the rest of renovation phase, which is quicker than the routine cleaning phase. See Figure 2‐3 for a

visual depiction of these phases.  

Interior renovations (dust‐generating and rest‐of‐renovation phases together), on average, were

estimated to last approximately 8 weeks. Estimates ranged from less than a day to 7 months (Mossman

et al., 2009). This phase varied depending on the type of activity following the lead‐based paint

renovation activity. For example, if four windows were removed from a room during the dust‐generating

phase, the length of the rest of renovation phase would equal the amount of time required to install

four new windows.  

The dust‐generating portions of exterior renovations, on average, were estimated to last approximately

1 week. Estimates ranged from 1 day to 4 weeks (Capouch 2011; Mossman et al., 2009). The rest of the

renovation phase was not explicitly modeled because this phase was not expected to affect exposure

levels at the receptor building where people are exposed. See appendix D for a further discussion of

renovation duration.  
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The time for media concentrations to return to background levels varied across the interior and exterior

analysis. The average and 95th percentile values for time to background in the exteriors analysis across

all scenarios were 88 and 464 days, respectively. The average and 95th percentile values for time to

background in the interiors analysis across all scenarios were 503 and 1,505 days, respectively. This

value was largely determined by the cleaning frequency and efficiency in the receptor building (which in

turn varied by floor and building type).

Figure 2‐3 illustrates this generic time series, using exposure to indoor dust as the primary exposure

pathway. Exposure to ambient air resulted in a similar peak during the dust‐generating phase of the

renovation activity but quickly returned to background after the renovation activity stopped. Exposure

to soil results in a similar peak during the dust‐generating phase, but the soil lead concentration

remained elevated over time due to lead’s persistence in the soil. Exposure to particles on hard surfaces

outdoors resulted in a similar peak during the dust‐generating phase but returned to the background

level more quickly than soil due to removal from rain events.  

Within this time series, activity patterns were varied by age group and building type based on the

Consolidated Human Activity Database (CHAD) database (US EPA 2014a). Exposure to adults and

children depended on how and when they interacted with these concentrations. The age of the person

and the amount of time they spend in the building affects their resulting blood lead levels. People

typically spend less time in P&CBs than they do in residences.  

Occupants in the receptor building spent varying amounts of time in the building, referred to in this

document as “time spent.” The time‐spent distributions were age and building‐type dependent, and the

person was exposed to renovation‐related lead concentrations only when they were in the affected

building. When they were away from the building (and prior to the renovation), they were assumed to

have age‐specific background lead exposure estimated from the NHANES survey (CDC 2012).

Figure 2‐3. Illustration of Renovation and Post‐Renovation Exposure Phases
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2.6. Exposure Metrics Selected for the Analysis

After individuals in receptor buildings are exposed to renovation‐related lead, an increase in overall

body burden of lead is likely. The key metrics examined in the Approach are blood lead and bone lead.

The half‐life of lead in blood is relatively short (approximately 30 days) while the half‐life of lead in bone,

depending on which bones are considered, is years to decades (US EPA 2013). The renovation

introduced a spike in blood lead, and then the blood lead concentrations returned to background levels,

as shown in Figure 2‐4. In contrast, bone represents a long‐term storage site for lead in the body. Thus,

bone lead can be interpreted as a record of past history of lead exposure. The renovation caused an

increase in bone lead that might stay with the individuals through the remainder of their lives, also

shown in Figure 2‐4. Figure 2‐4 is adapted from Figure 1 of Hu et al. (1998).  

Over a person’s lifetime, that person might be exposed to multiple renovation activities that would

follow the generic time series described above and shown in Figure 2‐3. Modeling exposures to multiple

renovations in multiple types of buildings over a person’s lifetime would introduce significant

uncertainty into model estimates. Instead, the Approach estimated incremental health effects from a

person’s most recent exposure to a renovation activity.

Figure 2‐4. Schematic of Renovation Effects on Blood and Bone Lead (adapted from Hu et al., 1998)
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2.7. Health Effects and Age Groups Covered in the Analysis

Based on a review of existing child and adult epidemiology studies, described in Section 6.1, the health

effects in Table 2‐3 were selected for inclusion in the analysis. Where appropriate, due to apparent

differences in the magnitude of an effect, or due to limitations in the applicability of a concentration‐

response function as taken from the literature, the selected health effects were modeled only for the

age ranges specified in Table 2‐3 (which is described further in Section 6.1).

Table 2‐3. Health Effects Included in the Approach

Health Effect

Age Ranges

Children

0–10

Adults

18–49

Adults

>50

IQ Changes 

Low Birth Weighta 

Kidney Effects  

Cardiovascular Disease Mortality (CVDM)  

a

Blood lead levels in women aged 18–49 were used to estimate the reduction in birth weight of their infants.  

The Approach modeled building occupants in three age ranges: 0–10, 18–49, and 50–80 years. The age

groups were defined based on the approximate range of ages in the health effect studies (0–10 for IQ

decrements, 18–49 for women with low birth weight infants, and 18–80 for cardiovascular disease

mortality and kidney effects). Children aged 11–17 will also be exposed to renovations that could result

in health effects. They were not modeled as part of the Approach. As described in Section 6, health

effect studies with strong concentration‐response relationships for this age‐group were not identified.

EPA might, however, explore the feasibility of estimating lead body burden and health endpoints (e.g.,

low‐birth‐weight infants for women aged 15–17) for this age group later.  

In the 0‐ to 10‐year age group, children will have diverse activity patterns, including varying amounts of

time spent in different buildings. EPA notes that buildings defined as child occupied facilities were not

assessed in this Approach. EPA bound the modeling parameter of time spent for young children, and will

continue to evaluate this over time to ensure that child occupied facilities are not modeled or assessed

as P&CBs.   

2.8. Incorporating Variability

Numerous factors influence the exposure from interior and exterior renovations. They include the

number of layers of paint and the lead content of the paint on the renovated building, type of

renovation activity performed, particle sizes released during the renovation activity, age of the exposed

individual at the time of the renovation, biokinetic characteristics of the individual, and building

characteristics affecting indoor lead dust levels such as cleaning frequency, cleaning efficiency, flooring
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type, air exchange rate, building size, and room size. Factors that further affect the exposure from

exterior renovations include the prevailing meteorology, distance from the renovated building, and

presence of obstructions between the exposed individual’s location and the renovated building.

These factors are expected to vary widely across building types and renovation events.  For this reason,

probabilistic modeling techniques (specifically, Monte Carlo analysis techniques) were used to estimate

distributions of health effect changes using distributions of key input variables. Where possible, the

overall modeling Approach built on and refined the analysis EPA performed for the 2008 residential

LRRP rule (US EPA 2008).

2.9. Monte Carlo Methods

In constructing the Monte Carlo‐based modeling Approach, a key distinction was made between

different sets of input variables. Scenario variables are variables that were treated discretely. Each

scenario variable had a predetermined set of possible values. Changes in health effect statistics were

independently estimated for all combinations of the scenario variable values. Each unique combination

of scenario variables is referred to as a scenario. Scenario variables are listed in Table 2‐1, and more

detail is provided in Appendices A (exteriors) and B (interiors).

For each scenario, several other variables were allowed to vary to capture differences across the

population. Each variable has a specified range of values and associated probabilities that these values

occur. Monte Carlo modeling was performed by calculating health effect changes using various

combinations of input variable values. Each independent calculation of an estimated blood lead value

and corresponding health effect change for a set of input values is known as a Monte Carlo iteration and

represents one hypothetical individual. During an iteration, a value was chosen (randomly sampled) for

each input variable, and the associated blood lead value and corresponding health effects were

calculated. This was done for both background exposure (without renovation) and renovation‐related

exposure (varied by scenario). One iteration resulted in a blood lead estimate and corresponding health

effect estimates for this hypothetical individual’s background exposure (without renovation),

renovation‐related exposure, and total exposure (background plus renovation‐related exposures).

Therefore, the renovation‐related effects are the difference between total and background values. This

procedure was then repeated thousands of times for each exposure scenario.  

To distinguish the variables that were allowed to vary across iterations from the variables that define a

scenario, the former variables are referred to as sampled variables. As opposed to scenario variables,

results from sampled variables were not aggregated in the analysis; instead, their variations were used

to take into account population‐level variability in the renovation‐related health effects. All sampled

variables are listed in Appendices A and B.  

A third set of variables was not varied; instead, these variables were set to their central tendency values

for all scenarios and iterations. These variables, referred to as fixed variables, are listed in the tables in
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Appendices A and B. These variables are not expected to substantially affect the estimates and generally

have limited data available.  

To perform the Monte Carlo analysis, each scenario was run thousands of times (where each run is

referred to as an “iteration”). Testing indicated that 10,000 iterations for exteriors and 3,000 iterations

for interiors were appropriate to optimize the combination of accuracy and run‐time efficiency. For each

iteration within each scenario, a value was selected randomly for every sampled variable from its

associated distribution.  

Each model iteration represents one hypothetical individual’s exposure within that scenario and

resulting blood lead and health effects. The distribution of characteristics provides an estimate for

thousands of hypothetical individuals per scenario.  

3. Estimating the Renovation‐Related Media Concentrations

during Exterior Renovations

When paint removal or another dust‐generating activity is performed on the exterior of a P&CB,

exposure can occur from several exposure pathways, as listed below and illustrated in Figure 3‐1:

Inside the Renovated Public and Commercial Building:

1. Airborne lead‐dust penetrates the building shell.

2. Lead‐dust near the perimeter is tracked into the building.

Inside Nearby Residences and Public and Commercial Buildings:

3. Airborne lead‐dust travels downwind and penetrates other building shells.

4. Airborne lead‐dust near the building travels downwind, deposits on the hard surface/soil, and is

tracked into the downwind buildings.

Near the Renovated Public and Commercial Building:

5. Airborne lead‐dust and deposited lead on hard surface/soil near the renovated building results

in exposure to building occupants when they are outside the renovated building.

6. Airborne lead‐dust and deposited lead on hard surface/soil near the downwind building results

in exposure to building occupants outside the downwind building.

Each exposure pathway results in elevated environmental concentrations, resulting in possible

exposures for individuals who spend time in these microenvironments.  

To capture exposure pathways in Cases 3, 4, and 6, a series of empirical data and peer‐reviewed models

was used to estimate the different media concentrations shown in red and italics for the downwind

building in Figure 3‐1: airborne lead dust, dust lead, and hard surface/soil lead. This section describes
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the overall methodology used to estimate the lead emission rates related to the renovation activity,

wind transport and deposition (fate and transport modeling), air and hard surface/soil concentrations,

and indoor lead dust levels in the downwind building. The exposure pathways to occupants inside and

directly outside the renovated building (Cases 1, 2, and 5) are covered via incorporation into the interior

modeling Approach (Section 4).  

Figure 3‐1. Illustration of Exposure Mechanisms for Exterior Renovations

3.1. Renovation Characteristics

To characterize the lead emitted during a renovation event, the renovated building and the renovation

job were defined for each exposure scenario. The renovated building and renovation activities were

characterized using the list of the variables described below. A discussion of how the values were

determined for this analysis is provided in Appendix A.

3.1.1. Characterizing the Renovated Building

To characterize the renovated building for a given exposure scenario, the key variables included:

 area of the building footprint,  

 number of stories in the building,  

 building vintage,  

 square footage of building components (painted wall, trim, windows/doors),

 average percentage of lead in the paint, and

 average number of layers of paint.
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The number of layers of paint and percentage of lead in the paint, in general, were correlated with the

building vintage.  Based on available data, the Approach estimates that older buildings had more layers

of paint, which had higher lead content (US EPA, 1998a, EPA 2007a). Thus, building vintage was

incorporated into the estimates for each of these variables.

This list does not include building use type. For the exterior analysis, EPA assumed that the overall

function of the building will not affect the emission of lead during renovation activities. The only

exception is agricultural buildings; these buildings are typically in rural rather than urban areas, so the

fate and transport modeling accounted for this difference, as discussed in Section 3.4.

3.1.2. Characterizing the Renovation Jobs

To characterize the renovation job for a given exposure scenario, the key variables included:

 renovation activity,

 fraction of paint removed,

 rate of renovation, and

 job duration.  

Job duration depends on the size of the building and the rate of renovation. In general, EPA assumed

that a renovation job is performed on the entire painted surface area of a building for a given

component. For example, if the job is a paint removal activity, EPA assumed that the estimated layers of

paint were removed from the wall surface area of the entire building. The exception is demolition; in

this case, the jobs were characterized as involving only one wall.

3.2. Lead Emission Rates during the Renovation Job

Figure 3‐2. Illustration of Emission from Renovation Activity
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The lead removed from the building exterior consists of a spectrum of particle sizes. For the purpose of

this assessment, three particle classes were defined and later grouped into two categories:

Category 1: Particulate Debris

1. Bulk debris: large particles that are readily seen and easily cleaned after the job, and

2. Particulate debris: intermediate particles that do not travel downwind but are more difficult to see

and clean after the renovation.

Category 2: Aerosolized Particles

3. Aerosolized particles: smaller particles of a size and density that enable them to aerosolize and

travel downwind. These particles can be deposited farther from the renovated building vicinity.

Thus, lead persists post‐renovation in two particulate classes (Figure 3‐2): aerosolized particles (upward

arrow) and particulate debris (downward arrow). This section describes how emissions were

characterized for both types of particles.

3.2.2. Aerosolized Particles

The emission rates for aerosolized particles were estimated based on the total amount of lead removed

during the job, the estimated fraction of the removed lead that becomes aerosolized, and the duration

of the job:

ൌ ݁ݐܴܽݏݏ݉݅ܧݎ݁ܣ

ݒ݊݋ܥݐܷ݊݅ ൈ ܽܿݎܨݎ݁ܣ ൈ ݁݀ݒ݋ܴ݁ܽ݀݁݉ܮ

݊݋݅ݐܽݎݑܦܾ݋ܬ

Where:

AerEmissRate  = the mass of lead emitted per hour (g/h)

LeadRemoved  = the total amount of lead in the paint that is removed (g)

AerFrac = the fraction of the total amount of removed lead that becomes aerosolized

JobDuration   = the total duration of the job (h)

UnitConv = a conversion factor to convert from hours to seconds and mg/cm2 to g/ft2.

AerFrac was estimated using information contained in the Draft Final Report on Characterization of Dust

Lead Levels After Renovation, Repair, Painting Activities (US EPA, 2007a), hereafter referred to as the

Dust Study. The Dust Study contains a series of experiments depicting renovation activities in homes and

schools with lead based paint and then sampling the resulting lead loadings. The AerFrac variable is
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independent of the lead content in the paint and the size of the job; instead, it characterizes the amount

of removed paint that becomes aerosolized. By characterizing emissions using this fraction, the Dust

Study experiments were normalized to account for differences in lead content in the paint, differences

in job size, and differences in job configuration to yield estimates of the aerosolized fraction for each

experiment. The estimation of this variable is documented in Appendix H.

The variable LeadRemoved was estimated using the amount of lead on the wall at the start of the job

per unit area, the fraction of paint removed during the job, and the total job surface area:

ݒ݊݋ܥݐܷ݊݅ ൈ ݁ܽݎܣܾ݋ܬ ൈ ݁݀ݒ݋ܴ݊݁݉݋݅ݐܽܿݎܨ ൈ ܨܴܺ ൌ ݁݀ݒ݋ܴ݁ܽ݀݁݉ܮ

Where:

LeadRemoved   =  the total amount of paint lead that is removed (g)

XRF =  the x‐ray fluorescence (XRF) measurement of lead in the paint per square

cm of wall (mg/cm2 wall)

FractionRemoved   =  the fraction of the total layers of paint that are removed during the job

JobArea =  the area of the building that was disturbed during the renovation (ft2

)

UnitConv =   a conversion term to convert from mg to g and cm2 to ft2

.

The amount of lead on the wall, also referred to as wall loading, at the start of the job (XRF) was

captured by XRF measurements, and a nationally representative data source (the National Survey of

Lead and Allergens in Housing, HUD 2002) was used to estimate the XRF distribution for the various

building vintages. This variable is described in Appendix C.1.7.  

The fraction removed (FractionRemoved) is intended to reflect that not all layers of paint are removed

during a repainting job. When a wall surface is repainted, it is usually first prepared by removing a

fraction of the existing paint rather than all the paint on the wall. This fraction can vary by the substrate

type (wood, metal, and stucco result in different surface preparation methods), the method used to

remove the paint (mechanized or heating techniques enable faster removal of more layers than scraping

methods), and the condition of the existing paint (more paint is likely to be removed if the paint is highly

degraded). A representative value of 15 percent was selected for the amount removed during surface

preparation, as described in Appendix H. For the other types of activities (e.g., trim removal, door

removal, demolition), the fraction was assumed to match the fractions estimated to have been removed

in these jobs in the Dust Study (US EPA 2007a).  

Both the JobArea and the JobDuration variables depend on assumptions about the size of the building.

These variables are discussed in Appendix D.
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The final emission rate equation, after substituting in the LeadRemoved equation, is;

ݒ݊݋ܥݐܷ݊݅ ൈ ܽܿݎܨݎ݁ܣ ൈ ݁ܽݎܣܾ݋ܬ ൈ ݁݀ݒ݋ܴ݊݁݉݋݅ݐܽܿݎܨ ൈ ܨܴܺ ൌ ݁ݐܴܽݏݏ݉݅ܧݎ݁ܣ

݊݋݅ݐܽݎݑܦܾ݋ܬ

Where:

AerEmissRate   = the mass of aerosolized lead emitted per hour (g/h)

XRF =  the XRF measurement of lead in the paint per square cm of wall (mg/cm2

wall)

FractionRemoved   = the fraction of total layers of paint removed during the job

JobArea =  the area of the building disturbed during the renovation (ft2

)

AerFrac =  the fraction of the total mass of paint removed that becomes aerosolized

UnitConv = a conversion factor to convert from hours to seconds and mg/cm2 to g/ft2

JobDuration   =  the total duration of the job in hours (h).

3.2.3. Particulate Debris

The mass emitted as particulate debris was estimated in a way similar to how aerosolized particulate

was estimated. Rather than an emission rate, however, the particulate debris calculation directly

estimates the amount of lead per square foot of ground after the renovation (the lead loading). No fate

and transport modeling is required for this particle class, so the loading can be directly estimated.

ݒ݊݋ܥݐܷ݊݅ ൈ ܽܿݎܨܾ݁ܦݐݎܲܽ ൈ ݁ܽݎܣܾ݋ܬ ൈ ܨܴܺ ൌ ݐ݉݅ܧݏݏܽܯܾ݁ܦݐݎܲܽݐݔܧ

Where:

ExtPartDebMassEmit = lead mass emitted to the hard surface from particulate debris (μg)

XRF =   the XRF measurement of lead in the paint per square cm of wall

(mg/cm2 wall)

JobArea =   the area of the building disturbed during the renovation (ft2

)

PartDebFrac =   the fraction of paint on the wall emitted as particulate debris

UnitConv   =   a factor to convert from g to g and cm2 to ft2
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The fraction of the paint emitted as particulate debris was estimated from a study on removal of lead

paint from bridges (Lee and Domanski 1999). The study evaluated the removal due to sandblasting and

the subsequent release of particles. The study estimated that, of the bulk material generated (bulk

debris and particulate debris, or the total particulate after subtracting the portion aerosolized), an

additional 6 percent would have an aerodynamic diameter less than 50 m. This estimate was judged to

be an appropriate definition for the particulate debris fraction in the absence of other data. Thus, the

PartDebFrac variable was estimated as:

Where:

PartDebFrac = fraction of paint on the wall emitted as particulate debris

AerosolFrac = fraction of paint on the wall emitted as aerosol.

3.3. Control Options

Figure 3‐3. Illustration of Control Options for Renovation Activity

Four options for controlling lead dust generated by exterior renovations of P&CBs were considered

(Figure 3‐3): (1) the use of horizontal containment around the renovation, (2) the use of vertical

containment, (3) the combined use of horizontal and vertical containment, and (4) the replacement of

high‐dust‐generating activities with low‐dust‐generating activities. Modeling was also done with none of

these control options in place.  

As discussed in Section 3.1, lead is emitted and persists post‐renovation in two different particulate size

categories: aerosolized particles and particulate debris. As a mitigating factor, the use of horizontal

containment further eliminates a large portion of the particulate debris from the renovation site. Using

PartDebFrac  (1 AerosolFrac)  0.06
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a vertical containment, a significant fraction of aerosolized lead is captured and deposits in the

immediate vicinity of the renovated building.  

Containment options were incorporated into the model via containment efficiencies applied to the lead

emission rates. Horizontal plastic was assumed to be placed around the perimeter of the renovated

building to a distance of 10 ft from the building edge along each wall. The dust that fell onto the

horizontal plastic was assumed to be evenly distributed on the plastic by the end of the renovation,

when the plastic and dust that remained on top were assumed to be removed. Not all deposited lead

mass will be captured by the horizontal plastic; the percentage of the dust depositing on the ground that

is captured by the horizontal plastic is referred to as the containment efficiency. This value was

estimated using dust loadings measured above (i.e., captured by plastic) and below (i.e., not captured by

the plastic) the horizontal plastic in the Dust Study. The overall average for all activities, 95 percent, was

used as the horizontal containment efficiency.  

Vertical containment also was modeled as constructed around the perimeter of the building 10 ft from

the building edge, resulting in the same building‐specific area of containment. Fewer data were available

to calculate the efficiency of dust capture by vertical containment. Iyiegbuniwe et al. (2006) presented

data for the efficiency of containments used during bridge de‐leading by abrasive blasting and found an

efficiency of 91 percent for lead particles. In the absense of additional data, this value was used for the

vertical containment efficiency for all renovation activities.  

When vertical containment was used alone, the captured dust was assumed to be evenly dispersed on

the ground within the area of containment at the end of the renovation. When vertical plastic was used

in conjunction with horizontal plastic, the captured dust was modeled as falling onto the horizontal

plastic and subsequently being removed with the plastic at the end of the renovation.

The effect of replacing higher dust‐generating activities with lower dust‐generating activities was

estimated by taking the difference of separate model runs for both the original and replacement

activities and determining the net reduction effect. In this Approach, the following differences were

estimated: needle gun without HEPA vs. needle gun with HEPA and power sanding without HEPA vs.

power sanding with HEPA. The with‐HEPA activities were assumed to have a 90 percent efficiency when

compared to their no‐HEPA activity counterparts. This is further described in Appendix D.  
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3.4. Fate and Transport Modeling

Figure 3‐4. Illustration of Transport and Deposition

The renovation‐related air lead concentrations and lead deposition rates (Figure 3‐4) were estimated

using the AERMOD dispersion model (version 13350; 12/16/2013) (US EPA 2009a). According to the

Revision to the Guideline on Air Quality Models (US EPA 2005), AERMOD represents the most robust air

quality model for source‐specific modeling when evaluated against monitoring data. To prepare the

meteorological data, both AERSURFACE (first version; 01/2008) and AERMET (version 13350;

12/16/2013) were used to preprocess the data (US EPA 2009b).  

AERMOD is a Gaussian air dispersion model. It is not a mass‐conserving fluid dynamics model nor does it

simulate feedback mechanisms, such as resuspension of deposited lead from soil. Theoretically, a

computational fluid dynamics (CFD) model could be a more appropriate selection for this application,

but no CFD model is readily available that can support this type of generalized assessment designed to

represent the range of conditions in the United States. Early scoping runs showed that resuspension

does not significantly influence the air concentrations and subsequent indoor dust loadings. Therefore,

air concentrations were estimated using the AERMOD values during the renovation and were set to zero

following the renovation.

AERMOD requires inputs that characterize the meteorological conditions; location, strength, and

duration of the emissions; particle size distribution (for estimating the deposition rate); and receptor

distance from the source, orientation, and height. To capture the natural variability in these input

parameters, the Approach was designed to allow these parameters to vary by scenario or

probabilistically or both. A set of AERMOD runs was performed to generate the necessary outputs for all

permutations of these inputs.  

Assumptions were made for each set of inputs to focus the analysis on a feasible number of AERMOD

runs while ensuring the model predictions were generalizable enough to capture the range of possible
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air concentrations of aerosolized lead‐dust experienced by the hypothetical individuals around the

renovated buildings. By varying all of the above parameters, more than 5,000 AERMOD simulations

were performed. Each simulation assumed a unit emission rate of 1 gram/second, and the complete set

of average air concentrations was compiled into a database used as inputs to the Monte Carlo model.

The unit emission rate was used so that the runs could be easily scaled later by multiplying by the actual

renovation‐specific emission rate. During each Monte Carlo iteration, probabilistic sampling was used to

select an AERMOD simulation to use for a given iteration.  

For the Approach, agricultural buildings were modeled with AERMOD’s rural option; all other receptor

building types were modeled with AERMOD’s urban option. AERMOD’s urban modeling option

parameterizes one of the effects of the urban heat island—the decreased nighttime cooling that creates

a “convective‐like” nighttime boundary layer with enhanced heating and turbulence compared to an

adjacent, nonurban area. Because the renovations occur only during the day and the urban effect occurs

primarily at night, the difference between simulated air concentrations using the urban versus rural

model setting is expected to be minimal. Early test runs indicated that differences in modeled air

concentrations were less than 1 percent in approximately 90 percent of the simulated scenario‐days. For

the small percentage of scenarios where this distinction does make a difference, EPA could consider

additional data sources that describe how often different building types occur in urban or rural locations

by adding this as a sampled variable.  

3.4.1. Receptor Buildings

Receptor buildings were assumed to be one of five unique types of buildings:

1. Residences

2. P&CB – Commercial Buildings

3. P&CB – Schools and Child Occupied Facilities

4. P&CB – Industrial Buildings

5. P&CB – Agricultural Buildings

Each different receptor building type was characterized with appropriate dimensions, air exchange, and

track‐in rates, as discussed in Appendix E.  

The locations were placed along 16 evenly spaced radials emanating from the center of the renovated

building. Receptor building locations upwind in a given hour of the renovation have no renovation‐

derived concentration of aerosolized lead‐dust for that hour, while receptor building locations

downwind of the renovation have the maximum concentrations. The assumption was that a receptor

building would be equally likely to be located along any of these radials, so the probability of each

occurring was 1/16 (or 0.0625) in the Monte Carlo model.  
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Receptor building locations were placed so that the average distance from the nearest wall of the

renovated building equaled each of 6 target distances along each radial: 5 ft, 50 ft, 150 ft, 300 ft, 650 ft,

and 800 ft. A schematic of the receptor building locations in relation to the renovated building is

presented in Figure 3‐5. These distances were selected to provide higher resolution close to the

renovation and to extend far enough to capture all locations where the change in dust loadings, blood

lead concentrations, and health effects due to renovation were at or approaching background levels.

The closest receptor distance at 5 ft is meant to capture the air concentrations and depositions for the

renovated building itself, and the results from these runs were incorporated into the interior modeling.  

   

Figure 3‐5. Summary schematic of example renovated building and locations of receptor buildings.

Early scoping results showed that track‐in of lead that is deposited on exterior surfaces around the

receptor building location contributes much less to combined exposures than atmospheric transport of

lead dust directly into the receptor location. In the case of receptor buildings that are very near to the

renovation building, however, track‐in can affect indoor loadings, particularly when considering debris

generated from the renovation that is too large to be atmospherically transported. As such, track‐in of

the bulk dust particulate debris and the dust that is captured within the containment area is considered

at the 5 ft receptor location.

The receptor building heights were simulated as 1, 2, or 3 or more stories. The DOE 2005 Residential

Energy Consumption Survey (RECS) (US EIA 2005) reports the frequency of different numbers of stories

in the U.S. housing stock. The Commercial Building Energy Consumption Survey (CBECS) reports the

frequency of different numbers of stories in commercial buildings (US EIA 2003). The height of each

story varied by building type and was derived from Department of Energy (DOE) reference buildings
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(Deru et al., 2011). Residences were assumed to have 8‐foot ceilings, commercial buildings were

assumed to have 9‐foot ceilings, schools were assumed to have 11‐foot ceilings, and agricultural and

industrial buildings were assumed to have 28‐foot ceilings (Deru et al., 2011). The heights in AERMOD of

individuals exposed within receptor buildings were set to correspond to the height of an individual (1.8

meters or 5.9 feet) above the height for each building story. For example, in one‐story buildings, the

breathing height for an individual in the building is 5.9 feet. For buildings greater than one story, the

individual’s breathing height is the sum of the individual’s height and the ceiling height of building,

which varies by building type.  

3.4.2. Meteorological Conditions

Meteorological conditions, most notably wind direction, wind speed, and turbulence, are key factors in

characterizing the transport of lead emissions across relatively small distances. Capturing all the

potential meteorological conditions near a renovated public/commercial building is not possible. The

meteorological conditions captured in this Approach, however, were designed to be generalizable to

renovations occurring during any season and in any part of the country.

An analysis was performed to determine sets of meteorological conditions that together would be

representative of the United States. Similar methodology has been used previously for other EPA

assessments, including modeling near roadway pollutants for the Office of Transportation and Air

Quality (US EPA 2011).  

Nine climate regions within the United States, as defined by the National Oceanographic and

Atmospheric Administration (NOAA), were selected (NCDC 2011). These regions are reproduced in

Figure 3‐6. The percentage of the U.S. population represented by each climate region is presented in

Table 3‐1. These percentages were used to construct the cumulative probability of selecting a given

climate region in the Monte Carlo model.  

Some of these climate regions contain coastal areas and others do not. For climate regions where the

selected representative station was coastal, available meteorological stations within the region were

analyzed to determine an additional inland representative. If a region was represented by two stations

(one coastal and one inland), each was given equal probability of occurrence in the Monte Carlo model.

These 14 stations were then processed using AERMET for use in the AERMOD air dispersion and

deposition model. The modeling for the current analysis used high‐frequency wind data and AERMINUTE

to reduce calm or missing wind values in the hourly wind data, and it also used other substitution

methods for missing values of important meteorological variables. The full data processing, analysis, and

preparation methods are further described in Appendix G.
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Figure 3‐6. NOAA Climate Regions

Table 3‐1. Population distribution by climate region.

Climate Region Percentage of Population in

Region

Cumulative Probability Used

in Monte Carlo Model

Upper Midwest 7.82% 0.0782

Northeast 20.25% 0.2807

Northwest 3.96% 0.3203

South 13.78% 0.4581

Southeast 18.11% 0.6392

Southwest 5.31% 0.6923

West 13.05% 0.8228

Northern Rockies and

Plains 1.59% 0.8387

Ohio Valley 16.13% 1.0000
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3.4.3. Obstruction Adjustment

The presence of obstructions between the source building and the receptor building can either increase

or decrease the amount of renovation‐derived lead‐containing dust to which individuals are exposed.

The windfield is influenced by the buildings such that emitted dust can be channeled around and over

the obstruction building and concentrated in a mixing “rotor” zone on the leeward side of the building.

Thus, individuals located near the obstruction might be exposed to either a larger concentration (in the

rotor zone) or smaller concentration (outside the rotor zone).

The Approach accounts for this potential obstruction effect by including an obstruction adjustment

factor. The distribution was determined from a set of test runs with different renovation‐building

obstruction‐building combinations with respect to distance and orientation. The distribution fit to the

test cases is:

For the 1st to 32nd percentiles:   ObsAdj = 0.15 × ln(perc)+1.15

For the 33rd to 87th percentiles: ObsAdj = 1

For the 88th to 100th percentile: ObsAdj = exp(perc25)

Where:

ObsAdj = obstruction adjustment

Perc    = percentile of distribution

The obstruction adjustment was also bound by the overall minimum and maximum found

experimentally, 0.1 and 5.3, respectively. More information about the derivation of this distribution is

available in Appendix G.
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3.5. Air Concentrations

Figure 3‐7. Illustration of Air Concentration

The air concentration time series at each receptor building during the renovation (Figure 3‐7) is

estimated as:

ሻ݂݂ܧ݅ܿݐݏ݈ܲܽݐݎܸ݁ െ 1ሺ ൈ ݆݀ܣݐܿݑݎݐݏܱܾ ൈ ݁ݐܴܽݏݏ݉݅ܧ ൈ ݎܽ݅ܦܱܯܴܧܣ ൌ ݊ܿ݋ܥݎ݅ܣݐ݌ܴ݁ܿ

Where:   

ReceptAirConc =  the air lead concentration time series at the receptor (g/m3

)

AERMODair   =  the modeled air concentration assuming unit emissions (1 g/s) in AERMOD

for the particular scenario and iteration (g/m3

)

EmissRate   =  the lead aerosol emission rate as in Section 3.2 (g/s)

ObstructAdj   =  the obstruction adjustment, as discussed in Section 3.4.3

VertPlasticEff   =  the containment efficiency of the vertical plastic, set to zero if there is no

containment or set to the renovation activity‐specific value if containment

is present.

The lists of all the AERMOD variable values for the particular scenario and for the particular Monte Carlo

iteration were used to select the correct AERMOD model output file. If the total job was assumed to be

performed on only a portion of the building (such as one wall), only a fraction of the AERMOD air

concentrations was used (i.e., 1⁄4 of the full run if only one wall was disturbed). The renovation duration

is also corrected by multiplying by the same fraction when estimating the emission rates.
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3.6. Outdoor Hard Surface and Soil Concentrations

Figure 3‐8. Illustration of Soil Lead Estimation

The primary source of exposure to renovation‐derived lead for occupants of buildings near the

renovation site is expected to be aerosolized particulates emitted during the renovation and later

incorporated into indoor dust. For children and adults living adjacent to the renovated building,

however, significant exposure might occur due to track‐in, depending on the type of containment used

during the renovation. To capture this potential exposure, the lead concentrations on hard surfaces

(such as sidewalks or entryways) and in soil around the renovated building are estimated (Figure 3‐8).

Track‐in was assumed to occur from walking on the ground and carrying the associated lead into an

adjacent receptor‐building location. This section discusses the equations used to construct the track‐in

lead concentration time series. Appendix E provides a discussion of how each input parameter value was

estimated.

To capture these processes, first the lead dust loadings on the hard surface from both aerosolized and

bulk particulate were calculated, taking into account the containment being used. No pedestrian foot

traffic was assumed to occur in the job site during the renovation. Thus, the available loading from

track‐in during the renovation was set to zero. The renovation aerosol lead remaining on the ground

near the renovated building and available for track‐in immediately after the renovation was estimated

as:

ݏݏܽܯ݈݋ݏ݋ݎ݁ܣݎ݋݅ݎ݁ݐݔܧ

ሻ݂݂ܧ݅ܿݐݏ݈ܲܽݎ݋ܪ െ 1ሺ ൈ ݂݂ܧ݅ܿݐݏ݈ܲܽݐݎܸ݁ ൈ ݎݑܦ݋ܴ݁݊ ൈ ݁ݐܴܽݏݏ݉݅ܧݎ݁ܣ ൌ

ݒ݊݋ܥݐܷ݊݅ ൈ

Where:  

ExteriorAerosolMass =   lead mass on hard surface from aerosolized particulate (μg)
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AerEmissRate = the lead aerosol emission rate (g/s)

RenoDur =   the total number of hours in the renovation job (determined by the

renovation rate/crew size and the number of blocks in the building) (h)

VertPlasticEff =   the containment efficiency of the vertical plastic, set to zero if there is

no containment or set to the renovation activity‐specific value

discussed in Section 2.5 if containment is present

HorPlasticEff =   the containment efficiency of the horizontal plastic, set to 0 if there is

no containment or set to the renovation activity‐specific value if

containment is present

UnitConv   = a factor to convert from h to sec and g to μg.

The renovation particulate debris lead remaining on the ground near the renovated building and

available for track‐in immediately after the renovation was estimated as:

ሻ݂݂ܧ݅ܿݐݏ݈ܲܽݎ݋ܪ െ 1ሺ ൈ ݐ݉݅ܧݏݏܽܯܾ݁ܦݐݎܲܽݐݔܧ ൌ ݏݏܽܯܾ݁ܦݐݎܲܽݐݔܧ

Where:

ExtPartDebMass = lead mass on hard surface from particulate debris (μg)

ExtPartDebMassEmit = lead mass emitted to the hard surface from particulate debris (μg)

HorPlasticEff =   the containment efficiency of the horizontal plastic, set to zero if there

is no containment or set to the renovation activity‐specific value if

containment is present.

This first equation builds on the AerEmissRate from Section 3.2.2, and the latter builds on

ExtPartDebMassEmit in Section 3.2.3.

The lead loading around the renovated building immediately following the renovation was then

calculated as:

ൌ ܽ݀݅݊݃݋ܮݎ݋݅ݎ݁ݐݔܧ

ݏݏܽܯܾ݁ܦݐݎܲܽݎ݋݅ݎ݁ݐݔܧ ൅ ݏݏܽܯ݈݋ݏ݋ݎ݁ܣݎ݋݅ݎ݁ݐݔܧ

݁ܽݎܣݐܽ݅݊݉݁݊ݐ݊݋ܥ

Where:

ExteriorLoading = lead loading on hard surfaces (g/ft2

)

ExteriorAerosolMass =   lead mass on hard surface from aerosolized particulate (μg)
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ExteriorPartDebMass =   lead mass on hard surface from particulate debris (μg)

ContainmentArea =   area over which renovation debris falls (ft2

).

This lead will settle on both hard surfaces (sidewalks, etc.) and the soil that surrounds the renovated

building. On hard surfaces, it will mix with other particles on the surface and slowly be washed away due

to runoff and other processes. In soil, it will mix into the upper soil layer and remain in the soil, given the

residence time of lead in soil is between 50 and 1000 years (Kaste et al., 2003; Klaminder et al., 2006).

For hard surfaces, the removal was assumed to occur at a specified frequency in discrete events (such as

rain events) that remove a specified fraction of lead during each removal event. The removal

frequencies and efficiencies are discussed in Appendix F. The hourly time series was constructed by:

 Repeating the loading for each subsequent hour until a removal event is scheduled to occur;  

 Multiplying the loading by the removal efficiency (TrackInRainEff) for the hour just after the removal

event; and  

 Repeating Steps (1) and (2) for the duration of the simulation or until the loading reaches a level

close to, but nominally above, 0 (selected to be 0.01 g/ft2

), whichever comes first. Because the

hard surface loading is exponentially decaying, it will not reach exactly 0, so a value very close to 0

was selected.

For soil, the removal frequency is set to zero so that the lead is not removed from the upper layer during

the course of the simulation.

The dust estimation equations and biokinetic blood lead model both require the track‐in levels to be in

terms of lead concentration rather than loading. Thus, the loading time series was converted to a lead

hard surface concentration time series by assuming a dust density, dust layer depth, and dust layer

porosity on the hard surface and soil and adjusting the units:

ܽ݀݅݊݃݋ܮݎ݋݅ݎ݁ݐݔܧ ൌ ݊ܿ݁݊݋ܥ݊ܫܽܿ݇ݎܶ

ݒ݊݋ܥݐܷ݊݅ ൈ ሻݕݐ݅ݏ݋ݎ݋݊ܲܫܽܿ݇ݎܶ െ 1ሺ ൈ ݕݐ݅ݏ݁݊ܦ݊ܫܽܿ݇ݎܶ ൈ ݄ݐ݌݁ܦ݊ܫܽܿ݇ݎܶ

Where:

TrackInConcen = exterior hard surface or soil lead concentration (μg/g)

ExteriorLoading   =   lead loading on hard surface or soil (μg/ft2

)
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TrackInDepth =   depth of particulate layer available for track‐in (m)

TrackInDensity =   density of particulate layer available for track‐in (g/m3

)

TrackInPorosity =   porosity of particulate layer available for track‐in (unitless)

UnitConv   =   conversion of ft2 to m2 (unitless).

The parameter values for the dust density and dust layer porosity are the same for both hard surfaces

and soil. The layer depth (TrackInDepth) differs between hard surface and soil, with hard surface set to a

nominal value of 1 mm and soil set to 1.5 cm. All three variables are discussed in Appendix F.

3.7. Indoor Dust Concentrations during the Renovation

This section describes how indoor dust loadings and concentrations (Figure 3‐9) were estimated after

incorporating the contributions from the air concentrations (via penetration), the soil/hard surface

concentrations (via track‐in), and the effect of routine cleaning.

Figure 3‐9. Illustration of Dust Loading Estimation

3.7.1. Indoor Dust Loadings

To estimate the indoor floor lead dust loading resulting from exterior renovations transported to nearby

buildings, a mechanistic mass‐balance model was developed. The model is an extension of the hybrid

mechanistic‐empirical model used in the lead National Ambient Air Quality Standard exposure and risk

assessments (US EPA 2007b). The results of the model have not previously been used in an EPA analysis,

although the model received positive reviews from the SAB when presented as part of the review of the

proposed approach for revising the lead hazard standard (US EPA 2010). This model accounts for the

two dominant sources of lead in indoor dust from exterior renovation activities: penetration of lead‐

containing ambient air into the indoor environment and tracking of lead from exterior soil or hard
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surfaces into the home. Removal occurs due to air exchange and routine cleaning. The model preserves

the total mass in the system and accounts for the accumulation of lead on the floor and in the indoor

air.

The indoor air mass balance equation accounts for penetration into the indoor environment, removal

due to air exchange, deposition from the air to the floor, and resuspension from the floor to the air. The

equation representing these processes is:

௕௉ܴܫܣܰܫ݀

݉݁ݑ݈݋ܸݎ݋ݐ݌ܴ݁ܿ݁ ൈ ݊ܿ݁݊݋ܥݎ݅ܣݐ݌ܴ݁ܿ݁ ൈ ݈݀݁ܲ݁݊݋ܯݐݏݑܦ ൈ ݁ݐ݄ܴܿܽ݊݃݁ܽݔܧݎ݅ܣ ൌ ݐ݀

ݏݑݏ݈ܴ݀݁݁݋ܯݐݏݑܦ ൅ ௕௉ܴܫܣܰܫ ൈ ݌݁ܦ݈݀݁݋ܯݐݏݑܦ െ௉௕ ܴܫܣܰܫ ൈ ݁ݐ݄ܴܿܽ݊݃݁ܽݔܧݎ݅ܣ െ

௕௉ܱܱܴܮܨ ൈ

Where:

dINAIRPb /dt =   change in time of the indoor air lead mass (μg/h)

AirExchangeRate =   air exchange rate (h−1

)

DustModelPen   =   penetration efficiency (unitless)

ReceptAirConcen =   concentration of lead in ambient air (μg/m3)

ReceptorVolume =   volume of the receptor location (m3

)

INAIPRPb =   indoor mass of lead in air (μg)

DustModelDep   =   deposition rate (h−1

)

DustModelResus =  resuspension rate (h−1

)

FLOORPb =   mass of lead on the floor (μg).

The floor mass balance equation accounts for deposition of lead from the air onto the floor,

resuspension of lead from the floor into the air, tracking of lead from outdoor soil, and removal of lead

due to routine cleaning:  

௕௉ܱܱܴܮܨ݀

݊ܿ݁݊݋ܥ݊ܫܽܿ݇ݎܶ	 ൅								 ௕௉ܱܱܴܮܨ ൈ ݏݑݏ݈ܴ݀݁݁݋ܯݐݏݑܦ െ௉௕ ܴܫܣܰܫ ൈ ݌݁ܦ݈݀݁݋ܯݐݏݑܦ ൌ ݐ݀

ݍ݁ݎܨ݈݁ܽ݊ܥ ൈ ݂݂ܧ݈݁ܽ݊ܥ െ 												ሻܽܿݎܨݐܽܯ െ 1ሺ ൈ ݁ݐܴܽܿ݇݅݊݃ܽݎܶݐݎܲܽ ൈ

௕௉ܱܱܴܮܨ ൈ

Where:
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dFLOORPb/dt =   change in time of the indoor floor lead mass (μg/h)

INAIPRPb   =   indoor mass of lead in air (μg)

FLOORPb =   mass of lead on the floor (μg)

DustModelDep =   deposition rate (h−1

)

DustModelResus =  resuspension rate (h−1

)

TrackInConcen =   concentration of lead in the tracked‐in particles (μg/g)

PartTrackingRate   =  rate at which particulate is deposited on front mat (g/h)

MatFrac =   fraction of total tracked material that is deposited on the front mat (as

opposed to the remainder of the house) (unitless)

CleanEff =   cleaning efficiency (unitless)

CleanFreq =   cleaning frequency (cleanings/h).

In most cases, the above terms represent first‐order transfer rates multiplied by the mass in the donor

compartment. For example, for the deposition from the air to the floor, the transfer term is the air mass

multiplied by the first‐order deposition rate. In some cases, an additional efficiency term is included to

indicate that only a certain percentage of the total mass is removed. For example, for the routine

cleaning, the mass is multiplied by both the removal rate (cleaning frequency) and the removal

efficiency (cleaning efficiency). The track‐in is characterized as a soil track‐in rate multiplied by the lead

soil concentration and an efficiency that represents the amount that remains on a mat rather than being

spread through the building.  

By converting the above mass‐balance differential equations to difference equations, the model can be

integrated forward in time to find the mass of lead on the floor and in the air at each time step using the

time‐varying air concentrations from AERMOD and the time‐varying outdoor hard surface or soil

concentrations. For the Approach, track‐in was assumed to occur from soil 50 percent of the time and

from hard surfaces the other 50 percent of the time.  

Appendices E and J together describe how the input values and distributions for the model were

determined. In each case, the variables were characterized separately for each receptor building type

(agricultural, industrial, public/commercial, school, and residence). Early scoping results indicated that

air exchange rate, cleaning frequency, track‐in rate, and the percentage of the receptor‐building that is

carpet were sensitive variables. Depending on whether adequate information was available in the

literature, sensitive variables were varied using probabilistic distributions, and the remaining values

were set to central tendency estimates.  
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Early scoping results showed that when compared to air penetration, track‐in of particles from soil and

hard surfaces contributed less to indoor dust loadings. Under circumstances where outdoor soil or hard

surface concentrations are elevated and more people continuously walk into a building, however, the

effect of track‐in will be amplified when compared to buildings where people enter less frequently (Hunt

et al., 2006). The approach taken to scale track‐in to different types of P&CBs is described in Appendix E.  

Some variables, including the cleaning efficiency and the resulting amount of lead removed by cleaning,

depend on the total amount of lead dust in a building rather than just the renovation‐derived portion.

For this reason, the background indoor dust loading was used as the initial condition in the model, and

after the completion of the renovation the cleaning is performed until the total dust loading is just lower

than the background level. This background is then subtracted to obtain the renovation‐related portion

of the dust loading.

3.7.2. Indoor Dust Concentrations

The indoor dust model used for both the exterior and interior approach, provides an estimate of the

lead loading in terms of mass of lead per square foot of floor. Biokinetic blood lead models require lead

concentrations, or the amount of lead per mass of particulate.  

Three data sets were combined to generate an empirical equation to convert lead dust loadings to lead

dust concentration (Adgate et al 1995, HUD 2002, Rasmussen et al 2013). The resulting best‐fitting

equation had the following form:  

ln (Conc) = Intercept + Slope × (Adj ln(Load)) + ErrorVariance

Where:  

Adj ln(Load)) = ln(Load) – Average (ln(Load)).

The average ln(Load) is the average of the natural logarithms of the measured loadings. The

ErrorVariance term was incorporated to allow the concentration estimates to vary in different Monte

Carlo iterations given the same lead loading input. This term was added to reflect that the underlying

data indicate significant spread related to household‐specific parameters such as the cleaning

frequency, cleaning efficiency, and contributions from outdoor soil versus indoor air. The data‐

processing and regression analysis are fully described in Appendix K.

4. Estimating the Renovation‐Related Media Concentrations

during Interior Renovations

When a dust‐generating activity is performed on the interior of a P&CB, exposure can occur under

several different settings, as listed below and illustrated in Figure 4‐1:
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1. Exposure to people who are in renovated rooms and inside the work area,

2. Exposure to people who are in renovated rooms and outside the work area,

3. Exposure to people who are in rooms adjacent to renovated rooms, and  

4. Exposure to people who are in other parts of the building.

In the diagram, the shaded areas indicate rooms that are being renovated, while the crosshatching

indicates where only part of a room is being renovated. For these interior renovations, we considered

three phases:

1. The dust‐generating phase, when the portion of the renovation job expected to generate lead‐

containing dust is performed. Any cleaning by the contractor occurs after the dust‐generating phase.

2. The rest‐of‐renovation phase, when the remainder of the renovation takes place.

3. The routine‐cleaning phase, when the renovation is complete and the dust levels are slowly

returning to background levels due to routine cleaning in the building.

So, lead dust levels could be elevated during all three phases until the lead loading returns to

background levels. This section details how the lead dust loading was calculated for different renovation

scenarios.

4.1. Renovation Characteristics

To characterize the lead emitted during a renovation event, the renovated building and the renovation

job were defined for each exposure scenario, just as in the exterior analysis. This section discusses the

characterization of the renovated building and the renovation job, with a complete discussion of the

derivation of each variable in Appendices B, C, and D.

4.1.1. Characterizing the Renovated Building

For the interior analysis, the renovated areas within P&CBs were characterized so that the variability

within the U.S. building stock was captured. In this analysis, the three variables used to capture the

building‐specific differences were:

 Building use type,

 Layout of the building, and  

 Room sizes and room characteristics in the building.
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Figure 4‐1. Example Building Layout with Different Renovation and Occupant Locations

This section describes how these variable values were assigned for this Approach.

Use Types

Unlike the exterior analysis, where only the exterior attributes of the building will strongly influence

exposure, the use type of an interior building will affect key attributes of both the building itself and the

amount of time per day people spend in the building. For example, a large supermarket will have one or

two large spaces with multiple smaller rooms, and patrons will visit the building only once or twice a

week. In contrast, hotels have numerous small rooms, and patrons might stay for days or months at a

time.  

The CBECS survey provides information about the number of buildings in the United States in different

use categories, as shown in Table 4‐1 (US EIA 2003). These CBECS categories were mapped to five

assessment categories for this Approach, where CBECS categories were grouped according to layouts,

room sizes, and expected time spent patterns. Five assessment categories were chosen to ensure

adequate coverage of the various building types and also to balance the resource constraints of the

Monte Carlo analysis.
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Table 4‐1. CBECS Buildings and Mapped Assessment Categories

CBECS Category DOE Categories Assessment Categories

Office

Small Office

Medium Office

Large Office 1. Office, outpatient healthcare, and

public order/safety Outpatient health care Outpatient Healthcare

Public order and safety    

Non‐refrigerated warehouse

Warehouse

2. Warehouse, food sales, religious

worship, and public assembly

Refrigerated warehouse

Food sales Supermarket

Religious worship None

Public assembly None

Food service Quick Service Restaurant

Full Service Restaurant

3. Food service, service, strip

shopping mall, enclosed mall, and

retail other than mall

Service None

Strip shopping mall Strip Mall

Enclosed mall None

Retail other than mall Stand‐alone Retail

Education School 4. Education  

Lodging Small Hotel

Large Hotel

5. Lodging, Nursing, inpatient health

care, and laboratory

Nursing None

Inpatient health care Hospital

Laboratory None

Other None

None

Vacant None

Building Layouts

Buildings in the United States can have numerous layouts, and the precise layout will influence how

many people are in proximity to a given renovation job. For this assessment, resource limitations

dictated that only a few representative layouts could be captured. As a starting point, the DOE reference

buildings (Deru et al., 2011) were used. These reference buildings provide layouts for 14 building types,

and each layout comprises a series of zones that correspond to a part of a room, a room, or a group of

rooms.  

In general, the layouts displayed a few characteristics:
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 Some had a single large room with multiple smaller rooms (e.g., food sales, warehouse, stand‐alone

retail, offices with open floor plans and cubicles), and  

 Others had repeating units connected by a hallway or walkway (e.g., schools, hospitals, hotels, strip

malls).

Thus, two different layouts were selected for the analysis, as shown in Figure 4‐2. The room sizes in the

layouts were varied to capture representative buildings in each use type. Each building layout has three

rooms.  In addition, repeating units of each layout can occur in the same building.

Configuration A Configuration B

Figure 4‐2. Representative Building Layouts Used in the Approach

Based on Dust Study renovation experiment data (see Appendix I), dust levels in rooms adjacent to

renovated rooms tend to be one or two orders of magnitude lower than the loadings in the renovated

room during the renovation. For this reason, and to constrain the analysis to a reasonable number of

scenarios, the following assumptions were made:

 Lead dust is unlikely to travel large distances in the HVAC system (turning off HVAC system and/or

covering air intakes during dust‐generating activities further prevents the spread of dust through the

HVAC system),  

 Lead dust loadings decrease exponentially with distance from the renovation activity, and  

 The renovation affects only those rooms directly adjacent to a renovated room and the renovated

room itself. All other rooms experience no increase in lead loading.

Based on these assumptions, only three rooms were used to estimate the exposure possibilities for a

person in the renovated building. If multiple rooms were renovated during a single job, and the total

exposure was estimated for a given person situated in one room. As in Figure 4‐1, the red hatched area

represents a renovated area smaller than a full room and the red solid area represents a fully renovated

room.  

Room Sizes and Characteristics

Each layout can comprise three rooms of varying sizes, and the DOE reference buildings were used to

characterize the set of room sizes for the Approach. The building types in the DOE data were mapped to

the assessment building categories, as shown in Table 4‐1.
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The data provided the size and frequency of each type of zone within the reference buildings. The zones

were mapped to rooms based on the expected room layouts in the different building types. Then, two to

four room sizes were estimated for each assessment category, as described in Appendix C.  Overall,

twelve room sizes were selected across the five building categories.   

In addition to room size, other characteristics were used to estimate the amount of paint disturbed

during a renovation job, including:

 Ceiling height,

 Painted wall area,  

 Percent of the room that is glass (window), and

 Fraction of the room covered in shelving attached to walls.

These variables were estimated by assuming that the width‐to‐length ratio of each room was 2:1 and by

using the CBECS and DOE reference building data, as described in Appendix C. The 2:1 ratio is based on

the assumption that most rooms are rectangular rather than square. This assumption is consistent with

the majority of layouts presented in the DOE reference building data.   

4.1.2. Characterizing the Renovation Job

To characterize the renovation job for a given exposure scenario, the key variables include:

 The type of renovation activity or activities,

 Rooms within the configuration that are being renovated,

 Floor area within the job area, and

 Fraction of paint removed.

The activities that comprise the job characterize each renovation. Activities can include a single type of

activity or a combination of activities (e.g., a paint removal activity and a window replacement). To

characterize the extent of the room that the renovation affects, each renovation job was given an

“intensity” in a scenario. This intensity characterizes the fraction of the wall area (paint removal and

cutouts) or the fraction of the room components (window replacement and shelving) the job affects.  

Including an intensity is particularly important for Configuration B, where one room is very large; in this

case, the renovation might affect only a corner, leaving most of the room relatively unaffected. For this

reason, a job area is also defined for each job, taking into account the intensity and assuming job areas

extend 10 ft out from the wall. This job area was assumed to be relatively isolated from the rest of the

room because of the inability of the lead dust to travel the entire extent of the room. The remainder of

the room was then treated as if it were an “adjacent” room.
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The renovation experiments in the Dust Study indicated that lead dust levels decay exponentially with

distance from the disturbed wall, and 10 ft was selected as a job area dimension based on inspection of

the shape of the experimental exponential functions across experiments (see Appendices H and I). Any

combination of rooms within a building configuration can be renovated, so different scenarios were

created to capture different renovation possibilities (US EPA 2007a).  

4.2. Lead Released During Renovation

The lead removed from the interior wall and building components consists of a spectrum of particle

sizes. As with the exterior analysis, different particle classes were defined:

1. Bulk and particulate debris: large particles that are readily seen and easily cleaned after the job,

and

2. Dust debris: intermediate and small particles that are more difficult to see and clean.

Thus, lead persists post‐renovation as dust debris, with the assumption that the renovation crew

removes all bulk debris and performs baseline cleaning to remove some of the dust debris before

leaving the job site. This section describes how the resulting post‐renovation lead loading was

characterized for the dust debris particles.

As in the exterior analysis, emissions were characterized using the fraction of wall paint removed that

ends up as dust on the floor at the end of the renovation. The equation is:

ൌ ܽ݀݋ܮݎ݋݋݈ܨ

ݎ݋ݐܽܿܨܿ݋ܮ ൈ ݎ݋ݐܽܿܨ݁ݏ݄ܲܽ ൈ ܽܿݎܨݐݏݑܦ ൈ ݁݀ݒ݋ܴ݁ܽ݀݁݉ܮ

݁ܽݎܣݎ݋݋݈ܨܾ݋ܬ

Where:

FloorLoad   = the floor lead loading after the renovation (g/ft2

)

LeadRemoved  =   the total amount of lead in the paint that is removed (g)

DustFrac =   the fraction of the total amount of removed lead that remains as dust at the

end of the renovation

PhaseFactor   =   a factor that adjusts the post‐dust‐generating phase room floor loading to the

post‐cleaning room floor loading, if applicable (see Section 4.2.4) adjacent

room floor loading, if necessary

LocFactor   =   a factor that adjusts the work room floor loading to the adjacent room floor

loading, if applicable (see Section 4.2.4)

JobFloorArea   =   the total job floor area (ft2

).
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The variable LeadRemoved was estimated using the amount of lead per unit area on the wall at the start

of the job, the fraction of that paint that is removed during the job, and the total job surface area:

ݒ݊݋ܥݐܷ݊݅ ൈ ݁ܽݎܣܾ݋ܬ ൈ ݁݀ݒ݋ܴ݊݁݉݋݅ݐܽܿݎܨ ൈ ܨܴܺ ൌ ݁݀ݒ݋ܴ݁ܽ݀݁݉ܮ

Where:

LeadRemoved   =  the total amount of lead that is removed (g)

XRF =  the XRF measurement of lead in the paint per square cm of wall (mg/cm2

wall)

FractionRemoved   =  the fraction of the total layers of paint that are removed during the job

JobArea =  the floor area over which lead dust debris falls (ft2

)

UnitConv =   a conversion factor to convert from h to sec and mg/cm2 to g/ft2

FractionRemoved is intended to reflect that not all layers of paint will be removed during a typical

repainting job. When a wall surface is repainted, it is usually first prepared by removing a fraction of the

existing paint rather than all the paint that is on the wall. This fraction can vary by the substrate type

(wood, metal, and stucco typically have different surface preparation methods), method used to remove

the paint (mechanized or heating techniques enable easier removal of more layers than scraping), and

condition of the existing paint (more paint is likely to be removed if the paint is highly degraded). A

representative value of 15 percent was selected for the amount removed during surface preparation, as

described in Appendix C. For other activities (e.g., trim removal, door removal, demolition), the

estimated fractions removed in these jobs were assumed to match those fractions in the Dust Study (US

EPA 2007a).

The amount of lead on the wall (XRF, also referred to as wall loading) at the start of the job was

captured by XRF measurements. A nationally representative data source (the National Survey of Lead

and Allergens in Housing, HUD 2002) was used to estimate the XRF distribution for different building

vintages. This variable is described in Appendix C.2.8.  

Both the JobArea and the JobFloor Area variables depend on assumptions about the size of the

renovation job and the renovated room (a combination of the wall length and the wall length affected

by the job). These variables are discussed in Appendix D.

As in the exterior analysis, the DustFrac is estimated using the Dust Study. This estimation process is

described below in Section 4.2.1 and in detail in Appendix I.  
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4.2.1. Dust Study Processing

The Dust Study (US EPA 2007a) was a series of experiments that performed renovation activities in

homes and schools with lead‐based paint and then sampled the resulting floor lead loadings. Each

experiment had differences in lead content in the paint, differences in job size, differences in job

configuration, and differences in the crew performing the work. For this analysis, the Dust Study data

were normalized to help account for these key differences before estimating an aggregate DustFrac

value for each renovation activity type; this is referred to as “processing.” The estimation of this variable

is documented in Appendix I.  

The processing steps for the Dust Study experiments are shown in Figure 4‐3. Experiment maps were

obtained from Battelle, the consulting firm that performed the Dust Study experiments. The maps

indicated where the job and the sample trays were located in the room during the experiment.  

Figure 4‐3. Dust Study Processing Steps

To prepare the data and normalize for the confounding variables, the following steps were performed:
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 Step 1: Organized raw Dust Study data (non‐bulk samples) by experiment.

 Step 2: The distance from the job wall to the lead sample was estimated based on the provided

experiment map.  

 Step 3: If the dust loading sample was not taken from directly in front of the job wall (i.e., it was

displaced to the side), the data were corrected to approximate what the loading would have been if

the sample had been taken from directly in front of the wall.  

 Step 4: Then, the lead loading was divided by the average percent of lead in the paint to estimate

the dust loading on the floor (rather than the lead loading).  

 Step 5: The dust loadings were divided by the total job area to estimate what the loading would

have been if only 1 ft2 had been disturbed.  

 Step 6: After the loading data were normalized, a regression equation was fit to the adjusted

loadings as a function of distance from the wall, assuming an exponential functional relationship.

 Step 7: Using the dimension of the room and integrating these regression equations, the total dust

loading in the room after the renovation was estimated.  

 Step 8: When the distances could not be used to derive regression equations, the room‐average

dust loading was multiplied by the floor area. The original amount of paint on the wall at the start of

the experiment was estimated based on the vintage of the building.

 Step 9: The ratio between the amount of dust on the floor and the amount of paint on the wall was

estimated.  

This final ratio provided an estimate of the DustFrac variable for each of the 60 interior experiments. To

obtain a single representative estimate for each renovation activity type (e.g., dry scraping or cabinet

removal), the estimates for a given job type were then aggregated by taking the geometric mean across

the relevant experiments. For two renovation activities, the fraction of paint that became dust was

different enough within the activity to justify creating subcategories: window replacement with and

without a saw and removal of paint via heat gun with a plaster and a nonplaster substrate.  

4.2.2. Other Data Sources Considered

The Dust Study provides information for 60 interior experiments across different renovation activity

types. To increase statistical power and potentially more robustly characterize the post‐renovation dust

loadings, other sources of data also were considered. A literature search was performed to find

literature that provided:

 Loading or air concentration data specifically collected during or after a renovation event, and  

 The necessary additional variables so that the experiment could be properly normalized by lead

content, job size, and room size to estimate the DustFrac variable.
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Both indoor air concentration and dust loading data were considered. The following equation was used

to estimate the dust loading after the renovation based on the air concentration during the renovation:

ݒ݊݋ܥݐܷ݊݅ ൈ ݐ݄݁݅݃ܪ݈݁݅݅݊݃ܥ ൈ ݊݋݅ݐܽݎݑܦ݌ݔܧ ൈ ݁ݐܴܽ݌݁ܦ ൈ ݊ܿ݋ܥݎ݅ܣ ൎ ܽ݀݋ܮݎ݋݋݈ܨ

Where:

FloorLoad =   the floor lead loading after the experiment (g/ft2

)

AirConc =   the average air concentration during the renovation activity (g/m3

)

DepRate   =   the particle deposition rate (h−1

)

ExpDuration =   the duration of the experiment (h)

CeilingHeight =   the ceiling height in the room (m)

UnitConv =   unit conversion to convert m2 to ft2

The deposition rate and duration were expected to vary by renovation activity. This approach was used

to incorporate additional data on cleaning efficiency of interior control options into the analysis, and the

results are further described in Appendix D (Grinshpun et al., 2002).  

The studies shown in Table 4‐2 were reviewed and provide exposure information for lead interior

renovation activities in a wide variety of buildings. These results were not, however, added to existing

Dust Study measurements to estimate DustFrac. Specifically, with the exception of the Grinshpun et al.

(2002) data source, few of the other data sources identified included all of the information needed to

estimate DustFrac by converting air concentrations to dust loading, (as described above with the Dust

Study experiments). Approximating the additional values required for this analysis would introduce

additional uncertainty, and for this reason, data from these alternative sources were not included in the

modeling. Table 4‐2 provides an overview of the data sources located. EPA also has requested additional

data from other federal agencies, the construction industry, and the public that could be incorporated

into the Approach. EPA, however, did not receive appropriate types of data or contextualizing

information as described below.

 The type of renovation activity being performed.  

 Total duration of the activity (length of time renovation activity was actually taking place).

 Sampling periods for air lead concentrations and descriptions of the task(s) performed during that

sampling period (during renovation, immediately after renovation, both).

 Type and location of the air sample (personal breathing zone or area air sample for primary or

bystander workers, next to the job in the renovated room, in the center of the renovated room, in

an adjacent room, etc.).
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 Location (floor, sill, or other surface of the renovated room or an adjacent room) and time when the

wipe sample was collected (before, during, after renovation activity, or after clean‐up activities).  

 Size of the renovation job including total area (ft2) of wall (or other building component) disturbed

during the renovation.  

 Description of the type of building and room being renovated, including the height of the ceiling in

the renovation room and the square footage of the room.  

 Measurements of the amount of lead in the paint.

Table 4‐2. Other Data Sources Considered for Interior Dust Loading Estimation

Study Data Type

Air Sampling Time

and Duration of

Activity

Size of Job

and Size of

Work Room

Lead Content

in the Paint

Jacobs et al. (1998) Air concentrations  X X

Lange et al. (2000) Air concentrations X X 

NAHB (2006) Lead loadings on floors and

air concentrations

 X X

Sussell et al. (1999) (previous

NIOSH HHEs)

Lead loadings on floors and

air concentrations

     X 

Sussell et al. (1998) (previous

NIOSH HHEs) Air concentrations  X 

NIOSH (2000) Air concentrations  X 

NIOSH (2005) Lead loadings on floors and

air concentrations

 X 

OSHA Site Visit Data Air concentrations  X 

Reames et al. (2001) Air concentrations  X X

Schirmer et al. (2012 Air concentrations X X 

Verma (2003) Air concentrations  X X

US EPA (1997)

(Environmental Field

Sampling Survey)

Lead loadings on floors and

air concentrations

 X 

Zhu et al. (2012) Air concentrations  X 

=yes and X=no

4.2.3. Dust Time Series

As mentioned at the beginning of Section 2, the interior analysis has three phases:
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 The dust‐generating phase, when the portion of the renovation job expected to generate lead‐

containing dust is performed. Any contractor cleaning occurs after the dust‐generating phase.

 The rest‐of‐renovation phase, when the remainder of the renovation takes place.

 The routine‐cleaning phase, when the renovation is complete and the dust levels are returning to

background levels due to routine cleaning in the building.

The Dust Study data (US EPA 2007a) were used to estimate the average work room loading during the

dust‐generating phase (“post‐work” in the Dust Study) and during the rest‐of‐renovation phase (“post‐

cleaning” in the Dust Study) using experiments where cleaning consisted only of dry‐sweep cleaning.

Comparisons between these loadings were used to estimate a phase factor so that the loading during

the rest‐of‐renovation phase could be estimated from the simulated dust‐generating phase using the

equation:

ݎ݋ݐܽܿܨ݁ݏ݄ܲܽ ൈ௘௡ ீ௨௦௧஽ܽ݀݋ܮݎ݋݋݈ܨ݇ݎ݋ܹ ൌ ௦௧௘ோܽ݀݋ܮݎ݋݋݈ܨ݇ݎ݋ܹ

Where:

WorkFloorLoadRest =   the work room floor lead loading during the rest‐of‐renovation phase

(g/ft2

)

WorkFloorLoadDustGen =   the work room floor lead loading during the dust‐generating phase

(g/ft2

)

PhaseFactor =   a factor applied to the work room dust‐generating loading to estimate

the corresponding rest‐of‐renovation loading

In addition to the post‐renovation work room loading, EPA also estimated loadings in the rooms directly

adjacent to the work room. The Dust Study captured data in “Observation Rooms” located near the

work room (US EPA 2007a). The average dust levels from observation rooms in the Dust Study

experiments were compared with the corresponding average work room loadings to estimate an

adjacent factor such that:

ݎ݋ݐܽܿܨ݆݀ܣ ൈ ܽ݀݋ܮݎ݋݋݈ܨ݇ݎ݋ܹ ൌ ܽ݀݋ܮݎ݋݋݈ܨ݆݀ܣ

Where:

AdjFloorLoad =  the floor lead loading during and after the renovation in the adjacent room

(g/ft2

)

WorkFloorLoad =  the floor lead loading during the renovation in the work room (g/ft2

)
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AdjFactor =   a factor applied to the work room loading to estimate the corresponding

adjacent room loading

Then, the routine cleaning phase time series was estimated by applying both a cleaning frequency and a

cleaning efficiency to the room. Cleaning frequency was assumed to vary from once every working day

to once every four weeks (see Appendix D). Then, on days when cleaning occurs,  

݂݂ܧ݈݁ܽ݊ܥ ൈ ௗ௟ைܽ݀݋ܮݎ݋݋݈ܨ ൌ ௪௘ேܽ݀݋ܮݎ݋݋݈ܨ

Where:

FloorLoadNew =   the floor lead loading just after the current cleaning (g/ft2

)

FloorLoadOld =   the floor lead loading just before the current cleaning (g/ft2

)

CleanEff =   the cleaning efficiency

The routine cleaning continues until the room lead loading returns to pre‐renovation background levels.

This same cleaning equation was applied in both the work room and the adjacent room in each scenario.

4.2.4. Location of the Exposed Individual

The location of the exposed individual within the renovated building is an important and sensitive

variable within the Approach. This variable affects the duration and magnitude of exposure based on

how much time an individual spends in various locations within a building. A wide range of combinations

are described below and further explored in the sensitivity analysis in Section 8.5.1. A subset of these

combinations was modeled in the Approach.  

As described in the beginning of Section 4 and in Figure 4‐1, individuals can be located:

1. Inside the work area of renovated rooms (work area),

2. Outside the work area of renovated rooms (work room),

3. In rooms adjacent to renovated rooms (adjacent room), and

4. In other parts of the building where exposure is assumed to be at background levels (other).  

And, based on whether the individual is allowed in the work area, work room, or adjacent room during

the renovation, exposure to an individual could start at the:

1. Beginning of the dust‐generation phase,

2. Beginning of the rest‐of‐renovation phase, or  

3. Beginning of the routine cleaning phase.

Finally, individuals might spend their time:
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1. In only one location in a building (one location), or  

2. In both a primary location and other locations within the building (multiple locations).

The full list of potential exposure scenarios when individuals are confined to one location is shown in
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Table 4‐3. Several different combinations are possible when individuals move through the building and

occupy multiple locations; these combinations are not listed because of the numerous possibilities, but

several cases that assume the individual is present in two distinct locations were explored in the

sensitivity analysis. Occupants included in the Approach were individuals who were located:

 In the work area, with exposure starting at the beginning of the dust‐generating phase (Case 1),

 In the work room (but outside the work area), with exposure starting at the beginning of the dust‐

generating phase (Case 3),  

 In the work area/work room, with exposure starting at the routine‐cleaning phase (Case 5), and

 In an adjacent room, with exposure starting at the beginning of the dust‐generating phase (Case 6).  

Dust loadings were estimated for each occupant location in a particular renovated building scenario.

Additional information on which combination is most likely to least likely to occur and is representative

of various types of individuals who spend time in P&CBs will need to be determined in the future.  
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Table 4‐3. Locations of Individuals and Timing of Exposure

Case

Number

of

Building

Locations

Room

Present

during

dust‐

generation

?

Present

during  

rest of

renovation?

Present

during

routine

cleaning?

Included

in

Approach

?

Case 1. One Location, Work

Area, Always Allowed in Room

During Renovation

One

Location Work Area Yes Yes Yes Approach

Case 2. One Location, Work

Area, Partially Allowed in Room

During Renovation

One

Location Work Area No Yes Yes Sensitivity

Case 3. One Location, Work

Room, Always Allowed in Room

During Renovation

One

Location Work Room Yes Yes Yes Approach

Case 4. One Location, Work

Room, Partially Allowed in

Room During Renovation

One

Location Work Room No Yes Yes Sensitivity

Case 5. One Location, Work

Area, Not Allowed in Room

During Renovation

One

Location

Work

Area/Work

Room

No No Yes Approach

Case 6. One Location, Adjacent

Room, Always Allowed in Room

During Renovation

One

Location

Adjacent

Room Yes Yes Yes Approach

Case 7. One Location, Adjacent

Room, Partially Allowed in

Room During Renovation

One

Location

Adjacent

Room No Yes Yes Sensitivity

Case 8. One Location, Adjacent

Room, Not Allowed in Room

During Renovation

One

Location

Adjacent

Room No No Yes Sensitivity

Case 9. One Location, Other

Room

One

Location Other Room No No No No

Exposure

Multiple Cases. Multiple

Locations

Multiple

Locations Various Various Various Various Sensitivity

4.3. Work Practice/Control Options

Information on the efficiency of interior work practice/control options is presented below. EPA will

consider the extent to which the work practice/control options are used during typical renovation jobs

to determine which, if any are baseline practices and which are potential control options.  

 One time contractor cleaning – broom sweeping

 HEPA vacuuming

 Wet mop cleaning with verification
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 Use of horizontal plastic in the work room

 Use of vertical plastic as a barrier to the spread of dust to the rest of the building

 Local exhaust ventilation on equipment

 Replacement of higher emitting activities with lower emitting activities  

Each control practice is associated with an efficiency that reduces the lead loading either after the

renovation in the work room (e.g., horizontal plastic or wet mop cleaning with verification) or during

and after the renovation in adjacent rooms (e.g., vertical plastic). So, for a particular control option,

ݎ݋ݐܽܿܨ݈݋ݎݐ݊݋ܥ ൈ௘ ௦஻௔ܽ݀݋ܮݎ݋݋݈ܨ ൌ ܽ݀݋ܮݎ݋݋݈ܨ

Where:

FloorLoad =   the floor lead loading in the Dust Study “post‐cleaning phase” after cleaning

has occurred or plastic has been removed (g/ft2

)

FloorLoadBase =   the floor lead during the Dust Study “post‐work phase,” before cleaning has

occurred or plastic has been removed (g/ft2

)

ControlFactor =   a factor applied to the post‐work loading to estimate the corresponding

control option loading.

Factors were developed for the different control option combinations shown in Table 4‐4.

Table 4‐4. Control Option Combinations Considered in the Approach

Control Option To Which Room Loading is the

Factor Applied? Efficiency

Dry sweeping only Work 0.943

Horizontal Floor Plastic with Dry

Sweeping

Work 0.956

Wet Mop Verification Cleaning Work 0.975

Horizontal Floor Plastic and Wet

Mop Verification Cleaning  

Work 0.993

4.4. Contributions from Exterior Renovations and Track‐in to

Renovated Building

The loadings in the different rooms inside the renovated P&CB depend on the renovation activities

inside the renovated building; however, if the building exterior is also renovated, occupants could track

in lead‐containing soil and further increase the interior lead loadings. In addition, elevated air
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concentrations during the exterior renovation could penetrate the building shell and lead to elevated

interior lead floor loadings.  

For any given renovated building, the outdoor soil concentration could be at background (no significant

renovation event on exterior of building) or any number of renovation‐related elevated concentrations.

The frequency of exterior and interior renovation activities occurring at the same time is expected to be

low. To account for this exposure pathway when it does occur, three exterior soil concentrations were

chosen, as shown in Table 4‐5.

Table 4‐5. Low and High Dust and Soil Levels from Track‐in to Same P&CB

Control Option Soil g/g

Background levels n/a

“Low” exterior renovation scenario 3.9

“High” exterior renovation scenario 27.5

The exterior scenarios provide estimates of the mean receptor building‐level outdoor soil levels

resulting from a given exterior renovation. The low and high scenarios were selected from the full set of

exterior scenario results by choosing the 25th and 75th percentiles for commercial and school receptor

buildings. Thus, to include the exterior renovation contribution to the interior of the renovated building,

each iteration in the Monte Carlo modeled sampled regardless of whether an exterior renovation job

was also present. Then, in the high and low cases, the mean soil levels for the closest exterior receptor

building in the background, low, and high exterior scenarios were selected for use in the interior model;

a track‐in term was added to the dust time series to account for the track‐in from these exterior jobs.

5. Selection and Update of the Leggett Model

The Leggett blood lead model was selected as the primary blood lead model for the Approach (Leggett

1993). Leggett model intake rates can be varied daily or weekly, enabling the model to capture

intermittent, short‐term exposure scenarios, such as renovation activities, more accurately than models

that can vary intakes only monthly (e.g., the IEUBK model).  The model also can be parameterized for

both children and adults, unlike other models such as IEUBK.  The Leggett blood lead model was

selected as the primary blood lead model for the 2008 LRRP Technical Approach for Residences, and this

decision was supported by the 2008 LRRP Clean Air Science Advisory Committee (CASAC) panel (EPA

2007c).  

EPA considered daily, weekly, biweekly, and monthly periods over which to average both background

and renovation‐related exposures in the Leggett model. Exposures to lead involve a mix of long‐term

exposures at relatively constant concentrations and possible intermittent short‐term exposures at

higher concentrations. Blood lead estimates vary over time in response to these different kinds of

exposures. One study used the Leggett model to evaluate model averaging times and suggests that use
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of averaging times that exceed the shortest exposure duration characterizing intermittent exposures

introduces uncertainty into model estimates (Lorenzana et al., 1998). EPA selected a biweekly period for

pre‐renovation exposure levels and a weekly period for renovation levels until blood lead returned to

background. The dust‐generating phase of exterior renovations, on average, were estimated to last

approximately 1 week. The dust‐generating phase of interior renovations, on average, were estimated

to last approximately 3 weeks.  Using a daily averaging time for renovation activities lasting less than 1

week could potentially improve model estimates, but does add significant time to model runs.    

Prior to implementing the Leggett model, several updates were made to the 1993 model based on

recent literature and data availability. In particular,

 Red blood cell volume, plasma volume, and skeletal and kidney masses were scaled to appropriate

values for small children through adolescents;

 Several measures of bone mineral transfer rates were evaluated and updated from those of the

1993 model;  

 The nonlinear red blood cell saturation level was revised to yield a linear model; and

 The model was evaluated and calibrated using NHANES data (children) and serial bone and blood

lead measurements in an occupational population of 209 lead smelters (Nie et al., 2005), as

described in Appendix M.

The model updates and testing are fully described in Appendix M.

Previous comparisons using the older Leggett model have shown that, under chronic exposure

conditions, the blood lead estimates obtained with the Leggett model are approximately two to three

times higher than those obtained with the IEUBK model (Pounds and Leggett 1998). These results

suggested that short‐term exposures in the Leggett model might be overestimated, and that lead uptake

levels to give selected blood lead levels might be underestimated. These issues are no longer the case

with the updated Leggett model, as shown in Appendix M, Figure M‐4.

The Leggett model does not include an exposure model and thus does not accept exposure

concentrations directly. Instead, the model uses total inhalation and ingestion intakes (administered

doses) as inputs. Thus, the exposure concentration time series are converted to intakes using intake

parameter inputs. To obtain estimates of blood lead, both the time series of the media inputs (air,

indoor dust, and outdoor soil) and the age‐specific exposure factor values that govern intake and

absorption processes must be specified. These intake values are described further in Appendix N.  

6. Estimating Health Effects in Children and Adults

After simulating the blood lead changes associated with each renovation activity, concentration‐

response functions were used to relate the incremental blood lead change to an associated health effect

in children and adults. The following section describes how the health effects were chosen, which
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Leggett blood lead model metric can be related most appropriately to the available health effects

literature, and how the concentration‐response functions were characterized. Further details for each

concentration‐response function characterization can be found in Appendix P.

6.1. Selection of Health Effects

Many health outcomes were considered for this analysis. The Integrated Science Assessment (ISA) for

Lead (US EPA 2013) and the National Toxicology Program (NTP) Monograph on Health Effects of Low‐

Level Lead (NTP 2012) were evaluated, and those effects reported to be “causal,” “suggestive,” or “likely

causal” in the ISA and “sufficient evidence of an association’" in the NTP report were considered for this

analysis. A weight‐of‐evidence approach was used to consider which health effects might be appropriate

for this analysis. The health effects on the following systems were selected for consideration: nervous

system, renal, reproductive and developmental, cardiovascular, immune, and hematologic effects. The

studies associated with each effect were evaluated based on the strength of the association, whether a

concentration‐response function was provided, the levels of exposure relevant to current population

exposures, and the biological significance of the effect.  

Both adults and children were considered in this analysis. For children, cognitive function as measured

by IQ met the above criteria. IQ is one of the most sensitive endpoints for children and it was also used

in the 2008 LRRP analysis for residences (US EPA 2008). The health effects selected for adults based on

these criteria were low birth weight, cardiovascular disease mortality (CVDM), and decreased kidney

function (glomerular filtration rate [GFR] < 60). Short descriptions of the studies are provided below, but

detailed analyses can be found in the ISA for Lead (US EPA 2013).  

The development of a concentration‐response function for adult health effects is complicated by the

lack of knowledge of whether the health effect is actually related to current blood lead levels or the

result of higher blood lead levels at earlier times in life. In the United States, exposures to lead were

much higher before lead was removed from gasoline and consumer paints. Blood lead levels in the

1970s were around 40 μg/dL and could have been as high as 60 μg/dL in the 1960s (Smith and Flegal

1992). According to NHANES, blood lead levels continue to decline each reporting period (CDC 2013).

From 1999 to 2010, adult blood lead levels (20 years and older) decreased 30 percent, and blood lead

levels of young children (1–5 years old) decreased 48 percent. The geometric mean blood lead levels

reported in NHANES 2009–2010 for all ages was 1.12 μg/dL (1.17 μg/dL for children 1–5 years and 1.23

μg/dL for adults). This reporting period (2009–2010) was the first where the geometric mean adult

blood lead levels have exceeded (albeit by a small increment) those in young children. Typical blood lead

levels in 2009–2010 were less than 1 μg/dL in children 6–19 years of age (geometric mean 0.84 μg/dL for

6–11 years and 0.68 μg/dL for 12–17 years). The blood lead level of the 95th percentile was 3.37 μg/dL

for children (1–5 years) and 3.57 μg/dL for adults. Generally, adults—and particularly older adults—had

much higher exposure to lead in the past. Blood lead levels in 2014 might or might not continue to

reflect past trends, leading to possible lower blood lead levels today than those reported in 2009–2010.  
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6.2. Selection of Lead Model Metric

The exposure scenario is an important consideration when analyzing the health outcomes that could be

used for this Approach. Both blood lead and, more recently, bone lead measures have been used as

biomarkers of exposure for lead in epidemiology studies. Blood lead reflects recent exposure

history. The half‐life of blood lead is approximately 30 days (Graziano 1994). In children, blood lead

provides a reasonable measure of recent exposure and possibly lifetime body burden because the

exchange between bone and blood lead is faster in children than in adults. On the other hand, the half‐

life of bone lead, depending on which bones are considered, is years to decades. Skeletal lead

represents the vast majority of lead body burden in adults and because of lead’s long residence time in

bone (particularly in the tibia), measurement of bone lead is an estimate of lifetime cumulative lead

dose (US EPA 2013). Bone can release its lead content into the bloodstream in the course of normal

bone metabolism and might release lead at increased rates during active bone demineralization in later

life and during pregnancy. The contribution of bone lead to blood lead changes depending on the

duration and intensity of the exposure, age, and various other physiological stressors (e.g., nutritional

status, pregnancy, menopause, extended bed rest, hyperparathyroidism) that can affect bone

remodeling, which normally and continuously occurs. Through release of lead from bone, adult bone

lead can maintain lead levels in blood long after external exposure has ceased (US EPA 2013).

The exposure scenario being modeled for renovation activities for P&CBs is a short‐term increase in

exposure during the dust generating phase of the renovation.  After the dust generation ceases, blood

lead levels from renovation activities will decline rapidly over the first few months and then there will be

a gradual, slow decline in blood lead concentrations over the next several years (US EPA 2013).

Therefore, the contribution of the exposure from short term LRRP activities would be better

characterized by blood lead levels. The contribution of a short‐term exposure to a health effect that is

exerted chronically is unknown. A single renovation activity will result in a short burst of exposure which

will be reflected in blood lead but will likely not be reflected in bone lead during the exposure

renovation period. For these reasons, blood lead was chosen as the appropriate metric for use in

assessing the health effects data for use in the Approach.

6.3. Concentration‐Response Characterization for IQ in Children

Section 4.3.11 of EPA’s ISA for Lead reports that epidemiologic studies consistently show that blood lead

levels measured during various lifestages or periods throughout childhood, as well as averaged over

multiple years during childhood, are associated with cognitive function decrements (US EPA 2013). An

international pooled analysis of seven prospective studies (Boston, MA; Cincinnati, OH; Rochester, NY;

Cleveland, OH; Mexico City, Mexico; Port Pirie, Australia; and Kosovo, Yugoslavia) and individual‐level

data from 1,333 children aged 4.8–10 years with a median concurrent blood lead level of 9.7 μg/dL,

found that increments in concurrent and peak blood lead levels were associated with a decrease in full‐

scale IQ (intelligence quotient [FSIQ] ), hereafter referred to as IQ (Lanphear et al., 2005). The pooled
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analysis demonstrated a blood lead‐associated decrement in IQ in a diverse population with application

of a uniform statistical model across cohorts.  

In the Lanphear et al. (2005) study, IQ was assessed at school age (mean age at IQ testing was 6.9 years).

All children were assessed with an age‐appropriate version of the Wechsler scales. Four measures of

lead exposure were examined: concurrent blood lead (blood lead level closest in time before the IQ

test), maximum blood lead level (peak blood lead measured at any time prior to the IQ test), average

lifetime blood lead (mean blood lead from 6 months to the concurrent blood lead test), and early

childhood blood lead (mean blood lead from 6 to 24 months). A pooled analysis of the relationship

between cord blood lead levels and IQ also was conducted in the subsample for which cord blood lead

tests were available.

Lanphear et al. explored regression models that adjusted for the effect of blood lead for study site,

maternal IQ, the home observation measurement for the environment (HOME) score (providing an

assessment of the stimulation and support available to a child at home), birth weight, and maternal

education. Statistical testing was used to assess the linearity or nonlinearity of the relationship between

blood lead levels and IQ. Regression diagnostics also were performed to ascertain whether lead

coefficients were affected by colinearity or influential observations. The fits of all four measures of

postnatal blood lead levels were compared based on R2 values. The blood lead measure with the largest

R2 (adjusted for the same covariates) was nominated a priori as the preferred blood lead index relating

lead exposure to IQ in subsequent inspections of the relationships. The primary analysis used a fixed‐

effects model, although a mixed model treating sites as random effects was also examined.

The median lifetime average blood lead concentration was 12.4 μg/dL (5th to 95th percentile, 4.1 to

34.8 μg/dL) with about 18 percent of the children having peak blood lead levels less than 10 μg/dL. The

5th‐ to 95th‐percentile concurrent blood lead levels ranged from 2.4 to 30 μg/dL. The mean IQ of all

children was 93.2 (SD=19.2) but this varied greatly between studies. All four measures of postnatal

exposure were highly correlated with IQ. The concurrent blood lead level, however, exhibited the

strongest relationship with IQ, as assessed by R2

. Nevertheless, the results of the regression analyses for

all blood lead measures were very similar. The best‐fitting model included the log of concurrent blood

lead, study site, maternal IQ, HOME Inventory scores, birth weight, and maternal education.

Various models, including a linear model, a cubic spline function, a log‐linear model, and a piecewise

linear model, were investigated in Lanphear et al. (2005). The shape of the concentration‐response

relationship was determined to be non‐linear; the log‐linear model was found to best fit to the data,

based on R2 values. Using the log‐linear models, the authors estimated a decrement of 1.9 points (95%

CI: 1.2, 2.6) in IQ for a doubling of concurrent blood lead. Thus, the IQ point decrements associated with

an increase in blood lead from less than 1 to 10 μg/dL compared to 10 to 20 μg/dL were 6.2 points (95%

CI: 3.8, 8.6) versus 1.9 points (95% CI: 1.2, 2.6). The individual effect estimates for the seven studies

used in the pooled analysis also generally indicate steeper slopes in studies with lower blood lead levels

compared to those with higher blood lead.
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Budtz‐Jørgensen et al. (2013) reanalyzed the Lanphear et al. data using linear mixed models and derived

a benchmark dose (BMD) estimate and a lower 95 percent confidence limit on the BMD for the effects

of childhood blood lead on IQ. Appendix P provides a more detailed discussion of Budtz‐Jørgensen et al.

(2013) reanalysis of data from the Lanphear et al. (2005) pooled study. After reviewing this reanalysis,

EPA chose to maintain the basic regression approach to estimating blood lead impacts on IQ originally

described in the Lanphear et al. (2005) publication.  

Crump et al. (2013) also reanalyzed the Lanphear et al. (2005) data, correcting what they considered to

be errors in blood lead reporting and IQ test results in the earlier analysis. They confirmed the basic

findings of highly significant log‐linear fits exactly reproduced Lanphear et al. (2005) findings using the

original data, and updated the log‐linear models using the “corrected” data (see Table 6‐1). Among the

changes made by Crump et al. was their use of the General Cognitive Index scores derived from tests

administered simultaneously with blood lead measurements at 57 months of age to estimate the effects

of concurrent exposures in the Boston (Bellinger et al., 1992) cohort. In the original analyses, Lanphear

had used blood lead measurements at 57 months as the “concurrent” blood lead estimate with the

results of Weshcler Intelligence Scale test administered at 120 months, a decision that Crump et al.

(2013) did not find appropriate. Even with this change, however, the estimated regression coefficient

and confidence limits for the impact of concurrent blood lead on IQ changed only minimally (Table 6‐1)

The recalculated coefficient (−2.65) and 95 percent upper and lower confidence limits (−1.61, −3.69) are

therefore used in the Monte Carlo simulation of IQ impacts based on concurrent blood lead. Similarly,

Crump et al. (2013) estimated a coefficient (−3.19) and confidence limits (−4.45, −1.94) for lifetime

blood lead that was not significantly different from that originally estimated by Lanphear et al. (2005).

Table 6‐1. Log‐linear Blood‐lead IQ Coefficients and Confidence Intervals Derived by Crump et al. (2013)

Based on Original and Corrected Data from Lanphear et al. (2005)

Blood Lead Metric Reproduced Using Original Data Results Using Corrected Data

Early Life  −2.04 (−3.27, −0.81) −2.21 (−3.38, −1.03)

Peak  −2.85 (−4.1, −1.6) −2.86 (−4.10, −1.61)

Lifetime   −3.04 (−4.33, −1.75) −3.19 (−4.45, −1.94) a

Concurrent  −2.70 (−3.74, −1.66) −2.65 (−3.69, −1.61)a

a

These values are used in the Approach

Based on the above analyses, EPA chose to estimate IQ reduction based on both concurrent and lifetime

average blood lead concentrations. In the Lanphear et al. study, “concurrent” refers to the blood lead

measured closest to the administration of the IQ test. However, in the context of this Approach,

“concurrent” is not used in the same context and instead refers to a point estimate of blood lead at a

given time point after the renovation. Based on the Lanphear et al. (2005) and Crump et al. (2013)

studies, both metrics were found to be significantly correlated with measured IQ. For concurrent blood

lead, the Monte Carlo analysis estimated potential IQ lost based on a single “measurement” (simulated

blood lead concentration) a predetermined (simulated) time after renovation, and IQ loss was estimated
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based on the regression coefficient from the Lanphear/Crump log linear model for concurrent blood

lead (see below). Lifetime average blood lead was estimated in EPA’s modeling based on the time‐

weighted average blood concentrations from birth until blood lead returns to background as simulated

by the Leggett model, and the lifetime average regression coefficients were used to estimate potential

IQ loss.  

Log‐linear models were used in preference to the piecewise linear models because (1) they provide

better qualitative fits across the range of blood lead being investigated (e.g., the log‐linear model

appeared to more closely follow the trend in the data than the piecewise model); and (2) incorporating

uncertainty in the log‐linear models into the IQ estimate process is more straightforward than for the

piecewise models. Lanphear et al. (2005) found that the log‐linear model provided excellent fits to the

data relating blood lead metrics and IQ in the pooled data (see previous discussion.) The form of this

equation is:

FSIQ = α + 0 * ln (blood lead+1) +  1*X1+ 2*X2 + ... + ε,

Where:

 =   the model intercept

 0 =   the coefficient for (log) blood lead  

 1,  2, etc. =   the coefficients for covariates (study site, maternal IQ, HOME Inventory, birth

weight, and maternal education), and

ε =   the normally distributed residual term.

This model form explains a large proportion of the variance for all the blood lead metrics used (0.62<R2

< 0.63), and the β coefficients for all four blood lead metrics (early life, concurrent, peak, and lifetime)

were highly significant and negative.  

As mentioned above, the median blood lead in the Lanphear et al. (2005) pooled data set was 9.7 μg/dL,

significantly higher than current background blood lead levels among U.S. children. The current

background values are, according to NHANES, on the order of 1 μg/dL. Despite the relatively high

median value, the Approach used equations based on the Lanphear et al. (2005) pooled analysis

because a sufficient number of children had blood leads in the range 1−5 g/dL to support the model fit

in this range (see Crump et al. (2013) Figure 2).   

Monte Carlo estimation of the changes in IQ associated with renovation were conducted for each

individual “sampled” in the simulation using both the concurrent and lifetime average metrics. Blood

lead “measurements” (outputs from the Leggett model) were generated for a given individual under

background exposure conditions (with variability determined by sampling from exposure‐associated
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input variables) and under identical conditions except that the blood lead measurement was also

affected by a renovation at some time prior to the measurement. Change in IQ was then estimated as:

ሻ .ݎݎ݁ ݀.ݐݏ 0,ሺܰ ∗ ߚ	 ൅ ܤܾܲ ln ∆ ൌ ܳܫ∆

Where:

∆݈ܾ݊ܲܤ ൌ ln	ሺ

݊݋݅ݐܽݒ݋݁݊ݎܤܾܲ

ሻ݊݀ݑ݋ݎܽܿ݇݃ܤܤܾܲ

The second term in the equation includes β, the regression coefficient from the concurrent and lifetime

average blood lead regressions and a normally distributed variable characterized by mean 0 and

standard error associated with the corresponding β estimate. As noted above, the β values for

concurrent and lifetime blood lead were −2.65 and −3.19, respectively. The standard errors of β (0.53 for

concurrent and 0.64 for lifetime average blood lead) were calculated from the 95 percent upper and

lower confidence limits given in Table 6‐1 using the normal approximation. Inclusion of this term

accounts for statistical uncertainty associated with the estimate of the beta coefficient; it does not

incorporate all sources of uncertainty in individual IQ responses to changes in blood lead.  

IQ changes were not estimated for individuals with simulated post‐renovation blood lead levels less than

1.0 g/dL, similar to the 2008 LRRP analysis and in agreement with the range of measured blood leads in

the Lanphear et al. (2005) pooled analysis. Less than 1 percent of the observations supporting the

Lanphear et al. (2005) statistical models were below this value, and thus estimating IQ changes in this

blood lead range was not justified. The overall output of the analysis is quantile estimates of IQ changes

associated with renovation derived from the Monte Carlo samples for each renovation scenario.  

6.4. Concentration‐Response Characterization for Low Birth Weight

Multiple studies were reported in the ISA for Lead that examined the association between maternal

blood lead and birth weight/fetal growth (US EPA 2013). The ISA considers the association between lead

exposure and low birth weight (LBW) to be “suggestive of a causal” relationship. The associations were

not always consistent across all studies considered in the ISA but a large retrospective study (n = 43,288)

conducted in New York state from 2003 to 2005 showed that increased maternal blood lead was

associated with decreased birth weight in infants (Zhu et al., 2010). This study provided data on low

blood lead levels (mean blood lead level was 2.1 μg/dL), adjusted for many confounders, and measured

a continuous birth weight instead of a dichotomous comparison of infants at/above and below 2500 g.  

Mothers, aged 15–49 years with blood lead level less than 10 μg/dL (over 99 percent of participants)

provided blood lead samples during pregnancy to the NY State Heavy Metals Registry. Included in this

analysis were 2,744 low birth weight infants. Atomic spectrometry was used to determine blood lead

levels and the detection limit for blood lead analysis was 1 μg/dL. Birth outcomes, including birth weight
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measured as a continuous variable, preterm birth defined as <37 weeks from date of last menstrual

period, and small for gestational age (SGA) defined as birth weight below the 10th‐percentile of birth

weight for gestational age were obtained from birth certificates. Potential confounders controlled for in

this analysis included maternal race and ethnicity, maternal age, maternal education, financial

assistance participation, self‐reported smoking during pregnancy, alcohol consumption during

pregnancy, illicit drug use during pregnancy, trimester when prenatal care started, timing of lead

sample, parity, sex of child, and pre‐pregnancy body mass index (Zhu et al., 2010).

The rates of low birth weight for the cohort were 6.3 percent, 8.1 percent for preterm birth and 9.5

percent for SGA. Adjusted odds ratios (ORs) of blood lead levels were estimated from logistic regression

with fractional polynomials. Adjusted ORs were not statistically significant for preterm birth and SGA.

Changes in birth weight estimates varied with blood lead levels, with the greater decreases in birth

weight occurring at the lower blood lead concentrations, without evidence of a threshold effect. Birth

weight reduction was estimated using log‐transformed blood lead in fractional polynomial regressions.

The best fit was obtained when blood lead was expressed using a square root transformation. An

estimated mean value of 27 g was reported for a 0‐ to 1‐μg/dL change in blood lead with smaller

reductions per μg/dL at higher blood lead concentrations (see Table 6‐2). After exclusion of mothers

with blood lead measurements less than 1 μg/dL, a linear model provided the best fit to the data, with a

1‐μg/dL increase in blood lead associated with a 7.0‐g decrease in birth weight (in the range 1–10 μg/dL)

(Zhu et al., 2010).   

Table 6‐2. Predicted Birth Weight Reductions Based on Fractional Polynomial Model1

Maternal Blood Lead

(g/dL)

Predicted Body Weight Reductions  

(grams) 95% UCL 95% LCL

0 −− −− −−

1 −27.4 −17.1 −37.8

2 −38.8 −24.1 −53.4

3 −47.5 −29.6 −65.4

4 −54.8 −34.2 −75.5

5 −61.3 −38.2 −84.4

6 −67.2 −41.8 −92.5

7 −72.5 −45.2 −99.9

8 −77.6 −48.3 −106.8

9 −82.3 −51.2 −113.3

10 −86.7 −54.0 −119.4

1

Source: Zhu et al. (2010), Table 3  

UCL = Upper Confidence Limit

LCL = Lower Confidence Limit
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Changes in birth weight were estimated using the fractional polynomial (square‐root) model that was

determined by Zhu et al. (2010) to provide the best fit to the data. As in the case of IQ loss, background

(non‐renovation) and post‐renovation blood lead concentrations were estimated for the same

hypothetical individuals using the exposure models described in the preceding chapters, and the Leggett

blood lead model. In this case, however, changes in blood lead were estimated for adult (18–49 year‐

old) women. Because of computational limitations, running a specific set of simulations using the

probability distribution for pregnancy between ages 18 and 49 years was not feasible. Instead, the age

distribution in the model was set equal to the general population distribution. The blood lead profile for

women within this age range in the Leggett model is similar, however, so age does not play a significant

role in determining the resulting estimated birth weight distribution.

For any maternal blood lead change, the reduction in birth weight predicted to be associated with

renovation was estimated as:

∆ܤܹ ൌ ∆√ܾܲܤ ൈ 27.4 ൅ ܰሺ0, ∆√ܾܲܤ ൈ 5.78ሻ      

Where:

ΔBW = the predicted change in birth weight, and

∆√ܾܲܤ ൌ ඥܾܲܤோ௘௡௢௩௔௧௜௢௡ െ ටܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

In the above equation, the value 27.4 equals the predicted birth weight reduction associated with a

change of maternal blood lead from a (hypothetical) level of 0 g/dL to 1 g/dL. The changes in birth

weight associated with further blood lead increments then scale as the square root of the increment

between pre‐ and post‐renovation blood lead levels. Similarly, 5.28 is the standard deviation of the

predicted changes in birth weight, derived from the confidence limits shown in Table 6‐2. The standard

deviation likewise scales with the square root of increasing blood lead increment. As for IQ change, the

notation N(0 , ∆√ܾܲܤ ൈ 5.78) indicates a random sample from the normal distribution with the

indicated mean and standard deviation. This random sampling is done for each iteration within all

Monte Carlo iterations and incorporates the variability in the estimated changes in birth weight, given a

specified change in blood lead.  

In estimating birth weight reductions, the relationship expressed in the above equation was limited to

Monte Carlo subjects where the post‐renovation blood lead value is estimated to be greater than 1.0

g/dL. When the post‐renovation blood lead was less than 1 g/dL, the estimated birth weight

reduction for that individual was estimated to be zero. This convention reflects the uncertainty in the

data and model results below 1 g/dL, the detection limit for blood lead values reported in the Zhu et al.

(2010) study. Similarly, where background blood lead values were estimated to be less than 1.0 g/dL,

the values were adjusted to 1 g/dL, again to restrict the estimation of birth weight reductions within
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the range of the data. To the extent that a substantial proportion of background concentrations are

below 1.0, this adds an element of uncertainty to the model results. Although estimating the degree of

uncertainty associated with the procedure is difficult, for any individual in the simulation with estimated

blood lead below 1 g/dL, the range of estimated birth weight reduction is bounded at the lower end by

zero and at the upper end by approximately the estimated birth weight reduction for women with a

blood lead level of 1 g/dl (27.4 grams.)  

In this analysis, EPA applied the C‐R function for low birth weight to hypothetical individuals aged 18–49.

The Zhu study included some participants 15–18 years of age, but the published paper does not include

sufficient information about the number or characteristics of these women to support analysis for these

women at this time. The paper reports that the minimum maternal age of the study participants was 15

and the 10th percentile was 20, of 43,288 participants for whom maternal age was known, but the

number of women under the age of 18 is unknown. In addition, although a simple averaged effect of age

was accounted for in the study, no analysis was reported that would control specifically for the well‐

established effect that young maternal age (ages 15–18, for example)  has on birth weight. The birth

weight effects reported are adjusted to be for a maternal age of 27.6 years, the average age in the

study, but are not necessarily appropriate for underage mothers.  

6.5. Concentration‐Response Characterization for Kidney Function

Several studies have reported an association between reduced kidney function and blood lead levels less

than 10 μg/dL (Lai et al., 2008; Hernandez‐Serrato et al., 2006; Goswami et al., 2005). Lower estimated

GFR is an indicator of chronic kidney disease. This endpoint was considered “suggestive of a causal”

relationship in the ISA for Lead (US EPA, 2013). Two studies were considered for this analysis (Muntner

et al., 2003; Navas‐Acien et al., 2009) but ultimately Navas‐Acien et al. (2009) was chosen to provide the

C‐R function. Both studies analyzed NHANES data but Muntner et al. (2003) used NHANES III data when

blood lead levels were much higher than they are now.  

Navas‐Acien et al. (2009) analyzed NHANES 1999‐2006, estimating relationships between reduced

kidney function and blood lead and cadmium concentrations.  The geometric mean blood lead level for

lead was 1.58 μg/dL in 14,778 participants 20 years and older.  Odds ratios were calculated for reduced

estimated GFR (eGFR) (<60 mL/min/1.73m2

), albuminuria, a (≥30 mg/g creatinine) and for individuals

having both symptoms simultaneously.  Odds ratios were adjusted for socio‐demographic factors (sex,

race/ethnicity), as well as body mass index, education, smoking status, urinary cotinine , current and

former alcohol intake, hypertension, and diabetes mellitus.  Analyses were conducted for either lead or

cadmium separately, and using models that included both.   

Logistic regression was applied to estimate the ORs for reduced eGFR across blood lead quartiles in men,

women, and in the entire cohort, using models with different degrees of adjustment. In addition,

regression models were estimated that included blood lead and cadmium levels as continuous variables.
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Blood lead and cadmium data were highly skewed, and both were log‐transformed when entered in the

regression models.  

For the study cohort as a whole, the trend in fully adjusted ORs (estimated using “Model 3” from Navas‐

Acien et al. (2009), which contains all the covariates listed above) for reduced eGFR was highly

significant across blood lead quartiles. Model 3 includes urinary cadmium as a covariate, so the resultant

estimates quantify the relationship between reduced eGFR and blood lead levels, controlling for

variations in cadmium exposure as well.  Adjusted ORs for the second and third quartiles (1.10, 1.36)

were elevated compared to the referent group, but not significantly at α=0.05 (Table 6‐3).  The

estimated OR for the fourth quartile (1.56) was significantly elevated.  Adjusted ORs for albuminuria

versus blood lead were not significantly elevated in the 2nd‐4th quartiles compared to the reference

group.   

Table 6‐3. Fully‐Adjusted (Model 3) Odds Ratios for Reduced eGFR (Navas‐Acien et al., 2009)

Quartile Blood Lead Range

(median)

Odds Ratio for Reduced GFR (95

Percent Confidence Limits)

Odds Ratio for Albuminuria (95

Percent Confidence Limits)

< 1.1 (0.8) 1 1

1.1–1.6 (1.3) 1.1 (0.80−1.51) 0.83 (0.66−1.04)

1.6−2.4 (1.9) 1.36 (0.99−1.85) 0.92 (0.76−1.12)

> 2.4 (3.2) 1.56 (1.17−2.08) 1.19 (0.96−1.47)

Navas‐Acien et al. also estimated relative risks of reduced eGFR based on the fully adjusted regression

models that included (log) blood lead and cadmium as continuous variables. For the entire cohort, the

estimated OR associated with an increase in blood lead from 1.1 to 2.4 g/dL (the 25th–75th percentiles

of the observed values) was 1.31 with 95 percent confidence limits of 1.16 and 1.47 (Table 6‐4). The

estimated OR was higher in women (1.44) than in men (1.13), and the latter value was not significantly

different from 1.

Table 6‐4. Odds Ratios for Reduced eGFR Associated with an Increase in Blood Lead Concentration

from 1.1 to 2.4 g/dL

Group Odds Ratio for Change in Blood lead from  

1.1−2.4 g/dL (95% confidence intervals)

Men 1.13 (0.96−1.33)

Women 1.44 (1.22−1.69)

Men + Women 1.31 (1.16−1.47)

Because the concentration‐response for albuminuria was found to be not statistically significant, EPA

has chosen the blood‐lead to reduced eGFR relationship as the endpoint for the simulation.  As
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discussed below, gender‐specific ORs and changes in prevalence will be estimated based on the

regression models shown in Table 6‐4.

In the Approach, the marginal increase in the prevalence of reduced GFR was calculated based on the

regression results presented by Navas‐Acien et al. (2009). Estimated ORs from the simulation were

multiplied by gender‐ and age‐specific background prevalence estimates for category 3‐5 kidney disease

for the U.S. population.  As for the other endpoints, marginal risks were calculated based on the

difference in blood lead levels seen in the same (simulated) individual experiencing background and

renovation exposures.  The absolute risk of an individual of a given sex and age having low eGFR was

estimated using the following equation:  

LGFR ൌ LGFR଴*eሺβାεሻ*Δ୪୬୔ୠ୆ܩܮܴ ൌ ܨܩܮܴ ∗ ଴݁ሺఉାఌሻ∗௱௟௡௉௕஻

߂݈ܾ݊ܲܤ ൌ lnሺሺܾܲܤோ௘௡௢௩௔௧௜௢௡ሻ /ሺܾܲܤ௢௥௚௞௖௔஻௨௡ௗሻሻ

Where:

LGFR0 =   national background prevalence of reduced GFR for an individual of a given

age and gender catalogued by the Centers for Disease Control and Prevention

(CDC)

(http://nccd.cdc.gov/CKD/default.aspxhttp://nccd.cdc.gov/CKD/default.aspx)

 =   the gender‐specific regression coefficient for reduced GFR derived from the

data presented in Table 6‐4

ε =   the normally distributed variable representing the uncertainty in the 

coefficient (calculated as described in Appendix P.5.1)

As the model is formulated in the above equation, the extra risk of reduced eGFR for a given individual is

LGFR ‐ LGFR0.  The exponential term in the equation above represents the OR for the exposed individual.  

In cases where the background incidence of the health outcome is low, the OR serves as an

approximation for the risk ratio. In this case, because low GFR is a relatively rare outcome, the

concentration response function estimates the risk ratio of reduced GFR as a function of both

concurrent and lifetime average blood lead concentrations.  (See Section 7.3.2 for more discussion of

the concurrent and lifetime average approaches.)

The 1999–2006 NHANES data used by Navas‐Acien et al. (2009) included blood lead measurements at

detection limits as low as 0.25 g/L, considerably lower than the detection limits in the other studies

that provided data for this Approach document. Little information was provided regarding potential

blood‐lead to reduced GFR relationship in the lower portion of the cohort; the first quartile, which has a

median blood lead level of 0.8 g/dL, was used as a reference group against which the other quartiles

were evaluated. Although the regression analysis with blood lead as a continuous variable included the
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entire data set down to the detection limits, much uncertainty is associated with the application of the

regression in that range. Thus, in this analysis, relative risks of 1.0 were assigned to any individual having

a renovation‐related blood lead level of less than 0.8 g/dL, the midpoint of the first quartile. Also,

background blood lead concentrations below this value were adjusted to 0.8 g/dL in the calculation.

Additional details concerning the calculation of eGFR risk in the Monte Carlo assessment are provided in

Appendix P.  

6.6. Concentration‐Response Characterization for Cardiovascular

Disease Mortality

Several studies examined the association between cardiovascular disease mortality (CVDM) and lead

exposure (Lustberg and Silbergeld, 2002; Schober et al., 2006; Cocco et al., 2007; Khalil et al., 2009; Lin

et al., 2011; Menke et al., 2006; Neuberger et al., 2009; Moller and Kristensen 1992; Weisskopf et al.,

2009). All but two of these studies reported an association between blood lead and CVDM (Moller and

Kristensen 1992; Weisskopf et al., 2009). Weisskopf et al. (2009), however did report an association

between bone lead and increased mortality.  

Menke et al. (2006) was chosen as the strongest study for this analysis, as described in Exhibit B of

Developing a Concentration‐Response Function for Pb Exposure and Cardiovascular Disease‐Related

Mortality (US EPA 2014b), also being peer reviewed with this Approach document and its appendices.

See this report for a detailed discussion of the CVDM concentration‐response function and discussion of

related issues. The EPA (2014b) report, and relevant details supporting the use of the study and the

application of the equations, are briefly summarized here.

The Menke et al. (2006) study was selected because it used a nationally representative NHANES III

sample of men and women whose blood lead concentrations were lower than many previous studies

(mean = 2.58 μg/dL), controlled for a wider range of potential confounders, tested for interactions with

lead with other covariates, considered concentration‐response relationships, and examined mortality

from specific cardiovascular diseases (CVDM) and other causes. The Menke et al. (2006) analysis thus

addresses some of the weaknesses noted in previous studies. Furthermore, the Menke et al. (2006)

findings were supported by a community‐based cohort study of women ages 65–87 years, in which

higher effect estimates were observed for mortality from cardiovascular disease than for all‐cause

mortality (Khalil et al., 2009).

Menke et al. (2006) examined all‐cause and cause‐specific mortality using NHANES III data, in

participants at least 18 years of age. They were followed for up to 12 years after their blood lead was

measured, and 1,661 deaths were identified from the National Death Index. The cause of death was

determined using either International Classification of Diseases (ICD) revision 9 or 10 (WHO 1990). The

mean blood lead level of participants at baseline was 2.58 μg/dL, the 80th percentile was 4.9 μg/dL, and

the maximum was 10 μg/dL. Participants were categorized into blood lead tertiles based on the
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weighted population distribution. Blood lead ranges for the tertiles were <1.93 μg/dL, 1.94–3.62 μg/dL,

and ≥3.63 μg/dL. To analyze the association between blood lead and mortality, follow‐up for each

participant was calculated as the time between their NHANES III examination and the date of death, the

date on which they turned 90 years of age, or December 31, 2000, whichever occurred first. The hazard

ratios and 95 percent confidence intervals were calculated by multivariable Cox regression models for

all‐cause cardiovascular, myocardial infarction, stroke, and cancer mortality by comparing each tertile

with the first (low‐lead) tertile (Table 6‐5). A hazard ratio (HR) is similar to an odds ratio. Both are

measures of a relative change in the risk of an event (such as heart disease incidence or eGFR > 60).

Both can control for the effect of other covariate characteristics. A HR is also specifically weighted based

on the time when events occurred.

Table 6‐5. Hazard Ratios and 95% Confidence Intervals of All‐Cause, Cardiovascular Disease, Myocardial

Infarction, and Stroke Mortality Associated with Tertile of Lead

Tertile 1

(<0.09 μmol/L or

<1.93 μg/dL)

Tertile 2

(0.09‐0.17 μmol/L

or

1.94‐3.62 μg/dL)

Tertile 3

(≥0.18 μmol/L

or

≥3.63 μg/dL)

ptrend

All‐cause mortality, n 252 470 939

Age, race‐ethnicity, and sex adjusted 1.00 0.97 (0.76–1.23) 1.37 (1.15–1.64) <0.001

Multivariable 1 adjusteda 1.00 0.93 (0.73–1.19) 1.30 (1.08–1.56) <0.001

Multivariable 2 adjustedb 1.00 0.91 (0.72–1.15) 1.25 (1.04–1.51) 0.002

Cardiovascular disease mortality, n 104 219 443

Age, race‐ethnicity, and sex adjusted 1.00 1.01 (0.68–1.51) 1.51 (1.07–2.14) 0.004

Multivariable 1 adjusteda 1.00 1.06 (0.70–1.60) 1.64 (1.14–2.35) 0.001

Multivariable 2 adjusted b 1.00 1.03 (0.69–1.55) 1.55 (1.08–2.24) 0.003

Myocardial infarction mortality, n 50 83 234

Age, race‐ethnicity, and sex adjusted 1.00 0.99 (0.55–1.79) 1.70 (0.99–2.90) 0.011

Multivariable 1 adjusted a 1.00 1.05 (0.56–1.97) 2.01 (1.12–3.61) 0.003

Multivariable 2 adjusted b 1.00 1.02 (0.55–1.89) 1.89 (1.04–3.43) 0.007

a

Adjustment included age, race‐ethnicity, sex, diabetes mellitus, body mass index (BMI), current or former

smoking, alcohol consumption, physical activity, low income, c‐reactive protein (CRP), total cholesterol, high school

education, urban residence, and post‐menopausal status. b

Adjustment includes variables in model 1, hypertension, and level of kidney function.

Sample sizes (n) refer to the number of events.

Source: Menke et al. (2006, Table 2).  

Menke et al. (2006) performed three analyses to adjust for various potential confounders, and CVDM

was significantly associated with blood lead levels in all three models. Tests for a linear trend across

tertiles of blood lead were computed by including tertile of lead as a continuous variable in the Cox
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regression models. This analysis found statistically significant increases in mortality risk as blood lead

levels increased for all causes of mortality analyzed except for cancer. The results of this analysis are

also presented in Table 6‐5 and support the finding of a concentration‐response relationship between

blood lead and CVDM.  

Menke et al. (2006) also conducted a series of regression analyses in which the log of blood lead was

included as a continuous variable. Analyses were conducted for the entire cohort and for subgroups of

the study subjects, controlling for same variables as in the tertile models. Changes in CVDM associated

with an increase in blood lead from 1.46 to 4.92 μg/dL (20th to 80th percentiles) were calculated for

each group. The estimated relative risk of CVDM associated with this change in blood lead was 1.53 (95

percent confidence interval was 1.21–1.94). The results of these models (with log [blood lead] included

as a continuous variable) were used as the basis for the CVDM concentration‐response function, as

discussed in the following section.

The hazard ratio estimates shown in Table 6‐6 were derived from covariate‐adjusted Cox regressions,

and the dependence of mortality on blood lead in these regressions is expressed in the regression

coefficients, (βs) estimated in the regressions. These β values can therefore be used to estimate the

changes in CVDM for changes in CVDM associated with the increased blood lead levels due to

renovation. The original Menke et al. (2006) article did not include β estimates, but the study authors

provided the values to EPA for the report, Developing a Concentration‐Response Function for Pb

Exposure and Cardiovascular Disease‐Related Mortality, and they also were used here.  

EPA’s approach for estimating CVDM due to increased blood lead used the β estimates derived

separately for men and women by Menke et al. (2006). Use of the gender‐specific values (rather than βs

for more narrowly defined subpopulations) was intended to provide estimates that are most

representative of the U.S. adult population as a whole. The β values and standard errors for men and

women from the Menke et al. (2006) analysis were 0.25 (0.20) and 0.40 (0.11), respectively.  

The Monte Carlo concentration‐response algorithm used to estimate individual CVDM risks was very

similar to that for changes in kidney function, as described in Section 6.5.1.  
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Table 6‐6. Summary of Adjusted Hazard Ratios for a 3.4‐Fold Change in Blood Lead

Subgroup Hazard Ratio of All−cause Mortality (95%

CI)

Hazard Ratio of Cardiovascular

Disease−related Mortality (95% CI)

Age (years)

<60 1.75 (1.25−2.44) 2.00 (1.24−3.22)

≥60 1.31 (1.08−1.58) 1.49 (1.12−1.99)

Race‐Ethnicity

Non‐Hispanic white 1.32 (1.09−1.60) 1.49 (1.12−1.99)

Non‐Hispanic black 1.23 (0.99−1.52) 1.13 (0.79−1.61)

Mexican‐American 1.17 (0.86−1.60) 1.55 (0.90−2.68)

Sex and Menopausal Status

Male 1.41 (1.11−1.78) 1.35 (0.84−2.18)

Female 1.24 (1.00−1.54) 1.63 (1.25−2.11)

Pre−menopausal 1.02 (0.54−1.95) 2.71 (0.93−7.91)

Post−menopausal 1.24 (1.00−1.54) 1.46 (1.04−2.03)

Residence

Rural 1.28 (1.05−1.54) 1.41 (1.01−1.96)

Urban 1.42 (1.18−1.72) 1.75 (1.19−2.56)

Smoking

Never 1.21 (0.93−1.58) 1.57 (1.10−2.24)

Former 1.61 (1.33−1.94) 2.07 (1.49−2.89)

Current 1.34 (0.96−1.87) 1.05 (0.54−2.04)

Body Mass Index (kg/m2)

<25 1.51 (1.16−1.96) 2.02 (1.32−3.11)

≥25 1.28 (1.03−1.58) 1.34 (0.94−1.91)

Hypertension

No 1.31 (1.08−1.58) 1.48 (0.96−2.26)

Yes 1.32 (1.09−1.60) 1.49 (1.15−1.94)

Diabetes

No 1.37 (1.19−1.58) 1.59 (1.31−1.92)

Yes 1.12 (0.73−1.71) 1.16 (0.67−2.00)

Estimated Glomerular Filtration Rate (mL/min/1.73m2)

<60 1.44 (1.01−2.06) 1.75 (1.06−2.88)
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Subgroup Hazard Ratio of All−cause Mortality (95%

CI)

Hazard Ratio of Cardiovascular

Disease−related Mortality (95% CI)

≥60 1.32 (1.12−1.56) 1.49 (1.18−1.89)

Overall 1.34 (1.16−1.54) 1.53 (1.21−1.94)

In the Monte Carlo simulation, blood lead levels were modeled twice for each hypothetical individual,

once with only background exposures, and again after renovation. Each individual was modeled as male

or female and blood lead measurements were assumed to occur at randomly chosen ages greater than

or equal to 18 years. EPA chose to estimate changes in CVDM as a function of both concurrent and

lifetime average blood lead concentrations. Risks were estimated based on the change in blood lead,

much like changes in kidney function, as:

஻௕௉௡௟∆∗ሺఉାఌሻ݁௚ ∗ ,௔,଴ܯܦܸܥ ൌ௚ ,௔ܯܦܸܥ

߂݈ܾ݊ܲܤ ൌ lnሺሺܾܲܤோ௘௡௢௩௔௧௜௢௡ሻ /ሺܾܲܤ௢௥௚௞௖௔஻௨௡ௗሻሻ

Where:

CVDMa,g =   the estimated age‐ and gender‐specific risk after renovation

CVDM0,a,g =   the age‐ and gender‐specific background (general population) CVDM risk

obtained from the CDC, as discussed further in Appendix P

 =   the regression coefficient

ε =   a random sample drawn from a normal distribution with mean zero and a

standard deviation equal to the estimated standard error of the coefficient.

Including ε in the equation incorporated the statistical uncertainty in the regression coefficient into the

estimation process, but, as noted previously, did not capture all sources of individual variability in the

analysis.  

The output of the analysis was an estimate of individual CVDM risk for an individual whose blood lead

level was affected by renovation. The sum of the individual risks across the simulation can be

interpreted as the total estimated CVDM in the population. The approach described above is essentially

the same as that described in US EPA (2014b), except that it estimates changes in risks for hypothetical

individuals and aggregates the results, rather than calculating risks for segments of the population. This

method allowed for the incorporation of heterogeneity of individual exposure scenarios into the

population risk estimate and allowed for differences in individual response to exposure (characterized

by the variability in the regression coefficients) to be included in the analysis. The quantity
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CVDMa,g/CVDM0,a,g represents the calculated hazard ratio for an exposed individual and CVDMa,g − 

CVDM0,a,g is the extra risk.  

Another source of uncertainty in the estimates is potential differences in the survival rates and disease

patterns in the U.S. general population compared to the pattern observed among the Menke et al.

(2006) subjects. The Menke et al. study estimated mortality risks occurring for a maximum follow‐up

duration of 12 years. Whether this same pattern of risk (that is, the estimated hazard ratios) would

remain the same for the entire lifespan of the exposed population is unknown.  

Because the detection limit for blood lead measurements in the Menke et al. (2006) study was 1.0

g/dL, CVDM risks were not estimated for blood lead concentrations below this level. A hazard ratio of

1.0 was assigned to individuals whose post‐renovation blood lead was less than 1 g/dL, and any

background estimates below this value were adjusted to 1 g/dL for the purposes of calculating

Δln(blood lead). Also, because of larger uncertainty associated with estimating CVDM at high blood lead

concentrations, risks were limited to post‐renovation blood lead values up to 5.98 g/dL (the 90th

percentile in the Menke et al. cohort). Post‐renovation blood lead estimates greater than this value

were adjusted downward to 5.98 g/dL when calculating Δln(blood lead).  

6.7. Summary of Health Effect Concentration‐Response Functions

Used in the Approach

Based on the discussion in Sections 6.1 to 6.6, the four health effects were represented using the

equations and parameters shown in Table 6‐7. Each equation depends on a comparison between the

post‐renovation blood lead and the background blood lead, although the form of the comparison varies.

For IQ, CVDM, and kidney effects, the comparison term is:

∆݈ܾ݊ܲܤ ൌ ln ቆ ܾܲܤோ௘௡௢௩௔௧௜௢௡

ܾܲܤ௢௥௚௞௖௔஻௨௡ௗቇ

while for low birth weight the comparison term is:

∆√ܾܲܤ ൌ ඥܾܲܤோ௘௡௢௩௔௧௜௢௡ െ ටܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

The difference in mathematical form of the comparison term is related to the form of the regression

equation that was used to fit the data in each of the health effect studies. For the Approach, the

“PbBRenovation” is the total (background plus renovation‐related) blood lead following the renovation. The

“PbBBackground” is the background blood lead at the given age of the individual. Then, using the equations

in Table 6‐7, the predicted metric is the renovation‐related change in the health effect. For IQ and LBW,

the change is measured as change in FSIQ (in IQ points) and change in birth weight (in grams).  For

CVDM and kidney effects, the change is represented using the hazard ratio.
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Table 6‐7. Summary of Concentration‐Response Functions for Approach Health Effects

Health Effect

and Reference Equation Parameters Blood Lead Rangeand Age Range

IQ

Lanphear et al.,

2005, with

parameter

adjustments

from   Crump et

al., 2013

ሻ .ݎݎ݁ ݀.ݐݏ 0,ሺܰ ∗ ߚ	 ൅ ܤܾܲ ln ∆ ൌ ܳܫ∆

∆݈ܾ݊ܲܤ ൌ ln	ሺ

ܾܲܤோ௘௡௢௩௔௧௜௢௡

ܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

ሻ

 = ‐2.7

std. err. = 0.5

Post‐Reno PbB > 1

g/dL;  If background

or post‐renovation

blood lead is less

than 1 g/dL, round

each up to 1 g/dL.

Age 0 to 10

LBW

Zhu et al., 2010

ଵ,଴ܹܤ∆ ൈ ܤܾܲ√∆ ൌ ܹܤ∆

൯ .ݒ݀݁ ݀.ݐݏ ൈ ܤܾܲ√∆ 0,൫ܰ൅ 

∆√ܾܲܤ ൌ ඥܾܲܤோ௘௡௢௩௔௧௜௢௡ െ ටܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

∆ܤܹ଴,ଵ= Change in body weight associated with

maternal PbB increase from 0 to 1 g/dL

∆ܤܹ଴,ଵ=2.74 g

std. dev = 2.58 (derived from

confidence limits)

Post‐Reno PbB > 1

g/dL; If background

or post‐renovation

blood lead is less

than 1 g/dL, round

each up to 1 g/dL.

If Post‐Reno PbB>10

g/dL, use the value

at 10 (‐86.7 g)

Age 18‐49

Kidney Function

Navas‐Acien et

al., 2009

ܨܩܮܴ ൌ ܨܩܮܴ ଴ൈ	݁൫ఉାேሺ଴,௦௧ௗ.௘௥௥.ሻ൯∗௱௟௡௉௕஻

∆݈ܾ݊ܲܤ ൌ ln	ሺ

ܾܲܤோ௘௡௢௩௔௧௜௢௡

ܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

ሻ

ܨܩܮܴ =௢national background prevalence of

reduced GFR for an individual of a given age and

gender

	 ൌ ݋݅ݐܴܽ	݇ݏܴ݅

ܴܨܩܮ

௢ܴܨܩܮ

 =0.157 (males); 0.467 (females)

std. err. = 0.083

Age

(yrs)

Female

LGFRo

Male

LGFRo

20‐29 0.29 0.24

30‐39 0.68 0.58

40‐49 1.97 1.67

50‐59 5.07 3.98

60‐69 13.05 9.27

70+ 25.60 21.06

Post‐Reno PbB > 1

g/dL; If background

or post‐renovation

blood lead is less

than 1 g/dL, round

each up to 1 g/dL.

Age 18‐49 and 50‐80

CVDM

Menke et al.,

2006

ܥܸܯܦ ൌ ܥܸܯܦ ଴ൈ ݁ሺఉାேሺ଴,௦௧ௗ.௘௥௥.ሻሻ∗∆௟௡௉௕஻

∆݈ܾ݊ܲܤ ൌ ln	ሺ

ܾܲܤோ௘௡௢௩௔௧௜௢௡

ܾܲܤ௢௥௚௞௖௔஻௨௡ௗ

ሻ

ܥܸܯܦ =௢national background prevalence of

CVDM for an individual of a given age and gender

	 ൌ ݋݅ݐܴ݀ܽݎܽݖܽܪ

ܯܦܸܥ

௢ܯܦܸܥ

 =0.25 (males); 0.40 (females)

std. err. = 0.20 (males); 0.11

(females)

Age

(yr)

Female

CVDMo

Male

CVDMo

20‐29 3.70E‐05 7.20E‐05

30‐39 1.20E‐04 2.40E‐04

40‐49 3.90E‐04 8.30E‐04

50‐59 9.40E‐04 2.20E‐03

60‐69 2.30E‐03 4.60E‐03

70‐80 7.60E‐03 1.20E‐02

Post‐Reno PbB > 1

g/dL; If background

or post‐renovation

blood lead is less

than 1 g/dL, round

each up to 1 g/dL.

Age 18‐49 and 50‐80
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

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The different equations will predict different renovation‐related health effect changes for different

background and renovation‐related blood leads. For example, if the background blood lead is 1 g/dL

and the renovation‐related blood lead is 9 g/dL, then the estimated mean IQ change would be  

∆݈ܾ݊ܲܤ ൌ ln	൬

9൅1

1 ൰ ൌ lnሺ10ሻ ൌ 2.3

∆ܫܳ ൌ ∆ ln ܾܲܤ	 ൅ ߚ ൌ 2.3 െ 2.7 ൌ െ0.4	݋݌݅݊ݏݐ

When the equations are applied in the Approach, the “renovation” blood lead is selected at a certain

time post‐renovation. Different time periods are selected to capture a range of different post‐

renovation blood leads, as discussed in Section 7.3.1. With the exception of IQ, the original health

studies capture blood lead values used that are point estimates of blood lead taken from typical adults;

thus, the blood lead values represent current blood lead levels and do not capture the past exposure

history of the individual. In many cases, the adults may have had much larger blood lead values in the

past when population‐level blood leads tended to be higher than today. In the case of the Approach, the

renovation is a relatively short (months to years) spike in blood lead followed by a return to background

levels. Thus, applying these health effect equations to a post‐renovation short‐lived blood lead spike as

opposed to a chronic lead exposure introduces uncertainty in the Approach, as discussed in Section

9.2.3.

Table 6‐8 provides examples of the results of the equations assuming a background blood lead of 1

g/dL and the renovation‐related blood lead shown in each row.  In the Monte Carlo simulations, the

background values and the renovation‐related values will both vary for each iteration, so this table

represents an example at a particular background blood lead. Figure 6‐1 depicts these predicted health

effect changes in graphical form, where the upper and lower 95th percent confidence intervals are also

shown.   

   
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Table 6‐8. Post‐Renovation Blood Lead Values and Associated Health Effect Magnitudes in Approach

Renovation‐Related

Blood Lead Change

(g/dL) *

IQ (IQ points) BWR (g)

IQ Lower 95th

Perc.

Upper 95th

Perc. BWR Lower 95thPerc.

Upper 95th

Perc.

0  (1) 0.0 1.0 −1.0 −27.4 −17.1  −37.8

1  (2)  −1.8  −0.8 −2.9 −38.8 −24.1  −53.4

2  (3)  −2.9  −1.9 −4.0 −47.5 −29.6  −65.4

3  (4)  −3.7  −2.6 −4.7 −54.8 −34.2  −75.5

4  (5)  −4.3  −3.2 −5.3 −61.3 −38.2  −84.4

5  (6)  −4.7  −3.7 −5.8 −67.2 −41.8  −92.5

6  (7)  −5.2  −4.1 −6.2 −72.5 −45.2  −99.9

7  (8)  −5.5  −4.5 −6.6 −77.6 −48.3  −106.8

8  (9)  −5.8  −4.8 −6.9 −82.3 −51.2  −113.3

9  (10)  −6.1  −5.1 −7.1 −86.7 −54.0  −119.4

Renovation‐Related

Blood Lead Change

(g/dL) *

CVDM Hazard Ratio Kidney Effects Risk Ratio

CVDM Lower 95th

Perc.

Upper 95th

Perc. GFR Lower 95thPerc.

Upper 95th

Perc.

0  (1) 1.00 0.61 1.39 1.00 0.84 1.16

1  (2) 1.19 0.80 1.58 1.11 0.95 1.28

2  (3) 1.32 0.92 1.71 1.19 1.03 1.35

3  (4) 1.41 1.02 1.81 1.24 1.08 1.41

4  (5) 1.50 1.10 1.89 1.29 1.12 1.45

5  (6) 1.57 1.17 1.96 1.32 1.16 1.49

6  (7) 1.63 1.23 2.02 1.36 1.19 1.52

7  (8) 1.68 1.29 2.07 1.39 1.22 1.55

8  (9) 1.73 1.34 2.12 1.41 1.25 1.57

9  (10) 1.78 1.39 2.17 1.44 1.27 1.60

* All equations use a renovation‐related blood lead as shown. The background blood lead is assumed to be 1 g/dL, close to

current adult background levels. The total post‐renovation blood lead is shown in parentheses.
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Figure 6‐1. Renovation‐Related Blood Lead Values and Associated Health Effect Magnitudes

7. Implementing the Monte Carlo Approach

Sections 3 through 6 provide the equations used to estimate the necessary media concentrations and

health effects for different renovation conditions. In general, the equations appear with individual

variable values (rather than as distributions). As discussed in Section 2.9, a Monte Carlo approach was

developed around these equations so that the variability in the necessary input parameters was

incorporated. Each variable was defined as either a:

 scenario variable,

 sample variable, or

 fixed variable.

This section describes how the variables were evaluated to choose which should be scenario variables

and which are expected to be the key Monte Carlo (sampled) variables. The section also describes how

activity patterns and exposure timing characteristics were combined with population‐level background

lead intakes to develop specific exposure scenarios from the general equations. Finally, the section

describes how the Monte Carlo model was tested prior to implementation of all the scenarios.
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7.1. Developing Exposure Scenarios

As discussed above and in Section 2.9, scenario variables are key variables that vary discretely so that

they can be evaluated in their various combinations after Monte Carlo modeling. For example, in

evaluating results, EPA requires information about the predicted health effects for separate types of

renovation activities, so the renovation activity is a scenario variable. Each combination of scenario

variables defines one scenario. For example, this might include “largest building size, earliest vintage,

trim replacement renovation activity, school building type, with the receptor building 50 ft away.”

Treatment of discrete scenario variables enables EPA to distinguish between this above scenario and the

same scenario with the receptor building 800 ft away.  

Sampled variables also vary but do not require explicit discrete treatment and cannot be evaluated

discretely after Monte Carlo modeling. For example, each receptor building type will have a different

building volume; the Approach varies the volume of the buildings according to a nationally

representative distribution but does not provide separate answers for each possible building volume, for

example, of an industrial building. Thus, building volume is a sampled variable.

To determine which variables would be scenario versus sampled variables, EPA balanced competing

priorities of flexibility to evaluate results for different values of a particular variable versus the overall

computational demands of the analysis. Each scenario variable added requires additional Monte Carlo

scenarios, each requiring thousands of iterations. To determine the number of scenarios and the

number of iterations, EPA struck a balance between adequate resolution of the effects of each variable

in the results and reasonable computing time.

Test cases were run to evaluate the accuracy of different numbers of iterations and to verify the

calculations being performed. Test cases were examined for quality assurance and to ensure the trends

were in the appropriate directions based on the trends in the input variables. The Monte Carlo modeling

code used a random number seed to ensure reproducibility of the model results.

Developing exposure scenarios also depends on the activity patterns of the simulated individuals,

including the time they spend in and outside the building and whether they are exposed to interior,

exterior, or both types of renovations. These considerations are discussed below in Section 7.3.

7.1.1. Exteriors

For the exterior analysis, the required scenario variables focused on key characteristics of the renovated

building (size and vintage), the renovation activity (activity performed and control options used), the

receptor building (building type and distance), and the age group of the person in the receptor building.

The sampled (Monte Carlo) variables focused on other variables describing the receptor building and

exposed individuals, including the background media concentrations, cleaning habits, amount of time

each person in a certain age group spends in the receptor building, etc.  
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The variables selected as scenario variables for the exterior analysis are shown in Table 7‐1Error!

Reference source not found. These variables were selected because they represent discrete

characteristics of the renovated building or receptor building that can be evaluated for a potential rule.

When multiplying the different combinations of variables (and accounting for the fact that horizontal

containment affects only the closest receptor), these combinations lead to a total of 28,152 scenarios.

These scenarios can be evaluated discretely and separately in the future.  

Table 7‐1. Scenario‐specific inputs.

Input Available Inputs Units

Renovation Activity Dry scraping, Wet scraping, Power sanding without HEPA, Power

sanding with HEPA, Heat gun, Needle gun without HEPA, Needle

gun with HEPA, Window/door replacement, Trim replacement,

Torching, Demolition

n/a

Distance from Renovation 5, 50, 150, 300, 650, and 800 ft

Size of Renovated Building F1T1, F1T2, F1T3, F2T1, F2T2, F2T3, F3T1, F3T2, F3T3 n/a

Type of Receptor Building Agricultural, Industrial, Commercial, Education, Residential n/a

Vintage of Renovation Pre‐1930, 1930‐1949, 1950‐1959, 1960‐1979 n/a

Horizontal Containment   Present or not present n/a

Vertical Containment   Present or not present n/a

Age Groups 0–10, 18–49, 50–80 n/a

The Monte Carlo (sampled) variables for the exterior analysis are shown in  

   
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Table 7‐2. All other variables are kept fixed in the analysis. These sampled variables were varied using

distributions because  

 They were expected to vary significantly,

 They play a relatively significant role on the predicted media concentrations and health effects, and

 Data sources could be identified to inform the choice of the distribution.

Of these variables, the receptor building volume, the air exchange rate, and the cleaning frequency

influenced the renovation‐related media concentrations because all three affect the residence time of

the lead‐containing dust in the receptor building. In addition, the time spent in the building will affect

the overall exposure to the renovation‐derived lead dust. These variables are examined further in the

sensitivity analysis in Section 8.
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Table 7‐2. Sampled Variables for Exterior Analysis

Parameter Units Distribution Type

Wall Loading (XRF) g/cm2 Lognormal

Receptor Building Vintage None Discrete

Receptor Building Height None Discrete

Receptor Building Volume m3 Lognormal

Receptor Building Air Exchange Rate hr‐1 Normal

Track‐in Rate g/day Lognormal

Fraction of Dust on Mat None Discrete

Track‐in from Soil or Hard Surface None Discrete

Percent Carpet None Discrete

Cleaning Frequency None Discrete

Meteorological Region None Discrete

Time Spent None Discrete

Age within Age Range None Discrete

Background Air Concentration g/m3 Lognormal

Background Soil Concentration g/g Lognormal

Background Dust Loading g/ft2 Lognormal

Loading to Concentration Conversion Parameters None Equation with error T‐distribution

7.1.2. Interiors

For the interior analysis, the selection of scenario variables was driven by key characteristics of the

renovated building (use type, vintage, room size, and carpeting status) and the renovation activity

(activity performed, how much of the room was affected, control options used, number of rooms

renovated, and location of the exposed individual in the building). Unlike the exterior analysis, however,

these combinations of variable values could not be constrained to a reasonable number of scenarios. For

example, assuming 5 use types, 5 vintages, 3 rooms sizes, 2 carpeting options, 100 combinations of paint

removal and other renovation activities, 12 options for how much of the rooms was affected (both

layers removed and fraction of room renovated), 4 control options, 3 age groups, and all the various

receptor locations results in more than 9 million unique scenarios, each of which would require between

1,000 and 10,000 iterations. This large number of scenarios is not computationally feasible but

accounting for this range of potential scenarios is required to cover the potential range of dust loadings

in the different buildings.
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For this reason, the interior analysis did not model all the scenarios directly; instead, it employed a

“lookup table approach.” The fundamental idea in this approach is that the exposure calculations can be

separated into two separate time periods:

1. Estimation of the dust loadings during the renovation (dust generation and rest‐of‐renovation) in

the room where the person is located, and  

2. Estimation of the dust loadings after the renovation (routine cleaning phase), along with the

resulting blood lead and health effect results.

The scenario variables were then broken into two accompanying categories:

1. Variables that affect only the during‐renovation dust loadings, and

2. Variables that affect the post‐renovation dust loadings.

These two different categories are shown in Finally, the rest‐of‐renovation durations were examined for

each of the dust‐generating duration bins. Again, the 5th, 25th, 50th, 75th, 90th, 95th, 99th, and 100th

percentiles were calculated and then aggregated when adjacent ones were within 24 hours (3 working

days) of each other.

This analysis gave a series of nested bins that specified a unique combination of dust‐generating phase

and rest‐of‐renovation loadings and durations. The analysis was repeated for each building type and

carpet status. The result was a total of 301,930 scenarios to be run in the Monte Carlo model.

Table 7‐3. In the lookup table approach, all the different loadings and renovation durations associated

with the different scenarios (the “Intermediates” column in Finally, the rest‐of‐renovation durations

were examined for each of the dust‐generating duration bins. Again, the 5th, 25th, 50th, 75th, 90th,

95th, 99th, and 100th percentiles were calculated and then aggregated when adjacent ones were within

24 hours (3 working days) of each other.

This analysis gave a series of nested bins that specified a unique combination of dust‐generating phase

and rest‐of‐renovation loadings and durations. The analysis was repeated for each building type and

carpet status. The result was a total of 301,930 scenarios to be run in the Monte Carlo model.

Table 7‐3) were calculated for every scenario. This resulted in millions of unique combinations of:

1. Dust‐generating phase dust loading in the room of occupancy,

2. Rest‐of‐renovation phase dust loading in the room of occupancy,

3. Duration of the dust‐generating phase, and

4. Duration of the rest‐of‐renovation phase.

Then, this complete list was used to select a representative number of combinations that cover the

range of dust loadings and durations. The representative dust generation phase dust loadings, rest‐of‐
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renovation dust loadings, dust‐generation phase durations, and rest‐of‐renovation duration values and

combinations were used to populate a “lookup table.” The values across these four variables resulted in

301,930 unique combinations. These unique combinations were run through the Monte Carlo model

(along with the sampled variables) to estimate health effects for each dust loading/duration

combination. As a final step, as shown in Figure 7‐1, the original scenario table was used to locate the

closest matching loadings and durations in the lookup table, and the results were linearly interpolated

to estimate the scenario results. This approach has the advantage of enabling millions of scenarios to be

estimated, using a smaller subset of representative scenarios.  

To arrive at the appropriate number of scenarios to run that would capture the millions of scenarios

without introducing large (e.g., >5 percent) errors, the loadings and durations were analyzed using the

SAS statistical software package (SAS Institute, 2012). The SAS code started with the dust‐generating

loading, as follows.

Dust‐Generating Loading Bins: The SAS code estimated percentiles 0th, 2nd, 4th, 6th, ...., 98th, and

100th. Then, the code looped back through these bins and collapsed bins that were within 5 g/ft2 of

each other. This tolerance value was selected because appreciable differences in the renovation‐related

health effect predictions were not expected to be influenced by this decision to set dust loading bins at

5 g/ft2 versus 2, 4, or 6 g/ft2

.  

For each Dust‐Generating Loading Bin, create Rest‐of‐Renovation Loading Bins: Next, the rest‐of‐

renovation loadings were examined for each dust‐generating bin. Again, the 0th, 2nd, 4th, 6th, ...., 98th,

and 100th percentiles were calculated and then aggregated when adjacent ones were within 5 g/ft2 of

each other.

For each Rest‐of‐Renovation Loading Bin, create Dust‐Generating Duration Bins: Next, for each dust‐

generating loading bin, the durations were used to estimate the 5th, 25th, 50th, 75th, 90th, 95th, 99th,

and 100th percentiles. The code then looped back through these bins and collapsed any that were

within 24 hours (3 working days) of each other. Again, 3 days was selected because appreciable

differences in the renovation‐related health effect predictions were not expected to be influenced by

this decision to set durations of exposure at 3 days versus 1, 2, or 4 days.  

For each Dust‐Generating Duration Bin, create Rest‐of Renovation Duration Bins: Finally, the rest‐of‐

renovation durations were examined for each of the dust‐generating duration bins. Again, the 5th, 25th,

50th, 75th, 90th, 95th, 99th, and 100th percentiles were calculated and then aggregated when adjacent

ones were within 24 hours (3 working days) of each other.

This analysis gave a series of nested bins that specified a unique combination of dust‐generating phase

and rest‐of‐renovation loadings and durations. The analysis was repeated for each building type and

carpet status. The result was a total of 301,930 scenarios to be run in the Monte Carlo model.
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Table 7‐3. Separation of Scenario Variables Into Categories for Lookup Table Approach

1. Variables That Only Affect

Renovation Exposure

Intermediates: Renovation Loadings

and Durations

2. Variables that Affect Post‐

Renovation Exposure

Scenario Table Intermediate Table Lookup Table

 Renovation activities and

intensities  

 Building configurations/room sizes  

 Rooms renovated

 Control option  

 Location of exposed individual  

 Work area sizes  

 Vintage  

 Loadings during dust generating

phase  

 Loadings during rest of renovation

phase  

 Duration of dust generating phase

 Duration of rest of renovation

phase

 Age group

 Carpet/Not Carpet, to determine

the cleaning efficiency  

 Building type, to determine the

time spent distribution  

 Exterior renovation soil

concentration and matching air

concentration

These variables are used to

estimate intermediates

These variables become inputs

to the Monte Carlo model Monte Carlo results

The Monte Carlo model samples from the sampled variables to estimate variation across the exposure

scenario. For the interior analysis, these variables are shown in  
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Table 7‐4. As with the exterior analysis, these Monte Carlo variables were varied using distributions

because

 They were expected to vary significantly,

 They play a relatively significant role on the predicted media concentrations and health effects, and

 Data sources could be identified to inform the choice of the distribution.

Of these variables, those associated with dust removal (cleaning frequency) and with the amount of

time spent in the building (time spent) are expected to have a significant effect on the results. These

variables are examined further in the sensitivity analysis described in Section 8.

Figure 7‐1. Illustration of the Lookup Table Method to Link Specific Scenarios with the Answers in the

Lookup Table
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Table 7‐4. Sampled Variables for Interior Analysis

Parameters Units Distribution Type

Wall Loading (XRF) g/cm2 Lognormal

Fraction of Paint Removed None Discrete

Building Floor Area m3 Lognormal

Track‐in Rate g/day Lognormal

Fraction of Dust on Mat None Discrete

Fraction of Exterior and Interior Renovations Occurring at the Same

Time None Discrete

Cleaning Frequency None Discrete

Time Spent None Discrete

Age within Age Range None Discrete

Background Air Concentration g/m3 Lognormal

Background Soil Concentration g/g Lognormal

Background Dust Loading g/ft2 Lognormal

Loading to Concentration Conversion Parameters None Equation with error T‐

distribution

7.2. Estimating Time‐Dependent Background Intakes

Once the various renovation scenarios are defined, the renovation‐related exposure estimates must be

placed within the context of background lead exposure for an individual. Because the current analysis

covered both children and adults up to age 80, background lead intakes (and thus background blood

lead) are highly variable over the lifetime of some modeled individuals. In particular, changes in

regulations related to lead in gasoline resulted in a dramatic change in intakes and blood lead values for

children, adolescents, and adults between 1980 and today. To place the magnitude of the renovation‐

related blood lead in the context of lifetime exposure, the full intake history from birth for each

individual was estimated and captured in the Approach.

To capture this change in intake, the NHANES blood lead data from each available NHANES year were

used to estimate the average blood lead for an individual for each year, ages 0 to 80. The available

NHANES years included 1976–1980, 1988–1991, 1991–1994, 1999–2000, 2001–2002, 2003–2004, 2005–

2006, 2007–2008, 2009–2010, and 2011–2012. Thus, the data gave average blood lead values for a

person aged 0, aged 1, aged 2, etc. in each available NHANES dataset. The population was then split into

age groups expected to have similar intakes over their lifetimes, including those born 1930–1954, 1955–

1966, 1967–1976, 1977–1986, 1987–1996, and Post 1996. These age groups were selected to group

individuals according to both overall age and age during rapidly changing intake time periods.  
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An initial estimate was then provided for a series of different periods for each age range. Intakes were

kept constant up until 1984 (for those born before 1984) and then were scaled based on the ratio of the

1984 blood lead to the current blood lead in 1993, 2000, 2005, and 2014. In this way, the intakes could

taper slowly from the leaded gasoline era values to the current 2014 estimates. The year 1984 was

selected as a time roughly in the middle of the U.S. lead gas phase out (1973–1996). The Leggett model

was run assuming the different intake values, and the sum of the mean square error terms between the

estimated and actual blood lead values from NHANES for each age group (males and females together)

were estimated. The initial intake was then changed and Leggett was rerun until the sum of the mean

square errors was at a minimum.  

The result was an intake profile for each individual based on birth year. The intakes changed in 1984,

1993, 2000, 2005, and 2014 so that the blood lead predicted in each year best matched the NHANES

blood lead values for individuals at that age in that year. Additional details about this analysis are

provided in Appendix N.4.

7.3. Building Time Series of Exposure and Blood Lead

The Approach description in Sections 2 through 7 focuses on the media concentrations (air, soil, hard

surface, indoor dust) for locations near interior and exterior renovations. To simulate exposure for

different individuals, these media concentrations were placed within the context of their overall lifetime

lead exposures. In particular, the media concentrations were matched with an age when the renovation

occurs, and other age‐specific exposure factors and activity patterns used to characterize the

hypothetical individual’s net lead intake related to the renovation concentrations.

7.3.1. Selection of Ages at Renovation and at Blood Lead Measurement

EPA evaluated health effects for individuals in three age ranges: 0‐10, 18‐49, and 50‐80. However, within

each age group, an individual can experience renovation at different ages. Thus, U.S. Census data were

used to find probability distributions for the population in each age range. These distributions are shown

in Appendix B. Then, for each Monte Carlo iteration, the age group distribution was sampled to

determine the age of the person at renovation during that iteration. The intakes were constructed so

that a person has background intake (see Section 7.2) up until the age at renovation and has background

intake and renovation‐related intake after the renovation. The blood lead model can then be run to

estimate blood lead at any point after the renovation.

The health effects used in the Approach, with the exception of IQ, are based on effects from cross‐

sectional epidemiology studies. As such, blood lead and health effect statistics are available at a single

age for an individual without knowledge of the prior history of exposure for that individual. To match

this study design, the blood lead post‐renovation would have to be saved in the modeling at a specific

point to be used in the health effect concentration‐response equations. The blood lead value, however,

will be highly dependent on the point after renovation selected for analysis. Ideally, the time chosen
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should be selected using information about the specific health effect and the biological window of

susceptibility or necessary duration of elevated blood lead to result in the effect. For lead exposure in all

of the known health effects, however, this information about the necessary duration of exposure is not

known and is not available in the literature. For low birth weight, the duration of pregnancy might be

the relevant biological window, whereas a longer‐term duration could be more relevant for chronic

health effects like kidney disease and CVDM.

To overcome this data shortfall, EPA captured the concentration‐response function health effect

predictions for a range of blood lead measurement times, where the “measurement time” is defined as

the time post‐renovation when the blood lead and health effect are assumed to be evaluated in the

modeled individual. To select the maximum measurement time to use, the Approach focuses on

evaluating the response to the most recent renovation experienced by the individual. During the

person’s lifetime, he or she will move through a variety of P&CBs, all of which will be renovated on

different frequencies and timescales. To establish the window of time during which the most recent

P&CB renovation took place, data were used that detail statistics for different types of renovation jobs

in P&CBs. As discussed in more detail in Appendix L, the average frequency of renovation for a given

building was between 1.3 years (low) and 4.2 years (high). These values are estimated from the

RSMeans Construction manual, and the high and low values account for assumptions about whether

different renovation activities happen concurrently or separately. The high value was used as an upper

bound for the time since the last renovation experienced by the individual in a P&CB. Thus, blood lead

values for the individual were captured at nine intermediate time‐since‐renovation values, with the

maximum value set to 4.2 years, as shown in Table 7‐5.  

Table 7‐5. Blood Lead Measurement Times Relative to the Renovation

Estimated Time Since Last Renovation

(Age at Blood Lead Measurement)

1 month

5 months

10 months

15 months

20 months

25 months

30 months

40 months

50 months (or 4.2 years)

Blood lead incremental changes and health effect changes related to the renovation were then

calculated for each individual at each of these time‐since‐renovation values. The Monte Carlo model
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returns statistics about each for the three age groups. Figure 7‐2 shows an example of two iterations,

one where the renovation occurs at 0.6 years old and one where it occurs at 4.8 years old. The dashed

lines represent the different times the blood lead values are captured and the health effect results are

estimated for these iterations.

Figure 7‐2. Illustration of Blood Lead Measurement Times (dashed lines) for Two Different Age‐at‐

renovation Values (0.6 and 4.8 years old) in the 0‐ to 10‐year‐old Age Group

As an additional blood lead metric, the lifetime average blood lead is also estimated for each iteration

and age group. This lifetime average is defined as the average from birth until the post‐renovation time

when blood lead returns to the background value. This metric, rather than being a biologically based

metric, is an exposure‐based one. Because the health effect studies and other literature do not provide

information on a biologically relevant window of susceptibility for the health effects, the exposure‐

based lifetime average definition is used as a surrogate. This lifetime average is depicted in the lower

panel of Figure 7‐3.
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Figure 7‐3. Diagram of Concurrent and Lifetime Average Blood Lead Metrics in the Approach

7.3.2. Age‐specific Activity Patterns and Exposure Factors  

In addition to the age at which the renovation occurred, other age‐specific exposure factors are used to

estimate exposure to specific renovation‐related media concentrations.  

Activity Patterns

The Approach incorporates the fraction of time spent in the P&CB. This fraction varies by age group and

by building type. To estimate the distributions, the CHAD database (US EPA 2014a) was used to

reconstruct fractions of time in various buildings based on the unique activity entries. The building types

used in the analysis were mapped to the CHAD building types, and many different percentiles of time

spent for four age groups and exterior and interior building types were calculated. Further details of

how these calculations were performed are available in Appendix L. These percentiles were then used as

a sampled variable in the Monte Carlo code to find the time spent in the renovated building for each age

group in each iteration. The effective exposure media concentrations were then estimated as time‐

weighted averages of background concentrations (when the person is away from the renovated building
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or receptor building) and background plus renovation‐related concentrations (when the person is inside

the renovated building or receptor building).

Combining Activity Inside and Outside the Receptor Building or Renovated Building

In addition to being in the renovated building, an individual could also spend time outside the building,

experiencing different exposure concentrations. For the exterior analysis, this case is incorporated in the

modeling. The exposure factors (discussed below) used in the interior and exterior renovation modeling

incorporate both time inside (dust ingestion) and time outside (soil ingestion). For example, a person

might exit the building for a period of time (e.g., to eat lunch) and then reenter the building to resume

activity. For the interior analysis, soil ingestion was again incorporated into the total particulate intake,

with the assumption that the relative soil and dust intake while in and around the renovated building is

the same as the relative soil and dust intake for the day as a whole as given in the EPA Exposure Factors

Handbook (US EPA 2011). In the case where the exterior of the building is not renovated, the soil lead is

expected to remain at background levels, so the person is exposed to background air and soil levels

when outside the building.

One final exposure scenario involves a building that is renovated on the exterior and in the interior

simultaneously. In that case, a person will be exposed to tracked‐in elevated soil lead. Then, they will be

exposed to the interior renovation‐related lead loading inside the building. The track‐in of elevated soil

was incorporated into the interior modeling during the routine‐cleaning phase.

Exposure Factors

To convert media concentrations to intakes, exposure factors were used. In general, these exposure

factors were taken from the age‐specific values in the EPA Exposure Factors Handbook (US EPA 2011),

and they include the inhalation rate, soil/dust ingestion rate, and fraction of soil/dust ingestion that is

from soil. These factors were multiplied by the lead concentrations in the media (air, soil, and dust) to

estimate the lead intakes for input in the Leggett blood lead model. The values used are all provided in

Appendix N.

7.4. Incorporating Population‐Level Biokinetic Differences  

The Leggett model was used to estimate the blood lead value for each iteration for the childhood and

adult age groups. After combining all iterations within a scenario, the resulting distribution of blood lead

estimates represents the variability in the range of hypothetical individuals from the media

concentration and lifestyle variable distributions included in the Monte Carlo analysis for that scenario.

The distribution of modeling results does not include variability from variables that were set to point

estimates in the model. One such set of variables is those that determine the exposure factors (e.g.,

inhalation and ingestion rates by age) and the biokinetic parameters (e.g., rate of movement of lead

from plasma to bone) in the blood lead model. These variables were not incorporated as distributions
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owing to the overall uncertainty in the parameter estimates themselves and the lack of reliable

literature data to use to estimate these distributions.

Thus, to account for these differences, additional variability was incorporated into the iteration‐specific

blood lead estimate. The Leggett model prediction for the given iteration was treated as the geometric

mean of the broader distribution of exposure factors and biokinetic differences. A representative

geometric standard deviation (GSD) was then selected and combined with this geometric mean to

estimate a series of new iteration‐specific blood lead values that accounts for these differences. In

particular, for each iteration, the Leggett value (interpreted as the geometric mean) and the GSD were

used to estimate 25 blood lead estimates. These 25 values were then used to estimate the blood lead

and health effect statistics returned for the scenario as a whole. This decision to use 25 values (as

opposed to, for example, 10, 100, or 1000) was made to balance model accuracy with computational

performance. Twenty‐five was deemed an appropriate number to resolve different parts of the

distribution without adding significant runtime and data storage burdens.

For the Approach, a value of 1.9 was selected for this GSD. This value is fairly constant across the

NHANES surveys since the year 2000. This value was identified as a reasonable value to use for a diverse

population, as discussed in Appendix O.  

For bone lead, no similar nationally representative survey spanning both children and adults could be

found. Because these data were not available, no GSD was applied to the bone lead estimates from the

Leggett model.

7.5. Metrics Saved for Incremental Media Concentrations, Blood Lead

and Health Effect Changes

After the blood lead estimates were made and the health effects were all estimated using the

concentration‐response functions, a series of model statistics was saved to serve as the analytical output

of the model.  
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Table 7‐6 shows the full suite of variables that were saved. In each case, the mean, median, standard

deviation, maximum, and 5th, 90th, 95th, 99th, 99.5th percentiles were saved to characterize the

distribution of model outputs.  These metrics allow for comparison across scenarios.

Of the metrics described in Table 7‐6, some of these have a counter metric called “fraction of iterations”

which shows how often iterations were above a certain values.  These allow for comparison within

scenarios.  Variables which have one or more “fraction of iteration” counters have a *next to them in

Table 7‐6.  Examples of this kind of metric, as distinct from summary statistics across scenarios, are

described in Table 8‐3 and Table 8‐10.     
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Table 7‐6. Model Metrics Saved from the Simulations

Variable

Do Not Depend  

on Age Age 0–10 Age 18–49 Age 50–80

Total Bkgnd

Reno

Only Total Bkgnd

Reno

Only Total Bkgnd

Reno

Only Total Bkgnd

Reno

Only

Reno Avg Air   

3‐month Avg Air*   

Max Soil Conc*   

Max Hard Surface

Loading*

  

Max Dust Loading*   

Dust Time to

Background

  

Blood Lead*         

Blood Lead Time to

Background

        

Bone Lead   

IQ Change* 

Low Birth Weight*   

Kidney Effects, Males,

Risk*

     

Kidney Effects, Males,

risk ratio*

     

Kidney Effects,

Females, Risk*

     

Kidney Effects,

Females, risk Ratio*

     

CVDM, Males, Risk*      

CVDM, Males, Hazard

Ratio*

     

CVDM, Females, Risk*      

CVDM, Females,

Hazard Ratio*

     

Key:

Shaded box: Not applicable

Checked box: Saved in analysis

Blank box: Does not match the health effect in questions based on the concentration‐response function

For each variable, three values were returned: (1) the total (renovation plus background) statistics,

(2) the background‐only statistics, and (3) the renovation‐related statistics. In each case, the total,
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background, and renovation‐related values were estimated for each iteration and the full set of

iterations was used to estimate the statistics for each of the three. For the health effects, the

renovation‐related contribution was determined in different ways for the different mathematical forms

of the equations. For IQ, the IQ change itself was interpreted as the renovation‐related contribution, so

no “total” or “background only” statistics were returned. This treatment reflects the functional form of

the concentration‐response function, which explicitly estimates IQ change relative to background. For

the low birth weight equations, predicted change in birth weight was estimated relative to a blood lead

of zero. Thus, the change was estimated for both the “total” blood lead and the “background only”

blood lead. The renovation‐related portion was estimated as the difference between the two. For

cardiovascular disease mortality and kidney effects, the model prediction was interpreted as the “total”

contribution, the population‐level average responses from CDC were interpreted as the “background

only” responses, as the renovation only was the difference between the two. The hazard or risk ratio

was also estimated for these health effects, and this ratio represents the ratio of the total to the

background‐only response.

7.6. Testing the Full Monte Carlo Models

Once the Approach equations and model parameters were defined, the code was implemented and

tested. This section describes the tests performed to ensure the model produced accurate and reliable

results.

7.6.1. Accuracy

The Approach outlined in this document was implemented using the Python programming language

(Version 2.7), including the Numpy, Scipy, Tweepy, and Cython add‐ins (Python Software Foundation,

2012). The complete code package developed for this Approach consisted of several modules that

performed the lookups, sampling, and calculations; text‐based input files specifying the distributions and

parameter values; the AERMOD air concentration files; and the Leggett model input tables. The code

was written with particular attention paid to the model speed so that all the scenarios and iterations

could be run using the minimum required computer resources. The lookup table for the interior

Approach was created using code written in the SAS programming language (SAS Foundation, version

9.3, 2012).

All the input parameters were collected in text files. For sampled parameters with defined distributions

(normal, lognormal, and uniform), upper and lower bounds were assigned so that the values could not

become unrealistically large or small. For normal distributions, the bounds were set at ±2 standard

deviations from the mean, while also ensuring that no sampled values were negative. For the lognormal

distribution, the bounds were set at ±2 standard deviations for the log‐transformed distribution.

Standard Python packages were used to sample from the distributions. For variables with discrete values

(such as the nine meteorology regions), the cumulative probability distribution of each variable was

used to select the appropriate value. For each variable value, the absolute probability of occurrence was
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assigned based on values in the literature or professional judgment, as described in the Appendices.

Then, the cumulative probability distribution is found by taking the sum of the probability of a given

value and all previous values. For example, if the probabilities of three different variable values were:

 Probability of A = 0.2

 Probability of B = 0.5

 Probability of C = 0.3

Then, the cumulative probability distribution would be:

 Cumulative Probability of A = 0.2

 Cumulative Probability of B = 0.7

 Cumulative probability of C = 1

The cumulative probability for each value is always between 0 and 1. Thus, a random number between 0

and 1 was selected, and the variable value with a cumulative probability greater than the random

number was then used in the Monte Carlo iteration. For example, if the random number were 0.3, then

variable value “B” would be selected, while if the random number were 0.8, then the variable value “C”

would be selected.

After the code was built, a full code review was performed, and then a series of quality assurance tests

was performed to ensure the code was performing correctly. Each lookup and calculation step was

designed as a separate function in the code. Python unit tests were used to test each function

separately to ensure that the function returned the correct output for a given input. A unit test is a

separate test for each function and module in the code to ensure the “unit” provides accurate solutions.

In addition, test cases were run in the model, and the trends across different model scenarios were

examined to ensure the direction of the trends matched the direction of the trends in the input values.

Finally, hand calculations were performed for portions of the code to ensure the code was performing

the correct calculations.

7.6.2. Convergence

Next, tests were performed to determine the number of model iterations necessary to ensure proper

convergence in both the mean and the 95th percentile statistics in the “worst case” scenario for both

the interior and exterior analyses. Full convergence implies that the model returns the same output

statistics each time the model is run. During the Monte Carlo sampling, random numbers were used to

select the input variable values during each iteration. In the computer code, the progression of random

numbers used in the model depends on the seed selected at the outset of the model run. The seed

determines the progression of random numbers. Convergence implies that if different seeds are used

(so that different random number progressions are used) in two separate model runs, the statistics will
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not differ beyond a certain pre‐specified tolerance. This will indicate the number of iterations is

sufficient so that all extreme values in the distributions are being adequately sampled and will return

approximately the same statistics each time. For this analysis, a ± 5 percent tolerance value for the

mean statistic was set for the convergence testing. This translates to an overall range of 10 percent in

the mean statistic.  

The exterior model was run with 10,000, 20,000, 30,000 and 50,000 iterations for each of the 15exterior

test cases. Each iteration case was run 10 times in the model using different initial seeds (i.e., a different

starting random number). Then, the range of means expressed as a percentage of the average across

the 10 runs was calculated for each number of iterations. The testing revealed that the 10,000‐iteration

case achieved a range of 5.6 percent for the mean childhood‐renovation‐related blood lead statistic and

2.4 percent for the 95th percentile. The 20,000 and 50,000 iteration cases did not offer significant

improvement but took much longer to run. Thus, 10,000 iterations was the selected size for the exterior

production runs.

For the interior analysis, the model was expected to converge in fewer iterations because fewer

variables were sampled. The interior model was run with 1,000, 2,000, 3,000, 4,000, 5,000, 7,500, and

10,000 iterations for each of the 15 interior test cases. The testing revealed that the 3,000‐iteration case

achieved a range of 6.4 percent for the mean childhood renovation‐related blood lead statistic and 7.6

percent for the 95th percentile. This was the smallest number of iterations meeting the ± 5 percent

tolerance, so 3,000 iterations was the selected size for the interior production runs.

All scenarios were then run through the model and the resulting blood lead level and health effects

statistics were estimated.

8. Results of the Monte Carlo Analysis  

8.1. Pre‐Screening to determine which Exterior and Interior Scenarios

require Monte Carlo

8.1.1. Exteriors

For the Exteriors analysis, each scenario was modeled using worst‐case assumptions for sampled

variables before deciding whether to include that scenario in the Monte Carlo analysis and run 10,000

iterations. The goal of this approach was to continue testing the model and to determine whether

certain scenarios did not require 10,000 Monte Carlo iterations because their worst‐case run results

indicated health effects very close to background levels (e.g., very low increase in health effects due to

renovation activities). Worst‐case runs tended to overestimate exposures when compared to maximum

or 99th percentile values from Monte Carlo iterations, as indicated during testing runs. Model results
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were evaluated relative to each other and compared to pre‐established criteria for blood lead and

health effects.  

The first criterion was lifetime blood lead for a 2‐year‐old child. If a scenario resulted in a lifetime blood

lead increase of less than 0.1 g/dL, it was flagged for possible exclusion from the analysis.

Approximately 20 percent of scenarios met this first criterion. While lifetime blood lead can be low for a

given scenario, concurrent blood lead directly following renovation (i.e., 1 month) and associated health

effects can remain elevated. For this reason, these scenarios were evaluated against health effect levels

for each endpoint. These levels were set to be very conservative, using 1‐month renovation‐related

outputs of <0.105 IQ change, <1.003 CVDM hazard ratio, <10 grams in birth weight change, and <1.05

kidney risk ratio.  These values were chosen as a point of comparison to limit the number of scenarios

and are not intended to reflect any health‐based threshold.    

The second criterion was whether the scenario resulted in an exceedance of any concurrent health

effect level. All scenarios resulted in an exceedance of at least one health effect level, again due to the

conservative nature of high exposure estimates and low health effect levels. Some scenarios, however,

exceeded only one of the four health‐effect thresholds.

For the third criterion was the number of time steps (out of nine total) that resulted in the exceedance

of a concurrent health effect level. If a scenario exceeded a health‐effect level, using only the one month

concurrent values and no other time steps, it was excluded.  Finally, health effect criteria were

interpreted such that if results were approaching health effect levels, they were considered for

inclusion. After consideration of these criteria, 3 percent of the scenarios were identified and did not

receive 10,000 Monte Carlo iterations. Maximum dust loadings for eliminated scenarios were also

considered and all loadings were very low with the maximum loading of eliminated scenarios being 5

g/ft2. These cases were considered when evaluating overall results by categorizing them as “near‐

background results.”

8.1.2. Interiors

For interiors, look‐up table values, as described in Section 7.1.2, for the various combinations of dust‐

generating phase loadings, rest‐of‐renovation loadings, dust‐generation phase durations, and rest‐of‐

renovation durations were evaluated. Testing runs showed that these variables ranged from very low to

very high. The very low loading values and the very low durations were evaluated to determine if a level

could be established where the combination of lower loadings and short durations would result in very

low incremental health effect changes and therefore the scenario could be reasonably excluded. Even

the shortest duration and lowest loading combinations, however, when combined with worst‐case

assumptions for other variables, resulted in incremental health effect changes above screening levels

described above for exteriors. Therefore, no loading or duration values were excluded.          
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8.2. Summary of Results for Exterior Monte Carlo Scenarios

This section summarizes the exterior results from the millions of iterations that were modeled to

characterize renovation‐related blood lead concentrations and incremental health effect changes for

children and adults. The purpose of this section is to provide some insight into the overall magnitude

and distribution of results, and the trends in results across different scenario variables.  

Throughout this section, blood lead concentrations and IQ changes are presented to one decimal point

(i.e., overall resolution of 0.1 g/dL blood lead and 0.1 IQ point). Hazard and risk ratios are presented to

the nearest 0.01 and birth weight is presented to the nearest 0.1 gram. This level of resolution was

selected to be consistent with the modeling capabilities. Results are summarized in this section in

various ways: a percentile (average, 95th) for a specific incremental environmental media, blood lead

concentration, or health effect within a scenario, comparisons of the percent of scenarios across all

scenarios that went above a value, and comparison of iterations within a scenario that went above a

value.

For all graphs and charts shown throughout Section 8, it is important to reiterate that the results apply

only to the exposure scenarios as described for purposes of the Approach. These results do not show

the estimated proportion of building types with an estimated media concentration or the estimated

proportion of the US population that is predicted to experience the specific blood lead change or

incremental health effect.  This is because the scenarios have not yet been evaluated to determine how

likely or unlikely they are to occur with respect to current work‐practices and renovation activities

within P&CBs.   

8.2.1. Trends across Scenario Variables
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Table 8‐1 provides a summary of trends observed in the exterior analysis across the scenario variables.

Additional graphical representation of these trends is provided in Section 8.3.1.
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Table 8‐1. Summary of Trends in Exterior Analysis

Trend

Category Trends Observed

Renovation

Activity Types

The blood lead changes associated with the renovation tend to decrease with decreasing

emission fraction.  Needle gun (no HEPA), torching, and power sanding (no HEPA) result in the

largest changes.  Trim replacement, window/door removal, and heat gun on wood result in the

smallest changes.  

At the closest receptor building, demolition results in the largest blood lead changes; more

particle debris and bulk material is created with this activity, which leads to larger track‐in and

higher indoor dust loadings.

Distance The blood lead changes decrease quickly with distance from the renovated building. At the

closest distance (0 ft, indicating that the renovated building is also the receptor building), 42%

of the scenarios estimated mean renovation‐related blood lead changes of at least 0.1 g/dL in

children after 5 months. However, at 50 ft that value drops to 15% and at 150 ft it drops to 6%.

By 800 ft, only 0.1% of the scenarios exceed a blood lead change of 0.1 g/dL after 5 months.

Size of

Renovated

Building Size

The size of the renovated building has a complex effect on the blood lead change. The

intermediate size (F1T2, for example) has the lowest blood lead level changes. For buildings with

a smaller footprint (F1T1, for example), blood lead changes increase. In this case, the building is

narrower with respect to any given receptor building radial; thus, the aerosolized dust is less

dilute and is channeled more along one given radial by the wind. For the bigger building (F1T3,

for example), the overall increase in lead emitted gives rise to larger blood lead changes in the

receptor building occupants.

When looking at changes in building size, taller buildings tend to result in lower blood lead

changes at the nearest receptor buildings and moderate increases at distant receptor buildings;

the wind field is faster at higher stories and carries the lead dust farther from the renovated

building.

Age of

Renovated

Building

The patterns associated with age of the building follow the trends in the amount of lead in the

paint for each vintage; thus, pre‐1930 buildings have the greatest blood lead changes followed

by 1930–1949, then 1960–1979, and finally 1950–1959.

Type of

Receptor

Building

The receptor building type affects the blood lead levels of the occupants according to time

spent, ceiling height, and cleaning frequency. For children, who spend most of their time either

in schools or in residences, the residential and school receptor buildings lead to the largest

blood lead changes. For adults, the largest blood lead changes are also observed when the

adults are in the residential receptor building, although the difference across building types is

more moderate in adults.

Containment

Options

Using containment plastic decreases blood lead changes. Vertical plastic decreases blood lead

changes at all but the closest receptor building (lower transport) while horizontal plastic

decreases blood lead changes at the closest receptor building (lower track‐in).

8.2.2. Maximum Hourly Dust

Figure 8‐1 summarizes the maximum hourly dust loadings across all the scenarios. The graph provides

the percentage of scenarios where the maximum‐in‐time dust loading in the receptor building exceeded
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each loading level on the x‐axis. These values are the mean, 95th, and 99th percentiles across all

iterations in the scenario.   

The time is takes loadings to return to background levels is another metric of interest that helps

contextualize maximum loading values and their exponential decrease over time.  Once routine cleaning

starts, the lead dust levels began to decrease exponentially, with 81.6% percent of scenarios returning

to background within an average of 6 months after the start of the renovation.  Average estimates of

time to background dust levels for exterior modeling ranged from 0‐93 weeks with an average of 13

weeks.   

Figure 8‐1. Maximum Hourly Dust Distribution for Exteriors  

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related loadings

for buildings across the U.S.

8.2.3. Renovation‐Related Blood Lead and Bone Lead

Figures and Tables presented in this section provide information comparing results to various blood lead

levels.  Blood lead values that are shown (0.1 ug/dl, 1 ug/dl, 10 ug/dl, etc) are for illustrative purposes

only and are not intended to evaluate potential hazards due to lead from renovations in P&CBs.  

Figure 8‐2 provides the percentage of scenarios that exceed each renovation‐related blood lead change

on the x‐axis. The graphs show the mean and 95th percentile blood lead change in children at both

5 months after the renovation and 15 months after the renovation. These time points are examples of

outputs generated from distributions from the Monte Carlo results.  These distributions can be
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generated from all of the post‐renovation time points (1 month, 5 month, 10 months, 15 months, 20

months, 25 months, 30 months, 40 months, 50 months).

Figure 8‐2. Renovation‐Related Blood Lead Distribution for Exteriors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related blood

lead values for individuals across the U.S.

Table 8‐2 provides a summary of the 95th percentile hypothetical blood lead changes across all exterior

scenarios for each age group at 1, 5, 20, and 50 months post‐renovation. It also provides the lifetime

average blood lead statistics (see Section 7.3.1). Table 8.2 shows the percent of scenarios where the 95th

percentile renovation‐related blood lead model estimates (for hypothetical individuals) exceed various

blood lead levels.  An additional filter was applied so that only those blood lead values that remained

elevated above pre‐renovation blood lead values for at least 4 weeks after the start of the renovation

are included in Table 8‐2. The overall pattern indicated small changes in blood lead levels in most

scenarios, with adults experiencing lower blood lead changes than children. Table 8‐3 shows the percent

of scenarios where either at least 1 percent (100 iterations) or 10 percent (1000 iterations) of the total

Monte Carlo iterations (10,000) for that scenario exceeded several example  blood lead levels  (0.1

ug/dL, 1 ug/dL, and 10 ug/dl, selected for illustrative purposes only).
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Table 8‐2. Percentage of Exterior Scenarios with 95th Percentile Renovation‐Related Blood Lead Change Levels Exceedi

Blood

Lead

Changes

(g/dL)

Age 0 – 10 Age 18‐49 Age 

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

m

0.1 49.38% 35.99% 16.74% 11.06% 15.32% 47.21% 28.86% 9.05% 6.76% 0.11% 47.21% 28.19% 80.5 22.06% 14.28% 5.68% 3.81% 3.29% 13.61% 2.94% 0.53% 0.53% 0.00% 13.44% 2.57% 0

1 12.06% 6.83% 1.90% 1.09% 1.00% 6.06% 0.17% 0.05% 0.06% 0.00% 5.76% 0.26% 02 5.03% 2.48% 0.50% 0.33% 0.23% 1.73% 0.00% 0.00% 0.00% 0.00% 1.78% 0.00% 03 2.93% 1.28% 0.23% 0.15% 0.08% 0.63% 0.00% 0.00% 0.00% 0.00% 0.68% 0.00% 04 1.88% 0.66% 0.12% 0.07% 0.03% 0.27% 0.00% 0.00% 0.00% 0.00% 0.35% 0.00% 05 1.29% 0.36% 0.07% 0.04% 0.01% 0.10% 0.00% 0.00% 0.00% 0.00% 0.15% 0.00% 0

10 0.22% 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 015 0.06% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 020 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0

*Percentages less than 0.01% are rounded to 0%
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Table 8‐3. Percentage of Exterior Scenarios with Either 1% or 10% of Iteration Blood Lead Changes Exceeding Spe

At Least 1% of Iterations Are Greater than Given Value

BloodLeadChanges(g/dL)

Age 0 – 10 Age 18‐49 Age 

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

m

0.1 65.48% 53.64% 31.08% 21.16% 29.80% 49.05% 38.04% 18.93% 13.60% 1.18% 47.88% 36.60% 1

1 25.59% 18.65% 8.63% 6.36% 6.04% 10.59% 2.78% 0.56% 0.62% 0.00% 10.07% 2.22% 0

10 1.52% 0.54% 0.10% 0.05% 0.04% 0.10% 0.00% 0.00% 0.00% 0.00% 0.10% 0.00% 0

At Least 10% of Iterations Are Greater than Given Value

BloodLeadChanges(g/dL)

Age 0 – 10 Age 18‐49 Age 

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

m

0.1 39.88% 26.40% 10.76% 7.18% 9.73% 26.29% 12.80% 2.68% 2.24% 0.01% 24.70% 11.57% 2

1 6.79% 3.26% 0.64% 0.40% 0.30% 2.20% 0.00% 0.00% 0.00% 0.00% 2.00% 0.03% 0

10 0.05% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0

*Percentages less than 0.01% are rounded to 0%
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Figure 8-3 expands on the results in Table 8‐2 provides a summary of the 95th percentile hypothetical

blood lead changes across all exterior scenarios for each age group at 1, 5, 20, and 50 months post‐

renovation. It also provides the lifetime average blood lead statistics (see Section 7.3.1). Table 8.2 shows

the percent of scenarios where the 95th percentile renovation‐related blood lead model estimates (for

hypothetical individuals) exceed various blood lead levels.  An additional filter was applied so that only

those blood lead values that remained elevated above pre‐renovation blood lead values for at least 4

weeks after the start of the renovation are included in Table 8‐2. The overall pattern indicated small

changes in blood lead levels in most scenarios, with adults experiencing lower blood lead changes than

children. Table 8‐3 shows the percent of scenarios where either at least 1 percent (100 iterations) or 10

percent (1000 iterations) of the total Monte Carlo iterations (10,000) for that scenario exceeded several

example  blood lead levels  (0.1 ug/dL, 1 ug/dL, and 10 ug/dl, selected for illustrative purposes only).
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Table 8‐2, showing the full distribution of the mean and 95th percentile blood lead changes at three

different months post‐renovation and for the lifetime average blood lead.

Figure 8‐3. Renovation‐related Blood Lead Distributions for All Exterior Scenarios

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related blood

lead values for individuals across the U.S.

Figure 8‐4 shows the percentage of the mean and 95th percentile bone lead changes that exceeded

each value on the x‐axis for both cortical bone (A) and trabecular bone (B). These values are shown at 50

months post‐renovation, which is the maximum time captured in the Approach. Overall, the bone lead

changes for most adults was small, less than 0.04 μg/g for cortical bone lead and 0.5 μg/g for trabecular

bone lead at the mean and the 95th percentile.  
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Figure 8‐4. Renovation‐related Bone Lead Distributions for Exteriors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related bone lead

values for individuals across the U.S.

8.2.4. Health Effects

Figure 8‐5 shows the percentage of scenarios that exceeded each IQ change value on the x‐axis for the

mean and 95th percentile scenario results and at both 5 and 15 months post‐renovation (panel A) and

for the lifetime‐average (panel B). Maximum IQ changes were greater than 6 IQ points, but the majority

of the mean predictions were less than 1 IQ point. The 95th percentile hypothetical individual, however,
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experienced at least a 1 IQ point change in 2 percent of the scenarios when examining the lifetime

average blood lead.  

Figure 8‐5. Renovation‐related IQ Change Distributions for Exteriors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related health

effects for individuals across the U.S.
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Figure 8‐6 provides the percentage of CVDM hazard ratios (panel A), GFR risk ratios (panel B), and birth

weight reduction (panel C) that exceeded each value on the x‐axis for the mean and the 95th percentile

cases. For the CVDM and GFR effects, 5 and 15 months were used while 5 and 10 months were selected

for birth weight reduction (to capture different times during pregnancy). The mean hazard or risk ratios

for both CVDM and GFR were low for most scenarios, although the 95th percentile reached hazard or

risk ratios above 1.10 for a small percentage of scenarios. The birth weight reduction values were low,

with the 95th percentile 10‐month value exceeding only 5 grams in 1 percent of the scenarios.

Figure 8‐6 applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related

health effects for individuals across the U.S.
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Figure 8‐6. Hazard Ratio (A), Risk Ratio (B), and Birth Weight Reduction (C) Distributions for Exteriors
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8.3. Sensitivity Analysis for an Exterior Scenario  

To assess the sensitivity of the blood lead estimates to the different input variables, several analyses

were performed, as follows.

 Categorical Sensitivity Analysis: Define a “base case” and vary one or two variables at a time to

determine the trends in the blood lead results. This analysis was performed for variables that are

“categories” rather than continuous numerical distributions (e.g., building type and building

vintage).

 Numerical Sensitivity Analysis: Define a “base case” and vary each variable value by 5 percent and

50 percent to determine the percentage change in the blood lead results. The 5 percent change

analysis represents a “local” sensitivity analysis; the small change in the input variables illustrates

how small changes in the current values would affect the model predictions. The 50 percent change

analysis represents a “global” sensitivity analysis; the large change in the input variables illustrates

how model predictions would vary over a broader range of input value assumptions. This analysis

was performed for variables that are either point estimates or continuous distributions (e.g., air

exchange rate and the amount of lead in the paint) (US EPA 2001).

This section describes the results from each of the analyses.

8.3.1. Categorical Sensitivity Analysis

For the categorical analysis, two types of categorical variables are addressed in the Approach: scenario

variables and sampled variables. These were treated separately in the sensitivity analysis. For the

scenario variables, the “base case” was defined by choosing the scenario variable values as shown in

Table 8‐4. This case was chosen because it would result in relatively high blood lead changes but would

not be the “worst‐case” scenario. Then, a series of additional scenarios was defined by changing one or

two scenario variables at a time. These cases were then run with 10,000 iterations and the renovation‐

related blood lead changes were compared.

   
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Table 8‐4. Categorical Scenario Variables Included in Exterior Sensitivity Analysis

Categorical

Sensitivity Case

Receptor

Building Type

Receptor

Location

Control

Option

Renovation

Activity Vintage Size

Base Commercial 50 ft None Power sanding Pre1930 Medium (F2T2)

Residential Residential 50 ft None Power sanding Pre1930 Medium (F2T2)

Agricultural Agricultural 50 ft None Power sanding Pre1930 Medium (F2T2)

Industrial Industrial 50 ft None Power sanding Pre1930 Medium (F2T2)

School School 50 ft None Power sanding Pre1930 Medium (F2T2)

0 ft Commercial 0 ft None Power sanding Pre1930 Medium (F2T2)

300 ft Commercial 300 ft None Power sanding Pre1930 Medium (F2T2)

800 ft Commercial 800 ft None Power sanding Pre1930 Medium (F2T2)

With plastic, 50 ft Commercial 50 ft Plastic Power sanding Pre1930 Medium (F2T2)

With plastic, 0 ft Commercial 0 ft Plastic Power sanding Pre1930 Medium (F2T2)

Trim, 800 ft Commercial 800 ft None Trim Replacement Pre1930 Medium (F2T2)

Trim 0 ft Commercial 0 ft None Trim Replacement Pre1930 Medium (F2T2)

Later Vintage Commercial 50 ft None Power sanding 1960‐1979 Medium (F2T2)

Small Building Commercial 50 ft None Power sanding Pre1930 Small (F1T1)

Big Building Commercial 50 ft None Power sanding Pre1930 Large (F3T3)

Figure 8‐7 shows the trends across the different sensitivity analysis cases for the categorical scenario

variables. Overall, the trends matched the direction of the trends in the input variables and reflected

those presented in  
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Table 8‐1. The renovation‐related blood lead change tended to decrease with distance from the

renovated building, with the implementation of plastic control options, and for lower‐emitting activities.

Changes in blood lead due to the renovation also tended to be smaller for adults than for children,

although this trend was also affected by the amount of time each age group spends in the receptor

building. For children, the largest blood lead changes were observed in residences where the children

spend much of their time. For adults, individuals in residences continued to have higher blood lead

changes, but the change across the receptor building types was smaller. For building size, both the

smaller and larger buildings led to higher blood lead changes when compared to intermediate sized

buildings.

Figure 8‐7. Categorical Scenario Variable Sensitivity Analysis Trends, Exteriors

For the Monte Carlo categorical variables, the residential scenario above was selected as the “base” case

and the Monte Carlo categorical variables were set to specific values within their distributions. Note that

setting the Monte Carlo variables to central tendency values means the residential sensitivity case has a

somewhat different value than in Figure 8‐7, where the Monte Carlo variables were all sampled. Then,

the value of each was changed one at a time, as shown in Table 8‐5. The biokinetic variability GSD was

also included in this analysis; a value of 1.6 (the Agency default in the IEUBK model) was compared with

the Approach value of 1.9.  
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Table 8‐5. Categorical Monte Carlo Variables Included in Exterior Sensitivity Analysis

Monte Carlo Categorical Variable Base Value Sensitivity Value

GSD 1.9 1.6

Receptor Vintage Pre‐1930 1960–1979

Receptor Height 1 Story 3 Stories

Percent Carpet Hard Surface Carpet

Cleaning Frequency Once a Week Once a Month

Age (years) 2, 35, and 65 10, 18, and 50

Time Spent (fraction) Median 95th Percentile

Figure 8‐8 shows the trends across the Monte Carlo categorical variable sensitivity analysis cases. For

this scenario, the GSD chosen (either 1.9 or 1.6) had a relatively limited effect on the mean blood lead

changes. The receptor building vintage and cleaning frequency led to large changes for children; for the

vintage, the later vintage had a lower background lead dust value, so cleaning to achieve background

value after the renovation took much longer. For this reason, children were exposed to renovation lead

longer and had a higher blood lead change (even though their absolute blood lead including background

was lower than a child in an older vintage residence). Blood lead changes tended to decrease with the

height of receptor building in this scenario, where the receptor building was assumed to be 50 ft from

the renovated building. At greater heights, the winds are stronger, so the lead dust spread farther and

faster, leading to lower dust levels at close receptor buildings and higher dust levels at greater distances.

Changing the ages from near the center of the age bins (2, 35, 65) to the edge of the bin (10, 18, 50)

tended to lead to decreases in the renovation‐related blood lead changes. Finally, increasing the time

spent percentile tended to increase the blood lead changes; in this scenario, the changes were relatively

modest because children already spend a large amount of time in residences in the median time spent

case (the base cases here in the sensitivity analysis). The relative change was larger for children in other

receptor building types.  Representative values for time spent were chosen for the sensitivity analysis.  

See Appendix L for a discussion of how time spent was estimated for the Approach.  
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Figure 8‐8. Categorical Monte Carlo Variable Sensitivity Analysis Trends for Exteriors

8.3.2. Numerical Sensitivity Analysis

As discussed above in the introduction to Section 8.4, the numerical sensitivity analysis included

variables that were either point estimates or continuous distributions in the Approach. Each value was

increased by both 5 percent and 50 percent to perform both a local and global sensitivity analysis. The

exception is the lead dust penetration factor, which is set to 1 in the Approach; this value was decreased

by 5 percent and 95 percent instead because the value cannot exceed 1. The variables included in the

analysis are shown in Table 8‐6.

Table 8‐6. Numerical Variables Included in Exterior Sensitivity Analysis

Variable Base Value

(Mean or Point Estimate) 50% Value 5% Value

Lead in Paint (XRF) (g/cm2) 0.0107 0.0160 0.0112

Obstruction Adjustment Factor 1 1.5 1.05

Aerosol Emission Fraction 0.112 0.168 0.118

Dust Model Penetration 1 0.5 0.95

Dust Model Deposition (hr−1) 0.65 0.98 0.68

Background Dust Loading (g/ft2) 13.4 20.1 14.1
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Variable Base Value

(Mean or Point Estimate) 50% Value 5% Value

Air Exchange Rate (hr−1) 0.63 0.95 0.66

Dust Model Resuspension (hr−1) 0.00014 0.00021 0.00015

Soil Density (g/m3) 2600000 3900000 2730000

Track in Soil Depth (m) 0.015 0.023 0.016

Track in Rain Efficiency 0.50 0.75 0.53

Track in Hard Surface Depth (m) 0.0010 0.0015 0.0011

Particulate Track‐in Rate (g/day) 0.93 1.40 0.98

Soil Porosity 0.20 0.30 0.21

Receptor Building Volume (m3) 508 762 533

MatFrac 0.13 0.20 0.14

After performing each simulation with 10,000 iterations, two key metrics were estimated:

 Elasticity: The percent change in the renovation‐related blood lead divided by the percent change in

the input variable. Elasticity is a measure of the percent change in the result for each percent

change in the input variable.

 Sensitivity score: Elasticity multiplied by the coefficient of variation (CV) of the variable. The

sensitivity score incorporates information about the spread in the distribution for a given variable.

Variables that have high elasticities (indicating high sensitivity) but that have relatively tight

distributions (low CVs) will not exert as strong an influence over the outcome as a variable with a

moderate elasticity but very wide distribution.

Table 8‐7 shows the elasticities for each age group for both the 50 percent and 5 percent sensitivity

cases, where the rows have been sorted to indicate the variables with the highest elasticity magnitudes

at the top of the table. Positive elasticities indicate the variable is positively correlated with the

renovation‐related blood lead, while negative elasticities indicate the variables are negatively

correlated. The analysis indicates that the amount of lead in the paint on the renovated building, the

obstruction adjustment factor, the fraction of removed paint that is aerosolized, the dust model

penetration, the dust model deposition rate, and the background dust loading had the highest

elasticities. Both the lead in the paint and the obstruction adjustment elasticities indicated that for every

1 percent change in these variables, the renovation‐related blood lead values for children change by

approximately 0.3 percent; elasticities tended to be much lower for adults.
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Table 8‐7. Elasticities for the Numerical Variables in the Exterior Sensitivity Analysis

Variable

50% 5%

Age 2 Age 35 Age 65 Age 2 Age 35 Age 65

Lead in Paint (XRF) (g/cm2) 0.314 0.050 0.059 0.285 0.046 0.055

Obstruction Adjustment Factor 0.314 0.050 0.059 0.285 0.046 0.055

Aerosol Emission Fraction 0.314 0.050 0.059 0.285 0.046 0.055

Dust Model Penetration 0.161 0.015 0.018 0.203 0.020 0.024

Dust Model Deposition (hr−1) 0.109 0.011 0.013 0.119 0.012 0.014

Background Dust Loading (g/ft2) ‐0.095 ‐0.010 ‐0.011 ‐0.150 ‐0.015 ‐0.018

Air Exchange Rate (hr−1) 0.069 0.007 0.008 0.084 0.008 0.010

Dust Model Resuspension (hr−1) ‐0.031 ‐0.003 ‐0.004 ‐0.036 ‐0.004 ‐0.005

Soil Density (g/m3) ‐0.008 ‐0.001 ‐0.001 ‐0.011 ‐0.001 ‐0.001

Track in Soil Depth (m) ‐0.005 ‐4.6E‐04 ‐0.001 ‐0.007 ‐0.001 ‐0.001

Track in Rain Efficiency ‐0.004 ‐3.5E‐04 ‐4.1E‐04 ‐0.006 ‐0.001 ‐0.001

Track in Hard Surface Depth (m) ‐0.003 ‐2.8E‐04 ‐3.3E‐04 ‐0.004 ‐3.6E‐04 ‐4.4E‐04

Particulate Track‐in Rate (g/day) 0.002 2.4E‐04 2.9E‐04 0.002 2.2E‐04 3.0E‐04

Soil Porosity 0.002 1.9E‐04 2.3E‐04 0.002 1.7E‐04 1.8E‐04

Receptor Building Volume (m3) ‐0.002 ‐1.7E‐04 ‐2.0E‐04 ‐0.002 ‐2.2E‐04 ‐2.3E‐04

MatFrac 0.000 ‐3.6E‐05 ‐3.6E‐05 0.000 ‐3.6E‐05 ‐5.2E‐05

These trends are depicted graphically in Figure 8‐9. Where possible (i.e., where a distribution for the

variable could be estimated), the sensitivity scores are also shown in the figure. The sensitivity scores

indicate that when the spread in the distributions were taken into account, the relative sensitivity of the

background dust loadings increased. Without knowledge of the accurate shape of the distribution of all

variables or estimates of the predicted error in each variable prediction, however, interpretation of the

sensitivity scores is difficult.
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Figure 8‐9. Numerical Variable Elasticities and Sensitivity Scores (Where Available), Exterior Analysis

8.4. Summary of Results for Interior Monte Carlo Scenarios  

This section summarizes the interior scenario results from the millions of iterations that were modeled

to characterize renovation‐related changes in blood lead concentrations and incremental health effect

changes for children and adults. This section provides some insight into the overall magnitude and

distribution of results and the trends in results across different scenario variables.  

Throughout this section, blood lead concentrations and IQ changes are presented to one decimal point

(i.e., overall resolution of 0.1 g/dL blood lead and 0.1 IQ points). Hazard or risk ratios are presented to

the nearest 0.01 and birth weight is presented to the nearest 0.1 gram. This level of resolution was

selected to be consistent with the modeling capabilities. Results are summarized in this section in

various ways: a percentile (average, 95th) for a specific incremental environmental media, blood lead, or
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health effect within a scenario, comparisons of the percent of scenarios across all scenarios that went

above a value, and comparison of iterations within a scenario that went above a value.

For all graphs and charts shown throughout Section 8, it is important to reiterate that the results apply

only to the exposure scenarios as described for purposes of the Approach. These results do not show

the estimated proportion of building types with an estimated media concentration or the estimated

proportion of the US population that is predicted to experience the specific blood lead change or

incremental health effect.  This is because the scenarios have not yet been evaluated to determine how

likely or unlikely they are to occur with respect to current work‐practices and renovation activities

within P&CBs.   

Nine unique time‐at‐blood‐measurement values (e.g. 1 month, 5 month, 10 months, etc) were chosen

and combinations of these are shown in the charts for health effects for relative comparison. There is

uncertainty about how long a person would need to be exposed to elicit a response. In many cases in

the interior analysis, the 1‐month post‐renovation blood lead and health effect estimates were very high

and exceeded the levels presented in the health effects papers themselves. EPA chose not to restrict

application of the IQ, CVDM, and eGFR equations to a particular blood lead threshold, because these

equations are parameterized with respect to the ratio of renovation blood lead and background blood

lead.  

EPA will evaluate all time‐at‐measurement values in the context of the exposure scenario, but the

1‐month post‐renovation estimates are likely not biologically feasible. The blood lead and health effect

changes should be considered in context. Blood lead levels remained elevated, especially during the

renovation, but quickly decreased over time. A high short‐term increase in blood lead could lead to an

effect later in life but whether that effect would be attributable to the high short‐term exposure or

lifetime exposure at varying background levels is unknown. In addition, before making a hazard

determination, EPA will put upper‐percentile exposures into context using the frequency of occurrence

of each scenario and each percentile in the scenario.  

8.4.1. Trends Across Scenario Variables
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Table 8‐19 provides a summary of trends observed in the interior analysis across the different scenario

variables. Additional graphical representation of these trends is provided in Section 8.5.1.

   
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Table 8‐8. Summary of Interior Trends Analysis

Trend Category Trends Observed

Loadings during

Dust Generation

Increases in the loadings during the dust‐generating phase and the rest‐of‐renovation phase

cause the renovation‐related blood lead changes to increase. In general, the dust

generating phase and rest‐of‐renovation loadings are correlated, since higher dust‐

generating loadings will correspond with high rest‐of‐renovation loadings. The relative

effect of the two loadings will depend in part on the durations of the two phases, with

changes in longer duration phases having a larger impact than changes to shorter duration

phases.  

Loadings during

Rest of Renovation

Duration of Dust

Generation

The duration of the dust‐generating affect blood lead levels because lead loadings increase

with the duration of renovation activities.  The duration of the rest of renovation also

affects blood lead levels because lead loadings can remain elevated through the rest of

renovation phase.  As these durations increase, the associated blood lead changes increase

as well. These values also tend to be correlated because the rest‐of‐renovation duration is

estimated using a multiplier applied to the dust‐generating duration.

Duration of Rest of

Renovation

Carpet vs. Not‐

carpet

In general, carpeting tends to decrease the overall blood lead changes compared with hard

floors. This trend is related to the cleaning efficiency; in the Approach, the minimum carpet

cleaning efficiency is higher than the minimum hard floor cleaning efficiency. Thus, for

cleaning episodes beyond about 4 or 5 cleanings, each carpet cleaning episode is more

efficient and the lead dust levels return to background faster than with hard floors. Looking

across the scenarios, identical scenarios without carpet tend to be 1.6 higher than the

carpeted scenarios on average when comparing the 10 month post‐renovation blood lead

changes in children. The ratio increases for increasing months post‐renovation.

Type of Renovated

Building

The building type impacts the blood lead changes primarily via the effect of time‐spent in

the building. Children’s largest average time‐spent is in schools, so the school building type

gives the largest blood lead changes for children age 0‐10.  Adults spend the most time on

average in Building Category 1 (Office, outpatient healthcare, and public order/safety), so

this building type gives the largest adult blood lead changes, with adults 18‐49 having

higher changes than adults 50‐80. The type of building also impacts the cleaning

frequencies, with food service/retail, schools, and hospitals/lodging being cleaned more

frequently on average than offices and warehouses. This tends to decrease the overall

blood lead changes in the buildings that are cleaned more frequently. Thus, in children,

there is a competing effect in schools between more frequent cleaning versus more time

spent in the building.

Room Size Room size has two competing effects on the indoor dust levels: increased wall surface area

leads to greater lead emission while increased floor space leads to a greater area over

which to spread the lead dust (dilution). Overall, increasing the room size while maintaining

the same ceiling height will tend decrease the blood lead change (dilution effect wins out).  

Job Intensity/

Layers of Paint

Removed

Both the job intensity and layers of paint removed affect the amount of lead released into

the room, so increasing either one increases the overall blood lead change.  

8.4.2. Maximum Hourly Dust

Figure 8‐10 summarizes the dust‐generating and rest‐of‐renovation loadings across all scenarios. The

graph provides the percentage of all scenarios in each loading bin. Some individuals were exposed to
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elevated lead loadings throughout the renovation while other individuals were exposed beginning with

the routine cleaning phase. For the dust‐generating phase loadings at <10 ug/ft2

, most of the values that

are 0 ug/ft2 and those reflect the scenarios where the person is not exposed during the dust‐generating

phase. The loadings just after the dust generating‐phase (blue bars) tended to be high, with 92 percent

of scenarios having loadings above 1,000 g/ft2. The loadings during the rest or renovation phase (red

bars) remained elevated but decrease, with 69 percent of scenarios having loadings above 1,000 μg/ft2

.

The time it takes loadings to return to background levels is another metric of interest that helps

contextualize maximum loading values and their exponential decrease over time.  Once routine cleaning

starts, the lead dust levels began to decrease exponentially, with 5.1% percent of scenarios returning to

background within an average of 6 months. Average estimates of time to background dust levels for

interior modeling ranged from 0 to 30 months with an average of 18 months.   

Figure 8‐10. Histogram of Scenario Dust‐Generating and Rest‐of‐Renovation Loading Distribution for

Interiors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related loadings

for buildings across the U.S.
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8.4.3. Renovation‐Related Blood Lead and Bone Lead

Figures and Tables presented in this section provide information comparing results to various blood lead

levels.  Blood lead values that are shown (0.1 ug/dl, 1 ug/dl, 10 ug/dl, etc) are for illustrative purposes

only and are not intended to evaluate potential hazards due to lead from renovations in P&CBs.  

Figure 8‐11 provides the percentage of scenarios that exceed each renovation‐related blood lead change

on the x‐axis. The graph plots the mean and 95th percentile blood lead change in children at both 5

months after the renovation and 15 months after the renovation. Overall, the blood lead changes were

larger than in the exterior analysis. These time points are examples of outputs generated from

distributions from the Monte Carlo results.  These distributions can be generated from all of the post‐

renovation time points (1 month, 5 month, 10 months, 15 months, 20 months, 25 months, 30 months,

40 months, 50 months).

Figure 8‐11. Renovation‐related Blood Lead Distribution for Interiors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related blood

lead values for individuals across the U.S.
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Table 8‐9 provides an additional summary of the 95th percentile blood lead changes for each age group

at 1, 5, 20, and 50 months post‐renovation. It also provides the lifetime average blood lead statistics

(see 7.3.1 for description of how lifetime blood lead was estimated). At 1 month post‐renovation, the

95th percentile blood lead changes were very high. Because the post‐renovation time period is defined

from the beginning of the renovation, however, some renovations were still ongoing at the 1‐month

mark. As with interiors, adults had lower estimated blood lead changes than children.  Examining the

results for lifetime average blood lead change for children, the 95th percentile exceeded 1 g/dL in

about 38 percent of the scenarios.  
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Table 8‐10 shows the percent of scenarios where at least 1 percent (30 iterations) or 10 percent (300

iterations) of the 3,000 interior Monte Carlo iterations exceeded several example blood lead levels (0.1

ug/dL, 1 ug/dL, and 10 ug/dl, selected for illustrative purposes only).
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Table 8‐9. Percentage of Interior Scenarios with 95th Percentile Renovation‐Related Blood Lead Change Levels Exceedi

Blood Lead

Changes

(g/dL)

Age 0 – 10 Age 18‐49 A

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

0.1 95.67% 95.49% 91.20% 64.62% 92.87% 95.52% 93.67% 66.43% 9.05% 15.96% 95.38% 92.36% 0.5 95.24% 92.23% 63.60% 9.89% 64.03% 91.37% 70.86% 4.46% 0.90% 2.76% 88.57% 61.25% 1.0 93.97% 85.17% 32.83% 2.85% 38.40% 83.06% 42.33% 2.06% 0.01% 1.18% 78.04% 33.22% 2.0 89.48% 66.82% 10.29% 1.31% 17.04% 67.72% 17.56% 0.29% 0.00% 0.05% 60.43% 13.07% 3.0 83.83% 51.60% 4.88% 0.51% 9.18% 55.19% 9.82% 0.03% 0.00% 0.00% 46.18% 7.26% 4.0 78.30% 40.82% 3.31% 0.26% 5.84% 44.87% 6.45% 0.01% 0.00% 0.00% 36.40% 4.68% 5.0 73.66% 33.07% 2.61% 0.13% 4.31% 36.86% 4.69% 0.01% 0.00% 0.00% 29.08% 3.45% 

10.0 56.34% 14.23% 1.28% 0.00% 2.37% 15.86% 2.03% 0.00% 0.00% 0.00% 11.15% 1.56% 15.0 44.00% 7.87% 0.38% 0.00% 1.94% 8.00% 1.19% 0.00% 0.00% 0.00% 6.47% 0.82% 20.0 35.29% 5.16% 0.16% 0.00% 1.34% 5.33% 0.71% 0.00% 0.00% 0.00% 4.68% 0.36% 

30.0 24.00% 2.90% 0.01% 0.00% 0.47% 3.48% 0.16% 0.00% 0.00% 0.00% 3.35% 0.10% 

40.0 16.47% 2.25% 0.00% 0.00% 0.14% 3.27% 0.02% 0.00% 0.00% 0.00% 3.21% 0.01% 

50.0 11.85% 1.97% 0.00% 0.00% 0.05% 3.13% 0.01% 0.00% 0.00% 0.00% 2.98% 0.00% 75.0 6.44% 1.18% 0.00% 0.00% 0.00% 2.89% 0.00% 0.00% 0.00% 0.00% 1.50% 0.00% 

100.0 4.37% 0.61% 0.00% 0.00% 0.00% 1.42% 0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 

*Percentages less than 0.01% are rounded to 0%
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Table 8‐10. Percentage of Interior Scenarios with Either 1% or 10% of Iteration Blood Lead Changes Exceeding Spe

At Least 1% of Iterations Are Greater than Given Value

Blood LeadChanges(g/dL)

Age 0 – 10 Age 18‐49 

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

0.1 95.70% 95.66% 95.21% 87.26% 95.29% 95.66% 95.38% 88.10% 55.62% 47.59% 95.64% 95.15% 

1 95.34% 93.37% 73.79% 18.41% 71.59% 91.72% 75.55% 9.78% 1.06% 2.81% 89.36% 67.11% 

10 74.39% 34.84% 2.92% 0.10% 4.86% 37.77% 5.18% 0.02% 0.00% 0.00% 30.49% 3.75% 

At Least 10% of Iterations Are Greater than Given Value

Blood LeadChanges(g/dL)

Age 0 – 10 Age 18‐49 

1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 20 mnths 50 mnths Life. 1 mnth 5 mnths 

0.1 95.64% 95.13% 85.12% 38.79% 88.94% 95.26% 95.26% 39.06% 3.51% 7.45% 94.91% 94.91% 

1 91.83% 91.83% 15.43% 1.88% 23.55% 74.61% 74.61% 0.79% 0.00% 0.21% 68.07% 68.07% 

10 44.05% 44.05% 0.42% 0.00% 1.83% 7.88% 7.88% 0.00% 0.00% 0.00% 6.53% 6.53% 

*Percentages less than 0.01% are rounded to 0%
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Figure 8‐12 expands on the results presented in Table 8‐9 to show the full distribution of the mean and

95th percentile blood lead changes at three different months‐post‐renovation and for the lifetime

average blood lead.  

Figure 8‐12. Renovation‐related Blood Lead Distributions for All Interior Scenarios

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related blood

lead values for individuals across the U.S.

Figure 8‐13 shows the percentage of the mean and 95th percentile bone lead changes that exceeded

each value on the x‐axis for both cortical bone (A) and trabecular bone (B). These values are shown at 50

months post‐renovation, which is the maximum time captured in the Approach. Overall, the bone lead

changes for adults were small, with most scenarios having changes less than 0.5 μg/g for cortical bone

lead, and less than 2.5 μg/g for trabecular bone lead at both the mean and 95th percentile.  
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Figure 8‐13. Renovation‐related Bone Lead Distributions for Interiors

Applies to hypothetical scenarios only.  Results do not show distribution of renovation‐related bone lead

values for individuals across the U.S.

8.4.4. Health Effects

Figure 8‐14 shows the percentage of scenarios that exceeded each IQ change value on the x‐axis for the

mean and 95th percentile scenario results and at both 5 and 15 months post‐renovation (panel A) and

for the lifetime‐average (panel B). The IQ changes in the interior analysis were much larger than in the

exterior analysis. In the 95th percentile case at 15 months after the renovation, nearly 65 percent of the

scenarios showed a renovation‐related IQ change of at least 1 point, whereas in the mean case the

percent drops to 6.3.  
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Figure 8‐14. Renovation‐related IQ Change Distributions for Interiors

Figures 8‐14 and 8‐15 apply to hypothetical scenarios only.  Results do not show distribution of

renovation‐related health effects for individuals across the U.S.

Figure 8‐15 provides the percentage of CVDM hazard ratios (panel A), GFR risk ratios (panel B), and birth

weight reduction (panel C) that exceeded each value on the x‐axis for the mean and 95th percentile

cases. The CVDM and GFR effects were examined at 5 and 15 months, while 10 and 20 months were

selected for birth weight reduction (to capture the pregnancy duration and a value 10 months later). The

hazard and risk ratios and birth weight reductions estimates were significantly greater than in the

exterior analysis.
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Figure 8‐15. Hazard Ratio (A), Risk Ratio (B), and Birth Weight Reduction (C) Distributions for Interior
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8.5. Sensitivity Analysis for an Interior Scenario  

Similar to the exterior analysis, sensitivity of the blood lead estimates to the input variables was

assessed using both a categorical and a numerical sensitivity analysis, as described in this section. An

additional sensitivity analysis was conducted to evaluate the effects of different combinations of the

location of the exposed individual, the time that exposure starts, and the amount of time spent in

different locations. This analysis is described on its own in Section 8.6.3.  

8.5.1. Categorical Sensitivity Analysis

For the categorical analysis, two types of categorical variables are included in the Approach: scenario

variables and Monte Carlo variables. These were treated separately in the sensitivity analysis. For the

scenario variables, the “base case” was defined by choosing the scenario variable values as shown in

Table 8‐11. Then, a series of additional scenarios were defined by changing one or two scenario

variables at a time. These cases were then run with 3,000 iterations and the renovation‐related blood

lead changes were compared.

Table 8‐11. Scenario Categorical Variables Included in Interior Sensitivity Analysis

Categorical Sensitivity

Case

Building

Type

Carpet

Type

Allowed/Not

Allowed

During‐

Loading

(g/ft2)

Post‐

Loading

(g/ft2)

During‐

duration

(weeks)

Post‐

duration

(weeks)

Base Office No Allowed 15000 350 2 1

Supermarket Supermarket No Allowed 15000 350 2 1

Retail Retail No Allowed 15000 350 2 1

School School No Allowed 15000 350 2 1

Lodging Lodging No Allowed 15000 350 2 1

Not Allowed Office No Not Allowed 15000 350 2 1

Low Loadings Office No Allowed 225 66 2 1

Low Loadings, Not

Allowed Office No Not Allowed 0 66 2 1

High Loadings Office No Allowed 200000 1050 2 1

Long During Duration Office No Allowed 15000 350 20 1

Long Post Duration Office No Allowed 15000 350 2 20

Long Both Durations Office No Allowed 15000 350 29 29

With Carpet Office Yes Allowed 15000 350 2 1

Figure 8‐16 shows the trends across the sensitivity analysis cases for the scenario categorical variables.

Overall, the renovation‐related blood lead change tended to decrease when the individual was not
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allowed in the workspace, when the loadings the individual was exposed to were lower, and when the

renovation durations were shorter. Trends across the building types reflected differences in the cleaning

frequencies and in the time spent in the building for the different age groups, schools representing the

highest childhood exposure building type, and offices (“base”) representing the highest adult exposure

building type. Also, in the loading range selected for this base case, individuals in the buildings with

carpet tended to have lower exposures than those in buildings with hard surface flooring owing to

differences in the cleaning efficiencies.

Figure 8‐16. Categorical Scenario Variable Sensitivity Analysis Trends, Interiors

For the Monte Carlo categorical variables, the office scenario above was selected as “base” case and the

Monte Carlo categorical variables were set to specific values within their distributions. Note that this

change means the office base case had a somewhat different value than in Figure 8‐16, where the

Monte Carlo variables were all sampled. Then, the value of each was changed one at a time, as shown in

Table 8‐12. The biokinetic variability GSD was also included in this analysis; a value of 1.6 (the Agency

default in the IEUBK model) was compared with the Approach value of 1.9.
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Table 8‐12. Monte Carlo Categorical Variables Included in Interior Sensitivity Analysis

Monte Carlo Categorical

Variable Base Value Sensitivity Value

GSD 1.9 1.6

Cleaning Frequency Once a Week Once a Month

Age 2, 35, and 65 10, 18, and 50

Time Spent Median 95th Percentile

Figure 8‐17 shows the trends across the sensitivity analysis cases for the various Monte Carlo categorical

variables. The cleaning frequency and time spent in the building tended to increase the blood lead

significantly, while the age and biokinetic GSD had only a modest effect on the mean renovation‐related

blood lead changes.  For time spent, representative values were chosen. See Appendix L for a

description for how time spent distributions were estimated for various age groups for the Approach.  

Figure 8‐17. Monte Carlo Categorical Variable Sensitivity Analysis Trends, Interiors

8.5.2. Numerical Sensitivity Analysis

As discussed above in the introduction to Section 8.4, the numerical sensitivity analysis included

variables that were either point estimates or were continuous distributions in the Approach. Each value
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was increased by both 5 percent and 50 percent to perform both a local and global sensitivity analysis.

The variables included in the analysis are shown in Table 8‐13. Only the 5 percent case was simulated for

the cleaning factor since it could not be increased above a value of 1. Unlike the dust penetration factor

in the exterior analysis, the elasticity was not likely symmetric for this variable with respect to being

increased versus decreased; thus, the variable was excluded from the global sensitivity analysis

altogether.

Table 8‐13. Numerical Variables Included in Interior Sensitivity Analysis

Variable

Base Value

(Mean or Point

Estimate)

50% Value 5% Value

Base N/A N/A N/A

Job Intensity 0.5 0.75 0.525

Fraction of Paint Removed 0.5 0.75 0.525

Room 1 Size (ft2) 60 90 63

Room 2 Size (ft2) 3900 5850 4095

XRF Sampled (g/cm2) 0.0063 0.0095 0.0066

Ceiling Height (ft) 9 13.4 9.44

Aerosol Emission Fraction   0.0119 0.0179 0.0125

Rate of "Dust Generating" Phase (ft2/hr) 360 540 378

Rest of Renovation Multiplier 2 3 2.1

Cleaning Factor 0.943 N/A 0.990

Adjacent Factor 0.0057 0.0086 0.0060

Background Dust Loading (g/ft2) 1.22 1.83 1.28

After performing each of these simulations with 10,000 iterations, several key metrics were estimated:

 Elasticity: The percent change in the renovation‐related blood lead divided by the percent change in

the input variable. Elasticity is a measure of the percent change in the result for each percent

change in the input variable.

 Sensitivity score: Elasticity multiplied by the coefficient of variation (CV) of the variable. The

sensitivity score incorporates information about the spread in the distribution for a given variable.

Variables that have high elasticities (indicating high sensitivity) but that have relatively tight

distributions (low CVs) will not exert as strong an influence over the outcome as a variable with a

moderate elasticity but very wide distribution.

Table 8‐14 shows the elasticities for each age group for both the 50 percent and 5 percent sensitivity

cases, where the rows have been sorted to indicate the variables with the highest elasticity magnitudes
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at the top of the table. Positive elasticities indicate the variable is positively correlated with the

renovation‐related blood lead, while negative elasticities indicate the variables are negatively

correlated. The analysis indicates the cleaning factor (i.e., the fractional efficiency of the cleaning and

other mitigation practices) was the most sensitivity variable with an elasticity of 0.55 (indicating a 0.5

percent change for each 1 percent change in the cleaning factor). Ceiling height, intensity of the job,

fraction of paint removed during the job, amount of lead in the paint, and background dust level

elasticities indicated the blood lead estimates are moderately sensitive to these variable values.

Table 8‐14. Elasticities for the Numerical Variables in the Interior Sensitivity Analysis

Variable

50% 5%

Age 2 Age 35 Age 65 Age 2 Age 35 Age 65

Cleaning Factor N/A N/A N/A –0.553 –0.572 –0.571

Ceiling Height 0.089 0.089 0.089 0.011 0.011 0.011

Intensity (Fraction of Walls Affected) 0.089 0.089 0.088 0.011 0.011 0.011

Fraction of Paint Removed 0.089 0.089 0.088 0.011 0.011 0.011

XRF Sampled 0.082 0.083 0.082 0.011 0.011 0.011

Paint Aerosol Emission Fraction 0.082 0.083 0.082 0.011 0.011 0.011

Background Dust –0.053 –0.057 –0.057 –0.006 –0.006 –0.006

Room 2 Size –0.046 –0.046 –0.046 –0.005 –0.005 –0.006

Rate of "Dust Generating" Phase   –0.004 –0.002 –0.002 –1.66E–04 –9.55E–05 –1.05E–04

Adjacent Factor 0.003 0.003 0.002 2.82E–04 2.55E–04 2.36E–04

Room 1 Size 5.00E–04 4.93E–04 4.42E–04 8.56E–05 9.98E–05 8.82E–05

These trends are depicted graphically in Figure 8‐18. Where possible (i.e., where a distribution for the

variable could be estimated), the sensitivity scores are also shown in the figure. The sensitivity scores

indicate that when the spread in the distributions are taken into account, the relative sensitivity of the

background dust loadings and the lead in the paint did not change relative to each other. In the absence

of knowledge about the accurate shape of the distribution of all variables, however, interpretation of

the sensitivity scores is difficult.
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Figure 8‐18. Numerical Variable Elasticities and Sensitivity Scores (Where Available), Interior Analysis

8.5.3. Sensitivity to Individual’s Location in Time and Space

As discussed in Section 4.2.4, an individual may be located in a number of different rooms in the

renovated building, they may be present during the full renovation or for only part of the renovation,

and they may either occupy only a single space or multiple spaces within the building during a single

visit. The Approach includes scenarios that cover many of these cases, and a sensitivity analysis was

conducted to explore the effects of the different assumptions about location and movement through

the building.  

Error! Not a valid bookmark self‐reference. shows the different sensitivity cases run. For this analysis,

the work area dust‐generating loading were assumed to be 100,000 g/ft2; the rest‐of‐renovation

loadings and adjacent room loadings were found by assuming baseline cleaning efficiency and adjacent

factors of 0.943 and 0.0057, respectively (see Appendix B). This gave rest‐of‐renovation loadings of

5,700 g/ft2

and adjacent loadings of 570 g/ft2. The “Other Room” loadings were assumed to be 0

mg/ft2. When a person traveled between two different areas, the loadings in the two areas were
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averaged to give the effective exposure loading (i.e., the person spends half of their time in each room).

Finally, the dust‐generating and rest‐of‐renovation durations were both set to 4 weeks. The building

type was an office building, where adults have a greater time‐spent than children.

Figure 8‐18 shows the average renovation‐related blood lead for the different age groups for each of the

sensitivity cases at 10 months after the start of the renovation. Cases that were included in the

Approach are shown in blue and scenarios only included in the sensitivity analysis are shown in red. For

the cases run, the cases where the individual is present only during the rest‐of‐renovation lie roughly in

the middle of the cases where they are present for the full renovation and where they are absent for the

full renovation. The cases included in the Approach tend to bound the other scenarios, with one

exception. Individuals who split their time between an adjacent room and another unaffected part of

the building will have lower blood lead changes than those captured in the Approach. Overall, for this

particular building, the location of the individual can change the renovation‐related blood lead by a

factor of 2 or 3.
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Table 8‐15. Sensitivity Analysis Cases That Explore Effects of Location and Exposure Timing

Case

Number

of

Building

Locations

Room

Present

during

dust‐

generatio

n?

Present

during  

rest of

renovation

?

Present

during

routine

cleaning

?

Case 1. One Location, Work Area, Always

Allowed in Room During Renovation

One

Location Work Area Yes Yes Yes

Case 2. One Location, Work Area, Partially

Allowed in Room During Renovation

One

Location Work Area No Yes Yes

Case 3. One Location, Work Room, Always

Allowed in Room During Renovation

One

Location

Work

Room Yes Yes Yes

Case 4. One Location, Work Room, Partially

Allowed in Room During Renovation

One

Location

Work

Room No Yes Yes

Case 5. One Location, Work Area, Not Allowed

in Room During Renovation

One

Location

Work

Area/Work

Room

No No Yes

Case 6. One Location, Adjacent Room, Always

Allowed in Room During Renovation

One

Location

Adjacent

Room Yes Yes Yes

Case 7. One Location, Adjacent Room, Partially

Allowed in Room During Renovation

One

Location

Adjacent

Room No Yes Yes

Case 8. One Location, Adjacent Room, Not

Allowed in Room During Renovation

One

Location

Adjacent

Room No No Yes

Case 9. One Location, Other Room One

Location

Other

Room No No No

Case 10. 50% work, 50% adjacent, always

allowed

Multiple

Locations

Work and

Adjacent No Yes Yes

Case 11. 50% work, 50% adjacent, partially

allowed

Multiple

Locations

Work and

Adjacent No No Yes

Case 12. 50% work, 50% adjacent, not allowed Multiple

Locations

Work and

Adjacent No No No

Case 13. 50% work, 50% other, always allowed Multiple

Locations

Work and

Other No Yes Yes

Case 14. 50% work, 50% other, partially allowed Multiple

Locations

Work and

Other No No Yes

Case 15. 50% work, 50% other, not allowed Multiple

Locations

Work and

Other No No No

Case 16. 50% adjacent, 50% other, always

allowed

Multiple

Locations

Adjacent

and Other No Yes Yes

Case 17. 50% adjacent, 50% other, partially

allowed

Multiple

Locations

Adjacent

and Other No No Yes

Case 18. 50% adjacent, 50% other, not allowed Multiple

Locations

Adjacent

and Other No No No
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Figure 8‐19. Location Sensitivity Analysis Results

Blue bars are cases included in the Approach while red bars are part of the sensitivity analysis

only.

9. Uncertainties in the Model Approach

The Approach and the resulting health effect predictions are subject to several uncertainties related to

input parameters, models, and equations used to estimate the media concentrations, blood

concentrations, and health effects. This section discusses these uncertainties qualitatively, building on

results from the sensitivity analysis to focus on key variables.  

In general, quantifying the uncertainty or predicting whether the uncertainty represents a low or high

bias in the simulated health effects is not possible. For every variable considered in this analysis, care

was taken to ensure that the selected values and approaches were neither overly conservative nor likely

to bias the results low. Instead, the focus was on finding values and distributions that represent the

variability in the U.S. population as a whole. Thus, any biases discovered were corrected, and the

direction of the remaining uncertainty cannot be predicted.  
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9.1. Input Variables

Any model‐based approach is limited by the availability and quality of its input data. The Approach

described here is data‐intensive, and thus, understanding the limitations and uncertainties associated

with the input data and assumptions is critical. In addition, any uncertainties and limitations of the data

will be propagated throughout the remainder of the downstream calculations and thus can have

implications for several different parts of the Approach.

9.1.1. The Dust Study and Estimation of the Emission Rates and Fraction

Emitted  

The EPA Dust Study (US EPA 2007a) represents the most complete study of lead loading related to

interior and exterior renovation activities. The study included several different renovation activities and

assessed the effect of various control options on the post‐renovation loading. The primary shortcoming

of this dataset is the relatively small sample size for each activity/control option combination. In

addition, the residences and schools had different job sizes, paint lead content, and contractor crews,

making comparison across control options and activities more difficult. Jobs were at times performed on

metal railings, wood ceilings, or other room components that might be difficult to compare to other

substrates. To account for the differences in job sizes and paint content, the Dust Study loadings were all

processed so that the emission rates for exteriors and the fraction emitted for interiors were estimated

based on the total particulate loading (rather than the dust loading) that was from a unit area on the

wall (see Appendices H and I).  

Because not every possible descriptive metric was recorded in the Dust Study, however, some data

required for the analysis was estimated in other ways.  For example, detailed samples that fully describe

the shape of the distribution of dust along the wall were not available.  But samples that were taken

were mapped and used for estimating emission rates and fraction emitted.   For the bulk material

fraction, the Approach assumes that the bulk material would fall (within 1 or 2 feet of the job wall) to

adjust the sample masses, but this assumption introduces uncertainty in the emission rates.  

Data for calculating lead dust emissions from the partial demolition of a building are also limited. The

study selected evaluated loadings after the demolition of a block of row houses. The overall emissions

were scaled to a per‐square‐foot‐demolished basis to be applied to a single wall of a renovated building

in the Approach. The extent to which applying data from a complete demolition to a partial demolition

might introduce bias to the results, however, is not possible.

Extrapolation of loadings in P&CBs from the fraction emitted metric derived from the dust study covered

a wide range across dust‐generation and rest of renovation phases ranging from very low to very high.  

The upper range of potential loading values present after a renovation, derived from wipe samples or

from converted air sampling, has not been well characterized. For interior experiments, the Dust Study

only performed the job on a portion of the room. This was usually a small (e.g., 10 to 30 ft2

) patch of wall
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Painting Activities in Public and Commercial Buildings

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or ceiling. Because the full room was not renovated, it is difficult to interpret our Approach loading

values within the context of the loadings found in the Dust Study. It is plausible that an upper bound

floor loading value may exist, but this data gap introduces uncertainty into the analysis.  

The Approach estimates the fraction of lead emitted during a renovation activity and is intended to be

the best possible estimate given the data available. The values were similar to the single available

emission fraction in the literature. Hence, the emission fractions represent an uncertainty in the

Approach results. The effect of this uncertainty (to either lower or raise incremental blood lead changes)

cannot be quantitatively assessed due to lack of data on actual emission fractions from renovation

activities.

9.1.2. Cleaning Frequency and Cleaning Efficiency

The modeled dust loadings in both the exterior and interior analysis are highly sensitive to both the

cleaning frequency and cleaning efficiency. Thorough literature searches were conducted for both

variables, but only limited data were available. In addition, the frequency and efficiency of cleaning

likely vary widely across the different building types and individual buildings. For this reason, these

variables introduce uncertainty in the Approach results. The effect of this uncertainty (to either lower or

raise incremental blood lead changes) cannot be quantitatively assessed.

9.1.3. Effect of Building Obstructions

In an urban environment, numerous buildings might lie between the renovated building and the

downwind receptor building. The effect of these obstructions was incorporated in the Approach by

simulating a number of obstruction scenarios in AERMOD using the AERMOD PRIME software. Based on

these test runs, a distribution of obstruction adjustment factors was created. To truly capture the

infinite combinations of building positions and orientations, however, is not possible. Thus, the

obstruction adjustment introduces additional uncertainty in the Approach and the effect of the potential

bias cannot be determined.

9.1.4. Frequency and Duration of Renovation Activities

Although information on the frequency of individual renovation activities is available, data on the

frequency of these activities in relationship to one another and across specific building types are limited.

A given building could experience multiple renovations, ranging from smaller to bigger jobs, over the

course of several years. Making assumptions about how often a person in a P&CB would be exposed to

multiple renovations over time would have introduced significant uncertainty due to lack of available

information. To partially account for this, many different combinations of multiple renovation activities

were modeled, but they were assumed to occur at the same time rather than at specified intervals.
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9.1.5. Time Spent and Location of the Exposed Individual

The amount of time spent in the renovated P&CB is a highly sensitive variable in the Approach. The

CHAD database provides a robust source of data to estimate the distribution of time spent in the various

building types. The Approach applies the time spent as a continuous pattern of entering the same

building, however, rather than attempting to estimate entry into different buildings of the same type.

For certain types of buildings, such as schools and offices, certain individuals are likely to enter only one

school or one office per day. For other types of buildings, such as retail buildings, certain individuals are

likely to enter different buildings of the same type on a given day. Given the complexity of human

activity patterns, accounting for the full range of variability is not possible. The length of time spent

between various microenvironments was analyzed, and this metric could be used to help differentiate

whether an individual spent time in one building and re‐entered that same building (short time‐

between), or if an individual spent time at different buildings (longer time‐between). Thus, the time‐

spent variable was applied in the same way for all exposed individuals, but has greater or lesser

uncertainty in the Approach depending on the type of building and the exposed individual.

In the interior analysis, exposure could start at many different times and occur at different locations.

Exposure could start at the beginning of the dust‐generating phase, the beginning of the rest‐of‐

renovation phase, or the beginning of the routine‐cleaning phase. Individuals also could be located in

the work area, in the work room, or in an adjacent room at any of these times. Individuals also could

either move between these spaces or remain in one space for the duration of estimated time spent.

Some, but not all, of these combinations have been explored in the Approach and the interior sensitivity

analysis. Based on what has been explored to date, this variable is highly sensitive, which could be

explored further, as it directly influences how long and at what levels individuals are exposed.     

9.2. Modeling Approaches

The modeling equations and tools in the Approach were selected with a focus on scientific rigor, peer‐

review status, and parsimony between the available detail in the input data and the detail in the model

equations. In cases where uncertainty was large, such as the Leggett model, efforts were made to

update the model algorithms to reflect the most up‐to‐date information in the literature. The

application of multiple modeling approaches, however, creates additional uncertainty in the analysis.  

9.2.1. Application of AERMOD

AERMOD is a Gaussian dispersion model and was selected to model the dispersion of renovation dust to

nearby locations. To fully capture the transport of mass, particularly in urban areas with more and larger

buildings, a full microscale, mass‐conserving, fluid dynamics model would be needed. The scope of the

current Approach, however, did not allow for the creation or application of such a model. In addition,

such a model inherently would be highly uncertain due to the effects of small‐scale turbulence and the

interaction between the windfield and the city buildings, in addition to the challenges associated with
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developing the required, highly detailed inputs for enough locations to suitably represent the United

States. AERMOD was selected as the most appropriate model for the Approach, but it has not been fully

validated for receptors very close to the release site and might introduce significant uncertainty to air

concentrations predicted at these distances. AERMOD does allow for land surface information (such as

roughness introduced by a city landscape) and windfield modifications due to obstructions, however, to

be modeled.  

Within AERMOD, modeling paint removal from the surface of an exterior wall as a series of point

sources, or stacks, with associated exit velocities and temperatures and stack diameters, is not an ideal

representation. Of the various options tested, this represented the best application of AERMOD for

exterior renovations. This approach could introduce uncertainty, particularly very close to the renovated

building.  

9.2.2. Loading to Concentration Conversion Equation

The extent of uncertainty associated with using the loading‐to‐concentration regression model is

difficult to estimate precisely. For this Approach, EPA used three data sources, resulting in an increased

sample size and overall decrease in the standard error. The three studies, however, used different

approaches for gathering loading data (wipe versus vacuum) and were conducted in different sampling

periods (years) and different countries. The overall confidence intervals in the empirical equation are

wide, indicating a wide spread in the relationship between lead dust loading and concentration.

Alternatively, considering the many covariates that could affect dust loading and lead concentration

(cleaning frequency, floor type, condition of interior paint, size and lead concentration of ambient air

particles, etc.), that the simple dust loading‐dust concentration regression has a significant slope and

explains over half the variance in the (logarithm of the) data is notable. In addition, the log‐log form of

the relationship has a physical basis, where the distribution of lead dust concentrations is taken as

arising from random multiple dilutions of dust from multiple sources (Ott, 1995). Extrapolation of the

relationship outside the range of loadings in the underlying data, however, will introduce additional

uncertainty.

9.2.3. Application of Adult Health Effect Equations and the Time Since

Renovation

It is recognized that both children and adults spend time in P&CBs, and the Approach should address

potential exposures and health impacts to both.  However, the application of the existing adult

concentration response functions for this Approach introduces uncertainty for the reasons described

below.   

Regulations related to lead in gasoline and in consumer paint have led to a steady decline in lead

exposure since the early 1980s. However, the existing health effects literature for lead exposure in

adults is based on cross‐sectional studies that examine blood or bone lead at a single time point.
Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and

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Because of this, a measure of blood lead level taken at the time when the study was conducted could

indicate blood lead levels that are much lower than a decade or two earlier in the adult’s life. The full life

history of lead in the adult’s blood cannot be assessed. Thus, the health effect might be correlated with

the blood lead at the time of assessment, but this blood lead level might not have a causal relationship

with the health effect; instead, it could simply indicate that people with higher‐than‐average blood lead

levels today likely also had higher‐than‐average blood lead levels in the past, and these past elevated

blood lead levels might have been the true cause of the health effect.  

In the Approach, a renovation event triggers an elevated blood lead level for a period of weeks to

several years before returning to background. To mimic the cross‐sectional design of the health effect

studies, a single point after the beginning of the renovation is selected as the blood lead measurement

and health effect assessment time. The results are highly sensitive, however, to the choice of how long

after renovation the blood lead is assessed. In addition, the past peak blood lead is not captured or

accounted for in the health effect equation. From a biological standpoint, no susceptibility window (in

terms of either length of exposure or timing of exposure) is known for these health effects, so the

sampling time cannot be set based on biological considerations. Lifetime average blood lead levels were

also used to estimate health effects in the approach; however, because the equations were not

originally developed using this metric, interpretation of these health effect estimates are difficult and

application of the equations might be inappropriate.

The health effect equations based on bone lead levels in part overcome this limitation. Bone lead more

accurately reflects lifetime exposure to lead rather than a single point estimate. As stated earlier in the

Approach, however, EPA is modeling effects from the most recent renovation and as such bone lead

estimates are an underestimate in situations where individuals have been exposed to more than one

renovation over time. Modeling multiple exposures over time might not be feasible given the wide

range of possibilities for timing, proximity to, and types of renovation activities within one person’s

lifetime.  

For the above reasons, the adult health effect predictions are highly uncertain. They are included in the

Approach because the adult time‐spent values are higher than the children time‐spent values, and

adults might represent a susceptible population for P&CB exposure. Application of the existing adult

concentration response functions, however, introduces significant uncertainty in the results.

9.2.4. Lookup‐Table Approach

The interior renovation analysis represents a particular challenge because of the number of possible

combinations of building use type, building configuration, room size, and occupant location. Even when

carefully constraining each variable to a reasonably low number of values, the combinations of scenarios

for the interior renovations exceeds 100 million. Such a large number of scenarios cannot be run using

available resources. The lookup‐table approach enables a smaller number of representative scenarios to

be run and then interpolation between the closest two to estimate the health effects. This approach
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introduces uncertainty into the calculations. Tests were performed on a subset of scenarios to ensure

that the predictions were within 5 percent of the true health effect predictions when run through the

Monte Carlo model, and in most cases results were well below 5 percent. Nevertheless, the approach

could introduce a small low or high bias for some ranges of lead loadings and durations.  

9.2.5. Correlated Variables

The Approach does not account for correlations between many of the input variables. For example, the

radial selected (primarily upwind or downwind) and the obstruction adjustment might be correlated. Or,

the background dust is likely correlated with cleaning frequency in different buildings, whereas the

Approach treats these as entirely separate variables. In some cases, efforts could be made to account

for correlations; for example, the lead content in the paint on the renovated building is related to the

vintage of the building in the Approach. In addition, the amount of time spent in the receptor building is

based on the building type. Owing to a lack of information about such correlations and the potential

biases possible with mischaracterizing these correlations in other variables, however, the Approach does

not attempt to capture correlations between other variables. By not allowing for correlations, however,

both the means and the 95th percentiles could be biased either high or low.

9.2.6. Point‐Estimate Variables and the Population Distribution

Many of the variables in the Approach are point estimates rather than distributions (sampled variables)

or a series of discrete values (scenario variables). In general, these variables are not expected to be the

most sensitive variables in the Approach; time spent, cleaning frequency and efficiency, and the lead

content of the paint (all highly sensitive variables) are represented as distributions based on the best

input data possible. Because not all variables are included as distributions and because of the

uncertainty in the distributions that are included, however, the distributions of the resulting health

effects and incremental blood lead changes must be interpreted carefully. Because of the intricacy of

the Approach and the complicated nature of the overall exposure assessment objective, how closely the

modeled results are related to true population distribution is unknown. The Approach presented here is

intended to represent the best estimate possible given the input data and modeling approaches

available.

9.2.7. Convergence of Upper Percentiles

The Monte Carlo simulations were performed with 10,000 (exteriors) and 3,000 (interiors) iterations to

resolve the mean blood lead changes to within ± 5 percent. True model convergence at all percentiles is

not possible, and larger uncertainty is associated with the 95th percentile estimates. In general,

however, the model was able to resolve the renovation‐related changes to within 0.1 g/dL blood lead,

0.1 IQ points, ORs to the nearest 0.01, and birth weight to the nearest 0.1 gram.
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Despite the uncertainties described above, EPA is confident that the approach taken provides

reasonable estimates of exposure input values and modeling outputs given the data sources available.

Background blood lead modeled results are very closely aligned with current and past U.S. population

blood lead levels, as reported by NHANES. Renovation‐related blood lead modeled results for most

scenarios indicated a modest increase above background blood lead levels, demonstrating a close

alignment between the modeled values and empirical values. The model estimates for the upper

percentiles were less certain, just as upper percentile empirical data in NHANES is less certain, but still

corresponded to the same general magnitude. This high level of correspondence between complicated

multistep modeling and empirical blood lead levels in the current U.S. population is evidence that the

many uncertainties in the analysis are likely not introducing specific overall bias for mid‐range

percentiles.  

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