# Appendices to the 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
Assisstance and support provided by ICF Intern ational under Contract number EP-C-14-001

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# Contents
Appendix A. Exterior Analysis Input Variable Values

.................................................. ..................... A-1 Appendix B. Interior Analysis Input Variable Values

.................................................. ...................... B-1 Appendix C. Renovated Building Characteristics ....

................................................... ....................... C-1 Appendix D. Renovation Characteristics ............

................................................... .......................... D-1 Appendix E. Receptor Building Characteristics .....

................................................... ......................... E-1 Appendix F. Soil and Hard Surface Parameters ......

................................................... ........................ F-1 Appendix G. AERMOD Variables ......................

................................................... ............................ G-1 Appendix H. Exterior Emission Rates ...............

................................................... ............................ H-1 Appendix I. Interior Fraction Emitted .............

................................................... ............................... I-1 Appendix J. Dust Model for Exterior Analysis ......

................................................... .......................... J-1 Appendix K. Loading to Concentration Conversion ...

................................................... .................... K-1 Appendix L. Estimates of Exposure Factors .........

................................................... .......................... L-1 Appendix M. Updates to the Leggett Model ..........

................................................... ...................... M-1 Appendix N. Background Media Concentrations and Int akes .............................................. ............. N-1 Appendix O. Population Biokinetic Variability .....

................................................... ......................... O-1 Appendix P. Concentration-Response Curves Supportin g Information ..................................... ........ P-1 Appendix Q. Approach to estimate universe of P&CBs

................................................... ................. Q-1

## blank page**Appendices to the Approach for Estimating Exposures and Incremental Health Effects from Lead due to Re**  **novation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft v July 28, 2014 

# Acronyms and Abbreviations

Acronym / Abbreviation Stands For
AERMOD American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) Regulatory Model Improvement Committee (AERMIC) Model
AIC Akaike Information Criteria
ASHRAE American Society of Heating, Refrigerating and Air Conditioning Engineers
BMD Benchmark Dose
BMDL Benchmark Dose Level
CBECS Commercial Buildings Energy Consumption Survey
CBP County Business Pattern
CBSA Core Based Statistical Areas
CDC Center for Disease Control
CDF Cumulative Distribution Function
COF Child Occupied Facility
CVDM Cardiovascular Disease Mortality
DOE Department of Energy (US Department of)
DOT Department of Transportation (US Department of)
EFSS Environmental Field Sampling Study
EPA Environmental Protection Agency
GFR Glomerular Filtration Rate
GSA General Services Administration
GSD Geometric Standard Deviation
GM Geometric Mean
HEPA High Efficiency Particulate Air
HUD Housing and Urban Development (US Department of)
HVAC Heating Ventilation and Air Conditioning
IEUBK Integrated Exposure Uptake Biokinetic Model for lea d
IQ Intelligence Quotient. Also Full Scale Intelligent Quotient (FSIQ)
LBP Lead Based Paint
LCL Lower Confidence Level
LEV Local Exhaust Ventilation
LRRP Lead Renovation Repair and Painting
MSA Metropolitan Statistical Areas**Appendices to the Approach for Estimating Exposures and Incremental Health Effects from Lead due to Re**  **novation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft vi July 28, 2014 Acronym / Abbreviation Stands For
MSE Mean Squared Error
NEXHAS National Human Exposure Assessment Survey
NHANES National Health and Nutrition Examination Survey
NLCD National Land Cover Database
NIST National Institute of Standards and Technology
P&CBs public and commercial buildings
QCLCD Quality Controlled Local Climatological Data
PbB blood lead
RECS Residential Energy Consumption Survey
SAB Science Advisory Board
SAMSON Solar and Meteorological Surface Observational Netw ork
SD Standard Deviation
SE Standard Error
UCL Upper Confidence Level
USGS United States Geological Survey
VF Ventilation Factor
TSCA Toxic Substances Control Act
XRF X-Ray Fluorescence

## blank page**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-1 July 25, 2014 

# Appendix A. Exterior Analysis Input Variable Values

Appendix A presents a summary of all inputs used by the exterior Monte Carlo model. All inputs, their values, and the methodology used to calculate the i nputs are discussed in detail in the remainder of t he Appendices. Table A-1 presents the inputs that are used to define a scenario, while Table A-3 presents the variables used in the Monte Carlo model. The “i nput 1” and “input 2” columns in Table A-3 correspond to different types of values depending o n the distribution type, as shown in Table A-2. For example, input 1 and input 2 are the “mean” and “st andard deviation” for a normal distribution and are the “geometric mean” and “geometric standard deviat ion (GSD)” for lognormal variables. For variables that use a discrete distribution in the Monte Carlo model or that vary by building type, the values an d probabilities used are shown in the tables in the r emainder of Appendix A.

Table A-1. Scenario-specific inputs.

Input Input name Available Inputs Unit
s

Renovation Activity RenoScenario Dry scraping, Wet Scraping, Power sanding without H EPA, Power sanding with HEPA, Heat gun, Needle gun witho ut HEPA, Needle gun with HEPA, Window/door replacement , Trim replacement, Torching, Demolition n/a

Distance from Renovation RenoDistance 5, 50, 150, 300, 650, and 800 ft Size of Renovation RenoClass F1T1, F1T2, F1T3, F2T1, F2T2, F2T3, F3T1, F3T2, F3T3 n/a Vintage of Renovation RenoVintage Pre-1930, 1930-1949, 1950-1959, 1960-197 9 n/a
Horizontal Containment HorPlasticOnOff Present or not present n/a
Vertical Containment VertPlasticOnOff Present or not present n/a
Receptor Building Types RecType Residence, Industrial building, Agricultural buildi ng, Public/Commercial building, or School. n/a

Age Groups Age 0-10 , 18-49, 50-80 n/a



### Table A-2. Key to Definition of “Input 1” and “Inpu t 2” in Table A-3 for Different Distribution Types.
Distribution Type Input 1 Input 2
Point estimate Value N/A Uniform Lower Limit Upper Limit Normal Mean Standard Deviation Lognormal Geometric Mean Geometric Standard Deviation Discrete Reference to Table where Values and Probabilities ar e Presented N/A**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-2July 25, 2014Table A-3. Exterior Analsyis Monte Carlo Inputs.ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputReno Bldg Point GlassFrac None Table A-4 NA N/A N/AAppendix C.1.3Reno Bldg Point NonGlassArea ft2Table A-4 NA N/A N/A Appendix C.1.3Reno Bldg Point WindowDoorArea ft2Table A-4 NA N/A N/A Appendix C.1.4Reno Bldg Point TrimArea ft2Table A-4 NA N/A N/A Appendix C.1.4Reno Bldg Point ContainmentArea ft2Table A-4 NA N/A N/A Appendix C.1.5Reno Bldg Lognormal WallLoadingp1930 (XRF) g/cm20.00607 2.90 0.0509 0.00072 Appendix C.1.7Reno Bldg Lognormal WallLoading19301949 (XRF) g/cm20.00377 2.38 0.0214 0.00067 Appendix C.1.7Reno Bldg Lognormal WallLoading19501959 (XRF) g/cm20.00253 2.30 0.0134 0.00048 Appendix C.1.7Reno Bldg Lognormal WallLoading19601979 (XRF) g/cm20.00335 2.34 0.0183 0.00061 Appendix C.1.7Reno Point RenoRate Days Table A-4 NA N/A N/A Appendix D.1.1Reno Point RenovationDuration Days Table A-4 NA N/AN/A Appendix D.1.2Reno Point FracPaintRemoved None Table A-5 NA N/A N/A Appendix D.1.2Reno Point FractionAerosolized None Table A-5 NA N/A N/AAppendix D.1.4,Appendix AReno Point FractionBulk None Table A-5 NA N/A N/A Appendix D.1.5Reno Point H_Plastic_Eff None 0.95 NA N/A N/A Appendix D.1.6Reno Point V_Plastic_Eff None 0.91 NA N/A N/A Appendix D.1.6Receptor Discrete ReceptorVintageResidential None Table A-6 NA N/A N/A Appendix E.3Receptor Discrete ReceptorVintageAgricultural NoneTable A-6 NA N/A N/A Appendix E.3Receptor Discrete ReceptorVintageIndustrial None Table A-6 NA N/A N/A Appendix E.3Receptor Discrete ReceptorVintageCommercial None Table A-6 NA N/A N/A Appendix E.3Receptor Discrete ReceptorVintageSchool None TableA-6 NA N/A N/A Appendix E.3**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-3July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputReceptor Discrete ReceptorHtResidential None TableA-6 NA N/A N/A Appendix E.4Receptor Discrete ReceptorHtAgricultural None TableA-6 NA N/A N/A Appendix E.4Receptor Discrete ReceptorHtIndustrial None Table A-6 NA N/A N/A Appendix E.4Receptor Discrete ReceptorHtCommercial None Table A-6 NA N/A N/A Appendix E.4Receptor Discrete ReceptorHtSchool None Table A-6 NA N/A N/A Appendix E.4Receptor Discrete CeilingHeightResidential M TableA-6 NA N/A N/A Appendix E.4Receptor Discrete CeilingHeightAgricultural M TableA-6 NA N/A N/A Appendix E.4Receptor Discrete CeilingHeightIndustrial M Table A-6 NA N/A N/A Appendix E.4Receptor Discrete CeilingHeightCommercial M Table A-6 NA N/A N/A Appendix E.4Receptor Discrete CeilingHeightSchool M Table A-6 NA N/A N/A Appendix E.4Receptor Lognormal HouseVolumeResidential m3390.5 2.06 1664.8 91.6 Appendix E.5Receptor Lognormal BldgVolumeAgricultural m35227 3.14 51459 531 Appendix E.5Receptor Lognormal BldgVolumeIndustrial m35227 3.14 51459 531 Appendix E.5Receptor Lognormal BldgVolumeCommercial m31371 3.00 12372 152 Appendix E.5Receptor Lognormal BldgVolumeSchool m32794 4.20 49206 159 Appendix E.5Receptor Normal AirExchangeRateResidential hr-10.63 0.65 1.93 0.001 Appendix E.6Receptor Normal AirExchangeRateAgricultural hr-11.5 0.87 3.24 0.001 Appendix E.6Receptor Normal AirExchangeRateIndustrial hr-11.5 0.87 3.24 0.001 Appendix E.6Receptor Normal AirExchangeRateCommercial hr-11.5 0.87 3.24 0.001 Appendix E.6Receptor Normal AirExchangeRateSchool hr-11.5 0.87 3.24 0.001 Appendix E.6Receptor Point DustModelPenetration None 1 NA N/A N/A Appendix E.7Receptor Point DustModelResuspension hr-10.00014 NA N/A N/A Appendix E.9Receptor Point DustModelDeposition hr-10.65 NA N/A N/A Appendix E.8**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-4July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputReceptor Lognormal PartTrackRateResidential g/day 0.61 2.52 3.85 0.096 Appendix E.10Receptor Lognormal PartTrackRateAgricultural g/day1.79 2.52 11.254 0.279 Appendix E.10Receptor Lognormal PartTrackRateIndustrial g/day 1.79 2.52 11.254 0.279 Appendix E.10Receptor Lognormal PartTrackRateCommercial g/day 62.0 2.52 390.5 9.7 Appendix E.10Receptor Lognormal PartTrackRateSchool g/day 29.9 2.52 188.4 4.7 Appendix E.10Receptor Discrete MatFrac None 0.13 or 0 NA N/A N/AAppendix E.Receptor Discrete PctCarpetResidential NoneTable A-7NA N/A N/A Appendix E.12Receptor Discrete PctCarpetAgricultural NoneTable A-7NA N/A N/A Appendix E.12Receptor Discrete PctCarpetIndustrial NoneTable A-7NA N/A N/A Appendix E.12Receptor Discrete PctCarpetCommercial NoneTable A-7NA N/A N/A Appendix E.12Receptor Discrete PctCarpetSchool None NA N/A N/AAppendix E.12**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-5July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputTable A-7Receptor Discrete CleanFreqResidential NoneTable A-7NA N/A N/A Appendix E.13Receptor Discrete CleanFreqAgricultural NoneTable A-7NA N/A N/A Appendix E.13Receptor Discrete CleanFreqIndustrial NoneTable A-7NA N/A N/A Appendix E.13Receptor Discrete CleanFreqCommercial NoneTable A-7NA N/A N/A Appendix E.13Receptor Discrete CleanFreqSchool NoneTable A-7NA N/A N/A Appendix E.13Receptor Point FloorCleanSlope None 0.113 NA N/A N/A Appendix E.14**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-6July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputReceptor Point FloorCleanInt None -0.5546 NA N/A N/A Appendix E.14Receptor Point CarpetCleanSlope None -0.4296 NA N/AN/A Appendix E.14Receptor Point CarpetCleanInt None 0.6584 NA N/A N/A Appendix E.14Receptor Point FloorCleanEffMin None 0.06 NA N/A N/A Appendix E.14Receptor Point CarpetCleanEffMin None 0.15 NA N/A N/A Appendix E.14Soil Point RainFreq DaysTableA-8NA N/A N/A Appendix F.1Soil Point TrackInRainEffHardSurface None 0.5 NA N/A N/A Appendix F.2Soil Point TrackInRainEffSoil None 0 NA N/A N/A Appendix F.2Soil Point SoilDensity g/m32600000 NA N/A N/A Appendix F.3Soil Point TrackInDepthHardSurface mm 1 NA N/A N/AAppendix F.4Soil Point TrackInDepthSoil cm 1.5 NA N/A N/A Appendix F.4Soil Point TrackInPorosityHardSurface None 0.2 NA N/A N/A Appendix F.5Soil Point TrackInPorositySoil None 0.2 NA N/A N/AAppendix F.5Air Discrete MetRegion None Table A-8 NA N/A N/A Appendix G.1Air Point ParticleSizeμmTable A-9 NA N/A N/A Appendix G.3Air Point ParticleDensity g/cm32 NA N/A N/A Appendix G.3Air Equation ObstructAdjust None Table A-10 NA N/AN/A Appendix G.5Load Conc Point LoadingConcenMean None 0.881 NA N/AN/A Appendix K.Load Conc Point LoadingConcenSlope None 0.421 NA N/A N/A 0Load Conc Point LoadingConcenInt None 5.145 NA N/AN/A Appendix K.Load Conc Point LoadingConcenDF None 1643 NA N/A N/A 0Load Conc Point LoadingConcenErrorVar None 0.544 NAN/A N/A Appendix K.Load Conc Point LoadingConcenIntSE None 0.0182 NA N/A N/A Appendix K.**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-7July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputLoad Conc Point LoadingConcenSlopeSE None 0.0094 NAN/A N/A Appendix K.Load Conc Point LoadingConcenT None 1.961 NA N/A N/A Appendix K.Activity Discrete TimeSpentValues None Table A-11 NA N/A N/A Appendix L.1Activity Uniform TimeSinceLastReno Years 0 4.2 N/AN/A Appendix L.1Activity Discrete AgeProb NoneTable A-12NA N/A N/A Appendix L.1Activity Point MaternalPbBμg/dL0.747 NA N/A N/A Appendix 0Bckground Lognormal BgAirConcμg/m30.02 5.52 0.61 0.0007 Appendix N.1Bckground Point BgSoilConcIndustrialμg/g197.3 NA N/A N/AAppendix N.1Bckground Point BgSoilConcCommercialμg/g49.97 NA N/A N/AAppendix N.1Bckground Lognormal BgSoilConcResidentialp1930μg/g367.4 3.53 4569.8 29.5Appendix N.1Bckground Lognormal BgSoilConcResidential19301949μg/g144.1 3.69 1958.3 10.6Appendix N.1Bckground Lognormal BgSoilConcResidential19501959μg/g79.6 3.77 1131.3 5.6Appendix N.1Bckground Lognormal BgSoilConcResidential19601979μg/g24.2 3.29 261.4 2.2Appendix N.1Bckground Lognormal BgSoilConcResidentialp1979μg/g15.8 2.32 85.3 2.9Appendix N.1Bckground Lognormal BgSoilConcAgriculturalμg/g17.1 1.75 52.4 5.6Appendix N.1Bckground Lognormal BgSoilConcSchoolμg/g28.0 3.00 252 3.1Appendix N.1Bckground Lognormal BgDustLoadResidentialp1930μg/ft23.97 4.76 89.81 0.18 Appendix N.1Bckground Lognormal BgDustLoadResidential19301949μg/ft21.94 7.76 116.80 0.03Appendix N.1Bckground Lognormal BgDustLoadResidential19501959μg/ft21.14 2.65 7.97 0.16Appendix N.1Bckground Lognormal BgDustLoadResidential19601979μg/ft20.79 4.25 14.27 0.04Appendix N.1Bckground Lognormal BgDustLoadResidentialp1979μg/ft20.49 3.38 5.55 0.04Appendix N.1Bckground Lognormal BgDustLoadNonResidentialμg/ft20.80 2.25 5.00 0.13Appendix N.1**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftA-8July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM, PointEstimate or Tablewith Values)Input 2(SD,GSD,orN/A)UpperBoundLowerBoundAppendixDescribing InputBckground Discrete Leggett_Background_Intakesμg/dayAppendix N.1 NA N/A N/AAppendix N.1Bckground Discrete Leggett_Background_PbBμg/dLAppendix N.1 NA N/A N/AAppendix N.1BloodLeadPoint Exposure Factors for Intake various AppendixL.4 NA N/A N/A Appendix L4BloodLeadPoint PbB_GSD None 1.9 NA N/A N/A Appendix O**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-9 July 25, 2014 

### Table A-4. Characteristics of the Renovated Buildin g Size.

Building ID 1 Fraction of

Surface Area that is Glass 2 Component Surface Areas Renovation Durations Containment Area Non-glass building surface area (ft 2 ) 2 Window &

door surface area (ft 2 ) 3 Trim

surface
area (ft 2 ) 3 Duration,

replace windows and doors (days) 4 Duration,

other activities (days) 4 Area inside

containment (ft 2 ) 5

F1T1 0.12 1,266 171 155 2 0.5 1,840 F1T2 0.13 2,502 354 321 4 1 3,280 F1T3 0.14 4,972 621 633 8 2 6,160 F2T1 0.15 2,439 280 323 4 1 1,840 F2T2 0.18 4,726 623 701 7 2 3,280 F2T3 0.18 9,451 1,146 1,381 12 4 6,160 F3T1 0.14 3,706 351 457 6 1.5 1,840 F3T2 0.17 7,177 799 1,000 12 3 3,280 F3T3 0.26 19,198 3,053 3,776 36 9 9,040

1 Building sizes were calculated from CBECS data (US EIA, 2003),
2 Glass surface area was calculated from CBECS micro- data (USE EIA, 2003)
3 Window, door, and trim areas were calculated using methodology provided by EPA
4 Durations were calculated using rate of removal fro m RSMeans (Mossman and Plotner, 2009) and (Capouch, 2011)
5 Containment area does not include the area of the r enovation building itself.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-10 July 25, 2014



### Table A-5. Activity-specific fraction of paint remo ved, aerosolized, and remaining as bulk.

Renovation Activity Fraction of Paint Removed ( FracRem ) Fraction of

Paint Aerosolized ( AerosolFrac ) 1 Fraction of Paint

Emitted as Bulk Particulate ( PartDebFrac ) 2 Power Sanding 0.15 0.248 0.051 Needle Gun 0.15 0.244 0.055 Torching 0.15 0.180 0.056 High Heat Gun 0.15 0.081 0.056
Low Heat Gun 0.15 0.077 0.059 Dry Scrape 0.15 0.070 0.060 Window/Door Replacement 0.15 0.069 0.060
Trim Replacement 0.15 0.070 0.060 Demolition 1.0 0.009 0.060

1 US EPA, 2007a and Farfel **et al.** , 2003
2 Lee and Domanski, 1999
3 Choe **et al.** , 2000



### Table A-6. Receptor Building Characteristics with P robabilities or Distributions

Receptor Type AERMO

D Type Cumulative Probabilities for Vintage Cumulative Probabilities for Height (stories) Ceil.

Ht. (m) Building Volume (m 3 )

Pre1930 1930 to 1949 1950 to 1959 1960 to 1979 Post to 1979 1 2 3 GM GSD

Agricultural Rural 0.07 0.12 0.18 0.41 1.00 1 0 0 8 .53 5227 1.94

Industrial Urban 0.07 0.12 0.18 0.41 1.00 1 0 0 8.5 3 5227 1.94 Commercial / Government Urban 0.13 0.22 0.31 0.61 1.00 0.64 0.89 1.00 2.74 1371 2.25

Schools Urban 0.05 0.14 0.30 0.61 1.00 0.73 0.88 1. 00 3.35 2794 1.86
Residences Urban 0.10 0.17 0.29 0.57 1.00 0.58 0.89 1.00 2.44 391 2.06**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-11 July 25, 2014 

### Table A-7. Receptor Cleaning Frequency, Carpet, Air Exchange Rate, and Track-in Rate with Probabilitie s or Distributions

Receptor Type Cumulative Probabilities for Cleaning Frequency Cumulative Probabilities for Percent Carpet AER (hr -1 ) Particulate Tracking Rate (g/day) Every working day (5 days/ week) Every

third day Every week (1 day/ week) Every

other week (1 day/ 2 weeks) Every

fourth week (1 day/ 4 weeks) All

Carpet All

Floor Mean SD Mean SD

Agricultural 0.1 0.3 0.7 0.9 1.0 0.08 1.0 1.5 0.87 1.77 2.52

Industrial 0.2 0.6 0.8 0.9 1.0 0.08 1.0 1.5 0.87 1.77 2.52
Commercial/ Government 0.2 0.6 0.8 0.9 1.0 0.45 1.0 1.5 0.87 61.49 2.52

Schools 0.4 0.6 0.8 0.9 1.0 0.27 1.0 1.5 0.87 29.67 2.52

Residences 0.1 0.3 0.7 0.9 1.0 0.52 1.0 0.63 0.65 0.61 2.52



### Table A-8. Meteorological Characteristics

Model Index Region Region

Name Inland

or Coastal Location (Surface

Station) Location (Upper-air

Station) Cumulative

Probability Based on Population in Each Region Number of

days between rain events (days)

M1 1 East North

Central Inland Iowa City IA Davenport IA 0.08 9

M2 2 Northeast Inland Pittsburgh PA Township PA 0.18 7 M3 Coastal Camp Springs MD Sterling VA 0.28 7 M4 3 Northwest Inland Idaho Falls ID Riverton WY 0.3 8 M5 Coastal Everett WA Salem OR 0.32 8 M6 4 South Inland Topeka KS Topeka KS 0.39 13 M7 Coastal Lake Charles LA Lake Charles LA 0.46 13 M8 5 Southeast Inland Atlanta GA Peachtree City GA 0.55 7 M9 Coastal New River NC Morehead City NC 0.64 7 M10 6 Southwest Inland Grand Junction CO Denver CO 0.69 16
M11 7 West Inland Las Vegas NV Flagstaff AZ 0.755 22 M12 Coastal Point Mugu CA Vandenberg AFB CA 0.82 22

M13 8 West North

Central Inland Sioux Falls SD Omaha NE 0.84 14 M14 9 Central Inland Rockford IL Davenport IA 1 7**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-12 July 25, 2014



### Table A-9. Particle Size Distribution Used in AERMO D Modeling.

Size Class Cumulative Probability

in Size Class < 2.5 μ m 0.02 2.5 – 10 μ m 0.74 10 – 20 μ m 1



### Table A-10. Summary statistics for the custom distr ibution for the obstruction adjustment.

Minimum 5 th Percentile Mean 95 th Percentile Maximum Adjustment factor 0.10 0.70 1 1.32 5.3



### Table A-11. Cumulative Distributions for Time Spent (Fraction of the Week) in Different Building Types for Each Age Range.

Building Type Cumulative

Probability Age Range

0-<5 5-<17 18-<49 50-80

Residence 0.05 0.58 0.52 0.41 0.46

0.10 0.65 0.57 0.47 0.52 0.25 0.79 0.66 0.55 0.61 0.52 0.90 0.79 0.68 0.81 0.75 0.98 0.92 0.86 0.94 0.90 1.00 0.99 0.96 1.00 0.95 1.00 1.00 1.00 1.00 1 1.00 1.00 1.00 1.00 Agricultural 0.05 0.01 0.02 0.01 0.01

0.10 0.01 0.02 0.01 0.02 0.25 0.02 0.04 0.04 0.03 0.52 0.05 0.07 0.13 0.10 0.75 0.08 0.14 0.36 0.20 0.90 0.10 0.18 0.49 0.34 0.95 0.13 0.19 0.54 0.36 1 0.13 0.31 0.67 0.65**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-13 July 25, 2014

Building Type Cumulative

Probability Age Range

0-<5 5-<17 18-<49 50-80

Industrial 0.05 0.01 0.01 0.01 0.01

0.10 0.01 0.01 0.02 0.02 0.25 0.01 0.01 0.17 0.14 0.52 0.01 0.03 0.34 0.34 0.75 0.02 0.14 0.38 0.38 0.90 0.02 0.19 0.44 0.42 0.95 0.02 0.33 0.47 0.46 1 0.02 0.33 0.53 0.57 Commercial/ Government 0.05 0.01 0.01 0.01 0.02

0.10 0.01 0.01 0.03 0.03 0.25 0.03 0.04 0.07 0.06 0.52 0.06 0.08 0.19 0.14 0.75 0.10 0.14 0.36 0.31 0.90 0.16 0.22 0.43 0.40 0.95 0.20 0.27 0.47 0.44 1 0.33 0.43 0.60 0.55 School 0.05 0.03 0.08 0.01 0.01

0.10 0.07 0.13 0.00 0.01 0.25 0.14 0.22 0.03 0.04 0.52 0.24 0.27 0.11 0.09 0.75 0.35 0.30 0.27 0.28 0.90 0.40 0.34 0.34 0.35 0.95 0.41 0.38 0.38 0.38 1 0.46 0.44 0.51 0.44**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft A-14 July 25, 2014 

### Table A-12. Cumulative Probabilities for Each Age R ange Age 0-10 Age 18-<50 Age 50-80
Age Cumulative Probability Age Cumulative

Probability Age Cumulative

Probability 

### 0 0.08 18 0.03 50 0.053 

### 1 0.17 19 0.07 51 0.101 

### 2 0.26 20 0.10 52 0.150 

### 3 0.35 21 0.14 53 0.198 

### 4 0.45 22 0.17 54 0.245 

### 5 0.54 23 0.20 55 0.293 

### 6 0.63 24 0.23 56 0.338 

### 7 0.72 25 0.26 57 0.381 

### 8 0.81 26 0.29 58 0.424 

### 9 0.91 27 0.32 59 0.464 

### 10 1.00 28 0.35 60 0.507

29 0.38 61 0.545 30 0.42 62 0.583 31 0.45 63 0.622 32 0.48 64 0.657 33 0.51 65 0.690 34 0.53 66 0.719 35 0.56 67 0.747 36 0.59 68 0.774 37 0.62 69 0.800 38 0.65 70 0.824 39 0.68 71 0.846 40 0.72 72 0.867 41 0.75 73 0.887 42 0.78 74 0.906 43 0.81 75 0.924 44 0.84 76 0.941 45 0.87 77 0.956 46 0.90 78 0.971 47 0.93 79 0.986 48 0.97 80 1.000 49 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# A.1. References for Appendix A

Capouch S. (2011). Personal Communication by email between Heidi Hubbard of ICF International and Scott Capouch, Construction Project Manager and Pro fessional Engineer with 10 years of experience, of US NIEHS. Title of email: Lead Pain t Removal – estimating info. February 2, 2011.
Choe K; Trunov M; Grinshpun S; Willeke K; Harney J; Trakumas S; Mainelis G; Bornschein R; Clark S; Friedman W. (2000). Particle settling after lead-ba sed paint abatement work and clearance waiting period. American Industrial Hygiene Associa tion Journal 61: 798-807.
Farfel M; Orlova A; Lees P; Rohde C; Ashley P; Chis olm J. (2003). A study of urban housing demolitions as sources of lead in ambient dust: demolition practi ces and exterior dust fall. Environmental Health Perspectives 111(1128-1234).
Lee and Domanski. (1999). Development of pollutant release estimates due to abrasive blasting for lead paint removal from New York City department of tran sportation steel bridges, Pittsburg, PA, Air and Waste Management Association.

Mossman M; Plotner S; Babbitt C; Baker T; Balboni B, Eds. (2009). RSMeans Facilities Construction Cos t Data.
US Energy Information Administration. (2003) Commer cial Building Energy Consumption Survey (CBECS). Available online at: http://www.eia.gov/emeu/cbecs/cbecs2003/public_use_ 2003/cbecs_pudata2003.html
US EPA. (2007a). Draft final report on characteriza tion of dust lead levels after renovation, repair, and painting activities. Prepared for EPA’s Office of P ollution Prevention and Toxcis (OPPT). Available online at: http://www.epa.gov/lead/pubs/duststudy01 -23-07.pdf**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-1 July 25, 2014 

# Appendix B. Interior Analysis Input Variable Values

Appendix B presents a summary of all inputs used by the interior Monte Carlo model. All inputs, their values, and the methodology used to calculate the i nputs are discussed in detail in the remainder of t he Appendices. Table B-1 presents the inputs that are used to define a scenario, while Table B-3 presents the variables used in the Monte Carlo model. The “i nput 1” and “input 2” columns in Table B-3 correspond to different types of values depending o n the distribution type, as shown in Table B-2. For example, input 1 and input 2 are the “mean” and “st andard deviation” for a normal distribution and are the “geometric mean” and “geometric standard deviat ion” for lognormal variables. For variables that use a discrete distribution in the Monte Carlo mode l or that vary by building type, the values and probabilities used are shown in the tables in the r emainder of Appendix B.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table B-1. Scenario-specific inputs.
Input Input name Available Inputs Units

Renovation Activity RenoScenario Dry scraping, Wet Scraping, Door Planing without HEPA, Door Planing with HEPA, Heat gun, Window Removal, Cabinet Removal, Cut

outs, Demolition n/a

Building Use Type BldgUse 1, 2, 3, 4, or 5 n/a Room Sizes and Configurations RoomSize 2 to 4 different sizes for each building use type ft 2

Carpeting in Renovated Rooms Carpet Present or Not Present n/a

Rooms Renovated RoomsRen Combinations of Room 1, Room 2, and/or Room 3 n/a
Location of Exposed Person in Building Loc Work room in work area, work room outside work area, adjacent room n/a
Intensity of Renovation Job JobIntens 5%, 25%, 50%, or 100% n/a

Fraction of Paint Removed FracPaint 15%, 50%, or 100% n/a

Vintage of Renovation RenoVintage Pre-1930, 1930-1949, 1950-1959, 1960-197 9 n/a

Horizontal Containment HorPlasticOnOff Present or not present n/a

Vertical Containment VertPlasticOnOff Present or not present n/a

Cleaning Type CleanType Baseline or specialized n/a

Age Groups Age 0-10, 18-49, 50-80 n/a



### Table B-2. Key to Definition of Variables by Distri bution Type. Distribution Type Input 1 Input 2 Point estimate Value N/A
Uniform Lower Limit Upper Limit Normal Mean Standard Deviation Lognormal Geometric Mean Geometric Standard Deviation

Discrete Reference to Table where Values and

Probabilities are Presented N/A**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftB-3July 25, 2014Table B-3. Interior Analsyis Monte Carlo Inputs.ModuleDistributionTypeParameterUnitsInput 1(Mean, GM,PointEstimate orTable withValues)Input 2 (SD,GSD, or N/A)Upper BoundLowerBoundAppendixDescribingInputReno Bldg Point CeilingHeightBldg1 ft Table B-4 NAN/A N/A Appendix C.2.3Reno Bldg Point CeilingHeightBldg2 ft Table B-4 NAN/A N/A Appendix C.2.3Reno Bldg Point CeilingHeightBldg3 ft Table B-4 NAN/A N/A Appendix C.2.3Reno Bldg Point CeilingHeightBldg4 ft Table B-4 NAN/A N/A Appendix C.2.3Reno Bldg Point CeilingHeightBldg5 ft Table B-4 NAN/A N/A Appendix C.2.3Reno Bldg Point FloorAreaBldg ftMapped toexteriorbuildingtypes, thenuseTableB-5NA N/A N/A Appendix C.1.2Reno Bldg Point GlassFrac None Table B-4 NA N/A N/AAppendix C.2.5Reno Bldg Point CabinetFrac None Table B-4 NA N/A N/A Appendix C.2.6Reno Bldg Lognormal WallLoadingp1930 g/cm20.00630 2.59320.00094 0.0424 Appendix C.2.8Reno Bldg Lognormal WallLoading19301949 g/cm20.00334 2.63470.00048 0.0232 Appendix C.2.8Reno Bldg Lognormal WallLoading19501959 g/cm20.00314 2.47250.00051 0.0192 Appendix C.2.8Reno Bldg Lognormal WallLoading19601979 g/cm20.00366 2.69820.00050 0.0266 Appendix C.2.8Reno Bldg Point JobArea ft210 ft outfrom wallNA N/A N/A Appendix D.2.3Reno Bldg Point RoomInRoomThreshold NoneRoom size ofat least3800 ft, jobNA N/A N/A Appendix D.2.3**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftB-4July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM,PointEstimate orTable withValues)Input 2 (SD,GSD, or N/A)Upper BoundLowerBoundAppendixDescribingInputarea nomore than5% of roomReno Point FractionEmitted NoneTableB-6NA N/A N/A Appendix D.2.7Reno Point HEPAEfficiency None 90% NA N/A N/A Appendix D.2.8Reno Point DustGeneratingRate ft2/hrTableB-6NA N/A N/A Appendix DReno Point RestofRenovationMultiplier NoneTableB-6NA N/A N/A Appendix DReno Point RuleCleaningEfficiency None Table B-7 NA N/A N/A Appendix D.2.8Reno Point HorizontalPlasticEfficiency None TableB-7 NA N/A N/A Appendix D.2.8Reno PointAdjacentRoomFactor, No VerticalPlasticNone 0.0057 NA N/A N/A Appendix D.2.8Reno PointAdjacentRoomFactor, WithVertical PlasticNone 0.0045 NA N/A N/A Appendix D.2.8Reno Point Sill Factor None 3.86 NA N/A N/A Appendix D.2.9Reno Point PercentExposureSill None 1% NA N/A N/AAppendix D.2.9Receptor Lognormal PartTrackRate g/day Table B-4 NAN/A N/A Appendix E.10Receptor Discrete MatFrac None 0.13 or 0 NA N/A N/AAppendix E.Receptor Point FloorCleanInt None -0.5546 NA N/A N/A Appendix E.14Receptor Point CarpetCleanSlope None -0.4296 NA N/AN/A Appendix E.14Receptor Point FloorCleanEffMin None 0.06 NA N/A N/A Appendix E.14Receptor Discrete CleaningFreq None NA N/A N/A Appendix E.14**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftB-5July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM,PointEstimate orTable withValues)Input 2 (SD,GSD, or N/A)Upper BoundLowerBoundAppendixDescribingInputTableB-8Load Conc Point LoadingConcenMean None 0.881 NA N/AN/A 0Load Conc Point LoadingConcenSlope None 0.421 NA N/A N/A 0Load Conc Point LoadingConcenInt None 5.145 NA N/AN/A 0Load Conc Point LoadingConcenDF None 1643 NA N/A N/A 0Load Conc Point LoadingConcenErrorVar None 0.544 NAN/A N/A 0Load Conc Point LoadingConcenIntSE None 0.0182 NA N/A N/A 0Load Conc Point LoadingConcenSlopeSE None 0.0094 NAN/A N/A 0Load Conc Point LoadingConcenT None 1.961 NA N/A N/A 0Activity Discrete TimeSpentValues None Table B-9 NAN/A N/A Appendix L.1Activity Uniform TimeSinceLastReno Months1, 5, 10, 15,20, 25, 30,40 or 50N/A N/A N/A Appendix L.3Activity Discrete AgeProb NoneTableA-12NA N/A N/A Appendix L.1Activity Point MaternalPbBμg/dL0.747 NA N/A N/A Appendix 0Bckground Lognormal BgDustLoadμg/ft20.8 2.5 5 0.13 Appendix N.1Bckground Discrete Leggett_Background_Intakesμg/dayAppendixN.4NA N/A N/A Appendix N.4Bckground Discrete Leggett_Background_PbBμg/dLAppendixN.4NA N/A N/A Appendix N.4BloodLeadPoint PbB_GSD None 1.9 NA N/A N/AMainDocumentBlood Point Exposure Factors for Intake various Appendix NA N/A N/A Appendix L4**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftB-6July 25, 2014ModuleDistributionTypeParameterUnitsInput 1(Mean, GM,PointEstimate orTable withValues)Input 2 (SD,GSD, or N/A)Upper BoundLowerBoundAppendixDescribingInputLeadL.4**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-7 July 25, 2014 

### Table B-4. Interior Analysis Renovated Building Var iables.

Building Types Room

Size 1 (ft2) Room Size 2 (ft2) Room Size 3 (ft2) Room Size 4 (ft2) Ave. Perc. Glass Percent of Wall that is Cabinet Ceiling

Height Track In

Rate GM (g/day) Track

In Rate GSD
1 Office, outpatient healthcare, and public order/safety 60 200 600 3900 20% 13% 9 15.45 2.52
2 Warehouse, food sales, religious worship, and public assembly 200 9100 N/A 20% 45% 28 0.52 2.52

3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 1800 3800 8400 N/A 20% 13% 28 100.79 2.52

4 Education 200 1300 12200 N/A 20% 13% 20 29.91 2.52 5 Lodging, Nursing, inpatient health care, and laboratory 300 1300 6200 N/A 20% 13% 11 28.44 2.52**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-8 July 25, 2014



### Table B-5. Mapping Interior Buildings to Exterior B uilding Sizes.

Building Types 

### F1T1 F2T1 F3T1 F1T2 F2T2 F3T2 F1T3 F2T3 F3T3

Floor Area (ft 2 ) 1,296 5,184 20,736 2,592 10,368 41,472 3,888 15,552 139,968 Cumulative Probability For each Interior Building T ype 1 Office, outpatient healthcare, and public order/safety 0.25 0.38 0.50 0.63 0.75 0.82 0.88 0.95 1.00

2 Warehouse, food sales, religious worship, and public assembly 0.05 0.25 0.38 0.50 0.75 0.82 0.88 0.95 1.00

3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 0.05 0.25 0.38 0.50 0.75 0.82 0.88 0.95 1.00

4 Education 0.05 0.25 0.38 0.50 0.58 0.67 0.75 0.95 1.00 5 Lodging, Nursing, inpatient health care, and laboratory 0.05 0.12 0.18 0.25 0.38 0.50 0.62 0.75 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-9 July 25, 2014



### Table B-6. Interior Analysis Renovation Activity Pa rameters.

Activity type Fraction of Paint That Remains as Lead Dust Rate of "dust

generating" phase in ft2/hr Rest of

Renovation Multiplier
Window, Saw 0.2137 42 0.5 Door Planing 0.1118 45 15 Heat Gun Plaster 0.0572 45 15 Dry Scrape 0.0380 45 15 Window, No Saw 0.0155 42 0.5 Heat Gun Wood 0.0119 45 15 Door Planing, with HEPA 0.0112 45 15 Cutouts 0.0056 45 2 Cabinet/Shelf Removal 0.0030 45 2 Demolition 0.0031 45 0.5 

### Table B-7. Interior Analysis Control Option Efficie ncies.

Control Option Horizontal Plastic / Rule Cleaning Efficiency Specialized Cleaning with Horizontal Plastic 

### 0.993 Base Cleaning with Horizontal Plastic 

### 0.956 Specialized Cleaning with No Horizontal Plastic 

### 0.975 Baseline Cleaning with No Horizontal Plastic 

### 0.943**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-10 July 25, 2014



### Table B-8. Interior Analysis Disbribution of Cleani ng Frequencies

Building Type Every working day (5 days/week) Every third

day (approx) (2 days/week) Every

week (1 day/week) Every

other week (1 day/2 weeks) Every

fourth week (1 day/4 weeks) 1 Office, outpatient healthcare, and public order/safety 0.2 0.6 0.8 0.9 1.0
2 Warehouse, food sales, religious worship, and public assembly 0.2 0.6 0.8 0.9 1.0
3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 0.4 0.6 0.8 0.9 1.0

4 Education 0.4 0.6 0.8 0.9 1.0 5 Lodging, Nursing, inpatient health care, and laboratory 0.4 0.6 0.8 0.9 1.0



### Table B-9. Cumulative Distributions for Time Spent (Fraction of the Week) in Different Building Types for Each Age Range.

Building Type Cumulative

Probability Age Range

0-<5 5-<18 18-<50 50-80 1 Office, outpatient healthcare, and public order/safety 0.05 0.01 0.01 0.01 0.01 0.10 0.01 0.01 0.03 0.02 0.25 0.01 0.01 0.16 0.08 0.52 0.02 0.03 0.32 0.25 0.75 0.04 0.08 0.36 0.35 0.90 0.07 0.18 0.41 0.40 0.95 0.12 0.35 0.44 0.43 1 0.12 0.42 0.53 0.50 2 Warehouse, food sales, religious worship, and public assembly 0.05 0.01 0.01 0.01 0.01 0.10 0.01 0.01 0.01 0.01 0.25 0.02 0.04 0.02 0.02 0.52 0.05 0.08 0.05 0.05 0.75 0.10 0.13 0.14 0.10 0.90 0.12 0.19 0.34 0.19 0.95 0.12 0.23 0.39 0.30 1 0.12 0.34 0.49 0.42**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft B-11 July 25, 2014

Building Type Cumulative

Probability Age Range

0-<5 5-<18 18-<50 50-80
3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 0.05 0.01 0.01 0.01 0.01 0.10 0.01 0.01 0.01 0.01 0.25 0.02 0.02 0.03 0.03 0.52 0.04 0.05 0.05 0.05 0.75 0.07 0.09 0.11 0.08 0.90 0.12 0.16 0.22 0.15 0.95 0.12 0.21 0.33 0.20 1 0.12 0.32 0.47 0.38 4 Education 0.05 0.01 0.08 0.01 0.01 0.10 0.04 0.13 0.01 0.01 0.25 0.11 0.21 0.03 0.04 0.52 0.12 0.27 0.13 0.09 0.75 0.12 0.30 0.28 0.28 0.90 0.12 0.33 0.34 0.35 0.95 0.12 0.38 0.39 0.38 1 0.12 0.43 0.49 0.45 5 Lodging, Nursing, inpatient health care, and laboratory 0.05 0.01 0.01 0.01 0.01 0.10 0.02 0.02 0.02 0.02 0.25 0.02 0.03 0.04 0.03 0.52 0.04 0.06 0.10 0.06 0.75 0.07 0.21 0.34 0.16 0.90 0.12 0.36 0.43 0.34 0.95 0.12 0.42 0.50 0.40 1 0.12 0.56 0.63 0.56**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft C-1 July 25, 2014 

# Appendix C. Renovated Building Characteristics

Appendix C includes information for how the renovat ed building characteristics were determined for the Approach. Each section includes information about t he selected data source and how (if at all) the dat a were processed to estimate point estimates or distr ibutions for the Monte Carlo model.


# C.1. Exteriors In this Approach, the modeled characterization of l ead emitted from renovation activities is dependent upon how the renovation building is characterized, which includes specifying its size (both the total building size and the size of different components) , height, and vintage. These variables in turn are used to estimate the amount of paint on the building and the lead content of that paint. This section describes the methodology used to select the buildi ng characteristics in this Approach.



## C.1.1. Building Block Approach To capture the emissions from a range of building s izes, a unit-based Approach was developed that characterized emissions from a specified amount of surface area on a renovated building. Then, each building is modeled as though one section (or “bloc k”) of the building is undergoing renovation at a time, with the renovation proceeding around the per imeter of the building. This method accounts for the fact that renovations are typically performed o n finite sections of a building at a given time rat her than on the entire surface area of the building at once.
In this Approach, the renovation block area was con structed to represent the typical wall surface area disturbed by a four-person crew in two hours when p erforming paint removal jobs. Each renovated building is assumed to have a square footprint, and the total square footage of a side of the building is broken up into incremental numbers of these renovat ion blocks. The building was also assumed to have windows (applied in the modeling as a fraction of t he block that is glass) estimated based on CBECS an d to have doors every 32.8 ft (10 m) around the perim eter of the building on the first floor.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure C-1. Largest renovation building with renov ation blocks and point sources.


### In this Approach, a separate fate and transport mod el run (see Appendix 2.3) is needed for each activi ty that has a different job rate. Job rates were colle cted for each activity from the RSMeans constructio n cost manual, a guide that provides typical crew out put (e.g., in square feet per day) for different renovation activities. (see Appendix C). Using the block approach above standardizes the rate of renovation for the paint removal and trim/door acti vities and greatly reduces the required number of fate and transport model simulations. The variabili ty and uncertainty associated with these rates is recognized.
For the purposes of the exterior renovation Approac h, the use type of the renovated building is not needed. It is assumed that only the exterior attrib utes of the building are necessary. However, when t he Approach models both exterior and interior renovati on activities simultaneously, the renovated buildin g is treated as both a “renovated” and “receptor” bui lding, and the use type is incorporated for track i n back into the same P&CB, as described in Section 4 of the Approach document.



## C.1.2. Building Sizes The renovated building size definitions were determ ined using the Commercial Building Energy Consumption Survey (CBECS) data (US EIA, 2003), a s urvey that represents the entire US commercial building stock. CBECS presents the overall building square footage based on building size (in eight bi ns) and number of floors in the building (in five bins) . The size bins ranged from “1,000 to 5,000 sf” to “Over 500,000 sf.” The metadata which included informatio n about malls was used for the analysis.
The number of floors were presented as “one”, “two” , “three”, “four to ten”, and “above 10.” It was estimated from these data that 67 percent of commer cial buildings in the US are 1 story, 22 percent ar e two stories, and 11 percent are more than two stori es. Based on these estimates, three building heigh t bins were selected to represent the commercial buil ding stock: “one”, “two”, and “three and above.”**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft C-3 July 25, 2014 

### To account for skewness of the dataset, a lognormal distribution was fit to the binned CBECS data separately for each of these three height categorie s, and a “small”, “medium”, and “large” building footprint was estimated for each of the height bins . The analysis proceeded in the following steps:

1. For the one story buildings, the upper bound of eac h CBECS size bin was noted. For “Over 500,000 sf”, no maximum value was available to use as an upper bound. Thus, the upper bound was set to a maximum of 1,000,000 square feet. Subs equent sensitivity analysis indicated the predictions were not heavily dependent on this numb er.

2. The cumulative probability was estimated for each s ize bin. This probability was found by finding the percentage of total buildings in the bin and pe rforming a cumulative sum of the probabilities for that bin and all lower bins.

3. An initial geometric mean and geometric standard de viation were assigned to the overall distribution across bins. The Excel® function “logn ormdist” was used to find the cumulative probability in the Lognormal corresponding to the u pper bound in each bin.

4. The “Goal Seek” Excel® function was used to adjust the geometric mean and geometric standard deviation until the sum of the squares of the error s between the actual and modeled cumulative probabilities across the bins was minimized.

5. This final geometric mean and geometric standard de viation pair was used to simulate 1,000 values. The means were taken in each tertile, and w ere used as the renovation building sizes.

6. The analysis was repeated for the two-story and thr ee-and-above-story buildings.
This gave a total of nine different renovation buil ding sizes. These building footprints were then rou nded to the nearest size that created an even number of renovation “blocks”.
Buildings that are taller than 3 stories are likely to have lower concentrations of lead due to larger ventilation factors, when compared to shorter build ings. However, taller buildings may transport lead longer distances due to fewer obstructions and prev ailing meteorological conditions between the renovated building and the receptor building. Tall er buildings, as well as potential future CBECS dat a, could be considered in the future.



## C.1.3. Percent Glass The fraction of the building that is glass was esti mated using CBECS microdata (US EIA, 2003). This dataset presents data for individual buildings, inc luding the size, number of stories, percent glass, and sampling weights used to make the data set represen tative of US commercial building stock. The CBECS data were mapped to the nine building sizes, as des cribed in Appendix C.1.2. From these data, an average percent glass was found for each building s ize. The weights from the CBECS survey were included to ensure the means were nationally repres entative.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft C-4 July 25, 2014**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-1. Characteristics of the RenovatedBuilding Size.

Building ID Fraction of

Surface Area that is Glass 2

F1T1 0.12 F1T2 0.13 F1T3 0.14 F2T1 0.15 F2T2 0.18 F2T3 0.18 F3T1 0.14 F3T2 0.17 F3T3 0.26



### The percent glass used for interiors (Appendix C.2. 5) is different than that used for exteriors. In in teriors, the percent glass is analyzed by different building types of all sizes, where variation proves to be s mall. For the exterior analysis, percent glass is analyze d not by building type but by building size, as sho wn here.



## C.1.4. Size of Renovated Building Components Three renovation activities (trim removal, four wal l window/door replacement, and one wall window/door replacement) are performed on specific building components rather than on the entire exterior painted surface area. Thus, emissions for these activities could not be estimated based solel y on the total surface area of the walls of the building . Instead, the surface area of these building components had to be estimated, as described below.
The area comprised of window frame trim was estimat ed by assuming that a typical window is 3.2 ft by 3.2 ft and has a 0.3 ft wide frame around the exter ior. The total number of windows was estimated by dividing the area that was glass (determined by mul tiplying the exterior surface area of the building by the percent glass) by the area of each window (10.2 4 ft 2 

### ) and rounding to the nearest whole number. The number of windows was multiplied by the area of frame associated with each window (4.20 ft 2 

### ) to calculate the total area of window trim. Doors were assumed to be located every 32.8 ft (10 m) around the perimeter of the building on the firs t floor. Each door was assumed to be 6.6 ft x 3.0 ft with a 0.3 ft wide trim on three sides. The area o f**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### other trim, not associated with windows and doors, was set equal to that of window and door trim plus an additional 5 percent of the non-glass exterior s urface area of the wall. A summary of the values selected for each of the ni ne building types is provided in Table C-2.
Table C-2. The parameters of buildings modeled with varying size of disturbance.

Building ID 1 Building footprint (sf) # of Stories Non-glass

building surface area (sf) Window &

door surface area (sf) Trim

surface area (sf) Total # of

blocks

F1T1 1,296 1 1,268 171 155 8 F1T2 5,184 1 2,504 354 321 16 F1T3 20,736 1 4,974 621 633 32 F2T1 1,296 2 2,429 284 326 16 F2T2 5,184 2 4,943 535 623 32 F2T3 20,736 2 9,429 1,155 1,389 64 F3T1 1,296 3 3,701 351 457 24 F3T2 5,184 3 6,874 925 1,111 48 F3T3 46,656 3 17,533 3,738 4,377 144

1 F1 = Floor 1, T1 = Tertile 1


## C.1.5. Containment Area The containment area captures the area covered by h orizontal plastic (if used) and the distance from t he building to the vertical plastic (if used). The App roach assumes that horizontal plastic is placed 10 ft out from the job wall. This distance was selected based on inspection of the Dust Study data. Typically, l ead dust loadings tended to drop to at least 2% of thei r value just next to the wall at a distance of 10 f t. If vertical plastic is used, it is assumed that the pl astic is 10 ft from the job wall. Using the total b uilding footprint area and the assumption that the building is square, the containment area is calculated usin g the building wall lengths and the assumption of 10 ft linear feet out from the job wall.


## C.1.6. Vintage of the Renovated Building The concentration of lead in the exterior paint of a building was assumed to be a function of the age of the building. This assumption is based on the fact that the concentration of lead in paint has decreas ed with time, particularly leading up to and following the ban on lead paint in residential structures in 1978. Buildings that were built in 1979 to present were n ot modeled due to the assumption that few, if any, of these building would contain lead-based paint. How ever, the number of newer buildings that may contain lead based paint is unknown. Four vintage b ins were considered for the renovated building: Pre

1930, 1930-1949, 1950-1959, and 1960-1979, which is consistent with the vintage bins used in the benefits analysis performed in support of the resid ential Lead Renovation, Repair and Painting (LRRP) rule.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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## C.1.7. Amount of Lead in the Paint on the Building X-Ray Fluorescence (XRF) measurements provide empir ical data about the amount of lead on the walls of buildings of various vintages. In general, XRF t echniques quantify lead mass per unit area through multiple layers of paint on the wall. For the Appro ach, the XRF distributions for the four oldest vint ages were estimated from the HUD National Housing Survey (US EPA, 1998a). In the HUD report, Table 2 provides descriptive statistics. The geometric mean for exterior components was used directly from the report; the geometric standard deviation was estima ted using the reported mean and geometric mean. The geometric mean, geometric standard deviation, a nd average lead content in paint (all vintages) are shown in Table C-3 below.

Table C-3. Lead XRF measurements by vintage for ext erior analysis.

Renovation vintage bin XRF Loadings (g/cm 2 )
Geometric mean Geometric standard deviation Average

Pre-1930 0.00607 2.90 0.01068 1930-1949 0.00377 2.38 0.00549 1950-1959 0.00253 2.30 0.00358 1960-1979 1 0.00331 2.34 0.0048
1 This vintage bin contains data from houses from 196 0-1978, but it is considered representative of the 1960-1979 bin.


### The geometric mean loading from the Pre-1930 vintag e results in an estimate of 19.6 percent lead in th e paint. This estimate can be compared to sources wh ich indicated that before 1940, paints were often 1 0 and sometimes up to 50 percent lead (NDHHS, 2011).
The lead content in the paint and the density of pa rticles are solved using the equations:



## t Res Density frac DensityPb frac Dens Paint frac

Dens Paint s PaintLayer UnitConv

Cov aint WallP XRF

×

− + × = = × × ×

) 1(

**XRF =** the XRF-measured amount of lead on the wall (g/cm 2 

### wall) **WallPaintCov =** the amount of area covered by a single gallon of pa int (ft 2 

### /gallon) **UnitConv =** a conversion factor to convert from gallons to cm 3 

### and from cm 2 

### to ft 2 

### PaintLayers = the number of layers of paint on the wall **PaintDens =** the density of the paint (g paint/cm 3 

### paint)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### frac = the fraction of paint which is lead by weight (unit less) **DensityPb =** the density of lead (g/cm 3 

### ) **DensityRest =** the density of the rest of the paint (other than le ad) (g/cm 3 

### )

All of the equation variables above were assumed to be fixed, aside from the XRF measurements. According to the EPA Wall Paint Exposure Model (USE PA 2001), the typical coverage of paint ( **WallPaintCov** ) is 400 ft 2 

### /gallon or 0.0025 gallons per ft2. The density of lead is 11.3 g/cm 3 

### , and the density of the rest of the paint is assumed to be 1 g/cm 3



### . 

# C.2. Interiors In this Approach, the modeled characterization of l ead emitted from renovation activities is dependent upon how the renovation building is characterized, which includes specifying its use, room sizes, heig ht, and vintage. These variables in turn are used to e stimate the amount of paint on the building and the lead content of that paint. This section describes the methodology used to select the building characteristics in the Approach.



## C.2.1. The Building Use Type As discussed in Section 5.1.1 of the main report, t he use type of a building will affect key attribute s of both the building itself and the time people spend in the building per day. For example, a large warehouse will have one or two larger spaces with m ultiple smaller rooms, and individuals will visit infrequently. In contrast, hotels will have numerou s small rooms, and individuals may stay for days or months at a time.
The CBECS survey provides information about the num ber of buildings in the U.S. in different use categories, as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-4. These CBECS categories were mapped to 5 assessment categories for this Approach, where CBECS categories were grouped according to layouts, room sizes, and expected time spent patterns. A total of 5 assessment categories were chosen to ens ure adequate coverage of different building types but also to balance the resource needs of the Monte Carlo analysis.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-4. CBECS Buildings and Mapped Assessment Ca tegories
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 Secondary 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



## C.2.2. Room Sizes**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-5 presents the representative room sizes fo r five types of model buildings. To develop these estimates, we began with 14 DOE Reference Buildings (Deru et al 2011). The DOE data provided the size and frequency of each type of zone within the Refer ence Buildings. In most cases, a zone was synonymous with room; however, we determined that s ome zones needed to be combined to form one room. For example, Stand-Alone Retail was composed of five zones. It was our judgment that four of the five zones would likely exist in a single room. Accordingly, we combined the Core Retail, Point o f Sale, Front Retail, and Front Entry zones into one Core Retail zone. Similarly, we determined that so me zones needed to be subdivided into multiple rooms. For example, zones used as offices were much larger than the average office size according to ot her sources (IFMA 2010) (Miller 2012). To address this, we divided the area of the office zones by th e average office size to determine the number of concurrent rooms within an office zone. In additio n, we determined that some zones needed to be eliminated. We removed from our data zones represe nting a building’s entire basement, zones that were not climate controlled, and zones with zero oc cupancy, meaning that individuals do not occupy those spaces.
Some of the sizes of the DOE reference buildings ap peared to be considerably larger than the average sized buildings observed in the CBECS data. In par ticular, the Warehouse, Supermarket, and StandAlone Retail DOE reference buildings all had rooms larger than the average sized building of that type according to CBECS. We adjusted the sizes of these buildings and their rooms so that the size of the building after the adjustments matched the size of the average building of that type in CBECS. The si zes of single large rooms were adjusted downward and wh en there were multiple smaller rooms the number of rooms was adjusted downward. For example , the DOE warehouse building had 14.3 offices that were 178 square feet, 1 bulk storage room that was 34,497 square feet and one fine storage room that was 14,998 square feet. This implies a buildin g size of 52,040 square feet when the average warehouse building size according to the CBECS data is only 21,603 square feet, 42 percent of the DOE reference building size. Thus, we adjusted the siz e of the two large rooms by multiplying their squar e footage by 42% and adjusted the number of offices d ownward by 41.5%. Thus, the new building had 5.9 offices that were 178 square feet, 1 bulk storage r oom that was 14,319 square feet and one fine storag e room that was 6,226 square feet.
After making the adjustments to the data described above, we calculated the prevalence of rooms by size for 5 categories of model buildings. To calcul ate these prevalence rates, each room within the DO E model building was weighted according to the weight assigned by DOE for that zone and each DOE building was weighted according to their frequency in the CBCES. Cut-points used to group the rooms into 2-4 size bins for each of the 5 model building categories were selected based on a visual inspect ion of the data so that no group represented less than 2 percent of the rooms in that model building (for example, the rooms in rows shaded the same color in were grouped together). Within each of these groups, the weighted average room size was calculat ed and rounded to the nearest 10ft 2 

### for room sizes below 100ft 2 

### and to the nearest 100ft 2 

### for rooms greater than 100ft 2 

### . The resulting room sizes and room frequencies are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-5.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft C-13 July 25, 2014



### Table C-5. Room Sizes For Each Assessment Building Category

Model Buildings Room Size ( ft 2 ) Percentage Weight Within Model Building Within All

Buildings

1 Office, outpatient healthcare, and public order/safety 60 2% 1% 200 89% 53% 600 5% 3% 3,900 4% 2% 2 Warehouse, food sales, religious worship, and public assembly 200 72% 4% 9,100 28% 1% 3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 1,800 72% 4% 3,800 18% 1% 8,400 11% 1%

4 Education 200 42% 7% 1,300 54% 9% 12,200 3% 1%
5 Lodging, Nursing, inpatient health care, and laboratory 300 80% 11% 1,300 16% 2% 6,200 4% 1%



## C.2.3. Designing Three-Room Renovation Units We can limit our modeling to three-room renovation units by making the simplifying assumption that exposures to individuals in rooms adjacent to more than 2 renovated rooms is equivalent to the exposures to individuals in rooms adjacent to 2 ren ovated rooms.
A renovation project might involve disturbing LBP i n multiple rooms that are also adjacent to other rooms. The exposure model is designed to account f or this by modeling the exposure in each affected room as a separate scenario. For example, consider a 5 room renovation in a hotel that can be represented by the configuration below in Table C-7

.

Table C-6: Example Configuration for 5 Room Hotel R enovation

(rooms sharing walls are adjacent)

Room A (not renovated) Room B

(not renovated) Room C

(renovated) Room D

(renovated) Room E

(renovated) Room F

(renovated) Room G

(renovated) Room H

(not renovated) Room I

(not renovated)


### We represented this renovation with 7 configuration s of 3 rooms (see Table C-7). The exposure was modeled for individuals in Room 2 only. For exampl e, although Room C appears in three different rows below in Table C-8, the exposures to the individual s in room C are only modeled in the second row.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Thus, this approach allows us to use a three room r enovation configuration to model a renovation that involves any number of rooms.

Table C-7: Example of 5 Room Hotel Renovation Repre sented by Three-Room Configurations

Room 1 (room adjacent to where occupants are) Room 2 (room with occupants) Room 3 (room adjacent to where occupants are) Room A (not renovated) Room B (not renovated) Room C (renovated) Room B (not renovated) Room C (renovated) Room D (renovated) Room C (renovated) Room D (renovated) Room E (renovated) Room D (renovated) Room E (renovated) Room F (renovated) Room E (renovated) Room F (renovated) Room G (renovated) Room F (renovated) Room G (renovated) Room H (not renovated) Room G (renovated) Room H (not renovated) Room I (not renovated)



## C.2.4. Ceiling Height The ceiling heights for each building type were est imated using the DOE reference buildings. For each building type in a given Approach building category , the reference buildings had a range of ceiling heights. Values corresponding to main rooms were fa vored over values in rooms deemed to have fewer occupants (e.g., for a quick service restaurant, th e dining room and kitchen ceiling heights were favo red over the “attic” ceiling height). A representative ceiling height was then chosen for each building category, as shown in Table C-8. Because the DOE bu ildings do not include either agricultural or industrial buildings, a representative value from a warehouse was used instead.

Table C-8. Ceiling Heights For Each Assessment Buil ding Category

Building Category Ceiling Height (ft) 1 (Office, outpatient healthcare, Public Order and Safety) 9 2 (Warehouses, Food Sales, Religious Worship, Public Assembly) 28
3 (Food Service, Service, Strip Mall, Retail) 28 4 (Education) 20
5 (Lodging, Nursing, Inpatient Health Care, Lab) 11**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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## C.2.5. Percent Glass

The percent glass is a variable collected from the CBECS. An analysis was conducted to determine if t he percent glass varied by building type. Only slight variation was found as all buildings reported approximately 20% glass surfaces. Percentages wer e derived by combining CBECS categories to match each of the Approach building categories. Then, the adjusted weight variable was used to estimate the nationally-representative fraction of glass in each Approach category. Percent glass as analyzed by building category for the interiors analysis differ s slightly from percent glass as analyzed by buildi ng size for the exteriors analysis.

Table C-9. Percent Glass for Each Approach Interior Building Category

Building Category Sum of Percent Glass Weight* ADJWEIGHT Sum of

ADJWeight Average

Percent Glass

0 (Vacant, Other) 49,175 252,552 19.5% 1 (Office, outpatient healthcare, Public Order and Safety) 205,167 1,015,125 20.2% 2 (Warehouses, Food Sales, Religious Worship, Public Assembly) 291,045 1,469,782 19.8% 3 (Food Service, Service, Strip Mall, Retail) 300,1 37 1,576,039 19.0% 4 (Education) 79,684 385,923 20.6%
5 (Lodging, Nursing, Inpatient Health Care, Lab) 28 ,839 159,329 18.1%



## C.2.6. Amount of Shelving The one data source found that quantifies the amoun t of shelving in a public building is from the Chil d Care Center Survey data (HUD 2003). Using the raw d ata, entries were separated into “wall” and “cabinet” categories. “Built in cabinets” were coun ted as “cabinets”; all other data types were not. T he total wall area and cabinet area reported for each room (of each childcare center) were calculated. Where both a cabinet and wall area existed for the same room, the cabinet to wall ratio was estimated (N=102). The area of all cabinets and walls were us ed in the calculation, regardless of whether they tested positive for lead or not. The resulting sta tistics are shown below in Table C-10. The standard deviation is larger than the mean, indicating signi ficant variability in this variable within the data

. Should additional data become available regarding t he amount of shelving by building type, EPA could make different assumptions. The generic high and m edium estimates derived below may not best represent the variability within a given building c ategory.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table C-10 Estimated Percentage of Interior Wall th at is Cabinet/Shelf in Child Care Centers

Statistic Cabinet to

Wall Ratio MIN 0.3% MAX 96.6% MEAN 13.1% STD 16.1%



### In order to estimate the amount of shelving in each room of each example building, buildings were mapped to specific cabinet “bins” defined using the CCC data:

 

### “High incidence of cabinetry/shelving”: use the mea n plus two standard deviations from the CCC data, or 45%

 

### “Medium incidence of cabinetry/shelving”: use the m ean from the CCC data, or 13%

 

### “Low incidence of cabinetry/shelving”: use the mean minus two standard deviations from the CCC data, or 0%. The mappings and values are shown in Table C-11. Table C-11 Estimated Percentage of Interior Wall th at is Cabinet/Shelf in Approach Building Categories

Building Types Cabinet/ Shelf Category Percent

of Wall that is Cabinet 1 Office, outpatient healthcare, and public order/s afety Medium 13%
2 Warehouse, food sales, religious worship, and pub lic assembly High 45%

3 Food service, service, strip shopping mall, enclo sed mall, and retail other than mall Medium 13%

4 Education Medium 13% 5 Lodging, Nursing, inpatient health care, and labo ratory Medium 13%



## C.2.7. Vintage of the Renovated Building The concentration of lead in the exterior paint of a building was assumed to be a function of the age of the building. This assumption is based on the fact that the concentration of lead in paint has decreas ed with time, particularly leading up to and following the ban on lead paint in residential structures in 1978. Four vintage bins were considered for the renovated building: Pre-1930, 1930-1949, 1950-1959, and**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### 1960-1979, which is consistent with the vintage bin s used in the benefits analysis performed in suppor t of the residential Lead Renovation, Repair and Pain ting (LRRP) rule.



## C.2.8. Amount of Lead in the Paint in the Building The distributions for the amount of lead in the pai nt for interior components were estimated as described in Section C.1.8, but the interior analys is used the interior component estimates for the HU D data source (US EPA, 1998a), Table 2, as shown in T able C-12.

Table C-12. Lead XRF measurements by vintage for in terior analysis.

Renovation vintage bin XRF Loadings (g/cm 2 )
Geometric mean Geometric standard deviation Average

Pre-1930 0.00630 2.59 0.00992 1930-1949 0.00334 2.63 0.00534 1950-1959 0.00314 2.47 0.00473 1960-1979 1 0.00366 2.70 0.00599
1 This vintage bin contains data from houses from 196 0-1978, but it is considered representative of the 1960-1979 bin.



# C.3. References for Appendix C: U.S. Department of Housing and Urban Development (H UD). 2003. First National Environmental Health Survey of Childcare Centers.”
Deru et al (2011). U.S. Department of Energy Commer cial Reference Building Models of the NationalBuilding Stock. Technical Report National R enewable Energy Laboratory. TP-5500-46861
IFMA, (2010), **Space and Project Management Benchmarks: IFMA Resea rch Report #34.**  NDHHS 2011
Miller, Norm, (2012), **Estimating Office Space per Worker.** Draft Report.
US EIA (2003) Commercial Building Energy Consumptio n Survey (CBECS). Available online at: http://www.eia.gov/emeu/cbecs/cbecs2003/public_use_ 2003/cbecs_pudata2003.html
US EPA. (1998a). Risk analysis to support standards for lead in paint, dust, and soil. Appendix G: Hea lth effects associated with exposure to lead and intern al lead doses in humans. (Report No. EPA 747-R-97-006). Washington, DC: US Environmental Pro tection Agency, Office of Pollution Prevention and Toxics. Available online at: http: //www.epa.gov/lead/pubs/toc.pdf.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### US EPA. (2001). Wall Paint Exposure Model Version 3 .2 Users Guide. Washington, DC: US Environmental Protection Agency, Office of Pollution Prevention a nd Toxics (OPPT). Available online at: http://www.epa.gov/opptintr/exposure/pubs/wpemman.p df.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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# Appendix D. Renovation Characteristics

Appendix D includes information for how the renovat ion job characteristics were determined for the Approach. Each section includes information abo ut the selected data source and how (if at all) the data were processed to estimate point esti mates or distributions for the Monte Carlo model.


# D.1. Exteriors In this Approach, the transport of lead downwind fr om the renovated building is determined by both the amount of lead emitted and the time over w hich the lead is emitted. The variables describing these two variables include the rate of renovation, the fraction of paint removed as both aerosol and bulk during the activity, and the efficacy of any containment options employed.



## D.1.1. Rate of Renovation Two different sources were considered to determine the rate of renovation for each of the renovation activities. The Dust Study (US EPA, 2007 a), which is used to estimate the fraction of paint emitted in each job (see Appendix H), reporte d the area of the wall and the associated job duration for each experiment. Rates of renovation f or each renovation activity type were estimated based on these data, as shown in Table D- 1. For activities with more than one experiment in the Dust Study, the standard deviatio n is also provided. In general, the standard deviations are large and indicate large variations in the rates of removal, in part stemming from the small number of experiments performed. In addi tion, three activities had only a single experiment and as a result are subject to large unc ertainty.
Owing to these large uncertainties, the RSMeans Fac ilities Construction Cost Manual (Mossman and Plotner, 2009) was used as an alternative to de termine the average rate of lead paint removal. For the paint removal activities, RSMeans reports that surface preparation for a one person crew is 86 ft 2 

### per day for aluminum siding, steel siding, and gyp sum board. RSMeans does not provide information on typical crew sizes. In lieu of reported data, personal communication with a professional engineer at the N ational Institute of Environmental Health Sciences, who is an expert in the field of construc tion project management, estimated that a typical crew for a commercial or public exterior re novation consists of approximately four workers (Capouch, personal communication, 2011). T he actual crew size may vary by building or job type. Additional information on this variab le would help inform the rate of renovation for exterior renovation activities with larger crews wo rking at a faster rate and smaller crews working at a lower rate. This translates to approxi mately 344 ft 2 

### per hour for the four person**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### crew assumed in the AERMOD modeling. This rate was rounded to 360 ft 2 

### per hour to ensure each of the modeled building panels could be finish ed within an increment of one day.

Table D

- 1

. Comparison of Dust Study and RSMeans removal rates

Dust Study Removal Rates (ft 2 /hr) RSMeans Removal Rates RSMeans

Removal Rates For a 4 Person Crew in an 8 hour day (ft 2 /hr) RSMeans

Removal Rates For a 4 Person Crew in an 8 hour day (ft 2 /day) Approach

Removal Rates (ft 2 /day)

No. Of Experiments (N) Mean Standard

Deviation Power Sanding 2 54 5 86 ft 2 /day 43 344 360
Needle Gun 1 3 N/A 86 ft 2 / day 43 344 360 Torching 2 35 2 86 ft 2 / day 43 344 360 High Heat Gun 1 6 N/A 86 ft 2 / day 43 344 360
Low Heat Gun 1 18 N/A 86 ft 2 / day 43 344 360 Dry Scrape 4 154 46 86 ft 2 / day 43 344 360 Replace Exterior Door 2 32 7 84 ft2/ day 42 336 360

Trim Replacement 2 96 119 64 ft/ day 16 128 180 Demolition N/A 900 ft 3 / day 38 300 360



### The trim removal rate was taken for the RSMeans est imate for “Trim”, which gives a value of 64 ft/day. To estimate the square footage for a 4 pers on crew, it was assumed that the trim had a width of 6 inches and the RSMeans value was multipl ied by 4*0.5=2. This yielded an estimate of 128 ft2/day. This value was rounded to 180 ft 2 

### per day to ensure the modeled building panel could be finished within an increment of half a day

. For door removal, a value of 84 ft 2 

### /day was taken from the RSMeans manual. Assuming a four person crew, this yields 336 ft 2 

### /day. Again, this value was rounded to 360 ft2/day to ensure a single building block could be renovated in increme nts of whole days.
Finally, the demolition rate was taken from the RSM eans with a value of 900 ft 3 

### /day. To convert to ft2/day, it was assumed that the walls were 4 in ches thick, giving a value of 300 ft 2 

### /day. This value was rounded to 360 ft2/day to ensure a single building block could be renovated in increments of whole days.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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## D.1.2. Job Duration For each building size, the total job duration can be calculated using the number of blocks on the building, the fraction of the block being renov ated, and rate of renovation. The Approach assumes that the crew works an 8 hour shift each da y during the day and does not work at night. Then,
	 = 
 × 	 × 

 × 
 	 × 8	h

where:



### JobDuration = total duration of the renovation job (days) **NumBlock =** the number of “blocks” in the building being renovated **BlockSize =** the size of each building block (ft 2 

### ) **ComponentFrac =** the fraction of the building block that is made up of the component being renovated (e.g., trim) (ft 2 

### ) **RenoRate =** the surface area that can be renovated by a four person crew in an hour

In order to simplify the estimation of the job dura tions and limit the total number of AERMOD simulations, similar renovation rates were set equa l (as discussed above in Section D.1.2) and a block was defined as the area from which a crew cou ld prepare the painted surface in 2 hours, as discussed in Section C.1.1.



## D.1.3. Fraction of Paint Removed For the modeling Approach, a representative surface preparation fraction is used for all the different possible substrate types and removal meth ods. This fraction was estimated using information in the RSMeans manual (Mossman and Plot ner 2009). RSMeans indicates that full removal of all exterior lead paint by a four perso n crew can be performed at a rate of 45 square feet per hour, while surface preparation rates vary between 240 (wood shingles) and 344 (aluminum siding, steel siding, and gypsum board) s quare feet per hour for different substrates. To estimate the fraction removed during surface pre paration, it was assumed that removing a certain mass of paint can be done at a constant rat e, but the surface preparation takes less time because less total paint is removed compared with t otal lead remediation. Thus, the surface preparation rates were divided by the lead removal rate to estimate the fraction of paint removed during surface preparation. This yielded a range of 13 to 19%. A representative value of 15% was selected for the modeling Approach. Beca use this value is expected to vary across**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### different substrates and removal methods, the selec tion of this value introduces uncertainty in the modeling Approach.



## D.1.4. Fraction of Paint Emitted As Aerosol By Activity The fraction of paint emitted as an aerosol was est imated using several different data sources. The primary data source is EPA’s Dust Study (US EPA , 2007a), which is the most comprehensive study of lead dust generation by renovation activit ies to date. Other data sources were used to supplement the Dust Study as noted in each subsecti on below in order to include additional exterior renovation activity types.



### D.1.4.1. Jobs Included in the Dust Study The Dust Study was used to characterize the fractio n of lead on the wall that is emitted from the eight exterior renovation activities studied in the report: paint removal by dry scraping, power sanding, torching, use of heat guns, and use of a n eedle gun, and replacement of exterior doors and trim (US EPA 2007a). In the Approach, particles may be either bulk particles that are larger and remain closer to the renovated building or aero sol particles that are in the size class that can be transported away from the renovated building . Using the Dust Study data, the fraction of removed paint that becomes aerosolized is estimated for each of these activities, as described in Appendix H.
After the activities were processed, emission fract ions for both the interior Dust Study experiments and the exterior Dust Study experiment were combined. Because the Dust Study used vertical plastic to encase the job area, the c onditions were likely similar to interior jobs. And, because the emission fraction calculations tak e into account differences in the amount of lead in paint in interior and exterior building env ironments, the experiments were deemed similar enough that they could be combined across t he interior and exterior experiments to estimate an overall fraction. Thus, the power sandi ng (exterior) and door planing (interior) experiments were combined and the geometric mean ac ross all the different experiments was used to describe a “sanding” emission fraction. Sim ilarly, the geometric mean was taken across the experiments for all other activity types as wel l.
In addition, the heat gun experiments for both inte rior and exterior experiments were also combined. The interior calculations revealed that t he emission fraction from heat gun activities performed on plaster substrates was considerably hi gher than the fraction for activities performed on wood substrates. Thus, heat gun experi ments were divided into separate plaster and wood activities and the geometric mean across t he interior and exterior experiments across each was used for the emission fractions. In the Mo nte Carlo model, these two different substrate types were sampled assuming equal probabi lity of occurrence to get health effect estimates for a generic heat gun activity.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-5 July 25, 2 014 

### D.1.4.2. Jobs that Include Local Ventilation (HEPA filters) In addition to the jobs examined in the Dust Study, the Approach also considers activities where the use of local ventilation (e.g., a HEPA filter) may reduce the emission of lead dust. The Battelle 2009 memo entitled “Effectiveness of Power Tool Shrouds to Reduce Dust Dispersion (DRAFT)” provides a summary of references found dur ing a 2009 literature search that measured the reduction in airborne dust levels or e xposure levels after implementation of HEPA filters for a variety of sawing, sanding, and grind ing activities. The studies looked at effectiveness when a variety of different surfaces were disturbed, with many studies looking at grinding concrete or mortar. Most of these activiti es are expected to produce small (e.g., < PM2.5) particles similar to the power sanding, door planing, and needle gun activities in the Dust Study.
The results are fairly consistent, with most result s showing efficiencies in the low 90% range. In particular, one study that looked at cut off saws i n the construction industry (Thorpe et al 1999). found efficiencies of 90%. Figure D-1 summarizes th e findings by study, where the lowest efficiency captured in the study is chosen as repre sentative. The green bar shows the Thorpe et al. study. Based on this range, 90% is deemed an ap propriate efficiency for power sanding, door planing, and needle gun activities. This is not at the high end of the range but instead is chosen to be representative of efficiency during actual (r ather than hypothetical “best”) use of the LEV equipment.
Some of the studies also looked at implementation o f wet techniques compared to dry techniques and found a similar efficiency, so 90% i s also deemed appropriate for wet activities.

In order to include these activities that include l ocal ventilation, the estimated emission fraction was -multiplied by (1-efficiency) = 0.10.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-6 July 25, 2 014



### Figure D-1. Summary of HEPA Efficiency Data in the Literature



### D.1.4.3. Demolition Demolition of building walls was not one of the ren ovation activities included in the Dust Study. However, the literature review report submi tted to EPA on January 21, 2011 included sources that measured dust emission from demolition activities. Two studies, Beck **et al** . (2003) and Stefani **et al** . (2005), measured dust generation from the implosi on of large structures that were not expected to be analogous to small scale de molition of building walls. Mucha **et al**

. (2009) collected dust samples near sites where the demolition of homes was occurring. However, they did not provide information on the si ze of the demolitions or the proximity of the samplers to the demolition, so it was not possible to calculate an emission rate.

Farfel **et al** . (2003) tracked the lead dust fall rate as measure d at 10 m from the renovation during the demolition of two blocks of row houses i n Baltimore. In a subsequent paper (Farfel **et al** ., 2005), levels of settled lead dust collected 100 m from demolition sites were measured before, immediately after, and 1 month after demoli tion were presented.
By subtracting the pre-demolition lead dust level f rom the measurement taken immediately post-demolition, it was possible to determine the l ead dust loading that was attributed to the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-7 July 25, 2 014 

### demolition. Farfel **et al** . (2003) provides detailed information on the demol ition itself, including the vintage of the rowhouses (pre-1950), the dates of the demolitions (10/27/99 and 4/19/2000), the duration (8 hrs), the size (40 m x 50 m, 38 m x 46 m), number of houses demolished (26, 27), and concentration of lead in t he paint on the row houses (ranging from below detect to above the limit of quantification, 9.9 mg/cm 2 

### .)
Because measurements at only 10 m and 100 m were av ailable, the methodology used to calculate emissions for the Dust Study activities c ould not be applied. Instead, AERMOD was used to estimate an emission rate which would give loading values consistent with the measured values. An AERMOD run was conducted with a unit emission rate which characterized the source using the averaged source data provided by Farfel **et al** . (2003) and meteorological data from http://www.weatherunderground.com for the dates of the demolition. Receptors were placed 10 m and 100 m downwind of the demoliti ons. The hourly deposition rate was calculated at both 10 m and 100 m distances downwin d, and these values were summed in time to estimate the cumulative loading after nine hours (eight hours of demolition plus one hour of settling time) to be consistent with measurement me thodology in Farfel **et al** . (2003).
The values calculated with the AERMOD run were comp ared to the Farfel **et al** . (2003) values to back-calculate the emission rates. It was determin ed that the values measured at 10 m from the renovation were likely much too low. Because 10 m i s in the near-field range, particularly considering the extremely large source, it is likel y that there is a large amount of variability in these numbers. Because only a limited number of me asurements were collected, it was determined that the emission rate based on the 100 m data was more reliable.
The values calculated with AERMOD gave the emission rate in units of mg of lead per second consistent with the demolition of the row houses. H owever, for a building of different size with a different demolition rate and total square footag e and with different lead content in the paint, the lead emission rate per second will be different . The rate of demolition of the row houses was determined by estimating the exterior wall surf ace area of the row houses using data provided in Farfel **et al** . (2003.) This was estimated at 7,818 m 2 

### for the average surface area of building demolished (averaged across the two differ ent demolition events.) The total duration of demolition was 8 hrs, yielding a rate of exterio r wall demolition of 2.92 ft 2 

### /s. Dividing the lead dust emission rate in mg/s by the demolition rate i n ft 2 

### /s, results in a lead dust emission rate in units of mg/ft 2 

### exterior wall.
The lead loadings measured on different row houses varied widely, as such, the geometric mean lead loading for houses built between 1930 and 1949 , 3.77 mg/cm 2 

### , was used. (The determination of the geometric mean wall lead loadi ngs by building vintage is discussed in Appendix 2.1.1.4.) By dividing the amount of lead e mitted, on a per square foot basis, by the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-8 July 25, 2 014 

### amount of lead on the wall, on a per square foot ba sis, the aerosol fraction of lead emitted was estimated to be 0.009.



### D.1.4.4. Summary and Benchmarking of Results The resulting fractions of paint emitted as aerosol for each renovation activity is presented in Table D-2. Lee and Domanski (1999) examined the re lease of lead and abrasives during sandblasting operations to remove lead paint from b ridges. They estimated that 9 percent of the lead on the bridge could be released as aerosol due to sandblasting. The emission fractions in this Approach tend to be on the order of this estim ate. Power sanding without HEPA, needle gun without HEPA, and torching all predict emission fractions higher than 9%. Overall, the Lee and Domanski (1999) study compares favorably with t he magnitudes of the emission fractions estimated here.

Table D

- 2

. Fraction of paint on the wall which is emitted as a erosolized particulate.
Job Type Fraction of Paint Aerosolized Power Sanding, without HEPA 

### 0.112 Power Sanding, with HEPA* 

### 0.011 Needle Gun, without HEPA 

### 0.244 Needle Gun, with HEPA* 

### 0.024 Torching 

### 0.180 Heat Gun, Plaster 

### 0.057 Heat Gun, Wood 

### 0.012 Dry Scrape 

### 0.038 Wet Scrape* 

### 0.0038 Window/Door Replacement 

### 0.069 Trim Replacement 

### 0.070 Demolition 

### 0.009 *These emission fractions are estimated using effic iencies applied to the activity above them, as discussed in Section D.1.6


## D.1.5. Fraction of Paint Emitted as Particulate Debris by Activity Small particulate removed during the renovation wil l be emitted as aerosolized particles. Larger bulk particles will fall to ground during the renov ation. The bulk particles themselves, however, may have a wide range of sizes. The largest of thes e, referred to here as “bulk debris,” are easily seen and picked up, and these will likely be remove d during post-job clean-up. In contrast, smaller bulk particles, referred to here as “partic ulate debris,” will be more difficult to see and clean. Particulate debris may remain on the ground after the job and be available for tracking indoors, depending on the use of horizontal plastic and the efficiency of the plastic in trapping the particulate.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-9 July 25, 2 014 

### Lee and Domanski (1999) evaluated the removal of le ad paint from bridges due to sandblasting and the subsequent release of particles. They estim ated that 9 percent of the lead in the paint on the bridge could be released as aerosolized part icles. In addition, of the bulk material generated, an additional 6 percent would have an ae rodynamic diameter less than 50 μ m.
In this Approach it was assumed that the amount of particulate debris available for track-in corresponded to this “less than 50 μ m” size class. In the absence of further informatio n about this fraction in other paint removal activities, th e 6 percent value was applied to all the renovation activities. To estimate the fraction of total paint on the wall released as particulate debris, the following equation was used:

where:



### PartDebFrac = fraction of paint on the wall emitted as particulat e debris **AerosolFrac =** fraction of paint on the wall emitted as aerosol
The final particulate debris fractions by renovatio n activity are shown in Table D-3.

Table D

- 3 . Fract ion of paint on the wall which is emitted as bulk particulate.

Job Type Fraction of Paint Emitted As Particulate Debris Power Sanding, without HEPA 

### 0.0593 Power Sanding, with HEPA 

### 0.0533 Needle Gun, without HEPA 

### 0.0585 Needle Gun, with HEPA 

### 0.0454 Torching 

### 0.0492 Heat Gun, Plaster 

### 0.0566 Heat Gun, Wood 

### 0.0593 Dry Scrape 

### 0.0577 Wet Scrape 

### 0.0598 Window/Door Replacement 

### 0.0559 Trim Replacement 

### 0.0558 Demolition 

### 0.0595 

### 06.0 ) 1( ×

− = c AerosolFra c PartDebFra**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-10 July 25, 2014 

## D.1.6. Containment Efficiencies As discussed in the main Approach document, both ho rizontal and vertical plastic were included as exposure control options. To estimate the horizo ntal containment efficiency, the Dust Study data for each activity type were used to find the r atio of the loading under the plastic to the loading on top of the plastic, as shown in Table D- 4. An average across all activities, or an efficiency of 0.95, was used as a representative ho rizontal plastic efficiency.

Table D

- 4 . Horizontal containment efficiency.

Renovation Activity Loading on

top of plastic ( μ g/ft 2 ) Loading

underneath plastic ( μ g/ft 2 ) Efficiency

Door Replacement 32653 32 0.999 Trim Replacement 44474 648 0.985 Dry Scrape 12004 89 0.992 Needle Gun 7713 270 0.966 High Heat Gun 37431 3149 0.922 Power Sanding 111388 4896 0.957 Torching 1 5447 8565 0.388 Low Heat Gun 18000 388 0.978

1 Torching data were assumed to be anomalous and not included in calculations.


### As discussed in the main document, fewer data were available to calculate the efficiency of dust captured by vertical containment. Iyiegbuniwe **et al.** (2006) presented data for the efficiency of containments used during bridge de-leading by abras ive blasting and found an efficiency of 91% for lead particles. In the absense of additional da ta, this value was used for the vertical containment efficiency for all renovation activitie s.


# D.2. Interiors In this Approach, the exposure to lead emitted duri ng interior renovation activities are characterized by the amount of lead emitted, the ar ea over which the lead spreads, and the amount of time the lead loading remains elevated. T he variables describing these two variables include floor area in the job area, the fraction of paint removed, the overall building size, the rate of renovation, the fraction of removed paint t hat remains as lead dust, the efficiencies of any control options used to mitigate the dust, and the relative exposure from floors versus window sills.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-11 July 25, 2014 

## D.2.1. Job Intensity The job intensity describes the fraction of the wal l surface area (painting jobs and cutouts jobs), the fraction of the cabinets/shelves (cabinet remov al jobs), or the fraction of the windows (window replacement jobs) in the room that are affe cted by the renovation.
For all jobs, a low, middle, and high intensity wer e selected. For painting, cabinetry, and windows, representative values of 15%, 50%, and 100 % are the representative intensity values. In addition, a very low case was added for painting to account for jobs that just require small paint removal and touch up work within a larger roo m. For this case, a value of 5% was selected.
For cutouts, it was more difficult to determine wha t an appropriate intensity range would be. The Approach assumes this activity covers renovatio n activities involving electrical receptacles and lighting fixtures as well as larger jobs involv ing creating new windows/doors or performing HVAC work. An internet and literature search was co nducted to find construction standards for light fixtures, thermostats, wall outlets, plumbing access, HVAC ductwork, windows, and doors. Very little information on construction standards f or these indoor construction additions was found.
However, standards from the International Code Coun cil and the U.S. Government GSA present minimum requirements and provide some guidance that was used to inform the Approach assumptions. Based on the information found for lig hting and power receptacle requirements, calculations were made to estimate the fraction of total wall square footage needed to rewire or relight a room. The estimates for lighting are assu med to capture the fraction of the ceiling area, but they are converted to an equivalent wall area f or ease of implementation in the Approach. For lighting, the fraction is between 0.004% to 0.2 % using the GSA ranges of required power density and the example lighting fixture sizes foun d in an internet search. For receptacles placed every 6 ft (a conservative assumption based on requ irements for a GSA “communication room”), the fraction of wall is 0.2%. Thus, cutouts for ins talling electrical outlets or ceiling lighting are expected to be less than 1% of the total wall area. Extensive information on cutout sizes for HVAC work could not be found, so an upper range of 10% of wall space was assumed. Comparing this range (< 1% to 10%) with the intensi ties for the other jobs, the assumption was made that the cutout intensities were 1/10 of the i ntensities in the other activities. This yielded low, middle, and high estimates of 1.5%, 5%, and 10 %.
The range of intensities are shown in Table D-5 In the Approach, renovation scenarios may include one or more job activity types. For the pur poses of calculating the fraction of components disturbed during the renovation, the ass umption was made that the entire job was either “low”, “middle”, or “high”. For painting job s only, the “very low” option was also included.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Table D-5. Job Intensities Describing Fraction of R oom Components Disturbed During Renovation

Activity Type Very Low Low Middle High

Paint removal 5% 15% 50% 100%
Cabinetry/shelves N/A 15% 50% 100%
Cutouts N/A 1.5% 5% 10%



## D.2.2. The Fraction of Paint Removed In addition to the intensity (fraction of wall area affected), the paint removal jobs also need the fraction of paint layers on the wall that are remov ed during the renovation. In preparing to paint, the crew may only prepare the surface by rem oving the degraded top layers of paint or they may remove the paint down to the substrate. To represent surface preparation, a fraction removed of 15% was selected based on the analysis d escribed in Appendix D.1.3. In addition, paint removal fractions of 50% and 100% were includ ed for each paint removal renovation job to cover the range of expected removal fractions.



## D.2.3. The Job Area and Exposure for Different Occupants Within the room being renovated, the effective job floor area over which the lead dust spreads may be equal either the entire room (for smaller ro oms and larger jobs) or only a portion of the room (for larger rooms and smaller jobs). To estima te the effective spread of the lead dust, the Dust Study data were examined. As described in Appe ndix I, experiments where the job area could be identified were used to estimate regressio n equations. These equations describe the exponential decrease in lead loading with distance from the job wall. These regression equations were examined to find the typical distanc e where the renovation-related lead loadings reach 5% of their value just next to the j ob wall. Based on this analysis, a representative value of 10 ft was selected to describe the length of a job out from the wall.
Then, for each renovated room size, the job area wa s estimated using the following equation, which calculates the floor area in the job area ass uming the length of the room is twice the width and making sure not to double count corners:

 = 	

	 × !	"h #6 × %
 2 − 4 × !	"h)

where:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-13 July 25, 2014 

### JobArea = Floor area next to the job walls over which the lea d dust spreads **intensity =** The fraction of the room components being renovated **jobwidth =** The length of the job area out from the job wall **RoomArea =** The area of the room being renovated

After estimating this job area, the job area was co mpared with the overall room area for the different room sizes and job intensities in the int erior analysis, as shown in Table D-6. For the job area to be considered different from the room a rea, a threshold of 3% was applied to the ratios. In these cases, it was deemed reasonable th at the job could be thought of as an isolated “room within a room” compared with the remainder of the room area. Based on this threshold, in any cases where the job intensity was 5% and the room size was greater than or equal to 3800 ft 2 

### , the job size was considered to be smaller than th e room size and the activity was treated as a “room within a room” scenario (as show n with the shading in Table D-6). Actual conditions where a “room within a room” scenario wo uld be present are unknown, but could be adjusted at a later time if additional information should become available suggesting either a higher or a lower threshold (i.e. 2%, 4%, 6%, 8%, o r 10%).
Table D-6. Comparison of Job Size to Floor Size

Room Floor Area (ft 2 ) Room Width (ft) Room Length (ft) Job Size 5% Ratio Job to Floor Size Job Size 25% Ratio Job to Floor Size Job Size 50% Ratio Job to Floor Size
60 5.5 11.0 3.0 5.0% 15.0 25.0% 30.0 50.0%
200 10.0 20.0 10.0 5.0% 50.0 25.0% 100.0 50.0%
300 12.2 24.5 16.7 5.6% 83.7 27.9% 167.4 55.8%
600 17.3 34.6 32.0 5.3% 159.8 26.6% 319.6 53.3% 1,300 25.5 51.0 56.5 4.3% 282.4 21.7% 564.9 43.5% 1,800 30.0 60.0 70.0 3.9% 350.0 19.4% 700.0 38.9% 3,800 43.6 87.2 110.8 2.9% 553.8 14.6% 1107.7 29.1% 3,900 44.2 88.3 112.5 2.9% 562.4 14.4% 1124.8 28.8% 6,200 55.7 111.4 147.0 2.4% 735.2 11.9% 1470.3 23.7% 8,400 64.8 129.6 174.4 2.1% 872.1 10.4% 1744.2 20.8% 9,100 67.5 134.9 182.4 2.0% 911.8 10.0% 1823.6 20.0% 12,200 78.1 156.2 214.3 1.8% 1071.5 8.8% 2143.1 17.6%


### For these “room within a room” scenarios, exposure was estimated for two different theoretical occupants, as shown in the right panel of Figure D- 2; in some cases, some occupants will be in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-14 July 25, 2014 

### the actual job area (either during or after the ren ovation event), so exposure is estimated using the lead dust emitted divided by the job area.

*"	*"	
+ ,-.	/01/	-2234/56 

### = 71/89:;6618 <-.=01/ 

### Equation D-1
Other occupants may be in the remainder of the roo m and have a relatively small interaction with the renovated-related lead dust. In these case s, the rest of the room is treated as if it were an adjacent room, so these occupants are exposed to the lead dust emitted divided by the rest of the room area and multiplied by the “adjacent fa ctor”.
*"	*"	
+ 01>6?-@?0--:	-2234/56 

### = 71/89:;6618 A1>6-@A--:=01/ 

### × "  Equation D-2
In cases where the job area is the full room area ( left panel of Figure D-2), exposure for only a single work room occupant is estimated:

*"	*"	
+ B-0C	0--:		-2234/56 

### = 71/89:;6618 A--:=01/ 

### Equation D-3
The location of occupants included in the Approach are:

 

### In work room, allowed in workspace throughout renov ation- exposure begins at the beginning of renovation activity

 

### In work room, not allowed in workspace throughout r enovation- exposure begins after the renovation activity

 

### In an adjacent room- exposure begins at the beginni ng of renovation activity Loadings are estimated for each occupant location i n a particular renovated building scenario.
*"	*"	
+ /8,/2156	0--:	-2234/56 

### = 71/89:;6618 =8,/2156A--:=01/ 

### × "  Equation D-4
The full range of exposed occupants is shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-15 July 25, 2014 

### Table D-7.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-16 July 25, 2014



### Table D-7. Different Room Occupants During Dust-Gen erating Phase
Allowed in work area during renovation ? Work room occupants Adjacent occupants Work area = room area Work area <> Room Area

Allowed - Work room occupant EQ D3 - Job area occupant EQ D-1 - Rest of room occupant EQ D-2 Adjacent occupant EQ D-4 Not Allowed - Work room occupant Exposure = 0 - Job area occupant Exposure = 0 - Rest of room occupant EQ D-2



### Small Room, Intensity = 5%: Job Area = Room Area Large Room, Intensity = 5%: Job Area < Room Area “Room within a Room”
Figure D-2. Diagram of work area with the renovated room.



## D.2.4. Mapping Interior Building Types to Exterior Buildin gs The exterior analysis covers receptors downwind fro m a renovated building. However, the exterior renovation can also affect occupants of th e renovated building itself. If interior and exterior renovations are going on simultaneously, t he occupants may be exposed to lead dust levels elevated by both exterior and interior renov ations.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-17 July 25, 2014 

### Thus, each interior scenario is simulated three way s:

 

### Assuming no exterior job is happening simultaneousl y,

 

### Assuming a “low” exterior job is happening simultan eously, and

 

### Assuming a “high” exterior job is happening simulta neously. For jobs where an exterior renovation is happening, the contribution is added in two different ways:

 

### The renovation-only dust loading value estimated fr om an exterior simulation is added in to the post-renovation loadings after the interior job , and

 

### The track-in of renovation-only soil lead is includ ed throughout the remainder of the interior simulation. Thus, representative low and high soil and dust lev els are needed to represent these exterior jobs. A subset of the full exterior simulations wer e examined and the 25 th 

### and 75 th 

### percentile values at the 0ft receptor were calculated to repre sent these low and high estimates, as shown in Table D-8. Table D-8. Representative Exterior Renovation-Only Dust and Soil Values for Use in the Interior Analysis Low (25 th percentile) High (75 th percentile) Dust ( μ g/ft2) Soil ( μ g/g) Dust ( μ g/ft2) Soil ( μ g/g)
6.3 

### 3.9 62.9 27.5
As in the exterior analysis dust model, the track- in loading contribution is estimated as



## ea BuildingAr MatFrac

te TrackingRa en PbSoilConc Loading racking T Pb 

## ) 1(

− × ×

=

where: 

### TrackingLoading = accumulation of tracked-in lead loading on the floor in a day (μg/ft 2 

### -day) **PbSoilConcen =** concentration of lead in the tracked-in soil (μg/g) **TrackingRate =** rate at which particulate is deposited on front mat s (g/day) **MatFrac =** fraction of total tracked material which is deposited on the front mat (as opposed to the remainder of the house) (unitless)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-18 July 25, 2014



### BuildingArea = the total floor area of the building over which the lead is tracked (ft 2 

### ) In order to implement this equation, the total buil ding square footage is needed; however, for the interior Approach, only three rooms of a buildi ng are modeled at a time based on the assumption used for this modeling approach that lea d dust does not travel further than one room away from the renovated room.
To estimate the total building square footages, the interior building types were mapped to the exterior building model sizes (F1T1, F1T2, F1T3, F2 T1, F2T2, F2T3, F3T1, F3T2, or F3T3). The CBECS data (US EIA, 2003) were used to estimate the fraction of total buildings in each building category that were in each size class. Then, these probabilities were cumulative summed to get the cumulative probability distribution, as shown i n **Table D-9** . In the Monte Carlo code, a random number is used to select a building size for each building type for each iteration where an exterior job is included.
Table D-9. Probability Distribution for Interior Bu ilding Types Mapped to Exterior Building Sizes

Buildings that use Layout Cumulative Probability
F1T1 F2T1 F3T1 F1T2 F2T2 F3T2 F1T3 F2T3 F3T3 SIZE (total floor area in ft 2 ) 1,296 5,184 20,736 2,592 10,368 41,472 3,888 15,552 139,968 1 Office, outpatient healthcare, and public order/safety 0.25 0.38 0.50 0.63 0.75 0.82 0.88 0.95 1.00 2 Warehouse, food sales, religious worship, and public assembly 0.05 0.25 0.38 0.50 0.75 0.82 0.88 0.95 1.00

3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 0.05 0.25 0.38 0.50 0.75 0.82 0.88 0.95 1.00

4 Education 0.05 0.25 0.38 0.50 0.58 0.67 0.75 0.95 1.00 5 Lodging, Nursing, inpatient health care, and laboratory 0.05 0.12 0.18 0.25 0.38 0.50 0.62 0.75 1.00



## D.2.5. Rate of Renovation – Dust Generating Phase As discussed in Appendix D.1.2, the RSMeans Facilit ies Construction Cost Manual (Mossman and Plotner, 2009) was used to determine the average ra tes for the renovation activities. The interior analysis utilizes the same surface prepara tion, window removal, and demolition as used in the exterior analysis. The values are repeated i n Table D-10.
Additional activity rates are needed for the interi or analysis. For the cabinet/shelf removal activity, the RSMeans “minor building deconstructio n, kitchen cabinets” entry was used. This**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-19 July 25, 2014 

### value is 18 linear feet for a 2 person crew per day . Assuming 3 ft high cabinets, this corresponds to a rate of 108 ft 2 

### /day.
For the cutouts activity, the RSMeans “receptacle a nd switch plates, electrical demolition, remove” entries were averaged. The units in RSMeans are “output per day” assuming one worker. Assuming the average receptacle is 0.12 ft2 , this gives an average rate of 4.1 ft 2 

### /day for a two person crew.

Table D-10. Rates of Dust-Generating Phase in Inter ior Analysis

Activity Type Rate for a Four Person Crew (ft 2 /day) Paint removal 360
Window Removal 336 Cabinet/shelf removal 108
Cutouts 4
Demolition 360



### The duration of each activity is then estimated as:

	D3>6E1510/6;5F 

### = 

	 × G

	 

where: 

### Duration **DustGenerating** 

### = Duration of the dust-generating phase of the renovation (days) **ComponentArea =** The area of the component being renovated (wall, cabinet, or window) **Intensity =** The fraction of the room components being renovated **Rate =** The rate of renovation (ft 2 

### /day)
After the duration is estimated for each separate r oom and each separate renovation activity, they are summed to give the total duration for the job.



## D.2.6. Rate of Renovation – Rest of Renovation Phase After the dust-generating phase is complete, the Ap proach assumes the crew proceeds with the rest of the renovation, including painting, install ation, etc. To estimate the rest of renovation duration, for each activity, the dust-generating ac tivity was paired with a complementary restof-renovation activity from RSMeans.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-20 July 25, 2014 

### For paint removal, RSMeans interior Paintings and C oatings activities were averaged to get a rate of 1,340 ft 2 

### /day for one worker. This gives a corresponding est imate of 5,360 ft2/day for a four person crew.
For windows, the RSMeans entries for “Windows, repl acement” were averaged to give an estimate of 7.2 windows per day for a two person cr ew. Assuming a window size of 10.24 ft2 as in other parts of the Approach, this gives a total of 148 ft 2 

### /day for a four person crew.

For cutouts, the RSMeans entries for installation o f “Receptacle Devices” were averaged to give a value of 9.0 receptacles per day for a one person crew. Again assuming a square footage of 0.21 ft2 for each receptacle, this gives an estimat e of 4.3 ft 2 

### /day for a four person crew.

For cabinets, the RSMeans entries for “Custom Cabin ets” were averaged to give a value of twenty cabinets per day for a two person crew. Usin g the accompanying cabinet dimensions in the RSMeans entries, this gave an average rate of 2 39 ft 2 

### /day for a four person crew.
For demolition, a general “rebuild” activity rate w as estimated using all the other rest-ofrenovation rates averaged with additional RSMeans e ntries for “Framing” (373 ft 2 

### /hr) and “Drywall installation” (70 ft 2 

### /hr). Averaging all the rates gave an overall estim ate of 1,776 ft 2 

### /day for a four person crew.
Each dust-generating and rest-of-renovation activit y with the corresponding estimate rates is shown in Table D-11. The rest-of renovation rates w ere divided by the dust-generating rates and rounded to yield the final “divisor” in the table. To estimate the rest-of-renovation duration, the dust-generating duration is divided by this number:

	A1>6-@A15-H/6;-5 

### = 	D3>6E1510/6;5F

### 	I	

=26;H;6J



### where: 

### Duration **RestofRenovation** 

### = Duration of the rest-of-renovation phase of the renovation (days) **Duration DustGenerating**  

### = Duration of the dust-generating phase of the renovation (days) **Divisor**

**Activity** 

### = The activity-specific divisor to relate the two

durations

As with the dust-generating durations, the rest-of- renovation durations are estimated separately for each room and each activity and then summed to give the total duration.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Table D-11. Rates of Rest-of-Renovation Phase in In terior Analysis

Dust-Generating Activity Type Rate for a

Four Person Crew (ft 2 /day) Rest-of-Renovation

Activity Type Rate for a

Four Person Crew (ft2/day) Divisor for

Rest-ofRenovation Duration Paint removal 360 Painting 5,360 15
Window Removal 336 Window Installation 148 0.5 Cabinet/shelf removal 108 Cabinet Installation 239 2 Cutouts 4 Receptacle Installation 4 1 Demolition 360 Rebuilding 1,776 5


## D.2.7. Fraction of Paint Emitted

The fraction of paint emitted as lead-containing du st was estimated using several different data sources. The primary data source is EPA’s Dust Stud y (US EPA, 2007a), which is the most comprehensive study of lead dust generation by reno vation activities to date. Other data sources were used to supplement the Dust Study, as noted in the subsections below, in order to include additional interior renovation activity typ es.



### D.2.7.1. Jobs Included in the Dust Study The Dust Study was used to characterize the fractio n of lead on the wall that is emitted from the six interior renovation activities studied in the r eport: paint removal by dry scraping, door planing, and use of heat guns; window removal; cabi net removal, and cut outs (US EPA 2007a). Using the Dust Study data, the fraction of removed paint that remains as small floor dust particulate is estimated for each of these activiti es, as described in Appendix I.
After the activities were processed, emission fract ions for both the interior Dust Study experiments and the exterior Dust Study experiment were combined. Because the Dust Study used vertical plastic to encase the job area, the c onditions were likely similar to interior jobs. And, because the emission fraction calculations tak e into account differences in the amount of lead in paint in interior and exterior building env ironments, the experiments were deemed similar enough that they could be combined across t he interior and exterior experiments to estimate an overall fraction. Thus, the power sandi ng (exterior) and door planing (interior) experiments were combined and the geometric mean ac ross all the different experiments was used to describe a “sanding” emission fraction. Sim ilarly, the geometric mean was taken across the experiments for all other activity types as wel l.
In addition, the heat gun experiments for both inte rior and exterior experiments were also combined. The interior calculations revealed that t he emission fraction from heat gun activities performed on plaster substrates was considerably hi gher than the fraction for activities**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### performed on wood substrates. Thus, heat gun experi ments were divided into separate plaster and wood activities and the geometric mean across t he interior and exterior experiments across each was used for the emission fractions. In the Mo nte Carlo model, these two different substrate types were sampled assuming equal probabi lity of occurrence to get health effect estimates for a generic heat gun activity.
Finally, inspection of the window removal experimen ts revealed a bimodal pattern, with a group of high emitting experiments and a group of lower e mitting experiments. Based on the experiment notes provided in the Dust Study, the hi gher emitting experiments used saws to remove the window, while the lower emitting experim ents used a crowbar or other method to remove the window. Thus, the activity was broken in to two separate activities: window, with saw and window, no saw. In the Monte Carlo model, e ach was assumed to occur with equal probability in a given window removal iteration.



### D.2.7.2. Jobs that Include Local Ventilation (HEPA filters) In addition to the jobs examined in the Dust Study, the Approach also considers activities where the use of local ventilation (e.g., a HEPA filter) may reduce the emission of lead dust. As described in Appendix D.1.4.2, a HEPA efficiency of 0.9 was used in the Approach. In the interior analysis, it was applied to the door planing activi ty.



### D.2.7.3. Demolition Demolition of building walls was not one of the ren ovation activities included in the Dust Study. However, the EFSS documents renovation activ ities performed in houses and the associated loadings (US EPA 1998). The report provi des nearly all the information needed to estimate the fraction of emitted paint that lands a s small dust particulate. As stated in the report, the demolition experiments were part of the Controlled Experimentally Designed (CED) experiments that were completed in row houses in Ba ltimore and single-family dwellings in Denver. The EFSS measured both the personal air mon itoring concentrations of the workers and the floor loadings after the experiment was complet ed. Then, an exponential distribution of the loading away from the demolished wall as a function of distance was estimated, and the resulting equations was used to estimate the total amount of lead within 6 feet of the wall.
This equation was then further adjusted in the EFSS report so that it represents the loading from a standardized work area within the room where demo lition experiments were conducted. The average amount of lead in the surface substrate in the demolished rooms was also specified in μ g/cm 2 

### . So, then, to get the fraction released as:

K 1:;6618 

### = *"	
+	 × L

L × M × N
	I

where:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-23 July 25, 2014



### frac **emitted** 

### = The fraction of paint removed from the wall that

remains on the floor as small dust particles **Loading =** The average lead dust loading on the floor ( μ g/ft 2 

### ) **WorkArea =** The work-area of the renovated room (ft 2 

### ) **WallArea =** The wall area of the renovated room (ft 2 

### ) **XRF =** The mass of lead on the wall per unit area ( μ g/cm 2 

### ) **UnitConv =** A term to convert from cm 2 

### to ft 2


### Unfortunately, the EFSS does not provide detailed i nformation about the definition of the “unit” room or about how exactly the conversion was done f rom experimental results to results for loadings presented in table 8C-10. Thus, the “Work **Area** ” and “ **WallArea** ” variables are not directly available in the EFSS. The loadings shown in 8C-3, and estimated in EFSS Table 8C-10 are representative of the six foot gradient work ar ea adjacent to the demolition activity.

It was also assumed that the rooms where the demoli tion activities took place were typical residential rooms with 8 foot ceilings of rectangul ar shape, 14 feet wide by 28 feet long. In determining the fraction emitted, the same answer i s calculated whether you assume the 14 foot wall or the 28 foot wall was demolished.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Figure D-3. Copy of EFSS Figure 8C-3 showing the le ad dust emitted as a function of distance from the wall for each activity.



### Variable Value for 14

ft Wall Value for 28

ft Wall Source

**Loading** 3,250 μ g/ft 2 

### 3,250 μ g/ft 2 

### EFSS Table 8C-10, converting to μ g/ft 2



### Work Area 84 ft 2 

### 168 ft2 84 ft2 = 14 ft. multiplied by 6 ft. 168 ft2= 28 ft. multiplied by 6 ft.

**WallArea** 104,048 cm 2 

### 208,096 cm2 104,048 cm2= 14 ft multiplied by 8 ft multiplied by 929 cm2/ft2 208,096 cm2= 14 ft multiplied by 8 ft multiplied by 929 cm2/ft2

**XRF** 840 μ g/cm 2 

### 840 ug/cm2 Geometric Mean from EFSS Table 8C-11

**frac emitted**  

### 0.0031 0.0031 Equation

So, the estimated fraction of lead on a wall that i s emitted as lead dust when the wall is demolished is 0.0031 (or 0.31%).



### D.2.7.4. Summary and Benchmarking of Results The resulting fractions of removed paint that remai n on the floor as lead dust for all the interior activities are shown in Table D-12. Activities usi ng a saw or power sander emit the most lead dust, while demolition and cutout jobs emit the lea st.

Table D-12. Fraction of paint removed that remains as small dust particulate.

Job Type Fraction of Paint Aerosolized
Window, With Saw 0.2137 Door Planing, without HEPA 0.1118 Heat Gun, Plaster 0.0572 Dry Scrape 0.0380 Window, No Saw 0.0155 Heat Gun, Wood 0.0119 Door Planing, with HEPA 0.0112 Cutouts 0.0056 Cabinet Removal 0.0030 Demolition 0.0031**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-25 July 25, 2014 

### As mentioned above, the different experiments in th e Dust Study (US EPA 2007a) were aggregated for a single activity type by taking the geometric mean across all experiment types. As a quality assurance step, these geometric means were used to try to back-calculate the loading in each room. Then, the estimated loading w as compared with the mean measured loading and the range of measured loadings in the r oom.

Figure D-4 shows the actual loadings versus the pre dicted loadings for the interior Dust Study experiments. Owing to the wide spread in the values , the graph is shown in log scale. The graph reveals there is no systematic bias between the pre dicted and experimental loadings. The overall R 2 

### (estimated using the raw data and not using a natu ral log transformation) is 0.61. In addition, of the 60 interior experiments, 50 of the predicted mean loadings lay within the overall range of loadings captured in the different sample trays for each experiment. The remaining 10 were examined, but no single reason was apparent fo r the over or under prediction of the loadings as they were spread across different types of renovation activities and experiment conditions. Overall, this quality assurance step in dicated the estimated emission fractions were reasonable and appropriate based on the jobs perfor med in the Dust Study.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Figure D-4. Actual Experimental Mean Loadings from the Dust Study as a Function of the Predicted Mean Loadings using the Geometric Mean Estimates of Fraction Emitted, Log-log scale


## D.2.8. Containment Efficiencies The Dust Study (US EPA 2007a) considered three diff erent control options to mitigate the occupant exposure to lead dust:

 

### Horizontal plastic in the work room, covering the f loor and removed after the renovation job,

 

### Vertical plastic over the doorways in the work room , isolating the room from the remainder of the building, and

 

### Wet mop cleaning with an additional verification st ep to ensure lead dust was removed to acceptable levels. These different activities were grouped into four d ifferent options:

1. Verification cleaning with horizontal and vertical plastic, 2. Non-Verification cleaning with horizontal and verti cal plastic, and 3. Verification cleaning without horizontal or vertica l plastic, 4. Non-Verification cleaning without horizontal or ver tical plastic. In the Approach, horizontal and vertical plastic ar e isolated from each other by assuming that horizontal plastic only affects the work room and v ertical plastic only affects the adjacent room. In addition, the cleaning step only affects the wor k room. Thus, control efficiencies for both the work room and adjacent room need to be calculated f or each of the four Dust Study control options. By comparing across two different options, the Approach can isolate the effects from either verification cleaning, horizontal plastic, o r vertical plastic.



### D.2.8.1. Work Room Control Option Efficiencies
To estimate the work room control option efficienci es, the “post work” and “post cleaning” room-average lead loading results from the Dust Stu dy were compared for each experiment. The post cleaning value represents the value after the room has been cleaned (non-verification or verification cleaning) and the horizontal plasti c has been removed (if present). The ratio of the “post cleaning” and “post work” values gives an efficiency factor for the cleaning (mitigation) step for each experiment.

In addition to this data source, an additional data source was located. Grinshpun et al. (2002) looked at different control options after different renovation activities in a chamber study. The authors were contacted and the original data from t he experiments were obtained. Air concentrations were continuously measured in a smal l chamber after doors containing lead based paint were either sanded for dry scraped. Ai r concentration values were converted to approximate loading values using the equation:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### *" ≈ 	 ×  × PQ	 × 	R	+h × N I

where: 

### FLOORLOAD **Pb** 

### = Floor lead loading at the end of the experiment (μg/m 2 

### ) **INAIRCONC**

**Pb** 

### = indoor concentration of lead in air during

experiment (μg/m 3 

### ) **DustDep =** deposition rate (h -1 

### ) **ExperimentDuration =** duration in hours of the experiment (h) **CeilingHeight =** height of the ceiling in the renovated room (m)
To estimate the deposition rate, a data source incl uding both air concentrations and dust loadings was used to optimize the depositions for d ifferent activity types. The Dust Study air concentrations were used to estimate the dust loadi ngs using the above equations. For each activity type, the depositions were then optimized to give the least mean square error between the predicted and actual mean floor loadings. This gave depositions of 0.09 hr -1 

### for sanding activities and 4 hr -1 

### for scraping activities.
These values were applied to the Grinshpun experime nts and the loadings were estimated. Estimated loadings agreed well with mea sured loadings taken at the corresponding time step. Then, the control options were mapped to the closest Dust Study control option, and the Grinshpun et al. (2002) efficiencies were estim ated.
Finally, the geometric mean across the different ef ficiencies for each control option for the aggregated Grinshpun et al. and Dust Study data wer e estimated. This gave final control option efficiencies shown in Table D-13. All the different work practice activities provide high efficiencies (>94%), although these efficiencies ar e applied to very large post-work loading values. Thus, even small differences in the efficie ncy factors can lead to large differences in exposure after the renovation job is complete.

Table D-13. Work Room Control Option Efficiencies.

Control Option Efficiency
Verification cleaning with horizontal plastic 0.993 Baseline cleaning with horizontal plastic 0.956 Verification cleaning without horizontal plastic 0.9 75 Baseline cleaning without horizontal plastic 0.943**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### D.2.8.2. Adjacent Room Control Option Multipliers For the adjacent room control options, only the Dus t Study (US EPA 2007a) data were used. In this case, the post-work values in the “observation room” and the work room were compared. The ratio was estimated for each experiment in each Dust Study control option and then the average across the experiments was calculated for e ach control option. Because the ratio was estimated before any cleaning practice was performe d, the difference between verification and non-verification cleaning in the work room had no e ffect. Thus, the final ratios were estimated by taking the geometric mean across control options 1 and 2 (both with vertical plastic) and control options 3 and 4 (both without vertical plas tic. The resulting adjustment factors, summarized in Table D-14, are applied to the post-w ork work room loadings to estimate the associated adjacent room loadings.
Table D-14. Adjacent Room Adjustment Factors

Control Option Efficiency
With vertical plastic 0.0045 Without vertical plastic 0.0047


## D.2.9. Exposure from Window Sills In the Dust Study, samples were taken both on the f loor and on the window sill after renovation experiments (US EPA 2007a). The window sills loadin gs tended to be much higher than the floor loadings, suggesting exposure to the window sills s hould be included in the exposure analysis.
First, the ratio between the sill loading and the f loor loading in the post-work experiment phase was estimated for each Dust Study experiment. The a verage across all the different experiments gave a multiplier of 3.86. Thus, in the Approach, t he sill loadings are estimated by multiplying the rest-of-renovation loadings by 3.86.
No literature exists describing the relative ingest ion from window sills versus floor dust for either children or adults. In lieu of this informa tion, the estimated ratio between the typical window sill surface area and the floor area of publ ic and commercial buildings was used. Assuming that the room is 20% glass (see Appendix C .2.5) and that the windows are of constant height, then the windows extend over 20% of the per imeter. Assuming 4” sills and varying the overall square footage of the room from 200 to 10,0 00 ft2, the sill to floor ratio is between 0.2% and 2%. Thus, a representative value of 1% was sele cted, and it was assumed the exposure ratio between sill and floor is the same as the ratio in square footage. Thus, the total loading exposure is estimated as:

*"	
+ STU /H1 

### = K @V--0 

### × *"	
+ @V--0 

### + K >;VV 

### × *"	
+ >;VV**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

Peer Review Draft D-29 July 25, 2014



### *"	
+ STU /H1 

### = 0.99 × *"	
+ @V--0 

### + 0.01 × 3.86 × *"	
+ @V--0



### where:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Loading **exp ave** 

### = Average lead dust loading the person is exposed to in the renovated building (μg/ft 2 

### ) **Loading**

**floor** 

### = Lead loading on the floor in the renovated building

(μg/ft 2 

### ) **Loading sill**  

### = Lead loading on the window sills in the renovated

building (μg/ft 2 

### ) **frac floor**  

### = The fraction of the ingestion of dust that is from

floors **frac sill**  

### = The fraction of the ingestion of dust that is from

sills



# D.3. References for Appendix D Beck C; Geyh A; Srinivasa A; Breysse P; Eggleston P ; Buckley T. (2003). The impact of a building implosion on airborned particulate matter in an Urb an community. Journal of the Air and Waste Management Association 54(10): 1256-1264.
Capouch S. (2011). Personal Communication by email between Heidi Hubbard of ICF International and Scott Capouch, Construction Proje ct Manager and Professional Engineer with 10 years of experience, of US NIEHS. Title of email: Lead Paint Removal – estimating info. February 2, 2011.
Farfel M; Orlova A; Lees P; Rohde C; Ashley P; Chis olm J. (2003). A study of urban housing demolitions as sources of lead in ambient dust: dem olition practices and exterior dust fall. Environmental Health Perspectives 111(1128-12 34).
Farfel M; Orlova A; Lees P; Rohde C; Ashley P; Chis olm Jr. J. (2005). A study of urban housing demolition as a source of lead in ambient dust on s idewalks, streets, and alleys. Environ Res 99(2): 204-213.
Grinshpun et al (2002). Efficiency of Final Cleanin g for Lead-Based Paint Abatement in Indoor Environments. Applied Occupational and Environment al Hygiene. Volume 17. Issue 3.
Iyiegbuniwe E; Conroy L; Scheff P. (2006). Emission factors development for the control of lead and other metals during bridge paint removal. Envir onmental Informatics 4:244-261.
Lee and Domanski. (1999). Development of pollutant release estimates due to abrasive blasting for lead paint removal from New York City departmen t of transportation steel bridges, Pittsburg, PA, Air and Waste Management Association

.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Mossman M; Plotner S; Babbitt C; Baker T; Balboni B, Eds. (2009). RSMeans Facilities Construction Cost Data.
Mucha A; Stites N; Evens A; MacRoy P (2009). Lead d ustfall from demolition of scattered site family housing: Developing a sampling methodology. Environmental Research 109(143- 148).
Thorpe et al 1999. Measurements of the effectiven ess of dust control on cut-off saws used in the construction industry. Ann Occup Hyg 43 (7): 44 3-456.
US EIA (2003) Commercial Building Energy Consumptio n Survey (CBECS). Available online at: http://www.eia.gov/emeu/cbecs/cbecs2003/public_use_ 2003/cbecs_pudata2003.html
US EPA. (1998). Risk analysis to support standards for lead in paint, dust, and soil. Appendix G: Health effects associated with exposure to lead and internal lead doses in humans. (Report No. EPA 747-R-97-006). Washington, DC: US Environmental Protection Agency, Office of Pollution Prevention and Toxics. Availab le online at: http://www.epa.gov/lead/pubs/toc.pdf.
US EPA. (2007a). Draft Final Report on Characteriza tion of Dust Lead Levels After Renovation, Repair, Painting Activities. Prepared for EPA’s Off ice of Pollution Prevention and Toxics (OPPT). Available at: http://www.epa.gov/lead/pubs/ duststudy01-23-07.pdf**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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# Appendix E. Receptor Building Characteristics

The dust model required inputs which were either sa mpled or which used central tendency estimates, depending on the available data and on t he expected sensitivity of the model results on each parameter. The variable values/distribution s are discussed further below.


# E.1. Receptor Distances For the exterior analysis, the receptors were assum ed to be at a series of distances from the renovated building. Initial scoping runs were done with receptors spaced at 50 ft, 100 ft, 150 ft, 200 ft, 300 ft, 450 ft, 650 ft, 900 ft, 1250 ft, an d 3500 ft. Inspection of results indicated very lo w air, soil, and dust concentrations at distances pas t 650 ft. For this Approach, a representative set of distances were set at 0ft, 50 ft, 150 ft, 300 ft , 650 ft, and 800 ft. The 0 ft receptor was added as a representation of the impacts on interior dust on the renovated building itself; in the model runs, it was set at a nominal distance of 5 ft from the building perimeter.


# E.2. Receptor Types For the exterior analysis, the receptor types need to cover all P&CBs, child-occupied facilities, and residences that might be located near a renovat ed building. For the exterior analysis, the buildings were broken into categories based on expe cted building size and characteristics of the amount of time spent in the building. Buildings nee d to be included whether they are likely to have lead based paint or not, since these receptor buildings are not the source of the renovation-related lead dust. The chosen categories included:

 

### Agricultural buildings

 

### Industrial buildings

 

### Commercial/Governmental buildings

 

### Schools, and

 

### Residences For interiors, the renovated and receptor buildings are the same building. The selection of building types for that analysis is discussed in Ap pendix C.2.1.


# E.3. Receptor Vintage The vintage of the receptor building helps determin e the background air, dust, and soil values to use in conjunction with the renovation-derived lead media. In this Approach, the background air, dust, and soil values are constant across all the public and commercial buildings owing to a general scarcity of more detailed information in th e literature. Thus, the receptor vintage is only**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### needed for the residential receptors. For those rec eptors, the Residential Energy Consumption Survey (RECS) was used to estimate the proportion o f U.S. homes in each of the analysis vintage bins. In estimating the proportion in each bin, the sample weights provided in the survey were incorporated to ensure the distribution is national ly representative. The resulting cumulative distribution function is shown in Table E-1.

Table E-1. Residential Building Vintage Cumulative Distribution

Cumulative Probabilities for Residential Vintage

Pre-1930 1930to

1949 1950 to

1959 1960 to

1979 Post1979
0.10 0.17 0.29 0.57 1.00



# E.4. Receptor Ceiling Height and Building Height

For the exterior analysis, the receptor building ce iling height is needed for the indoor dust model, while the building height is needed to selec t the appropriate AERMOD run from the choice of three different receptor heights. To sele ct the ceiling heights, the NIST CONTAM example buildings were used. For each building type , the example buildings were used to find a representative ceiling heights; because the example buildings do not cover agricultural or industrial buildings, warehouses were used as a pro xy.
The number of stories was estimated using the DOE 2 005 Residential Energy Consumption Survey (RECS) (US EIA, 2005) for residences and fro m the DOE 2003 Commercial Building Energy Consumption Survey (CBECS) (US EIA, 2003) for the p ublic and commercial buildings. The surveys provide information about the numbers of bu ildings in different story bins. The sample weights were used to estimate the proportion of bui ldings that are 1 story, 2 stories, or 3 or more stories. The resulting ceiling heights and dis tribution for number of stories are shown in Table E-2.
Table E-2. Receptor Ceiling Heights and Distributio n of Number of Stories

Receptor Type Ceil. Ht. (m) Cumulative Probabilities for Height (stories) 1 2 3 Agricultural 8.53 1 0 0

Industrial 8.53 1 0 0 Commercial / Government 2.74 0.64 0.89 1.00

Schools 3.35 0.73 0.88 1.00

Residences 2.44 0.58 0.89 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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# E.5. Receptor Volume The receptor volume (needed only for the exterior a nalysis) was taken from the DOE 2005 Residential Energy Consumption Survey (RECS) (US EI A, 2005) for residences and from the DOE 2003 Commercial Building Energy Consumption Survey (CBECS) (US EIA, 2003)for the public and commercial buildings. For CBECS, the different CBEC S building types were mapped to the analysis building types as shown in Table E-3. CBEC S does not separately report building parameters for agricultural or industrial buildings , so warehouses were used as a proxy for those two building types.
Table E-3. Mapping from Exterior Approach Building Categories to CBECS Categories

ICF Categories CBECS Categories

Agricultural and Industrial '05'='Nonrefrigerated warehouse' '11'='Refrigerated warehouse' '91'='Other'
Schools '14'='Education'

Commercial/Governmental '02'='Office' '04'='Laboratory' '06'='Food sales' '07'='Public order and safety' '08'='Outpatient health care' '12'='Religious worship' '13'='Public assembly' '15'='Food service' '16'='Inpatient health care' '17'='Nursing' '18'='Lodging' '23'='Strip shopping mall' '24'='Enclosed mall' '25'='Retail other than mall' '26'='Service'



### The survey reports the square footages, and these w ere multiplied by the ceiling heights in DOE 2005 Residential Energy Consumption Survey (RECS) ( US EIA, 2005) for residences and from the DOE 2003 Commercial Building Energy Consumption Sur vey (CBECS) (US EIA, 2003) for the public and commercial buildings. The surveys provid e information about the numbers of buildings in different story bins. The sample weigh ts were used to estimate the proportion of**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### buildings that are 1 story, 2 stories, or 3 or more stories. The resulting ceiling heights and distribution for number of stories are shown in Tab le E-2.

Table E-2 and converted to cubic meters to estimate the volumes. The sample weights in each survey were used when estimating the geometric mean and geometric standard deviation for each building type to ensure the distributions are nationally representative. The resulting volume distributions are shown in Table E-4

Table E-4. Receptor Building Characteristics with P robabilities or Distributions

Receptor Type Building Volume (m 3 )

GM GSD

Agricultural 5227 1.94

Industrial 5227 1.94

Commercial / Government 1371 2.25

Schools 2794 1.86

Residences 391 2.06



# E.6. Air Exchange Rate A literature search was conducted to find appropria te air exchange rates for different types of public and commercial buildings; a few sources were located that provided information, as shown below in Table E-5. However, the IMC and ASHR AE ventilation standards were rejected because they reported in units based on occupancy, a variable that is not captured in the Approach. All other sources were based on small sam ple sizes or were based on modeling rather than measurements. For this reason, the values from the EPA Exposure Factors Handbook for “non-residential buildings” were used for all the p ublic and commercial building types (industrial, agricultural, commercial/governmental, and schools) while the values from the EPA Exposure Factors Handbook for “residential building s” was used for residences (US EPA 2011). These values are shaded in Table E-5. For consisten cy across the two categories, the mean and standard deviation were used to estimate a normal d istribution (as opposed to using the geometric mean and geometric standard deviation to estimate a lognormal distribution for residences).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Table E-5. Sources Considered for the Air Exchange Rates



### Source Building Category N Mean StDev GM GSD Range

US EPA 2011 Exposure Factors Handbook (based on Turk et al., 1987) Non-residential buildings 1.5 0.87

US EPA 2011 Exposure Factors Handbook Residential buildings 0.63 0.65 0.46 2.25

Turk et al 1987 Educational 7 1.9 0.8 to 3.0 Office (<100k sf) 8 1.5 0.3 to 4.1 Office (>100k sf) 14 1.8 0.7 to 3.6 Library 3 0.6 0.3 to 1.0 Multiuse 53 1.4 0.6 to 1.9 Commercial (all) 40 1.5 0.87 0.3 to 4.1 EPA BASE Study Office buildings 100 1.8 2.1 Fradella et al., 2005 Warehouse 4 0.6 to 2.8

IMC Standards Findings are reported in L/s/person 

### Educational classroom 15 Public assembly space or theatre 15
General or office conference room 20 Office building office 20 Hotel, motel, resort 15

ASHRAE Standards Findings are reported as minimum cfm/person 

### Education 7.5-10 Office 5 Retail 7.5 Hotel 5 Public assembly 7.5-10



# E.7. Penetration Efficiency The penetration efficiency has been modeled for par ticles of various size classes and has been measured in a few field studies to be less than one (e.g., Dockery and Spengler, 1981; Freed **et al.**  , 1983; Liu and Nazaroff, 2001). However, unlike t he above studies, in a field study that simultaneously controlled for penetration and depos ition, the penetration efficiency ( **P** ) was found to be near 1 for all size classes (Thatcher a nd Layton, 1995); a similar result was also**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### reported for PM 2.5 

### for homes in California (Ozkaynak **et al.** , 1996). Thus, the penetration efficiency ( **P** ) was set to 1 for the mechanistic model.


# E.8. Deposition Rate The deposition rate ( **DustModelDep** ) was set to 0.65 h -1 

### based on information in the Exposure Factors Handbook (USEPA, 1997) based on a paper by Wallace (1996). The value for PM 10 

### was selected, as most of the suspended particulate in t he home is expected to fall within this size
range. The resuspension rate ( 

### Λ ) of 1.4 x 10 -4 

### s -1 

### was taken from values calculated from Thatcher and Layton (1995) for particles between 5 μ m and 10 μ m.


# E.9. Resuspension Rate The resuspension rate ( **R** ) varies strongly according to what activity is bei ng undertaken in the home. Resuspension rates during periods where human s are still or absent are lower than during periods of human activity. Vacuuming, in par ticular, introduces much higher resuspension. For the Approach model, an intermedia te value was taken from values calculated in Layton and Beamer (2009) for homes in the NHEXAS Midwest region (1.4 x 10 -4 

### h -1 

### ). This value incorporates the increased resuspension rate during an episode when one person was walking through the room.


# E.10. Particulate Tracking Rate

E.10.1. Residences The particulate tracking term represents the mass o f dust being tracked into a building during a day from all occupants who enter the building. For residences, this number was estimated from Von Lindern **et al.** (2003), who measured the amount of particulate dep osited on front mats in 276 houses in two locations near the Bunker Hill Su perfund Site. The lead levels reported in the paper are expected to be high-end and are not expec ted to represent general population exposures. However, the assumption is made that th e rate of accumulation of dust (as opposed to the lead in the dust) on doormats is not strongl y affected by the location and can be used to represent track-in to the receptor locations.
A distribution was developed by combining the data in the two locations in the 1998 site-wide analysis (their Table 4, number 4) and assuming the sample mat was 0.318 m 2 

### (as reported in Thatcher and Layton, 1995 for a similar type of ana lysis). The geometric mean and geometric standard deviation were estimated for the pooled da ta to be 0.0789 g/day and 2.52, respectively. Since this represents the amount of lead that remained on the doormat in the study, it was divided by the estimated fraction of dirt that remains on a doormat (see Appendix E.11) to get a final distribution with geometric me an and geometric standard deviation 0.61**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### g/day and 2.52, respectively. The tracking rate was sampled as part of the Monte Carlo analysis with a lognormal distribution.



## E.10.2. Public and Commercial Buildings This track in rate in residences was scaled to othe r types of P&CB using information inferred about the number of people per household within the 276 homes, how often those individuals entered their home on a given day, and how many peo ple enter a typical P&CB:

]	
+ ^&`a 

### = ]	
+ A1>;81521 

### × 
P
	 ^&`a 

### 
P
	 A1>;81521


### where: 

### TrackingRate **P&CB** 

### = Tracking rate into a public and commercial building (g/day) **TrackingRate Residence**  

### = Tracking rate into residences (g/day) **NumEntries P&CB**  

### = Number of people entering the public and commercial building each day **NumEntries Residence**  

### = Number of people entering the residence each day

Limited information is available within the Von Lin dern et al 2003 study on the activity patterns of individuals living within those homes. Informat ion on the number of people per household and the number of times they entered their home per day was estimated from other data sources. The homes were sampled in 1998 from the to wns of Smelterville, ID and Kellogg, ID. The American Community Survey has information on th e number of people per household for all cities and towns in the United States. However , this information was not collected in 1998. Census data for 1990, 2000, and 2010 on the number of people per household in the State of Idaho is also available. This information is consi stent with what is reported in Von Lindern et al 2003, that approximately 84% of the households had less than 3 adults living in the household, and approximately 26% of the households had more th an 3 adults. Homes with and without children were sampled, so the number of people per household rather than the number of people per family was chosen as a more appropriate metric. Information from these five data sources were averaged together an average value of 2.48 people per household was chosen, as shown in Table E-6.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Table E-6. Estimates of Numer of People Per Ho usehold in Smelterville and Kellogg, ID

Data Source People Per Household
ACS Smelterville. ID 2008-2013 1.97 ACS Kellogg, ID 2008-2013 2.29 Idaho US Census 2010 people per household 2.66 Idaho US Census 2000 people per household 2.69 Idaho US Census 1990 people per household 2.79 Average 2.48


### Information on real-time activity patterns of peopl e and how they move into and out of buildings is limited. Traditional activity pattern data shows how long individuals spend in different microenvironments based on self-reported questionnaires. One study using real-time GPS monitors found a difference in exposures for a full time worker who left and entered their home one time per day, and a stay-at-home parent wh o left and returned to their home two times per day. (Dons et al 2011). Another study mo nitored 265 elderly home owners, and found that they left and re-entered their homes an averag e of 2 times per day over 33 months. (Kaye et al 2011). For this analysis, it was assumed tha t each occupant of the residences enters and leaves the residence twice a day, so the total numb er of entries is


P
	 A1>;81521 

### = 2.48 × 2 = 4.96
Then, the estimates were made for the number of ent ries into public and commercial buildings of different use types. The methodologies are based on a 2010 U.S. Department of Energy (DOE) report analyzing door-opening frequency, **Energy Saving Impact of ASHRAE 90.1 Vestibule Requirements: Modeling of Air Infiltration Through**  **Door Openings** (Cho et al., 2010). The number of times a PnCB is entered is estimated to b e half of the door-opening frequency.
Table E-7 presents the door-opening frequency per d ay for each building type. Building sizes are calculated using the Commercial Buildings Energy Co nsumption Survey (CBECS). Peak frequencies (persons per hour) are obtained directl y from field data or are derived from building occupancy estimates. For retail and strip mall stor es, customers are assumed to use the entrance door two times within 1 hour (once when th ey enter and once when they leave). Therefore, the door-opening frequency during peak h ours is twice the occupancy. For all other building types, people are assumed to stay longer t han 1 hour in the building, and so the peak door-opening frequency is equal to the number of oc cupants. It is assumed that off-peak frequency (per hour) is one-tenth of the value duri ng the peak hour for all buildings except for restaurants and hospitals, which do not use peak an d off-peak hours in their frequency calculations. A schedule of peak and off-peak opera ting hours for each building was derived**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### from **Energy Saving Impact of ASHRAE 90.1 Vestibule Requi rements: Modeling of Air Infiltration**  **Through Door Openings** (Cho et al., 2010).
The average daily door-opening frequency is the sum of the frequency for all peak building hours and the frequency for all off-peak building h ours each day. Because the door-opening schedule differs for weekdays and weekends for some buildings, the average door-opening frequency is also weighted by day of the week.
Table E-8 presents estimates for the frequency of e ntries per day, which are assumed to be half of the total door-opening frequency, for each expos ure group. A building’s average frequency of entries per square foot per day is weighted by its relative prevalence within an exposure group, and scaled up to the average building size for the group to estimate average entries per day for each exposure group, with the exception of exposure group 2. For exposure group 2, a building’s entries per day is estimated without scaling becaus e the size of industrial and agricultural buildings is unknown. The derivation of the inputs used to estimate entries for each P&CB is described in more detail below.



### E.10.2.1. Office The number of workers for small office, medium offi ce, large office, and warehouse buildings are based on the Commercial Buildings Energy Consum ption Survey (CBECS) data by the Energy Information Administration (EIA) (EIA, 2003).



### E.10.2.2. Quick-Service and Sit-down Restaurant The number of customers visiting quick-service and sit-down restaurants is derived from a field study by Claar et al (1985). Claar et al. (1985) re ports an average of 898 daily customers for quick service restaurants and 284 daily customers f or sit-down restaurants. For quick-service restaurants, it is assumed that half of the custome rs will use the drive-through. Therefore, an estimated 449 customers (898/2) visit quick-service restaurants and 284 (568/2) customers visit sit-down restaurants daily. If customers enter once and leave once per visit, the door-opening frequency for customers is 898 for quick-service an d 568 for sit-down restaurants.
According to the Bureau of Labor Statistics Quarter ly Census of Employment and Wages, the average number of employees per establishment is 15 .37 for limited-service restaurants and 20.36 for full-service restaurants (BLS, 2013b; BLS , 2013c). If employees enter and leave the restaurant once per day, the door-opening frequency for employees is 30.74 (15.37 * 2) and 40.72 (20.36 * 2) for quick-service and sit-down re staurants, respectively.
The average door-opening frequency for quick-servic e and sit-down restaurants is the sum of the frequencies for customers and employees.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### E.10.2.3. Strip Mall and Stand-alone Retail Field data from Yuill (1996) indicate an average do or-opening frequency of 153 openings/hour for stand-alone retail stores. If it is assumed tha t customers do not stay longer than one hour, each customer will use the entrance door two times within one hour. Therefore, occupancy is estimated to be half of the average door-opening fr equency: 76.5.
The strip mall is assumed to have the same number o f people per entrance zone area as the stand-alone retail store. Based on the DOE retail p rototype, the entrance zone area is about 69.8 percent of the total store area (U.S. DOE, 2010). The area occupied per person for stand-alone retail is determined to be 91.4 ft 2 

### ([69.8% * 10,028 ft 2 

### ] / 76.5). Therefore, the occupancy for a stripmall is 177 (69.8% * 23,223 ft 2 

### ] / 91.4).



### E.10.2.4. Secondary School The number of staff for secondary schools is derive d from the CBECS data (EIA, 2003). The national average of teacher-to staff ratio in 2009 was 0.505, and the national average of student to teacher ratio was 15.4 (U.S. Department of Educa tion, 2012a; U.S. Department of Education, 2012b). The number of students is estimated using t hese two ratios, and the total school occupancy is the sum of the number of students and staff.



### E.10.2.5. Small and Large Hotel Based on the CBECES data, small hotels have an aver age of 4.82 staff members and 29.7 rooms available, and large hotels have an average of 47.5 staff members and 127 rooms available (EIA, 2003). It is assumed that the hotels have a 65 perc ent occupancy rate and 1.5 guests stay in each room (American Hotel and Lodging Association, 2007). Therefore, approximately 29.0 guests stay at the small hotel (29.7 * 65% * 1.5) a nd approximately 124 (127 * 65% * 1.5) stay in the large hotel. The occupancy of small and large h otels is the sum of the number of staff and guests.



### E.10.2.6. Hospital An average of 470 employees work during the main sh ift for inpatient care facilities (EIA, 2003). Because hospital employees work in shifts, the peak and off-peak operating schedule is not applied. We assume that staff work 8 hour shifts (i .e. three shifts per day) and that the number of staff working the night shift is half the number working the main shift. If each employee enters the hospital once and leaves once per day, t hen the door-opening frequency for workers is 2350 ((470 * 2 * 2) + (470 / 2 * 2)).
An average of 20.82 people are discharged from the hospital per day (U.S. Department of Health and Human Services, 2010). It is assumed that the n umber of people admitted per day is equal to the number of people discharged per day. Thus, t he door-opening frequency for non-ER patients is 41.64 (20.82 * 2).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### In addition, an average of 68.82 people visit the E mergency Department per day and 58.29 people are discharged daily (U.S. Department of Hea lth and Human Services, 2010). Therefore, 10.53 people per day are admitted to the hospital f rom the ER. These 10.53 people are assumed to be included in the number of people admitted to the hospital per day; therefore, to avoid double-counting, it is assumed that the number of p eople who are discharged from the ER is equal to the number of people who enter. Note that these ER discharges are distinct from the 20.82 non-ER discharges. Thus, the door-opening fre quency for ER patients is 116.58 (58.29 * 2).
The average door-opening frequency for the hospital is the sum of the frequencies for employees, non-ER patients, and ER patients.



### E.10.2.7. Outpatient Health Care The average door-opening frequency is estimated to be 123 openings/hour, based on field data from Yuill (1996).



### E.10.2.8. Warehouse- Industrial Buildings Employee and establishment data for the industrial sector are retrieved from the U.S. Census Bureau 2009 County Business Patterns (CBP) and a re port that estimated the universe of P&CB buildings, the methodology of their report is descr ibed in Appendix Q (U.S. Census Bureau, 2009). The CBP data indicate 12,879,161 employees i n the industrial sector. See Appendix Q for methods that estimated 2,400,604 industrial buildin gs. Occupancy is therefore 5.36 people per building.



### E.10.2.9. Warehouse- Agricultural Building According to the CBP data, there are 153,829 employ ees in the agricultural sector (U.S. Census Bureau, 2009). See Appendix Q for methods that esti mated 7,877,314 agricultural buildings. Therefore, the occupancy is 0.02 people per buildin g.



### E.10.2.10. Supermarket According to the Food Marketing Institute (FMI), me dian weekly sales and weekly sales per transaction are $318,170 and $35.01, respectively ( FMI, 2012). This implies an average of 1,298 customers per day ([$318,170/$35.01]/7), and .028 c ustomers per square foot per day (1,298/46,000 ft 2 

### ; FMI, 2012). We calculate the customers per day in an 8,314 ft 2 

### supermarket to be 234.65 (8,314 ft 2 

### *.028). Including an average of 10.94 employees ent ering the building once per day, the total door-opening frequency per day i s estimated to be 491.18 ([234.65 + 10.94] * 2; EIA, 2003).

It should be noted that some estimations in this an alysis make use of older data, such as the 1996 field-study by Yuill and the 1985 study by Cla ar et al. Due to industry changes since the time of data collection, it is possible that the cu rrent average occupancy of the buildings differs from the estimates used in this analysis. In additi on, the door-opening frequencies for some**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### buildings are likely to be greater than the estimat es listed in Table E-7. This is due to additional visitors that are not included in the peak occupanc y count. For example, hospital visitors, apartment visitors, and restaurant food suppliers a re not included in occupancy estimates. Furthermore, many large hotels frequently host conv entions, conferences, and other events which are not considered in the frequency estimates

.**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftE-13July 25, 2014Table E-7. Average Door-Opening Frequency for 16 PnCBsBuildingCategory1CBECSAverageFloorArea (ft2)Number of PeakHoursNumber of OffPeak HoursPeakOccupancy(persons)PeakFrequency(personsper hour)Off-PeakFrequency(personsper hour)AverageFrequencyper dayDerivation of Average FrequencyWeek-dayWeek-endWeek-dayWeek-end(a)(b)(c)(d)(e)(f)(g)=(f) x 0.1(h)SOFF 5,951 3 0 10 0 13.39 13.39 1.3393 38.27([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7MOFF 71,909 3 0 10 0 171.82 171.82 17.1825 490.93([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7LOFF 655,409 3 0 10 0 1,487.25 1,487.25 148.7249 4,249.28([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7OUTP 10,409 11 0 2 0 123.00 123.0012.3000984.00([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7WH 21,603 3 0 10 0 10.26 10.26 1.0255 29.30([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7WH (Ind.) N/A 3 0 10 0 5.36 5.36 0.5365 15.33([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7WH (Ag.) N/A 3 3 10 10 0.02 0.02 0.0020 0.08([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7SUP 8,314 N/A N/A N/A N/A N/A N/A N/A 491.182*(((((318,170/35.01)/46,000)/7)*8314) + 10.94)QSR 3,345 N/A N/A N/A N/A N/A N/A N/A 928.74(898*2/2)+ (15.37*2)FSR 6,585 N/A N/A N/A N/A N/A N/A N/A 608.72(284*2) + (20.36*2)SART 10,028 8 8 4 4 76.50 153.00 15.3000 1,285.20([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7STRIP 23,223 8 8 4 4 177.16 354.3335.43262,976.34([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7SSCL 37,024 2 0 9 0 235.98 235.9823.5981488.82([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7SHTL 14,990 3 3 13 13 33.77 33.77 3.3768 145.20([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7LHTL 97,102 3 3 13 13 171.34 171.34 17.1340 736.76([5*[(a)(f) + (c)(g)]] + [2*[(b)(f) + (d)(g)]])/7HOS 241,416 N/A N/A N/A N/A N/A N/A N/A 2,513.11(471*2*2) + (471/2*2) + (20.82*2) + (58.29*2)1SOFF — Small Office; MOFF — Medium Office; LOFF — Large Office; HOS — Hospital; WH — Warehouse (Ind.=Industrial; Ag.=Agricultural); SUP — Supermarket; QSR — Quick Service Restaurant; FSR — FastService Restaurant; STRIP — Strip Mall; SSCL — Secondary School; OUTP — Outpatient Healthcare; SHTL— Small Hotel; LHTL — Large Hotel; SART — Stand-alone RetailNote: Values may not calculate exactly due to rounding**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftE-14July 25, 2014Table E-8. Estimated Entries Per Day for Each Building CategoryBuildingCategory1CBECSAverageFloor Area(ft2)ExposureGroupAverageFrequency ofEntries per ft2per dayCBECS BuildingFrequencyBuildingWeightWeightedEntries per ft2per day2WeightedCBECS AverageFloor Area (ft2)Entriesper dayBuildingCategoryExposureGroupSOFF 5,95110.003215 756,965944,62480.13%0.008863 14,255 126.3MOFF 71,909 0.003414 61,875 6.55%LOFF 655,409 0.003242 4,965 0.53%OUTP 10,409 0.047266 120,819 12.79%WH 21,60320.000678 384,01210,747,9153.57%N/A 19,172 4.229WH (Ind.) N/A N/A 2,400,604 22.34%WH (Ag.) N/A N/A 7,877,314 73.29%SUP 8,314 .029539 85,985 0.80%QSR 3,34530.138846 78,190795,4519.83%0.067810 12,147 823.7FSR 6,585 0.046218 161,213 20.27%SART 10,028 0.064081 346,625 43.58%STRIP 23,223 0.064081 209,423 26.33%SSCL 37,024 4 0.006601 68,014 68,014 100.00% 0.006601 37,024 244.4SHTL 14,99050.004843 69,94997,44771.78%0.004661 49,845 232.4LHTL 97,102 0.003794 19,607 20.12%HOS 241,416 0.005205 7,890 8.10%1SOFF — Small Office; MOFF — Medium Office; LOFF — Large Office; HOS — Hospital; WH — Warehouse (Ind.=Industrial; Ag.=Agricultural); SUP — Supermarket; QSR — Quick Service Restaurant; FSR — FastService Restaurant; STRIP — Strip Mall; SSCL — Secondary School; OUTP — Outpatient Healthcare; SHTL— Small Hotel; LHTL — Large Hotel;SART — Stand-alone Retail2For exposure group 2, weighted entries per ft2per day cannot be calculated because the size of industrial and agricultural buildings is unknown.Note: Values may not calculate exactly due to rounding**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Based on the above information, the residential par ticulate track-in rate distribution was scaled for each of the analysis public and commercial buil ding types. The final distributions are shown in Table E-9.
Table E-9. Particulate Tracking Rate for Analysis R eceptor Types

Analysis Receptor Type Geometric Mean (g/day) Geometric

Standard Deviation

Exterior Agricultural 1.79 2.52

Industrial 1.79 2.52 Commercial/Governmental 61.99 2.52 Schools 29.91 2.52 Residences 0.61 2.52

Interior Office, service, outpatient healthcare, and public order/safety 15.45 2.52

Warehouse, food sales, religious worship, and publi c assembly 0.52 2.52 Food service, strip shopping mall, enclosed mall, a nd retail other than mall 100.79 2.52

Education 29.91 2.52 Lodging, Nursing, inpatient health care, and labora tory 28.44 2.52



# E.11. Fraction of Dirt Remaining on Floor Mat Some fraction of dirt tracked into a building will be deposited on a floor mat near the entry way. To estimate this fraction, the Approach uses Thatch er and Layton (1995), which measured the difference between particulate accumulation in trac ked and untracked areas in the home as well as the amount on the front mat. The assumption was made that 75 percent of the home will contain tracked dirt, and the other 25 percent cons ists of corners or other less accessible areas in which people do not walk as frequently. Using t his assumption and the information about the amount of mass on the front mat, the tracked areas of the home, and the untracked areas of the home in Thatcher and Layton (1995), an estimated 13 percent of tracked-in mass remains on the front mat and 87 percent is carried into the homes. To account for the variability within the universe of building types, it was assumed that som e buildings have floor mats and some do not. In the absence of data for this variable, it was as sumed that 50% of all buildings had front mats and 50% did not.


# E.12. Percentage of Building that is Carpeted The percent of a residence that is carpeted was der ived from two different data sources. First, data taken from the National Survey of Lead and All ergens in Housing indicate that nearly all**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### homes (over 90%) had some carpeting or rugs in one or more of the rooms sampled (HUD 2002). The arithmetic mean of the percent of area carpeted was 52% (EPA 2008).
The second method used a typical home layout and es timated upper and lower bounds of typical carpeting based on assigning each room as e ither carpeted, not carpeted, or having a specific probability of being carpeted. Two method s were used to estimate the overall square footage of a typical home. The first method used th e average whole house volume of 492 m 3 

### presented in the EPA 2011 Exposure Factors Handbook (EPA 2011). Estimates for room volumes are available from standard consumer exposure model s (EPA 2007), and were scaled up to match the 492 m 3 

### whole house volume. Room volumes were divided by 2.59 meters (assuming 8.5 ft. ceilings) and multiplied by 10.76 to conver t from meters squared to feet squared.
The second method used the generic floor plan for a single home presented in EPA’s Formaldehyde Indoor Air Model and estimated total s quare footage within different room types. Both methods assumed that kitchens, bathroo ms, and utility rooms are always noncarpeted, that living rooms/dens, bedrooms, and din ing rooms are always carpeted, that hallway and stair spaces have a 50% chance of being carpeted, and that storage closets, garages, and attics have a 25% chance of being carpeted. Th ese methods yielded percent carpeting estimates of 57% and 59% (Table E-10), both of whic h support the estimate derived from HUD National Survey of Lead and Allergens data. In the Approach, residences were represented as being carpeted 52% of the time and hard surface flo oring the other 48% of the time.

Table E-10. Estimate of Percent Carpet Using a Typi cal House Layout
Method 1

Room Type Room Volume Room

Size Number

of Rooms Total

Square Footage Carpet, Non-Carpet,

Mix Percent

Carpet Bedroom 36 150 3 449 Carpet

57% Living Room, Den 48 199 2 399 Carpet Bathroom 18 75 2 150 Non-Carpet Laundry Room, Utility Room 20 83 2 166 Non-Carpet Dining Room 20 83 1 83 Carpet Kitchen 36 150 1 150 Non-Carpet Hallway, Stairwells, Closets 70 291 1 291 Mix (Assu me 50%) Storage Closets, Garage, Attic 86 357 1 357 Mix (As sume 25%)

492 n/a n/a 2044**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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Room Type Square Feet Carpet, Non-Carpet, Mix Percent Carpet Bedrooms (3) 562 Carpet

59 

### % Living Room (1) 203 Carpet Bathroom (2.5) 154 Non-Carpet Laundry Room (1) 20 Non-Carpet Dining Room (1) 145 Carpet Kitchen (1) 150 Non-Carpet Hallway (3), Stairwells (1) 109 Mix (Assume 50%) Storage Closets (3), Garage (1), Attic (0) 489 Mix (Assume 25%) 1832


### For public and commercial buildings, the Commercial Flooring Maintenance Report (Ciprus, 2013) was used. The publisher provided estimates of the percentage of different building types that are carpeted in the United States. These esti mates were used to create cumulative distribution functions for the probability a buildi ng is carpet or hard surface flooring, as shown in Table E-11. These estimates were obtained for the e xterior analysis building types only; in the interior analysis, the carpeting status is treated as a scenario variable, with separate results provided for carpeted and non-carpeted floor types.

Table E-11. Cumulative Probability Distribution For Flooring Type in Public and Commercial Buildings and Residences

Receptor Type % Carpet All Carpet All Floor Data Source

Agricultural 0.08 1 Cirpus 2013

Industrial 0.08 1 Cirpus 2013

Commercial/Governmental 0.45 1 Cirpus 2013

Schools 0.27 1 Cirpus 2013 Residences 0.52 1 HUD 2002


# E.13. Cleaning Frequency Cleaning frequency ( **CleanFreq** ) represents a particularly sensitive variable, so a literature search was undertaken to identify all readily available da ta sources for residences and P&CBs.
In residences, there are two sources of data on cle aning frequency reported in the Exposure Factors Handbook (USEPA, 2011). The first is the N ational Human Activity Pattern Survey. One**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### of the questions in the follow-up questionnaire for this Survey was the “frequency that floors were swept or vacuumed.” There were 4,663 responde nts for this question, although a subset of these respondents comprised young children less than 11. Respondents in the top three age categories (12-17, 18-64, and >64) were believed to better represent individuals who most often clean. Thus, results are broken out by all respond ents and respondents over age 11.
Available survey responses were not a specific freq uency, but ranges: nearly every day, three to five times a week, once or twice a week, once or tw ice a month, less often, never, or don’t know. In order to estimate distributions of cleani ng frequency from the survey, each of these ranges was represented by a single point estimate i n the middle of the range, as shown below in Table 1. For the cases that were unbounded (less of ten, never, and don’t know), we chose to represent the ranges as “once every two months”, “o nce every year”, and “not available”, respectively. All individuals for which data were n ot available were excluded from the analysis. The data from respondents above the age of 11 were used estimate the average, median, and standard deviation values are presented in Table E- 13-1.
The second data source on cleaning frequency report ed in the Exposure Factors Handbook is the National Usage Survey of Household Cleaning Product s. Respondents were asked how frequently they performed various cleaning tasks. Two of the tasks were cleaning bathroom floors and cleaning kitchen floors. These are reas onable surrogates for cleaning frequency of floors. The results are shown in Table E-13-1.

In addition, there are two newer data sources that were not available at the time the 2011 version of the Exposure Factors Handbook was being compiled. The Michigan Department of Community Health initiated a Home-Based Environment al Intervention and Education program for Families with Pediatric Asthma in Michigan. 23 4 respondents were asked how frequently the vacuumed their child’s bedroom and other rooms of the home at baseline and after a six month intervention. The mean and standard deviatio n values are reported in Table E-13-1 (Largo et al 2011).
As part of the Study of Use of Products and Exposur e Related Behavior (SUPERB) study, California residents were asked questions about the ir cleaning habits. The population was comprised of two general groups: households with pa rents of young children and households with older adults. Results were further split base d on whether the primary cleaner was a man or a woman. The mean, standard deviation, median, a nd 90 th 

### percentile values are reported in Table E-12 (Moran et al 2012).
Across these four studies, there is a large range o f cleaning frequencies in residences. The standard deviations, when available are large compa red with the means, indicating significant**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### variability. The means are also larger than the med ians, suggesting a larger percentage of residences may be cleaned more frequently rather th an less frequently.
Table E-12. Summary of Cleaning Frequencies from Av ailable Data Sources for Residences. Times per month

Data Source and Activity Median Mean Standard Deviation Exposure Factors Handbook 2011: NHAPS, adults

only, "vacuum' 8 11.4 8.5 Exposure Factors Handbook 2011: National Usage Survey, Clean kitchen or bathroom floor 4 6

Moran et al 2012., women, houses with young children Dry Mop hard floors 4 7.2 7.6
Wet Mop hard floors 4 6.4 8.4
Sweep Hard floors 12 14.2 11.5

Vacuum hard floors 4 7.3 6.9

Vacuum carpet 5 9.0 8.0

Moran et al 2012., women, older adults Dry Mop hard floors 4 5.4 5.6 Wet Mop hard floors 4 4.5 5.6 Sweep Hard floors 4 8.6 8.0
Vacuum hard floors 4 5.2 5.8

Vacuum carpet 4 4.7 4.7

Largo et al 2011 Michigan Vacuuming floor in child's bedroom 3.2 2.1
Vacuuming floor in other rooms 3.8 1.9



### In public and commercial building, sources of clean ing frequency data include the ISSA, the worldwide cleaning industry association, the GSA, a nd the cleaning schedules from various universities. The information contained in the ISS A’s CompuClean calculator suggests that the maximum cleaning frequency of public and commercial building floors would be 260 days per year, every working day, or approximately 5 days pe r week. This value is generally supported by information available in GSA standard leases, which apply to most government buildings. Other types of buildings, including retail space and ware houses, may be cleaned more or less frequently for various reasons. However, data is g enerally not available for these types of buildings. Readily available information on cleani ng frequency is summarized in Table E-13.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Table E-13. Summary of Cleaning Frequencies from Av ailable Data Sources for Public and Commercial Buildings

Data Source Narrative Cleanings per Week The Carpet and Rug Institute’s Carpet Maintenance Guidelines For Commercial Applications lower traffic areas: vacuum 2 to 3 d/wk 2
The Carpet and Rug Institute’s Carpet Maintenance Guidelines For Commercial Applications high traffic area: vacuum daily 5
GSA Property Managers Book, COF sweep, mop, or scru b floor; thoroughly vacuum all carpet daily 5
GSA Standard Lease, Section 6-07 sweep, spot vacuum carpets; clean restroom fixtures, floors daily 5
ISSA 540 Cleaning Times As Used in CompuClean v10 Carpet spot cleaning, vacuuming, 260 d/y 5
Purdue University vacuum carpet, dust mop floors we ekly 1 Purdue University conference rooms: vacuum carpet, dust mop floors weekly 1
Purdue University libraries: vacuum carpet 2 d/wk 2 Purdue University Auditoriums: Vacuum 3 d/wk 3 Purdue University classrooms and conference rooms: daily (mopping floors; spot vacuuming) 5
Purdue University dust mop floors 2 d/wk; wet mop 1 d/wk 1
Purdue University vacuum, dust mop floors 3 d/wk 3 Purdue University Childcare facility: Vacuum and dus t mop daily 5
University of Minnesota vacuum and mop 1 d/wk 1 University of Minnesota classrooms and conference r ooms: vacuum and mop 1 d/wk 1
University of Minnesota autoscrub, mop public floor s daily 5 Kettering University vacuum, dust/spot mop hard flo ors in office suites & lounges 2 d/wk 2
Kettering University general cleaning 5 d/wk (vacuu m, mop) 5 Tulane University daily (mopping floors; spot vacuu ming) 5 ISSA 540 Cleaning Times As Used in CompuClean v10 Healthcare 365 days per year 7
ISSA 540 Cleaning Times As Used in CompuClean v10 Lodging 365 days per year 7
Suffield, CT Green Cleaning Program School: Clean c lassrooms 3 d/wk 3 Stratford, CT Green Cleaning Program Statement Scho ol: Clean classrooms daily 5 ISSA 540 Cleaning Times As Used in CompuClean v10 Schools School: 180 d/y 2



### An overall range of cleaning frequencies was constr ucted based on available data and matched to the different building types. To match each one , a central estimate was found using the most commonly reported cleaning frequency for that build ing type in the literature. Then each**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### building type was assigned to other surrounding fre quency bins according to the approximate range in the literature. In order to complete the d istribution, the probability that a given building is in each category must be assigned. Thes e weights were assigned assuming the central frequency had the highest probability and the proba bilities tapered linearly in both the lower and higher frequency directions. The distributions are shown in Table E-14.

Table E-14. Cleaning Frequency Distributions by Bui lding Type

Analysis Receptor Type Cleaning Frequency
Every working day (5 days/week) Every third

day (approx) (2 days/week) Every

week (1 day/week) Every

other week (1 day/2 weeks) Every

fourth week (1 day/4 weeks)

Exterior Agricultural 0.1 0.3 0.7 0.9 1.0

Industrial 0.2 0.6 0.8 0.9 1.0
Commercial/Governmental 0.2 0.6 0.8 0.9 1.0

Schools 0.4 0.6 0.8 0.9 1.0

Residences 0.1 0.3 0.7 0.9 1.0

Interior 1 Office, outpatient healthcare,

and public order/safety 0.2 0.6 0.8 0.9 1.0 2 Warehouse, food sales, religious worship, and public assembly 0.2 0.6 0.8 0.9 1.0
3 Food service, service, strip shopping mall, enclosed mall, and retail other than mall 0.4 0.6 0.8 0.9 1.0

4 Education 0.4 0.6 0.8 0.9 1.0 5 Lodging, Nursing, inpatient health care, and laboratory 0.4 0.6 0.8 0.9 1.0



# E.14. Cleaning Efficiency Cleaning efficiency ( **CleanEff** ) has been found to vary according to the type of f looring (carpeting versus hard floor) and the total amount of lead on the floor. After an exposure event introducing more lead into the environment, the fra ction of lead removed from a carpet will depend on the cleaning iteration, with higher effic iencies after the first cleaning iteration and lower efficiencies after multiple iterations due to electrostatic interactions which trap the particles within the carpet. For hard surfaces, the efficiency will depend on the total particle**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### loading on the floor, with higher efficiencies for higher loadings. Once higher loadings associated with a high exposure event have been cleaned away, the efficiencies associated with routine cleaning are expected to be low. The RRP Approach d erived cleaning efficiency equations based on the Clemson Environmental Technologies Laborator y (2001) efficiency data (USEPA, 2008a). For the hard floor equation, the efficiency depends on the loading values (in μ g/ft 2 

### ) according to the equation:


### Absolute cleaning efficiency = 0.113 * ln( Loading ) - 0.555
For the carpet equation, the efficiency depends on the cleaning iteration according to the equation:


### Absolute cleaning efficiency = -0.430 * ln( CleanIteration ) + 0.658
These equations are appropriate for high loadings a fter an interior renovation event. These equations are retained for this modeling effort.
However, because these equations are not applicable for low loadings or for numerous routine cleaning iterations, it is necessary to define a lo wer limit on the efficiency to prevent predictions of negative efficiencies. A lower limit of 6 percen t removal was selected for hard floors (at low loadings) and 15 percent efficiency was set for car pets (after numerous iterations). These values were based on the minimum cleaning efficiency repor ted for the final round of cleaning in the Clemson data.


# E.15. References for Appendix E American Hotel & Lodging Association. 2007. **The 2007 Lodging Industry Profile** . Washington, DC. Available at: http://www.ahla.com/uploadedFiles /AHLA/Press_Room/ Lodging_Industry_Profile/LIP2007.pdf

Cho,H., Gowri, K. and Liu, B. 2010. **Energy Saving Impact of ASHRAE 90.1 Vestibule Requirements: Modeling of Air Infiltration through**  **Door Openings.** U.S. Department of Energy, Pacific Northwest National Laboratory.

Claar, C.N., R.P. Mazzucchi, and J.A. Heidell. 1985

. **The Project on Restaurant Energy Performance—End-use Monitoring and Analysis**  , PNL-5462, Pacific Northwest National Laboratory, Richland, WA.
Dockery, D; Spengler, J. (1981). Indoor-outdoor rel ationships of respirable sulfates and particles. Atmospheric Environment. 15: 335-343.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### Energy Information Administration (EIA), 2003 Comme rcial Buildings Energy ConsumptionSurvey. Available at: http://www.eia.doe .gov/emeu/cbecs/
Food Marketing Institute (FMI). 2012, Supermarket F acts: Industry Overview 2012. Accessed February, 2014. Available at http://www.fmi.org/res earch-resources/supermarket-facts.
Freed J; Chambers T; Christie W; Carpenter C. (1983 ). Methods for assessing exposure to chemical substances: Volume 2. (Report No. EPA- 5 60/5-83/015 pp: 70-73.)Washington DC, Environmental Protection Agency, Office of Poll ution Prevention and Toxics (OPPT).
HUD (U.S. Department of Housing and Urban Developme nt). (2002). National Survey of Lead and Allergens in Housing: Final Report, Revision 6. Office of Lead Hazard Control. October 31. Available at: http://www.hud.gov/offices/lead/techstudies/survey. cfm#natsurvey
Largo et al 2011. Healthy Homes University: A home- based environmental intervention and education program for families with Pediatric Asthm a in Michigan. Public Health Reports. 2011 Supplement 1. Volume 126 (14-26).
Layton D; Beamer P. (2009). Migration of contaminat ed soil and airborne particles to indoordust. Envir. Science and Technol. 43: 8199-82 05.
Moran et al 2012. Frequency and longitudinal trends of household care product use. Atmospheric Environment 55 (2012) 417-424
Liu D; Nazaroff W. (2001). Modeling pollutant penet ration across building envelopes. Atmospheric Environment. 35: 4451-4462.
Ozkaynak H; Xue J; Spengler J; Wallace L; Pellizzar i E; Jenkins P. (1996). Personal exposure toairborne particles and metals: Results from the P article TEAM Study in Riverside, California. J.Expo. Anal. Environ. Epidemiol. 6(1): 57-78.
Thatcher T; Layton D. (1995). Deposition, rsuspensi on, and penetration of particles within a residence. Atmospheric Environment. 29(13): 1487-14 97.
U.S. Census Bureau. 2009. 2009 County Business Patt erns. Available at: http://www.census.gov/econ/cbp/index.html
U.S. Bureau of Labor Statistics (BLS). 2012. Occupa tional Outlook Handbook, 2012-13 Edition. Farmers, Ranchers, and Other Agricultural Managers. Accessed October 18, 2013. Available at: http://www.bls.gov/ooh/management/far mers-ranchers-and-otheragricultural-managers.htm**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### U.S. Bureau of Labor Statistics (BLS). 2013b. Quart erly Census of Employment and Wages. U.S. total, full-service restaurants, private ownership. Accessed October 18, 2013. Available at: http://www.bls.gov/cew/
U.S. Bureau of Labor Statistics (BLS). 2013c. Quart erly Census of Employment and Wages. U.S. total, limited-service restaurants, private ownersh ip. Accessed October 18, 2013. Available at: http://www.bls.gov/cew
U.S. Department of Education. 2012. National Center for Education Statistics, Digest of Education Statistics 2011, Table 86: Staff employed in public elementary and secondary school systems, by type of assignment and state or jurisdiction: Fall 2009. Accessed September 2013. Available at http://nces.ed.gov/pub s2012/2012001.pdf
U.S. Department of Education. 2012. National Center for Education Statistics, Digest of Education Statistics 2011, Table 71: Teachers, enro llment, and pupil/teacher ratios in public elementary and secondary schools, by state o r jurisdiction: Fall 2009. Accessed September 2013. Available at: http://nces.ed.gov/pu bs2012/2012001.pdf
U.S. Department of Energy. 2010a. Commercial Protot ype Building Models. Accessed September 2013. Available at:http://www.energycodes.gov/development/commercia l/90.1_models
U.S. Department of Energy. 2010b. Commercial Refere nce Buildings -Supermarkets. Accessed February 2014. Available at: http://energy.gov/eere /buildings/new-constructioncommercial-reference-buildings
U.S. Department of Health and Human Services. 2010. Healthcare Cost and Utilization Project. Accessed September 2013. Available at: http://hcupnet.ahrq.gov/HCUPnet.jsp
US EIA. (2005) Residential Energy Consumption Surve y (RECS). Available online at: http://205.254.135.24/consumption/residential/data/ 2005/
US EIA. (2003) Commercial Building Energy Consumpti on Survey (CBECS). Available online at: http://www.eia.gov/emeu/cbecs/cbecs2003/public_use_ 2003/cbecs_pudata2003.html
US EPA 1997. Exposure Factors Handbook. Available o nline at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid =12464

US EPA 2007: E-FAST User Guide. Available online a t: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?dei d=236252**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation**  , Repair, and Painting Activities in Public and Com mercial Buildings

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### US EPA 2011. Exposure Factors Handbook. Available o nline at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?dei d=236252
von Lindern I; Spalinger S; Bero B; Petrosyan V; v on Braun, M. (2003). The influence of soil remediation on lead in house dust. Sci. Total Envir on 303(1-2): 59-78.
Wallace L. (1996). Indoor particles: a review. Jour nal of the Air and Waste Management Association 46(2): 98-126.

Yuill, G.K. 1996. **Impact of High Use Automatic Doors on Infiltration** . Project 763-TRP, American Society of Heating, Refrigerating and Air-Condition ing Engineers, Inc., Atlanta, GA.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# Appendix F. Soil and Hard Surface Parameters

Several soil and hard surface parameters are needed to estimate the soil and hard surface loading concentration time series. The following Appendix d etails the sources for these parameters.


# F.1. Runoff Removal Frequency ( RainFreqDays ) The runoff removal frequency was needed for each of the nine climate regions. A report by the US DOT and US GS (2010) reports the average number of rain events per year in climate regions in the US, wher e an event is defined as a rain event resulting in at least 0.1 inches of rain and at least 6 hours betw een events. The data were digitized using GetData® soft ware and 365 days per year was divided by the mean days between events to get the average number of ev ents per year. The 15 climate regions correspond to US EPA “rain zones” and do not exactly match the climate regions. The rain zones were averaged, where appropriate, to approximately map the rain zo nes to the 9 climate regions. The resulting days between rain events is shown in Table F-1.

Table F

- 1

. Days between rain events

Climate Region Name Days Between Rain Events (days)

1 (East North Central) 9 2 (Northeast) 7 3 (Northwest) 8 4 (South) 13 5 (Southeast) 7 6 (Southwest) 16 7 (West) 22 8 (West North Central) 14 9 (Central) 7 

# F.2. Runoff Removal Efficiency ( TrackInRainEff ) During each rain event, some percentage of the stan ding dirt on the hard surface will be removed. It was assumed that 50 percent of the dirt layer was removed from the patio during each rain event (Batroney **et al.** , 2010). This does not apply to soil. It was assu med that once present in the soil, lead persists throughout the d uration of the exposure event.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# F.3. Dust Layer Density ( TrackInDensity )
McKone **et al.** (2001) report a typical soil density of 2,600 kg/m 3 

### . The particles on the hard surface will be a mix of particles from the renovation and soil particles. In the Approach, the hard surface partic le density was set equal to the soil density owing to a lack of more specific hard surface data.


# F.4. Dust Layer Depth ( TrackInDepth ) The thickness of the surface soil layer assumed in TRIM.FaTE model simulations performed for EPA OAQPS ranges from 1 cm for non-agricultural soils t o 20 cm for tilled agricultural soils (USEPA, 2009a ). In order to be adequately conservative, a yard surface soil layer thickness of 1.5 cm was assumed to remain consistent with the methodology used to coll ect soil samples for comparison to the soil lead hazard standards.
For an outdoor hard surface, the layer thickness re presents the thickness of the dirt layer on top of the hard surface. No appropriate value could be identif ied in the literature. Based on professional judgme nt, a value of 1 mm was selected for the hard surface l ayer thickness.


# F.5. Dust Layer Porosity ( TrackInPorosity ) McKone **et al.** provided an estimate for the soil porosity of 20 pe rcent or 0.2. In reality, the dust layer on the hard surface may be more or less porou s than the soil. In the absence of further data sources for hard surfaces, the soil value was used in the Approach.


# F.6. References for Appendix F Batroney T; Wadzuk B; Traver, R. (2010) A Parking D eck's First Flush. Journal of Hydrologic Engineerin g, 15(2): 123-128.
McKone T; Bodnear A; Hertwich E. (2001). Developmen t and evaluation of state-specific landscape data sets regional multimedia models. Lawrence Berkeley National Laboratory Report No. LBNL43722.
US DOT and USGS. (2010). Methods for Development o f Planning-Level Estimates of Stormflow at Unmonitored Stream Sites in the Conterminous United States. Office of Project Development and Environmental Review. Publication No. FHWA-HEP- 09-005. Washington, DC. Available online at: http://webdmamrl.er.usgs.gov/g1/FHWA/FHW A-HEP-09-005/FHWA-HEP-09-005.pdf
U.S. EPA (2009a). Risk and Technology Review (RTR) risk assessment methodologies: For review by the EPA’s Science Advisory Board. Case Studies – MACT I petroleum refining sources & portland**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### cement manufacturing. (Report No. EPA-452/R-09-006. ) Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Qual ity Planning and Standards.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# Appendix G. AERMOD Variables

G.1. Meteorology The methods used to process meteorology data for us e in the AERMOD runs of the current analysis are discussed in Appendix G.1.1, while the additional d etails about the sources of meteorology-related dat a are discussed in Appendix G.1.2.



## G.1.1. Methods for Estimating Meteorological Conditions To account for the fact that renovation may occur i n any part of the country, an analysis was performe d to determine sets of meteorological conditions that together would be representative of the entire US. Similar methodology has been used previously for ot her EPA research, including modeling near roadway pollutants for the Office of Transportation and Air Quality (US EPA, 2011). 14 stations were selected from 9 climate regions in the U.S.
EPA provides guidance for the use of meteorological data for air quality dispersion modeling in Append ix W to 40 CFR Part 51. The guidance specifies that th e meteorological data selected should be spatially and temporally representative of the transport and dispersion conditions in the area of concern. The meteorological data used in this public and commerc ial buildings analysis must be representative of th e conditions at the sources considered, yet general e nough to represent broad regions. While it would be ideal to formulate an entirely objective procedure for use in evaluating representativeness, no genera lly accepted analytical or statistical technique is ava ilable to determine representativeness of meteorological data (US EPA, 2000). Determining how well a given station represents the encompassing climate region is more difficult than determining h ow well a station represents a project site. Althou gh various methods could be used for this analysis, th e following Approach was chosen.



### G.1.1.1. Selecting Representative Stations As a starting point, the meteorology station(s) for each climate region were selected based on an analysis previously performed (US EPA, 2011). In th at 2011 analysis, as discussed in further detail in Appendix G.1.2, 392 active meteorological stations were identified in each climate region. To obtain hourly mixing heights, the (1) hourly surface, (2) twice-daily upper-air, and (3) monthly micrometeorological data were input into the AERMET preprocessor.
Next, the ventilation factor (VF) was calculated fo r each station. The VF is a combination of wind spe ed and mixing height that relates pollution dispersion potential to the local setting. More specifically, the VF is defined as the product of a representative wi nd speed and local mixing height and effectively**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### represents a measurement of the dispersion flux thr ough an idealized box around a source of pollution. In area per unit time, VF increases with either inc reasing mixing height or increasing wind speed, thu s increasing the idealized volume in which pollutants are allowed to mix and dilute. In US EPA (2011), i n order to estimate the VF consistently, all hourly s urface wind data were translated to a standard 2-m height by assuming a standard logarithmic wind prof ile. This corrects measurements made at different heights between stations to a single value to make inter-comparisons between datasets viable. It also establishes a consistent height within the mixing l ayer to use for comparisons. This height-corrected wind speed was multiplied by the AERMET-determined hourly mixing height, which was taken as the greater of the convective and mechanical mixing hei ght values.
Station representativeness was determined based on the range of VFs at a station relative to that of t he encompassing climate region. This method ensures th at the selected single station adequately represents the range of dispersive conditions prese nt in the mixing layer in a region. A time series o f hourly VFs was developed in US EPA (2011) spanning the full year of hourly records, minus any missing hours, for each station. A small number of stations were not used due to large amounts of missing data

. Specific quantiles of the cumulative distribution f unction (CDF) of each annual VF data series (the 5 th


### , 25 th 

### , 50 th 

### , 75 th 

### , and 95 th 

### percentiles) were determined and then averaged for each station, creating a mean and standard deviation in the annual estimate of the 5 th 

### , 25 th 

### , 50 th 

### , 75 th 

### , and 95 th 

### percentile values of VF for each station.
After each station was processed, the stations in a climate region were aggregated to estimate the means and standard deviations of climate-region-wid e 5 th 

### , 25 th 

### , 50 th 

### , 75 th 

### , and 95 th 

### percentile VFs.
Finally, the quantile data (x j 

### ) from each station in each climate region (g) was individually compared to that of the aggregate of all stations in the region (h), using a χ 2 

### -type analysis. The station that exhibited the smallest value of χ 2 

### was taken as representative of the region as a who le. This type of test is used to compare two sets of data to determine whether they are drawn from the same parent distribution. Its application is appropriate in this case because the statistic is being used in a relative sense to com pare between subsets of the full distribution.
For the current analysis, the same representative s tations identified in US EPA (2011) were used. In addition, for the five climate regions bordering th e Pacific or Atlantic Oceans or the Gulf of Mexico, if the selected station was near a coastline, then an addi tional station was included that was inland, or vic e versa. More specifically, the additional station se lected in each coastal region (a) was well inland i f the first station was coastal (or relatively coastal if the first station was inland), (b) was in a differ ent state from the first station, (c) experienced wind condit ions that were significantly different from the fir st**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### station, and (d) had high-frequency wind data avail able for the target period. Note that the χ 2 

### values were not considered for these additional stations.
Table G-1 shows the selected surface station(s) for each climate region, along with their correspondin g upper-air stations and the χ 2 

### values determined from comparing its CDFs of VF va lues against the entire climate region’s CDFs of VFs. Figure G-1 shows a map of the selected stations. Each of the χ 2 

### values for the first station selected per region was much less than one, indicating good relative agreement between the sets of VFs. The χ 2 

### values for four of the five additional selected st ations were greater than one, though the χ 2 

### values were not considered in selecting these addi tional stations.



### G.1.1.2. Processing the Data from the Selected Stations The 2007 through 2010 data (the analysis period for this project) for these 14 stations were then reprocessed for the purposes of the current dispers ion and deposition analyses. The hourly surface meteorological data were re-downloaded in a format friendlier to AERMET than the format used in the above analysis to select the 14 stations (see Appen dix G.1.2 for more information). Annual determinations of surface wetness were reevaluated using additional data. Then, several reprocessing methods were used to reduce the number of calm and missing wind values as well as the number of missing mixing height values, increasing the number of hours for which AERMOD can produce dispersion and deposition data.
The first method of replacing calm or missing wind data used the AERMINUTE preprocessor (version 11325 11/21/2011) with one-minute wind observations in order to reduce the number of calm winds compared to the typical hourly wind reports. The so urce of these one-minute data is discussed in Appendix G.1.2. One input to AERMINUTE is the date on which the ice-free wind sensor (i.e., sonic anemometer) was installed, as the sensitivity of th e instrumentation dictates how AERMINUTE processes light wind speeds. Table G-1 provides these installation dates. Note that the C amp Springs, MD station in the Northeast climate region is not a n ASOS station, so AERMINUTE was not used for that station. Using AERMINUTE outputs when running AERME T (version 13350; 12/16/2013) significantly reduced the occurrences of calm winds in most of th e data, and it also reduced the number of missing wind observations. AERMET was also run using setti ngs that randomized hourly wind direction observations (which are typically reported in tens of degrees) and avoided substitutions of missing cl oud cover data (which was known to be coded incorrectly in the version of AERMET being used).
After substitutions of wind data using AERMINUTE, t he second method of replacing missing data used methods similar to EPA’s guidelines for replacing m issing meteorology data, as described in US EPA (1992). The data replacement methods used here gene rally followed EPA’s “objective” steps, but those objective methods do not necessarily replace every instance of missing data – in these cases, EPA**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### suggests some subjective steps which rely to a larg e extent on professional judgment. Additional steps were developed for this analysis that identify reas onable replacements for missing data, to the extent that the existing data allows. In general, variable s were replaced so that an average of nearby hours is preferred, but otherwise the same hour of day from other neighboring days were used. This method was applied to missing mechanical mixing heights (d uring non-convective conditions), convective mixing heights (during convective conditions), and tempera ture data. For wind data, missing values were replaced by first preferring an average of nearby h ours, and when that was not successful the average was taken of other nearby hours. The averaging proc ess for wind direction values used the scalar components of the wind vector. Missing convective m ixing heights during non-convective hours, mechanical mixing heights during convective hours, and surface heat fluxes (which is the parameter used to determine whether or not the boundary layer is convective) were not replaced.
The resulting wind roses for the 14 selected statio ns are shown in Figure G-2. Note that wind roses indicate both wind speed and direction, while the V Fs used in US EPA (2011) to select the stations onl y include wind speed. Thus, elements of variation bet ween stations in each region (e.g., wind direction patterns) will occur that are not included in the r epresentativeness determination.



## G.1.2. Sources of Meteorological Data for AERMOD In a previous study (US EPA, 2011), meteorological data were collected from stations around the US and grouped into climate regions. The station data were then analyzed to select one to two stations per climate region. This Appendix gives further details about the sources of the meteorological data used to select the final 14 stations used in the current an alysis and then to process the data for those stati ons for use in this analysis.



### G.1.2.1. Surface Meteorological Data for Station Selection The Quality Controlled Local Climatological Data (Q CLCD) dataset used for this analysis is available f rom NOAA and covers thousands of surface meteorological stations within the US at an hourly resolution and includes all fields necessary for processing in the AERMOD Meteorological Processor (AERMET) and running AERMOD (NOAA, 2011a). However, no pre-exist ing tools were found capable of translating the QCLCD data into a form AERMET can read. Thus, scrip ts were developed in-house for US EPA (2011) to translate this QCLCD surface data in both of its fo rmats (the QCLCD data underwent a change in format in June 2007) into the Solar and Meteorological Sur face Observational Network (SAMSON) format accepted by AERMET. Some of the differences in fiel ds prior to and subsequent of the format change are noted in the QCLCD update documentation (NOAA, 2011 b).
To reduce processing time and meet the objectives o f this project, only the 392 stations in the QCLCD dataset that were active during the 2006 through 20 10 modeling period and within U.S. Census**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### urbanized areas were considered. Urbanized areas ar e defined as densely settled territories that conta in 50,000 or more people each (US Census Bureau, 2011) . Core-based statistical areas (CBSAs) or metropolitan statistical areas (MSAs) were not cons idered due to the fact that some CBSAs are MSAs (those containing at least one urbanized areas) whi le others are not (those containing one urban clust er of at least 10,000 people), and, unlike urbanized a reas, CBSAs can contain whole counties (US Census Bureau, 2011). To further reduce processing time, t he US EPA (2011) analysis to select stations representative of climate regions covered a three-y ear time frame (2008 through 2010), although the final processing for use in the current dispersion modeling analysis utilized years 2007 through 2010.
Each of the lower 48 states (except Delaware) and W ashington, DC contained at least one of these 392 surface stations. As an example of the distribution , four states (New Hampshire, South Dakota, Vermont , and Wyoming) and Washington, DC, each contained one station, while California contained 48 stations. Each of the nine climate regions contained at least 10 stations – ranging from 10 stations in Climate Region 9 (the Central region, covering the Ohio Val ley) to 84 stations in Climate Region 5 (the Southe ast region).
An additional input necessary for AERMET execution and for VF calculation is the anemometer height. For ASOS stations, these values were taken from a N OAA dataset (NOAA, 2011d) containing anemometer heights from 2009. Roughly 75 percent of the stations (298) considered here were included in this table. All remaining stations, as well as three stations where the anemometer height was reported as 0 m above ground level, were assumed to be at a standard 10 m height.



### G.1.2.2. Surface Meteorological Data for the Current Analysi s When the data for the 14 selected meteorological st ations were reprocessed for the purposes of the current analysis, the hourly surface data were redo wnloaded in Integrated Surface Hourly format (NCDC, 2013b), which works better with AERMET than does th e QCLCD data used in the station selection analysis. The one-minute wind data used for the cur rent analysis were obtained from NOAA (NOAA, 2013a).



### G.1.2.3. Upper-Air Meteorological Data Upper-air data was collected for US EPA (2011) from NOAA’s ESRL database (NOAA, 2011c). There were approximately 79 upper-air stations in the “lower 4 8” states active during the 2006 through 2010 perio d used to select representative stations. The upper-a ir station network has less coverage than for surfa ce stations. While most states have an upper-air stati on (some have multiple), nine states do not. Some surface stations are more than 100 km from the near est upper-air station, and the nearest upper-air station might not have the same geographic and clim ate characteristics as the surface station. Because of these differences between the surface and upper- air datasets, each surface station was first matche d**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### with its closest upper-air station. Then, each prox imate match was scrutinized to be sure that the surface station and upper-air station shared simila r geographic and meteorological characteristics. Fo r a surface station that did not share these characteri stics with its proximate upper-air station, that up perair station was replaced with the closest upper-air station that did share these characteristics. For example, a coastal surface station was matched with a coastal upper-air station even if an inland uppe rair station was physically closer. This resulted in 59 upper-air stations being matched with the 392 surface stations. This pairing was chosen to approp riately capture regional dispersion characteristics

. The same upper-air station data were used for the c urrent analysis.



### G.1.2.4. Micrometeorological Data for Station Selection Surface characteristics surrounding the surface met eorological station are also required for AERMET processing. AERMET requires the land use distributi ons of the study sites in order to estimate values of three important surface characteristics (surface ro ughness length, albedo, and Bowen ratio). Surface roughness length contributes to AERMOD’s estimation s of surface turbulence and boundary layer stability. Albedo affects the amount of solar radia tion absorbed by the surface, which affects boundar y layer height and stability. The Bowen ratio describ es how much surface heat is lost to the boundary layer through conduction and convection versus thro ugh evaporation, which affects the height and stability of the convective boundary layer.
AERMOD’s land-use preprocessor, AERSURFACE, was use d to calculate the distribution of land use types surrounding each station using the 1992 National La nd Cover Database (NLCD) (USGS, 2010). In order for AERSURFACE to tailor its calculations for a specifi c location, it also requires information on the loc ation’s climate (i.e., snowiness, season definitions, and a ridity). Using NCDC climate normal contour maps, monthly season assignments were made for US EPA (20 11) based on median frost/freeze dates and monthly average temperatures, the snowiness assignm ent was made based on average snow-day data, and aridity was determined using annual average rai nfall data. AERSURFACE has the ability to calculate separate surface roughness for a maximum of 12 dire ctional sectors out to a distance of up to 5 km, an d these sector roughness values are then assigned to each hour of modeling based on that hour’s wind direction. The maximum number of sectors, evenly di spersed, for every station (12 30-degree sectors starting with due North) along with the recommended default distance of 1 km, was used. In the US EPA (2011) analysis, all stations were assumed to have “average” wetness for moisture flux calculations. Each station was also characterized as an airport s tation in the preliminary analysis, which lowers th e surface roughness values for the commercial/industr ial/ transportation land cover type (which is otherwise assumed to have higher surface roughness values representative of a developed area with buildings). This is a reasonable assumption because most of the 392 surface stations are at airports. Using all of these data, AERSURFACE used look-up ta bles to identify the values of the three surface characteristics (see Appendix A of the AERSURFACE U ser’s Guide [USEPA, 2008b]).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### G.1.2.5. Micrometeorological Data for the Current Analysis To use data from the 14 selected station pairs in A ERMOD runs, the wetness and airport determinations were revisited to tailor them to the surface statio n sites. For wetness, the 2007 through 2010 annual rainfall amounts for each station were compared to the climate normal annual rainfall amount from the closest station in the 1981-2010 30-year NCDC clima te normal dataset. For a given year, if a station received at least 25 percent more rainfall than the climatological normal, it was processed in AERSURFACE as “wet” for that year; if a station rec eived at least 25 percent less rainfall, it was “dr y”; and, otherwise, it was “average”. Each of the 14 se lected station pairs was confirmed to be at an airp ort, and all other settings used in the station selectio n analysis were unchanged when rerunning AERSURFACE (first version; 01/2008).**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftG-8July 25, 2014Table G-1. Selected surface and paired upper-air stations representing each climate region.ClimateRegionSurface StationUpper-air StationVentilation FactorVF χ2WBANCallSignLocationCoastal or Inland,withapproximatedistances towater for coastalInstallation Date ofIce FreeWindSensorLatit.Longit.WBANLocationLatit.Longit.1(East NorthCentral)14937 KIOWIowa City,IAInland10/20/200541.633 -91.543 94982Davenport, IA41.6 -90.570.098(smallest in region)2(Northeast)13705 KADWCampSprings,MDCoastal (31 kmfrom ChesapeakeBay, 60 km fromAtl. Ocean)Not ASOS 38.811 -76.867 93734Sterling,VA38.98 -77.470.149(smallest in region)14762 KAGCPittsburgh, PAInland 4/3/2007 40.355 -79.922 94823Township,PA40.53 -80.230.764(13th smallest of 69stations in region)3(Northwest)24222 KPAEEverett,WACoastal (4 km fromPuget Sound, 50 kmfrom Salish Sea, 180km from Pac.Ocean)3/29/2007 47.908 -122.28 24232 Salem OR 44.92 -123.020.250(smallest in region)24145 KIDAIdahoFalls, IDInland 1/30/2007 43.516 -112.06 24061Riverton,WY43.06 -108.4716.087(largest of 21 stationsin region)4 (South)13920 KFOETopeka,KSInland 9/7/2006 38.95 -95.664 13996Topeka,KS39.07 -95.620.273(smallest in region)03937 KLCHLakeCharles,LACoastal (41 kmfrom Gulf ofMexico)4/19/2007 30.125 -93.228 03937LakeCharles,LA30.12 -93.222.734(28th smallest of 59stations in region)**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftG-9July 25, 2014ClimateRegionSurface StationUpper-air StationVF χ2WBANCallSignLocationCoastal orInland, withapproximatedistances towater forcoastalInstallation Dateof IceFreeWindSensorLatit.Longit.WBANLocationLatit.Longit.5(Southeast)93727 KNCANewRiver, NCCoastal (20 kmfrom Atl. Ocean)Unknowna34.7 -77.433 93768MoreheadCity, NC34.7 -76.80.379(smallest in region)13874 KATLAtlanta,GAInland 3/27/2007 33.64 -84.427 53819PeachtreeCity, GA33.35 -84.561.396(24th smallest of 84stations in region)6(Southwest)23066 KGJTGrandJunction,COInland 3/13/2007 39.134-108.53823062Denver,CO39.77 -104.880.459(smallest in region)7 (West)93111 KNTDPointMugu, CACoastal (2 kmfrom Pac. Ocean)10/1/2007 34.117-119.11093214Vandenberg AFB, CA34.75 -120.570.284(smallest in region)23169 KLASLas Vegas,NVInland 4/25/2007 36.079-115.15553103Flagstaff,AZ35.23 -111.824.253(33rd smallest of 52stations in region)8(West NorthCentral)14944 KFSDSiouxFalls, SDInland 8/22/2006 43.577 -96.754 94980Omaha,NE41.32 -96.370.377(smallest in region)9(Central)94822 KRFDRockford,ILInland 5/22/2007 42.196 -89.093 94982Davenport, IA41.6 -90.570.127(smallest in region)**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftG-10July 25, 2014Figure G-1. Map of selected surface and paired upper-air stations representing each climate region.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure G-2. Wind roses for each selected regionally representative station, 2007-2010.


# G.2. Emissions To account for the fact that renovations may occur during any season, the renovation emission progression around the building blocks was begun fo ur times during a model year, on January 1 st 

### , April 1 st 

### , July 1 st 

### , and October 1 st 

### . This progression was also repeated for four diffe rent years, each with different meteorological conditions (see below). Th en, the air concentrations from each renovation hou r for all of the 16 seasons (4 years × 4 seasons/year ) were aligned for each receptor location and avera ged to give a central tendency estimate of the air conc entrations across the seasons at that location. The n, the time series of these central tendency concentra tions were used in the Monte Carlo model.


# G.3. Deposition The size of the particles emitted during the renova tion will affect the deposition and air concentrati ons at each receptor in AERMOD. If the relative mix of particle sizes includes more coarse particles, the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### concentrations at the closest receptors will be lar ger and the concentrations at the farthest receptor s will be smaller because the larger particles will t end to deposit closer to the renovated building. AERMOD (version 13350; 12/16/2013), deposition was modeled using AERMOD deposition “method 1” algorithm (USEPA, 2009b), which requires the specif ication of the fraction of particulate matter in ea ch size category modeled and the density of the partic les. Thus, in order to accurately model the emissio ns due to the full set of renovation activities, diffe rent deposition values were used for different reno vation activities.
Before the selected size distributions and particle density are discussed, it is noted that the AERMOD modeling is intended to model the particulate conta ining lead (such as renovation-derived dust) rather than lead itself; then, the emission rates are used to estimate the lead mass emitted as part of that particulate. Thus, the deposition values should be consistent with the emitted lead-containing particulate (assumed primarily to be paint particle s) rather than the lead on the particulate.
Three size classes, 2.5, 10, and 20 μm, were select ed for all AERMOD runs. Choe **et al.** (2000) measured airborne particulate concentrations when doors with lead paint were dry scraped and dry sanded. They measured the mass fractions in sizes up to 20 μm be cause their preliminary experiments showed no appreciable mass above 20 μm after the renovation a ctivity. The line corresponding to the 0 th 

### hour after abatement in Choe **et al** ’s Figure 3 was digitized using the GetData ® 

### software program (GetData ® 

### , 2008). Then, the fraction of mass in each of the three siz e classes was estimated by (1) multiplying each concentration density by the dlog(d) to get the inc remental concentration at that diameter, (2) performing a cumulative sum across all diameters to get the cumulative concentration at each diameter, and (3) identifying the cumulative concentration fo r each of the three target size classes (2.5, 10, a nd 20 μm), and (4) finding the incremental concentration in each size bin. For dry scraping, 2 percent was l ess than 2.5 μm, 72 percent was between 2.5 and 10 μm, and 26 percent was between 10 and 20 μm. For sanding, 4 percent was less than 2.5 μm, 62 percent was between 2.5 and 10 μm, and 34 percent was between 10 and 20 μm. The two different particle si ze distributions were each run and determine to have less than a 2% effect on air or soil lead conc entrations. To reduce the number of AERMOD runs required all activities were modeled using the part icle size distribution resulting from dry scraping.
For the particle density, the density was set equal to 2 g/cm 3 

### to be representative of the density of leadcontaining paint. If the released particles consist of building substrate as well as paint, the partic le density may be somewhat larger or smaller. In addit ion, the exact density will depend on the fraction of lead in the paint, which varies by the year the pai nt was applied and from building to building. Thus, this assumption introduces uncertainty in the modeling. It was not computationally feasible, however, to perform AERMOD runs for every particle density. Ear ly scoping results indicated that AERMOD particle density did not strongly affect the indoor dust loa dings.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# G.4. Urban/Rural Each AERMOD modeling run for each combination of re novated building, renovation activity pattern, and meteorology data were run twice – once using AE RMOD’s urban setting with an arbitrary population of 1 million people, and once not using the rural setting.
For the Approach, agricultural buildings were model ed with AERMOD’s rural option; all other receptor building types were modeled with AERMOD’s urban opt ion. 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 e nhanced heating and turbulence compared to an adjacent, nonurban area. Because the renovations oc cur only during the day and the urban effect occurs primarily at night, the difference between simulate d 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 approxim ately 90 percent of the simulated scenario-days. Fo r the small percentage of scenarios where this distin ction does make a difference, EPA could consider additional data sources that describe how often dif ferent building types occur in urban or rural locat ions by adding this as a sampled variable.
Urban areas tend to experience increased daytime su rface heating and decreased nighttime surface cooling compared to rural areas. They experience su ch heating due both to densely distributed human activity and to ground cover and building materials that absorb solar radiation during the day and slo wly release it overnight. By contrast, rural areas with natural vegetation cover release daytime heating f airly quickly overnight, allowing rural areas to cool ove rnight relatively faster than urban areas. These ne arsurface heating and cooling aspects also affect sta bility, turbulence, and mixing heights, all of whic h affect the transport of chemicals in the air. The u rban setting in AERMOD parameterizes an urban area’ s decreased nighttime cooling by creating a nighttime boundary layer with a taller mixing height and enhanced heating and turbulence compared to an adja cent, non-urban area. The magnitude of these urban effects in AERMOD is partially controlled by a user-input value for urban population.
The urban option is not intended to affect daytime conditions, which in AERMOD is defined as hours when the atmosphere is unstable. However, there can be a sharp and artificial discontinuity in mixing height when daytime begins and the modeled urban ef fect shuts off, so the fifth AERMOD revision (version 11059, 02/28/2011) implemented a transitio n period that allows the urban mixing height adjustments to continue until the daytime mixing he ight becomes taller.
In some simplistic test runs with AERMOD, the urban option and any necessary daytime transition adjustments affected the modeled air concentration and deposition fluxes during approximately 10 percent of hours in the 9am-to-5pm renovation windo w used in the current analysis. During these**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### affected times, the urban option increases average air concentrations and deposition fluxes by approximately a factor of 2, though the effect aver aged across the whole renovation window is closer t o 10 or 20 percent. These simplistic AERMOD test runs and their results should not be interpreted as representative of all urban-setting scenarios or of building renovation activities in particular; the intent was only to show the conditions where the urban set ting impacts modeling results, with some notion of magnitude of impact.


# G.5. Obstruction Adjustment The presence of obstructions between the source bui lding and the receptor may either provide an enhancement or protection to the receptor from the renovation-derived lead-containing dust. The windfield will be influenced by the buildings such that emitted dust may be channeled around and over the obstruction building and concentrated in a mixi ng “rotor” zone on the leeward side of the building

. Thus, receptors located near the obstruction might be exposed to either a larger concentration (in the rotor zone) or smaller concentration (outside the r otor zone).
The Approach takes this potential obstruction effec t into account. However, it is not feasible quantitatively to model the effects of the infinite number of combinations of sources, obstructions, a nd receptors. Thus, a distribution of protection/enhan cement factors was developed and sampled during the Monte Carlo modeling. These factors are then ap plied to the AERMOD concentrations to account for potential obstruction effects.
Early scoping results evaluated the effects of 13 o bstruction scenarios were modeled using the BPIPPRIME algorithms in AERMOD to find the greatest pro tection and enhancement effects introduced by a third building, termed an obstruction, that is loca ted near to both the source and receptor location a nd disrupts the windfield around the receptor location . These exploratory model runs were performed with a grid of 1,381 downwind receptors, rather than the 160 receptors locations used in the Monte Carlo modeling. It was found that the greatest effect occ urred when an obstruction was located very near the source building and angled orthogonally to the sour ce building. In that scenario, the maximum modeled protective factor was approximately 0.1, meaning th e resulting air concentrations were 10 percent the level found in the case of no obstruction, and the maximum enhancement factor was approximately 5.3, or 530 percent greater than when no obstruction bui lding was present.
For this analysis, eight additional obstruction sce narios were modeled in an attempt to find the great est effect of an obstruction. The scenarios including variations on placing the obstruction closer to the source building, elongating the obstruction (i.e., a rectangular, rather than square, configuration), and placing obstructions in series (i.e., more than one obstruction). The protective and enhancement effec ts produced by the original orthogonal scenario tested for the transport analysis seemed to represent a**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### ceiling, as the additional tested scenarios produce d nearly identical magnitudes of protective and enhancement effects, but did not exceed those found in the original scenario. This is illustrated by t he results of three obstruction scenarios presented in Figure G-3.
From the obstruction scenario tests, the following general observations on the effects of obstructions on the modeled air concentration were found:

• For any given hour, the effect of the obstruction i s minimal to zero for at least half of the compass directions (i.e., the directions upwind of the source building are not affected by the obstruction).

• For most of the reasonable combinations of source b uildings and obstructions, the maximum protection and enhancement factors of an obstructio n were not less than 0.1 or greater than 5.3, respectively.

• Among the locations where the obstruction affected modeled air concentrations, the effects were relatively large at relatively few locations a nd relatively small, that is, near one, at a majority of locations. Given the above observations, a distribution was de veloped to describe the expected effect of an obstruction on modeled air concentrations. First, i t was assumed that all receptors upwind of the modeled renovation will experience no effect from a n obstruction on a given modeling hour. To replicate this, a number of receptors equal to the number modeled downwind but with no obstruction effect (protection/enhancement factor equal to one) were added to the results of each modeling run, resulting in 2,762 reported values. These values we re then sorted from smallest to largest for each scenario. The worst case and average of all obstruc tion cases were plotted, as shown in Figure G-3 and the distributions were described using the followin g equations:

For the 1 st 

### the 32 nd 

### percentiles: **ObsAdj** = 0.15*ln( **perc** )+1.15

For the 33 rd 

### to 87 th 

### percentiles: **ObsAdj** = 1

For the 88 th 

### to 100 th 

### percentile: **ObsAdj** = exp( **perc** 25 

### )

where **ObsAdj**

= obstruction adjustment

**Perc** = percentile of distribution
The obstruction adjustment was also bound by the ov erall minimum and maximum found experimentally, 0.1 and 5.3, respectively.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure G-3. Distribution used to describe the effec t of an obstruction building on air concentrations at a given receptor.


# G.6. References for Appendix G Choe K; Trunov M; Grinshpun S; Willeke K; Harney J; Trakumas S; Mainelis G; Bornschein R; Clark S;
Friedman W. (2000). Particle settling after lead-ba sed paint abatement work and clearance waiting period. American Industrial Hygiene Association Jou rnal 61: 798-807.

GetData ® 

### . (2008). GetData ® 

### Graphic Digitizer. http://www.getdata-graph-digitizer.com/download.php
NOAA (2011a). Quality Controlled Local Climatologic al Data (QCLCD [ 2.4.4]): Documentation. Effective January, 2005. Available online at: http://cdo.ncdc .noaa.gov/qclcd/qclcddocumentation.pdf.
NOAA (2011b). Quality Controlled Local Climatologic al Data (QCLCD [ 2.4.4]): Quality Controlled LCD Improvements/Differences/Updates. Available online at: http://cdo.ncdc.noaa.gov/qclcd/qclcdimprovements.pd f.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### NOAA (2011c). NOAA/ESRL Radiosonde Database. Availa ble online at:http://www.census.gov/geo/www/cob/ua_metadata.ht ml.
NOAA (2011d). National Weather Service Surface Obse rvations Program: Wind Tower Dataset. Availableonline at: http://www.weather.gov/ops2/Sur face/documents/windtower.xls.
US EPA. (2011). Development of a Microscale “Meta-m odel” for Estimating Near-road Impacts ofEmissions from Archetypal High-traffic Roads, Con tract EP-C-06-094, WA 4-06 Final Report, Prepared for
U.S. EPA, Office of Transportation and Air Quality, by ICF International, September 30, 2011.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-1 July 25, 2014



# Appendix H. Exterior Emission Rates

EPA’s Dust Study (US EPA, 2007a) is the most compre hensive study of lead dust generation by renovation activity to date. It was used to charact erize the lead-emissions from eight of the exterior renovation activities considered: paint removal by dry scraping, power sanding, torching, use of high and low temperature heat guns, and use of a needle gun; and replacement of exterior doors and trim. An emission rate was estimated for each of these renov ation activities using the methodology described below. The ninth activity, demolition, used a diff erent methodology described in Appendix B.10.
To design an analysis framework for estimating lead aerosol emissions due to renovation of a building from the Dust Study, it is necessary to understand the study methodology and the experimental variables captured in the study. Figure H-1 shows t he typical layout of an experiment in the Dust Stud y. The steps of the experiments were generally as foll ows:
An area of wall (or trim, door, railing, or porch c eiling) was selected and specified as the disturbed area.
The area around the disturbed area was prepared for the experiment. Plastic was placed on the ground and had a specified length along the wall (referred to as the plastic “width” or the “along-wall” direction) and a length perpendicular to the wall ( referred to as the plastic “distance from wall” or the “out-from-wall” direction). The plastic included “ rule plastic” closer to the job and “beyond rule pl astic” further from the job. In addition, vertical plastic was used at the edges of the ground plastic to enc ase the job and limit horizontal transport of dust outs ide the job area.


### The job was performed and the time needed to comple tely perform the renovation within the disturbance area was noted.
The dust loading in units of mass of lead per squar e foot was measured at six different distances from the wall for each experiment. To su pport the estimation of emission rates, the contractor who collected the data in the Dust Study , Battelle, provided maps for each experiment showing the geometry of the plastic and the placement of the sample trays. This information is not presented in the Dust Study repo rt but is provided in this Appendix in Sections 0 to H.14.
Either a single experiment or multiple experiments were performed for each activity, with six measurements of surface loading per experiment.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-2 July 25, 2014 

### To estimate the fraction of paint on the wall in th e Dust Study emitted during a specific activity, it is necessary to estimate the total amount of dus t that was removed and introduced into the air during the activity as a fraction of the total amount of paint that was on the wall. The total amount of emitted dust must be estimated from the l oading values in the Dust Study by accounting for spatial differences in the loading a nd integrating the loading over the area of the plastic. In this Approach, the emission calculation is achieved by

1. Along-wall distribution: Performing several convers ions on the loading data and estimating an alongwall distribution of dust, 2. Out-from-wall distribution: Generating a regression equation for the loading as a function of distance from the wall to estimate the out-from-wal l distribution of dust, 3. Total dust emitted: Integrating the loading on the plastic in the along-wall and out-from-wall directions to estimate the total dust emitted durin g the job, 4. Total paint on wall: Estimating the total amount of paint originally on the wall before the experiment began, and 5. Emission fraction: Estimating the fraction of paint on the wall that was emitted as aerosol particles.


### Each of these steps is described in general terms b elow. Then, specific details (including the experiment maps) are provided for each experiment i n the remainder of the Appendix.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-3 July 25, 2014



### Figure H-1. Schematic of the Dust Study experiment al set-up.


# H.1. Loading Data Conversions and Along-Wall Distributio n of Paint Dust Because each experiment was performed using differe nt total wall disturbance sizes, the fraction of paint emitted was calculated on a per square foot o f disturbed wall basis. Thus, each Dust Study lead loading was divided by the area disturbed in the ex periment to get the loading in units of lead mass p er square foot of plastic and per square foot of wall disturbed:

AreaDist LeadLoad LeadLoad unit

wall per 

### = 

### where: 

### LeadLoad **per wall unit** 

### = the total lead loading on the plastic per unit of w all area disturbed ( μ g/ft 2 

### /ft 2 

### ) **LeadLoad =** the total lead loading on the plastic ( μ g/ft 2 

### ) **AreaDist =** the total wall area disturbed during renovation (ft 2 

### )
In addition, because each experiment was performed on buildings with different compositions of lead within the paint, the fraction emitted was calculat ed based on the total amount of paint dust emitted rather than the total amount of lead emitted. This conversion assumes that each experiment within an activity should produce nearly uniform amounts of p aint dust per square foot disturbed, even though the amount of lead will vary according to the lead content in the paint. To make this conversion, each lead loading was divided by the percentage of lead in the paint specified in the Dust Study to Sample Trays

Plastic Containment Out-From-Wall Direction**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-4 July 25, 2014 

### approximate loadings in units of mass of paint part icles per square foot of plastic and per square foo t of wall disturbed (see equation below).



### mg g frac LeadLoad oad PaintDustL unit wall per unit wall per 

### / 1000 1 μ 

### × =
where: **LeadLoad per wall unit**  

### = the total lead loading on the plastic per unit of w all area disturbed (mg/ft 2 

### /ft 2 

### ) **PaintDustLoad**

**per wall unit** 

### = the total paint dust loading on the plastic per uni t of wall area disturbed (mg/ft 2 

### /ft 2 

### ) **frac =** the fraction of paint which is lead by weight (unit less)

Note that this conversion assumes that all the emit ted particles are paint particles and does not acco unt for the fact that the total emitted dust might incl ude wall substrate particles as well. Thus, the estimated paint dust emission rate will not represe nt the total dust emitted during the activity but i s intended to account for all the lead emitted, assum ing that there is lead in the paint but not within the substrate or other disturbed portions of the buildi ng.
Next, the along-wall distribution of dust was estim ated. Following inspection of the general trends in the Dust Study experiments, it was assumed that the loa dings were relatively constant over the width of th e job and then tapered linearly to zero at the edge o f the ground plastic in the along-wall direction (s ee Figure H-2). Thus, if measurements were made at lo cations wider than the job width (for example, Sample X in Figure H-2), these measurements were ad justed using a linear relationship to approximate the loading within the job area (for example, Sampl e X adj 

### in Figure H-2). In the event that experimentspecific dust distributions differed from this gene ral format, the distribution used is noted in the experiment-specific information in Sections 0 to H. 14.
To make this linear conversion, the following equat ion was used:

unit wall per unit wall per 

### oad PaintDustL e DistMeasur DistPlast DistPlast oadAdj PaintDustL

− =



### where: **PaintDustLoadAdj per wall unit**  

### = the total paint dust loading on the plastic per uni t of wall area disturbed adjusted to the level within the job area (mg/ft 2 

### /ft 2 

### ) **PaintDustLoad**

**per wall unit** 

### = the total paint dust loading on the plastic per uni t of wall area disturbed (mg/ft 2 

### /ft 2 

### ) **DistPlast =** the distance between the edge of the job and the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-5 July 25, 2014 

### containment plastic (ft) **DistMeasure =** the distance between the edge of the job and the measurement (ft)

All of these distances were calculated from the inf ormation in the Dust Study job maps provided by Battelle. Because the dust is measured over discre te areas 1 ft wide, the measurements are assumed to apply to the center of the measurement area. Thus, the loadings were assumed to taper to zero over a distance 1 ft wider than shown in the diagrams. Thi s assumption causes the loadings to fall to zero in the center of the 1 ft box next to the containment plas tic. The alternative assumption (loadings going to zero at the center of the box just inside the conta inment plastic) was not used, since non-zero measurements were available at those locations in t he Dust Study.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure H-2. Schematic of Assumed Along-Wall Distr ibution of Loading


# H.2. Regression of Dust Study Data by Renovation Activity To estimate the distribution of dust in the out-fro m-wall direction within the job area, a regression analysis was performed on the **PaintDustLoadAdj per wall unit**  

### values (or the **PaintDustLoad per wall unit**  

### values if no adjustment was necessary because the measurement was made within the job area) at each corresponding distance from the wall with Microsoft Excel® using the following equation for exponentia l decay:Out-FromWall Direction

Along-Wall Direction 

### Constant Loadings in Job Area Tapered To Zero Outside Job Area Tapered To Zero Outside Job Area



# x Distance to

Measurement Distance to Vertical Plastic

Job Area

Horizontal Rule Plastic Horizontal Beyond Rule Plastic

Vertical Containment Plastic 

# x adj



# x Dust Study Loading Measurement 

# x adj Adjusted Dust Study Loading MeasurementDust Loading perft2disturbed

Lateral Linear Taper Region**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-7 July 25, 2014 Dist Shape e Load egress PaintDustR × × = 0


### where:
**PaintDustRegress =**  paint dust loading (mg/ft 2 

### /ft 2 

### disturbed) **Load 0**  

### = loading just below the job at 0 ft (mg/ft 2 

### /ft 2 

### disturbed) **Shape =** shape of exponential regression (/ft) **Dist =** distance from renovation activity (ft)
Before performing the regressions, an adjustment wa s made to account for the presence of large bulk material in the Dust Study. Dust Study samples were collected and reported as “with bulk” and “without bulk”. Owing to the nature of the activities, some of the material removed during the renovation was expected to be too large to be released into the ai r. However, bulk samples in the Dust Study include samples in which either the particles were too larg e to be captured in a wipe sample (which should be excluded from this Approach) or where there was too much dust to be collected in a wipe sample (which should be included in this Approach). There is no w ay to separate these two different types of samples

. Thus, an empirical correction was made to account f or the large bulky particles. It was assumed that most of these bulky particles would fall within thr ee ft of the job wall (assuming they fell directly downward). Thus, any bulk samples collected within two ft of the job were excluded from the regression. In addition, any samples designated as “bulk” samples taken at three ft from the renovatio n wall were also excluded from the regression, since it was assumed that large debris could reach that distance for some of the activities. This correctio n allowed the samples at further distances (where b ulk is assumed to be absent) to define the shape of the regression curve. Comparing the regression parameters both before and after the correction, th e power sanding regression coefficients changed very little while the dry scraping coefficients cha nged dramatically. This trend is as expected and provides some indication that this Approach is appr opriate, as power sanding is expected to emit few bulky particles while dry scraping emissions could be dominated by bulky particles.


# H.3. Total Amount of Dust Emitted Once these regression equations were determined, th e total amount of dust on the plastic was estimated by combining the along-wall (Appendix H.1 ) and away-from-wall (Appendix H.2) distributions and integrating to get the total mass. This was don e by estimating the mass within the job area, estimating the mass outside the job area, and then adding the two together. For the mass within the jo b area, the mass was found by integrating the regress ion expression over the length of the plastic (outfrom-wall direction) and multiplying by the width o f the job (along-wall direction):**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-8 July 25, 2014 

# ( ) JobWidth e Shape Load assDust M LenPlast Shape inside ×

− × = × 1

0



### where:

**MassDust inside**  

### = paint dust mass on the plastic inside the job area (mg/ft 2 

### disturbed) **Load 0**  

### = loading just below the job at 0 ft (mg/ft 2 

### disturbed) **Shape =** shape of exponential regression (ft -1 

### ) **LenPlast =** length of plastic in the out-from-wall direction (f t) **JobWidth =** the width of the job in the along-wall direction (f t)
For the mass outside the job area, that is, collect ed on either side of the area disturbed, the mass w as found by finding the area within each linear taper region (one on each side) according to the equation :

Outside PlastWidth JobWidth side MassDustIn assDust side

outside 

### × = 5.0 M /



### where:

**MassDust outside/side**  

### = paint dust mass on each side of the plastic outside the job area (mg/ft 2 

### disturbed) **MassDust inside**  

### = paint dust mass on the plastic inside the job area (mg/ft 2 

### disturbed) **JobWidth =** the width of the job (ft) **PlastWidthOutside =** the width of the plastic beyond the job on one side only (ft)
The above equation reflects the assumption that in the region outside the job area, the total mass wil l taper linearly from being equal to that within the job area down to zero at the edge of the plastic. Finally,

side side outside inside total Number assDust M assDust M MassDust × + = /


### where:

**MassDust total**  

### = total paint dust mass on the plastic (mg/ft 2 

### disturbed) **MassDust inside**  

### = paint dust mass on the plastic inside the job area (mg/ft 2 

### disturbed) **MassDust outside/side**  

### = paint dust mass on the plastic outside the job area (mg/ft 2 

### disturbed) **Number**

**side** 

### = Number of linear taper areas adjacent to the job ar ea (0, 1, or 2)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-9 July 25, 2014 

# H.4. Amount of Paint on the Wall To estimate the total amount of paint on the wall f or each renovation experiment, first the paint dens ity and number of layers of paint were estimated. The a verage paint density for each activity was estimate d for the Dust Study jobs using the fraction of lead in paint according to the equation:

t Res Density frac DensityPb frac Dens Paint ×

− + × = )

1(



### where:

**PaintDens =** the density of the paint (g paint/cm 3 

### paint) **frac =** the fraction of paint which is lead by weight (unit less) **DensityPb =** the density of lead (g/cm 3 

### ) **DensityRest =** the density of the rest of the paint (other than le ad) (g/cm 3 

### )

The density of lead is 11.3 g/cm 3 

### , and the density of the rest of the paint is assum ed to be equal to 1 g/cm 3 

### consistent with the solid portion of modern paint (assumed to be lead-free), as calculated from Material Safety Data Sheets (MSDS) sheets.
The number of layers of paint on the wall at the ti me of the Dust Study job was estimated by assuming that painting had been done every seven years since the time the building was constructed. This value of seven years was selected based on information about typical renovation frequencies in child care centers (CCC data). For each of these previous pain ting jobs on the building, it was assumed that the surface was prepared for painting and then a layer of paint was applied. There is no way of knowing what method was used to remove paint and prepare th e surface in these prior painting jobs. Thus, the representative value of 15% removal for surface pre paration (Section D.1.3) was used in estimating the fraction of paint removed during each previous pain ting job. The Dust Study jobs were performed in 2006. Thus, the number of layers of paint in 2006 f or each Dust Study building construction date were estimated and are shown in Table H-1.
In some cases, the age of the renovated building wa s unknown. The Dust Study residences were built between 1900 and 1925, and a table provided within the Dust Study report indicates that nearby properties were built between 1900 and 1920. For t his analysis, a representative building year of 192 0 was selected as a year within that range.
For the actual paint removal jobs performed during the Dust Study, Battelle provided information about the degree of paint removal. Some jobs were surface preparation activities (removed only a fraction of the total paint on the wall), while others removed all the paint on the wall. The fraction removed ten ded**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-10 July 25, 2014 

### to depend on the method used to perform the job, as shown in Table H-2. Thus, the amount of paint removed during each Dust Study job was estimated by multiplying the number of layers of paint estimated to be on the building in 2006 by the esti mated fraction of paint removed during the particul ar job (100% for removal jobs and 15% for surface prep aration jobs).

Table H-1. Layers of Paint By Vintage and Building Type

Building Construction Date Estimated Layers of Paint

On Buildings in 2006 at the Start of the Dust Study

1900 6.2 1910 5.3 1918 5.0 1920 5.0 1925 4.7 1967 3.3



### Table H-2. Fraction of paint on the wall that is re moved in the

Dust Study experiments.

Job Type Fraction of Paint Assumed to Be

Removed During Dust Study Jobs Power Sanding Paint Removal; 100% Needle Gun Paint Removal; 100% Torching Paint Removal; 100% High Heat Gun Paint Removal; 100%
Low Heat Gun Paint Removal; 100% Dry Scrape Surface Preparation; 15% Trim Replacement Surface Preparation; 15% Door Replacement Surface Preparation; 15%



### These values were then used to estimate the total a mount of paint removed from the wall in the Dust Study job per square foot of wall:

d FracRemove nt ayersOfPai L UnitConv Dens Paint Cov aint WallP d aintRemove WallP × × × × =



### where:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-11 July 25, 2014 

### WallPaintRemoved = the total paint per square foot of wall removed dur ing the Dust Study job (mg/ft 2 

### wall) **WallPaintCov =** the amount of paint which covers a single square fo ot of wall (gallons/ft 2 

### ) **UnitConv =** a conversion factor to convert from gallons to cm 3 

### and from mg to g **PaintDens =** the density of the paint (g paint/cm 3 

### paint) **LayersOfPaint =** the number of layers of paint **FracRemoved =** the fraction of the total layers of paint that were removed during the Dust Study job 

# H.5. Percentage of Paint Emitted By Activity Finally, the fraction of paint on the wall emitted per activity is estimated by dividing the amount em itted during the activity (Appendix H.3) by the total amo unt on the wall (Appendix H.4).

WallPaint assDust M racEmitted F total



### =



### where:

**FracEmitted =**  fraction of paint removed from wall that is emitted as aerosol **MassDust total**  

### = total paint dust mass on the plastic (mg/ft 2 

### disturbed) **WallPaint =** the total paint per square foot of wall (mg/ft 2 

### wall)
Then, the fractions for the different experiments w ere combined with the fractions from any available interior experiments, and the geometric mean across the different experiments was used to represent the overall activity emission fraction. The resulti ng emission fractions are shown in Table D-2.
For comparison purposes, Lee and Domanski (1999) ex amined the release of lead and abrasives during sandblasting operations to remove lead paint from b ridges. They estimated that 9 percent of the lead o n the bridge could be released as aerosol due to sand blasting. The emission fractions in this Approach tend to be on the order of this estimate. Power san ding, torching, and needle gun activities have a higher emission fraction, and these activities are expected to have a high potential for emission. The Lee and Domanski (1999) study suggests the emission fra ctions estimated here are reasonable.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-12 July 25, 2014



### Table H-3. Fraction of paint on the wall which is e mitted as

aerosolized particulate.

Renovation Activity Fraction of Paint Removed ( FracRem ) Fraction of

Paint Aerosolized ( AerosolFrac ) 1 Fraction of

Paint Emitted as Bulk Particulate ( PartDebFrac ) 2 Power Sanding 0.15 0.248 0.051 Needle Gun 0.15 0.244 0.055 Torching 0.15 0.180 0.056 High Heat Gun 0.15 0.081 0.056
Low Heat Gun 0.15 0.077 0.059 Dry Scrape 0.15 0.070 0.060 Window/Door Replacement 0.15 0.069 0.060
Trim Replacement 0.15 0.070 0.060 Demolition 1.0 0.009 0.060


### The general approach above was applied to the exper iments in the Dust Study to compute the emission fractions. However, individual deviations from the generic layout and sampling protocol in Figure H-1 required additional assumptions in some experiments . The remainder of the Appendix details the assumptions, input data, regression results, and em ission fraction calculations for each experiment. The figures in each Appendix represent the maps provide d by Battelle, but all crosshatching (which represents regions where linear tapering is used to adjust loadings) were added during the estimation of the emission rates.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-13 July 25, 2014 

# H.6. Power Sanding

H.6.1. Experiment 2 The geometry of Experiment 2 (see Figure H-3) was s imilar to the generic geometry in Figure H-2. . Because the measurement in Sample 1 was very high e ven though it was far to the left of the job area, loadings were not tapered linearly to zero at the e dge of the plastic in this experiment. Instead, it was assumed that the dust generated during the power sa nding experiments was emitted with momentum and could therefore travel laterally to further dis tances. Thus, the loadings were assumed to be const ant across the entire width of the plastic (35 ft) and not just the width of the job (10 ft).
The measured lead loadings and associated distances are shown in Table H-4. No adjustments were made to the loadings since no linear tapering was a ssumed in this experiment. The loadings were divided by the size of the job (100 ft 2 

### ) and the fraction of lead in the paint (0.117) to estimate the dust loadings per ft 2 

### disturbed in Table H-4. Sample 1 was excluded fro m the regression because it was within 3 ft of the job. Then, the natural logs of a ll loadings were used with the distances to estimat e the regression parameters in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-14 July 25, 2014 

### Table H

- 5

. Regression Statistics in Experiment 2
. Here, **intercept**

is the intercept of the linear regression in log s pace, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( **intercept** ).
The integral was performed over the regression equa tion out to a distance of 17 ft, which was the extent of the non-rule plastic. Masses were estimat ed both within the job area and outside the job are a as shown in Table H-6. The estimated paint density using the lead fraction was 2.21 g/cm 3 

### . Because the renovated building was built in 1918, it was assume d to have 5.0 layers of paint at the time of the jo b. Power sanding was a paint removal activity, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.108

.

Figure H-3. Layout of Power Sanding Experiment 2

Table H-4. Dust Loading Measurements and Adjustment s in Experiment 2

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 1 NA **4,666,776** NA 398.87 None 1

2

3 6

5 4

1 Sample number Job Rule plastic Beyond-rule plastic Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-15 July 25, 2014 2 5 NA 328,358 NA 28.06 None 3 11 NA 102,291 NA 8.74 None 4 15 NA 56,460 NA 4.83 None 5 14 NA 82,678 NA 7.07 None 6 12 NA 63,192 NA 5.40 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-16 July 25, 2014 

### Table H-5. Regression Statistics in Experiment 2

Statistic Value Shape -0.17 Intercept 4.09 R 2 0.90 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 59.71



### Table H-6. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 2

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 3,306 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 8,266 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 11,572 Total Paint on Surface (mg/ft 2 disturbed) 107,206 Fraction Emitted 0.108


## H.6.2. Experiment 3 The geometry for Experiment 3 is shown in Figure H- 4. Similar to Experiment 2, the loadings in Experiment 3 were not tapered linearly to zero at t he edge of the plastic in this experiment. Again, i t was assumed that the dust generated during the powe r sanding experiments was emitted with momentum and could therefore travel laterally to fu rther distances. Thus, the loadings were assumed to be constant across the entire width of the plastic (36 ft) and not just the width of the job (12 ft).
The measured lead loadings and associated distances are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-17 July 25, 2014 

### Table H

- 7

. Dust Loading Measurements and Adjustments in Experi ment 3 . No adjustments were made to the loadings since no linear tapering was assumed in this experiment. The loadings were divided by the size of the job (1 00 ft 2 

### ) and the fraction of lead in the paint (0.131) to estimate the dust loadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-18 July 25, 2014 

### Table H

- 7

. Dust Loading Measurements and Adjustments in Experi ment 3 . Sample 1 was excluded from the regression becaus e it was within 3 ft of the job. Then, the natural logs of all loadings were used with the distances to est imate the regression parameters in Table H-8. Here, **intercept** is the intercept of the linear regression in log s pace, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( **intercept** ).
The integral was performed over the regression equa tion out to a distance of 17 ft, which was the extent of the non-rule plastic. Masses were estimat ed both within the job area and outside the job are a as shown in Table H-9. The estimated paint density using the lead fraction was 2.35 g/cm 3 

### . Because the renovated building was built in 1918, it was assume d to have 5.0 layers of paint at the time of the jo b. Power sanding was a paint removal activity, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.387

.

Figure H-4. Layout of Power Sanding Experiment 3

1

2

3

4 6

5

1 Sample number Job Rule plastic Beyond-rule plastic Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-19 July 25, 2014



### Table H-7. Dust Loading Measurements and Adjustment s in Experiment 3

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 3 NA **8,289,211** NA 632.76 None 2 8 NA 225,843 NA 17.24 None 3 12 NA 124,748 NA 9.52 None 4 14 NA 17,309 NA 1.32 None 5 17 NA 9,486 NA 0.72 None 6 15 NA 16,255 NA 1.24 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-8. Regression Statistics in Experiment 3

Statistic Value Shape -0.39 Intercept 6.17 R 2 0.89 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 476.65



### Table H-9. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 3
Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 14,748 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 29,496 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 44,244 Total Paint on Surface (mg/ft 2 disturbed) 114,229 Fraction Emitted 0.387



# H.7. Torching The torching experiments were conducted on the ceil ings of two different porches at residences. Scaffolding was used in both cases, and the scaffol ding had gaps in it (to allow material to fall to t he plastic during the experiment) and was dismantled p rior to sample collection (to allow material on the scaffolding to fall onto the plastic). Because of t hese two details in the experimental setup, the tor ching**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-20 July 25, 2014 

### loadings were noted to be very high at large distan ces from the porch, and many of the samples were bulk samples, making the regression calculations di fficult.
Thus, the torching experiments were treated differe ntly to account for the scaffolding presence and th e high bulk loadings. The high heat gun experiment (E xperiment 66, see Appendix H.9) was also conducted on a ceiling (the awning over a door at a school). Three of the measurements were bulk loadings. Thus, the regression results calculated when excluding th e bulk results from Experiment 66 were used to estimate the loadings at 0, 1, and 2 ft from the aw ning. Then, these predicted loadings (assumed to account for the non-bulk mass) were divided by the total bulk and non-bulk measurement at each distance to estimate the fraction that was bulk. T he results are shown in Table H-10. In general, the ratio was fairly consistent at the three distances with an average value of 0.50.
Because the high heat gun paint removal technique i s considered to be the most similar to the torching paint removal technique, this fraction was applied to the torching bulk measurements to estimate the non-bulk portion. This conversion is reflected in t he “Lead Loading (adjusted)” columns in Table H-11 and Table H-14. Then, all the measurements were inc luded in the regressions to estimate the emission fractions as described for each experiment below.

Table H-10. Torching Adjustment For Bulk Estimated from High Heat Gun Experiment

Dist. From Job (ft) Bulk Dust

Measurement Per ft 2 Disturbed (mg/ft 2 /ft 2 ) Regression

Prediction (mg/ft 2 /ft 2 ) Ratio

0 151.38 79.52 0.53 1 90.13 39.28 0.44
2 35.84 19.40 0.54 Average 0.50


## H.7.1. Experiment 37 As discussed above, Experiment 37 removed paint on a porch ceiling at a residence (see Figure H-5). Al l bulk measurements were adjusted by a factor of 0.50 as discussed above to approximate the non-bulk portion. Then, the loadings were divided by the siz e of the job (98 ft 2 

### ) and the fraction of lead in the paint (0.0518) to estimate the dust loadings per ft 2 

### disturbed in Table H-11. Because of the presence of the scaffolding that extended further than the porc h area, the loadings were assumed to be constant al l the way to the edge of the rule plastic, where very high bulk loadings were measured in the experiment

. Thus, the adjusted distance was estimated to be 0 f t for all measurements under the porch or on the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-21 July 25, 2014 

### rule plastic and the distance from the edge of the rule plastic to the measurement for the others. The n, the natural logs of loadings from all samples were used to estimate the regression parameters in Table H-12. Here, **intercept**

is the intercept of the linear regression in log s pace, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( **intercept** ).
The masses were estimated in two different regions as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-22 July 25, 2014



### Table H

- 13

. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 37
. Under the job and on the rule plastic, the mass w as estimated as the regression prediction at 0 ft ( Load at 0 ft) multiplied by the area of the porch and ru le plastic. Next, the mass on the beyond-rule plast ic is estimated by integrating the regression equation ov er the length of the plastic (4 ft) and multiplying by the width (20 ft). The estimated paint density usin g the lead fraction was 1.28 g/cm 3 

### . Although the vintage of the renovated building was unknown, near by buildings were believed to have been built at the same time and were built in 1920. Therefore, t he building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. Torching was a paint removal activity, so all 5.0 layers were assumed to be removed. These values we re used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.073

.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-23 July 25, 2014 

### Figure H-5. Layout of Torching Experiment 37

1

3

2

4 5 6

Sidewalk

1 Sample number Job Rule plastic Beyond-rule plastic Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-24 July 25, 2014



### Table H-11. Dust Loading Measurements and Adjustmen ts in Experiment 37

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead

Loading (adjusted) Dust Load

per ft 2 Disturbed 2 Adjustment Parameters

1 4.5 0 **1,134,829** 567,414 214.44 Distance adjusted to be from edge of rule plastic; Loading adjusted by factor of 0.5 to convert to non-bulk

2 12 0 919 NA 0.35 Distance adjusted to be from edge of rule plastic

3 10 0 **349,644** 174,822 66.07 Distance adjusted to be from edge of rule plastic; Loading adjusted by factor of 0.5 to convert to non

- bulk 4 15 2 144 NA 0.05 Distance adjusted to be from edge of rule plastic

5 15 2 190 NA 0.07 Distance adjusted to be from edge of rule plastic

6 15 2 145 NA 0.05 Distance adjusted to be from edge of rule plastic

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-12. Regression Statistics in Experiment 37

Statistic Value Shape -2.83 Intercept 2.83 R 2 0.67 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 17.01**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-25 July 25, 2014 

### Table H-13. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 37

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 4,422 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 120 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 4,543 Total Paint on Surface (mg/ft 2 disturbed) 62,054 Fraction Emitted 0.073


## H.7.2. Experiment 38 As discussed above, Experiment 38 removed paint on a porch ceiling at a residence (see Figure H-6). Al l bulk measurements were adjusted by a factor of 0.50 as discussed above to approximate the non-bulk portion. Measurements made outside the job width we re adjusted using a linear taper distance of 2 ft. This assumes the mass was the same on both sides bu t the loadings were somewhat higher on the left portion. Then, the loadings were divided by the siz e of the job (80 ft 2 

### ) and the fraction of lead in the paint (0.114) to estimate the dust loadings per ft 2 

### disturbed in Table H-14. Because of the presence of the scaffolding that extended further than the porc h area, the loadings were assumed to be constant al l the way to the edge of the rule plastic, where very high bulk loadings were measured in the experiment

. Thus, the adjusted distance was estimated to be 0 f t for all measurements under the porch or on the rule plastic and the distance from the edge of the rule plastic to the measurement for the others. The n, the natural logs of loadings from all samples were used to estimate the regression parameters in Table H-15. Here, **intercept**

is the intercept of the linear regression in log s pace, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( **intercept** ).
The masses were estimated in two different regions as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-26 July 25, 2014



### Table H

- 16

. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 38
. The mass in the job area includes both the mass u nder the job and on the rule plastic within the job area and the mass on the beyond-rule plastic within the job area. The mass under the job and on the rule plastic was estimated as the regression predic tion at 0 ft (Load at 0 ft) multiplied by the area of the porch (11 ft by 5 ft) and rule plastic (11 ft by 5 ft). Next, the mass on the beyond-rule plastic with in the job area was estimated by integrating the regressio n equation over the length of the plastic (4 ft) an d multiplying by the width (11 ft). Finally, the mass es outside the job width were tapered linearly assuming a taper distance of 2 ft to reflect the di stance on the left portion of the job. This assumes the mass was the same on both sides but the loadings we re somewhat higher on the left portion. The estimated paint density using the lead fraction was 2.18 g/cm 3 

### . Because the renovated building was built in 1900, it was assumed to have 6.2 layers of paint at the time of the job. Torching was a paint remo val activity, so all 6.2 layers were assumed to be remo ved. These values were used to estimate the total amount of paint on the wall, giving a final emissio n fraction of 0.287.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-27 July 25, 2014 

### Figure H-6. Layout of Torching Experiment 38 

### 1

3 2

6 5 4

Sidewalk

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-28 July 25, 2014



### Table H-14. Dust Loading Measurements and Adjustmen ts in Experiment 38

ID Dist. From Wall (ft) Adj.

Dist. (ft) Lead Loading

(Dust Study) 1 Lead

Loading (adjusted) Dust Load

per ft 2 Disturbed 2 Adjustment Parameters

1 2 0 **29,401,680** 14,700,840 1611.93 Distance adjusted to be from edge of rule plastic; Loading adjusted by factor of 0.5 to convert to non-bulk

2 10.5 0 **1,560,303** 780,152 85.54 Distance adjusted to be from edge of rule plastic; Loading adjusted by factor of 0.5 to convert to non-bulk

3 9 0 **1,819,556** 909,778 99.76 Distance adjusted to be from edge of rule plastic; Loading adjusted by factor of 0.5 to convert to non-bulk

4 16.5 4 632 1264 0.14 Distance adjusted to be from edge of rule plastic; Loading adjusted to estimate loading within job area

5 16.5 4 1,766 NA 0.19 Distance adjusted to be from edge of rule plastic

6 16.5 4 1,051 2,102 0.23 Distance adjusted to be from edge of rule plastic; Loading adjusted to estimate loading within job area
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-15. Regression Statistics in Experiment 38

Statistic Value Shape -1.79 Intercept 5.48 R 2 0.93 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 239.60**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-29 July 25, 2014 

### Table H-16. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 38

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 26,356 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 6,527 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 32,884 Total Paint on Surface (mg/ft 2 disturbed) 114,542 Fraction Emitted 0.287 

# H.8. Needle Gun

H.8.1. Experiment 65 Experiment 65 removed paint from a raised railing in a scho ol courtyard (see Figure H-7). Thus, assumptions had to be made to account for this geometry. Plas tic extended all around the job. It was assumed that the total masses emitted both in front of and behi nd the railing were equal. On the outer edge to the left of the job, the loadings were tapered to z ero using a distance to the edge of the plastic of 8 ft. Sample 2 was adjusted to estimate the associat ed loading within the job area, and the adjusted loadings are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-30 July 25, 2014 

### Table H

- 17

. Dust Loading Measurements and Adjustments in Experiment 65

. The loadings were divided by the size of the job (30 ft 2 

### ) and the fraction of lead in the paint (0.053) to estimate the dust loadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-31 July 25, 2014 

### Table H

- 17

. Dust Loading Measurements and Adjustments in Experiment 65 . Samples 1, 2 and 3 were excluded from the regression sinc e they were within 3 ft of the job. Then, the natural logs of all loadings were used with the adjusted dista nces to estimate the regression parameters in Table H-18. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 12 ft (the length in front of the job) with a job width of 23 ft and then multiplied by two t o account for the plastic behind the job. The estimated paint density using the lead fraction was 1.55 g /cm 3 

### . Because the renovated building was built in 1967, it was assumed to have 3.3 layers of paint a t the time of the job. Needle gun was a paint removal activity, so all 3.3 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall in Table H-19, giving a fi nal emission fraction of 0.244.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-32 July 25, 2014 

### Figure H-7. Layout of Needle Gun Experiment 65 Vertical wall of school

4

1

2

`

3

5

6 width = 16 Vertical wall of school

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment V

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Peer Review Draft H-33 July 25, 2014



### Table H-17. Dust Loading Measurements and Adjustmen ts in Experiment 65

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 3 NA **381,586** NA 239.99 None 2 3 NA 21,158 NA 13.31 None 3 2 NA **888,167** NA 558.60 None 4 6 NA 20,344 NA 12.79 None 5 6 NA 8,431 NA 5.30 None 6 12 NA 1,327 NA 0.83 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-18. Regression Statistics in Experiment 65

Statistic Value Shape -0.38 Intercept 4.40 R 2 0.90 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 81.29



### Table H-19. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 65

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 9,699 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 1,687 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 11,386 Total Paint on Surface (mg/ft 2 disturbed) 46,717 Fraction Emitted 0.244



# H.9. High Heat Gun

H.9.1. Experiment 66 Experiment 66 removed paint on an awning above a door in a sc hool courtyard (Figure H-8). Thus, assumptions had to be made to account for this geometry. Plas tic extended to the left of the job. There**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-34 July 25, 2014 

### was a raised railing to the left of the job, but because the job was done on an awning at a height of 7 to 15 ft, it was assumed that the dust could travel over the r ailing and spread to the vertical school wall. In addition, it was assumed that the dust could spread the full lengt h to the containment plastic in front of the job. The total masses emitted both in front of and just t o the left of the awning were assumed to decrease exponentially with distance from the awning edge. In the remaining portion of plastic, it was assumed that loadings tapered both from the upper area and right a rea, tapering to zero over a length of 19 ft (from the upper portion to the containment plastic) and over a length of 15 ft (from the right portion over to the vertical wall of the school).
The loadings were divided by the size of the job (30 ft 2 

### ) and the fraction of lead in the paint (0.183) to estimate the dust loadings per ft 2 

### disturbed in Table H-20. Sample 6 was adjusted to approximat e the value of the loading within the width of the job. Samples 1, 2 and 3 were excluded from the regression since they were within 3 ft of the job. Then, the natural logs of loadings from Samples 4, 5, and 6 were used to estimate the regression parameters in Table H-21. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed l oading at 0 ft calculated as exp( 

### intercept ).
The masses were estimated in three different regions. Under the job, the mass was estimated as the regression prediction at 0 ft (Load at 0 ft) multiplied by the area of the awning (5 ft by 9 ft). Next, the masses within the two job areas (lower and to the left of the job) were estimated by integrating the regression out to 15 ft and multiplying by a width of 5 ft (left portion) and integrating the regression out to 19 ft and multiplying by a width of 9 ft (lower portion) . Finally, the mass in the remaining taper region was estimated by tapering the left and lower portions of t he within-job loadings to zero over the remaining area. The estimated paint density using the lead frac tion was 2.89 g/cm 3 

### . Because the renovated building was built in 1967, it was assumed to have 3 .3 layers of paint at the time of the job. High heat gun was a paint removal activity, so all 3.3 la yers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.081.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-35 July 25, 2014 

### Figure H-8. Layout of High Heat Gun Experiment 66 

### Vertical wall of school

3

2

1

4

6

5

Approximate placement of vertical containment

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment V

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Peer Review Draft H-36 July 25, 2014



### Table H-20. Dust Loading Measurements and Adjustmen ts in Experiment 66

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading (Dust Study) 1 Lead

Loading (adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment Parameters

1 7 2 **196,760** NA 35.84 Distance adjusted to be from edge of awning

2 5 1 **494,822** NA 90.13 Distance adjusted to be from edge of awning

3 3 0 **831,062** NA 151.38 Distance adjusted to be from edge of awning
4 11 6 14,124 NA 2.57 Distance adjusted to be from edge of awning

5 16 11 318 NA 0.06 Distance adjusted to be from edge of awning

6 13 8 349 407 0.07 Distance adjusted to be from edge of awning; Loading adjusted to estimate loading within job area

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-21. Regression Statistics in Experiment 66

Statistic Value Shape -0.71 Intercept 4.38 R 2 0.70 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 79.52**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-37 July 25, 2014 

### Table H-22. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 66

Estimated Masses and Emission Fractions Value

Mass of Dust Under Job Area (mg/ft 2 disturbed) 3,578 Mass of Dust Within Job Area (mg/ft 2 disturbed) 1,578 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 1,917 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 7,073 Total Paint on Surface (mg/ft 2 disturbed) 87,288 Fraction Emitted 0.081 

# H.10. Low Heat Gun

H.10.1. Experiment 64 Experiment 64 removed paint from a door at the bottom of a stairwell in a school courtyard (see Figure H-9). Thus, assumptions had to be made to account for this geo metry. The space at the bottom of the stairs was confined and loadings measured there (even at distanc es greater than 3 ft from the door) were very high and included bulk material (Samples 2 and 4). Bulk samples 1, 2 and 4 were excluded from the regression. Sample 3 was not excluded because the presence of the corner was assumed to mediate the transfer of bulk material from the door to t hat location. Finally, it was assumed that because the job was performed at the bottom of a flight of stairs, the dust would not travel further than the edge of the plastic.
The loadings were divided by the size of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.315) to estimate the dust loadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-38 July 25, 2014 

### Table H

- 23

. Dust Loading Measurements and Adjustments in Experiment 64 . Samples 3, 5, and 6 were adjusted to estimate the assoc iated loading within the job area, and the adjusted loadings are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-39 July 25, 2014 

### Table H

- 23

. Dust Loading Measurements and Adjustments in Experiment 64 . Then, the natural logs of loadings from Samples 3, 5, and 6 were used to estimate the regression parameters in Table H-24. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ).
The mass within the job area was estimated by integrating the regression equation over a length of 8 ft and multiplying by the job width of 5 ft. The mass outside the jo b area was estimated by calculating the integrated mass between 1 ft and 8 ft (to account for the pr esence of the corner) and tapered linearly assuming a taper distance of 6 ft. The estimated paint densit y using the lead fraction was 4.26 g/cm 3



### . Because the renovated building was built in 1967, it was assume d to have 3.3 layers of paint at the time of the job. Low heat gun was a paint removal activity, so all 3.3 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.077.

Figure H-9. Layout of Low Heat Gun Experiment 64 Vertical wall of school 1

3

2

6 4

5

Vertical wall - bordering work area at bottom of st airs

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment

Vertical wall of school V

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Peer Review Draft H-40 July 25, 2014



### Table H-23. Dust Loading Measurements and Adjustmen ts in Experiment 64

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment Parameters

1 1 NA **130,674,045** NA 5,531.18 None 2 5 NA **1,133,090** NA 47.96 None 3 3 NA **2,538,052** NA 107.43 None 4 7 NA **2,692,069** NA 113.95 None 5 8 NA 26,587 NA 1.13 None 6 7 NA 16,235 NA 0.69 None

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-24. Regression Statistics in Experiment 64

Statistic Value Shape -0.83 Intercept 7.16 R 2 0.99 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 1290.60



### Table H-25. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 64

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 7,800 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 2,045 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 9,845 Total Paint on Surface (mg/ft 2 disturbed) 128,483 Fraction Emitted 0.077**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-41 July 25, 2014 

# H.11. Dry Scrape

H.11.1. Experiment 1 The geometry of Experiment 1 (see Figure H-10) was sim ilar to the generic geometry in Figure H-1. The beyond-rule plastic spread far to the left of the job. Howev er, it was assumed that in the dry scrape experiment, the dust would tend to be largely bulk pieces and would not spread far in the lateral direction. Thus, the right width (3 ft) was used to taper the l oadings to zero.
The measured lead loadings and associated distances are shown in Table H-26. No adjustments were necessary to approximate loadings within the job area, since all measurements were taken within the job width. The loadings were divided by the size of the job (9 0 ft 2 

### ) and the fraction of lead in the paint (0.135) to estimate the dust loadings per ft 2 

### disturbed in Table H-26. Sample 1 was excluded from the regression because it was within 3 ft of the job. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table H-27. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the ass umed loading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 17 ft, which was the extent of the non-rule plastic. Masses were estimated both within the job area (8 ft wide) and outside the job area as shown in Table H-28. The estimated paint densi ty using the lead fraction was 2.40 g/cm 3



### . Because the renovated building was built in 1918, it was assume d to have 5.0 layers of paint at the time of the job. Dry scrape was a surface preparation activity , so 15% of the paint layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.015.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-42 July 25, 2014 

### Figure H-10. Layout of Dry Scrape Experiment 1 1

2

3 4

5

6

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-43 July 25, 2014



### Table H-26. Dust Loading Measurements and Adjustmen ts in Experiment 1

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters
1 1 NA 12,516,130 NA 1,030.13 None 2 5 NA 48,865 NA 4.02 None 3 7 NA 8,227 NA 0.68 None 4 8 NA 4,018 NA 0.33 None 5 12 NA 916 NA 0.08 None 6 17 NA 893 NA 0.07 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-27. Regression Statistics in Experiment 1

Statistic Value Shape -0.31 Intercept 1.97 R 2 0.78 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 7.17



### Table H-28. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 1

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 185 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 69 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 254 Total Paint on Surface (mg/ft 2 disturbed) 17,435 Fraction Emitted 0.015



## H.11.2. Experiment 21 The geometry of Experiment 21 (see Figure H-11) was si milar to the generic geometry in Figure H-1. The job was not centered with respect to the plastic. The larg er width on the left (6 ft) was used to taper the within-job loadings to zero at the edge of the plastic. Thi s reflects the assumption that the total amount**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-44 July 25, 2014 

### of dust will be the same on both sides, but the loadings to the r ight will be somewhat higher since the dust spreads over a smaller distance. This is reflected in t he diagram with darker crosshatching on that side.
The measured lead loadings and associated distances are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-45 July 25, 2014 

### Table H

- 29

. Dust Loading Measurements and Adjustments in Experiment 21 . No adjustments were necessary to approximate loadings within the job area, since all measurements were taken within the job width. The loadings were divided by the size of the job (150 ft 2 

### ) and the fraction of lead in the paint (0.013) to estimate the dust l oadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-46 July 25, 2014 

### Table H

- 29

. Dust Loading Measurements and Adjustments in Experiment 21 . Sample 1 was excluded from the regression because it was within 3 ft of the job. Then, the natural logs of all loadings were used with the distances to estimate t he regression parameters in Table H-30. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 18 ft, which was the extent of the non-rule plastic. Masses were estimated both within the job area (16 ft wide) and outside the job area as shown in Table H-31. The estimated paint densi ty using the lead fraction was 1.13 g/cm 3



### . Because the renovated building was built in 1920, it was assume d to have 5.0 layers of paint at the time of the job. Dry scrape was a surface preparation activity , so 15% of the paint layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.041.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-47 July 25, 2014 

### Figure H-11. Layout of Dry Scrape Experiment 21

1

2

3

4

6

5

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-48 July 25, 2014



### Table H-29. Dust Loading Measurements and Adjustmen ts in Experiment 21

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 1 NA **1,463,974** NA 750.76 None 2 7 NA 2,266 NA 1.16 None 3 11 NA 401 NA 0.21 None 4 13 NA 263 NA 0.13 None 5 18 NA 165 NA 0.08 None 6 15 NA 552 NA 0.28 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-30. Regression Statistics in Experiment 21

Statistic Value Shape -0.20 Intercept 1.18 R 2 0.72 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 3.25



### Table H-31. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 21

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 248 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 93 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 342 Total Paint on Surface (mg/ft 2 disturbed) 8,255 Fraction Emitted 0.041



# H.12. Experiment 75 The geometry of Experiment 75 (see Figure H-12) was si milar to the generic geometry in Figure H-1. The job was not centered with respect to the plastic. The larg er width on the left (6 ft) was used to taper the within-job loadings to zero at the edge of the plastic. Thi s reflects the assumption that the total amount of dust will be the same on both sides, but the loadings to the r ight will be somewhat higher since the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-49 July 25, 2014 

### dust spreads over a smaller distance. This is reflected in t he diagram with darker crosshatching on that side.
The measured lead loadings and associated distances are shown in Table H-32. No adjustments were necessary to approximate loadings within the job area, since all measurements were taken within the job width. The loadings were divided by the size of the job (1 00 ft 2 

### ) and the fraction of lead in the paint (0.013) to estimate the dust loadings per ft 2 

### disturbed in Table H-32. Sample 1 was excluded from the regression because it was within 3 ft of the job. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table H-33. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the ass umed loading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 16 ft, which was the extent of the non-rule plastic. Masses were estimated both within the job area (16 ft wide) and outside the job area as shown in Table H-34. The estimated paint densi ty using the lead fraction was 1.13 g/cm 3



### . Because the renovated building was built in 1920, it was assume d to have 5.0 layers of paint at the time of the job. Dry scrape was a surface preparation activity , so 15% of the paint layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.188.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-50 July 25, 2014 

### Figure H-12. Layout of Dry Scrape Experiment 75

Table H-32. Dust Loading Measurements and Adjustmen ts in Experiment 75

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 1 NA **1,961,779** NA 1509.06 None 2 4 NA 13,794 NA 10.61 None 3 7 NA 1,774 NA 1.36 None 4 8 NA 1,159 NA 0.89 None 5 15 NA 183 NA 0.14 None 6 12 NA 469 NA 0.36 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type 1

2

3

4

6

5

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-51 July 25, 2014 

### Table H-33. Regression Statistics in Experiment 75

Statistic Value Shape -0.36 Intercept 3.24 R 2 0.93 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 25.61



### Table H-34. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 75

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 1,130 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 424 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 1,553 Total Paint on Surface (mg/ft 2 disturbed) 8,255 Fraction Emitted 0.188



## H.12.1. Experiment 40 The geometry of Experiment 40 (see Figure H-13) was si milar to the generic geometry in Figure H-1. The job extended over the entire width of the plastic, so no li near tapering or loading adjustments were necessary.
The loadings were divided by the size of the job (250 ft 2 

### ) and the fraction of lead in the paint (0.157) to estimate the dust loadings per ft 2 

### disturbed in Table H-35. Sample 1 was excluded from the regr ession because it was within 3 ft of the job. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table H-36. Her e, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed l oading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 12 ft, which was the extent of the non-rule plastic. Masses were estimated wit hin the job area (20 ft wide). The estimated paint density using the lead fraction was 2.62 g/cm 3 

### . Because the renovated building was built in 1920, it was assumed to have 5.0 layers of paint at the time of t he job. Dry scrape was a surface preparation activity, so 15% of the paint layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emissi on fraction of 0.038.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-52 July 25, 2014 

### Figure H-13. Layout of Dry Scrape Experiment 40

Table H-35. Dust Loading Measurements and Adjustmen ts in Experiment 40

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 6 NA 13,796 NA 0.35 None 2 2 NA 11,480,352 NA 292.49 None 3 4 NA 268,359 NA 6.84 None 4 11 NA 7,303 NA 0.19 None 5 9 NA 11,701 NA 0.30 None 6 10 NA 15,959 NA 0.41 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type 

### 2

3

1

5 6 4

1 Sample number Job Rule plastic Beyond-rule plastic Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-53 July 25, 2014 

### Table H-36. Regression Statistics in Experiment 40

Statistic Value Shape -0.41 Intercept 2.70 R 2 0.70 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 14.92



### Table H-37. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 40

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 721 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 0 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 721 Total Paint on Surface (mg/ft 2 disturbed) 19,091 Fraction Emitted 0.038



# H.13. Trim Replacement

H.13.1. Experiment 4 Experiment 4 removed trim from the side of a home and was pe rformed on two separate sides of the building (see Figure H-14). Thus, assumptions had to be made to account for this geometry. It was assumed that the loadings were constant on both walls within the job area. On the outer edges of the job on both walls, the loadings were tapered to zero using a distance to the edge of the plastic of 5 ft. Between the two sections of job wall, the loadings were a ssumed to taper linearly from both directions using a width of 7 ft in both directions. In actuality, the dis tance was 8 ft for the upper portion of the job. Thus, this assumption reflects that the total mass co ntribution will be the same from both directions even though the mass from the upper portion can spread over a wider distance. Samples 1, 2, and 6 were adjusted to estimate the associated loading within the job area, and the adjusted loadings are shown in Table H-38. Sample 1 was also adjusted so that t he distance reflected the distance to the corner of the wall (3 ft).
The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.153) to estimate the dust loadings per ft 2 

### disturbed in Table H-38. Samples 2 and 3 were excluded from t he regression since they were within 2 ft of the job. Sample 1 was included in the regression. It is within 3 ft**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-54 July 25, 2014 

### of the job but was not a bulk measurement. Then, the natural lo gs of all loadings were used with the adjusted distances to estimate the regression parameters in Table H-39. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ). In this case, the regression coefficient is low, indi cating there is additional uncertainty in this emission estimate.
The integral was performed over the regression equation o ut to a distance of 7 ft, which was the extent of the non-rule plastic in the lower portion of the job. The same distance was used for the upper portion, reflecting the assumption that the total mass contr ibution will be the same from both directions even though the mass from the upper portion can spread over a wider distance. Masses were estimated both within the job area and outside the job area (the two edge taper regions and the middle “double taper” region) as shown in Table H-40. The estimated paint density using the lead fraction was 2.58 g/cm 3 

### . Because the renovated building was built in 1920, it was ass umed to have 5.0 layers of paint at the time of the job. Trim replacement was a surface prepar ation activity, so 15% of the paint layers were assumed to be removed. These values were used to est imate the total amount of paint on the wall, giving a final emission fraction of 0.070.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-55 July 25, 2014 

### Figure H-14. Layout of Trim Replacement Experiment 4 

### 2 1 3

4 6 5

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-56 July 25, 2014



### Table H-38. Dust Loading Measurements and Adjustmen ts in Experiment 4

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment Parameters

1 2 3 3,951 NA 0.65 Distance adjusted to be linear distance from job; Loading adjusted to estimate loading within job area

2 1 NA **1,652,382** 2,065,478 337.50 Loading adjusted to estimate loading within job area 3 2 NA 110,776 NA 18.10 None 4 5 NA 59,027 NA 9.64 None 5 6 NA 26,040 NA 4.25 None

6 5 NA 52,742 263,710 43.09 Loading adjusted to estimate loading within job area

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-39. Regression Statistics in Experiment 4

Statistic Value Shape 0.86 Intercept -2.32 R 2 0.38 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 0.10



### Table H-40. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 4

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 747 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 560 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 1,307 Total Paint on Surface (mg/ft 2 disturbed) 18,790 Fraction Emitted 0.070**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-57 July 25, 2014 

## H.13.2. Experiment 36 Experiment 36 removed trim from the edge of a porch. The geometry is shown in Figure H-15. Three of the samples were collected within 3 ft of the job and were excluded from the regression (see Table H-41). However, the remaining three samples were all co llected at the same distance from the edge of the porch (7 ft). Thus, in the absence of data at multiple dis tances, no regression can be performed. This experiment was excluded from the emission rate calculation.

Figure H-15. Layout of Trim Replacement Experiment 36

2 3 1

4 5 6

Sidewalk

1 Sample number Job Rule plastic Beyond-rule plastic Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-58 July 25, 2014



### Table H-41. Dust Loading Measurements and Adjustmen ts in Experiment 36

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment

Parameters 1 3 NA 154,825 NA 15.36 None 2 2 NA **2,991,198** NA 296.75 None 3 2 NA **1,794,377** NA 178.01 None 4 7 NA 18,536 NA 1.84 None 5 7 NA 128,549 NA 12.75 None 6 7 NA 209,520 NA 20.79 None
1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bold type 

# H.14. Door Replacement

H.14.1. Experiment 34 Experiment 34 replaced a door at a home. The geometry is shown in Figure H-16. Three of the samples were collected within 3 ft of the job and were excluded from the regression (see Table H-42). However, the remaining three samples were all collected at the same distance from the edge of the porch (6 ft). Thus, in the absence of data at multiple distances, no regressi on can be performed. This experiment was excluded from the emission rate calculation.

Figure H-16. Layout of Door Replacement Experiment 34 1

2 3

4 5 6

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-59 July 25, 2014



### Table H-42. Dust Loading Measurements and Adjustmen ts in Experiment 34

ID Dist. From Wall (ft) Adj.

Dist. (ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load per

ft 2 Disturbed 2 Adjustment Parameters

1 1 NA **217,108** 248,123 94.52 Loading adjusted to estimate loading within job area

2 3 NA 76,118 86,992 33.14 Loading adjusted to estimate loading within job area

3 3 NA 13,886 27,772 10.58 Loading adjusted to estimate loading within job area 4 6 NA 30,489 NA 11.61 None

5 6 NA 81,048 129,677 49.40 Loading adjusted to estimate loading within job area

6 6 NA 1,086 8,688 3.31 Loading adjusted to estimate loading within job area

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bold type 

## H.14.2. Experiment 35 The geometry of Experiment 35 (Figure H-17) was simila r to the generic geometry in Figure H-1. The job was not centered with respect to the plastic. The larger width on the left (8 ft) was used to taper the within-job loadings to zero at the edge of the plastic. Thi s reflects the assumption that the total amount of dust will be the same on both sides, but the loadings to the r ight will be somewhat higher since the dust spreads over a smaller distance. This is reflected in t he diagram with darker crosshatching on that side.
The measured lead loadings and associated distances are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-60 July 25, 2014 

### Table H

- 43

. Dust Loading Measurements and Adjustments in Experiment 35 . Samples 1, 4, and 6 were adjusted to approximate the loa dings at those distances within the job width. These adjusted loadings are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-61 July 25, 2014 

### Table H

- 43

. Dust Loading Measurements and Adjustments in Experiment 35 . The loadings were divided by the size of the job (25 ft 2 

### ) and the fraction of lead in the paint (0.112) to estimate the dust loadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-62 July 25, 2014 

### Table H

- 43

. Dust Loading Measurements and Adjustments in Experiment 35 . Samples 1 and 3 were excluded from the regression because they were within 2 ft of the job. Sample 2 was within 3 ft of the job, but it was not a bulk measurement so it was retained in the regression. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table H-44. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ). In this case, the regression coefficient is low, indicating there is additional uncertainty in this emission est imate.
The integral was performed over the regression equation o ut to a distance of 8 ft, which was the extent of the non-rule plastic. Masses were estimated both within the job area (4 ft wide) and outside the job area as shown in Table H-45. The estimated paint density usi ng the lead fraction was 2.16 g/cm 3



### . Because the renovated building was built in 1925, it was assume d to have 4.7 layers of paint at the time of the job. Door replacement was a surface preparation act ivity, so 15% of the paint layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.069.

Figure H-17. Layout of Door Replacement Experiment 35 3 1

2

4 6 5

1 Sample number Job Rule plastic Beyond-rule plastic Lateral linear taper region Approximate placement of vertical containment**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-63 July 25, 2014



### Table H-43. Dust Loading Measurements and Adjustmen ts in Experiment 35

ID Dist. From Wall (ft) Adj. Dist.

(ft) Lead Loading

(Dust Study) 1 Lead Loading

(adjusted) Dust Load

per ft 2 Adjustment Parameters

1 1 NA 28,904 43,356 15.48 Loading adjusted to estimate loading within job area 2 3 NA 42,285 NA 15.10 None 3 1 NA 145,898 NA 52.11 None
4 5 NA 3,648 5,472 1.95 Loading adjusted to estimate loading within job area 5 5.5 NA 29,256 NA 10.45 None
6 5.5 NA 16,914 19,330 6.90 Loading adjusted to estimate loading within job area

1 Measurements that include bulk material are denote d in italics
2 Measurements included in regression denoted in bol d type



### Table H-44. Regression Statistics in Experiment 35

Statistic Value Shape -0.32 Intercept 3.45 R 2 0.19 Load at 0 ft (mg/ft 2 plastic/ft 2 disturbed) 31.57



### Table H-45. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 35

Estimated Masses and Emission Fractions Value

Mass of Dust Within Job Area (mg/ft 2 disturbed) 361 Mass of Dust Not Within Job Area (mg/ft 2 disturbed) 722 Total Mass of Dust on Plastic (mg/ft 2 disturbed) 1,083 Total Paint on Surface (mg/ft 2 disturbed) 15,705 Fraction Emitted 0.069**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft H-64 July 25, 2014 

# H.15. References for Appendix H Beck C; Geyh A; Srinivasa A; Breysse P; Eggleston P; Buckley T (2003). The impact of a building implosion on airborned particulate matter in an Urban community. Journal of the Air and Waste Management Association 54(10): 1256-1264.
Capouch S., personal communication, 2012. Personal Communicat ion by email between Heidi Hubbard of ICF International and Scott Capouch, Construction Project Manager with 10 years of experience, of US NIEHS. Title of email: Painting fre quency. February 15, 2012.
Farfel M; Orlova A; Lees P; Rohde C; Ashley P; Chisolm J (2003). A study of urban housing demolitions as sources of lead in ambient dust: demolition practices and exte rior dust fall. Environmental Health Perspectives 111(1128-1234).
Farfel MR; Orlova AO; Lees PS; Rohde C; Ashley PJ; Chis olm Jr. J (2005). A study of urban housing demolition as a source of lead in ambient dust on sidewalks, streets, and alleys. Environ Res 99(2): 204-213.
Lee M. and Domansky J. (1999). Development of pollutant re lease estimates due to abrasive blasting for lead paint removal from New York City department of transpo rtation steel bridges, Pittsburg, PA, Air and Waste Management Association.
Mucha A; Stites N; Evens A; MacRoy P (2009). Lead dustfa ll from demolition of scattered site family housing: Developing a sampling methodology. Environmental Res earch 109(143-148).
Stefani D; Wardman D; Lambert T (2005). The implosion of the Calgary General Hospital: Ambient air quality issues. Journal of the Air and Waste Management Asso ciation 55: 52-59.
US EPA. (2007a). Draft final report on characterizatio n of dust lead levels after renovation, repair, and painting activities. Prepared for EPA’s Office of Polluti on Prevention and Toxcis (OPPT). Available online at: http://www.epa.gov/lead/pubs/duststudy01-23-07.pdf
Weather Underground. (2011). History for Baltimore, MD. A vailable online at: http://www.weatherunderground.com**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-1 July 25, 2014



# Appendix I. Interior Fraction Emitted

I.1. Loading Data Conversions Because each experiment was performed using different total w all disturbance sizes, the fraction of paint emitted was calculated on a per square foot of disturbed w all basis. Thus, as in the exterior analysis, each Dust Study post-work lead loading was divided by the area disturbed in the experiment to get the loading in units of lead mass per square foot of floo r and per square foot of wall disturbed:



### AreaDist LeadLoad LeadLoad unit

wall per 

### = where: 

### LeadLoad **per wall unit** 

### = the total lead loading on the floor per unit of wal l area disturbed ( μ g/ft 2 

### /ft 2 

### ) **LeadLoad =** the total lead loading on the floor ( μ g/ft 2 

### ) **AreaDist =** the total wall area disturbed during renovation (ft 2 

### )
In addition, because each experiment was performed on buildings wit h different compositions of lead within the paint, the fraction emitted was calculated based on t he total amount of paint dust emitted rather than the total amount of lead emitted. This conversi on assumes that each experiment within an activity should produce nearly uniform amounts of paint dust per s quare foot disturbed, even though the amount of lead will vary according to the lead content in the paint. To make this conversion, each lead loading was divided by the percentage of lead in the paint specified in the Dust Study to approximate loadings in units of mass of paint particles per square foot of floor and per square foot of wall disturbed (see equation below).



### mg g frac LeadLoad oad PaintDustL unit wall per unit wall per 

### / 1000 1 μ 

### × =
where: 

### LeadLoad **per wall unit** 

### = the total lead loading on the floor per unit of wall area disturbed (mg/ft 2 

### /ft 2 

### ) 

### PaintDustLoad

**per wall unit** 

### = the total paint dust loading on the floor per unit of wall area disturbed (mg/ft 2 

### /ft 2 

### ) 

### frac = the fraction of paint which is lead by weight (unitless)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-2 July 25, 2014 

### Note that this conversion assumes that all the emitted parti cles are paint particles and does not account for the fact that the total emitted dust might include wall substrate particles as well. Thus, the estimated paint dust emission rate will not represent the to tal dust emitted during the activity but is intended to account for all the lead emitted, assuming that th ere is lead in the paint but not within the substrate or other disturbed portions of the building.
Next, the along-wall distribution of dust was estimated. F ollowing inspection of the general trends in the Dust Study experiments, it was assumed that the loadings wer e relatively constant over the width of the job and then tapered linearly to zero at the edge of the room in the along-wall direction (see Figure I-1). (US EPA 2007a) Thus, if measurements were made at locat ions wider than the job width (for example, Sample X in Figure I-1), these measurements were adjusted using a linear relationship to approximate the loading within the job area (for example, Sample X adj 

### in Figure I-1).
To make this linear conversion, the following equation was used:

unit wall per unit wall per 

### oad PaintDustL e DistMeasur DistPlast DistPlast oadAdj PaintDustL

− =



### where: 

### PaintDustLoadAdj **per wall unit** 

### = the total paint dust loading on the floor per unit of wall area disturbed adjusted to the level within the job area (mg/ft 2 

### /ft 2 

### ) 

### PaintDustLoad

**per wall unit** 

### = the total paint dust loading on the floor per unit of wall area disturbed (mg/ft 2 

### /ft 2 

### ) 

### DistPlast = the distance between the edge of the job and the edge of the room (ft) 

### DistMeasure = the distance between the edge of the job and the measurement (ft)

All of these distances were calculated from the informati on in the Dust Study job maps provided by Battelle. Because the dust is measured over discrete area s 1 ft wide, the measurements are assumed to apply to the center of the measurement area. Thus, the loadings were assumed to taper to zero over a distance 1 ft wider than shown in the diagrams. This assumption causes the loadings to fall to zero in the just at the edge of the room. The alternative assumption (l oadings going to zero at the center of the box just inside the wall) was not used, since non-zero measurem ents were available at those locations in the Dust Study.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-3 July 25, 2014 

### Figure I-1. Schematic of Interior Dust Study Expe riments


# I.2. Regression of Dust Study Data by Renovation Activity To estimate the distribution of dust in the out-from-wall dir ection within the job area, a regression analysis was performed on the 

### PaintDustLoadAdj **per wall unit** 

### values (or the 

### PaintDustLoad **per wall unit** 

### values if no adjustment was necessary because the measurement was made within the job area) at each corresponding distance from the wall with Microsoft Excel® using the following equation for exponential decay:

Dist Shape e Load egress PaintDustR × × = 0


### where:


### PaintDustRegress **=** paint dust loading (mg/ft 2 

### /ft 2 

### disturbed) 

### Load **0** 

### = loading just below the job at 0 ft (mg/ft 2 

### /ft 2 

### disturbed) 

### Shape = shape of exponential regression (/ft) 

### Dist = distance from renovation activity (ft)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-4 July 25, 2014 

# I.3. Total Amount of Dust Emitted Once these regression equations were determined, the total amount of dust on the floor was estimated by integrating the regression equation over the length of the r om to get the total mass (out-from-wall direction) and multiplying by the width of room (along-wall dir ection): 

# ( ) RoomWidth e Shape Load assDust M LenRoom Shape 

## ×

− × = × 

## 1

0



### where:



### MassDust **inside** 

### = paint dust mass on the floor inside the job area (mg/ft 2 

### disturbed) 

### Load **0** 

### = loading just below the job at 0 ft (mg/ft 2 

### disturbed) 

### Shape = shape of exponential regression (ft -1 

### ) 

### LenRoom = length of room in the out-from-wall direction (ft) 

### RoomWidth = the width of the room in the along-wall direction (ft)
For some experiments, the exact location of the job could not be determined based on experiment maps provided; these included small cutout jobs on the wall a nd ceiling and door planing jobs where the door was placed at an undisclosed location in the middle of the room on sawhorses. For these experiments, the regression could not be performed because the distance from the job wall for each sample was unknown. In these cases, the loadings per ft2 dist urbed were averaged and multiplied by the area of the room to estimate the total amount of mas s. As a check of the regression method, this “averaging” method and the regression method predictions of tot al dust were compared, and in general the predictions were within 25% of each other (average = 12 %).


# I.4. Amount of Paint on the Wall To estimate the total amount of paint on the wall for eac h renovation experiment, first the paint density and number of layers of paint were estimated. The average pai nt density for each activity was estimated for the Dust Study jobs using the fraction of lead in paint ac cording to the equation:

t Res Density frac DensityPb frac Dens Paint ×

− + × = )

1(



### where:



### PaintDens = the density of the paint (g paint/cm 3 

### paint) 

### frac = the fraction of paint which is lead by weight (unitless) 

### DensityPb = the density of lead (g/cm 3 

### )**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-5 July 25, 2014 

### DensityRest = the density of the rest of the paint (other than lead) (g/cm 3 

### )

The density of lead is 11.3 g/cm 3 

### , and the density of the rest of the paint is assumed to be e qual to 1 g/cm 3 

### consistent with the solid portion of modern paint (assumed to be lead-free), as calculated from Material Safety Data Sheets (MSDS) sheets.
The number of layers of paint on the wall at the time of the Dust Study job was estimated by assuming that painting had been done every seven years since the time the building was constructed. This value of seven years was selected based on information about typical renovation frequencies in child care centers (CCC data). For each of these previous painting jo bs on the building, it was assumed that the surface was prepared for painting and then a layer of paint was applied. There is no way of knowing what method was used to remove paint and prepare the surface in these prior painting jobs. Thus, the representative value of 15% removal for surface preparat ion (Section D.1.3) was used in estimating the fraction of paint removed during each previous painting job. The Dust Study jobs were performed in 2006. Thus, the number of layers of paint in 2006 for each Dus t Study building construction date were estimated and are shown in Table H-1. (EPA 2007a)
In some cases, the age of the renovated building was unknown. The Dust Study residences were built between 1900 and 1925, and a table provided within the Dust Study report indicates that nearby properties were built between 1900 and 1920. For this analys is, a representative building year of 1920 was selected as a year within that range.
For the actual paint removal jobs performed during the Dust S tudy, Battelle provided information about the degree of paint removal. Some jobs were surface prepara tion activities (removed only a fraction of the total paint on the wall), while others removed all the pa int on the wall. Thus, the amount of paint removed during each Dust Study job was estimated by multiplying t he number of layers of paint estimated to be on the building in 2006 by the estimated fract ion of paint removed during the particular job (100% for removal jobs and 15% for surface preparation jobs).

These values were then used to estimate the total amount of paint removed from the wall in the Dust Study job per square foot of wall:

d FracRemove nt ayersOfPai L UnitConv Dens Paint Cov aint WallP d aintRemove WallP × × × × =



### where:



### WallPaintRemoved = the total paint per square foot of wall removed during the**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-6 July 25, 2014 

### Dust Study job (mg/ft 2 

### wall) 

### WallPaintCov = the amount of paint which covers a single square foot of wall (gallons/ft 2 

### ) 

### UnitConv = a conversion factor to convert from gallons to cm 3 

### and from mg to g 

### PaintDens = the density of the paint (g paint/cm 3 

### paint) 

### LayersOfPaint = the number of layers of paint 

### FracRemoved = the fraction of the total layers of paint that were remo ved during the Dust Study job 

# I.5. Percentage of Paint Emitted By Activity Finally, the fraction of paint on the wall emitted per activ ity is estimated by dividing the amount emitted during the activity (Appendix I.3) by the total amount on the w all (Appendix I.4).



### WallPaint assDust M racEmitted F =

where:



### FracEmitted **=** fraction of paint removed from wall that is emitted as aerosol 

### MassDust **=** total paint dust mass on the floor (mg/ft 2 

### disturbed) 

### WallPaint = the total paint per square foot of wall (mg/ft 2 

### wall) Then, the fractions for the different experiments were combi ned with the fractions from any available exterior experiments, and the geometric mean across the di fferent experiments was used to represent the overall activity emission fraction. The resulting emi ssion fractions are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-7 July 25, 2014 

### Table I-1.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-8 July 25, 2014 

### Table I-1. Interior Analysis Fraction of Paint Emit ted.

Activity type Fraction of Paint That Remains as Lead Dust Window, Saw 0.2137 Door Planing 0.1118 Heat Gun Plaster 0.0572 Dry Scrape 0.0380 Window, No Saw 0.0155
Heat Gun Wood 0.0119 Door Planing, with HEPA 0.0112 Cutouts 0.0056 Kitchen Gut (Cabinet/Shelf Removal in the Approach) 0.0030



### The general approach above was applied to the experiments in t he Dust Study to compute the emission fractions. The remainder of the Appendix details the assumptions , input data, regression results, and emission fraction calculations for each experiment. The figures in each Appendix represent the experiment maps provided by Battelle.


# I.6. Window Replacement

I.6.1. Experiment 41 The geometry of Experiment 41 is shown in Figure I-2, and the measured lead loadings and associated distances are shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-9 July 25, 2014



### Table I-2. Post-Work Loading Measurements and Adjus tments in Experiment 41 

### . Samples 2 and 4 were adjusted using the taper assumption sinc e they were outside the lateral extent of the job. The loadings were divided by the size of the job (10 ft 2 

### ) and the fraction of lead in the paint (0.009) to estimate the dust loadings per ft 2 

### disturbed in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-10 July 25, 2014



### Table I-2. Post-Work Loading Measurements and Adjus tments in Experiment 41 

### . Then, the natural logs of all loadings were used with the dis tances to estimate the regression parameters in Table I-3. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept ).
The integral was performed over the regression equation o ut to a distance of 15 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-4. The estimated paint density using the lead fraction was 1.09 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.538.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-11 July 25, 2014 

### Figure I-2. Layout of Window Replacement Experiment 41 4 12 18

17

8 Stairs 7 3 16 11

15 2 6 10 14 9 1 5 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-12 July 25, 2014



### Table I-2. Post-Work Loading Measurements and Adjus tments in Experiment 41

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 61,134 61,134 679.27 2 5 1,852 2,777 30.86 3 8 9,948 9,948 110.53 4 15 1,387 3,698 41.09



### Table I-3. Regression Statistics in Experiment 41

Statistic Value Shape -0.15 Intercept 5.74 R 2 0.37 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 310.9



### Table I-4. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 41

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 29,254 Total Paint Removed From Surface (mg/ft 2 disturbed) 54,343 Fraction Emitted 0.538



## I.6.2. Experiment 42 The geometry of Experiment 42 is shown in Figure I-3, and the measured lead loadings and associated distances are shown in Table I-5. Samples 1 and 4 were adj usted using the taper assumption since they were outside the lateral extent of the job. The loadings w ere divided by the size of the job (10 ft 2 

### ) and the fraction of lead in the paint (0.019) to estimate the dus t loadings per ft 2 

### disturbed in Table I-5. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-6. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-13 July 25, 2014 

### The integral was performed over the regression equation o ut to a distance of 15 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-7. The estimated paint density using the lead fraction was 1.2 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.326.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-14 July 25, 2014 

### Figure I-3. Layout of Window Replacement Experiment 42
Table I-5. Post-Work Loading Measurements and Adjus tments in Experiment 42

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 18,575 26,004 136.87 2 4 19,100 19,100 100.53 3 8 10,251 10,251 53.95 4 12 252 1,767 9.30 

### 18

4 12 8 17 11 Stairs

16 3 7 15

14 6 2

10 1 9 13 5

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-15 July 25, 2014 

### Table I-6. Regression Statistics in Experiment 42

Statistic Value Shape -0.28 Intercept 5.86 R 2 0.95 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 349.50



### Table I-7. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 42

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 19,387 Total Paint Removed From Surface (mg/ft 2 disturbed) 59,485 Fraction Emitted 0.326



## I.6.3. Experiment 43 The geometry of Experiment 43 is shown in Figure I-4, and the measured lead loadings and associated distances are shown in Table I-8. Samples 1, 3, and 4 were adjusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (10 ft 2 

### ) and the fraction of lead in the paint (0.019) to estimate the dust loadings per ft 2 

### disturbed in Table I-8. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-9. Here, 

### intercept is the intercept of the linear regression in log space, and Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 15 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-10. The estimated paint density using the lead fraction was 1.2 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.172.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-16 July 25, 2014 

### Figure I-4. Layout of Window Replacement Experiment 43
Table I-8. Post-Work Loading Measurements and Adjus tments in Experiment 43

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 26,831 29,514 155.34 2 2 18,223 18,223 95.91 3 10 1,180 1,573 8.28 4 14 571 1,141 6.01 

### 4 18

8 17 Stairs 12 3

7 11

16

15

14 10 6 13 2 9 1 5

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-17 July 25, 2014 

### Table I-9. Regression Statistics in Experiment 43

Statistic Value Shape -0.26 Intercept 5.13 R 2 0.96 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 169.15



### Table I-10. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 43

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 10,215 Total Paint Removed From Surface (mg/ft 2 disturbed) 59,485 Fraction Emitted 0.172


## I.6.4. Experiment 44 The geometry of Experiment 44 is shown in Figure I-5, and the measured lead loadings and associated distances are shown in Table I-11. Samples 3 and 4 were ad justed using the taper assumption since they were outside the lateral extent of the job. The loadings w ere divided by the size of the job (10 ft 2 

### ) and the fraction of lead in the paint (0.041) to estimate the dus t loadings per ft 2 

### disturbed in Table I-11. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-12. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 15 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-13. The estimated paint density using the lead fraction was 1.42 g/cm 3 

### Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.069.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-18 July 25, 2014 

### Figure I-5. Layout of Window Replacement Experiment 44
Table I-11. Post-Work Loading Measurements and Adju stments in Experiment 44

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 28,492 28, 492 69.49 2 3 12,960 12,960 31.61 3 7 1,786 2,084 5.08 4 11 1,084 2,528 6.17 18

12 17 8 4 Stairs 11 16

3 7 15

5 1 2 14 6 13 9 10

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-19 July 25, 2014 

### Table I-12. Regression Statistics in Experiment 44

Statistic Value Shape -0.28 Intercept 4.47 R 2 0.72 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 87.42



### Table I-13. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 44

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 4,905 Total Paint Removed From Surface (mg/ft 2 disturbed) 70,797 Fraction Emitted 0.069


## I.6.5. Experiment 9 The geometry of Experiment 9 is shown in Figure I-6, and the measured lead loadings and associated distances are shown in Table I-14



### . Samples 1, 2, and 3 were adjusted using the taper assumption s ince they were outside the lateral extent of the job. The loading s were divided by the size of the job (9 ft 2 

### ) and the fraction of lead in the paint (0.073) to estimate the dust loadings per ft 2 

### disturbed in Table I-14. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-15. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 14 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-16. The estimated paint density using the lead fraction was 1.75 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sa me time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.013.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-20 July 25, 2014 

### Figure I-6. Layout of Window Replacement Experiment 9
Table I-14. Post-Work Loading Measurements and Adju stments in Experiment 9

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 11,634 17,450 26.56 2 3 403 1,610 2.45 3 6 1,344 10,754 16.37 4 12 561 561 0.85



### 17

4

8 12 16

15 11

3 7 14

2 13 10 5 1

6 9

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-21 July 25, 2014 

### Table I-15. Regression Statistics in Experiment 9

Statistic Value Shape -0.25 Intercept 3.13 R 2 0.48 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 22.82



### Table I-16. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 9

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,160 Total Paint Removed From Surface (mg/ft 2 disturbed) 87,250 Fraction Emitted 0.013


## I.6.6. Experiment 10 The geometry of Experiment 10 is shown in Figure I-7, and the measured lead loadings and associated distances are shown in Table I-17. Samples 2 and 4 were ad justed using the taper assumption since they were outside the lateral extent of the job. The loadings w ere divided by the size of the job (9 ft 2 

### ) and the fraction of lead in the paint (0.033) to estimate the dust l oadings per ft 2 

### disturbed in Table I-17. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-18. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 14 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-19. The estimated paint density using the lead fraction was 1.34 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.134.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-22 July 25, 2014 

### Figure I-7. Layout of Window Replacement Experiment 10
Table I-17. Post-Work Loading Measurements and Adju stments in Experiment 10

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 15,817 15,817 53.25 2 3 14,746 16,384 55.17 3 6 15,031 15,031 50.61 4 13 113 132 0.44 

### 4 17 12

8 16

15 11

3 14

7

1 2 10 9 5 13 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-23 July 25, 2014 

### Table I-18. Regression Statistics in Experiment 10

Statistic Value Shape -0.49 Intercept 5.81 R 2 0.92 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 333.70



### Table I-19. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 10

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 8,920 Total Paint Removed From Surface (mg/ft 2 disturbed) 66,683 Fraction Emitted 0.134


## I.6.7. Experiment 11 The geometry of Experiment 11 is shown in Figure I-8, and the measured lead loadings and associated distances are shown in Table I-20. No adjustments were ma de to the loading since all samples were within the width of the job. The loadings were divided by the siz e of the job (18 ft 2 

### ) and the fraction of lead in the paint (0.101) to estimate the dust loadings per ft 2 

### disturbed in Table I-20. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-21. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 14 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-22. The estimated paint density using the lead fraction was 2.04 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.006.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-24 July 25, 2014 

### Figure I-8. Layout of Window Replacement Experiment 11
Table I-20. Post-Work Loading Measurements and Adju stments in Experiment 11

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 11,211 6.17 2 2 16,203 8.91 3 6 3,783 2.08 4 11 2,204 1.21



### 17 10

16 2 9 12 15 3 5 1 14 8 4 6 13 11 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-25 July 25, 2014 

### Table I-21. Regression Statistics in Experiment 11

Statistic Value Shape -0.19 Intercept 2.20 R 2 0.89 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 9.0



### Table I-22. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 11

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 609.42 Total Paint Removed From Surface (mg/ft 2 disturbed) 101,646 Fraction Emitted 0.006


## I.6.8. Experiment 12 The geometry of Experiment 12 is shown in Figure I-9, and the measured lead loadings and associated distances are shown in Table I-23. No adjustments were ma de to the loading since all samples were within the width of the job. The loadings were divided by the siz e of the job (11 ft 2 

### ) and the fraction of lead in the paint (0.072) to estimate the dust loadings per ft 2 

### disturbed in Table I-23. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-24. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-25. The estimated paint density using the lead fraction was 1.74 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.005.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-26 July 25, 2014 

### Figure I-9. Layout of Window Replacement Experiment 12
Table I-23. Post-Work Loading Measurements and Adju stments in Experiment 12

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 9,757 12.32 2 2 1,559 1.97 3 6 2,748 3.47 4 9 585 0.74



### 15

10 9 8 2 6 1 4 14

12 5 3 13

11 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-27 July 25, 2014 

### Table I-24. Regression Statistics in Experiment 12

Statistic Value Shape -0.23 Intercept 2.20 R 2 0.40 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 9.05



### Table I-25. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 12

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 472.30 Total Paint Removed From Surface (mg/ft 2 disturbed) 86,736 Fraction Emitted 0.005 

# I.7. Cut Outs

I.7.1. Experiment 22 The geometry of Experiment 22 is shown in Figure I-10, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (1 3 ft 2 

### ) and the fraction of lead in the paint (0.036) to estimate the dust loadings per ft 2 

### disturbed in Table I-26. Then, the loadings were averaged and multiplied by the area of the room (190 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-27. The estimated paint density using the lead fracti on was 1.37 g/cm3. Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was ass umed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.011.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-28 July 25, 2014 

### Figure I-10. Layout of Cut Outs Experiment 22

Table I-26. Post-Work Loading Measurements and

Adjustments in Experiment 22

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 84 0.18 2 1,642 3.51 3 1,454 3.11 4 3,935 8.41 

### 4 2 17 6

8 16 3 15 7

11 5 12 14 10 9

1 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-29 July 25, 2014 

### Table I-27. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 22

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 722 Total Paint Removed From Surface (mg/ft 2 disturbed) 68,226 Fraction Emitted 0.011


## I.7.2. Experiment 23 The geometry of Experiment 23 is shown in Figure I-11, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (14 ft 2 

### ) and the fraction of lead in the paint (0.036) to estimate the dust loadings per ft 2 

### disturbed in Table I-28. Then, the loadings were averaged and multiplied by the area of the room (190 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-29. The estimated paint density using the lead fracti on was 1.37 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was as sumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.015.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-30 July 25, 2014 

### Figure I-11. Layout of Cut Outs Experiment 23

Table I-28. Post-Work Loading Measurements and

Adjustments in Experiment 23

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 4,174 8.28 2 3,469 6.88 3 313 0.62 4 3,146 6.24

17 7 8 4

16 15 1 11 10

14 9 12 5 2 3 13 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-31 July 25, 2014 

### Table I-29. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 23

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1046 Total Paint Removed From Surface (mg/ft 2 disturbed) 68,226 Fraction Emitted 0.015



## I.7.3. Experiment 24 The geometry of Experiment 24 is shown in Figure I-12, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.05) to estimate the dust loadings per ft 2 

### disturbed in Table I-30. Then, the loadings were averaged and multiplied by the area of the room (190 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-31. The estimated paint density using the lead fracti on was 1.52 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was ass umed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The ex periment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.0002



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-32 July 25, 2014 

### Figure I-12. Layout of Cut Outs Experiment 24

Table I-30. Post-Work Loading Measurements and

Adjustments in Experiment 24

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 92 0.05 2 473 0.24 3 48 0.02 4 103 0.05 17

10 3 16

9 1 15 8 14 2 4 5 12

13 7 6 11

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-33 July 25, 2014 

### Table I-31. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 24

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 17 Total Paint Removed From Surface (mg/ft 2 disturbed) 75,424 Fraction Emitted 0.0002



## I.7.4. Experiment 25 The geometry of Experiment 25 is shown in Figure I-13, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (14 ft 2 

### ) and the fraction of lead in the paint (0.036) to estimate the dust loadings per ft 2 

### disturbed in Table I-32. Then, the loadings were averaged and multiplied by the area of the room (190 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-33. The estimated paint density using the lead fracti on was 1.37 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was ass umed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.0113



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-34 July 25, 2014 

### Figure I-13. Layout of Cut Outs Experiment 25

Table I-32. Post-Work Loading Measurements and

Adjustments in Experiment 25

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 127 0.25 2 2,667 5.29 3 887 1.76 4 4,524 8.98

17 8 12 16

1 4 15

9 2 11 10

14 3

7

5 13 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-35 July 25, 2014 

### Table I-33. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 25

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 733 Total Paint Removed From Surface (mg/ft 2 disturbed) 68,226 Fraction Emitted 0.011



## I.7.5. Experiment 45 The geometry of Experiment 45 is shown in Figure I-14, a nd the measured lead loadings and associated distances are shown in Table I-34. Sample 2 was adjusted us ing the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.018) to estimate the dust l oadings per ft 2 

### disturbed in Table I-34. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-35. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-36. However, the R2 was very low, so the average method was used instead of t he integral method. The estimated paint density using the lead fraction was 1.19 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5.3 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.0264.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-36 July 25, 2014 

### Figure I-14. Layout of Cut Outs Experiment 45
Table I-34. Post-Work Loading Measurements and Adju stments in Experiment 45

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 2,101 2,101 19.45 2 3 110 137 1.27 3 6 909 909 8.41 4 12 621 621 5.75

12 17

6 4 16 1 9 15 11 2

10 5 8 7 14

3

13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job Cabinets**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-37 July 25, 2014 

### Table I-35. Regression Statistics in Experiment 45

Statistic Value Shape -0.02 Intercept 1.89 R 2 0.01 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 6.64



### Table I-36. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 45

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,558 Total Paint Removed From Surface (mg/ft 2 disturbed) 58,971 Fraction Emitted 0.011



## I.7.6. Experiment 46 The geometry of Experiment 46 is shown in Figure I-15, a nd the measured lead loadings and associated distances are shown in Table I-37. Sample 2 was adjusted using the taper assumption since it was outside the lateral extent of the job. The loadings were di vided by the size of the job (7 ft 2 

### ) and the fraction of lead in the paint (0.018) to estimate the dust l oadings per ft 2 

### disturbed in Table I-37. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-38. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-39. The estimated paint density using the lead fraction was 1.19 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5.3 layers of paint at the time of the jo b. The experiment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, givi ng a final emission fraction of 0.013.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-38 July 25, 2014 

### Figure I-15. Layout of Cut Outs Experiment 46
Table I-37. Post-Work Loading Measurements and Adju stments in Experiment 46

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 1,586 1,586 12.59 2 2 529 794 6.30 3 6 216 216 1.71 4 11 90 90 0.71 17

9

16 5 4 15 3 1 10 7 14 6 8 2 12 11 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job Cabinets**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-39 July 25, 2014 

### Table I-38. Regression Statistics in Experiment 46

Statistic Value Shape -0.29 Intercept 2.76 R 2 0.85 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 15.75



### Table I-39. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 46

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 781.02 Total Paint Removed From Surface (mg/ft 2 disturbed) 58,971 Fraction Emitted 0.013



## I.7.7. Experiment 71 The geometry of Experiment 71 is shown in Figure I-16, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.008) to estimate the dust loadings per ft 2 

### disturbed in Table I-40. Then, the loadings were averaged and multiplied by the area of the room (115 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-41. The estimated paint density using the lead fracti on was 1.08 g/cm 3 

### . The building was built in 1900, thus it was assumed to have 6.2 layers of paint at t he time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.001



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-40 July 25, 2014 

### Figure I-16. Layout of Cut Outs Experiment 71

Table I-40. Post-Work Loading Measurements and

Adjustments in Experiment 71

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 59 1.23 2 16 0.34 3 7 0.15 4 16 0.34



### Table I-41. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 71

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 59 Total Paint Removed From Surface (mg/ft 2 disturbed) 63,279 Fraction Emitted 0.001 1 10 15

14 9 2 3 8

6 11 4 7 13 5 12

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Cabinets**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-41 July 25, 2014 

## I.7.8. Experiment 72 The geometry of Experiment 72 is shown in Figure I-17, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (7 ft 2 

### ) and the fraction of lead in the paint (0.01) to estimate the dust loadings per ft 2 

### disturbed in Table I-42. Then, the loadings were averaged and multiplied by the area of the room (115 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-43. The estimated paint density using the lead fracti on was 1.10 g/cm 3 

### . The building was built in 1900, thus, it was assumed to have 6.2 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.0208



### .

Figure I-17. Layout of Cut Outs Experiment 72 12 1 15

5 14 7 2 3 4 10 11 9 8 6 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-42 July 25, 2014 

### Table I-42. Post-Work Loading Measurements and

Adjustments in Experiment 72

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 2,995 42.78 2 55 0.79 3 40 0.58 4 174 2.48



### Table I-43. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 72

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1341 Total Paint Removed From Surface (mg/ft 2 disturbed) 64,488 Fraction Emitted 0.021


## I.7.9. Experiment 52 The geometry of Experiment 52 is shown in Figure I-18, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.095) to estimate the dust loadings per ft 2 

### disturbed in Table I-44. Then, the loadings were averaged and multiplied by the area of the room (120 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-45. The estimated paint density using the lead fracti on was 1.98 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 3.3 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.003



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-43 July 25, 2014 

### Figure I-18. Layout of Cut Outs Experiment52

Table I-44. Post-Work Loading Measurements and

Adjustments in Experiment 52

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 1,101 1.93 2 1,154 2.02 3 453 0.79 4 220 0.39



### Table I-45. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 52

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 154 Total Paint Removed From Surface (mg/ft 2 disturbed) 61,859 Fraction Emitted 0.003 8 4

12 11 3 15

14 13 7 10 9 2 5 1 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room Vertical containment plastic**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-44 July 25, 2014 

## I.7.10. Experiment 53 The geometry of Experiment 53 is shown in Figure I-19, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.074) to estimate the dust loadings per ft 2 

### disturbed in Table I-46. Then, the loadings were averaged and multiplied by the area of the room (120 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-47. The estimated paint density using the lead fracti on was 1.77 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 3.3 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.005



### .

Figure I-19. Layout of Cut Outs Experiment 53 2 15 6 10 5 1 9 3 7

11 14 12 8 13 4

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Vertical containment plastic**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-45 July 25, 2014 

### Table I-46. Post-Work Loading Measurements and

Adjustments in Experiment 53

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 880 1.98 2 3,005 6.77 3 234 0.53 4 128 0.29



### Table I-47. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 53

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 287 Total Paint Removed From Surface (mg/ft 2 disturbed) 55,082 Fraction Emitted 0.005


## I.7.11. Experiment 54 The geometry of Experiment 54 is shown in Figure I-20, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings



### . The loadings were divided by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.074) to estimate the dust loadings per ft 2 

### disturbed in Table I-48. Then, the loadings were averaged and multiplied by the area of the room (120 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-49. The estimated paint density using the lead fracti on was 1.77 g/cm 3



### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 3.3 layers were assumed to be removed. These values were used to estimate the total amount of pa int on the wall, giving a final emission fraction of 0.006



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Commerci al Buildings

Peer Review Draft I-46 July 25, 2014 

### Figure I-20. Layout of Cut Outs Experiment 54

Table I-48. Post-Work Loading Measurements and

Adjustments in Experiment 54

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 1,991 4.48 2 1,381 3.11 3 712 1.60 4 841 1.90 5 9 2 6 1 10

7 3 13 11

4 15 12 14 8

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room Vertical containment plastic**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-47 July 25, 2014 

### Table I-49. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 54

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 333 Total Paint Removed From Surface (mg/ft 2 disturbed) 55,082 Fraction Emitted 0.006


## I.7.12. Experiment 55 The geometry of Experiment 55 is shown in Figure I-21, but the exact location of the cutouts is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (6 ft 2 

### ) and the fraction of lead in the paint (0.074) to estimate the dust loadings per ft 2 

### disturbed in Table I-50. Then, the loadings were averaged and multiplied by the area of the room (120 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-51. The estimated paint density using the lead fracti on was 1.77 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 3.3 layers were assumed to be removed



### . These values were used to estimate the total amount of pai nt on the wall, giving a final emission fraction of 0.005



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-48 July 25, 2014 

### Figure I-21. Layout of Cut Outs Experiment 55

Table I-50. Post-Work Loading Measurements and

Adjustments in Experiment 55

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 1,793 4.04 2 1,655 3.73 3 271 0.61 4 124 0.28



### 2 1 14

6 5 15 9 10 13 3 7 11

12 8 4

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification smaple Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-49 July 25, 2014 

### Table I-51. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 55

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 260 Total Paint Removed From Surface (mg/ft 2 disturbed) 55,082 Fraction Emitted 0.005 

# I.8. Door Plane

I.8.1. Experiment 77 The geometry of Experiment 77 is shown in Figure I-22, but the exact location of the door is unknown. Thus, no regression could be performed and the emission fractio n is based on the average loadings. The loadings were divided by the size of the job (26 ft 2 

### ) and the fraction of lead in the paint (0.017) to estimate the dust loadings per ft 2 

### disturbed in Table I-52. Then, the loadings were averaged and multiplied by the area of the room (180 ft 2 

### ) to get the total mass of dust on the floor, as shown i n Table I-53. The estimated paint density using the lead fraction was 1.18 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5.3 layers of paint at the ti me of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assumed to be removed



### . These values were used to estimate the total amount of paint on t he wall, giving a final emission fraction of 0.216



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-50 July 25, 2014 

### Figure I-22. Layout of Door Plane Experiment 77

Table I-52. Post-Work Loading Measurements and

Adjustments in Experiment 77

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 31,648 71.60 2 36,137 81.76 3 34,162 231.87 4 21,771 73.88

17 3 12 9 6 1

16 11 2 10

15 5 8 14

4 13 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-51 July 25, 2014 

### Table I-53. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 77

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 12,596 Total Paint Removed From Surface (mg/ft 2 disturbed) 58,457 Fraction Emitted 0.216



## I.8.2. Experiment 78 The geometry of Experiment 78 is shown in Figure I-23, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. The loadings were divided by the size of the job (25 ft 2 

### ) and the fraction of lead in the paint (0.02) to estimate the dust loadings per ft 2 

### disturbed in Table I-54. Then, the loadings were averaged and multiplied by the area of the room (180 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-55. The estimated paint density using the lead f raction was 1.21 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5.3 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assumed to be removed



### . These values were used to estimate the total amount of pai nt on the wall, giving a final emission fraction of 0.161



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-52 July 25, 2014 

### Figure I-23. Layout of Door Plane Experiment 78

Table I-54. Post-Work Loading Measurements and

Adjustments in Experiment 78

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 36,423 72.85 2 38,237 114.71 3 27,352 54.70 4 4,999 15.00 4 17 11 2 10 6

3 16 7

15 12 9 14 5 1

8 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-53 July 25, 2014 

### Table I-55. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 78

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 9,631 Total Paint Removed From Surface (mg/ft 2 disturbed) 59,999 Fraction Emitted 0.161


## I.8.3. Experiment 79 The geometry of Experiment 79 is shown in Figure I-24, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. Sample 2 was adjusted using the taper assumption since it was outside the lateral extent of the job



### . The loadings were divided by the size of the job (32 ft 2 

### ) and the fraction of lead in the paint (0.021) to estimate the dust loadings per ft 2 

### disturbed in Table I-56. Then, the loadings were averaged and multiplied by the area of the room (140 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-57. The estimated paint density using the lead f raction was 1.22 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings wer e believed to have been built at the same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.120.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-54 July 25, 2014 

### Figure I-24. Layout of Door Plane Experiment 79

Table I-56. Post-Work Loading Measurements and

Adjustments in Experiment 79

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 72,601 108.04 2 31,651 70.65 3 22,105 32.89 4 13,484 20.07

16 2 7 9 15 12

6 3 5 14 4 1

10 8

13 11

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-55 July 25, 2014 

### Table I-57. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 79

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 7,283 Total Paint Removed From Surface (mg/ft 2 disturbed) 60,513 Fraction Emitted 0.120



## I.8.4. Experiment 80 The geometry of Experiment 80 is shown in Figure I-25, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. No adjustments were made to the loa ding since all samples were within the width of the job. The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.059) to estimate the dust loadings per ft 2 

### disturbed in Table I-58. Then, the loadings were averaged and multiplied by the area of the room (180 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-59. The estimated paint density using the lead fracti on was 1.61 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was as sumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.095



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-56 July 25, 2014 

### Figure I-25. Layout of Door Plane Experiment 80

Table I-58. Post-Work Loading Measurements and

Adjustments in Experiment 80

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 49,254 20.87 2 166,383 70.50 3 140,057 59.35 4 41,628 17.64 12 7 17

4 16 8 15 2 1

11 5 14

3 9 6 10 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-57 July 25, 2014 

### Table I-59. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 80

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 7,576 Total Paint Removed From Surface (mg/ft 2 disturbed) 80,051 Fraction Emitted 0.095


## I.8.5. Experiment 47 The geometry of Experiment 47 is shown in Figure I-26, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. Sample 2 was adjusted using the taper assumption since it was outside the lateral extent of the job



### . The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.039) to estimate the dust loadings per ft 2 

### disturbed in Table I-60. Then, the loadings were averaged and multiplied by the area of the room (180 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-61. The estimated paint density using the lead f raction was 1.40 g/cm 3 

### . The building was built in 1910, thus 

### , it was assumed to have 5.3 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assumed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.109



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-58 July 25, 2014 

### Figure I-26. Layout of Door Plane Experiment 47

Table I-60. Post-Work Loading Measurements and

Adjustments in Experiment 47

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 209,798 134.49 2 30,305 38.85 3 11,958 7.67 4 11,267 7.22

17

12 5 16

10 15 1 9 6 3

7 14 4 11 8 13 2

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-59 July 25, 2014 

### Table I-61. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 47

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 7,596 Total Paint Removed From Surface (mg/ft 2 disturbed) 69,768 Fraction Emitted 0.109


## I.8.6. Experiment 48 The geometry of Experiment 48 is shown in Figure I-27, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.078) to estimate the dust loadings per ft 2 

### disturbed in Table I-62. Then, the loadings were averaged and multiplied by the area of the room (180 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-63. The estimated paint density using the lead f raction was 1.81 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5.3 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.3 layers were assumed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.039.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-60 July 25, 2014 

### Figure I-27. Layout of Door Plane Experiment 48

Table I-62. Post-Work Loading Measurements and

Adjustments in Experiment 48

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 81,228 26.03 2 57,536 18.44 3 68,305 21.89 4 38,101 30.53 17

16 10

7 2 6 15 11 1 5 8 3 14

12 13 9 4

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-61 July 25, 2014 

### Table I-63. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 48

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 3,536 Total Paint Removed From Surface (mg/ft 2 disturbed) 89,821 Fraction Emitted 0.039


## I.8.7. Experiment 73 The geometry of Experiment 73 is shown in Figure I-28, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. The loadings were divided by the size of the job (31 ft 2 

### ) and the fraction of lead in the paint (0.012) to estimate the dust loadings per ft 2 

### disturbed in Table I-64. Then, the loadings were averaged and multiplied by the area of the room (115 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-65. The estimated paint density using the lead f raction was 1.12 g/cm 3 

### . The building was built in 1900, thus 

### , it was assumed to have 6.2 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.107



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-62 July 25, 2014 

### Figure I-28. Layout of Door Plane Experiment 73

Table I-64. Post-Work Loading Measurements and

Adjustments in Experiment 73

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 16,511 44.38 2 29,248 78.62 3 17,936 48.21 4 27,380 73.60



### 15 2 3 9 10 6 14 4 1 11

13 8 12 5 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-63 July 25, 2014 

### Table I-65. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 73

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 7,039 Total Paint Removed From Surface (mg/ft 2 disturbed) 65,697 Fraction Emitted 0.117


## I.8.8. Experiment 74 The geometry of Experiment 74 is shown in Figure I-29, but the exact location of the door during the experiment is unknown. Thus, no regression could be performed and t he emission fraction is based on the average loadings. The loadings were divided by the size of the job (26 ft 2 

### ) and the fraction of lead in the paint (0.032) to estimate the dust loadings per ft 2 

### disturbed in Table I-66. Then, the loadings were averaged and multiplied by the area of the room (115 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-67. The estimated paint density using the lead f raction was 1.33 g/cm 3 

### . The building was built in 1900, thus 

### , it was assumed to have 6.2 layers of paint at the time of the job



### . The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.040



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-64 July 25, 2014 

### Figure I-29. Layout of Door Plane Experiment 74

Table I-66. Post-Work Loading Measurements and

Adjustments in Experiment 74

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 17,982 21.61 2 26,293 31.60 3 13,378 16.08 4 33,363 40.10



### 15 10 2 11 8 5 3

9 14

1 4 12 6 13 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-65 July 25, 2014 

### Table I-67. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 74

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 3,145 Total Paint Removed From Surface (mg/ft 2 disturbed) 77,785 Fraction Emitted 0.040 

# I.9. Dry Scrape

I.9.1. Experiment 5 The geometry of Experiment 5 is shown in Figure I-30, and t he measured lead loadings and associated distances are shown in Table I-68. No adjustments were m ade to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (90 ft 2 

### ) and the fraction of lead in the paint (0.016) to estimate the dust loadings per ft 2 

### disturbed in Table I-68. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-69. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 13 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-70. The estimated paint density using the lead fraction was 1.17 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.003.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-66 July 25, 2014 

### Figure I-30. Layout of Dry Scrap Experiment 5
Table I-68. Post-Work Loading Measurements and Adju stments in Experiment 5

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 3,067 2.13 2 4 1,289 0.90 3 5 1,122 0.78 4 12 1,289 0.90 17 4 12 16 8 11 15

7 3 6 2 14

1 9 10 13 5

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-67 July 25, 2014 

### Table I-69. Regression Statistics in Experiment 5

Statistic Value Shape -0.06 Intercept 0.39 R 2 0.27 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 1.48



### Table I-70. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 5

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 178 Total Paint Removed From Surface (mg/ft 2 disturbed) 57,943 Fraction Emitted 0.003


## I.9.2. Experiment 6 The geometry of Experiment 6 is shown in Figure I-31, and, the measured lead loadings and associated distances are shown in Table I-71. No adjustments were ma de to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (90 ft 2 

### ) and the fraction of lead in the paint (0.027) to estimate the dust loadings per ft 2 

### disturbed in Table I-71. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-72. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 13 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-73. The estimated paint density using the lead fraction was 1.28 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layer s) were removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.032.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-68 July 25, 2014 

### Figure I-31. Layout of Dry Scrap Experiment 6
Table I-71. Post-Work Loading Measurements and Adju stments in Experiment 6

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 7,357 3.03 2 1 4,158 1.71 3 7 2,886 1.19 4 9 5,358 2.20



### Table I-72. Regression Statistics in Experiment 6

Statistic Value Shape -0.03 Intercept 0.81 R 2 0.13 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 2.24 17 12 2 6 16 3 9 5 8 10 15 7 4

14 11 1 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-69 July 25, 2014 

### Table I-73. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 6

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 305 Total Paint Removed From Surface (mg/ft 2 disturbed) 9,540 Fraction Emitted 0.032


## I.9.3. Experiment 7 The geometry of Experiment 7 is shown in Figure I-32, and t he measured lead loadings and associated distances are shown in Table I-74. No adjustments were ma de to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (70 ft 2 

### ) and the fraction of lead in the paint (0.017) to estimate the dust loadings per ft 2 

### disturbed in Table I-74. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-75. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 13 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-76. The estimated paint density using the lead fraction was 1.18 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layer s) were removed. These values were used to estimate the total amount of paint on the wall, giving a f inal emission fraction of 0.062.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-70 July 25, 2014 

### Figure I-32. Layout of Dry Scrap Experiment 7
Table I-74. Post-Work Loading Measurements and Adju stments in Experiment 7

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 15.427 12.96 2 1 5,949 5.00 3 7 3,146 2.64 4 10 256 0.21



### Table I-75. Regression Statistics in Experiment 7

Statistic Value Shape -0.37 Intercept 2.74 R 2 0.79 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 15.50 2 5 1 9 17 10

16 6 15

3 7 11 14 4

12 8 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-71 July 25, 2014 

### Table I-76. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 7

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 543 Total Paint Removed From Surface (mg/ft 2 disturbed) 8,768 Fraction Emitted 0.062


## I.9.4. Experiment 8 The geometry of Experiment 8 is shown in Figure I-33, and t he measured lead loadings and associated distances are shown in Table I-77. No adjustments were ma de to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (110 ft 2 

### ) and the fraction of lead in the paint (0.017) to estimate the dust loadings per ft 2 

### disturbed in Table I-77. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-78. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 13 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-79. The estimated paint density using the lead fraction was 1.18 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layer s) were removed. These values were used to estimate the total amount of paint on the wall, giving a f inal emission fraction of 0.059.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-72 July 25, 2014 

### Figure I-33. Layout of Dry Scrap Experiment 8
Table I-77. Post-Work Loading Measurements and Adju stments in Experiment 8

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 5,710 3.05 2 2 4,886 2.61 3 4 48,440 25.90 4 9 69 0.04



### Table I-78. Regression Statistics in Experiment 8

Statistic Value Shape -0.65 Intercept 3.25 R 2 0.60 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 25.86

8 17 5 7 16 1

15 3

11 12 14 10 9 4 6

2

13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-73 July 25, 2014 

### Table I-79. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 8

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 521 Total Paint Removed From Surface (mg/ft 2 disturbed) 8,768 Fraction Emitted 0.059


## I.9.5. Experiment 26 The geometry of Experiment 26 is shown in Figure I-34, a nd the measured lead loadings and associated distances are shown in Table I-80. Sample 3 was adjusted us ing the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (60 ft 2 

### ) and the fraction of lead in the paint (0.021) to estimate the dust l oadings per ft 2 

### disturbed in Table I-80. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-81. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-82. The estimated paint density using the lead fraction was 1.22 g/cm3. Althoug h the vintage of the renovated building was unknown, nearby buildings were believed to have been built at t he same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not a ll paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layers) were removed. These values were used to estimate the total amount of paint on the wall, givi ng a final emission fraction of 0.048.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-74 July 25, 2014 

### Figure I-34. Layout of Dry Scrap Experiment 26
Table I-80. Post-Work Loading Measurements and Adju stments in Experiment 26

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 2,623 2,623 2.08 2 2 4,668 4,668 3.71 3 4 2,254 3,006 2.39 4 13 2,033 2,033 1.61



### Table I-81. Regression Statistics in Experiment 26

Statistic Value Shape -0.04 Intercept 1.06 R 2 0.47 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 2.90

8 4 17 12

16 15

7 3 11 5 14 2 6 1 10 Stairs

13 9

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-75 July 25, 2014 

### Table I-82. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 26

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 434 Total Paint Removed From Surface (mg/ft 2 disturbed) 9,077 Fraction Emitted 0.049


## I.9.6. Experiment 27 The geometry of Experiment 27 is shown in Figure I-35, a nd the measured lead loadings and associated distances are shown in Table I-83. No adjustments were ma de to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (64 ft 2 

### ) and the fraction of lead in the paint (0.025) to estimate the dust loadings per ft 2 

### disturbed in Table I-83. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-84. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 16 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-85. The estimated paint density using the lead fraction was 1.26 g/cm 3



### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layer s) were removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.021.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-76 July 25, 2014 

### Figure I-35. Layout of Dry Scrap Experiment 27
Table I-83. Post-Work Loading Measurements and Adju stments in Experiment 27

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 14,503 9.06 2 3 1,105 0.69 3 5 1,389 0.87 4 10 107 0.07



### Table I-84. Regression Statistics in Experiment 27

Statistic Value Shape -0.49 Intercept 2.07 R 2 0.88 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 7.90

11 17 12 5 16 8 2 3 4

15

1 14 10 7 9 13 Stairs 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-77 July 25, 2014 

### Table I-85. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 27

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 194 Total Paint Removed From Surface (mg/ft 2 disturbed) 9,385 Fraction Emitted 0.02


## I.9.7. Experiment 28 The geometry of Experiment 28 is shown in Figure I-36, a nd the measured lead loadings and associated distances are shown in Table I-86. No adjustments were ma de to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (65 ft 2 

### ) and the fraction of lead in the paint (0.025) to estimate the dust loadings per ft 2 

### disturbed in Table I-86. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-87. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-88. The estimated paint density using the lead fraction was 1.26 g/cm3. Althoug h the vintage of the renovated building was unknown, nearby buildings were believed to have been built at t he same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layers) were removed. These values were used to estimate the total amount of paint on the wall, givi ng a final emission fraction of 0.215.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-78 July 25, 2014 

### Figure I-36. Layout of Dry Scrap Experiment 28
Table I-86. Post-Work Loading Measurements and Adju stments in Experiment 28

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 44,197 27.20 2 2 43,302 26.65 3 8 4,241 2.61 4 12 458 0.28



### Table I-87. Regression Statistics in Experiment 28

Statistic Value Shape -0.42 Intercept 3.97 R 2 0.98 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 53.16

1 10 2 17 5 9 6 16

15

11 3 7

14 8

4 Stairway 12 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-79 July 25, 2014 

### Table I-88. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 28

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 2,021 Total Paint Removed From Surface (mg/ft 2 disturbed) 9,385 Fraction Emitted 0.215


## I.9.8. Experiment 29 The geometry of Experiment 29 is shown in Figure I-37, a nd the measured lead loadings and associated distances are shown in Table I-89. Sample 4 was adjusted us ing the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (72 ft 2 

### ) and the fraction of lead in the paint (0.026) to estimate the dust l oadings per ft 2 

### disturbed in Table I-89. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-90. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 16 ft, (the length of the room), and the total mass of dust on the floor was estim ated as shown in Table I-91. The estimated paint density using the lead fraction was 1.27 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15% of the paint (or .8 layer s) were removed. These values were used to estimate the total amount of paint on the wall, giving a f inal emission fraction of 0.028.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-80 July 25, 2014 

### Figure I-37. Layout of Dry Scrap Experiment 29
Table I-89. Post-Work Loading Measurements and Adju stments in Experiment 29

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 4 3,989 3,989 2.13 2 6 2,410 2,410 1.29 3 11 1,575 1,575 0.84 4 13 386 965 0.52



### Table I-90. Regression Statistics in Experiment 29

Statistic Value Shape -0.14 Intercept 1.24 R 2 0.95

17 9 6 12 3 16 5

7 10

2 1

15 11 14 8

13 Stairway 4

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-81 July 25, 2014 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 3.45



### Table I-91. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 29

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 263 Total Paint Removed From Surface (mg/ft 2 disturbed) 9,463 Fraction Emitted 0.028 

# I.10. Kitchen Gut

I.10.1. Experiment 49 The geometry of Experiment 5 is shown in Figure I-30, but t he exact location of the removed kitchen components is unknown. Thus, no regression could be performed and the emission fraction is based on the average loadings. The loadings were divided by the size of the job (55 ft 2 

### ) and the fraction of lead in the paint (0.017) to estimate the dust loadings per ft 2 

### disturbed in Table I-92. Then, the loadings were averaged and multiplied by the area of the room (80 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-93. The estimated paint density using the lead f raction was 1.18 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings wer e believed to have been built at the same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed



### . These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.008.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-82 July 25, 2014 

### Figure I-38. Layout of Kitchen Gut Experiment 49

Table I-92. Post-Work Loading Measurements and

Adjustments in Experiment 49

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 4,010 4.29 2 2,291 2.45 3 14,229 15.22 4 1,433 1.53



### Table I-93. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 49

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 470 Total Paint Removed From Surface (mg/ft 2 disturbed) 58,457 Fraction Emitted 0.008 8 2 15 6 12

1 5 11 4 14 13 7 3 10 9

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-83 July 25, 2014 

## I.10.2. Experiment 50 The geometry of Experiment 50 is shown in Figure I-39, but the exact location of the removed kitchen components is unknown. Thus, no regression could be performed and the emission fraction is based on the average loadings. The loadings were divided by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.023) to estimate the dust loadings per ft 2 

### disturbed in Table I-94. Then, the loadings were averaged and multiplied by the area of the room (80 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-95. The estimated paint density using the lead f raction was 1.24 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings wer e believed to have been built at the same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.006.

Figure I-39. Layout of Kitchen Gut Experiment 50 6 3 1 11 15 4 14 2 12 8

5 7

13 10 9

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-84 July 25, 2014 

### Table I-94. Post-Work Loading Measurements and

Adjustments in Experiment 50

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 162 0.18 2 1,414 1.54 3 14,560 15.83 4 481 0.52



### Table I-95. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 50

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 361 Total Paint Removed From Surface (mg/ft 2 disturbed) 61,542 Fraction Emitted 0.006


## I.10.3. Experiment 51 The geometry of Experiment 51 is shown in Figure I-40, but the exact location of the removed kitchen components is unknown. Thus, no regression could be performed and the emission fraction is based on the average loadings. The loadings were divided by the size of the job (66 ft 2 

### ) and the fraction of lead in the paint (0.035) to estimate the dust loadings per ft 2 

### disturbed in Table I-96. Then, the loadings were averaged and multiplied by the area of the room (80 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-97. The estimated paint density using the lead f raction was 1.36 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings wer e believed to have been built at the same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.001



### .**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-85 July 25, 2014 

### Figure I-40. Layout of Kitchen Gut Experiment 51

Table I-96. Post-Work Loading Measurements and

Adjustments in Experiment 51

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 1,896 0.82 2 481 0.21 3 3,139 1.36 4 2,624 1.14



### Table I-97. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 51

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 70 Total Paint Removed From Surface (mg/ft 2 disturbed) 67,712 Fraction Emitted 0.001 5 15 11 9 2 8 14 12 6 3 13 7 10 4 1

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-86 July 25, 2014 

## I.10.4. Experiment 76 The geometry of Experiment 76 is shown in Figure I-41, but the exact location of the removed kitchen components is unknown. Thus, no regression could be performed and the emission fraction is based on the average loadings. The loadings were divided by the size of the job (60 ft 2 

### ) and the fraction of lead in the paint (0.016) to estimate the dust loadings per ft 2 

### disturbed in Table I-98. Then, the loadings were averaged and multiplied by the area of the room (80 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-99. The estimated paint density using the lead f raction was 1.17 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings wer e believed to have been built at the same time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.018.

Figure I-41. Layout of Kitchen Gut Experiment 76

5 1 8 9 10 2 11 15

3 14 6 4 12 7 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-87 July 25, 2014 

### Table I-98. Post-Work Loading Measurements and

Adjustments in Experiment 76

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 10.171 10.59 2 137 0.14 3 16,409 17.09 4 20,074 20.91



### Table I-99. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 76

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 975 Total Paint Removed From Surface (mg/ft 2 disturbed) 55,183 Fraction Emitted 0.02


## I.10.5. Experiment 67 The geometry of Experiment 67 is shown in Figure I-42, a nd the measured lead loadings and associated distances are shown in Table I-100. Sample 2 was adjuste d using the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (40 ft 2 

### ) and the fraction of lead in the paint (0.012) to estimate the dust l oadings per ft 2 

### disturbed in Table I-100. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-88 July 25, 2014 

### Table I-101. Regression Statistics in Experiment 67 

### . Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of13 ft, (the adjusted approximate length of the room), and the total mass of dust o n the floor was estimated as shown in Table I-102. The estimated paint density using the lead fracti on was 1.12 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1920. Therefore, the building was ass umed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The expe riment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assum ed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.001.

17 9 10 12 8 3

16 1 4 15 5

7 11 14

13 2 6

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Cabinets Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-89 July 25, 2014 

### Figure I-42. Layout of Kitchen Gut Experiment 67
Table I-100. Post-Work Loading Measurements and Adj ustments in Experiment 67

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 474 474 0.99 2 2 162 405 0.84 3 7 10 7 0.01 4 10 10 7 0.01**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-90 July 25, 2014 

### Table I-101. Regression Statistics in Experiment 67

Statistic Value Shape -0.57 Intercept 0.86 R 2 0.90 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 2.36



### Table I-102. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 67

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 62 Total Paint Removed From Surface (mg/ft 2 disturbed) 55,886 Fraction Emitted 0.001


## I.10.6. Experiment 68 The geometry of Experiment 68 is shown in Figure I-43, a nd the measured lead loadings and associated distances are shown in Table I-103. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (34 ft 2 

### ) and the fraction of lead in the paint (0.025) to estimate the dust loadings per ft 2 

### disturbed in Table I-103. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-91 July 25, 2014 

### Table I-104. Regression Statistics in Experiment 68 

### . Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 16 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-105. The estimated paint density using the lead fraction was 1.26 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.003.

17 12 11 16 4

9 15

3 5 6 10 7 14 8 2 1 13 Stairs

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Cabinets Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-92 July 25, 2014 

### Figure I-43. Layout of Kitchen Gut Experiment 68
Table I-103. Post-Work Loading Measurements and Adj ustments in Experiment 68

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 3,133 3.69 2 1 7,386 8.69 3 4 222 0.26 4 9 12 0.01**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-93 July 25, 2014 

### Table I-104. Regression Statistics in Experiment 68

Statistic Value Shape -0.75 Intercept 2.29 R 2 0.96 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 9.86



### Table I-105. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 68

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 197 Total Paint Removed From Surface (mg/ft 2 disturbed) 62,570 Fraction Emitted 0.003


## I.10.7. Experiment 69 The geometry of Experiment 69 is shown in Figure I-44, a nd the measured lead loadings and associated distances are shown in Table I-106. Sample 3 was adjuste d using the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (69 ft 2 

### ) and the fraction of lead in the paint (0.052) to estimate the dust l oadings per ft 2 

### disturbed in Table I-106. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-107. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept). The R 2 

### of the regression was very low, so the average approach was used instead.

The total mass of dust on the floor was estimated as show n in Table I-108. The estimated paint density using the lead fraction was 1.54 g/cm 3 

### . The building was built in 1910, thus, it was assumed to have 5 .3 layers of paint at the time of the job



### . The experiment notes indicated that all paint was removed duri ng the renovation, so all 5.3 layers were assumed to be re moved. These values were used to estimate the total amount of paint on the wall, giving a final emission f raction of 0.002.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-94 July 25, 2014 

### Figure I-44. Layout of Kitchen Gut Experiment 69
Table I-106. Post-Work Loading Measurements and Adj ustments in Experiment 69

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 3,265 3,265 0.91 2 15 911 911 0.25 3 7 196 391 0.11 4 9 5,238 5,238 1.46 17 5 1 9 7 11 16 3 15

4

8 14

6 12 10 13 2

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Cabinets Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-95 July 25, 2014 

### Table I-107. Regression Statistics in Experiment 69

Statistic Value Shape -0.07 Intercept -0.27 R 2 0.08 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 0.76



### Table I-108. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 69

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 121 Total Paint Removed From Surface (mg/ft 2 disturbed) 76,452 Fraction Emitted 0.002


## I.10.8. Experiment 70 The geometry of Experiment 70 is shown in Figure I-45, a nd the measured lead loadings and associated distances are shown in Table I-109. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (61 ft 2 

### ) and the fraction of lead in the paint (0.008) to estimate the dust loadings per ft 2 

### disturbed in Table I-109. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-96 July 25, 2014 

### Table I-110. Regression Statistics in Experiment 70 

### . Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept). The R 2 

### of the regression was very low, so the average approach was used instead.

The total mass of dust on the floor was estimated as show n in Table I-111. The estimated paint density using the lead fraction was 1.08 g/cm 3 

### . The building was built in 1900, thus, it was assumed to have 5 .0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be re moved. These values were used to estimate the total amount of paint on the wall, giving a final emission f raction of 0.008.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-97 July 25, 2014 

### Figure I-45. Layout of Kitchen Gut Experiment 70
Table I-109. Post-Work Loading Measurements and Adj ustments in Experiment 70

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 2,940 2,940 6.02 2 2 1,704 1,704 3.49 3 6 18 18 0.04 4 8 3,513 3,513 7.20 6 3 15 10 4 1 5 11

14 8 7

2 9 13 12

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Cabinets Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-98 July 25, 2014 

### Table I-110. Regression Statistics in Experiment 70

Statistic Value Shape -0.21 Intercept 1.34 R 2 0.08 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 3.83



### Table I-111. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 70

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 482 Total Paint Removed From Surface (mg/ft 2 disturbed) 63,279 Fraction Emitted 0.008 

# I.11. Heat Gun

I.11.1. Experiment 30 The geometry of Experiment 30 is shown in Figure I-46, but the exact location of the heat gun activity is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadingsThe loadings were divided by the size of the job (65 ft 2 

### ) and the fraction of lead in the paint (0.1) to estimate the dust loadings per ft 2 

### disturbed in Table I-112. Then, the loadings were averaged and multiplied by the area of the room (150 ft 2 

### ) to get the total mass of dust on the floor, as shown i n Table I-113. The estimated paint density using the lead fraction wa s 2.03 g/cm 3 

### . The building was built in 1900, thus 

### , it was assumed to have 6.2 layers of paint at the time o f the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amount of paint on t he wall, giving a final emission fraction of 0.010.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-99 July 25, 2014 

### Figure I-46. Layout of Heat Gun Experiment 30

Table I-112. Post-Work Loading Measurements and Adjustments in Experiment 30

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 30,100 4.63 2 24,194 4.65 3 53,599 21.99 4 91,472 56.29 11 16

2 6 1

15 10 7 12 9 14 8

5 3 4 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-100 July 25, 2014 

### Table I-113. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 30

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,150 Total Paint Removed From Surface (mg/ft 2 disturbed) 118,886 Fraction Emitted 0.010


## I.11.2. Experiment 31 The geometry of Experiment 31 is shown in Figure I-47, but the exact location of the heat gun activity is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (65 ft 2 

### ) and the fraction of lead in the paint (0.051) to estimate the dust loadings per ft 2 

### disturbed in Table I-114. Then, the loadings were averaged and multiplied by the area of the room (150 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-115. The estimated paint density using the lead fracti on was 1.53 g/cm 3 

### . The building was built in 1900, thus, it was assumed to have 6.2 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.016.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-101 July 25, 2014 

### Figure I-47. Layout of Heat Gun Experiment 31

Table I-114. Post-Work Loading Measurements and Adjustments in Experiment 31

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 23,124 7.97 2 26,394 15.92 3 43,516 13.13 4 32,040 77.32

16 12 5 3

15 7 9 6 1

10 14 2 11 8 13 4

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-102 July 25, 2014 

### Table I-115. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 31

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,415 Total Paint Removed From Surface (mg/ft 2 disturbed) 89,269 Fraction Emitted 0.0168


## I.11.3. Experiment 32 The geometry of Experiment 32 is shown in Figure I-48, but the exact location of the heat gun activity is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (69 ft 2 

### ) and the fraction of lead in the paint (0.085) to estimate the dust loadings per ft 2 

### disturbed in Table I-116. Then, the loadings were averaged and multiplied by the area of the room (150 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-117. The estimated paint density using the lead fracti on was 1.88 g/cm 3 

### . The building was built in 1900, thus, it was assumed to have 6.2 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.021.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-103 July 25, 2014 

### Figure I-48. Layout of Heat Gun Experiment 32

Table I-116. Post-Work Loading Measurements and Adjustments in Experiment 32

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 66,896 18.25 2 114,711 19.56 3 144,393 98.48 4 36,275 12.37

7

16 15

2 11 6 5 9 1 14 8 4 10 13 3 12

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-104 July 25, 2014 

### Table I-117. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 32

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 2,316 Total Paint Removed From Surface (mg/ft 2 disturbed) 109,820 Fraction Emitted 0.02


## I.11.4. Experiment 33 The geometry of Experiment 33 is shown in Figure I-49, but the exact location of the heat gun activity is unknown. Thus, no regression could be performed and the emission f raction is based on the average loadings. The loadings were divided by the size of the job (60 ft 2 

### ) and the fraction of lead in the paint (0.035) to estimate the dust loadings per ft 2 

### disturbed in Table I-118. Then, the loadings were averaged and multiplied by the area of the room (150 ft 2 

### ) to get the total mass of dust on the floor, as shown in Table I-119. The estimated paint density using the lead fracti on was 1.36 g/cm 3 

### . The building was built in 1900, thus, it was assumed to have 6.2 layers of paint a t the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 6.2 layers were assumed to be removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.034.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-105 July 25, 2014 

### Figure I-49. Layout of Heat Gun Experiment 33

Table I-118. Post-Work Loading Measurements and Adjustments in Experiment 33

ID Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)
1 16,454 12.54 2 41,859 19.93 3 87,849 52.29 4 5,774 6.87

6 16 4

3 2 15 9 5 12

11 1 14 8 10 7 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-106 July 25, 2014 

### Table I-119. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 33

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 2,713 Total Paint Removed From Surface (mg/ft 2 disturbed) 79,599 Fraction Emitted 0.034


## I.11.5. Experiment 13 The geometry of Experiment 13 is shown in Figure I-51, a nd the measured lead loadings and associated distances are shown in Table I-120. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (53 ft 2 

### ) and the fraction of lead in the paint (0.081) to estimate the dust loadings per ft 2 

### disturbed in Table I-120. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-121. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of half the length and half the width for each of the four walls, since the job encompasse d the whole room. The total mass of dust on the floor was estimated as shown in Table I-122. The es timated paint density using the lead fraction was 1.84 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1 920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate t he total amount of paint on the wall, giving a final emission fraction of 0.014.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-107 July 25, 2014 

### Figure I-50. Layout of Heat Gun Experiment 13
Table I-120. Post-Work Loading Measurements and Adj ustments in Experiment 13

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 40,378 9.41 2 2 27,630 6.44 3 5 25,391 5.91 4 6 24,874 5.79



### Table I-121. Regression Statistics in Experiment 13

Statistic Value Shape -0.08 Intercept 2.21 R 2 0.52 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 9.07 17

16 5 2 15

7 10

6 14 12 4 9 3 8 1 11 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-108 July 25, 2014 

### Table I-122. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 13

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,292 Total Paint Removed From Surface (mg/ft 2 disturbed) 91,363 Fraction Emitted 0.001


## I.11.6. Experiment 14 The geometry of Experiment 14 is shown in Figure I-52, a nd the measured lead loadings and associated distances are shown in Table I-123. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.02) to estimate the dust loadings per ft 2 

### disturbed in Table I-123. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-124. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of half the length and half the width for each of the four walls, since the job encompasse d the whole room. The total mass of dust on the floor was estimated as shown in Table I-125. The es timated paint density using the lead fraction was 1.21 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the same time and were built in 1 920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate t he total amount of paint on the wall, giving a final emission fraction of 0.007.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-109 July 25, 2014 

### Figure I-51. Layout of Heat Gun Experiment 14
Table I-123. Post-Work Loading Measurements and Adj ustments in Experiment 14

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 5 427 0.28 2 1 5,204 3.47 3 2 6,223 4.15 4 1 9,359 6.24 

### 8

7 16 4 3 15

14 1 12 6 9 10 5 2

11 13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-110 July 25, 2014 

### Table I-124. Regression Statistics in Experiment 14

Statistic Value Shape -0.71 Intercept 2.41 R 2 0.93 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 11.16



### Table I-125. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 14

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 426 Total Paint Removed From Surface (mg/ft 2 disturbed) 59,999 Fraction Emitted 0.007


## I.11.7. Experiment 15 The geometry of Experiment 15 is shown in Figure I-53, a nd the measured lead loadings and associated distances are shown in Table I-126. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.102) to estimate the dust loadings per ft 2 

### disturbed in Table I-126. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-127. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 14 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-128. The estimated paint density using the lead fraction was 2.05 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes indicated that all paint was removed during the renovation, so all 5.0 layers were assumed to be removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.007.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-111 July 25, 2014 

### Figure I-52. Layout of Heat Gun Experiment 15
Table I-126. Post-Work Loading Measurements and Adj ustments in Experiment 15

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 1 101,104 13.22 2 1 34,976 4.57 3 4 180,281 23.57 4 6 6,096 0.80

17 2 1 5 7

16

15 12 6 11 10

8 14 9 4 3

13

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-112 July 25, 2014 

### Table I-127. Regression Statistics in Experiment 15

Statistic Value Shape -0.32 Intercept 2.71 R 2 0.27 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 15.07



### Table I-128. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 15

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 679 Total Paint Removed From Surface (mg/ft 2 disturbed) 102,160 Fraction Emitted 0.001


## I.11.8. Experiment 16 The geometry of Experiment 16 is shown in Figure I-53, a nd the measured lead loadings and associated distances are shown in Table I-129. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.04) to estimate the dust loadings per ft 2 

### disturbed in Table I-129. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-130. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 13 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-131. The estimated paint density using the lead fraction was 1.41 g/cm 3 

### . Although the vintage of the renovated building was unknown, nearby buildings were believed to have been built at the sam e time and were built in 1920. Therefore, the building was assumed to have been built in 1920 and to have 5.0 layers of paint at the time of the job. The experiment notes do not specify the am ount of paint removed during the renovation, so all 5.0 layers were assumed to be remove d. These values were used to estimate the total amount of paint on the wall, giving a final emission fractio n of 0.005.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-113 July 25, 2014 

### Figure I-53. Layout of Heat Gun Experiment 16
Table I-129. Post-Work Loading Measurements and Adj ustments in Experiment 16

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 3,802 1.27 2 4 5,505 1.83 3 2 4,393 1.46 4 2 5,905 1.97



### Table I-130. Regression Statistics in Experiment 16

Statistic Value Shape 0.09 Intercept 0.26 R 2 0.19 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 1.29 11 9 15

12 8 4 1 10 5 6 3 2 14

13 7

Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-114 July 25, 2014 

### Table I-131. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 16

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 376 Total Paint Removed From Surface (mg/ft 2 disturbed) 70,282 Fraction Emitted 0.001


## I.11.9. Experiment 60 The geometry of Experiment 60 is shown in Figure I-54, a nd the measured lead loadings and associated distances are shown in Table I-132. Samples 2 and 4 were a djusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.029) to estimate the dust loadings per ft 2 

### disturbed in Table I-132. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-133. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 10 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-134. The estimated paint density using the lead fraction was 1.30 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The e xperiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.027.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-115 July 25, 2014 

### Figure I-54. Layout of Heat Gun Experiment 60
Table I-132. Post-Work Loading Measurements and Adj ustments in Experiment 60

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 3,014 3,014 1.39 2 2 2,611 3,481 1.60 3 5 1,443 1,443 0.66 4 9 193 1,546 0.71



### Table I-133. Regression Statistics in Experiment 60

Statistic Value Shape -0.12 Intercept 0.58 R 2 0.66 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 1.78 4

6 16

10 15 2 9 7 8

14 3

5 13 12 1 11

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-116 July 25, 2014 

### Table I-134. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 60

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 166 Total Paint Removed From Surface (mg/ft 2 disturbed) 6,084 Fraction Emitted 0.027


## I.11.10. Experiment 61 The geometry of Experiment 61 is shown in Figure I-55, a nd the measured lead loadings and associated distances are shown in Table I-135. Sample 3 was adjuste d using the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.022) to estimate the dust l oadings per ft 2 

### disturbed in Table I-135. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-136. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 10 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-137. The estimated paint density using the lead fraction was 1.23 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.017.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-117 July 25, 2014 

### Figure I-55. Layout of Heat Gun Experiment 61
Table I-135. Post-Work Loading Measurements and Adj ustments in Experiment 61

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 3,624 3,624 2.20 2 3 1,351 1.351 0.82 3 4 95 191 0.12 4 9 51 51 0.03

16 4 10 1

2 15

5 11 7

9 14 12 8 3

13 6

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-118 July 25, 2014 

### Table I-136. Regression Statistics in Experiment 61

Statistic Value Shape -0.56 Intercept 1.26 R 2 0.82 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 3.53



### Table I-137. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 61

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 100 Total Paint Removed From Surface (mg/ft 2 disturbed) 5,745 Fraction Emitted 0.017


## I.11.11. Experiment 62 The geometry of Experiment 62 is shown in Figure I-56, a nd the measured lead loadings and associated distances are shown in Table I-138. Samples 1 and 3 were a djusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (73 ft 2 

### ) and the fraction of lead in the paint (0.022) to estimate the dust loadings per ft 2 

### disturbed in Table I-138. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-139. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 10 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-140. The estimated paint density using the lead fraction was 1.23 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.042.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-119 July 25, 2014 

### Figure I-56. Layout of Heat Gun Experiment 62
Table I-138. Post-Work Loading Measurements and Adj ustments in Experiment 62

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 5,001 8,001 4.98 2 1 3,676 3,676 2.29 3 5 478 764 0.48 4 7 1,974 1,974 1.23 

### 15

10 6 1 3 15 11 7

5

8

14

2 13 4 9 12

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-120 July 25, 2014 

### Table I-139. Regression Statistics in Experiment 62

Statistic Value Shape -0.24 Intercept 1.36 R 2 0.42 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 3.88



### Table I-140. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 62

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 239 Total Paint Removed From Surface (mg/ft 2 disturbed) 5,745 Fraction Emitted 0.042


## I.11.12. Experiment 63 The geometry of Experiment 63 is shown in Figure I-57, a nd the measured lead loadings and associated distances are shown in Table I-141. Sample 1 was adjuste d using the taper assumption since it was outside the lateral extent of the job. The loadings were divi ded by the size of the job (75 ft 2 

### ) and the fraction of lead in the paint (0.026) to estimate the dust l oadings per ft 2 

### disturbed in Table I-141. Then, the natural logs of all loadings were used with the distances to estimate the regression parameters in Table I-142. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 10 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-143. The estimated paint density using the lead fraction was 1.27 g/cm3. The sc hool was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The e xperiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.040.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-121 July 25, 2014 

### Figure I-57. Layout of Heat Gun Experiment 63
Table I-141. Post-Work Loading Measurements and Adj ustments in Experiment 63

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 1,914 4,305 2.21 2 1 10,384 10,384 5.33 3 5 471 471 0.24 4 10 65 65 0.03

2 16 8

3

10 4

15 5 12 14 6

9 1

11 7 13

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-122 July 25, 2014 

### Table I-142. Regression Statistics in Experiment 63

Statistic Value Shape -0.58 Intercept 2.16 R 2 0.96 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 8.64



### Table I-143. Estimated Total Dust Emitted, Total Pa int on Wall, and

Emission Fraction in Experiment 63

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 238 Total Paint Removed From Surface (mg/ft 2 disturbed) 5,939 Fraction Emitted 0.040 

# I.12. Low Heat

I.12.1. Experiment 56 The geometry of Experiment 56 is shown in Figure I-58, a nd the measured lead loadings and associated distances are shown in Table I-144. Samples 3 and 4 were a djusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (50 ft 2 

### ) and the fraction of lead in the paint (0.022) to estimate the dust loadings per ft 2 

### disturbed in Table I-144. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-145. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-146. The estimated paint density using the lead fraction was 1.23 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The e xperiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amount of paint on the wall, giving a final emission fraction of 0.159.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-123 July 25, 2014 

### Figure I-58. Layout of Low Heat Experiment 56
Table I-144. Post-Work Loading Measurements and Adj ustments in Experiment 56

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 7,837 7,837 7.12 2 1 16,046 16,046 14.59 3 8 325 1,298 1.18 4 12 295 590 0.54



### Table I-145. Regression Statistics in Experiment 56

Statistic Value Shape -0.31 Intercept 2.89 R 2 0.98 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 17.94

14 2 13 6 5 1 9 10

15 17 7 11 3 12

16

8 4

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-124 July 25, 2014



### Table I-146. Estimated Total Dust Emitted, Total Pa int on Wall, and

Emission Fraction in Experiment 56

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 912 Total Paint Removed From Surface (mg/ft 2 disturbed) 5,745 Fraction Emitted 0.159


## I.12.2. Experiment 57 The geometry of Experiment 57 is shown in Figure I-59, a nd the measured lead loadings and associated distances are shown in Table I-147. Samples 1 and 2 were a djusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (50 ft 2 

### ) and the fraction of lead in the paint (0.024) to estimate the dust loadings per ft 2 

### disturbed in Table I-147. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-148. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-149. The estimated paint density using the lead fraction was 1.25 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.189.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-125 July 25, 2014 

### Figure I-59. Layout of Low Heat Experiment 57
Table I-147. Post-Work Loading Measurements and Adj ustments in Experiment 57

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 3 5,768 13,459 2.42 2 3 2,374 8,310 1.94 3 7 730 730 -0.50 4 12 120 120 -2.30 14 5 10 1 2 6 9 17

3 15 7 13 11 12 8

16 4

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-126 July 25, 2014 

### Table I-148. Regression Statistics in Experiment 57

Statistic Value Shape -0.51 Intercept 3.56 R 2 0.97 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 35.03



### Table I-149. Estimated Total Dust Emitted, Total Pa int on Wall, and

Emission Fraction in Experiment 57

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,103 Total Paint Removed From Surface (mg/ft 2 disturbed) 5,842 Fraction Emitted 0.189


## I.12.3. Experiment 58 The geometry of Experiment 58 is shown in Figure I-60, a nd the measured lead loadings and associated distances are shown in Table I-150. Samples 3 and 4 were a djusted using the taper assumption since they were outside the lateral extent of the job. The loading s were divided by the size of the job (50 ft 2 

### ) and the fraction of lead in the paint (0.028) to estimate the dust loadings per ft 2 

### disturbed in Table I-150. Then, the natural logs of all loadings were used with the dista nces to estimate the regression parameters in Table I-151. Here, 

### intercept is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 12 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-152. The estimated paint density using the lead fraction was 1.29 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The experiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.020.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-127 July 25, 2014 

### Figure I-60. Layout of Low Heat Experiment 58
Table I-150. Post-Work Loading Measurements and Adj ustments in Experiment 58

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Lead Loading in

μ g/ft 2 

### (adjusted) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 2,759 2,759 1.97 2 1 4,145 4,145 2.96 3 6 198 330 0.24 4 10 65 162 0.12 

### 10 4 7 14 3 15 6

8 11 1 5 16

2 13 12 9

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-128 July 25, 2014 

### Table I-151. Regression Statistics in Experiment 58

Statistic Value Shape -0.38 Intercept 1.32 R 2 0.95 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 3.76



### Table I-152. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 58

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 119 Total Paint Removed From Surface (mg/ft 2 disturbed) 6,036 Fraction Emitted 0.020


## I.12.4. Experiment 59 The geometry of Experiment 13 is shown in Figure I-61, a nd the measured lead loadings and associated distances are shown in Table I-153. No adjustments were made to the loadings since all samples were within the width of the job. The loadings were divided by the siz e of the job (50 ft 2 

### ) and the fraction of lead in the paint (0.13) to estimate the dust loadings per ft 2 

### disturbed in Table I-153. Then, the natural logs of all loadings were used with the distances to estim ate the regression parameters in Table I-154. Here, 

### intercept

is the intercept of the linear regression in log space, a nd Load at 0 ft is the assumed loading at 0 ft calculated as exp( 

### intercept).
The integral was performed over the regression equation o ut to a distance of 10 ft, (the length of the room), and the total mass of dust on the floor was estima ted as shown in Table I-155. The estimated paint density using the lead fraction was 2.34 g/cm 3 

### . The school was built in 1967, thus, it was assumed to have 3.3 layers of paint at the time of the job. The e xperiment notes indicated that not all paint was removed during the renovation, so it was assumed that only 15 % of the paint (or 0.5 layers) were removed. These values were used to estimate the total amo unt of paint on the wall, giving a final emission fraction of 0.128.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-129 July 25, 2014 

### Figure I-61. Layout of Heat Experiment 59

18 12 3

10 17 11 4

16

2 9 7 15

14 8 5 1 6 13

Vertical containment plastic Sample number for post-work sample Sample number for post-cleaning sample Sample number for post-verification sample Sample number for additional sample, if needed Wall of room Job**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft I-130 July 25, 2014



### Table I-153. Post-Work Loading Measurements and Adj ustments in Experiment 59

ID Dist. From Wall (ft) Lead Loading in

μ g/ft 2 

### (Dust Study) Dust Load per

ft 2 

### Disturbed (mg/ft 2 

### floor/ft 2 

### wall)

1 2 65,994 10.15 2 2 59,156 9.10 3 6 11,403 1.75 4 8 1,302 0.20



### Table I-154. Regression Statistics in Experiment 59

Statistic Value Shape -0.60 Intercept 3.55 R 2 0.95 Load at 0 ft (mg/ft 2 floor/ft 2 disturbed) 34.93



### Table I-155. Estimated Total Dust Emitted, Total Paint on Wall, and Emission Fraction in Experiment 59

Estimated Masses and Emission Fractions Value

Total Mass of Dust on Floor (mg/ft 2 disturbed) 1,402 Total Paint Removed From Surface (mg/ft 2 disturbed) 10,973 Fraction Emitted 0.13 

# I.13. References for Appendix I

US EPA. (2007a). Draft final report on characterizatio n of dust lead levels after renovation, repair, and painting activities. Prepared for EPA’s Office of Polluti on Prevention and Toxcis (OPPT). Available online at: http://www.epa.gov/lead/pubs/duststudy01-23-07.pdf**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-1 July 25, 2014 

# Appendix J. Dust Model for Exterior Analysis

In order to estimate the time-dependent indoor dust loading ari sing from exterior renovation activities, a mechanistic model was developed to incorporate the rele vant source and removal terms for lead in a typical indoor environment. The mechanistic model is designed t o capture the physical transfer of mass from one medium to another under the assumption of mass bal ance. Previous studies have also built mass balance models of indoor dust. Allott et al. (1994) constructed a mass balance model to estimate the residence time of contaminated soil particles in the i ndoor environment based on observations in four homes in England contaminated by the Chernobyl incident. Tha tcher and Layton (1995) constructed an indoor mass balance model of a home in Califo rnia to estimate deposition rates, resuspension rates, and infiltration factors. Recognizing the key role of tracked-in soil on indoor dust loadings, Johnson (2008) built the DIRT model simulating the spa tial pattern of tracked-in soil for a given total soil mass flux into the home. And Layton and Beamer ( 2009) built a model simulating tracked-in soil and penetration of outdoor air and the subsequent physical proce sses governing indoor dust loadings. The model here is a general set of equations that follows the same mass balance methods as in these papers.
The general form of the mass balance equation for a single c ompartment of interest is:



### Out Mass of Flux In Mass of Flux dt Mass d

−

= ] [

where: 

### d[Mass]/dt = change over time of the mass

**Flux of Mass In =** flux of mass into the compartment **Flux of Mass Out =** flux of mass out of the compartment
In the dust model, two “compartments” of interest are defined: the indoor air and the floor. Both of these compartments contain particulates associated with indoo r dust, and by parameterizing the processes that govern the flux of mass to and from each com partment, the model can provide an inventory of dust in the air and on the floor through time.

The relevant sources and sinks of lead to the indoor dust for this Approach include:

 

### ambient air particles which penetrate the indoor environment and settle on the floor,

 

### outdoor soil particles which are tracked into the home, and**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-2 July 25, 2014  

### the routine cleaning that remove lead from the floor.
Figure J-1 shows a schematic of the various lead and partic ulate mass flux terms used in the mechanistic model to account for all sources and sinks of lead.

Figure J-1. Mechanistic Indoor Dust Model Schemati c



### For the indoor air compartment, the fluxes for mass include penetration of air and particles from outdoors, ventilation of indoor air back to the outdoor envir onment, deposition of mass out of the air, and resuspension of accumulated mass on the floor back into the air 1 

### :

Pb Pb Pb Pb Pb 

### Flux on Resuspensi Flux Deposition Flux n Ventilatio Flux n Penetratio dt dINAIR +

− − =

1 The presence of an HVAC system will tend to re-circ ulate indoor air, passing the air through a filter with each circulation. This system will tend to remove mass from the indoor environment (both in the air and on the floor) and act as a further sink. However, because the ci rculation rate and filtration efficiency of such sy stems has not been comprehensively described in the literature an d because use of such systems changes across the se asons and different geographic regions, removal of mass durin g recirculation is not included in the mechanistic model.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-3 July 25, 2014 

### where: 

### dINAIRPb/dt = change in time of the indoor air lead mass

**Penetration Flux =** penetration of lead-containing particles from outdo ors **Ventilation Flux =** ventilation of indoor air back to the outdoor envir onment **Deposition Flux =** deposition of mass out of the air **Resuspension Flux =** resuspension of mass back to the air In general, each flux is parameterized as the mass of the " donor" compartment multiplied by the rate (expressed in reciprocal time) of the physical exchange pro cess. In some cases, an efficiency factor is also included to account for the fractional efficiency of the removal process. For the Penetration Flux,



### b
	Q ^. 

### = P × b × b	 × c

where: 

### Penetration Flux **Pb** 

### = penetration of air lead from outdoors (μg/h)

**AER =** air exchange rate (h -1 

### ) **P =** penetration efficiency (unitless) **PbAIR =** concentration of lead in ambient air (μg/m 3 

### ) **V =** volume of the building (m 3 

### ) Because the air exchange rate ( **AER** ) specifies the number of times the indoor air is r eplaced by outdoor air in a given hour, it represents both the rate of penetration in and ventilation out. The ventilati on flux out of the building is thus given by:



### c
		Q ^. 

### = P × GG ^.


### where: 

### Ventilation Flux **Pb** 

### = ventilation of indoor lead in air back to the outdo or environment (μg/h) **AER =** air exchange rate (h -1 

### ) **INAIRPb =** indoor mass of lead in air (μg) The deposition flux (Deposition Flux) is defined as the amount of mass in the air times a deposition r ate:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-4 July 25, 2014

### 		Q ^. 

### =  × GG ^.


### where: 

### Deposition Flux **Pb** 

### = deposition of lead out of the air (μg/h)

**DepRate =** deposition rate (h -1 

### ) **INAIRPb =** indoor mass of lead in air (μg)

So, using the penetration, ventilation, deposition fluxes, and indoor source terms, the equation for t he change in time of the indoor air lead mass is:

Pb Pb Pb 

### INAIR DepRate INAIR AER V PbAIR P AER dt dINAIR ×

− ×

− × × × =



### where: 

### dINAIRPb/dt = change in time of the indoor air lead mass (μg/h)

**INAIPRPb =** indoor mass of lead in air (μg) **FLOORPb =** mass of lead on the floor (μg) **PbAIR =** concentration of lead in ambient air (μg/m 3 

### ) **AER =** air exchange rate (hour -1 

### ) **P =** penetration efficiency (unitless) **V =** volume of the building (m 3 

### ) **DepRate =** deposition rate (h -1 

### )
For the indoor floor dust compartment ( **FLOOR** ), the fluxes include deposition of lead from the a ir 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:

Pb Pb Pb Pb Pb 

### Flux Cleaning Flux on Resuspensi ux TrackingFl Flux Deposition dt dFLOOR

− − + =

where: 

### dFLOOR/dt = change in time of the indoor floor mass

**Deposition Flux =** deposition of mass out of the air onto the floor **Tracking Flux =** tracking of soil inside from outdoors **Resuspension Flux =** resuspension of mass back to the air **Cleaning Flux =** removal of lead due to routine cleaning**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-5 July 25, 2014 

### The deposition flux ( **Deposition Flux** ) and resuspension flux ( **ResuspensionFlux)** retain the same form as in the **INAIR** equations.
The tracking flux (TrackingFlux) is parameterized s pecifically according to the limited data available about the process. Von Lindern (2003) measured the amount of particulate deposited on front mats in 276 houses in two locations near the Bunker Hill Su perfund Site. The lead levels reported in the pape r are expected to be high-end and are not expected to represent general population exposures. However, the assumption is made that the rate of accumulatio n of dust (as opposed to the lead in the dust) on doormats is not strongly affected by the location a nd can be used to represent a national population o f homes. In addition, Thatcher and Layton (1995) meas ured the difference between particulate accumulation in tracked and untracked areas in the home as well as the amount on the front mat. From these two data sources, it is possible to estimate a distribution of mat particulate accumulation rate s as well as the fraction of material that remains on th e mat compared to being tracked into the home. For this reason, the tracking is parameterized as:

]	
+Q ^. 

### = b	
 × ]	
+ × (1 − e)

where: 

### TrackingFluxPb = accumulation of tracked-in lead on the floor (μg/h)

**PbSoilConcen =** concentration of lead in the tracked-in soil (μg/g) **TrackingRate =** rate at which particulate is deposited on front mat s (g/h) **MatFrac =** fraction of total tracked material which is deposit ed on the front mat (as opposed to the remainder of the build ing) (unitless) The cleaning flux ( **Cleaning Flux** ) is parameterized assuming a cleaning efficiency ( **CE** ) and cleaning frequency ( **CF** ) and multiplying these by the mass on the floor ( **FLOOR** ):

	
+Q ^. 

### = P ×  × *gg ^.


### where: 

### Cleaning FluxPb = removal of lead due to routine cleaning (μg/h)

**CE =** cleaning efficiency (unitless) **CF =** cleaning frequency (cleanings/h) **FLOORPb =** mass of lead on the floor (μg)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Combining the floor fluxes then gives:

Pb Pb Pb 

### FLOOR CF CE MatFrac te TrackingRa en PbSoilConc INAIR DepRate dt dFLOOR × ×

− − × ×

− × = ) 1(

where: 

### 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) **DepRate =** deposition rate (h -1 

### ) **PbSoilConcen =** concentration of lead in the tracked-in soil (μg/g) **TrackingRate =** rate at which particulate is deposited on front mat s (g/h) **MatFrac =** fraction of total tracked material which is deposit ed on the

front mat (as opposed to the remainder of the build ing) (unitless) **CE =** cleaning efficiency (unitless) **CF =** cleaning frequency (cleanings/h) The above equations can be converted to difference equations by assuming a discrete time step and the model can be integrated forward in time to describe the lead and particulate accumulation at any
moment.


# J.1. References for Appendix J Allott et al 1994. A model of environmental behavio ur of contaminated dust and its application to determining dust fluxes and residence times. Atmosp heric Environment. (28) 4. 679-687.
Johnson D. (2008). A first generation dynamic ingre ss, redistribution, and transport model of soil tra ckin: DIRT. Environ Geochemistry Health 30: 589-596.
Layton D; Beamer P. (2009). Migration of contaminat ed soil and airborne particles to indoor dust. Envi r. Science and Technol. 43: 8199-8205.
Thatcher T; Layton D. (1995). Deposition, rsuspensi on, and penetration of particles within a residence

. Atmospheric Environment. 29(13): 1487-1497.
von Lindern I; Spalinger S; Bero B; Petrosyan V; v on Braun, M. (2003). The influence of soil remediat ion on lead in house dust. Sci. Total Environ 303(1-2): 59-78.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft J-7 July 25, 2014**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft K-1 July 25, 2014 

# Appendix K. Loading to Concentration Conversion

The indoor dust model provides an estimate of the l ead loading in terms of mass of lead per square foo t of floor. However, the selected biokinetic blood le ad model, Leggett, cannot accept lead loadings as inputs. Instead, it requires lead concentrations, o r the amount of lead per mass of ingested dust.
EPA previously analyzed the available evidence on t he relationship between dust-lead loading statistic s and dust-lead concentrations and developed an empir ical approach that uses a regression relationship between dust-lead loading and dust-concentration me asurements (US EPA, 2008c) A log-log model was fitted to data from HUD’s National Survey of Lead-B ased Paint in Housing (“HUD Survey Data”), which are provided in Appendix C-1 of EPA’s risk assessm ent for TSCA Appendix 403 (US EPA, 1998).
EPA further considered the loading to concentration relationship when evaluating possible hazard standards for residences and public and commercial buildings in 2011. A mechanistic model and an empirical approach were presented and described in Appendix E of both documents. Based on SAB feedback, the empirical model was used.
Considering the previous use of and SAB preference for an empirical approach, EPA searched for additional data sources to supplement the HUD Surve y Data. There were two goals of identifying additional data sources. The first was to determin e if other data sources showed a similar relationsh ip between lead dust loading and concentration, furthe r validating use of this approach. The second was to increase the overall sample size and improve the overall fit of the regression.
Three data sources were pooled together to develop a more robust loading to concentration relationship. The HUD Survey data measured floor d ust loadings based on vacuum samples and used an empirical equation to estimate the associated wipe loadings. It is anticipated that the wipe samples better capture the total lead present in the home; the vacuum samples are subject to vacuum collection efficiencies. Thus, the estimated wipe lead loading s were paired with the Blue Nozzle lead concentrations at each home to develop the loading- to-concentration statistical relationship. Blue Nozzle sampling is an older sampling technique, whi ch assumes that the concentration is roughly uniform across all particles and the particles coll ected by the Blue Nozzle device are representative of the true average concentration. Summary statistics from HUD survey data are provided below in Table K-1. In general, the spread in the data is large an d covers loadings up to 375 μ g/ft 2 

### and concentrations up to 50,400 μ g/g. (HUD et al 2002)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft K-2 July 25, 2014 

### Table K-1. Statistics from the HUD survey data.

Statistic Loading

( μ g/ft 2 ) Concentration

( μ g/g) Average 19.48 520 Min 0.51 0.09 Max 375.00 50,400 50 th percentile 6.35 190


### A study by Adgate et al in 1995 examined the relati onship between dust loading, lead loading, and lead concentration in house dust in 216 homes in Jersey City, New Jersey. The total sample size was 444 da ta points. The data points from the scatterplot in Fi gure 7A were digitized using the GetData®; because some of the data points were located on top of each other, only 312 of the 444 data points were captured during the digitization. The R 2 

### in Adgate Figure 7A corresponding to the log/log r elationship between lead loading and lead concentration was 0.6 5 Wipe samples were collected for lead loadings and were compared to dust concentrations collected by a vacuum cleaner from wall to wall carpet and area rugs greater than 48 square feet. The total am ount of dust was adjusted for vacuum collection efficiency using an algorithm developed by Wang et al 1995. Summary statistics from the Adgate data are provided below in Table K-2. In general, the s pread in the data is large and covers loadings up t o 9,626.97 ug/ft2 and concentrations up to 33,113.40 ug/g. (Adgate et al 1995)

Table K

- 2 . Statistics from the Adgate data.

Statistic Loading

( μ g/ft 2 ) Concentration

( μ g/g) Average 96.34 1,004.65 Min 0.38 18.64 Max 9,626.97 33,113.40 50 th percentile 22.32 468.69


### The Canadian House Dust Study (CHDS) collected dust samples from 1,025 homes across 13 cities and was designed to estimate nationally representative urban house dust metal concentrations and metal loadings. Trained technicians followed a vacuum sa mpling protocol to collect a composite sample of active dust from all dry living areas of each house . Participants were instructed not to clean for at least one week prior to sample collection. Fine (<80 μm) and coarse (80–300 μm) particle size fractions were collected and were combined to calculate loading va lues based on: measurements of the area sampled, dust mass, dust lead concentration, and time elapse d since cleaning (details in Rasmussen et al., 2013 ).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### As area measurements were not available for three h ouses, the overall sample size for the loading calculation was 1,022. The distributions of metal l oads obtained using the CHDS vacuum sampling protocol are consistent with population-based distr ibutions obtained using wipe sampling methods from other studies. Summary statistics from the Canadia n House are provided below in Table K-3. In genera l, the spread in the data is smaller and covers concentrations up to 7,800 ug/g (Rasmussen et al 2013).

Table K

- 3 . Statistics from the Canadian House Dust Study

Statistic Loading

( μ g/ft 2 ) Concentration

( μ g/g) Average 2.88 210 Min 0.019 14.2 Max 89.4 7,800 50 th percentile 0.77 100


### These three data sources were pooled together and a n outlier analysis was conducted. Three outliers were detected and excluded from the pooled sample r esulting in an overall sample size of 1,643. The summary statistics for the pooled data set are prov ided below in Table K-4.

Table K

- 4 . Statistics for the Pooled Data Set

Statistic Loading

( μ g/ft 2 ) Concentration

( μ g/g) Average 22.86 370.10 Min 0.019 2.01 Max 9,626.9 14,935.96 50 th percentile 2.02 137



### From the raw data, each loading and concentration w as transformed by taking the natural log, and the average of the natural log of the loadings across a ll data points was subtracted from each loading. Th e latter step re-centers the data and makes the estim ated slope and intercept uncorrelated. A regression was performed on these data using the add-on Regres sion function in Excel ® 

### to calculate a predictive distribution as detailed in Qian (2010). The regre ssion was used to calculate the degrees of freedom, slope, intercept, error variance, and estimated sta ndard deviation of the slope and intercept using th e following regression equation:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft K-4 July 25, 2014



### ln( **Conc** ) = **Intercept**

+ **Slope*** (Adj ln( **Load** )) + **ErrorVariance**
where: Adj ln( **Load** )) = ln( **Load** ) – Average(ln( **Load)** )
The regression modeling treated the lead data as a simple random sample and did not take the survey weights and survey design in the HUD data into acco unt. Each individual measurement is assumed to give equal information about the relationship betwe en loading and concentration. The results of the regression modeling are presented below in Table K- 5.

Table K

- 5 . Regression results for loading

- concentration conversion.

Parameter Value Degrees of Freedom 1641 Intercept 5.145 Slope 0.421 Error variance 0.544 Estimated standard deviation (standard

error) of intercept 0.018 Estimated standard deviation (standard

error) of slope 0.009
R Square 0.55


### The error variance term represents the true variabi lity around the regression line, which captures the reality that a ln( **Load** ) can be associated with a range of ln( **Conc** ). Because the regression is calculated using 1643 concentration and loading data pairs, th ere is some uncertainty in the predicted intercept and slope, captured as the estimated standard devia tion, or standard error, of the intercept and slope

. However, both of these values were reduced when usi ng the larger pooled data set when compared to using the HUD Survey data set alone. The standard error of the intercept was reduced from 0.04 to 0.018, the standard error of the slope was reduced from 0.05 to 0.009, and the R Square was improved from 0.465 to 0.55. The overall uncertainty and va riability within the predicted **ln(Conc)** is represented by the sum of the error variance (modeled as a chi- squared distribution) in the predicted intercept an d the variance in the predicted slope (each represen ted by Normals). The Monte Carlo model samples from each distribution for each realization when co nverting from loading to concentration.
To bound the range of concentrations that the Monte Carlo model can predict from a known loading, 90 percent prediction intervals were calculated using a T-distribution with 1641 residual degrees of freedom (based on the full regression data set).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft K-5 July 25, 2014 

# K.1. References for Appendix K Adgate et al 1995. Lead in House Dust. Relationship s between Exposure Metrics. Environmental Research (70) 134-147.
HUD (U.S. Department of Housing and Urban Developme nt). (2002). National Survey of Lead and Allergens in Housing: Final Report, Revision 6. Of fice of Lead Hazard Control. October 31. Available at: http://www.hud.gov/offices/lead/ techstudies/survey.cfm#natsurvey
Rasmussen et al (2013). Canadian House Dust Study: Population-based concentrations, loads and loading rates of arsenic, cadmium, chromium, copper , nickel, lead, and zinc inside urban homes. Science of the Total Environment 443 (2013) 520–529
Qian S. (2010). Environmental and ecological statis tics with R. Chapman and Hall, Section 9.2
US EPA. (1998). Risk analysis to support standards for lead in paint, dust, and soil. Appendix G: Heal th effects associated with exposure to lead and intern al lead doses in humans. (Report No. EPA 747-R-97-006). Washington, DC: US Environmental Pro tection Agency, Office of Pollution Prevention and Toxics. Available online at: http: //www.epa.gov/lead/pubs/toc.pdf.
US EPA. (2008a). Economic analysis for the TSCA lea d renovation, repair, and painting program Final Rule for Target Housing and Child-Occupied Faciliti es. Washington, DC: US Environmental Protection Agency, Office of Pollution Prevention a nd Toxics (OPPT).
US EPA. (2010a). Proposed approach for developing l ead dust hazard standards for residences. SAB Consultation Draft. Washington, DC: US Environment al Protection Agency, Office of Pollution Prevention and Toxics (OPPT). Prepared for July 6-7 , 2010 consultation**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-1 July 25, 2014



# Appendix L. Estimates of Exposure Factors

L.1. Age Groups and Ages Within Each Group For the analysis, a set of age groups were selected for which the different health effects would be estimated in aggregate. It was necessary to group t ogether individuals of different ages in order to create a reasonable number of scenarios in the mode l. In order to ensure the age group was representative of the individual members, the group s were selected by examining the selected health effect papers to determine the population ages in t he studies (Table L-1). In general, the age groups were selected to cover the populations in the diffe rent papers as best as possible. For low birth weig ht, additional consideration of the population distribu tion of maternal age at pregnancy in the United States was also considered. In addition, cohort d istributions provided in the studies were considere d to ensure that pertinent data were not excluded from t he analysis.

Table L-1. Age Ranges in the Health Effect Study Su bjects. Health Effect and Study Age Range of Study Subjects IQ effects, Lanphear et al. 2005 0 to 10 years old Low Birth Weight, Zhu et al. 2010 15 to 49 years ol d Kidney effects, Navas-Acien et al. 2009 20 to 90 ye ars old (NHANES population) Cardiovascular disease mortality, Menke et al.2006 20 to 90 years old (NHANES population)


### Based on these considerations, the following age gr oups were selected to closely match the ages noted in the study population:

 

### Age 0-10: IQ effects

 

### Age 18-49: Low birth weight, kidney, and cardiovasc ular disease mortality effects

 

### Age 50-80: Kidney and cardiovascular disease mortal ity effects The Monte Carlo model requires a cumulative probabi lity distribution for each age group; that distribution is sampled during each iteration to de termine the age of the person within the group for the blood lead and health effect calculations. The Amer ican Community Survey data from 2010-2012 ( http://www.census.gov/acs/www/ ) were used to estimate these distributions. (ACS 2012) In each age group, the total population in that group was used to calculate the probability of occurrence of each age**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-2 July 25, 2014 

### in increments of years. Table L-2 and Table L-3 sho w the distributions for the children and adults, respectively.

Table L-2. Probability Distribution for Children.

Age Population Probability Cumulative Probability Age 0-10 Under 1 year 3,728,928 0.08 0.08
1 Year 3,920,811 0.09 0.17 2 Years 4,070,061 0.09 0.26 3 Years 4,128,393 0.09 0.35 4 Years 4,177,518 0.09 0.45 5 Years 4,174,548 0.09 0.54 6 Years 4,062,915 0.09 0.63 7 Years 4,114,308 0.09 0.72 8 Years 4,016,352 0.09 0.81 9 Years 4,111,269 0.09 0.91 10 years 4,178,124 0.09 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-3 July 25, 2014 

### Table L-3. Probability Distributions for Adults

Age Population Probability Cumulative Probability Age Population Probability Cumulative Probability Age 18-49 Age 50-80 18 years 4,597,878 0.03 0.03 50 years 4,826,172 0.0 5 0.05 19 years 4,374,663 0.03 0.07 51 years 4,464,354 0.0 5 0.10 20 years 4,782,129 0.04 0.10 52 years 4,463,964 0.0 5 0.15 21 years 4,694,160 0.03 0.14 53 years 4,377,144 0.0 5 0.20 22 years 4,375,500 0.03 0.17 54 years 4,385,244 0.0 5 0.25 23 years 4,171,068 0.03 0.20 55 years 4,360,065 0.0 5 0.29 24 years 4,029,537 0.03 0.23 56 years 4,115,214 0.0 4 0.34 25 years 4,379,898 0.03 0.26 57 years 4,024,680 0.0 4 0.38 26 years 4,178,904 0.03 0.29 58 years 3,875,571 0.0 4 0.42 27 years 4,217,025 0.03 0.32 59 years 3,729,201 0.0 4 0.46 28 years 4,204,608 0.03 0.35 60 years 3,872,127 0.0 4 0.51 29 years 4,147,719 0.03 0.38 61 years 3,541,116 0.0 4 0.55 30 years 4,462,401 0.03 0.42 62 years 3,519,627 0.0 4 0.58 31 years 4,111,062 0.03 0.45 63 years 3,495,222 0.0 4 0.62 32 years 4,117,977 0.03 0.48 64 years 3,272,820 0.0 4 0.66 33 years 3,916,908 0.03 0.51 65 years 3,017,097 0.0 3 0.69 34 years 3,896,793 0.03 0.53 66 years 2,613,507 0.0 3 0.72 35 years 4,093,446 0.03 0.56 67 years 2,604,534 0.0 3 0.75 36 years 3,826,857 0.03 0.59 68 years 2,503,611 0.0 3 0.77 37 years 3,887,709 0.03 0.62 69 years 2,354,469 0.0 3 0.80 38 years 4,013,214 0.03 0.65 70 years 2,246,490 0.0 2 0.82 39 years 4,039,923 0.03 0.68 71 years 1,995,159 0.0 2 0.85 40 years 4,633,614 0.03 0.72 72 years 1,925,682 0.0 2 0.87 41 years 4,197,336 0.03 0.75 73 years 1,838,373 0.0 2 0.89 42 years 4,178,478 0.03 0.78 74 years 1,696,614 0.0 2 0.91 43 years 4,072,578 0.03 0.81 75 years 1,667,943 0.0 2 0.92 44 years 4,109,589 0.03 0.84 76 years 1,548,294 0.0 2 0.94 45 years 4,323,045 0.03 0.87 77 years 1,453,188 0.0 2 0.96 46 years 4,333,803 0.03 0.90 78 years 1,368,168 0.0 1 0.97 47 years 4,454,691 0.03 0.93 79 years 1,317,393 0.0 1 0.99 48 years 4,510,179 0.03 0.97 80 years 1,317,870 0.0 1 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-4 July 25, 2014 

### For the low birth weight health effect, the effects are restricted to pregnant women and their newborn s rather than the general population. The distributio n for the general population between ages 18-50 is fairly uniform while the distribution for pregnancy peaks in the 20’s and tapers significantly during the late 30’s and 40’s ( Error! Reference source not found. , http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09. pdf).

Figure L-1 Table L-4. Probability Distribution for Pregnancy by Age

Age Probability Cumulative Probability Age Probability Cumulative Probability 18 0.0221 0.0221 34 0.0525 0.8475 19 0.0346 0.0567 35 0.0245 0.8719 20 0.0475 0.1042 36 0.0245 0.8964 21 0.0475 0.1516 37 0.0245 0.9209 22 0.0475 0.1991 38 0.0245 0.9453 23 0.0475 0.2466 39 0.0245 0.9698**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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24 0.0475 0.2941 40 0.0057 0.9754 25 0.0582 0.3523 41 0.0057 0.9811 26 0.0582 0.4105 42 0.0057 0.9868 27 0.0582 0.4687 43 0.0057 0.9925 28 0.0582 0.5269 44 0.0057 0.9981 29 0.0582 0.5851 45 0.0003 0.9985 30 0.0525 0.6376 46 0.0003 0.9988 31 0.0525 0.6900 47 0.0003 0.9991 32 0.0525 0.7425 48 0.0003 0.9994 33 0.0525 0.7950 49 0.0003 0.9997 34 0.0525 0.8475 50 0.0003 1.0000



### However, the Leggett blood lead model does not pred ict significantly different blood lead values between age 20 and age 50. This is illustrated in F igure L-1 where the model was run with constant intakes at the current background level from birth to an age of 80. The model does not respond to intake significantly differently during the adult y ears. Owing to computational resource limitations, the low birth weight health effect distribution was sim ulated using the general population distribution (i .e., the same distribution used for the other adult heal th effects). However, because the blood leads do no t vary significantly during this portion of adulthood , this assumption is not expected to dramatically a ffect the predicted distribution.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-6 July 25, 2014 

### Figure L-1. Blood Lead Profile for an Individual wi th Constant Background Intake from Birth to Age 80 using Leggett model


# L.2. Time Spent The amount of time that children and adults spend i n different types of Public and Commercial Building s is highly variable and is a sensitive variable in e stimating the exposure to lead from either an inter ior or exterior P&CB renovation activity. The Consolidated Human Activity Database (CHAD) contains the most robust human activity data available (CHAD). The C HAD database contains activity information from survey respondents who logged their whereabouts for one or multiple days. The database contains this information for individuals on different days and f or people ranging from young children to adults.
The most up-to-date CHAD database was obtained on M arch 6, 2014. The database contains information from different surveys, and all data we re used in the analysis. As a first step, an initia l quality control step was performed. The number of u nique entries in the database was determined to**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-7 July 25, 2014 

### be 1,901,301. The number of unique entries in the database after removing entries where field QCMiss > 60 (either activity or location is unknown for mo re than 1 hr/day) and field qcsleep is missing (no sleep time entered) was 1,633,914. The corresponding uni que number of activity days captured in the database is 42,090.
Then, the location types in the CHAD database were mapped to the interior and exterior receptor building types. The mapping is shown in Table L-5 f or interiors and**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-8 July 25, 2014 

### Table L-6 for exteriors. Other CHAD codes were not included in the analysis and generally covered individuals who were spending time traveling from o ne place to another or spending time outside. Then, percentiles were estimated for each age group and f or each analysis category. The percentiles were rounded to the nearest 15-minute (quarter hour) inc rement; if a percentile was 0.0 hours per day, then it was rounded to 15 minutes to account for noise a t the low percentiles. Finally, the data in hours p er day were divided by 24 hours to give the average fr action of the day. Table L-7 and Table L-8 give the resulting hours per day and fractions for each perc entile. In the Monte Carlo model, the percentiles were used as the cumulative distribution function a nd the fraction of the day values were sampled for each iteration and for each age group.
In estimating the percentiles, special consideratio n was given for “child occupied facilities” (COFs) which are defined as a “building, or a portion of a build ing, constructed prior to 1978, visited regularly b y the same child, under 6 years of age, on at least two d ifferent days within any week, provided that each day’s visit lasts at least 3 hours, and the combine d weekly visits last at least 6 hours, and the comb ined annual visits last at least 60 hours.” Daycare fac ilities and preschools are typically thought of as childoccupied facilities, but the definition above is no t limited to only those types of buildings. Other types of public and commercial buildings qualify as child occupied facilities if all of the conditions in th e definition are met. However, if any one of these c onditions is not met, the building would not qualif y as a child-occupied facility.
For children 5 and under, only the schools building category had a 50 th 

### percentile time spent of greater than 3 hours per days. Other building types exceed ed 3 hours per day, but only at the upper percentiles. For children 5 and under, the upper a nd lower percentiles of the original distribution f or the interior analysis categories were altered so th at estimates tended toward the 50 th 

### percentile value and do not account for visits longer than 3 hours p er day. This is the same as removing longer time spent values above the previously defined COF thres hold.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table L-5. Mapping from Interior Analysis Codes to CHAD Codes Interior Building Type CHAD Codes Mapped to Interior Building Type 1i: Office, Public assembly, Public safety, Religious Worship, Outpatient healthcare 32100 : Office building / bank / post office 33400 : At work : no specific location, moving amon g location

2i: Warehouse, Industrial, Agricultural, Supermarket 32200 : Industrial plant / factory / warehouse 32900 : Public building / library / museum / theate r 32920 : Library / courtroom / museum / theater 33700 : At Church 32300 : Grocery store / convenience store

3i: Food sales, mall, strip mall, Services, Non-mall retail 32400 : Shopping mall / non-grocery store 32500 : Bar / night club / bowling alley 32510 : Bar / Night Club 32520 : Bowling alley 32600 : Repair shop 32610 : Auto repair shop /gas station 32620 : Other repair shop 32700 : Indoor gym / sports or health club 32910 : Auditorium, sport's arena / concert hall 33100 : Laundromat 33300 : Beauty parlor / barber shop / hair dresser' s 33600 : At Restaurant 33900 : At Dry cleaners 4i : Education 33500 : At School 5i: Lodging, hospital 33200 : Hospital / health care facility / doctor's office 33800 : At Hotel /Motel**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table L-6. Mapping from Exterior Analysis Codes to CHAD Codes
Exterior Building Type CHAD Codes Mapped to Exterior Building Type
1e: Agricultural 36200 : Farm 2e: Industrial 32200 : Industrial plant / factory / warehouse

3e: Commercial/ Public/ Government 32100 : Office building / bank / post office

33400 : At work : no specific location, moving among locat 32900 : Public building / library / museum / theater 32920 : Library / courtroom / museum / theater 33700 : At Church 32300 : Grocery store / convenience store 32400 : Shopping mall / non-grocery store 32500 : Bar / night club / bowling alley 32510 : Bar / Night Club 32520 : Bowling alley 32600 : Repair shop 32610 : Auto repair shop /gas station 32620 : Other repair shop 32700 : Indoor gym / sports or health club 32910 : Auditorium, sport's arena / concert hall 33100 : Laundromat 33300 : Beauty parlor / barber shop / hair dresser's 33600 : At Restaurant 33900 : At Dry cleaners 33200 : Hospital / health care facility / doctor's office 33800 : At Hotel /Motel 34200 : Laboratory

4e: Schools 33500 : At School

5e: Residences 30000 : Residence, General

30010 : Your Residence 30020 : Other's Residence 30100 : Residence, indoor 30120 : Your residence, indoor 30121 : Kitchen 30122 : Living room / family room 30123 : Dining room 30124 : Bathroom 30125 : Bedroom 30126 : Study / Office 30127 : Basement 30128 : Utility room / Laundry room 30129 : Other indoor 30130 : Other's residence, indoor 30131 : Other's Kitchen 30132 : Other's living room / family room 30133 : Other's Dining room 30134 : Other's Bathroom 30135 : Other's Bedroom 30136 : Other's Study / Office 30137 : Other's Basement 30138 : Other's utility room / laundry room 30139 : Other indoor 30200 : Residence, Outdoor 30210 : Your residence, Outdoor 30211 : Your residence - Pool, spa 30219 : Your residence - Other outdoor 30220 : Other's residence, outdoor 30221 : Other's residence - Pool, spa 30229 : Other's residence - Other outdoor 30300 : Garage 30310 : Indoor garage 30320 : Outdoor garage 30330 : Your garage 30331 : Your indoor garage 30332 : Your outdoor garage 30340 : Other's garage 30341 : Other's indoor garage 30342 : Other's outdoor garage 30400 : Other, residence 32810 : Childcare facility, house**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftL-1 July 25, 2014Table L-7. Percentiles for Time Spent in DifferentInterior Building TypesInterior Analysis, Hours per DayPnCBCategoryUnder 5 years old, Percentiles5-17 years old, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1i 0.25 0.25 0.25 0.5 1.00 1.75 2.9 2.9 0.25 0.25 0.25 0.75 2.00 4.25 8.5 102i 0.25 0.25 0.5 1.25 2.5 2.9 2.9 2.9 0.25 0.25 1.00 2.00 3.00 4.5 5.5 8.253i 0.25 0.25 0.5 1.00 1.75 2.9 2.9 2.9 0.25 0.25 0.5 1.25 2.25 3.75 5.00 7.754i 0.25 1.00 2.75 2.9 2.9 2.9 2.9 2.9 1.00 3.00 5.00 6.5 7.25 8.00 9.0 10.255i 0.25 0.5 0.5 1.00 1.75 2.9 2.9 2.9 0.25 0.5 0.751.5 5.00 8.75 10.00 13.5PnCBCategory18-49 years old, Percentiles 50 years old and above, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1i 0.25 0.75 3.75 7.75 8.75 9.75 10.5 12.75 0.25 0.5 2.0 6.0 8.5 9.5 10.25 122i 0.25 0.25 0.5 1.25 3.25 8.25 9.25 11.75 0.25 0.25 0.5 1.25 2.5 4.5 7.25 103i 0.25 0.25 0.75 1.25 2.75 5.25 8 11.25 0.25 0.250.75 1.25 2.0 3.5 4.75 9.04i 0.25 0.25 0.75 3.0 6.75 8.25 9.25 11.75 0.25 0.25 1.0 2.25 6.75 8.5 9.0 10.755i 0.25 0.5 1.0 2.5 8.25 10.25 12.0 15.0 0.25 0.5 0.75 1.5 3.75 8.25 9.5 13.5Interior Analysis, Fraction of WeekPnCBCategoryUnder 5 years old, Percentiles5-17 years old, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1i 0.01 0.01 0.01 0.02 0.04 0.07 0.12 0.12 0.01 0.01 0.01 0.03 0.08 0.18 0.35 0.422i 0.01 0.01 0.02 0.05 0.10 0.12 0.12 0.12 0.01 0.01 0.04 0.08 0.13 0.19 0.23 0.343i 0.01 0.01 0.02 0.04 0.07 0.12 0.12 0.12 0.01 0.01 0.02 0.05 0.09 0.16 0.21 0.324i 0.01 0.04 0.11 0.12 0.12 0.12 0.12 0.12 0.08 0.13 0.21 0.27 0.30 0.33 0.38 0.435i 0.01 0.02 0.02 0.04 0.07 0.12 0.12 0.12 0.01 0.02 0.03 0.06 0.21 0.36 0.42 0.56PnCBCategory18-49 years old, Percentiles 50 years old and above, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1i 0.01 0.03 0.16 0.32 0.36 0.41 0.44 0.53 0.01 0.02 0.08 0.25 0.35 0.40 0.43 0.502i 0.01 0.01 0.02 0.05 0.14 0.34 0.39 0.49 0.01 0.01 0.02 0.05 0.10 0.19 0.30 0.423i 0.01 0.01 0.03 0.05 0.11 0.22 0.33 0.47 0.01 0.01 0.03 0.05 0.08 0.15 0.20 0.384i 0.01 0.01 0.03 0.13 0.28 0.34 0.39 0.49 0.01 0.01 0.04 0.09 0.28 0.35 0.38 0.455i 0.01 0.02 0.04 0.10 0.34 0.43 0.50 0.63 0.01 0.02 0.03 0.06 0.16 0.34 0.40 0.56**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftL-2 July 25, 2014Table L-8. Percentiles for Time Spent in DifferentExterior Building TypesExterior Analysis, Hours per DayPnCBCategoryUnder 5 years old, Percentiles5-17 years old, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1e 0.10 0.20 0.60 1.20 2.00 2.40 2.90 2.90 0.50 0.50 1.00 1.75 3.25 4.25 4.50 7.502e 0.30 0.30 0.30 0.30 0.50 0.60 0.60 0.60 0.25 0.25 0.25 0.75 3.25 4.50 8.00 8.003e 0.30 0.40 0.80 1.40 2.50 3.80 4.70 7.90 0.25 0.25 1.00 2.00 3.25 5.25 6.50 10.254e 0.70 1.80 3.30 5.80 8.40 9.50 9.90 11.00 2.00 3.00 5.25 6.50 7.25 8.25 9.00 10.505e 13.90 15.60 19.00 21.50 23.40 24.00 24.00 24.0012.50 13.75 15.75 19.00 22.00 23.75 24.00 24.00PnCBCategory18-49 years old, Percentiles 50 years old and above, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1e 0.25 0.25 1.00 3.00 8.75 11.75 13.00 16.00 0.250.50 0.75 2.50 4.75 8.25 8.75 15.502e 0.25 0.50 4.00 8.25 9.00 10.50 11.25 12.75 0.250.50 3.25 8.25 9.00 10.00 11.00 13.753e 0.25 0.75 1.75 4.50 8.75 10.25 11.25 14.50 0.500.75 1.50 3.25 7.50 9.50 10.50 13.254e 0.00 0.00 0.75 2.75 6.50 8.25 9.00 12.25 0.00 0.25 1.00 2.25 6.75 8.50 9.00 10.505e 9.75 11.25 13.25 16.25 20.75 23.00 24.00 24.00 11.00 12.50 14.75 19.50 22.50 24.00 24.00 24.00Exterior Analysis, Fraction of WeekPnCBCategoryUnder 5 years old, Percentiles5-17 years old, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1e 0.01 0.01 0.03 0.05 0.08 0.10 0.12 0.12 0.02 0.02 0.04 0.07 0.14 0.18 0.19 0.312e 0.01 0.01 0.01 0.01 0.02 0.03 0.03 0.03 0.01 0.01 0.01 0.03 0.14 0.19 0.33 0.333e 0.01 0.02 0.03 0.06 0.10 0.16 0.20 0.33 0.01 0.01 0.04 0.08 0.14 0.22 0.27 0.434e 0.03 0.08 0.14 0.24 0.35 0.40 0.41 0.46 0.08 0.13 0.22 0.27 0.30 0.34 0.38 0.445e 0.58 0.65 0.79 0.90 0.98 1.00 1.00 1.00 0.52 0.57 0.66 0.79 0.92 0.99 1.00 1.00PnCBCategory18-49 years old, Percentiles 50 years old and above, Percentiles5th 10th 25th 50th 75th 90th 95th 99th 5th10th 25th 50th 75th 90th 95th 99th1e 0.01 0.01 0.04 0.13 0.36 0.49 0.54 0.67 0.01 0.02 0.03 0.10 0.20 0.34 0.36 0.652e 0.01 0.02 0.17 0.34 0.38 0.44 0.47 0.53 0.01 0.02 0.14 0.34 0.38 0.42 0.46 0.573e 0.01 0.03 0.07 0.19 0.36 0.43 0.47 0.60 0.02 0.03 0.06 0.14 0.31 0.40 0.44 0.554e 0.01 0.01 0.03 0.11 0.27 0.34 0.38 0.51 0.01 0.01 0.04 0.09 0.28 0.35 0.38 0.445e 0.41 0.47 0.55 0.68 0.86 0.96 1.00 1.00 0.46 0.52 0.61 0.81 0.94 1.00 1.00 1.00**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-3 July 25, 2014 

# L.3. Length of Time Between Renovation and Blood Lead Measurement In the Approach, all renovation activities are assu med to occur in 2014 so that incremental health effects could be aggregated and attributed to renov ation activities in a given year. The Approach samples the population age distribution discussed i n Section L.1 to determine the individual’s age at the time of renovation. Based on that age, the individu al’s birth year (relative to 2014) is calculated, a nd they are assigned the correct background intake pro file to match that birth year.

To be consistent with the methods in the health eff ect papers, blood lead and health effect status was determined at a specific point in time post-renovat ion. Similar to the NHANES survey (on which some o f the health effect studies are based), it is assumed in the Approach assumes that the health effect ris k is based on this single point estimate of blood lead.
People tend to enter different public and commerci al buildings numerous times each week, and they will encounter numerous pubic and commercial buildi ng renovations over the period of their lives. In this Approach, it is not feasible to try to model e very renovation; the possible combinations of build ings and ages during the renovation are nearly infinite and cannot be incorporated into the model. Instead, the Approach models the most recent renovation that occurred in a building with lead-containing paint.
Thus, the Approach assumes that a given person ente rs a given public and commercial building as part of their day-to-day activities. They may spend eith er a large or small amount of time in the building, and that will be accounted for separately in the “time spent” variable. That one given building will be renovated using different activities with varying f requency. To quantify the frequency, the Approach uses the 2011 RSMeans Facilities Maintenance & Repa ir Cost Data (Mossman and Plotner 2011). The RSMeans data book includes information on the frequ ency (in years) for a variety of systems, including interior finishes, exterior closures, plumbing, HVA C, electrical systems, and window and door replacements. We decided to consider a range of yea rs for each event type because the number of years can vary widely based on material and size. For exa mple, the frequency of painting concrete block wall s (exteriors) is every 25 years; however, finished wo od shingles (exteriors) are painted every 5 years.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-4 July 25, 2014 

### Table L

- 9 . Frequency of Renovations in Commercial and Public Buildings, by Event Type presents the range of frequencies, by event type, for selected data from RSMeans. The total annual frequency of exposure from renovations in public an d commercial buildings was calculated in two different ways. To calculate the numbers in the fi rst total row we assumed that the probability of performing each renovation activity is independent of the probabilities of performing the other renovation activities. For example, we assume that the probability of replacing pipes is independent of the probability that walls are repainted. Thus, if the annual frequency of interior painting is 0.2 a nd the annual frequency of replacing pipes is 0.01, the pr obability of doing either of these jobs in a given year is the sum of the two annual frequencies, or 0.21. Ho wever, it may be more realistic to assume that the probabilities of performing different renovation ac tivities are not independent. For example, if the wall must be disturbed to replace pipes it’s a good time to repaint the room at the same time. The second total row in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-5 July 25, 2014 

### Table L

- 9 . Frequency of Renovations in Commercial and Public Buildings, by Event Type accounts for this by taking the most frequent inte rior activity and assuming that the less frequent interior activities will be performed at the same t ime as the most frequent activity. For example, if interior painting is done every 4 years and HVAC sy stems are replaced every 20 years, we assume that the building owner will choose to coincide the HVAC replacement with one of the painting events, so the HVAC will be replaced at the same time as the i nterior painting (i.e., pipes are replaced at the same time as every 5 th 

### interior painting job).

As shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-6 July 25, 2014 

### Table L

- 9

. Frequency of Renovations in Commercial and Public B uildings, by Event Type
, the annual renovation frequency is estimated to be between 0.24 and 0.70. In other words, the average person will be exposed to renovations that may disturb lead-based paint between every 1.4 to 4.2 years.
However, the person in question is assumed to be pr esent during the renovation and their blood lead is measured at some later date. As a proxy for when a person’s blood is measured, it is assumed in the Approach that the person will have been in a buildi ng being renovated at least within the last 4.2 yea rs. In reality, they enter numerous buildings that each may or may not have lead based paint and that may or may not have been recently renovated. However, t he assumption that a renovation happened at least 4.2 years ago states that “the person enters at least one public and commercial building that contains lead-based paint, and that building was re novated sometime within the last 4.2 years’.
To implement this assumption in the Approach, the r enovation is always assumed to occur in 2014. Then, the concurrent blood lead is recorded at nine different time periods after the renovation: 1 month, 5 months, 10 months, 15 months, 20 months, 2 5 months, 30 months, 40 months, and 50 months, where 50 months is approximately 4.2 years. This sampling pattern is shown in Figure L-2 in th e top panel. Capturing all the different time periods allows analysis of the health effects over the ran ge of assumed time periods since the renovation.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-7 July 25, 2014 

### Table L

- 9 . Frequency of Renovations in Commercial and Public Buildings, by Event Type

Event Frequency (in Years) Annual

Frequency Low High Low High Interior Painting 5 4 0.2 0.25 Exterior Painting 25 5 0.04 0.20 Replace plumbing pipes and fixtures 75 10 0.01 0.10 Replace HVAC Systems 75 20 0.01 0.05 Replace Electrical System and Fixtures 20 20 0.05 0 .05 Window and Door Replacement Events 60 20 0.02 0.05 TOTAL (Assuming Renovation Types are Performed Separately ) 3.0 a 1.4 b 0.33 a 0.70 b

TOTAL (Assuming Renovation Interior Activities are perfor med together and Exterior Activities are performed toge ther. For example, HVAC and other interior activities are performed at the same time as interior repainting; window replacement and other exterior activities ar e performed at the same time as exterior repainting). 4.2 c 2.2 d 0.24 c 0.45 d

a. The 0.33 total annual frequency is calculated by su mming the annual frequencies of the individual events above (0.2+0.04+0.01+0.01+0.05+0. 02=0.33). The annual frequency is converted to a frequency in years by taking its rec iprocal (1/0.33=3). b. The 0.70 total annual frequency is calculated by su mming the annual frequencies of the individual events above (0.25+0.2+0.1+0.05+0.05+0.0 5=0.7). The annual frequency is converted to a frequency in years by taking its rec iprocal (1/0.7=1.4).

c. The 0.24 total annual frequency is calculated by su mming the most frequent interior activity frequency (0.2) and the most frequent exterior acti vity frequency (0.04) annual frequencies of the individual events above (0.2+0.04=0.24). Th e annual frequency is converted to a frequency in years by taking its reciprocal (1/0.24 =4.2). d. The 0.45 total annual frequency is calculated by su mming the most frequent interior activity frequency (0.25) and the most frequent exterior act ivity frequency (0.2) annual frequencies of the individual events above (0.25+0.2=0.45). Th e annual frequency is converted to a frequency in years by taking its reciprocal (1/0.45 =2.2).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-8 July 25, 2014 

### Figure L-2. Diagram of Concurrent and Lifetime Aver age Blood Lead Metrics in the Approach


### As an additional blood lead metric, the lifetime av erage blood lead is also estimated for each iterati on and age group. This lifetime average is defined as the average from birth until the post-renovation ti me when blood lead returns to the background value. Th is metric, rather than being a biologically-based metric, is an exposure-based one. Because the healt h effect studies and other literature do not provid e information on a biologically relevant window of su sceptibility for the health effects, the exposurebased lifetime average definition is used as a surr ogate. This lifetime average is depicted in the low er panel of Figure L-2.


# L.4. Exposure Factors for Lead Renovation Intake Estimat es
The Leggett model requires intakes as inputs rather than media concentrations. To convert the renovation-related media concentrations to intakes for each age group, the following equations were used: gG
 = 	G
+ × ( 1 ' 	f  

G
G
  c
		  	**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### where: 

### OralIntake = oral intake of renovation-related dust ( μ g/day)

**DustSoilIngestRate =** ingestion rate of dust and soil per day (g/day) **FracSoil =** fraction of dust and soil intake that is soil **DustConcen =** lead concentration in dust ( μ g/g) **InhalIntake =** inhalation intake of renovation-related particulate ( μ g/day) **VentilationRate =** inhalation rate of air (m 3 

### /day) **AirConcen =** lead concentration in air ( μ g/m 3 

### )
Table L-10 shows the values used for the exposure f actors in the Approach. It also shows the absorptio n fraction used in the Leggett model to determine how much of the oral intake enters the blood stream.

Table L-10. Exposure Factors Used in Approach

Parameter Source 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-10 18-80

Soil and Dust Ingestion Rate (g/day) Exposure

Factors Handbook (US EPA, 2011) 0.06 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.05

Fraction of soil and dust ingestion that is soil Exposure

Factors Handbook (US EPA, 2011) 0.45

Ventilation Rate (m 3 /day) Exposure

Factors Handbook (US EPA, 2011) 5.4 8 9.5 10.9 10.9 10.9 12.4 14.4 13.3

Absorption Fraction in Leggett Model Leggett

(1993) 0.45 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.15



# L.5. References for Appendix L: American Community Survey ACS 2012. Estimates of U S Population by Age. Available online at ( http://www.census.gov/acs/www/ )**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft L-10 July 25, 2014 

### Lanphear BP, Hornung R, Khoury J, et al. (2005). Lo w-level environmental lead exposure and children’s intellectual function: an international pooled anal ysis. Environ Health Perspect, 113, 894–9.
Leggett RW. (1993). An age-specific kinetic model o f lead metabolism in humans. **Environmental HealthPerspectives**  101:598–616.
Menke et al (2006). Blood lead below 0.48 micromol/ L (10 microg/dL) and mortality among US adults. Circulation. 114:1388–1394.

Mossman M; Plotner S; Babbitt C; Baker T; Balboni B, Eds. (2011). RSMeans Facilities Construction Cos t Data
Navas-Acien et al (2009). Blood Cadmium and Lead a nd Chronic Kidney Disease in US Adults: A Joint Analysis. Am. J. Epidemiol. (2009) 170 (9): 1156-1164.
US EPA, 2011. Exposure Factors Handbook. Available at http://www.epa.gov/ncea/efh/pdfs/efhcomplete.pdf

. Zhu et al (2010). Maternal Low-Level Lead Exposure a nd Fetal Growth. Environmental Health Perspectives. Volume 118. Number 10.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-1 July 25, 2014 

# Appendix M. Updates to the Leggett Model

The Leggett biokinetic model (Leggett, 1993) synthe sized a wide variety of sometimes disparate sources of information related to the biokinetics of lead i n humans. The Leggett model was informed by:

1. Lead tracer studies of injection, ingestion, and in halation in healthy adult humans, 2. Measurements of lead in environmentally exposed men , women, and children at autopsy, 3. Lead mass-balance studies on adult humans, 4. Bioassay and autopsy measurements on occupationally exposed subjects, 5. Lead studies in laboratory animals ad different lif e stages 6. Experimental, occupational, environmental, and medi cal data on the biokinetics of elements that serve as physiological analogues of lead, and 7. Basic physiological information on the human body. As such, the Leggett model structure is a minimal s ystem of body compartments and mass exchange terms needed to synthesize all these data sets. The modular form of the model allows investigators to modify specific parameter values to address special problems in lead toxicology or to incorporate new information related to lead biokinetics. The origin al Leggett model included:

a. Input file : an ASCII input file containing information descri bing the lead exposure scenario and the age-dependent lead transfer rates for each comp artments, and

b. Model code : an executable FORTRAN program which reads the inp ut file and performs the prescribed calculations, and writes the outputs to an ASCII file.
This approach was designed to provide maximum flexi bility and versatility rather than user-friendlines s.
The original Leggett model has, on several occasion s, been translated onto other software platforms or into other programming languages. These adaptation s include porting the FORTRAN code to a C++ version by Michael Korsch (personal communication), a MatLab version (LeggettPlus) by the OEHHA (2013), and Excel scripts to support an exposure mo dule by ICF and SRC (personal communication). In each case, these adaptations were undertaken to pro vide a more user friendly interface or to support iterative analyses.
In addition, the original FORTRAN code has been cha nged to create four distinct versions of the model, and these versions have been distributed to researc hers in academia and government (e.g. EPA) upon**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-2 July 25, 2014 

### request. It is important to note that these four FO RTRAN versoins did not alter the algorithms and equations of the model itself, but rather facilitat e output of new variables:

 

### Version 1 : ICRPv001.FOR added line#53 in the input file and added a module to the FORTRAN code for chelation (R. Leggett, 1996)

 

### Version 2: ICRPv002.FOR created output options to convert CRTC ON and TRBCON (μg lead/g wet bone) to CORTCONBM and TRBCONBM (μg lead/g bone min eral) to better simulate XRF measurements of bone lead( J. Pounds, 1997)

 

### Version 3: ICRPv003.FOR converted output time from DAYS to YEA RS (J. Pounds, 1997)

 

### Version 4: ICRPv004.FOR added output options for UPTAKEGI, UPT AKERI, UPTAKE and added additional output preceison to YEARS (J. Pounds and R. Leggett, 1997).
With respect to bone (the storage site of lead in t he body), the Leggett model includes a series of compartments that exchange lead mass with plasma. T his compartmental structure includes more exchange compartments than other existing models, a s shown in Figure M-1. The transfer rates help determine the relative storage of lead in the bone and the overall half-life of lead in the body. Thus , these parameter values are highly sensitive variabl es.
The Approach requires accurate simulation of both c hildren and adult blood lead values at typical background exposure levels and over short-term reno vation related changes. Thus, as part of the development of this Approach, Version 4 (ICRPv004.F OR) of the original Leggett Lead Model (Leggett, 1993) was updated to Version 5 (ICRPv005.FOR) by J. Pounds to resolve some limitations of the original model and to incorporate new input data available i n the literature. The tasks undertaken included:

1. Scaling tissue lead concentrations for children and adolescents, 2. Evaluating and updating bone lead transfer rates, a nd 3. Evaluating and calibrating the model using human da ta sets for children and adults.
These updates were undertaken so that the model rem ains backward-compatible with existing input files. That is, when possible, only the parameter v alues in the model input file (*.DAT) were modified . If necessary, FORTRAN code was updated, or both the co de and the default input file were edited to incorporate the changes. In instances for which rel iable data were not available and thus required judgment, the minimal numerical changes from the or iginal Leggett model were employed. The changes are discussed in detail below, and the FORTRAN code with an example input file (*.DAT) are provided as attachments.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-3 July 25, 2014 

### The California Office of Environmental Health Hazar d Assessment (OEHHA) also recently revisited the Leggett model (OEHHA, 2013), building the “Leggett Plus” model. Their changes were reviewed and considered during this effort. However, because the ir focus was on adult occupational exposure, additional changes were made to ensure model approp riateness for the hypothetical individuals (children and adults experiencing typical non-occup ational background lead exposure with the addition of a short-term renovation-related increase in lead exposure) being considered in this present analysi s.

Figure M-1. The Skeletal and blood compartments of representative biokinetic models for lead metabolis m

in humans. a). Rabinowitz et al. 1983; b) Nie et al . 2005, c) Leggett, 1993.


# M.1. Scaling Model Parameters for Children The Leggett model is a mass-balance model, meaning it keeps track of the input, movement, and output of lead from the body. In order to estimate concent rations for each simulated body compartment (e.g., blood lead), the lead mass (μg) in each compartment is divided by the age-dependent volume (or weight) of the compartment. In the original Leggett model, some tissue volumes were limited to adult**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### values. Thus, although some tissue concentrations w ere calculated for all ages, some outputs were only valid for adults. For the new version of the Legget t model that is presented here, the kidney mass (KIDWT), total skeletal mass (TSKELWT), fraction of skeletal mass that is trabecular bone (FRTRAB), trabecular bone mass (TTRABWT), cortical bone mass (TCORTWT), red blood cell volume (RBCVOL), and plasma volume (PLSVOL) were updated to allow lead c oncentrations to change from birth to adulthood. The RBC saturation values were also revisited as di scussed below. The revisions for scaling consisted of 1. Identifying age-dependent tissue volumes/weights at 14 different ages from birth to 75 years of age, and 2. Editing the FORTRAN code to read these values and i nterpolate to ages that are not explicitly defined consistent with the interpolation approach used by the Leggett model (1993).



## M.1.1. Kidney mass, total skeletal mass, red blood cell vo lume, and plasma volume Not all tissues are scaled for children in the Legg ett versions 1-4. Thus, model outputs for lead concentration in these tissues are only valid for a dults. The age-dependent compartmental volumes for kidney, total skeleton, red blood cell, and plasma were estimated using values in ICRP Publication 89 “Basic Anatomical and Physiological Data for Use in Radiological Protection Reference Values” (Valenti n, 2002). The kidney and skeletal values were used dir ectly while the red blood cell and plasma volumes were estimated from age-dependent hematocrit and to tal blood values. In each case, the agedependent values for males were selected. This sele ction was done to provide consistency with parameter selection in original Leggett model (Legg ett, 1993) which evolved from the ICRP model for radioisotopes of lead in the workplace. The imple mentation of gender-dependent computation and outputs is beyond the scope of the present effort. The implementation of gender-dependent computation and outputs is beyond the scope of the present effort. The most critical limitation of the updated Leggett model with respect to gender is dur ing and menopause; that is, the current version of the Leggett model (like all previous versions) does not consider gender dependent changes in bone mass, bone mineral metabolism, and skeletal mass du ring adolescence and menopause. The Leggett-93 approach is justified by the poor availability of e xperimental studies on lead kinetics in females compared to males. The consequence of applying the Leggett models to simulate blood lead concentrations in females has not been evaluated. S uch evaluation would require information describing gender-dependent lead intake and uptake in addition to age-dependent physiological data.
The updated compartmental volume values were hard-c oded into the FORTRAN code at specific ages, and linear interpolation was performed between the age bins to determine intermediate values consistent with Leggett model handling of age-depen dent parameters. The updated parameter values are shown in Table M-1.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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## M.1.2. Fraction of Skeleton that is Trabecular Bone The age-dependent trabecular and cortical bone volu mes were estimated using the total skeletal volumes and multiplying by an age-dependent fractio n that accounts for the fraction of the skeleton that is trabecular bone. Infants are born with near ly all of their body consisting of trabecular bone while adult’s trabecular bone is approximately 20% of ske letal mass (Brinckmann et al., 2002). An extensive literature search was undertaken, but reliable, age -dependent changes between these two extremes with age could not be located. In the absence of su ch information, it was assumed that the body is hal f trabecular and half cortical bone at age 5 (Brinckm ann et al. 2002) and the other values were interpolated to get the full distribution with age. The values are shown in Table M-1.

Table M-1. Updated Age-Dependent Model Compartment Volumes

Age (yr) Kidney Mass (g) Skeletal

Mass (g) Fraction

Skeleton that is Trabecular Total

Blood Volume (dL) Hematocrit Red Blood Cell Volume (dL) Plasma

Volume (dL)

0 

### 25 370 0.95 2.7 0.575 1.55 1.15 0.27 

### 36 642 0.90 3.1 0.37 1.15 1.95 1 

### 70 1170 0.85 5 0.35 1.75 3.25 5 

### 110 2430 0.50 14 0.38 5.32 8.68 10 

### 180 4500 0.34 24 0.38 9.12 14.88 15 

### 250 7950 0.23 45 0.4 18.00 27.00 18 

### 310 10500 0.20 53 0.42 22.26 30.74 24 

### 310 10500 0.20 53 0.45 23.85 29.15 30 

### 310 10500 0.20 53 0.45 23.85 29.15 40 

### 310 10500 0.20 53 0.45 23.85 29.15 45 

### 310 10500 0.20 53 0.45 23.85 29.15 55 

### 310 10500 0.20 53 0.45 23.85 29.15 65 

### 310 10500 0.20 53 0.45 23.85 29.15 75 

### 310 10500 0.20 53 0.45 23.85 29.15


## M.1.3. RBC saturation The nonlinear accumulation of lead by erythrocytes at high blood lead levels is widely accepted (USEPA , 2013). Moreover, non-linear binding saturation is c onceptually logical as high affinity lead-binding s ites on proteins and other molecules will become saturat ed at high lead levels. The original Leggett model set a threshold and a maximum saturation of 25 μg/d L whole blood and 350 μg/dL RBC respectively. The OEHHA implementation of the Leggett model (OEHHA, 2 013) set the threshold to zero and the maximum limit of saturation to 270 μg /dL), since t he mechanistic and empirical basis for a threshold is**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### not well defined. The difference between using thr eshold values of 0 versus 25 μ g/dL and using saturation values of 270 and 350 μg /dL are shown i n Figure M-2. Overall, blood lead levels using no threshold and a saturation value of 270 are lower t han blood lead levels using the original Legget (19 93) parameters. The difference, as determined in test runs as part of the current effort, is a decrease b y approximately 33% at both 20 μ g/dL and 30 μ g/dL, with smaller differences at lower blood lead levels.
For this effort, the parameters in the input file ( input line 17) were changed to reflect threshold an d maximum saturation of 0 and 270, respectively, and the nonlinearity in the model was turned on. It should be recognized that the issue of threshold an d saturation is not necessarily settled but awaits better experimental data and insight to establish t hese parameters.

Figure M-2. The Effect of Different Nonlinear Thres hold and Saturation Value Assumptions. The “1” in the figure legend indicates that the non linearity in the model was turned on.


## M.1.4. Additional Parameter Changes Other parameters were changed during the course of model calibration to support age-dependent model outputs of lead concentrations and to improve simulation of blood lead in children. Both the deposition fraction from plasma to red blood cells and the transfer rate from red blood cells to plasm a were adjusted to improve agreement between the mode led blood lead for children ages 1 to 7 years and the NHANES average blood leads for the same age range, as discussed in Section M.3.
The selection of the adult values used by Leggett ( 1993) were obtained from a limited radiotracer stud y conducted in adults only, and thus no age-dependent transfer rates were available. Leggett (1993) concluded that “approximately” 24% of plasma lead e ntered the RBC and this variable was not modified for age in Leggett-93. The transfer rates between p lasma and red blood cells were empirically updated**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### to provide more accurate simulation of blood lead l evels in children. The adult transfer rates were al so adjusted slightly to provide better agreement with the occupational calibration data set. The updated values are shown in Table M-2. No contemporary stud ies were identified to support an objective definition of these parameters.
We also reviewed and updated the default maternal b lood lead concentration to be more appropriate to contemporary blood lead data and to better simul ate blood leads in children. The Leggett-93 model set the maternal blood lead at 10 μg lead/dL at tim e of birth (input file line #18). This variable in turn defines the starting blood and tissue lead concentr ations at birth (input file line #19). This variabl e was updated following review of maternal blood leads fr om the 2011-2012 NHANES survey (CDC, 2012). The average blood lead was estimated across the female population age 18 to 50. An average value of 0.7 μg lead/dL was calculated and thus 0.7 μg lead/dL was used as the new default maternal blood lead in the Leggett input file.

Table M-2. Additional Parameters Updated in the Leg gett Model

Age Deposition

Fraction to RBC Transfer Rate

from RBC***
0 0.562* 0.20 0.27 0.562* 0.20 1 0.562* 0.20 5 0.277 0.20 10 0.277** 0.21 15 0.139 0.22 18 0.139 0.22 24 0.139 0.22 30 0.139 0.22 40 0.139 0.22 45 0.139 0.22 55 0.139 0.22 65 0.139 0.22 75 0.139 0.22 * Changed from 0.462 ** Changed from 0.139 *** All changed from 0.24 

# M.2. Bone Transfer Rates There are two major kinds of structural bone, trabe cular and cortical. Cortical bone is much more dens e and stronger than trabecular bone and comprises abo ut 80% of the adult skeletal mass. Trabecular bone is more vascular, more flexible, less dense, and ha s a higher surface/mass ratio. The vascularity and**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### surface to mass ratio facilitate more rapid deposit ion and exchange of Ca 2+ 

### or Pb 2+ 

### and other boneseeking metals. Kinetic models for bone mineral met abolism, including lead, generally include compartments for both bone types with or without ki netic sub-compartments. The bone transfer rates for the Leggett (and other) models are derived from available data from human and animal studies with priority given to data derived from humans. This pr imary objective for reevaluation of the bone-lead transfer (in addition to scaling for children and a dolescents) is to incorporate more information on t he skeletal lead metabolism in the aged human. That is , the fractional skeletal transfer rates in the ori ginal Leggett model were not adjusted after age 25. This adjustment is important as age (with diet, gender, exercise, genetics, etc.) determines the rate of bo ne formation, resorption, bone turnover, and bone loss.
**Difficulty in defining bone-lead transfer rates.** There is excellent potential to apply new data, no t available to Leggett in 1993, to inform revision of the Leggett model bone-lead transfer rates. These new data include (a) more elaborate non-invasive measur es of lead in cortical and trabecular bone by XRF, (b) development of blood and urinary biomarkers of bone formation and resorption, and (c) more widely applied studies of Dual Energy X-ray absorptiometry (DEXA) measures of bone density and bone mass. Electronic searches using Medline, Quertle, and Goo gle Scholar were conducted to identify useful papers.
It must be recognized that biokinetic compartments and transfer integrate many chemical and biological processes. For example, the transfer of lead from t rabecular bone to plasma (RTRAB) is the sum of the chemical loss by displacement of lead from hydroxya patite, bone matrix proteins, or bone cells by Ca 2+ 

### or H + 

### . Pb2+ may also be lost from bone as the mineralize d and proteinaceous bone matrix is degraded by osteoclasts.
The last two decades have seen the identification, development, and clinical application of numerous biomarkers for anabolic and catabolic skeletal biol ogy. The use of these biomarkers is limited as, for example, a marker of bone resorption, does not dist inguish between turnover of trabecular and cortical bone. Moreover, bone resorption or turnover as defi ned by a biomarker may not be equivalent to loss of Pb as Pb 2+ 

### may be simply redistributed to another site in bon e.
Finally, it is not appropriate to directly compare, or utilize, bone-lead transfer rates from other bi okinetic or PBPK models of lead as the number of transfer ra tes varies considerably. Twelve fractional transfer rates and six compartments comprise the skeletal co mponent of the Leggett model. In contrast, the Rabinowitz model includes a single compartment and two transfer rates for skeletal lead (Rabinowitz et al. 1976). Other kinetic models lie between these t wo extremes (see Figure M-1).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Values and data sources for trabecular and cortical bone-lead transfer rates were evaluated. Bone formation and turnover rates from different sources were reviewed including:

 

### Age-dependent bone formation and turnover rates as defined by tracer studies of Ca, Pb, Sr, or other bone-volume-seeking elements.

 

### Age-dependent bone formation and turnover rates as defined by blood and urinary biomarkers were also reviewed.

 

### Age-dependent bone-lead transfer rates described in the scientific literature since 1993 including values in PBPK models or other compartmental models

. The use of blood or urinary concentrations of Osteo calcin, bone-specific alkaline phosphatase, procollagen I extension peptides, acid phosphatase, telopeptides of type I collagen, pyridinoline and deoxypyridinoline crosslinks are used as biomarkers of bone formation, resorption, and turnover. Unfortunately, the medical studies reviewed were fo cused on individuals at risk for skeletal disease(s ) and did not encompass age-dependent studies of a ge neral population suitable to inform modification of bone-lead transfer parameters. This literature h owever, has promise to inform age-dependent bonelead transfer parameters and should be re-evaluated in the future. Several biokinetic and PBPK lead models published s ince the Leggett 1993 publication included bonelead transfer rates. The values of a single represe ntative parameter, transfer from trabecular bone to plasma, are illustrated in Figure M-3. For this eff ort, the values used in the Coon et al. (2006) implementation of the Leggett model, originally fro m Rowland (1964), were selected. These values include bone lead transfer rates for trabecular and cortical bone to age 70 yr, including changes in b one mineral metabolism associated with senectitude, as shown in Table M-3. The published bone-lead parameters from more recent publications were not used to update the Leggett model in the current effort because (a) the y did not include age-dependent parameters (e.g. Ni e et al. 2005; OEHHA, 2013; O’Flaherty, 1998), (b) th e model structure was incompatible with the Legge tt model, so the transfer parameters could not be dire ctly compared, and/or (c) the description of bone turnover and skeletal mass did not reflect well kno wn changes in bone mineral metabolism associated with aging (all except O’Flaherty, 1998).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure M-3. Bone Transfer Rates Used in Different P BPK Models Since 1993.

Table M-3. Bone Transfer Rates Used in the Model

Age Cortical

Bone Transfer Rates Trabecular

Bone Transfer Rates 0 0.0102 0.0102 0.27 0.00822 0.00822 1 0.00288 0.00288 5 0.00154 0.00181 10 0.00089 0.00132 15 0.000512 0.000956 18 0.000370 0.000781 24 0.000082 0.000493 30 0.000041 0.000247 40 0.000041 0.000247 45 0.000041 0.000274 55 0.000041 0.000301 65 0.000041 0.000329 75 0.000041 0.000356**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# M.3. Evaluaton and Calibration of the Updated Model Two different exercises were conducted to compare t he simulated blood lead values with literature data and to calibrate the model to provide adequate agre ement. These two separate exercises, one for children and one for adults, are described below.



## M.3.1. Comparison for Children The childhood portion of the updated Leggett model was compared to NHANES data from the 2007- 2008 survey (CDC, 2012). This particular survey was selected as the best match to the available intake estimates.The NHANES values were the average values across each different age bin (0-<1, 1-<2, 2-<3, 3

- <4, 4-<5, 5-<6, and 6-<7), as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table M-4. Media intake rates were based on values in the Exposure Factors Handbook (EPA, 2011) while media concentrations were set to likely backg round values, as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table M-4. The concentrations and intake rates were used to calculate the daily lead intakes by inhalation and ingestion. Then, the Leggett model w as run from birth to age 7 yr. The IEUBK model was also run to provide a comparison. The original Legg ett model predicted blood lead values a factor of 2 to 3 higher than those predicted by the IEUBK or repor ted by NHANES; however, after adjusting the RBC deposition fraction and performing the scaling of m odel parameters, the adjusted Leggett model agrees very closely to the NHANES data. Figure M-4Figure M-4 compares the blood lead concentrations from NHANES and model simulations using IEUBK, Leggett ( version 4) and Leggett (version 5). The decrease in blood lead between ages 0.5 to 1 year is because th e intestinal absorption fraction gradually decrease s from 45 to 30 percent by age 1 yr. The rapid increa se in blood lead at age 1 yr is due to the substant ial and abrupt 67% increase in lead intake (see**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table M-4). Version 5 of the Leggett model agrees w ell with the NHANES data. It should be noted, however, that this calibration only applies to chro nic exposures; no data were available to perform analysis on shorter-term exposures such as those ex perienced during a renovation event.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table M-4. Background Media Concentrations and Inta ke Rates Used for the Childhood Model Callibration

Parameter Source Age 0-<1 Age

1-<2 Age

2-<3 Age 3-<4 Age 4-<5 Age 5-<6 Age 6-<7 Soil and Dust Ingestion Rate (g/day) Exposure Factors Handbook (US EPA, 2011) 0.06 0.1 0.1 0.1 0.1 0.1 0.1
Fraction of soil and dust ingestion that is soil Exposure Factors Handbook (US EPA, 2011) 0.45

Background Residential Dust Concentration ( μ g/g) HUD (2002), averaged across vintages and converted to concentration 75.7

Background Residential Soil Concentration ( μ g/g) HUD (2002), averaged across vintages 44.6

Dietary Lead Intake ( μ g/day) Exposure Factors Handbook (US EPA, 2011) and Total Diet Survey (US FDA, 2007) 2.1 3.9 4.7 4.4 4.4 4.4 4.5

Water Intake (L/day) Exposure Factors Handbook (US EPA, 2011) 0.32 0.27 0.32 0.33 0.33 0.33 0.41
Water Lead Concentration ( μ g/L) Total Diet Survey (FDA, 2007) 1.0
Ventilation Rate (m 3 /day) Exposure Factors Handbook (US EPA, 2011) 5.4 8 9.5 10.9 10.9 10.9 12.4

Air Lead Concentration ( μ g/m 3 ) EPA’s Air Quality Systems (AQS) database (USEPA, 2010) 0.02

Total Inhalation Intake ( μ g/day) 0.108 0.16 0.19 0.218 0.218 0.218 0.248

Total Gastrointestinal Intake ( μ g/day) 6.16 10.31 11.17 10.91 10.91 10.91 11.07

NHANES 2007-2008 blood lead values ( μ g/dL) 

### 1.4 1.7 1.3 1.2 1.0 1.1 0.8**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure M-4. Comparison of NHANES, IEUBK, Leggett (v ersion 4) and Leggett (version 5) blood lead levels

. 

## M.3.2. Evaluation of Updated Leggett Model in Occupational ly Exposed Adults In order to evaluate the performance of the Leggett model for adults, data were obtained from subjects in three longitudinal bone lead studies performed i n 1994 (n = 381), 1999 (n = 313), and 2008 (n = 497 ) (Nie et al., 2005; 2008 data not yet published). In these studies, there were 209 subjects who participated in all three studies, so these subject s were used for the evaluation. For each participan t, tibia (cortical bone) and calcaneus (trabecular bon e) lead was measured **in vivo** using 109 

### Cd K X-ray fluorescence. Blood lead measurements and a cumulat ive blood lead index were also available. Finally, the study population was characterized by a discont inuity in lead exposure from July 1990 to May 1991 during a plant strike.
The dataset does not provide measures of lead expos ure or intakes, so intake estimates were optimized iteratively using the Leggett model. An initial gue ss was made for each subject, and the intake was assumed to decrease in proportion to the blood lead decreases. The assumption is that an individual subject’s work place habits, diet, personal workpla ce activity (job), personal hygiene and physiology (e.g. diet, age, gender) that modulate lead exposure, lea d intake, and lead uptake were consistent within a subject. The biokinetic parameters of the model wer e not further adjusted. Thus, only lead intake was**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### adjusted was adjusted until the mean square error ( MSE) between the simulated and actual blood lead and a bone lead measurements was minimized as shown below.
MSE = (((cortical94 - tibia94i) * 3 + (trabecular9 4 - calcaneus94i) * 2 + (pbb94 - pbb94i)) ^ 2 + ((cortical99 - tibia99i) * 3 + (trabecular99 - calc aneus99i) * 2 + (pbb99 - pbb99i)) ^ 2 + ((cortical08 - tibia08i) * 3 + (trabecular08 - calc aneus08i) * 2 + (pbb08 - pbb08i)) ^ 2)/3
This error term gives more weight to cortical bone lead (as the most stable body store) and less to bl ood lead (as the most labile body compartment). This we ighting is similar to that used by Coon et al. 2006

.

Figure M-5. Comparison of Mean Square Error between Simulated and Actual Blood and Bone Lead both Before and After the Intake Optimization.


### shows examples of the mean square error before and after optimization. In general, the mean square error was decreased by a factor of 10 during the op timization. After the optimization was performed, the data were examined for each subject. In general , inspection indicated that cortical bone lead leve ls were biased low while blood lead levels were biased high. The bone and RBC transfer rates were adjusted in order to provide the best overall fit i n the different subjects.
Figure M-6 compares the blood lead, cortical bone l ead, and trabecular bone lead estimates from Version 4 of the Leggett model, Version 5 of the Le ggett model, and the actual data from one occupational worker. The model Version 5 was calibr ated using the bone and blood lead data, and the cumulative blood lead index (CBLI) serves as an add itional check of the model fit. For this individual , the model updates reduce the over prediction of blood l ead and provide better fits to the data.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure M-5. Comparison of Mean Square Error between Simulated and Actual Blood and Bone Lead both Before and After the Intake Optimization.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure M-6. Comparison of Blood and Bone Lead Predi cted Values to Measured Values in One

Occupationally Exposed Adult. CBLI = Cumulative Blo od Lead Index


# M.4. Issues to consider at future date Additional activities can be undertaken in the futu re to further improve the model performance and reliability:

 

### The modifications resulting in Version 5 of the mod el should be published in a peer-reviewed journal to facilitate communication and critical review of the model modifications and updates.

 

### The age-dependent parameters newly included in the FORTRAN code should be moved to a revised input file. This revision will increase transparenc y for these parameters, and facilitate modification and further testing. Such revision will, of course, essentially decouple the input files used over the last twenty years from the current version of the L eggett model.

 

### Opportunities should be developed to use stable iso tope tracers in humans to better define key agedependent parameters of lead metabolism. Such an ef fort should be focused on gastrointestinal bioavailability and on skeletal processes.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### The clinical measures of bone mineral density and b one mass should continue to be monitored for data to inform the model’s age-dependent skeletal d escription.

 

### The Health and Welfare has conducted a study measur ing blood lead and bone lead (K-XRF) on nonoccupationally exposed population in Toronto. This study population includes children and the aged (David Chettle, personal communication). This is a valuable data set to calibrate and validate the updated Leggett model.

 

### The Leggett model should be updated to consider gen der.

 

### The model could be incorporated in EPA agency-wide efforts to create an All Ages Lead Model (AALM). 

# References for Appendix M

Brinckmann P; W Frobin; Gunnar Leivseth. Musculoske letal biomechanics. 2002. Published by Stuttgart, New York). Centers for Disease Control and Preventi on, (2012). National Health and Nutrition Examination Survey 2010-2012.U.S. Department of Hea lth and Human Services, Centers for Disease Control and Prevention.
Coon, S, A Stark, E Peterson, A Gioi, G Kortsha, J Pounds, D. Cheattle, and J Gorrell. 2006. Whole-bod y lifetime occupational lead exposure and risk of Par kinson’s Disease. Environmental Health Perspectives Vol. 114, No. 12, Dec., 2006.

Centers for Disease Control and Prevention. 2012. N ational Health and Nutrition Examination survey. Available at http://www.cdc.gov/nchs/nhanes.htm.
HUD, 2002. National Survey of Lead and Allergens in Homes. http://www.niehs.nih.gov/research/atniehs/labs/lrb/ enviro-cardio/studies/nslah/index.cfm
Leggett RW. (1993). An age-specific kinetic model o f lead metabolism in humans. Environmental Health Perspectives 101:598–616.
Nie, L, Chettle DR, Webber CE, Brito JA, O'Meara JM , McNeill FE. 2005. The study of age influence on human bone lead metabolism by using a simplified mo del and X-ray fluorescence data. J Environ Monit. 2005 Nov;7(11):1069-73.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### OEHHA. 2013. Estimating Workplace Air and Worker Bl ood Lead Concentration Using an Updated Physiologically-based Pharmacokinetic (PBPK) Model. Available at http://oehha.ca.gov/air/risk_assess/PBPK2013.pdf

. O’Flaherty, E. 1998. A physiologically based kinet ic model for lead in children and adults. Environ Health Perspect. Dec 1998; 106(Suppl 6): 1495–1503.
Rabinowitz, MB, G W Wetherill, and J D Kopple. 1976 . Kinetic analysis of lead metabolism in healthy humans. J Clin Invest. Aug 1976; 58(2): 260–270.
Rowland, RE. Resorption and bone physiology. pp. 33 5-351 in H. M. Frost, ed., Bone Biodynamics, Boston:Little, Brown, and Company; 1964.
US EPA, 2010. EPA’s Air Quality Systems (AQS) datab ase. Avaialble at http://www.epa.gov/ttn/airs/airsaqs/detaildata/down loadaqsdata.htm.
US EPA, 2011. Exposure Factors Handbook. Available at http://www.epa.gov/ncea/efh/pdfs/efhcomplete.pdf

. US EPA. (2013). Integrated Science Assessment for L ead. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/075F.
US FDA, 2007. Total Diet Survey. Available at http://www.fda.gov/Food/FoodScienceResearch/TotalDi etStudy/default.htm

. Valentin, 2002. Basic Anatomical and Physiological Data for Use in Radiological Protection Reference Values. ICRP Publication 89. Ann. ICRP 32 (3-4).**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# Appendix M Attachment 1. Leggett Version 5 Code

C UNITS: all times are in days, but ages are in ye ars. C Pb is in micrograms. C INMODE is intake mode. C INMODE=0 is injection (direct input) into diff usible plasma, C 1 is inhalation, 2 is ingestion, 3 is any com bination of 0, 1, and 2. C IACUTE=1 is acute intake & 2 is chronic intake

. C The chronic intake rate may be varied with tim e in a stepwise fashion. C The chronic injection rate is the variable CHR , which is later converted to CRONIC. C The chronic inhalation rate is the variable BR ETH, which is later converted C to BRTCRN. The chronic ingestion rate is eat , later converted to EATCRN. C ICPR.for received from Rich Leggett 9/93 C ICRPv001 (chelat.for) add line#53 for chelatio n 10/96; rwl C ICRPv002 convert to crtconbm & trbconbm bone m ineral density; jgp C ICRPv003 convert output time from DAYS to YEAR S; 6/97; jgp C ICRPv004 add UPTAKEGI, UPTAKERI, UPTAKE; corre ct trbconbm conversion; C add decimal to YEARS; 11/97 jgp & rwl C ICRPv005 add age-dependent scaling for RBCONC,RENCON,CRTCON,TRBCON,ASHCON,TORBC c April 2014, JGP C ICRPv005a adjust Pb distribution to cort-trab bone; fraction trab-cort bone DIMENSION DELTA(10),ICYC(10),Q(50) DIMENSION CHAGE(14),ENDPT(1000),CHR(1000),BRE TH(1000),EAT(1000), $ AGSCAL(14),ARCORT(14),ARTRAB(14),ATBONE(14), ATFRAC(14), $ AF1(14),ARCS2B(14),ARTS2B(14),ARCSDF(14),ART SDF(14), $ ARBLAD(14),ARLVR2(14),ARKDN2(14),ARBRAN(14), ARRBC(14), $ ARD2CS(14),ARD2TS(14),ARD2DC(14),ARD2DT(14), ATBRAN(14), $ ATSOF0(14),ATSOF1(14),ATSOF2(14),AAMTBL(14), RDIFF(14), $ FLONG(14),AKIDWT(14),ATSKELWT(14),ATTRABWT(1 4),ATCORTWT(14), $ FRTTRAB(14),ARBCVOL(14),APLSVOL(14),ABLDHCT( 14),ATORBC(14) REAL INTACT,LAMOUT OPEN(30,FILE='POUNDS.DAT') OPEN(50,FILE='TEMP.DAT') READ(30,*) EXPAGE READ(30,*) NDELT,DELT0,NCYCLE,ENDDAY READ(30,*) (DELTA(I),I=1,NDELT) READ(30,*) (ICYC(I),I=1,NDELT) READ(30,*) ISKIP READ(30,*) I1,I2,I3,I4,I5 READ(30,*) INMODE,IACUTE,LINPUT C READ # CHRONIC INTAKE STEPS UP TO 50 AND CONSECUT IVE C ENDPOINTS IN DAYS, STARTING WITH DAYS=0.0.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-23 July 25, 2014 

### READ(30,*) NCHRON IF (IACUTE .EQ. 2) READ(30,*) (ENDPT(I),I=1,N CHRON) IF (IACUTE .EQ. 2) READ(30,*) (CHR(I),I=1,NCH RON) IF (IACUTE .EQ. 2) READ(30,*) (BRETH(I),I=1,N CHRON) IF (IACUTE .EQ. 2) READ(30,*) (EAT(I),I=1,NCH RON) IF (IACUTE .NE. 2) READ(30,*) DUMMY IF (IACUTE .NE. 2) READ(30,*) DUMMY IF (IACUTE .NE. 2) READ(30,*) DUMMY IF (IACUTE .NE. 2) READ(30,*) DUMMY READ(30,*) NUMAGE,XMXAGE READ(30,*) (CHAGE(I),I=1,NUMAGE) READ(30,*) (AF1(I),I=1,NUMAGE) READ(30,*) RDECAY READ(30,*) IRBC,RBCNL,SATRAT,POWER READ(30,*) IFETAL,BLDMOT,BRATIO READ(30,*) SOFIN,RBCIN,BONIN,RENIN,HEPIN,BRAN IN READ(30,*) R1,R2,R3,R4,CILIAR READ(30,*) BR1,BR2,BR3,BR4 READ(30,*) RSTMC,RSIC,RULI,RLLI READ(30,*) (AGSCAL(I),I=1,NUMAGE) READ(30,*) (ARCORT(I),I=1,NUMAGE) READ(30,*) (ARTRAB(I),I=1,NUMAGE) READ(30,*) (ARCS2B(I),I=1,NUMAGE) READ(30,*) (ARTS2B(I),I=1,NUMAGE) READ(30,*) (ARCSDF(I),I=1,NUMAGE) READ(30,*) (ARTSDF(I),I=1,NUMAGE) READ(30,*) (RDIFF(I),I=1,NUMAGE) READ(30,*) (FLONG(I),I=1,NUMAGE) READ(30,*) RLVR1,RKDN1 READ(30,*) (ARBLAD(I),I=1,NUMAGE) READ(30,*) (ARLVR2(I),I=1,NUMAGE) READ(30,*) (ARKDN2(I),I=1,NUMAGE) READ(30,*) RSOF0,RSOF1,RSOF2 READ(30,*) (ARBRAN(I),I=1,NUMAGE) READ(30,*) TOURIN,TOFECE,TOSWET,S2HAIR READ(30,*) (ATBONE(I),I=1,NUMAGE) READ(30,*) (ATFRAC(I),I=1,NUMAGE) READ(30,*) TOLVR1,H1TOH2,H1TOSI,H1TOBL READ(30,*) TOKDN1,TOKDN2 READ(30,*) (ATSOF0(I),I=1,NUMAGE) READ(30,*) (ATSOF1(I),I=1,NUMAGE) READ(30,*) (ATSOF2(I),I=1,NUMAGE) READ(30,*) (ATBRAN(I),I=1,NUMAGE) DO 6 I=1,NUMAGE**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-24 July 25, 2014 

### ARD2CS(I)=(1.-FLONG(I))*RDIFF(I) ARD2TS(I)=(1.-FLONG(I))*RDIFF(I) ARD2DC(I)=FLONG(I)*RDIFF(I) ARD2DT(I)=FLONG(I)*RDIFF(I) 6 CONTINUE READ(30,*) TORBC READ(30,*) TOEVF,SIZEVF READ(30,*) RPLAS,TOPROT,RPROT READ(30,*) (ARRBC(I),I=1,NUMAGE) READ(30,*) XRBCVOL,XPLSVOL C ADJRBC=RBCVOL/(RBCVOL+PLSVOL) moved to below READ(30,*) (AAMTBL(I),I=1,NUMAGE) READ(30,*) ICHEL,CHLEFF,CHEL1,CHEL2 IF (IACUTE .EQ. 1 .AND. LINPUT .EQ. 1) WRITE( *,11) IF (IACUTE .EQ. 2 .AND. LINPUT .EQ. 1) WRITE( *,12)
C Age-Dependent Kidney Mass from ICRP v89, p148 C WHERE AGES 0,0.27,1,5,10,15,18,24,30,40,45,55,65 and 75 yr AKIDWT (1) = 25. AKIDWT (2) = 36. AKIDWT (3) = 70. AKIDWT (4) = 110. AKIDWT (5) = 180. AKIDWT (6) = 250. AKIDWT (7) = 310. AKIDWT (8) = 310. AKIDWT (9) = 310. AKIDWT (10) = 310. AKIDWT (11) = 310. AKIDWT (12) = 310. AKIDWT (13) = 310. AKIDWT (14) = 310.
C AGE-DEPENDENT TOTAL SKELETAL MASS (g) from ICRP v89, page 170 C WHERE AGES ARE 0,0.27,1,5,10,15,18,24,30,40,45,55 ,65 and 75 YEARS ATSKELWT (1) = 370. ATSKELWT (2) = 642. ATSKELWT (3) = 1170. ATSKELWT (4) = 2430. ATSKELWT (5) = 4500. ATSKELWT (6) = 7950. ATSKELWT (7) = 10500. ATSKELWT (8) = 10500. ATSKELWT (9) = 10500.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-25 July 25, 2014 

### ATSKELWT (10) = 10500. ATSKELWT (11) = 10500. ATSKELWT (12) = 10500. ATSKELWT (13) = 10500. ATSKELWT (14) = 10500.

C AGE-DEPENDENT Fraction of total skeletal ma ss comprised of trabecular bone C WHERE AGES ARE 0,0.27,1,5,10,15,18,24,30,40,45,55 ,65 and 75 YEARS C (changed from 99,96,87,) FRTTRAB (1) = .95 FRTTRAB (2) = .90 FRTTRAB (3) = .85 FRTTRAB (4) = .50 FRTTRAB (5) = .34 FRTTRAB (6) = .23 FRTTRAB (7) = .20 FRTTRAB (8) = .20 FRTTRAB (9) = .20 FRTTRAB (10) = .20 FRTTRAB (11) = .20 FRTTRAB (12) = .20 FRTTRAB (13) = .20 FRTTRAB (14) = .20

C CALCULATE AGE-DEPENDENT MASS OF TRABECULAR AN D SKELETAL BONE AT 14 AGES ATTRABWT=ATSKELWT*FRTTRAB ATCORTWT=ATSKELWT*(1-FRTTRAB)
C Age-dependent ARBCVOL and APLSVOL calculated from age-dependent hematocrit and total blood (7) .48->.42 ABLDHCT (1) = .575 ABLDHCT (2) = .37 ABLDHCT (3) = .35 ABLDHCT (4) = .38 ABLDHCT (5) = .38 ABLDHCT (6) = .40 ABLDHCT (7) = .42 ABLDHCT (8) = .45 ABLDHCT (9) = .45 ABLDHCT (10) = .45 ABLDHCT (11) = .45 ABLDHCT (12) = .45 ABLDHCT (13) = .45**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-26 July 25, 2014 

### ABLDHCT (14) = .45

ARBCVOL=AAMTBL*ABLDHCT APLSVOL=AAMTBL*(1-ABLDHCT) C ADJRBC=RBCVOL/(RBCVOL+PLSVOL) Calculation rep laced by BLDHCT
C Age-dependent depostion fraction to RBC ATORBC (1) = 0.20 ATORBC (2) = 0.20 ATORBC (3) = 0.20 ATORBC (4) = 0.20 ATORBC (5) = 0.21 ATORBC (6) = 0.22 ATORBC (7) = 0.22 ATORBC (8) = 0.22 ATORBC (9) = 0.22 ATORBC (10) = 0.22 ATORBC (11) = 0.22 ATORBC (12) = 0.22 ATORBC (13) = 0.22 ATORBC (14) = 0.22
11 FORMAT(' Enter acute input.') 12 FORMAT(' Enter chronic input to blood.') IF (IACUTE .EQ. 1 .AND. LINPUT .EQ. 1) READ(* ,*)ACPLAS IF (IACUTE .EQ. 2 .AND. LINPUT .EQ. 1) READ(* ,*)CHR(1) IF (IACUTE .EQ. 1 .AND. LINPUT .NE. 1) AACUTE =1.0 IF (IACUTE .EQ. 1 .AND. LINPUT .EQ. 1) AACUTE =ACPLAS C IF (IFETAL .NE. 1 .OR. EXPAGE .GT. 0.01 .OR. $ IACUTE .NE. 2) GO TO 15 YSOF2=SOFIN*(BLDMOT*BRATIO*3./RBCIN) YRBC=RBCIN*(BLDMOT*BRATIO*3./RBCIN) YCVOL=0.8*BONIN*(BLDMOT*BRATIO*3./RBCIN) YTVOL=0.2*BONIN*(BLDMOT*BRATIO*3./RBCIN) YKDN2=RENIN*(BLDMOT*BRATIO*3./RBCIN) YLVR2=HEPIN*(BLDMOT*BRATIO*3./RBCIN) YBRAN=BRANIN*(BLDMOT*BRATIO*3./RBCIN) 15 CONTINUE HOWOLD=EXPAGE DAYS=0.0 BTEMP=0.0 C BEGIN CALCULATIONS. DO 1000 N=1,NCYCLE**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-27 July 25, 2014 

### C CHRONIC INTAKE SWITCH IS IACUTE. BRTCRN=0.0 IF (IACUTE .NE. 2) GO TO 38 DO 35 I=2,NCHRON+1 IF (DAYS .GT. ENDPT(I-1)-1.0E-09) GO TO 35 CRONIC=CHR(I-1) BRTCRN=BRETH(I-1) EATCRN=EAT(I-1) GO TO 38 35 CONTINUE 38 CONTINUE IF (DELT0 .GT. 0.0) THEN DELT = DELT0 ELSE DO 52 ISTEP = 1, NDELT IF (N .LE. ICYC(ISTEP)) THEN DELT = DELTA(ISTEP) GO TO 53 END IF 52 CONTINUE 53 CONTINUE END IF HOWOLD=HOWOLD+DELT/365. DAYS=DAYS+DELT TMINS=1440.*DAYS XMINS=1440.*DAYS HOURS=24.*DAYS YEARS=DAYS/365. IF (DAYS .GT. 1.0) TMINS=DAYS C IN THE FOLLOWING THE AGE-DEPENDENT VARIABLES ARE INTERPOLATED C TO GET VALUES FOR AGE HOWOLD. C SKIP THE FOLLOWING STEPS FOR PERSONS OLDER THAN XMXAGE YEARS. IF (HOWOLD .GE. XMXAGE) GO TO 500 C DO 200 JAGE=1,NUMAGE IF (HOWOLD .GE. CHAGE(JAGE)) GO TO 200 K=JAGE-1 GO TO 300 200 CONTINUE C 300 CONTINUE C L=K+1 U=HOWOLD-CHAGE(K)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-28 July 25, 2014 

### V=CHAGE(L)-HOWOLD W=V/(U+V) Z=U/(U+V) C GO TO 600 C C FOR THE CASE IN WHICH HOWOLD IS AT LEAST XMXAGE YEARS: 500 K=NUMAGE L=NUMAGE W=1.0 Z=0.0 C 600 CONTINUE C C DETERMINE BY INTERPOLATION THE RATES AND FRACTIO NS AT AGE HOWOLD. C F1=W*AF1(K)+Z*AF1(L) AMTBLD=W*AAMTBL(K)+Z*AAMTBL(L) RCORT=W*ARCORT(K)+Z*ARCORT(L) RTRAB=W*ARTRAB(K)+Z*ARTRAB(L) TFRAC=W*ATFRAC(K)+Z*ATFRAC(L) TBONE=W*ATBONE(K)+Z*ATBONE(L) TOSOF0=W*ATSOF0(K)+Z*ATSOF0(L) TOSOF1=W*ATSOF1(K)+Z*ATSOF1(L) TOSOF2=W*ATSOF2(K)+Z*ATSOF2(L) TOBRAN=W*ATBRAN(K)+Z*ATBRAN(L) RCS2B=W*ARCS2B(K)+Z*ARCS2B(L) RTS2B=W*ARTS2B(K)+Z*ARTS2B(L) RCS2DF=W*ARCSDF(K)+Z*ARCSDF(L) RTS2DF=W*ARTSDF(K)+Z*ARTSDF(L) RDF2CS=W*ARD2CS(K)+Z*ARD2CS(L) RDF2TS=W*ARD2TS(K)+Z*ARD2TS(L) RDF2DC=W*ARD2DC(K)+Z*ARD2DC(L) RDF2DT=W*ARD2DT(K)+Z*ARD2DT(L) RLVR2=W*ARLVR2(K)+Z*ARLVR2(L) RKDN2=W*ARKDN2(K)+Z*ARKDN2(L) RBLAD=W*ARBLAD(K)+Z*ARBLAD(L) RBRAN=W*ARBRAN(K)+Z*ARBRAN(L) RRBC=W*ARRBC(K)+Z*ARRBC(L) GSCALE=W*AGSCAL(K)+Z*AGSCAL(L) KIDWT=W*AKIDWT(K)+Z*AKIDWT(L) TSKELWT=W*ATSKELWT(K)+Z*ATSKELWT(L) TRABWT=W*ATTRABWT(K)+Z*ATTRABWT(L) CORTWT=W*ATCORTWT(K)+Z*ATCORTWT(L)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-29 July 25, 2014 

### RBCVOL=W*ARBCVOL(K)+Z*ARBCVOL(L) PLSVOL=W*APLSVOL(K)+Z*APLSVOL(L) BLDHCT=w*ABLDHCT(K)+Z*ABLDHCT(L) TORBC= w*ATORBC(K)+Z*ATORBC(L)
C ADJRBC=W*AADJRBC(K)+Z*AADJRBC(L)

C TEVF=TOEVF AGESCL=(1.0-TEVF-TBONE)/(1.0-TEVF-ATBONE(NUMA GE)) TURIN=AGESCL*TOURIN TFECE=AGESCL*TOFECE TSWET=AGESCL*TOSWET TSOF0=AGESCL*TOSOF0 TSOF1=AGESCL*TOSOF1 TSOF2=AGESCL*TOSOF2 TBRAN=AGESCL*TOBRAN TLVR1=AGESCL*TOLVR1 TKDN1=AGESCL*TOKDN1 TKDN2=AGESCL*TOKDN2 TRBC=AGESCL*TORBC TPROT=AGESCL*TOPROT C DECRBC=YRBC/RBCVOL DECPLS=YPLAS/PLSVOL BLDVOL=RBCVOL+PLSVOL DECLTR=YBLUD/BLDVOL TOORBC=TRBC IF (IRBC .NE. 1 .OR. RBCONC .LE. RBCNL) GO TO 610 TOORBC=TRBC*(1.-((RBCONC-RBCNL)/(SATRAT-RBCNL )))**POWER IF (TOORBC .LT. 0.0) TOORBC=0.0 610 CONTINUE TSUM=TOORBC+TEVF+TPROT+TBONE+TURIN+TFECE+TSWE T+TLVR1 $ +TKDN1+TKDN2+TSOF0+TSOF1+TSOF2+TBRAN CF=(1.-TOORBC)/(1.-TRBC) RPLS=TSUM*RPLAS REVF=TEVF*RPLS/SIZEVF IF (ICHEL. EQ. 1 .AND. DAYS .GE. CHEL1 .AND. DAYS .LE. CHEL2) $ THEN TEVF=(1.-CHLEFF)*TEVF TFECE=(1.-CHLEFF)*TFECE TSWET=(1.-CHLEFF)*TSWET TSOF0=(1.-CHLEFF)*TSOF0 TSOF1=(1.-CHLEFF)*TSOF1**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-30 July 25, 2014 

### TSOF2=(1.-CHLEFF)*TSOF2 TBRAN=(1.-CHLEFF)*TBRAN TLVR1=(1.-CHLEFF)*TLVR1 TKDN1=(1.-CHLEFF)*TKDN1 TKDN2=(1.-CHLEFF)*TKDN2 TPROT=(1.-CHLEFF)*TPROT TBONE=(1.-CHLEFF)*TBONE TOORBC=(1.-CHLEFF)*TOORBC TURIN=1.0-TOORBC-TEVF-TPROT-TBONE-TFECE-TSWET -TLVR1 $ -TKDN1-TKDN2-TSOF0-TSOF1-TSOF2-TBRAN END IF C IF (INMODE .EQ. 0 .OR. INMODE .EQ. 2) GO TO 5 5 C____________________________________________ C LUNG 1 COMPARTMENT Y0=YR1 IF (IACUTE .EQ. 1 .AND. N .EQ. 1) Y0=R1*AACUT E P=0.0 IF (IACUTE .EQ. 2) P=R1*BRTCRN LAMOUT=RDECAY+BR1 YR1=ACTVTY(Y0,P,LAMOUT,DELT) YR1W=INTACT(Y0,P,LAMOUT,DELT) C_______________________________________________ C LUNG 2 COMPARTMENT Y0=YR2 IF (IACUTE .EQ. 1 .AND. N .EQ. 1) Y0=R2*AACUT E P=0.0 IF (IACUTE .EQ. 2) P=R2*BRTCRN LAMOUT=RDECAY+BR2 YR2=ACTVTY(Y0,P,LAMOUT,DELT) YR2W=INTACT(Y0,P,LAMOUT,DELT) C_______________________________________________ C LUNG 3 COMPARTMENT Y0=YR3 IF (IACUTE .EQ. 1 .AND. N .EQ. 1) Y0=R3*AACUT E P=0.0 IF (IACUTE .EQ. 2) P=R3*BRTCRN LAMOUT=RDECAY+BR3 YR3=ACTVTY(Y0,P,LAMOUT,DELT) YR3W=INTACT(Y0,P,LAMOUT,DELT) C_______________________________________________ C LUNG 4 COMPARTMENT Y0=YR4 IF (IACUTE .EQ. 1 .AND. N .EQ. 1) Y0=R4*AACUT E**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-31 July 25, 2014 

### P=0.0 IF (IACUTE .EQ. 2) P=R4*BRTCRN LAMOUT=RDECAY+BR4 YR4=ACTVTY(Y0,P,LAMOUT,DELT) YR4W=INTACT(Y0,P,LAMOUT,DELT) YLUNG=YR1+YR2+YR3+YR4 55 CONTINUE C____________________________________________ IF (INMODE .EQ. 0) GO TO 65 C STOMACH CONTENTS (STMC) COMPARTMENT Y0=YSTMC IF (INMODE .EQ. 2 .AND. IACUTE .EQ. 1 .AND. N .EQ. 1) $ Y0=AACUTE P=0.0 IF (INMODE .GT. 1 .AND. IACUTE .EQ. 2) P=EATC RN IF (INMODE .EQ. 1 .OR. INMODE .EQ. 3) P=EATCR N+ $ CILIAR*(BR1*YR1W+BR2*YR2W+BR3*YR3W+BR4*YR4 W)/DELT LAMOUT=RDECAY+GSCALE*RSTMC YSTMC=ACTVTY(Y0,P,LAMOUT,DELT) YSTMCW=INTACT(Y0,P,LAMOUT,DELT) 65 CONTINUE IF (INMODE .EQ. 0 .AND. N .EQ. 1) GO TO 66 C___________________________________________ C SMALL INTESTINE CONTENTS (SIC) COMPARTMENT Y0=YSIC P=(GSCALE*RSTMC*YSTMCW+H1TOSI*RLVR1*YLVR1W+TF ECE*CF*BTEMP)/DELT LAMOUT=RDECAY+GSCALE*RSIC YSIC=ACTVTY(Y0,P,LAMOUT,DELT) YSICW=INTACT(Y0,P,LAMOUT,DELT) 66 CONTINUE C--------------------------------------- C PLASMA (PLS); THIS IS DIFFUSIBLE PLASMA PB; C DOES NOT INCLUDE RELATIVELY SLOWLY EXCHANGEABLE PLASMAC PROTEIN-BOUND PB, WHEN THE LATTER COMPARTMENT I S USED. Y0=YPLS IF (INMODE .EQ. 0 .AND. N .EQ. 1 .AND. IACUTE .EQ. 1) $ Y0=AACUTE P1=(RPROT*YPROTW+RRBC*YRBCW+REVF*YEVFW+RSOF0* YSOF0W $ +(1.0-S2HAIR)*RSOF1*YSOF1W+RSOF2*YSOF2W $ +H1TOBL*RLVR1*YLVR1W+RLVR2*YLVR2W $ +RKDN2*YKDN2W+RCS2B*YCSURW+RTS2B*YTSURW $ +RCORT*YCVOLW+RTRAB*YTVOLW+RBRAN*YBRANW $ +F1*GSCALE*RSIC*YSICW)/DELT IF (IACUTE .EQ. 2) P1=P1+CRONIC**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-32 July 25, 2014 

### IF (INMODE .EQ. 1 .OR. INMODE .EQ. 3) P1=P1+ $ (1.0-CILIAR)*(BR1*YR1W+BR2*YR2W+BR3*YR3W+BR 4*YR4W)/DELT LAMOUT=RPLS+RDECAY UPTAKEGI=(F1*GSCALE*RSIC*YSICW)/DELT UPTAKERI=(1.0-CILIAR)*(BR1*YR1W+BR2*YR2W+BR3* YR3W+BR4*YR4W)/DELT UPTAKE=UPTAKEGI+UPTAKERI C ACTIVITY AND INTEGRATED ACTIVITY YPLS=ACTVTY(Y0,P1,LAMOUT,DELT) YPLSW=INTACT(Y0,P1,LAMOUT,DELT) BTEMP=RPLS*YPLSW C__________________________________ C PLASMA-PROTEIN BOUND PB (PROT) Y0=YPROT P=TPROT*CF*BTEMP/DELT LAMOUT=RPROT+RDECAY YPROT=ACTVTY(Y0,P,LAMOUT,DELT) YPROTW=INTACT(Y0,P,LAMOUT,DELT) YPLAS=YPLS+YPROT YPLASW=YPLSW+YPROTW C______________________________________ C C RED BLOOD CELLS (RBC) Y0=YRBC C NOTE THAT CF HAS BEEN REMOVED FROM FOLLOWING: P=TOORBC*BTEMP/DELT LAMOUT=RRBC+RDECAY YRBC=ACTVTY(Y0,P,LAMOUT,DELT) YRBCW=INTACT(Y0,P,LAMOUT,DELT) YBLUD=YPLAS+YRBC SUMRBC=SUMRBC+YRBCW C______________________________________ C EVF Y0=YEVF P=TEVF*CF*BTEMP/DELT LAMOUT=REVF+RDECAY YEVF=ACTVTY(Y0,P,LAMOUT,DELT) YEVFW=INTACT(Y0,P,LAMOUT,DELT) C_______________________________________________ C FAST TURNOVER SOFT-TISSUE COMPARTMENT (SOF0) Y0=YSOF0 P=TSOF0*CF*BTEMP/DELT LAMOUT=RSOF0+RDECAY YSOF0=ACTVTY(Y0,P,LAMOUT,DELT) YSOF0W=INTACT(Y0,P,LAMOUT,DELT)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft M-33 July 25, 2014 

### C _____________________________________ C INTERMEDIATE TURNOVER SOFT-TISSUE COMPARTMENT (S OF1) Y0=YSOF1 P=TSOF1*CF*BTEMP/DELT LAMOUT=RSOF1+RDECAY YSOF1=ACTVTY(Y0,P,LAMOUT,DELT) YSOF1W=INTACT(Y0,P,LAMOUT,DELT) C _____________________________________ C SLOW TURNOVER SOFT-TISSUE COMPARTMENT (SOF2) Y0=YSOF2 P=TSOF2*CF*BTEMP/DELT LAMOUT=RSOF2+RDECAY YSOF2=ACTVTY(Y0,P,LAMOUT,DELT) YSOF2W=INTACT(Y0,P,LAMOUT,DELT) C _____________________________________ C BRAIN (BRAN) Y0=YBRAN P=TBRAN*CF*BTEMP/DELT LAMOUT=RBRAN+RDECAY YBRAN=ACTVTY(Y0,P,LAMOUT,DELT) YBRANW=INTACT(Y0,P,LAMOUT,DELT) C__________________________________________ C CORTICAL SURFACE (CSUR) Y0=YCSUR P=(TBONE*(1.0-TFRAC)*CF*BTEMP+RDF2CS*YCDIFW)/ DELT LAMOUT=RCS2B+RCS2DF+RDECAY YCSUR=ACTVTY(Y0,P,LAMOUT,DELT) YCSURW=INTACT(Y0,P,LAMOUT,DELT) C______________________________________ C EXCHANGEABLE CORTICAL VOLUME (CDIF) Y0=YCDIF P=RCS2DF*YCSURW/DELT LAMOUT=RDF2CS+RDF2DC+RDECAY YCDIF=ACTVTY(Y0,P,LAMOUT,DELT) YCDIFW=INTACT(Y0,P,LAMOUT,DELT) 620 CONTINUE C-------------------------------------------------- C NONEXCHANGEABLE CORTICAL VOLUME (CVOL) Y0=YCVOL P=RDF2DC*YCDIFW/DELT LAMOUT=RCORT+RDECAY YCVOL=ACTVTY(Y0,P,LAMOUT,DELT) YCVOLW=INTACT(Y0,P,LAMOUT,DELT) C--------------------------------------------------**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### C TRABECULAR SURFACE (TSUR) Y0=YTSUR P=(TBONE*TFRAC*CF*BTEMP+RDF2TS*YTDIFW)/DELT LAMOUT=RTS2B+RTS2DF+RDECAY YTSUR=ACTVTY(Y0,P,LAMOUT,DELT) YTSURW=INTACT(Y0,P,LAMOUT,DELT) C-------------------------------------------------- C EXCHANGEABLE TRABECULAR VOLUME (CDIF) Y0=YTDIF P=RTS2DF*YTSURW/DELT LAMOUT=RDF2TS+RDF2DT+RDECAY YTDIF=ACTVTY(Y0,P,LAMOUT,DELT) YTDIFW=INTACT(Y0,P,LAMOUT,DELT) 630 CONTINUE C-------------------------------------------------- C NONEXCHANGEABLE TRABECULAR VOLUME (TVOL) Y0=YTVOL P=RDF2DT*YTDIFW/DELT LAMOUT=RTRAB+RDECAY YTVOL=ACTVTY(Y0,P,LAMOUT,DELT) YTVOLW=INTACT(Y0,P,LAMOUT,DELT) C------------------------------------------- C LIVER 1 (LVR1) Y0=YLVR1 P=TLVR1*CF*BTEMP/DELT LAMOUT=RLVR1+RDECAY YLVR1=ACTVTY(Y0,P,LAMOUT,DELT) YLVR1W=INTACT(Y0,P,LAMOUT,DELT) C-------------------------------------------- C LIVER 2 (LVR2) Y0=YLVR2 P=H1TOH2*RLVR1*YLVR1W/DELT LAMOUT=RLVR2+RDECAY YLVR2=ACTVTY(Y0,P,LAMOUT,DELT) YLVR2W=INTACT(Y0,P,LAMOUT,DELT) YLIVR=YLVR1+YLVR2 C-------------------------------------------------- C KIDNEYS 1 Y0=YKDN1 P=TKDN1*CF*BTEMP/DELT LAMOUT=RKDN1+RDECAY YKDN1=ACTVTY(Y0,P,LAMOUT,DELT) YKDN1W=INTACT(Y0,P,LAMOUT,DELT) C--------------------------------------------------**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### C KIDNEYS 2 Y0=YKDN2 P=TKDN2*CF*BTEMP/DELT LAMOUT=RKDN2+RDECAY YKDN2=ACTVTY(Y0,P,LAMOUT,DELT) YKDN2W=INTACT(Y0,P,LAMOUT,DELT) C YKDNE=YKDN1+YKDN2 C-------------------------------------------------- C BLADDER Y0=YBLAD P=(TURIN*CF*BTEMP+RKDN1*YKDN1W)/DELT LAMOUT=RBLAD+RDECAY YBLAD=ACTVTY(Y0,P,LAMOUT,DELT) YBLADW=INTACT(Y0,P,LAMOUT,DELT) C------------------------------------------------- C UPPER LARGE INTESTINE CONTENTS Y0=YULIC P=(1.0-F1)*GSCALE*RSIC*YSICW/DELT LAMOUT=GSCALE*RULI+RDECAY YULIC=ACTVTY(Y0,P,LAMOUT,DELT) YULICW=INTACT(Y0,P,LAMOUT,DELT) C------------------------------------------------- C LOWER LARGE INTESTINE CONTENTS Y0=YLLIC P=GSCALE*RULI*YULICW/DELT LAMOUT=GSCALE*RLLI+RDECAY YLLIC=ACTVTY(Y0,P,LAMOUT,DELT) YLLICW=INTACT(Y0,P,LAMOUT,DELT) C-------------------------------------------------- C URINE U0=YURIN Y0=YURIN P=RBLAD*YBLADW/DELT YURIN=Y0+P*DELT UTEMP=P*DELT URIN=YURIN-U0 C-------------------------------------------------- C FECES Y0=YFECE P=GSCALE*RLLI*YLLICW/DELT YFECE=Y0+P*DELT FTEMP=P*DELT C--------------------------------------------------**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### C SWEAT Y0=YSWET P=TSWET*CF*BTEMP/DELT YSWET=Y0+P*DELT C-------------------------------------------------- C HAIR, NAILS, DESQUAMATED SKIN Y0=YHAIR P=S2HAIR*RSOF1*YSOF1W/DELT YHAIR=Y0+P*DELT C-------------------------------------------------- C SIGMA=YPLAS+YRBC+YEVF+YSOF0+YSOF1+YSOF2+YBRAN $ +YCVOL+YTVOL+YCSUR+YTSUR+YCDIF+YTDIF+YKDNE+Y BLAD+YLIVR $ +YR1+YR2+YR3+YR4+YSTMC+YSIC+YULIC+YLLIC $ +YURIN+YFECE+YSWET+YHAIR TBODY1=YPLAS+YRBC+YEVF+YSOF0+YSOF1+YSOF2+YBRA N $ +YCVOL+YTVOL+YCSUR+YTSUR+YCDIF+YTDIF+YKDNE+Y LIVR TBODY2=TBODY1+YR1+YR2+YR3+YR4+YBLAD $ +YSTMC+YSIC+YULIC+YLLIC TOTEXC=YURIN+YFECE+YSWET+YHAIR YSKEL=YCVOL+YTVOL+YCSUR+YTSUR+YCDIF+YTDIF YTRAB=YTSUR+YTDIF+YTVOL YCORT=YCSUR+YCDIF+YCVOL YSOFT=YSOF0+YSOF1+YSOF2 IF (TBODY1 .NE. 0.0) BONFRC=YSKEL/TBODY1 IF (TBODY1 .NE. 0.0) BRNFRC=YBRAN/TBODY1 IF (TBODY1 .NE. 0.0) HEPFRC=YLIVR/TBODY1 IF (TBODY1 .NE. 0.0) BLDFRC=YBLUD/TBODY1 IF (TBODY1 .NE. 0.0) RENFRC=YKDNE/TBODY1 IF (TBODY1 .NE. 0.0) OTHFRC=YSOFT/TBODY1 IF (YBLUD .NE. 0.0) PLSRBC=YPLAS/YBLUD IF (AMTBLD .NE. 0.0) BLCONC=YBLUD/AMTBLD C IF (RBCVOL .NE. 0.0) RBCONC=YRBC/(ADJRBC*AMT BLD) replaced by next line IF (RBCVOL .NE. 0.0) RBCONC=YRBC/(BLDHCT*AMTB LD)

RENCON=YKDNE/KIDWT CRTCON=YCORT/CORTWT TRBCON=YTRAB/TRABWT ASHCON=YSKEL/TSKELWT CRTCONBM=CRTCON*1.8 TRBCONBM=TRBCON*3.*1.8
IF (N .GT. 1) CLEAR=(URIN/DELT)/YPLAS IF (N .GT. 1) BCLEAR=100.*(URIN/DELT)/YBLUD**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### IF (YBLUD .NE. 0.0) PCENT=100.*YPLAS/YBLUD Q(1)=YPLAS Q(2)=YRBC Q(3)=YBLUD Q(4)=YSKEL Q(5)=YCORT Q(6)=YTRAB Q(7)=YLIVR Q(8)=YKDNE Q(9)=YSOFT Q(10)=YBRAN Q(11)=YURIN Q(12)=YFECE Q(13)=TBODY2 Q(14)=TOTEXC Q(15)=BONFRC Q(16)=BRNFRC Q(17)=HEPFRC Q(18)=BLDFRC Q(19)=RENFRC Q(20)=OTHFRC Q(21)=BLCONC Q(22)=RBCONC Q(23)=RENCON Q(24)=CRTCON Q(25)=TRBCON Q(26)=ASHCON Q(27)=CLEAR Q(28)=BCLEAR Q(29)=PCENT Q(30)=YLUNG Q(31)=Q(28)*Q(3)/100. Q(32)=CRTCONBM Q(33)=TRBCONBM Q(34)=UPTAKEGI Q(35)=UPTAKERI Q(36)=UPTAKE IF (MOD(N,ISKIP) .NE. 0) GO TO 1000 C WRITE(*,991)YEARS,SIGMA,Q(I1),Q(I2),Q(I3),Q( I4),Q(I5) WRITE(50,991)YEARS,SIGMA,Q(I1),Q(I2),Q(I3),Q( I4),Q(I5) IF (DAYS .GT. ENDDAY) GO TO 1001 991 format(' ', 1p7e10.3) 1000 CONTINUE 1001 CONTINUE**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### STOP END C REAL FUNCTION INTACT(Y,P,X,D) DX=D*X IF (DX .GT. 50.0) GO TO 10 INTACT=((1.0-DEXP(-DBLE(X*D)))/X)*(Y-(P/X))+P *D/X GO TO 20 10 INTACT=(1.0/X)*(Y-P/X)+P*D/X 20 CONTINUE RETURN END C FUNCTION ACTVTY(Y,P,X,D) DX=D*X IF (DX .GT. 50.0) GO TO 10 ACTVTY=(Y-(P/X))*DEXP(-DBLE(X*D))+P/X GO TO 20 10 ACTVTY=P/X 20 CONTINUE RETURN



# Appendix M Attachment 2: Sample Input File for Model Version 5 Based on the model updates and the updated maternal blood lead value, an input file template was generated for the analysis and is reproduced below. All the parameter values in italics vary according to the specific iteration and and when the renovation begins; the other values remain constant across all the different renovation and background intake simu lations. These inputs include the age-dependent absorption rates, transfer rates, and deposition ra tes used in the model. They are listed in a series of 14 numbers on each input line of the file, where the 1 4 numbers correspond to the ages 0.27, 1, 5, 10, 15 , 18, 24, 30, 40, 45, 55 65, and 75 years old.**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review Draft M-1July 25, 2014Table M-5. Input File Template for Analysis0. LINE # 1 Template for PnCB exposure analysis simulations0.05,0,**10000000,25550**# 2 # DIFF STEP LENGTHS; USE FIXEDSTEP LENGTH? MAXIMUM # STEPS; LAST DAY0.01 # 3 DELTi; CALCULATIONS FIRST BASED ONSTEP LENGTHS DELT1, THEN DELT2, ETC**1000,1900,2800,6700,1000000**# 4 ICYCi; USE TIME STEP DELT1 UP TO STEP ICYC1, DELT2 UP TO ICYC2, ETC1 # 5 WRITE TO SCREEN ONLY ON THESE STEPS (E.G., EVERY 100TH TIME STEP)21,24,25,22,23 # 6 FIVE SELECTED OUTPUTS3,2,0 # 7 MODE OF INTAKE; SWITCH FOR ACUTE/CHRONIC; SWITCH FOR MANUAL INPUT (1)7 # 8 NCHRON, #TIME STEPS IN CHRONIC INTAKE FUNCTION (UP TO 50)**365,730,1095,1460,1825,2190,10950,18250,25550**# 9 ENDPT, ENDPOINTS OF THE CHRONIC INTAKE TIME STEPS7*0.0 # 10 UP TO 1000 CHRONIC INTAKE RATES DIRECTLY TO BLD ON STEPS,**0.108,0.16,0.19,0.218,0.218,0.218,0.248**# 11 UP TO 1000 CHRONIC LUNG DEPOSITION RATES (PER DAY)**6.16,10.31,11.17,10.91,10.91,10.91,11.07**# 12 UP TO 1000 CHRONIC INGESTION INTAKE RATES (PER DAY)14,75. # 13 NUMBER OF AGES AT WHICH PARAM VALUES ARE SPECIFIED & MAX OFTHESE0.,0.274,1.,5.,10.,15.,18.,24.,30.,40.,45.,55.,65.,75. # 14 AGES (Y) AT WHICH PARAMETER VALUES ARE GIVEN EXPLICITLY2*0.45,4*0.3,8*0.15, # 15 GASTROINTESTINAL ABSORPTION FRACTION (F1)0.0 # 16 RADIOLOGICAL DECAY RATE OF PB PER DAY1,0.0,270.,1.5, # 17 1 FOR NONLINEAR MODEL; 3 NONLINEAR PARAMETER VALUES1,0.7,0.85 # 18 1 FOR FETAL EXPOSURE; MOTHER'S BLOOD CONC; FETUS:MOTHER BLD RATIO (10-->0.7)0.50,0.07,0.32,0.01,0.055,0.045, # 19 FRACS OF BODYPB AT BIRTH IN SOFT TISS, RBC, BONE, KIDNEY, LIVER, BRAIN0.25,0.35,0.30,0.10,0.04, # 20 FRACS OF DEPOSITED AMT ASSIGNED TO 4 LUNG COMPS; FRAC MOVEMENTTO GI16.6,5.54,1.66,0.347, # 21 BR1,BR2,BR3,BR424.,6.,1.85,1.0, # 22 ADULT TRANSFER RATES FROM STOMACH AND INTESTINAL SEGMENTS4*1.66667,1.33333,9*1.0 # 23 SCALES RATE OF MOVEMENT THRU GI TRACT AT 10 AGES0.0102,0.00822,0.00288,0.00154,0.00089,0.000512,0.000370,0.000082,6*0.000041 # 24 CORT BONE from Rowland0.0102,0.00822,0.00288,0.00181,0.00132,0.000956,0.000781,0.000493,2*0.000247,0.000274,0.000301,0.000329,0.000356 # 25 TRAB BONE5*0.40,9*0.35 # 26 FRAC TRANSFERCORT SURF TO BLOOD5*0.60,9*0.55 # 27 FRAC TRANSFERTRABECULAR SURFACE TO BLOOD**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review Draft M-2July 25, 20145*0.55,9*0.60 # 28 FRAC TRANSFERCORT SURFACE TO VOLUME5*0.40,9*0.40 # 29 FRAC TRANSFERTRAB SURFACE TO VOLUME14*0.023105 # 30 TOTAL TRANSFER RATE FROM EXCH BONE VOLUME14*0.2 # 31 FRAC TRANSFER FROM EXCH VOL TO NONEXCH0.0693,0.139 # 32 TRANSFER RATES FROM LIVER 1 AND KIDNEY 12*12.,15.,11.,8.,9*7. # 33 TRANSFER RATE FROM U BLADDER TO URINE4*0.00693,10*0.0019 # 34 TRANSFERRATE FROM LIVER 24*0.00693,10*0.0019 # 35 TRANSFERRATE FROM KIDNEY 22.079,0.00693,0.00038 # 36 TRANSFERRATES FROM 3 SOFT TISS COMPS14*0.00095, # 37 TRANSFER RATE FROM BRAIN0.15,0.006,0.0035,0.4 # 38 DEP FRACS IN URINE,FECES,SWEAT; FRAC TRANS ST1 TO EXCRETION2*0.24,0.144,0.128,0.179,0.237,8*0.08 # 39 DEPOSITION FRACTION IN BONE0.2,0.2,0.2,0.222,0.25,0.279,8*0.556 # 40 FRACTION OF BONE DEPOSITION GOING TO TRABECULAR0.04,0.1,0.45,0.45 # 41 DEP FRAC INLIVER 1; FRACS LIVER 1 TO LIVER 2, SI, & PLASMA0.02,0.0002 # 42 DEP FRACSIN KIDNEY 1 AND KIDNEY 23*0.08345,3*0.08375,8*0.08875 # 43 DEPOSITIONFRACTION IN ST03*0.01,3*0.01,8*0.005 # 44 DEPOSITIONFRACTION IN ST13*0.001,3*0.001,8*0.001 # 45 DEPOSITION FRACTION IN ST23*0.00045,11*0.00015, # 46 DEPOSITIONFRACTION IN BRAIN0.20, # 47 DEPOSITION FRACTION IN RBC (superceeded by age-dependent data in code)0.5000,3. # 48 DEPOSITION FRACTION IN EVF; SIZE OF EVF RELATIVE TO PLASMA2000.,0.0004,0.139, # 49 TRANSFER RATE FROM PLASMA; DEP FRAC & RATE OF LOSS FOR PLASMAPROTEINS3*0.562,2*0.277,9*0.139 # 50 TRANSFERRATE FROM RRBC22.,30. # 51 AMOUNT OF RBC AND PLASMA (DL) IN REFERENCE ADULT MALE (superceeded by code)2.7,3.1,5.0,14.0,24.0,45.0,8*53.0 # 52 AMOUNT OF BLOOD (DL) AT 14 AGES0,0.4,1243.,1250. # 53 ICHEL, CHLEFF, CHEL1, CHEL2**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# Appendix N. Background Media Concentrations and Intakes

N.1. Dust Background lead-dust loading estimates were found s eparately for public and commercial buildings (as a whole) and for residences. This section provides an overview of the data used to define the lognormal distributions used for each.



## N.1.1. Public and Commercial Buildings Little information is available about the levels of lead in dust in public and commercial buildings. A literature search was conducted but few sources wer e found. The data source found was the First National Health Survey of Child Care Centers (herea fter the CCC Report; Westat, 2003). The survey provides information on the geometric mean and geom etric standard deviation from 336 samples of lead floor loading. Because of the relatively low n umber of samples in each vintage bin when the data were separated into the different bins, an overall distribution across all bins was used. The geometri c mean and geometric standard deviation were taken fr om Table 5-5 from the Westat report, giving values of 0.8 μ g/ft 2 

### and 2.25, respectively.

Table N-1. Public/Commerical Background Lead Dust L oadings

Vintage Geometric

Mean ( μ g/ft 2 ) Geometric

Standard Deviation All 0.8 2.25



## N.1.2. Residences For residences, the background dust loading values were estimated using. The American Healthy Homes Survey (AHHS) (HUD, 2011). AHHS is a follow-up to the National Survey of Lead and Allergens in Housin g (NSLAH) conducted from 1997 through 2000 and monito red lead dust levels in homes. These data were favored over other sources (e.g., NHANES) because t he raw survey data with vintage data were available so that different background dust distributions cou ld be developed for each Approach housing vintage.
The raw survey data were obtained from HUD, and sam ples taken within a single home were averaged to provide one estimate per residence. If vintage i nformation was not available for a given home, it w as assigned to the home using random sampling from the overall distribution in the dataset. Then, the geometric mean and geometric standard deviation acr oss the dataset was estimated in the SAS software**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### package, utilizing the sample weights in the data s o that the estimates were nationally representative

. The final distributions by vintage are shown in Tab le N-2.

Table N-2. Residential Background Lead Dust Loading s by Vintage.

Vintage Geometric

Mean ( μ g/ft 2 ) Geometric

Standard Deviation Pre 1930 3.97 4.76 1930-1949 1.94 7.76 1950-1959 1.14 2.65 1960-1979 0.79 4.25 Post 1979 0.49 3.38



# N.2. Soil Similar to dust, background soil lead concentration estimates were found separately for public and commercial buildings (as a whole) and for residence s. This section provides an overview of the data us ed to define the lognormal distributions used for each

. 

## N.2.1. Public and Commercial Buildings As with dust, limited information was available abo ut soil lead concentrations around public and commercial buildings. This section provides a summa ry of data used for each building type. A summary of the input values is provided in Table N-3.

Table N-3. Public/Commerical Background Lead Soil C oncentrations

Building Type Point Estimate ( μ g/g) Geometric

Mean ( μ g/g) Geometric Standard Deviation Industrial 197.3 N/A N/A
Commercial 49.97 N/A N/A Agricultural N/A 17.1 1.75 Schools N/A 28.0 3.00



### Government/Commercial Buildings and Industrial Buil dings For government/commercial and industrial soils, a m eta-analysis study of soil samples from 1970 to 2012 in different land use areas was used (Datko-Wi lliams, 2013). The data were limited to measurements within the last 15 years based tempora l trends in the Datko-Williiams analysis, which**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### indicated an approximately 25-75% decrease in soil lead levels every 10-20 years. The studies were further limited to those in the “commercial” and “i ndustrial” land use categories. The resulting 9 stu dies were from Florida, NY, Ohio, and Maryland. For each , the average was estimated and then the weighted average across studies using the number of samples in the study was estimated. These averages resulted in soil concentration estimates of 50 μ g/g for government/commercial buildings and 197 μ g/g for industrial buildings. Owing to the aggregated p resentation of data in the meta-analysis study, the results were not further parsed into distributions, and a single point estimate was used for backgroun d soil.
Agricultural Buildings For agricultural soils, the USGS background soil su rvey (http://www.epa.gov/superfund/lead/background.htm#b ackground) provides estimates from 2004- 2007, and, while not a structured survey like NSLAH or NHANES, is relatively nationally representative

. Lead statistics based on 1,556 soil samples collect ed from the top 5 cm of soil that was “planted/cultivated” were calculated, resulting in a geometric mean of 17.1 μ g/g and a geometric standard deviation of 1.75.

Schools For schools, the same data source used for public a nd commercial building background dust (the CCC Survey) was used for soil as well. Values presented in Table 6-5 of that survey indicated a geometric mean of 28 μ g/g and a geometric standard deviation of 3.



## N.2.2. Residences Background soil values were estimated using the Nat ional Survey of Lead and Allergens in Housing (NSLAH) survey data (HUD, 2002). The NSLAH survey e xamined allergen and contaminant levels in 831 housing units and was designed to be nationally rep resentative. The data were binned into the differen t Approach building vintages, and survey weights were incorporated to estimate the geometric mean and geometric standard deviation, as shown in**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table N-4.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table N-4. Residential Background Lead Soil Concent rations by Vintage.

Vintage Geometric Mean ( μ g/ft 2 ) Geometric

Standard Deviation Pre 1930 367.4 3.53 1930-1949 144.1 3.69 1950-1959 79.6 3.77 1960-1979 24.2 3.29 Post 1979 15.8 2.32



# N.3. Air The background lead concentration data for ambient and indoor air used in this approach are based on a review of the 2009 annual average total suspended p articulate (TSP) monitoring data for lead contained in EPA’s Air Quality Systems (AQS) database (USEPA, 2010b,c).
The range of concentrations reported in this databa se was large, with a 10 th 

### percentile concentration of 0.01 μg/m 3 

### and a 90 th 

### percentile concentration of 0.18 μg/m 3 

### . Based on these data, the geometric mean (0.02 μg/m 3 

### ) and geometric standard deviation (5.52) were sel ected to represent the range of background ambient air concentrations. Note, howev er, that this value may overestimate the ambient air concentration because lead monitors are often l ocated in areas with nearby lead emission sources.


# N.4. Intake, Blood Lead, and Bone Lead The Approach estimates health effects for both chil dren and adults. Because of historical changes in blood lead after changes in the lead content of gas oline and paint, population blood lead levels have been declining over the last few decades. Thus, the approach must take into account the blood lead history of the individual and simulate different 20 14 background intakes, blood leads, and bone leads for different ages of adults.
To make these estimates, the NHANES survey estimate s were compiled, starting with NHANES II and continuing through the latest measurements in 2012 (CDC, 2012). The population-average estimates of blood lead for each age and for each survey were es timated and compiled, as shown in Table N-5.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-6 July 25, 2014 

### Table N-5. Average Blood Lead Levels Estimated from NHANES II through NHANES 2011-2012.

Age 1976- 1980 1988- 1991 1991- 1994 1999- 2000 2001- 2002 2003- 2004 2005- 2006 2007- 2008 2009- 2010 2011- 2012 

### 0 16.8 1 17.3 5.3 4.3 3.5 2.7 2.6 2.0 2.4 1.7 1.4 2 16.9 5.2 4.0 2.8 2.5 2.5 2.1 1.9 1.5 1.7 3 16.9 4.4 3.5 3.1 2.1 2.3 1.6 2.1 1.3 1.3 4 15.8 4.6 3.5 2.5 1.8 2.0 1.7 1.6 1.4 1.2 5 15.8 4.1 2.9 2.3 1.9 1.7 1.6 1.5 1.4 1.0 6 14.5 3.5 3.4 2.1 1.7 1.7 1.6 1.5 1.2 1.1 7 14.8 3.5 2.7 1.8 1.7 1.7 1.3 1.1 1.0 0.8 8 12.8 3.4 2.4 2.1 1.5 1.5 1.2 1.2 1.0 0.9 9 13.1 3.0 2.3 1.5 1.5 1.4 1.1 1.1 0.9 0.8 10 13.4 2.8 2.3 1.8 1.4 1.4 1.1 1.1 0.9 0.7 11 12.5 2.8 2.2 1.6 1.5 1.2 1.1 1.0 0.8 0.7 12 11.7 2.8 2.6 1.8 1.3 1.3 1.1 1.1 0.8 0.7 13 11.6 2.7 1.9 1.5 1.3 1.1 1.0 1.1 0.8 0.7 14 11.2 2.3 2.2 1.2 1.1 1.1 1.0 0.9 0.8 0.6 15 11.8 2.4 1.8 1.3 1.0 1.0 0.9 0.9 0.7 0.6 16 11.7 2.1 1.7 1.2 1.0 1.1 0.9 0.8 0.7 0.6 17 12.9 2.2 1.5 1.1 1.0 1.3 0.9 0.8 0.8 0.7 18 13.1 2.9 1.8 1.4 1.1 1.1 1.1 0.8 1.0 0.7 19 12.7 2.2 2.1 1.4 1.3 1.2 0.9 1.1 0.9 0.7 20 12.8 3.3 2.0 1.5 1.1 1.1 1.1 1.1 0.9 0.8 21 13.4 2.9 2.4 1.5 1.6 1.6 1.2 1.1 1.0 1.1 22 13.2 2.7 1.9 1.3 1.4 1.3 1.1 1.0 1.1 0.8 23 13.6 3.3 1.8 1.5 1.2 1.4 1.1 1.0 1.0 0.8 24 13.6 3.0 2.3 2.0 1.3 1.2 1.0 1.3 1.1 0.8 25 13.7 2.9 2.8 1.5 1.5 1.3 1.1 1.0 0.9 1.1 26 14.9 2.9 2.1 1.6 1.6 1.4 1.2 1.3 1.1 1.2 27 13.0 3.2 2.6 1.7 1.3 1.2 1.2 1.4 1.2 1.1 28 12.9 3.5 2.2 1.8 1.6 1.4 1.2 1.2 1.3 0.9 29 12.9 3.2 2.0 1.8 1.3 1.5 1.1 1.1 1.1 1.0 30 13.4 3.4 2.7 1.8 1.3 1.5 1.4 1.4 1.7 0.8 31 14.0 3.0 3.0 1.8 1.4 1.7 1.1 1.4 1.1 0.8 32 14.6 3.6 2.6 1.4 1.9 1.9 1.3 1.5 1.1 0.8**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-7 July 25, 2014

Age 1976- 1980 1988- 1991 1991- 1994 1999- 2000 2001- 2002 2003- 2004 2005- 2006 2007- 2008 2009- 2010 2011- 2012 

### 33 13.8 3.8 2.4 1.9 1.5 1.4 1.8 1.3 1.1 1.2 34 14.6 3.3 2.7 2.9 1.6 1.5 1.3 1.4 1.2 1.0 35 14.5 5.1 3.5 1.4 1.4 1.5 1.4 1.3 1.4 1.0 36 14.8 4.5 2.7 2.0 1.7 1.7 1.4 1.4 1.1 1.1 37 15.0 4.0 2.7 2.2 1.8 1.5 1.3 1.4 1.3 1.0 38 14.2 4.0 3.0 2.1 2.6 1.6 1.4 1.2 1.2 1.8 39 15.7 3.7 2.4 1.7 1.9 2.0 1.4 1.7 1.1 1.1 40 13.6 3.5 2.8 2.0 2.2 1.5 1.5 1.2 1.3 1.5 41 16.1 3.8 2.9 2.2 1.6 1.6 1.6 1.3 1.3 1.0 42 14.8 3.6 3.3 1.8 1.7 1.7 1.4 1.4 1.3 1.3 43 14.1 3.6 2.9 2.1 2.3 2.0 1.4 1.6 1.3 1.1 44 13.8 3.4 2.7 2.1 2.4 1.9 1.6 1.6 1.2 1.4 45 15.8 3.6 3.1 2.0 2.1 2.1 1.7 1.4 1.6 1.3 46 14.1 3.6 3.3 2.3 2.1 1.8 1.5 1.7 1.7 1.6 47 14.6 4.5 3.6 2.6 2.2 1.7 1.9 1.6 1.6 1.7 48 14.9 5.1 3.5 2.4 2.0 2.0 2.1 2.0 1.7 1.5 49 15.6 4.6 3.4 2.1 2.3 2.0 2.1 1.7 1.7 1.4 50 16.1 4.4 3.0 2.6 2.3 2.1 1.6 1.7 2.1 1.5 51 14.9 4.5 3.5 2.5 2.1 1.9 1.9 1.8 1.7 2.0 52 15.9 5.8 3.2 2.3 2.2 2.1 2.0 2.0 1.6 2.4 53 14.9 4.7 4.1 2.4 2.2 2.1 2.1 2.4 1.7 1.7 54 15.6 5.9 3.3 2.5 2.1 2.5 2.1 2.3 2.1 1.6 55 14.4 4.4 3.7 2.7 2.3 2.1 2.1 2.3 1.9 1.5 56 15.1 5.6 3.8 2.2 2.2 2.0 2.3 2.1 1.8 1.6 57 14.4 5.4 3.5 3.2 2.1 1.9 2.3 2.2 2.4 1.7 58 16.0 4.2 3.7 2.8 2.2 2.2 2.0 1.9 2.1 1.7 59 15.3 4.4 3.1 2.4 2.3 1.9 2.3 2.1 1.6 2.2 60 14.4 6.1 4.0 2.7 2.5 2.2 2.0 2.0 1.9 2.0 61 14.7 4.3 3.3 5.2 2.2 2.3 2.6 2.0 1.8 1.6 62 14.3 4.3 3.9 2.4 2.5 2.3 2.3 1.8 1.8 1.5 63 13.9 4.9 3.6 2.5 2.1 2.6 2.2 2.2 1.9 1.8 64 14.6 4.2 4.5 2.6 2.2 2.2 2.4 2.0 1.7 1.8 65 15.3 4.7 3.8 2.7 2.3 2.3 2.7 1.9 1.9 1.5 66 14.7 5.0 3.9 3.0 2.3 2.2 2.2 1.9 1.9 2.0**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-8 July 25, 2014

Age 1976- 1980 1988- 1991 1991- 1994 1999- 2000 2001- 2002 2003- 2004 2005- 2006 2007- 2008 2009- 2010 2011- 2012 

### 67 14.1 4.7 4.1 2.8 2.4 2.1 2.4 1.9 1.8 2.0 68 14.7 5.0 4.1 2.5 2.2 2.2 1.9 2.0 1.9 3.2 69 14.4 4.8 4.4 3.1 2.1 2.4 2.5 1.8 1.8 1.9 70 15.5 4.1 3.6 3.0 2.8 2.3 2.2 2.2 2.3 1.6 71 13.4 4.3 4.6 2.4 2.6 2.8 2.3 2.1 1.9 1.6 72 14.0 4.3 4.5 3.0 3.7 2.2 2.7 2.4 2.0 2.0 73 12.4 5.7 3.6 2.5 2.5 2.9 2.2 2.0 1.8 2.0 74 13.9 4.6 4.3 2.7 2.8 2.4 2.2 2.3 1.9 1.7 75 13.1 5.3 4.5 2.7 2.4 2.1 2.4 2.5 2.0 1.9 76 4.5 4.0 2.5 2.7 2.3 2.7 2.4 1.7 1.8 77 4.7 3.0 2.6 2.2 2.2 2.2 2.1 2.0 1.8 78 4.8 4.2 3.8 2.6 3.1 2.1 2.8 2.1 1.7 79 5.8 5.2 3.1 2.5 2.3 2.8 2.5 2.2 1.9 80 4.7 3.8 2.5 2.6 2.7 2.9 2.4 2.2 2.1 81 4.8 4.3 2.8 2.6 2.2 2.7 82 4.6 4.6 3.1 2.4 3.1 2.7 83 5.4 3.9 2.5 2.4 2.7 2.5 84 4.8 4.5 2.8 2.5 2.6 2.8 85 4.0 4.0 3.2 3.0 3.1 2.8 86 5.3 4.9 87 5.0 4.4 88 5.0 4.7 89 5.5 4.5 90 5.0 3.6
The different age groups were then grouped by takin g the following into account:

 

### Childhood blood leads tend to be higher than adults , so age ranges cannot be so broad that they span children and adults,

 

### Legislation that limited lead in gasoline lead to a rapid decline in blood lead during the 1980’s and 1990’s, and

 

### Blood lead levels have continued to decline through the 2000’s through today, although at a slower
rate.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-9 July 25, 2014 

### Because biokinetics vary with age and media concent rations have varied with calendar year, birth year is used to track different individuals and their intak e values. Their biokinetics will then vary in the L eggett model with their age, but intake changes related to changes in media concentrations will happen in fix ed calendar years.
Based on these observations, six different age rang es were defined based on birth year. Then, the average NHANES predictions for each survey and for each defined age range were estimated. The values are shown in Table N-6. Next, intake rates were ass umed to be constant but then allowed to change at pre-defined years based on inspection of the rate o f change of blood leads. The years selected include : 1984, 1993, 2000, and 2005. An initial guess at lea d intake was arbitrarily set to 100 μ g/day for the first of these intake year ranges. Then, the initial gues s was scaled during the subsequent years using the ratio of the blood lead in the first year range and the year range in question. For example, for the o ldest individuals assumed to be born between 1930 and 195 4, their intake from birth to 1984 was set to 100 mg/day. Then, their intake from 1984 to 1993 was sc aled using the ratio of NHANES blood leads in 1992 (the closes survey year to 1993) and the NHANES mea surements in closest to 1984 (1978). Looking at Table N-6, this ratio is 3.3/14.3 = 0.23. Multiply ing by the original guess of 100 μ g/day gives an intake guess from 1984 to 1993 of 23 μ g/day. This scaling assumes that the intake will sc ale approximately linearly with blood lead; although this assumption is not strictly true, blood lead tends to trend lin early with intake over narrow regions of intake levels, a nd this assumption allows the initial guesses to incorporate information known about blood lead tren ds.
Next, the Leggett model was used to back-calculate the intake levels for each age using the initial guesses in Table N-6. The blood leads resulting fro m running the initial guesses were compared to the NHANES values by calculating the mean squared error (MSE) between the predicted (Leggett) and actual (NHANES) values. Lead intakes were optimized based on a gradient-descent method by iteratively adjusting the initial lead intakes by a factor unti l the MSE was minimized. The intakes corresponding to the minimized MSE were selected as the representati ve intakes for the selected birth year. This proce ss was repeated for each birth year within each age ra nge.**Appendices to the Approach for Estimating Exposures and Increment** **al Health Effects from Lead due to Renovation**, Repair, and Painting Activities in Public andCommercial BuildingsPeer Review DraftN-10 July 25, 2014Table N-6. Age Ranges, Years When Intakes Change, Initial Intake Guesses, and Blood Lead Levels Used in Intake Optimization CalculationBirth Year: 1930-1954Birth Year: 1955-1966Birth Year: 1967-1976YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL)YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL)YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL)1984 100 1978 14.3 1984 100 1978 12.5 1984 100 197814.61993 23 1989 4.4 1993 21 1989 3.3 1993 14 1989 2.62000 19 1992 3.3 2000 16 1992 2.6 2000 12 1992 2.02005 16 2000 2.7 2005 14 2000 2.1 2005 9 2000 1.72014 13 2002 2.3 2014 13 2002 2.1 2014 9 2002 1.52004 2.2 2004 1.8 2004 1.62006 2.3 2006 1.7 2006 1.42008 2.1 2008 1.7 2008 1.42010 1.9 2010 1.7 2010 1.32012 1.9 2012 1.7 2012 1.3Birth Year: 1977-1986Birth Year: 1987-1996Birth Year: Post 1996YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL))YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL)YearIntakeChangesInitial IntakeEstimate(μg/day)BloodLeadYearNHANESBlood Lead(μg/dL)1984 1001993 68 1989 3.5 1993 100 1989 5.22000 38 1992 2.4 2000 39 1992 3.6 2000 1002005 33 2000 1.3 2005 28 2000 1.9 2005 52 2000 3.02014 28 2002 1.2 2014 22 2002 1.4 2014 31 2002 2.12004 1.3 2004 1.2 2004 2.02006 1.1 2006 1.0 2006 1.52008 1.2 2008 1.0 2008 1.52010 1.2 2010 0.9 2010 1.12012 1.0 2012 0.8 2012 0.9**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-11 July 25, 2014 

### Figure N-1 shows an overview of the consolidated av erage NHANES data used for each age range, the individual NHANES estimates for each age within the age range, and the fit data. Overall, the optimization routine provided good fits between the predicted and actual blood lead estimates.
The optimized intakes were used to estimate the bac kground bone lead and blood lead values in the year 2014 and then projected forward with constant intake 5 additional years. The renovation was then assumed to occur in 2014 and the blood lead with re novation was simulated and compared to the background-only blood lead. A summary of select int ake, blood lead, and bone lead estimates is provided in Table N-7. The full table was not inclu ded owing to the length of the table.
This approach assumes that intake is constant from the year of birth until 1984, which means that intakes for both children and adults are similar.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-12 July 25, 2014 Line = Data Used for Optimization; Red = Actual NHA NES Data; Black = Fit Model Data



### Figure N-1. NHANES Data and Fit Background Blood Le ad Estimates**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-13 July 25, 2014 

### Table N-7. Estimated Intake Rates for Select Birth Years

Birth Year: 1930-1954 Birth Year: 1955-1966 Birth Year: 1967-1976

Year Intake Changes Intake

for BY 1930 ( μ g/day) Intake

for BY 1942 ( μ g/day) Year

Intake Changes Intake

for BY 1955 ( μ g/day) Intake

for BY 1960 ( μ g/day) Year

Intake Changes Intake

for BY 1967 ( μ g/day) Intake

for BY 1972 ( μ g/day) 1984 44.48666 45.82987 1984 39.46613 35.15088 1984 61.88535 46.47278
1993 10.36069 10.67351 1993 8.242491 7.341253 1993 8.619557 6.472853
2000 8.40406 8.657807 2000 6.501917 5.790994 2000 7 .267323 5.457393
2005 7.114232 7.329035 2005 5.332247 4.749217 2005 5.857615 4.398774
2014 5.891762 6.069654 2014 5.323517 4.741441 2014 5.330255 4.002753 Birth Year: 1977-1986 Birth Year: 1987-1996 Birth Year: Post 1996

Year Intake Changes Intake

for BY 1977 ( μ g/day) Intake

for BY 1982 ( μ g/day) Year

Intake Changes Intake

for BY 1987 ( μ g/day) Intake

for BY 1990 ( μ g/day) Year

Intake Changes Intake

for BY 1996 ( μ g/day) Intake

for BY 2000 ( μ g/day) 1984 13.22644 12.2373
1993 9.055472 8.378259 1993 9.139481
2000 5.085096 4.704807 2000 3.519678 2.752592 2000 12.22738
2005 4.323468 4.000137 2005 2.526356 1.975756 2005 6.312934 4.290547
2014 3.73722 3.457732 2014 2.027376 1.585525 2014 3 .796646 2.580367**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft N-14 July 25, 2014 

# N.5. References for Appendix N

Centers for Disease Control and Prevention. 2012. N ational Health and Nutrition Examination survey. Available at http://www.cdc.gov/nchs/nhanes.htm.
Datko-Williams, L et al 2014. Analysis of U.S. soi l lead (Pb) studies from 1970 to 2012. Science of t he Total Environment. 854-63 (468-469).
HUD 2011. American Healthy Homes Survey.
HUD, 2002. National Survey of Lead and Allergens in Homes. http://www.niehs.nih.gov/research/atniehs/labs/lrb/ enviro-cardio/studies/nslah/index.cfm
U.S. Environmental Protection Agency (U.S. EPA). 20 10c. Air Quality System (AQS) Database. Available online at: http://www.epa.gov/ttn/airs/airsaqs/aqsw eb/aqswebwarning.htm.
USGS 2011. Background soil survey. Available onlin e at http://www.epa.gov/superfund/lead/background.htm#ba ckground

Westat. 2003. First National Environmental Health S urvey of Child Care Centers. Final Report, Rockvill e, MD, www.hud.gov/offices/lead/techstudies/survey.cfm

.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft O-1 July 25, 2014 

# Appendix O. Population Biokinetic Variability

In the Approach, numerous variables are sampled in order to capture the expected range in the U.S. population. However, the biokinetic parameters in t he Leggett model (e.g., transfer rates from blood t o bone and total mass of bone) are not sampled. Data are not available in the literature to accurately develop distributions of these parameters for the U .S. population.
Instead, biokinetic variability is included after t he blood lead is estimated from the Leggett model. This modeled blood lead is treated as a central tendency estimate (geometric mean), and a population-level geometric standard deviation (GSD) is assigned. The n, the lognormal distribution defined by the geometric mean Leggett blood lead prediction and GS D is sampled twenty five times to estimate the variability in the blood lead arising from biokinet ic variability.
To determine the appropriate GSD to use, several fa ctors were considered. The NHANES survey represents the most nationally-representative surve y of blood lead across all age groups (CDC, 2012). Table O-1 shows the GSD estimated for each NHANES s urvey since the year 2000. The GSD values are consistent across the different surveys in spite of an overall decrease in blood lead over this period . For the Approach, a value of 1.9 was selected. The NHAN ES GSD represents variability associated with both the biokinetic differences in individuals as well a s their differences in environmental media concentrations and intakes. For that reason, the lo wer of the two GSDs represented in NHANES was selected to represent the biokinetic variability.

Table O-1. NHANES Survey Population-level Blood Lea d Geometric Standard Deviations

NHANES Survey Year Population GSD 1999-2000 1.9 2001-2002 2.0 2003-2004 1.9 2005-2006 1.9 2007-2008 1.9 2009-2010 1.9 2011-2012 2.0


### The selected GSD is larger than the recommended age ncy default in the IEUBK model (1.6). The IEUBK model is intended for use at contaminated sites, wh ere the exposed population may be more**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### homogeneous. (US EPA 2010) NHANES is expected to gi ve the best national-level estimate of the population-level GSD, so these values were preferre d over the IEUBK default value.


# O.1. References for Appendix O Centers for Disease Control and Prevention. 2012. N ational Health and Nutrition Examination survey. Available at http://www.cdc.gov/nchs/nhanes.htm.
U.S. Environmental Protection Agency (U.S. EPA). 20 10. Integrated Exposure Uptake Biokinetic Model for Lead in Children, IEUBKwin version 1.1, build 2 64. http://www.epa.gov/superfund/health/contaminants/le ad/products.htm#ieubk.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft P-1 July 25, 2014 

# Appendix P. Concentration-Response Curves Supporting

Information

P.1. IQ (Lanphear et al. (2005) Pooled Analysis) Lanphear et al. (2005) reported the results of seve n studies that measured full-scale IQ in school ag e children (mean age at IQ testing was 6.9 years) in four U.S. cities (Boston, Cincinnati, Cleveland, an d Rochester), as well as in Australia, Mexico, and Yu goslavia. All children were assessed using ageappropriate versions of the Wechsler scales. Four measures of lead exposure were examined: concurrent blood lead (PbB closest in time to the I Q test), maximum blood lead level (highest PbB measured at any time prior to the IQ test), average lifetime blood lead (time-weighted mean PbB incorporating the results of all blood lead measure ments from 6 months to the concurrent sample), and early childhood blood average (mean PbB across all samples taken between ages 6 to 24 months). A pooled analysis of the relationship between cord bl ood lead levels and IQ also was conducted in the su bsample for which cord blood samples were available.
Lanphear et al. explored regression models that a djusted forthe effect of blood lead for site, as we ll as 10 common potential confounders. These included HOM E Inventory scores, birth weight, maternal education and IQ, and prenatal substance abuse. St atistical testing was employed to assess the linear ity or nonlinearity of the relationship between blood l ead levels and FSIQ. Regression diagnostics also w ere performed to ascertain whether lead coefficients we re affected by co-linearity or influential observations. The fits of all four measures of pos tnatal blood lead levels were compared based on R 2 

### values. The blood lead measure with the largest R 2 

### (adjusted for the same covariates) was nominated **a priori**  as the preferred blood lead index relating lead ex posure to IQ in subsequent inspections of the relationships. The primary analysis used a fixed-e ffects model, although a mixed model treating sites as random effects was also examined.
The median lifetime average blood Pb concentration in the seven cohorts that were studied was 12.4 μg/dL (5 th 

### to 95th percentile, 4.1 to 34.8 μg/dL), with about 18 percent of the children having peak blood Pb levels below 10 μg/dL. The 5th to 95th pe rcentile concurrent blood Pb levels ranged from 2.4 to 30 μg/dL. The mean IQ of all children was 93.2 [Standard Deviation (SD) 19.2] but this varied grea tly between studies. All four measures of postnatal ex posure were highly correlated. However, the concurrent blood Pb level exhibited the strongest a ssociation with IQ, as estimated by R 2 

### . Nevertheless, the results of the regression analyses for all bloo d Pb measures were very similar. The best-fitting model included the log of concurrent blood Pb, stud y site, maternal IQ, HOME Inventory scores, birth**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### weight, and maternal education. No significant int eractions were observed between PbB and study site or other covariates.
Various models, including a linear model, a cubic s pline 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 l og-linear model was found to best fit to the data, based on R 2 

### values. Using the log-linear models, the authors estimated a decrement of 1.9 points (95 percent CI: 1.2, 2.6) in FSIQ for a doubling of co ncurrent blood lead. Thus, the IQ point decrements associated with an increase in blood lead from belo w 1 to 10 μg/dL compared to 10 to 20 μg/dL were 6.2 points (95 percent CI: 3.8, 8.6) versus 1.9 po ints (95 percent CI: 1.2, 2.6). The individual ef fect estimates for the seven studies used in the pooled analysis also generally indicate steeper slopes in studies with lower blood lead levels compared to th ose with higher blood lead.
Table P-1. Log-Linear Regression Coefficients Estim ated by Lanphear et al. (2005) and Recalculated by Crump et al. (2013)



### Blood Lead Metric Lanphear et al. (2005) Crump et al. (2013) Estimates Derived 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) Concurrent

- 2.70 (

- 3.74,

- 1.66)

- 2.65 (

- 3.69,

- 1.61) 1



### Lanphear et al. also estimated piecewise linear mod els relating the concurrent blood lead to IQ change s above and below “cutpoints” of 7.5 and 10 ug/dL. T hey found that the coefficients for IQ change below and above 7.5 ug/dL (-2.95 and -0.16, respectively ) were both significantly negative and significantl y different from one another (p = 0.015.) The coeffi cients for concurrent PbB below and above 10 ug/dL PbB were also reported to both be significantly neg ative (-0.80 and -0.13) but not significantly diffe rent from one another (p = 0.103.)


# P.2. IQ (Crump et al. (2013) Reanalysis of Lanphear et a l. Data) Crump et al. (2013) subsequently obtained the raw d ata set supporting the Lanphear et al. pooled analysis, attempted to replicate the results of the original study, and sought to identify problems wi th the data and statistical methods that were used to analyze them, and to “evaluate the robustness of their conclusions.”
In their analysis, Crump et al. implemented several adjustments to the treatment of the data:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft P-3 July 25, 2014  

### They developed a revised site-specific scheme for l inking specific blood lead sampling rounds to specific IQ measurements.

 

### Derived time-weighted average blood lead metrics to substitute for the unweighted metrics used by Lanphear et al., and added PbB measurements at 24 m onths into the calculation of the averages.

 

### Corrected the equation used in the log transformati on of blood lead data from the Boston cohort.

 

### Substituted the McCarthy Scales of Children’s Abili ties General Cognitive Index measured at 57 months for the Wexler full-scale IQ at 120 months t hat had been used as the “concurrent” IQ measure for blood lead measured in the Boston cohor t at 57 months (see Section 7.3.1.) For all other groups, Wexler full-scale IQ measurements wer e selected.

 

### Identified IQ measurement in 10 children from the M exican Cohort where the closest “concurrent” measurement was at age 78 months rather than 72 mon ths. Crump et al. then attempted to replicate the earlie r regression estimates derived by Lanphear et al., using the original data and the data that had been “corrected” based on the adjustments noted above. When estimating the log-linear models, they obtaine d the same results as seen in the earlier analysis. When models were fit to the “corrected” data (adjus ted as described above), they obtained slightly different coefficient values from those derived Lan phear et al. (Table P-1), but the differences in coefficients were not significant, and the basic re sults of the analysis (negative and highly signific ant loglinear coefficients in the range from -2.2 to -3.2 for all blood lead metrics) were unchanged.
Crump et al. also estimated piecewise regressions f or concurrent PbB values above and below the 7.5 and 10 ug/dL cutoff values. For these models, they obtained different results from the earlier analys is. Their slope coefficient for PbB < 7.5 ug/dL (2.54 I Q points/dL) was slightly smaller than that estimat ed by Lanphear et al. (-2.94), but both estimates were si gnificantly less than zero. The estimated slope coefficient above 7.5 ug/dl (-0.15 ug/dL) was very close to that estimated by Lanphear (-0.16), and confidence intervals indicated that both values wer e significantly negative.
Crump et al. found that the piecewise linear regres sion coefficient for subjects with concurrent blood lead levels below 10 μg/dL were not significant, in contrast to the results reported by Lanphear et al ., and that the difference between slope coefficients for subjects above and below this cutoff was likewi se not significant. Using the “corrected” data, they e stimated a blood lead-IQ slope of -1.06 (C.I. = 2.2 5, 0.118) below 10 μg/dL, compared to Lanphear et al.’ s original -0.88 (C.I. = -2.13, 0.38.) While the v alue from the revised analysis becomes more negative, th e confidence interval still includes zero; Crump et al. calculated a marginally significant p-value of 0.078.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Crump et al. also experimented with different cutpo ints (i.e., the blood lead at which the slope of th e IQblood lead relationship changes) and blood lead met rics in an attempt to identify the lowest blood lea d concentrations at which slope coefficients retained significance. They found a generally consistent pattern of most slope coefficients remaining negati ve down to the lowest cutoff point analyzed (2 μg/dL.) However, the coefficients for blood lead c utoffs less than 5-7 μg/dL lost statistical signifi cance, consistent with the decreasing numbers of subjects involved.
Crump et al. indicated “Although we found some smal l errors and questionable decisions by Lanphear et al. that, taken alone, could cause doubt in their c onclusions, our reanalysis tended to support their conclusions. We concluded that there was statistica l evidence that the exposure-response is non-linear over the full range of BPb evaluated in these studi es, which implies that, for a given increase in blo od lead, the associated IQ decrement is greater at low er BPb levels.” They noted, however, after fitting piecewise models to the various blood lead metrics, that the IQ loss could be “adequately modeled” at PbB levels less than 10 ug/dL. In addition, they c oncluded that the evidence support the existence of exposure-response relationship for IQ loss at peak PbB values as low as 7 ug/dL, and for concurrent Pb B at 5 ug/dL. As noted above, however, these latter conclusions were based on linear regressions incorporating only small proportions of the data, a nd thus had less power to detect significant relationships.
As noted in Section 6.3, after reviewing the Crump et al. (2013) reanalysis of the Lanphear et al. (20 05) study, EPA is retaining the basic log-linear regres sion approach to the prediction of IQ loss after renovation, while using the revised regression coef ficients calculated by Crump et al.


# P.3. IQ (Budtz-Jørgensen et al. (2013) Estimation of BMDs and BMDLs) Budtz-Jørgensen et al. (2013) estimated blood lead- IQ relationships from the Lanphear et al. (2005) da ta using a set of linear mixed models of the general f orm:

IQ i j 

### = α

j 

### − f ( d i,j 

### ) 

### + γ 1 

### x 1 

### i,j 

### +

. . . 

### + γ p 

### x p i 

### j 

### + ε i j 

### This approach allows for the intercept of the model (α j 

### ) to vary across the studies, whereas the effects o f covariates ( γ 1



### - γ

p ) 

### are assumed to be constant across the studies. The expression f ( d i,j 

### ) captures the form of the blood lead-IQ dependence; both linear a nd log-linear transformations were used. It is not clear to what extent the form of the models used in this analysis differed from those used in the Lanphear 2005 study; the covariates were the same a nd there was no discussion of whether the problems with the data identified by Crump et al. w ere addressed.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### An important difference from the previous analysis was that likelihood methods (rather than linear regression) were used to optimize the models. This allowed relative goodness of fits and model parsimony to be compared using differences in AIC ( Akaike Information Criteria.) Another important difference was that Budtz-Jørgensen et al. applied the results of their models to derive BMD and BMDL estimates for a change of 1.0 IQ points for each bl ood lead metric. They also employed the “hybrid method” to estimate BMD and BMDL values for changes of 1, 2.5, and 5 percent changes in IQ assuming a background incidence of 5 percent. Finally, they compared the fits of linear and log-linear models (using AIC values), as well as estimating the same piecewise linear models (cutoffs at 10 and 7.5 μg/d L) fitted in the original analysis.
For concurrent blood lead, the log-linear model aga in provided a superior fit to both the linear and breakpoint models (lower AIC value.) For the other blood lead metrics, the log-linear model provided superior fits to the linear models and comparable o r slightly inferior fits to data in the low-exposur e range (below the cutoffs.) The BMD values for a 1. 0 point change in IQ score derived from the log-lin ear models for different blood lead metrics fell into a relatively narrow range (0.355-0.558 μg/dL), and t he BMDLs ranged from 0.260 to 0.343 μg/dL. The lowest values were estimated from concurrent blood lead and the highest for early childhood blood lead concentrations. BMDs/ BMDLs for piecewise linear models with breakpoints at 7.5 μg/dL ranged from 0. 712/0.434 μg/dL for peak blood lead to 1.647/0.980 μg/dL for concurrent lead. BMDs/ BMDLs for models with breakpoints at 10 μg/dL are somewhat higher ranging from 1.034/0.689 (concurren t) to 3.769/1.610 (early childhood.)
EPA has decided not to employ the Budtz-Jørgensen e t al. results in estimating IQ loss associated with renovation, for several reasons. First, use of the continuous regression models from the Lanphear/Crump et al. analysis does not require tha t selection of a cutoff value for reduced IQ and wi ll produce a continuous distribution of IQ loss as a f unction of exposure. In contrast, the Budtz-Jørgen sen BMDs and BMDLs are calculated for specified values of IQ loss (1 point, 1 percent, etc.), so the outpu t of a risk assessment using these values would be an es timate of what proportion of the exposed population exceeds the selected cutoff values. EPA believes that the regression approach will provide more useful information about the distribution of o utcomes (predicted IQ changes) than would be obtainable using the BMDs/BMDLs from the Budtz-Jørg ensen et al. study without considerable adaptation. In addition, the Lanphear et al. (2005 ) and Crump et al. (2013) analyses directly provide well-documented regression coefficients that have b een subject to extensive quality assurance and peer review, while the equations and model fits from the Budtz-Jørgensen et al. study have not been made publically available and would require independent review by EPA scientists for use in regulatory analysis. Finally, many of the BMDL values estimat ed by Budtz-Jørgensen et al. are less than 1 ug/dL,**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### and EPA is reluctant to predict risks in this range because there are so few observation below 1 ug/dL in the Lanphear et al. data set.


# P.4. Estimation of Birthweight Reductions from Zhu et al. (2010) Model As discussed in Section 6.4, Zhu et al. (2010) teste d a number of fractional polynomial models relating observed variations in birthweight to maternal bloo d lead concentrations and multiple covariates, including gestational age, maternal age, race, Hisp anic ethnicity, education, smoking, alcohol drinkin g, drug abuse, in wedlock, participation in special fi nancial assistant program, parity, infant sex, and timing of the blood lead test relative to birth. They fou nd that a model incorporating a square root transformation of maternal blood lead provided the best fit to the data. While they did not provide t he coefficients and standard errors for their model, t he article includes estimates of the mean and 95% upper and lower confidence limits on the predicted body weight reductions at blood lead concentrations up to 10 ug/dL (Table P-2).

Table P-2. Predicted Birthweight Reductions Based on Fractional Polynomial Model from Zhu et al. (201 0)



### PbB BWR

(gms) 95% UCL 95% LCL Mean-LCL UCL-Mean Effective Std. Dev. 1 

### 0 -- -- -- -- -- -- 1 -27.4 -17.1 -37.8 10.4 10.3 5.28 2 -38.8 -24.1 -53.4 14.6 14.7 7.47 3 -47.5 -29.6 -65.4 17.9 17.9 9.13 4 -54.8 -34.2 -75.5 20.7 20.6 10.54 5 -61.3 -38.2 -84.4 23.1 23.1 11.79 6 -67.2 -41.8 -92.5 25.3 25.4 12.93 7 -72.5 -45.2 -99.9 27.4 27.3 13.95 8 -77.6 -48.3 -106.8 29.2 29.3 14.92 9 -82.3 -51.2 -113.3 31.0 31.1 15.84 10 -86.7 -54.0 -119.4 32.7 32.7 16.68

1. Estimated as (UCL-LCL)/3.92, assuming normal dist ribution


### As shown in Figure P-1, the projected change in bir thweight is linearly related to the square root PbB (this is inherent in the specified form of the mode l.) The slope of the relationship, as discussed in Section 7.4.1, is -27.4 grams per one unit change i n the square root of maternal blood lead; this valu e is used in the estimation of mean change in body weigh t in Equation 7.1, repeated below.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### ∆L = 	 ∆i ( b ) ∗ 27.4 + (0, ∆i ( b ) ∗ 5.28) (7.1)
Variation in the predicted change around the mean w as modeled as a normal distribution, based again on the inherent form of the Zhu et al. model, with a n effective “standard deviation” modeled from the upper and lower confidence limits in Table P-2. It can be seen from the table that, at each PbB concentration, the confidence limits are symmetrica l around the mean; assuming a normal distribution, and that the confidence limits are 1.96 standard de viations above and below the mean allows the estimation of the effective standard deviations as (UCL-LCL)/3.92, shown in the last column of Table P -2. As was the case for the mean birthweight reduction, the effective standard deviation also varies linea rly with the square root of maternal PbB. As shown in Figure P-2, the slope of this relationship is 5.28 gm birthweight for each change of one unit in the squa re root of maternal PbB, this result provides the multiplicative coefficient used in the second term of Equation 7.1.

Figure P-1. Dependence of Predicted Mean Birth Weig ht Reduction on the Square Root of Maternal Blood Lead y = -27.431x + 0.0221

R2 = 1

-90.0 -80.0 -70.0 -60.0 -50.0 -40.0 -30.0 -20.0

1.0 1.5 2.0 2.5 3.0 3.5Predicted Mean Birth Weight Loss, gm

Square Root(Maternal Blood Lead, ug/dL)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Figure P-2. Dependence of Variation in Prediction B irthweight Reduction on the Square Root of Maternal Blood Lead



### Coefficients from the Zhu et al. model were estimate d in models that included the large number of covariates listed above. Thus, the estimated chang es in birthweight obtained using these coefficients reflect the observed characteristics of the study p opulation, which may not precisely match the distribution of these characteristics of the pregna nt population in the U.S. as a whole. By fixing th e age distribution of the Monte Carlo model to be similar to that among the Zhu et al. study participates, th at source of variation can at least be accounted for. The uncertainty associated with differences in distributions of the other covariates between the s tudy population and the general U.S. population cannot be estimated directly.


# P.5. Reduced Glomerular Filtration Rate (GFR) Based on N avas-Acien et al. (2009) Study

P.5.1. Estimation of Regression Coefficient and Standard E rror As discussed in Section 6.5, Navas-Acien et al. (20 09) conducted an analysis of the relationship betwe en risk of “category 3-5 kidney disease” (GFR < 60 ml/ minute/1.73 M 2 

### ) in a sample of almost 15,000 adult subjects from the 1999-2006 NHANES. In logistic re gression models that included adjustments for a wide range of covariates (and for simultaneous expo sures to cadmium), they found statistically significant elevations in the adjusted odds ratios (ORs) for low GFR in the upper quartile of the stud y population compared to the referent group, accompan ied by a statistically significant positive trend i n y = 5.2762x - 0.0001 R2 = 1

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

1.0 1.5 2.0 2.5 3.0 3.5ffective Standard Deviation of BWReduction

Square Root(Maternal Blood Lead, ug/dL)**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### OR across the quartiles. Using a model containing log-transformed blood lead as a continuous variable they estimated the odds ratios associated with an i ncrease in PbB from 1.1 to 2.4 ug/dL (the 25 th 

### – 75 th 

### percentiles of the observed ranges, as shown in Tab le P-3. As discussed in Section 6.5, the models us ed to estimate the odds ratios also included urinary c admium as a covariate, so the estimated association with lead exposure takes into account the effects o n kidney function of simultaneous exposure to cadmium, which is also a known kidney toxin.
Table P-3. Navas-Acien et al. (2009) Regression Res ults, Estimated Coefficient Values, and Standard Er rors 

### Gender-Specific Regression Results
Group PbB

Range OR LCL UCL
M 1.1-2.4 1.13 0.96 1.33 F 1.1-2.4 1.44 1.22 1.69 M+F 1.1-2.4 1.31 1.16 1.47
Because only the ORs were presented, it was necessa ry to back-calculate the β values and standard errors based on the ORs and changes in PbB, as illu strated in Table P-4. 2 

### Given the exponential form of the logistic regression, the β values for men and w omen can be estimated as ln(OR)/ΔlnPbB, where ΔlnPbB is ln(2.4/1.1), the 75 th 

### /25 th 

### percentile blood lead values from the study.
Table P-4. Navas-Acien et al. (2009) Estimated β Va lues 

### Group ΔlnPbB ln(RR) β M 0.780 0.122 0.157 F 0.780 0.365 0.467 M+F 0.780 0.270 0.346 The standard errors were estimated for each of the coefficients based on the upper and lower confidence limits provided in the article as shown in

2 The authors of study have been contacted to provid e confirmation of the numerical derivations present ed in this appendix.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table P-5. Using a normal approximation (after con firming that the confidence limits were logsymmetrical), the standard errors were calculated a s Standard Error = ln (UCL/LCL) / 3.92. (The 95 percent upper and lower confidence limits are assum ed to be 1.96 times larger or smaller than the estimated β values.) The standard errors were used to represent the variability in the β estimate as described below.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table P-5. Estimation of Standard Errors Associated with the β Coefficients 

### Group LCL UCL ln(UCL/LCL) Std. Err. Men 0.96 1.33 0.3260 0.083 Women 1.22 1.69 0.3259 0.083
Men+Women 1.16 1.47 0.2368 0.060



## P.5.2. Estimation Low GFR Risk As explained in Section 7.5, the absolute risk for a given individual in the Monte Carlo simulation wa s estimated to be:



### *n = *n o 

### ∗  ( pqr ) ∗sV5^.a


### , with ΔlnPbB being the natural log of the ratio of t he renovation and background blood lead estimates. As for birthweight, statistical uncertainty in the β variable was represented by adding an “ε” term, w hich takes the value of a normal distribution with a mea n zero and standard deviation equal to the estimate d standard errors from Table P-3. At each Monte Carl o iteration, an independent random sample is drawn from this distribution and added to the β estimate. As shown in the above equation, the risk of low G FR associated with renovation is calculated relative t o background relative risks, which represent the average prevalences in different age groups and gen ders in the U.S. population. Background prevalence rates in the U.S. 2012 population provided by the V eterans Affairs Administration were obtained from the CDC ( http://nccd.cdc.gov/CKD/detail.aspx?QNum=Q391 ) and are provided in Table P-6.
Table P-6. Prevalence of Chronic Kidney Disease (GF R <60 ml/minute/1.73 M 2 

### ) in Adults, 2012 (percent) 

### Age Female Male 20-29 years 0.29 0.24 30-39 years 0.68 0.58 40-49 years 1.97 1.67 50-59 years 5.07 3.98 60-69 years 13.05 9.27 70+ years 25.6 21.06**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# P.6. Menke et al. (2006) Regression: Background Cardiova scular Mortality Risks The risk of cardiovascular disease mortality will b e estimated, as discussed in Section 6.6 and in US EPA 2014c as:

ce /,F 

### = ce o,/,F 

### ∗  ( pqr ) ∗∆V5^.a



### The exponential term on the right-hand side of the equation calculates the hazard ratio for a simulate d subject of a given age and gender, while CVDM 0,a,g 

### represents that age- and gender-specific backgroun d risk of CVD mortality in the U.S. population. Valu es for CVDM 0,a,g 

### have been obtained from CDC-curated data (Centers for Disease Control 2010), as shown i n Table P-7.
Table P-7. Background Cardiovascular Mortality Risk s for the U.S. Population (CDC 2010)

Age (years) Gender Annual CVD Mortality

50-59 M 2.20E-03 F 9.40E-04

60-69 M 4.60E-03 F 2.30E-03

70-80 M 1.20E-02 F 7.60E-03



# P.7. References for Appendix P

Budtz-Jørgensen E, Bellinger D, Lanphear B, Grandje an P. (2013). An international pooled analysis for obtaining a benchmark dose for environmental lead e xposure in children. Risk Anal. 2013;33:450–61.
Crump et al (2013). A statistical reevaluation of t he data used in the Lanphear et al. (2005) pooledanalysis that related low levels of blood lead to i ntellectual deficits in children. Critical Review Toxicology. October:43 (9): 785-99.
Lanphear BP, Hornung R, Khoury J, et al. (2005). Lo w-level environmental lead exposure and children’s intellectual function: an international pooled anal ysis. Environ Health Perspect, 113, 894–9.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Menke et al (2006). Blood lead below 0.48 micromol/ L (10 microg/dL) and mortality among US adults. Circulation. 114:1388–1394.
Navas-Acien et al (2009). Blood Cadmium and Lead a nd Chronic Kidney Disease in US Adults: A Joint Analysis. Am. J. Epidemiol. (2009) 170 (9): 1156-1164.
Zhu et al (2010). Maternal Low-Level Lead Exposure a nd Fetal Growth. Environmental Health Perspectives. Volume 118. Number 10.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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# Appendix Q. Approach to estimate universe of P&CBs

National data on the universe of P&CBs and their sp atial relationships both to each other and to residential buildings is limited. Due to this limit ation, methods were developed to estimate the unive rse of P&CBs in the United States. These estimates were developed using the 2011 Maryland Department of Planning (MDP) MDProperty View dataset (MDP 2011). This appendix presents estimates for the number of P&CBs, as well as supplemental information about building types, and distances between P&CBs and all other building types.
The analysis used four general different building t ype categories: (1) agricultural, (2) commercial, ( 3) government, and (4) industrial. Agricultural, comme rcial, and industrial buildings generally included all buildings with the corresponding zoning designation s on parcels that were not government owned. Government buildings generally included all buildin gs on government owned parcels that were zoned commercial or industrial.
Results are disaggregated according to the distance between a P&CB and other building categories, including residences. The distance categories consi dered in this analysis correspond to distances modeled and described in Section 3 of the Approach document. The universe of P&CBs and proximity analysis presents estimates of the number of reside nces located near P&CBs, and estimates of the number of non-residential buildings located near P& CBs.
There are no publically available sources of inform ation on the national number of buildings for properties that are zoned industrial or agricultura l. However, the U.S. Department of Energy’s 2003 Commercial Building Energy Consumption Survey (CBEC S) includes national estimates for the number of commercial buildings (DOE 2008). In addition, hist orical and current data on the national amount of commercial floor space are available from the U.S. Department of Energy, so these data are used to validate the corresponding estimates derived from t he MDProperty View data.
The MDP (2011) data are particularly useful because they include geographical information (latitude an d longitude coordinates) for each property. Therefor e, these data can be used to estimate three critica l elements of the analysis related to the proximity o f P&CBs to residences and other P&CBs:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### (1) the number of buildings with at least one resid ence within a given distance from PnCBs,
(2) the number of residences near PnCBs, and
3) the number of other PnCBs near PnCBs.
The MDP data have geographic and property informati on collected by the State Department of Assessments and Taxation (SDAT) for 2.2 million par cels in the state of Maryland, including parcel ownership and address information, and basic detail s about the parcel and structures associated with i t. While the database does not contain information on the number of buildings on a parcel, it tracks othe r information that may indicate whether buildings may be present on the parcel.
The approach grouped the parcels into categories an d then visually inspected a random sample of parcels. This visual inspection was used to estima te the average number of parcels with buildings and the average number of P&CBs per parcel. The number of P&CBs in Maryland was then calculated as the sum of the product of the number of parcels with bu ildings and the average number of P&CBs per parcel across the different parcel categories. National e stimates are then extrapolated from the Maryland estimates based on the ratios of the total number o f employer establishments in the United States to the total number in Maryland for the industries com prising the four building categories included in th e analysis.
The number of buildings per parcel is not included in the MDProperty View dataset. Therefore, a random sample of parcels was drawn for each parcel type and the satellite images of the sampled parcels were visually examined using ArcGIS to iden tify the number of buildings on the parcel. The vis ual examination was complicated by the fact that parcel boundaries were not clearly delineated (parcels were denoted by centroids - single points represent ing the parcel’s geometric center) and some parcels were obscured by tree cover or fuzzy satellite imag ery. Best judgment was used to determine the parcel’s boundaries, (e.g., physical landmarks, com parison of the parcel’s acreage data to area measur ed by ArcGIS measurement tool, etc.), and the number o f buildings on the parcel (cross-referencing with Google Maps). The methods used to estimate the aver age number of P&CBs per parcel varied for the different types of parcels. Another limitation of the analysis is the assumption that the Maryland da ta are representative of the number of buildings per p arcel nationally.
For commercial, government, and industrial parcels, the average number of buildings per parcel was estimated by examining a sample of randomly selecte d parcels with buildings in each building category. Agricultural parcels were grouped into 1 of 13 size categories. Approximately 10 parcels were randomly selected for each size-residence category, with the exception of some of the smallest and largest size

-**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### residence categories which had fewer than 10 parcel s. The averages from the agricultural sample were then weighted by the proportion of parcels with bui ldings in each size-residence category.
The first building on an agricultural parcel with a residence is assumed to be the residence and all o ther buildings on that parcel are assumed to be agricult ural buildings – only the agricultural buildings ar e included in the analysis since residential building s are already regulated under the residential provi sions of the LRRP program. For parcels with mixed commer cial and residential use, it is assumed that all buildings are P&CBs. Buildings that were mixed-use were also identified. For example, a two story building with retail space on the first floor and a partments on the second floor.
The number of P&CBs in Maryland is estimated as the product of the total number of parcels with at least one building and the average number of buildi ngs per parcel. National estimates are then extrapolated from the estimates for Maryland using the ratio of national employer establishments in th e affected sectors to employer establishments in Mary land. Note that while other indicators could be used to extrapolate from Maryland to the U.S. (e.g. , population, output, all employer and non-employer establishments), an important advantage of using em ployer establishments is that these data are available at the Zip Code level.
This allows for estimating the number of buildings by Zip Code, where the number of buildings can be combined with estimates for the proximity of the bu ildings to housing units. Note that the ratio of Maryland to U.S. commercial establishments was used to extrapolate from Maryland government P&CBs to the national number of P&CBs. This ratio is used because nearly all government parcels are zoned as commercial. It is also worth noting that the Maryl and to United States ratio for both commercial establishments and population are both 1.87% (U.S. Census Bureau 2011a).

As indicated in Table O-1, there are an estimated 1 5.7 million P&CBs in the United States. Agricultura l buildings account for approximately 50.3 percent (7 .9 million) of the total, commercial buildings acco unt for 28.4 percent (4.4 million), government building s account for 6.0 percent (0.9 million), and indust rial buildings account for 15.3 percent (2.4 million).
The steps used to combine the MDP data with Census data to develop national estimates of the proximity between P&CBs and residences are summarized as foll ows:**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### 1. Use decennial Census data to group ZIP Code Tabulati on Areas (ZCTAs) into **ZIP Code Groups** , so we can match those areas outside of Maryland with a reas in Maryland that are likely to be similar in terms of the proximity between P&CBs and residences. 2. Use the MDP data to estimate the proximity of P&CBs to residences and other P&CBs within each Maryland ZIP Code Group. 3. Use Census County Business Patterns data compiled b y ZIP Code on employer establishments to assign a weight to each Maryland ZIP Code Group, so that 4. Extrapolate from the Maryland data to national esti mates for the parameters listed above. 5. Estimate the national parameters for proximity betw een P&CBs, residences, and other P&CBs as the weighted average of these parameters for the Ma ryland ZIP Code Groups.**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

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### Table O-1: Estimated Universe of P&CBs in Maryland and the United States

Building Category 1 Ind . For

Dwellin
gs 2,3 Structure Square Footage Reported Class

Code 4 Maryland National

Total Parcels with P&CBs P& CBs Per Parcel 5 Total Number of P&CBs Number

of Establish. 5 Number

of Establish. 6 Ratio of

MD to U.S. 6 Estimated Number of P&CBs

Agricultural Yes - - 17,086 3.3 56,384 192 21,679 0 .886% 6,363,883

No

- - 5, 8 3 0 2. 3 13 , 4 0 9 1, 5 13 , 4 3 1 **Subtotal 22,916**  **3.0 69,793**  7,877,314

Commercial Y e s
- - 3, 0 0 3 1.3 3, 9 0 4 131,721 7,041,758 1.871% 20 8 , 6 5 8

No Y e s
- 43 , 7 0 1 56 , 8 1 1 3, 0 36 , 3 9 8

- No

- 17 , 1 7 4 22 , 3 2 6 1, 1 93 , 2 6 6 **Subtotal 63,878**  **83,041** 4,438,322
Government

- Y e s
- 2, 8 8 8 1.4 4, 0 4 3

- - 21 6 , 0 8 8

No Y e s
- 6, 7 8 2 9, 4 9 5 50 7 , 4 8 3

-

- P a r k /R e c 1, 4 0 8 1, 9 7 1 10 5 , 3 4 5 - Public

W o r k s 1,525 2,135 114,110

**Subtotal 12,603**  **17,644** 943,026
Industrial

- Y e s
- 11 , 9 6 2 1.6 19 , 1 3 9 3,506 353,143 0.993% 1, 9 27 , 3 9 2

- No

- 2, 9 3 7 4, 6 9 9 47 3 , 2 1 2 **Subtotal 14,899**  **23,838** 2,400,604
Total 114,296 194,316 15,659,266**Appendices to the Approach for Estimating Exposures and Increment**  **al Health Effects from Lead due to Renovation** , Repair, and Painting Activities in Public and Comme rcial Buildings

Peer Review Draft Q-2 July 25, 2014 Notes: 1. The “Agricultural” land use category used in the Maryland database includes some parcels tha t would be considered residential under the LRRP program. To account for this, the agricultural parcel counts have been adju sted by 66.5 percent to account for the difference between the total area of farmland in Maryland according to the Census of Agriculture (2,051,756 acres) and the total area of the parcels classified as agricultural in the Mary land dataset (3,084,052 acres). 2. Agricultural and commercial parcels were broken out by whether they contain residential structures because these structures are already covered under the LRRP program under the residential rule. There were onl y three industrial parcels with records indicating there we re residential structures and all of them appear to be categorized incorrectly. Thus, industrial parcel c ounts are not broken out by the presence of residen tial dwellings. 3. A parcel is assumed to have a residence if it ha s a nonzero value in the total number of dwellings field. 4. Class codes provide detail on the parcel’s owne rship type (public, state, private, etc.) and use ( hospital, park, school). Parcels without housing and/or building da ta were grouped by class code and examined separate ly. Government parcels in the (1) Parks and Recreation and (2) Public Works class codes were identified as potentially different from other parcel types with respect to the likelihood of having a building on t he parcel. 5. Estimated from a sample of parcels that were vis ually inspected to obtain information not contained in the original MDP dataset. See Appendix 1 for a descript ion of how these parameters were estimated and the estimated confide nce intervals. 6. “Agricultural” establishments include those from NAICS 11; “Commercial” establishments include thos e from NAICS 23, 42, 44 ,48, 51, 52, 53, 54, 55, 56, 61, 6 2, 71, 72, and 81; “Industrial” establishments incl ude those from NAICS “21, 22, and 31. **Sources: MDP 2011, USDA 2009, U.S. Census Bureau 20 11a, b** 


# Q.1. References for Appendix Q Maryland Department of Planning and Maryland State Department of Assessments and Taxation (MDP). 2011. **MdProperty View 2011 Edition.** Maryland Department of Planning is the owner of the MDProperty View Product, parcel x,y reference point s, property maps and land use/land cover data. Maryland State Department of Assessments and Taxation owns the parcel dataset records in the MdProperty View Product.

U.S. Census Bureau. 2011a. **2010 Census of Poulation and Housing Summary Files for All States (Machine**  **Readable Files)**

. U.S. Department of Energy (U.S. DOE). 2008. **2003 Commercial Buildings Energy Consumption Survey :**  **Public Use Microdata Files**

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