NOVEMBER 13, 2007

REVISED FINAL REPORT ON CHARACTERIZATION OF DUST LEAD LEVELS AFTER RENOVATION, REPAIR, AND PAINTING ACTIVITIES

Prepared By

BATTELLE 505 King Avenue Columbus, Ohio 43201

EPA Contract No. EP-W-04-021

Prepared For

Sineta Wooten, Project Officer Ronald Morony, Work Assignment Manager Samuel Brown, Deputy Work Assignment Manager

Program Assessment and Outreach Branch National Program Chemicals Division Office of Pollution Prevention and Toxics

U.S. Environmental Protection Agency 1200 Pennsylvania Avenue NW (7404T) Washington, D.C. 20460Acknowledgements

Many people contributed to the completion of this study. At EPA, Mr. John Schwemberger served as the EPA Work Assignment Manager as sisted by Mr. Samuel Brown. At Battelle, Mr. Tim Pivetz served as the project manage r supported by a large team of people. In alphabetical order, those w ho contributed significantly include Mr. Wesley Baxter, Ms. Rona Boehm, Mr. Peter Bujnak, Dr. Cynthia Cooper, Mr. Steven Gomori, Mr. Brad Goodwin, Mr. Pernell Horton, Ms. Nicole-Iro z Elardo, Mr. Darrell Joseph, Ms. Michelle Lobdell, Ms. Kathya Mahadevan, Mr. Ian MacG regor, Mr. Mark Misita, Ms. Susan Morriss, Ms. Jyothi Nagaraja, Mr. Henr y Pham, Ms. Cyndi Reese, Mr. Adam Ridenbaugh, Dr. Jessica Sanford, Dr. Ketra Schmitt, Ms. Elizabeth Slone, Mr. Warren Strauss, Mr. Joseph Tabor, Ms. Rena Thomas, Ms. Stephanie Weber, Mr. Zachary Willenberg, and Ms. Jennifer Zewatsky.

The study team expresses sincere thanks to th e many property owners who made their houses available for renovation work under this study, as well as to Columbus Public Schools who provided access to one of their vacant buildi ngs. The study team also thanks the many subcontractors who supported the study includin g police officers in Columbus and Pittsburgh, renovation firms and the renovation workers who implemented the study protocols, abatement cleaning firms, environmental testing firms wh o provided XRF testing and clearance testing services, the analytical laborat ory, and staff at Healthy Housi ng Solutions, Inc. who supported the search for potential units. The study also th anks those staff at the Ohio Department of Health, Columbus Health Department, Pennsyl vania Department of Health, and Allegheny County Health Department who provided guidance and assistance along the way.

Thanks also to other staff and subcontractors who provided support to the study but are not named above. The study could not have been completed without the full effort of everyone on the team.i TABLE OF CONTENTS

Page

GLOSSARY OF TERMS............................................................................................................. . ...............vii

1.0 INTROD UCTION............................................................................................................... ............1-1 1.1 BACKGROUND AND PU RPOSE OF STUDY................................................................. 1-1 1.2 PROPOSED RULE SUMMARY.......................................................................................1-1 1.3 STUDY OBJE CTIVES ......................................................................................................1-2

2.0 SUMMARY OF CONCLUSI ONS AND PEER R EVIEWS.............................................................2-1 2.1 CONCLUSION S SUMMARY ...........................................................................................2-1 2.2 PEER REVIEWS OF STUDY D ESIGN............................................................................ 2-2 2.3 HUMAN SUBJEC TS REVIEW.........................................................................................2-3

3.0 STUDY DESIGN .............................................................................................................. .............3-1 3.1 SITE SELECTION PROC ESS AND REQUIR EMENTS ..................................................3-1 3.2 SAMPLING METHODS ...................................................................................................3-2 3.3 LABORATORY AN ALYSIS METH ODS........................................................................... 3-3 3.4 FIELD SAMPLIN G PROTOCOL S....................................................................................3-4 3.4.1 Inte rior Jobs ........................................................................................................3- 4 3.4.2 Exteri or Jobs .......................................................................................................3-9 3.5 PARTICIPATING CONTRACTORS (RRP WORKERS, CLEANING, CLEARANCE TE CHNICIAN S) ......................................................................................3-10 3.6 ENVIRONMENTAL SAFET Y AND HEALTH PLAN.......................................................3-10

4.0 FIELD WO RK SUMMARY ........................................................................................................ ....4-1 4.1 OVERVIEW OF SITE SEL ECTION/SCREENIN G PROCESS........................................4-1 4.2 NUMBER AND TYPE OF EXPER IMENTS COMP LETED..............................................4-3 4.3 PROTECTION AND CL EANING ROUT INES..................................................................4-4 4.4 ENVIRONMENTAL SAMPLES COLLE CTED .................................................................4-6 4.5 PROTOCOL/SAMPLING I SSUES AND RESOLU TIONS ...............................................4-7 4.6 ADHERENCE TO ENVIRONMENTAL SAFETY AND HEALTH PROTOCOLS............4-10 4.7 RECORD KEEPING........................................................................................................ 4-11

5.0 STATISTICA L ANALYSIS PLAN ................................................................................................. .5-1 5.1 DATA ORGA NIZATION ...................................................................................................5-1 5.2 DESCRIPTI VE ANALYSES .............................................................................................5-1 5.3 STATISTICA L MODELS ..................................................................................................5-2

6.0 DESCRIPT IVE ANALYSES ...................................................................................................... ....6-1 6.1 HOUSING UNIT/EXPERIMENT SUMMARY INFORM ATION.........................................6-1 6.2 PAINT LEAD LEVELS .....................................................................................................6-5 6.3 PRE-WORK CLEAR ANCE LEVEL S................................................................................6-6 6.4 BULK DEBR IS SAMPLES .............................................................................................6-10 6.5 INTERIOR DU ST SAMPLES .........................................................................................6-11 6.5.1 Interior Fl oor Sample s.......................................................................................6-11 6.5.2 Interior Sill Sample s ..........................................................................................6-17 6.5.3 Hallway/Ex it Sample s .......................................................................................6-22 6.5.4 Exterior Post-Work Tray Samp les during Window Replacements ...................6-23 6.6 INTERIOR AIR SAMPL ES.............................................................................................6-24 6.7 EXTERIOR DU ST SAMPLES........................................................................................6-25ii Page

6.8 SOIL SAMPLES.............................................................................................................6 -29 6.8.1 Soil Sampling fo r Interior Jobs.......................................................................... 6-29 6.8.2 Soil Sampling fo r Exterior Jobs......................................................................... 6-32 6.9 LENGTH OF AC TIVITY DA TA.......................................................................................6-34 6.9.1 Duration of Po st-work Clea ning ........................................................................ 6-34 6.9.2 Duration of Cleani ng Verifica tion ...................................................................... 6-35 6.9.3 Duration of Pr e-Work Se t Up ............................................................................ 6-36 6.9.4 Duration of In terior RRP Work .......................................................................... 6-37 6.10 CLEANING VERIFI CATION DA TA................................................................................6-38 6.10.1 Verification of Floor Zones................................................................................ 6-38 6.10.2 Verification of Window Sills............................................................................... 6-47

7.0 STATISTICAL MODE LING RESU LTS .........................................................................................7-1 7.1 POST-WORK DUST LEAD LEVEL S...............................................................................7-2 7.1.1 Post-Work Work Room Dust Lead Levels ..........................................................7-2 7.1.2 Post-Work Tool Room Dust Lead Levels ............................................................7-4 7.1.3 Post-Work Observati on Room Dust Le ad Levels ...............................................7-6 7.2 POST CLEANING STAGE – DUST LEAD LEVELS........................................................7-7 7.2.1 Post-Cleaning Work Room Dust Le ad Levels ....................................................7-7 7.2.2 Post-Cleaning Tool Room Dust Lead Levels....................................................7-10 7.2.3 Post-Cleaning Observat ion Room Dust Lead Levels .......................................7-13 7.3 POST-VERIFICATION STAG E DUST LEAD LEVELS..................................................7-16 7.3.1. Post-Verification Work Room Dust L ead Levels ...............................................7-16 7.3.2. Post-Verification Tool Room Dust L ead Levels ................................................7-18 7.3.3. Post-Verification Observ ation Room Dust Lead Level s....................................7-20 7.4 COMPARISON OF RULE PRACTIC ES AND BASELINE PRACTICES-POST-JOB DUST LEAD LEVELS ....................................................................................................7-22 7.4.1. Post-Job Work Ro om Dust Lead Levels...........................................................7-22 7.4.2. Post-Job Tool R oom Dust Lead Levels ............................................................7-23 7.4,3. Post-Job Observation Room Dust L ead Levels ................................................7-24 7.5 EXTERIOR DUST LEAD LEVEL S.................................................................................7-26 7.6 POST-VERIFICATION PREDICTORS OF CLEARANCE FAILURE.............................7-28 7.6.1 Single Covariate Modeling Resu lts................................................................... 7-29 7.6.2 Multivariate Modeling Resu lts...........................................................................7-45 7.6.3 Summary of Results..........................................................................................7-47

8.0 QUALITY ASSURANCE AND QU ALITY CONTROL RESULTS ..................................................8-1 8.1 FIELD AUDI T RESULT S .................................................................................................8-1 8.1.1 Site Sele ction Audit.............................................................................................8-1 8.1.2 Field Impl ementatio n...........................................................................................8-1 8.1.3 EPA Site Visits ....................................................................................................8-2 8.2 LABORATORY A UDIT RESULT S...................................................................................8-2 8.3 QC SAMPLE RESULTS, I SSUES, AND RESO LUTIONS...............................................8-2 8.4 DATA AUDIT PROC ESS AND RESU LTS.......................................................................8-4 8.5 SOFTWARE REVIEWS ...................................................................................................8-4

9.0 CONC LUSIONS............................................................................................................... .............9-1 9.1 OBJECTIVE 1: EFFECT OF INTENS ITY LEVEL ON DUST- LEAD LEVELS .................9-1 9.2 OBJECTIVE 2: EFFECT OF USE OF INTERIOR PLASTIC ...........................................9-1 9.3 OBJECTIVE 3: EFFECT OF USE OF PROPOSED RULE CLEANING METHOD .........9-2 9.4 OBJECTIVE 4: ANALYSIS OF DUST MIGR ATION .......................................................9-2iii 9.5 OBJECTIVE 5: EFFECT OF PLASTIC GROUND COVERING DURING EXTERIOR WORK .......................................................................................................................... ..9-3 9.6 OBJECTIVE 6: EFFECTS OF CL EANING VERIFICAT ION PROCESS.........................9-3 9.7 OBJECTIVE 7: COMPARISON OF RULE PRACTICES AND BASELINE PRACTICES 9-6

APPENDIX A: EXPERIMENT-SPEC IFIC REPO RTS .............................................................................A-1

APPENDIX B: DETAILED DESCRIPTIVE ANAL YSES OF EXTERIOR DUST LEAD LOADINGS (INCLUDING BULK D EBRIS SAMPLES)........................................................................B-1

APPENDIX C: DETAILED DESCRIPTIVE AN ALYSES OF INTERIOR FLOOR DUST LEAD LO ADINGS ........................................................................................................... C-1

APPENDIX D: DETAILED DESCRIPTIVE AN ALYSES OF INTERIOR WINDOW SILL DUST LEAD LOAD INGS .......................................................................................................... D-1

APPENDIX E: DETAILED DESCRIPTIVE ANAL YSES OF EXTERIOR DUST LEAD LOADINGS (WITHOUT BULK DEBR IS SAMPLES)...........................................................................E-1

APPENDIX F: DETAILED DESCRIPTIVE ANALYSES OF INTERIOR AIR LEAD CONCENT RATIONS.............................................................................................F-1

APPENDIX G: DETAILED DESCRIPTIVE ANAL YSES OF AVERAGE POST-WORK WORK ROOM FLOOR DUST LEAD LOADING, AVERAG E PAINT LEAD LOADING, AND AREA DISTURBED ................................................................................................................... G-1

APPENDIX H: DETAILED DESCRIPTI VE ANALYSES OF INTERIOR HALLWAY DUST LEAD LO ADINGS ................................................................................................ H-1

APPENDIX I: DETAILED STATISTICAL MODE LING RESULTS OF RESIDENTIAL FLOOR LEAD LEVELS ..................................................................................................................I -1

APPENDIX J: DETAILED STATISTICAL MODELI NG RESULTS OF RESIDENTIAL WINDOW SILL LEAD LEVELS......................................................................................................... J-1

APPENDIX K: DETAILED STATISTICAL MODELING RESULTS OF COF FLOOR LEAD LEVELS .................................................................................................................K- 1

APPENDIX L: DETAILED STATISTICAL MODE LING RESULTS OF COMBINED HOUSING UNIT/COF FLOOR L EAD LEVEL S..................................................................................L-1

APPENDIX M: DETAILED STATISTICAL MODE LING RESULTS OF COMBINED HOUSING UNIT/COF WINDOW SILL LEAD LEVEL S.....................................................................M-1

APPENDIX N: DETAILED STATISTICAL MODELING RESULTS OF EXTERIO R DUST LEAD LEVELS FOR COMBINED RESIDENT IAL AND COF DATA ....................................................... N-1

APPENDIX O: HISTOGRAMS OF RESIDUA LS OF BEST FITTI NG MODE LS .................................... O-1

LIST OF TABLES

Table 4-1. Housing Unit Distributi on by City .................................................................................. ...4-1 Table 4-2. Interior RRP Jobs Ac ross Three Levels of RRP Work.....................................................4-3 Table 4-3. Exterior RRP Jobs Ac ross Three Levels of RRP Wo rk ...................................................4-4iv Table 4-4. Interior Jobs and Associated Approaches for Con ducting Exper iment ...........................4-5 Table 4-5. Exterior Jobs Co nducted in Each City .............................................................................4-6 Table 4-6. Total Number of Envi ronmental Samples Collecte d .......................................................4-7 Table 6-1 Housing Un it Information ............................................................................................. ....6-1 Table 6-2 Interior Experi ment Summary Table ................................................................................6-2 Table 6-3 Exterior Experi ment Summary Table ...............................................................................6-4 Table 6-4 Summary of the Pre-wo rk Clearance In formation............................................................6-8 Table 6-5 Interior Bulk Samples by J ob Type ................................................................................6-10 Table 6-6 Exterior Bulk Samples by J ob Type ...............................................................................6-10 Table 6-7 Exterior Dust Wipe Samp les During Window Replacem ent .......................................... 6-24 Table 6-8 Number (and Percentage) of Interi or Air Dust Lead Concentration Measurements ( μ g/m 3 ) Below Detection Limit Measur ements by Room and Stage ..............................6-24 Table 6-9 Distance of Exte rior Trays fr om Work............................................................................6-26 Table 6-10 Average Back ground Dust Loading for Exterior Experiments, by Job Ty pe..................6-29 Table 6-11 Pre- and Post-Work Soil Sample Re sults (μg/g) by Inte rior Experi ment .......................6-30 Table 6-12 Average Post-work/Pre- work Ratios for Interior Work Soil Samples by Job Type ........6-32 Table 6-13 Pre- and Post-Work Soil Sample Re sults (μg/g) for Exte rior Experiments....................6-33 Table 6-14 Ratio of Post-work to Pre-work Soil Lead Level in Exterior Soil Samples by J ob Type.................................................................................................................. ..6-34 Table 6-15 Summary of Post-work Cleaning Times, by Phase (in minutes ) ...................................6-34 Table 6-16 Summary of Post-work Cleani ng Times, by Job (in minutes )........................................6-35 Table 6-17 Summary of Post-work Cleaning Times, by Contractor (in minu tes).............................6-35 Table 6-18 Duration of Post-work Cleaning Ve rification Stages, by P hase (in minut es).................6-35 Table 6-19 Duration of Post-work Cleaning Ve rification Stages, by J ob (in minut es) .....................6-36 Table 6-20 Duration of Post-work Cleaning Verifi cation Stages, by Contractor (in minutes) ..........6-36 Table 6-21 Duration of Interior Pre-work Set-up (i n minutes ) ..........................................................6-36 Table 6-22 Duration of Interior Pr e-work Set-up, by Contract or ......................................................6-36 Table 6-23 Duration of Interior Pre-work Set-up, by Building Ty pe (in minut es) .............................6-37 Table 6-24 Duration of Interior Work, by Job (i n minute s) ...............................................................6-37 Table 6-25 Duration of Exterior Work, by Job (i n minutes ) ..............................................................6-38 Table 6-26 Floor Verification Information by Phase .........................................................................6-40 Table 6-27 Post-cleaning and Post-verificat ion Floor Lead Levels for Experiments with Zero and ≥ 1 Verification Failu res by Ph ase................................................................... 6-40 Table 6-28 Floor Verification Information by Job Type ....................................................................6-41 Table 6-29 Floor Verification In formation by C ontractor ..................................................................6-42 Table 6-30 Floor Verification In formation by Fl oor Type ..................................................................6-42 Table 6-31 Floor Verification Info rmation by Floor Conditi on........................................................... 6-42 Table 6-32 Floor Verification Information by Job Type, Floor Type, and Floor Condition ...............6-43 Table 6-33 Floor Zones That Failed Second Wet Cloth Ve rification................................................6-43 Table 6-34 Verification Informati on for Interior Ex periment s............................................................6-45 Table 6-35 Window Sill Verificati on Information by Phas e ..............................................................6-47 Table 6-36 Window Sill Verification Information by In tensity Lev el..................................................6-47 Table 6-37 Verification Informati on for Interior Ex periment s............................................................6-47 Table 6-38 Post-cleaning and Post-verificat ion Sill Lead Levels for Experiments with Zero and ≥ 1 Verification Failu res by Ph ase................................................................... 6-48 Table 6-39 Window Sill Verification In formation for Interi or Experim ents........................................6-49 Table 7-1 Post-Work Work Ro om Modeling Re sults........................................................................7-4 Table 7-2 Post-Work Tool R oom Modeling Re sults.........................................................................7-5 Table 7-3 Post-Work Observati on Room Modeling Results ............................................................7-7 Table 7-4 Post-cleaning Work Room Modeling Results ..................................................................7-9 Table 7-5 Post-cleaning Tool Room Modeling Results..................................................................7-12 Table 7-6 Post-cleaning Observat ion Room Modeli ng Result s .....................................................7-14 Table 7-7 Post-verification Work Room Modeling Re sults.............................................................7-17 Table 7-8 Post-verification Tool Room Modeling Results ..............................................................7-19 Table 7-9 Post-verification Observ ation Room Modeli ng Result s..................................................7-21 Table 7-10 Post-job Work Room Rule vs. Non-Rule Mode ling Result s...........................................7-23v Table 7-11 Post-job Tool Room Rule vs. Non-Rule Mode ling Result s ............................................7-24 Table 7-12 Post-job Observation Room Ru le vs. Non-Rule Mo deling Resu lts................................7-25 Table 7-13 Comparison Exterior Dust Pan Levels Within Each Job with Bulk Debris Samples Included (Model Based Estimate Differences an d Ratios ) ............................................7-26 Table 7-14 Comparison Exterior Dust Pan Lev els Within Each Job Excluding Bulk Debris Samples (Model Based Estimate Differences an d Ratios )............................................7-27 Table 7-15 Single Covariate Logistic Regression Modeling Estimates for Proportion of Failed Post-verification Fl oor Wipe Sa mples............................................................................7-32 Table 7-16 Single Covariate Model Results for Pr oportion of Failed Post-verification Floor Wipe Samples acro ss All J obs................................................................................................ 7-33 Table 7-17 Single Covariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across Non-restrict ed Jobs ....................................................................7-34 Table 7-18 Single Covariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across Non-restricted Jobs Usin g Rule Clea ning.................................. 7-35 Table 7-19 Single Covariate Logistic Regression Modeling Results for Probability of Failed Post-verificati on Floor Wi pe ...........................................................................................7-38 Table 7-20 Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe ........................................................................................................................ ..7-39 Table 7-21 Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe across Non-re stricted Jobs...................................................................................7-40 Table 7-22 Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe across Non-restricted Jobs Using Rule Cleaning .................................................7-41 Table 7-23 Single Covariate Logistic Regression Modeling Results for Probability of Failed Window Sill Cleani ng Verifica tion .................................................................................. 7-43 Table 7-24 Single Covariate Model Results for Pr obability of Failing a Single Post-verification Window Sill Wipe Samp le ..............................................................................................7-44 Table 7-25 Single Covariate Model Results for Pr obability of Failing a Single Post-verification Window Sill Wipe Sample ac ross Non-restri cted Jobs ..................................................7-44 Table 7-26 Single Covariate Model Results for Pr obability of Failing a Single Post-verification Window Sill Wipe Sample across Non-re stricted Jobs Using Rule Clea ning................7-45 Table 7-27 Multivariate Model Results for Pro portion of Failed Post-verification Floor Wipe Samples across a ll Experim ents.................................................................................... 7-45 Table 7-28 Multivariate Model Results for Pro portion of Failed Post-verification Floor Wipe Samples across Non- restricted Jobs .............................................................................7-46 Table 7-29 Multivariate Model Results for Pro bability of Failed Post-verification Floor Wipe across all Experiments...................................................................................................7-46 Table 7-30 Multivariate Model Results for Pro bability of Failed Post-verification Floor Wipe across Non-re stricted Jobs ............................................................................................7-47 Table 8-1 Summary of Exte rnal QC Re sults....................................................................................8-3 Table 8-2 Schneider Laboratorie s Quality Cont rol Data ..................................................................8-3 Table 9-1 Post-Job Floor Lead Levels and Number Of Samples Failing Clearance in the Work Room ........................................................................................................................ ....9-4 Table 9-2 Post-Job Floor Lead Levels And Number Of Samples Failing Clearance (Excluding Door Planing And High Heat Gun Jobs)...................................................................................9-4 Table 9-3 Post-Job Floor Lead Levels and Number of Samples Failing Clearance (Door Planing and High Heath Gun Jobs Only) in t he Work R oom.................................9-5 Table 9-4 Post-job Window Sill Lead Levels an d Number of Samples Failing Clearance ..............9-5 Table 9-5 Post-job Window Sill Lead Levels and Number of Samples Failing Clearance For Sampled Wi ndow S ills ...............................................................................................9-6 Table 9-6 Post-job Window Sill Lead Levels and Number of Samples Failing Clearance Sampled Dust Co llection Tr ays........................................................................................9-6 Table 9-7 Post-Job Average Floor Lead Level s and Failed Samples by Job Type.........................9-9 Table 9-8 Post-job Window Sill Average Lead Levels and Failed Sample s by Job Type .............9-10vi LIST OF FIGURES

Figure 3-1 Flowchart of Interior Field Sampling Activities .................................................................3-8 Figure 6-1 Scatter Plot of Area Disturbed vs. Average Post-work Work Room Floor Lead Loading ................................................................................................................. ..6-6 Figure 6-2 Post-work Work Room Floor Lead Levels, by Job.........................................................6-11 Figure 6-3 Post-Cleaning Work Room Floor Lead Levels, by Job and Plastic Use........................6-12 Figure 6-4 Post-Cleaning Work Room Floor Lead Levels, by Job and Cleaning Method...............6-13 Figure 6-5 Post-Job Work Room Floo r Lead Levels, by J ob and Phas e ........................................6-14 Figure 6-6 Post-Job Tool Room Floor Lead Levels, by Job and Phas e..........................................6-16 Figure 6-7 Post-Job Observation Room Floor Lead Levels, by Job and Phas e.............................6-16 Figure 6-8 Post-Cleaning Work Room Sill Lead Levels, by Job a nd Plastic Use ...........................6-18 Figure 6-9 Post-Cleaning Work Room Sill Lead Levels, by Job and Cleaning Me thod..................6-19 Figure 6-10 Post-Job Work Room Sill Lead Levels, by Job and Rule Us e.......................................6-20 Figure 6-11 Post-Job Work Room Sill Lead Levels, by J ob and Phas e............................................6-20 Figure 6-12 Post-Job Tool Room Sill L ead Levels, by Job and Rule Us e ........................................6-21 Figure 6-13 Post-Job Observation Room S ill Lead Levels, by Job and Rule Us e............................6-22 Figure 6-14 Post-work Air Lead Concentratio ns in the Work R oom, by Jo b.....................................6-25 Figure 6-15 Exterior Dust Lead Levels on and Under Rule Plastic, by Job (w/bulk samples) ............................................................................................................6 -28 Figure 6-16 Exterior Dust Lead Levels on and Under Rule Plastic, by Job (w/bulk samples) ............................................................................................................6 -28 Figure 6-17 Time Series of Soil Sample s Near the Front Po rch at H0 9 ...........................................6-31 Figure 6-18 Post-verification Fl oor Lead Loadings by Phase ...........................................................6-39 Figure 6-19 Post-verification Floo r Lead Loadings by Job Type .......................................................6-41 Figure 6-20 Number of Experiments by Post-clean ing and Post-verification Floor Lead Loadings..6-44 Figure 6-21 Number of Experiments by Post-clean ing and Post-verificati on Sill Lead Loadings .....6-48 Figure 9-1 Post-Job Work Room Floor Lead Levels, by Job and Rule Us e .....................................9-7 Figure 9-2 Post-Job Work Room Sill Lead Levels, by Job and Rule Us e.........................................9-7vii 

### Glossary of Terms for the 

### Field Study to Characteri ze Dust Lead Levels After Renovation, Repair, and Painting

Baseline Cleaning – a series of steps to remove dust that are representative of standard cleaning practices used by RRP contractor s without a final EPA rule fo r Renovation, Repair, and Painting. These include sweeping and vacuuming with a non-HEPA vacuum.

Baseline Practices – the set of protection and clean-up pr actices (no use of protective plastic during work and use of baseline cleaning follo wing work) representing typical work practices utilized for a renovation job without a final EPA rule for Renovation, Repair, and Painting.

Child-Occupied Facility (COF) – a building, or portion of a vacant building, constructed before 1978 that could be used by children under si x years old, such as a daycare center or early year kindergarten at a schoo l, and that could conceivably me et the formal definition of a Child-Occupied Facility in 40 CFR part 745.223.

Dust Lead Loading – the amount of lead obtained from in a given sample area. For this report, dust lead loading is presented in μg/ft 2

.
Experiment – full implementation of a single occurre nce of a renovation job using one set of protection and clean-up methods ; includes all work, cleaning, and environmental sampling.

Housing Unit – a structure or portion of a structure th at is typically occupied by one or more persons as living quarters.

Job – a specific renovation, repair, or painting activity that is of in terest for the risk assessment and economic cost-benefit analysis, such as installing a new window.

Lead-based paint – paint or other coating with l ead content at or above 1.0 mg/cm 2 or at or above 0.5% by weight (5,000 μ g/g).

Level or Intensity-level – one of three job cate gories (low, medium, or high), determined based on pre-study expectations of the amount of dus t expected to be gene rated by each activity; because analysis of post-work lead levels indi cated that these pre-assigned levels were not entirely consistent with actual dust generated, the primary analyses presented in the report summarize the renovation activit ies by individual job.

Observation Room – an interior room adjacent to the T ool Room but not the Work Room that represents other areas of a house impacted by interior RRP work.

Phase – one of four iterations of a job specified by the use or non-use of containment and a particular cleaning method (Phase I=plastic shee ting with specialized cleaning, Phase II=plastic sheeting with baseline cleaning, Phase III=no pl astic sheeting with spec ialized cleaning, andviii Phase IV=no plastic sheeting with baseline cleaning), each implemented under comparable conditions so the effect of containment and cleaning method can each be assessed.

Replicate – a repetition of a specific j ob, including all four phases or iterations associated with the job (e.g., a second set of window replacements).

RRP – an acronym that stands for Renovation, Repair, and Painting.

Rule Practices – the set of protection a nd clean-up methods (use prot ective plastic during work, specialized cleaning following work, and cleaning verification) representing work practices under EPA’s Proposed Rule for Renovation, Repair, and Painting.

Sites - housing units or COFs where one or more study experiments were conducted.

Specialized (Rule) Cleaning – cleaning required by the EPA Proposed Rule for Renovation, Repair, and Painting; comprised of HEPA vacuuming and wet-mopping with two-bucket method. Note that neither phosphate nor ethyle nediamine tetraacetic acid (EDTA) was used in the wash waters.

Stage (of Sampling) – the four different points of tim e within an experiment at which environmental samples were collected (after completion of the RRP job, after completion of cleaning, after completion of the last wet cloth in the cleaning verification, and after completion of the last dry cloth in the cleaning verification).

Study Location – a city or other geographic area participating in the study through identification, recruitment, and part icipation of housing units and COFs.

Tool Room – an interior room immediately adjacent to the work room where workers might place equipment and materials needed for a job.

Work Area – the location within the work room or on the exterior perimeter where the actual RRP work was performed. Note that the interi or work area could cro ss the boundaries of the work room, e.g. if a window was being replaced from the inside, the work area might have included portions of the exterior.

Work Room – the room in the housing unit or COF wh ere the RRP work was to be performed.1-1 

### 1. Introduction

1.1. Background and Purpose of Study

Many residences built prior to 1978 c ontain lead-based paint that, if disturbed, is likely to create lead hazards for residents of the home. Data fr om the National Survey of Lead and Allergens in Housing indicate that lead-based paint was used in 24% of the housing constructed between 1960 and 1978, in 69% of housing constructed be tween 1940 and 1959, and in 87% of housing constructed prior to 1940. 1 Because of formulation change s, not only was lead more common among older homes but the lead content was higher as well. Actions that involve the disturbance or removal of lead-based paint, including ma ny renovation and repair ac tivities, can result in high localized concentrations of lead dust in the air and on exposed surfaces within the home, as well as in surrounding soils. 2

In support of the Federal government’s goal of eliminating childhood lead poisoning by 2010, EPA proposed a rule establishing requirements to protect residents of pre-1978 housing units from lead hazards due to Renovation, Repair, and Painting (RRP) activities. The proposed rule, issued under the authority of §402(c)(3) of th e Toxic Substances Control Act (TSCA), was published in the Federal Register on January 10, 2006. 3

In an effort to support a thorough risk assessment and cost-benefit analysis of the proposed rule, a field study was designed and conducted to characte rize dust lead levels during various stages of RRP activities. Results of that field study are presented in this report.

1.2. Proposed Rule Summary

EPA’s proposed rule establishes training, certifica tion, and accreditation requirements, as well as work practice standards, for contractors perf orming RRP work in housing built prior to 1978. Specifically, the proposal would establish require ments for training renovators and dust sampling technicians; certifying renovato rs, dust sampling technician s, and renovation firms; and accrediting providers of renovation and dust sampli ng technician training. The proposal would also require renovation professiona ls to follow certain lead-safe work practice standards. EPA developed these requirements, in part, based on conclusions drawn from a previous renovation and remodeling field study that identified rela tionships between certain renovation activities and elevated blood-lead levels. 4 The requirements address EPA’s concern that RRP work conducted by untrained and uncertified contractors may create new lead hazards, increas ing the risk of lead exposure to the residents of homes containing lead-based paint.

EPA has proposed a phased approach for implementa tion of this rule. In itially, the rule would apply only to RRP work performed in target housing built before 1960 – both rental and

1 U.S. Department of Housing and Urban Development (HUD). 2002. National Survey of Lead and Allergens in Housing, Volume I: Analysis of Lead Hazards, Final Report, Revision 7.1. 2 U.S. EPA. 2006. Air Quality Criteria for Lead (Second External Review Dra ft), Volume I of II. EPA/600/R-5/144aB. 3 U.S. EPA. 40 CFR 745, Lead; Renovat ion, Repair, and Painting Program; Proposed Rule. Federal Register (71 FR 1588, January 10, 2006). 4 U.S. EPA. 1999. Lead Exposure A ssociated with Renovation and Remode ling Activities: Phase III, Wisconsin Childhood Blood-Lead Study. EPA 747-R-99-002.1-2 owner-occupied – where a child with an elevated bl ood-lead level resides, unl ess, with respect to owner-occupied target housing, th e renovation professional obtains a signed statement from the owner-occupant indicating that the renovation will occur in the owner’s residence and that no child under age 6 resides there. By the end of the phase-in period, the app licability of the rule would be expanded to include all rental and owner-occupied target housing built between 1960 and 1977 and occupied by a child under age six. “Target housing” is de fined in §401 of TSCA as “any housing constructed before 1978, excep t housing for the elderly or persons with disabilities (unless any child under ag e 6 resides or is expected to reside in such housing) or any 0-bedroom dwelling.” EPA is also proposing to authorize interestedstates, U.S. Territories, and Indian Tribes to administer and enforce al l elements of the new renovation provisions.

1.3. Study Objectives

The study was designed to compare environmental lead levels at appropriate stages after various types of RRP activities were c onducted on the interior and exteri or of residential housing units and child-occupied facilities (COFs) . All jobs disturbed more than 2 square feet of lead based paint, which is the de minimus amount of disturbe d area, to which the propos ed rule applies. Of particular interest was the imp act of using specific work prac tices that renovation contractors would be required to follow under the proposed rule (e.g., the use of plastic containment in the work area and a multi-step cleaning protocol). The RRP activities conducted represented the range of activities permitted under the proposed rule, including work practices that are restricted or prohibited under 40 CFR part 745.227(e)(6). These restricted or prohibited work practices included:

• Open-flame burning or torching of lead-based paint;

• Machine sanding or grinding or abrasive bl asting or sandblasting of lead-based paint without a High Efficiency Particul ate Air (HEPA) exhaust control;

• Dry scraping of more than 2 square feet of interior lead-based paint in any one room and dry scraping totaling more than 20 square feet on exte rior surfaces; and

• Operating a heat gun on lead-based pain t at temperatures above 1100 degrees Fahrenheit.

The study design was tailored to generate data that would permit evaluation of the following seven study objectives:

Objective 1 : What is the effect of low-, medium -, and high-level RRP work on post-work, post-cleaning, and post-verifica tion dust-lead levels (int erior and exterior)? Objective 2 : Are there significant differences in l ead levels at the post-cleaning and/or post-verification phases from the use of heavy- duty polyethylene plastic sheeting during the work activity? Is there an interaction between level of RRP work and the use of plastic? Objective 3 : Are there significant differences in post-cl eaning and/or post-veri fication lead levels between surfaces cleaned with the proposed ru le cleaning method and surfaces cleaned with baseline cleaning methods? Is there an intera ction between level of RRP work and cleaning method used?1-3 Objective 4 : Are there differences in the amount of l ead dust migration from the Work Room to adjacent rooms between different levels of RRP work, use and non-use of plastic, and use and non-use of proposed ru le cleaning methods? Objective 5 : Does the use of plastic ground coverings during exterior work reduce the amount of dust lead falling onto the ground? Objective 6 : Are there significant differences in the lead levels remaining after the two steps of the cleaning verification process, i.e., th e wet cloth step and the dry cloth step?

Objective 7 : Are there significant differences in post -job lead levels between jobs conducted using proposed rule practices (use of plastic, specialized cleani ng, and cleaning verification) and those conducted using baseline practices (no plastic and baseline cleaning)?2-1 

### 2. Summary of Conclusions and Peer Reviews

2.1. Conclusions Summary

Objective 1 – Effect of Job Intensit y and Job Type on Lead Levels Generated The results indicated that both initial intensity level and job type impacted post-work, postcleaning, and post-verificati on floor and sill lead levels. Da ta collected by the study indicated significant variability in the lead levels generated across th e different jobs conducted. Using a power planer to remove paint from doors without exhaust controls generated significantly more lead than other interior jobs. On the other ha nd, cutting into walls to allow electrical, plumbing, or other work, generated significantly less lead than other jobs. On the exterior, jobs such as power sanding paint from walls generated large am ounts of lead while dry scraping paint from a wall generated relatively less; however, these findings differe d depending on whether the bulk debris and excessive dust gene rated by a job was included in th e sample results. Removal of paint by torch burning left large amounts of lead in the dust collection tr ays as big chunks of paint were scraped from porch ceilings following heating with the torch; however, only one percent of the lead created by th is work activity was measured by a dust wipe with the rest being work debris picked up by hand.

Objective 2 – Effects of Use of In terior Plastic Protective Sheeting The results indicated that use of plastic resulted in somewhat lower average lead levels in the Work room; however, this effect was not statis tically significant. Use of plastic did not consistently result in lower work room floor lead levels across job t ypes at the post-cleaning phase. The use of plastic sheeti ng resulted in lower geometric mean lead levels for door planing and high heat gun jobs than when plastic was not used, as measured at the post-cleaning stage. Consistently lower geometric mean floor lead levels were fou nd in the Tool and Observation rooms, respectively, with the use of plasti c across all three sampling stages although the differences were relatively small. Hypothesis testing of differences in floor and sill lead levels based on use of plastic had mixed results with some statistically significant differences and some not statistically significant.

Objective 3 – Effect of Use of Proposed Rule Cleaning Method The results indicated that the proposed Rule cl eaning method led to much lower geometric mean lead levels remaining in the Work room fo llowing post-work cleaning, which was confirmed by statistical modeling. Little impact of cleaning method was observed in either of the two nonwork rooms.

Objective 4 – Analysis of Dust Migration The results indicated that measurably lower leve ls of lead were measured in the Tool and Observation rooms when plastic was used dur ing the work, however, mixed results were obtained in regards to the statis tical significance of those differe nces. The cleaning method used in the work room did not have a significant im pact on lead levels in the non-study rooms.

Objective 5 – Effects of Plastic Gro und Covering During Exterior Work The use of plastic ground coverings captured la rge amounts of lead which were considerably larger than the lead that was below the plastic coverings. When considering all the dust and2-2 debris that was captured on top of the Rule plastic, predicted rati os of lead measured on top of the Rule plastic to lead measured under the Rule plastic ranged from 66:1 to 2,291:1 across exterior jobs. The lowest lower 95% confiden ce interval bounds on thos e ratios were 12:1 and 6:1, respectively, for the power sanding and the torc h burning jobs. Some jobs resulted in high lead levels measured in areas just outside the Rule plastic.

Objective 6 – Effects of Clea ning Verification Process The cleaning verification process as stated in the propos ed rule resulted in decreases in lead levels, but under the conditions of the study was not always accura te in identifying the presence of levels above EPA standards for floors and sills. Factors such as floor condition, contractor performance, job type, and dust particle char acteristics impacted the cleaning verification process in the study; however, floor condition of post-work and post-cleaning lead levels were found to best explain cleaning veri fication failures. All interior experiments did result in final passed cleaning cloths for all floor zones and for all window sills, but nearly half of the experiments ended with average Work r oom floor lead levels above 40 μg/ft 2 on floors. Lead levels for the cases above the floor standard we re distributed as follo ws: 10 between 40 and 69, 6 between 70 and 99, 5 between 100 and 199, 3 between 200 and 499, and 5 greater than 500. The eight highest levels were a ssociated with high heat gun or door planing jobs and floors in poor condition. Of the 29 experiments where final lead levels were above 40 μg/ft 2 on floors, 20 were on floors in poor condition. Excluding the door planing and high heat gun jobs, which had higher post-job lead levels than the others and wh ich appeared to generate fine particle size dust and, in the case of the door planing, a white dust that was hard to detect on a white cloth, postjob averages when the proposed rule cleaning was used were below clearance levels. For window sills, approximately 20 percent of the experiments ended with lead levels above 250 μg/ft 2 . Almost all of the high leve ls, well above the standard, were associated with door plane and high temperature heat gun jobs. The st udy did not yield much evidence to evaluate differences in lead levels after the wet and dry cleaning verification steps because the dry cleaning verification was only requir ed in three experiments.

Objective 7 – Comparison of Rule Practices and Baseline Practices The results indicated that the proposed rule practi ces resulted in lower geometric mean floor and window sill lead levels in the work room at the completion of a job, and these results were confirmed by statistical modeli ng. There was evidence of proposed rule practices resulting in lower lead levels in the non-wo rk rooms; however, these differences were not consistently statistically significant.

2.2. Peer Reviews of Study Design

The design and quality assurance pr oject plan of this study were peer reviewed. The peer review was conducted from May 24, 2006 to June 11, 2006. Experts in fields related to the study reviewed the design and quality assurance plan independently and provided written comments to EPA. The major comments of the revi ewers as a group are summarized below.

Overall, most reviewers commented favorably on the study design. In response to a charge question asking for recommendations for alternativ e approaches, most reviewers indicated the design as proposed was satisfactory. Neverthele ss, a number of reviewers recommended that2-3 more samples be collected. In response, more dust samples were added in the Work Room, more exterior soil samples were included for both interior and exterior renovation jobs, and background samples for exterior dust co llection tray sampling were added.

Some reviewers suggested adding more intens ive jobs to the study, and some reviewers questioned whether the less intensive jobs in the st udy would result in a sufficient amount of lead dust. In response, more intensive jobs, such as power sanding without a HEPA attachment and open flame burning, were added to the study. Le ss intensive jobs remained in the study, however, because the goal of the study was to examine the range of activities expected to be covered by the EPA rule. Approximate square foot age of lead-based paint disturbed by each job was measured in each experiment, and all jobs in the study disturbed more than the de minimus 2 square feet in the proposed Re novation, Repair, and Painting rule.

A number of reviewers commente d that the impact of “real world” renovation conditions, such as the presence of furniture, occupants, pets, pre-existing lead dust, and so on, would not be captured by the study. For health and safety re asons, the study was conducted in vacant housing units and other vacant units. The study results ma y underestimate the levels of dust that would result from a renovation job due to the absence of these “real world” factors, but the study will achieve its goal of providing comparative data on the difference in lead dust levels when leadbased paint is disturbed under proposed ru le versus baseline work practices.

A reviewer questioned the use of the phrase “n ormal cleaning” in the design documentation to describe dry broom sweeping and shop vacuum ing. Dry broom sweeping and shop vacuuming were included in the study for comparative purpo ses as the cleaning that would likely be done if there were no EPA rule for Renovation, Repair, and Painting. The phrase “normal cleaning” was changed to “baseline cleaning.”

Some reviewers recommended the use of ASTM st andards. ASTM standards were adopted for dust and soil sampling. An ASTM standard for pa int chip collection was included in the design at the time of the peer review.

EPA has established an electronic record of the peer review of the study design. This record includes the comments from the reviewers and EP A’s responses to those comments. The peer review record for the review of the study desi gn can be found in the public docket for the final rule.

2.3. Human Subjects Review

The study was assessed by EPA as to whether it met the definition of a Human Subjects study as defined by 40 CFR Part 26 – Protection of Human Subjects. EPA made th e determination that the study was not a Human Subjects study because; (1) the only human subjects monitoring in the study, via the collection of personal air m onitoring samples and the collection of blood samples from the RRP workers, was conducted so lely to ensure comp liance with all OSHA requirements in 29 CFR Part 1926.62 – Lead E xposure in Construction, and (2) the ensuing personal air monitoring data and blo od lead data were not to be an alyzed or generalized as part2-4 of the study analysis. The persona l air monitoring data and blood lead data were reported to appropriate parties as require d by 29 CFR Part 1926.62.

Representatives of the Battelle Institutional Review Board (IRB) reviewed the study design and data collection plans early in the design stage and again once the study protocols were fully established. From both reviews, they determin ed that the study did not meet the regulatory definition of “human subjects research” according to the definitions at 45 CFR 102 (f)(2) (Common Rule) and 40 CFR 26.102 (f)(2) (EPA speci fic regulation). As such, the study was not subject to the Human Subject s Protections regulations.

Personal exposure monitoring was performed in acco rdance with health and safety requirements and was not used for purposes of research , but rather to determine th e amount of airborne lead dust, if any, to which workers might be exposed and to confirm that the appropriate personal protective equipment (PPE) was used for the RRP workers. Thus, the IRB’s opinion was that the personal exposure monitoring was not related to the research objectives and, therefore, did not invoke human subjects protections , but, rather, was a personnel pr otection activity. In addition, the IRB did not determine the RPP field study to be an “intentional exposure” study in accordance with the provisions of 40 CFR Part 26.1102(1), “ Research involving intentional exposure of a human subject means a study of a substance in which the exposure to the substance experienced by a human subject partic ipating in the study would not have occurred but for the human subject's participation in the study.”

.3-1 

### 3. Study Design

3.1. Site Selection Process and Requirements

As noted previously, the project was conducted to characterize the dust lead levels after various types and intensity of renovation, repair, or painting jobs in vacant housing units and vacant child-occupied facilities with l ead-based paint (LBP). Both interior and exterior jobs were considered when evaluating sites for selection.

Potential sites were evaluated for the 60 interi or experiments (48 at housing units and 12 at COFs) and 15 exterior experiments (12 at hous ing units and 3 at COFs) for a total of 75 experiments. Since multiple experiments could be performed at a single site, 75 individual sites were not required. To allow for multiple experiments at a site, however, there needed to be lead-based paint in sufficient quantities on the desired building components, ability to conduct the experiments in the available timeframe, a nd ability to avoid cros s-contamination through cleaning and verification between experi ments conducted at the same site.

The following characteristics summarize the requirem ents of potential sites for inclusion in the study:

• Built before 1978;

• Cleanable before work began, in between, and at the completion of all study activities;

• Vacant and accessible during the study data collection period and available to schedule work according to the study’s needs;

• Presence of lead based paint at the required levels (defined as dried paint film that has a lead content at or exceeding 1.0 mg/cm 2 or 0.5 percent by weight ) and of sufficient size [2 square feet (ft 2 ) or more] within a single room or on specific desired components of the prospective site;

• Ability of potential interior sites to meet the desire d three room study layout by containing three sequentially adjacent room s to allow sampling in a Work Room, a Tool Room, and an Observation Room; and

• Adequate space at potential exterior sites in the yard or around the exterior of the building to allow desired sample collec tion and containment of dust and debris.

Additional characteristics were also desirable but not required in poten tial sites, including:

• Availability of electricity;

• Selection of a cross section of building ages (e.g., 2 pre-1920, 2 built between 1920 and 1950, and 2 built between 1950-1978) was ideal , but this desire was balanced by other study considerations; and

• Sites within close proximity to each other so as to maximize site coordination efforts.3-2 3.2. Sampling Methods

For interior work, the study-impacted areas of pa rticipating housing units or COFs were defined as a set of three rooms – one undergoing the RRP activity, an adjacent room for tool storage, and an observation room adjacent to the tool storag e room. In addition, post-experiment samples were obtained from a hallway running between th ese areas and the entrance of the unit used for conducting and exiting the job.

Paint Chip Samples – Following initial identification of lead-based paint using an X-ray fluorescence instrument, paint chip samples were collected from prospective components for laboratory analysis in order to obtain an accurate measurement of the lead in the paint that was potentially going to be disturbed by the RRP wo rk. The method used to collect paint samples was the cold-scraping method described in ASTM E1729, “Standard Practice for Field Collection of Dried Paint Samples for Subsequent Lead Determination.” The size of the samples obtained was approximately two square inches a nd included all paint down to the substrate, minimizing the amount of substrate material in th e sample. Lead-based paint is defined by EPA as paint or other surface coatings that contain l ead greater than or equal to 0.5 percent by weight or greater than or equal to 1.0 mg/cm 2

. Interior Dust Wipe Samples – The interior sampling protocol i nvolved collecting four floor dust wipe samples and one window sill dust wipe sa mple from the Work Room and two floor dust wipe samples and one window sill dust wipe sa mple from the other two study rooms (Tool and Observation rooms) at each stage of the e xperiment – post-work, post-cleaning, and postcleaning verification. In addition, if a floor verification zone in the Work Room failed two wet cloth verifications, an additional post-wet cloth verification sample was collected for that floor zone before proceeding with the dry verificati on process. All dust wipes were collected in accordance with ASTM 1728, “Standard Practice fo r Collection of Settled Dust Samples Using Wipe Sampling Methods for Subsequent Lead Determination.”

The locations of the floor and window sill dust wipe samples in all three study rooms were randomly selected prior to the experiment and speci fied in experiment-specific sampling plans. This ensured that the locations of the samples we re not biased by the field technician, and that no two samples were collected from the same area. The randomized sample selection in each room was stratified as necessary, to ensure samples were obtained from different sections of a room. If window sills were badly damaged, inadequately sized, or missing, a dust collection tray was placed in the study room, and all window sill sa mples were collected from the tray. Dust collection trays also replaced ac tual window sills when sills could not be cleaned below 250
μ g/ft 2 . For the window replacement experiments, a dust collection tray was placed on the horizontal surface directly below the outside of the window and sampled after the work was completed.

Note that total dust loadings were not obtained as part of this study. Total dust loading would have enabled calculation of concentrat ion of lead per unit of total dust.3-3 Exterior Dust Wipe Samples – For exterior work, nine dust colle ction trays were set up prior to the RRP work – three on top of the Rule pl astic coverings, three in corresponding positions underneath the Rule plastic coverings, and th ree near the Rule plas tic and on top of the protection plastic. Dust wipe samples were ta ken from each tray following completion of the work. Background lead levels in the air at exterior job sites we re measured by setting out a dust collection tray for a number of hours and subsequently taki ng a dust wipe of the tray.

Bulk Debris Samples – Where necessary, field technicians were instructed to collect excess debris that could not be colle cted with a dust wipe from each sampling location during the postwork sampling stage. The debris was collected by glove-covered hands and tools such as chisels that could scoop up material and placed in a plastic bag or centrifuge tube for further processing and laboratory analysis. For jobs that generated such large amounts of dust that it could not all be picked up using a dust wipe (such as the door planing job), techni cians also used glovecovered hands to collect and place the excessive dust in a plastic bag.

Ambient Air Samples – Air samples were collected as part of this study following NIOSH Method 7082. For interior work, air samplers were placed in all three study rooms on tripods approximately five feet off the ground, so as to a pproximate levels of dust lead available to be inhaled. A flow rate of 3 L/min was used and ke pt relatively constant (within the limits of the calibrator) for consistency throughout the study.

Soil Samples – Soil samples were collected accord ing to ASTM E1727-05, “Standard Practice for Field Collection of Soil Samples for Subsequent Lead Determination,” before and after both interior and exterior work. The soil samples se rved to measure potential track-in contamination during work (interior) and backgr ound lead contamination of the property (exterior), as well as potential contamination of the property by study activities.

3.3. Laboratory Analysis Methods

Paint Chip Samples – Laboratory sample preparation was performed according to EPA Method 3050B, and the subsequent atomic absorption analys is of the determinatio n of lead followed EPA Method 7420. The analysis provided meas urements of lead concentrations in μ g/g, with a minimum detection limit of 20 μg total lead per sample.

Dust Wipe Samples – All interior and exterior dust wipe samples were prepared according to EPA Method 3050B and analyzed for lead conten t using EPA Method 7420 (atomic absorption). The analysis provided measurements of lead concentrations in μ g/ft 2 , with a minimum detection limit of 10 μg total lead per sample, which was re quested to provide more specificity at lower levels of lead.

Bulk Debris Samples –Battelle technicians recorded the total mass of each bulk debris sample. Subsequently, any non-paint chip/dust debris was removed (screws, nails, pieces of wood, etc.), the samples were homogenized (paint, dust, and other material crushed and mixed with a clean mortar and pestle), and two subsamples of at least 1 g each were obtained. Each was labeled appropriately and sent to the la boratory for analysis as paint chip samples. The laboratory reported percentage of lead by mass in each of the two subsamples. The mean lead mass percent3-4 was calculated; this mean multiplied by the tota l bulk mass gives the total lead in the bulk sample. The total lead mass in the bulk sample is added to the total lead mass measured in the associated dust wipe sample to obtain total mass per square foot ( μ g/ft 2 ) in the sampled area.

Ambient Air Samples – Air filter samples were analyzed using NIOSH Method 7082 (flame atomic absorption spectrophotometry). The an alysis provided measurements of lead concentrations in μ g/m 3 , with a minimum detection limit of 2 μg total lead per filter.

Soil Samples – EPA Method 3050B was used to prepar e the soil samples for analysis. EPA Method 7420 (atomic absorption) was used to analy ze the samples for lead. The analysis of soil lead concentrations was reported in μg/g, with a minimum detection limit of 20 μg total lead per sample.

3.4. Field Sampling Protocols

3.4.1. Interior Jobs

Prior to the start of an experiment, paint chip samples were collected from building components that would potentially be disturbed during RRP activ ities to evaluate the lead concentrations in the paint. In addition, three composite soil samp les were collected, where applicable, from bare soil nearest to (1) the entryway to the building used by the workers, (2) the walkway from the entryway to the street, and (3) a window closest to the work area. If the unit was used for multiple interior experiments, the post-experiment soil samples of the previous experiment were used as the pre-experiment soil samples for th e next interior experime nt, where appropriate.

All areas of the unit not being used in the study were barricaded us ing plastic coverings. All air vents in floors, walls, and ceilings in any of the areas impacted by the study were also covered with plastic. A thorough cleaning was conducted by a lead abatement firm with experience cleaning after lead abatement activities in accord ance with the U.S. Department of Housing and Urban Development (HUD) Guidelines on abatemen t cleaning and clearance. An independent licensed clearance technician then collected five dust wipes (4 floor, 1 sill) from the Work Room and three dust wipes (2 floor and 1 sill) each fr om the Tool and Observation Rooms, plus up to three additional samples in hall ways or other areas impacted by the study. If the lead loading on the clearance wipes was no greater than 40 μ g/ft 2 on floors and 250 μ g/ft 2 on window sills, the unit was declared clean. If one or more samples failed to meet these clearance standards, then the room in which that sample was colle cted was re-cleaned and re-sampled.

Deviations from this plan did occur in order to maintain progress. There were some cases where one or more samples in a room were measured above 40 μ g/ft 2 , but because the room average was below 40, the decision was made to proceed w ith work. There were a few cases where the room average was above 40 μ g/ft 2 , but work occurred because of schedule constraints. When floors in Tool and Observations were unable to achieve clearance, they we re covered with clean plastic, which provided a clean surface with whic h to start the experiment. When window sills were in poor condition and/or could not be cleaned below 250 μ g/ft 2 , clean dust collection trays were used in place of the actual window sill in almost all cases.3-5 If the interior protection/clean- up (P/CU) phase to be implemented involved containment, plastic coverings were set up in the Work Room by the RRP contractor according to the proposed rule. This included closing and seali ng all doors into the Work Room; covering all doors within the work area that had to be used while the job was being performed with plastic sheeting in a manner that allowed workers to pass through, while confining dust and debris to the work area; covering the entire floor surface with taped-down plastic sheeting in the Work Room; and taping plastic to the outside of the window frame, if a window was being replaced as part of the job, so that no debris could fall to the ground outside the window.

An area just outside of the Work Room (either in the hallway or the Tool Room) was identified to serve as the primary decontamination area where workers removed all dust and debris from their person, their equipment, and the exterior of any waste containers using a HEPA vacuum. The floor of the primary decontamination area was securely covered with plastic.

A secondary decontamination area was set up just in side or outside of the main entrance to the unit. This area was used to remove and dispose of the Tyvek suits and booties worn for worker protection while inside the unit. Respirat ors were also removed in the secondary decontamination area. Personnel also cleaned of f any debris remaining on equipment, clothing, or the exterior of waste containers prior to le aving the study site. A tack pad was set up to remove any remaining dust from street shoes before leaving the property.

The hired RRP contractors conducted their job as they normally would, with in the specifications of the study. Following job completion, study pe rsonnel waited for one hour before collecting post-work dust wipe and air sample s. According to Choe et al. 2000 5 , about 90 percent of airborne lead mass settled with in one hour of lead abatement work. Thus, one hour wait times were utilized by the study protocol as it would allow sett ling of most if the lead dust but also be short enough to allow completion of an experi ment in a single day. In instances where significant debris covered the pre-designated sa mpling location, the debris was collected in a plastic bag prior to wiping the area with a dust wi pe. The bulk samples were sent separately to the lab for analysis.

Once post-work environmental samples were collect ed, the RRP contractor misted and picked up the plastic sheeting covering the fl oor, if present. The sheeting isolating the Work Room from the other areas of the unit remained in place until all cleaning activities were completed. RRP contractors were instructed to follow one of two cleaning me thods – a baseline cleaning or the proposed rule cleaning. For a baseline cleani ng effort, the RRP contractor swept the entire Work Room with a broom and dustpan to collec t large amounts of debr is. Subsequently, the RRP contractor vacuumed the work area with a Shop Vac-type vacuum. For the rule cleaning, the RRP contractor followed the guidelines detailed in the proposed rule to clean the entire Work Room. Large pieces of debris were collected and disposed of prior to cleaning. All walls were vacuumed with a HEPA vacuum starting from the ceiling and working down to the floor. The remaining surfaces and objects in the work area, including floors and any fixtures, were then vacuumed with a HEPA vacuum. The floors we re then thoroughly mopped using a 2-bucket

5 Choe, KT, Trunov, M, Grinshpun, SA, Willeke, K, Harney, J, Trakumas, S, Mainelis, G, Bornschein, R, Clark, S, and Friedman, W. Particle Settling after Lead Based Pa int Abatement Work and Clear ance Waiting Period. AIHAJ 61:798-807. November/December 2000.3-6 mopping method. Simple Green detergent was used as the cleaning solution in all experiments. Neither phosphate nor ethylenedia minetetraacetic acid (EDTA) were used for cleaning purposes. If containment was used in the experiment, afte r completion of all appropriate cleaning activities the plastic covering the door separating the Work Room from the other areas of the unit was taken down. This was performed by first mis ting and, then removing the plastic carefully folding it up, and placing it in a heavy duty garbage bag.

After the cleaning was complete, all study pers onnel waited for one hour before collecting post-cleaning dust wipe and air samples. Once all post-cleaning samples were collected, the RRP contractor began the cleaning verification process described in detail in the proposed rule. For both the window sills and floor zones, each area was wiped with a wet cleaning verification cloth and compared to photograp hic standards contained on the verification card provided as guidance for determining the cleanliness of the clot h. If the RRP contractor determined that the cloth used was dirtier than the photographic stan dard for a clean cloth, then the component was re-cleaned following the proposed rule cleani ng method (i.e., using a HEPA vacuum and wet mop) and wiped again with a new wet cloth. Any re-cleanings were done w ith the proposed rule cleaning method in order to get more informa tion regarding the clean ing cloth performance under proposed rule methods.

If the second wet cloth failed verification, then the component was re-cleaned and allowed to dry. The component was then wiped with a dry, white verification cloth, which was compared to the dry cloth photographic standard on the verification card provided. If the first dry cloth failed, i.e. was dirtier than the phot ographic standard of a clean, dr y cloth, then the component was wiped with a second dry verificati on cloth. The process was repeat ed for a maximum of four dry verification cloths. All windows ills were verified as clean before moving on to the floor verification. The floor was separa ted into zones approximately 40 ft 2 in area for the verification process. In the cases where a window sill or floo r zone failed two wet verification tests, a postwet verification sample was collected from that zone before proceeding with re-cleaning the component and dry cloth verification. The colle ction of post-verification dust wipe and air samples took place immediately foll owing the cleaning verification.

After collection of the cleaning verification samples from the three study rooms, up to three additional 1 ft 2 floor dust wipe samples from a hallway connecting the study rooms to the unit’s entrance were collected. In addition, three co mposite soil samples were collected in locations near those taken during pre-experiment sampling.

Following completion of an experiment, the ar eas of the unit impacted by the study were re-cleaned. If the unit was to be used in a s ubsequent experiment, the plastic sheeting protecting the parts of a housing unit not impacted by the stu dy were left in place. A licensed clearance technician then collected five dust wipes (4 floor, 1 sill) from the Work Room and three dust wipes (2 floor and 1 sill) from the Tool and Observation Rooms, plus up to three additional samples in hallways or other areas impacted by the study. If the lead in the clearance wipes met the study’s clearance standards, the unit was declar ed clean. If not, then each room that failed was re-cleaned and re-sampled.3-7 If all interior work at the unit was complete, the plastic sheeting protecting the parts of the housing unit not impacted by the study was taken down, and the entire unit underwent an abatement style cleaning. A cer tified inspector/risk assessor or clearance technician then collected samples from one floor, one window sill, and one window trough, where accessible, from four rooms in the unit, in accordance with state procedures for clearance tes ting after lead abatement. If one or more samples were measured with dust lead levels over the clearance level, the area from which that sample was taken underwent re-cleaning. Following re-cleaning, another clearance sample was obtained and re-ana lyzed. Clearance levels were achieved in all study housing units, with the excep tion of some window troughs. Some window troughs did not get below 400 μ g/ft 2 , but these were either unimpacted by study activities or were in a deteriorated condition pointed out to the property owner. Final clearance results were provided to all property owners.

Figure 3-1 presents a flowcharts of the steps in volved with conducting th e interior experiments including all related sampling activities.3-8 Figure 3-1. Flowchart of Interior Field Sampling Activities3-9 3.4.2. Exterior Jobs

Prior to the start of an experiment, paint chip samples were collected from building components that were potentially going to be disturbed during RRP activities to evaluate the lead concentrations in the paint. In addition, thr ee composite soil sample s were collected, where applicable, from bare soil (1) near the foundation of the housing unit/COF and within the area to be covered by the Rule plastic, (2) at the back edge (farthest from the housing unit/COF) of the Rule plastic, and (3) at the back edge (farth est from the housing unit/COF) of the containment plastic. If the unit was used in multiple exteri or experiments, the post- experiment soil samples from the previous experiment were used as the pre-experiment soil samples for the next interior experiment, where appropriate. One or more dus t collection trays were set out prior to work commencing to evaluate the background level of dust lead in the air at a housing unit.

The approximate size and location of the “Rule containment” was estimated in advance by the study team in order to identify pr e-work soil sample locations. Th e exact size and location of the “Rule containment’ was determined by the site supervisor in consultation with the RRP contractor, taking into account a ny physical constraints such as property lines, fences, nearby houses and the placement of the vertical containment structure. Before exterior work began, the RRP contractor set up “protecti on containment,” which included a large area of plastic covering the ground and vertical containment spanning th e entire distance between ground level and the maximum height of the com ponent undergoing RRP work.

The Rule containment was set up subsequently by the RRP contractor in accordance with the proposed rule. This included lying securely taped- or weighed-down plastic on top of the containment plastic extending out from the edge of the building a reasonable distance to collect falling paint debris. All doors a nd windows within 20 feet of and below the work area were closed. Dust collection pans were placed by st udy technicians under, on top of, and near the Rule plastic covering.

A decontamination area was se t up immediately outside of th e work area, where all study personnel removed and disposed of their protecti ve suits and booties, and removed all dust and debris from tools and supplies.

The hired RRP contractors conducted their job as they normally would, with in the specifications of the study. Following an up to one-hour wait after j ob completion, field technicians obtained dust-wipe samples from the dust collection pa ns on top of the Rule plastic sheeting. Subsequently, the RRP contractor misted and fold ed the plastic sheeting inward to trap dust and debris inside. The dust wipe samples from the pans under the Rule plastic were sampled after the plastic was removed. The trays placed near the rule plastic were also sampled at this time. Technicians collected three com posite soil samples, consisting of three sub-samples each, in locations similar to those used for the pre-experiment soil sa mpling. Once all study-related activities were complete, the RRP contract or removed the vertical containment.3-10 3.5. Participating Contractors (RRP Wor kers, Cleaning, Clearance Technicians)

Study implementation required the pa rticipation of multip le contractors in both Columbus, Ohio, and Pittsburgh, Pennsylvania. In Columbus, th e study utilized two licensed firms to conduct initial cleanings, recleanings, and final cl eanings of the Columbus housing units and child-occupied facility. All XRF inspections an d dust lead clearance tests were performed by ATC Associates, Inc. (ATC), also licensed by the state of Ohio to conduct these activities. Three separate contractors provided the renovation work support for the field operations at the Columbus housing units and school. All had at least one person who had receiv ed training in the EPA/HUD course in Lead Safety for Remodeling, Repair, and Painting. In some cases, property owners worked with study pe rsonnel to identify pr eferred contractors who would conduct the renovation work on their properties.

In Pittsburgh, the study utili zed services of one abatement cleaning firm, one certified environmental firm to conduct XRF inspections a nd dust lead clearance te sts, and one renovation contractor. The renovation workers in Pittsburgh received training in the EPA/HUD course in Lead Safety for Remodeling, Repair, and Painting prior to starting work.

All participating renovation work ers underwent study-specific trai ning that reviewed the study health and safety plan, reviewed study protocols, and introduced and demonstrated the cleaning verification process. In additi on, the study arranged for respirator training and fit testing for those renovation workers who were not equippe d with the necessary respirator protection.

Site security during working hours was provided by special-duty police officers in Pittsburgh and off-duty police officers in Columbus.

3.6. Environmental Safety and Health Plan

The health and safety requirements for the study were applicable to all RRP workers and study personnel. A Health and Safety Plan was drafte d to specify the precautions required to comply with the OSHA Lead in Construction standa rd (29 CFR 1926.62); to prevent study personnel from injuring themselves, their families, or neighbors; and to prot ect the study property and surrounding properties. During stud y activities, standard safe wo rk practices were followed to avoid falls, burns, lacerations, and other injuries.

All RRP contractors were trained in accordance w ith the Lead in Construction standard, as well as the HAZCOM standard (29 CFR 1910.1200) with re gard to lead. In addition, at least one member of the RRP work crew present on site was required to have Lead Safe Renovation training. Half- or full-face respirators were worn during all work activi ties in accordance with the observed personal exposure results for each t ype of study-related activity performed. To ensure that workers had adequate respiratory protection prior to job- specific data being available, the study conservatively assigned respirator type s to each job at the beginning of the study so that for many jobs a higher degree of protecti on was utilized beyond what might typically be used. For the experiments where paint was removed by open-flame torching, the RRP workers and study personnel wore supplied air respirators fo r increased protection ag ainst lead inhalation.3-11 A blood lead surveillance program was implemen ted to monitor the pre- and post- study blood lead content of all RRP workers and study personnel.

For both interior and exterior work, personal air samples were collected to ensure compliance with OSHA requirements; however, th e results are not analyzed as part of the study. Personal air sampling results were monitored closely to ensu re that respirators used for each job were adequate to provide sufficient protection. All personal air mo nitoring and blood lead results were provided to the workers.

Great effort was taken to limit the potential for lead contamination outside of the study area. Workers and study personnel were required to w ear hooded protective clothing made of Tyvek, or another appropriate material , while performing study activities . Upon exiting the work area, all protective clothing and equipment were cleane d with a HEPA vacuum, and, in some cases, a wet cloth. Protective booties were worn to prot ect against contamination of the study areas from the outside, as well as to protect the property from contamination due to the work-generated dust. Before exiting the property, the pr otective clothing was vacuumed, again, and discarded. A tack pad was used to remove any residual dust from the bottoms of shoes. Workers and study personnel were required to wash exposed skin with soap and water immediately after exiting the property. All waste generated during study activ ities was removed from the properties and transported to a municipal or construction landfill to be disp osed of as household waste.4-1 

### 4. Field Work Summary

4.1. Overview of Site Selection/Screening Process

The study team developed a list of potential lo cations and contacts for identifying pr ospective housing units and COFs to enroll in the study. Initi ally, the study sought to identify locations in or around Columbus, Ohio because of their proxim ity to Battelle and because Columbus is home to a large scale effort by various housing agencies, neighborhood associations, non-profit agencies, and private developers to rehabilita te older, low-income housing. The study team thought such organizations would likely have vacant housing and would be open to collaborating on the study to have work done that is benefi cial to their ongoing efforts in housing renovation and remodeling activities. Leads identified throu gh various sources in other cities were explored including Milwaukee, Detroit, Los Angeles, Birmingham, Philadelphia, Chicago, Baltimore, Arlington (VA), and Washington, DC.

The site selection process i nvolved contacting organizations thought to have information on ongoing RRP activities. During the initial contact, study represen tatives explained the study to the contact, offered to provide them with a F act Sheet summarizing the study, and asked whether they knew of RRP activities planned for housing in their area or of housing fitting the study requirements that would be a good candidate for participation in the study. If they were interested in supporting the st udy, detailed discussions about potential candidate housing units were held. If phone conversations concerning potential sites seemed to satisfy all the necessary study requirements and the owner was amenable to working within the study requirements for available experiments and study schedule, then a site visit was scheduled to inspect the property.

Permission to inspect and test units or buildings was obtained from site owners or managers prior to any inspection or testing taking place. The permission included an agreement as to whether and how the results of the inspection and testi ng would be transmitted to the site owner or manager. The required study form was used to obtain formal permission of the property owner to conduct the XRF inspection and visual assess ment of a property. Each prospective housing unit or COF underwent a visual inspection to en sure that it met the study requirements beyond the presence of LBP – three sequential rooms, v acant, cleanable, etc. If the visual inspection confirmed that a prospective housing unit met the study requirements, a full lead screening inspection was scheduled with a portable XRF device to measure le ad content of various painted components in the house – walls, window sills, tr im, doors, etc. Inspectors obtained a large number of XRF measurements throughout a house, which were recorded by the field operations coordinator for the area, or their representative, on the appropriate data collection forms. As the study progressed and experiments were assigned to prospective units to be gin work, subsequent site inspections were targeted to the components for experiments th at still needed to be assigned.

Following review of the XRF measurements at a prospective housing unit, study planners determined whether there were one or more room s that could serve as the Work Room for an interior job or whether there was more than one exterior side of the house that could serve as the work area for an exterior job. Permission to co llect paint chip samples was obtained from the property owner using the proper form. Paint samp les were then collected from components in prospective rooms and exterior sides for la boratory analysis, as necessary. Based on4-2 confirmation of sufficient levels of lead based paint in the pros pective housing site components, matching experiments were assigned and the housing owners were informed of their inclusion in the study and of the intended sche dule of renovation activities to be performed at their housing unit.

In Columbus, Ohio and Pittsburgh, Pennsylvania, the study was able to conduct experiments in vacant housing owned by local non-profit agencies that own older housing needing renovation. Additionally, private citizens in Columbus w ho owned vacant housing containing lead-based paint also made their homes available to the study. A suitable COF wa s identified through the Columbus Public Schools system. A vacant school that contained sufficient amounts of lead-based paint was utilized fo r all COF experiments. Therefore, the study was able to use housing units in both Columbus and Pittsburgh and a COF in Columbus to provide some measure of geographic diversity while still being able to allo w economy of site coordination efforts.

A total of 35 housing units were sufficient candi dates to warrant a visu al inspection of the properties. Of these 35 units, 27 properties were in good enough condition and satisfied the study requirements to warrant having an XRF screening performed. A number of the housing units that were excluded from the study posed safety hazards to study personnel such as missing stairwells and caved-in ceilings. Units were not excluded based solely on floor condition and many experiments were performed using floors in poor condition.

Based on the XRF measurements, the subsequent paint chip sampling, and a matching of the units to the study experiments, the study ultim ately identified 15 housing units that met the selection criteria for use in the characterization of environmental le ad levels associated with low, medium, and high levels of interior and exte rior RRP activity. Table 4-1 summarizes the distribution of housing units among the two cities and interior/exterior experiments. As mentioned above, in addition to the housing units, one school was utilized to conduct 12 interior and 3 exterior experiments.

Table 4-1. Housing Unit Distribution by City.

Columbus Pittsburgh Interior Only 3 4 Exterior Only 3 1 Interior/Exterior 1 3 TOTAL 7 8

The buildings studied do not represent a stat istically-representative sample of housing on a national basis, as this was not an objective of th is research study. Howe ver, although most of the housing used in the study was built pre-1930, the di stribution of paint lead levels on components worked on covered a broad range - from 0.8% to 10.2% lead by weight for in terior jobs and from 1.3% to 16.8% lead by weight for ex terior jobs. These ranges of pa int lead levels are consistent with the types of distributions reported in the Na tional Survey of Lead and Allergens in Housing for components with l ead-based paint.4-3 4.2. Number and Type of Experiments Completed

The study conducted 12 interior jobs at housing units and 3 interior jobs at a COF and, similarly, 12 exterior jobs at housing units an d 3 exterior jobs at a COF. Ta ble 4-2 identifies each of the interior and exterior jobs perf ormed at housing units across three work intensity levels specified prior to work taking place, while Table 4-3 lists the inte rior and exterior work performed at the COF. In parentheses at the end of each job de scription is the number of times each job was conducted. For interior jobs, each replicate of a job was to be done four times under each phase of plastic protection and cleaning method. T hus, interior housing un it jobs actually were conducted eight times to achieve the two full job replicates. As noted in Section 1.3, practices restricted by 40 CFR 745.227(e)(6) we re included among the jobs condu cted as part of the study. These are noted with roman numerals within th e table corresponding to the four prohibited activities specified in 40 CFR 745.227(e)(6).

Table 4-2. Interior RRP Jobs across Three Levels of RRP Work.

Type Low Level Work Medium Level Work High Level WorkHOUSING UNITS Make up to three cut-outs, each of a 2 foot or more section of wall with LBP disturbing approximately 6 ft 2 of LBP. (2) Replace window from inside unit, disturbing at least 2 ft 2 of LBP. (2) Scrape deteriorating LBP from interior walls, scraping 50-75 ft 2 of painted surf aces [iii]. (2)

Plane 20-40 ft 2 of LBP from an interior door [ii]. (2) Remove paint from 75-100 ft 2 of lead-based painted components by using a heat gun over 1100° Fahrenheit held at one inch or the distance specified from paint [iv]. (2) Gut out a kitchen, disturbing 100 ft 2 or more of LBP. (2)COF Make three cut-outs, each of a 2 foot or more section of wall with LBP disturbing approximately 6 ft 2 of leadbased paint. (1) Remove paint from 50 ft 2 of lead-based painted components by using a heat gun UNDER 1100° Fahrenheit held at one inch or the distance specified from paint. (1) Remove paint from 75 ft 2 of lead-based painted components by using a heat
gun OVER 1100° Fahrenheit held at one inch or the distance specified from paint [iv]. (1)4-4 Table 4-3. Exterior RRP Jobs across Three Levels of RRP Work.

Type Low Level Work Medium Level Work High Level WorkHOUSING UNITS Replace an exterior door and doorway, disturbing 25-50 ft 2 of lead-based paint. (2)

Replace fascia boards, soffits, and other exterior trim on one side of the structure, disturbing approximately 50 ft 2 of lead-based paint. (2) Remove lead-based paint from exterior components by dry scraping, disturbing approximately 100 ft 2 of leadbased paint [iii]. (4) Remove paint by power sanding or grinding at least 100 ft 2 of lead-based paint on exterior wood components on one side of the structure [ii]. (2)

Remove lead-based paint by torching or open-flame burning on at least 100 ft 2 of lead-based paint from wood porch ceilings [i]. (2)COF Remove approximately 50 ft 2 of LBP from an exterior component using a needle gun. (1) Remove paint from 50-75 ft 2 of lead-based painted components by using a heat gun UNDER 1100° Fahrenheit held at one inch or the distance specified from paint. (1) Remove paint from 75-100 ft 2 of lead-based painted components by using a heat gun OVER 1100° Fahrenheit held at one inch or the distance specified from paint [iv]. (1)

4.3. Protection and Cleaning Routines

Interior Jobs – For each interior job, the study evaluate d four work area protection/clean-up (P/CU) routines or phases – the four combinati ons of (1) use of plas tic coverings/no use of plastic coverings and (2) baseli ne cleaning after work completion/cleaning per the proposed rule after work completion. Thus, th e four P/CU routines were:

• Phase I - Use of plastic coverings on floor a nd doorways during work combined with rule cleaning after work completion;

• Phase II - Use of plastic coverings on floor and doorways during work combined with baseline cleaning after work completion;

• Phase III - No plastic coverings on floor or doorways during work combined with rule cleaning after work completion; and

• Phase IV - No plastic coverings on floor or doorways during work combined with baseline cleaning after work completion.

Within each job, the study attempted to characterize the amount and spread of dust under each of the four P/CU routines. The preferred method fo r achieving this was to split the job into four phases of equal activity to be performed in one room – the 1 Unit/1 Room Approach – and conduct the experiment independently four times so that each experiment corresponded to one of the four P/CU routines. In the instances where the job was unable to be replicated four times in the same room, the next alternat ive was to conduct the work acr oss two rooms – applying two of the four P/CU routines in one room and the othe r two routines in another room. For jobs that could not be replicated in one or two rooms, such as the kitchen gutting job, the study used a 4 Room approach where each P/CU routine was conducted in a different room. These rooms could be in the same unit or diffe rent units. The approaches requi ring multiple rooms or units to complete a full job produced data for which comparability depends on the degree to which similar room layouts with lead-based paint on the same components could be found. Table 4-4 lists the jobs performed with the ap proach used to complete that job.4-5 Table 4-4. Interior Jobs and Associated Approaches for Conducting Experiment.
Activity

Level Job Job # Columbus Pittsburgh
1 2 Units, 2 Rooms Cut outs 2 2 Units, 2 Rooms 1 2 Units, 2 Rooms Low Replace window from inside house 2 1 Unit, 3 Rooms 1 1 Unit, 1 Room Scrape deteriorating lead-based paint from a flat interior component 2 1 Unit, 1 Room

1 2 Units, 3 Rooms Medium Scrape or plane 20-40 ft 2 of lead-based paint from an interior door 2 2 Units, 2 Rooms
1 1 Unit, 1 Room Remove paint from 75-100 ft 2 of leadbased painted components in a room by using a heat gun at or over 1100 degrees Fahrenheit 2 1 Unit, 4 Rooms

1 4 Units, 4 Rooms High

Gut out a kitchen 2 2 Units, 2 Rooms 2 Units, 2 Rooms

The window replacement job in Columbus was conducted in one housing unit, but the windows were spread across three rooms with one room having two windows replaced. The other job that required three rooms, one door planing job, used two doors from a room in one house and two doors from another house, but the doors in the second house were in different rooms.

At the school, the three jobs we re conducted in two separate cl assrooms, with six experiments conducted in each classroom. A ll four Cut-out experiments were conducted in one room, while the four low heat gun jobs were conducted in the second classroom. Th e high heat gun job was split between the two classrooms with two e xperiments conducted in each room. These assignments were made based on wall sp ace available to have paint removed.4-6 Exterior Jobs – For exterior jobs, the primary protecti on technique under evaluation was the use of plastic ground covering. To investigate this, plastic coverings were set up under an exterior job, as required by the proposed rule, with dust collection trays under, on top of, and near the plastic coverings. After completion of the exteri or work, dust wipe samples were obtained from the dust collection trays. Thus, the exterior work and associated sampling were performed in one phase. Table 4-5 shows the distribution of the exterior jobs across city and the number of housing units required. The three ex terior jobs at the COF were all conducted at the school in Columbus.

Table 4-5. Exterior Jobs Conducted in Each City.
Activity

Level Job (# Jobs) Columbus Pittsburgh

Replace exterior door (2) 2 Units Low Replace soffit, fascia and trim (2) 1 Unit 1 Unit

Medium Scrape deteriorating lead-based paint from a flat exterior component (4) 3 Units

Remove paint from 100 ft 2 of lead-based painted components by power sanding (2) 1 Unit High Remove paint from 100 ft 2 of lead-based painted components by open flame burning (2) 2 Units

4.4. Environmental Samples Collected

Dust wipe and air samples were collected during each stage of the interior experiments. In addition, dust clearance samples were gathered fo llowing the pre-experiment (initial clearance) and post-experiment (final clearance) cleanings for interior jobs to ensure (1) that each phase of work began in a clean environment and (2) that housing units and buildin gs did not contain any residual lead hazards when work was complete d. Table 4-6 summarizes the total number of environmental samples that were collected, incl uding those collected for clearance purposes. The quality control samples included in the tabl e include field spike a nd field blank samples inserted into the sample stream for quality assurance purposes.4-7 Table 4-6. Total Number of Environmental Samples Collected

Housing Units (12) COF (3) Experiment Type Sample Type # of Samples # of Samples TOTAL

Dust Lead Clearance Wipe 806 132 938

Dust Wipe 1,699 469 2,168

Air 576 146 722

Bulk 120 37 157

Soil 170 0 170 Interior

Paint Chip 187 22 209
Dust Wipe 137 28 165

Air 13 4 17

Bulk 24 11 35

Soil 72 0 72 Exterior

Paint Chip 29 4 33
Wipe QC 201 51 252 Quality Control Air QC 96 25 121
TOTAL 4,130 929 5,059

4.5. Protocol/Sampling Issues and Resolutions

Throughout the duration of field wo rk, various circumstances impacted the performance of study protocols. Such circumstances generally result ed in minor adaptations to field activities or sampling procedures. Any adaptations were pl anned by field personnel, in consultation with study organizers whenever possible, to preserve the validity of each experiment. Summarized below are various protocol variations and sa mpling issues noted by field personnel over the course of the field study.

Protocol Variations

Obstructed floor samples – All sampling plans were prepared prior to sampling without access to detailed photographs of the study rooms. As a result, sampling plans sometimes included floor samples in areas occupied by air vents and other obstructions. When this occurred, field technicians moved the affected floor sample to an adjacent location not already used or designated for sampling.

Reduced wait times – The study design called for field pers onnel to wait for one hour following completion of the RRP work and cleaning stages before collecting dust wipe and air samples from the study rooms. Some RRP jobs took sign ificantly longer than an ticipated to complete, pushing the first one-hour wait period well into th e afternoon. On several such occasions, site4-8 supervisors opted to reduce the pos t-cleaning wait time in an effo rt to finish the experiment before dark or before site security personnel le ft for the day (typically at 5:00 pm). No postwork wait times were reduced. When post-cl eaning wait times were reduced, which only occurred on approximately five occasions, wait times ranged from zero (once) to 50 minutes. During exterior work, one hour post-work wait times also were planned to allow dust to settle before sampling. In two cases, anticipated bad w eather or increasing winds led site supervisors to shorten wait times in order to collect samples before weather conditions might lead to loss of samples.

Extreme weather conditions (during interior experiments) – In general, field personnel used a front porch or other non-study ar ea of a housing unit to set up a staging area for all study-related equipment and supplies. Several experiments we re conducted in Columbus on extremely cold or rainy days at housing units with no available inte rior space, aside from the study rooms, to set up a staging area. To protect study equipment and personnel from the elements, field technicians used a portion of the housing unit’s Observation Ro om to hold study equipment. In these cases, which are noted in the experiment-level reports (see Appendix A), technicians first ensured that adequate space was available in the Observation Room to serve as a staging area and satisfy sampling requirements for that room. All equi pment and cleaning supplies were protected under plastic whenever necessary to prevent contam ination from RRP work and other study-related activities. Floor samples were moved, when n ecessary, to the open area nearest the planned sampling location to avoid sampling from areas pa rtially or fully covered by study equipment.

Vacuuming protocol (Baseline cleaning) – Early in the st udy, field personnel in Pittsburgh were confused as to whether contractors should be using the Shop-Vac to v acuum the Work Room walls during baseline cleaning. In some experime nts the walls had been vacuumed, and in others they had not. Field personnel consulted with stu dy organizers in Columbus and determined that the walls should not be vacuumed during baseli ne cleaning, since the baseline scenario was intended to represent a less inte nsive cleaning effort than Rule cleaning. For all subsequent experiments, contractors were directed to vacuum the walls only dur ing Rule cleaning.

Mopping protocol (Rule cleaning) – The application of the two bucket mopping method differed across the RRP contractors. Some contractors preferred to mop the en tire Work Room with soapy water first, then remove the soapy bucket from the room and mop the entire floor again with the rinse water. Other contractors used both buckets simultaneously – mopping several square feet with soapy water a nd then going over the same area with rinse water. Since both approaches are consistent with the text in the proposed rule and as RRP contractors were expected to perform the job as they would under normal conditions, both approaches were represented in the study.

Sampling Issues

Use of sampling trays in place of window sills – During many experiments, sampling trays were used in place of actual window sills. This was done for a number of reasons including insufficient sill surface area for repeated sampling, poor condition of a sill, and inability to clean a sill so it would pass clearance. Out of 60 inte rior experiments, sampling trays were used in approximately 30 Work Rooms, 39 Tool Rooms (w hich often were hallways with at most one4-9 available window), and 26 Observation Rooms. Us e of trays as a stand-in for actual window sills allowed the study to determine the amount of lead generated by the RRP activity and how much of that remained behind afterward, wit hout confounding the results with background levels or deteriorated sill conditions. On a small nu mber of occasions in Pittsburgh, sampling trays were not put into position before RRP work began, when they were supposed to be used. In these instances, field technicians taped off a rectangular section of floor in an area where no floor samples were supposed to be taken and sampled dire ctly from the floor instead of the tray. On several other occasions when sampling trays should have been used in place of the unit’s original window sills, field technicians inadvertently sample d from the sills instead of sampling trays. All such instances are noted in the experi ment-level reports (s ee Appendix A).

Soil sample locations – Soil samples were intended to be taken from the same general locations before and after each experiment to facilitate a comparative analysis of soil-lead levels. In practice, field technicians occasionally took post-wo rk samples from areas that were not sampled prior to work. A variety of field personnel was used in Columbus, and the same individuals did not always work at the same housing units. As a result, the individual s collecting the samples were occasionally uncertain about the location of pre-work samples and/or the most appropriate location of post-work samples.

Use of Plastic to cover tool and observation floors – The design of the study called for sampling to occur on actual floor surfaces, which had been cleaned prior to a renovation job occurring. Because of floors that were in poor condition an d proved difficult to clean to a level at which they would pass clearance testing, during approxim ately 11 of the 60 interior experiments plastic sheeting was used to cover tool and observation room floors in orde r to provide a clean surface at the beginning of the experiment. As floors in th e tool and observation rooms were not cleaned as part of the study protocol, this should not impact the results. All sampling in the work room was performed on actual floor surfaces (excluding the post-work samp ling when plastic containment was used).

Cleaning Verification - Some RRP contractors appeared more likely than others to consider discolored wet verificati on cloths to be “clean,” attributing discolorations to factors other than residual paint dust (with no apparent basis for such conclusions). The impact of this on the study is that there may have been fewer cleaning ve rifications than there should have been, and potentially higher post-verification lead levels than would have b een achieved if re-cleaning was performed and additional veri fication cloths used.

Field personnel also observed th at the use of Simple Green detergent could contribute to discoloration of the verification cloths, as on multiple occasions verification cloths used on zones that were re-cleaned with Simple Green app eared progressively more soiled after each recleaning. Field personnel eventu ally concluded that the cleani ng solution itself was causing the grayish discoloration. Subsequent analysis of floor samples taken from these zones confirmed that the discoloration was not caused by lead-based paint particulates.4-10 4.6. Adherence to Environmental Safety and Health Protocols

As described in Section 3, a site -specific health and safety pl an was prepared for each housing unit and COF used for field work. The field work crews in Columbus and Pittsburgh kept copies of these plans on-site at all times. Each plan co ntained detailed health an d safety protocols to be followed by contractors and field technicians. The site supervisor at each job site was responsible for ensuring that a ll crew members adhered to these guidelines, which could be grouped into the general categories described below.

Study unit containment (to prevent lead contamination of non-study areas) – Contractors were directed to seal off non-study areas of each housi ng unit/COF with plastic sheeting and tape prior to beginning work. If cuts, holes, or gaps we re observed in the containment at any time, contractors or field tech nicians quickly took action to re-tape or otherwise re-seal the plastic so that non-study areas would continue to be protected from lead dust and other potential hazards.

Personal safety during all stages of field work and sampling – On each day of field work, prior to beginning the RRP activity, the site supervisor held a safety meeting to communicate potential hazards associated with the job (e.g., lead e xposure, physical hazards) and remind the crew members about the personal protective equipment that would be required for the day’s work. During work, contractors were routinely reminded to inform the site supervisor if they needed a break. Field crews had access to a fire extinguisher, first-aid ki d, and emergency air horn at all times during the study.

Respiratory protection and monitoring – All crew members we re required to wear NIOSH/MSHA-approved respirators when inside the work areas. The type of respirator initially required for each job was based on the OSHA re quirements and an assessment of the air monitoring data available for the jobs in the study. Half-mask, air-purifying respirators were required for activities expected to generate relatively small amounts of leaded dust. Full-mask, air-purifying respirators or ha lf-mask, supplied-air respirators were required for activities expected to generate significant dust or lead vapors. Air sampli ng for personal exposure to lead was performed during each of the three stag es of the study – renovation work, post-work cleaning, and cleaning verification. Personal air sampling results were reviewed after each job and used to modify respirator requirements for subsequent jobs . For example, personal air samples collected during the cleaning verificati on stage were consistently below the analyzing laboratory’s detection limits; therefore, contra ctors and field technicians in Pittsburgh and Columbus were not required to wear respiratory protection duri ng the cleaning verification stage after the first two weeks of field work.

Hygiene – Site-specific health and safety plans calle d for handwashing facilities to include hot water, soap, and towels. Contr actors in Columbus in Pittsbur gh were responsible for providing these facilities, which ty pically consisted of two handheld pr essure sprayers (one filled with soapy water, one with clear wate r) and a bucket to collect used wa ter. Workers in the COF had access to a sink with running water and often elected to use hand soap at the sink instead of the pressure sprayers. Because much of the field wo rk was conducted in cold weather, it was not always possible to use hot water for the handw ashing stations set up at the housing units.4-11 Contractors had no way of heating th e water or maintaining its temperature in the field. On days when outdoor temperatures were below freezing, some crew members elected to use baby wipes to clean their hands and faces in lieu of th e cold water in the pressure sprayers.

Medical Surveillance – Medical surveillance was c onducted on all field personnel and renovation workers. Each worke r’s blood lead level was measured before field work began and was or will be measured again following completion of work on the study.

4.7. Recordkeeping

Detailed records were kept of all field data co llection activities. The information recorded by site supervisors and field technicians on data collection forms included:

• Clearance sampling locations and chain of custody records;

• Site wind speed and direction data for each day of exterior work;

• Precipitation activity during exterior work;

• P/CU phase of each experiment;

• Description of RRP work and duration of all RRP activities, including setting up plastic coverings, conducting cleaning, and conducting cleaning verification;

• Unit ID, location of study rooms or exteri or work area, and specific location of samples obtained;

• Surface type and size of dust wipe samples;

• Air sampling locations and air fl ow rates for each sampler;

• Soil sample locations and associated sample IDs

• Location of the decontamination area wher e booties and other prot ective gear were removed upon exiting the work area;

• Paint lead levels on compone nts undergoing work activity;

• Regular sample and QC sample identification information;

• Chain-of-custody records; and

• Signatures and initials of sample collectors.

When data collectors made or discovered errors on their data collection forms, they crossed out the incorrect information, inserted the correct information, and adde d their initials and the date next to the change.

In addition to written records, site supervisors obtained digital photographs and video recordings of various activities during the study across a subset of the housing units and experiments performed. Activities photogra phed or recorded include:

• initial inspection of the properties,

• RRP contractors conducting the work,

• plastic coverings being set up as containment,

• post-work cleaning,

• collection of environmental samples, and

• cleaning verification.4-12 All digital photographs and video recordings were downloaded to computers for electronic storage.

Laboratory results were returned from the labor atory in electronic spreadsheet and Portable Document Format (PDF) files. All laboratory results were entere d into a Microsoft (MS) Access 2003 database and exported into MS Excel 2003 and SAS v9.1 for st atistical analyses. Data collection forms were archived for use in verify ing the results entered in the database and in checking the electronic files receive d from the laboratory. These fo rms are not included in this final report in order to maintain the privacy of individual property owners.5-1 

### 5. Statistical Analysis Plan

5.1. Data Organization

A MS Access database was developed to manage the various types of data generated by the study. Throughout the study, paint chi p, initial and final soil, init ial and final dust clearance, dust wipe, air and bulk samples were collected, w ith respective collection information recorded on data collection forms (see Section 4). In a ddition to the environmental samples, property information, room designations and XRF results were recorded. The information recorded on the forms was entered by a data entry specialist or other professional into a customized interface that mirrored the look of the actual forms so that the information could be entered exactly as it was seen on the individual forms, thus reducing the potential for data entry errors. Laboratory analysis results were submitted electronically by the laboratory in MS Excel format. After review, they were directly uploaded into the data base. The two sources of information could be linked by the unique sample ID. The informati on from the data collection sheets provided supplemental information to the lab results, includ ing the collection date, location of the sample, and any issues encountered wh en collecting the sample.

5.2. Descriptive Analyses

The descriptive analysis section presents summary results for each type of data collected in the order listed below:

• Housing unit/experiment summary data,

• Paint lead levels,

• Pre-work clearance levels,

• Bulk debris samples,

• Interior dust samples,

• Interior air samples,

• Exterior dust samples with and without bulk debris samples included,

• Soil samples,

• Activity duration data, and

• Cleaning verification information.

For the environmental sample data, explorator y descriptive analyses were generated to summarize sampling results summarized by a variet y of covariates of interest. The study was performed in multiple housing units in two different cities by four different renovation contractors, providing multiple sources of varian ce; thus, results are summarized by Unit, City, and Contractor. Results are also presented with respect to the type of job performed and the expected intensity level of the work. Jobs are fu rther aggregated into those that did and did not use restricted practices. For inte rior experiments, the effect of the P/CU phase used during the experiment is investigated. The P/CU analysis is further broken down into the use or non-use of plastic coverings and whether the RRP contractors followed th e rule cleaning or baseline cleaning guidelines. Additionally, floor and sill wi pe results are summarized by work room floor type and condition.5-2 For the various categorical variables invest igated, detailed descriptive analyses in Appendices B-E contain box plots and summary tables, which illustrate and summarize the distribution of the data across the various factor s. A box plot displays the median (represented by the center horizontal line), the 25 th percentile (represented by the bottom of the box), and the 75 th percentile (represented by the top of the box). The vertical lines, or whiskers, are drawn from the box to the most extreme point within 1. 5 * interquartile range. (An interquartile range is the distance between the 25 th and the 75 th percentiles.) Any value more extreme than this, is identified individually with stars. The data are plotted using a log-base 10 scale. The summary statistics provided in the tables are sample size, arithmetic average, geometric average, minimum, 25 th percentile, median, 75 th percentile, maximum, and, for interior wipe data, the percentage of measurements exceedi ng the federal clearance standard.

Continuous variables investigat ed in the exploratory analys es include the paint lead concentration and size of the disturbed area for bo th interior and exteri or work; pre- and postwork soil lead concentrations for both interior and exterior work; and floor dust lead loadings at clearance and average post-work work room floor lead level for interior work. For these variables, scatter plots of dust lead loadings are presented to i llustrate the distri bution of the data as a function of the various characteristics. Fi tted linear regression lines are included on those scatter plots with associated r- square values to provide inform ation on the correlation between the data and the characteristic of interest.

The detailed descriptive analyses (Appendices B-E) also present analysis of the distribution of the various types of data befo re and after transformation.

Due to laboratory limitations, some environmental samples were reported as having levels below the detection limit, which were 10 μ g for dust wipes and 2 μ g for air samples. In order to include these values in summary analyses, the lead level for all sample types was set to one half of the reporting limit of the laboratory. Appendices B-E contains summaries of the amount of data reported below the detection limit. None of the exterior dust wipes fell below the detection limit.

The dust wipes in the descriptive analyses are presented as both wipe only and wipe plus bulk results. The wipe only results represent the amo unt of lead dust remaining on the floor after RRP work was performed and larger pieces of debris and excessive amounts of dust were picked up; a probable scenario for real-world RRP work. Th e inclusion of bulk samp les provides a value of the total lead in the dust and debris, and thus total lead contamination, resulting from the work.

5.3. Statistical Models

Effectiveness of the proposed cleaning and inte rior plastic and exterior plastic protocols was analyzed using general linear models with mi xed (fixed and random) effects. The response (log e -concentrations) was modeled to be linearly related to comb inations of categorical and continuous covariates. The lead concentrations were log e -transformed prior to modeling so that residuals were approximat ely normally-distributed. Prior to analyzing the lead dust loadings, the underlying distribution of the response data was examined for normality using Shapiro-Wilks tests, a standard test for normality. Pre- and post-transformed data were reviewed. To a large5-3 extent, the log transformations yielded data that were approx imately normal; however, in all cases the log-transformed data we re used in the analyses.

Key fixed effects included indicato rs for rule- or baseline cleani ng and use of containment plastic on interior jobs. On exterior j obs, the key fixed effects included tray position on, under or near a sheet of plastic. A random effect was include d for experiment number (grouped by Work-room number nested within housing unit) in or der to model covariance among the multiple subsamples to correctly assess significance and confid ence limits. That is, floor-wipes within an experiment are treated as sub-samples (exchang eable repeated measures) on the experimental unit. Dustpan loadings in each exterior expe riment are also treated as sub-samples on the experimental unit.

Besides key fixed effects, other covariates (both cate gorical and continuous) were analyzed to assess the extent that different fa ctors explain differences in log e lead concentrations in the samples. More detailed descriptions follow. Detailed model results across all objectives is available in Appendices I through N.

The first objective is assessed using only Work-room wipes at the Post-work (PW) stages. The effects of plastic and cleaning (the sec ond and third objectives) on reduction of log e lead levels subsequent to cleaning are a ssessed using Work-room wipes fr om Post-cleaning (PC) and Post-verification (PV) stages. The effects of plastic and cleaning on migration (the fourth objective) are assessed on wipes from Post-cleani ng (PC) and Post-verifica tion (PV) stages from the Observation and Tool rooms. Objective 5 ad dresses exterior plastic and involves only the exterior dust pan log e loadings.

Additional statistical modeling of the study data was pursued to determine what factors were predictive of post-verification clearance failure in the Work Room. Attention was focused on post-verification clearance failures on floor and window sill surfaces, in which post-verification dust-wipe samples were compared to the standards of 40 μ g/ft 2 and 250 μ g/ft 2 , respectively. Logistic regression models were used to asse ss the predictive ability of various candidate predictor variables in single covariate models, and to build a multivariate statistical model. Likelihood statistics from the singl e covariate models were used to assess the predictive ability of each potential variable, and the multivariate model was built by sequentially adding variables to the model in order of predictive ability. Inte raction terms were explor ed to the degree thatthey made physical sense. Since multiple (floor) dust-wipe samples were collected in the postverification phase within each experiment, l ogistic regression models were developed corresponding to two differe nt response variables:

1. The proportion of post-verification dust-wipe sa mples that exceed the clearance standard – in which the response becomes a binomial proportion (number exceeding standard / number of dust samples collected); and 2. The probability that any one post-verification dust-wipe sample will exceed the clearance standard – in which the response becomes a bi nary outcome. Note that dust wipes failing clearance are analogous to obtaining a fals e negative response from a cleaning verification cloth, as each clean ing verification zone had to pass visual inspection before post-verification dust wipe testing could commence.5-4 Since only one post-verification window-sill dust-lead loading measure was obtained from the workroom in each experiment, the logistic regr ession analysis defaulted to a single binary
response.6-1 

### 6. Descriptive Analyses

6.1. Housing Unit/Experiment Summary Information

Table 6-1 summarizes the 15 housing units and one COF utilized to complete the 75 experiments. The four apartments (H31-H33, H35) were located in two attached buildings and the two duplex units (H16-H17) were both halves of the same building. Thus, 12 separate buildings were used for the study, with most of the buildings built between approximately 1900 and 1925. The only exception was the COF school building, which was built in 1967.

Table 6-1. Housing Unit Information

Unit ID City Year Built Building Type Approximate Interior Square Footage H01 Columbus, Ohio 1918 Single Family Exterior Only H02 Columbus, Ohio OLD* (Pre-1920) Single Family Exterior Only H03 Columbus, Ohio OLD* (Pre-1920) Single Family 1,500 H08 Columbus, Ohio 1910 Single Family 1,744 H09 Columbus, Ohio OLD* (Pre-1920) Single Family 2,404 H10 Pittsburgh, Pennsylvani a 1920 Single Family 2,508 H13 Pittsburgh, Pennsylvania 1900 Single Family Exterior Only H16 Pittsburgh, Pennsylvania 1925 Duplex 1,472 H17 Pittsburgh, Pennsylvania 1925 Duplex 1,472 H19 Columbus, Ohio 1920 Garage Exterior Only H31 Pittsburgh, Pennsylvania 1925 Apartment 550 H32 Pittsburgh, Pennsylvania 1925 Apartment 550 H33 Pittsburgh, Pennsylvania 1925 Apartment 550 H35 Columbus, Ohio 1900 Single Family 1,538 H36 Pittsburgh, Pennsylvania 1925 Apartment 550 C01 Columbus, Ohio 1967 School Not available * Only information on Year Built from tax assessor records is “OLD.” Nearby properties indicate that these houses

were likely built between 1900 and 1920.

Tables 6-2 and 6-3 summarize vari ous characteristics of the interi or and exterior experiments, respectively. For the interior experiments, Tabl e 6-2 contains the averag e paint lead, amount of surface disturbed and type of surface disturbed. Average paint lead ranged from 0.8 to 13.0 percent by weight across the 60 interior experiments. For th e work room, floor type and condition and size are presented. The size of the tool and observation rooms is also included, as well as the distance from the observation room to the work room. This was calculated as the distance from the observation room entrance to th e work room entrance. In some cases, these distances are relatively small, such as four and five feet. In those cases, the work room was a second floor bedroom and the observation room was another second floor bedroom. The hallway separating the bedrooms served as the tool room.6-2Table 6-2. Interior Experiment Summary TableJob-Related InformationWork RoomTool RoomObservation RoomExp.#UnitIDJobPhaseAvg. PaintLead(% by wt.)DisturbedPaint (ft2)SubstrateBehind PaintRoom#FloorTypeFloorConditionSize(ft2)Room#Size(ft2)Room#Size(ft2)Distance toWorkRoom (ft)5 H03 Dry Scrape I 1.6 90 Plaster 201 Wood Poor 160 205 70 203 100 156 H03 Dry Scrape II 2.7 90 Plaster 201 Wood Poor 160 205 70 203 100 157 H03 Dry Scrape IV 1.7 70 Plaster 201 Wood Poor 160 205 70 203 100 158 H03 Dry Scrape III 1.7 110 Plaster 201 Wood Poor 160 205 70 203 100 159 H09 Window Repl. III 7.3 9 Wood 201 Wood Poor 180 204 100 203 200 510 H09 Window Repl. IV 3.3 9 Wood 201 Wood Poor 180 204 100 203 200 511 H09 Window Repl. II 10.1 18 Wood 203 Wood Poor 200 204 100 201 180 512 H09 Window Repl. I 7.2 11 Wood 206 Wood Poor 150 204 100 203 200 1513 H09 Heat Gun II 8.1 53 Wood/Stone 201 Wood Poor 180 204 100 203 200 514 H09 Heat Gun III 2.0 75 Wood 202 Wood Poor 140 204 100 203 200 515 H09 Heat Gun IV 10.2 75 Wood/Stone 203 Wood Poor 200 204 100 206 150 1516 H09 Heat Gun I 4.0 75 Wood 206 Wood Poor 150 204 100 203 200 1522 H16 Cut-outs IV 3.6 13 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1423 H16 Cut-outs I 3.6 14 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1424 H17 Cut-outs III 5.0 40 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1425 H16 Cut-outs II 3.6 14 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1426 H17 Dry Scrape III 2.1 60 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1427 H17 Dry Scrape IV 2.5 64 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1428 H17 Dry Scrape I 2.5 65 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1429 H17 Dry Scrape II 2.6 72 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1430 H10 Heat Gun IV 10 65 Wood 201 Wood Poor 150 206 50 202 105 431 H10 Heat Gun II 5.1 65 Wood 201 Wood Poor 150 206 50 202 105 432 H10 Heat Gun III 8.5 69 Wood 201 Wood Poor 150 206 50 202 105 433 H10 Heat Gun I 3.5 60 Wood 201 Wood Poor 150 206 50 202 105 441 H16 Window Repl. I 0.9 10 Wood 101 Wood Poor 230 102 215 103 190 1442 H16 Window Repl. II 1.9 10 Wood 101 Wood Poor 230 102 215 103 190 1443 H17 Window Repl. IV 1.9 10 Wood 101 Wood Fair 230 102 215 103 190 1444 H17 Window Repl. III 4.1 10 Wood 101 Wood Fair 230 102 215 103 190 1445 H08 Cut-outs IV 1.8 6 Plaster 103 Vinyl Tile Poor 180 102 220 101 200 1546 H08 Cut-outs III 1.8 7 Plaster 103 Vinyl Tile Poor 180 102 220 101 200 1547 H08 Door Plane IV 3.9 40 Wood 103 Vinyl Tile Poor 180 102 220 101 200 1548 H08 Door Plane I 7.8 40 Wood 103 Vinyl Tile Poor 180 102 220 101 200 1549 H31 Kitchen Gut II 1.7 55 Plaster 101 Vinyl Tile Fair 80 106 80 102 110 1050 H32 Kitchen Gut IV 2.3 40 Plaster 101 Vinyl Tile Fair 80 106 80 102 110 106-3Table 6-2. (continued) Interior Experiment Summary TableJob-Related InformationWork RoomTool RoomObservation RoomExp.#UnitIDJobPhaseAvg. PaintLead(% by wt.)DisturbedPaint (ft2)SubstrateBehind PaintRoom#FloorTypeFloorConditionSize(ft2)Room#Size(ft2)Room#Size(ft2)Distance toWorkRoom (ft)51 H33 Kitchen Gut I 3.5 66 Plaster 101 Vinyl Tile Fair 80 106 80 102 110 1052 C01 Cut-outs III 9.5 6 Plaster 102 Tile Good 120 112 560 120 50 2553 C01 Cut-outs II 7.4 6 Plaster 102 Tile Good 120 112 560 120 50 2054 C01 Cut-outs IV 7.4 6 Plaster 102 Tile Good 120 112 560 120 50 1555 C01 Cut-outs I 7.4 6 Plaster 102 Tile Good 120 112 560 120 50 1556 C01 Low Heat Gun II 2.2 50 Plaster 105 Tile Good 190 115 550 125 120 2057 C01 Low Heat Gun IV 2.4 50 Plaster 105 Tile Good 190 115 550 125 120 1558 C01 Low Heat Gun III 2.8 50 Plaster 105 Tile Good 145 115 600 125 120 1559 C01 Low Heat Gun I 13.0 50 Plaster 105 Tile Good 240 115 500 125 120 2560 C01 Heat Gun II 2.9 75 Plaster 105 Tile Good 160 115 580 125 120 2561 C01 Heat Gun III 2.2 75 Plaster 105 Tile Good 160 115 580 125 120 2562 C01 Heat Gun IV 2.2 73 Plaster 102 Tile Good 160 112 520 120 50 2563 C01 Heat Gun I 2.6 75 Plaster 102 Tile Good 160 112 520 120 50 2567 H16 Kitchen Gut I 1.2 40 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1468 H17 Kitchen Gut III 2.5 34 Plaster 103 Vinyl Tile Good 190 102 215 101 230 1469 H08 Kitchen Gut IV 5.2 69 Plaster 103 Vinyl Tile Poor 180 102 220 101 200 1570 H35 Kitchen Gut II 0.8 61 Plaster 101 Tile Fair 115 110 50 104 180 1071 H35 Cut-outs II 0.8 6 Plaster 101 Tile Fair 115 110 50 104 180 1072 H35 Cut-outs I 1.0 7 Plaster 101 Tile Fair 115 110 50 104 180 1073 H35 Door Plane II 1.2 31 Wood 101 Tile Fair 115 110 50 104 180 1074 H35 Door Plane III 3.2 26 Wood 101 Tile Fair 115 110 50 104 180 1076 H36 Kitchen Gut III 1.6 60 Plaster 101 Vinyl Tile Fair 80 106 80 102 110 1077 H08 Door Plane II 1.7 26 Wood 103 Vinyl Tile Poor 180 102 220 101 200 1578 H08 Door Plane IV 2.0 25 Wood 103 Vinyl Tile Poor 180 102 220 101 200 1579 H09 Door Plane I 2.1 32 Wood 202 Wood Poor 140 204 100 203 200 580 H09 Door Plane III 5.9 40 Wood 201 Wood Poor 180 204 100 203 200 56-4Table 6-3. Exterior Experiment Summary TableExp. #JobUnitIDWorkLocationPaintedMaterialAverage PaintLead (% bywt.)DisturbedPaint (ft2)Length of RulePlastic from WorkLocation (ft)Width of RulePlastic (ft)Length of ContainmentPlastic Beyond RulePlastic (ft)1 Dry Scrape H01 North Wall Wood 13.5 90 7 12 102 Power Sanding H01 North Wall Wood 11.7 100 12 35 53 Power Sanding H01 North Wall Wood 13.1 100 12 25 54TrimReplacementH02 Rear Porch Wood 15.3 40 5 20 221 Dry Scrape H09 North Wall Wood 1.3 150 12 25 634ReplaceExterior DoorH16 South Wall Wood 10.5 25 4 8 435ReplaceExterior DoorH17 North Wall Wood 11.2 25 4 8 436TrimReplacementH10 Front Porch Wood 16.8 60 13 24 437 Torching H10 Porch Ceiling Wood 2.7 98 13 20 438 Torching H13 Porch Ceiling Wood 11.4 80 10 15 440 Dry Scrape H19 North Wall Wood 15.7 250 6 20 664 Low Heat Gun C01Door Trim inCourtyardWood 31.5 75 5 10 365 Needle Gun C01Railing inCourtyardMetal 5.3 30 5 30 Varied*66 High Heat Gun C01Awning inCourtyardMetal 18.3 30 8 12 875 Dry Scrape H09 North Wall Wood 1.3 100 6 25 10* The containment plastic did not extend anidentical amount in all directions aroundthe railing. The railing was bordered onone side by a staircase and a vertical wall,which limited the size of the rule and containment plastic on that side.6-5 6.2. Paint Lead Levels

Once the presence of lead-based paint was established based on XRF measurements, paint chips were obtained from each building component that w ould be disturbed by the renovation work. In all cases, the laboratory reported l ead levels in the paint chips c onsistent with the definition of LBP. In general, higher levels of lead were found on exterior surfaces with average paint lead levels ranging from 1 percent to 32 percent by weight across the 15 exterior experiments with an overall average of approximately 12 percent. Ac ross the 60 interior experiments, overall average paint lead levels ranged from 1 to 13 percent with an average of approximately 4 percent. As noted in Section 4.1, the distri bution of paint lead levels on components undergoing work covered a broad range and were consistent with th e types of distributions reported in the National Survey of Lead and Allergens in Housi ng for components with lead-based paint.

The lead content of the paint in a particular experiment could affect the resulting dust lead levels. To investigate this within Appendix G, Section G2 contains detailed summaries of the average paint lead levels by intensity level, city, job, use of plastic and cleaning method, and other experimental characteristics. The bullets below summarize some findings of interest from those analyses:

• Average paint lead levels were similar across the three intensity levels for both interior and exterior work. Average inte rior levels were 3 or 4 perc ent across the three levels for interior work and 11 to 13 percent across the three levels for exterior work (see Figure/Table G2.1a).

• Average paint lead levels differed a bit acro ss jobs (see Figure/Tab le G2.3a). Average paint lead levels ranged from 2 to 5 per cent across different interior jobs with dry scraping and kitchen gut/cabinet replacement ha ving the lowest average levels. Average paint lead levels ranged from 5 to 32 percent across different ex terior jobs with the needle gun, torching, and dry scraping being the only jo bs averaging less than 10 percent. The low heat gun job on an exterior door and doorwa y at the school was the job with a paint lead average of 32 percent lead, much higher th an the next highest si ngle job average of 18 percent.

• Average paint lead level did not differ base d on plastic use, clean ing method, or phase (see Figures/Tables G2.5a-2.7a).

• There appeared to be only a slight associ ation between paint lead levels and average post-work floor lead loadings in the work room (see Figure G2.11).

• Paint lead levels and post-wor k soil lead levels after interior work appeared to have some association with an r-squa re of 0.11 (see Figure G2.14).

In addition to the average paint lead level, the amount of pain t disturbed during the work was another factor that might influe nce the lead levels resulting fro m the job. Following work, field technicians recorded the approximate number of square feet that were disturbed by each job. Area disturbed ranged from 25 to 250 ft 2 across exterior jobs and 6 to 110 ft 2 across interior jobs. Appendix G, Section G3 explores the association of square foot age disturbed with the various other study characteristics of interest. Findings of interest are listed below:6-6 • Low level interior and exterior jobs disturbe d significantly less area than the medium and high intensity jobs, which is consistent w ith the study design (see Figure/Table G3.1a)

• Of the interior jobs, the cut outs and window replacement jobs disturbed the least amount of paint (see Figure/Table G3.2a).

• Average area disturbed did not differ much based on plastic use, cleaning method, or phase (see Figures/Tables G3.5a-2.7a).

• For interior restricted jobs, average area disturbed was 61 ft 2 compared to 26 ft 2 for non-restricted jobs (see Figure/Table G3.8a).

• As seen in Figure 6-1 below, average area di sturbed did appear to be associated with average post-work floor lead leve ls in the work room with an r-square of 0.28 (see also Figure G3.11). Average area disturbed may be associated with job intensity, since the disturbed area increased as the (ini tial) job intensity increased.Disturbed Area (Sq. Ft)

0 10 20 30 40 50 60 70 80 90 100 110

Floor - Avg. Post-Work Workroom Floor Lead Loading (w/ Bulk) (R-Square = 27.73%) 10 100 1000 10000 100000 1000000 10000000 o o o

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Figure 6-1. Scatter Plot of Area Disturbed Versus Average Post-work Work Room Floor Lead Loading

6.3. Pre-work Clearance Levels

The goal at the beginning of each experiment was to obtain values of <10 μ g/ft 2 for all pre-work floor clearance samples and <62.5 μ g/ft 2 for all pre-work window sill clearance samples. After reviewing the dust lead results from the initia l cleanings and subsequent re-cleanings of candidate properties, it became clear that such stringent clearance levels were going to be difficult to achieve and could seriously hinder the progress of the study. Due to the initial difficulties in achieving the proposed clearance le vels, the pre-work clearance standards were changed to target an average of <40 μ g/ft 2 for floors and < 250 μ g/ft 2 for sills, the current EPA clearance levels for lead in dus t. For most experiments these targets were achieved; however, there were a few cases where they were not.6-7 As seen in Table 6-4, which presents the average clearance levels for all experiments, there were three experiments that started with averag e work room floor l ead levels above 40 μ g/ft 2 , ranging from 44 to 151 μ g/ft 2 . Tool room and observation room floor lead levels started above 40 μ g/ft 2 once in each room type. Additi onally, exit hallways started above 40 μ g/ft 2 four times. There were also a handful of cases where a window sill was used for sampling after failing to get below clearance levels. For most experiments, dust co llection trays replaced window sills when they were in poor condition or could not pass clearance.

Pre-work clearance levels did not appear to be associated with other endpoints. Within Appendix G, pre-work clearance levels are compared to average post-work lead levels and little or no correlation is evident with an r-square of 0.03 (see Figure G1.12). Figures C3.2a-c within Appendix C explore the correlation between pr e-work clearance levels and post-work, post-cleaning, and post-verifi cation floor lead levels. No correl ation was apparent in any of the three rooms at any of the three sampling stages . Figures D3.2a-c with in Appendix D present these analyses for sill lead levels with similar results.6-8Table 6-4.Summary of Pre-Work Clearance InformationFinal Pre-work Floor Lead LevelsFinal Pre-work Sill Lead Levels **Work RoomTool RoomObserv. RoomHallwayWork RoomTool RoomObserv. RoomExp. #JobPhaseAvg.(μg/ft2)# ofCleans^Avg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleans5 Dry Scrape I 28.4 1 29.8 1 25.7 1 <10 1 323.1T1 n/a 1,980T16 Dry Scrape II 27.4 2 15.3 1 22.1 1 <10 1 - - - - - -7 Dry Scrape IV 35.0 1 32.3 1 17.8 1 <10 1 - - - - - -8 Dry Scrape III 7.1 2 8.3 1 < 10 1 <10 1 - - - - - -9 Window Repl. III 30.0 2 10.5 1 19.6 1 20.6 1 120.8 2 < 10 1 58.5 210 Window Repl.IV < 10 1 55.8* 1 44.7* 1 8.4 1 30.9 1 756.9T1 44 111 Window Repl.II 6.8 3 < 10 1 < 10 2 4.1 1 46.3 1 7,737.4T1 228.8 112 Window Repl.I43.9 2 < 10 1 < 10 1 <10 1 346.9T2 172.0T1 404.7T113 Heat Gun II 10.4 1 < 10 1 8.7 157.6 1 18.3T1 481.3T1 80.5T114 Heat Gun III < 10 2 28.2 1 32.5 1 9.9 1 1684.4T1 1096.6T1 74.7T115 Heat Gun IV < 10 1 < 10 1 < 10 145.7 1 89.7T1 453.3T1 552.3T116 Heat Gun I < 10 1 < 10 1 < 10 1 6.0 1 374.3T1 1,828.1T1 7.3T122 Cut-outs IV < 10 1 < 10 1 < 10 1 - - 61.1 1 960.6T1 35.8 123 Cut-outs I < 10 1 < 10 1 < 10 1 - - <15.9 1 73.3 1 <20.0 124 Cut-outs III < 10 2 12.7 1 21.4 1 - - <76.9 1 88.3 1 94 125 Cut-outs II < 10 1 < 10 1 < 10 1 - - <15.9 1 63.2 1 <20.0 126 Dry Scrape III < 10 1 < 10 1 8.7 1 - - < 20 1 143.7 1 24.4 127 Dry Scrape IV < 10 1 < 10 1 < 10 1 - - 25.9 1 78.8 1 19.7 128 Dry Scrape I < 10 1 < 10 1 < 10 1 - - < 20 1 18.9 1 29.4 129 Dry Scrape II < 10 1 < 10 1 < 10 1 - - < 20 1 < 20 1 16.5 130 Heat Gun IV 11.0 2 < 10 2 < 10 2 29.3 2 - - - - - -31 Heat Gun II 17.8 2 < 10 2 < 10 2 <10 2 - - - - - -32 Heat Gun III 18.3 1 < 10 1 < 10 1 12.0 1 - - - - - -33 Heat Gun I < 10 1 < 10 1 < 10 1 10.0 1 - - - - - -41Window Repl.I < 10 1 < 10 1 < 10 1 - - < 16.1 1 45.4 1 < 16.1 142Window Repl.II < 10 1 < 10 1 < 10 1 - - < 16.1 1 < 16.1 1 13 143Window Repl.IV < 10 1 < 10 1 < 10 1 - - 20.2 1 65.3 1 < 16.1 144Window Repl.III < 10 1 < 10 1 < 10 1 - - 18.3 1 < 16.1 1 < 16.1 145 Cut-outs IV95.0 2 20.7 2 18.8 2 - - 1,217.50 2 29.6 1 < 20.8 146 Cut-outs III 19.0 2 39.3* 1 146.1* 1 - - 336.8T2 45.0T1 < 20.8T147 Door Plane IV < 10 1 < 10 1 < 10 1 - - 107.1 1 <10.9 1 <1.8 16-9Table 6-4.(continued) Summary of Pre-Work Clearance InformationFinal Pre-work Floor Lead LevelsFinal Pre-work Sill Lead Levels **Work RoomTool RoomObserv. RoomHallwayWork RoomTool RoomObserv. RoomExp. #JobPhaseAvg.(μg/ft2)# ofCleans^Avg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleansAvg.(μg/ft2)# ofCleans48 Door Plane I150.7 1 708.7* 1 250.3* 1 - -516.8 1599.7 1 55.7 149 Kitchen Gut II < 10 1 < 10 1 < 10 1 - - <30.3 1 - - <13.3 150 Kitchen Gut IV < 10 1 < 10 1 < 10 1 - - <30.3 1 - - < 10 151 Kitchen Gut I < 10 1 < 10 1 < 10 1 - - <30.3 1 - - 154.2 152 Cut-outs III < 10 1 < 10 1 19.3 1 < 10 1 - - - - - -53 Cut-outs II < 10 1 < 10 1 < 10 1 8.9 1 - - - - - -54 Cut-outs IV < 10 1 < 10 1 < 10 1 < 10 1 - - - - - -55 Cut-outs I 6.8 1 < 10 1 < 10 1 < 10 1 - - - - - -56 Low Heat Gun II 7.9 1 10.8 1 < 10 1 < 10 1 - - - - - -57 Low Heat Gun IV 6.8 1 8.6 1 8.6 1 12.3 1 - - - - - -58 Low Heat Gun III 18.4 1 12.5 1 9.1 1 < 10 1 - - - - - -59 Low Heat Gun I 9.4 1 14.4 1 < 10 1 < 10 1 - - - - - -60 Heat Gun II 14.1 182.0 1 < 10 1 < 10 1 - - - - - -61 Heat Gun III 12.5 1 11.2 1 < 10 1 < 10 1 - - - - - -62 Heat Gun IV < 10 1 < 10 1 < 10 1 < 10 1 - - - - - -63 Heat Gun I < 10 1 < 10 1 < 10 1 < 10 1 - - - - - -67 Kitchen Gut I < 10 1 < 10 1 < 10 1 - - < 16.1 1 131.3 1 < 16.1 168 Kitchen Gut III < 10 1 < 10 1 <10 1 - - 152.1 1 19.2 1 66.1 169 Kitchen Gut IV < 10 1 < 10 1 <10 1 - -264.8 1 55.1 1 < 15.4 170 Kitchen Gut II 8.4 1 < 10 1 <10 169.2 1 < 30.3 1 - - 27.4 171 Cut-outs II < 10 1 8.0 1 < 10 1141.1 1 22.4 1 - - 28.9 172 Cut-outs I < 10 1 < 10 1 < 10 1 17.2 1 < 18.5 1 - - < 13.2 173 Door Plane II < 10 1 < 10 1 < 10 1 <10 1 <18.9 1 - - 13.4 174 Door Plane III < 10 1 < 10 1 < 10 1 <10 1 <14.5 1 - - 23.2 176 Kitchen Gut III < 10 1 < 10 1 < 10 1 - - <30.3 1 - - 404.6T177 Door Plane II 7.5 1 < 10 1 < 10 1 - - 3,304.9T1 25.4 1 25.1 178 Door Plane IV 18.0 1 < 10 1 < 10 1 - - 1,161.1T1 90.5 1 10.4 179 Door Plane I < 10 1 < 10 148.9 1 8.2 1 1,691.9T1 1,078.8T1 104.7 180 Door Plane III 9.6 1 < 10 1 38.6 1 37.0 1 417.9T1 7.8 1 7.4 1* Clean plastic was put over the floor prior to the start of work** Where the window sillclearance sample is missing ( - ), or has a ' T ' (T), a dust collection tray wasused instead ofthe window sill^ # of Cleans represents the number of times that a unit was cleaned prior to an experiment. If one or more rooms did not pass clearance, re-cleaning andadditional clearance testing was required.6-10 6.4. Bulk Debris Samples

Many of the intensive renovation jobs generated la rge amounts of debris and dust. When larger pieces of debris and/or excessive dust were pres ent, technicians collected it in a sample bag or container prior to collecting the dust wipe. For in terior work, all these samples were taken in the work room during the post-work stage. For exte rior work, all bulk samples were taken on top of the rule plastic. In later analys es, the bulk sample results are larg ely excluded from interior data analyses based on the concept that renovators woul d typically pick up larger debris and excessive dust. Table 6-5 contains a summ ary of the bulk debris samples co llected on each interior job. Each bulk sample was collected from a specific sa mpling area, and the average lead in the wipes that correspond to those sampling areas is pres ented, as well. For th e exterior analyses, descriptive analyses of the exte rior dust results are presented both with and without the bulk debris samples. Table 6-6 cont ains summary data by job for the exterior jobs. Similar to the interior experiments, the averag e lead in the wipes represents the trays from which the bulk samples were collected.

Table 6-5. Interior Bulk Samples by Job Type

Interior Job # of Bulk Samples Average Lead in Corresponding Wipes (μg/ft 2 ) Average Lead in Bulk Sample (μg/ft 2 ) Average Total Lead in Sample Area (μg/ft 2 )

Cut-outs 17 1,810 255,734 257,544 Window Replacement 8 10,771 84,976 95,748 Dry Scrape 19 7,659 1,946,558 1,954,217 Door Plane 19 59,802 1,685,460 1,745,262 Low Heat Gun 8 20,510 630,304 650,814

Kitchen Gut 9 3,363 74,610 77,973

Heat Gun 34 32,780 1,558,268 1,591,049
TOTAL 114 23,751 1,164,304 1,188,055

Table 6-6. Exterior Bulk Samples by Job Type

Exterior Job # Bulk Samples Average Lead in Corresponding Wipes (μg/ft 2 ) Average Lead in Bulk Sample (μg/ft 2 ) Average Total Lead in Sample Area (μg/ft 2 )
Replace Exterior Door 1 5,286 212,025 217,311 Trim Replacement 3 58,424 2,087,562 2,145,986 Needle Gun 2 21,466 613,410 634,876 Dry Scrape 5 58,975 5,479,144 5,538,119 Low Heat Gun 4 65,884 34,193,431 34,259,314 Power Sanding 2 46,608 6,431,386 6,477,994 High Heat Gun 3 17,384 490,164 507,548 Torching 5 25,297 6,827,905 6,853,202
TOTAL 25 42,150 8,813,751 8,855,9016-11 6.5. Interior Dust Samples

The exploratory analyses of the interior dust samples include da ta for both the housing units and COF combined. The bulk debris samples, which all were in the work room at the post-work stage, are not included in these analyses.

6.5.1. Interior Floor Samples Appendix C contains numerous box pl ots, summary tables, and scatte r plots of the interior floor dust lead loadings across the three study rooms. The bullets below lis t highlights of those analyses separately for the Work room and the Tool/Observation rooms.

Work Room

• Figure and Table C2.1a summarize the work room data by intensity level. The geometric means (GM) and medians for the medium inte nsity job are higher th an the high intensity jobs at all three sampling stages. The low intensity jobs do have lower GMs and median floor lead levels compared to the other two intensity levels.

• Floor lead levels are similar between the wo rk in Columbus and Pittsburgh, as seen in Figures and Tables C2.2a to C2.2c.

• The door planing and heat gun >1100 degrees Fahrenheit jobs ge nerated the highest amounts of post-work lead dust in the work r oom, as seen in Table C2.3.a and Figure 6-2 below.

Post-Work Work Room Floor Lead Levelsby Job Type

422 3,993 2,686 7,737

958 2,080 32,644

1 10 100 1,000 10,000 100,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100 Interior JobsGM Floor Lead Level(μg/ft2)

Figure 6-2. Post-work Floor Lead Levels in the Work Room, by Job The error bars represent the 95% confidence interval computed using the geometric standard deviation. The values presented on the graph are the geometric mean floor lead levels.6-12 • There are differences in work room floor l ead levels across the th ree contractors that performed interior work with post-cleaning GMs ranging from 27 μ g/ft 2 to 90 μ g/ft 2 and post-verification GMs ranging 18 μ g/ft 2 to 53 μ g/ft 2 . (Figure/Table C2.4a)

• When plastic protective sheeting was used as required in the proposed EPA rule, GM post-cleaning floor lead levels in the work room are 41 μ g/ft 2 compared to 62 μ g/ft 2 when plastic was not used. Figure 6-3 compares wo rk room post-cleaning floor lead levels by use of plastic and by job. Use of plastic di d not lead to lower pos t-cleaning floor lead levels in the Work Room for all jobs.

Post-Cleaning Work Room Floor Lead Levelsby Job Type and Plastic Use

123

44

25 77 78 66

7 411

18 25 85

18 31 220

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Level(μg/ft2)

Plastic No Plastic
Figure 6-3. Post-Cleaning Work Room Floor Lead Levels, By Job and Plastic Use. The error bars represent the 95% confidence interval computed using the geometric standard deviation. The values presented on the graph are the geometric mean floor lead levels.

• When rule cleaning was performed following work, post-cleaning GM and median floor lead levels in the work room were lower (see Figure/Table C2.7a), at less than half the levels as when baseline cleaning was used. Across all jobs, the GM post-cleaning lead level was 34 μ g/ft 2 with rule cleaning and 75 μ g/ft 2 with baseline cleaning. There is variability in this difference by job, as seen in Figure 6-4, but except for the door planing job, rule cleaning consistently ach ieved lower post-cleaning levels.6-13 Post-Cleaning Work Room Floor Lead Levels - by Job and Cleaning Method

122 59

28 236

26 54

12 10 245

86 214

30 51

13

1 10 100 1000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Level(μg/ft2)

Proposed Rule Cleaning Baseline Cleaning
Figure 6-4. Post-Cleaning Work Room Floor Lead Levels, By Job and Cleaning Method. The error bars represent the 95% confidence interval computed using the geometric standard deviation. The values presented on the graph are the geometric mean floor lead levels.

• Table C2.8a reports that the phases using rule cleaning (I and III) le d to lower GM floor lead levels in the work room following post- work cleaning, with Phase IV (no plastic and baseline cleaning) having a GM nearly twice that of the nearest phase – 105 μ g/ft 2 compared to 54 μ g/ft 2 for baseline cleaning with plasti c. When analyzing how the four phases compare, it is appropriate to use post-cleaning data for the baseline cleaning phases and post-verification data for the ru le cleaning phases, since under the proposed rule the rule cleaning would be followed by the cleaning verification process. These data are referred to as “post-job” levels throughout this and subs equent analyses. Figure 6-5 compares those data for each job, illustrating the consistently lower levels achieved by the proposed rule practices compared to the baseline practices. Regarding the phases that combined rule and baseline practices, in fi ve of seven jobs no plastic/rule cleaning achieved noticeably lower GM floor lead leve ls than plastic/baseline cleaning, indicating that rule cleaning may be more important th an the use of plastic sheeting for lowering lead levels on floors in the Work Room. Large differences are evident for a few jobs such as heat gun over 1100 degrees with GM post-job floor lead levels of 36 μ g/ft 2 using proposed rule methods and 445 μ g/ft 2 using baseline methods.6-14 Post-job Work Room Floor Lead Levels, by Job and Phase

5 33

12 24 148

30 36

22 135

58 41 450

66 445

8 110

45
23 102 113 135

8 70

15

7 353

11 89

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices
Plastic/Baseline Clean No Plastic/Rule Clean

Figure 6-5. Post-Job Work Room Floor Lead Levels, By Job and Phase

• When grouping jobs together by whether a rest ricted practice was used, the non-restricted practice jobs achieved significantl y lower GM and median work room floor lead levels at each of the post-work, post-cleaning, and post-verification phases (see Figure/Table C2.9a).

• Higher lead levels were measured on wood floors compared to tile floors (see Figure/Table C2.10a); however, because the post-work levels are measurably higher on wood floors, this may be correlated with th e types of jobs done on wood floors and the paint lead level and square footag e disturbed in those rooms.

• Similarly, floor in poor condition also had higher floor lead levels across the three sampling stages; however, because post-work levels are measurably higher, the differences at the subsequent two stages may be caused by higher intensity work (see Figure/Table C2.11a).

• There is some correlation between the av erage paint lead concentration of the components worked on and the work room floor lead levels measured at the three main sampling stages (see Figure C3.1a). R-s quare values range from 0.10 to 0.15 across the post-work to post-verification stages. Work r oom floor lead levels also appear to be correlated with average post-work work room floor lead levels (including bulk debris samples) with r-square values of 0.24 at pos t-cleaning and 0.16 at post-verification (see Figure C3.6a).

• Work room floor lead levels do not appear to be correlated with pre-work clearance levels, initial and final soil l ead levels, and area disturbed.6-15 Tool/Observation Rooms

• Similar to the Work room, the Medium intensit y jobs led to a higher GM floor lead level in both the Tool and Observat ion rooms at all three samplin g stages – post-work, postcleaning, and post-verificati on (see Figure/Table C2.1b-c).

• Tool and Observation room floor lead levels vary by job performed, w ith the jobs leading to higher work room levels (Door planing and high heat gun) also ha ving higher levels in the non-work rooms (see Figure/Table C2.3b-c).

• Lower GM floor lead levels were achieved w ith plastic than wit hout plastic across the three sampling stages (see Figures/Tables C2.6b-c). On the other hand, the levels measured in the non-work rooms did not va ry much based on cleaning method used (see Figures/Tables C2.7b-c). Thus, for Tool and Observation rooms, use of plastic sheeting for the Work Room as required by the Proposed Rule appears to be more important than the cleaning method used in the Work Room.

• Comparing proposed rule practices to baseli ne practices in the Tool room, post-job (post-cleaning for baseline cleaning phases and post-verification for rule cleaning phases) GM floor lead levels are 36 μ g/ft 2 using proposed rule practices and 66 μ g/ft 2 using baseline practices (see Table C2.8b). In the Observation room, post-j ob floor lead levels are 20 μ g/ft 2 using proposed rule practices and 34 μ g/ft 2 using baseline practices (see Table C2.8c).

• Figures 6-6 and 6-7 plot th e post-job (post-cleaning for baseline cleaning phases and post-verification for rule clean ing phases) floor lead levels for Phase I and Phase IV by job. The differences in the non-work r ooms become more pronounced in the higher intensity jobs. Further evidence of this is seen when comparing restricted practice jobs to non-restricted practice jobs (see Figure/Table C2.9b-c). The re stricted practice jobs have higher GM floor lead levels across the thre e sampling stages in both non-work rooms, although the difference is la rger in the Tool room.6-16Post-Job Tool Room Floor Lead Levelsby Job Type and Phase

32 39 33 228

11 64

10 39 32 76 1,379

19 95

11

1 10 100 1,000 10,000 100,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Levels(μg/ft2)

Phase I-PV Phase IV-PC
Figure 6-6. Post-Job Tool Room Floor Lead Levels, By Job and Phase. The error bars represent the 95% confidence interval computed using the geometric standard deviation. The values presented on the graph are the geometric mean floor lead levels.

Post-Job Observation Room Floor Lead Levels- by Job Type and Phase

20 24 138 20

6 10 10 26 58 420

26 14

10 10

1 10 100 1,000 10,000 100,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Levels(μg/ft2)

Phase I Phase IV
Figure 6-7. Post-Job Observation Room Floor Lead Levels, By Job and Phase. The error bars represent the 95% confidence interval computed using the geometric standard deviation. The values presented on the graph are the geometric mean floor lead levels.

• Tool and Observation room floor lead leve ls are not correlated with most of the continuous covariates considered (soil lead levels, paint l ead levels, square footage);6-17 however they do appear to be correlated with post-work work room fl oor lead levels, i.e., the more lead dust stirred up by the work, th e more lead dust escapes to nearby rooms (see Figures C3.6b-c).

6.5.2. Interior Sill Samples

Appendix D contains an identical set of exploratory analyses to that generated for the floor dust data. As seen in Table D1.1, a higher percenta ge of non-detect measurements was obtained for the window sill data. For example, over 70 percent of Observation Room samples were below the detection limit at the post-cl eaning and post-observation stages. Note that the sample sizes for most of the window sill samples were less than 1 square foot. With less surface area to work with on actual window sills and dus t collection trays serving as window sills, the size of some sill samples is around 0.25-0.35 ft 2 . With a reporting limit of 10 μ g of lead and a sample size of .2 ft 2 , the reported measurement would be <50 μ g/ft 2 . As noted earlier, half the detection limit was used for analysis purposes. Thus, a <50 μ g/ft 2 sample was reported as 25 μ g/ft 2 for the following analyses.

The bullets below contain selected results of in terest from the detailed Appendix D analyses.

• The window sill data are much closer to a normal distribution following the log transformation (see Figures D1.1a-D1.3b). Th e large number of non-detects still skews the distribution, but use of the log-tr ansformed data should be acceptable.

• The post-work sill samples in the Work room do not have a consistent relationship across sampling stages when analyzed by intensity le vel. At post-work, me dium intensity jobs yielded higher sill levels; how ever at post-cleaning, the hi gh level jobs yielded higher GM sill lead levels (see Figure/Table D2.1a).

• Jobs in Pittsburgh appear to have yielded higher sill lead levels across the three study rooms and all sampling stages than jobs in Columbus (see Figures/Tables D2.2a-D2.2c).

• The door planing job generated higher lead dust on window sills as measured at each sampling stage in all three study rooms (Figur e/Tables D2.3a-D2.3c). Generally, the high heat gun job and window replacement job also led to somewhat higher levels than the other jobs.

• Contractor C2’s jobs led to lower sill lead levels at all sampling stages across the three rooms (see Figure/Tables D2.4a-D2.4c). This may be correlated with the job types conducted by each contractor.

• The sill lead levels are highly variable across housing units. Unit H10 yielded much higher post-cleaning and post-verifi cation sill lead levels in the work room with GMs of 4,067 μ g/ft 2 and 1,367 μ g/ft 2 at the two stages, respectiv ely (see Figure/Tables D2.5aD2.5c). The next highest GM at both stages in the Work room belonged to H08 at 367
μ g/ft 2 and 196 μ g/ft 2 , respectively.

• Use of plastic is associated with lowe r post-cleaning and post-verification GM and median sill lead levels across all three room s (see Figure/Tables D2.6a-D2.6c). Sill lead levels in the Tool room are also lower when plastic was used with a post-cleaning GM of6-18 67 μ g/ft 2 with plastic and 124 μ g/ft 2 without plastic. When focused on post-cleaning levels by job in the Work room, five of seven jobs yielded similar or lower sill lead levels when plastic was used, as illustrated in Figure 6-8.



### Post-cleaning Work Room Sill Lead Levels, By Job and Use of Plastic

37 134

30 198 209

47 238

52 139 282

72 682

39 351

0 100 200 300 400 500 600 700 800

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Plastic No Plastic
Figure 6-8. Post-Cleaning Work Room Sill Lead Levels, By Job and Plastic Use

• Use of rule cleaning appears to be asso ciated with lower post-cleaning and postverification sill lead levels in the Work room (see Figure/Tables D2.7a). Figure 6-9 displays Work room sill lead levels by job and cleaning method. Similarly, lower sill lead levels were found in the Tool room at all three sampling stages with GMs of 74, 81, and 54 μ g/ft 2 across the three stages using rule cleaning and 117, 103, and 125 μ g/ft 2 using baseline cleaning (see Fi gure/Tables D2.7b). Not much difference is observed in the Observation room by cleaning me thod (see Figure/Tables D2.7c).6-19 Post-cleaning Work Room Sill Lead Levels, by Job and Cleaning Method

25 49 63 54 392

23 126 78 381

132 265 365

81 660

0 100 200 300 400 500 600 700

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Cleaning Baseline Cleaning

Figure 6-9. Post-Cleaning Work Room Sill Lead Levels, By Job and Cleaning Method

Analyzing the work room sill lead levels by phase, the largest post-cleaning and postverification differences are betw een Phases I and IV, with the proposed rule practices having the lowest GM and the baseline practices havi ng the highest GM (see Figure/Tables D2.8a). Figure 6-10 presents the work room datafrom these two phases by job, using postverification levels for Phase I a nd post-cleaning levels for Phase IV. Figure 6-11 presents the work room data for all four phases using pos t-verification levels for Phases I and III and post-cleaning levels for Phases II and IV. The largest differe nces are usually between the Proposed Rule Practices and the Baseline prac tices; however, in the dry scraping and high heat gun jobs, the plastic/baseline cleaning routin e yielded higher GM sill lead levels than no plastic/baseline cleaning. [Note that there was only one Heat Gun < 1100°F experiment that used no plastic/rule cleaning and the correspond ing post-verification window sill sample in the Work Room was not received by the la boratory, which explai ns the missing bar in Figure 6-11.]6-20 Post-job Work Room Window S ill Lead L evels, by Job and Rule Use

28 63

21 21 197

17 72 120 568 429 247 707

61 601

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices

Figure 6-10. Post-Job Work Room Sill Lead Levels, By Job and Rule Use

Post-job Work Room Sill Lead Levels, by Job and Phase

28 63

21 21 197

17 72

120 568 429 247 707

61 601

51 256

41 285 188 108 726

43 75 60 411

24 108

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices
Plastic/Baseline Clean No Plastic/Rule Clean

Figure 6-11. Post-Job Work Room Sill Lead Levels, By Job and Phase

• GM Tool room sill lead levels are also lo wer across the three sampling stages under the Proposed Rule Practices (Phase I) compared to the Baseline Practi ces (Phase IV) – 57
μ g/ft 2 vs. 127 μ g/ft 2 at post-work, 55 μ g/ft 2 vs. 131 μ g/ft 2 at post-cleaning, and 41 μ g/ft 2
vs. 150 μ g/ft 2 at post-verification. Figure 6-12 disp lays the post-work Tool room lead levels by job for these two phases.6-21 Post-job Tool Room S ill Lead Levels, by Job and Rule Use

21 40 43 21 265

11 66 65

23 143
57 1,986

84 222

1 10 100 1,000 10,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices

Figure 6-12. Post-Job Tool Room Sill Lead Levels, By Job and Rule Use

• There is not as much difference between GM Observation room sill lead levels under the Proposed Rule Practices (Phase I) compared to the Baseline Practi ces (Phase IV) – 53
μ g/ft 2 vs. 58 μ g/ft 2 at post-work, 35 μ g/ft 2 vs. 52 μ g/ft 2 at post-cleaning, and 35 μ g/ft 2 vs. 49 μ g/ft 2 at post-verification (see Fi gure/Table D2.8c). Figure 6-13 displays the post-job Observation room lead levels by job for th ese two phases. There are mostly minor differences in the non-restricted practice jobs, whereas the Proposed Rule Practices appear to have led to lower post-job lead levels from the restricted practice jobs.6-22 Post-job Observation Room Sill Lead Levels, by Job and Rule Use

22 21 58

21 142

11 56

23 40 33 19 306

26 127

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices

Figure 6-13. Post-Job Observation Room Sill Lead Levels, By Job and Rule Use

• Use of restricted practices is associated with higher GM sill lead levels in each of the three study rooms. Note that the window sills in the Observation and Tool rooms are not cleaned between each stage of the experiment , indicating that the higher post-work sill lead levels observed after restricted practices translates to higherpost-cleaning and postverification levels, as well (s ee Figure/Tables D2.9a-D2.9c).

6.5.3. Hallway/Exit Samples

The study design specified collection of three sa mples at the end of each experiment along the path from the work room to the entrance used by workers. These samples provide data on both the track-in of lead dust from the outside and tr acking of lead dust from the work room through the house as workers enter and exit. The locat ion of the samples was not pre-specified in sampling plans. Technicians were instructed to obtain one sample n ear the entrance of the house, one from an area closer to the work room , and one in between. In houses where work was done on the second floor, the hallway samples were generally taken from the bottom of the steps, near the entrance, and in between these two areas. The samples were taken following the collection of all post-verification samples in the three main study rooms. At this point, the workers would have removed most if not all of their job-related equipment, and remaining traffic through the house mainly involved rem oval of study-related equipment.

Appendix H contains a set of expl oratory analyses similar to thos e generated for the other data types. As with the other dust wi pe data, the data were log-transf ormed to get closer to a normal distribution. The bullets below present some key results seen in Appendix H.

• Higher lead levels are seen inexit samples taken after medi um intensity jobs with a GM of 507 μ g/ft 2 for medium intensity jobs compared to 212 μ g/ft 2 for high intensity and 73 μ g/ft 2 for low intensity (see Figure/Table H2.1a).6-23 • The GM exit sample lead level for Door Planing jobs was 500 μ g/ft 2 , much higher than all other jobs (see Figure/Tab le H2.3a). GMs of the other six jobs range from 21 to 83 μ g/ft 2 , with the high heat gun job having th e next highest level below the door planning job.

• As seen in Figure/Table H2.6a, there is little difference in GM lead levels between jobs that used plastic in the Work room during the work (67 μ g/ft 2 ) and those that did not (69 μ g/ft 2 ).

• The difference in GM lead levels is relati vely small between jobs using rule cleaning (59 μ g/ft 2 ) compared to jobs using baseline cleaning (78 μ g/ft 2 ).

• The baseline practices (no plastic/baseline cl eaning) yielded higher GM exit levels of 88 μ g/ft 2 compared to a range of 55 μ g/ft 2 to 69 μ g/ft 2 for the other three P/CU routines (see Figure/Table H2.8a).

• Higher hallway lead levels were measured fo llowing restricted practi ce jobs compared to non-restricted jobs with GM of 136 μ g/ft 2 versus 34 μ g/ft 2 (see Figure/Table H2.9a).

• When the work room floors were in poor c ondition, hallway lead levels were higher than when work room floors were in good condition with GMs of 143 μ g/ft 2 for poor condition floors and 18 μ g/ft 2 for floors in good condition (see Figure/Table H2.11a).

• Hallway/exit lead levels were not associated with pre-work or post-work soil lead levels (see Figures H3.4 and H3.5).

• Hallway/exit lead levels do appear to be co rrelated with average post-work work room floor lead levels with an r-squa re value of 0.11 (see Figure H3.7).

6.5.4. Exterior Post-Work Tray Sample s During Window Replacements

For the experiments where a window was replaced from the interior of a housing unit, one dust collection tray was placed for sa mpling on top of plastic sheeting on the exterior of the unit, directly below the window. This sampling was done to evaluate potential exterior contamination when performing a window replacement from the inside of the house. Prior to conducting the window replacements, the RRP workers covered th e window with plastic from the outside to prevent falling debris from contaminating the gr ound below. After the replacement was finished, the RRP workers carefully removed the exterior plastic sheeting. The dust collection tray was sampled immediately after the work was comple ted and the plastic removed. The results of those exterior dust collec tion tray samples are shown in Table 6-7. The results are not consistent, even when considering variabil ity due to contractor, housing unit, and tray location. For experiments 11 and 42, a significant amount of debr is was observed falling from a pocket at the bottom of the external plastic sh eeting when the RRP workers were removing it. The debris fell onto the ground covering and into the dust collection tray. For so me of the other experiments, small paint chips were present in the tray when sampled, but no signifi cant debris was observed falling out of the protective plastic.6-24 Table 6-7. Exterior Dust Wipe Samples During Window Replacement

Experiment Concentration (μg/ft 2 ) Location
9 291 Porch roof under second story window 10 232 Porch roof under second story window 11 31,715 Ground under second story window 12 12 Ground under second story window 41 17.0 Porch floor under first story window 42 20,591 Porch floor under first story window 43 44 Porch floor under first story window 44 338 Porch floor under first story window

6.6. Interior Air Samples

Indoor air samplers collected samples during each work stage (work, cleaning, and cleaning verification) in each of the three study rooms. The work and cleaning stages included a one-hour wait time following completion of activity to allo w settling of airborne du st. The air monitors operated from the beginning of the activity unt il the end of the one-hour wait time. Field technicians recorded information on flow rate at the beginning a nd end of the sampling period in order to calculate a mean flow rate. In a fe w cases, multiple air filters were needed during a single sampling stage because of a filter getti ng clogged with dust. In these cases, the concentrations obtained for the two samples were averaged and that valu e used in subsequent analyses.

Appendix F contains detailed explor atory analyses of the indoor air filter samples. Laboratory analysis of the air filter samples had a 2 μ g detection limit. With ma ny of the filt ers not running for long, e.g. during the cleaning verification stage that sometim es only lasted 10-15 minutes, this made it more challenging to measure e nough lead to be over the detection limit. Additionally, with only small amounts of air mon itored, laboratory measurements are relatively variable. The air sampling time could not have b een increased, as it then would not have been representative of the length of time and exposur e for each stage of the experimental protocol. Reported levels range anywhere from <2.7 to <20 to <666 μ g/m 3 . Table 6-8 (also Table F1.1) presents the number and percentage of samples that contained lead belo w the detection limit. Appendix F presents analyses using the half th e reporting limit for each sample that was below the detection limit; thus, the three example values listed above are included as 1.35, 10, and 333
μ g/m 3 in the analyses.

Table 6-8. Number (and Percentage) of Interior Air Dust Lead Concentration Measurements (μg/m 3 ) Below Detection Limit Measurements by Room and Stage

Room Stage Work Tool Observation Post-Work 29 (48.3%) 43 (71.7%) 45 (75.0%) Post-Cleaning 50 (82.0%) 53 (88.3%) 54 (90.0%) Post-Verification 59 (98.3%) 59 (98.3%) 58 (96.7%)6-25 Below are a few findings from the air lead analys es. Since the Tool and Observation rooms have at most 28% reported values in any given stage, no results from those analyses are presented. Similarly, because the post-cleaning and post-veri fication data in the Work room have no more than 18 percent reported measur ements, no results from those an alyses are discussed. Because the post-work stage in the work room is the only set of data with more than half of its values actually being reported measurements, only those data are discussed.

• Air lead concentrations in the work room dur ing Medium intensity jobs were higher than the High and Low intensity jobs.

• The finding regarding intensity level is driv en largely by the door planing job, which has a GM air lead concentration far greater than any other job – 300 μ g/m 3 compared to the next highest job, heat gun above 1100 degrees, with 13 μ g/m 3 (see Figure/Table F2.3a).

• The GM work room air lead level when plastic was used is higher than when plastic was not used - 12 μ g/m 3 compared to 8 μ g/m 3 (see Figure/Table F2.6a). During phases with protective plastic used, plastic sheeting c overed floors and the doorway into the Work Room. Presence of the airlock that did not al low airborne lead to escape the Work Room likely contributed to higher Work Room air lead levels in these cases.

• The GM work room air lead level was higher during restricted pract ice jobs than other jobs, which is not surprising si nce the door planing is defined as a restricted practice job (see Figure/Table F2.9a). Figure 6-14 plot s air lead concentrations for each job.

Post-work Air Lead Concentration in Work Room, by Job

5 6 3 10 300

2 13

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Air Lead Level (μg/m3)

Air Lead Level

Figure 6-14. Post-work Air Lead Concentration in the Work Room, by Job

6.7. Exterior Dust Samples

The exterior study design specified collection of three types of dust collection tray samples – on the Rule plastic, under-the-Rule pl astic, and near-the-Rule plastic. Three samples were obtained from each location during each experiment. The size of the Rule plastic varied from job to job. The proposed rule does not identify specific areas of ground to be covered during exterior work,6-26 so the size of the rule plastic was determined fo r each experiment within the constraints of the dimensions of the property being worked on and the need to collect samples near the Rule plastic as well. For this study, vertical containmen t and additional ground plastic were set up as a precaution to capture dust and debris that might not be captured on the Rule plastic. Table 6-9 contains distances from the work location for a ll on and near Rule plastic samples, as well as associated results. The under Rule plastic samples were obtained from similar locations to those used for the on Rule plastic samples.

Table 6-9. Distance of Exterior Trays from Work.
On top of Rule Plastic Near Rule Plastic Exp.

# Job Type Unit ID Sample # Loading* (μg/ft 2 ) Dist. From Work (ft) Loading* (μg/ft 2 ) Dist. From Work (ft)** 1 12,516,131 1 4,178 8 2 50,819 5 953 12 1 Dry Scrape H01 3 8,556 7 929 17 1 4,666,776 1 58,718 15 2 341,492 5 85,985 14 2 Power Sanding H01 3 106,383 11 65,719 12 1 8,289,211 3 18,002 14 2 234,877 8 9,865 17 3 Power Sanding H01 3 129,738 12 16,905 15 1 4,109 2 61,388 5 2 1,652,382 1 27,082 6 4 Trim Replacement H02 3 115,207 2 54,851 5 1 1,463,974 1 274 13 2 2,357 7 172 18 21 Dry Scrape H09 3 417 11 574 15 1 217,311 1 31,708 6 2 79,163 3 84,290 6 34 Replace Exterior Door H16 3 14,441 3 1,129 6 1 30,060 1 3,793 5 2 43,976 1 30,426 5.5 35 Replace Exterior Door H17 3 151,734 1 17,590 5.5 1 161,018 3 19,278 7 2 2,991,198 2 133,690 7 36 Trim Replacement H10 3 1,794,377 2 217,900 7 1 1,134,829 4.5 150 15 2 956 12 198 15 37 Torching H10 3 349,644 10 151 15 1 29,401,680 2 658 16.5 2 1,560,303 10.5 1,836 16.5 38 Torching H13 3 1,819,556 9 1,093 16.5 1 14,348.0 6 7,596 11 2 11,480,352 2 12,169 9 40 Dry Scrape H19 3 268,359 4 16,597 106-27 Table 6-9. (continued) Distance of Exterior Trays from Work.
On top of Rule Plastic Near Rule Plastic Exp.

# Job Type Unit ID Sample # Loading* (μg/ft 2 ) Dist. From Work (ft) Loading* (μg/ft 2 ) Dist. From Work (ft)** 1 130,674,045 1 2,692,069 7 2 1,133,090 5 27,650 8 64 Low Heat Gun C01 3 2,538,052 3 16,884 7 1 381,586 3 21,158 6 2 22,004 1 8,768 6 65 Needle Gun C01 3 888,167 2 1,380 12 1 196,760 7 14,689 11 2 494,822 5 331 16 66 High Heat Gun C01 3 831,062 3 363 13 1 1,961,779 1 1,205 8 2 14,345 4 190 15 75 Dry Scrape H09 3 1,845 7 488 12 * Where applicable, the loading represents the sum of the dust wipe plus the bulk sample. ** Distance from work represents the perpendicular distance from the work wall/area to the collection tray

Appendix B contains detailed expl oratory analyses of the exte rior tray samples by location summarized by intensity level, c ity, job, contractor, and housing un it. The bulk debris samples are included in these analyses. Appendix E cont ains the same analyses, but excludes the bulk debris samples. Figures B1.1a -1.3b and Figures E1.1a-1.3b displa y histograms of the raw and log-transformed data collected for each locati on – on, under, and near Rule plastic. The logtransformation does lead to a data more clos ely approximating normality. The following bullets describe selected findings obser ved from Appendices B and E:

• The GM and median lead levels measured on the Rule plastic across all jobs is much higher than the other two locations with GM of 241,977 μ g/ft 2 and median 268,359
μ g/ft 2 , compared to GM of 479 μ g/ft 2 and median of 322 μ g/ft 2 under-the-Rule plastic and GM of 5,771 μ g/ft 2 and median of 9,865 μ g/ft 2 near-the-Rule plastic. Excluding the bulk debris samples reduced the t op of Rule plastic GM to 21,292 μ g/ft 2 and median to 22,004 μ g/ft 2 . No bulk debris samples were co llected near or under rule plastic.

• The high intensity jobs resulted in higher GM lead levels on and under-the-Rule plastic, when the bulk samples are included, but the high and low level jobs have similar GMs when bulk samples are excluded. The low leve l jobs yielded higher levels near-the-Rule plastic (see Figure/Tables B2.1a -c). A possible explanation for this is that the Rule plastic covered less area in the low-level jobs (trim/soffit and door replacements and needle gun) because of space constraints.

• The low heat gun job yielded the bulk samples with the most amount of lead, which gave this job a much higher GM on the Rule plastic than all other jobs. With bulk samples excluded, the power sanding job yielded a much higher GM on the Rule plastic. Figures 6-15 and 6-16 present the on and under Rule plastic samples both with and without the bulk debris samples, respectivel y, by each job. All jobs yielded much higher lead levels on the plastic than under the pl astic regardless of wh ether the bulk samples are included, except the torch burning job. The torching job has a GM on the Rule plastic of 562,462 μ g/ft 2 with the bulk samples included and only 5,447 μ g/ft 2 with the6-28 bulk samples excluded. The GM of the under Ru le plastic samples for the torching jobs is 8,565 μ g/ft 2

.

Exterior Lead Measured on Top of and Under Rule Plastic, by Job (w/ bulk debris samples)

32 648 270 3,149

89 4,896 8,565

388

60,662 296,251 195,372 7,216,358

71,540 591,491 562,462 432,520

1 10 100 1,000 10,000 100,000 1,000,000 10,000,000

Door Repl. Trim/Soffit Repl. Needle Gun Heat gun <1100 Dry Scrape Pow er Sanding Torching Heat gun >1100GM Lead Levels (ug/ft2)

Under Rule Plastic Top of Rule Plastic

Figure 6-15. Exterior Dust Lead Levels On and Under Rule Plastic, by Job (w/ Bulk Samples)

Exterior Lead Levels on Top of and Under Rule Plastic, by Job (excluding debris samples)

32 648 270 3149

89 4896 8565

388 32,653 44,474

7,713 37,431 12,004 111,388

5,447 18,000

1 10 100 1,000 10,000 100,000 1,000,000

E1-Door Repl. E2- Trim/Soffit Repl. E3-Needle Gun E4-Heat gun <1100 E5-Dry Scrape E6-Pow er Sanding E7- Torching E8-Heat gun >1100GM Lead Levels (ug/ft2)

Under Rule Plastic Top of Rule Plastic

Figure 6-16. Exterior Dust Lead Levels On and Under Rule Plastic, by Job (excl. bulk samples)

• There does appear to be an association be tween paint lead levels and lead levels measured on and near-the-Rule plastic with r-square values of 0.16 and 0.17, respectively6-29 (see Figure B3.1). No association between paint lead level and under Rule plastic samples was found.

• No association is observed between initial soil lead levels and lead measured in any of the three exterior tr ay locations (see Figure B3.2). While no association is observed between post-work soil lead levels and lead measured on or under Rule plastic, there does appear to be some correlation between post-work soil levels and lead levels measured in trays near-the-Rule plastic (see Figure B3.3).

• There does not appear to be an associati on between amount of area disturbed by the RRP work and lead measured at any of th e three locations (see Figure B3.4).

Prior to performing exterior experiments, one or more dust collection trays were set out in the area of the rule plastic to meas ure the background concentration of lead at the property. Trays were exposed for periods ranging from two and a quarter hours to 10 hours, with two-thirds exposed between four and five hours. E xposure was less than four hours for only one experiment, caused by theft of th e initial set of sampling trays at the location. The number of samples collected for each job type and associat ed lead levels and sampling times are presented in Table 6-10. In the instances where more than one experiment was performed at the same unit (e.g. the trim replacement and torching at H10), the results of the background tray(s) are included for both experiments.

Table 6-10. Average Background Dust Loading for Exterior Experiments, by Job Type

Exterior Job # Samples Average Background Lead Level (μg/ft 2 ) Exposure Time (hr) Trim Replacement 6 13.9 4.0, 5.8 Door Replacement 6 < 9.6 9.0 Dry Scrape 5 16.4 2.25, 4.25 Power Sanding 2 51.2 4.25 Torching 6 32.8 4.0, 10.0

6.8. Soil Samples

6.8.1. Soil Sampling for Interior Jobs

For interior experiments, soil samples were coll ected before work began and again after work was completed at three sampling locations: near the main entrance to the unit used during the experiment, near the path traveled by the work ers and study personnel from their vehicles to the unit, and from the ground immediately below a wi ndow that led into the Work Room. See Table 6-11 for the all soil lead measurements for each interior experiment. The soil samples were collected to evaluate the possible contamination of the soil by study activities, as well as to get an idea of the potential track-in from the outside . There was no soil or dirt at any of the sampling locations for H31, H32, H33, H36 and C 01, thus no soil samples were collected for experiments performed at those units.6-30Table 6-11. Pre- and Post-Work Soil SampleResults (μg/g) by Interior Experiment05 - H0306 - H0307 - H0308 - H0309 - H0910 - H09Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 903 1,635 1,635 737 737 856 856 709 679 391 391 408Near Path 96 125 125 129 129 86 86 74 401 273 273 317Under Window 141 385 385 288 288 44 44 105 705 700 700 2,07711 - H0912 - H0913 - H0914 - H0915 - H0916 - H09Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 5,551 2,245 3,460 2,508 5,006 2,074 4,540 3,348 3,026 5,352 2,508 8,044Near Path 276 229 343 291 241 289 367 288 323 230 291 268Under Window 10,361 11,226 1,746 2,117 2,411 2,845 10,097 8,643 5,672 15,150 2,117 2,44222 - H1623 - H1624 - H1725 - H1626 - H1727 - H17Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 1,213 910 910 485 752 677 485 575 616 571 571 685Near Path 408 429 429 286 334 204 286 274 213 272 272 287Under Window 1,136 1,410 1,410 1,772 622 564 1,772 2,355 644 911 911 61328 - H1729 - H1730 - H1031 - H1032 - H1033 - H10Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 685 286 286 1,105 1,230 929 2,258 1,033 1,033 957 957 886Near Path 287 652 652 329 773 940 700 426 426 877 877 544Under Window 613 541 541 879 n/a 1,441 n/a 4,031 4,031 1,505 1,505 1,15441 - H1642 - H1643 - H1744 - H1745 - H0846 - H08Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 8,120 8,167 8,167 2,921 529 622 622 1,138 359 308 308 940Near Path 457 399 399 473 278 203 203 542 133 146 146 121Under Window n/a 386 386 444 n/a 521 521 346 367 336 336 51947 - H0848 - H0867 - H1668 - H1769 - H0870 - H35Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 450 390 390 359 575 1,178 677 616 940 347 496 383Near Path 218 170 170 133 274 344 204 213 121 118 411 355Under Window 482 204 204 367 2,355 3,650 564 644 519 416 752 47871 - H3572 - H3573 - H3574 - H3577 - H0878 - H08Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Entrance 471 413 413 772 383 476 476 471 347 395 395 311Near Path 481 500 500 502 355 337 337 481 118 175 175 139Under Window 554 567 567 1,260 478 426 426 554 416 n/a n/a 47879 - H0980 - H09Pre-Work Post-WorkPre-Work Post-WorkNear Entrance 684 1,659 1,659 3,098Near Path 35 49 49 308Under Window 11,121 16,547 n/a 1,3876-31 If work was performed in the same unit within the same Work Room, the post-work soil samples from the previous experiment were used as the pre-work soil samples fo r the next experiment. For two experiments performed at H09 and one experiment each at H16 and H17, the Work Room changed from experiment to experiment and pre-soil samples were not collected to correspond to the correct Work Room window. At H10, exterior experiments were performed alternately with interior experi ments, resulting in two experime nts where there were no pre-work soil samples collected under the Work Room win dow, and the post-work sample of the previous experiment could not be used due to possible contamination by the exterior experiment.

The samples collected near the main entrance and under the Work Room window were typically higher than the near path samples, due to thei r proximity to the foundation of the house. Large variations in the lead content of the soil were observed when samples were collected within the same general area. When examining an increase from pre-work soil lead to post-work soil lead, the variability became evident as subsequent samples collected in the same location were consistent with the lower, pre-work samples. An example of this for samples collected near the front porch of H09 is given in Figure 6-17. In cases where post-work samples were collected in the same general area but different locations than th e pre-work samples, it is difficult to discern the change due to natural variability and change due to study activities.

Figure 6-17. Time series of soil samples near th e front porch at H09. These samples correspond to the near entrance samples and some under the Wo rk Room window samples. Where more than one sample is collected on the same day, the plot sh ows whether the sample was pre-work or post-work.6-32 Table 6-12 lists the number of collected samples and average post-work to pre-work soil lead level ratio. Instances where the two samples were collected from inconsistent locations are not included in this table. The job type, job intens ity, and sample location do not appear to play a significant role in determining the change in soil l ead level. This table also depicts the effect of variability as some locations across the study experien ced an overall decrease in soil lead levels.

Table 6-12. Average Post-work/Pre-work Ratios for Interior Work Soil Samples, by Job Type Near Main Entrance Near Path Under Work Room Window Interior Job # Ratio # Ratio # Ratio

Cut-outs 8 1.25 8 0.91 8 1.30 Window Replacement 8 0.89 8 1.12 6 1.35 Dry Scrape 8 1.33 8 1.12 8 1.33 Door Plane 8 1.28 8 1.74 5 1.18 Kitchen Gut 4 1.02 4 1.03 4 1.03

Heat Gun 8 1.15 8 1.02 6 1.17

Figures C3.3a-c and C3.4a-c within Appendix C pl ot pre-work and post-work soil lead levels, respectively, against floor dust lead loadings in the work, tool, and observation rooms. Little if any association is evidenced from these plots be tween soil lead levels and interior floor lead levels. The highest r-square value (.06) is se en in Figure C3.4a showing slight association between post-work soil lead level and post-cleaning floor lead levels in the work room. Identical analyses are presented in Figures D3.3a-c and D3.4a-c for sill lead le vels and no correlation between sill lead levels and so il lead levels is indicated.

There is some evidence of correla tion between paint lead levels in the work room and soil lead levels. Within Appendix G, Figures G2.13 and G2.14 plot pre-work and post-work soil lead levels, respectively, by average paint lead levels . The association betw een pre-work soil lead and average paint lead yielded an r-square of .08 while the asso ciation between post-work soil lead and average paint lead yielded an r-square of 0.11.

6.8.2. Soil Sampling for Exterior Jobs

Similar to the interior experiments, soil samples were collected for all exterior experiments at three distinct locations: near the foundation of the house, near the back edge of the rule containment plastic, and near the back edge of the plastic used for property containment purposes. See Table 6-13 for the actual soil lead le vels for each exterior experiment. These soil samples were collected to evaluate the possibl e contamination of the soil resulting from study activities. The samples collected near the foundati on of the house were gene rally higher than the other locations, as the dete riorating paint from house components are more likely to collect there. For H02, H16 and H17, there were no soil samp les collected near the foundation due to a concrete sidewalk resting against the house.6-33Table 6-13. Pre- and Post-Work Soil SampleResults (μg/g) for Exterior Experiments01 - H0102 – H0103 – H0104 - H0221 – H09Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Foundation 2,347 2,880 2,733 16,540 4,590 1,782 n/a n/a 657 373Near Edge of RulePlastic 371 853 111 279 422 480 218 253 352 1,882Near Edge ofContainment Plastic299 480 178 97 258 336 194 147 550 46734 – H1635 – H1736 – H1037 – H1038 – H13Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-Work Pre-Work Post-WorkNear Foundation n/a n/a n/a n/a 1,075 1,230 181 2,258 1,123 1,529Near Edge of RulePlastic 2,953 8,120 562 529 944 773 548 700 543 678Near Edge ofContainment Plastic438 457 294 278 316 358 213 335 439 41640 - H1975 – H09Pre-Work Post-WorkPre-Work Post-WorkNear Foundation 3,164 4,522 373 382Near Edge of RulePlastic306 371 1,882 1,592Near Edge ofContainment Plastic 146 226 467 4346-34 Variability in the soil lead levels also played a role in the change in pre-work to post-work soil samples for exterior experiments. Samples were sometimes not collected at the same location, making it more difficult to determine the study e ffect. The number of collected samples and average post-work to pre-work ex terior soil lead level ratio fo r each sampling location, by job, are contained in Table 6-14. Note that the exterior experiments were only performed two or four times, so the averages include a small number (1-2) of values. The largest change is observed near the foundation, while the medium and high level jo bs appear to have a larger change in soil lead than the low intensity jobs.

Table 6-14. Ratio of Post-work to Pre-work Soil Lead Level in Exterior Soil Samples, by Job Type

Near Foundation Near Edge of Rule Plastic Near Edge of Containment Plastic Trim Replacement 1.144 .990 .945 Door Replacement n/a 1.846 .994 Dry Scrape 2.124 4.852 2.466 Power Sanding 3.220 1.825 .924 Torching 6.918 1.263 1.260

6.9. Length of Activity Data

Field technicians recorded the length of time required to perfo rm various activities throughout each experiment including pre-work set up, reno vation work, post-work cleaning, and cleaning verification. The tables included below pr esent summary data on cleaning and cleaning verification activities.

6.9.1. Duration of Post-work Cleaning

The summary data on post-work cleaning times doe s indicate a small difference in the length of time required to conduct Rule and baseline cleaning – an average of 45 minutes for rule cleaning and 36 minutes for baseline cleaning. Table 6-15 indicates that there is not a difference in cleaning time based on the use of plastic. Some differences are evident across jobs, with higher dust producing jobs (door planing, heat gun) resulting in higher clean-up times, as seen in Table 6-16. Table 6-17 reports that one contractor took approximately 8 minutes longer on average to conduct post-work cleaning than the other two.

Table 6-15. Summary of Post-work Cleaning Times, by Phase (in Minutes)

P/CU Phase # Obs. Avg. Std. Dev. Min Max Phase I - Plastic/Rule cleaning 15 44.7 18.0 20.0 80.0 Phase II - Plastic/Baseline cleaning 14 35.6 12.3 18.0 60.0 Phase III - No Plastic/Rule cleaning 15 45.3 12.9 30.0 70.0 Phase IV - No Plastic/Baseline cleaning 15 36.1 12.3 19.0 60.0

Total 59 40.5 14.5 18.0 80.06-35 Table 6-16. Summary of Post-work Cleaning Times, by Job (in Minutes)

Job # Obs. Avg. Std. Dev. Min Max
Cut-outs 12 38.8 12.7 18.0 62.0
Door Plane 7 45.3 20.1 25.0 80.0 Dry Scrape 8 40.1 19.9 20.0 80.0 High Heat Gun 12 44.9 12.6 29.0 70.0
Kitchen Gut 8 35.9 11.1 22.0 55.0
Low Heat Gun 4 42.5 12.6 30.0 60.0 Window Replacement 8 36.4 14.1 19.0 65.0

Total 59 40.5 14.5 18.0 80.0

Table 6-17. Summary of Post-work Cleaning Times, by Contractor (in Minutes)

Job # Obs. Avg. Std. Dev. Min Max
Contractor 2 20 45.9 13.8 25.0 80.0
Contractor 3 17 38.1 16.5 18.0 80.0
Contractor 4 22 37.5 12.6 20.0 65.0

Total 59 40.5 14.5 18.0 80.0

6.9.2. Duration of Cleaning Verification

The length of the cleaning verifica tion process was measured from its start to when the last floor verification zone was declared cl ean. This included time spent recleaning zones if any failed and performing both the wet cloth veri fication and dry cloth verificat ion, if any were necessary. Across all experiments, the clean ing verification process averaged 21 minutes with a range from 5 minutes to 135 minutes. As seen in Table 6- 18, the average cleaning verification length was longer when baseline cleaning was used follo wing work as opposed to Rule cleaning. Summarizing the cleaning verifi cation durations by job, as repor ted in Table 6-19, the jobs taking longest on average to pa ss cleaning verification were th e kitchen gut (35.5. minutes) and cut out (24.5 minutes) jobs, whic h were relatively low intensity in terms of dust production. On average, Contractor 3 took longer to conduct the cleaning verifications , as seen in Table 6-20.

Table 6-18. Duration of Cleaning Verification Stages, by Phase (in Minutes)
Phase # Obs. Avg. Std. Dev. Min Max Phase I - Plastic/Rule cleaning 15 12.9 5.3 7.0 25.0 Phase II - Plastic/Baseline cleaning 14 21.8 33.4 5.0 135.0 Phase III - No Plastic/Rule cleaning 15 15.5 10.6 6.0 45.0 Phase IV - No Plastic/Baseline cleaning 15 35.0 28.0 10.0 95.0
Total 59 21.3 23.4 5.0 135.06-36 Table 6-19. Duration of Cleaning Verification Stages, by Job (in Minutes)

Job # Obs. Avg. Std. Dev. Min Max Cut-outs 12 24.5 24.4 5.0 95.0 Door Plane 7 21.0 20.8 6.0 60.0 Dry Scrape 8 22.9 29.4 8.0 95.0 Heat Gun 12 11.1 4.9 5.0 25.0 Kitchen Gut 8 35.5 41.4 10.0 135.0 Low Heat Gun 4 16.3 7.5 10.0 25.0 Window Replacement 8 18.8 12.5 10.0 45.0
Total 59 21.3 23.4 5.0 135.0

Table 6-20. Duration of Cleaning Verification Stages, by Job (in Minutes)
Job # Obs. Avg. Std. Dev. Min Max Contractor 2 20 18.7 19.5 5.0 95.0 Contractor 3 17 31.2 35.7 6.0 135.0 Contractor 4 22 16.0 9.2 8.0 40.0
Total 59 21.3 23.4 5.0 135.0

6.9.3. Duration of Pre-work Set Up

Before work could begin prior to a job, renova tion workers had to prepare for the work by unloading tools and equipment, setting up genera tors, setting up plastic containment if it was necessary, etc. Table 6-21 reports that experi ments that required plastic containment took more time on average to prepare for. There was little difference by contractor, as seen in Table 6-22, or by type of building as seen in Table 6-23.

Table 6-21. Duration of Interior Pre-work Set Up, by Phase (in Minutes)

Phase # Obs. Avg. Std. Dev. Min Max Phase I - Plastic/Rule cleaning 15 96.2 27.3 60.0 140.0 Phase II - Plastic/Baseline cleaning 14 96.6 24.8 65.0 140.0 Phase III - No Plastic/Rule cleaning 15 75.6 25.1 30.0 135.0 Phase IV - No Plastic/Baseline cleaning 15 64.3 13.6 45.0 95.0
Total 59 83.0 26.6 30.0 140.0

Table 6-22. Duration of Interior Pre-work Set Up, by Contractor (in Minutes)

Contractor # Obs. Avg. Std. Dev. Min Max
C2 20 87.0 27.3 45.0 140.0
C3 17 81.6 24.9 40.0 135.0
C4 22 80.3 28.0 30.0 135.06-37 Table 6-23. Duration of Interior Pre-work Set Up, by Building Type (in Minutes) Unit Type # Obs. Avg. Std. Dev. Min Max Housing Unit 47 83.8 27.4 30.0 140.0
COF 12 79.6 24.1 45.0 120.0

The pre-work set up times for exterior work encompassed a significant amount of study-required set up including vertical containment set up a nd laying out containmen t plastic on the ground. Thus, the exterior pre-work set up times are not indicative of set up required by the proposed rule, which would primarily be laying out the rule plastic, and are not presented.

6.9.4. Duration of Interior RRP Work

The length of time that it took to conduct each job for the study varied significantly. A summary of interior work duration times is re ported in Table 6-24. The heat gun jobs took the longest averaging three hours for the heat gun <1 100 degrees job and about three and half hours for the heat gun >1100 degrees job. On the other hand, the jobs averaging the shortest amount of time were the cut outs and kitchen gut jobs. There is high variability among the cut out jobs as some experiments only involved the cutting of th e holes in the wall, but not any electrical or plumbing work, while others included actual installation of wiring, outlets, or lights.

Table 6-24. Duration of Interior Work, by Job (in Minutes)

Job # Obs. Avg. Std. Dev. Min Max Cut-outs 12 49.8 48.7 7.0 170.0 Window Replacement 8 90.9 46.6 45.0 188.0 Dry Scrape 8 95.4 44.0 65.0 201.0 Door Plane 7 60.4 31.0 30.0 125.0 Heat Gun < 1100 degrees 4 180.0 24.8 145.0 200.0 Kitchen Gut 8 50.0 23.5 25.0 90.0 Heat Gun > 1100 degrees 12 214.8 44.4 145.0 300.0
Total 59 105.2 75.7 7.0 300.0

A summary of exterior work dur ation times is reported in Table 6-25. The paint removal jobs conducted at the COF took the longest, while th e door replacement and dry scraping jobs took less time on average than the others.6-38 Table 6-25. Duration of Exterior Work, by Job (in Minutes)
Job # Obs. Avg. Std. Dev. Min Max Replace Exterior Door 2 47.5 10.6 40.0 55.0 Trim Replacement 2 115.0 134.4 20.0 210.0 Needle Gun 1 239.0 n/a 239.0 239.0 Dry Scrape 4 70.0 63.5 35.0 165.0 Heat Gun <1100 degrees 1 245.0 n/a 245.0 245.0 Power Sanding 2 112.5 10.6 105.0 120.0 Heat Gun > 1100 degrees 1 305.0 n/a 305.0 305.0 Torching 2 152.5 31.8 130.0 175.0
Total 15 128.3 91.5 20.0 305.0

6.10. Cleaning Verification Data

The analyses contained in this section explore the impact of the cleani ng verification process on lead levels remaining in the Work room at the completion of an RRP job. The analyses explore the association of different factors with post-ve rification floor and sill lead levels, including phase, job type, contractor, floor type and c ondition. Of interest were the relation of post-verification levels to EPA clearance standards.

6.10.1. Verification Of Floor Zones

As detailed in Section 3, each interior experime nt contained a cleaning ve rification stage, where the RRP workers performed cleaning verification in accordance with the proposed rule. In an effort to keep the cleaning verification process as close as possible to what the RRP workers would do if the rule was enacted, they were given training on how to do the cleaning verification, and minimal guidance was provided by study pers onnel during the execution. The decision to pass or fail a verification cloth was left solely up to the RRP workers, although the study field supervisors were instructed to overrule any cl early unwarranted determinations. Information on the cleaning verification for all in terior experiments is seen in Table 6-34. All average values presented in this section refer to the Work Room only.

Floor Cleaning Verification by Phase Figure 6-18 displays the post-verifi cation (PV) floor lead levels for all interior experiments by Phase. Note that the sample results below the detection limit of the labor atory are set to 5 μg/ft 2

, one half of the detection limit, for plotting and analysis purposes.6-39 Figure 6-18. Post-verification Floor Lead Loadings by Phase. The reference line is set at the EPA clearance standard of 40 μg/ft 2 for floor surfaces.

When considering the percentage of floor zones that failed cleaning veri fication on the first wet verification cloth, Table 6-26 reports that 48.6% of floor zones that were tested after Phase IV (no plastic and baseline cleaning) failed. The two phases wh ich involved no use of plastic had the highest average post-verification floor lead levels (313.5 and 192.7 μg/ft 2 ). When plastic was used and the rule cleaning method performed, the average post-verificatio n floor lead level was the closest to the EPA clearance standard of 40 μg/ft 2 . One problem that arose during the study was that after the use of insufficiently diluted Si mple Green solution during the rule cleaning and all cleanings after the first wet verification failur e, the verification cloths had a gray color to them, making them visibly darker than the verifica tion card. This led to the failure of some floor zones, although the post-cleaning (PC) and post-ve rification (PV) floor le ad levels were below clearance standards. This was especially true for the experiments performed at C01-the school.6-40 Table 6-26. Floor Verification Information by Phase

Phase Avg. PC Floor Lead Level

(μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth Failure Avg. PV Floor Lead Level

(μg/ft 2 ) I – Plastic/Rule Cleaning 69.0 9.4% 0.0% 55.1 II – Plastic/Baseline Cleaning 172.8 10.9% 4.7% 121.2 III – No plastic/Rule Cleaning 305.9 4.5% 0.0% 313.5 IV – No plastic/Baseline Cleaning 476.8 48.6% 2.9% 192.7

As seen in Table 6-27, the aver age percent change (PV versus PC) in floor lead level for Phase III is positive due to an unusual value for experiment 76 where the average post-cleaning floor lead level was less than 10 μg/ft 2 and the average post-veri fication lead level was 89.6 μg/ft 2 . If this experiment is excluded, the overa ll average percent change in floor lead level across all experiments between the post-cleanin g and post-verification stages results in a decrease of 29.1%. A greater decrea se in the floor lead levels is observed when one or more of the floor zones failed and were re-cleaned at l east once. For phases i nvolving base line cleaning, this meant that the floor was cleaned according to the rule method following the first failure and any subsequent failures, as called for by the desi gn. This shift in cleaning methods for cleaning verification should be considered in interpreting the data from the cleaning verification portion of the experiments.

Table 6-27. Post-cleaning and Post-verification Floor Lead Levels for Experiments with Zero and ≥ 1 Verification Failures by Phase
Phase I II III IV # of Verification Failures 0 ≥ 1 0 ≥ 1 0* ≥ 1 0 ≥ 1
Average PC Floor Lead Level (μg/ft 2 ) 79.1 41.3 204.1 47.6 369.442 75.4 752.8 235.3
Average PV Floor Lead Level (μg/ft 2 ) 69.0 17.0 145.7 23.0 374.192 61.6 328.2 74.2
Avg. % Change per Experiment -18.6% -27.2% -19.5% -50.7% -17.4% -41.5% -35.1% -60.2%
Overall Avg. Change -12.7% -58.9% -28.6% -51.6% 3.2% -18.3% -56.4% -68.4% * excludes one experiment with <10 at po st-cleaning and 89.6 at post-verification

Floor Cleaning Verification by Job Figure 6-19 and Table 6-28 displa y the post-verification floor l ead levels for all interior experiments by job type. The jobs with the hi ghest average floor lead level after cleaning verification were the door plan ing and the high heat gun jobs. The door plane job resulted in a significant amount of fine dust di stributed around the entire Work Room, while the other jobs resulted in larger paint chips and pieces of de bris. This could be the cause of the high postverification lead levels, as the fi ne dust was difficult to clean, especially when the floor surface was not in good condition. The fine dust was also more difficult to see on the verification cloths than any dark, soot-like residue.6-41 Figure 6-19. Post-verification Floor Lead Loadings by Job Type. The reference line is set at the EPA clearance standard of 40 μg/ft 2 for floor surfaces.

Table 6-28. Floor Verification Information by Job Type
Job Type Average PC Floor Lead Level (μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth Failure Average PV Floor Lead Level (μg/ft 2 ) Cut-outs 19.2 33.3% 2.1% 12.1 Window Replacement 109.1 11.9% 0.0% 65.0 Dry Scrape 81.3 15.0% 0.0% 38.6
Door Plane 431.7 28.6% 2.9% 284.0 Heat Gun <1100 degrees 50.4 20.0% 0.0% 26.2
Kitchen Gut 45.2 20.0% 10.0% 37.0 Heat Gun >1100 degrees 799.8 6.1% 0.0% 549.4

Floor Cleaning Verification by Contractor Across the study, the cleaning verification was perf ormed variably by the RRP workers. Some of the observed differences included the amount of force applied to the verification cloth, the speed of wiping the verification zones, the cleaning performed if a particular zone or zones failed6-42 verification, and the determinati on of whether a zone passed or failed. Table 6-29 reports that RRP workers in Pittsburgh were less likely to fail a floor zone, and had an average PV floor lead level of more than double that of Columbus contractors.

Table 6-29. Floor Verification Information by Contractor

City Contractor # of Exp. Avg. PC Floor Lead Level

(μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth Failure Avg. PV Floor Lead Level

(μg/ft 2 ) C2 20 175.5 28.6% 6.5% 65.9 Columbus C3 18 220.0 22.4% 0.0% 147.7 Pittsburgh C4 22 365.6 8.8% 0.0% 291.1

Floor Cleaning Verification by Floor Type and Condition Tables 6-30 and 6-31 report th e floor verification summary da ta by floor type and floor condition, respectively. Table 6-30 reports that the first verifica tion cloths taken on floors were more likely to fail on vinyl tile and tile floor s even though the post-cleaning levels on average are lower on the tile floors as opposed to the wood floors. The vinyl and tile floors were located in residential kitchens and in the classrooms at the COF. Table 6-31 repo rts that the floors in poor condition had significantly higher post-verification floor lead leve ls. Note that nearly all of the wood floors (20 of 22 experime nts) were in poor condition.

Table 6-30. Floor Verification Information by Floor Type

Floor Type # of Exp Avg. PC Floor Lead Level

(μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth Failure Avg. PV Floor Lead Level

(μg/ft 2 ) Wood 22 543.5 10.6% 0.0% 384.5 Vinyl 21 123.8 23.7% 2.1% 61.4
Tile 17 47.7 25.4% 4.8% 28.8

Table 6-31. Floor Verification Information by Floor Condition

Floor Type # of Exp Avg. PC Floor Lead Level

(μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth Failure Avg. PV Floor Lead Level

(μg/ft 2 ) Good 22 35.8 19.4% 0.0% 16.8
Fair 11 76.5 15.4% 7.7% 58.8
Poor 27 508.9 15.4% 1.6% 341.5

Table 6-32 provides information regarding how the interaction between job type and floor type and condition impacts cloth failure s and post-verification floor lead levels. Certain jobs occurred only in rooms with certain floor types and conditions, which highlights some differences between and within jobs. While door planing resu lted in relatively high post-verification floor lead levels as seen in Table 6-28, the two j obs that occurred on wood floors in poor condition performed significantly worse with a post-verification average of 619 μ g/ft 2 than the other six jobs that occurred in rooms with vinyl or tile floors. Within the cut out and kitchen jobs, although relatively low post-verifica tion lead levels were found across all jobs, higher levels were found for the jobs performed in rooms with poor floor condition.6-43 Table 6-32. Floor Verification Information by Job Type, Floor Type, and Floor Condition

Job Type Floor Type/ Condition # of Exp Avg. PC Floor Lead Level (μg/ft 2 ) % 1st Wet Cloth

Failure % 2nd Wet Cloth Failure Avg. PV Floor Lead Level (μg/ft 2 ) Tile-Good 4 10.5 50.0% 0.0% 5.0 Vinyl-Good 4 16.4 30.0% 0.0% 7.2

Tile-Fair 2 11.6 0.0% 0.0% 12.4 Cut-Outs
Vinyl-Poor 2 49.9 40.0% 10.0% 36.1 Wood-Fair 2 96.4 0.0% 0.0% 82.2 Window Replacement Wood-Poor 6 113.4 16.7% 0.0% 59.2 Vinyl-Good 4 92.3 0.0% 0.0% 42.6 Dry Scrape Wood-Poor 4 70.2 30.0% 0.0% 34.6 Tile-Fair 2 196.6 0.0% 0.0% 134.5 Vinyl-Poor 4 443.8 50.0% 5.0% 191.2 Door Plane Wood-Poor 2 642.4 0.0% 0.0% 619.2 Heat Gun <1100 degrees Tile-Good 4 50.4 20.0% 0.0% 26.2
Vinyl-Good 2 14.8 0.0% 0.0% 8.6

Tile-Fair 1 71.5 100.0% 100.0% 41.4 Vinyl-Fair 4 40.2 25.0% 0.0% 36.9 Kitchen Gut
Vinyl-Poor 1 99.7 0.0% 0.0% 89.6 Tile-Good 4 19.8 18.8% 0.0% 7.3 Heat Gun >1100 degrees Wood-Poor 8 1,189.8 0.0% 0.0% 820.4

Impact of Dry Cloth Verification Table 6-33 shows the corresponding average post -cleaning, post-wet verification and postverification floor lead levels for each of the five floor z ones that failed the second wet verification cloth. For each experi ment, the number of dry verification cloths used is also listed. Each of these three experiments occurred with a tile floor using the kitchen as the work room.

Table 6-33. Floor Zones That Failed Second Wet Cloth Verification
Exp # - Job Floor Zone PC Floor Lead Level (μg/ft 2 ) WV Floor Lead Level (μg/ft 2 ) # of Dry Cloths PV Floor Lead Level (μg/ft 2 ) 45 – Cut outs 2 91.5 61.5 2 37.3 47 – Door planing 2 1,292.1 550.8 1 643.6 1 n/a 150.7 1 77.9 2 46.2 < 10 2 < 10 70 – Kitchen gut 3 147.1 < 10 1 n/a6-44 Comparison of Post-cleaning and Post-verification Lead Levels Figure 6-20 plots the number of e xperiments in various categories offloor lead levels at the postcleaning and post-verification stag es. With floors, over 50 percent of the experiments ended with average post-verifica tion levels less than 40 μ g/ft 2 . The number of experiments with average levels over 100 μ g/ft 2 dropped from 20 at the post-cleaning stage to 13 at postverification.

Figure 6-20. Number of Experiments by Post-cleaning and Post-verification Floor Lead Loadings6-45Table 6-34. Verification Information for Interior ExperimentsExp.#UnitIDJobPhaseFloorTypeFloorCond.Cont.# ofFloorZonesAvg. PCFloor LeadLevel (μg/ft2)# Failed1st WetCloths# Failed2nd WetClothsPost-WetVerif.(μg/ft2)# FailedDryClothsAvg. PVFloor LeadLevel (μg/ft2)# PVSamples>40 μg/ft2Verif.Time(min)*23 H16 Cut-outs I Vinyl Good C4 5 6.6 1 0 - 0 < 10 0 1955 C01 Cut-outs I Tile Good C2 3 < 10 1 0 - 0 < 10 0 1572 H35 Cut-outs I Tile Fair C3 3 14.8 0 0 - 0 < 10 0 725 H16 Cut-outs II Vinyl Good C4 5 11 0 0 - 0 8.7 0 1553 C01 Cut-outs II Tile Good C2 3 7.8 0 0 - 0 < 10 0 571 H35 Cut-outs II Tile Fair C3 3 8.5 0 0 - 0 19.8 0 1224 H17 Cut-outs III Vinyl Good C4 5 31.1 0 0 - 0 9.9 0 1546 H08 Cut-outs III Vinyl Poor C3 5 15.9 0 0 - 0 14.9 0 1552 C01 Cut-outs III Tile Good C2 3 17.8 2 0 - 0 < 10 0 2022 H16 Cut-outs IV Vinyl Good C4 5 16.7 5 0 - 0 < 10 0 7045 H08 Cut-outs IV Vinyl Poor C3 5 84 4 1 61.5 1 57.2 1 9554 C01 Cut-outs IV Tile Good C2 3 11.3 3 0 - 0 < 10 0 3548 H08 Door Plane I Vinyl Poor C3 5 180.9 0 0 - 0 290.4 4 879 H09 Door Plane I Wood Poor C3 4 139.1 0 0 - 0 87.4 4 1073 H35 Door Plane II Tile Fair C3 3 233.9 0 0 - 0 116.3 3 777 H08 Door Plane II Vinyl Poor C3 5 50.7 0 0 - 0 31.4 1 874 H35 Door Plane III Tile Fair C3 3 159.4 0 0 - 0 152.6 4 680 H09 Door Plane III Wood Poor C3 5 1,145.8 0 0 - 0 1,150.9 4 1047 H08 Door Plane IV Vinyl Poor C3 5 1,372.7 5 1 550.8 0 395.5 4 55*78 H08 Door Plane IV Vinyl Poor C3 5 171 5 0 - 0 47.6 2 405 H03 Dry Scrape I Wood Poor C2 5 31.6 1 0 - 0 28.9 2 2028 H17 Dry Scrape I Vinyl Good C4 5 104.4 0 0 - 0 39.5 1 86 H03 Dry Scrape II Wood Poor C2 5 180.5 0 0 - 0 58.7 4 1529 H17 Dry Scrape II Vinyl Good C4 5 156.1 0 0 - 0 38.1 1 108 H03 Dry Scrape III Wood Poor C2 5 12.9 0 0 - 0 9.4 0 1526 H17 Dry Scrape III Vinyl Good C4 5 23.2 0 0 - 0 19.3 0 107 H03 Dry Scrape IV Wood Poor C2 5 55.9 5 0 - 0 41.2 3 3527 H17 Dry Scrape IV Vinyl Good C4 5 85.5 0 0 - 0 73.6 3 1016 H09 Heat Gun>1100 I Wood Poor C2 3 54 0 0 - 0 65.5 4 1033 H10Heat Gun>1100I Wood Poor C4 4 191.8 0 0 - 0 147.5 4 106-46Table 6-34. (continued) Verification Information for Interior ExperimentsExp.#UnitIDJobPhaseFloorTypeFloorCond.Cont.# ofFloorZonesAvg. PCFloor LeadLevel (μg/ft2)# Failed1st WetCloths# Failed2nd WetClothsPost-WetVerif.(μg/ft2)# FailedDryClothsAvg. PVFloor LeadLevel (μg/ft2)# PVSamples>40 μg/ft2Verif.Time(min)*63 C01Heat Gun>1100I Tile Good C2 4 < 10 0 0 - 0 < 10 0 1013 H09Heat Gun>1100II Wood Poor C2 5 97.2 0 0 - 0 205.3 3 1031 H10Heat Gun>1100II Wood Poor C4 4 1,393.0 0 0 - 0 1,122.9 4 1060 C01Heat Gun>1100II Tile Good C2 4 30.2 0 0 - 0 12.9 0 514 H09Heat Gun>1100III Wood Poor C2 4 84.9 0 0 - 0 76.4 3 832 H10Heat Gun>1100III Wood Poor C4 4 2,876.6 0 0 - 0 2,976.1 4 1061 C01Heat Gun>1100III Tile Good C2 4 11.3 0 0 - 0 < 10 0 1015 H09Heat Gun>1100IV Wood Poor C2 5 2,669.6 0 0 - 0 678.9 4 1030 H10Heat Gun>1100IV Wood Poor C4 4 2,151.0 0 0 - 0 1,290.7 4 3062 C01Heat Gun>1100IV Tile Good C2 4 32.7 3 0 - 0 6.4 0 2551 H33 Kitchen Gut I Vinyl Fair C4 3 45.4 0 0 - 0 42.3 2 1067 H16 Kitchen Gut I Vinyl Good C4 5 < 10 0 0 - 0 < 10 0 1049 H31 Kitchen Gut II Vinyl Fair C4 3 45.8 3 0 - 0 10.5 0 3568 H17 Kitchen Gut III Vinyl Good C4 5 24.6 0 0 - 0 12.2 0 3476 H36 Kitchen Gut III Vinyl Fair C4 3 < 10 0 0 - 0 89.6 1 1550 H32 Kitchen Gut IV Vinyl Fair C4 3 64.7 0 0 - 0 < 10 0 2570 H35 Kitchen Gut II Tile Fair C3 3 71.5 3 3150.7<10<10 1 41.4 1 13569 H08 Kitchen Gut IV Vinyl Poor C3 5 99.7 0 0 - 0 89.6 3 2059 C01 Low Heat Gun I Tile Good C2 6 122.2 3 0 - 0 29.1 2 2556 C01 Low Heat Gun II Tile Good C2 5 25.6 1 0 - 0 17.2 0 1558 C01 Low Heat Gun III Tile Good C2 4 9.5 0 0 - 0 7.5 0 1057 C01 Low Heat Gun IV Tile Good C2 5 44.2 0 0 - 0 51 2 1012 H09 Window Repl. I Wood Poor C3 3 60.5 0 0 - 0 29.9 1 1041 H16 Window Repl. I Wood Poor C4 6 69 0 0 - 0 41.4 3 1511 H09 Window Repl. II Wood Poor C3 5 96.9 0 0 - 0 33.5 1 1542 H16 Window Repl. II Wood Poor C4 6 183.4 0 0 - 0 96.2 4 109 H09 Window Repl. III Wood Poor C3 5 133 1 0 - 0 118.2 4 4544 H17 Window Repl. III Wood Fair C4 6 38.1 0 0 - 0 56.1 2 1010 H09 Window Repl. IV Wood Poor C3 5 137.8 4 0 - 0 36 1 3043 H17 Window Repl. IV Wood Fair C4 6 154.7 0 0 - 0 108.3 3 15* Verification time includes window sill and floor verification, re-cleaning, and wait times.6-47 6.10.2. Verification of Window Sills

Along with the floor zones, each window sill in the Work Room was subject to cleaning verification. As stated previous ly, all window sills had to pass clearance before the RRP workers moved on to the cleaning verification of the floor. Overall, only three window sills failed the first wet cloth verification, despite the fact that nineteen window sills had post-cleaning lead levels of greater than 250 μg/ft 2 . Tables 6-35, 6-36, and 6-37 list the post-cleaning and postverification average sill lead leve ls by phase, job intensity level, and contractor respectively. Table 6-39 reports window sill cleaning verifi cation information across all experiments.

Table 6-35. Window Sill Verification Information by Phase

Phase PC Sill Lead Level (μg/ft 2 ) PV Sill Lead Level (μg/ft 2 ) # 1 st Wet Cloth Failures I – Plastic/Rule Cleaning 151.8 198.7 1 II – Plastic/Baseline Cleaning 761.6 119.6 0 III – No plastic/Rule Cleaning 1439.0 490.5 1 IV – No plastic/Baseline Cleaning 601.4 467.4 1

Table 6-36. Window Sill Verification Information by Intensity Level

Intensity Level PC Sill Lead Level (μg/ft 2 ) PV Sill Lead Level (μg/ft 2 ) # 1 st Wet Cloth Failures Low 159.9 155.7 2 Medium 366.1 451.9 1

High 1689.3 347.6 0

Table 6-37. Window Sill Verification Information by Contractor

City Contractor # of Exp Avg. PC Sill Lead Level

(μg/ft 2 ) % 1st Wet Cloth Failure % 2nd Wet Cloth

Failure Avg. PV Sill Lead Level

(μg/ft 2 ) C2 20 142.0 0.0% 0.0% 39.3 Columbus C3 18 469.2 7.7% 0.0% 526.8 Pittsburgh C4 22 1,500.9 3.1% 0.0% 381.1

No window sills failed a second we t cloth verification. For two of the three window sills that failed the first wet verification cloth, ther e are no corresponding post-cleaning or postverification samples. This is because the sampli ng protocol called for one window sill sample at each sampling stage for interior experiments, wh ile the cleaning verification protocol requires the testing of all window sills within the Work Room. For the one window sill that does correspond to the window used in the three-st age sampling, the post-cleaning lead level was 44.5 μg/ft 2 and the post-verificati on level was 20 μg/ft 2

. Comparison of Post-cleaning and Post-verification Lead Levels Figure 6-21 plots the number of e xperiments in various categories of sill lead levels at the post-cleaning and post-verification stages. The nu mber of experiments with sill lead levels below the clearance standard of 250 μ g/ft 2 increased from 41 to 48 between post-cleaning and post-verification. Table 6-38 pres ents the percent change in wi ndow sill lead levels from6-48 post-cleaning to post-verification by phase and whether a cloth failure was identified. It is worth noting that lead levels from 24 experiments were below the detection limit at post-cleaning and 34 experiments are post-verifica tion, with 21 being below the dete ction limit at both stages (9 from Phase I).

Figure 6-21. Number of Experiments by Post-cleaning and Post-verification Sill Lead Loadings

Table 6-38. Post-cleaning and Post-verification Sill Lead Levels for Experiments with Zero and ≥ 1 Verification Failures by Phase.
Phase I II III IV # of Verification Failures 0 ≥ 1 0 ≥ 1 0 ≥ 1 0 ≥ 1
Average PC Sill Lead Level (μg/ft 2 ) 156.5 85.8 761.6 n/a 1,538.6 44.5 614.9 411.5

Average PV Sill Lead Level (μg/ft 2 ) 208.8 57.5 118.8 n/a 526.7 20.0 438.4 873.3
Avg. % Change per Experiment 1.7% -33.0% -52.5% n/a 106.1%* -55.1% -44.2% 112.2%
Overall Avg. Change 33.4% -33.0% -84.4% n/a -65.8% -55.1% -28.7% 112.2%

* with exclusion of 2 experiments that changed from below detection limit to 166 and 282 μg/ft 2 , average percent change would be -14.2%6-49 Table 6-39. Window Sill Verification Information for Interior Experiments

Exp.

# Unit ID Job Phase Sill Type Cont. # of Sills PC Sill Lead Level (μg/ft 2 ) # Failed 1st Wet Cloths PV Sill Lead Level (μg/ft 2 ) # PV Samples >250 μg/ft 2

23 H16 Cut-outs I SILL C4 2 < 40 0 < 40 0

55 C01 Cut-outs I TRAY C2 1 < 41.6 0 < 41.6 0

72 H35 Cut-outs I SILL C3 2 < 100 0 < 100 0

25 H16 Cut-outs II SILL C4 2 < 52.6 0 < 52.6 0

53 C01 Cut-outs II TRAY C2 1 48.7 0 < 45.5 0

71 H35 Cut-outs II SILL C3 2 101.1 0 < 62.5 0

24 H17 Cut-outs III SILL C4 2 < 50 0 166.4 0

46 H08 Cut-outs III TRAY C3 1 < 41.6 0 < 41.6 0

52 C01 Cut-outs III TRAY C2 1 < 45.5 0 < 45.5 0

22 H16 Cut-outs IV SILL C4 2 198.2 0 < 40 0

45 H08 Cut-outs IV SILL C3 1 416.9 0 276.6 1

54 C01 Cut-outs IV TRAY C2 1 < 41.6 0 < 41.6 0
48 H08 Door Plane I SILL C3 1 521.3 0 1,707.9 1

79 H09 Door Plane I TRAY C3 1 104.2 0 < 45.5 0

73 H35 Door Plane II SILL C3 3 127.1 0 115.7 0

77 H08 Door Plane II TRAY C3 1 278.6 0 61.7 0

74 H35 Door Plane III SILL C3 2 170.4 0 < 100 0
80 H09 Door Plane III TRAY C3 1 2,546.1 0 3,378.2 1
47 H08 Door Plane IV SILL C3 1 1,798.7 0 2,625.3 1

78 H08 Door Plane IV TRAY C3 1 277.9 0 96.5 0
5 H03 Dry Scrape I TRAY C2 1 < 10 0 < 10 0 28 H17 Dry Scrape I SILL C4 2 85.8 1* 57.5 0
6 H03 Dry Scrape II TRAY C2 1 < 50 0 < 25 0
29 H17 Dry Scrape II SILL C4 2 467 0 115 0
8 H03 Dry Scrape III TRAY C2 1 < 45.5 0 < 45.5 0
26 H17 Dry Scrape III SILL C4 2 < 52.6 0 < 52.6 0
7 H03 Dry Scrape IV TRAY C2 1 < 50 0 < 50 0
27 H17 Dry Scrape IV SILL C4 2 150.8 0 < 52.6 0

16 H09 Heat Gun I TRAY C2 1 < 41.6 0 < 41.6 0

33 H10 Heat Gun I TRAY C4 1 1005.3 0 790.2 1

63 C01 Heat Gun I TRAY C2 1 < 45.5 0 < 45.5 0

13 H09 Heat Gun II TRAY C2 1 253.7 0 81.4 0
31 H10 Heat Gun II TRAY C4 1 8,838.8 0 927.2 1

60 C01 Heat Gun II TRAY C2 1 170.4 0 < 45.5 0

14 H09 Heat Gun III TRAY C2 1 < 45.5 0 < 45.5 0
32 H10 Heat Gun III TRAY C4 1 18,132.8 0 2,669.6 1

61 C01 Heat Gun III TRAY C2 1 < 41.6 0 < 41.6 0
15 H09 Heat Gun IV TRAY C2 1 1,352.4 0 182.7 0
30 H10 Heat Gun IV TRAY C4 1 1,697.6 0 1,784.5 16-50 Table 6-39. (continued) Window Sill Verification Information for Interior Experiments

Exp.

# Unit ID Job Phase Sill Type Cont. # of Sills PC Sill Lead Level (μg/ft 2 ) # Failed 1st Wet Cloths PV Sill Lead Level (μg/ft 2 ) # PV Samples >250 μg/ft 2

62 C01 Heat Gun IV TRAY C2 1 94.4 0 < 45.5 0

51 H33 Kitchen Gut I SILL C4 1 < 45.5 0 < 45.5 0

67 H16 Kitchen Gut I SILL C4 2 < 40 0 < 40 0

49 H31 Kitchen Gut II SILL C4 1 < 100 0 < 100 0

70 H35 Kitchen Gut II SILL C3 2 < 66.6 0 < 66.6 0

68 H17 Kitchen Gut III SILL C4 2 396.5 0 < 52.6 0

76 H36 Kitchen Gut III SILL C4 1 86.2 0 138.3 0

50 H32 Kitchen Gut IV SILL C4 1 130.3 0 < 41.6 0
69 H08 Kitchen Gut IV SILL C3 1 1,414.2 0 73.5 0

59 C01 Low Heat Gun I TRAY C2 1 137.7 0 < 41.6 0

56 C01 Low Heat Gun II TRAY C2 1 285.1 0 133.1 0 58 C01 Low Heat Gun III TRAY C2 1 < 41.6 0 n/a 1

57 C01 Low Heat Gun IV TRAY C2 1 246.8 0 70.4 0 12 H09 Window Repl. I TRAY C3 1 < 45.5 0 < 45.5 0 41 H16 Window Repl. I SILL C4 1 218.1 0 176 0 11 H09 Window Repl. II SILL C3 1 106.9 0 < 45.5 0 42 H16 Window Repl. II SILL C4 1 611.9 0 126.2 0 9 H09 Window Repl. III SILL C3 2 44.5 1 < 40 0 44 H17 Window Repl. III SILL C4 1 < 52.6 0 281.5 1 10 H09 Window Repl. IV SILL C3 2 411.5 1* 873.3 1 43 H17 Window Repl. IV SILL C4 1 784.9 0 892.5 1 * The failed verification cloth does not correspond to the sill from which the samples were collected.7-1 

### 7. Statistical Modeling Results

The following sections provide the results of stat istical models applied to floor and window sill dust-lead results from the RRP Field Study. The results are organized by Stage (Post Work, Post Cleaning, and Post Verification) and Room (Wor k, Tool, and Observation) – with separate statistical models being specified within ea ch Stage-by-Room combination to address the multiple inferential objectives mentioned earlier in Section 1. Section 7.1 corresponds to all results obtained in the Post Work Stage of fi eld data collection, with subsections 7.1.1 – 7.1.3 corresponding to results in the Work Room, Tool Room and Observation Room, respectively. Similarly, Sections 7.2 and 7.3 (and their associ ated sub-sections) correspond to the Post Cleaning and Post Verification Stages. Section 7 .4 corresponds to a cross-stage analysis that compares full rule implementation (use of plas tic sheeting and RRP rule cleaning in the Postverification Stage) to baseline act ivities or no rule implementation (no use of plastic sheeting and baseline cleaning in th e Post-cleaning Stage).

The results summarized in the following sections provide an overview of the statistical modeling results for floor and window sill dust-lead loadings from interior RRP jobs for two subsets of study data: (1) data limited to re sidential units only, and (2) Comb ined data for all interior RRP jobs (both residential and COFs). All analyses that were conduc ted on the second subset of data include a term in the model that adjusts for di fferences between residential units and COFs. Thus, a COF variable appears in a ll models of the Combined data.

The rationale for presenting the analysis results for each Stage-by-Room combination is that the series of statistical models that are fit to a subset of data (e.g. post- cleaning floor dust-lead loadings in the Observation room) are directly co mparable using a likelihood ratio test statistic. Differences in reported likelihood fit statistics (-2 L og-Likelihood) between two models applied to the same dataset can be assessed by comparing to the Chi-Squared ( χ 2 ) distribution with degrees of freedom matching the difference in th e number of fixed eff ects included in the two models. Generally, the lower likelihood ratio statis tic indicates the better fitting model. Within each subset of data, a potentia l best model is identified base d on evaluation of the likelihood ratio along with the number of variables includ ed and the significance of additional variables.

A mixed models analysis of va riance approach including both fi xed and random effects was used to fit the statistical models pres ented in this section and test the various study hypotheses, using Proc Mixed in SAS®. Each of the models explai ned dust-lead loadings on floors or window sills as a function of one or more fixed effects vari ables (intensity-level, jo b-type, use of plastic sheeting, post-work cleaning method, average pain t-lead loading on disrupted component(s), square feet of lead-based paint disturbed, etc.) – with some models investigating interactions between some of these fixed effect variables to address specific an alysis objectives.

Adjustments were made in the model to account for the data not being independent. There is anticipated positive correlation between the multiple samples of each type taken within the same room during the same experiment, within the sa me room across experiments, and within the same housing unit across experiments. To account for this, the models applied to floor dust-lead loadings included random effect terms that adju sted for anticipated positive correlation among multiple results observed from within the sa me unit, among multiple results observed from7-2 within the same room (within a unit), and among multiple results observed from within the same RRP experiment. The models applied to wi ndow sill dust-lead loadings included a random effect for the anticipated positive correlation among multiple results observed from within the same room within a unit (a random effect fo r experiment was unnecessary because there was only one window sill sample obtained from each Stage-by-Room combination within an experiment).

Because the statistical models a ssume the data are normally distri buted and because the dust lead loading data were non-normal (as indicated by va rious figures in Appedices B-E), all floor and sill data underwent log-transformation to more cl osely approximate normality, which generally is done with environmental lead data. Although mo st sets of transformed data still differ significantly enough from normality to be identified as non-normal by the test statistic presented in the appendices, the statistical models utilized are robust e nough to account for this level of non-normality. Plots of residuals from each set of data are presented within the section to illustrate the suitability of the models used.

In the sections that follow, we provide summary tables for the models fit for each Stage-by-Room combination of data. The summary tables provide an overview of the p-value associated with each explanatory factor and th e likelihood fit statistic for model fit to that particular subset of data. The p-values for cat egorical variables are based on an F-test for the combined effect across multiple levels. Statistical significance is generally attributed to p-values less than 0.05. P-values between .05 and about .10 are referred to as borderline significant.

Additional detail on the specific pa rameter estimates and standard errors from each model can be found in Appendices I through N. Residential floor and sill results are in Appendices I and J; COF floor results are in Appendix K; combined interior results are in Appendices L and M; and detailed exterior model results ar e in Appendix N. Residual plots for the best fitting models are contained in Appendix O. In a few places in this section, we compare the specific parameter estimates for a variable of interest and calcula te the model-predicted difference in geometric mean levels based on those estimates. This is done using the formula 100*(1-e x ), with “x” being the difference in parameter estimates.

7.1. Post-Work Dust Lead Levels

The following three subsections provide an overvie w of statistical modeling results for floor and window sill dust lead loading results in the post work stage of th e field study, in the Work room (subsection 7.1.1), Tool room (subsection 7.1.2), and Observation room (subsection 7.1.3).

7.1.1. Post-Work Work Room Dust Lead Levels

Table 7-1 provides an overview of results from statistical models developed to describe postwork floor and window sill dust-lead loadings in the Work room across the two subsets of data described earlier (residential units only and all un its combined). The results are repeated using the wipe-only dust-lead results, as well as the results that also integrated the bulk samples.7-3 Objective 1-Model 1 assesses the total effect of Intensity-Le vel (High, Medium, and Low) on floor and window sill dust lead loadings, which wa s shown to be statistically significant in all models. Objective 1-Model 2 assesses the total e ffect of Job-Type (a 6-level variable in the residential unit dataset, and a 7-level variable in the all units combined dataset), which explained additional variability in floor and window sill dust -lead loadings as anticipated. Job-type is nested within intensity level by design.

In addition to the models assessing the total eff ect of intensity level a nd job type, Objective 1- Models 3 and 4 investigate whether these two variab les are still statisticall y significant predictors of floor and window sill dust lead loadings afte r adjusting for the paint-lead loading on the surface being disturbed and a measure of the square feet of lead-based paint disturbed during the RRP activity. The summary of results provided in Table 7-1 demonstrate that both IntensityLevel and Job-Type are statistically significant predictors of post-work floor and window sill dust-lead loadings in the work room after adju sting for the effects of paint-lead loading and square footage disturbed, and the addition of these two variables does not always improve the predictive ability of these mode ls. This suggests that these two additional terms may compete for explanatory power with inte nsity-level and job-type. A co mparison of the wipe-only dustlead loadings with the measures that integrat e the bulk samples did not yield any meaningful differences in interpretation of the data.

The highlighted rows in Table 7-1 indicate the models that appear to fit the data best. Objective 1-Model 2, which accounted only for job type and COF, performs best for the residential floor data and both sets of window sill data. Within the combined floor data, the inclusion of area disturbed and average paint lead level improved the model fit slightly with average paint lead significantly impacting the floor lead levels. Section O1 of Appendix O contains the residual plots from these four best fitting models. The floor model residuals appe ar to be approximately normally distributed. The residuals from the sill models appear to contain some skewness.7-4 Table 7-1: Post-work Work Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood

COF . 756.3 0.20 945.5 . 214.8 0.28 263.8 Objective 1: Model 1 Objective 1: Model 1 Intensity level <.01 <.01 <.01 <.01
COF

. 726.7 0.05 921.3

. 192.5 0.35 231.3 Objective 1: Model 2 Job type <.01 <.01 <.01 <.01
COF . 755.5 0.05 943.2 . 215.2 0.16 269.4 Objective 1: Model 3 Objective 1: Model 3 Square feet disturbed 0.34 0.76 0.35 0.95 Objective 1: Model 3 Avg. paint lead <.01 <.01 <.01 0.10 Objective 1: Model 3 Intensity level <.01 <.01 <.01 <.01
COF . 731.7 0.03 915.4 . 197.2 0.34 236.1 Objective 1: Model 4 Square feet disturbed 0.58 0.82 0.33 0.21 Avg. paint lead 0.04 <.01 0.35 0.19 Job type <.01 <.01 <.01 <.01
COF . 954.5 0.67 1222.9 . 228.4 0.28 278.4 Objective 1: Model 1, with bulk w Intensity level <.01 <.01 <.01 <.01
COF . 931.0 0.29 1195.3 . 205.9 0.34 247.1 Objective 1: Model 2, with bulk wodel 2 Job type <.01 <.01 <.01 <.01
COF . 952.1 0.20 1222.3 . 228.1 0.15 283.1 Square feet disturbed 0.57 0.20 0.34 0.89 Objective 1: Model 3, with bulk Avg. paint lead <.01 <.01 <.01 0.07 Objective 1: Model 3 Intensity level <.01 <.01 <.01 0.01
COF . 934.7 0.18 1193.7 . 209.8 0.33 251.1 Objective 1: Model 4, with bulk : Model 4 Square feet disturbed 0.55 0.94 0.32 0.22 Objective 1: Model 4 Avg. paint lead 0.03 <.01 0.33 0.16 Objective 1: Model 4 Job type <.01 <.01 <.01 <.01

7.1.2. Post-work Tool Room Dust Lead Levels

Table 7-2 provides an overview of results from statistical models developed to describe postwork floor and window sill dust-lead loadings in the Tool room across the two subsets of data described earlier (residential units only and all units combined). The results are provided for the wipe samples only (as there were no bul k samples found in the Tool room).

Objective 1-Model 5 assesses the total effect of Intensity-Level on floor and window sill dust lead loadings, which was shown to be statistically significant in all models. Model 6 assesses the total effect of Job-Type, which explained additio nal variability in floor and window sill dust-lead loadings as anticipated. In addi tion to the models assessing the total effect of intensity level and job type, Objective 1-Models 7 a nd 8 investigate whether these two variables are still statistically significant predictors of floor and window sill dust lead loadings after adjusting for the paint-lead loading on the surface being disturbed and a measur e of the square feet of lead-based paint disturbed during the RRP activity.

The summary of results provided in Table 7-2 demonstrate that both Intensity-Level and JobType are statistically significant predictors of post-work floor and window sill dust-lead loadings7-5 in the Tool room after adjusting for the eff ects of paint-lead load ing and square footage disturbed. The addition of these two variables do es not always improve the predictive ability of these models, which suggests that these two additional terms may co mpete for explanatory power with intensity-level and job-type.

Models were also developed to assess whether the use of plastic sheeting reduced post-work dust-lead loadings observed in the Tool room. Objective 1–Models 9 an d 10 provide the results of these analyses. Although the use of plastic sheeting did not always result in statistically significant reductions in post-work dust lead lo adings in the Tool room – inspection of the parameter estimates of these models (found in Appe ndices I through M) reveal s that the effect of using plastic sheeting results in lower estimated po st-work dust lead loadi ngs for the Tool Room across all models. The effects of Job-Type and Intensity-Level were found to be statistically significant, after adjusting for th e use of plastic sheeting in al l models applied to dust lead loadings in the Tool room at the post-work stage.

Objective 1–Model 6, which accounts for job type and the differences between residential units and the COF, is highlighted in Table 7-2 as the be st fitting model for all four data sets. Although the log-likelihoods are slightly lower for Objective 1–Model 10, th at model also included an additional variable, which did not lead to much gain in the likelihood statistic and was not significant. Although use of plasti c was not statistically significant, the detailed results of this model in Table L2 report that combined floor lead levels are 0.32 units lower on the log scale when plastic was used, which equates to geom etric means predicted to be 27% lower when plastic was used than when it was not used. Section O2 of Appendix O contains the residual plots from these best fitting models, which all indicate reasonable normal distributions.

Table 7-2: Post-Work Tool Room Modeling Results

Floors Sills
All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 5 COF . 328.1 0.36 436.3 . 176.4 0.32 212.0 Objective 1: Model 5 Intensity level <.01 <.01 0.03 0.01 Objective 1: Model 6 COF

. 306.5 0.31 413.4

. 164.0 0.21 198.2 Job type <.01 <.01 0.02 0.02 Objective 1: Model 7 COF . 334.6 0.21 441.9 . 172.0 0.07 211.1 Objective 1: Model 7 Square feet disturbed 0.78 0.78 0.09 0.18 Objective 1: Model 7 Avg. paint lead 0.06 0.02 <.01 <.01 Objective 1: Model 7 Intensity level <.01 <.01 <.01 0.01 Objective 1: Model 8 COF . 311.7 0.25 417.2 . 164.9 0.12 199.6 Objective 1: Model 8 Square feet disturbed 0.07 0.32 0.50 0.72 Objective 1: Model 8 Avg. paint lead 0.49 0.01 <.01 <.01 Objective 1: Model 8 Job type <.01 <.01 0.04 0.03 Objective 1: Model 9 COF . 326.0 0.36 435.2 . 175.2 0.32 212.0 Objective 1: Model 9 Plastic 0.12 0.19 0.32 0.60 Objective 1: Model 9 Intensity level <.01 <.01 0.02 0.01 Objective 1: Model 10 COF . 305.0 0.31 412.7 . 163.1 0.22 198.3 Objective 1: Model 10 Plastic 0.16 0.20 0.37 0.66 Objective 1: Model 10 Job type <.01 <.01 0.02 0.027-6 7.1.3. Post-Work Observation Room Dust Lead Levels

Table 7-3 provides an overview of results from statistical models developed to describe postwork floor and window sill dust-lead loadings in the Observation room across both residential units only and all units combined. The results are provided for the wipe samples only as there were no bulk samples found in the Observation room.

As in the other two rooms, both intensity level and job type are statisti cally significant when assessed by themselves – in Objective 1-Mode l 11, and Objective 1-Mo del 12, respectively. Again Job Type performs better than intensity level in explaini ng more variability in the floor and window sill dust-lead loadings as antici pated, achieving lower log-likelihood ratios. Objective 1-Models 13 and 14 investigate whethe r intensity level and job type were still statistically significant predictors of floor and window sill dust le ad loadings after adjusting for the paint-lead loading on the surface being dist urbed and area disturbed during the RRP activity. The summary of results provided in Table 7-3 demonstrate that both Intensity-Level and JobType are statistically significant predictors of post-work floor and window sill dust-lead loadings in the work room after adjusting for the eff ects of paint-lead loading and square footage disturbed. The addition of these two variables improved the predic tive ability for floors, but not for window sills. Furthermore, average paint-l ead loading on components disrupted by the RRP activity was a statistically signifi cant predictor of dust-lead levels found in the Observation room in the post-work stage in most of the models.

Models were also developed to assess whether the use of plastic sheeting reduced dust-lead loadings found in the Observati on room in the post work stage. Objective 1–Models 15 and 16 provide the results of theses analyses. The us e of plastic sheeting re sulted in statistically significant reductions in post-work floor lead loadings in the Ob servation room for residential units only. This effect was only borderline signif icant in the combined data (p-value=.10), which included the COFs.

The detailed results in Table L3 of Appendix L re port floor lead levels from the combined model 0.46 units less when plastic was us ed, which equates to a predicte d GM 36.9% lower than when plastic was not used. The use of plastic sheet ing did not significantly lower window sill dust lead loadings in the Observation room at the post-work stage. The effects of Job-Type and Intensity-Level were found to be statistically si gnificant, after adjusting for the use of plastic sheeting in all models applied to dust lead load ings in the observation room at the post-work stage.

The best-fitting models differed across data sets for the post-work Observation room data. As highlighted in Table 7-3, the model adjusting for plastic use in addition to job type performed best for residential floors, while in the combined data the model adjusting for paint lead level and area disturbed performed better. For window si lls, the model adjusting only for job type performed best. Section O3 of Appendix O cont ains the residual plots from these best fitting models, which indicate reasonable normal distributi ons for floors and some skewness for sills. If additional analyses are performed, these can be investigated further to determine impact of outliers in the window sill data.7-7 Table 7-3: Post-Work Observation Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 11 COF . 304.3 0.78 368.6 . 161.7 0.23 191.3 Objective 1: Model 11 Intensity level <.01 <.01 0.02 0.01 Objective 1: Model 12 COF . 285.8 0.85 350.1

. 151.9 0.35 179.0 Job type <.01 <.01 0.04 0.02 Objective 1: Model 13 COF . 300.6 0.24 362.9 . 165.3 0.09 197.5 Objective 1: Model 13 Square feet disturbed 0.53 0.58 0.24 0.64 Objective 1: Model 13 Avg. paint lead <.01 <.01 <.01 0.02 Objective 1: Model 13 Intensity level <.01 <.01 0.03 0.03 Objective 1: Model 14 COF . 285.3 0.54 344.5 . 158.1 0.23 184.5 Square feet disturbed 0.18 0.50 0.97 0.79 Avg. paint lead <.01 <.01 0.09 0.03 Job type <.01 <.01 0.09 0.03 Objective 1: Model 15 COF . 298.8 0.78 366.1 . 161.9 0.24 191.9 Objective 1: Model 15 Plastic 0.02 0.09 0.83 0.79 Objective 1: Model 15 Intensity level <.01 <.01 0.02 0.02 Objective 1: Model 16 COF

. 280.4 0.86 348.1 . 152.1 0.35 179.6 Plastic 0.02 0.10 0.80 0.81 Job type <.01 <.01 0.04 0.03

7.2. Post Cleaning Stage - Dust Lead Levels

The following three subsections provide an overvie w of statistical modeling results for floor and window sill dust lead loadings in the post-cleanin g stage of the field study, in the Work room (subsection 7.2.1), Tool room (subsection 7.2.2), and Observation room (subsection 7.2.3).

7.2.1. Post-Cleaning Work Room Dust Lead Levels

Table 7-4 provides an overview of results from statistical models developed to describe postcleaning floor and window sill dust lead loadings in the work room for residential units only and all units combined. The results are provided fo r the wipe samples only (as there were no bulk samples after the post-work stage). Below are so me highlights of some of the models run for these data.

• Objective 1-Models 17 and 18 a ssess the total effects of In tensity-Level and Job-Type, respectively, on floor and window sill dust lead loadings in the Work room during the post-cleaning stage. Intensity-Level and J ob-Type were shown to be statistically significant in all models.

• Objective 1-Models 19 and 20 investigate whet her intensity level and job type are still statistically significant predic tors of floor and window sill dust lead loadings after adjusting for the paint lead loading on the surface being disturbed and a measure of the square feet of lead-based pa int disturbed during the RRP activ ity. The results in Table 7- 4 demonstrate that both Intensity-Level and Job-Type remain statistically significant predictors of post-cleaning floor dust lead load ings in the Work room after adjusting for the effects of paint lead loading and square footage disturbed. While average paint lead loading was found to be a significant predicto r of post-cleaning floor dust lead loadings7-8 in the Work room – Models 19 and 20 did not improve the prediction over Models 17 and 18, as evidenced by a comparison of the log-lik elihoods. Models 19 and 20 applied to the window sill dust lead loadings demonstrate that the addition of these two variables (average paint-lead loading and square footag e disturbed) competes with intensity-level and job-type for predictive power , without improving the model fit.

• Objective 2–Model 1 assesses the total effect of using plastic sh eeting on post-cleaning dust lead levels in the work room, which wa s not a statistically si gnificant predictor (on its own) in any of the mode ls applied to floor and window sill dust lead levels. Inspection of the parameter estimates from these models in Appendices I through M, however, demonstrates that use of plastic shee ting was consistently associated with lower predicted post-cleani ng dust lead levels in the Work room.

• Objective 2–Models 2 through 5 assess whethe r the use of plastic is significant after adjusting for intensity-level or job-type, and whether there is any significant interaction between the use of plastic and either intensity-level or job- type when predicting floor and window sill dust lead loadings following cleaning in the work room. These results follow the same general trend as observed in Obj ective 2–Model 1, in which the use of plastic sheeting was not statistically significant, but inspection of the parameter estimates is suggestive of a modest protective effect.

• Objective 3–Model 1 assesses the total e ffect of using RRP rule cleaning on postcleaning dust-lead levels in the Work room, which was highly statis tically significant in lowering dust lead loadings in all of the models.

• Objective 3–Models 2 through 5 assess whet her the use of RRP rule cleaning is significant after adjusting for intensity-leve l or job-type, and wh ether there is any significant interaction between the use of RRP rule cleaning and either intensity-level or job-type when predicting floor and window s ill dust lead loadings following cleaning in the Work room. The use of RRP rule cleani ng was highly significant in all models, even after adjusting for intensity-l evel or job-type. The addi tion of the interaction term between RRP rule cleaning and either intensit y-level or job-type did not significantly improve the prediction of the model – sugges ting that the effectiveness of RRP rule cleaning does not change as a functio n of intensity level or job type.

• The remaining models fit to the post-cleani ng stage in the Work room investigate the simultaneous effects of using plastic sheet ing and RRP rule clean ing by adding both of these terms and their interaction to the mode l. Objective 1 – Models 21 and 22 explore the interactive effects of plastic sheeting and RRP rule cleaning on dust lead loadings after adjusting for the effects of intensity-lev el (21) and job-type (22). Objective Y – Models 1 and 2 build further on these models by also adjusting for average paint lead loading, which was found to be a significant pr edictor in previously described models.

The results suggest that Model 22, which explains floor dust lead loading as a function of jobtype, plastic sheeting, RRP rule cleaning, and th e interaction between plastic sheeting and RRP rule cleaning, provides the best f it for the floor data. In this model, post-cleaning work room floor dust lead loadings were lowest in the experiments that combined both proposed rule cleaning and the use of plastic sheeting, next lowe st for experiments that just the proposed RRP rule cleaning but not plastic, followed by experi ments that only employed the use of plastic. Each of these three combinations resulted in significantly lower floor dust lead loadings than7-9 found in experiments that had base line cleaning and no use of plasti c, as seen in the detailed parameter estimates found in Table I4 of Appendix I and Table L4 of Appendix L.

For window sills, the results sugge st that Objective 3–Model 5 provi des the best fit by adjusting for job type, cleaning method, and their interactio n. It makes sense that use of plastic would have less impact on window sills, as the plastic did not cover the sills as it did floors. Table J4 of Appendix J and Table M4 of Appendix M contai n the detailed parameter estimates for this model. Looking at some of the models that e xplores the differences between phases, such as Objective Y-Model 2, the results indicate that th e window sill dust lead loadings were only found to be significantly lower for the experiments that employed proposed ru le cleaning and use of plastic on its own did not yield statistically si gnificant lower window sill dust lead loadings.

Section O4 of Appendix O contai ns the residual plots from so me of the best-fitting models, which indicate reasonably normal distributions for floors and some skewness for sills. If additional analyses are performed, these can be i nvestigated further to determine potential causes of normality in these residuals.

Table 7-4: Post-Cleaning Work Room Modeling Results

Floors Sills
All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 17 COF . 529.6 0.16 646.6 . 173.6 0.45 208.5 Objective 1: Model 17 Intensity level <.01 <.01 0.14 0.07 Objective 1: Model 18 COF . 513.4 <.01 634.4 . 164.1 0.21 197.8 Objective 1: Model 18 Job type <.01 <.01 0.14 0.15 Objective 1: Model 19 COF . 529.2 0.02 646.5 . 178.8 0.33 216.1 Objective 1: Model 19 Square feet disturbed 0.60 0.90 0.18 0.32 Objective 1: Model 19 Avg. paint lead <.01 <.01 0.05 0.16 Objective 1: Model 19 Intensity level <.01 <.01 0.10 0.08 Objective 1: Model 20 COF . 516.8 <.01 630.5 . 171.2 0.15 205.7 Objective 1: Model 20 Square feet disturbed 0.89 0.65 0.54 0.55 Objective 1: Model 20 Avg. paint lead <.01 <.01 0.26 0.21 Objective 1: Model 20 Job type <.01 <.01 0.25 0.17 Objective 1: Model 21 COF . 510.2 0.01 623.0 . 162.8 0.29 195.6 Objective 1: Model 21 Avg. paint lead <.01 <.01 0.09 0.06 Objective 1: Model 21 Clean*Plastic <.01 <.01 0.02 <.01 Objective 1: Model 21 Intensity level <.01 <.01 0.11 0.03 Objective 1: Model 22 COF

. 499.0 <.01 607.4 . 153.4 0.15 184.1 Avg. paint lead <.01 <.01 0.29 0.10 Clean*Plastic <.01 <.01 0.02 <.01 Job type <.01 <.01 0.12 0.05 Objective 2: Model 1 COF . 542.7 0.19 661.6 . 176.8 0.43 213.8 Objective 2: Model 1 Plastic 0.38 0.37 0.22 0.48 Objective 2: Model 2 COF . 527.2 0.17 644.9 . 171.4 0.44 208.0 Objective 2: Model 2 Intensity level <.01 <.01 0.10 0.06 Objective 2: Model 2 Plastic 0.08 0.11 0.15 0.37 Objective 2: Model 3 COF . 521.6 0.18 639.2 . 165.3 0.45 202.9 Objective 2: Model 3 Intensity level <.01 <.01 0.08 0.05 Plastic 0.03 0.05 0.15 0.37 Intensity level*Plastic 0.15 0.10 0.25 0.287-10 Table 7-4: (continued) Post-Cleaning Work Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 2: Model 4 COF . 511.7 0.01 633.4 . 162.1 0.21 197.5 Objective 2: Model 4 Job type <.01 <.01 0.12 0.15 Objective 2: Model 4 Plastic 0.14 0.18 0.17 0.41 Objective 2: Model 5 COF . 495.2 0.01 614.8 . 147.1 0.23 179.5 Job type <.01 <.01 0.14 0.16 Plastic 0.15 0.52 0.16 0.44 Job type*Plastic 0.10 0.13 0.56 0.46 Objective 3: Model 1 COF . 538.1 0.21 656.1 . 171.8 0.41 203.5 Objective 3: Model 1 Clean 0.02 <.01 0.01 <.01 Objective 3: Model 2 COF . 523.5 0.20 639.5 . 167.0 0.42 197.5 Objective 3: Model 2 Intensity level <.01 <.01 0.12 0.04 Objective 3: Model 2 Clean <.01 <.01 0.01 <.01 Objective 3: Model 3 COF . 521.2 0.21 636.6 . 161.6 0.42 194.3 Objective 3: Model 3 Intensity level <.01 <.01 0.14 0.05 Objective 3: Model 3 Clean <.01 <.01 0.01 <.01 Objective 3: Model 3 Intensity level*Clean 0.76 0.41 0.27 0.54 Objective 3: Model 4 COF . 506.1 <.01 627.0 . 156.1 0.19 185.2 Objective 3: Model 4 Job type <.01 <.01 0.07 0.05 Objective 3: Model 4 Clean <.01 <.01 <.01 <.01 Objective 3: Model 5 COF . 494.9 <.01 614.0

. 140.9 0.19 169.5 Job type <.01 <.01 0.08 0.08 Clean <.01 <.01 <.01 <.01 Job type*Clean 0.57 0.58 0.36 0.47 Objective Y: Model 1 COF . 509.9 0.11 621.8 . 155.4 0.72 186.3 Objective Y: Model 1 Intensity level 0.74 0.75 0.49 0.61

Objective Y: Model 1 Avg. PostWork Work Floor Lead <.01 <.01 <.01 <.01 Objective Y: Model 1 Clean*Plastic 0.01 <.01 0.03 <.01 Objective Y: Model 2 COF . 500.4 0.02 615.6 . 148.4 0.37 177.4 Objective Y: Model 2 Job type 0.08 0.64 0.55 0.57

Objective Y: Model 2 Avg. PostWork Work Floor Lead 0.04 <.01 0.03 <.01 Objective Y: Model 2 Clean*Plastic 0.01 <.01 0.02 <.01

7.2.2. Post-Cleaning Tool Room Dust Lead Levels

Table 7-5 provides an overview of results from statistical models developed to describe postcleaning floor and window sill dust-lead loadings in the Tool room across residential units only and all units combined. The results provided ar e for wipe samples only as there were no bulk samples found in the Tool room. The bullets below summarize the various models tested.

• Objective 1-Models 23 and 24 a ssess the total effects of In tensity-Level and Job-Type, respectively, on floor and window sill dust lead loadings in the Tool room during the post-cleaning stage. Intensity-Level and J ob-Type were shown to be statistically significant in all models.

• Regarding Objective 1-Models 25 and 26, the results provided in Table 7-5 demonstrate that both Intensity-Level and Job-Type remain statistically significant predictors of post7-11 cleaning floor dust-lead loadings in the Tool room after adju sting for the effects of paint lead loading and square footage disturbed. While average paint- lead loading was found to be a significant predictor of post-cleaning floor and window sill dust lead loadings in the Tool room for most of the models – M odels 25 and 26 did not improve the prediction over Models 23 and 24, as evidenced by a comparison of the log-likelihoods.

• Objective 2–Model 6 assesses the total effect of using plastic sh eeting on post-cleaning dust lead levels in the tool room, which was not a statistically significant predictor (on its own) in any of the models applied to floor a nd window sill dust lead levels. Inspection of the parameter estimates from these models in Appendices I through M, however, demonstrates that use of plastic sheeting is consistently associated with lower predicted post-cleaning dust-lead levels in the Tool room.

• Objective 2–Models 7 through 10 assess whethe r the use of plastic is significant after adjusting for intensity-level or job-type, and whether there is any significant interaction between the use of plastic and either intensity-level or job- type when predicting floor and window sill dust lead loadings following cleaning in the Work room. The results of these analyses suggest that there may be a significan t interaction between the use of plastic and job-type when explaining both floor and window sill dust-lead loadings in the tool room following cleaning – and that these models provid ed the best fit of the data compared to any of the remaining models described below.

• Objective 3–Model 6 assesses th e total effect of using th e proposed Rule cleaning on post-cleaning dust lead levels in the Tool room, and Ob jective 3–Models 7 through 10 assess this effect after adjusting for intens ity and job-type and their interaction with cleaning method. As anticipated, the method of cleaning applied in the Work room did not influence floor or window sill dust lead levels in the Tool room.

• The remaining models fit to the post-cleani ng stage in the Tool room investigate the simultaneous effects of using plastic sheeti ng and proposed rule cleaning by adding both of these terms and their interaction to the model. These models did not improve upon the models that simply adjusted for the intera ction between use of plastic and job-type.

As noted above, Objective2-Model 10 provided the be st fit for all four data sets – residential floors and sills and combined floors and sills. Although use of plastic is not found to be significant by this model, there are significant or borderline significant in teractions between job type and use of plastic for the floor data. Sec tion O5 in Appendix O contains residual plots for these models, which all are reasonably normally distributed.7-12 Table 7-5: Post-Cleaning Tool Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 23 COF . 315.6 0.40 411.6 . 171.4 0.25 204.9 Objective 1: Model 23 Intensity level <.01 <.01 0.05 0.04 Objective 1: Model 24 COF . 297.9 0.39 392.8 . 157.9 0.24 189.8 Objective 1: Model 24 Job type <.01 <.01 0.02 0.02 Objective 1: Model 25 COF . 319.0 0.08 415.4 . 168.7 0.05 205.8 Objective 1: Model 25 Square feet disturbed 0.91 0.97 0.06 0.10 Objective 1: Model 25 Avg. paint lead <.01 <.01 <.01 <.01 Objective 1: Model 25 Intensity level <.01 <.01 <.01 0.01 Objective 1: Model 26 COF . 300.2 0.26 392.2 . 160.8 0.15 194.0 Objective 1: Model 26 Square feet disturbed 0.08 0.20 0.97 0.83 Objective 1: Model 26 Avg. paint lead 0.11 <.01 0.02 0.02 Objective 1: Model 26 Job type <.01 <.01 0.03 0.03 Objective 1: Model 27 COF . 309.2 0.21 405.9 . 161.0 0.08 197.5 Objective 1: Model 27 Avg. paint lead 0.01 <.01 <.01 <.01 Objective 1: Model 27 Clean*Plastic 0.54 0.52 0.36 0.31 Objective 1: Model 27 Intensity level <.01 <.01 0.02 0.01 Objective 1: Model 28 COF . 294.3 0.27 385.5 . 151.1 0.12 184.3 Objective 1: Model 28 Avg. paint lead 0.05 <.01 0.02 0.01 Objective 1: Model 28 Clean*Plastic 0.53 0.40 0.44 0.32 Objective 1: Model 28 Job type <.01 <.01 0.03 0.02 Objective 2: Model 6 COF . 328.0 0.41 424.5 . 176.4 0.23 210.3 Objective 2: Model 6 Plastic 0.34 0.42 0.20 0.20 Objective 2: Model 7 COF . 312.5 0.40 410.3 . 168.7 0.24 202.7 Objective 2: Model 7 Intensity level <.01 <.01 0.03 0.02 Objective 2: Model 7 Plastic 0.07 0.18 0.11 0.12 Objective 2: Model 8 COF . 305.6 0.41 404.3 . 160.7 0.25 198.2 Objective 2: Model 8 Intensity level <.01 <.01 0.04 0.03 Objective 2: Model 8 Plastic 0.16 0.29 0.24 0.18 Objective 2: Model 8 Intensity level*Plastic 0.14 0.15 0.10 0.36 Objective 2: Model 9 COF . 295.5 0.39 391.9 . 155.5 0.24 187.9 Objective 2: Model 9 Job type <.01 <.01 0.01 0.02 Objective 2: Model 9 Plastic 0.10 0.20 0.12 0.13 Objective 2: Model 10 COF

. 277.4 0.42 370.5

. 137.8 0.27 168.7 Job type <.01 <.01 0.01 0.02 Plastic 0.30 0.52 0.40 0.44 Job type*Plastic 0.06 0.04 0.16 0.21 Objective 3: Model 6 COF . 329.0 0.42 425.2 . 178.1 0.25 212.0 Objective 3: Model 6 Clean 0.96 0.77 0.77 0.97 Objective 3: Model 7 COF . 315.8 0.41 412.1 . 171.2 0.25 205.3 Objective 3: Model 7 Intensity level <.01 <.01 0.05 0.04 Objective 3: Model 7 Clean 0.91 0.72 0.61 0.90 Objective 3: Model 8 COF . 311.7 0.41 408.2 . 168.0 0.26 202.9 Objective 3: Model 8 Intensity level <.01 <.01 0.07 0.05 Objective 3: Model 8 Clean 0.83 0.73 0.68 0.91 Objective 3: Model 8 Intensity level*Clean 0.57 0.43 0.97 0.987-13 Table 7-5: (continued) Post-Cleaning Tool Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 3: Model 9 COF . 298.1 0.41 392.9 . 158.0 0.24 190.2 Objective 3: Model 9 Job type <.01 <.01 0.02 0.02 Objective 3: Model 9 Clean 0.81 0.40 0.89 0.79 Objective 3: Model 10 COF . 285.6 0.41 379.3 . 146.5 0.24 176.7 Objective 3: Model 10 Job type <.01 <.01 0.03 0.03 Objective 3: Model 10 Clean 0.97 0.70 0.97 0.68 Objective 3: Model 10 Job type*Clean 0.62 0.69 0.92 0.85 Objective Y: Model 3 COF . 308.5 0.51 402.0 . 165.0 0.43 197.5 Objective Y: Model 3 Intensity level 0.25 0.12 0.80 0.92

Objective Y: Model 3 Avg. PostWork Work Floor Lead <.01 <.01 0.01 <.01 Objective Y: Model 3 Plastic 0.23 0.22 0.19 0.12 Objective Y: Model 4 COF . 296.9 0.48 391.2 . 155.5 0.36 186.1 Objective Y: Model 4 Job type 0.04 0.06 0.39 0.43

Objective Y: Model 4 Avg. PostWork Work Floor Lead 0.62 0.08 0.21 0.05 Objective Y: Model 4 Plastic 0.14 0.24 0.18 0.14

7.2.3. Post-Cleaning Observation Room Dust Lead Levels

Table 7-6 provides an overview of statistical modeling results post-cleaning floor and window sill dust-lead loadings in the Observation room across the residential units only and all units combined data sets. The results are provide d for wipe samples only as there were no bulk samples found in the Observation room. The bu llets below summarize fi ndings from the various models.

• Objective 1-Models 29 and 30 a ssess the total effects of In tensity-Level and Job-Type, respectively, on floor and window sill dust lead loadings in the work room during the post-cleaning stage. Intensity-Level and J ob-Type were shown to be statistically significant in all models.

• Objective 1-Models 31 and 32 investigate whet her intensity level and job type are still statistically significant predic tors of floor and window sill dust lead loadings after adjusting for the paint lead loading on the surface being disturbed and a measure of the square feet of lead-based paint disturbe d during the RRP activity. The summary of results provided in Table 7-6 demonstrates th at both Intensity-Level and Job-Type remain statistically significant predictors of post- cleaning floor dust-lead loadings in the Observation room after adjusting for the effect s of paint lead loading and square footage disturbed. While average pain t-lead loading was found to be a significant predictor of post-cleaning floor and window sill dust lead load ings in the Observation room in all of the models – these more complex models did not always improve the prediction over Models 29 and 30, suggesting that these terms may compete with intensity-level and jobtype for explanatory power.

• Objective 2–Model 11 assesses the total effect of using plastic sheeting on post-cleaning dust-lead levels in the observation room, which a statistically significant predictor for floors in residential units (p -value = 0.03, estimate of -0.97) and nearly significant for all7-14 units (p-value = 0.06). The total effect of plastic on lowering window sill dust lead levels in the Observation room was borderline signi ficant (p-value of 0.10 in residential units and 0.12 in all units combined).

• Objective 2–Models 12 through 15 assess whethe r the use of plastic was significant after adjusting for intensity-level or job-type, and whether there wa s any significant interaction between the use of plastic and either intensity-level or job- type when predicting floor and window sill dust lead loadings following cleani ng in the work room. Although the F-test associated with the interaction term was not statistically significan t across any of these models, Objective 2 – Model 15 provided the best fit of the data across floors and window sills within both subsets of data explored.

• Objective 3–Model 11 assesses the total eff ect of using the proposed Rule cleaning method on post-cleaning dust-lead levels in the Observation room, and Objective 3– Models 12 through 15 assess this effect after ad justing for intensity and job-type and their interaction with cleaning method. As in the Tool room, the method of cleaning applied in the Work room did not influence floor or window sill dust lead levels in the Observation

room.

• The remaining models fit to the post-cleaning stage in the Observation room investigate the simultaneous effects of using plastic sh eeting and RRP rule cleaning by adding both of these terms and their interaction to the model. These models did not significantly improve upon the models that simply adjusted for the interaction be tween use of plastic and job-type.

As noted above, Objective2-Model 15 provided the be st fit for all four data sets – residential floors and sills and combined floors and sills. Although use of plastic is not found to be significant by this model, there are significant or borderline significant in teractions between job type and use of plastic for the floor data. Sec tion O6 in Appendix O contains residual plots for these models, which all are reasonably normally distributed.

Table 7-6: Post-Cleaning Observation Room Modeling Results

Floors Sills
All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 29 COF . 298.7 0.61 363.3 . 154.6 0.22 179.5 Objective 1: Model 29 Intensity level <.01 <.01 0.08 0.07 Objective 1: Model 30 COF . 281.6 0.50 349.0 . 137.5 0.33 157.1 Objective 1: Model 30 Job type <.01 <.01 <.01 <.01 Objective 1: Model 31 COF . 294.3 0.26 356.8 . 154.2 0.07 185.1 Objective 1: Model 31 Square feet disturbed 0.28 0.23 0.19 0.43 Objective 1: Model 31 Avg. paint lead <.01 <.01 <.01 0.01 Objective 1: Model 31 Intensity level <.01 <.01 0.04 0.09 Objective 1: Model 32 COF . 283.6 0.36 344.6 . 138.5 0.28 160.0 Objective 1: Model 32 Square feet disturbed 0.79 0.80 0.15 0.03 Objective 1: Model 32 Avg. paint lead <.01 <.01 0.04 0.09 Objective 1: Model 32 Job type <.01 <.01 <.01 <.01 Objective 1: Model 33 COF . 277.9 0.24 343.1 . 145.0 0.09 175.5 Objective 1: Model 33 Avg. paint lead <.01 <.01 <.01 0.02 Clean*Plastic 0.01 0.03 0.26 0.25 Intensity level <.01 <.01 0.03 0.047-15 Table 7-6: (continued) Post-Cleaning Observation Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood p-value -2 Log Likelihood Objective 1: Model 34 COF . 265.7 0.37 330.4 . 130.9 0.25 155.8 Avg. paint lead <.01 <.01 0.02 0.05 Clean*Plastic <.01 0.03 0.26 0.22 Job type <.01 <.01 <.01 <.01 Objective 2: Model 11 COF . 311.1 0.69 376.7 . 156.8 0.19 182.2 Plastic 0.03 0.06 0.10 0.12 Objective 2: Model 12 COF . 288.9 0.59 358.1 . 150.5 0.22 176.7 Objective 2: Model 12 Intensity level <.01 <.01 0.02 0.03 Objective 2: Model 12 Plastic <.01 0.02 0.03 0.06 Objective 2: Model 13 COF . 286.3 0.60 355.1 . 147.2 0.22 174.1 Objective 2: Model 13 Intensity level <.01 <.01 0.05 0.05 Objective 2: Model 13 Plastic <.01 0.03 0.07 0.09 Objective 2: Model 13 Intensity level*Plastic 0.75 0.63 0.61 0.55 Objective 2: Model 14 COF . 270.8 0.72 343.4 . 133.4 0.34 154.0 Objective 2: Model 14 Job type <.01 <.01 <.01 <.01 Objective 2: Model 14 Plastic <.01 0.01 0.03 0.04 Objective 2: Model 15 COF

. 259.7 0.74 326.6

. 120.9 0.35 139.4 Job type <.01 <.01 <.01 <.01 Plastic <.01 0.06 0.14 0.20 Job type*Plastic 0.62 0.27 0.36 0.22 Objective 3: Model 11 COF . 316.1 0.72 380.2 . 159.3 0.21 184.4 Objective 3: Model 11 Clean 0.75 0.81 0.60 0.60 Objective 3: Model 12 COF . 298.5 0.62 363.6 . 154.7 0.22 180.1 Objective 3: Model 12 Intensity level <.01 <.01 0.08 0.07 Objective 3: Model 12 Clean 0.49 0.61 0.54 0.55 Objective 3: Model 13 COF . 294.2 0.62 360.7 . 151.6 0.22 178.1 Objective 3: Model 13 Intensity level <.01 <.01 0.11 0.09 Objective 3: Model 13 Clean 0.50 0.61 0.49 0.51 Objective 3: Model 13 Intensity level*Clean 0.44 0.66 0.66 0.69 Objective 3: Model 14 COF . 281.9 0.50 349.6 . 138.3 0.33 158.3 Objective 3: Model 14 Job type <.01 <.01 <.01 <.01 Objective 3: Model 14 Clean 0.66 0.84 0.92 0.95 Objective 3: Model 15 COF . 269.3 0.51 335.9 . 127.8 0.30 147.9 Objective 3: Model 15 Job type <.01 <.01 0.02 <.01 Objective 3: Model 15 Clean 0.63 0.76 0.77 0.85 Objective 3: Model 15 Job type*Clean 0.55 0.71 0.65 0.68 Objective Y: Model 5 COF . 284.4 0.92 351.4 . 148.5 0.36 173.1 Objective Y: Model 5 Intensity level 0.02 0.04 0.81 0.84

Objective Y: Model 5 Avg. PostWork Work Floor Lead <.01 <.01 0.03 0.01 Objective Y: Model 5 Plastic <.01 0.01 0.08 0.06 Objective Y: Model 6 COF . 272.4 0.99 340.2 . 134.6 0.44 155.1 Objective Y: Model 6 Job type <.01 0.02 0.04 0.01

Objective Y: Model 6 Avg. PostWork Work Floor Lead 0.51 0.02 0.32 0.23 Objective Y: Model 6 Plastic <.01 0.01 0.05 0.047-16 7.3. Post-Verification Stage Dust Lead Levels

The following three subsections provide an overvie w of statistical modeling results for floor and window sill dust lead loading results in the post-ve rification stage of the field study, in the Work room (subsection 7.3.1), Tool room (subsection 7.3.2), and Observation room (subsection 7.3.3).

7.3.1. Post-Verification Work Room Dust Lead Levels

Table 7-7 provides an overview of results from statistical models developed to describe postverification floor and window sill dust lead loadi ngs in the work room across the two subsets of data described earlier (residential units only and all units combined). Detailed floor and sill modeling results are contained in Appendices I through M. It is im portant to keep in mind when considering the Work room data at the post-verifi cation stage that zones of the work room that failed a cleaning verification step underwent recleaning using the proposed Rule cleaning method, regardless of which cleaning method was us ed at the post-cleaning stage. The bullets below summarize the various models run to ex plore the post-verification Work room data.

• Objective 1-Models 35 and 36 indicate that In tensity-Level and Job-Type were shown to be statistically significant predictors of post- verification floor dust lead loadings – but not significant predictors of wi ndow sill dust-lead loadings.

• Objective 1-Models 37 and 38 demonstrate that intensity-level and job-type are still statistically significant predicto rs of floor dust lead loadings after adjusting for the paintlead loading on the surface being disturbed and a measure of the square feet of lead-based paint disturbed during the RRP activity. Aver age paint-lead loading was found to be a significant predictor of post-verification floor dust lead loadings in the Work room. None of the variables in Models 37 and 38 appear ed significantly predictive of window sill dust-lead loadings in the Wo rk room following verification.

• The remaining models fit to the post-verifica tion dust lead loadings in the Work room were largely unremarkable – with the rema ining variables (use of plastic, cleaning method, etc.) failing to explain a major portion of variability in post-verification dust lead results. This result suggests that the verification step acts as an equalizer – removing the factors that previously explained elevated dust lead le vels in earlier phases of the experiment.

• The fact that intensity-level, job-type, and average paint lead loading are still predictive of post-verification floor dust-lead levels ma y be a concern. On the other hand, over half of all experiments ended with the average post-verification floor lead levels below 40
μ g/ft 2 indicating that the various cleaning techniques uti lized were able to return floor levels to a clean condition in the majority of experiments.

Objective 3-Model 20 provided the be st fit for three of the four da ta sets – residential floors and residential and combined sills. This model adjusted for cleaning method, job type, and their interaction. This model performe d well for the combined floor data set as well, but Objective 1- Model 40, which adjusted for phase (Clean*Plastic ) and average paint lead, may have performed slightly better. Cleanin g method was no longer significant at the post-verification stage. Section O7 in Appendix O contains residual plots for thes e models. The residuals from the floor models are normally distributed, although there does appear to be an outlier on the upper end of the distribution. The sill models appear to contain some skewness in the residuals.7-17 Table 7-7: Post-Verification Work Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 1: Model 35 COF . 542.0 0.16 640.6 . 167.9 0.33 194.3 Objective 1: Model 35 Intensity level <.01 <.01 0.27 0.10 Objective 1: Model 36 COF . 520.1 <.01 627.8 . 155.6 0.12 180.5 Objective 1: Model 36 Job type <.01 <.01 0.08 0.06 Objective 1: Model 37 COF . 536.3 0.02 639.8 . 174.5 0.30 204.3 Objective 1: Model 37 Square feet disturbed 0.92 0.53 0.94 0.70 Objective 1: Model 37 Avg. paint lead <.01 <.01 0.09 0.50 Objective 1: Model 37 Intensity level <.01 <.01 0.28 0.13 Objective 1: Model 38 COF . 522.4 <.01 623.8 . 161.5 0.17 187.8 Objective 1: Model 38 Square feet disturbed 0.81 0.91 0.12 0.09 Objective 1: Model 38 Avg. paint lead <.01 <.01 0.69 0.90 Objective 1: Model 38 Job type <.01 <.01 0.08 0.03 Objective 1: Model 39 COF . 526.9 0.02 629.3 . 164.4 0.27 195.1 Objective 1: Model 39 Avg. paint lead <.01 <.01 0.09 0.40 Objective 1: Model 39 Clean*Plastic 0.27 0.10 0.48 0.44 Objective 1: Model 39 Intensity level <.01 <.01 0.24 0.14 Objective 1: Model 40 COF . 514.3 <.01 614.3 . 153.5 0.12 181.1 Avg. paint lead <.01 <.01 0.40 0.59 Clean*Plastic 0.42 0.17 0.37 0.35 Job type <.01 <.01 0.13 0.07 Objective 2: Model 16 COF . 556.5 0.19 658.9 . 170.0 0.30 198.0 Objective 2: Model 16 Plastic 0.40 0.51 0.29 0.34 Objective 2: Model 17 COF . 539.9 0.17 639.9 . 166.5 0.33 193.4 Objective 2: Model 17 Intensity level <.01 <.01 0.22 0.08 Objective 2: Model 17 Plastic 0.09 0.18 0.22 0.23 Objective 2: Model 18 COF . 535.4 0.17 637.4 . 163.1 0.33 191.1 Objective 2: Model 18 Intensity level <.01 <.01 0.22 0.09 Objective 2: Model 18 Plastic 0.06 0.16 0.22 0.24 Objective 2: Model 18 Intensity level*Plastic 0.24 0.47 0.89 0.93 Objective 2: Model 19 COF . 518.0 <.01 627.5 . 154.3 0.12 179.8 Objective 2: Model 19 Job type <.01 <.01 0.07 0.06 Objective 2: Model 19 Plastic 0.08 0.26 0.22 0.26 Objective 2: Model 20 COF . 508.2 <.01 617.0 . 142.2 0.12 166.8 Objective 2: Model 20 Job type <.01 <.01 0.10 0.08 Objective 2: Model 20 Plastic 0.10 0.39 0.27 0.33 Objective 2: Model 20 Job type*Plastic 0.69 0.89 0.86 0.92 Objective 3: Model 16 COF . 556.6 0.21 658.2 . 170.8 0.30 198.2 Objective 3: Model 16 Clean 0.42 0.26 0.56 0.38 Objective 3: Model 17 COF . 542.1 0.19 640.2 . 167.8 0.32 194.2 Objective 3: Model 17 Intensity level <.01 <.01 0.28 0.11 Objective 3: Model 17 Clean 0.41 0.20 0.58 0.417-18 Table 7-7: (continued) Post-Verification Work Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 3: Model 18 COF . 540.2 0.20 639.2 . 164.3 0.32 191.8 Objective 3: Model 18 Intensity level <.01 <.01 0.33 0.13 Objective 3: Model 18 Clean 0.41 0.20 0.62 0.45 Objective 3: Model 18 Intensity level*Clean 0.92 0.92 0.73 0.81 Objective 3: Model 19 COF . 520.2 <.01 627.5 . 155.0 0.12 179.7 Objective 3: Model 19 Job type <.01 <.01 0.07 0.05 Objective 3: Model 19 Clean 0.39 0.25 0.39 0.24 Objective 3: Model 20 COF

. 505.6 <.01 612.7

. 142.8 0.13 165.5 Job type <.01 <.01 0.10 0.09 Clean 0.49 0.39 0.45 0.18 Job type*Clean 0.11 0.27 0.76 0.68 Objective 7: Model 7 COF . 534.9 0.31 632.9 . 156.5 0.48 185.3 Objective Y: Model 7 Intensity level 0.90 0.59 0.39 0.45

Objective Y: Model 7 Avg. PostWork Work Floor Lead <.01 <.01 <.01 <.01 Objective Y: Model 7 Clean*Plastic 0.67 0.35 0.64 0.47 Objective 7: Model 8 COF . 517.6 <.01 623.9 . 148.8 0.21 175.4 Objective Y: Model 8 Job type <.01 0.01 0.42 0.43

Objective Y: Model 8 Avg. PostWork Work Floor Lead 0.17 <.01 0.04 0.04 Objective Y: Model 8 Clean*Plastic 0.42 0.48 0.54 0.41

7.3.2. Post-Verification Tool Room Dust Lead Levels

Table 7-8 provides an overview of results from models developed to de scribe post-verification floor and window sill dust lead loadings in th e Tool room. The bullets below summarize the various models run.

• Objective 1-Models 41 and 42 a ssess the total effects of In tensity-Level and Job-Type, respectively, on floor and window sill dust lead loadings in the Tool room during the post-verification stage. Job-Type was show n to still be a statistically significant predictor of post-verification fl oor dust lead loadings – but not a significant predictor of window sill dust lead loadings. Intensity-lev el was not a significant predictor of postverification floor or window sill post-verifi cation dust lead levels in the Tool room.

• In the remaining models fit to the data, average paint lead loading appeared to be predictive of window sill dust lead levels – and models that accounted for the combined effects of job type and cleaning method app eared to be most predictive of window sill dust-lead loadings. Similarly, average pain t lead levels appeared only marginally predictive of floor dust-lead loadings, and models that accounted for the combined effects of job-type and use of plastic provided the best fit for the post-verification floor dust lead models for the Tool room.

Objective 2-Model 25, which adjusted for job type, plastic, and their interaction, appeared to be the best-fitting model for the floor lead data. Us e of plastic was significa nt in residential units, but not significant when including the COF data in the analysis. Note that the COF variable was7-19 significant indicating differences between the residential units a nd the school. In the school, the classroom being worked on was separated into bo th the Work room and Tool room whereas in houses the Tool room was an adjacent room usually accessed through a doorway. This difference may impact the Tool room results. Interestingly, Objectiv e3-Model 25 fits the sill data best and found a borderline significance for cleaning method in the combined data set. Section O8 in Appendix O contains residual plots for these models. As with most of the other analyses, the residuals from the floor models ap pear normally distributed while the residuals from the sill models are somewhat skewed.

Table 7-8: Post-Verification Tool Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 1: Model 41 COF . 351.2 0.42 452.6 . 181.6 0.29 217.2 Objective 1: Model 41 Intensity level 0.08 0.03 0.25 0.13 Objective 1: Model 42 COF . 334.5 0.06 436.8 . 172.7 0.43 206.4 Objective 1: Model 42 Job type <.01 <.01 0.28 0.23 Objective 1: Model 43 COF . 357.7 0.14 461.2 . 179.8 0.09 222.2 Objective 1: Model 43 Square feet disturbed 0.96 0.90 0.19 0.33 Objective 1: Model 43 Avg. paint lead 0.09 0.10 <.01 0.02 Objective 1: Model 43 Intensity level 0.10 0.06 0.08 0.12 Objective 1: Model 44 COF . 342.5 0.04 444.5 . 173.0 0.32 212.5 Objective 1: Model 44 Square feet disturbed 0.67 0.98 0.89 0.64 Objective 1: Model 44 Avg. paint lead 0.74 0.16 <.01 0.08 Objective 1: Model 44 Job type 0.02 0.02 0.28 0.32 Objective 1: Model 45 COF . 342.7 0.13 447.3 . 170.2 0.04 209.9 Objective 1: Model 45 Avg. paint lead 0.14 0.10 <.01 <.01 Objective 1: Model 45 Clean*Plastic 0.08 0.09 0.41 0.13 Objective 1: Model 45 Intensity level 0.03 0.02 0.17 0.14 Objective 1: Model 46 COF . 328.0 0.03 431.7 . 162.4 0.19 200.3 Objective 1: Model 46 Avg. paint lead 0.79 0.15 <.01 0.02 Objective 1: Model 46 Clean*Plastic 0.07 0.10 0.40 0.13 Objective 1: Model 46 Job type <.01 <.01 0.25 0.28 Objective 2: Model 21 COF . 354.7 0.37 457.8 . 185.1 0.25 221.3 Objective 2: Model 21 Plastic 0.12 0.16 0.65 0.49 Objective 2: Model 22 COF . 347.4 0.39 450.0 . 181.0 0.28 216.7 Objective 2: Model 22 Intensity level 0.04 0.02 0.24 0.12 Objective 2: Model 22 Plastic 0.06 0.09 0.56 0.41 Objective 2: Model 23 COF . 341.9 0.40 445.7 . 176.4 0.28 213.4 Objective 2: Model 23 Intensity level 0.04 0.02 0.29 0.15 Objective 2: Model 23 Plastic 0.06 0.10 0.66 0.47 Objective 2: Model 23 Intensity level*Plastic 0.41 0.46 0.71 0.88 Objective 2: Model 24 COF . 330.3 0.05 434.2 . 172.1 0.43 206.0 Objective 2: Model 24 Job type <.01 <.01 0.28 0.23 Objective 2: Model 24 Plastic 0.04 0.09 0.60 0.45 Objective 2: Model 25 COF

. 313.3 0.05 417.3 . 158.0 0.42 191.0 Job type <.01 <.01 0.38 0.34 Plastic 0.03 0.16 0.81 0.70 Job type*Plastic 0.29 0.59 0.89 0.957-20 Table 7-8: (continued) Post-Verification Tool Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 3: Model 21 COF . 355.6 0.39 458.7 . 184.2 0.22 219.2 Objective 3: Model 21 Clean 0.20 0.29 0.26 0.10 Objective 3: Model 22 COF . 349.2 0.41 451.7 . 180.4 0.25 215.1 Objective 3: Model 22 Intensity level 0.08 0.03 0.29 0.15 Objective 3: Model 22 Clean 0.16 0.27 0.31 0.13 Objective 3: Model 23 COF . 344.4 0.42 447.1 . 176.5 0.26 211.7 Objective 3: Model 23 Intensity level 0.07 0.03 0.31 0.15 Objective 3: Model 23 Clean 0.18 0.31 0.30 0.14 Objective 3: Model 23 Intensity level*Clean 0.55 0.38 0.88 0.74 Objective 3: Model 24 COF . 332.6 0.06 435.9 . 171.1 0.38 203.6 Objective 3: Model 24 Job type <.01 0.01 0.27 0.19 Objective 3: Model 24 Clean 0.17 0.28 0.24 0.09 Objective 3: Model 25 COF . 318.2 0.06 420.3

. 155.9 0.41 185.6 Job type <.01 0.02 0.24 0.16 Clean 0.18 0.38 0.27 0.06 Job type*Clean 0.66 0.71 0.62 0.45 Objective Y: Model 9 COF . 346.6 0.45 448.7 . 180.9 0.35 217.4 Objective Y: Model 9 Intensity level 0.57 0.27 0.94 0.62

Objective Y: Model 9 Avg. PostWork Work Floor Lead 0.03 0.04 0.12 0.21 Objective Y: Model 9 Plastic 0.11 0.10 0.64 0.42 Objective Y: Model 10 COF . 331.5 0.07 435.0 . 173.1 0.46 207.4 Objective Y: Model 10 Job type 0.05 0.10 0.74 0.58

Objective Y: Model 10 Avg. PostWork Work Floor Lead 0.82 0.31 0.75 0.80 Objective Y: Model 10 Plastic 0.05 0.09 0.64 0.45

7.3.3. Post-Verification Observation Room Dust Lead Levels

Table 7-9 provides the results from the statistical models develope d to describe post-verification floor and window sill dust-lead loadings in the Observation room. Objective 1-Models 47 and 48 assess the total effects of In tensity-Level and Job-Type, re spectively, on post-verification floor and window sill dust lead loadings. Both were shown to still be statistically significant predictors of post-verification fl oor and window sill dust lead loadi ngs in the Observation room. In the remaining models fit to the post-verification Observation room data, average paint lead loading appeared to be predictive of both floor and window sill dust-lead le vels, and models that accounted for the combined effects of job type and use of plastic pr ovided the best fit.

Objective 2-Model 30 is highlighted in Table 7-9 as providing the best fit for each of the four data sets; however, the results for these data are not as clear. Use of plastic is found to be borderline significant in the reside ntial data but not is significant in the co mbined data. When the job type by plastic use interac tion is not included, the effect of plastic use is significant for the residential data and for window sills in the combined data. The residual plots in Section O9 of Appendix O appear approximately normal for floors and window sills.7-21 Table 7-9: Post-Verification Observation Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 1: Model 47 COF . 273.0 0.84 346.8 . 151.4 0.25 176.2 Objective 1: Model 47 Intensity level <.01 <.01 0.07 0.05 Objective 1: Model 48 COF . 249.1 0.80 325.7 . 138.6 0.35 159.7 Objective 1: Model 48 Job type <.01 <.01 0.02 <.01 Objective 1: Model 49 COF . 267.1 0.38 348.5 . 145.9 0.03 179.2 Objective 1: Model 49 Square feet disturbed 0.82 0.56 0.10 0.25 Objective 1: Model 49 Avg. paint lead <.01 <.01 <.01 <.01 Objective 1: Model 49 Intensity level <.01 <.01 0.02 0.05 Objective 1: Model 50 COF . 244.6 0.48 323.0 . 135.2 0.24 161.8 Objective 1: Model 50 Square feet disturbed 0.03 0.10 0.46 0.10 Objective 1: Model 50 Avg. paint lead <.01 <.01 <.01 0.01 Objective 1: Model 50 Job type <.01 <.01 <.01 <.01 Objective 1: Model 51 COF . 253.9 0.37 339.0 . 134.6 0.08 168.4 Objective 1: Model 51 Avg. paint lead <.01 <.01 <.01 <.01 Objective 1: Model 51 Clean*Plastic 0.08 0.31 0.06 0.12 Objective 1: Model 51 Intensity level <.01 <.01 <.01 0.02 Objective 1: Model 52 COF . 235.5 0.48 317.4 . 123.8 0.18 153.8 Objective 1: Model 52 Avg. paint lead <.01 <.01 <.01 <.01 Objective 1: Model 52 Clean*Plastic 0.04 0.39 0.10 0.14 Objective 1: Model 52 Job type <.01 <.01 <.01 <.01 Objective 2: Model 26 COF . 292.2 0.87 366.5 . 152.8 0.21 178.8 Plastic 0.26 0.59 0.05 0.08 Objective 2: Model 27 COF . 268.8 0.83 346.4 . 145.8 0.24 172.8 Objective 2: Model 27 Intensity level <.01 <.01 0.01 0.02 Objective 2: Model 27 Plastic 0.03 0.29 0.01 0.04 Objective 2: Model 28 COF . 266.4 0.83 344.4 . 141.4 0.24 169.1 Objective 2: Model 28 Intensity level <.01 <.01 0.03 0.03 Objective 2: Model 28 Plastic 0.05 0.32 0.04 0.07 Objective 2: Model 28 Intensity level*Plastic 0.95 0.99 0.32 0.32 Objective 2: Model 29 COF . 244.7 0.80 325.6 . 133.4 0.37 156.2 Objective 2: Model 29 Job type <.01 <.01 <.01 <.01 Objective 2: Model 29 Plastic 0.03 0.32 0.02 0.03 Objective 2: Model 30 COF

. 230.8 0.83 306.3

. 118.9 0.38 140.3 Job type <.01 <.01 <.01 <.01 Plastic 0.06 0.85 0.08 0.13 Job type*Plastic 0.17 0.07 0.21 0.18 Objective 3: Model 26 COF . 293.0 0.88 366.7 . 155.6 0.24 180.6 Objective 3: Model 26 Clean 0.44 0.65 0.30 0.25 Objective 3: Model 27 COF . 272.5 0.84 347.3 . 150.4 0.26 175.5 Objective 3: Model 27 Intensity level <.01 <.01 0.06 0.04 Objective 3: Model 27 Clean 0.33 0.58 0.22 0.19 Objective 3: Model 28 COF . 269.8 0.83 344.4 . 147.9 0.25 173.8 Objective 3: Model 28 Intensity level <.01 <.01 0.08 0.06 Objective 3: Model 28 Clean 0.37 0.60 0.22 0.19 Objective 3: Model 28 Intensity level*Clean 0.86 0.59 0.85 0.817-22 Table 7-9: (continued) Post-Verification Observation Room Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood pvalue -2 Log Likelihood Objective 3: Model 29 COF . 248.9 0.79 326.6 . 138.5 0.36 159.9 Job type <.01 <.01 0.02 <.01 Clean 0.33 0.84 0.40 0.33 Objective 3: Model 30 COF . 239.3 0.77 313.6 . 126.9 0.35 147.6 Objective 3: Model 30 Job type <.01 <.01 0.05 0.02 Objective 3: Model 30 Clean 0.32 0.92 0.33 0.26 Objective 3: Model 30 Job type*Clean 0.82 0.63 0.55 0.51 Objective 4: Model Y COF . 266.3 0.96 342.3 . 143.9 0.39 168.9 Objective Y: Model 11 Intensity level <.01 0.02 0.93 0.97

Objective Y: Model 11 Avg. PostWork Work Floor Lead 0.02 <.01 0.02 <.01 Objective Y: Model 11 Plastic 0.08 0.34 0.05 0.05 Objective 4: Model Y COF . 246.8 0.88 326.3 . 133.8 0.50 156.5 Objective Y: Model 12 Job type <.01 <.01 0.19 0.16

Objective Y: Model 12 Avg. PostWork Work Floor Lead 0.79 0.20 0.19 0.11 Objective Y: Model 12 Plastic 0.03 0.34 0.04 0.04

7.4. Comparison of Rule Practices and Baseline Practices - Post-Job Dust Lead Levels

The following three subsections provide an overvie w of statistical modeling results for floor and window sill dust lead loadings in a cross-stage analysis compari ng full rule implementation (use of plastic sheeting and proposed Rule cleaning at the Post-Ver ification Stage) to no rule implementation (no use of plastic sheeting and baseline cleaning at the Post-Cleaning Stage).

7.4.1. Post-Job Work Room Dust Lead Levels

Table 7-10 provides an overview of results from the various statistical models applied across stages within the Work room - comparing full rule implementation to no rule implementation. Model 1 assesses the total effect of rule im plementation, which was shown to be highly significant in both floors and window sills in the tw o subsets of data explored. Models 2 through 5 assess the effect of full rule implementation in the Work room, after adjusting for intensitylevel or job type and their intera ction with full rule implementati on. In each of these adjusted models, full rule implementation was associated w ith significantly lower dust-lead loadings in the Work room. Model 5 appears to fit the data best fo r each of the four data sets. The residual plots from the floor models appear reasonable, while the residual plots from the sill models appear to be heavy-tailed.7-23 Table 7-10: Post-Job Work Room, Rule vs. Non-Rule Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 7: Model 1 COF . 245.1 0.22 293.4 . 78.1 0.31 92.8 Objective X: Model 1 Rule 0.01 <.01 0.02 <.01 Objective 7: Model 2 COF . 238.8 0.12 285.5 . 76.4 0.33 91.3 Objective X: Model 2 Rule <.01 <.01 0.03 <.01 Objective X: Model 2 Intensity level 0.06 0.01 0.93 0.77 Objective 7: Model 3 COF . 234.3 0.11 280.9 . 71.4 0.33 87.8 Objective X: Model 3 Rule <.01 <.01 0.04 <.01 Objective X: Model 3 Intensity level 0.17 0.02 0.69 0.61 Objective X: Model 3 Rule*Intensity level 0.49 0.23 0.58 0.74 Objective 7: Model 4 COF . 215.9 <.01 268.0 . 64.7 0.19 76.7 Objective X: Model 4 Rule <.01 <.01 0.02 <.01 Objective X: Model 4 Job type <.01 <.01 0.35 0.34 Objective 7: Model 5 COF

. 202.7 <.01 253.2

. 49.6 0.33 59.4 Rule <.01 <.01 0.10 0.02 Job type <.01 <.01 0.61 0.52 Rule*Job type 0.35 0.68 0.66 0.60

7.4.2. Post-Job Tool Room Dust Lead Levels

The model results from comparing full rule impl ementation to no rule implementation within the Tool room are presented in Table 7-11. Model 6 assesses the total effect of rule implementation, which was not a statistically signifi cant predictor of dust lead loadi ngs in the Tool room. Models 7 through 10 assess the effect of full rule implementation in the Work room, after adjusting for intensity-level or job type and their interaction with full rule implementation. These models suggest that full rule implementation was associat ed with a marginally significant decrease in window sill dust lead levels, afte r adjusting for the effects of job-type (Model 9). Model 10 appears to provide the best fit, although by adding the rule use by job type interaction the effect of the rule was no longer significant. Section O11 presents the residual plots for Model 10, which appear to be normally distributed except for the residential sill data.7-24 Table 7-11: Post-Job Tool Room, Rule vs. Non-Rule Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 7: Model 6 COF . 180.4 0.47 233.2 . 91.0 0.45 107.6 Objective X: Model 6 Rule 0.46 0.46 0.24 0.13 Objective 7: Model 7 COF . 171.5 0.33 224.8 . 83.4 0.40 102.1 Objective X: Model 7 Rule 0.22 0.23 0.18 0.10 Objective X: Model 7 Intensity level <.01 <.01 0.14 0.20 Objective 7: Model 8 COF . 165.9 0.37 220.3 . 76.7 0.40 96.7 Objective X: Model 8 Rule 0.35 0.31 0.23 0.13 Objective X: Model 8 Intensity level 0.05 0.03 0.12 0.17 Objective X: Model 8 Rule*Intensity level 0.78 0.84 0.45 0.59 Objective 7: Model 9 COF . 144.2 0.23 205.1 . 69.2 0.54 85.8 Rule 0.08 0.23 0.09 0.06 Job type <.01 <.01 0.07 0.16 Objective 7: Model 10 COF

. 131.3 0.22 186.5

. 52.2 0.46 66.6 Rule 0.17 0.36 0.19 0.14 Job type <.01 0.10 0.30 0.36 Rule*Job type 0.97 0.99 0.80 0.91

7.4.3. Post-Job Observation Room Dust Lead Levels

Table 7-12 contains the model results from co mparing full rule implementation to no rule implementation across stages within the Observati on room. Model 11 assesses the total effect of rule implementation, which was not a statistically significant pred ictor of dust lead loadings. Models 12 through 15 assess the eff ect of full rule implementation, after adjusting for intensitylevel or job-type and their inte raction with full rule implementation. The effect of full rule implementation was not a significant predictor of dust-lead loadings in any of these adjusted models within the Observation room.

Model 15 seems to provide the best fit to the da ta, but the variable a dded for the interaction between rule use and job type is not significan t at all. The residuals plots for Model 15 contained in Section O12 of Appendix O are ap proximately normal except for the residential floor data.7-25 Table 7-12: Post-Job Observation Room, Rule vs. Non-Rule Modeling Results

Floors Sills All Residential Combined All Residential Combined

Model Variables Included in Model p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood p-value -2 Log Likelihood Objective 7: Model 11 COF . 167.4 0.68 201.2 . 78.3 0.33 91.7 Objective X: Model 11 Rule 0.43 0.66 0.43 0.56 Objective 7: Model 12 COF . 154.4 0.49 191.5 . 74.0 0.29 88.0 Objective X: Model 12 Rule 0.23 0.40 0.38 0.50 Objective X: Model 12 Intensity level <.01 0.01 0.32 0.27 Objective 7: Model 13 COF . 149.2 0.50 186.9 . 69.6 0.31 84.4 Objective X: Model 13 Rule 0.23 0.41 0.43 0.54 Objective X: Model 13 Intensity level 0.02 0.03 0.44 0.38 Objective X: Model 13 Rule*Intensity level 0.77 0.79 0.90 0.85 Objective 7: Model 14 COF . 135.1 0.49 174.9 . 58.9 0.34 70.7 Objective X: Model 14 Rule 0.10 0.26 0.23 0.32 Objective X: Model 14 Job type <.01 <.01 0.06 0.07 Objective 7: Model 15 COF

. 122.5 0.55 157.9

. 44.9 0.35 56.0 Rule 0.14 0.35 0.35 0.47 Job type <.01 0.12 0.33 0.32 Rule*Job type 0.96 1.00 0.80 0.937-26 7.5. Exterior Dust Lead Levels

The main comparisons of interest for the exterior experiments are (1) the levels of lead measured on top of the Rule plastic compared to levels me asured under the Rule plastic, and (2) the levels of lead measured on top of the Rule plastic compar ed to levels measured near the Rule plastic. The first comparison focuses on the effectiveness of the Rule plastic at preventing dust from spreading to the ground underneath it, while the second focuses on whether the Rule plastic was adequate to prevent contamination of the gr ound a bit further away from the work area. Appendix N contains the detailed model results fo r the mixed models run on both sets of exterior data. Table 7-13 presents the ratio s of the model-predicted lead le vels measured at each of the three locations: near (N) Rule plas tic vs. on (O) Rule plastic; near (N) Rule plastic vs. Under (U) Rule plastic; and on (O) Rule pl astic vs. Under (U) Rule plastic . Bulk debris samples are included in Table 7-13; however, th e same statistics are presented in Table 7-14 without the bulk debris samples.

Table 7-13. Comparison Exterior Dust Pan Levels within Each Job with Bulk Debris Samples Included (Model Based Estimate Differences and Ratios)
95% Confidence Interval on the Ratio Job Type Location 1 Location 2 Estimate (Log. Scale) StdErr of Estimate (Log. Scale) Ratio
Lower Upper Near On -1.50 1.19 0.2 0.0 2.4 Near Under 6.03 1.19 417.0 39.6 4,386.7 Door Replacement On Under 7.53 1.19 1,869.9 177.7 19,672.5
Near On -1.58 1.19 0.2 0.0 2.2 Near Under 4.54 1.19 94.0 8.9 989.3 Trim/Soffit Replacement On Under 6.13 1.19 457.1 43.5 4,809.4
Near On -3.43 1.68 0.0 0.0 0.9 Near Under 3.16 1.68 23.6 0.8 657.0 Needle Gun On Under 6.59 1.68 724.9 26.0 20,213.6
Near On -4.20 1.68 0.0 0.0 0.4 Near Under 3.53 1.68 34.3 1.2 955.5 Heat gun under 1100 degrees On Under 7.74 1.68 2,291.3 82.2 63,895.1
Near On -4.03 0.84 0.0 0.0 0.1 Near Under 2.65 0.84 14.2 2.7 75.0 Dry Scrape On Under 6.69 0.84 802.0 151.9 4,235.4
Near On -2.93 1.19 0.1 0.0 0.6 Near Under 1.86 1.19 6.5 0.6 67.9 Power Sanding On Under 4.79 1.19 120.8 11.5 1,271.1
Near On -7.19 1.19 0.0 0.0 0.0 Near Under -3.00 1.19 0.1 0.0 0.5 Torching On Under 4.18 1.19 65.7 6.2 690.9
Near On -5.88 1.68 0.0 0.0 0.1 Near Under 1.14 1.68 3.1 0.1 86.8 Heat gun over 1100 degrees On Under 7.02 1.68 1,113.8 39.9 31,058.67-27 The Estimate column within tables 7-13 and 7- 14 provides the predicted difference from the model for Location 1 – Location 2. Thus, the predicted difference of On-Under for the door replacement job (including bulk debris samples) is 6.9 units on the log scale. This translated to a ratio of 1,870:1 with a lower bound on a 95% conf idence interval of 178:1. For the O:U comparison, a ratio greater than one indicates si gnificantly higher levels on the plastic than under, which was expected. For th e N:O comparison, a ratio less than one indicates that the lead levels measured near the Rule plastic are lower than those on the Rule plastic.

Table 7-14. Comparison Exterior Dust Pan Levels within Each Job Excluding Bulk Debris Samples (Model Based Estimate Differences and Ratios)
95% Confidence Interval on the Ratio Job Type Location 1 Location 2 Estimate (Log. Scale) StdErr of Estimate (Log. Scale) Ratio
Lower Upper Near On -0.88 0.92 0.41 0.07 2.58 Near Under 6.03 0.92 416.95 66.92 2598.06 Door Replacement On Under 6.91 0.92 1006.52 161.53 6271.66
Near On 0.32 0.92 1.37 0.22 8.54 Near Under 4.54 0.92 94.03 15.09 585.93 Trim Soffit Replacement On Under 4.23 0.92 68.63 11.01 427.62
Near On -0.19 1.30 0.82 0.06 10.94 Near Under 3.16 1.30 23.56 1.77 313.20 Needle Gun On Under 3.35 1.30 28.62 2.15 380.44
Near On 0.01 1.30 1.01 0.08 13.48 Near Under 2.49 1.30 12.05 0.91 160.24 Heat gun under 1100 degrees On Under 2.48 1.30 11.88 0.89 158.01
Near On -2.25 0.65 0.11 0.03 0.38 Near Under 2.65 0.65 14.21 3.90 51.80 Dry Scrape On Under 4.90 0.65 134.58 36.91 490.70
Near On -1.26 0.92 0.28 0.05 1.77 Near Under 1.86 0.92 6.46 1.04 40.22 Power Sanding On Under 3.12 0.92 22.75 3.65 141.77
Near On -2.55 0.92 0.08 0.01 0.49 Near Under -3.00 0.92 0.05 0.01 0.31 Torching On Under -0.45 0.92 0.64 0.10 3.96
Near On -2.70 1.30 0.07 0.01 0.89 Near Under 1.14 1.30 3.11 0.23 41.37 Heat gun over 1100 degrees On Under 3.84 1.30 46.35 3.49 616.23

The results in Table 7-13, which include the bulk debris samples, report that O:U ratios range from 66:1 to 2,291:1 with the power sanding the to rch burning jobs having the lowest lower confidence interval bounds at 12:1 and 6:1, respec tively. Generally, this indicates that only a small percentage of the lead levels that land ed on top of the Rule plastic were measured underneath the plastic. Note that the 95% confidence bounds ar e very wide because of the relatively small number of samples available fo r each job. The N:O ratios are less than 0.2:1 across all jobs, although the upper confidence bound on the door replacement and trim/soffit replacement jobs exceeded 1:1.7-28 Because the impact of excluding the bulk debris samples was to significantly lower the amounts of lead measured on top of the Rule plastic, th e results in Table 7-14 differ somewhat from those in Table 7-13. The estimated O:U ratio of the torch burning job is below 1:1, as over 99 percent of the on Rule plastic samples were comprised of the bulk debris. The O:U ratios for the other jobs declined, but the lowest predicted ratio ot her than the torch burning was 12:1 for the heat gun job below 1100 degrees. Similarly, the lowe r 95% confidence bounds decreased with the torch burning and heat gun <1100 degrees below 1: 1. The N:O ratios increased with the decline in the lead levels on the Rule plastic causing tw o jobs exceed 1:1 – the trim/soffit replacement and the heat gun under 1100 degrees. The upper confidence bounds on five jobs exceeds 1:1 with the exclusion of the bulk debris samples.

Note that the lead levels measur ed near the Rule plastic are so mewhat impacted by the amount of Rule plastic that could be used for a job given the property cons traints. Both door replacement jobs only had seven or eight feet of space betw een the door being worked on and a fence, which limited the size of the Rule plastic that could be used. Similarly, one of the trim/soffit replacement jobs had about the same distan ce between the porch being worked on and a neighboring fence. For that job especially, whic h occurred on components about 10 feet off the ground, in a real-world situation more rule pl astic would be used than was used in the experiment.

7.6. Post-verification Predicto rs of Clearance Failure

As discussed in Section 5.3, statistical modeli ng was performed to inve stigate causes of postverification lead levels above th e clearance standards on floors and sills. The following predictor variables were explored in these analyses:

• Job Type

• Cleaning Method

• Use of Plastic

• Phase - interaction of clea ning method and plastic use

• Restricted job - groups jobs by restricted and non-restricted status (28 of 60 experiments were done using restricted practices)

• Intensity level – based on initial assignment

• Building type - Housing vs. COF

• Contractor – 3 differe nt contractors perfor med interior work

• Floor type – Vinyl , Tile, and Wood

• Floor condition – rated as Good, Fair, Poor

• Paint-Lead Levels

• Area of Painted Surface Disturbed

• Post-Work Dust Lead Loading in the Work Room – analyzed both arithmetic average and geometric mean

• Post-Cleaning Dust Lead Loading in the Work Room - analyzed both arithmetic average and geometric mean

We planned to conduct the analyses two ways to consider cleaning method differently. The initial analyses used data from all experiments whether they used the Baseline or Rule cleaning method. In these models, Cleaning Method was cons idered as a variable. Once the data set was7-29 reduced to only non-restricted jobs, we also cr eated a subset of experiments in which Rule Cleaning was used to specifically focus on factor s influencing cleaning ve rification success when the proposed rule cleaning methods were used. The first set of analyses provides results on whether the cleaning method was a factor in whether the subs equent cleaning verification
process was a success.

Generalized Estimating Equations were used to account for the potential positive correlation among the dust-lead loading outcomes that are a ssessed from within the same housing unit or COF and within the same room.

7.6.1. Single Covariate Modeling Results

Two sets of models were run to analyze cleaning verification failure on floors. Model Set 1 used proportion of dust wipes > 40 μ g/ft 2 as the outcome measure, while Model Set 2 used probability of a single post-verification dust wipe failing clear ance. Within each model set, three groups of data were analyzed – data fromall jobs, data from non-restricted jobs only, and data from nonrestricted jobs at which the Rule Cleaning me thod was used. Cleaning verification failure on window sills were analyzed using the probability of a post-verification dust wipe sample > 250
μ g/ft 2 . Thus, the nine sets of output are listed below:

• Model Set 1a – proportion of failing floor wipes, data from all jobs

• Model Set 1b - proportion of failing floor wipe s, data from non-restricted jobs only

• Model Set 1c - proportion of failing floor wipes, data from non-restricted jobs that used Rule Cleaning

• Model Set 2a – probability of one or more post-verification floor wipes failing, data from all jobs

• Model Set 2b - probability of one or more post-verification floor wipes failing, data from non-restricted jobs only

• Model Set 2c - probability of one or more post-verification floor wipes failing, data from non-restricted jobs that used Rule Cleaning

• Model Set 3a - probability of a post-verifica tion sill wipe failing, data from all jobs

• Model Set 3b - probability of a post-verificati on sill wipe failing, data from non-restricted jobs only

• Model Set 3c - probability of a post-verificati on sill wipe failing, data from non-restricted jobs that used Rule Cleaning

Model Set 1a – Proportion of Failing Floor Dust Wipes, All Jobs Each variable being considered was included in a single covariate logis tic regression model to assess its predictive ability on proportion of dust wipes failing clearance. From review of Table 7-15 that presents p-values indicating the ove rall statistical significance of each variable considered, the variables that were predictive of proportion of dust wipes failing clearance (at a .05 significance level) were:

• Job type

• Restricted status

• Floor type • Floor condition

• Post-work average and GM floor lead levels

• Post-cleaning average floor l ead levels (although GM floor lead levels had a p-value of .065)7-30 Table 7-16 presents the comparisons between the different responses or levels within each of the variables considered. Within th e Restricted Job model, the estimate of 1.69 for restricted jobs indicates a higher proportion of floor dust wipes failing clearan ce compared to non-restricted jobs. Within Floor Type, Tile and Vinyl have estimates of -2.64 and -2.07, respectively, with pvalues <.001 indicating that proportion of floor dust wipes failing clearance on those surfaces is significantly lower compared to wood floors. Within Floor Condition, floors in Good condition have a parameter estimate of - 2.94 and floors in Fair condition ha ve a parameter estimate of - 1.52 compared to those in Poor condition w ith p-values of .001 and <.0001, respectively.

Model Set 1b – Proportion of Failing Floor Dust Wipes, Non-restricted Jobs Subsetting the data set to only the 32 experiments that used non-restricted jobs eliminates about 47 percent of the data. As a result, the only va riables in Table 7-15 indicated as statistically significant predictors of proportion of floor dus t wipes failing clearance are Post-work Average and GM Floor Lead Levels and Post-cleaning Aver age and GM Floor Lead Levels. Job Type is borderline significant with a p- value of .06. Floor Type and Floor Condition are borderline significant as well with p-values of .09. Note that the Restrict ed Job model was not run as only non-restricted jobs were in cluded in the data set.

Although the overall significance of Job Type, Floor Type, and Floor Condition did not reach the .05 level, Table 7-17 indicates that there were significant differ ences between levels of these variables.

• Within Job Type, the estimate of -2.38 for Cut Outs indicates a significantly lower proportion of floor wipe failu res compared to the Kitche n Gut job (p-value of .0001) while the estimate of 1.85 for Window Repl acement indicates a significantly higher proportion of failures compared to the Kitchen Gut job (p-value of .007).

• Within Building Type, the estimate of -2.40 for COFs and a p-value of .004 indicates a significantly lower proportion of floor wipe failures compared to residential housing.

• Within Floor Type, the estimate of -2.43 and -2.33 for Tile and Vinyl, respectively, with p-values of .001 indicate si gnificantly lower proportion of floor wipe failures compared to Wood floors.

• Within Floor Condition, the estimate of -2.48 fo r Good floors with an associated p-value of .01 indicates a significantly lower proportion of floor dust wipe failures compared to floors in Poor condition.

Model Set 1c – Proportion of Failing Floor Du st Wipes, Non-restricted Jobs with Rule Cleaning Subsetting the data set of non-restricted jobs further to include only those done with Rule Cleaning reduces the data set to only 16 experime nts or about 27 percent of the data used in Model Set 1a. Because of these changes, Job T ype and Cleaning Method were not assessed. Table 7-15 indicates that using this limited data se t no variables met the .05 level of significance. Post-work Average and GM Floor Lead Levels and Post-cleaning Average and GM Floor Lead Levels were all borderlin e significant with p-values ranging from .051 to .061.

Similar to Model Set 1b, although the overall sign ificance of Floor Type and Floor Condition did not reach the .05 level, Table 7- 18 indicates that there were significant differences between levels of these variables.7-31 • Within Floor Type, the estimate of -2.71 and -2.91 for Tile and Vinyl, respectively, with p-values <.01 indicate signi ficantly lower proportion of floor wipe failures compared to Wood floors.

• Within Floor Condition, the estimate of -2.56 fo r Good floors with an associated p-value of .03 indicates a significantly lower proportion of floor dust wipe failures compared to floors in Poor condition.7-32 Table 7-15. Single Covariate Logistic Regression Modeling Estimates for Proportion of Failed Post-verification Floor Wipe Samples

Model Source DF Chi Square Estimate p-value

Job Type 6 66.83 <.0001 Cleaning Method 1 0.14 0.7054
Plastic Use 1 0.17 0.677
Phase 3 1.2 0.7537
Restricted 1 11.74 0.0006 Intensity Level 2 3.68 0.1586 Building type 1 8.27 0.004
Contractor 2 1.77 0.4128 Floor Type 2 20.12 <.0001
Floor Condition 2 26.96 <.0001
Disturbed Area 1 1.91 0.1675
Paint Lead Level 1 2.17 0.1412 Post-work Avg. Floor Lead 1 14.08 0.0002
Post-work GM Floor Lead 1 11.26 0.0008 Post-cleaning Avg. Floor Lead 1 64.19 <.0001 Set 1a: All Jobs

Post-cleaning GM Floor Lead 1 51.82 <.0001 Job Type 3 29.83 <.0001 Cleaning Method 1 0.16 0.6893
Plastic Use 1 0.26 0.6086
Phase 3 0.81 0.8464 Intensity Level 2 0.43 0.8071 Building type 1 1.07 0.3001
Contractor 2 1.44 0.4868 Floor Type 2 16.17 0.0003
Floor Condition 2 7.45 0.0242
Disturbed Area 1 0 0.9837
Paint Lead Level 1 0.08 0.7811 Post-work Avg. Floor Lead 1 2.39 0.1217
Post-work GM Floor Lead 1 2.37 0.1237 Post-cleaning Avg. Floor Lead 1 38.17 <.0001 Set 1b: Restricted Jobs

Post-cleaning GM Floor Lead 1 28.01 <.0001
Plastic Use 1 0 1
Phase 1 0.04 0.849 Intensity Level 2 2.3 0.3161 Building type 1 0.76 0.3845
Contractor 2 1.04 0.5959 Floor Type 2 12.68 0.0018
Floor Condition 2 4.6 0.1001
Disturbed Area 1 0.31 0.5804
Paint Lead Level 1 0.95 0.3302 Post-work Avg. Floor Lead 1 4.71 0.0299
Post-work GM Floor Lead 1 4.39 0.0362 Post-cleaning Avg. Floor Lead 1 10.74 0.001 Set 1c: Restricted Jobs with Rule Cleanings

Post-cleaning GM Floor Lead 1 15.74 <.00017-33 Table 7-16. Single Covariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across All Jobs

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.47 0.56 -2.57 -0.37 -2.61 0.009
I1-Cut Outs -2.38 0.62 -3.60 -1.17 -3.85 0.0001 I2-Replace Windows 1.85 0.68 0.51 3.18 2.7 0.0069

I3-Scrape Surface 1.22 0.61 0.03 2.40 2.01 0.0447

I4-Scrape Door 2.93 0.95 1.06 4.80 3.07 0.0021 I5-Heat gun < 1100 degrees 0.37 0.56 -0.73 1.47 0.65 0.5127 I6-Heat gun > 1100 degree s 1.98 1.02 -0.02 3.97 1.94 0.0519 Job Type

I7-Kitchen Gut 0.00 0.00 0.00 0.00 Intercept -0.41 0.41 -1.21 0.40 -0.99 0.323
0=Baseline Method 0.14 0.36 -0.57 0.85 0.38 0.7054 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept -0.41 0.39 -1.16 0.35 -1.05 0.2942 0 – No plastic 0.14 0.33 -0.51 0.78 0.42 0.677 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept 0.00 0.42 -0.83 0.83 0 1 I – Plastic/Rule Cleaning -0.14 0.35 -0.84 0.55 -0.4 0.6863 II – Plastic/Baseline Cleaning -0.55 0.50 -1.53 0.44 -1.09 0.2752 III – No Plastic/Rule Cleaning -0.55 0.77 -2.05 0.96 -0.71 0.4772 Phase

IV – No Plastic/Baseline Cleaning 0.00 0.00 0.00 0.00 Intercept -1.18 0.40 -1.97 -0.40 -2.97 0.003

1-yes 1.69 0.49 0.73 2.66 3.43 0.0006 Restricted Job

2-no 0.00 0.00 0.00 0.00 Intercept -1.10 0.57 -2.22 0.03 -1.92 0.0554

1-High 0.90 0.70 -0.48 2.28 1.28 0.2013
2-Medium 1.30 0.68 -0.03 2.63 1.92 0.055 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept 0.00 0.32 -0.62 0.62 0 1

COF -2.40 0.83 -4.03 -0.76 -2.88 0.004 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept -0.34 0.62 -1.55 0.88 -0.54 0.5868
C2 -0.34 0.86 -2.03 1.35 -0.39 0.6948
C3 0.45 0.67 -0.86 1.75 0.67 0.5019 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 1.10 0.33 0.45 1.75 3.31 0.0009

Tile -2.64 0.71 -4.02 -1.25 -3.74 0.0002 Vinyl -2.07 0.62 -3.28 -0.86 -3.35 0.0008 Floor Type

Wood 0.00 0.00 0.00 0.00 Intercept 0.87 0.33 0.22 1.51 2.63 0.0085

Fair -1.52 0.46 -2.44 -0.61 -3.28 0.001
Good -2.99 0.60 -4.16 -1.81 -4.98 <.0001 Floor Condition

Poor 0.00 0.00 0.00 0.00 Intercept -0.84 0.48 -1.78 0.11 -1.73 0.0834 Disturbed Area Disturbed Area 0.01 0.01 0.00 0.03 1.38 0.16757-34 Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -0.89 0.36 -1.59 -0.20 -2.52 0.0119 Avg. Paint Lead Level Avg. Paint Lead Level 0. 14 0.10 -0.05 0.33 1.47 0.1412 Intercept -1.49 0.38 -2.23 -0.75 -3.93 <.0001 Post-work Avg.

Floor Lead Post-work Avg. Floor Le ad 0.00 0.00 0.00 0.00 3.75 0.0002 Intercept -1.31 0.36 -2.02 -0.60 -3.62 0.0003 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.00 3.36 0.0008 Intercept -2.56 0.37 -3.28 -1.83 -6.93 <.0001 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Lead 0.03 0.00 0.02 0.03 8.01 <.0001 Intercept -2.54 0.38 -3.29 -1.80 -6.67 <.0001 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.03 0.00 0.02 0.04 7.2 <.0001

Table 7-17. Single Covariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across Non-restricted Jobs

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.47 0.56 -2.57 -0.37 -2.61 0.009
I1-Cut Outs -2.38 0.62 -3.60 -1.17 -3.85 0.0001 I2-Replace Windows 1.85 0.68 0.51 3.18 2.7 0.0069 I5-Heat gun < 1100 degrees 0.37 0.56 -0.73 1.47 0.65 0.5127 Job Type

I7-Kitchen Gut 0.00 0.00 0.00 0.00 Intercept -1.27 0.47 -2.19 -0.35 -2.71 0.0067
0=Baseline Method 0.17 0.44 -0.68 1.03 0.4 0.6893 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept -1.37 0.61 -2.57 -0.16 -2.23 0.026 0 – No plastic 0.35 0.68 -0.99 1.69 0.51 0.6086 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept -0.79 0.47 -1.72 0.14 -1.66 0.0963 I – Plastic/Rule Cleaning -0.31 0.58 -1.44 0.82 -0.54 0.592 II – Plastic/Baseline Cleaning -0.68 0.84 -2.32 0.96 -0.81 0.4174 III – No Plastic/Rule Cleaning -0.48 0.87 -2.19 1.22 -0.56 0.5776 Phase

IV – No Plastic/Baseline Cleaning 0.00 0.00 0.00 0.00 Intercept -1.10 0.57 -2.22 0.03 -1.92 0.0554

1-High -0.37 0.80 -1.93 1.20 -0.46 0.6452
2-Medium 0.00 0.57 -1.12 1.12 0 1 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept -0.99 0.44 -1.86 -0.12 -2.23 0.0257

COF -0.96 0.92 -2.76 0.85 -1.04 0.3001 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept -1.00 0.68 -2.34 0.34 -1.46 0.1444
C2 -0.95 1.06 -3.02 1.13 -0.89 0.3709
C3 0.15 0.81 -1.44 1.75 0.19 0.8527 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 0.38 0.39 -0.38 1.14 0.98 0.328

Tile -2.43 0.75 -3.91 -0.96 -3.23 0.0012 Vinyl -2.33 0.73 -3.75 -0.90 -3.19 0.0014 Floor Type

Wood 0.00 0.00 0.00 0.007-35 Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept 0.00 0.44 -0.87 0.87 0 1

Fair -1.25 0.76 -2.74 0.24 -1.65 0.0997
Good -2.48 0.98 -4.41 -0.56 -2.53 0.0113 Floor Condition

Poor 0.00 0.00 0.00 0.00 Intercept -1.19 0.75 -2.67 0.28 -1.58 0.1131 Disturbed Area Disturbed Area 0.00 0. 02 -0.03 0.03 0.02 0.9837 Intercept -1.32 0.66 -2.61 -0.02 -1.99 0.0463 Avg. Paint Lead Level Avg. Paint Lead Level 0. 03 0.12 -0.20 0.26 0.28 0.7811 Intercept -1.93 0.53 -2.97 -0.89 -3.65 0.0003 Post-work Avg.

Floor Lead Post-work Avg. Floor Lead 0.00 0.00 0.00 0.00 1.55 0.1217 Intercept -1.86 0.51 -2.86 -0.86 -3.65 0.0003 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.00 1.54 0.1237 Intercept -3.19 0.50 -4.16 -2.22 -6.44 <.0001 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Lead 0.03 0.00 0.02 0.04 6.18 <.0001 Intercept -3.03 0.50 -4.01 -2.05 -6.07 <.0001 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.03 0.01 0.02 0.05 5.29 <.0001

Table 7-18.. Single Covariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across Non-restricted Jobs Using Rule Cleaning

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.27 0.59 -2.43 -0.12 -2.16 0.0309 0 – No plastic 0.00 0.95 -1.87 1.87 0 1 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept -1.27 0.74 -2.72 0.18 -1.72 0.0857 I – Plastic/Rule Cleaning 0.17 0.92 -1.62 1.97 0.19 0.849 Phase III – No Plastic/Rule Cleaning 0.00 0.00 0.00 0.00 Intercept -1.10 0.63 -2.32 0.13 -1.76 0.079

1-High -0.85 0.75 -2.32 0.63 -1.13 0.2598
2-Medium 0.00 0.63 -1.23 1.23 0 1 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept -1.10 0.54 -2.17 -0.03 -2.02 0.0436

COF -0.85 0.97 -2.76 1.06 -0.87 0.3845 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept -1.10 0.60 -2.28 0.08 -1.82 0.0682
C2 -0.85 1.01 -2.82 1.13 -0.84 0.4005
C3 0.31 1.13 -1.90 2.52 0.27 0.7834 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 0.51 0.60 -0.66 1.68 0.86 0.3916

Tile -2.71 1.02 -4.71 -0.70 -2.65 0.0081 Vinyl -2.91 0.90 -4.68 -1.14 -3.22 0.0013 Floor Type

Wood 0.00 0.00 0.00 0.00 Intercept 0.00 0.79 -1.55 1.55 0 1

Fair -1.10 0.92 -2.90 0.71 -1.19 0.2326
Good -2.56 1.20 -4.91 -0.22 -2.15 0.0319 Floor Condition

Poor 0.00 0.00 0.00 0.007-36 Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.03 0.81 -2.61 0.55 -1.28 0.2001 Disturbed Area Disturbed Area -0.01 0.02 -0.04 0.02 -0.55 0.5804 Intercept -1.83 0.79 -3.38 -0.27 -2.3 0.0213 Avg. Paint Lead Level Avg. Paint Lead Level 0. 11 0.12 -0.12 0.34 0.97 0.3302 Intercept -1.85 0.58 -2.99 -0.71 -3.18 0.0015 Post-work Avg.

Floor Lead Post-work Avg. Floor Le ad 0.00 0.00 0.00 0.00 2.17 0.0299 Intercept -1.86 0.60 -3.03 -0.69 -3.12 0.0018 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.00 2.09 0.0362 Intercept -3.14 0.63 -4.38 -1.90 -4.97 <.0001 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Lead 0.04 0.01 0.02 0.06 3.28 0.001 Intercept -3.40 0.70 -4.78 -2.03 -4.84 <.0001 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.06 0.01 0.03 0.08 3.97 <.0001

Model Set 2a – Probability of Single Floor Dust Wipe Failing Clearance, All Jobs The analyses presented for Model Sets 1a-1c were repeated using probability of one or more floor dust wipes failing clearance within the multi ple post-verification floor samples collected from within the work room of each experiment. The previous models focused on the proportion of post-verification floor dust-wipe samples from the workroom that failed clearance (which is a slightly different response variable). As before , each variable being considered was included in a single covariate logistic regressi on model to assess its predictive ab ility on probability of a floor dust wipe failing clearance. Analyses by Job Type are not included in th e results because there were too many categories for the amount of data av ailable. From review of Table 7-19 that presents p-values indicating the overall statistica l significance of each variable considered, the variables identified as statistically significant are si milar to those identified in the first model set. Variables that were predictive of probability of a floor dust wipe failing clearance (at a .05 significance level) were:

• Restricted status (p-value=.01)

• Building type (p-value<.01)

• Floor type (p-value<.01)

• Floor condition (p-value<.01)

• Post-work average (p-value=.04) and GM (p-value<.01) floor lead levels

• Post-cleaning average (p-val ue<.01) and GM (p-value< .01) floor lead levels

Within Floor Type, there is evidence that bo th Tile and Vinyl floor s had significantly lower probability of clearance floor wipe failure th an Wood floors with estimates of -3.92 and -2.84, respectively. Similarly, both Good and Fair floor conditions had significantly lower probability of failure than floors in Poor condition. Although the overall significance of Intensity Level did not reach the .05 level, Table 7- 20 indicates that there were significant differences between levels of this variable. The estimate of 1.59 fo r Medium intensity indicat es a significantly higher probability of floor wipe failure compared to the Low intensity jobs (p-value of .0493).

Model Set 2b – Probability of Single Floor Dust Wipe Failing Clearance, Non-restricted Jobs As noted previously, limiting the analysis to on ly the 32 experiments that used non-restricted jobs eliminates about 47 percent of the data. Table 7-19 indicates that the statistically significant7-37 predictors of probability of floor dust wipe clearance failure are Floor Condition, Post-work Average and GM Floor Lead Levels, and Post-c leaning Average and GM Floor Lead Levels. Within Floor Condition the estimate of - 3.78 for Good floors has a significantly lower probability of failure than floors in Poor condi tion; however the comparison of Fair condition floors to Poor condition floors results in a p-value of .087, onl y borderline significant likely because of the limited data set.

Model Set 2c – Probability of Single Floor Dust Wipe Failing Clearance, Non-restricted Jobs with Rule Cleaning Following narrowing of the data set further to in clude only those non-restri cted jobs done with Rule Cleaning, only Post-work Average and GM Floor Lead Levels were le ft as statistically significant predictors of probabi lity of floor wipe failure as Floor Condition and Post-cleaning Average and GM Floor Lead Levels were all borde rline significant with p-values ranging from .064 to .075.7-38 Table 7-19. Single Covariate Logistic Regression Modeling Results for Probability of Failed Post-verification Floor Wipe

Model Source DF Chi Square Estimate p-value

Cleaning Method 1 0.77 0.3812
Plastic Use 1 0.09 0.7693
Phase 3 1.36 0.714
Restricted 1 6.29 0.0121 Intensity Level 2 4.54 0.1033 Building type 1 7.47 0.0063
Contractor 2 5.71 0.0574 Floor Type 2 11.98 0.0025
Floor Condition 2 20.99 <.0001
Disturbed Area 1 1.24 0.2651
Paint Lead Level 1 0.85 0.3576 Post-work Avg. Floor Lead 1 4.25 0.0392
Post-work GM Floor Lead 1 19.19 <.0001 Post-cleaning Avg. Floor Lead 1 7.5 0.0062 Set 2a: All Jobs

Post-cleaning GM Floor Lead 1 8.63 0.0033 Cleaning Method 1 0.21 0.6484
Plastic Use 1 0.14 0.7051
Phase 3 1.59 0.6608 Intensity Level 2 0.1 0.9528 Building type 1 1.36 0.244
Contractor 2 3.12 0.2096
Floor Condition 2 8.84 0.0121
Disturbed Area 1 0.64 0.4241
Paint Lead Level 1 0.18 0.6711 Post-work Avg. Floor Lead 1 10.78 0.001
Post-work GM Floor Lead 1 7.61 0.0058 Post-cleaning Avg. Floor Lead 1 6.41 0.0114 Set 2b: Restricted Jobs

Post-cleaning GM Floor Lead 1 6.21 0.0127
Plastic Use 1 0.21 0.6462
Phase 1 0.55 0.4591 Intensity Level 2 0.37 0.8311 Building type 1 0.91 0.3414
Contractor 2 1.23 0.5403
Floor Condition 2 5.19 0.0748
Disturbed Area 1 0.77 0.3816
Paint Lead Level 1 0.64 0.4245 Post-work Avg. Floor Lead 1 6.45 0.0111
Post-work GM Floor Lead 1 5.37 0.0205 Post-cleaning Avg. Floor Lead 1 3.18 0.0746 Set 2c: Restricted Jobs with Rule Cleanings

Post-cleaning GM Floor Lead 1 3.44 0.06357-39 Table 7-20. Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept 0.27 0.43 -0.58 1.11 0.62 0.5336
0=Baseline Method 0.42 0.49 -0.53 1.38 0.88 0.3812 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept 0.55 0.51 -0.45 1.54 1.08 0.2811 0 – No plastic -0.14 0.48 -1.08 0.80 -0.29 0.7693 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept 1.01 0.75 -0.46 2.48 1.35 0.1784 I – Plastic/Rule Cleaning -0.10 0.54 -1.15 0.96 -0.18 0.8594 II – Plastic/Baseline Cleaning -0.61 0.69 -1.96 0.75 -0.88 0.3806 III – No Plastic/Rule Cleaning -1.15 1.00 -3.10 0.81 -1.15 0.2515 Phase

IV – No Plastic/Baseline Cleaning 0.00 0.00 0.00 0.00 Intercept -0.13 0.46 -1.03 0.78 -0.27 0.7853

1-yes 1.42 0.57 0.31 2.54 2.51 0.0121 Restricted Job

2-no 0.00 0.00 0.00 0.00 Intercept -0.20 0.65 -1.47 1.07 -0.31 0.7572

1-High 0.61 0.67 -0.72 1.93 0.9 0.3691
2-Medium 1.59 0.81 0.00 3.17 1.97 0.0493 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept 0.99 0.43 0.15 1.83 2.3 0.0214

COF -2.60 0.95 -4.46 -0.74 -2.73 0.0063 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept 0.49 0.59 -0.67 1.64 0.82 0.4095
C2 -0.69 0.88 -2.41 1.04 -0.78 0.4349
C3 1.12 0.79 -0.42 2.67 1.43 0.1542 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 3.04 0.92 1.23 4.85 3.3 0.001

Tile -3.92 1.14 -6.15 -1.69 -3.44 0.0006 Vinyl -2.84 1.09 -4.99 -0.70 -2.6 0.0093 Floor Type

Wood 0.00 0.00 0.00 0.00 Intercept 2.53 0.54 1.47 3.58 4.7 <.0001

Fair -1.97 0.74 -3.41 -0.52 -2.67 0.0075
Good -3.69 0.81 -5.28 -2.10 -4.55 <.0001 Floor Condition

Poor 0.00 0.00 0.00 0.00 Intercept 0.01 0.65 -1.27 1.29 0.02 0.9863 Disturbed Area Disturbed Area 0.01 0. 01 -0.01 0.03 1.11 0.2651 Intercept 0.00 0.46 -0.91 0.91 0 0.9977 Avg. Paint Lead Level Avg. Paint Lead Level 0. 13 0.14 -0.14 0.40 0.92 0.3576 Intercept -1.18 0.68 -2.51 0.15 -1.74 0.0823 Post-work Avg.

Floor Lead Post-work Avg. Floor Le ad 0.00 0.00 0.00 0.00 2.06 0.0392 Intercept -1.82 0.57 -2.94 -0.70 -3.18 0.0015 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.00 4.38 <.0001 Intercept -3.70 1.30 -6.26 -1.15 -2.84 0.0045 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Lead 0.09 0.03 0.03 0.16 2.74 0.0062 Intercept -4.24 1.55 -7.28 -1.21 -2.74 0.0061 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.13 0.04 0.04 0.21 2.94 0.00337-40 Table 7-21. Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe across Non-restricted Jobs

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -0.25 0.54 -1.32 0.82 -0.46 0.6443
0=Baseline Method 0.25 0.55 -0.83 1.33 0.46 0.6484 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept -0.25 0.57 -1.37 0.86 -0.44 0.6583 0 – No plastic 0.25 0.66 -1.05 1.55 0.38 0.7051 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept 0.51 0.78 -1.02 2.05 0.65 0.5142 I – Plastic/Rule Cleaning -0.22 0.81 -1.81 1.36 -0.28 0.7823 II – Plastic/Baseline Cleaning -1.02 0.91 -2.80 0.76 -1.12 0.2612 III – No Plastic/Rule Cleaning -1.02 1.03 -3.05 1.00 -0.99 0.3226 Phase

IV – No Plastic/Baseline Cleaning 0.00 0.00 0.00 0.00 Intercept -0.20 0.65 -1.47 1.07 -0.31 0.7572

1-High 0.20 0.91 -1.57 1.98 0.22 0.8246
2-Medium 0.20 0.65 -1.07 1.47 0.31 0.7572 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept 0.17 0.54 -0.89 1.23 0.31 0.757

COF -1.27 1.09 -3.40 0.86 -1.16 0.244 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept -0.15 0.76 -1.65 1.34 -0.2 0.8401
C2 -0.94 1.21 -3.32 1.43 -0.78 0.4364
C3 1.00 0.99 -0.94 2.94 1.01 0.3125 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 2.08 0.85 0.42 3.74 2.45 0.0142

Fair -1.86 1.08 -3.98 0.27 -1.71 0.0866
Good -3.78 1.28 -6.28 -1.28 -2.97 0.003 Floor Condition

Poor 0.00 0.00 0.00 0.00 Intercept -0.51 0.81 -2.10 1.08 -0.63 0.5316 Disturbed Area Disturbed Area 0.01 0. 02 -0.02 0.05 0.8 0.4241 Intercept -0.38 0.65 -1.65 0.89 -0.58 0.5613 Avg. Paint Lead Level Avg. Paint Lead Level 0. 06 0.15 -0.23 0.35 0.42 0.6711 Intercept -1.75 0.66 -3.04 -0.45 -2.65 0.0081 Post-work Avg.

Floor Lead Post-work Avg. Floor Le ad 0.00 0.00 0.00 0.00 3.28 0.001 Intercept -1.90 0.65 -3.17 -0.63 -2.93 0.0034 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.00 2.76 0.0058 Intercept -3.23 1.23 -5.65 -0.82 -2.62 0.0087 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Lead 0.07 0.03 0.02 0.13 2.53 0.0114 Intercept -3.61 1.48 -6.51 -0.72 -2.44 0.0145 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.11 0.04 0.02 0.19 2.49 0.01277-41 Table 7-22. Single Covariate Model Results for Probability of Failed Post-verification Floor Wipe across Non-restricted Jobs Using Rule Cleaning

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept 0.00 0.79 -1.55 1.55 0 1 0 – No plastic -0.51 1.11 -2.69 1.67 -0.46 0.6462 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept -0.51 0.78 -2.05 1.02 -0.65 0.5142 I – Plastic/Rule Cleaning 0.80 1.08 -1.32 2.91 0.74 0.4591 Phase III – No Plastic/Rule Cleaning 0.00 0.00 0.00 0.00 Intercept -0.41 0.69 -1.75 0.94 -0.59 0.5552

1-High 0.41 1.07 -1.69 2.50 0.38 0.704
2-Medium 0.41 0.69 -0.94 1.75 0.59 0.5552 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept 0.00 0.67 -1.31 1.31 0 1

COF -1.10 1.15 -3.36 1.16 -0.95 0.3414 Building Type Residential Housing 0.00 0.00 0.00 0.00 Intercept 0.29 0.90 -1.47 2.05 0.32 0.7486
C2 -1.39 1.30 -3.94 1.17 -1.06 0.2869
C3 -0.29 1.34 -2.92 2.35 -0.21 0.8305 Contractor

C4 0.00 0.00 0.00 0.00 Intercept 1.10 1.15 -1.16 3.36 0.95 0.3414

Fair 0.00 1.63 -3.20 3.20 0 1
Good -2.89 1.51 -5.85 0.07 -1.92 0.0555 Floor Condition

Poor 0.00 0.00 0.00 0.00 Intercept -0.75 0.90 -2.51 1.01 -0.84 0.4025 Disturbed Area Disturbed Area 0.02 0. 02 -0.02 0.06 0.87 0.3816 Intercept -0.88 0.97 -2.77 1.01 -0.91 0.3625 Avg. Paint Lead Level Avg. Paint Lead Level 0. 14 0.17 -0.20 0.48 0.8 0.4245 Intercept -5.55 2.67 -10.79 -0.31 -2.07 0.038 Post-work Avg.

Floor Lead Post-work Avg. Floor Le ad 0.00 0.00 0.00 0.00 2.54 0.0111 Intercept -4.59 1.21 -6.97 -2.22 -3.79 0.0001 Post-work GM

Floor Lead Post-work GM Floor Lead 0.00 0.00 0.00 0.01 2.32 0.0205 Intercept -3.23 2.00 -7.15 0.69 -1.62 0.1063 Post-cleaning Avg. Floor Lead Post-cleaning Avg. Floor Le ad 0.10 0.06 -0.01 0.21 1.78 0.0746 Intercept -3.40 1.98 -7.28 0.49 -1.71 0.0868 Post-cleaning GM Floor Lead Post-cleaning GM Floor Le ad 0.14 0.07 -0.01 0.28 1.86 0.06357-42 Model Set 3a – Proportion of Failing Sill Dust Wipes, All Jobs Because only one post-verification window sill sample was obtained during the study, the analyses of cleaning verification effectiveness on window sills are limited to analyzing the probability of obtaining a pos t-verification wipe >250 μ g/ft 2 . Additionally, because fewer data points were available for analysis, a smaller num ber of the variables unde r consideration were able to be modeled.

When the full set of interior experiments was an alyzed, Table 7-23 reports that only Post-work Average and GM Floor Lead Levels and Post-cl eaning Average and GM Floor Lead Level were found to be significant predictors of probability of window sill wi pe failure based on the single covariate logistic regression modeling. Each of the four variables yielded p-values <.001. Unlike the floor modeling, Plastic Use and Phase were found to be borderline significant with pvalues of .05 and .07, respectively. As seen in Table 7-24, the probabilityof achieving a postverification sill sample above 250 μ g/ft 2 was higher in No Plastic e xperiments as indicated by the parameter estimate of 1.19 compared to the expe riments using Plastic. Within the Phase variable, there is evidence that both the Pl astic/Rule Cleaning and Plastic/Baseline Cleaning options yielded lower probability of clearance si ll wipe failure than the No Plastic/Baseline Cleaning option with estimates of -1.10 and -1.95 , respectively and associated p-values of .059 and .042.

Model Set 3b – Proportion of Failing S ill Dust Wipes, Non-restricted Jobs When limiting the window sill data to non-restricted jobs only, none of the variables considered reach the .05 level of significance for predicting probability of a post-verification sill sample above 250 μ g/ft 2 . On the other hand, Disturbed Area (p-value=.076), Paint Lead Level (pvalue=.086), Post-cleaning averag e floor lead level (p-value=.099) , and Post-cleaning GM floor lead level (p-value=.099) were all borderline significant.

Model Set 3c – Proportion of Failing Sill Dust Wipe s, Non-restricted Jobs with Rule Cleaning After limiting the window sill data further to non-re stricted jobs that used Rule Cleaning, three variables were found to be significant predictors of probability of a post- verification sill sample above 250 μ g/ft 2 - Disturbed Area (p-value=.004), Post-c leaning average floor lead level (pvalue=.030), and Post-cleaning GM fl oor lead level (p-value=.030).7-43 Table 7-23. Single Covariate Logistic Regression Modeling Results for Probability of Failed Window Sill CV

Model Source DF Chi Square Estimate p-value

Cleaning Method 1 1.14 0.2847
Plastic Use 1 3.76 0.0525
Phase 3 7.05 0.0704
Restricted 1 1.05 0.3063 Intensity Level 2 0.19 0.9085
Disturbed Area 1 0.23 0.6333
Paint Lead Level 1 2.24 0.1342
Post-work Sill Lead 1 14.51 0.0001 Set 3a: All Jobs

Post-cleaning Sill Lead 1 12.05 0.0005 Cleaning Method 1 2.01 0.1561
Disturbed Area 1 3.15 0.0761
Paint Lead Level 1 2.94 0.0864
Post-work Sill Lead 1 0.44 0.5057 Set 3b: Restricted Jobs

Post-cleaning Sill Lead 1 2.72 0.0992

Disturbed Area 1 8.15 0.0043
Paint Lead Level 1 0.32 0.5724 Set 3c: Restricted Jobs with Rule Cleanings Post-cleaning Sill Lead 1 4.7 0.03027-44Table 7-24. Single Covariate Model Results for Probability of Failing a Single Postverification Window Sill Wipe Sample

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.61 0.59 -2.76 -0.46 -2.75 0.006
0=Baseline Method 0.22 0.21 -0.19 0.63 1.07 0.2847 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept -2.20 0.79 -3.74 -0.65 -2.79 0.0053 0 – No plastic 1.19 0.61 -0.01 2.38 1.94 0.0525 Plastic Use

1 – Plastic 0.00 0.00 0.00 0.00 Intercept -0.69 0.51 -1.69 0.31 -1.36 0.174 I – Plastic/Rule Cleaning -1.10 0.58 -2.24 0.04 -1.89 0.0587 II – Plastic/Baseline Cleaning -1 .95 0.96 -3.82 -0.07 -2.03 0.0419 III – No Plastic/Rule Cleaning -0.69 0.46 -1.59 0.21 -1.51 0.1304 Phase

IV – No Plastic/Baseline Cleaning 0.00 0.00 0.00 0.00 Intercept -1.95 0.65 -3.21 -0.68 -3.01 0.0026

1-yes 0.85 0.83 -0.78 2.47 1.02 0.3063 Restricted Job

2-no 0.00 0.00 0.00 0.00 Intercept -1.39 0.68 -2.72 -0.06 -2.04 0.0412

1-High 0.00 1.21 -2.37 2.37 0 1
2-Medium -0.35 0.82 -1.95 1.25 -0.43 0.6698 Intensity-level

3-Low 0.00 0.00 0.00 0.00 Intercept -1.16 0.71 -2.56 0.24 -1.63 0.1037 Disturbed Area Disturbed Area -0.01 0.02 -0.04 0.03 -0.48 0.6333 Intercept -2.18 0.60 -3.35 -1.01 -3.66 0.0003 Avg. Paint Lead Level Avg. Paint Lead Level 0. 16 0.11 -0.05 0.37 1.5 0.1342 Intercept -2.01 0.58 -3.15 -0.87 -3.46 0.0005 Post-work Sill Lead Post-work Sill Lead 0.00 0.00 0.00 0.00 3.81 0.0001 Intercept -2.94 0.63 -4.18 -1.70 -4.64 <.0001 Post-cleaning Sill Lead Post-cleaning Sill Lead 0.00 0.00 0.00 0.00 3.47 0.0005

Table 7-25. Single Covariate Model Results for Probability of Failing a Single Postverification Window Sill Wipe Sample across Non-restricted Jobs

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -2.71 1.05 -4.77 -0.65 -2.58 0.0099
0=Baseline Method 1.24 0.88 -0.47 2.96 1.42 0.1561 Cleaning Method

1=Rule Method 0.00 0.00 0.00 0.00 Intercept -0.25 1.01 -2.23 1.74 -0.24 0.8083 Disturbed Area Disturbed Area -0.12 0.07 -0.26 0.01 -1.77 0.0761 Intercept -1.21 0.81 -2.80 0.37 -1.5 0.1334 Avg. Paint Lead Level Avg. Paint Lead Level -0.22 0.13 -0.46 0.03 -1.71 0.0864 Intercept -2.02 0.61 -3.21 -0.83 -3.33 0.0009 Post-work Sill Lead Post-work Sill Lead 0.00 0.00 0.00 0.00 0.67 0.5057 Intercept -2.46 0.71 -3.86 -1.07 -3.46 0.0005 Post-cleaning Sill Lead Post-cleaning Sill Lead 0.00 0.00 0.00 0.00 1.65 0.09927-45Table 7-26. Single Covariate Model Results for Probability of Failing a Single Postverification Window Sill Wipe Sample across Non-restricted Jobs Using Rule Cleaning

Single Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Intercept -1.44 1.21 -3.81 0.93 -1.19 0.2333 Disturbed Area Disturbed Area -0.08 0.03 -0.13 -0.03 -2.85 0.0043 Intercept -2.51 1.11 -4.69 -0.33 -2.26 0.024 Avg. Paint Lead Level Avg. Paint Lead Level -0.05 0.08 -0.21 0.11 -0.56 0.5724 Intercept -1.76 1.19 -4.08 0.56 -1.49 0.1374 Post-cleaning Sill Lead Post-cleaning Sill Lead -0.03 0.01 -0.05 0.00 -2.17 0.0302

7.6.2. Multivariate Modeling Results

Based on the results of the single covariate logi stic regression modeling, multivariate logistic regression models were built to assess the combined effect of the multiple factors influencing the post-verification clearance failures. Multivariate models were built for the full experiment and non-restricted jobs analyses, but not for the non-re stricted jobs with Rule cleaning data because of the limited amount of data. The window sill , as well, did not support development of a multivariate model because of the limited amount of data.

Model Set 1 - Proportion of Failed Post-verification Floor Wipe Samples Table 7-27 presents the multivariate regression m odel develop for Model Set 1a for proportion of failed post-verification floor wipe samples using the full set of experiments. Job Type, Floor Condition, and Post-cleaning Average Floor Lead we re the three variables included in the final model based on this data set. The Type III p-va lues for Floor Condition (p-value=.03) and Postcleaning Average Floor Lead (p-value=.01) indi cate that these two vari ables are significant predictors of proportion of failed post-verificatio n floor wipe samples. Job Type, although many of the jobs differ significantly from the Kitchen Gut job in terms of their predictive ability, did not reach the .05 level of significance overall with in the model. Its inclusion in the model, however, provides a better fit.

Table 7-27. Multivariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across all Experiments

Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Type III p-value Intercept Intercept -2.15 0.51 -3.14 -1.16 -4.24 <.0001
I1-Cut Outs -1.42 0.41 -2.21 -0.62 -3.48 0.0005 I2-Replace Windows 0.39 0.68 -0.93 1.72 0.58 0.5612

I3-Scrape Surface 1.55 0.63 0.31 2.78 2.46 0.014

I4-Scrape Door 0.31 0.91 -1.49 2.10 0.34 0.7373 I5-Heat gun < 1100 degree s 2.53 0.66 1.23 3.83 3.82 0.0001 I6-Heat gun > 1100 degree s 1.70 0.76 0.21 3.18 2.24 0.0252 Job Type

I7-Kitchen Gut 0.00 0.00 0.00 0.00 <.0001

Fair -0.29 0.44 -1.15 0.57 -0.66 0.5086
Good -2.72 0.48 -3.66 -1.78 -5.66 <.0001 Floor Condition

Poor 0.00 0.00 0.00 0.00 <.0001

Post-cleaning

Floor Lead Post-cleaning Avg. Floor Lead 0.02 0.00 0.01 0.03 6.03 <.0001 <.00017-46 Table 7-28 presents the multivariate regression model developed for Model Set 1b for proportion of failed post-verification floor wipe sample s using experiments done with non-restricted practices. Floor Condition dropped out of this model leaving only Job Type and Post-cleaningGM Floor Lead as the two explanatory variables included in the final model. The Type III pvalues for Job Type and Post-cleaning GM Fl oor Lead were both at about .06 indicating borderline statistical significance.

Table 7-28. Multivariate Model Results for Proportion of Failed Post-verification Floor Wipe Samples across Non-restricted Jobs

Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Type III p-value Intercept Intercept -2.48 0.51 -3.47 -1.48 -4.86 <.0001
I1-Cut Outs -1.88 0.60 -3.06 -0.70 -3.11 0.0019 I2-Replace Windows 0.71 0.77 -0.80 2.22 0.92 0.3555 I5-Heat gun < 1100 degrees 0.32 0.42 -0.50 1.14 0.76 0.4463 Job Type

I7-Kitchen Gut 0.00 0.00 0.00 0.00 .0482

Post-cleaning

Floor Lead Post-cleaning GM Floor Lead 0.02 0.01 0.01 0.04 2.61 0.0091 .0091

Model Set 2 – Probability of a Post-verification Floor Wipe Sample Exceeding 40 μg/ft 2 Table 7-29 presents the multivariate regression m odel developed for Model Set 2a for probability of a post-verification floor wipe sample faili ng clearance. Restricted Status, Floor Condition, and Post-work Average Floor Lead were the thr ee variables included in the final model. The Type III p-value for Floor Condition (p-value=.03) indicated it as a significant predictors of probability of a post-verification floor wipe sample failing clearance, although the p-value for Post-work Floor Lead was ju st a bit over the .05 level.

Table 7-29. Multivariate Model Results for Probability of Failed Post-verification Floor Wipe across all Experiments

Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Type III p-value Intercept Intercept 0.90 0.36 0.19 1.61 2.48 0.013
Fair -1.63 0.74 -3.09 -0.17 -2.19 0.0283
Good -2.96 0.71 -4.36 -1.56 -4.15 <.0001 Floor Condition

Poor 0.00 0.00 0.00 0.00 .0001

Post-work Floor Lead Post-work Avg. Floor Lead 0.00 0.00 0.00 0.00 1.94 0.0519 .0519

Table 7-30 presents the multivariate regression m odel developed for Model Set 2a for probability of a post-verification floor wipe sample faili ng clearance using only experiments done with nonrestricted practices. Floor Condition and Post-w ork Average Floor Lead were the two variables included in the final model. The Type III p-va lues for Floor Condition (p-value=.053) and Postwork Floor Lead (p-value=.07) were a bit over the .05 level, but these tw o covariates performed best at explaining the variability of the probability of a post-verification floor wipe sample failing clearance.7-47 Table 7-30. Multivariate Model Results for Probability of Failed Post-verification Floor Wipe across Non-restricted Jobs

Covariate Levels Estimate Standard Error Lower CL Upper CL Z p-value Type III p-value Intercept Intercept 0.68 0.52 -0.34 1.71 1.3 0.1929
Fair -2.19 0.87 -3.89 -0.49 -2.53 0.0116
Good -3.88 0.87 -5.58 -2.17 -4.46 <.0001 Floor Condition

Poor 0.00 0.00 0.00 0.00 <.0001

Post-work Floor Lead Post-work Avg. Floor Lead 0.00 0.00 0.00 0.00 3.38 0.0007 .0007

7.6.3. Summary of Results

Between the single covariate and multivariate mo deling of post-verifica tion floor wipes that exceed 40 μ g/ft 2 , the primary variables found to be best predict these failures were Floor Condition and Post-work and Post-cleaning Lead Leve ls. Job Type and the Restricted Status of jobs also had some predictive ability within some of these models. Although other potential covariates such as Building Type, Floor Type, and Intensity Level were found to have some relationship to post-verificat ion floor wipes exceeding 40 μ g/ft 2 , they were not good predictors when considered together with th e other covariates. Thus, it could be that covariates such as Job Type, Restricted Status, and Intensity Level were ultimately correlated with Post-work and Postcleaning lead levels, which is the best predictor of whether actual lead levels remaining on floors following the cleaning verification process were below the clearance level.

The window sill post-verification clearance failure re sults were not as clear. Similar to the floor models, post-work and post-cleaning sill lead level appeared to be one of the best predictors of the probability of achieving a post- verification window sill value > 250 μ g/ft 2 . A number of other potential covariates such as amount of area disturbed and pa int lead level, indicated some predictive ability but did not reach a level of statistical significance.

Development of multivariate models in order to identify factors that are predictive of postverification clearance failures in the Work Room was challenged by the relatively small number of experiments completed in the RRP Field Stud y (particularly when limiting the analyses to non-restricted work practices). The single cova riate models, however, provide important insight into the factors that merit further investigation (as well as those factors that do not seem to be predictive, such as the use of rule cleaning or plastic).8-1 

### 8. Quality Assurance and Quality Control Results

8.1. Field Audit Results

8.1.1. Site Selection Audit

The Battelle Quality Assurance officer conducted audits of the X-ray fluorescence (XRF) measurement procedure and the methodology for co llection of paint chip samples on September 11, 2006, and October 24, 2006, respectively. Both audits were performed by observing actual work done in the field. The procedures as pe rformed by the study technicians, including Battelle staff and subcontractors, followe d those outlined in the Site Selection QAPP. Only minor suggestions, usually concerning do cumentation, were offered to th e field staff, who immediately implemented the corrective actions as appropriate.

8.1.2. Field Implementation

The Battelle Quality Assurance officer conducted three on-site field audits of renovation work and related environmental sampling. The first au dit was of an exterior paint removal job in Columbus on October 19, 2006. Observed was the c onstruction of the containment structure, use of personal protective equipmen t (PPE), manual paint removal, and sampling by collection of dust and paint chips accumulated on the dust collection trays, followed by subsequent take down of the containment structure. The procedures as performed by the field crew, including Battelle staff and subcontractors, follo wed those outlined in the Full Study QAPP. Personnel for the onsite RRP contractor were reminded that they must use the appropriate PPE, and they complied immediately; however, no major de ficiencies were identified.

An on-site audit of an interior window remova l job (Phase III) in Columbus was performed on October 20, 2006. The Battelle auditor observed, among other tasks, removal and replacement of the window, personal and area air sampling, ru le cleaning using HEPA vacuums followed by wet mopping, and cleaning verification. Specifi cally inspected was the method of dust wipe collection in the work, tool, and observation rooms. It was confirmed th at the study technicians were following ASTM guidelines to properly coll ect wipe samples by using a proper “S” pattern and using as much of the wipe surface area as possible in order to maximize dust collection efficiency. The procedures as performed by the field crew, incl uding Battelle staff and subcontractors, followed those outlin ed in the Full Study QAPP.

A third on-site audit was conducted in Pittsburgh during an interior kitchen gutting (Phase II) on November 6, 2006. Essentially the sa me activities were observed as for the on-site field audit in Columbus on October 19, 2006, for the window re moval job. Specifically inspected was the method of dust wipe collection in the work, tool , and observation rooms. It was confirmed that the study technicians were followi ng ASTM guidelines to properly collect wipe samples. In general, the procedures as performed by th e field crew, includi ng Battelle staff and subcontractors, followed those outlin ed in the Full Study QAPP.8-2 During all three field audits, no major deficienci es were identified; only minor suggestions, usually concerning documentation, were offered to the field staff, who i mmediately implemented corrective actions as appropriate.

8.1.3. EPA Site Visits

EPA staff made four separate vi sits to observe the field work in the study, and provided written and oral reports to the Principa l Investigator on their observations . The Principal Investigator reviewed the observations in these reports and responded prom ptly to any concerns.

8.2. Laboratory Audit Results

The Battelle Quality Assurance officer conduc ted an on-site, day-long inspection of the analytical laboratory, Schneider Laboratories , Incorporated (SLI), in Richmond, VA, on November 13, 2006. Reviewed were: all pertin ent documentation, the lab’s organization and project staff responsibilities, elements of Qu ality Assurance and Qua lity Control, the staff training program, lab safety, document control, as well as a general review of the lab facilities, equipment, and software. In addition, a thor ough review was performe d of the procedures employed to prepare and analyze samples. During the course of the audit, no significant issues were identified that required follow-up by the Batt elle Quality Assurance officer. A formal audit report was prepared and forwarded to Schne ider staff and to project leaders.

8.3. QC Sample Results, Issues, and Resolutions

In general, the results of the analysis of the qua lity control (QC) samples revealed not only that field staff were handling sample s appropriately, but also that the results from the contract analytical laboratory, Schneider Laboratories, Inc., (SLI) were accurate and reproducible.

A number of different external QC samples were prepared. Blank wipes were prepared by field technicians who donned clean gloves, opened the wi pe packet, and placed the unused wipe into a clean, prelabeled sample vial. Blank air ca ssettes were flow checked by passing a nominal amount of air through them (no more than approx imately 6 L), then were capped and labeled. Spiked wipes and air cassettes were prepared by the Department of Environmental Services (DES) at the University of Cincinnati using National Institutes of Standards and Technology (NIST) Standard Reference Materials. The spikes were then inserted into the sample stream as blind performance evaluation samples to verify th e integrity of the analytical results provided by SLI.

Table 8-1 contains the results from analyzi ng the external quality control samples. As seen in Table 8-1, for both the spiked wipes and air cassette blanks, more than 95% of the external QC samples came back within the desire d tolerance. Approxima tely 91% of the blank wipes showed values less than the laboratory’s reporting limit (10 μ g lead), and over 95% of the blank wipes had lead amounts less than 20 μ g.8-3 Table 8-1. Summary of External QC Results

Media QC Type Total Number Acceptable Tolerance Percent Within Tolerance Number Out Of Tolerance Percent Out Of Tolerance
Wipe Spike 117 Bias < 20% 95.7 5 4.3 Wipe Blank 113 Amount < RL † 91.2 10 8.8

Air Spike 46 Bias < 20% 82.6 8 17.4
Air Blank 66 Amount < RL 97.0 2 3.0

† RL = reporting limit; for wipes, 10 μ g lead; for air cassettes, 2 μ g lead

A total of eight of the spiked air cassettes were recovered bey ond the acceptable bias range of 20%; of these, four consecutively numbered samples (as labeled by UC DES) showed lead levels less than the reporting limit of 2 μ g. DES was contacted and the most likely explanation was an error by the preparation laboratory - that blank cassettes were ina dvertently mistaken for spiked cassettes. Excluding these four samples, 90% (42 of 46) of the external QC air cassettes demonstrated acceptable recoveries.

In all cases in which the laboratory measured external QC samples outside the acceptable tolerance ranges, it was requested that samples be reanalyzed in order to confirm the original results. In all cases, th e initial results were confir med upon repeat analysis.

An analysis of laboratory’s inte rnal quality control data is sh own in Table 8-2. These results indicate that the labora tory’s internal QC pro cedures were adequate and, except for the matrix spike, met the study’s QC requirements.

Table 8-2. Schneider Laboratories Quality Control Data Schneider Laboratories QC Type Number of Samples QC Tolerance # Out of Tolerance % Out of Tolerance Reagent Blank 908 < RL a 0 0 Wipe Blank 248 < RL 0 0 Soil Blank 61 < RL 0 0 Paint Blank 72 < RL 0 0 Air Filter Blank 74 < RL 0 0 Paint Control Sample 74 Bias < 20% 0 0 Wipe Control Sample 248 Bias < 20% 0 0 Soil Control Sample 61 Bias < 20% 0 0 Air Filter Control Sample 148 Bias < 20% 0 0 Calibration Verification 904 Bias < 15% 0 0 Duplicates 133 RPD b < 25% 6 4.5% Replicates 454 RPD b < 25% 0 0

a RL = Reporting Limit ; for most analyses, RL = 10 μ g; for air, RL = 2 μ g b Relative percent deviation: RPD = |x 1 -x 2 |/[(x 1 +x 2 )/2]*100%

Duplicate and replicate tolerances were calculated as relative percent deviation (RPD). A replicate analysis is one in which the sample ex tract is analyzed twice and is a measure of the repeatability of a measurement (instrument reproducibility). This is substantially different from a duplicate analysis in which a given sample is homogenized and split into two subsamples. Duplicate samples could only be pr epared for matrices that coul d be subsampled, i.e., only for8-4 paint chip and soil samples. Results from duplicate analyses provide a measure of the reproducibility of the sample handling and ex traction procedures, superimposed onto any instrumental variability. Approximately 13% of the replicate analyses showed lead concentrations below the applicable reporting lim it. In all cases, both sample and replicate values were below the applicable reporting lim it; as such, all were w ithin tolerance.

Matrix spikes are prepared in a manner similar to a duplicate sample, with the exception that one of the subsamples was subsequently spiked with 200 μ g of lead. In most cases, a sample to be prepared for duplicate analysis was also used for the matrix spike; thus a total of three subsamples were collected: one for the primary sample, one for the duplicate, and one for the matrix spike. The spike amount of 200 μ g of lead, however, was generally only a small fraction of the total lead concentr ation in the samples. Thus, small vari ations in the amount of lead in the samples, due primarily to the heterogeneity of pa int and soil samples, resulted in matrix spike recoveries generally outside of th e specified tolerance. For exam ple, one particular paint chip sample extract was found to contain 124,206 μ g of lead and its duplicate sample showed 139,579
μ g lead (RPD = 11.7 %). The matrix spike should have had 124,406 μ g of lead, but instead 130,495 μ g was found, which calculates to a spike recovery of (130,495 – 124,206 μ g)/200 μ g * 100% = 3,144%. Given the relatively low sp ike amount, the ordinary variation between duplicate subsamples resulted in poor spike recoveries.

8.4. Data Audit Process and Results

Battelle’s Quality Assurance offi cer traced approximately 2% of the data (169 field and QC samples) acquired during the field study and subse quent laboratory analyses from the initial acquisition, through data reduction and statistical analysis to ensure the integrity of the results. All calculations performed on these data were verified as correct. Comments that stemmed from the data audit are listed below.

• The need for recording actual batch IDs for environmental samples in the database was identified, in order to allo w linking of QC results to samples in a given batch. If necessary, environmental data associated w ith out-of-tolerance QC samples could be flagged as potentially suspect.

• Field blank and field spike samples were includ ed in the database table containing all the environmental data. It seemed appropriate to move those data to the table containing QA/QC samples.

8.5. Software Reviews

EPA prepared test data sets of interior and exteri or data that were proces sed using the descriptive and statistical summary programs used to summar ize the study data. EP A reviewed the output resulting from the analysis of th e test data sets and determined that the analytical programs performed as expected.9-1 

### 9.0 Conclusions

Conclusions are presented in this section for each of the seven objectives specified in Section 1.3.

9.1 Objective 1: Effect of Intens ity-level on Dust Lead Levels

The results indicated that both initial intensity level and job type impacted post-work, postcleaning, and post-verificati on floor and sill lead levels. Da ta collected by the study indicated significant variability in the lead levels generated across th e different jobs conducted. Using a power planer to remove paint from doors without exhaust controls generated significantly more lead than other interior jobs. On the other ha nd, cutting into walls to allow electrical, plumbing, or other work, generated significantly less lead than other jobs. On the exterior, jobs such as power sanding paint from walls generated large am ounts of lead while dry scraping paint from a wall generated relatively less; however, these findings differe d depending on whether the bulk debris and excessive dust gene rated by a job was included in th e sample results. Removal of paint by torch burning left large amounts of lead in the dust collection tr ays as big chunks of paint were scraped from porch ceilings following heating with the torch; however, only one percent of the lead created by th is work activity was measured by a dust wipe with the rest being work debris picked up by hand.

9.2. Objective 2: Effect of Heavy Duty Polyet hylene Plastic Sheeting on Interior Lead Levels

Figure 6-3 indicates that use of plastic did not consistently resu lt in lower geometric mean work room floor lead levels across j ob types at the post-cl eaning phase. The use of plastic sheeting, however, did lower geometric means for door plani ng and high heat gun jobs as measured at the post-cleaning phase. Similar results are found w ith window sills, as seen in Figure 6-8. Modeling and hypothesis testing of the effect of plastic on lead levels on floors in the Work room was not statistically signi ficant at either the post-clean ing (p-value = .15) or postverification stages (p-value = .10) for housing units when adjusted for job and interactions with job type. For window sills, sta tistical modeling and hypothesis tes ting also showed that the use of plastic was not statistically si gnificant at either the post-cleaning or psot-verification stages for housing units. Using the combined housing unit a nd COF data, similar results were obtained.

Conclusions about protective plas tic are different when consider ing other parts of a house or building. Figure/Table C2.6b and C2.6c reported c onsistently lower geometric mean floor lead levels in the Tool and Observat ion rooms, respectively, with the use of plastic across all three sampling stages – although the diffe rences were relatively small. For example, Tool room floor geometric mean lead levels were 41, 43, and 39 μ g/ft 2 across post-work, post-cleaning, and postverification stages with plas tic compared to 57, 61, and 79 μ g/ft 2 without plastic; Observation room geometric mean levels were 18, 17, and 22 μ g/ft 2 with plastic compared to 30, 38, and 31
μ g/ft 2 without plastic.

When modeling the Tool room floor levels, use of plastic was not signifi cant at the post-work stage (p-value=.20, estimate=-0.32, combined data) ad justing for job type and was not significant9-2 at the post-cleaning stage (p-val ue=.52, combined data) adjusting for job type and interactions with job type. At the post-verification stage, use of plastic was border line significant using the combined data (p-value=.09, adjusted for job type ) and significant (p-value=.03, adjusted for job type and post-work lead levels) using only the housing unit data.

Findings for the Observation room floor levels differ somewhat depending on whether COF data are included in the analysis. For residentia l units only, modeling Observation room levels resulted in significantly lower leve ls when using plastic at all thr ee stages with p-values of .02 at post-work (adjusted for job type) and <.01 at pos t-cleaning and post-verifi cation (adjusted for job type and interactions with job type). When analyzing the combined da ta, the model predicts lower levels using plastic but the difference is borderline sign ificant (p-value =.10, estimate= -0.46) at the post-work stage; si gnificant at the post-cleaning stage (p-value=.01, estimate=-0.71) when adjusted for job type and average post-work work room floor lead; and not significant at post-verification (p-value=.34) using that same model.

The statistical modeling did not fi nd that the use of plastic signif icantly impacted sill lead levels in the Tool room at any of the three sampli ng stages. There is evidence, however, when analyzing the residential data of use of plastic l eading to lower sill lead levels in the Observation room at post-cleaning (p-value = .03) and post-verification (p-value =.02) when adjusting for job type or post-work work room floor lead levels.

9.3. Objective 3: Effect of Cleaning Method on Interior Lead Levels

The data do provide evidence that the proposed rule cleaning method of HEPA vacuuming followed by wet mopping with a cleaning solution yielded lower lead leve ls than the baseline cleaning method. Analyzing differences in clea ning method is focused on the Work room, as little impact of cleaning method was observed in either of the two non-work rooms, which did not undergo any cleaning. Table C2.7a reports GM post-cleaning floor lead across all jobs of 34
μ g/ft 2 for the Rule cleaning and 75 μ g/ft 2 for the baseline cleaning. Figure 6-4, which plots postcleaning GM floor lead levels by job and cleani ng method, reports lower le vels across all job types except door planing. Figure 6-9 shows sim ilar results for window sills. The statistical models verify these trends, repor ting significantly lower post-cleaning floor lead levels after rule cleaning (p-value <.01). No significant difference is found be tween cleaning methods for floors at the post-verification stage. It should be noted that any cleaning verification failures after baseline cleaning led to recleani ngs by rule cleaning method in th is study. Significantly lower work room window sill lead levels are also f ound at the post-cleaning (p-value<.01) stages following rule cleaning. Similar to floor lead levels, however, no significant difference based on cleaning method is found at the post -verification stage. Thus, it a ppears that the differences in lead levels between the two cleaning methods re maining after the initia l cleaning stage were removed by the cleaning verification process co nducted for all experi ments regardless of cleaning method used.

9.4. Objective 4: Migration of Lead Dust from Work Room to Adjacent Rooms

The results indicated that lower levels of lead were measured in the Tool and Observation rooms when plastic was used during the work; however, mixed results were obtained in regards to the9-3 statistical significance of those differences. The cleaning method used in the work room did not have a significant impact on lead levels in the non-study rooms.

9.5. Objective 5: Effectiveness of Plasti c Ground Coverings during Exterior Work

The use of plastic ground coverings captured la rge amounts of lead which were considerably larger than the lead that was below the plastic coverings. When considering all the dust and debris that was captured on top of the Rule plastic, predicted rati os of lead measured on top of the Rule plastic to lead measured under the Rule plastic ranged from 66:1 to 2,291:1 across exterior jobs. The lowest lower 95% confiden ce interval bounds on thos e ratios were 12:1 and 6:1, respectively, for the power sanding the torch bur ning jobs. Some jobs resulted in high lead levels measured in areas just outside the Rule plastic.

9.6. Objective 6: Lead Levels Remaining Fo llowing Cleaning Verification Process

The cleaning verification process as stated in the propos ed rule resulted in decreases in lead levels, but under the conditions of the study was not always accura te in identifying the presence of levels above EPA standards for floors and sills. Factors such as floor condition, contractor performance, job type, and dust particle char acteristics impacted the cleaning verification process in the study. As indica ted in Section 7.6, the logistic regression modeling of postverification floor wipe s that exceed 40 μ g/ft 2 found that the primary variables found to be best predict these failures were Floor Condition and Po st-work and Post-cleani ng Lead Levels. All interior experiments did result in final passed cleaning cloths for all floor zones and for all window sills, but nearly half of the experiments e nded with average Work room floor lead levels above 40 μg/ft 2 on floors. Lead levels for the cases above the floor standard were distributed as follows: 10 between 40 and 69, 6 between 70 and 99, 5 between 100 and 199, 3 between 200 and 499, and 5 greater than 500. The eight highest levels were associated with high heat gun or door planing jobs and floors in poor condition. Of the 29 experiments where final lead levels were above 40 μg/ft 2 on floors, 20 were on floors in poor condition. Excluding the door planing and high heat gun jobs, which had higher post-job lead levels than the others and which appeared to generate fine particle size dust and, in the ca se of the door planing, a white dust that was hard to detect on a white cloth, post-job averages wh en the proposed rule cleaning was used were below clearance levels. For wi ndow sills, approximately 20 per cent of the experiments ended with lead levels above 250 μg/ft 2 . Almost all of the high levels , well above the standard, were associated with door plane and high temperature heat gun jobs. The study did not yield much evidence to evaluate differences in lead levels after the wet a nd dry cleaning verification steps because the dry cleaning verification was only required in three experiments.

Table 9-1 summarizes the arithmetic average post-v erification lead levels and number of samples > 40 μg/ft 2 for rule cleaning phases and post-cleaning lead levels and number of samples > 40 μg/ft 2 for baseline cleaning phases. Note that for Phases II and IV, which included baseline cleaning, any recleanings required were done by rule cleaning methods. Within the no plastic/rule cleaning phase, expe riment 32 achieved an average pos t-verification floor lead level of 2,976 μg/ft 2 . If that experiment were excluded, th e average post-verifica tion floor lead level for phase III would become 123.4 μg/ft 2 . Factors such as contract or, floor condition, and floor type impacted the performance of the cleaning ve rification process. One contractor obtained9-4 higher post-verification lead levels than the others due in part to this contractor being more willing to pass a verification cloth.

Table 9-1. Post-job Floor Lead Levels and Number of Samples Failing Clearance in the Work Room
RULE CLEANING - Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II - Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 55.1 313.5* 172.8 305.9 Average # of Post-job Dust Wipes > 40 μg/ft 2 ** 1.8 1.4 2.5 2.9
* Excluding Experiment 32, which had a very high post-verification floor level, the average lead level is 123.4 μg/ft 2 ** 4 post-job floor dust wipes were obtained from the Work Room, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 4
The door planing and high heat gun jobs resulted in the highest post-job lead levels. Table 9-2 summarizes post-job floor lead levels with these two jobs excluded. Significantly lower levels than those presented in Table 9-1 were achieved with these two jobs ex cluded. Without the two jobs, the post-verification averag es for the rule cleaning phases are below clearance, while the baseline cleaning post-cleaning averages are between 75 and 80 μg/ft 2 . For comparison, Table 9- 3 presents the summary data for the door plan ing and high heat gun j obs only, both of which generated a lot of fine dust. The distribution acr oss the four P/CU phases is different for these two jobs, with the combination of use of plasti c and baseline cleaning associated with lower post-job levels than no plastic and baseline cl eaning. Again, one very high post-verification value (2,976.1) from a Phase III heat gun job imp acts that average signif icantly. With that sample excluded, the post-verif ication average for phase III for door planes and heat guns becomes 346.2 μg/ft 2

.
Table 9-2. Post-job Floor Lead Levels and Number of Samples Failing Clearance (excluding Door Planing and High Heat Gun)
RULE CLEANING - Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II – Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 23.1 34.2 78.7 75.5 Average # of Post-job Dust Wipes > 40 μg/ft 2 * 1.1 0.6 2.1 2.7
* 4 post-job floor dust wipes were obtained from the Work Room, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 49-5 Table 9-3. Post-job Floor Lead Levels and Number of Samples Failing Clearance (Door Planing and High Heat Gun Jobs Only ) in the Work Room
RULE CLEANING – Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II – Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 119.2 872.2 361.0 1,279.4

Average # of Post-job Dust Wipes > 40 μg/ft 2 * 3.2 3.0 3.2 3.4
* 4 post-job dust wipes were obtained from the Work Room, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 4

Similar to the post-job floor le vels and number of samples a bove the clearance standard, Table 9-4 summarizes the average post-job lead levels and number of samples > 250 μg/ft 2 for rule and baseline cleaning phases. Again, post-job refers to the post-veri fication sampling stage for rule cleaning phases (I and III), and to the post-cleaning sampling stage for baseline cleaning phases (II and IV). Overall, the two phases involving rule cleaning resulted in lower post-job window sill lead levels than the two phases involving baseline cleaning.

Table 9-4. Post-job Window Sill Lead Levels and Number of Samples Failing Clearance
RULE CLEANING - Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II - Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 198.7 490.5 761.6 601.4

Average # of Post-job Dust Wipes > 250 μg/ft 2 * 0.1 0.4 0.3 0.5

* 1 post-job dust wipe was obtained from the Work Room window sill, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 1

Due to the configuration of the study rooms a nd above clearance pre-work lead levels, dust collection trays were substituted fo r window sills in the Work Room for half (30) of the interior experiments. Tables 9-5 and 9-6 show the co mparison of post-job lead levels and number of failures by cleaning method for sampled window sills and dust collection trays, respectively. The average lead level across the phases appears to be lower when sills were sampled, however, 16 out of 20 door plane and heat gun experime nts, noted above as having the most dust generated, were completed with dust colle ction trays serving as window sills.9-6 Table 9-5. Post-job Window Sill Lead Levels and Number of Samples Failing Clearance for Sampled Window Sills
RULE CLEANING - Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II - Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 293.4 101.3 190.5 663.2

Average # of Post-job Dust Wipes > 250 μg/ft 2 * 0.1 0.3 0.1 0.6

* 1 post-job dust wipe was obtained from the Work Room window sill, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 1

Table 9-6. Post-job Window Sill Lead Levels and Number of Samples Failing Clearance for Sampled Dust Collection Trays
RULE CLEANING - Post Verification BASELINE CLEANING - Post Cleaning
Phase I - Plastic III - No plastic II - Plastic IV - No plastic Average Lead Level (μg/ft 2 ) 115.7 879.7 1,414.3 530.7

Average # of Post-job Dust Wipes > 250 μg/ft 2 * 0.1 0.6 0.4 0.4

* 1 post-job dust wipe was obtained from the Work Room window sill, thus for each experiment the minimum # of wipes ≥ 40 is 0 and maximum number of wipes ≥ 40 is 1

9.7 Objective 7: Comparing Post-job Levels between the Proposed Rule Package and the Baseline Package

Interior Application of the package of plastic prot ective sheeting, HEPA vacuuming and wet mopping, and cleaning verification practices in EPA’s proposed rule did result in lower lead levels at the end of a job than were achieved using baseline practices (no plastic protective sheeting and cleaning with broom and a shop-vacuum vacuum). Descriptive analyses in Section 6 of this report display the lower geometric mean levels achieved consistent ly across jobs on work room floors and sills (see Figures 9-1 and 9-2). Statis tical modeling presented in Section 7 confirms a statistically significant differen ce between proposed rule and base line practices for work room floors. The associated hypothesis test had a p- value <.01 from models that compared proposed rule practices to baseline practices adjusting fo r job type and the intera ction between job type and use of rule practices for housing units onl y and for all units combined. The difference between the proposed rule practic es and baseline practices was significant (p-value=<.01) for window sills in the Work room, using the combined housing unit and COF data and adjusting for job type and COF. This comparison was also sign ificant using only the residential data (p-value =.02) and adjusting for job type.9-7 5 33 135 24 30 148

24
12 41

66 450

41 58

22

1 10 100 1,000 10,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Floor Lead Levels(μg/ft2)

Proposed Rule Practices Baseline Practices Figure 9-1. Post-Job Work Room Floor Lead Levels, By Job and Rule Use

28 63

21 21 197

17 72

120 568 429 247 707

61 601

1 10 100 1,000

Cut-outs Window Repl. Kitchen Gut Heat Gun <1100 Door Planing Dry Scraping Heat Gun >1100GM Sill Lead Level (μg/ft2)

Proposed Rule Practices Baseline Practices Figure 9-2. Post-Job Work Room Sill Lead Levels, By Job and Rule Use

For the Tool and Observation room floors, diffe rences between the rule package and baseline package were smaller, not consistent across all jo bs, and not statistically significant. Figures 6-6 and 6-7 display smaller and non-uniform differences between use of proposed rule practices and baseline practices in the two non-work rooms for floors, with the rule package being lower than the baseline package in five of seven jobs in e ach room. For some jobs, Tool and Observation room geometric mean floor lead levels at the end of a job were above the EPA standard, even when the rule package was followed. Application of the same statistical model applied to the Work room achieved a non-signi ficant p-value of .17 in the Tool room and .14 in the Observation room for the difference between rule and baseline practices when looking just at housing units. These p-values were even higher with the combined housi ng unit and COF data. Figures 6-12 and 6-13 display post-job geometric mean lead levels on window sills in the Tool and Observation Rooms, respectively. The differe nces between rule and baseline practices were non-uniform. Rule practices had lo wer levels than baseline practi ces in five of the seven job types for the Tool room, and for four of the seven job types in the Observation room. In the Tool room, the difference between proposed rule pr actices and baseline practices was borderline significant for window sills (p-value=.06 combin ed, p-value=.09 housing units only) adjusting for job type and COF. In the Observation room , the difference between proposed rule practices and baseline practices was not significant for window sills (p-value=.32 combined, p-value=.23 housing units only) in models accounting for job type and COF in the combined model and job type in the housing units only model. Tool a nd Observation room geometric mean sill lead levels were below the EPA standard for all job types ex cept door planing.9-8 While the rule package resulted in lower lead leve ls than the baseline pa ckage in the work room, the final lead levels measured under the rule package were not always lower than the EPA standards. Table 9-7 and Table 9-8, for floors and sills respectively, show the arithmetic mean post-job lead levels and average number of sa mples above the EPA standard by job type and study phase. The post-job results displayed includ e post-verification results for phases with rule cleaning and post-cleaning results for phases with baseline cleaning. The arithmetic average post-job floor lead level over all samples collected in the work room for Phase I (plastic and rule cleaning) is less than 40 μ g/ft 2 for all job types except Door Planing and Heat Gun >1100 Degrees. The average number of samples above or equal to 40 μ g/ft 2 was two or less out of four in five of the seven job types. The arithmetic average post-job window sill lead level over all samples collected in the work r oom for Phase I is less than 250 μ g/ft 2 for all job types except Door Planing and Heat Gun >1100 Degrees, with the latter job just beyond that clearance standard at 278 μ g/ft 2 . Consistent with this result is that no post-job sill samples above 250
μ g/ft 2 were collected in Phase I except in the Door Planing and High Heat Gun jobs.9-9 Table 9-7. Post-job* Average Floor Lead Levels and Failed Samples by Job Type

Job Type Phase # of Exp. Post-Job* Average Lead Level (μg/ft 2 ) Post-Job* Average # of Samples > 40 (μg/ft 2 ) I (plastic/rule) 3 5.0 0.0 II (plastic/baseline) 3 9.1 0.0 III (no plastic/rule) 3 9.9 0.0 Cut-outs

IV (no plastic/baseline) 3 37.3 1.3 I (plastic/rule) 2 35.7 2.0 II (plastic/baseline) 2 140.2 4.0 III (no plastic/rule) 2 87.2 3.0 Window Replacement
IV (no plastic/baseline) 2 146.2 4.0 I (plastic/rule) 2 34.2 1.5 II (plastic/baseline) 2 168.3 3.5 III (no plastic/rule) 2 14.4 0.0 Dry Scrape

IV (no plastic/baseline) 2 70.7 3.5 I (plastic/rule) 2 188.9 4.0 II (plastic/baseline) 2 142.3 3.5 III (no plastic/rule) 2 651.8 4.0 Door Plane

IV (no plastic/baseline) 2 771.9 4.0 I (plastic/rule) 1 29.1 2.0 II (plastic/baseline) 1 25.6 1.0 III (no plastic/rule) 1 7.5 0.0 Heat Gun<1100

Degrees IV (no plastic/baseline) 1 44.2 2.0 I (plastic/rule) 2 23.7 1.0 II (plastic/baseline) 2 58.6 2.5 III (no plastic/rule) 2 50.9 0.5 Kitchen Gut

IV (no plastic/baseline) 2 82.2 3.0 I (plastic/rule) 3 72.7 2.7 II (plastic/baseline) 3 506.8 3.0 III (no plastic/rule) 3 1,019.2 2.3 Heat Gun>1100

Degrees IV (no plastic/baseline) 3 1,617.8 3.0 * Post-job refers to the post-verification values for phases that involve the rule cleaning method (I and III) and the post-cleaning values for phases that i nvolve the baseline cleaning method (II and IV).9-10 Table 9-8. Post-job* Windo w Sill Average Lead Levels and Failed Samples by Job Type.

Job Type Phase # of Exp. Post-Job* Average Lead Level (μg/ft 2 ) Post-Job* Average # of Samples > 250 (μg/ft 2 ) I (plastic/rule) 3 30.3 0.0 II (plastic/baseline) 3 58.7 0.0 III (no plastic/rule) 3 70.0 0.0 Cut-outs

IV (no plastic/baseline) 3 212.0 0.3 I (plastic/rule) 2 99.4 0.0 II (plastic/baseline) 2 359.4 0.5 III (no plastic/rule) 2 150.8 0.5 Window Replacement IV (no plastic/baseline) 2 598.2 1.0 I (plastic/rule) 2 31.3 0.0 II (plastic/baseline) 2 246.0 0.5 III (no plastic/rule) 2 24.5 0.0 Dry Scrape

IV (no plastic/baseline) 2 87.9 0.0 I (plastic/rule) 2 865.3 0.5 II (plastic/baseline) 2 202.9 0.5 III (no plastic/rule) 2 1,714.1 0.5 Door Plane

IV (no plastic/baseline) 2 1,038.3 1.0 I (plastic/rule) 1 20.8 0.0 II (plastic/baseline) 1 285.1 1.0 III (no plastic/rule) 1 n/a 1.0 Heat Gun <1100 Degrees IV (no plastic/baseline) 1 246.8 0.0 I (plastic/rule) 2 21.4 0.0 II (plastic/baseline) 2 41.7 0.0 III (no plastic/rule) 2 82.3 0.0 Kitchen Gut

IV (no plastic/baseline) 2 772.3 0.5 I (plastic/rule) 3 277.9 0.3 II (plastic/baseline) 3 3,087.6 0.7 III (no plastic/rule) 3 904.4 0.3 Heat Gun >1100 Degrees IV (no plastic/baseline) 3 1,048.1 0.7 * Post-job refers to the post-verification values for phases that involve the rule cleaning method (I and III) and the post-cleaning values for phases that i nvolve the baseline cleaning method (II and IV).

Exterior The use of plastic as a ground covering during exte rior jobs captured large amounts of leaded dust. Figure 6-14 and Figure 6-15 compare the amount of lead on top of the rule plastic to the amount under the rule plastic, with Figure 6-14 including bulk debris samples and Figure 6-15 excluding the bulk debris samples. For most job types, there is a substa ntial difference between the amount of lead captured by the rule plastic and the amount under the rule plastic. One notable special case is Torching. Without the bul k debris samples, the amount of lead under the plastic exceeds the amount on top. For all 8 job t ypes, the ratio of the am ount of lead on top of the rule plastic to the amount underneath was statistical ly significant when bulk debris samples were included. This changed to 6 out of th e 8 job types when bulk debris samples were excluded.9-11 For some job types, substantial amounts of lead were measured in collection trays just outside the rule plastic. The job types for which substan tial amounts of lead were measured just outside the rule plastic were trim/s offit replacement, power sa nding, and door replacement.