# WORLD TRADE CENTER RESIDENTIAL DUST

CLEANUP PROGRAM

Final Report

December 2005

Prepared by: United States Environmental Protection Agency, Region 2 New York City Response and Recovery Operations 290 Broadway New York, NY 10007-1866

With technical assistance from: Syracuse Research Corporation 301 Plainfield Road, Suite 350 Syracuse, NY 13212

Approved by:

-------------------------Final Report December 2005 Page ii 

# ACKNOWLEDGEMENTS

EPA is grateful for the generous contribution of the residents of Manhatta n who provided access to EPA to sample their dwelling units, and of the build ing owners who provided access to the common space areas used in this Study.

This project was designed and implemented by EPA w ith the support of the Indoor Air Work Group of EPA’s Indoor Air Task Force. The work group organiza tions directly involved in the development of the project included the New York City Department of Environmental Protection; New York City Department of Health and Mental Hygiene; Ne w York City Mayor’s Office of Environmental Coordination; New York City Office of Emergency Ma nagement, New York State Department of Health; Agency for Toxic Substances and Disease Registry; O ccupation Safety and Health Administration, EPA’s Office of Solid Waste and Emergency Response and EPA Region 2.

Note: This interim final report is a working document that will be subject to further Agency and third-party review. EPA intends to excerpt, a nd possibly expand, portions of this report for inclusion in manuscripts that will be submitted to scientific journals for review and consid eration for publishing.Final Report December 2005 Page iii



# CONTENTS

WORLD TRADE CENTER RESIDENTIAL DUST CLEANUP PROGRAM............................................ i

ACKNOWLEDGEMENTS...............................................................................................................

........... ii

CONTENTS.......................................................................................................................

.......................... iii

LIST OF FIGURES ................................................................................................................

..................... vi

LIST OF TABLES.................................................................................................................

...................... ix

GLOSSARY ....................................................................................................................... ........................xii

ACRONYMS.......................................................................................................................

....................... xv

EXCUTIVE SUMMARY............................................................................................................... .............. 1

1.0 INTRODUCTION ..............................................................................................................

................... 4

2.0 WTC DUST CLEANUP PRORAM COMPONENTS ........................................................................ 10

3.0 WTC DUST CLEANUP PROGRAM ................................................................................................. 22 3.1 Data Summary ..............................................................................................................

................... 22 3.1.1 Summary of TEM (PCMe) data ..............................................................................................

. 22 3.1.2 Summary of TEM data.....................................................................................................

........ 24 3.1.3 Summary of Dust Wipe data ...............................................................................................

..... 24 3.2 Efficacy of the Dust Cleanup Program......................................................................................

...... 37 3.2.1 Reductions in the Ra te of PCMe Exceedances ........................................................................ 37 3.2.2 Reduction in Dust Lead Loading..........................................................................................

.... 39 3.2.3 Reduction in Dust Dioxin Loading ........................................................................................

.. 40 3.2.4 Reduction in Dust Antimony Loading and Dust Mercury Loading ......................................... 42 3.3 Summary of the Spatial Analysis of PCMe Exceedances ............................................................... 42 3.4 Comparison of WTC Indoor Dust Program and EPA Background Study....................................... 43

REFERENCES .....................................................................................................................

...................... 51

APPENDIX A. Statistics ........................................................................................................ .................A-1

APPENDIX B. World Trade Center Indoor Dust Cleaning Program Monitoring Contract Scope of Work.................................................................................................... ....................... B-1

APPENDIX C. Evaluation Procedures to Determine the Presence of World Trade Center (WTC)-Related Dust and Debris in Residential Ventilation Systems in Lower Manhattan ........................................................................................... ........................... C-1 C.1.0 Scope ........................................................................................... ........................... C-1 C.2.0 Purpose ......................................................................................... .......................... C-1 C.3.0 Applicable Documents ............................................................................ ............... C-2 C.4.0 Professional, Health and Safety Requirements for Individuals Performing the Evaluations .................................................................................. ............................ C-3Final Report December 2005 Page iv

C.4.1 Evaluation Team ........................................................................ .................... C-3 C.4.2 HVAC/Electrical Professional - Requirements ............................................ . C-3 C.4.3 Sheet Metal Professional - Requirements ................................................ ...... C-5 C.4.4 Environmental Professional - Requirements .............................................. ... C-5 C.5.0 Evaluation Procedures ........................................................................... ................. C-6 C.5.1 General ................................................................................ .......................... C-6 C.5.2 Documentation of Existing Mechanical Conditions ...................................... C- 7 C.5.3 Recommended Supplies and Equipment ..................................................... .. C-7 C.5.4 Typical System Components for Each Equipment Category ........................ C-7 C.5.4.1 Ventilators, Wall Air Conditioning Units and Window Air Conditioning Units in Common Space ..................................... ......... C-8 C.5.4.2 Fan Coil/Heat Pump Units in Common Spaces ................................. C- 8 C.5.4.3 Central Air System .......................................................... .................. C-8 C.5.5 Evaluation Procedures .................................................................. ................. C-9 C.5.5.1 Accessibility ............................................................... ....................... C-9 C.5.5.1.1 Locate System Components Accessible for Visual Inspection .............................................. ............................. C-9 C.5.5.1.2 Methods of Access ............................................. .............. C-10 C.5.5.2 Methods of Evaluation ....................................................... ............. C-10 C.5.5.2.1 Visual Inspection Procedure ................................... .......... C-10 C. 5.5.2.2 Assessment of Conditions ...................................... .......... C-11 C.5.5.2.3 Environmental Sampling Procedure .............................. ... C-12 C.6.0 Post-cleaning Visual Inspection .................................................................. .......... C-13

APPENDIX D. World Trade Center Indoor Dust Cleaning Program Cleaning Contract Scope of Work.................................................................................................... .......................D-1 D.1.0 Introduction .................................................................................... ........................D-1 D.2.0 Roles and Responsibilities ...................................................................... ...............D-2 D.3.0 Qualifications .................................................................................. .......................D-3 D.4.0 Specifications .................................................................................. .......................D-3 D.4.1 Scope of Work A ........................................................................ ...................D-4 D.4.2 Scope of Work B ........................................................................ ...................D-8

APPENDIX E. Analysis of the Asbestos PCMe, Dust Lead Loading and Dust Dioxin Loading Data... E-1 E.1.0 Summary of Data Collected.................................................................................... E-1 E.1.1 Summary of TEM (PCMe) Data ..................................................................... E-1 E.1.2 Summary of TEM Data................................................................................... E-2 E.1.3 Summary of Wipe Data................................................................................... E-2 E.1.3.1 Lead and Dioxin Wipe Data................................................................. E-2 E.1.3.1.1 Lead Wipe Data..................................................................... E-3 E.1.3.1.2 Dioxin Wipe Data.................................................................. E-3 E.1.3.2 Wipe Data for Other Metals................................................................. E-4 E.2.0 Efficacy of the Dust Cleanup Effort........................................................................ E-4 E.2.1 Reductions in the Rate of Asbestos PCMe Exceedances................................ E-4 E.2.2 Reductions in Dust Lead Loadings ................................................................. E-5 E.2.2.1 Reductions in Dust Lead Load ings in Residences withPre-Cleanup

Levels greater than the Health-Based Benchmarks for Lead Loading .................................................................. ........................... E-14 E.2.2.2 Effect of Floor Level / Pre-Cleanup Average Dust Lead Loading on the Reduction in Dust Lead Levels ..................................... ......... E-16 E.2.3 Reductions in Dust Dioxin Loadings ..................................................... ...... E-19 E.3.0 Spatial Pattern of PCMe Exceedances .................................................................. E-26Final Report December 2005 Page v E.3.1 Analytical Approach .................................................................................... E-26 E.3.1.1 Purpose .............................................................................................. E-26 E.3.1.2 Analytical Methods and Spatial Resolution....................................... E-26 E.3.1.3 PCMe Exceedance as a Spatial Poisson Process................................ E-27 E.3.2 Spatial Analysis ............................................................................................ E-31 E.3.2.1 Site-Level (Global) Pattern of PCMe Exceedance............................. E-32 E.3.2.2 SSA-Level Pattern of PCMe Exceedance .......................................... E-38 E.3.2.3 Building-Level Patte rn of PCMe Exceedance ................................... E-45 E.3.2.4 Site-Level Vertical Pattern of PCMe Exceedance ............................. E-56

APPENDIX F. Othe r Fiber Analyses .............................................................................................. .........F-1

APPENDIX G. Calculation of the Standard Deviational Ellipse.............................................................G-1

APPENDIX H. Spatial Autoregression Analysis of the PCMe Data.......................................................H-1 H.1.0 Test Only Data ................................................................................ .....................H-1 H.2.0 Clean and Test Data ........................................................................... ................H-12 H.3.0 Clean and Test Data Subset .................................................................... ............H-22

APPENDIX I. Evaluation of Wipe Sampling Data ................................................................................. .I-1Final Report December 2005 Page vi



# LIST OF FIGURES

1-1. Site location map ........................................................................................................ .......................... 5

1-2. Illustration of the four components that comprised the WTC Dust Cleanup Program......................... 6

2-1. Simulation of WTC plume on the morning of the attack ................................................................... 14

2-2. Simulation of WTC plume in the afternoon the day after the attack .................................................. 15

3-1. The number of samples (i.e., not residences) that exceeded health-based benchmarks, for contaminants that had at least one exceedance of their respective health-based benchmark.............. 41

3-2. Comparison of airborne asbestos concentrations from WTC Dust Cleanup Program and Background Study to concentrations re ported by the Health Effects Institute (HEI, 1991)............... 47

3-3. Comparison of dust lead loading levels from WTC Dust Cleanup Program and Background Study to loadings reported in the HUD Survey of Lead and Allergens in U.S. Housing database (HUD, 2001) ............................................................................................................ ........................... 49

3-4. Comparison of dust dioxin loading levels from WTC Dust Cleanup Program and Background Study to loadings measured in an office building in Binghamton, NY by the New York State Department of Health (NYSDOH, 2002) .................................................................................... ....... 50

E-1. Boxplots for residential dust lead loading ( μ g/ft 2 ), by sample type ................................................. E-7

E-2. Histogram for 10,000 randomly generated sa mples from a standard normal distribution............... E-9

E-3. Boxplot for 10,000 randomly generated sa mples from a standard normal distribution................. E-10

E-4. Boxplot for the reduction in residential average dust lead loading ( μ g/ft 2 ) ................................... E-13

E-5. Scatter plot of log-transformed pre-clea nup residential average dust lead loadings (verti cal axis), by floor.............................................................................................. ...................... E-18

E-6. Boxplots for residential dust dioxin loading ( μ g/ft 2 ), by sample type............................................ E-25

E-7. Boxplot for the reduction in reside ntial average dioxin wipe reductions....................................... E-25

E-8. Histogram for 10,000 randomly generated samples from a Poisson distribution with mean = 2 .. E-29

E-9. Boxplot for 10,000 randomly generated sample s from a Poisson distribution with mean = 2 ...... E-30

E-10. Centrographic statistics for the clean and test data...................................................................... E-33

E-11. Centrographic statistics for the test only data............................................................................... E-34

E-12. Centrographic statistics for the unique test only buildings........................................................... E-36

E-13. Spatial distribution of PCMe exceedance rates for the test only data, by statistical summaryFinal Report December 2005 Page vii

areas ............................................................................................................... .............................. E-39

E-14. Spatial distribution of PCMe exceedance rates for the clean and test data, by statistical summary areas ....................................................................................................... ...................... E-40

E-15a. Significant differences between estimated exceedance rates for test only data.......................... E-46

E-15b. Significant differences betwee n estimated exceedance rates for clean and test data................. E-47

E-16. Nearest neighbor analysis for the PCMe asbestos data................................................................ E-53

E-17. Calculation of the Ripley K function .................................................................................... ....... E-54

E-18. Ripley’s K plot for the test only PCMe exceedance event data ................................................... E-57

E-19. Ripley’s K plot for the clean and test PCMe exceedance event data........................................... E-58

F-1. Scatter plot between PCMe results and AHERA results...................................................................F-5

F-2. Scatter plot between PCMe results and total fibers results ............................................................... F-5

F-3. Scatter plot between AHERA results and total fibers results............................................................F- 6

F-4. Scatter plot between PCMe resu lts and non-asbestos fibers results..................................................F-6

F-5. Scatter plot between AHERA results and total fibers results............................................................F- 7

F-6. Scatter plot between PCMe results and total fibers results ............................................................... F-7

F-7. Scatter plot between PCMe resu lts and non-asbestos fibers results..................................................F-8

H-1. Left: MC scatterplot for the rate of exceedance.......................................................................... .....H-3

H-2. Geographic distribution of relevant eigenvectors......................................................................... ...H-4

H-3. Significant differences between estimated exceedance rates for test only data, with Statistical Su mmary Areas labeled ................................................................................................. ..................H-9

H-4. Left: quantile plot for LN(asbestos + 0.004). Right: quantile plot for LN(rate + 0.0001)...........H-14

H-5. Left: MC scatterplot for the rate of ex ceedance. Right: MC scatte rplot for the average measure of asbestos................................................................................................... .....................H-14

H-6. Geographic distribution of relevant eigenvectors......................................................................... .H-16

H-7. Significant differences between estimated exceedance rates for clean and test data, with Statistical Summary Areas labeled ..................................................................................... ...........H-21

I-1. Zone A: Lead vs. Year Constructed ....................................................................................... ............I-5

I-2. 

### Zone B: Lead vs. Year Constructed ...............................................................................................I-6Final Report December 2005 Page viii I-3. Zone A: Lead Measurements >= 25 ug/ft2 vs. Year Constructed ......................................................I-7

I-4. Zone B: Lead Measurements >= 25 ug/ft2 vs. Year Constructed ......................................................I-8Final Report December 2005 Page ix



# LIST OF TABLES

3-1. Summary of Availabl e Asbestos PC Me Results...............................................................................

.. 23

3-2. Number of Samples of Residential Airborne Asbestos Analyzed by Transmission Electron Microscopy (TEM) and By Asbestos Type Detected ......................................................................... 24

3-3. Statistics for All Lead Wipe Data Combined ( μ g/ft 2 )......................................................................... 25

3-4. Statistics for Lead Wipe Clean and Test Data.................................................................................... 27

3-5. Statistics for Lead Wipe Clean and Test Data with Outliers Removed.............................................. 28

3-6. Statistics for All Dioxin (TEQ) Wipe Data (ng/m 2 ) ........................................................................... 28

3-7. Dust Wipe Sample Data ....................................................................................................

................. 29

3-8. Health-based Benchmarks and Screening Values for Chemicals of Potential Concern (COPCs) in Settled Dust...................................................................................................................

...................... 38

3-9. Comparison of descriptive statistics from the USEPA WTC Indoor Dust Program and the USEPA Upper Manhattan B ackground Study...............................................................................................

... 45

A-1. Statistics for Lead Wipe Clean and Test Data.................................................................................A-1 A-1a. Statistics for Lead Wipe Clean and Test Data – Outliers Removed .......................................A-2

A-2. Statistics on the Reduction in Average Lead Wipe Loadings (Pre- and Post-cleanup) ...................A-3

A-3. Statistics on Reduction in Average Lead Wipe Lo adings (Pre- and Post-cleanup) in Residences With Pre-cleanup Averages Greater Than the HUD Screening Level of 25 μ g/ft 2 ..........................A-4

A-4. Statistics for Pre-cleanup Residential Average Dust Lead Loading by Floor Group. .....................A-5

A-5. Statistics for Dioxin Wipe Clean and Test Data..............................................................................A-6 A-5a. Statistics for Dioxin Wipe Clean and Test Data-One Outlier Removed.................................A-7

E-1. Reduction in Average Lead Wipe Loadings (Pre- and Post-cleanup) ( μ g/ft 2 ) ............................... E-12

E-2. Statistics on Reduction in Average Lead Wipe Loadings (Pre- and Post-cleanup) ( μ g/ft 2 ) in Residences with Pre-cleanup Greater Than the Health-based Benchmark of 25 ug/ft 2 ................. E-15

E-3. Statistics for Average Pre-cleanup Residential Dust Lead Loading by Floor Group ( μ g/ft 2 ) ........ E-17

E-4. Reduction in Dust Lead Loading by Floor Group ( μ g/ft 2 ) ............................................................. E-20

E-5. Percent Reduction in Average Residen tial Dust Lead Loading by Floor Group ( μ g/ft 2 )............... E-21

E-6. Statistics for Dioxin Wipe Clean and Test Data (ng/m 2 ) ............................................................... E-22

E-7. Reduction in Average Dioxin Wipe Loadings (TEQ) a (Pre- and Post-cleanup) (ng/m 2 )............... E-24Final Report December 2005 Page x

E-8. Model Estimation Results for the Log-transformed Test Only PCMe Exceedance Rates ............. E-42

E-9. Model Estimation Results for the Log-transformed Clean and Test PCMe Exceedance Rates..... E-43

E-10. Nearest Neighbor Statistics for the PCMe Exceedances.............................................................. E-52

E-11. Contingency table for test only PCMe exceedances, on a sample-basis ...................................... E-59

E-12. Contingency table for clean and test PCMe exceedances, on a sample-basis ............................. E-60

E-13. Contingency table for test only PCMe exceedances, on a residence-basis .................................. E-61

E-14. Contingency table for clean and test PCMe exceedances, on a residence-basis.......................... E-61

F-1. Pearson Correlations..................................................................................................... .....................F-4

F-2. Spearman Correlations .................................................................................................... ..................F-4

F-3. Phase-Contrast Microscopy E quivalent (PCMe) Measurements ......................................................F-9

H-1. Selected model estimation results........................................................................................ ............H-2

H-2. Selected model estimation results when spatial dependence is included.........................................H-5

H-3a. Test only SSAs pairs having significant pairwise comparisons of exceedance rates...................H-10

H-3b. Distribution of significant difference of means by MCBG, Test Only ........................................H-11

H-4. Selected constant mean mode l estimation results for rates............................................................H-13

H-5. Selected model estimation results for ra tes when spatial dependence is included ........................H-15

H-6a. SSAs pairs having significant pa irwise comparisons of rates......................................................H-19

H-6b. Distribution of significant difference of means by MCBG, Cl ean & Test Data..........................H-20

H-7. Selected constant mean mode l estimation results for rates............................................................H-22

H-8. Selected model estimation results for ra tes when spatial dependence is included ........................H-23

I-1. Building Construc tion Year by Zone ...................................................................................... ...........I-2

I-2. Overview of key results for these contaminan ts in relation to the health based cleaning Benchmarks established for the program ................................................................................ ...........I-3

I-3. Lead Results by EPIC Zone ............................................................................................... ................I-4

I-4. Lead Results by Distance from WTC Zone .................................................................................. .....I-4

I-5. The Means (ug/ft2) of Lead Sample Results by Floor and EPIC Zone ..............................................I-9Final Report December 2005 Page xi I-6. The Means (ug/ft2) of Lead Sample Results by Floor and Building Age ..........................................I-9

I-7. 95 % UCL on Building-Specific Results in Relation to EPIC Zones ..............................................I-11Final Report December 2005 Page xii



# GLOSSARY

Average residence dust lead (dioxin) loading: The arithmetic mean of the results from multiple (typically three) dust wipe samples that were collect ed from each residence before (i.e., pre-cleanup dust lead/dioxin loading) and after (i.e., post-cleanup dust lead/dioxin loading) cleaning.

Centrographic statistics: The two dimensional counterparts to the tr aditional univariate statistics that are used to describe the location (e.g., mean, median) and dispersion (e.g., standard deviation) of a single variable. Centrographic statistics are used to descr ibe the geographic center of a collection of objects, their distribution in space, and the orientation of the di stribution; e.g., buildings with PCMe exceedances.

Clean and test buildings: Buildings that contain one or more residences or common areas that were cleaned and then tested for airborne asbestos; a subset of these residences were also tested for metals and dust dioxin loading (mass/unit area). Many clean and test buildings also contain residences that were tested but not cleaned, at the request of the residents.

Clean and test data: Consists of the results of samples collected from residences and common areas that were cleaned and then tested for airborne asbestos; a se lected subset of the residences was also tested for dust lead loading and dust dioxin loading.

Common areas: Areas of residential buildings that are acc essible to all building occupants; e.g., hallways, laundry rooms, stairwells.

Count data: A type of categorical data that represent the number of times something occurs within an interval of time, space or volume; e.g., the number of PCMe exceedance within the potentially effected area surrounding the WTC site.

CSR: Complete spatial randomness

Dust dioxin loading: Nanograms of dioxin per square meter of sampled surface (ng/m 2 ).

Dust dioxin loading exceedance: Dust dioxin loadings that excee d the health-based benchmark of 2 ng/m 2

. Dust lead loading: Micrograms of lead per square foot of sampled surface (μg/ft 2 ).

Dust lead loading exceedance: Dust lead loadings that exceed the HUD screening level of 25 μg/ft 2

. Dust wipe samples: Samples of residential dust that were collected from residences and common areas. Samples were typically collected from three different surfaces within an apartment (e.g., walls, floors, counter tops).

Nearest neighbor distance (NNd): Used in the point pattern analysis to assess the spatial distribution of PCMe exceedance. The NNd is the average distan ce between a PCMe exceedance and the closest other PCMe exceedance. The NNd is compared against the distance that is expected if the PCMe exceedances are randomly distributed in space. Values less than the expected distance indicate spatial clustering, values greater than the expected distance indicate dispersion.

PCMe: Asbestos phase contrast microscopy equivalent (PCMe) concentrations measured by TEM. Phase Contrast Microscopy equivalence (PCMe) is a process to identify asbestos fibers by TEM analysis that would also be visible by PCM.Final Report December 2005 Page xiii

PCMe exceedance: PCMe results that exceeded the health-based benchmark of 0.0009 fibers per cubic centimeter (f/cc) of air.

Point pattern analysis: A statistical analysis in which the emphasis is on the location of events (e.g., PCMe exceedance), rather than the magnitude of the da ta (e.g., PCMe concentration). The focus of point pattern analysis is often to test the null hypoth esis of complete spatial randomness (CSR) (i.e., the distribution of events follow a homogeneous spatial Poisson process). The nonparametric hypothesis of spatial randomness is also tested in this report, using computer simulation methods.

Poisson distribution (Poisson model): Used to describe the occurrence of rare events. The Poisson distribution is used throughout this report to describ e the distribution of PCMe exceedances. The Poisson distribution is typically used to m odel the occurrence of an event during a fixed period of time or within a fixed region of space.

Positive spatial autocorrelation: The tendency for samples collected near each other to have similar values.

Ripley’s K function: Used in the point pattern analysis to assess the spatial distribution of PCMe exceedance. Ripley’s K function counts the number of ot her events that occur within a certain distance of an event. The count is repeated for each event. Ripley’s K function equals the sum of the counts. Typically, Ripley’s K function is calculated for several distance intervals and the values are plotted versus the distance intervals. Values greater than zero indi cate spatial clustering, values less than zero indicate dispersion.

Spatial autoregression: A type of statistical regression analysis that considers, explicitly, the spatial autocorrelation exhibited by the data, if any.

Spatial clustering: The tendency for PCMe exceedance to be spaced closer together than is likely if the exceedances were randomly distributed in space (i.e., randomly distributed among the sampled buildings).

Spatial dispersion: The tendency for PCMe exceedance to be spaced further apart on average than is likely if the exceedances were randomly distribut ed in space (i.e., randomly distributed among the sampled buildings). A square grid is an example of a spatial dispersion.

Spatial resolution: Refers to the coarseness of geographic aggreg ation. In this report, PCMe data are analyzed at two levels of spatial resolution: at the building level and at the statistical summary area (SSA) level.

Spatial scale: Refers to the geographic extent over which an analysis is performed. In this report, the spatial scale is Lower Manhattan, south of Canal Street.

TEM: Transmission electron microscopy; an analytical method to identify and count the number of asbestos fibers present in a sample.

Test only buildings: Buildings that contain one or more residences that were tested for one or more of the following: airborne asbestos, dust lead loading, dust dioxin loading, but were not cleaned, at the request of the residents. Most test only buildings also contain residences or common areas that were cleaned and tested.

Test only data: Results of samples collected from residences that were tested for one or more of the following: airborne asbestos, dust lead loading, dust dioxin loading, but were not cleaned, at the request of the residents.Final Report December 2005 Page xiv Unique test only buildings: Buildings that contain one or more residences that were tested for one or more of the following: airborne asbestos, dust lead loading, dust dioxin loading, but were not cleaned, at the request of the residents. Unique test only buildings do not contain residences or common areas that were cleaned and tested.Final Report December 2005 Page xv



# ACRONYMS

PCMe phase contrast microscopy equivalent

TEM transmission electron microscopy

CV coefficient of variation TEQ toxicity equivalent quotient

SSAs statistical summary areas

iid independent and identically distributed

NNd nearest neighbor distance

NNI nearest neighbor index

N total number of events

A area of the site

CSR complete spatial randomness

CI confidence interval

S-W statistic Shapiro-Wilk statistic

f/cc fibers/cubic centimeter

μ g/ft 2 micrograms per square foot ng/m 2 nanograms per square meterFinal Report December 2005 Page 1 

# EXECUTIVE SUMMARY

Introduction and Background

This report presents and summarizes the results of EPA ’s World Trade Center Dust Cleanup and Testing. Under the authority of the Robert T. Stafford Disaster Relief and Emergency Assistance Act (Stafford Act), EPA formed an Indoor Air Task Force in February 2002. In April 2002, the Mayor of the City of New York requested that EPA serve as the lead agency for addressing potential effects of WTC dust on residences in lower Manhattan. EPA subsequen tly developed and implemented a comprehensive program, with broad interagency input at federal, st ate and local levels, to ensure that lower Manhattan residents were protected from potential expos ures to WTC-related dust and debris.

The WTC dust cleanup and testing program allowed r esidents living south of Canal Street in lower Manhattan to have their homes professi onally cleaned and tested or just tested free of charge. In addition to offering this service to residents, EPA conducte d three supporting projects, also funded by FEMA under the Stafford Act. The projects were:

• A Contaminants of Potential Concern (COPC) Report established health-based benchmarks for contaminants in support of cleanup efforts.

• A Confirmation Cleaning Building Study evaluated the effectiveness of various cleaning techniques on WTC-related dust.

• A Background Study provided data on contaminants in indoor air and settled dust in residences North of 78th Street.

Overview of WTC Dust Cleanup and Testing Program

All residents of lower Manhattan living below Canal St reet were given a choice of services. Residents could chose to have their residence professionally cl eaned, followed by confirmatory testing, or they could choose to just have their homes tested. Cer tified professional contractors cleaned and tested the homes, under the direction of EPA. Owners and managers of residential buildings and coop boards could also have their building's common areas cleaned and HVAC system evaluated and cleaned, if necessary. The cleaning and monitoring contractors cleaned a nd tested common areas such as the building lobby, hallways, stairways and elevator interiors. The contractors evaluated other common areas, including laundry rooms, utility rooms, compactor rooms, and elevator shafts and cleaned as needed.Final Report December 2005 Page 2 Residences were cleaned using standard asbestos cleanup methods – using HEPA-filtered vacuums and wet wiping all horizontal hard surfaces (i.e. floors, ceilings, ledges, trims, furnishings, appliances, equipment, etc.). Vertical and soft surfaces were HEPA vacuumed two times. EPA did not require workers to wear personal protection equipment dur ing these routine cleanups because OSHA determined that such equipment was not necessary. As an adde d precaution, contractors isolated the areas containing visible dust and wore personal protection equipment.

Depending upon the size of the residence, from three to five air samples were co llected and analyzed for asbestos using transmission electron microscopy (T EM) and phase contrast microscopy (PCM). In a subset of the residences, pre- and post-cleanup dust wi pe samples were collected (e.g., from floors, walls, and furniture) and analyzed for dioxin, mercury, lead , and 21 other metals. The results of this sampling, along with interpretation through a comparison with health-based benchmarks, were shared with occupants of the residences. Residences that did not meet the health-based benchmark of 0.0009 fibers per cubic centimeter for asbestos in one or more sa mples were encouraged to have their residences recleaned and tested until they met the benchmark. In a few cases, residents chose not to have their

residences recleaned. There were a number of outcom es that resulted in inconclusive results. Filter

overload was the most common. Filter overload occurs when too many dust particles are captured on the

filter. The filter becomes obscured so technicians examining it under a microscope cannot separate out individual fibers. This causes an inconclusive result, which is discarded. Other causes of inconclusive results are blown or damaged filters. Residents with more than one inconclusive result were encouraged to have their apartments re-cleaned and re-tested. A total of 28,702 valid sample results was analyzed, 22,497 from residential units and 6,205 from common areas within residential buildings (e.g., hallways, laundry rooms).

Results

Asbestos The number of samples that exceeded the health-based benchmarks for airborne asbestos was very small – about 0.4% of the asbestos samples taken. In those cases where the benchmark was exceeded in both residences and in common spaces, the cleanup program was successful in ach ieving the health-based benchmark for asbestos after the first cleaning approximately 99% of the time.

Wipe Samples Contractors collected wipe samples from 263 apartments in 156 buildings. Approximately 14% of the pre-cleanup samples exceeded the U.S. Housing a nd Urban Development ( HUD) screening level of

25 μ g/ft 2 , while only about 3% of the post-cleanup sampl es exceeded the screening level. This showedFinal Report December 2005 Page 3 that the cleanup methods were effective in reducing l ead. The percent of apar tments that exceeded the lead health-based benchmark was greater than the percentages of apartments that had exceedances for other metals, mercury and dioxin. The level w as consistent, however, with data from the HUD on housing stock in the Northeast United States. This f actor makes it difficult to distinguish between lead

from World Trade Center dust and other sources, especially in older buildings.

There were very few exceedances of the health-b ased screening values measured for any of the other 22 metals. The screening value of 627 μ g/m 2 for antimony was exceeded in 2 pre-cleanup samples (0.1% of all samples); the maximum measured value was 1,180 μ g/m 2 . The screening value of 157 μ g/m 2 for mercury was exceeded in 5 pre-clea nup samples (0.4% of all samples).

Reductions in dust dioxin loadings were modest due to the low pre-cleanup levels. Only 8 of the 1,535 (approximately 0.5%) of the combined samples (i.e., test only and clean and test ) exceeded the

health-based benchmark for residen tial dust dioxin loading of 2 ng/ m 2

.

An analysis of the location of asbestos exceedances does not demonstrate a special pattern of exceedances relative to WTC proximity. Apparent groups of asbest os exceedances could be explained by the location of the sampled buildings and the variability in the number of samples that were collected from each building.Final Report December 2005 Page 4



# 1.0 INTRODUCTION

The U.S. Environmental Protection Agency (EPA) suppor ted federal, state, and New York City efforts to recover from the federally declared disaster resu lting from the September 11, 2001 attack on the World Trade Center (WTC). These actions were taken under the authority of the Robert T. Stafford Disaster Relief and Emergency Assistance Act (Stafford Act) and in accordance with the applicable procedures and policies of the National Oil and Hazardous Substance Po llution Contingency Plan, 40 C.F.R. Part 300 (the NCP) (EPA, 1990). This report provides a summa ry of the actions taken by EPA to cleanup the

indoor environment in Lower Manhattan.

The cleanup of the WTC site and surrounding ambien t (i.e., outdoor) environment proceeded through the winter of 2001-2002. Early investigations indicated th at an indeterminate number of residences located in the vicinity of the WTC complex were contaminated with dust and debris following the WTC attack; and there was growing concern in the re-occupied r esidential communities of Lower Manhattan regarding potential long-term health problems associat ed with residual WTC-related indoor dust (Figure 1-1) . EPA formed an Indoor Air Task Force in February, 2002 and by request of the Mayor of the City of New York, EPA was designated the lead governmental agency fo r addressing the indoor environment in April, 2002. EPA’s focus in this regard was to address indoor air concerns through an indoor dust cleanup and air sampling program for residential spaces in Lower Manhattan. This comprehensive program was implemented to ensure that Lower Manhattan resi dents were protected from potential exposures to

harmful dust and debris residuals. EPA developed this program with br oad interagency input at federal,

state and local levels. EPA utilized all the t ools available, including appropriate aspects of the NCP, to achieve this goal as expeditiously as possible.

EPA implemented three programs related to indoor ai r in Lower Manhattan residences. These programs were funded by FEMA under the Stafford Act, specifically Sections 403 (Essential Assistance) and 407 (Debris Removal) (Figure 1-2) . First, EPA directed a Confirmation Cleaning Building Study (EPA, 2003a) by collecting samples in a building that had on ly minimal cleaning after the attack, employing and evaluating various cleaning techniques on WTC-related dust. Second, EPA directed a Background Study (EPA, 2003b) to provide monitoring data on indoor air contaminants in residences north of 78th Street, which were minimally affected by the collapse of the WTC, so that such data could be compared with

data obtained in residences in Lower Manhattan. Th ird, EPA, along with the New York City Department of Environmental Protection (NYCDEP), provided fo r the monitoring and cleaning of Lower Manhattan residences through the Indoor Air Residential Assistance Program-WTC Dust Cleanup.Final Report December 2005 Page 5Final Report December 2005 Page 6

Under this program residents were given the option of requesting either cleaning followed by sampling, or sampling alone. Pursuant to intera gency agreements under the Stafford Act, FEMA provided funds to the city, which used its emergency contracting authority to enter into contracts for a hotline contractor to register residents for the indoor dust cleaning program, and with certified asbestos cleanup contractors to professionally clean apartments, as well as with Project Monitors to oversee the cleaning contractors and conduct air monitoring. The actual cleaning and m onitoring was carried out by NYCDEP contractors, under the direction and oversight of EPA, in coordination with the city. The samples collected by the NYCDEP contractors were analyzed by EPA contractors.

EPA evaluated the information and data that were gathered in the Confirmation Study (EPA, 2003a) and the Background Study (EPA, 2003b), as well as the results of a peer review of a draft technical document

on World Trade Center Contaminants of Potential Concern (EPA, 2003c), as the residential cleaning and monitoring activities proceeded. This document provid ed the health-based benchmarks for indoor air and

settled dust. The data from the Confirmation and Background studies, and the COPC benchmarks, were used to determine whether any program adjustments or modifications were needed. This approach of conducting studies and cleanups in parallel was nece ssary because of the scientific complexities of dealing with indoor environments and the need for time ly response to the potential threat to public health Confirmation Cleaning Study Upper Manhattan Background Study

WTC Indoor Dust Program Contaminants of Potential Concern Report

WTC Dust Cleanup Program Confirmation Cleaning Study Upper Manhattan Background Study

WTC Indoor Dust Program Contaminants of Potential Concern Report

WTC Dust Cleanup Program

Figure 1-2 . Illustration of the four components that comprised the WTC Dust Cleanup Program.Final Report December 2005 Page 7 and welfare. For the indoor environment in NYC, EPA had limited indoor sampling protocols, health benchmarks, background data for urban areas (esp ecially Manhattan), correlations of dust to air exposures, etc. This degree of scientific uncertain ty made defining a cleanup program very difficult. Cleanup methods that are effective for asbestos and fibrous materials cleanup were employed; these methods were expected to be effective for any ot her WTC particulate matter that might pose health concerns. Sampling was performed for airborne asbest os in every residence EPA was asked to clean or test, and samples for metals and dioxin were collect ed from a subset of residences. This provided additional information on the contaminants of potential concern. If the results from the studies indicated the need to modify the cleanup approach, EPA did so. Again, the cleanup efforts and study efforts were performed concurrently by EPA to complete the cl eanup of residential spaces as soon as possible. EPA believed this was appropriate given the urgency and scope of the actions needed to help restore Lower Manhattan to pre-9/ll conditions. In developing th e Indoor Air Residential Assistance Program-WTC Dust Cleanup, EPA relied on the existing data, th e intergovernmental collaboration process, and discussions with scientific, technical, and medical pr ofessionals and concerned community members.

The Indoor Air Residential Assistance Program- WTC Dust Cleanup responded to a disaster involving a release that was most certainly not typical, not only be cause of the terrorist act that led to the release but also because of the unique challe nges posed by the presence and potential presence of WTC dust in thousands of Lower Manhattan apartments. When the WTC collapse occurred, there was a release of asbestos, a hazardous substance, to the environment. The debris and pulverized dust from the collapse affected many structures in Lower Manhattan to vary ing degrees. This release was documented by bulk dust sampling done by EPA; approximately 35% of bulk dust samples outdoors contained greater than 1% asbestos, which is a regulatory definition of asbestos -containing material (ACM) under federal, state and

local statutes.

Limited investigation of residential indoor environm ents was conducted in the weeks and months after the WTC collapsed. Two notable studies were the Ag ency for Toxic Substances and Disease Registry (ATSDR)/ New York City Department of Health and Mental Hygiene (NYCDOHMH) Study, “Final Report of the Public Health Investigation to A ssess Potential Exposures to Airborne and Settled Surface

Dust in Residential Areas of Lower Manhattan” (ATSDR/NYCDOHMH, 2002) and the Ground Zero Taskforce Report, “Characterization of Particulate Found In Apartments After Destruction of the World Trade Center” (Ground Zero Taskforce, 2001). The ATSDR/NYCDOHMH Study (2002) was larger (30 study buildings and 4 background [i.e., comparis on] buildings) and included both re-occupied and unoccupied apartments. Sampling took place from N ovember 4 through December 11, 2001. The report was released in September 2002; after the EPA indoor air cleanup program was underway. This studyFinal Report December 2005 Page 8 showed that total fiber counts of air samples take n in Lower Manhattan were similar to the comparison areas above 59th Street sampled during this investigation. The six Lower Manhattan areas that had elevated total fiber counts were re-examined by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The TEM and SEM resu lts indicated that neither asbestos nor synthetic vitreous fibers (SVF) contributed to the elevated fi ber counts. However, low levels of asbestos were found in some settled surface dust, primarily below Chambers Street. Many of the Lower Manhattan locations sampled had been previously cleaned prior to this investigation. No asbestos was detected in the comparison indoor dust samples taken north of 59th Street. The Ground Zero Taskforce Report (2001) was limited to three apartments that were still undisturbed when sampled on 9/18/01. Samples demonstrated significantly elevated levels of asbestos in the settled dust.

The ACM was deposited in a very variable manner, that is, samples of bulk dust/debris, taken virtually adjacent to each other, had differing levels of asbestos . EPA believes that the dust materials that reached

the interiors of structures were likewise variable in its deposition. In addition, some of the material may have contained asbestos at levels of concern for long -term risk, even though it may not have exceeded 1% ACM. Given that there are over 20,000 residentia l units in Lower Manhattan, specifically identifying which of them were affected by amounts of dust pote ntially causing long-term health effects would have been time- and resource-intensive. In addition, making risk or exposure assessments in indoor environments is very complex. The age, building materials, house keeping practices, past and current usage of the space may all impact exposure. The variability of the WTC debris/dust material and the manner in which it affected building interior s adds another layer of complication.

In deciding upon a cleanup program for Lower Manha ttan residences EPA considered the following:

• The complexity of sampling dust material for qua ntities of hazardous substances and the lack of scientific consensus on how to do so;

• The absence of standards that have broad scientif ic support which relate airborne exposure routes to dust containing hazardous substances; and

• The absence of health- or risk -based standards for dust.

In addition, EPA had to consider how to gauge the residual impacts of cleanups already undertaken by

residents who returned to their homes. All of the above have substantial uncertainty or controversy surrounding them.

Federal, state, and city health and medical professi onals supported a program that addressed the need for cleanup assurances without the time, expense, and un certainties associated with a location-specificFinal Report December 2005 Page 9 sampling and risk assessment approach. EPA also c onsulted with environmental health and science experts in the academic and research sector on the cl eanup approach described above. Though there were many questions and a desire for more data collecti on, they generally acknowledged that a broad-based cleanup program was an appropriate response.

For these reasons, and in consultation with FEMA, New York City, and New York State, EPA determined that rather than taking a risk-based a pproach to each residential unit or building, cleanup would be performed in any Lower Manhattan apartment based on residents' request.

The attack on and collapse of the WTC was a truly unprecedented event, far different from every other federally declared disaster. As such, EPA believe it warranted a unique response that supplemented FEMA relocation and cleanup assistance programs, was bi ased towards immediate action to protect the health and safety of the residents of Lower Manha ttan, was consistent with federal disaster plan guidelines, and adhered to the applicable a nd appropriate provisions of the NCP.

Therefore, in response to the WTC collapse, EPA set in motion a program that moved its components (which might otherwise be implemented sequentia lly) along parallel tracks for the purpose of initiating residential cleanups as soon as possible. These co mponents included: development of health-based

benchmarks for indoor air and settle d dust (EPA, 2003c); a site-speci fic characterization of background (EPA, 2003b); and, a study to assess the effectiveness of cleaning methods (EPA, 2003a). Each of these components informed the most important part of the program - the timely cleanup of residential dwellings. In developing the WTC Dust Cl eanup Program EPA relied on existing data, the intergovernmental collaboration process, and discu ssions with scientific, technical and medical professionals and concerned community members. The concurrent program components, which are schematically illustrated in Figure 1-2 were designed in such a way that adjustments and modifications to the Indoor Dust Program could be implemented based on information, as it became available, from these other initiatives. The material that follows provi des a summary of efforts to develop health-based benchmarks, characterize background, and assess the effi cacy of cleaning methods, (detailed reports on these efforts have already been issued and are available on the EPA website http://www.epa.gov/wtc/ ); and a detailed analysis of the results of the WTC Dust Cleanup Program.Final Report December 2005 Page 10



# 2.0 WTC DUST CLEANUP PROGRAM COMPONENTS

As noted in the Introduction of this report, a num ber of initiatives were undert aken concurrently to expedite the cleanup of residences in Lowe r Manhattan; they are described below.

Contaminants of Potential Concern Report (COPC)

The first component was an evaluation, conducted by a multi-agency task force headed by EPA, to evaluate indoor environments for the presence of cont aminants that might pose l ong-term health risks to local residents (EPA, 2003c). As part of this ev aluation, a task force sub-committee was established (COPC Committee) to identify contaminants of potentia l concern (COPC) that are likely associated with

the WTC disaster and establish health-based benchmarks for those contaminants in support of planned residential cleanup efforts in Lower Manhattan. A sy stematic risk-based approach was used to select COPC. The goal was to identify those contaminants mo st likely to be present within indoor environments at levels of health concern. The following chem icals were identified as COPC: Dioxin, PAHs, Lead, Asbestos, Fibrous Glass, and Crystalline Silica.

Health-based benchmarks were deve loped to be protective of long-te rm habitability of residential dwellings. A hierarchical approach was employed for developing benchmark values, including relevant and appropriate environmental standa rds/regulations (HUD standard for lead in indoor dust); calculation of health-based benchmarks employing conventiona l environmental risk assessment paradigms and guidance (for asbestos, dioxin and PAHs); and adaptati on of occupational standards with additional safety factors (fibrous glass and crystalline silica).

Confirmation Cleaning Study

The second component was an effort to confirm th at the cleaning methods recommended to the public were effective in reducing contaminants from dust ge nerated from the WTC collapse and recovery efforts (EPA, 2003a). EPA, with support from FEMA and New York City, studied cleaning methods in a heavily contaminated building on Liberty Street, just south of the WTC site. The cleaning confirmation study examined various cleaning and vacuuming meth ods that were likely to be used by or were recommended to residents and professional cleaning comp anies to clean dust and debris from residential living areas in the aftermath of the attacks.

EPA contractors cleaned homes and a few commercial spaces in the building that contained a complex mixture of contaminants, including cons truction debris and fire-related compounds.Final Report December 2005 Page 11 Eleven cleaning methods were selected and assigned to the residential units within the building according to the levels of observed dust. Each cleaning met hod was tested in units with significant and minimal levels of dust. The following cleaning methods were used:

• Residential quality upright vacuums and shop vacuums

• Residential quality upright vacuums with the a ddition of an air filtration device (AFD)

• HEPA-filtered upright and shop vacuums

• HEPA-filtered upright and shop vacuums with the addition of an AFD

• Industrial quality HEPA-filtered vacuums

• Industrial quality HEPA-filtered vacuum s with the addition of an AFD

• Wet wiping of all horizontal and/or vertical surfaces with soap and water

• Carpet cleaning

• Standard cleaning procedures used by professiona l duct cleaning companies for the cleaning of air conditioning (A/C) systems, ducts and related equipment

• Use of water only for wet wipe of horizontal and/or vertical surfaces

• Scope A cleaning procedures developed by EPA and New York City for the cleaning of residential units in Lower Manhattan (EPA, 2003a)

Results were compared to health-based benchmarks for COPCs identified above to determine if the cleaning was successful. A summary of the significant conclusions of the study is provided below. These

include observations about the extent of WTC-re lated contamination within the building and the effectiveness of the cleaning methods tested in the study.

• The observation of WTC dust is a reasonable indicator that WTC contaminants may be present and that the amount of WTC dust correlat es with the level of contamination.

• Concentrations of some contaminants in th e WTC dust were elevated above health-based benchmarks.

• The use of a standard cleaning method of vacuum ing and wet wiping significantly reduced levels of WTC-related contamination with each cleaning event and was successful in reducing concentrations to levels belo w health based benchmarks.

• In some cases, multiple cleaning sessions (2 or 3) were necessary to reduce contamination. The methods were highly effective in reducing all COPC below health-based benchmarks.

• Asbestos in air is a reasonable indicator of wh ether additional cleaning is needed. Based on the compounds and testing methods chosen, the data suggests that using asbestos air samples as an indicator for additional cleaning is the most sensitive of the testing methods, as it resulted in the largest percentage of additional cleanings.

• Standard HVAC cleaning methods reduced the c oncentrations of WTC contaminants in HVAC systems.Final Report December 2005 Page 12 WTC USEPA Background Study

The third component of the USEPA’s WTC response was a background study (EPA, 2003b) to determine concentrations of building-related materials and combustion byproducts in residential dwelling. A background study was initiated because limited info rmation was available in the literature for the analytes that were identified in WTC-related dust. Characterization and evaluation of the degree of impact to the indoor environments required knowledge regarding pre- attack concentrations of the potential indoor contaminants. Although the COPC identified in WTC-related dust have been used extensively in building construction or studied as environmental contaminants, there is limited information available that reports background concen trations of these compounds in urban residential

indoor environments.

The objective of the background study was to determin e the indoor concentrations of building-related

materials and materials found in fire-related com bustion byproducts, including asbestos, synthetic vitreous fiber (SVF), fibrous glass, crystalline s ilica (as alpha-quartz), calcite, gypsum, dioxin, and polycyclic aromatic hydrocarbons (PAHs). The resu lts from the background study were to provide a basis upon which to make risk management decisions if the health-based benchmarks could not be realistically achieved. The estimated background values that were derived from the background study

were not used because the health-based be nchmarks were consistently achieved.

Volunteers residing in Upper Manhattan locations that were minimally impacted by the WTC collapse were recruited for the study. The outer boundary of the affected area was determined from a preliminary dispersal and dilution model using meteorological data on September 11, 2001 (EPA, 2001) and shortly thereafter. Computer modeling results showed that Upper Manhattan locations north of 78 th Street, approximately eight kilometers or five miles from the WTC site, would be minimally affected by WTC fallout dust. The computer model predicted that the concentration of fallout particulate matter for areas

north of 78 th Street would be from 1,000 – 10,000 times less than that at the WTC site ( Figures 2-1 and

2-2 ). Air and settled dust samples were collected from 25 residential units and 9 common areas within 14 buildings. The sampled buildings were approximately 8 – 19 kilometers (5 – 12 miles) northeast of the WTC site. When possible, samples were colle cted from two residential units and from one common area, such as the lobby, hallway, stairwell, or shar ed laundry facility in each building. The comparison of the results from the background study to the data fro m the WTC site did not include formal tests to determine if the concentrations were statistically si gnificant due to the disparity in the number of samples that were collected from each building, and the large number of samples that were collected in each study with results below detection limits (i.e., high rate of non-detects ).Final Report December 2005 Page 13

Evaluation of the data collected from the WTC U SEPA Background Study was able to: provide estimates of baseline levels or background concentrations of compounds that were identified as COPCs related to the World Trade Center collapse; show that the estimat ed background concentrations were consistent with other background studies and historical data, when co mparison data were available; and, provide a source of data to help address data gaps in the scientific literature on background c oncentrations of buildingrelated materials.

Overview of the WTC Dust Cleanup Program

Registration for the WTC Dust Cleanup Program was open from June 05 through December 28, 2002. EPA conducted a public outreach initiative to inform r esidents about the Program. Components of this initiative included: distribution of pamphlets at r esidential buildings, subway stations and local businesses; meetings with community groups; operati on of a registration hotline; establishment of a website for on-line registration; maili ngs; and, newspape r advertisements.

The WTC Dust Cleanup Program was open to all residents living below Canal Street. Upon signing up, residents had a choice of receiving a cleaning with confir matory testing, or, in the event the residence was already professionally cleaned and/or not significantl y impacted by the WTC collapse, testing only. A brief description of the protocols that were followed is provided below. The protocols are provided in Appendices B-D

.

Cleanup work was conducted by contractors and work ers (the Cleanup Contractor) certified by New York State and New York City. Separate, third-party cont ractors, also licensed by New York State, oversaw the cleanup work and conducted all testing (the Projec t Monitor). Further direct oversight was provided by EPA personnel. All personnel involved in this program carried appropriate photo identification.Final Report December 2005 Page 14

Figure 2-1. Simulation of WTC plume on the morning of the att ack. National Oceanic and Atmospheric Administration meteorological stations are indicated as: Ne wark (EWR), Teterboro (TEB), LaGuardia Airport (LGA), Central Park (NYC) and John F. Kennedy Airport (JFK). Numbers in red are the hourly average concentratio n of particulate matter ≤ 2.5 μ m in size in μ g/m 3

. Plume direction is towards the south-southeast and dilution of the plume varies from less than 500 to approximately 1,000,000.Final Report December 2005 Page 15

Figure 2-2. Simulation of WTC plume in the afternoon the day af ter the attack. National Oceanic and Atmospheric Administration meteorological sta tions are indicated as: Newark (EWR), Teterb oro (TEB), LaGuardia Airport (LGA), Central Park (NYC) and John F. Kennedy Airport (J FK). Numbers in red are the hourly aver age concentration of particulate matter ≤ 2.5 μ m in size in μ g/m 3 . Plume direction is primarily towards the northeast.Final Report December 2005 Page 16 The Project Monitor contacted residents requesti ng assistance to confirm and schedule cleanup and testing. The Project Monitor had access to a transla tion service to assist with the process for those whose primary language is not E nglish. There were three phases to the ac tual work: 1) Pre-cleaning inspection; 2) Cleaning; and 3) Testing.

Owners and managers of residential buildings and co op boards could request to have their building's common areas cleaned and HVAC system evaluated a nd cleaned, if necessary. After receiving the request, common areas such as the building lobby, hallw ays, stairways and elevator interiors would be cleaned. Other common areas, including laundry rooms, utility rooms, compactor rooms, and elevator

shafts were evaluated and cleaned as needed.

During a pre-cleaning inspection for an individual residence, the Project Monitor met with the occupant(s) to assess conditions, discuss procedures a nd testing options, determine any special concerns or needs, and answer questions. The Project M onitor obtained written access and authorization, and scheduled the cleaning work. Residents were given information about preparing for cleaning including the handling of valuable personal items, the presence of pets, etc. The Project Monitor discussed the level of cleanup required (see below) and reside nt's options for post-cleanup testing.

Damage to a building as a result of the WTC collap se may have resulted in the growth of mold in apartments. As part of the Cleaning Program, th e Project Monitor contacted the NYCDOHMH if mold was observed in a residence or residential building. The NYCDOHMH then contacted the building owner to provide recommendations on how to address the affect ed areas. Further information regarding mold

can be found in the NYCDOHMH fact sheet entitled "Facts About Mold" (NYCDOHMH, 1994). ( http://www.ci.nyc.ny.us/html/doh/html/epi/epimold.html )

If the Project Monitor identified the presence of pot ential friable asbestos type insulation in areas requested to be cleaned, it was reported to NYCDEP for evaluation and appropriate follow up action. Likewise, if the Project Monitor identified potential peeling, flaking or chalking paint, the NYCDOHMH was notified for evaluation and appropriate follow up action.

Cleaning Scope

Following the assessment, the Project Monitor determ ined the appropriate cleanup approach. Most residences were addressed under EPA's "Scope A" cl eanup. Residences (typically unoccupied) where there was still significant amounts of WTC dust and/or debris were dealt with under EPA's "Scope B" cleanup which adds precautions to require further worker protection and techniques to minimizeFinal Report December 2005 Page 17 spreading of possible contamination while re moving the dust/debris. Areas where localized

accumulations of WTC dust were found in a residen ce which otherwise had minimal dust (i.e., between windows, inside air conditioners), were addressed under a Scope B cleanup, wherein the areas containing

the dust were isolated from the remainder of the r esidence prior to removal. Residents (or their representatives) may have been present (but did not have to be) during Scope A cleanings. Residents were not allowed to be present during Scope B cl eanings, unless the Scope B cleanup applied to only parts of the residence. In most cases, cleaning operations took no more than two days.

In a Scope A cleanup, all horizontal hard surfaces, in cluding floors, ceilings, ledges, trims, furnishings, appliances, equipment, etc., were HEPA vacuumed and wet wiped. Vertical and soft surfaces were HEPA vacuumed two times. Dry sweeping was pr ohibited. A detailed description of the minimum cleaning requirements is listed below (field experien ce may have resulted in the modification of these procedures):

• Terraces, balconies, exterior window sills, wi ndow wells and window guards that are accessible from the interior of the dwelling, will be cleaned.

• Interior windows, screens, window sills and window guards will be cleaned.

• Vacuuming will begin with the ceiling, conti nue down the walls and include the floor.

• Impermeable walls and floors will be wet wiped using disposable wipes, after consultation with and approval by owner. Wet wiping will not be conducted if it is determined that it would cause damage to the surface.

• Curtains, fabric window treatments, upholstery and other materials that cannot be cleaned by wet wiping shall be HEPA vacuumed two times. Fabr ic covered furniture will be vacuumed using a stiff brush attachment.

• Carpets will be cleaned with a water extraction cl eaner equipped with a motorized agitator brush. Water extraction cleaning will not be conducted if it is determined that it would cause damage to the carpet.

• Paperwork and books w ill be HEPA vacuumed.

• Electrical outlets will be vacuumed.

• Window air conditioners will be vacuumed th en removed from their position and vacuumed internally. Filters will be HEPA vacuumed and reinstalled. Ai r conditioners will be reinstalled after cleaning.

• Intake/discharge registers of HVAC systems (if present) will be removed/cleaned. The first foot of duct work will also be vacuumed, then the regist er will be reinstalled a nd covered with plastic.Final Report December 2005 Page 18

• Appliances such as refrigerators and stoves will be cleaned and moved. The floor footprint of the appliances will be cleaned and the appliance w ill be reinstalled in its original position.

• Refrigerator cooling tubes will be brushed and vacuumed.

• Stove exhaust fan filters will be cleaned.

• The first foot of all exhaust duct work (includi ng stove, dryer and bathroom vents) will be vacuumed. Exhaust fans will be vacuumed and wiped.

• Closet floors will be vacuumed.

• Solid objects (electrical equipment, exercise equi pment, etc.) will be wet wiped, moved to allow cleaning of the underlying surface and will be returned to their original location.

• Dishwasher toe plates will be removed and the floor beneath the appliance will be cleaned.

• Baseboard heaters will be cleaned. Protective covers on finned radiant heaters and baseboard heaters will be removed to expose heat elements. Fins are to be brushed and vacuumed to remove dust.

• All cleaning equipment will be vacuumed and/ or wet wiped for use on the residence.

In a Scope B cleanup, the areas containing dust and/or debris were sealed off and exhaust fans equipped with HEPA filters were used to lower the air pressure within the sealed off area so that no dust escaped. Dust and debris were bagged and sealed for removal. Workers wore protective gear and residents were not allowed within the sealed off area. Scope B work could be applied to an entire residence or to portions of a residence where remnan ts of bulk dust were discovered.

Testing Protocol

Sampling was conducted no later than 24 hours after clean-up was completed. For test only apartments sampling was conducted in the absence of a cleaning ev ent. Air samples, that were analyzed for both asbestos (separate counts for long, i.e., >5 um length, versus total fibers) and non-asbestos fibers, were obtained from all residences. Generally, one sample was obtained from each room in an apartment or from contiguous areas in common spaces. A subset of clean and test (approximately 200) and “test only” (approximately 50) apartments received wipe sa mpling for 23 metals plus dioxin. A description of the testing (sampling and analysis) protocol is summarized below. When cleaning was completed, the Project Monitor di d a visual inspection. If dust was observed, the

residence was re-cleaned as necessar y. Once the visual inspection found the residence to be dust free, final air sampling was authorized.Final Report December 2005 Page 19 This final testing phase took approximately eight hour s and was completed within one day (24 hours) of the completion of cleanup work. Residents had a choi ce between two forms of airborne asbestos testing, modified-aggressive and aggressive. Modified-aggressi ve testing simulates the normal air movement in a room where a fan or air conditioner is running. In aggressive testing, a one-horsepower leaf blower was

used to blast air into all corners of the residence be fore testing was begun. From that point on, the two tests are identical. Any air conditio ners were turned on and 20-inch fa ns (one per 10,000 cubic feet of room space) were run at low speeds for the duration of the test. Depending on the number of rooms in a residence, from three to five air samplers were locat ed in the residence and run for approximately eight hours. These samplers draw in a measured volume of room air and collect dust from the air on a filter. The collected dust is then examined in a laboratory for asbestos using transmission electron microscopy (TEM). Additional analysis by phase contrast micr oscopy (PCM) was conducted to obtain a count of

non-asbestos fibrous material.

Residents may have occupied their home during modi fied-aggressive testing but were cautioned to be prepared for noise and disruption. Occupants with know n allergies, asthma or other health concerns were advised to consider contacting their health care pr ovider to determine whether it was advisable to be present while cleaning and/or testing was underway. R esidents were required to relocate during, and for 48 hours after, aggressive testing. The Red Cross ag reed to provide financial assistance to defray costs for relocation expenses. Information from the Re d Cross was provided as needed. Occupants were required to remove or secure objects, including pi ctures and artwork that could be blown over or otherwise damaged.

The Project Monitor conducted a post-cleaning inspec tion of the apartment with the resident at the completion of modified-aggressive sampling, or upon re-entry after aggressive sampling. During this inspection the project monitor and the resident dete rmined whether cleaning/monitoring activities were completed and whether any property damage or loss ha d occurred. The resident then signed a Project Completion Form.

At a limited number of “clean and test” residences (approximately 200), the Project Monitor conducted pre- and post-cleanup wipe sampling for dioxi n, mercury and metals. Approximately 50 test only residences received a single round of wipe sampling fo r dioxin, mercury and metals. Generally, wipe samples were obtained from three discrete surfaces within an apartment. Results of this sampling, along with interpretation through a comparison with health -based benchmarks, were shared with occupants of

the residences.Final Report December 2005 Page 20

EPA notified residents and owners of the results of the post-cleanup airborne asbestos testing. Notification letters included an interpretation of th e TEM results for long (>5 um) asbestos fibers through comparison with EPA’s cleanup criteria (see below). Additional information was provided on the results of total asbestos fibers (>0.5 um) by TEM and tota l non-asbestos fibers by PCM analysis. Residencespecific test results were not made public. Residences were re-cleaned and re-t ested if any post-cleanup samples registered levels of asbestos in excess of EPA's cleanup criteria. For test only apartments, residents were eligible for cleaning if any airborne asbestos samples exceeded EPA’s cleanup criteria. A technical discussion of asbestos air sampling and metals/dioxin wipe sampling can be accessed at www.epa.gov/wtc (See EPA’s WTC Residential Confirmation Cleaning Study; EPA, 2003a).

Interpretation of test results

Clearance criteria were developed for evaluating airborne asbestos sampling results. A health-based

value of 0.0009 f/cc was established based on TEM an alysis of phase contrast microscopy equivalent (PCMe) fibers. The TEM analysis protocol was adapted from the Asbestos Hazard Emergency Response act (AHERA) and modified to count only asbestos fibers greater than 5 microns in length, with an aspect ratio greater than 5:1, and no minimum width require ment. The basis for the clearance criteria of 0.0009 f/cc (PCMe) is detailed in the COPC Report.

As previously stated, residences were re-cleaned and re-tested if the post-cleanup testing found levels of asbestos in excess of the cleanup criteria of 0.0009 f/cc (PCMe). There were a number of outcomes that

resulted in inconclusive results. Filter overload (defined as dust deposition obscuring more than 10–25% of the filter), which compromises the ability to accura tely count asbestos fibers, was the most common

cause of inconclusive results. Other causes of inconclu sive results included blown and/or damaged filters. Apartments with overloaded and/or blown/damaged filte rs were re-cleaned and re-tested. In addition, sampling results for total asbestos fibers greater than 0.5 microns in length (as per AHERA counting rules), and total non-asbestos fibers by PCM analysis were evaluated on a case-by-case basis.

Wipe Sampling

The wipe samples taken as part of the Dust Cl eanup Program supplemented the findings of the WTC Confirmation Cleaning Study by providing additional information obtained under actual field conditions. While this single sampling event, conducted approxi mately 18 months after the release, could not reconstruct the collective exposure incurred since 9/ 11, it could serve to put into context the existingFinal Report December 2005 Page 21 contaminant levels in settled dust by comparing the results of the sampling to health-based benchmarks (see EPA, 2003a, Appendix Z , Table Z.3 ) developed for the WTC Clean-up Program.

Wipe samples were collected and analyzed in accordance with the pr ocedures and methods presented in the Quality Assurance Project Plan (QAPP). Procedures for the collecti on of wipe samples are detailed in Appendix F of the QAPP (EPA, 2003a). Samples were collected and analyzed for 22 metals, mercury and dioxin (EPA, 2003a, Appendix Z, Table Z.3 ). Of these, dioxin and lead were identified as COPCs that are likely associated with the WTC disaster. A summary of the wipe sample results is presented in Appendix

Z . Detailed results for lead and dioxin are provided in Section 3.0

.Final Report December 2005 Page 22



# 3.0 WTC DUST CLEANUP PROGRAM

Data analyzed in this report were extracted from th e Residential database on September 10, 2003. A copy of the data set, with data necessary to protect the privacy of individual participants in the program redacted, is available from the EPA Region 2 Records Center. Appendix E contains a detailed discussion of the results presented in this section. Appendix F contains additional information on other fiber analyses that were conducted.

Overall, the data indicate a low rate of exceedance of the health-based benchmarks that were established for the WTC cleanup effort. The exceedance rates for airborne asbest os, and the exceedances rates for dust loading for the 21 metals other than lead, were less than 0.5% on a sample-basis. The exceedance rate for dust lead loading decreased from approximate ly 14% before cleanup, to 3% after cleanup, on a sample-basis. The exceedance rate for dust dioxin loadi ng was less than 1% before and after cleanup.



### 3.1 D ATA 

### S UMMARY



### 3.1.1 Summary of TEM (PCMe) data

Table 3-1 summarizes the sample results for asbestos. The data described in this section and Section 3.1.2 are results for asbestos phase contrast microscopy equivalent (PCMe) concentrations measured by transmission electron microscopy (TEM). A total of 28,702 sample results were available for asbestos by PCMe; 22,497 from residential units, and 6,205 from common areas within residential buildings (e.g., hallways, laundry rooms). Samples for PCMe analysis were collected from residential units that were tested only, as well as from residential units and comm on areas that were cleaned and tested. Results by PCMe were compared to the health-based benchm ark of 0.0009 f/cc (fibers/cubic centimeter) to

determine the status of the residential units/common areas.

The asbestos clearance criteria for the WTC Indoor Air Clean-up Program were based on long (i.e.,

≥ 5 μ m) fiber counts. The use of a minimum fiber length of 5 μ m for carcinogenic activity represents

current scientific consensus and reflects the criteria in EPA’s Integrated Risk Information System (IRIS) toxicity data base for attributing carcinogenic potency.Final Report December 2005 Page 23

Phase Contrast Microscopy equivalence is a process to identify asbestos fibers by TEM analysis that would also be visible by PCM. The optical resolution of the phase contrast microscope is approximately 5 microns in length and 0.25 microns in width for fibe r analysis. Historically, most of the occupational studies available (and reviewed by IRIS) to estimat e the cancer potency of asbestos, employed PCM analysis. Therefore, in cases where TEM is used for asb estos analysis, fiber counts need to be adjusted to

PCMe.

The asbestos counting rules employed for the WTC Indoor Cleanup Program were designed to record PCMe fibers. Thus, TEM analyses were performed and fibers were then counted following AHERA (Asbestos Hazard Emergency Response Act) counting rules. Fibers ≥ 5 μ m (AHERA also stipulates a minimum 5:1 aspect ratio) were distinguished from total (i.e., >0.5 μ m) fiber counts, although total fiber counts were also recorded. To maximize analyti cal capacity for a large sampling event, no minimum width requirement was employed. This may have resulted in a modest over counting bias by not eliminating extremely thin fibers (i.e., <0.25 μ m) from the count. However, the potential bias attributed to this counting procedure would be protective of human health. Modification was made to AHERA (by obtaining larger samples volumes) in order to achieve the lower detection limits required by the use of a

risk-based clearance criteria. Table 3-1. Summary of Available Asbestos PCMe Results

Summary of residential airborne asbest os data. The data represent phase contrast microscopy equivalent (PCMe) concentrations measured by transmission electron microscopy (TEM). The health-based benchmark of 0.0009 fibers/cubic centimeter was exceeded in a very small fraction of the samples. Occupants of residences with one or more exceedance for PCMe were offered recleaning. Sample Type Residential Sa mples Common Area Samples Samples collected 22,497 6,205 Number of samples >0.0009 a (exceeds) 102 21

Percent exceeds 0.45% 0.34% Maximum concentration 0.0204 0.0042

Minimum concentration Not Detected b Not Detected

a The health-based benchmark for asbestos is 0.0009 fibers/cubic centimeters. b Detection limit ranged from 0.0 004 to 0.0005 fibers/cubic centimeters.Final Report December 2005 Page 24 

### 3.1.2 Summary of TEM data

Table 3-2 lists the types of asbestos that were detected by TEM in the airborne asbestos samples from

residences and common areas. Asbestos was detect ed in approximately 4% of the available TEM samples. Chrysotile asbestos was detected in approxim ately 92% of the samples included in this subset of the data; amosite was detected in approximately 3%. Other forms of asbestos that were detected included actinolite, anthophyllite, tr emolite, and crocidolite.



### 3.1.3 Summary of Dust Wipe data Summary of Dust Lead Wipe Data

The database contained 1,540 wipe samples for dust l ead loading that were collected from 263 residences, located in 157 buildings. Summary statistics for the data are provided in Table 3-3 . Samples that were

below the detection limit of 1.86 μ g/ft 2 were set equal to the detection limit. Review of existing environmental standards/regulations identified an app licable and relevant standard to set a health-based

benchmark for lead in interior dust. The Reside ntial Lead-Based Paint Hazard Reduction Act (Title X) Final Rule (40 CFR, Part 745, 1/5/01) established unifo rm national standards for lead in interior dust.

Table 3-2. Number of Samples of Residential Airborne Asbestos Analyzed by Transmission Electron Microscopy (TEM) and By Asbestos Type Detected For samples where asbestos was detect ed; Chrysotile was encountered in approximately 92% of the residential samples, and in 91% of the samples collected from common areas. The next most frequently identified type of asbestos was Amosite (3% in residential, 4% in common areas).

Asbestos Type Residential Samples Common Area Samples Not detected 21,543 5,926 Actinolite 9 1 Amosite 31 10 Amosite/Chrysotile 3 2 Amosite/Chrysotile/Crocidolite 1 0 Amphibole 1 3 Anthophyllite 6 3 Anthophyllite/Chrysotile 3 1 Chrysotile 878 255 Chrysotile/Actinolite 6 0 Chrysotile Amphibole 0 2 Chrysotile/Tremolite 3 0 Crocidolite 1 0 Gypsum fibers present a 7 0 Tremolite 5 2 Total 22,497 6,205

a Non asbestos fibers.Final Report December 2005 Page 25

Thus, both EPA and the United States Department of Housing and Urban Development (HUD) have set a

dust standard for lead of 40 μ g/ft 2 for floors (including carpeted floors), and 250 μ g/ft 2 for interior window sills. To support the development of a du st standard, EPA performed an analysis of the Rochester Lead-in-Dust Study (HUD, 1995). At 40 μ g/ft 2 , a multimedia analysis shows a 5.3% probability that a child’s blood lead level would exceed 10 μ g/dL. Thus, this standard meets the criteria established by EPA (i.e., 95% probability to be below 10 μ g/dL) (EPA, 1994) for managing environmental lead hazards. However, an additiona l increment of protectiveness was added by setting the

health-based benchmark for lead in settled dust at the more stringent HUD screening level of 25 μ g/ft 2

. Approximately 9% of all lead wipe samples (i.e., test only and clean and test ) were above the HUD screening level of 25 μ g/ft 2 (Table 3-3) ; approximately 14% of the pre-cleanup samples exceeded the HUD screening level , while approximately 3% of th e post-cleanup samples exceeded the screening level ( Tables 3-4 and 3-5 ). Approximately 6% of the samples were above the HUD benchmark of 40 μ g/ft 2 m ( Table 3-3)

.

Summary of Dust Dioxin Wipe Data

The database contained 1,535 wipe samples for dust dioxin loading that were collected from 263 residences, located in 157 buildings. B asic statistics for the data are provided in Table 3-6 . The dioxin results were modified using a toxicity equi valency quotient (TEQ), which takes into account the toxicity differences between 17 congener groups

. The results are reported in 2,3,7,8-tetrachlorodibenzop-dioxin (TCDD) equivalents. Th e TEQ value reported in the table represents the estimated maximum potential concentration (EMPC). The TEQ EMPC va lue used data that indicated the presence of a Table 3-3. Statistics for All Lead Wipe Data Combined ( μ g/ft 2 )

This table provides summary statisti cs for residential dust lead loading data that was collected from reside nces that were cleaned and tested (both before and after cleanup), and tested only. Apartments sampled a 263 Buildings sampled 157 Number of samples 1540 Nondetects 264 (17.1%) Exceedances @ 25 μ g/ft 2 a 136 (8.8%) Exceedances @ 40 μ g/ft 2 b 95 (6.2%)

a The database contains matching data (i .e., pre- and post-cleanup data) for 214 apartments, and unmatched data (i.e, only pre-cleanup or only post-cleanup) for 49 apartments, for a total of 263 apartments.. b Exceedance: lead wipe samples that exceeded the health-based benchmark of 25 μ g/ft 2Final Report December 2005 Page 26 congener above zero ng/m 2 even if all of the QA/QC reporting level criteria were met for that sample. This value represents the highest potential concentrati on of dioxin that may have been present. At least one of the 17 congeners was detected in 1,136 of the samples; the remaining 399 samples were reported as below the detection limit for each congener. On ly 8 of the 1,535 (approximately 0.5%) of the combined samples (i.e., test only and clean and test ; Table 3-6 ) exceeded the health-based benchmark for residential dust dioxin loading of 2 ng/m2 (Table 3-6)

.

Summary of Dust Wipe Data for Other Metals

Data for 21 metals, in addition to lead, were collected. Statistics for the 21 metals (plus lead and dioxin), and the reduction in the average dust loading rates for each, are provided in Table 3-7 . The data are grouped into three categories in Table 3-7 : samples collected from residences and common areas that were cleaned and tested ( clean and test samples ), samples that were collected from residences that were tested only ( test only samples ), and the combination of these two categories ( all samples ).Final Report December 2005 Page 27

Table 3-4. Statistics for Lead Wipe Clean and Test Data

The clean and test subset of the data exhibit very high positive skewness and high variability. The raw data and log- transformed pre- and post-cleanup data fail the S-W test for normality (log-transformed data [pre-/post-]: S-W statistic=0.89/0.85, p<0.0001/p<0.0001). This table includes two observations that have been treated as outliers in subsequent analyses (see Section 3.4.1 for details). Statistics for the data set, after removal of the two outliers, are provided in Tables 3-7 and A-1a

. Statistic Pre-cleanup Post-cleanup Apartments sampled 214 214 Buildings sampled 145 145 Number of samples 680 674 Nondetects 101 (14.8%) 140 (20.8%) Exceedances @ 25 μ g/ft 2 a 93 (13.7%) 21 (3.1%) Exceedances @ 40 μ g/ft 2 b 67 (9.9%) 12 (1.8%) Minimum 1.86 1.86 Median 7.32 6.38 Mean 35.46 19.03 Maximum 6790 7250 Standard deviation 286.03 279.64 Skewness 20.56 25.77 CV c 8.07 14.70 S-W Statistic d 0.07 0.03 Prob Normal e <0.0001 <0.0001

a Exceedance: lead wipe samples that exceeded the health-based screening level of 25 μ g/ft 2

b Exceedance: lead wipe samp les that exceeded the HUD h ealth-based benchmark of 40 μ g/ft 2

c CV=coefficient of variation= standard deviation/mean d S-W Statistic: Shapiro-Wilk statistic

e Prob Normal: probability the data are from a normal distributionFinal Report December 2005 Page 28

Table 3-5. Statistics for Lead Wipe Clean and Test Data with Outliers Removed

The clean and test subset of the data exhibit very high positive skewness and high variability. The raw data and log-transfor med pre- and post-cleanup data fail the S-W test for normality (log-transformed data [pre-/post-]: S-W statistic0.90/0.89, p<0.0001/p<0.0001). This table excludes two observations that have been treated as outliers (see Section 3.4

. 1 for details). Statistic Pre-cleanup Post-cleanup Apartments sampled 214 214 Buildings sampled 145 145 Number of samples 679 673 Nondetects 101 (14.9) 140 (20.8) Exceedances @ 25 μ g/ft 2 a 92 (13.5) 20 (3.0) Exceedances @ 40 μ g/ft 2 b 66 (9.7%) 11 (1.6%) Minimum 1.86 1.86 Median 7.32 6.37 Mean 25.52 8.28 Maximum 2530 394 Standard deviation 121.00 19.79 Skewness 15.24 13.89 CV c 4.74 2.39 S-W Statistic d 0.15 0.21 Prob Normal e <0.0001 <0.0001

a Exceedance: lead wipe samples that exceeded the health-based screening level of 25 μ g/ft 2

b Exceedance: lead wipe samp les that exceeded the HUD h ealth-based benchmark of 40 μ g/ft 2

c CV=coefficient of variation= standard deviation/mean d S-W Statistic: Shapiro-Wilk statistic

e Prob Normal: probability the data are from a normal distribution

Table 3-6. Statistics for All Dioxin (TEQ) Wipe Data (ng/m 2 )

This table provides summary statistics for residential dust dioxin loading data that was collected from residences that were cleaned and tested (both before and after cleanup), and tested only. Apartments sampled a 263 Buildings sampled 157 Number of samples 1535 Nondetects 399 (26.0%) Exceedances b 8 (0.52%)

a The database contains matching da ta (i.e., pre- and post-cleanup data) for 214 apartments, and unmatched data (i.e, only pre-cleanup or only post-cleanup) for 49 ap artments, for a total of 263 apartments. b Exceedance: dioxin wipe samples that exceeded the health-based benchmark of 2 ng/m 2 TEQ EMPC (ND = 1⁄2).Final Report December 2005Page 29Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesAluminumNumberPercentagesAluminumNumberPercentagesAluminumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1536 99.5% Detects1329 99.5% Detects 102 99.0%Nondetects 8 0.5% Nondetects7 0.5% Nondetects 1 1.0%Nondetects @ 200 7 87.5% Nondetects@ 200 7 100.0% Nondetects @ 200 0 0.0%Nondetects @ 1000 1 12.5% Nondetects @1000 0 0.0% Nondetects @ 1000 1 100.0%Max 319000 Max 296000 Max 45500Min ND @ 200 Min ND @ 200 Min ND @ 248Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 3258.97Avg Post-Means 1093.05Avg % Reduction 35.44AntimonyNumberPercentagesAntimonyNumberPercentagesAntimonyNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 13 0.8% Detects 7 0.5% Detects 4 3.9%Nondetects 1531 99.2% Nondetects1329 99.5% Nondetects 99 96.1%Nondetects @ 80 1526 99.7% Nondetects@ 80 1329 100.0% Nondetects @ 80 94 94.9%Nondetects @ 400 5 0.3% Nondetects@ 400 0 0.0% Nondetects @ 400 5 5.1%Max 1180 Max 1180 Max 404Min ND @ 80 Min ND @ 80 Min ND @ 80Exceedances 2 0.1% Exceedances 2 0.1% Exceedances 0 0.0%Avg Pre-Means 84.38Avg Post-Means 80.01Avg % Reduction 1.47ArsenicNumberPercentagesArsenicNumberPercentagesArsenicNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 34 2.2% Detects 30 2.2% Detects 1 1.0%Nondetects 1510 97.8% Nondetects1306 97.8% Nondetects 102 99.0%Nondetects @ 20 1505 99.7% Nondetects@ 20 1306 100.0% Nondetects @ 20 97 95.1%Nondetects @ 100 5 0.3% Nondetects@ 100 0 0.0% Nondetects @ 100 5 4.9%Max 286 Max 268 Max 100Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 21.21Avg Post-Means 20.06Avg % Reduction 2.09Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’ sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location.. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7,B-7aandB-13).Final Report December 2005Page 30Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesBariumNumberPercentagesBariumNumberPercentagesBariumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 245 15.9% Detects210 15.7% Detects 17 16.5%Nondetects 1299 84.1% Nondetects1126 84.3% Nondetects 86 83.5%Nondetects @ 200 1294 99.6% Nondetects@ 200 1126 100.0% Nondetects @ 200 81 94.2%Nondetects @ 1000 5 0.4% Nondetects @1000 0 0.0% Nondetects @ 1000 5 5.8%Max 23400 Max 23400 Max 5510Min ND @ 200 Min ND @ 200 Min ND @ 200Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means: 367.84Avg Post-Means: 215.12Avg % Reduction: 13.92BerylliumNumberPercentagesBerylliumNumberPercentagesBerylliumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 0 0.0% Detects 0 0.0% Detects 0 0.0%Nondetects 1544 100.0% Nondetects1336 100.0% Nondetects 103 100.0%Nondetects @ 20 1539 99.7% Nondetects@ 20 1336 100.0% Nondetects @ 20 98 95.1%Nondetects @ 100 5 0.3% Nondetects@ 100 0 0.0% Nondetects @ 100 5 4.9%Max ND @ 100 Max ND @ 20 Max ND @ 100Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 20.00Avg Post-Means 20.00Avg % Reduction 0.00CadmiumNumberPercentagesCadmiumNumberPercentagesCadmiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 68 4.4% Detects50 3.7% Detects 12 11.7%Nondetects 1476 95.6% Nondetects1286 96.3% Nondetects 91 88.3%Nondetects @ 20 1471 99.7% Nondetects@ 20 1286 100.0% Nondetects @ 20 86 94.5%Nondetects @ 100 5 0.3% Nondetects@ 100 0 0.0% Nondetects @ 100 5 5.5%Max 1180 Max 906 Max 1180Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 22.34Avg Post-Means 20.28Avg % Reduction 3.13Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location. Similarly, the lead and dioxin samplenumbers do not match the sample numbers shownin Tables B-7, B-7aor B-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005Page 31Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesCalciumNumberPercentagesCalciumNumberPercentagesCalciumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 4050000 Max 4050000 Max 474000Min 1440 Min 1680 Min 1440Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 43239.15Avg Post-Means 24571.60Avg % Reduction 28.95ChromiumNumberPercentagesChromiumNumberPercentagesChromiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 855 55.4% Detects723 54.2% Detects 63 61.2%Nondetects 689 44.6% Nondetects613 45.8% Nondetects 40 38.8%Nondetects @ 20 684 99.3% Nondetects@ 20 613 100.0% Nondetects @ 20 35 87.5%Nondetects @ 100 5 0.7% Nondetects@ 100 0 0.0% Nondetects @ 100 5 12.5%Max 1900 Max 1050 Max 1900Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 48.01Avg Post-Means 28.23Avg % Reduction 21.45CobaltNumberPercentagesCobaltNumberPercentagesCobaltNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 4 0.3% Detects 2 0.1% Detects 1 1.0%Nondetects 1540 99.7% Nondetects1334 99.9% Nondetects 102 99.0%Nondetects @ 200 1535 99.7% Nondetects@ 200 1334 100.0% Nondetects @ 200 97 95.1%Nondetects @ 1000 5 0.3% Nondetects @1000 0 0.0% Nondetects @ 1000 5 4.9%Max 1000 Max 654 Max 1000Min ND @ 200 Min ND @ 200 Min ND @ 200Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 200.92Avg Post-Means 200.00Avg % Reduction 0.29Notes: The ‘Total’ sample numbers do not equal the sum of the‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location. Similarly, the lead and dioxin samplenumbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005Page 32Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesCopperNumberPercentagesCopperNumberPercentagesCopperNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 14500 Max 14500 Max 3700Min 36 Min 36 Min 108Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 387.98Avg Post-Means 226.82Avg % Reduction 18.73IronNumberPercentagesIronNumberPercentagesIronNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 228000 Max 212000 Max 168000Min 207 Min 462 Min 207Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 5438.09Avg Post-Means 1689.07Avg % Reduction 34.77LeadNumberPercentagesLeadNumberPercentagesLeadNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1280 82.9% Detects1090 81.6% Detects 89 86.4%Nondetects 264 17.1% Nondetects246 18.4% Nondetects 14 13.6%Nondetects @ 1.86 260 98.5% Nondetects @1.86 246 100.0% Nondetects @ 1.86 10 71.4%Nondetects @ 9.29 4 1.5% Nondetects @9.29 0 0.0% Nondetects @ 9.29 4 28.6%Max 7250 Max 7250 Max 1380Min ND @ 1.86 Min ND @ 1.86 Min ND @ 1.86Exceedances 136 8.8% Exceedances 112 8.4% Exceedances 12 11.7%Avg Pre-Means 24.40Avg Post-Means 16.21Avg % Reduction 8.19Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location. Similarly, the lead and dioxin samplenumbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005Page 33Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesMagnesiumNumberPercentagesMagnesiumNumberPercentagesMagnesiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 1550000 Max 1550000 Max 83400Min 2920 Min 4650 Min 3560Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 15852.43Avg Post-Means 11540.41Avg % Reduction 12.46ManganeseNumberPercentagesManganeseNumberPercentagesManganeseNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1455 94.2% Detects1263 94.5% Detects 95 92.2%Nondetects 89 5.8% Nondetects73 5.5% Nondetects 8 7.8%Nondetects @ 20 85 95.5% Nondetects@ 20 73 100.0% Nondetects @ 20 4 50.0%Nondetects @ 100 4 4.5% Nondetects@ 100 0 0.0% Nondetects @ 100 4 50.0%Max 4410 Max 4410 Max 2390Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 117.82Avg Post-Means 51.19Avg % Reduction 30.18MercuryNumberPercentagesMercuryNumberPercentagesMercuryNumberPercentagesSamples 1517 Samples 1298 Samples 100Detects 593 39.1% Detects469 36.1% Detects 64 64.0%Nondetects 924 60.9% Nondetects829 63.9% Nondetects 36 36.0%Nondetects @ 0.4 885 95.8% Nondetects @0.4 793 95.7% Nondetects @ 0.4 36 100.0%Nondetects @ 1.6 8 0.9% Nondetects @1.6 7 0.8% Nondetects @ 1.6 0 0.0%Nondetects @ 40 2 0.2% Nondetects@ 40 2 0.2% Nondetects @ 40 0 0.0%Nondetects @ 2 20 2.2% Nondetects@ 2 19 2.3% Nondetects @ 2 0 0.0%Nondetects @ 4 9 1.0% Nondetects@ 4 8 1.0% Nondetects @ 4 0Max 248 Max 248 Max 15.8Min ND @ 0.4 Min ND @ 0.4 Min ND @ 0.4Exceedances 6 0.4% Exceedances 5 0.4% Exceedances 0 0.0%Avg Pre-Means 4.71Avg Post-Means 2.24Avg % Reduction 0.84Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched pre- and postcleanup samples collected from the same location. Similarly, thelead and dioxin sample numbers do not match the sample numbers shown inTables B-7, B-7aor B-13becauseTables B-7, B-7aandB-13inlcudeall pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aand B-13).Final Report December 2005Page 34Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesNickelNumberPercentagesNickelNumberPercentagesNickelNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 613 39.7% Detects523 39.2% Detects 57 55.3%Nondetects 931 60.3% Nondetects813 60.8% Nondetects 46 44.7%Nondetects @ 20 928 99.7% Nondetects@ 20 813 100.0% Nondetects @ 20 43 93.5%Nondetects @ 100 3 0.3% Nondetects@ 100 0 0.0% Nondetects @ 100 3 6.5%Max 3160 Max 3160 Max 492Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 62.56Avg Post-Means 27.13Avg % Reduction 23.15PotassiumNumberPercentagesPostassiumNumberPercentagesPostassiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 239000 Max 239000 Max 100000Min 1350 Min 1350 Min 8140Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 24749.34Avg Post-Means 20235.38Avg % Reduction 10.67SeleniumNumberPercentagesSeleniumNumberPercentagesSeleniumNumberPercentagesSamples 1544 Samples 1319 Samples 103Detects 1204 78.0% Detects984 74.7% Detects 102 99.0%Nondetects 340 22.0% Nondetects334 25.3% Nondetects 1 1.0%Nondetects @ 20 280 82.4% Nondetects@ 20 277 82.9% Nondetects @ 20 1 100.0%Nondetects @ 40 60 17.6% Nondetects@ 40 57 17.1% Nondetects @ 40 0 0.0%Max 590 Max 590 Max 559Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 137.85Avg Post-Means 240.49Avg % Reduction -38.53Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location. Similarly, the lead and dioxin samplenumbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005Page 35Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesSilverNumberPercentagesSilverNumberPercentagesSilverNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 26 1.7% Detects 24 1.8% Detects 1 1.0%Nondetects 1518 98.3% Nondetects1312 98.2% Nondetects 102 99.0%Nondetects @ 20 1512 99.6% Nondetects@ 20 1311 99.9% Nondetects @ 20 97 95.1%Nondetects @ 100 5 0.3% Nondetects@ 100 0 0.0% Nondetects @ 100 5 4.9%Nondetects @ 200 1 0.1% Nondetects@ 200 1 0.1% Nondetects @ 200 0 0.0%Max 1400 Max 1400 Max 268Min ND @ 20 Min ND @ 20 Min ND @ 20Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 22.49Avg Post-Means 650.77Avg % Reduction -3151.96SodiumNumberPercentagesSodiumNumberPercentagesSodiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1538 99.6% Detects1333 99.8% Detects 101 98.1%Nondetects 6 0.4% Nondetects3 0.2% Nondetects 2 1.9%Nondetects @ 400 2 33.3% Nondetects@ 400 1 33.3% Nondetects @ 400 1 50.0%Nondetects @ 4000 2 33.3% Nondetects @4000 1 33.3% Nondetects @ 4000 1 50.0%Max 2 33.3% Max 1 33.3% Max 0 0.0%Min 557000 Min 557000 Min 222000Exceedances ND @ 400 Exceedances ND @ 400 Exceedances ND @ 400Avg Pre-Means 63441.36Avg Post-Means 51980.14Avg % Reduction 11.33Dioxin (TEQ ND=1/2)NumberPercentagesDioxin (TEQ ND=1/2)NumberPercentagesDioxin (TEQ ND=1/2)NumberPercentagesSamples 1538 Samples 1322 Samples 103Detects 1136 73.9% Detects938 71.0% Detects 96 93.2%Nondetects 402 26.1% Nondetects384 29.0% Nondetects 7 6.8%Max 75.3 Max 2.29 Max 3.01Min 0.265 Min 0.265 Min 0.349Exceedances 8 0.5% Exceedances 3 0.2% Exceedances 1 1.0%Avg Pre-Means 0.65Avg Post-Means 0.64Avg % Reduction 0.01Notes: The ‘Total’ sample numbers do not equal the sum of the ‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched preand post-cleanup samples collected from the same location. Similarly, the lead and dioxin samplenumbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B7aandB-13inlcude all pre-and post-cleanup samples(i.e., residences with only pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005Page 36Table 3-7. Dust Wipe Sample DataTotal SamplesMatched Pre- and Post-Cleaning SamplesTest Only SamplesThalliumNumberPercentagesThalliumNumberPercentagesThalliumNumberPercentagesSamples 1544 Samples 938 Samples 103Detects 0 0.0% Detects 0 0.0% Detects 0 0.0%Nondetects 1544 100.0% Nondetects938 100.0% Nondetects 103 100.0%Nondetects @ 200 1539 99.7% Nondetects@ 200 938 100.0% Nondetects @ 200 98 95.1%Nondetects @ 1000 5 0.3% Nondetects @1000 0 0.0% Nondetects @ 1000 5 4.9%Max ND @ 1000 Max ND @ 200 Max ND @ 1000Min ND @ 200 Min ND @ 200 Min ND @ 200Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 235.80Avg Post-Means 195.51Avg % Reduction 3.85VanadiumNumberPercentagesVanadiumNumberPercentagesVanadiumNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 7 0.5% Detects 3 0.2% Detects 1 1.0%Nondetects 1537 99.5% Nondetects1333 99.8% Nondetects 102 99.0%Nondetects @ 200 1532 99.7% Nondetects@ 200 1333 100.0% Nondetects @ 200 97 95.1%Nondetects @ 1000 5 0.3% Nondetects @1000 0 0.0% Nondetects @ 1000 5 4.9%Max 1000 Max 539 Max 1000Min ND @ 200 Min ND @ 200 Min ND @ 200Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 236.67Avg Post-Means 641.60Avg % Reduction -218.87ZincNumberPercentagesZincNumberPercentagesZincNumberPercentagesSamples 1544 Samples 1336 Samples 103Detects 1544 100.0% Detects1336 100.0% Detects 103 100.0%Nondetects 0 0.0% Nondetects0 0.0% Nondetects 0 0.0%Max 78900 Max 78900 Max 67400Min 372 Min 539 Min 380Exceedances 0 0.0% Exceedances 0 0.0% Exceedances 0 0.0%Avg Pre-Means 2196.83Avg Post-Means 1419.72Avg % Reduction 16.89Notes: The ‘Total’ sample numbers do not equal the sum of the‘Clean and Test’ and ‘Test Only’sample numbers because the ‘Matched Pre- and Post-Cleaning Samples’ include only the matched pre- andpost-cleanup samples collected from the same location. Similarly,the lead and dioxin sample numbers do not match the sample numbers shown inTables B-7, B-7aorB-13becauseTables B-7, B-7aandB-13inlcude all pre-and post-cleanup samples (i.e., residences withonly pre- or post-cleanup samples are not excluded fromTables B-7, B-7aandB-13).Final Report December 2005 Page 37 The database contained 1,517 results fo r mercury, and 1,544 results for a ll of the other metals. The rate of detection (based on all samples) varied widely fro m 0 for beryllium and thallium, to 100% for calcium, copper, iron, magnesium, potassium and zinc. Eight of the 21 metals had detection rates of less than or equal to 5%; 4 had detection rates between 6 and 60%. Results for each metal were compared against risk-based screening levels (Table 3-8) . Very few exceedances of the risk-based screening values were measured for any of the metals. The screening value of 627 μ g/m 2 for antimony was exceeded in 2 precleanup samples (0.1% of all samples); the maximum measured value was 1,180 μ g/m 2 . The screening

value of 157 μ g/m 2 for mercury was exceeded in 5 pre-clea nup samples (0.4% of all samples). No residence had an average antimony dust loading or mercury dust loading greater than their respective

health-based benchmarks.



### 3.2 E FFICACY OF THE 

### D UST 

### C LEANUP 

### P ROGRAM



### 3.2.1 Reductions in the Rate of PCMe Exceedance

The efficacy of the asbestos cleanup effort was assessed using PCMe exceedances for clean and test data.

One measure of effectiveness is the overall rate of exceedances, which equals the number of exceedances divided by the total number of samples that were collected. The overall exceedance rate on sample-basis for the WTC Cleanup Program was approximately 0.00418, or 0.42%.

An alternative measure of efficacy is the number of times a residence or a common area within a building (e.g., hallway, stairwell, laundry) had to be recleaned to achieve the clearance criteria of 0.0009 f/cc. Residences were recleaned if one or more samples exceeded the health-based be nchmark for asbestos, or one or more samples could not be analyzed in the la boratory due to excessive dust on the air filter (i.e.,

overloads ). The cleanup effort was effective in achievi ng the clearance criteria for PCMe approximately

99% of the time in residential units and common ar eas. The PCMe clearance criterion was not achieved in 35 out of 3,387 (1.03%) residences and in 11 out of 785 common areas (1.40%) after the first cleaning. The probability of achieving the clearance on the second attempt in residential units that did not achieve clearance after the first cleaning was approximately 1 (>0.999; 2 out of the 25 residences that were recleaned did not achieve clearance after the second cleaning - 10 residents elected not to have their residences recleaned, or were unresponsive). These resu lts suggest that the cleaning methods used were effective in reducing asbestos con centrations in residential air.Final Report December 2005 Page 38

A modified aggressive sampling procedure was used in most of the apartments (EPA, 2003a). The modified-aggressive sampling procedure was adapte d from the aggressive sampling procedure described in AHERA. The aggressive sampling procedure had a tendency to overload the sampling filter with dust, preventing the samples from being an alyzed by the laboratory (EPA, 2003a). The modified aggressive sampling is thought to be more representative of typical household activity patterns (EPA, 2003a). The rate of exceedance varied between the two sampling procedures. On a sample basis, the exceedances

rates in test only residences were 0.50 and 0.49% for the a ggressive and modified aggressive sampling Table 3-8. Health-based Benchmarks and Screening Values for Chemicals of Potential Concern (COPCs) in Settled Dust

Chemical of Potential Concern Health-based Benchmark/ Screening Value Aluminum 1567888 Antimony 627 Arsenic 387 Barium 109752 Beryllium 3136 Cadmium 1557 Chromium 4704 Cobalt 31358 Copper 62716 Iron 940733 Lead a 25 Manganese 31358 Mercury 157 Nickel 31358 Selenium 7839 Silver 7839 Thallium 110 Vanadium 10975 Zinc 470366 Dioxin a 2

Table is based on in EPA, May 2003 COPC report. All benchmarks are μ g/m 2 , except for lead, which is in μ g/ft 2 , and dioxin, which is ng/m 2

.

a The health-based benc hmark for lead is 40 μ g/ft 2 ; however, the more stringent screening HUD screening value of 25 μ g/ft 2 was used (see Section 3.5.1 for details).

b Health-based benchmark is for toxicity equivalent (TEQ), which is a weighted summation of 17 types (congeners) of dioxin, where the weights represent the relative toxicity for each specific congener.Final Report December 2005 Page 39 procedures, respectively; th e exceedances rates for the clean and test residences were 3.4 and 0.20% for the aggressive and modified aggressive sampling procedures, respectively. The test only exceedances rates were not significantly different by Fisher’s exact test (p>0.99); the clean and test exceedances rates were statistically significant by Fisher’s exact test (p<0.01). On a residence-basis (i.e., one or more sample result from the residence equal or exceeded the benchmark for asbestos), the exceedances rates in test only residences were 3.0 and 1.1% for the aggressive and modified aggressive sampling procedures, respectively; the ex ceedances rates for the clean and test residences were 6.4 and 0.64% for the aggressive and modified aggressive sampling procedures, respectively. The test only exceedances rates were not significantly different by Fisher’s exact test (p>0.34); the clean and test exceedances rates were statistically significant by Fisher’s exact test (p<0.01).



### 3.2.2 Reduction in Dust Lead Loading

The methods used were effective in reducing levels of lead as measured by wipe samples. The indoor environment is considered to be a complex and dynami c system that is influenced by many interacting factors (physical, chemical, thermodynamic conditions, human activity, building design, building materials, HVAC system, etc.). Therefore, it is not uncommon to find variability in the amount of contaminants in settled dust within a building, and certa inly from one building to the next. In addition to WTC proximity, high variability is also likely due to the wide range of diversit y in the housing stock, contents of the residences and common areas, and pree xisting conditions, or pr evious activity, at these

sites.

To assess the effectiveness of the cl eanup program, the wipe data were divided into two groups: samples

that were collected before th e apartments were cleaned ( pre-cleanup) , and samples that were collected after the apartments were cleaned ( post-cleanup) . Pre-cleanup lead wipe samples and post cleanup samples were collected from 214 apar tments, located in 145 buildings.

The cleanup program reduced the average dust lead loading in residential units by approximately 16 μ g/ft 2 (20%) ( Section E.4.1 ).

Thirty-six residences had pre-cleanup average dust le ad loadings greater than the HUD screening of

25 μ g/ft 2 . Average post-cleanup dust lead loading in resi dences with average pre-cleanup loadings above the HUD screening level of 25 μ g/ft 2 were approximately 85 μ g/ft 2 lower than pre-cleanup loadings. The cleanup program was successful in reducing the average dust lead loading in 31 of the 36 residences to

below the 25 μ g/ft 2 screening level, a success rate of approximately 86% (Section E.4.2)

.Final Report December 2005 Page 40

Twenty-three residences had pre-cleanup average dust lead loadings greater than the HUD benchmark of

40 μ g/ft 2 . Average post-cleanup dust lead loading in resi dences with average pre-cleanup loadings above

the HUD benchmark of 40 μ g/ft 2 were approximately 120 μ g/ft 2 lower than average pre-cleanup loadings. The cleanup program reduced the average dust lead lead ing in 21 out of the 23 residences, a success rate of approximately 91%.

Residences located on the third floor or lower te nded to have higher pre-cleanup average loadings (39.52 μ g/ft 2 ) than residences located on floors 4-10 (21.08 μ g/ft 2 ) and floors higher than 10 (14.18 μ g/ft 2 ). Reduction in average dust lead loading also varied by building floor level. On average, dust lead loadings were reduced by 33.1 μ g/ft 2 (43.5%) for residences on floors 3 and lower, by

11.1 μ g/ft 2 (23.1%) for residences on floors 4–10, and by 6.9 (8.6%) for residences located on floors 11 and higher (Section E.4.3)

.

The number of exceedances of dust lead loading on a sample-basis is shown in Figure 3-1 . Two sets of bars are also shown for dust lead loading exceedan ces, corresponding to two different benchmarks for dust lead loading. The first set of bars corresponds to the WTC screening level of 25 μ g/ft 2 ; the second set corresponds to the HUD health-based benchmark of 40 μ g/ft 2 . Regardless of the benchmark that is used, the reduction in the number of exceedan ces (on a sample-basis) is approximately 85%.



### 3.2.3 Reduction in Dust Dioxin Loading The measurable effect of the cleanup program on dust dioxin loadings was less than it was for lead due primarily to low pre-cleanup dust dioxin loadings, wh ich limits the usefulness of the dioxin data to assess the efficacy of the dust cleanup program. Pre-cleanup and post cleanup dust wipe samples for dioxin were collected from 212 apartments, located in 145 build ings. Reductions in dust dioxin loadings were modest due to the low pre-cleanup levels. The mean of the average pre-cleanup dust dioxin loading in each residence was 0.65 ng/m 2 ; all residential average dust dioxin loadings were less than the healthbased benchmark of 2 ng/m 2 . The cleanup program reduced the residential average dust dioxin loading by approximately 0.01 ng/m 2 (Section E.4.4)

.Final Report December 2005 Page 41

The number of exceedances of dust dioxin loading on a sample-basis is shown in Figure 3-1 . Two sets of bars are shown for dioxin, corresponding to two methods for treating the dioxin sample results that were reported as below detection limit ( nondetects ) by the laboratory. The first set of numbers (i.e., ND=1/2) corresponds to the method that was used in th is report (nondetects were set equal to 1⁄2 of the detection limit); the second set of numbers corresponds to an alternative method of treating nondetects (setting nondetects equal to 0 ng/m 2 ). The number of exceedances is low regardless of the method that is

used to treat the nondetects. 

### 0 20 40 60 80 100 120 140

A

nti mo n

y

D

ioxin (ND = 1/2 )

D

io

x i n

(N

D = 0) Me r c

u

ryExceedances 

### Pre-Cleanup Post-Cleanup

Figure 3-1. The number of samples (i.e., not residences) that exceeded health-based benchmarks, for contaminants that had at least one exceedance of their respective health-based benchmark. Two sets of bars are shown for dioxin, corresponding to two methods for treating the dioxin sample results that were reported as below detection limit ( nondetects ) by the laboratory. The first set of numbers (i.e., ND=1/2) corresponds to the method that was used in this repor t (nondetects were set equa l to 1⁄2 of the detection limit); the second set of numbers corresponds to an alternative method of tr eating nondetects (setting nondetects equal to 0 ng/m 2 ). The number of exceedan ces is low regardless of the method that is used to treat the nondetects. Two se ts of bars are also shown for dust l ead loading exceedances, corresponding to two different benchmarks for dust le ad loading. The first set of bars corresponds to the WTC screening level of 25 μ g/ft 2 ; the second set corresponds to the HUD health-based benchmark of 40 μ g/ft 2 . The reductions in the number of exceedances (pre-clea nup, post-cleanup) are as follows: antimony (2, 0); dioxin (ND=1/2) (4, 4); dioxin (ND=0) (2, 1); lead (>25 μ g/ft 2 ) (115, 21); lead ( ≥ 40 μ g/ft 2 ) (84, 12); mercury (5, 1).Final Report December 2005 Page 42 

### 3.2.4 Reduction in Dust Antimony Loading and Dust Mercury Loading A comparison of the number of exceedances in pre-cleanup samples ( not residences) to the number of exceedances in post-cleanup samples for antimony and mercury is provided in Figure 3-1 . The number of dust antimony exceedances was reduc ed from 2 in the pre-cleanup samples to 0 in the post-cleanup samples; the number of dust mercur y exceedances was reduced from 5 in the pre-cleanup samples to 1 in the post-cleanup samples.



### 3.3 S UMMARY OF THE 

### S PATIAL 

### A NALYSIS OF 

### PCM E 

### E XCEEDANCES

One hundred twenty two of the 28702 samples collected had exceedances of the health based standard (PCMe > 0.0009 f/cc) for asbestos. The comparison of the rates of PCMe exceedances between SSAs

was restricted to a subset of the SSAs that had a sample size of 30 or more. Sample sizes less than 30 were considered to small to yield reliable results . The existence of a spatial pattern in the PCMe exceedances is not supported by the spatial analyses:

1. Analysis of the site-level (global) patte rn of PCMe exceedances indicates that the geographic centers of the exceedance events for the test only and clean and test buildings tended to be located south of the geographic center of the sampled buildings, and east of the WTC site. Except for one location, the test only exceedance locations occurred along

an east-west line located south of the WTC site. There is no obvious pattern to the clean

and test exceedances. Interpretation of the exceed ance locations is complicated by the variability in the number of samples that were collected in buildings (Section E.3.2.1)

.

2. The analysis of PCMe exceedances at the st atistical summary area (SSA) level indicated that the rate of PCMe exceedances varied over the sampled area:

a. SSAs with similar PCMe exceedance rates tended to be located near each other (i.e., the rates exhibit positi ve spatial autocorrelation) (Section E.3.2.2)

.

b. Comparison of the rates of PCMe excee dances across the SSAs indicated that

SSAs with exceedance rates that were si gnificantly greater than the other SSAs were located east ( test only data), and north and east ( clean and test data) of the

WTC site (Section E.2.2.2)

.

3. Analysis of the building-level (local) pa ttern of PCMe exceedances, using nearest neighbor methods, suggests the pattern is c onsistent with a spatially random process (Section E.3.2.3)

.Final Report December 2005 Page 43

4. Analysis of the building-level (local) patte rn of PCMe exceedances, using Ripley’s K function, also failed to reject the hypothesi s that the PCMe exceedances were generated by a spatially random process (Section E.3.2.3)

.

5. Analysis of the site-level vertical dist ribution of PCMe exceedances, on a residencebasis, did not find any statistically significant pattern for residences that were tested (i.e., test only residences) or residences that were cleaned and tested (i.e., clean and test residences) (Section E.3.2.4)

.

6. Analysis of the site-level vertical distri bution of PCMe exceedances, on a sample-basis, indicates samples collected from clean and test residences and common areas that were located on lower floors (i.e., ≤ 3 rd floor) were approximately 2 times more likely to

exceed the health-based benchmark for airbor ne asbestos than were samples collected

from clean and test residences and common areas located on upper floors (floors 10 and higher). No significant differences were found between clean and test samples collected on middle floors (floors 4–9) and upper floors. The rate of PCMe exceedances was found to differ between floor groups for the test only samples, although comparisons between the floor groups were not statistically significant (Section E.3.2.4)

.



### 3.4 C OMPARISON OF 

### WTC I NDOOR 

### D UST 

### P ROGRAM AND 

### EPA B ACKGROUND 

### S TUDY

As described earlier, a background study was c onducted in Upper Manhatta n to determine indoor concentrations of selected analytes that were identif ied in WTC-related dust. Several of the analytes, specifically asbestos, lead, and dioxin, that were meas ured in Upper Manhattan were also measured in the WTC Indoor Dust Program. An evaluation was conducted with these three analytes to determine if the

concentrations detected in Lower Manhattan one to tw o years after the collapse of the WTC were similar to those measured in Upper Manhattan. The evaluati on consists of comparing the frequency of detection, the range of values reported (i.e., minimum and maxi mum), and the percentage of samples that were above the health-based benchmark for each analyte (Table 3-9)

.

The most appropriate measurements for comparison ar e the frequency of detection and the percentage of samples that exceeded the health-b ased benchmark. The minimum and maximum values are not the most reliable method for comparing the two studies due to the variability in the detection limits and the substantial difference in sample size between the tw o studies. As sample size increases there is aFinal Report December 2005 Page 44 tendency for the range of values detected to increase, which limits the reliability of comparing maximum

values from the two studies.

In addition to comparing the two studies with each ot her, the results from the studies were compared to

values obtained from the literature. Studies were id entified that reported concentrations from indoor environments for these three analytes using similar sampling and analytical methodologies. The minimum, maximum, mean, median, and 90 th percentile values from the literature were compared with the values reported from the EPA studies. The literatu re values were reported using censored data. In order to make the comparison the EPA data compatible with the literature values, the EPA data sets for each analyte were censored using the same method as reported in the literature. This was done for comparison purposes only and the censoring method employed does not provide any additional insight into what the actual values from the EPA studies may have been. The censoring method used, as well as detailed information from each literature study that was chosen for comparison, are presented in the discussion of each analyte.

Asbestos - The frequency of detection from samples coll ected in the two distinctly different geographic locations were similar, with a detection rate of 2% in Lower Manhattan and 5% in Upper Manhattan. The

minimum concentrations from both areas were identic al, while the maximum detected concentration in Lower Manhattan was higher than the maximum detect ed concentration in Upper Manhattan. Although

the maximum detected concentrations were not simila r between the two areas, the percentage of samples

that exceeded the health-based crite rion was similar, with 0.5% in Lower Manhattan and 0.0% in Upper

Manhattan.Final Report December 2005 Page 45

Table 3-9. Comparison of Descriptive Statisti cs from the USEPA WTC Indoor Dust Program and the USEPA Upper Manhattan Background Study

Comparison of airborne asbestos, dust lead loading and dust dioxin loading measured in Lower Manhattan to concentrations measu red in in Upper Manhattan (‘backgr ound’). The most appropriate measurements for comparison are the frequency of detection and the percentage of samples that exceeded the health-based benchmark. Comparis on of the minimum values is confounded by the variability in the detection limits. Comparison of the maximum values is confounded by the variability in sample sizes; as sample size increases there is a tendency for the maximum value to increase.

USEPA WTC Indoor Dust Program USEPA Upper Manhattan Background Study Analyte

n % det. a min max % above b n % det. a min max % above b

Asbestos (s/cc) c 20,887 2% <0.0004 0.0204 0.5% 62 5% <0.0004 0.0004 0.0%

Lead ( μ g/ft 2 ) 1812 78% <1.86 2530 7.6% 114 50% <0.5 49 0.9%

Dioxin (ng/m 2 ) d 1549 74% 0.292 5.14 0.5% 114 77% 0.475 1.66 0%

s/cc: structures per cubic centimeters; : g/ft 2 = micrograms of lead per square foot of surface; ng/m 2 = nanograms of dioxin per square meter of surface

a % det.: percent of samples that contained the contaminant at levels above the detection limit b % above: percent of sample measurements that were greater than the health-bas ed benchmark (health-based benchmarks: asbestos: 0.0009 f/cc; lead: 25 μ g/ft 2 ; dioxin 2 ng/m 2 :).

c Phase contrast microscopy equivalent (P CMe) results; see glossary for definition d International toxicity equivalent quotient ( TEQ) see glossary for definition; all congene rs that were not de tected were set equ al to 1⁄2 their detection limit.

A summary paper by the Health Effects Institute presen ted asbestos results for several different types of buildings, including schools, residences, and public/c ommercial spaces (HEI-AR, 1991). The asbestos

measurements were made using TEM analysis, and counted fibers that were ≤ 5 μ m, which is the same

method that was utilized in the EPA studies. Th e values reported in the summary paper were leftcensored; a value of zero was substituted for sample s that were reported as being below the detection limit. Values reported for residential spaces, and for all buildings combined (i.e., minimum, mean, median, and 90 th percentile) in this summary paper were plotted beside the same values from the EPA

studies (Figure 3-2) . The horizontal axis reports the results from the test-only data set from Lower Manhattan (LM-Pre), the clean and test data set from Lower Manhattan (LM-Post), the Upper Manhattan data set (UM), the residential data set (Residence) and the data set from all buildings (All) from the HEI summary paper. The results of this comparison indicate that all of the values that were plotted fall below

the health-based benchmark that was established for the WTC Indoor Dust Cleanup program. The onlyFinal Report December 2005 Page 46 exception is the maximum values that were reported for the Lower Manhattan data set, which were above

the health-based benchmark. The maximum values from the literature were not reported, so a comparison

cannot be made. The mean values from the literature are higher than those reported in the EPA studies (after replacing non-detects with 0). This may be in part due to the high number of non-detect samples that were present in the EPA studies. If the de tection limit were substituted for the EPA non-detect samples, the means from the EPA studies would be near 0.0005 s/cc. It is likely that the true mean asbestos concentration in Manhattan, based on data from the EPA studies, lies somewhere between 0 and 0.0005 s/cc. The middle of the range, which is 0.00025 s/cc, is quite similar to the mean reported in the HEI summary paper.

Lead - The frequency of detection, the maximum detect ed concentration, and the percentage of samples that exceeded the health-based criteria were higher in Lower Manhattan when compared to the results from Upper Manhattan 1 . If only the post-cleaning samples from the clean and test apartments are used for the comparison, the percentage above the health -based criterion falls from 7.6% to 2.5%, which is more similar to the Upper Manhattan rate (0.9%).

1 Two data points were removed fr om the Lower Manhattan data set for this analysis , as they were identified as outliers in the

lead data set.Final Report December 2005 Page 47

The best comparison data set that was identified for lead was the 2001 Housing and Urban Development database for lead and allergens in U.S. housing (HUD, 2001). This database provides data on lead in settled dust from urban residences in four regions of the United States (i.e., Northeast, Midwest, South, and West), by building age. Information on the distribution of lead loadings for carpeted and uncarpeted floors in housing stock ranging in age from pre- 1939 to 1998 for the Northeast was queried from the HUD database and descriptive statistics were generated. Values for samples that were identified as being

below the detection limit were substituted with 1⁄2 of the detection limit. The minimum, maximum, median, mean, and 90 th percentile values were plotted beside the same values from the USEPA studies (Figure 3-3) . The horizontal axis reports the results fro m the test-only data set from Lower Manhattan Asbestos 0.0204 0.0037

0 0.0005

0.001 0.0015

0.002Concentration, s/cc Median Mean 90th Max Min

LM-Pre LM-Post UM Residence All Benchmark (0.0009 s/cc)

Figure 3-2. Comparison of airborne asbestos concentr ations from WTC Dust Cleanup Program and Background Study to concentrations reported by the Health Effects Institute (HEI, 1991). The table includes data from the test-only data (LM-Pre), and clean and test data (LM-Post) from the Lower Manhattan Dust Cleanup Program ; Upper Manhattan data (i.e., background) (UM) from the WTC Background Study (EPA, 2003b); and, the residential data (Residence) and the data from all buildings (All) from the HEI summary paper (HEI, 1991). The results of th is comparison indicate that all of the values fall below the health-based benchmark that was established for the WTC Indoor Dust Cleanup program. The only exception is the maximum values that were repor ted for the Lower Manhattan data set, which were above the health-based benchmark. The maximum va lues from the literature were not reported, so a comparison cannot be made. The mean values from th e literature are higher than those reported in the USEPA studies (after replacing non- detects with 0). This may be inpart due to the high number of nondetect samples that were present in the USEPA studi es. If the detection limit were substituted for the USEPA non-detect samples, the means from the USEPA studies would be n ear 0.0005 s/cc. Its likely that the true mean asbestos concentra tion in Manhattan, based on data from the USEPA studies, lies somewhere between 0 and 0.0005 s/cc An estimate that is in th e middle of the that range, e.g., 0.00025 s/cc, is quite similar to the mean reported in the HEI summary paper.Final Report December 2005 Page 48 (LM-Pre), the clean and test data set from Lower Manhattan (LM-Post), the Upper Manhattan data set (UM), and the HUD data set. The maximum values de tected in the LM-Pre and HUD data sets were similar, although the LM-Pre value was higher. The means from the four data sets were all below the health-based benchmark. The LM-Pre mean was ju st under the benchmark, the LM-Post and HUD means were similar, and the UM mean was the lowest. The comparison indicates that the maximum detected

concentrations varied between st udies, the means, medians, and 90 th percentile values for the LM-Post, UM, and HUD were below the benchmark, and all but the 90 th percentile for the LM-Pre data set were

below the benchmark.

Dioxin - The frequency of detection in the two areas were similar with a rate of 74% in Lower Manhattan and 77% in Upper Manhattan. The minimum detected concentrations were also similar, and the

maximum detected concentration from Lower Manha ttan was slightly higher than Upper Manhattan 2

. The percentage of samples that were above the h ealth-based criterion was similar between the two areas

with a rate of 0.5% in Lower Ma nhattan and 0.0% in Upper Manhattan.

There was limited information in the literature for dioxi n wipe samples that could be used for comparison. The New York State Department of Health ( NYSDOH) reported on post-occupancy environmental sampling from an office building that was impacted by a fire that released polychlorinated biphenyls and dioxin (NYSDOH, 2002). This report presented data (Binghamton data) for wipe samples that were collected and analyzed for dioxin using similar met hods as those used in the EPA studies. The data represents the seventh round of post-occupancy samp ling, which occurred in 1999, 18 years after the building fire. This was the last round of sampli ng because the dioxin concentrations were very low throughout the building. The values presented in th e paper were reported in Toxicity Equivalents Quotients (TEQs) where congeners that were below th e detection limit were set to 1⁄2 of the detection limit. The minimum, maximum, median, mean, and 90 th percentile values from this study were plotted

beside the same values from the EPA studies (Figure 3-4) . The minimum, median, and mean values from the three studies were very similar. Ther e was a slightly higher value for the 90 th percentile from the NYSDOH data set (NYSDOH, 2002). Th e minimum, median, mean, and 90 th percentile values were all

below the health-based benchmark. The maximum detected concentrations from the Lower Manhattan data sets (LM-Pre and LM-Post) were margina lly higher than the Upper Manhattan (UM) and Binghamton data sets. With the exception of the UM da ta set, all of the maximum values were above the

2 One data point was removed from the Lower Manhattan data set for this analysis, as it was identified as outliers in the dioxin

data set.Final Report December 2005 Page 49

health-based benchmark. This indicates that the diox in concentrations observed in the WTC Indoor Dust Program were similar to background, and si milar to values reported in the literature.

In summary, a comparison between analytical r esults from Lower Manhattan and Upper Manhattan show that the number of samples that ex ceed health-based criteria for three an alytes one to two years after the collapse of the WTC are similar. Additionally, valu es reported in the literature for these analytes indicate that the mean, median, and 90 th percentile values are similar to those reported in the EPA studies, with the exception of maximum detected concentrations which were generally higher than those reported in the

literature. Lead 1845 394 2350

0 10 20 30 40 50 60Concentration, ug/ft2 Median Mean 90th Max Min

LM-Pre LM-Post UM HUD Benchmark (25 ug/m 2 )

Figure 3-3. Comparison of dust lead loading levels from WTC Dust Cleanup Program and Background Study to loadings reporte d in the HUD Survey of L ead and Allergens in U.S. Housing database (HUD, 2001). Data for carpeted and uncarpe ted buildings in the Northeast constructed from pre-1939 to 1998 were used in this analysis. The figure includes data from the test-only data (LM-Pre), and clean and test data (LM-Post) from the Lower Manhattan Dust Cleanup Pr ogram; Upper Manhattan da ta (i.e., background) (UM) from the WTC Background Study (EPA, 2003b); and, the data from the HUD database (HUD, 2001). Values for samples that were identified as being belo w the detection limit were substituted with 1⁄2 of the detection limit. The maximum values detected in the LM-Pre and HUD da ta sets were similar, although the LM-Pre value was higher. The means from the four data sets were all below the health-based benchmark. The LM-Pre mean was just under th e benchmark, the LM-Post and HUD m eans were similar, and the UM mean was the lowest. The comparison indicates that the maximum detected concentrations varied between studies, the means, medians, and 90 th percentile values for the LM-P ost, UM, and HUD were below the benchmark, and all but the 90 th percentile for the LM-Pre data set were below the benchmark.Final Report December 2005 Page 50

Dioxin

0 1 2 3 4 5 6Concentration, ng/m2 Median Mean 90th Max Min

LM-Pre LM-Post UM Binghamton Benchmark (2 ng/m 2 )

Figure 3-4. Comparison of dust dioxin loading levels from WTC Dust Cleanup Program and Background Study to loadings measured in an office building in Binghamton, NY by the New Yo rk State Department of Health (NYSDOH, 2002). The figure includes data from the test-only data (LM-Pr e), and clean and test data (LM-Post) from the Lower Manhattan Dust Cleanup Program; Upper Ma nhattan data (i.e., background) (UM) from the WTC Background Study (EPA, 2003b); and, data from (NYSDOH, 2002). The minimum, median, and mean values from the three studies were very similar. There was a slightly higher value for the 90 th percentile from the NYSDOH data set (N YSDOH, 2002). The minimum, median, mean, and 90 th percentile values were all below the health-based benchmark. The maximum detected concentrations from the Lower Manhattan data sets (LM-Pre and LM-Post) were marginally higher than the Upper Manhattan (UM) and Binghamton data sets. With the exception of the UM data set, all of the maximum values were above the health-based benchmark. This i ndicates that the dioxin concentrations observed in the WTC Indoor Dust Program were simila r to background, and similar to values reported in the literature.Final Report December 2005 Page 51 

# References

Agency for Toxic Substances and Disease Registry/New York City Department of Health and Mental Hygiene (ATSDR/NYCDOHMH). 2002. Final Report of the Public Health Investigation to Assess Potential Exposures to Airborne and Settled Surf ace Dust in Residential Areas of Lower Manhattan, September.

Bailey, T.C. and A.C. Gatrell. 1995. Interactive Sp atial Data Analysis. Prentice Hall. Harlow, England.

Cameron, A. and P. Trivedi. 1998. Regression anal ysis of count data. Cambridge University Press.

Clark, P.J, and F.C. Evans. 1954. Distance to N earest Neighbor as a Measure of Spatial Relationships in Populations. Ecology (35)4.

Cressie, N.A.C. 1993. Statistics for Spatial Da ta. John Wiley & Sons, Inc. New York, N.Y.

Dixon, P.M. 2001. Nearest Neighbor Methods. Department of Statistics, Iowa State University. Preprint #01-19 December 1.

Ebdon, D. Statistics in Geography. 1988. Basil Blackwell, Ltd., Oxford, UK.

Ground Zero Taskforce. 2001. Characterization of Pa rticulate Found In Apartments After Destruction of the World Trade Center, October.

Griffith, D. and J. Layne. 1999. A casebook for spatial statistical data analysis. Oxford University Press, NY.

Griffith, D. 2002. A spatial filtering specification for the auto-Poisson model. Statistics & Probability Letters 58:245-251.

Health Effects Institute – Asbestos Research (HEI -AR). 1991. Asbestos in Public and Commercial Buildings: A literature review and synthesis of current knowledge. http://www.asbestosinstitute.ca/reviews/hei-ar/hei-ar.html.

Hope, A. (1968). A simplified Monte Carlo significance test procedure, J. of the Royal Statistical Society , 30B: 582-598.

Levine, N. 2002. CrimeStat II: A Spatial Statis tics Program for the Analysis of Crime Incident Locations. Ned Levine & Associates, Houston, TX, a nd the National Institute of Justice, Washington, D.C, May.

New York City Department of Health and Mental Hygiene (NYCDOHMH). 1994. Facts About Mold. Available online: http://www.ci.nyc.ny.us/html/doh/html/epi/epimold.html.

New York State Department of Health (NYSDOH). 2002. Binghamton State Office Building: Postoccupancy environmental sampling, Final Round, October

Skellam, J. (1946). The frequency distribution of the difference between two Poisson variates belonging to different populations, J. of the Royal Statistical Society , 10B: 257-261.

U.S. Department of Housing and Urban Development (HUD). 1995. The Relation of LeadContaminated House Dust and Blood Lead Among Ur ban Children. Final Report to U.S. HUD from theFinal Report December 2005 Page 52 University of Rochester School of Medicine (Roch ester, NY) and the National Center for Lead Safe Housing (Columbia, MD).

U.S. Department of Housing a nd Urban Development (HUD). 2001. National Survey of Lead and Allergens in Housing, Final Report, Volume I: Anal ysis of Lead Hazards, Revision 6.0. Washington, D.C.

U.S. Environmental Protection Agency (EPA). 1990. National Oil and Hazardous Substances Pollution Contingency Plan . Final Rule 40 CFR 300. 55 Federal Regi ster, 8666–8865, March 8. Washington, DC.

U.S. Environmental Protection Agency (EPA). 1994 . Revised Interim Soil Lead Guidance for CERCLA Sites and RCRA Corrective Action Facilities. OSWER Directive 9355.4-12. Washington D.C.

U.S. Environmental Protection Agency (EPA). 2001. Exposure and Human Health Evaluation of Airborne Pollution from the World Trade Center Di saster. External Review Draft. NCEA-W-1395. EPA/600/P-2/002A, October.

U.S. Environmental Protection Agency (EPA). 2003a. Interim Final WTC Residential Confirmation Cleaning Study. Volume 1. May.

U.S. Environmental Protection Agency (EPA). 2003b. Background Study Report. Interim Final. April.

U.S. Environmental Protection Agency (EPA). 2003c. World Trade Center Indoor Environment Assessment: Selecting Contaminants of Poten tial Concern and Setting Health-Based Benchmarks. Contaminants of Potential Concern Committee of the World Trade Center Indoor Air Task Force Workgroup. May.Final Report December 2005 Page A-1



# APPENDIX A.

Additional Statistics

Table A-1. Statistics for Lead Wipe Clean and Test Data

The clean and test subset of the data exhibit very high positiveskewness and high variability. The raw data and log-transformed preand post-cleanup data fail the S-W test for normality (log-transformed data [pre-/post-]: S-W statistic=0. 89/0.85, p<0.0001/p<0.0001). This table includes two observations that have been treated as outliers in subsequent analyses (see Section E.2.2 for details). Statistics for the data set after removal of the two outliers are provided in Table A-1a

. Pre-Cleanup Post-Cleanup apartments sampled 214 214 buildings sampled 145 145 number of samples 680 674 nondetects 101(14.8%) 140 (20.8%) exceedances a 93 (13.7%) 21 (3.1%)

mean 35.46 19.03 standard deviation 286.03 279.64 skewness 20.56 25.77 CV b 8.07 14.70 variance 81812.09 78199.03 maximum 6790 7250 99 th percentile 470 83.2 95 th percentile 76.30 16.00 90 th percentile 37.30 11.00 75 th percentile 12.50 8.40 median 7.32 6.38 25 th percentile 3.23 2.47 10 th percentile 1.86 1.86 5 th percentile 1.86 1.86 1 st percentile 1.86 1.86 minimum 1.86 1.86 S-W Statistic c 0.07 0.03 Prob Normal d <0.0001 <0.0001

a Exceedance: lead wipe ( μ g/ft 2 ) samples that exceeded th e HUD screening level of 25 μ g/ft 2

b CV=coefficient of variation= standard deviation/mean

c SW-Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-2

Table A-1a. Statistics for Lead Wipe Clean and Test Data – Outliers Removed

The clean and test subset of the data exhibit very high positiveskewness and high variability. The raw data and log-transformed preand post-cleanup data fail the S-W test for normality (log-transformed data [pre-/post-]: S-W statistic=0. 90/0.89, p<0.0001/p<0.0001). This table excludes two observations that have been treated as outliers (see Section E.2.2 for details). Pre-Cleanup Post-Cleanup apartments sampled 214 214 buildings sampled 145 145 number of samples 679 673 nondetects 101 (14.9) 140 (0.21) exceedances a 92 (13.5) 20 (3.0) mean 25.52 8.28 standard deviation 121.00 19.79 skewness 15.24 13.89 CV b 4.74 2.39 variance 14641.04 391.80 maximum 2530 394 99 th percentile 366.00 83.20 95 th percentile 73.00 15.50 90 th percentile 37.25 10.90 75 th percentile 12.50 8.40 median 7.32 6.37 25 th percentile 3.22 2.47 10 th percentile 1.86 1.86 5 th percentile 1.86 1.86 1 st percentile 1.86 1.86 minimum 1.86 1.86 S-W Statistic c 0.15 0.21 Prob Normal d <0.0001 <0.0001

a Exceedance: lead wipe ( μ g/ft 2 ) samples that exceeded th e HUD screening level of 25 μ g/ft 2

b CV=coefficient of variation= standard deviation/mean

c SW-Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-3

Table A-2. Statistics on the Reduction in Average Lead Wipe Loadings (Preand Post-cleanup)

Statistics for average pre-cleanup and post-cleanup residential dust lead loading ( μ g/ft 2 ) measured by wipe samples are shown, and statistics for the reduction in the average dust lead loading. The sta tistics describe the distribution of precleanup and post-cleanup average lead loadings ( μ g/ft 2 ) that were measured in each residence. The average dust lead lo adings and the reduction in the averages, continue to display substantial departur es from normality; a log-transformation of the data fails to improve the fit of a norma l distribution to the data (S-W statistic for reductions=0.37 0, p<0.0001; pre-cleanup averages: S-W statistic 0.95, p; post-cleanup averages: S-W statistic= 0.92, p<0.0001). Outliers were removed from the dataset (see Section E.2.2 for details).

Statistic Reduction in Average Lead Wipe Loading Average PreCleanup Lead Wipe Loading Average PostCleanup Lead Wipe Loading

n 214 214 214

mean 16.21 24.40 8.19 standard deviation 65.16 66.34 17.10 skewness 7.23 7.73 12.17 CV a 4.02 2.727 2.096 variance 4245.98 4401.40 292.29 maximum 708.21 748.95 241.67 99 th percentile 289.03 294.33 39.25 95 th percentile 81.65 92.44 16.08 90 th percentile 31.37 44.73 11.77 75 th percentile 9.50 18.44 8.44 median 1.77 8.66 6.79 25 th percentile 0.00 4.94 3.01 10 th percentile -1.31 2.34 1.86 5 th percentile -3.35 1.86 1.86 1 st percentile -21.50 1.86 1.86 minimum -163.27 1.86 1.86 S-W Statistic b 0.33 0.15 0.22 Prob Normal c <0.0001 <0.0001 <0.0001

a CV=coefficient of variation= standard deviation/mean b S-W Statistic: Shapiro-Wilk statistic

c Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-4

Table A-3. Statistics on Reduction in Average Lead Wipe Loadings (Pre- and PostCleanup) in Residences With Pre-cl eanup Averages Greater Than the HUD Screening Level of 25 μ g/ft 2

The statistics describe the distribution of pre-cleanup and post-cleanup average lead loadings ( μ g/ft 2 ) that were measured in each residenc e. The average dust lead loadings and the reduction in the averages show less variation and are less skewed than the complete distribution of average residential dust lead loadings. A log-transformation of the data improves the fit of a normal distribution to the data (S-W statistic for reductions=0.71, p<0.0001; pre-cleanup aver ages: S-W statistic=0.89, p<0.0001; postcleanup averages: S-W statistic=0.88, p<0.0001); however, significant departures from the normal distribution model remain. Outlie rs were removed from the dataset (see Section E.2.2 for details).

Statistic Reduction in Average Lead Wipe Loading Average PreCleanup Lead Wipe Loading Average PostCleanup Lead Wipe Loading

n 36 36 36

mean 84.84 102.12 17.28 standard deviation 140.85 138.37 39.62 skewness 2.90 3.47 5.49 CV a 166.01 135.50 229.28 variance 19838.34 19147.47 1569.75 maximum 708.21 748.95 241.67 99 th percentile 708.21 748.95 241.67 95 th percentile 408.96 413.87 40.74 90 th percentile 199.74 209.36 31.73 75 th percentile 88.03 101.78 11.56 median 38.82 48.52 8.08 25 th percentile 25.26 34.74 6.39 10 th percentile 19.92 26.86 3.78 5 th percentile 10.35 25.98 2.08 1 st percentile -163.27 25.86 1.86 minimum -163.27 25.86 1.86 S-W Statistic b 0.64 0.56 0.32 Prob Normal c < 0.0001 < 0.0001 < 0.0001

a CV=coefficient of variation= standard deviation/mean b S-W Statistic: Shapiro-Wilk statistic

c Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-5

Table A-4. Statistics for Pre-cleanup Residential Average Dust Lead Loading by Floor Group

Average dust lead loadings ( μ g/ft 2 ) tend to decrease with increasing floor level, and variance tends to increase with increasing average dust lead loading. Wh en grouped by floor level, the average pre-cleanup dust lead loadings show less variation and are less skewed than the complete distribution of average residential dust lead loadings. A log-transformation of the averages substantially improves the fit of a normal distribution to the data (S-W statistic for lower floor group=0.95, p=0.0149; middle: S-W statistic=0.96, p=0.0127; upper: S- W statistic=0.90, p=0.0002). Outliers were removed from the dataset (see Section E.2.2 for details). Floor group a Statistic Lower Middle Upper

n 61 93 60

mean 39.52 21.08 14.18 standard deviation 102.71 46.41 37.98 skewness 5.84 6.97 7.06 CV b 2.60 2.20 2.68 variance 10549.74 2154.14 1442.33 maximum 748.95 413.87 294.33 99 th percentile 748.95 413.87 294.33 95 th percentile 175.15 78.40 39.48 90 th percentile 73.45 38.62 21.90 75 th percentile 27.23 18.44 9.54 median 9.20 10.03 7.25 25 th percentile 5.46 5.27 3.80 10 th percentile 2.17 2.32 2.56 5 th percentile 1.86 1.96 1.98 1 st percentile 1.86 1.86 1.86 minimum 1.86 1.86 1.86 S-W Statistic c 0.36 0.34 0.25 Prob Normal d <0.0001 <0.0001 <0.0001

a Floor groups were defined as follows; lower: ≤ floor 3; middle: floors 4 – 10 inclusive; upper: > floor 10 b CV=coefficient of variation= standard deviation/mean

c S-W Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-6

Table A-5. Statistics for Dioxin Wipe Clean and Test Data

The data summarized in this table are dioxin toxicity equivalency quotients (TEQs) (ng/m 2 ), which are the sum of 17 different chemical forms (congeners) of dioxin. The clean and test subset of the data exhibit high positive skewness but lo w variance. Very few exceedances were observed for dioxin. The raw data and log-transformed pre- and post-cleanup data fail the S-W test fo r normality (log-transformed data [pre-/post-]: S-W statistic=0.71/0.89, p<0.0001/p<0.0001). This table excludes one observation that has b een treated as an outlier in the analysis of the dioxin wipe data (see Section E.2.3 for details). Statistics for the data set after removal of the outlier are provided in Table A-5a

. Pre-Cleanup Post-Cleanup apartments sampled 212 212 buildings sampled 145 145 number of samples a 674 668 nondetects 0 0 exceedances 3 (0.4%) 4 (0.6%) mean 0.81 0.65 standard deviation 3.95 0.28 skewness 25.75 5.27 CV b 0.49 0.42 variance 15.62 0.08 maximum 75.4 4.34 99 th percentile 1.85 1.76 95 th percentile 1.11 1.17 90 th percentile 0.90 0.90 75 th percentile 0.68 0.68 median 0.60 0.59 25 th percentile 0.56 0.54 10 th percentile 0.47 0.46 5 th percentile 0.43 0.43 1 st percentile 0.34 0.34 minimum 0.27 0.27 S-W Statistic c 0.03 0.62 Prob Normal d <0.0001 <0.0001

a Exceedance: lead wipe samples that exceeded the HUD screening level of 25 μ g/ft 2

b CV=coefficient of variation= standard deviation/mean

c S-W-Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page A-7

Table A-5a. Statistics for Dioxin Wipe Clean and Test Data-One Outlier Removed

The data summarized in this table are dioxin toxicity equivalency quotients (TEQs) (ng/m 2 ), which are the sum of 17 different chemical forms (congeners) of dioxin. The clean and test subset of the data exhibit high positive skewness but lo w variance. Very few exceedances were observed for dioxin. The raw data and log-transformed pre- and post-cleanup data fail the S-W test fo r normality (log-transformed data[pre-/post-]: S-W statistic=0.88/0.89, p<0.0001/p<0.0001). One precleanup observation with a value of 75.3 ng/m 2 was removed as an outlier (see Section E.2.3 for details). Pre-Cleanup Post-Cleanup apartments sampled 212 212 buildings sampled 145 145 number of samples a 673 668 nondetects 0 0 exceedances 2 4 mean 0.66 0.65 standard deviation 0.29 0.28 skewness 6.79 5.27 CV b 0.44 0.42 variance 0.09 0.08 maximum 5.14 4.34 99 th percentile 1.81 1.76 95 th percentile 1.10 1.17 90 th percentile 0.90 0.90 75 th percentile 0.68 0.68 median 0.60 0.59 25 th percentile 0.56 0.54 10 th percentile 0.47 0.46 5 th percentile 0.43 0.43 1 st percentile 0.34 0.34 minimum 0.27 0.27 S-W Statistic c 0.57 0.62 Prob Normal d <0.01 <0.01

a Exceedance: lead wipe samples that exceeded the HUD screening level of 25 μ g/ft 2

b CV=coefficient of variation= standard deviation/mean

c S-W-Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are from a normal distribution according to S-W testFinal Report December 2005 Page B-1



# APPENDIX B.

World Trade Center Indoor Dust Cleaning Program Monitoring Contract Scope of Work

DRAFT - 8/8/02

A. Introduction

All work performed under this contract entered in to between the New York City Department of Environmental Protection (DEP) and the Monitoring Cont ractor must be in compliance with all applicable laws and regulations, including but not limited to re gulations issued by the United States Environmental Protection Agency (EPA), the United States Depart ment of Labor Occupational Safety and Health Administration (OSHA), the New York State Department of Labor (NYS DOL), New York State Department of Health (NYS DOH) Environmental Laboratory Accreditation Program (ELAP) and the New York City Department of Environmental Protection (DEP).

The EPA and DEP will solicit participation in a dir ect assistance program for residents and residential building owners south of Canal Street who are concerned that their residences may have debris/dust from the collapse of the World Trade Center (WTC). A DEP/EPA telephone hotline has been established to receive requests from the public. The assistance will in clude various options, including cleaning within residences and common spaces by licensed asbestos c ontractors with follow-up asbestos sampling, asbestos air sampling only, and providing high efficiency particulate air (HEPA) vacuums.

NYS DOL licensed asbestos contractors shall perform cleaning activities for residents who wish to have

their homes cleaned.

The purpose of this contract is to acquire project monitoring services including site inspections, surveys and assessments, scheduling, coordi nating and monitoring the clean-up of dust/debris, and collecting samples from residences impacted by the collapse of the World Trade Center. The scope of work for the monitoring activities is identified below. The mon itoring contractor shall supply all equipment and supplies necessary to perform the wo rk specified in this contract.Final Report December 2005 Page B-2 B. Roles and Responsibilities

As provided in the instant contract, EPA’s World Tr ade Center Dust Cleanup Field Personnel shall have the authority of Project Manager, in addition to DEP’ s Project Manager. As such, the authority of EPA World Trade Center Dust Cleanup Field Personnel sh all include the following authority related to monitoring work conducted under this contract: 1. The authority to stop work for health and safety reasons. 2. The authority to stop work for non-compliance with the Scope of Work. 3. The authority to give technical direction to the contractor in the performance of the work. 4. The authority to review and approve or disa pprove of the qualifications of the monitoring personnel involved in the work. 5. The right to inspect and accept or reject any work.

The Monitoring Contractor shall be responsible for: 1. Scheduling and coordinating the cleaning and m onitoring work with residents, building owners, the Cleaning Contractor and EPA; 2. HVAC system evaluation and inspection; 3. Oversight of the Cleaning Contractor; 4. Inspection of the Cleaning Contractor’s work; 5. Area air monitoring; 6. Wipe sampling; and 7. Personal air monitoring.

C. Qualifications

Proof of qualifications must be av ailable on-site during the performan ce of work and shall be presented upon request.

Project Monitors

The Project Monitors shall be employees of the Mo nitoring Contractor. The Project Monitors must possess valid NYS Asbestos Project Monitor certificat es. The Project Monitors must have served as a third party project monitor on at least 25 asbestos abatement projects. Project Monitor must have performed final clearance inspections on at least 25 asbestos abatement projects. The Project Monitor

must have access to translation services to schedule pre-cleaning unit inspections with residents. The Project Monitor shall be familiar with NYC DOH’s Mold Assessment and Remediation Guidelines.

Air Monitoring Technicians

Air monitoring technicians must possess valid NYS Asbestos Project Sampling Technician (APST) certificates. Technicians must have performed air sampling for at least 6 months.Final Report December 2005 Page B-3 Wipe Sampling Technicians

Wipe sampling technicians must have 1 year experience in wipe sampling for a variety of contaminants. Employees shall have received specific training in wipe sampling methods.

HVAC System Inspectors

HVAC assessments and cleanliness insp ections shall be conducted by qua lified personnel. At a minimum such personnel shall have an understanding of HVAC system opera tion and experience in utilizing accepted indoor environmental sampling practices, current industry HVAC cleaning procedures, and applicable industry standards. The HVAC System In spection contractor shall be a certified member of National Air Duct Cleaners Association (NADCA).

Background Investigations

All contractors and subcontractors involved in the mon itoring activities under this contract are responsible for completing a background check on their employ ees and for screening unacceptable candidates from the pool of on-site workers. Background checks sha ll be completed with 30 days of the award of the contract. Thereafter, background checks shall be co mpleted prior to employees beginning on-site work. EPA will provide guidelines for evaluating the background information collected. Contractors are required to maintain records of background checks for 4 years and to make them available to the DEP and EPA when requested. At a minimum, the background check must include: 1. Law enforcement checks (5 years)

2. Professional license and certification

D. Specifications

This contract between DEP and the monitoring contr actor shall be in force for 24 consecutive months from the commencement date. The monitoring contractor must be prepared to mobilize within 72 hours of the contract award. Cleaning activities shall be sche duled for 20 residences per day. Air sampling shall be scheduled and completed within 24 hours of successf ul visual inspection by the project monitor. Air sampling shall also be provided at an additional 10 to 20 residences per day where cleaning activities are not scheduled (i.e., where residents have requested sa mpling only). Work shall be scheduled 7 days per week. A sufficient number of properly trained a nd certified personnel shall be available for project monitoring and sampling.

Electricity and water necessary to conduct the work required under this contract will be provided by the owner or occupant of the work area.Final Report December 2005 Page B-4 The Monitoring Contractor shall have available suffi cient quantities of sampling equipment to provide the amount and type of samples required for this project. At a minimum the following equipment is required: air-sampling pumps with maximum flow rate capaci ties of 15 liters per minute, tripods, rotometers, sample cassettes with mixed cellulose ester filters having pore size of 0.8 micron.

The Monitoring Contractor shall attend the mandatory pre-bid meeting and project kick off meeting prior to the start of work. The agenda fo r the kick off meeting shall include scope of work, sampling logistics and resource requirements and procedures for data ac quisition and submittal. The monitoring contractor will demonstrate data acquisition procedures and software to be employed for this project. In addition, the Monitor Contractor shall attend meetings or conf erence calls with EPA to coordinate field activities, as requested.

The Monitoring Contractor shall adopt and follow th e Quality Assurance Project Plan prepared by EPA for all environmental data collection activities performed under this contact. All appropriate data, original field forms/data sheets, and chain-of-custody forms shall be collected and completed in accordance with the instructions contained in the contract and provid ed to EPA. This information shall be provided via FormsII Lite (F2L) or an alternative electronic format that is accessible by the laboratory. All clearance air samples and copies of necessary documentation shall be hand delivered daily to EMSL Laboratories

at: 307 W. 38 th St., New York, New York, 212-290-0051. All wipe samples and copies of necessary documentation shall be shipped daily by courier for next day delivery to Paradigm Analytical Laboratories at: 2627 Northchase Parkway SE, Wilmington NC 28405, 910-350-2839.

Copies of all invoices for work conducted under this cont ract shall be provided to EPA. EPA will review

submitted invoices to confirm that the work has b een completed and forward the invoices to DEP for processing. Decisions regarding the reimbursement of costs will be made by DEP.

Deliverables

All deliverables shall be provided via F2L or an ag reed upon alternative electronic format approved by EPA and DEP. The Project Monitor shall also ma intain a copy of each deliverable and all field

documentation submitted under this contract for 365 days. The Project Monitor shall review all deliverables prior to submission to EPA. The review shall assure that each deliverable is accurate and complete, technically sound, and free of clerical errors. The Project Monitor shall direct and coordinate all services and report all findings to EPA. The scope of this project will be as follows:Final Report December 2005 Page B-5 1. The Project Monitor shall be respons ible for accessing a secure EPA Website (url, user name and password to be provided by EPA) to obtain request or information (address, building identification numbers, etc.). In coordination with the EPA, the Proj ect Monitor will access residential services requested from the Website, schedule pre-clean ing inspections and schedule cleaning activities and air sampling with the tenants/owners, asbestos abatement contractors and air monitoring technicians for the various residences. The Proj ect Monitor will then supply relevant information on activities for the EPA Indoor Air database via the secure EPA website. Data to be input may include, but is not limited to start and completion dates for the cleaning, sampling dates and sample identification numbers. All data shall be maintained as confidential.

The Project Monitor shall provide validated a ddresses and scheduling information into the EPA Indoor Air web database via F2L or an agreed upon alternative electronic format or website approved by EPA within 24 hours of activity.

2. The Monitoring Contractor will schedule and cond uct pre-cleaning or pre-sampling inspections of the residences, common spaces and heating, ve ntilation and air conditi oning systems (HVAC) based on the requests for assistance from individual residents and building owners made through the DEP/EPA hotline.

a. Inspections are to be conducted in accord ance with inspection checklists prepared by EPA.

b. Residents will be provided with a fact sh eet and information prepared by EPA regarding sampling options (aggressive and modified a ggressive air sampling protocols) and the handling of valuable personal items (money, je welry, heirlooms, etc.) and fragile objects during work.

c. The Project Monitor will obtain written au thorization for access for all work to be performed for this project including inspec tion, sampling and cleaning of residences, common spaces and HVAC systems using the Access Agreement (to be provided).

d. The Project Monitor will advise EPA and NYC DOH or DEP accordingly if any of the following conditions are observed:

i. mixed residential and commerc ial use within a building;

ii. dust and/or debris in common spaces;

iii. the presence of mold on building components (walls, support beams, ceiling tiles, HVAC systems)(NYC DOH notification);

iv. the presence of peeling, flaking or chalking paint (NYC DOH notification);

v. the presence of potential asbestos-type insulation (e.g., fibrous materials)(NYC DOH notification if damaged);

vi. residents with special needs or medical conditions that may be aggravated by exposure to airborne contaminants; or

vii. other circumstances that may require deviation from the procedures specified in the scopes of work for the cleaning and monitoring contracts.Final Report December 2005 Page B-6 e. In consultation with EPA, the Project Monitor will make a determination of the appropriate Cleaning Contract Scope of Work (i.e. Scope A or B) to be implemented. This determination is to be made based on the descriptions included in the Cleaning Contract Scope of Work and best professional judgment.

All deliverables shall be provided in the EPA I ndoor Air web database via F2L or an agreed upon alternative electronic format or website approved by EPA within 24 hours of activity. The Project Monitor shall record all pre-cleaning and pre-samp ling inspection information in the EPA Indoor Air database or using an agreed upon electronic form at. This information shall include, but is not limited to, building cleaned or inspected by DEP (Y/N), type of HVAC system (central, individual or none), HVAC impacted (Y/N), occupant present (Y/N), written access obtained (Y/N), space occupied (Y/N), dust present (low /high), presence of mold on building components (Y/N), peeling /flaking/chalking paint present (Y /N), suspected ACM present (Y/N), residential use (commercial/residential), residents with special needs, cleaning method (Scope A or B). Dates and times shall be associated with a ll activities including start and finish date.

3. In the event that mold or flaking, peeling or chalking paint is observed on building components (walls, support beams, ceiling tiles, HVAC systems) in areas where work h as been requested, the Monitoring Contractor will notify EPA and fax a written request for assistance in evaluating potential health hazard to NYC DOH at 212-442-33 78. Cleaning will not proceed in areas where mold or flaking, peeling or chalking paint is observed until potential hazards are evaluated and addressed as necessary. The Project Monitor shall be familiar with NYC DOH’s Mold Assessment and Remedi ation Guidelines. Deliverables

The Project Monitor shall record if mold or fl aking, peeling or chalking paint is present and notify the NYCDOH and EPA. This information shall be provided via EPA Indoor Air web database or an agreed upon alternative electroni c format or website approved by EPA within 24 hours of activity.

4. The presence of in-place materials suspected to contain asbestos shall be identified and quantified. The condition of the material shall be evaluated to identify damaged, deterioration, delamination, etc.

5. In the event that damaged, deteriorated, de laminated, etc. suspected ACM is observed, the monitoring contractor will notify EPA and fax wr itten notification to the NYC DOH. Cleaning or air monitoring will not proceed in areas where su ch suspected ACM is observed until instructed otherwise.

The Project Monitor shall record if damaged, de teriorated, delaminated, etc. suspected ACM is observed and notify EPA and fax written notificatio n to the NYC DOH. This information shall be provided via EPA Indoor Air web database or an agreed upon alternative electronic format or website approved by EPA within 24 hours of activity.

6. The Monitoring Contractor shall evaluate HVAC systems in residential buildings where cleaning will take place to determine if these systems ha ve been impacted by dus t or debris from the collapse of the World Trade Center and if these systems require cleaning.Final Report December 2005 Page B-7 a. In cases where cleaning is requested for an individual residence, portions of the HVAC system dedicated to that residence will be evaluated.

b. In cases where cleaning is requested for a whole building, the building’s HVAC system will be evaluated as a whole.

c. HVAC assessments shall be conducted in accordance with the National Air Duct Cleaners Association (NADCA) General Specification for the Cleaning of Commercial Heating, Ventilating and Air Conditioning Systems and the NADCA Assessment, Cleaning and Restoration Standard (ACR 2002).

d. The results of the whole building HVAC system evaluations shall be documented in a letter report. This report shall be provi ded to EPA within 2 business days of the completion of the evaluation. If the whole sy stem requires cleaning, a copy of the report shall be sent to DEP within 2 business days of the completion of the evaluation. This report shall include the name and qualifications of inspector, date of inspection, building location, a description of the HVAC system, th e basis for the determining whether or not the system was impacted by the collapse of th e WTC and if the system requires cleaning.

e. If a HVAC system requires cleaning, then the Monitoring Contractor shall prepare a scope of work for the cleaning the HVAC system or portion thereof. The scope of work shall be provided to EPA for approval within 2 business days of the completion of the HVAC system evaluation. A copy of the scope of work shall also be provided to DEP.

f. If the Monitoring Contractor is not a member of the NADCA, a subcontractor that is a member may perform the HVAC system evaluation and inspection.

The Project Monitor shall record if the HVAC systems have been impacted by dust or debris from the collapse of the WTC, if the HVAC system h as been evaluated and cleaned subsequent to September 11, 2001 (if yes, then when and by whom) and if the HVAC system requires cleaning. This information shall be provided via F2L or an agreed upon alternative electronic format or website approved by EPA within 24 hours of activity.

7. HVAC systems that have been impacted by dus t or debris from the collapse of the World Trade Center will be cleaned. In the event that a HVAC system for an entire building requires cleaning, a separate, site specific contract will be awarded by DEP for this work. If only a portion of a HVAC system requires cleaning, then the Cleaning Contractor will conduct the cleaning utilizing specialized labor trained and experienced in duct cleaning.

a. The monitoring contractor shall schedule and oversee the cleaning of HVAC systems.

b. The Monitoring Contractor shall inspect HVAC systems or portions thereof cleaned to verify cleanliness. If visible dust or othe r contaminants are evident through visual inspection or testing, then the Monitoring Contractor will direct the HVAC Cleaning Contractor to reclean those portions of the system where dust or other contaminants are present and reinspect.

c. HVAC cleaning, and cleanliness inspections shall be conducted in accordance with the National Air Duct Cleaners Association (NADCA) General Specification for the Cleaning of Commercial Heating, Ventila ting and Air Conditioning Systems and the NADCA Assessment, Cleaning and Restoration Standard (ACR 2002).Final Report December 2005 Page B-8 8. Sampling of residences and common spa ces where cleaning has not been requested:

a. Based on consultation with resident, aggressi ve or modified aggressive air sampling will be conducted.

i. Before beginning aggressive air sampling, a 1 horsepower electric leaf blower shall be used to direct exhaust air ag ainst walls, ceilings, floors and other surfaces. This shall continue for at l east five minutes per 1,000 square feet of floor. When directing the exhaust, caution shall be taken to minimize disturbance and potential damage to furnis hings and personal belongings

ii. Residents shall not be present in their residence during aggressive air sampling activities.

iii. Personal protective equipment includi ng disposable clothing, gloves, and respirators shall be worn during aggressive air sampling activities.

iv. HVAC systems shall be shut down or isolated locally during aggressive air sampling.

v. Room and window air conditioners shall not be operated during aggressive air sampling.

vi. Leaf blowers shall not be employed for modified aggressive air sampling.

vii. All other procedures are identical for aggressive and modified aggressive air sampling, unless otherwise noted.

b. At least one 20-inch fan shall be placed in the center of each room sampled. One fan per 10,000 cubic feet of room space shall be used . The fans shall be operated on slow speed and pointed toward the ceiling. The fans sha ll run for at least 15 minutes prior to the start of sampling. The fans shall operate c ontinuously throughout sampling and shall not be turned off until sampling is completed.

c. For studio apartments 3 air samples shall be collected. For 1-bedroom apartments and above, 5 air samples shall be collected. Sampling equipment shall be placed in living areas and away from obstructions. The sampling cassette must be placed on a tripod, not taped to existing surface and should be directed downward at a 45 degree angle.

d. Common spaces will be sampled without the use of forced air devices (fans, leaf blowers etc). For small spaces, less than 160 square feet, 3 samples will be collected. For large spaces greater than 160 square feet and less than 25,000 square feet 5 samples will be collected. For spaces greater than 25,000 square feet, 1 sample will be collected for each 5,000 square feet. Sampling equipment sh all be placed away from obstructions. The sampling cassette must be placed on a tripod, not taped to existing surfaces and directed downward at a 45 degree angle.

e. Air samples will be collected in accordan ce with the procedures specified in NIOSH 7400 Method and analyzed by phased contrast microscopy (PCM) followed by Asbestos Hazard Emergency Response Act (AHERA) transmission electron microscopy (TEM). The flow rate for collection of air samples shall be at least 10 liters per minute and no more than 15 liters per minute. Air sampli ng cassettes need to be monitored to ensureFinal Report December 2005 Page B-9 that cassettes are not clogging by monitoring flow rates. The minimum sample volume required is 3600 liters. These air samples and copies of necessary documentation shall be hand delivered daily to EMSL Laboratories at: 307 W. 38 th St., New York, New York, 212-290-0051.

f. Wipe samples will be collected at 10 per cent of the residences where sampling only has been requested, up to a maximum of 13 reside nces, as instructed by EPA. This sampling will consist of the collection of 3 wipe sampl es each for dioxin, total metals and mercury (Note: a separate wipe sample is required for each parameter at each location). Metals and mercury wipe samples will be collected in accordance with procedures specified in HUD Appendix 13.1. Dioxin wipe samples w ill be collected in accordance with the procedures specified in ASTMD 6661-01. Wipe samples and copies of necessary documentation will be packed, sealed and shippe d daily by courier for next day delivery to Paradigm Analytical Laboratories at: 2627 Northchase Parkway SE, Wilmington NC 28405.

g. 24-hour turn-around time shall be specified for the analysis of all samples collected using the aggressive air sampling method. Standard turn-around time shall be specified for analysis of modified aggressive air samples and wipe samples.

h. The Monitoring Contractor will documen t all the necessary information regarding sampling activities, fill out all field logs, data sheets, and chain-of-custody forms. The Monitoring Contractor will use F2L for all sa mple and chain-of-custody documentation. Samples must be labeled and accompanied with completed chain-of-custody forms before shipping to the designated laboratory. The label must include the EPA Project Tracking Number.

i. All information must be maintained usin g the secure EPA website (url, user name and password to be provided by EPA). Informa tion on sampling activities (date, sample ID) shall be provided to EPA, via the website, w ithin 24 hours of completion of sampling. All data shall be maintained as confidential.

The Project Monitor will keep a field notebook, doc ument the size of the sampled area, sampling locations (including a field sketch) and equipm ent used during the collection of samples (leaf blower, fan, etc.). In addition, date, start and completion dates for the cleaning, sample media, flow media type, flow rates (start/final), flow rate (average), time (start/finish), total elapsed time (min), calculated sample volume (L), pump fault, weather, quality assurance samples (lot blank) sampling dates and sample identification numbers (sample IDs), complete chain-of-custody forms, EPA Project Tracking Numbers and laborat ory address shall be entered into an agreed upon electronic format or website approved by EPA within 24 hours of activity.

9. Pre-cleaning and Post-cleaning Wipe Sampling Procedures:

a. The Project Monitor will collect pre-clea nup and post-cleanup wipe samples at 5 percent of the residences cleaned up to a maximum of 50 residences as instructed by EPA. This sampling will consist of the collection of 3 pre cleaning and 3 post cleaning wipe samples for dioxin, total metals and mercury (Note: a separate wipe sample is required for each parameter). Metals and mercury wipe sa mples will be collected in accordance with procedures specified in HUD Appendix 13.1. Dioxin wipe samples will be collected in accordance with the procedures specified in ASTMD 6661-01.Final Report December 2005 Page B-10 b. The Monitoring Contractor will documen t all the necessary information regarding sampling activities; will ensure personnel comple te all field logs, data sheets, and chainof-custody forms. The Monitoring Contractor shall use F2L for all sample and chain-ofcustody documentation. Samples must be labeled and accompanied with completed chain-of-custody forms before shipping to the designated laboratory. The label must include the EPA Project Tracking Number. Samples will be packed, sealed and shipped daily by courier for next day delivery to Paradigm Analytical Laboratories at: 2627 Northchase Parkway SE, Wilmington NC 28405

c. Personnel collecting wipe samples shall be familiar with and experienced in the collection of wipe samples using the specified sampling method.

The personnel collecting the samples will keep a field notebook and document sampling locations (including a field sketch). The personnel collec ting the samples shall complete chain-of-custody forms and record the number of wipe samples, EPA Project Tracking Numbers, the laboratory address, date, and sample IDs in an agreed upon electronic format or website approved by EPA within 24 hours of activity.

10. The Project Monitor shall schedule and ove rsee the cleaning operations of the Cleaning Contractor. Prior to initiating the cleaning of any residence, common space or HVAC system the Monitoring Contractor shall verify that the exterior of the buildi ng has been cleaned or has been inspected and found to be free of debris from the collapse of the WTC or shall obtain written authorization from EPA to proceed. No residen tial or common space shall be cleaned until the HVAC system has been evaluated and cleaned as necessary. If a scheduling problem arises, the Project Monitor should contact EPA. The Project Monitor shall coordinate daily access for cleaning and sampling with residents, owners, the Cleaning Contractor and sampling technicians.

11. The Monitoring Contractor shall provide sufficient personnel to provide periodic (2-4 hours/unit) oversight of the Cleaning Contractor to verify that work is being conducted in accordance with the Cleaning Contract Scope of Work.

12. The Project Monitor shall verify that the Cleaning Contractor is using properly trained and certified NYS DOL and NYC DEP asbestos certified workers

.

13. The Project Monitor shall conduct routine quality assurance inspections during the course of cleaning and concur by initialing the Cleaning Checklist (to be provided by EPA) for tasks completed in compliance with the Cleaning Contract Scope of Work.

The Project Monitor shall maintain informati on on the status of cleaning activities using the secure EPA website (url, user name and passwor d to be provided by EPA). Cleaning tasks completed in accordance with the Cleaning Contr act Scope of Work shall be documented on the Cleaning Checklist and reported to EPA via the we bsite within 24 hours. The Project Monitor shall record daily activities in the field notebook.

14. Personal Air Monitoring:

Personal air monitoring shall be performed by th e Monitoring Contractor in accordance with US DOL OSHA requirements. The person who conducts sampling shall possess a valid NYS DOLFinal Report December 2005 Page B-11 APST certificate. Personal air monitoring will be performed on a minimum of one employee of the Cleaning Contractor per shift per residence during the first 6 weeks of cleaning operations. This may be extended based on a determination made by EPA and OSHA. Thereafter personal air monitoring samples will be taken at one samp le per day. Air samples will be collected in accordance with the procedures specified in NIOSH 7400 Method and analyzed by phased contrast microscopy (PCM) followed by Asb estos Hazard Emergency Response Act (AHERA) transmission electron microscopy (TEM). Analysis of these samples shall be the responsibility of the Monitoring Contractor. Results of analysis shall be provided to EPA by the Monitoring Contractor in an approved electronic format within 24 hours of collection. Copies of the results shall also be sent immediately to OSHA and the Cleaning Contractor. The Cleaning Contractor will make these results available to the employ ees or their designated representatives for their review in accordance with 29 CFR 1910.1020 and 29 CFR 1926.1101.

The Monitoring Contractor shall document a ll the necessary information regarding sampling activities, fill out all field logs, data sheets, and chain-of-custody forms. The Monitoring Contractor shall use F2L for all sample and chain- of-custody documentation. This data shall be provided to EPA within 24 hours of completion of the sampling.

The Project Monitor shall submit upon request a fi eld package consisting of field sheets, field notes (copy), and chain-of-custody forms (copy), wh ich shall be delivered to the EPA via fax, or an alternative format agreed upon. The Monitoring Contractor must sign and date all field sheets, field logbooks and chain-of-custody forms. This data shall be provided upon request.

15. The Project Monitor shall perform final visual inspections within 2 hours of the completion of the cleaning. If all areas are not dust free at that time, then the Project Monitor will direct the Cleaning Contractor to reclean as necessary. If all areas are dust free, then the Project Monitor will authorize final air sampling. Final insp ections shall be documented using the Cleaning Checklist.

The Project Monitor shall submit to EPA, upon request, copies of the field notebook pages via email, fax, or alternative agreed upon format.

16. Post Cleaning Air Sampling Procedures:

a. Sampling shall not begin until a visual insp ection confirms the absence of visible dust and debris. Post cleaning air sampling shall be performed upon successful visual inspection and completed within 24 hours. A successful visual inspection shall be an inspection that verifies the absence of dust and debris.

b. All surfaces must be completely dry prior to the start of sampling.

c. Upon completion of the cleaning, air sampli ng will be conducted to ve rify attainment of the clean-up criteria. Based on consultation w ith the resident or owner, aggressive or modified aggressive air sampling will be conducted. Residents shall not be present during aggressive air sampling.

i. Before beginning aggressive air sampling, a 1 horsepower electric leaf blower shall be used to direct exhaust air ag ainst walls, ceilings, floors and other surfaces. This shall continue for at l east five minutes per 1,000 square feet of floor area. When directing the exhaust, caution shall be taken to minimizeFinal Report December 2005 Page B-12 disturbance and potential damage to furnishings and personal belongings

ii. Residents shall not be present in their residence during aggressive air sampling activities.

iii. Personal protective equipment includi ng disposable clothing, gloves, and respirators shall be worn during aggressive air sampling activities.

iv. HVAC systems shall be shut down or isolated locally during aggressive air sampling.

v. Room and window air conditioners shall not be operated during aggressive air sampling.

vi. Leaf blowers shall not be employed for modified aggressive air sampling.

vii. All other procedures are identical for aggressive and modified aggressive air sampling, unless otherwise noted.

d. At least one 20-inch fan shall be placed in the center of each room sampled. One fan per 10,000 cubic feet of room space shall be used . The fans shall be operated on slow speed and pointed toward the ceiling. The fans sha ll run for at least 15 minutes prior to the start of sampling. The fans shall operate con tinuously throughout sampling and shall not be turned off until sampling is completed.

e. For studio apartments 3 air samples shall be collected. For 1-bedroom apartments and above, 5 air samples shall be collected. Sampling equipment shall be placed in living areas and away from obstructions. The sampling cassette must be placed on a tripod, not taped to existing surface and should be directed downward at a 45 degree angle.

f. Common spaces will be sampled without the use of forced air devices (fans, leaf blowers etc). For small spaces, less than 160 square feet, 3 samples will be collected. For large spaces greater than 160 square feet and less than 25,000 square feet 5 samples will be collected. For spaces greater than 25,000 square feet, 1 sample will be collected for each 5,000 square feet. Sampling equipment sh all be placed away from obstructions. The sampling cassette must be placed on a tripod directed downward at a 45 degree angle and not taped to existing surfaces.

g. Air samples will be collected in accordan ce with the procedures specified in NIOSH 7400 Method and analyzed by phased contrast microscopy (PCM) followed by Asbestos Hazard Emergency Response Act (AHERA) transmission electron microscopy (TEM). The flow rate for collection of air samples shall be at least 10 liters per minute and no more than 15 liters per minute. Air sampli ng cassettes need to be monitored to ensure that cassettes are not clogging by monitoring flow rates. The minimum sampling volume required is 3600 liters. All clearance air samples and copies of necessary documentation shall be hand delivered daily to EMSL Laboratories at: 307 W. 38 th St., New York, New York, 212-290-0051.

h. 24-hour turn-around time shall be specified for the analysis of all post-cleaning air samples for Scope B cleanups and for all samples collected using the aggressive air sampling method. Standard turn-around time shall be specified the analysis of air samples collected for Scope A cleanups us ing the modified aggressive air samplingFinal Report December 2005 Page B-13

method.

i. The Monitoring Contractor shall docum ent all the necessary information regarding sampling activities, fill out all field logs, data sheets, and chain-of-custody forms. The Monitoring Contractor shall use F2L for all sa mple and chain-of-custody documentation. Samples must be labeled and accompanied with completed chain-of-custody forms before shipping to the designated laboratory. The label must include the EPA Project Tracking Number.

j. All information must be maintained usin g the secure EPA website (url, user name and password to be provided by EPA). Dates for monitoring and/or cleaning shall be provided to EPA electronically in an EPA approved format within 24 hours. Information on sampling activities (date, sample ID) will be provided to EPA, via the website, within 24 hours of completion of evaluation. All da ta shall be maintained as confidential.

The Project Monitor shall record all post-cleani ng information in the EPA Indoor Air database or an agreed upon electronic format. This informa tion shall include, but is not limited to sampling technique (aggressive or modified aggressive), vi sually cleaned (Y/N), damage claim (Y/N), and post cleaning status (pass/fail). Dates and times shall be associated with all activities including start and finish date.

Additional information including but not limited to, absence of dust/d ebris, equipment used (leaf blower, fan etc.), size of space, start and completion dates for the cleaning, sample media, flow rates (start/final, flow rate (average), time (s tart/finish), total elapsed time (min), calculated sample volume (L), pump fault, weather, units, QC samples (lot blank), sampling dates, sample IDs, complete chain-of-custody forms, EPA Pr oject Tracking Numbers and laboratory name shall be entered into an agreed upon electronic format or website approved by EPA within 24 hours of activity. Dates and times shall be associated with all activities including start and finish date.

17. The residence will be recleane d and retested if the clean-up criteria of 0.0009 fibers/cc (PCME measured by TEM) is not achieved or if determined necessary by EPA. This clean-up criterion may be reevaluated and revised, if determined n ecessary based on field conditions and analytical limitations.

18. The Project Monitor will conduct a post-cleaning inspection of the residence with the resident at the completion of the sampling. The resident should inspect the residence and identify any damage at this time and sign-off on the Project Completion Form. Any damage or loss to a residence or its contents sha ll be documented on this form.

The Project Monitor shall submit upon request to the EPA copies of the field notebook as requested via fax or alternative format agreed upon and within two days of request. Final air sampling shall be performed with 24 hours of fina l visual inspection. The Project Monitor shall ensure that the Project Completion Form is co mpleted for each residence cleaned or sampled.

19. The Project Monitor will complete a daily re port which documents site observations, cleaning starts, continuations, completions, air sampling, recleaning and any delays or difficulties encountered.Final Report December 2005 Page B-14 The Project Monitor shall enter appropriate da ta from the completed Project Completion Form into the EPA Indoor Air web database or an agr eed upon alternative electronic format or website approved by EPA within 24 hours of activity.

20. EPA will notify residents of the results of samp ling conducted as part of this project. No other party is authorized to release residential or common area sampling results.

21. Any damage to or loss of private property that occurs during sampling or is caused by the Monitoring Contractor or its employees while enga ged in activities covered by this scope of work is the responsibility of the Monitoring Contractor. The Monitoring Contractor is not responsible for damage or loss caused by the acts of third pa rties not involved in activities covered by this scope of work.

22. Copies of all reports, Cleaning Checklist, Inspection Checklists, Project Completion Forms and chain-of-custody forms shall be submitted daily to the EPA.Final Report December 2005 Page C-1



# APPENDIX C.

Evaluation Procedures to Determine the Presence of World Trade Center (WTC)-Related Dust and Debris in Residential Ventilation Systems in Lower Manhattan

C.1.0 SCOPE

The procedures contained in this document provi de guidance for determining the presence of WTCrelated dust in residential ventilation systems in Lo wer Manhattan. This documen t is solely concerned with determination of the presence of WTC-related dus t in residential ventilation systems. Determining

the exact nature of all hazardous air contaminants a nd contaminants other than airborne dust that may have been released during the WTC collapse and that may have potentially impacted ventilation systems operating at the time of the collapse is beyond the scope of this document.

This document provides general guidance to address the following with respect to residential ventilation systems impacted by WTC-related dust:

• Professional, health and Safety Requirements for Individuals performing the evaluations

• Evaluation Procedures (Visual Assessment, Hist orical Assessment, and Sampling Procedures)

• Post-Cleaning Visual Inspection Procedures

This document is limited in scope to evaluation of environmental conditions within the ventilation systems to determine whether they have been impa cted by the WTC collapse. This document is not concerned with the following:

• Mechanical operation of the systems

• Environmental conditions, contaminants, or other cond itions within the systems that are not related to the WTC collapse

• Recommendations and procedures that by thei r nature must be contaminant-specific



### C.2.0 PURPOSE

The purpose of this document is to provide pro cedures for inspecting and evaluating residential ventilation systems in Lower Manhattan to determine if such systems have been impacted by airborneFinal Report December 2005 Page C-2 dust from the WTC collapse, and to provide guidelin es for the qualifications of personnel accomplishing such inspection and evaluation. WTC-related dust is generally c onsidered to have common, cons istent, and readily observable characteristics visually and tactilely differentiati ng it from common dust. WTC-related dust generally contains extremely fine particles similar in consistency to talcum powder, is light-colored, contains pulverized concrete and/or gypsum wall board, and may contain asbestos fibers.

Ventilation systems are reservoirs for environmental dust and dirt. Therefore, in some cases, it may not be possible to visually differentiate between WTC-re lated dust and environmental dust that was present in the ventilation system prior to or after the WTC co llapse. In these cases, bulk dust sampling will be performed.



### C.3.0 APPLICABLE DOCUMENTS

This section provides full bibliography for references made within this document. Evaluations should be conducted in a manner that is fully compliant with the guidance provided in the following documents, to the extent applicable.

ACR 2002, Assessment, Cleaning and Restoration of HVAC Systems , National Air Duct Cleaning Association, Washington, D.C. (2002).

Section 3 of ACR 2002 includes procedures for performing a visual assessment of HVAC systems required in Section C.5.5.2.1 of this document.

NADCA Standard 97-05, Requirements for the Installation of Service Openings in HVAC Systems, National Air Duct Cleaning Association, Washington, D.C. (1997).

NADCA 97-05 includes procedures for installing service openings in HVAC systems and construction and material specifications for replace ment panels, plates or access doors to cover such openings as required under Section C.5.5.1.2 of this document.

SMACNA HVAC Duct Constructi on Standards – Metal and Flexible , Sheet Metal and Air Conditioning Contractors’ National Association, Inc., 2 nd Edition (1995).

The SMACNA standard includes cons truction and material specificatio ns for access doors for covering service openings as required under Section C.5.5.1.2 of this document.

SMACNA Fibrous Glass Duct Construction Standards, Sheet Metal and Air Conditioning Contractors’ National Association, Inc., 6 th Edition (1992).

The SMACNA standard includes cons truction and material specificatio ns for access doors for covering service openings as required under Section C.5.5.1.2 of this document.Final Report December 2005 Page C-3 NFPA Standards 90A, Standard for the Installation of Air- Conditioning and Ventilating Systems, and 90B, Standard for the Installation of Warm Ai r Heating and Air-Conditioning Systems, National Fire Protection Association (1999 Edition).

The NFPA standards include cons truction and material specificati ons for replacement coverings on service openings as required under Section C.5.5.1.2 of this document.

OSHA Regulations 29 CFR 1910, Occupational Health and Safety Standards

The OSHA regulations specify health and safety requirements for protecting employees during the inspection procedures.



### C.4.0 PROFESSIONAL, HEALTH AND SAFETY REQUIREMENTS FOR INDIVIDUA LS PERFORMING THE EVALUATIONS

Many older ventilation system components contain both friable and nonfriable asbestos and may contain various contaminants such as mercury, PCBs, lead and microbial contamination. Aside from these contaminants, asbestos-containing pipe insulation, plaster and other asbestos-containing building materials may be disturbed during evaluation of ve ntilation equipment. Safety hazards such as fall hazards, electrical hazards, and mechanical hazards also may be encountered during evaluation of ventilation equipment. Due to the potential presence of these health and safety hazards, this section specifies minimum professional requirements for indivi duals performing the evaluations, as well as health and safety requirements pertinent to conducting the evaluati ons. It is not the intention of this document to provide all applicable health and safety requirements. It is expected that the entities performing work are knowledgeable in all federal, state and local health and safety requirements and standards pertinent to conducting the evaluations. This doc ument references several key OSHA standards that are relevant to

this work.



### C.4.1 Evaluation Team

All evaluations shall be performed in teams consis ting of a qualified HVAC/Electrical Professional and a qualified Environmental Professional. For large central air systems, it may be helpful to supplement the

team with a Sheet Metal Professional.



### C.4.2 HVAC/Electrical Prof essional - Requirements

1. The HVAC/Electrical Professional shall be an employee of a professional, licensed mechanical ventilation contracting or engineering firm.Final Report December 2005 Page C-4

2. The HVAC/Electrical Professional shall be ab le to demonstrate competency and document experience in the following areas: air ha ndling equipment identification and access, identification of system components, and installation of service openings in sheet metal and fibrous glass ducts in accordance with NFPA, NADCA and SMACNA guidelines and NYC building codes.

3. The HVAC/Electrical Professional shall have received training from their employer for reasonably anticipated hazards during HVAC work including training required under OSHA standards, including but not limited to lockout/ta gout, fall protection, and personal protective equipment standards.

NOTE: If necessary, a licensed electrician shall be subcontracted to de-energize electrically operated equi pment in accordance with OSHA’s lockout/tagout requirements.

4. The minimum personal protective equipment required for use by the HVAC/Electrical Professional includes:

a. a supply of disposable protective coveralls b. a supply of disposable protective gloves

c. safety glasses d. respiratory protection as specified in item 5 below

5. The HVAC/Electrical Professional shall be capab le of wearing and sha ll be provided with a P100 air purifying respirator with appropriate me dical determination, fit testing and training as required under OSHA’s personal pr otective equipment standard.

6. The HVAC/Electrical Professional shall have received 2-hour asbestos awareness training.

7. The HVAC/Electrical Professional shall be responsible for:

a. the lockout/tagout of electrical or mechanical hazards required to safely perform the evaluations;

b. the HVAC/Electrical Professional’s firm shall provide sound equipment as needed to meet OSHA’s fall protection requirements that may be applicable to parts of the evaluations and the HVAC/Electrical Professional shall be res ponsible for implementing the use of such equipment;

c. locating and identifying ventilation system com ponents to be included in the evaluation;

d. any disassembly of any ventilation equipment and components required to complete the evaluation, and proper re-assembly following the evaluation; and

e. assist the Environmental Professional in making determinations required in Section C 4.4

.Final Report December 2005 Page C-5



### C.4.3 Sheet Metal Prof essional – Requirements

Requirements for the Sheet Metal Professional, if part of the evaluation team, are identical to those listed

in Section C.4.2 for the HVAC/Electrical Professional.



### C.4.4 Environmental Professional - Requirements

The Environmental Professional shall hold a current EPA accreditation as an AHERA Building Inspector. The primary purpose of the asbestos certification require ment is to be able to identify asbestos-containing materials and asbestos-related hazards in order to a void the disturbance of asbestos-containing materials during the evaluations.

1. The Environmental Professional shall have current EPA accreditation as an AHERA Building Inspector in any U.S. state.

2. The minimum personal protective equipment required for use by the Environmental Professional includes:

a. a supply of disposable protective coveralls b. a supply of disposable protective gloves

c. safety glasses d. respiratory protection as specified in item 3 below

3. The Environmental Professional shall be capable of wearing and shall be provided with a P100 air purifying respirator with appropriate medical determination, fit testing and training as required under OSHA’s pers onal protective equipment standard.

4. The Environmental Professional is responsible for:

a. ensuring that no asbestos-containing materials are disturbed during the evaluations;

b. determining which personal protective equipment will be used by the HVAC/Electrical Professional, the Sheet Metal Professional and by the Environmental Professional during the evaluation; and

c. collecting any bulk, wipe, microvac or tape-lift samples that are necessary to complete the evaluation;

d. making determinations required in Section C.5.3 ; and

e. cleaning up any debris that may be disturbed as a result of the evaluation using a HEPA

vacuum.Final Report December 2005 Page C-6



### C.5.0 EVALUATION PROCEDURES

C.5.1 General

Ventilation systems are likely to vary widely in t ype, configuration and complexity. This evaluation procedure applies considers three general categories of ventilation systems that may be encountered in residential buildings in lower Manhattan:

1. Ventilators, wall air conditioning units and window air conditioning units in common spaces ; 2. Fan coil or heat pump units in common spaces ; and

3. Central systems with heating and/or cooling capabilities. Wall air conditioning units and window air conditioning units which serve an individual residence are not included in this evaluation procedure. These units will be cleaned during the cleaning of residential

spaces.

Note that exhaust systems such as bathroom and kitchen exhaust fans that directly remove room air to the outdoors are not included in this evaluation procedure. Components of direct exhaust systems typically include exhaust grilles, exhaust duc t, exhaust fan, and rooftop or wall exhaust outlet devices. If information suggests that contamination of direct e xhaust systems may be present, an evaluation may be performed using the same principles outlined for items 1, 2 , and 3 above. Similarly, any ventilation equipment encountered that does not fall into any of these categories can be inspected using principles

outlined in this section.

The locations within the ventilation system equipmen t expected to have the greatest impact from WTCrelated dust include air intakes and in take ducts, intake air dampers, inta ke air filters and various system components located downstream of the intake air filters , depending on the system’s filtration efficiency. Dust may collect at potential impingement points such as duct terminations, tran sitions and elbows, and interior system components such as control devices, dampers, thermal coils, turning vanes, fans, etc.

One factor that may be considered in performi ng the evaluation is whether or not the ventilation equipment operated during the WTC collapse and in the weeks immediately following the collapse. Equipment that was not operating due to power loss, or due to concerns about entrained dust, may not have been impacted as heavily as equipment that operated throughout the collapse and immediate cleanup response.Final Report December 2005 Page C-7

Section C.5.3 includes a listed of recommended equipment for performing the evaluation.

Section C.5.4 includes a list of system components for each equipment category.

Section C.5.5 includes the evaluation procedures that may be applied to each system component.



### C.5.2 Documentation of Existing Mechanical Conditions

Prior to the start of the evalua tion, the HVAC/Electrical Professional shall ensure that the ventilation system is cycled and that there are no obvious exis ting deficiencies affecting proper mechanical operation of the system for which the evaluation team may later be held responsible.



### C.5.3 Recommended Supplies and Equipment

− Personal protective equipment (see Section C.4.0 )

− Sampling supplies (see Section C.5.5.2.3 )

− Disposal bags (see Section C.5.5.2.3 )

− Spray bottle containing soapy water

− Cleaning cloths

− Ladders (as needed)

− Lifts or scaffolding (as needed)

− Extension cords

− Hand tools (screw driver, pliers, etc.)

− Rotary metal cutting saw

− For covering service openings, sheet metal plates, panels or access doors meeting NADCA 9705, NFPA 90A/90B and SMACNA specifications

− Telescoping inspection mirrors and flashlights

− Boroscope

− HEPA vacuum



### C.5.4 Typical System Components for Each Equipment Category

The ventilation systems may contain, but may not n ecessarily be limited to combinations of the listed components in each category.Final Report December 2005 Page C-8



### C.5.4.1 Ventilators, Wall Air Conditioning Units and Window Air Conditioning Units in Common Spaces

− Outside air intake louvers, grates and screens

− Outside air duct

− Outside air dampers

− Return air grille

− Return air plenum

− Filter rack

− Filter media

− Coils (evaporator)

− Blower assembly

− Condensate drain pan

− In-line electrical resistance st rip heaters (in supply ducts connected to unit ventilators)

− Fire dampers

− Turning vanes

− Supply plenum or supply duct liner

− Supply air diffuser



### C.5.4.2 Fan Coil/Heat Pump Units in Common Spaces

− Return grille

− Return air plenum

− Filter rack

− Filter media

− Blower assembly

− Thermal coils

− Supply plenum

− Supply diffusers



### C.5.4.3 Central Air System

− Outside air intake louvers, grates and screens

− Outside air duct

− Outside air dampers

− Return air grilles

− Return air plenum

− Return air plenum damper

− Return air ducts

− Turning vanes

− Mixing chambers

− Filter rack

− Filter banks/media

− Pre-heat coils

− Cooling coils

− Re-heat coilsFinal Report December 2005 Page C-9

− Humidification and/or air cleaning equipment

− Fire dampers

− In-line re-heat coils

− Interior insulation

− Duct connectors

− Blower assembly including blower wheel, blower housing, air vanes, in-line noise attenuators, acoustical treatment s (e.g., baffles, duct linings)

− Condensate drain pan

− Condensate accumulator

− Supply air plenum

− Supply air plenum damper

− Supply plenum or supply duct linings

− Supply air ducts (high and low pressure)

− Supply air diffusers

− Terminal boxes

− Open or ducted passive ventilation shafts



### C.5.5 Evaluation Procedures

C.5.5.1 Accessibility

C.5.5.1.1 Locate System Components Accessible for Visual Inspection

The HVAC/Electrical Professional shall make an as sessment of the accessibility of the various components of the system. For example, component s may be enclosed within permanent sheet metal panels, or may be located above or be hind solid plaster ceilings and walls.

Determine the components that are accessible. At minimum, representative surfaces of the following system components should be inspected:

− air intake (outdoor or return)

− air intake dampers

− return air grilles

− return air plenum

− horizontal surfaces

− impingement points (e.g. turning vanes, elbows, transitions)

− filter racks and filter media

− blower

− thermal coils

− interior surfaces of the supply air ducts

− horizontal surfaces

− impingement points (e.g. turning vanes, elbows, transitions)

− volume dampers

− terminal boxes

− supply diffusersFinal Report December 2005 Page C-10



### Note that depending on the size and complexity of the HVAC system, access may require the use of ladders, lifts or scaffolds using appropriate methods of fall protection.

C.5.5.1.2 Methods of Access

The following list summarizes methods of accessi ng HVAC system components for inspection:

− through existing service openings (i.e., access doors and panels)

− by disassembly of housing

− by installation of service openings (may range from 1” diameter holes to access doors)

The HVAC/Electrical Professional shal l assess the accessibility of each HV AC system component to be inspected. If an HVAC system component is not accessible, the Environmental Professional shall be

consulted to determine whether installation of a service opening will likely disturb asbestos-containing materials. After such consultation, if approved by the Environmental Professional, the HVAC/Electrical Professional shall install service openings as need ed to inspect the HVAC components listed in Section

C.5.5.1.1

.

NOTE : Disassembly of housing and installation of service openings may only be performed by the HVAC/Electrical Professional and replacement plates, panels or access doors shall be installed in accordance w ith NFPA, NADCA and SMACNA standards and NYC building codes.

The Environmental Professional shall repair or seal an y interior/exterior duct insulation disturbed by the installation of service openings.



### C.5.5.2 Methods of Evaluation

C.5.5.2.1 Visual Inspection Procedure

The HVAC/Electrical Professional and the Environmental Professional should jointly perform the visual inspection. At minimum, the components listed in Section C.5.5.1.1 shall be inspected.

The visual inspection shall be accomplished us ing one or more of the following methods:

− direct examination

− telescoping inspection mirrors and flashlights inserted through service openings

− boroscopes inserted through supply air diffusers or other existing openings

− remotely operated video cameraFinal Report December 2005 Page C-11



### C.5.5.2.2 Assessment of Conditions

Visual Assessment

All required interior surfaces in contact with the air st ream shall be inspected for visible accumulations of dust and/or debris. Inspect all surfaces in contact with the air stream. Information indicates that some of the defining characteristics of WTC-related dust are that it contains extremely fine particles similar to talcum powder in consistency, is light-colored, contains pulverized c oncrete and/or gypsum wallboard, and may contain asbestos fibers. Th e visual inspection shall document:

− A general description of the appearance of interior surfaces of the various system components. The description for each component will include, but may not be limited to:

• interior duct/fan housing surfaces are porous/non-porous

• interior duct and fan housing surfaces are lined with insulation

• interior duct and fan housing surfaces are doubl e-walled (i.e. interior insulation with perforated metal cover)

• filter loading, condition of filters and filter rack

• interior surfaces are free/not free of visible dust and debris or suspect WTC-related dust and debris

• description of dust color, level of dust loading that may include:

− the depth of dust observed on each component (e.g., less than 1/16 inch, greater than or equal to 1/16 inch.).

− the depth and location of dust on ducts and fan housing (i.e., on interior bottom, top and sides of ducts)

− visually estimated percentage of surfa ce area with suspect WTC-related dust

• whether or not there are materials that are likely not associated with WTC-related dust such as building-related asbestos-containing materials, animal carcasses, delaminating lining material, visible mold growth, water damage, fecal matter, feathers or other evidence of animals, etc.

Historical Assessment

The evaluation team shall attempt to describe any ot her available information from site occupants or building managers, such as the known status of sy stem operating conditions at the time of the WTC collapse, ventilation system maintenance (i.e., cleani ngs, filter changes, or replacement since the WTC collapse).Final Report December 2005 Page C-12

NOTE: Based on these assessments, to the best of hi s/her ability the Environmental Professional will state a general impression of the overall cleanliness of each component, and whether or not it appears to be impacted by WTC-related dust.



### C.5.5.2.3 Environmental Sampling Procedure

If the evaluation team determines that the ventilation equipment appears to have been impacted by WTCrelated dust, cleaning will be recommended and envi ronmental sampling will not be required. This information shall be included in the HVAC system evaluation report. The HVAC system evaluation report must be sent to EPA electronically in PDF format.

In the absence of environmental sampling that indica tes otherwise, WTC-related dust shall be assumed to be an asbestos-containing material and cleaning pr ocedures shall be performed in accordance with procedures for asbestos decontamination (see Section C.4.0 ).

If dust or debris is present and it is not apparent that it is related to the collapse of the WTC, a maximum of 5 bulk samples will be collected and analyzed for pH, fibrous glass and crystalline silica. These samples and copies of necessary documentation shall be sh ipped daily by courier for next day delivery to EMSL Analytical Inc., 107 Haddon Avenue, Westmont, NJ 08108 (Tel: 856-858-4800). This data will be used to determine if the lapse of the WTC. Dust will be considered WTC-rela ted if: pH is 9 or above; fibrous glass content is between 30 and 40 % and crystalline silica conten t is 5% or greater. Laboratory data will be reported back to th e Monitoring Contractor and shall be included in the HVAC system evaluation report. The HVAC system evaluation report must be sent to EPA electronically in PDF

format.

Sampling Procedures

1. Sampling of interior dust may only be performed by the Environmental Professional.

2. Minimum personal protective equipment used by the Environmental Professional shall include disposable protective coveralls, a P100 respirator, safety glasses, and disposable gloves.

3. A HEPA vacuum shall be on-site for clean-up, if needed.

4. Dust may be collected from any visible deposit. Frequently, dust will be more likely to accumulate on horizontal surfaces, although this may not always be the case.Final Report December 2005 Page C-13 5. Sample collection and analysis:

Bulk samples . Bulk dust samples may be scooped or scraped into a sealable container using an appropriate sampling device. A minimum of 20 gram s of sample will be collected. Wipe the device with a clean, wet cloth between samples to prevent cross-contamination. Wipe the exterior of the sample container to remove excess dust. Label th e container with the sample identification. At minimum, for each sample the date, building name, and specific location of the sample, including whether or not the sample is located upstream or downstream of the air filters, shall be recorded.

6. Completion of sampling:

The Environmental Professional shall repair or seal any interior/exterior duct insulation disturbed as a result of sample collection.

At the completion of sampling, the Environmenta l Professional shall wipe the outside of his/her respirator with a clean, wet, disposable cloth and shall place the respirator into a clean, sealed plastic bag. Clean any suspect debris or contaminati on resulting from the sampling activities using a HEPA vacuum or wet wiping methods. All wiping cloths, disposable protective suits and gloves, and drop cloths shall be placed into a sealed polyethylene bag for proper disposal.



### C.6.0 POST-CLEANING VISUAL INSPECTION

Cleanliness verification shall be performed by the evaluation team consisting of a HVAC/Electrical

Professional and an Environmental Professional as described in Section C.4.0 of this document after cleaning of one or more ventilation system components has been completed.

Following cleaning, the Environmental Professional sha ll ensure that all interior ventilation system components that were subject to the cleaning proce dures are visibly clean. An interior surface will be considered visibly clean when it is fr ee from non-adhered subs tances and debris.

To determine whether a surface if visibly clean, a thorough and comprehensive visual inspection and assessment of all cleaned components shall be performed in accordance with visual procedures

established in Sections C.5.5.2.1 and C.5.5.2.2 of this document. In order to observe locations that are difficult to clean, additional access openings shall be installed as needed to conduct a comprehensive post-cleaning visual inspection.Final Report December 2005 Page D-1



# APPENDIX D.

World Trade Center Indoor Dust Cleaning Program Cleaning Contract Scope of Work

DRAFT 8/8/02

D.1.0 INTRODUCTION

All work performed under this contract entered in to between the New York City Department of Environmental Protection (DEP) and the cleaning contr actor must be in compliance with all applicable laws and regulations, including but not limited to re gulations issued by the United States Environmental Protection Agency (EPA), the United States Depart ment of Labor Occupational Safety and Health Administration (OSHA), the New York State Department of Labor (NYS DOL), and the New York City Department of Environmental Protection (DEP).

The EPA and DEP will solicit participation in a dir ect assistance program for residents and residential building owners south of Canal Street who are concerned that their residences may have debris/dust from the collapse of the World Trade Center (WTC). A DEP/EPA telephone hotline has been established to receive requests from the public. The assistance will in clude various options, including cleaning within residences and common spaces by licensed asbestos c ontractors with follow-up asbestos sampling, asbestos air sampling only, and providing high efficiency particulate air (HEPA) vacuums.

NYS DOL licensed asbestos contractors shall perform cleaning activities for residents who wish to have their homes cleaned. An independent Monitoring Contractor will schedule and monitor the work, perform a visual inspection, and perform asbestos air sampling when the cleaning is completed.

The purpose of this contract is to acquire the servi ces of a NYS DOL licensed asbestos contractor with DEP and NYS DOL certified workers for the performan ce of cleaning activities at residential buildings impacted by the collapse of the World Trade Center . The scope of work for the cleaning activities is attached. The cleaning contractor shall supply all equipment and supplies necessary to perform the work specified in this contract.Final Report December 2005 Page D-2



### D.2.0 ROLES AND RESPONSIBILITIES

As provided in the instant contract, EPA’s World Tr ade Center Dust Cleanup Field Personnel shall have the authority of Project Manager, in addition to DEP’ s Project Manager. As such, the authority of EPA World Trade Center Dust Cleanup Field Personnel sh all include the following authority related to cleaning work conducted under this contract:

- The authority to stop wo rk for health and safety reasons. - The authority to stop work fo r non-compliance with the Scope of Work. - The authority to give technical direction to the contractor in the performance of the work. - The authority to review and approve or disa pprove of the qualifications and performance of the cleaning personnel involved in the work. - The right to inspect and accept or reject any work .

An independent Monitoring Contractor will perform air monitoring. The Personal air monitoring will be performed by the Monitoring Contractor on a minimum of one employee of the Cleaning Contractor per shift per apartment during the first 6 weeks of clean ing operations. Thereafter, personal air samples will be taken randomly at a rate of one sample per da y. The results of this sampling shall be sent immediately to EPA, OSHA and the cleaning contractor. The cleaning contractor shall make these results available to the employees or their designa ted representatives for their review in accordance with

29 CFR 1910.1020 and 29 CFR 1926.1101.

The Monitoring Contractor is responsible for: - Scheduling and coordinating the cleaning and monitoring work with residents, building owners, the Cleaning Contractor and EPA; - HVAC system evaluation and inspection; - Oversight of the Cleaning Contractor; - Inspection of the Cleaning Contractor’s work; - Area air monitoring; and - Personal air monitoring. The Cleaning Contractor is responsible for clean ing residences, common spaces and portions of HVAC systems identified by the Monitoring Contractor.Final Report December 2005 Page D-3 

### D.3.0 QUALIFICATIONS

This contract requires that only a NYS DOL licen sed asbestos contractor and only NYC DEP and NYS DOL certified workers will be allowed to perform any of the cleaning activities under this contract. This requirement also applies to any subcontractors invol ved in the cleaning. A copy of these licenses and certificates must be available on-site during the perfo rmance of work and must be presented upon request.

The HVAC system cleaning contractor shall be a certified member of the National Air Duct Cleaners Association (NADCA) or shall maintain membersh ip in a nationally recognized non-profit industry organization dedicated to the cleaning of HVAC systems. If the cleaning contractor is not a member of the NADCA, a subcontractor that is a member may perform the HVAC system cleaning.

All contractors and subcontractors involved in the cl eaning activities under this contract are responsible for completing a background check on their employ ees and for screening unacceptable candidates from the pool of on-site workers. Background checks sha ll be completed with 30 days of the award of the contract. Thereafter, background checks shall be completed prior to employees beginning on-site work. EPA will provide guidelines for evaluating the background information collected. Contractors are required to maintain records of background checks for 4 years and to make them available to the DEP and EPA when requested. At a minimum, the background check must include:

1. Law enforcement checks (5 years)

2. Professional license and certification



### D.4.0 SPECIFICATIONS

This contract between DEP and the cleaning contract or shall be in force for 24 consecutive months from the commencement date. The cleaning contractor must be prepared to mobilize within 72 hours of the

contract award. The contractor shall have on staff and assigned to this contract a sufficient number of properly trained and certified workers to clean 20 units simultaneously and complete the cleaning activities in each residence within no more than 2 days. The contractor shall be prepared to work 7 days per week. Activities shall be coordinated with the Monitoring Contractor to ensure that the visual inspection is performed within two hours of the completion of cleaning activities. Cleaning activities shall be considered completed upon successful visual in spection by the Monitoring Contractor. Copies of

all invoices for work conducted under this contract sh all be provided to EPA. EPA will review submittedFinal Report December 2005 Page D-4 invoices to confirm that the work has been comp leted and forward them to DEP for processing. Decisions regarding the reimbursement of costs will be made by DEP.

Electricity and water necessary to conduct the work required under this contract will be provided by the owner or occupant of the work area.

All cleaning operations will be conducted in accordan ce with either Scope A or Scope B as described

below.

HEPA means a filter system capable of trapping and retaining 99.97% of all mono-dispersed particles of

0.3 micrometers in diameter.

Vacuum or HEPA vacuum means a vacuum cleaner e quipped with a HEPA filter, with a minimum static

water lift of 95 inches.

Water extraction cleaner means a water extraction carpet cleaner equipped with a motorized agitator brush for carpet cleaning and upholstery nozzle with a minimum static water lift of 95 inches.

The cleaning contractor shall attend the mandatory pre- bid meeting and project kick off meeting prior to the commencement of work. The cleaning contractor sh all as requested, attend meetings or participate in

conference calls with EPA to coordinate field activities.

The cleaning contractor shall apply for any and all necessary permits and applications necessary to complete the work.



### D.4.1 Scope of Work A

Application: These procedures apply to the cleaning of minimal dust accumulations (light coating). If a visual inspection indicates the presence of significan t accumulations of dust and/or debris from the collapse of the WTC in residences or comm on spaces (including elevator shafts), Scope B procedures shall be applied (refer to Scope of Work B ). Residents may be present during Scope A cleaning procedures.Final Report December 2005 Page D-5 Cleaning of Common Spaces

Common spaces including hallways, stairways and the interior of elevator cars shall be cleaned, if requested by the building owner. The Monitoring Contractor in consultation with EPA, or EPA’s designee will evaluate and determine if other common areas including utility rooms, laundry rooms, compactor rooms, elevator shafts require cleaning. Work will begin from the entrance and continue through all common spaces in an orderly fashion. A detailed description of the minimum cleaning requirements for common space is as follows: - Vacuuming will begin with the ceiling, c ontinue down the walls and include floors. A vacuum equipped with a motorized agitator bar will be used to vacuum carpets.

- Impermeable walls and floors will be wet wiped, after consultation with and approval by owner. Wet wiping will not be conducted if it is determined that it would cause damage to the surface.

- Carpets will be cleaned with a water extracti on cleaner after consultation with and approval by owner. After cleaning, red rosin construction pape r will be applied to high traffic areas to protect carpets from soiling. Water extrac tion cleaning will not be conducted if it would cause damage to the carpet.

- Surfaces that are not cleaned by wet met hods (wet wiping and water extraction cleaner) will be vacuumed two times.

Cleaning of HVAC Systems

HVAC systems that are determined by the Monitoring Contractor to be impacted by dust or debris from the collapse of the World Trade Center will be cleaned in accordance with the site -specific scope of work prepared by the Monitoring Contractor and approve d by EPA. HVAC systems cleaning, if warranted, shall be completed prior to the initiation of the clean ing of common space or residences within an affected building. In the event that the HVAC system for an entire building re quires cleaning, a separate, site specific contract will be awarded by DEP for this wo rk. If only a portion of an HVAC system requires cleaning, then the cleaning contractor will conduct the cleaning utilizing specialized labor trained and experienced in duct cleaning.

HVAC cleaning shall be conducted in accordance with National Air Duct Cleaners Association (NADCA) General Specification for the Cleaning of Commercial Heating, Ventilating and Air Conditioning Systems and the NADCA Assessment , Cleaning and Restoration Standard (ACR 2002)

. Verification of the effectiveness of HVAC system cleaning will be determined by the Monitoring Contractor. If dust or other contaminants are evident through visual inspection, those portions of theFinal Report December 2005 Page D-6 system where dust or other contaminants are present sh all be recleaned and subjected to reinspection for

cleanliness

. If the cleaning contractor is not a member of the NADCA, a subcontractor that is a member may perform this portion of the work.

Cleaning of Residential Spaces

Residences will be cleaned using HEPA vacuums, water extraction cleaners and wet wiping as described

below. Surfaces to be cleaned include but are not limited to walls, floors, ceilings, ledges, trims, furnishings, appliances, equipment, etc. Encapsula ting agents shall not be applied. Dry sweeping is prohibited. The cleaning contractor will not clean inside of drawers, cabinets, breakfronts and similar enclosed storage or display pieces, however, the ex terior of these pieces will be cleaned. Cleaning of clothing and accessories (handbags, shoes etc.) shall be the responsibility of the occupant. A detailed description of the minimum cleaning requirements is as follows: - Terraces, balconies, exterior window sills, window wells and window guards that are accessible from the interior of the dwelling, shall be cleaned.

- Interior windows, screens, window sills and window guards will be cleaned.

- Vacuuming will begin with the ceiling, c ontinue down the walls and include the floor. A vacuum equipped with a motorized agitator bar will be used to vacuum carpets.

- Impermeable walls and floors will be wet wiped, after consultation with and approval by owner/resident. Wet wiping will not be conducted if it is determined that it would cause damage to the surface.

- Carpets will be cleaned with a water extracti on cleaner after consultation with and approval by owner/resident. After cleani ng, red rosin construction paper w ill be applied to high traffic areas to protect carpets from soiling. Water extraction cleaning will not be conducted if it would cause damage to the carpet.

- Fabric covered furniture will be vacuumed and then cleaned with a water extractions cleaner after consultation with and if approved by owner/resident. Water extraction cleaning will not be conducted if it would cause damage to the furniture.

- All surfaces including but not limited to floors, walls, curtains, fabric window treatments, upholstery and other materials that are not cleaned by wet methods (wet wiping and water extraction cleaning) will be HEPA vacuumed two times. Fabric covered furniture that is not cleaned by wet methods w ill be vacuumed using an appr opriate brush attachment.

- Intake/discharge registers of HVAC systems (if present) will be removed/cleaned. The first foot of duct work will also be vacuumed; then the register will be reinstalled and covered with a layer of 6 mil polyethylene sheeting.Final Report December 2005 Page D-7

- Window and room air conditioners will be vacuumed, wet wiped and removed from their housing to allow access to internal portions of the air conditioner. The internal portions of the air conditioner a nd housing will then be va cuumed. Filters will be vacuumed and reinstalled. Air conditioners w ill be reassembled and reinstalled after cleaning.

- Paperwork and books will be HEPA vacuumed.

- Electrical outlets will be vacuumed.

- Appliances such as refrigerators and stoves will be cleaned and moved. The floor footprint of the appliances will be cleaned a nd the appliance will be reinstalled in their original positions.

- Refrigerator cooling tubes will be brushed and vacuumed.

- The first foot of all exhaust duct work (including stove, dryer and bathroom vents) where accessible, will be v acuumed. Exhaust fans will be vacuumed and wiped.

- Unobstructed closet floors will be vacuumed. - Solid objects (electrical equipment, exerci se equipment, etc.) will be wet wiped, moved to allow cleaning of the underl ying surface and will be returned to their original location.

- Dishwasher toe plates will be removed and the floor beneath the appliance will be cleaned.

- Baseboard heaters will be cleaned. Protectiv e covers on finned radiant heaters and baseboard heaters will be removed to e xpose heat elements. Fins are to be brushed and vacuumed to remove dust.

- All cleaning equipment will be vacuumed a nd/or wet wiped after completion of the cleaning and before removal from the work area.

- HEPA air filtration devices (AFDs) will run continuously during all cleaning activities, as appropriate given site conditions. AFDs shall be installed and operated to provide a minimum of one air change every 15 minutes. Make up air should be derived from a non-impacted source (i.e. open window or common spaces previously cleaned).

- A minimum of one asbestos supervisor sh all be present in each bu ilding during work.

- A Cleaning Checklist (to be developed by EPA) will be completed by the cleaning contractor as tasks are completed to document the progress of the cleaning.

- The cleaning contractor shall notify the monitoring contractor immediately upon completion of the cleaning. The Monitori ng Contractor will conduct a thorou gh visual inspection to verify the absence of visible dust a ccumulations. If dust is observed the cleaning contractor will reclean as necessary at no additional cost.

- Air sampling shall be performed by the Monitoring Contractor after the area is free of dust accumulations as determined by the Monitoring Contractor. The residence will be recleaned and retested if the clean-up criteria of 0.0009 fibers/cc (PCME measured by TEM) is not achieved or if determined ne cessary by EPA. This clean-up criterion may be reevaluated and revised, if determined nece ssary based on field conditions and analytical limitations.Final Report December 2005 Page D-8 - Any damage or loss that occurs during cleaning is the responsibility of the cleaning contractor. The cleaning contractor is not responsible for damage or loss caused by the acts of third parties not involved in the cleaning activities.

- Owner/residents may identify and tag certain furnishings (e.g. carpets and fabric covered furniture) for disposal rather than cleaning. Disposal of residents’ personal property shall not occur without prior written authorization by the owner.

- Disposal of all wastes generated duri ng the cleaning shall be the responsibility of the contractor. All waste genera ted shall be treated as asbestos-containing material (ACM). Transportation and disposal of ge nerated waste shall be in compliance with all applicable rules and regulations.

- If mold or peeling, flaking or chalking paint is observed in the work area, the cleaning contractor shall immediately contact the Monitoring Contractor.

- If in-place materials suspected to contain asbestos are observed the Cleaning Contractor shall immediately notify the Monitoring Co ntractor. The Monitoring Contractor will evaluate the condition of the material to identify damaged, deterioration, delamination, etc. The Cleaning Contractor shall wrap suspect ACM that is in good or excellent condition with 6-mil polyethylene sheeting and seal air tight with duct tape or equivale nt method prior to cleaning or air monitoring.

- In the event that damaged, deteriorated, delaminated, etc. suspected ACM is observed, the Cleaning Contractor will notify the Monitoring Contractor. Cleaning or air monitoring will not proceed in areas where such suspected ACM is observed until instructed otherwise.



### D.4.2 Scope of Work B

Application: A visual inspection was performed and large or significant accumulations of dust or debris from the collapse of the WTC was observed in comm on spaces, residences or portions thereof (such as windows, terraces or balconies)

.

Residents will not be allowed in the work area. R esidents may be present in the residence during cleaning

in cases where the work area can be is olated by the erection of isola tion barriers. In all other applications of Scope B it is assumed that residents will not be present in the residence. A detailed description of the minimum cleaning requirements is as follows:

- The cleaning contractor shall meet with EPA or the Project Monitor if requested, to discuss site specific procedures for debris removal, isolation of the work space and worker decontamination.Final Report December 2005 Page D-9 - An asbestos project notification form (NYC form ACP-7) shall be submitted, as required by Title 15, Chapter 1 of the Rules of the City of Ne w York (RCNY), directly to the NYC DEP Asbestos Control Program prior to the start of work. NYS DOL notification may also be required.

- At least one asbestos supervisor shall be present at each work place (work place is defined in Title 15 , Chapter 1 of RCNY as the work area and th e decontamination enclosure system).

- Personal protective equipment including dispo sable clothing, gloves, and respirators shall be worn during this cleaning activity.

- Warning signs shall be posted at all of the approach to the work area.

- A decontamination enclosure system shall be installed at the entrance to the work area.

- The shower room shall be equipped with at least a 6-foot flexible hose for waste decontamination. A remote holdi ng area with a lockable door for waste shall be located at the site and shall comply with all a pplicable storage rules and regulations. Waste removal shall not occur during worker shift changes or when workers are showering or changing.

- An entry/exit log in compliance with the requirements set forth in Title 15, Chapter 1 of RCNY shall be maintained in the clean room.

- A remote decontamination enclosur e system shall be considered when appropriate, i.e. inability to comply with the provision due to space limitation or othe r agency rules, such as for compliance with New York City Fire Department egress requirements.

- HVAC systems shall be shut down and locked out or isolated locally.

- Isolation barriers shall be installed with two layers of 6-mil polyethylene sheeting and sealed with tape.

- Negative pressure ventilation equipment (air filtration devices (AFDs)) shall be installed and operated during all cleaning activities. Equipment shall run continuously until clearance air monitoring. A minimum of one air change every 15 minutes shall be provided. When ducting to the outside is not possibl e, a second negative pressure ventilation unit compatible with the primary unit may be connected in series.

- When conducting cleaning of common space in apartment buildings, the elevator control shall be modified to bypass the work area.

- Prior to any cleaning of common spaces, isolation barriers (i.e. sealing off of all openings, including but not limited to windows, corridors, doorways, barriers, skylights, ducts, grills, diffusers, and any other penetra tions of the workplace) shall be installed with two layers of 6- mil plastic sheeting sealed with tape. All seams of HVAC or other system components that pass through the work place shall also be sealed. All openings shall be HEPA vacuumed prior to installing the isolation barrier.Final Report December 2005 Page D-10

- All WTC debris shall be misted and doubl e-bagged. Accumulation of water is prohibited. Water misting shall be su fficient to wet the material without water accumulations.

- After the removal of debris, all surfaces will be cleaned in accordance with the procedures specified in Scope A. Af ter all surfaces have been cleaned, a second cleaning shall be performed. This results in two full cleanings of all surfaces, with the following exception. Water extraction cleaning of car pets and fabric covered furnitu re will be conducted only once. Surfaces include but are not limited to walls, floors, ceilings, ledges, trims, appliances, equipment and furnishings.

- Residents may identify and tag certain furnis hings (e.g. carpets and fabric covered furniture) for disposal rather than cleani ng. Disposal of residents’ personal property shall not occur without prior written authorization.

- Disposal of all wastes generated duri ng the cleaning shall be the responsibility of the contractor. All waste genera ted shall be treated as asbestos-containing material (ACM). Transportation and disposal of ge nerated waste shall be in compliance with all applicable rules and regulations.

- A Cleaning Checklist (to be provided by EPA) will be completed by the cleaning contractor as tasks are completed to document the progress of the cleaning.

- An activity log will be maintained by the site supervisor.

- The cleaning contractor shall notify the monitoring contractor immediately upon completion of the cleaning. The Monitori ng Contractor will conduct a thorou gh visual inspection to verify the absence of visible dust a ccumulations. If dust is observed, the cleaning contractor will reclean as necessary at no additional cost.

- Air sampling shall be performed by the Monitoring Contractor after the area is free of dust accumulations as determined by the Monitoring Contractor. The residence will be recleaned and retested if the clean-up criteria of 0.0009 fibers/cc (PCME measured by TEM) is not achieved or if determined ne cessary by EPA. This clean-up criterion may be reevaluated and revised, if determined nece ssary based on field conditions and analytical limitations.

- After successful clearance air monitoring, is olation barriers shall be removed in conjunction with the use of a HEPA vacuum.

- Any damage or loss that occurs during cleaning is the responsibility of the cleaning contractor. The cleaning contractor is not responsible for damage or loss caused by the acts of third parties not involved in the cleaning.

- All work shall be in compliance with all othe r applicable requirements of Title 15, Chapter 1 of the RCNY and New York Industrial Code Rule 56.

- If mold or peeling, flaking or chalking paint is observed, the cleaning contractor shall immediately contact the Monitoring Contractor.Final Report December 2005 Page D-11 - If in-place materials suspected to contain asbestos are observed the Cleaning Contractor shall immediately notify the Monitori ng Contractor. The Monitoring Contractor will evaluate the condition of the material to identify damaged, deterioration, delamination, etc. The Cleaning Contractor shall wrap su spect ACM that is in good or excellent condition with 6-mil polyethylene sheeting and seal air tight with duct tape or equivale nt method prior to cleaning or air monitoring.

- In the event that damaged, deteriorated, delaminated, etc. suspected ACM is observed, the Cleaning Contractor will notify the Monitoring Contractor. Cleaning or air monitoring will not proceed in areas where such suspected ACM is observed until instructed otherwise.Final Report December 2005 Page E-1



# APPENDIX E.

Analysis of the Asbestos PCMe, Dust Lead Loading and Dust Dioxin Loading Data

Data analyzed in this report were extracted from th e Residential database on September 10, 2003. A copy of the data set, with data necessary to protect the privacy of individual participants in the program redacted, is available from the EPA Region 2 Records Center.



### E.1.0 S UMMARY 

### O F 

### D ATA 

### C OLLECTED



### E.1.1 Summary of TEM (PCMe) Data

Table 3-1 summarizes the sample results for asbestos. The data described in this section and Section

E.1.2 are results for asbestos phase contrast microscopy equivalent (PCMe) concentrations measured by transmission electron microscopy (TEM). A total of 28,702 sample results were available for asbestos by PCMe; 22,497 from residential units, and 6,205 from common areas within residential buildings (e.g., hallways, laundry rooms). Samples for PCMe analysis were collected from residential units that were tested only, as well as from residential units and comm on areas that were cleaned and tested. Results by PCMe were compared to the health-based benchm ark of 0.0009 f/cc (fibers/cubic centimeter) to determine the status (i.e., exceeds or does not exceed benchmark) of the residential units/common areas.

The asbestos clearance criteria for the WTC Indoor Air Clean-up Program were based on long (i.e., ≥ 5 um) fiber counts. The use of a minimum fiber length of 5 μ m for carcinogenic activity represents

current scientific consensus and reflects the criteria in the EPA Integrated Risk Information System (IRIS) for attributing carcinogenic potency.

Phase Contrast Microscopy equivalence is a process to identify asbestos fibers by TEM analysis that would also be visible by PCM. The optical resolution of the phase contrast microscope is approximately 5 microns in length and 0.25 microns in width for fibe r analysis. Historically, most of the occupational studies available (and reviewed in IRIS), from which es timates of cancer potency of asbestos are derived,Final Report December 2005 Page E-2

employed PCM analysis. Therefore, in cases where TEM is used for asbestos analysis, fiber counts need to be adjusted to estimate a PCMe.

The asbestos counting rules employed for the WTC Indoor Clean-up Program were designed to record PCMe fibers. Thus TEM analyses were performed, and fibers were then counted following AHERA (Asbestos Hazard Emergency Response Act) counting rules. Fibers ≥ 5 μ m (AHERA also stipulates a minimum 5:1 aspect ratio) were distinguished from total (i.e., > 0.5 μ m) fiber counts, although total fibers counts were also recorded. To maximize analyti cal capacity for a large sampling event, no minimum width requirement was employed. This may resulted in a modest over counting bias by not eliminating extremely thin fibers (i.e., <0.25 um) from the count . However, the potential bias attributed to this counting procedure would be protective of human health. Modification was made to AHERA (by obtaining larger samples volumes) in order to achieve the lower detection limits required by the use of a

risk-based clearance criteria.



### E.1.2 Summary of TEM data

Table 3-2 lists the types of asbestos that were detected by TEM in the airborne asbestos samples from

residences and common areas. Asbestos was detect ed in approximately 4% of the available TEM samples. Chrysotile asbestos was the detected in approximately 92% of the samples included in this subset of the data; amosite was detected in approximat ely 3%. Other forms of asbestos that were detected include actinolite, anthophyllite, tremolite, and crocidolite.



### E.1.3 Summary of Wipe data

E.1.3.1 Lead and Dioxin Wipe Data

This section of the report describes lead and di oxin dust wipe data that were collected from 263 residences that were located in 157 buildings. Wi pe data that were used to assess efficacy of the cleanup program are discussed in Section E.4.0Final Report December 2005 Page E-3 

### E.1.3.1.1 Lead Wipe Data

The database contained 1,540 wipe samples for dust l ead loading that were collected from 263 residences, located in 157 buildings. Summary statistics for the data are provided in Table 3-3 . Samples that were

below the detection limit of 1.86 μ g/ft 2 were set equal to the detection limit. Review of existing environmental standards/regulations identified an app licable and relevant standard to set a health-based

benchmark for lead in interior dust. The Reside ntial Lead-Based Paint Hazard Reduction Act (Title X) Final Rule (40 CFR, Part 745, 1/5/01) established unifo rm national standards for lead in interior dust. Thus, both EPA and the United States Department of Housing and Urban Development (HUD) have set a

dust standard for lead of 40 μ g/ft 2 for floors (including carpeted floors), and 250 μ g/ft 2 for interior window sills. To support the development of a du st standard, EPA performed an analysis of the Rochester Lead-in-Dust Study (HUD, 1995). At 40 μ g/ft 2 , a multimedia analysis shows a 5.3% probability that a child’s blood lead level would exceed 10 μ g/dL. Thus, this standard meets the criteria established by EPA (i.e., 95% probability to be below 10 μ g/dL) (EPA, 1994) for managing environmental lead hazards. However, an additiona l increment of protectiveness was added by setting the

health-based benchmark for lead in settled dust at the more stringent HUD screening level of 25 μ g/ft 2

. Approximately 9% of all lead wipe samples (i.e., test only and clean and test ) were above the HUD screening level of 25 μ g/ft 2 ( Table 3-3 ); approximately 14% of the pre-cleanup samples exceeded the HUD screening level , while approximately 3% of th e post-cleanup samples exceeded the screening level ( Tables 3-4 and 3-5 ). Approximately 6% of the samples were above the HUD benchmark of 40 μ g/ft 2 ( Table 3-3 ).



### E.1.3.1.2 Dioxin Wipe Data

The database contained 1,535 wipe samples for dust dioxin loading that were collected from 263 residences, located in 157 buildings. Summa ry statistics for the data are provided in Table 3-6 . The dioxin results were modified using a toxicity equi valency quotient (TEQ) that takes into account the toxicity differences between 17 congener groups

. The results are reported in 2,3,7,8-tetrachlorodibenzop-dioxin (TCDD) toxicity equivalents. The TEQ values reported in Table 3-6 represent the estimated maximum potential concentration (EMPC). The TEQ EMPC value used data that indicated the presence of a congener above zero mg/m 2 even if the sample did not meet all of the QA/QC reporting level criteria. This value represents the highest potential concentrati on of dioxin that may be present. At least one of the 17 congeners were detected in 1,136 of th e samples; the remaining 399 samples were reported asFinal Report December 2005 Page E-4

below the detection limit for each congener. Only 8 of the 1,535 (approximately 0.5%) of the combined samples (i.e., test only and clean and test ; Table 3-6 ) exceeded the hea lth-based benchmark for residential dust dioxin loading of 2 ng/m2 ( Table 3-6 ).



### E.1.3.2 Wipe Data for Other Metals

Statistics for the 21 metals, and the reduction in the average dust loading rates for each, are provided in

Table 3-7 . The data are grouped into three categories in Table 3-7 : samples collected from residences and common areas that were cleaned and tested ( clean and test samples ), samples that were collected from residences that were tested only ( test only samples ), and the combination of these two categories ( all samples ).

The database contained 1,517 results fo r mercury, and 1,544 results for a ll of the other metals. The rate of detection (based on all samples) varies widely from 0, for beryllium and thallium, to 100%, for calcium, copper, iron, magnesium, potassium and zinc. Eight of the 21 metals had detection rates of less than or equal to 5%; 4 had detec tion rates between 6 and 60%. Results for each metal were compared against risk-based screening levels ( Table 3-8 ). Very few exceedances of the risk-based screening values were measured for any of the metals. The screening value of 627 μ g/m 2 , for antimony, was exceeded in 2 pre-cleanup samples (0.1% of all samples); the maximum measured value was 1,180 μ g/m 2 . The screening value of 157 μ g/m 2 for mercury was exceeded in six samples (0.4% of all samples).



### E.2.0 E FFICACY OF THE 

### D UST 

### C LEANUP 

### E FFORT



### E.2.1 Reductions in the Rate of Asbestos PCMe Exceedances

The efficacy of the asbestos cleanup effort was assessed using PCMe exceedances for clean and test data.

One measure of effectiveness is the overall rate of exceedances, which equals the number of exceedances divided by the total number of samples that were collected (i.e., rate on a sample basis). The overall exceedance rate on a sample-basis for the WTC cleanup pr ogram was approximately 0.00418, or 0.42%.

An alternative measure of efficacy is the number of times a residence or a common area within a building (e.g., hallway, stairwell, laundry) had to be recleaned to achieve the clearance criteria of 0.0009 f/cc. The cleanup effort was effective in achieving the clearance criteria for PCMe approximately 99% of the timeFinal Report December 2005 Page E-5

in residential units and common areas. The PCMe clearance criterion was not achieved in 35 out of 3,387 (1.03%) residences, and in 11 out of 785 common areas (1.40%) after the first cleaning. The probability of achieving the clearance on the second atte mpt in residential units that did not achieve clearance after the first cleaning approached 1 (>0.999 ; 2 out of the 25 residences that were recleaned did not achieve clearance after the second cleaning - 10 residents elected not to have their residences recleaned, or were unresponsive). The cleaning me thods used were effective in reducing asbestos

concentrations in residential air.

A modified aggressive sampling procedure was used in most of the apartments (EPA, 2003a). The modified-aggressive sampling procedure was adapte d from the aggressive sampling procedure described in AHERA. The aggressive sampling procedure had a tendency to overload the sampling filter with dust, preventing the samples from being an alyzed by the laboratory (EPA, 2003a). The modified aggressive sampling is thought to be more representative of typical household activity patterns (EPA, 2003a). The rate of exceedance varied between the two sampling procedures. On a sample basis, the exceedances

rates in test only residences were 0.50 and 0.49% for the a ggressive and modified aggressive sampling procedures, respectively; th e exceedances rates for the clean and test residences were 3.4 and 0.20% for the aggressive and modified aggressive sampling procedures, respectively. The test only exceedances rates were not significantly different by Fisher’s exact test (p>0.99); the clean and test exceedances rates were statistically significant by Fisher’s exact test (p<0.01). On a residence-basis (i.e., one or more sample result from the residence equal or exceeded the benchmark for asbestos), the exceedances rates in test only residences were 3.0 and 1.1% for the aggressive and modified aggressive sampling procedures, respectively; the ex ceedances rates for the clean and test residences were 6.4 and 0.64% for the aggressive and modified aggressive sampling procedures, respectively. The test only exceedances rates were not significantly different by Fisher’s exact test (p>0.34); the clean and test exceedances rates were statistically significant by Fisher’s exact test (p<0.01).



### E.2.2 Reductions in dust lead loadings

The indoor environment is considered to be a comple x and dynamic system that is influenced by many interacting factors (physical, chemical, therm odynamic conditions, human activity, building design, building materials, HVAC system, etc.) Therefore, it is not uncommon to find variability in the amount of contaminants in settled dust within a building, and certainly from one building to the next. In additionFinal Report December 2005 Page E-6

to WTC proximity, the large CV is also likely due to the wide range of diversity in the housing stock, contents of the residences and common areas, and pree xisting conditions, or pr evious activity, at these

sites.

To assess the effectiveness of the cl eanup program, the wipe data were divided into two groups: samples

that were collected before th e apartments were cleaned ( pre-cleanup) , and samples that were collected after the apartments were cleaned ( post-cleanup) . Pre-cleanup lead wipe samples and post-cleanup samples were collected from 214 apar tments, located in 145 buildings. Samples that were below the

detection limit of 1.86 μ g/ft 2 were set equal to the detection limit. Table 3-4 provides statistics for the pre-cleanup and post-cleanup lead wipe data; a mo re complete set of statistics is provided in Appendix A , Table A-1 . On average, three pre-cleanup and three post -cleanup wipe samples were collected from each apartment (see Section 2.2 for an overview of the cleanup program).

The data are highly positively skewed with a very la rge coefficient of variation (CV). The high positive skewness indicates that a few lead wipe samples containe d much higher levels of lead than the majority of the samples ( Figure E-1 ). The large CV indicates the data are hi ghly variable; i.e., the lead wipe samples indicate the dust lead loadings vary over a wide range of values. One factor that is a likely contributor to variability in lead wipe sampling results is the pr esence of lead-based paint in older (i.e., pre 1950) housing. This factor is exemplified in the case of th e two highest recorded lead wipe samples in the data set. These two samples were pre- (6,790 μ g/ft2) and post-cleaning (7,250 μ g/ft2) wipes obtained from the top of a storage chest. The lead loading measured in the two other pre-cleanup lead wipe samples

collected from this residence was 3.57 and 91.8 μ g/ft 2 , and the lead loading in the two other post-cleanup samples was 7.41 and 7.56 μ g/ft 2 . The extremely high lead loads in these two matched samples prompted additional investigation which determined that the ches t surface was remarkable for flaking paint. A paint chip sample was analyzed and contained 14% (140,000 μ g/kg) lead, thus, providing a plausible explanation for the aforementioned sampling results. Table 3-5 provides statistics for the pre-cleanup and post-cleanup lead wipe data with the above two values removed, as outliers; a more complete set of statistics is provided in Appendix A , Table A-1a . Although the above two outliers were excluded from the remainder of the analyses of the lead wipe data, thei r inclusion would not have changed the results of the

statistical tests that are described later in this section.Final Report December 2005 Page E-7

The high CV and skewness often mask differences be tween subsets of the data; e.g., between pre-cleanup and post-cleanup dust lead levels. The issue of high CV and skewness are closely related to the issue of normality. Many statistical methods for comparing two or more sets or subsets of data are based on an assumption that the data are derived from a normal distribution. As shown in Figures E-2 and E-3 , a normal distribution is a bell-shaped curve that is symmetric about the mean (i.e., skew ness=0). One method of reducing the skewness and CV, thereby improving the fit of a normal distribution to the data, is to take the logarithms of the da ta. Log-transformation of the data reduced the skewness to 1.17 and 0.71 for the pre-cleanup and post-cleanup data, respectivel y, and the CV to 0.57 and 0.48, respectively. Likewise, tests for normality 3 indicate the log-transformation improved the fit of a normal distribution to

3 The following tests for normality were performed in each case: Anderson Darling, Cramer-von Mises, Kolmogorov-Smirnoff, and Shapiro-Wilk.

Figure E-1

. Boxplots for residential dust lead loading ( μ g/ft 2 ), by sample type. The distributions of the pre- and post-cleanup average dust lead loadings exhibit extreme positive skewness. Typically (see Figure 3-15a ), a boxplot consists of a box with a line emanating from each end. The upper part of the box is drawn at the 75 percentile (%) and the lower part of the box is drawn at the 25 th %; the median is indicated by a line drawn through the box and the mean is indicated by a ‘+’ sign. For a normal distribution, the mean and median will coincide. The line drawn out of the bottom of the box te rminates at the minimum value; the line drawn from the top terminates at the maximum value. The extreme positive skewness exhibited by all the combined lead data results in the box appearing as a single line in the above figure. A log-transformation greatly improves the fit of a lognormal distribution to the data, however, the data continue to exhibit subs tantial positive skewness.Final Report December 2005 Page E-8

the pre-cleanup and post-cleanup data (S-W statistic increased to 0.90 and 0.89, respectively) however, the log-transformed data continue to display s ubstantial departures from normality (p<0.0001 for both subsets).

The cleanup program reduced the overall number of ex ceedances from 92 (13.5 % of samples) to 20 (3.0% of samples). The mean and median of the combined post-cleanup data are less than the precleanup data ( Table 3-5 ).

One approach to assessing the effectiveness of the clea nup program would be to compare the distribution of the 680 pre-cleanup samples to the 673 post-cleanup samples, taking into account various factors that effect lead loading in residential areas (e.g., the amount and condition of lead-based paint, the amount of carpeted floors, the amount of upholstered furniture). This approach would provide the ability to estimate

the effects of these various factors on the effectiv eness of the cleanup program; however, data for these various factors are not readily available for the cleanup program.

An alternative to the above approach would be to calculate the difference between the mean pre-cleanup and mean post-cleanup lead wipe loadings for each of the 214 residences, and comparing the 214 differences. The advantage of analyzing the differe nces between pre-cleanup and post-cleanup loadings on a residence-by-residence basis is that it takes in to consideration the multiple factors (e.g., age of buildings) that effect the dust lead loadings in an apartment, without the need to explicitly incorporate these factors in the analyses. Statistical comparis ons between the pre-cleanup and post-cleanup dust lead loadings using the second approach will tend to be more powerful than those made using the first approach when multiple factors affect the dust lead loading 4 . Therefore, the second approach was adopted for this analysis.

4 The addition of factors in the analyses decreases the degrees of freedom that are available to compare the pre-cleanup dust lead loadings with the post-cleanup loadings.Final Report December 2005 Page E-9

Figure E-2 . Histogram for 10,000 randomly generated samp les from a standard normal distribution. The value of the sample is shown on the x-axis, th e y-axis shows the percent of the 10,000 samples that have the value indicated on the x-axis. For example, approximately 10% of the 10,000 samples have a value of approximately 0. The normal dist ribution is a bell-shaped curve that is symmetric about the mean, which equals 0 for the standard no rmal distribution. For any normal distribution, approximately 66% of the observations occur within a distance of 1 standard deviation of the mean; approximately 95% occur within a distance of 2 standard deviations of the mean. For example, approximately 66% of the 10,000 simulated values fall within the interval bounded by -1 and +1 and, approximately 95% of the values fall within the interval bounded by -2 and +2.Final Report December 2005 Page E-10

Table E-1 presents statistics on the average pre-clea nup and post-cleanup dust lead loadings, on a residence-by-residence basis, and the reduction in the average dust lead load ing for the 214 cleaned residences. A more complete set of statis tics for lead wipe reductions is provided in Appendix A , Table A-2 . The dust lead loadings shown in Table E-1 are average loadings for each residence that were estimated with approximately three pre- and three post-cleanup samples collected from each residence. The distribution of the reductions is positively skewed with high variance ( Figure E-4 ). Tests for normality indicate the normal distribution provides a poor fit to the data. A log-normal transformation of the data fails to substantially im prove normality (S-W=0.37, p<0.0001). The evaluation to determine the efficacy of the cleanup program, presented below, considers the high variance, skewness and deviation from normality exhibited by the data. Figure E-3. Boxplot for 10,000 randomly generated samp les from a standard normal distribution. The values of the samples (i.e., z-values or standard normal deviates) are shown on the y-axis. The boxplot is another method of illustrating the distri bution of a sample. As shown above, the boxplot for a normal distribution is symmetric a bout the mean/median. The median (indicated by horizontal line that is located with in the box) and mean (indicated by black ‘+ ’) of a normal distribution are equal, and located at the center of the box. The 25 th percentile of the distribution (indicated by the bottom of the box) and the 75 th percentile (indicated by the top of the box) are equidistant from the median/mean; the extreme values (indicated by the short horizontal lines at the end of the vertical lines that emanate from the box) as are also approximate ly equidistant from the median/m ean. The skewness of a normal distribution equals zero; a positivel y skewed population is characterized by a few observations that are much larger than the rest of the observation; see Figure 3-1 for an example of extreme positive skewness.Final Report December 2005 Page E-11

On average, the cleanup program reduced the av erage dust lead loading in each residence by approximately 16 μ g/ft 2 (95% confidence interval [CI] 5 , 10.0, 29.4%). The reduction in the mean dust lead loading was found to be statistically significant by the t-test (t=3.64, p=0.0003). The t-test assumes the differences are normally distributed, which is a questionable assumption for this data ( Table E-1 ). As a check on the apparent violation of the normality ass umption, the significance of the reduction in dust lead loadings was also tested using the nonparame tric sign test. The sign test assumes only that the differences between the pre-cleanup and post-cl eanup means are independent random variables 6 . The sign test considers only the direction of the differen ce (i.e., positive or negative), not the magnitude of the differences, which removes the effects of the extreme measure ments (which produce the positive skewness) on the test results. The sign test also i ndicated the reduction in the dust lead loadings were significant (M=53.5, p<0.0001).

5 Confidence interval was determined by bootstrapping, using the bias-c orrected accelerated (BCa) method (Efron and Tibshirani, 1993). The BC bootstrap method does not rely on an assumption of normality for the distribution of the mean of the reduction in dust lead loadings, and is therefore preferred over the typical method (i.e., using the t-distribution) for this data.

6 This assumption is also not strictly va lid because random sampling met hods were not used to sele ct which residences were cleaned (it was a voluntary program) and random sampling methods were not used to select the subset of the cleaned residences where dust wipe samples were collected.Final Report December 2005 Page E-12

Table E-1. Reduction in Average Lead Wipe Loadings (Pre- and Post-cleanup) ( μ g/ft 2 )

Statistics for average pre-cleanup and post-cleanup residential dust lead loading measured by wipe samples are shown, and statistics for the reduction in the average dust lead loading. The average dus t lead loadings and the reduction in theaverages, continue to display substantial departures from normality; a logtransformation of the data fails to improve the fit of a normal distribution to the data (S-W statistic for reductions=0. 37, p<0.0001; pre-cleanup averages: S-W statistic=0.95, p<0.0001; post-cleanup av erages: S-W statistic=0.92). Outliers were removed from the dataset (see Section 3.4.1 for details).

Statistic Reduction in Average Lead Wipe Loading Average Pre-Cleanup Lead Wipe Loading Average Post-Cleanup Lead Wipe Loading n 214 214 214 Mean 16.21 24.40 8.19 Standard deviation 65.16 66.34 17.10 Skewness 7.23 7.73 12.17 CV a 4.02 2.727 2.096 Var 4245.98 4401.40 292.29 Maximum 708.21 748.95 241.67 Median 1.77 8.66 6.79 Minimum -163.27 1.86 1.86 S-W Statistic b 0.33 0.15 0.22 Prob Normal c <0.0001 <0.0001 <0.0001

a CV=coefficient of variation= standard deviation/mean b S-W Statistic: Shapiro-Wilk statistic

c Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk test.Final Report December 2005 Page E-13

The effect of cleaning can also be expressed as a percent decrease in the dust lead loading (Equation E13 ). The mean percent reduction in dust lead loading was 20.4% (95% CI 3 , 8.8, 27.9%). The data set included two extreme negative percent decreases; an increase in mean dust lead loading of 485% that corresponded to an increase from a pre-cleanup mean loading of 4.9 μ g/ft 2 to a post-cleanup loading of

27.1 for a 22 nd floor residence, and an increase from 4.4 to 25.9 μ g/ft 2 for a 5 th floor residence. After these observations were removed from the data, the mean percent reduction in dust lead loading was 25.0% (95% CI, 17.7, 31.3%).

Percent decrease 

# ( )

pre post pre

x x x

−

⋅ = 100 Equation E-13

Where,

= pre x average pre-cleanup dust lead loading

= pre x average post-cleanup dust lead loading Figure E-4

. Boxplot for the reduction in residential average dust lead loading ( μ g/ft 2 ). The short ‘box’ indicates the majority of the reductions occur within a very short range of values; 50% of the reductions are between 0 and 9.50 μ g/ft 2

.Final Report December 2005 Page E-14

Another method for analyzing the effectiveness of the cleanup program in reducing dust lead loadings is to determine the rate (i.e., proportion of residences) at which the average dust lead loadings were reduced by the cleanup. In 9 of the residences, all of the average pre- and post-cleanup dust lead loading

measurements were less than the detection limit. Of the remaining 205 residences, the post-cleanup average dust lead loading was greater than the pre- cleanup average in 49 residences, and lower in the other 156 residences, yielding a rate of reduction of approximately 76%. The sign test discussed in the previous paragraph is based on the number of reducti ons (i.e., not the magnitude of the reductions) and, therefore, provides a test for the statistical signi ficance for the rate of reduction (i.e., the number of

reductions / the number of cleaned and tested residences). Therefore, the sign test indicates that the rate at which the cleanup program lowered the average reside ntial dust lead loading is statistically significant (M=53.5, p<0.0001).



### E.2.2.1 Reductions in dust lead loadings in reside nces with pre-cleanup levels greater than the health-based benchmarks for lead loading

The effectiveness of the cleanup program at reducing dust lead loading in the 36 residences that had precleanup dust lead loadings greater than the HUD screening level of 25 μ g/ft 2 was assessed. Table E-2 provides statistics for dust lead loading for these reside nces; a more complete set of statistics is provided

in Appendix A, Table A-3 . The dust lead loadings shown in Table E-2 are average loadings for each residence that were estimated with approximately three pre- and three post-cleanup samples collected from each residence. As expected , the distributions of the average pre-cleanup, post-cleanup and dust lead loading reductions for this subset of the da ta are less skewed and have lower CVs than the distribution of the lead loadings for all 214 sampled apartments. The log-transformation of the reductions in the average loading does not substantially impr ove normality (S-W statistic=0.71, p<0.0001).

Thirty-six residences had pre-cleanup average dust le ad loadings greater than the HUD screening of

25 μ g/ft 2 . The cleanup program reduced the average dust l ead loading in the residences with average precleanup loadings above the HUD scr eening level by approximately 85 μ g/ft 2 (95% CI, 3 71.2, 173.6%). The reduction in the average dust lead loading was found to be statistically significant (t-test, t=3.61, p=0.0009; sign test, M=17, p<0.0001).

The cleanup program was successful in reducing average dust lead loading in 31 of the 36 residences to

below the 25 μ g/ft 2 HUD screening level, a success rate of approximately 86%. In four other residences,Final Report December 2005 Page E-15

the average post-cleanup dust lead loading was substantially reduced, but remained above 25 μ g/ft 2 ; from 749.0 to 40.7, 149.3 to 28.8; 120.9 to 39.2; 83.2 to 40.7; and 61.6 to 31.7 μ g/ft 2 . The post-cleanup average increased from 78 to 242 μ g/ft 2 in one residence. In three cases, a residence with a pre-cleanup average dust lead loading less than the screening level had a post-cleanup average that exceeded the screening level. The increases in post-cleanup aver age dust lead loadings could reflect sampling variability or site-specific factors that can not be assessed with data that are currently available.

Twenty-three residences had pre-cleanup average dust lead loadings greater than the HUD benchmark of

40 μ g/ft 2 . Average post-cleanup dust lead loading in resi dences with average pre-cleanup loadings above

the HUD benchmark of 40 μ g/ft 2 were approximately 120 μ g/ft 2 lower than average pre-cleanup loadings. The cleanup program reduced the average dust lead lead ing in 21 out of the 23 residences, a success rate of approximately 91%.

Table E-2. Statistics on Reduction in Average Lead Wipe Loadings (Pre- and Post-Cleanup) ( μ g/ft 2 ) in Residences with Pre-cleanup Greater than the Health-based Benchmark of 25 ug/ft 2

The average dust lead loadings and the reduction in the averages show less variation and are less skewed than the complete distribution of av erage residential dust lead loadings. A logtransformation of the averages slightly improves th e fit of a normal distribution to the data (S-W statistic for reductions=0.71, p<0.0001; pre-clea nup averages: S-W statistic=0.89, p<0.0001; postcleanup averages: S-W statistic=0.88, p<0.0001); however, significant departures from the normal distribution model remain. Outliers were removed from the dataset (see Section 3.4.1 for details).

Statistic Reduction in Average Lead Wipe Loading Average Precleanup Lead Wipe Loading Average Post-Cleanup Lead Wipe Loading n 36 36 36 Mean 84.84 102.12 17.28 Standard deviation 140.85 138.37 39.62 Skewness 2.90 3.47 5.49 CV a 166.01 135.50 229.28 Var 19838.34 19147.47 1569.75 Maximum 708.21 748.95 241.67 Median 38.82 48.52 8.08 Minimum -163.27 25.86 1.86 S-W Statistic b 0.64 0.56 0.32 Prob Normal c <0.0001 <0.0001 <0.0001

a CV=coefficient of variation= standard deviation/mean b S-W Statistic: Shapiro-Wilk statistic

c Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk testFinal Report December 2005 Page E-16 

### E.2.2.2 Effect of Floor Level / Pre-Cleanup Average Dust Lead L oading on the Reduction in Dust Lead Levels

The reduction in lead loadings (on a percent change basis) was related to building floor number, through an effect of floor number on pre-cleanup mean dust l ead loadings. Lower floors tended to have higher pre-cleanup lead loadings 7 and, therefore, showed greater reduc tion in loading (discussed further in

Section E.4.3 ). Of the 36 residences with pre-cleanup means greater than the HUD screening level of

25 μ g/ft 2 , 17 of them were located on lower floors (i.e., ≤ 3 rd floor), 14 on floors between the 4 th and 10 th floors, and 5 at floors greater than the 10 th (two at 11 and one at 12). Figure E-5 shows a plot of the logtransformed pre-cleanup means vs. floor number; highe r average pre-cleanup loadings tended to occur in

residences that are located on floors 10 and lower.

In the following analysis, floor numbers are used as a surrogate for pre-cleanup average concentration. Typically, 30 observations or more are desired for making statistical comparisons between two or more groups of data. None of the floor levels had 30 observations and just six floor levels had 10 or more observations (i.e., differences between pre- and post-clea nup dust lead loadings). Therefore, floor levels were combined into three groups: the first group ( lower ) consisting of basement through 3rd floor residences, second group ( middle ) consisting of floors 4 through 10, and the third group consisting of all residences in floors 11 and up ( upper ). Statistics for the pre-cleanup av erage dust lead loadings for each floor group are shown in Table E-5 ; Table A-4 provides additional statistics fo r this data. The data show moderate to high variability, and large positive sk ewness; log-transformation substantially improved normality for all three floor groups. The differences between the means of the pre-cleanup average dust lead loading between the lower and upper floor groups (25.3 μ g/ft 2 ) is statistically significant (t-test with log-transformed data = 2.60, p=0.0104); the differen ce between the medians of the lower and upper floor groups is statistically significant by the Mann-Whitney test (W=1,375, p=0.0185).

7 Older buildings in lower Manhattan tend to have fewer floors than newer buildings. The tendency for lower floors to contai n higher pre-cleanup lead loadings may be attributable, at least in part, to the age of the building.Final Report December 2005 Page E-17

Table E-3. Statistics for Average Pre-Cleanup Residential Dust Lead Loading by Floor Group ( μ g/ft 2 )

The tendency for average dust lead load ings to decrease with increasing floor level is indicated by the statistics s hown in the table. Also shown is a tendency for the variance to increase with increasing average dust lead loading. When grouped by floor level, the average pre-cleanup dust lead loadings show less variation and are less skewed than the complete distribution of average residential dust lead loadings. A log-transformation of the averages substantially improves the fit of a normal distribution to the data (S-W statistic for lower floor group=0.95, p=0.0149; middle: S-W statistic=0.96, p=0.0127; upper: S- W statistic=0.90, p<0.0002). Outliers were removed from the dataset (see Section 3.4. 1 for details). Floor Group a Statistic Lower Middle Upper n 61 93 60 Minimum 1.86 1.86 1.86 Maximum 748.95 413.87 294.33 Median 9.20 10.03 7.25 Mean 39.52 21.08 14.18 Standard deviation 102.71 46.41 37.98 Skewness 5.84 6.97 7.06 CV b 2.60 2.20 2.68 S-W Statistic c 0.36 0.34 0.25 Prob Normal d <0.0001 <0.0001 <0.0001

a Floor Group: Lower=floors ≤ 3; Middle=3 < floors ≤ 10; upper=floors >10 b CV=coefficient of variation= standard deviation/mean

c S-W Statistic: Shapiro-Wilk statistic d Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk testFinal Report December 2005 Page E-18

The difference between the means of th e middle and upper floor groups (6.9 μ g/ft 2 ) is statistically significant (t-test with log-transformed data = 2.22, p=0.0281); the difference between the medians of the middle and upper floor groups is statistically significa nt by the Mann-Whitney test (W=2,067, p=0.0069).

As expected, the reduction in dust lead loading in μ g/ft 2 , and on a percent decrease basis, varied by floor level (i.e., pre-cleanup average loading) ( Tables E-4 and E-5 ). The differences in the reductions in dust lead loading between the lower and upper floor grou ps, and the middle and upper floor groups were found to be significant by the nonparametric Mann-Whitney test (W=986.0, p=<0.0001; W=2034.5, p=<0.0048, respectively). Prior to estimating the difference in th e percent reduction in dust lead loadings between the different floor groups, two observations with extrem e negative percent reductions were removed from the data set (see Section E.4.1 for details). The differences in the percent reductions in dust lead loading between the lower and upper floor groups, and the mi ddle and upper floor groups of were found to be significant by the Mann-Whitney test (W=934.5, p=<0.0001; W=1978.5, p=<0.0051, respectively). The Figure E-5. Scatter plot of log-transformed pre-cleanup residential av erage dust lead loadings (ver tical axis), by floor. The plot indicates that the pr e-cleanup average residential dust lead loading decreases with increasing floor levels up to approximately floor level of 15. The mean and variab ility in pre-cleanup average dust lead loading is fairly constant at floors higher than 15.Final Report December 2005 Page E-19

cleanup program was successful in reducing the dust lead loading in residences with the highest precleanup average loadings (i.e., located on floor numbers 3 and lower) by approximately 33.1 μ g/ft 2 , or 43.5 % (95% CI, 3 17.8, 78.9 μ g/ft 2 ; 29.71, 53.39%). Average residentia l dust lead loadings in the middle floors were reduced on average by 11.1 μ g/ft 2 , or 23.1 % (95% CI, 3 4.40, 27.14 μ g/ft 2 , 17.42, 39.55 %, respectively). The dust lead loading in floors higher than 10 were reduced on average by 6.9 μ g/ft 2 , or 8.6% (95% CI, 3 1.44, 28.54 μ g/ft 2 , -2.70, 18.35 %, respectively).



### E.2.3 Reductions in Dust Dioxin Loadings

The measurable effect of the cleanup program on dust dioxin loadings was less than it was for lead due primarily to low pre-cleanup dust dioxin loadings, wh ich limits the usefulness of the dioxin data to assess the efficacy of the dust cleanup program. For this r eason, the analysis of the dioxin results is less extensive than the analys is of the lead results.

Pre-cleanup and post-cleanup dust wipe samples for dioxin were collected from 212 apartments, located in 145 buildings. Table E-6 provides statistics for the pre-cleanup and post-cleanup dioxin wipe data; a more complete set of statistics is provided in Appendix A, Table A-5 and A-5a . On average, three precleanup and three post-cleanup wipe samples we re collected from each apartment (see Section 2.2 for a description of the cleanup program). The dioxin loading measured in one sample had a value of 75.3 ng/m 2 , approximately 20 times higher than the next highest value (5.14 ng/m 2 ). This sample was removed from the data set as an outlier. The pre- and pos t-cleanup data exhibit mode rate variance and positive skewness, and the normal distribution is found to be a poor fit to the data ( Figure E-6 ). A log-normal transformation of the data fails to substantially im prove normality. Given the low levels of dioxin that were found prior to cleanup, the mean and median of the post-cleanup data are very similar to the mean and median of the pre-cleanup data.Final Report December 2005 Page E-20

Table E-4. Reduction in Dust Lead Loading by Floor Group ( μ g/ft 2 )

The mean reduction in average residential dust lead loading varies by floor level, as expected given the large variation in average pre-cleanup dust lead loadings between the floor groups. The difference in the reduction between the lower and upper floor groups, and between the middle and upper floor groups, are statistically significant (see Section 3.3.2.3 for details). A logtransformation of the averages fails to improve the fit of a normal distribution to the data (S-W statistic for lower floor group=0.41, p<0.0001; middle: S-W statistic=0.41, p<0.0001; upper: S-W statistic= 0.27, p<0.0001). Outliers were removed from the dataset (see Section 3.4.1 for details). Floor Group a Statistic Lower Middle Upper n 61 93 60 Minimum -6.18 -163.27 -22.18 Maximum 708.21 408.96 289.03 Median 3.94 2.37 0.51 Mean 33.11 11.10 6.94 Standard deviation 97.77 48.91 38.14 Skewness 5.85 5.61 7.10 CV 2.95 4.40 5.50 S-W Statistic 0.35 0.37 0.25 Prob Normal <0.0001 <0.0001 <0.0001

a Floor Group: Lower=floors ≤ 3; Middle=3 <floors ≤ 10; upper=floors >10 b CV=coefficient of variation= standard deviation/mean

c S-W Statistic: Shapiro-Wilk statistic d Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk testFinal Report December 2005 Page E-21

Table E-5. Percent Reduction in Average Residential Dust Lead Loading by Floor Group ( μ g/ft 2 )

The percent reduction in average residential dust lead loading varies by floor level. The difference in the reduction between the lower and upper floor groups, and between the middle and upper floor groups, are statistically significant (see Section 3.3.2.3 for details). The negative skewne ss for each floor level is due to a few increases in average dust lead loadi ng after cleanup. In addition to the two observations that were removed as outliers (see Section 3.4.1 ), two residences were removed from the this analysis as outliers; the average post-cleanup dust lead loading in these residences were 484% and 448% higher than the precleanup average (i.e., increased from 4.4 to 25.9, and 4.9 to 27.1 μ g/ft 2

, respectively). Floor Group a Statistic Lower Middle Upper n 61 92 59 Minimum -180.10 -208.25 -149.79 Maximum 95.41 98.82 98.20 Median 57.39 29.24 6.94 Mean 43.48 23.13 8.64 Standard deviation 47.02 54.51 41.71 Skewness -1.84 -1.69 -0.70 CV 1.08 2.36 4.83 S-W Statistic 0.83 0.87 0.95 Prob Normal <0.0001 <0.0001 <0.0085

a Floor Group: Lower=floors ≤ 3; Middle=3 < floors ≤ 10; upper=floors >10 b CV=coefficient of variation= standard deviation/mean

c S-W Statistic: Shapiro-Wilk statistic d Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk testFinal Report December 2005 Page E-22

Table E-7 presents statistics on the average pre-cleanup, average post-cleanup and average reduction in dust dioxin loadings, on a residence-by-residence basis. The distribution of the reductions is moderately negatively skewed with high variance ( Figure E-7 ). Tests for normality indicate the normal distribution provides a poor fit to the data. A log-normal tran sformation of the data fails to improve normality (SW=0.85, p<0.0001).

On average, the cleanup program reduced the average dioxin loading by approximately 0.01 ng/m 2 (95% CI, 3 –0.0161, 0.0327%). The inclusion of zero within the CI indicates that the reduction in dust dioxin loading is not significant. However, the sign test (M =14, p=0.06) and, particularly, the Wilcoxon signed rank test (S=1,790, p=0.05), indicate that the reduction in dust dioxin loadings was significant. The Table E-6. Statistics for Dioxin Wipe Clean and Test Data (ng/m 2 )

The data summarized in the above table are dioxin toxicity equivalents (TEQs), which are the sum of 17 different chemical forms (congeners) of dioxin. The clean and test subset of the data exhibit high positive skewness but low variance. Few exceedances were observe d for dioxin. The raw data and logtransformed pre- and post-cleanup data fail the S-W test for normality (logtransformed data [pre-/post-]: S-W statistic=0.71/0.89, p<0.0001/p<0.0001). Statistic Pre-Cleanup Pre-Cleanup a Post-Cleanup Apartments sampled 213 213 213 Buildings sampled 145 145 145 Number of samples 674 673 668 Nondetects 162 (24.0%) 162 (24.1%) 228 (34.1%) Exceedances b 3 (0.4%) 2 (0.3%) 4 (0.6%) Minimum 0.27 0.27 0.27 Median 0.60 0.60 0.59 Mean 0.81 0.66 0.65 Maximum 103 5.14 4.34 Standard deviation 3.95 0.29 0.28 Skewness 25.75 6.79 5.27 CV c 0.49 0.44 0.42 S-W Statistic d 0.03 0.57 0.62 Prob Normal e <0.0001 <0.0001 <0.0001

a Statistics for pre-cleanup data with one outlier removed (see Section E.2.3 for details).

a Exceedance: dioxin wipe samples that exceeded the health-based benchmark of 2 ng/m 2 TEQ EMPC (ND=1/2). b CV=coefficient of variation= standard deviation/mean

c SW-Statistic: Shapiro-Wilk statistic d Prob Normal: probability the data are fro m a normal distributi on deviation/meanFinal Report December 2005 Page E-23

Wilcoxon signed rank test will tend to be more power ful at detecting differences between the pre-cleanup and post-cleanup average dust dioxin loadings than the sign test provided that the distribution of the differences in dust dioxin loading is symmetric (but not necessarily conforming to a normal distribution) (Conover, 1999). Based on Figure E-7 , the assumption of symmetry appears to be reasonable.

The post-cleanup average dioxin loading was greater th an the pre-cleanup average in 92 residences, and lower in the other 120 residences, yielding a rate of reduction of approximately 57 %. The sign test (see preceding paragraph) indicates that the rate at whic h the cleanup program lowered the average residential dust dioxin loading is statistically significant.

To assess the effectiveness of the dust cleanup program for residences that had measurable pre-cleanup dust dioxin loading, the comparison between reside ntial average pre-cleanup dust dioxin loadings and residential average post-cleanup dust dioxin loadings was limited to residences where all the pre-cleanup

measurements for dioxin were above the detection limit. There were 124 residences In 93 buildings that met this criterion. The pre-cleanup measurement of 75.3 ng/m 2 was not included in this data set (see preceding section). The cleanup program reduced the residential average dust dioxin loading in these 124 residences by approximately 0.01 ng/m 2 , the same average reduction that was observed in the

212 residences.Final Report December 2005 Page E-24

Table E-7. Reduction in Average Dioxin Wipe Loadings (TEQ) a (Pre- and Post-Cleanup) (ng/m 2 )

The reductions in average residential dust di oxin loadings are more modest than the reductions achieved for average dust lead load ing, primarily due to the low average precleanup dust dioxin loadings. Analysis of th e reduction in the residential average dust dioxin loading in a subset of the 212 residences, where all pre-cleanup sample measurements results were greater than the detection limit, also indicated an average reduction of 0.01 ng/m 2

.

Statistic Reduction in Average Dioxin

Wipe Loading Average PreCleanup Dioxin

Wipe Loading Average PostCleanup Dioxin

Wipe Loading n 212 212 212 Mean 0.01 0.65 0.64 Standard deviation 0.18 0.18 0.19 Skewness -0.97 1.88 2.06 CV b 19.02 0.28 0.30 Var 0.03 0.03 0.04 Maximum 0.63 1.61 1.36 Median 0.01 0.60 0.60 Minimum -0.81 0.33 0.32 S-W Statistic c 0.85 0.83 0.78 Prob Normal d <0.0001 <0.0001 <0.0001

a TEQ: toxicity equivalent quotient. b CV=coefficient of variation= standard deviation/mean

c S-W Statistic: Shapiro-Wilk statistic d Prob Normal: probability the da ta are from a normal distribution by Shapiro-Wilk testFinal Report December 2005 Page E-25

Figure E-6. Boxplots for residential dust dioxin loading ( μ g/ft 2 ), by sample type. The distributions of the preand post-cleanup average dust dioxin loadings also exhi bit extreme positive skewness. No pre- or post-cleanup average dust dioxin loadings exceeded the health-based benchmark of 2 ng/m 2 . One observation, with a value of 103 ng/m r , was removed from the data as an outlier.

Figure E-7. Boxplot for the reduction in residential averag e dioxin wipe reductions. The short length of the ‘box’ indicates the most of the reductions are cl ose to zero. The small reductions are due to the low pre-cleanup average dust dioxin loadings in all but one of the residences (the residence with the one high pre-cleanup dioxin dust loading of 103 ng/m 2 ). One observation, with a value of 103 ng/m r , was removed from the data as an outlier. This observation was collected from the mantle of a fireplace.Final Report December 2005 Page E-26



### E.3.0 SPATIAL PATTERN OF PCMe EXCEEDANCES

E.3.1 Analytical Approach

E.3.1.1 Purpose

Data were analyzed to detect the possible presence of spatial, or geographic patterns in the occurrence of PCMe exceedances. In this report, a PCMe exceedan ce is defined as a sample result that exceeded the

heath-based benchmark of 0.0009 fibers/cc. Detec tion of spatial patterns in the exceedances could be used to identify possible sources of the exceedances, or lead to expl anations for the exceedances. The latter could be used to improve future cleanup and/or monitoring efforts. The data were divided into two groups: data from residences and building common areas th at were cleaned and tested (clean and test data), and data from residences that were tested only (test only data). All common areas (e.g., lobbies, laundry rooms, hallways, stairwells) were cleaned and tested.



### E.3.1.2 Analytical Methods and Spatial Resolution

The methods that are used to detect and measur e spatial patterns depend upon the spatial scale and resolution at which the spatial patterns are analyzed. The spatial scale refers to the geographic extent over which the analysis is performed. In this report, the geographic scale is lower Manhattan, south of Canal Street ( Figure 1-1 ). Regarding geographical scale, the pattern of PCMe exceedance could be analyzed by examining the buildings where the health-based benchmark was exceeded, or by examining the number, or rate of exceedances over larger geographic areas. As resolution decreases, the data must be aggregated (e.g., summed, averaged) over the chosen geographic units (e.g., zip codes), which results in some loss of geographic information (i.e., the exact location wher e the individual exceedance s occurred). However, aggregating the data tends to reduce variability, whic h may then reveal spatial patterns that had been obscured by small-scale variability in the data (i.e., fluctuations in the data over short distances).

The appropriate spatial scale and resolution depends upon the objectives of the analysis. For example, one of the objectives of this analysis was to dete rmine if the geographic location of PCMe exceedances were clustered geographically. For this objective, the PCMe data were analyzed at the site level and the building level (the latter being the smallest ge ographic unit reported). The second objective was to determine if the rate of PCMe exceedance (i.e., number of exceedances/number of samples analyzed) varied across the area that was potentially affected by the collapse of the WTC buildings. For this objective, the PCMe data were aggregated over statistical summary areas (SSAs) ( Figure 1-1 ). StatisticalFinal Report December 2005 Page E-27

summary areas were based on census-block groups th at were modified by EPA for the purposes of describing the PCMe data.

Spatial pattern analyses at the site level and building level were performed using methods of point pattern analysis (Cressie, 1993; Bailey and Gatrell, 1995). In point pattern analysis, the focus is on the location of exceedances. The goal is to determine if there ar e any geographic patterns exhibited by the location of the exceedances. In general, there are two types of geographic patterns that are possible: clustering and dispersion (or regularity). Clustering is exhibited by the tendency for points to be located in clumps, while dispersion refers to the tendency for points to be more regularly distributed than would be expected, based on chance. An example of a point pattern that exhibits dispersion is a square grid. The primary focus in this study is on identifyi ng clusters of exceedances, which could indicate an asbestos source, or otherwise lead to an explanation for the elevated air borne asbestos concentrations.

Analysis of the PCMe data at the SSA scale was performed using methods from spatial autoregression analysis. Spatial autoregression is a type of statis tical regression analysis that considers the spatial autocorrelation exhibited by the data, if any. In th e present context, (positive) spatial autocorrelation is the tendency for SSAs with similar rates of PCMe ex ceedances to be located near each other. Classical regression analysis assumes the data are independent and identically distributed (iid) 8 . Data that exhibit spatial autocorrelation violate the independent portio n of this assumption. Therefore, using classical regression methods with data that exhibits spatial au tocorrelation will affect the accuracy of statistical tests that are made with the data; for example, testing the rates of PCMe exceedances between SSAs

could lead to erroneous conclusions.



### E.3.1.3 PCMe Exceedance as a Spatial Poisson Process

The spatial analysis of the PCMe exceedances that is presented in Sections E.3.2.2 and E.3.2.3 is based on the assumption that the exceedances can be modeled as a Poisson distribution ( Figures E-8 and E-9 ). The rationale for this assumption is as follows.

8 Many methods of classical sta tistical analysis are developed ma thematically based, in part, on the assumption that the data ar e independent and identically distributed. The assumption of inde pendence requires that the probab ility that an observation from the sample takes on a given value is not dependent upon the values of any of the other observations. The identically distribut ed assumption requires that all of the obser vations are members of the same population (i.e., the same di stribution function).Final Report December 2005 Page E-28

The spatial analysis of the PCMe data focuses on th e spatial distribution of PCMe exceedances, rather than on the spatial distribution of the PCMe concentra tion. When analyzed in this way, the PCMe data are converted into one of two values: one (concentrat ions > 0.0009 f/cc) or zero (concentrations < 0.0009 f/cc). In this format, the data can be mode led with a binomial distribution (DeGroot, 1989):



## ( ) 

# ( ) ( ) x n x x

n 

### p

p p n x f −



### − = 1 , | Equation E-1

The expression on the left hand side of Equation E-1 is interpreted as the probability of observing x exceedances out of n air samples (i.e., tests), given the proba bility of observing an exceedance from any given test ( p) . The variable x therefore is limited to positive in tegers between zero and the sample size (i.e., x = 0, 1, 2, ...., n ). The parameter p is estimated from the data; it is the total number of exceedances divided by the number of samples ( n) . The 

# ( ) x

n symbol represents the number of combinations of n objects taken x at a time 9 . Assuming a binomial distribution provides a good fit to the PCMe exceedance data, the expected number of exceedances, given n samples and probability p is:

np X E = ] [ Equation E-2

and its variance of x is:

) 1 ( ] [ p np X Var

− = Equation E-3

Notice that the number of exceedances will tend to increase with sample si ze. The variance also increases with sample size. The relationship between vari ance and sample size must be considered when comparing exceedance rates between areas with different sample sizes; this point is discussed further in

Section E.3.2.2

.

9 In the present context, it represents the number of ways that x exceedances could be observed in n samples, when order is not important. The right hand side of equatio n 1A equals the number of ways that x exceedances could be observed in a sample of

size n, multiplied by the probability of observing an exceedance for any given test.Final Report December 2005 Page E-29

Figure E-8 . Histogram for 10,000 randomly generated samples from a Poisson distribution with mean = 2. The value of the sample is shown on the x-axis, the y-axis s hows the percent of the 10,000 samples that have the value indicated on th e x-axis. A Poisson distribution is commonly used to model the occurrence of rare events with in a fixed period of time or space. The Poisson distribution with mean = 2 is posi tively skewed; as the mean of a Poisson random variable increases, its distribution approaches a normal distribution.Final Report December 2005 Page E-30

The binomial distribution can be approximated with a normal distribution. The advantages of modeling

the exceedances with a normal distribution is that ther e are many statistical procedures that are based on the assumption of normality, and the variance of a normally distributed variable does not depend upon sample size (i.e., the variance is constant ). The normal approximation improves as n increases and the

value of p approaches 0.5 (DeGroot, 1989). However, the estimates for p for the test only and clean and

test data (0.00487 and 0.00418, respectively), make the normal approximation untenable. For example, the normal approximation would generally be considered acceptable for a comparison of the exceedance rates between SSAs provided the following relati onship was satisfied for each of the SSAs:

5 > × SSA SSA rate exceedance n

where, n SSA is the number of samples in the SSA, and exceedance rate SSA is the number of exceedances located in the SSA divided by n SSA . This requirement would be satisfied for just one SSA for the test only data, and five SSAs for the clean and test data. Figure E-9. Boxplot for 10,000 randomly generated samp les from a Poisson distribution with mean = 2. The values of the randomly drawn samples ar e shown on the y-axis. As shown above, the boxplot for a Poisson distribution is positively skewed. As the mean of a Poisson random variable increases, its distribution approaches a normal distribution.Final Report December 2005 Page E-31

The Poisson distribution was developed for modeling rare events, such as the exceedance rates observed in the WTC cleanup program. When n is large and p is very small, the binomial distribution can be approximated by a Poisson distribution (DeGroot, 1989):



## ( ) ! | x e x f x λ λ λ

−

= Equation E-4

The variable x is limited to positive integers betw een zero and the sample size (i.e., x = 0, 1, 2, ...., n ). The parameter λ is estimated from the data; it is the total number of exceedances divided by the number of samples ( n) (same as the binomial distribution). The mean and variance of a Poisson distribution are equal to the parameter λ , and do not depend upon the sample size. In Section E.3.2.2 the Poisson distribution is shown to provide a better fit to the data than the binomial.

Possible differences in the intensity of exceedan ce events across the site is assessed in Section E.3.2 , using methods from point pattern analysis ( Section E.3.2.1 ) and spatial autoregression E.3.2.2 ); possible differences on a smaller scale (e.g., within SSAs) are assessed using additional methods from point pattern analysis ( Section E.3.2.3 ). The effect of sample size (i.e., number of samples per building) on

PCMe exceedance is also considered in Section E.3.2

.



### E.3.2 Spatial Analysis

The locations of PCMe exceedance were described by first assessing the overall ( global ) pattern of the exceedances using methods from point pattern analysis. The data were then aggregated by SSAs and analyzed using methods from spatial autoregression to describe the spatial distribution of PCMe exceedances at the SSA-scale, and to estimate the di fferences in the rate of PCMe exceedances between the SSAs. The local pattern of the exceedances wa s assessed by measuring the level of spatial autocorrelation, or spatial dependence, exhibited by the data, using additional methods from point pattern analysis. Finally, the vertical distribution of PCMe exceedances is analyzed at the site level using frequency tables and Poisson regression. When interpreting the results of this analysis, it should be kept in mind that participation in the WTC Dust Cleanup Pr ogram was on a voluntary basis. Therefore, the

data were not derived from a random sample, nor do they represent a census of all the buildings and residences within the sampled area. (In the context of point pattern analysis, point patterns derived from a random sample and census are referred to as sampled point patterns and mapped point patterns ,Final Report December 2005 Page E-32
respectively.) With this in mind, the global and local patterns of PCMe exceedance are interpreted in relation to the location of the sampled buildings.



### E.3.2.1 Site-Level (Global) Pattern of PCMe Exceedance

For the point pattern analysis, the PCMe data were aggregated at the building level by counting the number of sample results that exceeded the heath-ba sed benchmark of 0.0009 fi bers/cc for each building (i.e., the number of exceedances). The term exceedance ev ent is used to refer to buildings that contain at least one PCMe exceedance. Consistent with the appro ach used in the analysis of the dust wipe data, the PCMe exceedances were grouped into clean and test and test only categories. Figures E-10 and E-11 show the location (centroids) of the 408 buildings that contain at least one residence or common area that was cleaned and tested (clean and te st buildings), and the 219 buildings that contain at least one residence that was tested only (test only buildings), respectivel y. Note that the two groups of buildings are not mutually exclusive: approximately 39% of the clean a nd test buildings contain at least one residence that was tested only, and approximately 75% of the test only buildings contain at least one common area or

residence that was cleaned and tested.

Centrographic statistics were used to describe the first order, or global pattern of the distribution of the exceedance events. The centrographic statistics that are described here are similar to the traditional

univariate statistics that are used to describe th e location (e.g., mean, median) and distribution (e.g., standard deviation, skewness) of a single variable . Centrographic statistics were calculated using the geographic coordinates of the centroids of the buildin gs. Centrographic statistics were used to describe the geographic center of the exceedance events, their distribution in space, and the orientation of the distribution. The centrographic statistics for th e exceedance events were then compared to the centrographic statistics for the buildings that were sampled.Final Report December 2005 Page E-33

Figure E-10. Centrographic statistics for the clean and test data. Clean and test data refers to samples collected from residences where the residents had requested EPA to clean th eir residences and test their indoor air fo r asbestos. Centrogr aphic statistics are the two dimensional counterparts of common one-dimen sional summary statistics; they describe gl obal characteristics of the data. Polyg ons represent statistical summary areas (SSAs); hatching indicates SSA s where PCMe data was not coll ected. Two-dimensional locatio nal statistics are indi cated by stars (mean center, or average of X and Y coor dinates) and diamonds (media n center, median of X and Y coordinates). The figure indicates the geographic center of the location of the 37 exceedances is shifted towards the south re lative to the geographic center of the clean and test buildings. Comparison of the median center and the arithmetic mean center for the exceedances indicates that the location of the exceedances is ‘skewed’ slight ly towards the north. Comparison of the standard deviational ellipses, which illustrate the dispersion of events around their mean centers, indi cates that the pattern of ex ceedances is more evenly d istributed across lower Manhattan than the pattern of clean and test building locations. The ellipse for the clean and test buildings is more elongated in the north-south direction, indicating that the build ing locations are more dispersed in the north-south direction than they are in the east-west direction. The medi an number of samples collected from clean and test buildings that had at leas t one exceedance (119) is an order of magnitude higher than the median number of samples collected from clean and test buildings that had no exceedances (12). The shades of green assigned to the statistical summary areas (SSAs) indicate the number of samples collected from each SSA. T he four shades of green correspond to quartiles of the number of sample s; the darkest green is assigned to SSAs with the largest number of samples (i.e., 4 th quartile). There is a strong relations hip between the sample size and the lo cation of exceedances; 36 of the 38 exceedances are located in SSAs with sample sizes above the median.Final Report December 2005 Page E-34

Figure E-11. Centrographic statistics for the test only data. Test only data refers to samples collected at residents where residences requested to have their indoor ai r tested for asbestos but declined to have their residences cleaned. The figure in dicates that the geographic center of the location of the 8 exceedances is shifted towards the south relative to the geographic center of the test only buildings. Comparison of the median center and the arithmetic mean center for the exceedances indicates that the location of the exceedances are ‘skewed’ slightly towards the east. The standard deviational ellipse for the test only buildings shows that the exceedances are more dispersed in the north-south direction, while the exceedances are dispersed more in the eas twest direction. The east-west trend may be attributable to the higher sample sizes associated with buildings where the exceedances were measured. The median number of samples in the 8 test only buildings that had at least one exceedance is 19.5 (range of 9 to 38 samples); the median number of samples for the test only buildings without exceedances is 7 (range of 3 to 256 samples). The shades of green assigned to the statistical summa ry areas (SSAs) indicate the number of samples collected from each SSA. The four shades of green correspond to quartiles of th e number of samples; the darkest green is assigned to SSAs wit h the largest number of samples (i.e., 4 th quartile). There is a strong relationship between the sample size and the location of exceedances; all of the exceedances are located in SSAs with sample sizes above the median.Final Report December 2005 Page E-35

Figures E-10 , E-11 , and E-12 show the mean centers, median cente rs, and standard deviational ellipses

for the clean and test buildings and the clean and test exceedance events, test only buildings and test only exceedance events, and the unique test only buildings (described below), respectively. The X and Y coordinates of the mean center equal the mean of th e X coordinates and the mean of the Y coordinates, respectively, of the building centroids. The coordina tes of the median center equal the median of the X and Y coordinates of the building centroids. The median is less influenced by geographic outliers (buildings that are located far from the median or mean center of buildings) than the mean. The median is

often used when there are a few extreme locations that could greatly influence the mean and distort what might be considered the geographic center of the building locations.

A standard deviational ellipse is a measure of the dispersion of the buildings around the mean center in two dimensions. Comparing the standard deviational ellipse for the exceedance events to the standard deviational ellipse for the location of the sample d buildings provides a qualitative comparison between their geographic centers, and the magnitude and direction of their dispersion. The method for calculating

the standard deviational ellipse is described in Appendix G

.

Figure E-11 shows the locations of the 219 test only buildings and 8 exceedance events (one exceedance event is obscured by the symbol for the median center of the exceedance events). The mean center for the exceedance events, which is shifted to the east and north of the median center, is influenced by the two

exceedance events that are located near the eastern boundary of the potentially affected area, and the one event near the northern boundary. The location, shape and approximately north-south orientation of the standard deviational ellipse for the test only buildings reflect the high density of sampled buildings that are located northeast, east and southwest areas of the WTC site. In contrast, the spatial pattern of the test only exceedances events approaches an east-west orient ed line; the lone exceedan ce event located near the northern boundary of the site has a very large influe nce on the shape of the ellipse. The east-west trend

indicated in Figure E-11 may be attributable to the higher sample sizes associated with buildings where

the exceedances occurred. The medi an number of samples in the 8 test only buildings that had at least one exceedance is 19.5 (range of 9 to 38 samples); the median number of samples for the test only buildings without exceedances is 7 (range of 3 to 256 samples).Final Report December 2005 Page E-36

Figure E-12. Centrographic statistics for the unique test only buildings. Unique test only buildings are buildings that do not contain any residences or common areas that were cleaned. The unique test only buildings tend to be located north of the test only buildings, and are dispersed in a northeast-southwest direction. No PCMe exceedances were measured in any unique test only building. The lack of exceedance events could be attributed, in part, to the low number of samples collected from these buildings. The average number of samples collected from the unique test only buildings is 6.5, with a minimum of 3 and a maximum of 12; twenty-three of the 54 unique test only buildings had 5 or fewer sample s, and 47 had fewer than 10. In contrast, the 8 test only buildings with one or more exceedance had an av erage of 22 samples, with a minimum of 6 and a maximum of 38. The shades of green assigned to the statistical summary areas (SSAs) indicate the number of samples collected from each SSA. The f our shades of green correspond to quartiles of the number of samples; the darkest green is assigned to SSAs with the larges t number of samples (i.e., 4 th quartile).Final Report December 2005 Page E-37

Figure E-12 shows the location of the 60 buildings that contain exclusively test only residences ( unique test only ; i.e., no clean and test common areas or residences). There were no exceedances in the unique test only buildings. The geographic center of the unique test only buildings shows that these buildings

tend to be located north of the test only buildings. It should be noted that the lack of exceedance events could be attributed, in part, to the low number of samples collected from these buildings. The median number of samples collected from the unique test only buildings is 6, with a minimum of 3 and a maximum of 21; 24 of the 60 unique test only buildings had 5 or fewer samples collected from them, and

52 had fewer than 10.

Figure E-10 shows the locations of the 408 clean and test buildings and the 37 exceedance events. The geographic center of the clean and test buildings is located northeast of the WTC site. The geographic

center of the clean and test buildings that had at least one exceedan ce is located east of the WTC site, and south of the geographic center of the clean and test buildings. The standard deviational ellipse for the

clean and test buildings and the clean and test exceedances both indicate a north-south orientation. The width of the standard deviational ellipse for the clean and test exceedances is wider than the ellipse for

the clean and test buildings, indicating the distribution of exceed ances are more dispersed in the east-west

direction than are the clean and test building locations. The intensity of exceedances appears to be greater south and east of the WTC site compared to th e areas north of the WTC site. Again, the apparent pattern may be attributable, at least in part, to diffe rences in sample size. The median number of samples

collected from clean and test buildings that had at least one exceedance (119) is approximately 10 times higher than the median number of samples collected from clean and test buildings that had no exceedances (12).

The geographic center of the exceedance events for the test only and clean and test buildings tend to be located south of the geographic center of the sampled buildings ( Figure E-10 ). Except for one location,

the test only exceedance locations occur along an east-west line that extends across lower Manhattan ( Figure E-11 ). No obvious pattern to the clean and test exceedances is evident. Interpretation of the exceedance locations is complicated by the variabilit y in the number of samples that were collected between buildings.

The possible differences in intensity of exceedance ev ents across the site were further addressed using methods from spatial autoregression ( Section E.3.2.3 ) and using additional methods from point patternFinal Report December 2005 Page E-38
analysis ( Section E.2.2.3 ). The effect of sample size (i.e., num ber of samples per building) on PCMe exceedance is also considered in both of the analyses.



### E.3.2.2 SSA-Level Pattern of PCMe Exceedance

Spatial distribution of PCMe Exceedance

The primary objective of this analysis is to describe the spatial distribution of PCMe exceedances at the SSA-scale, and to estimate the differences in the ra te of PCMe exceedances between the SSAs. Samples

from test only and clean and test residences were collected from 36 and 38 SSAs, respectively. Rates

were calculated for each SSA as the number of exce edances within the SSA divided by the number of

results for PCMe for the SSA. Rates were used to account for the large difference in sample sizes

between the SSAs.

Exceedance rates varied from 0 to 0.060 for the test only data and from 0 to 0.058 for the clean and test

data. More than one-half of the SSAs had no exceedances for the test only (30/37, or 81% with 0 exceedances) and clean and test data (23/39, or 60% with 0 exceedances). The spatial distribution of the

PCMe exceedance rates for the test only and clean and test data are shown in Figures E-13 and E-14 , respectively. For the test only data, the SSAs with the highest rate s (upper quartile) coincide with the distribution of the exceedance events; every SSA with one or more exceedance falls in the upper quartile of the exceedance rate, which further indicates the rareness of the exceedance events. SSAs that fell within the upper quartile contained 1 – 9 exceedance events.

For the clean and test data, SSAs that fell within the upper quartile of exceedance rates for the clean and

test data contained 2 to 32 exceedance events. All but 4 SSAs had exceedance rates less than 1%; the highest rate of exceedances was 6%. Statistical summ ary areas with the highest rates are located north

and east of the WTC site. Figure E-14 indicates there is a tendency for SSAs with similar rates to be located near each other (i.e., positive spatial autoco rrelation). Measuring spatial autocorrelation in the PCMe exceedances is made difficult by the low rate of exceedances and the lack of data for some SSAs (discussed further in Appendix H ).Final Report December 2005 Page E-39

Figure E-13 . Spatial distribution of PCMe exceedance rates for the test only data, by statistical summary areas. Test only data refers to samples collected at residents where re sidences requested to have their indoor air tested for asbestos but declined to have their residences cleaned. The exceedance rate for each statistical summary area (SSA) equals the number of PCMe results for the SSA that exceeded the health-based benchmark, divided by the number of samples collected from the SSA. Quartiles of the PCMe exceedance rate are shown. Statistical summary areas with one or more PCMe exceedance fall in the upper quartile of the exceedance rate, which indicates the rareness of the exceedance events. Six of the seven SSAs that had one or more exceedance are located east and north of the WTC site; the seventh SSA, which is located south west of the WTC had one exceedance.Final Report December 2005 Page E-40

Figure E-14. Spatial distribution of PCMe exceedance rates for the clean and test data, by statistical summary

areas. Clean and test data refers to samples coll ected from residences where the residents had requested EPA to clean their residences and test their indoor air for asbestos . Quartiles of the distribution of PCMe exceedance rates are shown. Statistical summary areas (SSAs) with one or more exceedan ces fall in the upper two quartiles, indicating the rareness of the exceedance events. Sta tistical summary areas with exceedance rates in the upper quartile of the distribution of PCMe exceedances are lo cated north and east of the WTC site. Modest positive spatial autocorrelation in the exceedance rates is indicated by the tendency for SSAs with similar rates to be located near each other.Final Report December 2005 Page E-41
Fitting Poisson models to PCMe exceedances

The binomial and Poisson distributions are reasonabl e statistical models for the PCMe exceedances ( Section E.2.1.3 ). Binomial and Poisson distributions were fit to log-transformed rates 10 for the test only

and clean and test data (second and third columns of Tables E-8 and E-9 , respectively). The assumption of equal mean and variance, which is a feature of the Poisson model ( Section E.2.1.3 ), was assessed by fitting a negative binomial model to the data ( Appendix H ). The estimates of the dispersion parameters for the fitted negative binomial models ( Tables E-8 and E-9 ) indicated that the assumption of equidispersion may be very poor for the test only and clean and test exceedance rates (i.e., the mean and variance of the rates may not be constant across the SSAs). Violation of the equidispersion assumption

has affects similar to violations of the constant variance assumption with a normal distribution model (Cameron and Trivedi, 1998) 11 . A consequence of the violation is a tendency for a loss of power to detect

actual differences in PCMe rates be tween SSAs (Griffith and Layne, 1999). Similarly, failure to consider spatial autocorrelation present in data can lead to a loss of statistical power (Griffith and Layne, 1999).

Very often, remedial measures designed to reduce one type of model violation also reduce the violation of other assumptions. With this in mind, a spatial filter approach was used to account for the spatial autocorrelation present in the data; th e approach is described briefly in Appendix H and thoroughly in (Griffith, 2002). Estimates of the parameters for the binomial and Poisson models with the spatial autocorrelation filter added, for the test only and clean and test data, are shown in the last three columns

of Tables E-8 and E-9 , respectively. The parameter estimates for the binomial and Poisson models are very close for both sets of exceedance rates; however , the Poisson model provides a much better fit for

the test only exceedance rates; the fit is approximately the same for the clean and test exceedance rates.

Five buildings accounted for 6,470 (27%) of the clean and test sample results. A subset of the clean and

test data was created by removing these 6,470 meas urements from the database. The binomial and

Poisson models were refit to the data to assess the ef fect of these five buildings on the estimates of the model parameters, and their effect on the goodness-o f-fit of the models to the data. The parameter estimates differed slightly, and both models conti nued to account for approximately 40% of the variance

10 The Poisson models were actually fit to log-transformed counts of exceedances, with the log of the number of samples included in the model as an offset variable. This is mathema tically equivalent to fitting the Poisson model to the log-transfo rmed rates; see Appendix H for further explanation.

11 Violation of the constant variance a ssumption with the normal distribution model affects the significance level (p-values) reported for statistical tests, such as the comparison of the PCMe rates between SSAs. The actual error rates (i.e., type I er ror rate, α ) will tend to be larger than the intende d error rate (Griffith and Layne, 1999).Final Report December 2005 Page E-42

in the data. Based on these results, the spatial auto correlation-filtered Poisson m odels with parameters - 5.94 and -6.06 (log-transformed rates of ex ceedances) were used to describe the clean and test and test only data, respectively.

Table E-8. Model Estimation Results for the Log-transformed Test Only PCMe Exceedance Rates

The binomial and Poisson distributions are plausi ble models for the PCMe exceedances as both distributions can be used to describe count data. A spatial filter derived from the spatial autocorrelation that is expressed by the data was added to both models. Addition of the filter has a substantial impact on the parameter estimates. The apparent violation of the equidispersion assumption (i.e., equal mean and variance) of the Poisson model was re ndered inconsequential after the spatial filter was added (see Appendix H for details). The Poisson model is more appealing for the PCMe exceedances due to the rari ty of their occurrence and provides a better fit to the data, accounting for approximately twice th e variance that is explained by the binomial model. Prior to Considering Spatial Autocorrelation a With Spatial Filter Added to Models a

Model Parameter Estimate Equidispersion Parameter Estimate Equidispersion % Variance Accounted for Binomial -5.3183 NA -6.0572 NA 30% Poisson -5.3232 NA -6.0625 NA 60% Negative binomial b -5.0964 4.6066 -6.1506 0.4476

a Spatial autocorrelation is accounted for in the statistical models using an ei genfunction spatial f ilter (Griffith, 2002);

see Appendix E for details. b A negative binomial distribution was fi t to the PCMe exceedances to assess th e assumption of equidispersion (equal mean and variance), which is a feat ure of a Poisson random variable.Final Report December 2005 Page E-43

Table E-9. Model Estimation Results for the Log-transformed Clean and Test PCMe Exceedance Rates

The binomial and Poisson distributions are plausi ble models for the PCMe exceedances as both distributions can be used to describe count data. A spatial filter derived from the spatial autocorrelation that is expressed by the data wa s added to both models. The apparent violation of the equidispersion assumption (i.e., equal me an and variance) of the Poisson model was reduced by the addition of the spatial filter to the model (see Appendix H for details). The Poisson model is more appealing for the PCMe exceedances due to the rarity of their occurrence; both models explain approximately th e same percent of the variance in the data. Prior to Considering Spatial Autocorrelation a With Spatial Filter Added to Models a

Model Parameter Estimate Equidispersion Parameter Estimate Equidispersion % Variance Accounted for Binomial -5.4713 NA -5.9347 NA 40% Poisson for rates -5.4756 NA -5.9383 NA 40% Negative binomial b for rates -5.2098 2.8692 - c

a Spatial autocorrelation is accounted for in the statistical models using an ei genfunction spatial f ilter (Griffith, 2002);

see Appendix E for details. b A negative binomial distribution was fi t to the PCMe exceedances to assess th e assumption of equidispersion (equal mean and variance), which is a feat ure of a Poisson random variable.

c The negative binomial not estimable; however, the deviance measure for the Poisson model (1.38) indicates overdipsersion has been reduced.Final Report December 2005 Page E-44

Comparison of PCMe Exceedance Rates

Exceedance rates for each SSA with a sample size of 30 or more were compared to each other to assess whether or not statistically significant differences exist. Aggregate sample sizes less than 30 were considered too small to include in the comparisons. The sample size restriction left 22 SSAs for the test only data and 32 for the clean and test data. Comparisons were base d on the spatial autocorrelationfiltered Poisson models described above. These comparisons essen tially consist of calculating the difference between the rates for two SSAs, and determ ining if the absolute value of the difference is statistically different from zero. In general, the differences in th e exceedance rates will approach a

normal distribution as the means for the rates increase s. The normal approximation is very good when the

number of exceedance for each SSA exceeds 4. The low number of exceedances in most SSAs indicated the normal approximation would be poor. This was c onfirmed by a simulation experiment which showed

that the normal distribution would not be reasonable for either the test only or clean and test exceedance rates comparisons. Therefore, the significance of each of the pairwise comparisons between SSA exceedance rates was determined by nonparametric si mulation analysis. The simulation experiments are

described in Appendix H

.

Pairwise comparisons that were si gnificant at type I error rates ( α ) of 0.01, 0.05 and 0.10 are shown in Appendix H , Tables H-1 and H-2 . The type I error rates reported ar e global error rates that take into consideration the multiple comparisons that are being made. When performing multiple statistical tests, the probability of rejecting the null hypothesis when it is true (Type I error, α ) increases. In the present context, this means the probability of incorrectly concluding that a difference exists between the exceedance rates for two SSAs would be greater than intended, unless the error rate was adjusted to compensate for the multiple tests. The error rates reported in Tables H-1 and H-2 reflect a Bonferroni adjustment to account for the multiple tests (see Appendix H for details). The comparisons of the test only exceedance rates between SSAs indi cate there are three SSAs with

exceedance rates that are statisti cally significantly different (at α = 0.01) than the exceedance rates observed in approximately one-half of the other SSAs ( Figure E-15a )

Results of the comparison of the clean and test exceedance rates between SSAs are indicated in Figure

E-15b . The number of significant pairwise comparisons at α = 0.01 are shown for SSAs that had one or

more exceedance. Three SSAs that differ from the ma jority of the other SSAs are located east of the

WTC. The number of exceedance s for these three SSAs range from 17 – 32; the exceedance rates range

from 0.006 to 0.059.Final Report December 2005 Page E-45

The SSA-level analysis has shown that the Poiss on model provides a reasonable model for the PCMe

exceedance rates after the model is modified to acc ount for the positive spatial autocorrelation that is exhibited by the exceedance rates. The comparisons of the exceedance rates indicate that the rates are not constant across the SSAs. Statistical summary areas having the highest rate of PCMe exceedances are

located east of the WTC site.



### E.3.2.3 Building-Level Pattern of PCMe Exceedance

Two methods for testing for the pres ence of clusters in the exceedance events, Nearest Neighbor distances and Ripley K functions, are briefly described in this section. Both methods can be used to produce plots of the spatial distribution of sample locations, and the spatial distribution of PCMe exceedance locations. Visual comparison of these plots can provide useful qualitative information regarding the presence or absence of spatial clustering of the PCMe exceedance events. A formal statistical test for spatial randomness is available for the nearest neighbor distan ce. A semi-quantitative, graphical method is used with the Ripley K function to test for spatial randomness. The underlying assumption behind both methods, as they are employed in this analysis, is that PCMe exceedances follow a homogeneous spatial Poisson process as described in Section E.2.1.3

.

The location of the test only and clean and test buildings are not evenly distributed across the potentially affected area. For example, the buildings can be gr ouped into five sub areas. The largest dimension of these areas varies from approximately 750–1,500 meters . Therefore interpretation of these analyses should be limited to distances of 500–750 meters, as distances greater than these may be overly influenced by global trends in the events, rather than the local spatial dependence between events.Final Report December 2005 Page E-46

Figure E-15a. Significant differences between estimated exceedance rates for test only data. Estimates are based on the spatially-filtered Poisson model (see Section 3.2.3.2 and Appendix H for details). The number of significant pairwise comparis ons at an experiment-wise α = 0.01 (with a Bonferroni adjustment) are shown for SSAs that had one or more exceedances. Comparisons with SSAs with sample sizes less than 30 (indicated in figure by cross-hatching, and in figure legend by “n <30”) were deemed unreliabl e and were therefore not included in the analysis. The 3 SSAs that were found to have the most number of significant comparisons are located east of the WTC. The numbers of exceedances for these three SSAs range from 2 to 9; their exceedance rates range from 0.021 to 0.060. The spatial pattern exhibite d above is similar to the pa ttern of exceedance rates that is shown in Figure 3-13 however, 4 of the 7 SSAs with exceedance rates in the 4 th quartile ( Figure 3-13 ) were found to be significantly different from 5 or fewer of the other SSAs.Final Report December 2005 Page E-47

Figure E-15b. Significant differences between estimated exceedance rates for clean and test data. Estimates are based on the spatially-filtered Poisson model (see Section 3.2.3.2 and Appendix H for details). The number of significant pairwise comp arisons at an experiment-wise α = 0.01 (with a Bonferroni adjustment) are shown for SSAs that had one or more exceedances. Co mparisons with SSAs with sample sizes less than 30 (indicated in figure by cross-hatching, and in figure legend by “n<30”) were deemed unreliable and were therefore not included in the analysis. Three of th e SSAs that were found to have the most number of significant comparisons are located east of the WTC. The numbers of exceedances for these three SSAs range from 17 to 32; their exceedance rates range from 0.006 to 0.059. The spatial pattern exhibited above is similar to the pattern of exceedance rates that is shown in Figure 3-14 however, 3 of the 9 SSAs with exceedance rates in the 4 th quartile ( Figure 3-14 ) were found to be significantly diffe rent from 4 or fewer of the other SSAs.Final Report December 2005 Page E-48
Nearest Neighbor Method

In nearest neighbor analysis, the focus is on the di stance between the exceedance events. The observed nearest neighbor distance 

### d N N is the average distance between each exceedance events and its closest neighbor (i.e., another exceedance event). It is calcu lated by determining the distance between each event and its nearest neighbor, then taking the average of the distances. The observed 

### d N N is compared to the average distance between nearest neighbors that wo uld be expected if the events were randomly distributed in space (i.e., if they followed a spatial Poisson process). The expected distance is provided by:



### λ ˆ 2 1 ) ( =



### NNd E ; 

### A N / ˆ

= λ Equation E-5

where, E(NNd)=expected average distance between nearest neighbors, under the assumption that the events follow a spatial Poisson process; λ =mean, or intensity, of the spa tial Poisson process, which is estimated by the total number of events (N), divided by the area of the site (A). The ratio of the observed nearest neighbor distance to the expected nearest ne ighbor distance yields the nearest neighbor index (NNI): 

# ( ) [ ] NNd E d N N NNI = Equation E-6

Nearest neighbor indexes equal to one indicate comp lete spatial randomness (CSR; i.e., homogeneous Poisson process); NNIs less than one indicate spa tial clustering, and NNIs greater than one indicate dispersion, or regular spacing (e.g., a square grid).

An important concern in this analysis is how much lo wer (greater) than one does the NNI have to be to conclude the events are clustered (dispersed). A test for the significance of NNI (i.e., lack of clustering or dispersion in the location of PCMe exceedances) ma y be performed by computing the standardized estimate of the NNd (Z) ( Equation E-7 ) and then comparing the calculated Z to a table of the standard normal distribution (Clark and Evans, 1954):

d N N 

### SE NNd E d N N Z ] [

− = Equation E-7Final Report December 2005 Page E-49

Where d N N SE =standard error of the estimate of the mean nearest neighbor distance:



## ( )

N 4 4 SE d N N 

### π π

− = Equation E-8

A shortcoming of the above test is that it assume s the data are a random sample from the population (Bailey and Gatrell, 1995; Dixon, 2001), which has alr eady been determined to be invalid for the PCMe data. A second shortcoming of the test is that it ignores the correlation between nearest neighbor distances (Cressie, 1993; Dixon, 2001). An extreme cas e of the correlation is two exceedance events that are the nearest neighbor of each other (i.e., reflexive nearest neighbors ). Under the assumption of complete spatial randomness (CSR) in two dimensions , approximately 62% of the events of a spatial point pattern are reflexive nearest neighbors (Dixon, 2001). Finally, nearest neighbor analysis assumes that exceedance events are from a continuous, isotro pic surface. The geographic distribution of the sampled buildings represent a distribution of discrete objects rather than a continuous surface, and it is not equal in all directions (i.e., the distribution is anisotropic).

Given the shortcomings of the above approach, a num erical simulation approach was used to test the significance of the NNd. The simulation approach generates a list of possible ways of assigning N

exceedance ‘labels’ to B buildings, where N equals the number of exceedance events (i.e., N =8 for test only and 37 for clean and test data) and B equals the number of sampled buildings (i.e., B =219 for test only and 408 for clean and test data). The observed pattern of exceed ance events is then compared to the list of possible patterns to test the hypothesis that the exceedance events are randomly distributed geographically (the average NNd for the observed patt ern of events is compared to the ranked list of NNDs for the simulated values). If the observed NNd is typical of the simulated values, the null hypothesis of first order spatial randomness is not rejected ; if the observed value is smaller or larger than most of the simulation NNds, the null hypothesis is rejected. Another advantage of the simulation test is it remove s the assumption that the exceedance events follow a random spatial Poisson process. The simulation test detects departures from spatial randomness, rather than departures from a specific type of random process.

The numerical simulation was executed by randomly selecting N buildings (without replacement) from

the list of B buildings that were sampled for PCMe . The NNd was then calculated for the N randomlyFinal Report December 2005 Page E-50
selected buildings and saved. This process was repeated 9,999 times, producing 9,999 NNds. The NNd

that was calculated for the actual data was then a dded to the list of 9,999 simulated values. The 10,000 NNds were then ranked from lowest to highest. A two-sided test of the null hypothesis that the

exceedance events are consistent with a first order spatial random process can be made by comparing the rank of the observed NNd divided by 10,000 to (1– α /2), where α is the chosen level of significance, and rejecting the null hypothesis if the simulated p-value is greater than (1– α /2).

In addition to calculating a NNI for the distances be tween the closest nearest neighbors (i.e., first order nearest neighbors), it is often informative to calculate NNIs for second, third, ..., K-th nearest neighbors. For example, the k=2 ( second order ) NNI is the ratio of the aver age distance between each PCMe exceedance and its second nearest neighbor 

# ( ) 2 = k D N N , and the expected NND for k=2 

## [ ] ( ) 2 = k NNd E :



## [ ] ( ) A N K K K NNd E K k 2

2 ) ! 2 ( )! 2 (

=

= Equation E-9

Evaluating the average nearest neighbor distance at orde rs greater than one provides a description of the interaction between events at increasing separation distances. Equations B-8 and B-9 are appropriate for first order NNds; significance tests for higher order NNds have not been developed.

The NNIs for the exceedance events should be compar ed to the NNIs for the sampled buildings to account for the nonrandom sampling methods that were employed. A relative NNI is calculated as the

ratio between the NNI for the exceedance events a nd the NNI for the sampled buildings. Relative NNIs less than (greater than) one indicate clustering (dispersi on) of events that is not explained by the spatial distribution of the sampled buildings. The relative NNI is a qualitative measure; statistical tests for significance are not available.

Nearest Neighbor Results

The simulation test for the significance of the test only NNd failed to reject the null hypothesis of first order spatial randomness, although the small number of exceedances (8) should be considered. The

simulation test for the clean and test exceedance events also failed to re ject the null hypothesis of first order spatial randomness (p=0.33). The p-value i ndicates that 33% of the simulated patterns of clean andFinal Report December 2005 Page E-51

test exceedance events had NNds smaller than the observed NNd. These results argue against significant spatial clustering of the PCMe exceedances at the site (i.e., more than would be expected by chance).

Table E-10 shows the NNI and relative NNI for the first 5 ‘ord ers’ of neighbors. The table indicates that

the test only events are more dispersed than the test only buildings. These results should be interpreted

with caution due to the small number of test only exceedance events (8). The clean and test exceedance events exhibit clustering that is consistent with the clustering observed in the sampled buildings. Figure

E-16 shows the NNI for the first 20 orders of neighbors. The test only and clean and test events plot above the sampled buildings, indicating that the events are not clustered. At higher orders of neighbors,

the clean and test events are slightly more dispersed relativ e to the spatial distribution of sampled buildings. The results for higher orders also should be interpreted with caution due to the low number of exceedances (37). Overall, results from the nearest neighbor method lead to a rejection of the null hypothesis that the exceedance events are clustered.

Ripley’s K Function

Ripley’s K function (K function) is another met hod for assessing whether the exceedance events are

clustered. While the NNd looks at the distance be tween nearest events at increasing orders, the K function looks at the number of neighbors at increasing distances. The number of neighbors is determined by drawing a circle of radius r around each event and counting the number of other events (‘neighbors’) that fall within the circle ( Figure E-17 ). This is repeated for every event. Ripley’s K

function for distance r equals the total number of neighbors th at were counted over all the events:



## ( ) 

# ( ) 

# ∑ ∑

=

= = N

j ij N

i d I N A r K

1 1 2 Equation E-10Final Report December 2005 Page E-52

Table E-10. Nearest Neighbor Statistics for the PCMe Exceedances

The nearest neighbor distance (NNd) for order 1 is the average distance between the location of each PCMe exceedance and its nearest neighbor. Second order NNds correspond to the average distance between the location of each PCMe exceedance and its second nearest neighbor, etc. Nearest neighbor indexes (NNIs) equal the NNd divided by E[NNd]. NNIs less than (gr eater than) 1 indicate spatial clustering (dispersion) of PCMe exceedances. The NNI s for the spatial distribution of sampled buildings indicate the buildings tend to be clustered, which is typical for the geographical distribution of buildings in an urban environment. Proper interpretation of the NNIs for the exceedances requires comparing the nearest neighbor indexes (NNIs) for the exceedances to the NNI for the sampled buildings. The relative NNIs for the clean and test and test only PCMe exceedances indicate a lack of spatial clustering (i.e., they are greater than 1). The results shown are approximate; the E[NNd] assumes the PCMe data were gath ered using random sampling methods, or the that the entire population was measured; neither assumption is valid given the data were obtained by voluntary particip ation in the WTC dust cleanup program. Test Only Buildings Test Only Exceedances Order NNd a E[NNd] b NNI c NNd a E[NNd] b NNI c Rel-NNI d 1 50.87 62.10 0.82 406.42 324.91 1.25 1.53 2 73.80 93.15 0.79 725.19 487.37 1.49 1.88 3 91.54 116.44 0.79 910.49 609.21 1.49 1.90 4 110.03 135.84 0.81 1071.27 710.74 1.51 1.86 5 124.26 152.82 0.81 1253.63 799.59 1.57 1.93 Clean and Test Buildings Clean and Test Exceedances Order NNd a E[NNd] b NNI c NNd a E[NNd] b NNI c Rel-NNI d 1 33.74 45.50 0.74 118.45 151.08 0.78 1.06 2 52.59 68.24 0.77 170.72 226.62 0.75 0.98 3 65.61 85.31 0.77 224.41 283.28 0.79 1.03 4 76.22 99.52 0.77 278.31 330.49 0.84 1.10 5 85.79 111.96 0.77 326.16 371.80 0.88 1.14

a NNd: nearest neighbor distance (meters) b E[NNd]: expected nearest neighbor distance, unde r assumption of complete spatial randomness (CSR)

c NNI: nearest neighbor index d Rel-NNI: relative nearest neighbor index= NNI for exceedances/NNI for all buildingsFinal Report December 2005 Page E-53

Figure E-16. Nearest neighbor analysis for the PC Me asbestos data. The nearest nei ghbor index (NNI) is the ratio of the observed nearest neighbor distance (NNd; average distance betw een each PCMe exceedance and its closest neighbor) to the expected value of the NNd, under the assumption of complete spatial randomness (CSR). A NNI of 1 indicates a random spatial distribution of events; NNIs < 1 indi cate clustering, NNIs >1 i ndicate dispersion (e.g., sp atial distribution of PCMe exceedances on a square grid). The x-axis of the figure indicates the average distance between neighbors of increasing orders ; e.g., the NNI of order =2 is the ratio of the average distance between each PCMe exceedance and its second closest neighbor, and the expected distance between neighbors of order = 2. The NNIs for the building lo cations indicate spatial clustering at small spatial scales (i.e., low orders ). The buildings approach a random distribution (i .e., NNI = 1) at larger spatial scales (i.e., higher orders). This pattern is typical of the geographic distribution of buildi ngs in an urban landscape. The NNIs for the c lean and test exceedances are very similar to the NNIs for the clean a nd test buildings;, up to order =5, indicating a lack of spati al clustering of the exceedances, relative to the building locations; clean and test exceedances events appear to be randomly distributed among the sampled clean and test building locations. At orders greater than 5, the clean and test exceedance event s appear to be spaced further apart on aver age than expected for a random distribution. However, given the small number of clean and test exceedances (37), the NNIs at higher orders should be interpreted with caution. The test only exceedance events appear to dispersed; however the very low number of test only exceedance events (8) preclude drawing definitive conclusions. All of the test only exceedances occurred in buildings that also contained at least one residence that was also cleaned and tested. Furthermore, the analysis of spatial trends (further discussed in Section 3.2 of the report) indicate that buildings with only test only residences ( unique test only ) tend to be located north of th e buildings that also contained clean and test residences. This difference between the spatial distribution of the test only and unique test only buildings probably contributes to the dispersion indicated by the NNI for the test only exceedance events. 

### 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

0 5 10 15 20 

### OrderNearest Neighbor Index 

### Test only bldgs Test only exceedances Clean & test bldgs Clean & test exceedance Unique test only bldgsFinal Report December 2005 Page E-54



# i

j

Figure E-17. Calculation of the Ripley K function. The Ripley K function is estimated by counting the number of other exceedance ev ents that are located within a distance r of an exceedance event

. The calculation is repeated for every event, i , j , .... N , where N = the number of events. The Ripley K function for separati on distance r is the sum of all counts over all events ( Equation 6, Section 3.2.2.2.3 ). Ripley’s K function is ty pically repeated for increasing separation distances and plotted vs the separation distances (e.g., Figures 3-11, 3-12 ). Shown above is the calculation for two events, i and j , for six separation distances (corresponding to the six circles). The concentric circles represent increasing separation distances ( r ). For example, Event i : 0 other events (i.e., other exceedances ) within distance of 1 unit, 7 other events within a distance of 6 units; event j: 1 other event within a di stance of 3 units, and 3 other events within a distance of 6 units.Final Report December 2005 Page E-55

Where, r=radius of circle that is used to define neighbors, A =area of site, N =number of exceedance events, d ij =distance between points i and j , I =indicator variable that=1 if dij < r and 0 otherwise.

This calculation is repeated, each time increasing the radius r of the circle that is used to define neighbors , up to the desired maximum value of r

.

Interpretation of the K function is typically perform ed by plotting a conversion of K(r), L(r), versus r :



## ( ) ( )

r r K r L

− = π Equation E-11

The conversion to L(r) is made to make the plot easier to interpret. Values of L(r) greater than 0 indicate clustering; values less than 0 indicate dispersion.

Under the assumption that the exceedance events are distributed according to a random spatial Poisson process, the expected number of events within distance r of a given event is:

2 )] ( [ r A N r K E π = Equation E-12

where, N =number of events, A =area of site, and r =radius of circle that is used to define neighbors.

The expected value of the K-function, after convers ion to L(r) (Equation E-11), plots as a horizontal line at L(d)=0. If the number of other exceedance events found within a distance r from an exceedance event is greater than E(K[r]), clustering is indicated at that distance; conversely, if the number of events at r is less than the expected value, dispersion is indicated.

The weighted Ripley’s K function was estimated fo r the two groups of exceedance events, where the events are weighted by the number of samples that were collected from each building. The weights account for the increased likelihood of measuring an exceedance in buildings where more samples are collected (Levine, 2002).

The sampling distribution of K(r) has not been determ ined. Therefore, a test for CSR was performed using a simulation approach that is similar to the one that was used to test the significance of the NNI. The numerical simulation was executed by randomly selecting N buildings (without replacement) fromFinal Report December 2005 Page E-56

the list of B buildings that were sampled for PCMe. The values of L(r) were then calculated for the N randomly selected buildings for different values of r and saved. This process was repeated 9,999 times, producing 9,999 estimates of L(r) at each distance, r

. Simulation envelopes were created by plotting extreme values of the simulated L(r) at each distan ce. The significance of the estimated K-function at

each distance r was made by comparing it to the s imulation envelopes

.

Ripley’s K Function Results

Figures E-18 and E-19 show the K-function for the test only and clean and test events plot below the test only and clean and test buildings, respectively, which indicates that the exceedance events are more dispersed than the geographic distribution of the samp led buildings. The exceedances also appear to be dispersed relative to the location of the sampled build ings, after the Ripley K function is adjusted to consider the number of samples that were collected fr om each building. At separation distances greater than approximately 400 feet, the curv e for the exceedances falls at or be low the curve that corresponds to

the 5 th percentile of the simulated Ripley K values, indicating that the pattern of test only exceedances may be more dispersed than expected based on chan ce alone for a spatially random process. However, given the small sample number of exceedances ( n =8), these results should be interpreted with caution.

The Ripley K function for the clean and test exceedances indicates that th e exceedances are slightly dispersed relative to the location of the sampled build ings. Some slight clustering of exceedance events may be indicated at the smallest separation distance considered (i.e., approximately 100 meters) when the exceedances are compared to the Ripley K function fo r the sampled buildings after it is adjusted to consider the number of samples that were collect ed from each building. However, the curve for

exceedance events falls between the 5 th and 95 th percentile of the simulated Ripley K values, indicating that the pattern of exceedances does not differ significan tly from a spatially random process. Overall, the analyses provide no convincing evidence of clustering in either the clean and test or test only exceedance

events.



### E.3.2.4 Site-Level Vertical Pattern of PCMe Exceedance

Analysis of the vertical pattern of PCMe exceedan ces was performed using contingency tables and by fitting Poisson regression models to the data. Floor le vels were used as a surrogate for elevation. Early attempts at fitting a Poisson model using individual floor levels were unsuccessful due to the rarity of exceedances. To address this problem, floors were grouped into three categories: lower floors (floors 3Final Report December 2005 Page E-57

and lower), middle floors (floors between 4 and 9, inclusive), and upper floor s (floors 10 and higher). The analysis was performed in two ways. The firs t approach was performed at the sample level ( samplebasis ); each sample result was used in the an alyses (i.e., the exceedance events were not aggregated at the building level). In the second approach ( residence-basis ), the data were aggregated at the residence level; any residence that had one or more ex ceedance was treated as an exceedance.

Figure E-18. Ripley’s K plot for the test only PCMe exceedance event data. A Ripley’s K plot is used to compare the number of neighbors for each exceedance event (i.e., location of bu ilding with at least one PCMe results > 0.0009 f/cc) to an expected number of neighbors base d upon the null hypothesis that the events are randomly distributed across the geographic landscape according to a ho mogenous spatial Poisson process (see Section 3.2.1.3 for explanation). The number of neighbors is determined by drawing a circle of radius ‘r’ around each event and counting the number of other events (‘neighbors’) that fall within the circle. This is repeated for every event. Ripley’s K function for radius ‘r’ equals the total number of neighbors that were counted over all the events. This calculation is re peated, each time in creasing the size of the circle that is used to define neighbors ; the increasing radius is shown on the x-axis. A conversion of K(r) to L(r) (see Section 3.2.2.2.3 for definition) is made to make th e plot more linear (i.e., easier to interpret). Values of L(d) greater than 0 indicate clus tering; values less than 0 indicate dispersion. Ripley’s K for the test only PCMe events is consistent w ith the nearest neighbor plot ( Figure 3-7 ); the geographical distribution of the test only events exhibit less clustering than the test only buildings, respectively. A weighted Ripley’s K function was estimated for the sampled buildings, where the events are weighted by the number of samples that were collected from each building. The weights account for the increased likelihood of measuring an exceedance in buildings where more samples are collected. A comparison of the Ripley K function for the test only events to the weighted Ripley K indicates that the exceedances are dispersed relative to the sampled buildings. The location of the Ripley K plot for the exceedances within the simulation envelope (see Section 3.2.2.2.3 for details), which is defined by the 5 th and 95 th percentile of the simulated Ripley K function at each distance interval ( r ), supports a conclusion that there is insufficient evidence to indicate clustering of the test only exceedance events. 

### -400 -300 -200 -100 0 100 200 300 400

0 200 400 600 800 1000 1200 

### Distance between points (r, meters)L(r) 

### Test only bldgs 5th % of simulated values Wgt test only bldgs 95th% of simulated values Test only exceedsFinal Report December 2005 Page E-58

Sample-Basis Analysis

Contingency tables for the test only and clean and test data are provided as Tables E-11 and E-12

.

All of the exceedance rates are less than 1%. Fisher’s exact test for the test only data indicates the difference in exceedance rates between floors is marg inally significant (p=0.08, 2-sided). A higher exceedance rate was observed for the middle floor gr oup (0.73%) then either the lower (0.11%) and upper (0.37%) floor groups. Additional tests were perform ed between the floor groups to determine which exceedance rates were significantly differen t, if any. The difference between the lower floor group and

the middle floor group was found to be significant by Fisher’s exact test (p=0.04, 2-sided); differences between the middle and upper, and lower and upper floor groups were not statistically significant by Figure E-19. Ripley’s K plot for the clean and test PCMe exceedance event data. The Ripley’s K plot for the clean and test PCMe events is consistent w ith the nearest neighbor plot ( Figure 3-7 ); the geographical distribution of the clean and test events exhibit less clustering than the clean and test buildings. A comparison of the Ripley K function for the clean and test events to the weighted Ripley K function indicates that the exceedance events appear to be slightly more clustered than the sampled buildings, particularly at short distances. The location of the Ripley K plot for the exceedances within the simulation envelope (see Section 3.2.2.2.3 for details), which is defined by the 5 th and 95 th percentile of the simulated Ripley K function at each distance interval ( r ), fails to support a conclusion th at the exceedance events are clustered. 

### -300 -250 -200 -150 -100

-50 0 50 100 150 200

0 200 400 600 800 1000 1200 

### Distance between points (r, meters)L(r) 

### Clean & test bldgs 5th % of simulated values Wgt clean & test bldgs 95th % of simulated values Clean & test exceedsFinal Report December 2005 Page E-59

Fisher’s exact test (p=0.17 and p=0.43, respectively; 2-sided). The Poisson model for the test only data was not significant.

Fisher’s exact test for the clean and test data indicates the difference in exceedance rates between floors is statistically significant (p=0.02, 2-sided). The exceedance rate was highest for the lower floor group (0.66%), lower for the middle floor group (0.44%), and the lowest for the upper floor group (0.32%). Additional tests were performed between the floor groups to determine which exceedance rates were significantly different. The difference between the lower and upper floor groups was found to be statistically significant by Fisher’s exact test (p=0.01; 2-sided). The differences between the lower and

middle floor group, and the middle and upper floor group were found to be not significant by Fisher’s exact test (p=0.12, p=0.20, respectively; 2-sided). The odds ratios for the Poisson model indicate lower floors are twice as likely to have exceedances as th e upper floors (95% CI 1.2, 3.4) (p-value for chisquare test for parameter = 0.01).

Table E-11. Contingency Table for Test Only PCMe Exceedances, on a Sample-Basis

The exceedance rate in the middle floor group is higher than the exceedance rates observed in the lower and upper floor groups. The p-value for Fisher’s exact test is 0.08, indicating the differences between the floor groups is marginally statistically significant. Floor Group Not PCMe Exceedances PCMe Eceedances Totals

lower 873 a 99.89 b 1 c 0.11 d 874

middle 1768 99.27 13 0.73 1781

upper 1617 99.63 6 0.37 1623

Totals 4258 99.53 20 0.47 e 4278 e

a Number of samples that did not exceed health-based benchmark for asbestos b Percent of samples that did not exceed health-based benchmark for asbestos

c Number of samples that exceeded the health-based benchmark for asbestos b Percent of samples that exceeded the health-based benchmark for asbestos

e The table does not include samples where the floor was not provided in the database, therefore sample sizes and percent of exceedances will differ from those provided elsewhere in the report.

e The table does not include samples where the floor was not provided in the database, therefore sample sizes and percent of exceedances will differ from those provided elsewhere in the report.Final Report December 2005 Page E-60

Table E-12. Contingency Table for C lean and Test PCMe Exceedances, on a Sample-Basis

The observed exceedance rate increases with floor le vel. The p-value for Fisher’s exact test is 0.02, indicating the differences between the floor gr oups is statistically significant at the 0.05 level. Floor Group Not PCMe Exceedances PCMe Exceedances Totals

lower 4233 a 99.34 b 28 c 0.66 d 4261

middle 8971 99.56 40 0.44 9011

upper 10488 99.68 34 0.32 10522

Totals 23692 99.57 102 0.43 e 23794 e

a Number of samples that did not exceed health-based benchmark for asbestos b Percent of samples that did not exceed health-based benchmark for asbestos

c Number of samples that exceeded the health-based benchmark for asbestos b Percent of samples that exceeded the health-based benchmark for asbestos

e The table does not include samples where the floor was not provided in the database, therefore sample sizes and percent of exceedances will differ from those provided elsewhere in the report.Final Report December 2005 Page E-61

Residence-Basis Analysis Contingency tables for the test only and clean and test data are provided as Tables E-13 and E-14

. Fisher’s exact test indicates the exceedance rates do not differ significantly between floor groups for the test only and clean and test data (p=0.74 and 0.84, respectively), when the data are analyzed at the

residence level.

Table E-13. Contingency Table for Test Only PCMe exceedances, on a Residence-Basis

The exceedances are extremely rare across the fl oor groups. The p-value for Fisher’s exact test is 0.74, indicating the differences between the floor groups is not statistically significant. Floor Group Not PCMe Exceedances PCMe Exceedances Totals

lower 147 a 100 b 0 c 0 d 147

middle 303 99.02 3 0.98 306

upper 292 99.32 2 0.68 294

Totals 742 99.33 5 0.67 e 747 e

a Number of samples that did not exceed health-based benchmark for asbestos b Percent of samples that did not exceed health-based benchmark for asbestos

c Number of samples that exceeded the health-based benchmark for asbestos b Percent of samples that exceeded the health-based benchmark for asbestos

e The table does not include samples where the floor was not provided in the database, therefore sample sizes and percent of exceedances will differ from those provided elsewhere in the report.

Table E-14. Contingency Table for C lean and Test PCMe exceedances, on a Residence-Basis

Very little difference in the exceedance rate is obs erved between floor groups. The p-value for Fisher’s exact test is 0.84, indicating the differe nces between the floor groups is statistically significant. Floor Group Not PCMe Exceedances PCMe Exceedances Totals

lower 534 a 99.07 b 5 c 0.93 d 539

middle 1306 99.24 10 0.76 1316

upper 1497 99.27 11 0.73 1508

Totals 3337 99.23 26 0.77 e 3363 e

a Number of samples that did not exceed health-based benchmark for asbestos b Percent of samples that did not exceed health-based benchmark for asbestos

c Number of samples that exceeded the health-based benchmark for asbestos b Percent of samples that exceeded the health-based benchmark for asbestos

e The table does not include samples where the floor was not provided in the database, or common areas, therefore sample sizes and percent of exceedances will differ from those provided elsewhere in the report.Final Report December 2005 Page F-1



# APPENDIX F.

Other Fiber Analyses

The WTC Clean-up Program recorded three distinct fi ber measurements in indoor air. Residents were provided a report that detailed the results of each of these three analyses. One measurement was employed specifically to provide a risk-related basis for clearing residential dwelling. The other two measurements provide ancillary information on a wide r range of asbestos and other man-made vitreous fibers. Listed below is a summary of each fiber measur ement protocol with applicable reference values.

1. The clearance criteria for the WTC Clean-up Program was based on phase-contrast microscopy equivalent (PCMe) measurements of asbestos in ind oor air. This counting method employs transmission electron microcopy (TEM) for analysis but registers onl y asbestos fibers greater than 5 microns in length with a minimum length-to-width ratio of 5:1. Th e minimum length of 5 um represents the current scientific consensus that attributes cancer-causing poten tial to long (i.e. >5 microns) asbestos fibers. The

health-based benchmark for the PC Me measurement is .0009 f/cc, wh ich equates to an excess lifetime

cancer risk of one-in-ten thousand based on the cons ervative assumption of 30 years of continuous exposure at the benchmark concentration.

Phase Contrast Microscopy (PCM) was used for airb orne asbestos analysis in the preponderance of occupational epidemiology studies which provide th e robust dose-response data base on asbestos-related carcinogenesis. However, advancemen ts in asbestos analysis by electron microscopy have made TEM the analytical method of choice. Consequently, EPA counts fibers by TEM, but employs a fiber counting metric derived from phase contrast microscopy (PCM) for quantifyi ng cancer risk associated with asbestos exposure. TEM analysis is typically performed under a magnification of 20,000 X, whereas PCM analysis is performed at 400 X magnification. In order to make any statement regarding quantitative cancer risk associated with exposures estimated via TEM fiber counts (as directed by AHERA) a PCM “equivalence” count must first be obtaine d. In effect, PCM “equivalence” is an exercise

to convert TEM counts to structures that would be vi sualized had the analysis been done by PCM. (It

should be noted that the correlation between total PCM fiber and TEM fiber counts is not straightforward and varies between analysts; therefore, no standard co nversion factor exists for these two measurements.)Final Report December 2005 Page F-2

In addition to evaluating cleaning techniques against the health-based clearance criteria for asbestos in air (.0009 f/cc, PCMe), two additional analyses we re conducted to measure airborne fibers.

2. The first is similar to the analytical approach (i .e., TEM) used to establish the clearance criteria except

that in this case all asbestos fibe rs greater than 0.5 microns are counted. This is the asbestos fiber counting method used in the Asbestos Hazard Emerge ncy Response Act (AHERA). All fibers greater than (>) 0.5 um are counted as per AHERA counting me thods for school reentry. This is the method used

under EPA standards to determine whether an asbe stos clean-up project has left behind residual contamination. The standard for school clean-ups is 0.022 fibers/cubic centimeter (f/cc) as converted

from 70 structures/mm2.

3. Airborne fibers were also measured using phase contrast microscopy (PCM). A light microscope is used to identify the presence of fibers in the air. The fibers are stained to help in their identification. This method cannot reliably visualize fibers that are thinne r than .25 microns in width. Consequently, based on the typical aspect ratio range of asbestos fibers, most fibers less than 5 microns in length cannot be seen. Nor can PCM accurately identify different types of fibers. This is the method used by OSHA to determine compliance with its asbestos standard for wo rkers of 0.1 f/cc of air. Because it is not specific for asbestos, the PCM analysis also serves as a meas ure of non-asbestos fibrous material (such as fibrous glass) that was released from the WTC disaster. The OSHA workplace standard for fibrous glass is 1

f/cc. There are no environmental standards for fibrous glass, but a PCM concentration of .01 f/cc (derived by adding a safety factor of one hundred (100X) to th e occupational standard) is believed to be protective of the general public.Final Report December 2005 Page F-3



## Syracuse Research Corporation 301 Plainfield Road, Suite 350 Syracuse, NY 13212 

# Memo

To: Dennis Santella, Mark Maddaloni and Pat Evangelista, Region 2

From: Bill Thayer, Syracuse Research Corporation

Date: April 9, 2004

Re: Correlation Analysis for PCMe, AHERA and fibers

Per your direction, the Pearson and Spearman correlation coefficients were calculated for the PCMe, AHERA, total fiber (PCM) and non-asbestos fiber results, using data from the WTC database. Tables F-1 and F-2 indicate that the correlation between PCMe a nd AHERA results is high, and a weak relationship is indicated between non-asbestos fibers and PCMe. There does not appear to be a linear or nonlinear relationship among the other pairs of results. Scatter plots ( Figures F-4 ) are consistent with these results. Correlations were calculated using only the records wh ere both pairs of measurements were detected. This greatly reduced the number of total pairs that c ould be included in the calculation, particularly the correlations between PCMe and tota l fibers, and between AHERA results and ‘total fibers’. However, visual inspection of scatter plots that were prepared using all of the data showed that the fiber results varied (approximately) from 0 to 0.06 f/cc when asbe stos fibers were not detected by the AHERA and PCMe methods ( Figures F-5 and F-6 ), which is approximately the same range in total fibers that was observed when asbestos was detected with PCMe and AHERA methods. Non-asbestos fibers ranged from approximately 0 to 0.24 f/cc ( Figure F-7 ) when asbestos fibers were not detected by the PCMe method, which is also approximately equal to the total range of measured non-asbestos fibers.

Please feel free to contact me at 315-452-8424 or thayer@syrres.com with any questions or comments.Final Report December 2005 Page F-4

Table F-1. Pearson Correlations

Variable PCMe AHERA Fibers Non-Asbestos Fibers

PCMe 1.00 0.86 (<0.0001) 579 0.12 (0.003) 579 0.32 (<0.0001) 532

AHERA 0.86 (<0.0001) 579 1.00 0.08 (0.006) 1215 0.25 (<0.0001) 1087

Fibers 0.12 (0.003) 579 0.08 (0.26)

1215 1.00 0.15 (<0.0001) 24,706

Non-asbestos fibers 0.32 (<0.0001) 532 0.25 (<0.0001) 1087 0.15 (<0.0001) 24,706 1.00

The Pearson correlation coefficient measures the linear relationship between two variables. The correlation between a given pair of observations is provided in the cell that occurs in the row and column for that pair of observations; e.g., the correlation between the PCMe and AHERA measurements = 0.86. The first number in each cell is the estimated correlation coefficient, the second number (in parenthesis) is the probability for the null hypothesis that the correlation = 0, and the last number is the number of pairs that were used to estimate th e correlation. Low correlations are often found to be significantly different from zero when the sample size is large (e.g., the Spearman correlation between ‘Fibers’ and ‘AHERA’).

Table F-2. Spearman Correlations

Variable PCMe AHERA Fibers Non-Asbestos Fibers

PCMe 1.00 0.73 (<0.0001) 579 0.12 (0.003) 579 0.17 (<0.0001) 532

AHERA 0.73 (<0.0001) 579 1.00 0.06 (0.03)

1215 0.13 (<0.0001) 1087

Fibers 0.12 (0.003) 579 0.06 (0.03)

1215 1.00 0.19 (<0.0001) 24,706

Non-asbestos fibers 0.17 (<0.0001) 532 0.13 (<0.0001) 1087 0.19 (<0.0001) 27,706 1.00

The Spearman correlation coefficien t measures the nonlinear relationship between tw o variables; it is calculated using the ranks of the data. The correlation between a given pair of observations is provided in the cell that occurs in the row and column for that pair of observations; e.g., the correlation between the PCMe and AHERA measurements = 0.73. The first number in each cell is the estimated correlation coefficient, the second number (in parenthesis) is the probability for the null hypothesis that the correlation = 0, and the last number is the number of pairs that were used to estimate th e correlation. Low correlations are often found to be significantly different from zero when the sample size is large (e.g., the Spearman correlation between ‘Fibers’ and ‘AHERA’).Final Report December 2005 Page F-5

Figure F-1. Scatter plot between PCMe results (struc tures/cubic centimeter) (vertical axis) and AHERA results (structures/cubi c centimeter). The plot indi cates a good linear relationship between the two measurements; the Pearson correlation coefficient = 0.86 (p < 0.0001).

Figure F-2. Scatter plot between PCMe results (str uctures/cubic centimeter) (vertical axis) and total fibers results (fibers/cubic centimeter) . The plot indicates a lack of a relationship between the two measurements; the Pearson correlation coefficient = 0.12 (p = 0.003).Final Report December 2005 Page F-6

Figure F-3. Scatter plot between AHERA results (s tructures/cubic centimeter) (vertical axis) and total fibers results (fibers/cubic centimeter) . The plot indicates a lack of a relationship between the two measurements; the Pearson correlation coefficient = 0.08 (p = 0.006).

Figure F-4. Scatter plot between PCMe results (str uctures/cubic centimeter) (vertical axis) and non-asbestos fibers results (fibers/cubic centimeter). A relationship between the two measurements is not apparent from the plot; the Pearson correlation coefficient = 0.32 (p < 0.001).Final Report December 2005 Page F-7

Figure F-5. Scatter plot between AHERA results (s tructures/cubic centimeter) (vertical axis) and total fibers results (fibers/cubic centimeter) . Non-detects for both m easurements were set = -0.1 to allow plotting. Total fiber results va ried from approximately 0 to 0.03 (f/cc) when asbestos fibers were not de tected by the AHERA method.

Figure F-6. Scatter plot between PCMe results (str uctures/cubic centimeter) (vertical axis) and total fibers results (fibers/cubic centimeter) . Non-detects for both measurements were set = -0.1 to allow plotting. Total fiber results varied from approximately 0 to 0.03 (f/cc) when asbestos fibers were not detected by PCMe.Final Report December 2005 Page F-8

Figure F-7. Scatter plot between PCMe results (str uctures/cubic centimeter) (vertical axis) and non-asbestos fibers results (fibers/cubic cen timeter). Non-detects for both measurements were set = -0.1 to allow plotting. The non-asbe stos fiber results vari ed from approximately 0 to 0.24 (f/cc) when asbestos fibe rs were not detected by PCMe.Final Report December 2005 Page F-9

Table F-3. Phase-Contrast Microscopy Equivalent (PCMe) Measurements

Count Fibers Concentration Analyte Clean Or Test Sampling Procedure Samples Detects Average Max Average Max

PCMe Clean and Test aggressive 1396 96 2.90 17 0.0017 0.0129 Clean and Test modaggressive 17162 290 1.41 22 0.0008 0.0204 Test aggressive 212 2 1.50 2 0.0008 0.0011 Test modaggressive 4356 78 1.44 7 0.0007 0.0037

TEM Clean and Test aggressive 1396 135 6.96 62 0.0040 0.0403 Clean and Test modaggressive 17162 663 2.00 48 0.0012 0.0732 Test aggressive 212 6 1.17 2 0.0006 0.0011 Test modaggressive 4356 148 2.24 20 0.0011 0.0101

PCM Clean and Test unknown 20 20 12.85 31.5 0.0019 0.0040 Clean and Test aggressive 1396 1319 10.90 112 0.0021 0.0150 Clean and Test modaggressive 17162 15113 13.68 136 0.0026 0.0440 Test unknown 16 14 17.68 42 0.0029 0.0060 Test aggressive 212 198 11.44 84 0.0020 0.0110 Test modaggressive 4359 3824 16.14 185 0.0031 0.0570

Nonasbestos Clean and Test Unknown 20 10 5.90 11 0.0030 0.0053 Fibers Clean and Test Aggressive 1396 835 10.32 204 0.0057 0.1747 Clean and Test Modaggressive 17161 8562 7.38 482 0.0038 0.2345 Test Unknown 16 9 10.56 34 0.0055 0.0177 Test Aggressive 212 137 20.26 119 0.0103 0.0609 Test Modaggressive 4359 2414 8.74 330 0.0045 0.1619

Note: The averages reported above for fibers and concentrations do not include the non detects. For example the average PCMe ( aggressive sampling) fiber count of 2.9 is the average for the 96 samples where asbestos was detected. The 1300 non detects were not used to determine an average value for all the PCMe samples.Final Report 9/19/2007 Page G-1 

# APPENDIX G.

Calculation of the Standard Deviational Ellipse

The standard deviational ellipse is derived from the bivariate distribution (Ebdon, 1988):

Bivariate Distribution = 

### 2 2 2 y

x 

### SQRT σ σ + Equation 1

The standard deviational ellipse is calculated in two steps (Ebdon, 1988). In the first step, the orientation of the axes of the ellipse is determined by minimi zing the sum of squares of the distances between the building locations and the x and y:

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ∑ ∑ ∑ ∑ ∑ ∑

= = = = = =

− − ⎥ ⎥ ⎥ ⎦ ⎤

⎢ ⎢ ⎢ ⎣ ⎡

⎪ ⎭ ⎪ ⎬ ⎫ ⎪ ⎩ ⎪ ⎨ ⎧ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − − + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − −

= N

i i i N

i i i N

i N

i i i N

i i N

i i

y y x x y y x x y y x x y y x x

Arc

1 2 / 1 2

1 2

1 1 2 2

1 2

1 2

2 4

tan θ Equation 2

The y-axis is rotated clockwise through angle θ . The standard deviations are then calculated using the rotated x- and y-axes:



## ( ) ( ) 

## [ ] ⎭ ⎬ ⎫ ⎩ ⎨ ⎧

− − − − = 

# ∑

= N

i i i x N Sin y y x x S

1 2 2 cos 2 θ θ Equation 3



## ( ) ( ) 

## [ ] ⎭ ⎬ ⎫ ⎩ ⎨ ⎧

− − − − = 

# ∑

= N

i i i y N y y x x S

1 2 2 cos sin 2 θ θ Equation 4Final Report December 2005 Page H-1



# APPENDIX H.

Spatial Autoregression Analysis of the PCMe Data

Daniel A. Griffith, Department of Geography, University of Miami



### H.1.0 TEST ONLY DATA

Description of Sample

A total of 4,316 samples were collected, of which 10 have no geographic labels. The total number of samples with a value that exceeds the threshold level is 21. These sample asbestos measurements were aggregated by location into 45 statistical summary areas (SSAs) for lower Manhattan (south of Canal Street). One of these SSAs is the site that housed the WTC; no data were collected for this plus an

additional 8 SSAs.

Initial Data Analysis

The rareness of exceedances suggests that these data ma y be described by a Poisson model. One feature of a Poisson random variable is that its mean, 

### μ , and its variance are equal (equidispersion), a property frequently violated by real world data. "Failure of the Poisson assumption of equidispersion has similar qualitative consequences to failure of the assumption of homoskedasticity" associated with the Gaussian distribution (Cameron and Trivedi, 1998, p. 77). The standard way of accommodating overdispersion (the presence of more variation than is expected fo r a Poisson random variable) is by replacing a Poisson random variable with a negative bi nomial random variable—which can be viewed as a gamma mixture of Poisson random variables. In doing so, the distribut ion of counts is viewed as either (1) having missing variables for the mean specification, or (2) being de pendent (i.e., the occurrence of an event increases the probability of further events occurring). The most popular implementation of the negative binomial probability model specifies the variance as being quadratic in the mean, or



### μ + 2 

### ημ = (1 + 

### ημ ) 

### μ ,Final Report December 2005 Page H-2 with the dispersion parameter, 

### η , to be estimated. The magnitude of 

### η may be interpreted as follows (after Cameron and Trivedi, 1998, p. 79):



### η = 0 implies no overdispersion;



### μ ≈ η 1 implies a modest degree of overdispersion; and,



### μ ≥ η 2 implies considerable overdispersion.

In other words, if 0 

### μ < η ≤ 5

. 0 , a spatial analyst may consider overdispersion detected in georeferenced

data to be inconsequential, with little to be gained by replacing a Poisson with a negative binomial model specification. Meanwhile, recognizing that these ex ceedances are constrained by the number of samples collected suggests that these data may be desc ribed by a binomial model. Recoding counts of exceedances to a binary (0-1) presence/absence measur ement suggests that these data may be described by a logistic model. Simple estimation results for each of these four models appear in Table H-1

.

Table H-1. Selected Model Estimation Results Model Intercept Equidispersion

Poisson -5.3232 NA Negative binomial -5.0964 4.6066

Binomial -5.3183 NA Logistic 1.4213 NA

One important implication from the tabulated results appearing in Table H-1 is that a Poisson model description of rates may suffer from a marked viola tion of the equidispersion assumption. The following evidence supports this claim:

≈ 

### = 58333

. 0 2

ˆ 2 μ 3.42857 < 4.6066.

In other words, the mean and variance may not be constant across the 36 SSAs.Final Report December 2005 Page H-3 Accounting for Spatial Autocorrelation

A conventional spatial autocorrelation analysis is hinde red by two features of the collected data. One is

the rareness of exceedance. In order to further e xplore spatial dependency in this context, average measures of asbestos also were analyzed. The other drawback is the absence of data for 9 SSAs. Because these areal units are dispersed across the study re gion, computing a Moran Coefficient (MC) becomes problematic.

MC scatterplots appear in Figure H-1 . No conspicuous geographic pattern is apparent for either rates or averages, in part because of the presence of a large number of zeroes.

Figure H-1. Left: MC scatterplot for the rate of exceedance. Ri ght: MC scatterplot for the average measure of asbestos.

Latent map patterns also can be assessed with eigenvectors derivable from a MC. Here four of the 11 eigenvectors (E 1 , E 8 , E 10 , E 22 ; these were selected using the stepwise options for PROC LOGISTIC in SAS, and SWPOIS in STATA) denoting consequentia l positive spatial autocorrelation help describe the geographic distribution of exceedance rates. Maps of these synthetic geographic variables appear in Figure H-2.Final Report December 2005 Page H-4

Figure H-2. Geographic distribution of rele vant eigenvectors. Top left: E 1 . Top right: E 8 . Bottom left: E 10 . Bottom right: E 22 .Final Report December 2005 Page H-5 The MC values for the four eigenvectors range from 0. 37 to 0.97. Estimation results that include these eigenvectors in the specifications of each of the four models appearing in Table H-1 are reported in Table

H-2 . The Poisson model with an assumption of equi dispersion appears to be reasonable here. This specification accounts for nearly 60% of the variation in the geographic distribution of rates. The binomial model specification accounts for about 30% . The logistic model description seems inappropriate.

One important finding that can be gleaned from Table H-2 is detected overdispersion accompanying the simple Poisson model description principally is attrib utable to latent spatial autocorrelation (0.4476 <<



### 8571

. 0 58333

. 0 5

. 0 ≈ ). Accordingly, these data can be well described with a Poisson model when the

model specification captures spatial dependencies.

Pairwise Comparisons Between SSAs

Pairwise comparisons of SSA asbestos exceedance sampling results were made to assess whether or not statistically significant differences exist. Aggreg ate sample sizes less than 30 are considered too unreliable, and were not included in this assessment. The outcome of this sample size restriction is 22 SSAs with a sufficient number of samples, allowing (22 

### × 21/2=) 231 pairwise comparisons.

Pairwise Comparisons Statistical Theory

The rareness of exceedances suggests that an analysis of differences of means cannot easily be based upon a binomial model. For a normal approximation to be reasonable here, each SSA sample would need to satisfy the constraint of (sample size) 

### × (exceedance rate) > 5. Table H-2. Selected Model Estimation Results When Spatial Dependence is Included Model Intercept Equidispersion E 1 E 8 E 10 E 22 Poisson -6.0625 NA 4.6209 -9.2072 3.9665 NA Negative binomial -6.1506 0.4476 3.8439 -8.9830 4.5748 NA

Binomial -6.0572 NA 4.6680 -9.3192 3.9896 NA Logistic 2.0052 NA NA NA -9.3199 -6.6709Final Report December 2005 Page H-6 Although both the binomial and the Poisson regressi on models produce very similar descriptive results for the lower Manhattan asbestos data, the Poiss on model seems to furnis h a better model-based

inferential basis.

Consider the difference between two Poisson random variables with means 1 

### μ and 2 

### μ . Mathematical statistical theory states that the expected value of the difference of any two random variables equals the difference of their expected values. Therefore, the difference of means for two Poisson random variables equals 2 1 

### μ

− μ . If these two Poisson random variables ar e independent, then their difference has a

known statistical distribution (Skellam, 1946). The respective sampling variance of each is

1 1



### n μ and

2 2



### n μ ;

the sampling variance of their difference is

2 2

1 1



### n n μ + μ , which parallels a standard result for normal curve

theory. As the two means, 1 

### μ and 2 

### μ , increase to infinity, the distribu tion of the difference of these two independent Poisson variables ra pidly converges to normality. Convergence on a normal probability distribution is quick, with a very good approximation attained once 1 

### μ >4 and 2 

### μ >4. But for small

values of 1 

### μ and/or 2 

### μ this normal approximation is poor. In these latter cases, the difference of two

Poisson random variables still tends to conform to a Poisson distribution.

When multiple comparisons are being made, the overa ll level of significance often should be adjusted downward to compensate for an increase in chance null hypothesis rejections (i.e., Type I errors). For example, in the single WTC asbestos study for whic h 231 difference of means null hypotheses are being evaluated, each hypothesis with a single test , setting the global Type I error probability at 

### α =0.05 means that at least one in twenty of the hypotheses test ed will turn up significan t, merely due to chance fluctuation. In other words, there is a very good ch ance of finding at least one test (and as many as 11 or 12) to be statistically significant solely due to sampling variability, incorrectly concluding that a difference exists in the population. The Bonferroni correction/adjustment is the most basic procedure for modifying 

### α to compensate for this increase in Type I error probability. When the samples are

independent, the modification becomes 

### tests of # α . For the WTC study, and a two-tailed test, this

becomes 

### 231 005

. 0 for an overall 

### α =0.01, 

### 231 025

. 0 for an overall 

### α =0.05, and 

### 231 05

. 0 for an overall 

### α =0.10.

As correlation between the samples increases, the denom inator of this adjustment effectively decreasesFinal Report December 2005 Page H-7 toward 1. Uncorrelated variables require a full Bonf erroni adjustment, perfectly correlated variables require no adjustment, and partially correlated va riables required an adjustment between these two

extremes.

Differences of Exceedance Rates

The estimated spatially filtered Poisson model produ ces sample mean estimate s for uncorrelated Poisson variables. These models include LN (# of cases) as an offset variable. Therefore, dividing both sides of the estimated equation for i 

### ˆ μ (i.e., the mean rate for areal unit i) by the corresponding number of samples

yields the set of estimated rates, assu ming an underlying Poisson process, of

i i



### n ˆ μ , i=1, 2, ..., 22. The

accompanying set of null hypotheses becomes

H o : j j

i i



### n n μ

− μ =0 , i 

### ≠ j, i=1, 2, ..., 22 and j=i+1, i+2, ..., 22 .

The estimated standard error for this difference of rates test is given by 2 j j 2 i i



### n ˆ

n ˆ μ + μ

.

A simulation experiment involving 50,000 di fference of means replications (total=231 

### × 50,000) was conducted using the spatially filtere d Poisson model estimation results . The simulated Poisson random variable, Y, then was used in a bivariat e linear regression analysis, which yielded

j j

i i



### n ˆ

n ˆ μ

− μ = -0.00000 + 1.00012 j j

i i



### n y

n y

− + e , R 2 = 1.00, and

2 j j 2 i i



### n ˆ

n ˆ μ + μ = -0.00000 + 1.00031

j n j y

i n i y 

### s

− + e , R 2 = 1.00.

In both cases, the intercept is not significantly differe nt from 0, and the slope is not significantly different from 1. These simulations confirmed the preceding theoretical results.Final Report December 2005 Page H-8

The model-based mean estimates range from roughly 0.01 to 7.22, implying that most all of the difference of rates sampling distributions should be non-normal . Each simulated dataset was subjected to a diagnostic Kolmogorov-Smirnov goodness-of-fit test for a normal distribution, producing test statistics ranging from roughly 0.48 to 0.53. In other words, the simulated sampling distributions fail to conform to normal distributions. Consequently, the pairwise di fference of rates assessments are based upon a Hopetype nonparametric simulation analysis, involvi ng 99,999 replications coupl ed with each observed difference. The simulated distribution is based on a pa ir of Poisson random variables, each with the same

mean of

2 1 1 2 2 1



### n n 2 n n μ + μ , which yields a null hypothesis difference of 0 and the correct theoretical variance

of

2 2

1 1



### n n μ + μ . Because a two-tailed test is employed here , an observed rank of 1-2 or 99,999-100,000

results in a rejection of the null hypothesis for 

### α =0.01, an observed rank of 3-12 or 99,990-99,998 results in a rejection of the null hypothesis for 

### α =0.05, and an observed rank of 12-22 or 99,979-99,989 results in a rejection of the null hypothesis for 

### α =0.10. Based on these criteria, 21 pairs of exceedance rates are significantly different at the 10% level, 17 pairs are significantly different at the 5% level, and 48 pairs are significantly different at the 1% level. Basical ly, roughly 37% of the extreme MCGB mean pairs tend to be significantly different. These differences aris e from four clusters of mean sizes. The first is dominated by the largest MCBG mean of roughly 9 (MCBG 10015022). The second is dominated by the second and third largest means of approximately 2-3 (MCBG 10008002, MCBG 10015021). The third is dominated by the medium mean of approximate ly 1.5 (MCBG 10015012). The fourth group is dominated by the relatively small mean of roughl y 0.1 (MCBG 10317019D). Significant pairwise contrasts appear in Tables H-3a and H-3b , and Figure H-3

.

These results need to be moderated by keeping in mind that the estimated Poisson model accounts for only about 50% of the variance in the observed exceedances.Final Report December 2005 Page H-9

Figure H-3. Significant differences between estimated exceedance rates for test only data, with Statistical Summary Areas labeled. Estimates are based on the spatially-filter ed Poisson model (see Section 3.2.3.2 and Appendix D for details). The number of significant pairwise comp arisons at an experiment-wise α = 0.01 (with a Bonferroni adjustment) are shown for SSAs that had one or more exceedances. Comparisons with SSAs with sample sizes less than 30 (indicated in figure by cross-hatching, and in figure legend by “n<30”) were deemed unr eliable and were therefore not included in the analysis. The 3 SSAs that were found to have the most number of significant comparisons are located east of the WTC. The numbers of exceedances for these three SSAs range from 2 to 9; their exceedance rates rang e from 0.021 to 0.060. The spatial pattern exhibited above is similar to the patter n of exceedance rates that is shown in Figure 3-13 however, 4 of the 7 SSAs with exceedance rates in the 4 th quartile ( Figure 3-13 ) were found to be significantly differen t from 5 or fewer of the other SSAs.Final Report December 2005 Page H-10

Table H-3a. Test only SSAs Pairs Having Significant Pairwise Comparisons of Exceedance rates

Significantly Different Means at the 

### α =0.10 Level 10008002 10015011 10015011 10033001B 10008002 10039004 10015011 10033001A 10015011 10039001A 10033003B 10033001A 10015011 10033002B 10033002B 10033001A Significantly Different Means at the 

### α =0.05 Level 10007002 10015012 10015011 10039004 10008002 10021001 10015011 10039001B 10013002 10015011 10015011 10033003B 10013003 10015011 10015011 10317019A Significantly different means at the 

### α =0.01 level 10007002 10015022 10015012 10027001 10008002 10013002 10015012 10039004 10008002 10013003 10015012 10039001B 10008002 10015022 10015012 10039001A 10008002 10021002 10015012 10033003B 10008002 10039001B 10015012 10033002B 10008002 10039001A 10015012 10033001B 10008002 10033003B 10015012 10033001A 10008002 10033002B 10015012 10317019A 10008002 10033001B 10015012 10317019C 10008002 10033001A 10015012 10317019D 10008002 10317019A 10015021 10015022 10008002 10317019C 10015022 10021001 10008002 10317019D 10015022 10021002 10013002 10015012 10015022 10025001 10013002 10015022 10015022 10027001 10013003 10015012 10015022 10039004 10013003 10015022 10015022 10039001B 10015011 10015012 10015022 10039001A 10015011 10015022 10015022 10033003B 10015011 10021002 10015022 10033002B 10015011 10317019C 10015022 10033001B 10015011 10317019D 10015022 10033001A 10015012 10015021 10015022 10317019A 10015012 10021001 10015022 10317019C 10015012 10021002 10015022 10317019D

a See figure H-3 for a map of the statistical summary areas (SSAs).Final Report December 2005 Page H-11

Table H-3b. Distribution of Significant Difference of Means by MCBG, Test Only

MCBG Number of Significant Differences MCBG Number of Significant Differences 10007002 2 10021002 4 10008002 19 10025001 3 10008003 2 10027001 5 10009001 1 10033001A 5 10013002 4 10033002B 8 10013003 8 10033003B 5 10015011 5 10039001A 5 10015012 17 10039001B 6 10015021 17 10317019A 9 10015022 21 10317019C 8 10021001 4 10317019D 14

a See figure H-3 for a map of the statistical summary areas (SSAs).Final Report December 2005 Page H-12



### H.2.0 CLEAN AND TEST DATA

Description of Sample

A total of 24,375 samples were collected, of which 17 have no geographic labels. The total number of samples with a value that exceeds th e threshold level is 102. These sa mple asbestos measurements were aggregated by location into 45 statistical summary areas (SSAs) for lower Manhattan (south of Canal Street). One of these SSAs is the site that housed the WTC; no data were collected for this plus an

additional 6 SSAs.

Initial Data Analysis

The rareness of exceedances suggests that these data ma y be described by a Poisson model. One feature of a Poisson random variable is that its mean, 

### μ , and its variance are equal (equidispersion), a property frequently violated by real world data. "Failure of the Poisson assumption of equidispersion has similar qualitative consequences to failure of the assumption of homoskedasticity" associated with the Gaussian distribution (Cameron and Trivedi, 1998, p. 77). The standard way of accommodating overdispersion (the presence of more variation than is expected fo r a Poisson random variable) is by replacing a Poisson random variable with a negative bi nomial random variable—which can be viewed as a gamma mixture of Poisson random variables. In doing so, the distribut ion of counts is viewed as either (1) having missing variables for the mean specification, or (2) being de pendent (i.e., the occurrence of an event increases the probability of further events occurring). The most popular implementation of the negative binomial probability model specifies the variance as being quadratic in the mean, or



### μ + 2 

### ημ =(1 + 

### ημ ) 

### μ ,

with the dispersion parameter, 

### η , to be estimated. The magnitude of 

### η may be interpreted as follows (after Cameron and Trivedi, 1998, p. 79):



### η = 0 implies no overdispersion;



### μ ≈ η 1 implies a modest degree of overdispersion; and,



### μ ≥ η 2 implies considerable overdispersion.Final Report December 2005 Page H-13

In other words, if 0 

### μ < η ≤ 5

. 0 , a spatial analyst may consider overdispersion detected in georeferenced

data to be inconsequential, with little to be gained by replacing a Poisson with a negative binomial model specification. Meanwhile, recognizing that these ex ceedances are constrained by the number of samples collected suggests that these data may be desc ribed by a binomial model. Recoding counts of exceedances to a binary (0-1) presence/absence measur ement suggests that these data may be described by a logistic model. Simple estimation results for each of these four models appear in Table H-4

.

Table H-4. Selected Constant Mean Model Estimation Results for Rates Model Intercept Equidispersion

Poisson for rates -5.4756 NA Negative binomial for rates -5.2098 2.8692

Binomial -5.4713 NA Logistic 0.3185 NA

Note: rates were modeled by including the log of the number of cases as an offset variable.

One important implication from the tabulated results appearing in Table H-4 is that a Poisson model description of rates may suffer from a dramatic viol ation of the equidispersion assumption. The following evidence supports this claim:

≈ 

### = 68421

. 2 2

ˆ 2 μ 0.74510 << 2.8692.

In other words, the mean and variance may not be constant across the 38 SSAs.

Accounting for Spatial Autocorrelation

A conventional spatial autocorrelation analysis is hinde red by two features of the collected data. One is

the rareness of exceedance. In order to further e xplore spatial dependency in this context, average measures of asbestos also were analyzed. Both th e rates and the average measures were transformed, using a logarithmic (i.e., Box-Cox 0 power) transformati on with a translation parameter, to better conform to a bell-shaped curve (see Figure H-4 ). The other drawback is the absence of data for 7 SSAs. BecauseFinal Report December 2005 Page H-14 these areal units are dispersed across the study re gion, computing a Moran Coefficient (MC) becomes problematic.

MC scatterplots appear in Figure H-5 . A conspicuous geographic pattern of positive spatial autocorrelation is apparent for the averages, and a possible positive spatial autocorrelation pattern may be present for the rates. Both patterns are corr upted by the presence of a number of zeroes.

Figure H-4. Left: quantile plot for LN( asbestos + 0.004). Right: quantile plot for LN(rate + 0.0001).

Figure H-5. Left: MC scatterplot for the rate of exceedance. Ri ght: MC scatterplot for the average measure of asbestos.Final Report December 2005 Page H-15 Latent map patterns also can be assessed with eigenvectors derivable from a MC. Here five of the 11eigenvectors (E 2 , E 3 , E 8 , E 17 , E 22 ; these were selected using the stepwise options for PROC LOGISTIC in SAS, and SWPOIS in STATA) denoting conseque ntial positive spatial autoco rrelation help describe the geographic distribution of exceedance rates fo r the Poisson and binomial models. The negative binomial model failed to be estimable, yielding a negative maximum likelihood estimate for dispersion; but, the deviance measure for the estimated Poiss on model is 1.36, suggesting a lack of serious overdispersion. One eigenvector (E 10 ) relates to the logistic version of the variable. Maps of three of the five synthetic geographic variables appear in Figure H-6

.

The MC values for the five eigenvectors range from 0. 38 to 0.93. Estimation results that include these eigenvectors in the specifications of each of the four models appearing in Table H-4 are reported in Table

H-5 . The Poisson model with an assumption of equi dispersion appears to be reasonable here. This specification accounts for roughly 40% of the variation in the geographic distribution of rates. The binomial model specification renders very similar results. The logistic model description seems inappropriate.

One important finding that can be gleaned from Table H-2 is even detected modest overdispersion accompanying the Poisson model description largely is attributable to latent spatial autocorrelation.

Table H-5. Selected model estimation results for rates when spatial dependence is included Variable Poisson model Negative binomial model Binomial model Logistic model intercept -5.9383 -5.9347 0.2773 equidispersion NA NA NA

E 2 -4.2422 -4.2812 NA

E 3 4.4056 4.4575 NA

E 8 -5.4424 -5.5115 NA

E 10 NA NA -4.4191

E 17 -2.2640 -2.2961 NA

E 22 4.2200 Failed to be estimable

4.2615 NAFinal Report December 2005 Page H-16

Figure H-6. Geographic distribution of rele vant eigenvectors. Top left: E 2 . Top right: E 3 . Bottom left: E 17 .

Pairwise Comparisons Between SSAs

Pairwise comparisons of SSA asbestos exceedance sampling results were made to assess whether or not statistically significant differences exist. Aggreg ate sample sizes less than 30 are considered too unreliable, and were not included in this assessment. The outcome of this sample size restriction is 32 SSAs with a sufficient number of samples, allowing (31 

### × 30/2=) 465 pairwise comparison.Final Report December 2005 Page H-17

Differences of Exceedance Rates

The estimated spatially filtered Poisson model produ ces sample mean estimate s for uncorrelated Poisson variables. These models include LN(# of cases) as an offset variable. Therefore, dividing both sides of the estimated equation for i 

### ˆ μ (i.e., the mean rate for areal unit i) by the corresponding number of samples

yields the set of estimated rates, assu ming an underlying Poisson process, of

i i



### n ˆ μ , i=1, 2, ..., 31. The

accompanying set of null hypotheses becomes

H o : j j

i i



### n n μ

− μ = 0 , i 

### ≠ j, i=1, 2, ..., 31 and j=i+1, i+2, ..., 31 .

The estimated standard error for this difference of rates test is given by 2 j j 2 i i



### n ˆ

n ˆ μ + μ

.

A simulation experiment involving 50,000 di fference of means replications (total=496 

### × 50,000) was conducted using the spatially filtere d Poisson model estimation results . The simulated Poisson random variable, Y, then was used in a bivariat e linear regression analysis, which yielded

j j

i i



### n ˆ

n ˆ μ

− μ = -0.00000 + 0.99955 j j

i i



### n y

n y

− + e , R 2 = 1.00, and

2 j j 2 i i



### n ˆ

n ˆ μ + μ = 0.00001 + 0.99873

j n j y

i n i y 

### s

− + e , R 2 = 1.00.

In the second case, the intercept is significantly di fferent from 0, and in both cases the slope is significantly different from 1. These may well be size effect results, since substa ntively both intercepts effectively are zero, and both slopes effectively are 1.

The model-based mean estimates range from roughly 0.10 to 32.60, implying that at least some of the difference of rates sampling distributions should be n on-normal. Each simulated dataset was subjected toFinal Report December 2005 Page H-18 a diagnostic Kolmogorov-Smirnov goodness- of-fit test for a normal distribution, producing test statistics ranging from roughly 0.01 to 0.35. In other words, the simulated sampling distributions fail to conform to normal distributions. Consequently, the pairwise di fference of rates assessments are based upon a Hopetype nonparametric simulation analysis, involvi ng 99,999 replications coupled with each observed difference. The simulated distribution is based on a pa ir of Poisson random variables, each with the same

mean of

2 1 1 2 2 1



### n n 2 n n μ + μ , which yields a null hypothesis difference of 0 and the correct theoretical

variance of

2 2

1 1



### n n μ + μ . Because a two-tailed test is employ ed here, an observed rank of 1 or 100,000

results in a rejection of the null hypothesis for 

### α =0.01, an observed rank of 2-5 or 99,996-99,999 results in a rejection of the null hypothesis for 

### α =0.05, and an observed rank of 6- 11 or 99,990-99,995 results in a rejection of the null hypothesis for 

### α =0.10. Based on these criteria, six pairs of exceedance rates are significantly different at the 10% level, 14 pairs are significantly different at the 5% level, and 122 pairs are significantly different at the 1% level. Basical ly, roughly 33% of the extreme MCGB mean pairs tend to be significantly different. These differences arise from four clusters of mean sizes. The first is the extreme MCBG mean of nearly 33 (MCBG 10015022). The second is the third largest mean of approximately 16 (MCBG 10015012). The third is the somewhat small mean of 0.64 (MCBG 10016004) which more than likely is being amplified by its sm all sample size of 32. The remaining 28 MCGBs form a set whose sample-size-weighted absolute differences of means range from nearly 0 to almost 0.1. Primarily, significant differences are bet ween the extremes within this group (see Tables H-6a and 6b , and Figure H-7 ).

These results need to be moderated by keeping in mind that the estimated Poisson model accounts for only about 50% of the variance in the observed exceedances.Final Report December 2005 Page H-19

Table H-6a. SSAs Pairs Having Significant pairwise Comparisons of Rates a

Significantly Different Means at the α = 0.10 Level 10009002 10027001 10015011 10033002A 10027001 10317019C 10009002 10033003B 10015021 10317019C 10039004 10317019C Significantly Different Means at the α = 0.05 Level 10007002 10009002 10013003 10027001 10021001 10317019C 10008002 10021001 10021001 10021002 10027001 10039001A 10009001 10027001 10021001 10025001 10033003B 10317019C 10009002 10015021 10021001 10039001B 10039004 10033003B 10013003 10021001 10021001 10039004 Significantly Different Means at the α = 0.01 Level 10007002 10009001 10015011 10021001 10015021 10021002 10007002 10013002 10015011 10021002 10015021 10025001 10007002 10015011 10015011 10025001 10015021 10039001B 10007002 10015012 10015011 10027001 10015021 10039004 10007002 10015022 10015011 10029002 10015021 10317019A 10007002 10021002 10015011 10031001 10015021 10317019D 10007002 10039004 10015011 10033001A 10015022 10016004 10007002 10317019D 10015011 10033001B 10015022 10021001 10008002 10015011 10015011 10033002B 10015022 10021002 10008002 10015012 10015011 10033003A 10015022 10025001 10008002 10015022 10015011 10033003B 10015022 10027001 10008002 10027001 10015011 10039001A 10015022 10029002 10008003 10015012 10015011 10039001B 10015022 10031001 10008003 10015022 10015011 10039003 10015022 10033001A 10009001 10015011 10015011 10039004 10015022 10033001B 10009001 10015012 10015011 10317019A 10015022 10033002A 10009001 10015021 10015011 10317019C 10015022 10033002B 10009001 10015022 10015011 10317019D 10015022 10033003A 10009001 10021001 10015012 10015021 10015022 10033003B 10009001 10033003B 10015012 10015022 10015022 10039001A 10009001 10317019C 10015012 10016004 10015022 10039001B 10009002 10015011 10015012 10021001 10015022 10039003 10009002 10015012 10015012 10021002 10015022 10039004 10009002 10015022 10015012 10025001 10015022 10317019A 10009002 10021001 10015012 10027001 10015022 10317019C 10009002 10317019C 10015012 10029002 10015022 10317019D 10013002 10015011 10015012 10031001 10021001 10039001A 10013002 10015012 10015012 10033001A 10021001 10317019A 10013002 10015021 10015012 10033001B 10021001 10317019D 10013002 10015022 10015012 10033002A 10021002 10027001 10013002 10021001 10015012 10033002B 10021002 10033003B 10013002 10027001 10015012 10033003A 10025001 10027001 10013002 10033003B 10015012 10033003B 10027001 10039001B 10013002 10317019C 10015012 10039001A 10027001 10039004 10013003 10015011 10015012 10039001B 10027001 10317019A 10013003 10015012 10015012 10039003 10027001 10317019D 10013003 10015021 10015012 10039004 10033003B 10317019A 10013003 10015022 10015012 10317019A 10033003B 10317019D 10015011 10015012 10015012 10317019C 10039001B 10033003B 10015011 10015021 10015012 10317019D 10317019C 10317019D 10015011 10015022 10015021 10015022

a See Figure H-7 for a map of the statistical summary areas (SSAs).Final Report December 2005 Page H-20

Table H-6b. Distribution of Significant Difference of Means by MCBG, Clean & Test Data

MCBG Number of Significant Differences MCBG Number of Significant Differences 10007002 9 10029002 3 10008002 5 10031001 3 10008003 2 10033001A 3 10009001 9 10033001B 3 10009002 9 10033002A 3 10013002 9 10033002B 3 10013003 6 10033003A 3 10015011 28 10033003B 12 10015012 30 10039001A 5 10015021 14 10039001B 7 10015022 30 10039003 3 10016004 2 10039004 9 10021001 16 10317019A 7 10021002 8 10317019C 12 10025001 6 10317019D 9 10027001 16

a See Figure H-7 for a map of the statistical summary areas (SSAs).Final Report December 2005 Page H-21

Figure H-7. Significant differences between estimated exceedance rates for clean and test data, with Statistical Summary Areas labeled. Estimates are based on the spatially-filtered Poisson model (see Section 3.2.3.2 and Appendix D for details). The number of significant pairwise comparisons at an experiment-wise α = 0.01 (with a Bonferroni adjustment) are shown for SSAs that had one or more exceedances. Comparisons with SSAs with sample sizes less than 30 (indicated in figure by cross- hatching, and in figure legend by “n<30”) were deemed unreliable and were therefore not include d in the analysis. Three of the SSAs that were found to have the most number of significant comparisons are located east of the WTC. The numbers of exceedances for these three SSAs range from 17 to 32; their exceedance rates range from 0.006 to 0.059. The spatial pattern exhibited above is similar to the pattern of exceedance rates that is shown in Figure 3–14 however, 3 of the 9 SSAs with exceedance rates in the 4 th quartile ( Figure 3–14 ) were found to be significantly different from 4 or fewer of the other SSAs.Final Report December 2005 Page H-22



### H.3.0 CLEAN AND TEST DATA SUBSET

Description of Sample

When sampling results for five intensivel y sampled buildings are removed from the clean and test dataset, a total of 17,905 samples remain, of which 17 have no geographic labels. The total number of samples

with a value that exceeds the threshold level is 92. These sample asbestos measurements were aggregated by location into 45 modified census block groups (SSA s) for lower Manhattan. One of these SSAs is the site that housed the WTC; the modified database cont ains no data for this plus an additional 7 SSAs.

Initial Data Analysis

Simple estimation results for each of the four mode ls (i.e., Poisson, negati ve binomial, binomial and logistic) that parallel those for the complete dataset appear in Table H-7 . These results are very similar to those obtained with the complete dataset, too.

Table H-7. Selected Constant Mean Model Estimation Results for Rates Model Intercept Equidispersion

Poisson for rates -5.2701 NA Negative binomial for rates -5.1409 3.1819

Binomial -5.2649 NA Logistic 0.4964 NA

Note: rates were modeled by including the log of the number of cases as an offset variable

.

Accounting for Spatial Autocorrelation

Identified prominent latent map patterns also are very similar (E 3 , E 8 , and E 17 are common to the rates models; and again were selected using the stepwise options for PROC LOGISTIC in SAS, and SWPOIS in STATA). One of the eigenvectors identified with the complete dataset disappears here (E 2 ). One model difference now is that the binomial model links to eigenvector E 22 , whereas the Poisson model links to eigenvector E 27 . The negative binomial model yielded a di spersion parameter estimate of 0 here, making it indistinguishable from a Poisson model. As before, the same single eigenvector (E 10 ) relates to the logistic version of the variable. The Poisson model with an assumption of equidispersion appears to be reasonable here. This specification accounts for ro ughly 40% of the variation in the geographic

distribution of rates.Final Report December 2005 Page H-23

Table H-8. Selected model estimation results for rates when spatial dependence is included Variable Poisson model Negative binomial model Binomial model Logistic model intercept -5.8875 -5.8827 0.5075 equidispersion NA NA NA

E 1 2.2292 2.2311 NA

E 3 4.1741 4.2493 NA

E 8 -3.4133 -3.4547 NA

E 10 NA NA -5.3629

E 17 -2.8619 -2.8848 NA

E 22 NA NA NA

E 27 -3.3557 Failed to be estimable

-3.4310 NAFinal Report December 2005 Page I-1 

# APPENDIX I.

Evaluation of Wipe Sampling Data Prepared by: U.S. Environmental Protection Agency ORD’s National Center for Environmental Assessment and Region 2 for the November 15, 2004, Meeting of the World Trade Center Expert Technical Review Panel

Background

During the 2002 Region 2 Cleanup Program, over 1500 pre- and post-cleaning wipe samples were taken in a sample of 263 (out of a total of 4200) apartments in the clean up program in 165 buildings. These samples were measured for 24 contaminants, includi ng lead and other metals, and dioxin. Wide geographic coverage was sought by attempting to iden tify volunteer apartments in as many buildings as possible. This evaluation focuses on the approximate 1000 pre-cleaning sample results, with an emphasis

on lead.

The spatial analysis described earlier in this report was completed before EPA received a final draft photo analysis describing the distribution of visible dust resulting from the collapse of the WTC. The figure below indicates the location of sampled apartments with respect to EPIC Zones of Confirmed (Red), Probable (Orange), Possible (Violet), and No (Blank) impact by dust and debris from the collapse of the

WTC Towers.Final Report December 2005 Page I-2

We then obtained from New York City in formation on the age of the buildings we sampled and matched this information to the sample results so that th e possible relationship between building age and sample results could be assessed. A summary of the distributi on of building ages by EPIC zone is provided in the

Table I-1

.

Table I-1. Building Construction Year by Zone

Date Built Zones

N A B C D

Before 1920 60 37 22 1 0

1920 - 1950 13 0 11 0 2

After 1950 25 19 6 0 0

No date 58 35 10 1 12

Totals 156 91 49 2 14 Notes: A = confirmed dust; B = proba ble; C = possible; D = no dust

N= number of samples, approximately 4 sa mples per apartmentFinal Report December 2005 Page I-3 The public expressed continued concern that Merc ury, Dioxin and Lead are contaminating indoor

environments. Below is the Table I-2 , which an overview of key results for these contaminants in relation to the health based cleaning benchm arks established for the program

Table I-2. Overview of Key Results for these Cont aminants in Relation to the Health Based Cleaning Benchmarks Esta blished for the Program Analytes N Health-based benchmark # > BM; (range) Overall Mean Mercury 915 157 ug/m2 5 (161-248) 4 Dioxin 859 2 ng/m2 6 (2 – 5; 75) 0.8 Lead 995 25 ug/ft2 6 (2 – 5; 75) 37 Lead * 993 25 ug/ft2 115(25 – 6790) 22

Notes: * These are lead results not including two high outliers at 6790 and 2530 ug/ft2 - 1 or 2 exceedances found for 20 other contaminants - the high measuremen t of dioxin of 75 ng/m2 was found above a fireplace - # > BM: number of measurements greater than the health benchmark. Range of the values exceeding the bench mark is shown in parentheses.

Only in the case of lead were there a significant number of measurements (12% ) above the health-based

benchmarks. The results were then examined as a func tion of three factors that may affect measured lead concentrations: floor of building where measurement was taken, age of building, and location of building: i.e., EPIC Zone and distance from WTC. Also, the proposed criteria for building cleanup includes a link to WTC dust via signature analysis and requires th e 95% upper confidence limit on the building mean to be greater than the 25 ug/ft2 benchmark. An anal ysis was performed to dete rmine how many buildings

would meet this concentration criteria. The Lead results by EPIC zone and distance from the WTC are

described in the Tables I-3 and I-4 below:Final Report December 2005 Page I-4 Table I-3. Lead Results by EPIC Zone

Zone

Meters A A1 B C D

N 625 623 285 7 78 Mean, ug/ft2 37 22 21 26 14 Median, ug/ft2 7 7 2 17 9 Max, ug/ft2 6790 1380 1160 48 208

Notes: A = confirmed dust; A1 = confirmed with two high measurements of 2530 a nd 6790 ug/ft2 deleted;

B = probable; C =possible; D = no dust

Table I-4. Lead Results by Distance from WTC

Meters 0-250 250-500 500-750 750-1000 >1000

N 102 388 276 145 82 Mean, ug/ft2 20 29 (23)* 23 66 (18)** 20 Median, ug/ft2 7 7 6 4 9 Max, ug/ft2 861 2530 1160 6790 208

Notes: * mean for 250-500 with outlier of 2530 removed ** mean for 750-1000 with outlier of 6790 removed

The information gathered on buildin g age was then used to evaluate whether there was any association

between the lead results and the build ing age. The New York City record s provided to us did not have the ages of every building in which samples were collected. Thus the graphs below Figures I-1, I-2, I-3 and

I-4 report results only for buildings in zones A and B. Two sets of information were graphed: the concentration of lead in samples vs. the building age, and the number of values greater than the health screening benchmark of 25ug/ft2 vs. building age.Final Report December 2005 Page I-5

Figure I-1 . Zone A: Lead vs. Year Constructed 1900 1920 1940 1960 1980 2000 Year Constructed 0 400 800 1200Lead Measurement (ug/ft2) 

### Zone A: Lead vs Year Constructed (Two large lead outliers removed)Final Report December 2005 Page I-6

Figure I-2. Zone B: Lead vs. Year Constructed 1900 1920 1940 1960 1980 2000 Year Building Constructed 0 200 400 600 800 1000 1200Lead Measurment (ug/ft2) Zone B: Lead vs Year ConstructedFinal Report December 2005 Page I-7

Figure I-3. Zone A: Lead Measurements >= 25 ug/ft2 vs. Year Constructed 1900 1920 1940 1960 1980 2000 Year Building Constructed 0 400 800 1200Lead Measurements (μg/ft2 ) 

### Zone A Lead Measurements ≥ 25 μ g/ft2 vs Year Constructed (Two large lead outliers removed)Final Report December 2005 Page I-8

Figure I-4. Zone B: Lead Measurements >= 25 ug/ft2 vs. Year Constructed 1900 1920 1940 1960 1980 Year Building Constructed 0 200 400 600 800 1000 1200Lead Measurement (μg/ft2) 

### Zone B Lead Measurements ≥ 25 μ g/ft2 vs Year ConstructedFinal Report December 2005 Page I-9 We next looked at the relationship between the floor in the building where the samples were collected and the building age. Table I-5 below describes the means (ug/ft2) of lead sample results by floor and EPIC

Zone. Table I-6 describes the means (ug/ft2) of lead sample results by floor and building age.

Table I-5. The Means (ug/ft2) of Lead Sa mple Results by Floor and EPIC Zone

Zone Floor Overall Mean (n) A A1 B C D Basement 113 (4) --- --- 113 --- ---

1 st 14 (29) 5 5 26 --- --

2 nd 117 (124) 168 56 9 --- 9

3 rd 29 (127) 38 38 42 24 6

4 th 21 (114) 10 10 36 23 13

5 th 25 (80) 12 12 80 --- 2 6-10 14 (227) 13 13 12 --- 22 11-20 21 (141) 29 29 11 --- 10 >20 8 (149) 6 6 5 --- 7

Notes: All results are the mean in ug/ft2

A1: Zone A mean for 2nd floor measurements calculated wit hout two outliers

Table I-6. The Means (ug/ft2) of Lead Sa mple Results by Floor and Building Age

Zone Floor Overall Mean (n) <1920 1921-50 >1950 No Year Basement 113 (4) 113 --- --- ---

1 st 14 (29) 19 --- 2 5

2 nd 117 (124) 204 (41) * 34 9 8

3 rd 29 (127) 53 15 5 16

4 th 21 (114) 35 24 7 10

5 th 25 (80) 14 7 10 45 6-10 14 (227) 17 4 14 14 11-20 21 (141) 31 1 14 25 >20 8 (149) 39 17 6 5

Notes: * mean of 41 calculated with two ou tliers of 6790 and 2530 ug/ft2 removed All results are the mean in ug/ft2;Final Report December 2005 Page I-10

Observations on Full Data Set

When the two lead outliers are deleted, the overall re sults do not appear meaningfully different among the four zones, and among the five distance categories. Th ere is a suggestion that higher concentrations are found on lower building floors, across zones although th is may be confounded with the presence of older buildings across zones. The clearest relationship is bet ween lead concentrations and age of building, i.e., older buildings tend to have the higher concentrations and older buildings have higher concentrations on lower floors. Some high lead concentrati ons were also observed in newer buildings.

An examination of the highest measurements for lead may suggest trends of note. There are 23 measurements higher than 200 ug/ft2:

15 of these 23 measurements are in "confirme d" zone; only 1 in "no dust" zone: 17 of the top 23 measurements are found in the 5th floor or lower: 18 of top 23 are in buildings built 1926 and earlier; 4 have no date and 1 built in 1971 11 of top 13 are in "confirmed" zone, but 9 of these 13 are in buildings built 1920 and earlier, with the remaining 4 in buildings of unknown age.

Taken together, this suggests that all three factors (z one, floor and age) may be related to the observation of the highest measurements of lead in this sampli ng program. Site specific factors may help to explain results from particular buildings. For example: high measurements were made in three buildings on

Chambers Street that were located near a renovati on project. High measurements were also made in a building on Liberty Street that was observed to have remaining WTC dust lodged outside in window ledges. The building with the highest measurement, 6790 ug/ft2, was built in 1900, the earliest year identified for building date in this sample set. (Ho wever, this measurement may be suspect because it is so much larger than the others.)

Finally we looked at the 95% UCL on building-specific results in relation to EPIC Zones, which are

shown in the Table I-7 below. To obtain an estimate of how many buildings might require a lead clean up if the HUD screening level of 25ug/ft2 were used as a clean up benchmark.Final Report December 2005 Page I-11 Table I-7. 95 % UCL on Building-Specific Results in Relation to EPIC Zones

Zone Description A B C D Number buildings 91 49 2 17

# blds mean > 25 18 10 1 0

# blds 95% UCL > 25 32 17 2 4

# blds max > 25 42 18 2 3 Notes: A = confirmed dust; B = pr obable; C = possible; D = no dust

# blds mean > 25 : # of buildings with mean > 25 ug/ft2 # blds 95% UCL > 25: # of buildings with 95% UCL on mean > 25 ug/ft2 # blds max > 25: # of buildings with maximum observed value > 25 ug/ft2

Overall Results

Measurements of contaminants above health benchmarks were infrequent with the exception of lead. The clearest relationship is found between lead concentrati ons and age of building, suggesting lead paint as a cause for high lead measurements in Lower Manhattan. Proximity and floor of building seemed to be, at best, weakly related to measured lead levels. However , an examination of the highest measurements does suggest that, on a case-by-case basis, these factors as well as direct WTC impact, may be important. Building-specific results suggest that a substantial pe rcentage of buildings may meet the partial criteria for building cleanup of 95% UCL of mean being greate r than health screening benchmark of 25 ug/ft2.