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 Manhattan who provided access to EPA 

to sample their dwelling units, and of the building owners who provided access to the common space 

areas used in this Study. 

This project was designed and implemented by EPA with the support of the Indoor Air Work Group of 

EPA’s Indoor Air Task Force. The work group organizations 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; New York City Mayor’s Office of Environmental 

Coordination; New York City Office of Emergency Management, New York State Department of Health; 

Agency for Toxic Substances and Disease Registry; Occupation 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, and possibly expand, portions of this report for inclusion in manuscripts 

that will be submitted to scientific journals for review and consideration 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 Rate 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-3 
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 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 Loadings 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-26 
Final Report December 2005 

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 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 Pattern of PCMe Exceedance ................................... E-45 

E.3.2.4 Site-Level Vertical Pattern of PCMe Exceedance ............................. E-56 

APPENDIX F. Other 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-1 
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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 reported 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 (μg/ft2

), by sample type ................................................. E-7 

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

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

E-4. Boxplot for the reduction in residential average dust lead loading (μg/ft2

) ................................... E-13 

E-5. Scatter plot of log-transformed pre-cleanup residential average dust lead loadings 

 (vertical axis), by floor.................................................................................................................... E-18 

E-6. Boxplots for residential dust dioxin loading (μg/ft2

), by sample type............................................ E-25 

E-7. Boxplot for the reduction in residential 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 samples 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 summary 
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 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 between 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 results 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 results 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 

 Summary 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 exceedance. Right: MC scatterplot 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-6 
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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-8 
Final Report December 2005 

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LIST OF TABLES 

3-1. Summary of Available Asbestos PCMe 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 (μg/ft2).........................................................................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/m2) ...........................................................................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 Background 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 Loadings (Pre- and Post-cleanup) in Residences 

 With Pre-cleanup Averages Greater Than the HUD Screening Level of 25 μg/ft2

..........................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) (μg/ft2

) ............................... E-12 

E-2. Statistics on Reduction in Average Lead Wipe Loadings (Pre- and Post-cleanup) (μg/ft2) in 

 Residences with Pre-cleanup Greater Than the Health-based Benchmark of 25 ug/ft2

................. E-15 

E-3. Statistics for Average Pre-cleanup Residential Dust Lead Loading by Floor Group (μg/ft2

) ........ E-17 

E-4. Reduction in Dust Lead Loading by Floor Group (μg/ft2

) ............................................................. E-20 

E-5. Percent Reduction in Average Residential Dust Lead Loading by Floor Group (μg/ft2

)............... E-21 

E-6. Statistics for Dioxin Wipe Clean and Test Data (ng/m2

) ............................................................... E-22 

E-7. Reduction in Average Dioxin Wipe Loadings (TEQ)a

 (Pre- and Post-cleanup) (ng/m2)............... E-24 
Final Report December 2005 

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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 Equivalent (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 model estimation results for rates............................................................H-13

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

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

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

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

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

I-1. Building Construction Year by Zone .................................................................................................I-2 

I-2. Overview of key results for these contaminants 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-9 
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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-11 
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GLOSSARY 

Average residence dust lead (dioxin) loading: The arithmetic mean of the results from multiple 

(typically three) dust wipe samples that were collected 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 traditional 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 describe the geographic center of a collection of objects, 

their distribution in space, and the orientation of the distribution; 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 selected subset of the residences was also tested for 

dust lead loading and dust dioxin loading. 

Common areas: Areas of residential buildings that are accessible 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/m2

). 

Dust dioxin loading exceedance: Dust dioxin loadings that exceed the health-based benchmark of 

2 ng/m2

. 

Dust lead loading: Micrograms of lead per square foot of sampled surface (μg/ft2

). 

Dust lead loading exceedance: Dust lead loadings that exceed the HUD screening level of 25 μg/ft2

. 

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 distance 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 data (e.g., PCMe concentration). The focus of point 

pattern analysis is often to test the null hypothesis 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 describe the distribution of PCMe exceedances. The Poisson 

distribution is typically used to model 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 other 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 indicate 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 distributed 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 aggregation. 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. 
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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 

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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 

μg/ft2

 micrograms per square foot 

ng/m2 nanograms per square meter 
Final 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 subsequently developed and implemented a comprehensive 

program, with broad interagency input at federal, state and local levels, to ensure that lower Manhattan 

residents were protected from potential exposures to WTC-related dust and debris. 

The WTC dust cleanup and testing program allowed residents living south of Canal Street in lower 

Manhattan to have their homes professionally cleaned and tested or just tested free of charge. In addition 

to offering this service to residents, EPA conducted 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 Street were given a choice of services. Residents 

could chose to have their residence professionally cleaned, followed by confirmatory testing, or they 

could choose to just have their homes tested. Certified 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 and 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 during these routine cleanups because OSHA determined 

that such equipment was not necessary. As an added 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 collected and analyzed for 

asbestos using transmission electron microscopy (TEM) and phase contrast microscopy (PCM). In a 

subset of the residences, pre- and post-cleanup dust wipe 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 samples 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 outcomes 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 achieving 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 and Urban Development (HUD) screening level of 

25 μg/ft2

, while only about 3% of the post-cleanup samples exceeded the screening level. This showed 
Final Report December 2005 

Page 3 

that the cleanup methods were effective in reducing lead. The percent of apartments 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 was consistent, however, with data from the HUD on 

housing stock in the Northeast United States. This factor 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-based screening values measured for any of the other 

22 metals. The screening value of 627 μg/m2

 for antimony was exceeded in 2 pre-cleanup samples (0.1% 

of all samples); the maximum measured value was 1,180 μg/m2

. The screening value of 157 μg/m2 for 

mercury was exceeded in 5 pre-cleanup 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 residential dust dioxin loading of 2 ng/ m2

. 

An analysis of the location of asbestos exceedances does not demonstrate a special pattern of exceedances 

relative to WTC proximity. Apparent groups of asbestos 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) supported federal, state, and New York City efforts to 

recover from the federally declared disaster resulting 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 Pollution Contingency Plan, 40 C.F.R. Part 300 

(the NCP) (EPA, 1990). This report provides a summary of the actions taken by EPA to cleanup the 

indoor environment in Lower Manhattan. 

The cleanup of the WTC site and surrounding ambient (i.e., outdoor) environment proceeded through the 

winter of 2001-2002. Early investigations indicated that 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 residential communities of Lower Manhattan regarding 

potential long-term health problems associated 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 for 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 residents were protected from potential exposures to 

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

state and local levels. EPA utilized all the tools available, including appropriate aspects of the NCP, to 

achieve this goal as expeditiously as possible. 

 

EPA implemented three programs related to indoor air 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 only 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. Third, EPA, along with the New York City Department 

of Environmental Protection (NYCDEP), provided for the monitoring and cleaning of Lower Manhattan 

residences through the Indoor Air Residential Assistance Program-WTC Dust Cleanup. 
Final Report December 2005 

Page 5 
Final 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 interagency 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 monitoring 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 provided 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 necessary because of the scientific complexities of 

dealing with indoor environments and the need for timely 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 (especially Manhattan), correlations of dust to air 

exposures, etc. This degree of scientific uncertainty 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 other WTC particulate matter that might pose health 

concerns. Sampling was performed for airborne asbestos in every residence EPA was asked to clean or 

test, and samples for metals and dioxin were collected 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 cleanup 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 the Indoor Air Residential Assistance Program-WTC 

Dust Cleanup, EPA relied on the existing data, the intergovernmental collaboration process, and 

discussions with scientific, technical, and medical professionals 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 because of the terrorist act that led to the release but 

also because of the unique challenges 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 varying 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 environments was conducted in the weeks and months after the 

WTC collapsed. Two notable studies were the Agency 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 Assess 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., comparison] buildings) and included both re-occupied and 

unoccupied apartments. Sampling took place from November 4 through December 11, 2001. The report 

was released in September 2002; after the EPA indoor air cleanup program was underway. This study 
Final Report December 2005 

Page 8 

showed that total fiber counts of air samples taken 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 results indicated that neither asbestos nor synthetic 

vitreous fibers (SVF) contributed to the elevated fiber 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 residential units in Lower Manhattan, specifically identifying 

which of them were affected by amounts of dust potentially 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 interiors adds another layer of complication. 

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

• The complexity of sampling dust material for quantities of hazardous substances and the lack of 

scientific consensus on how to do so; 

• The absence of standards that have broad scientific 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 professionals supported a program that addressed the need for 

cleanup assurances without the time, expense, and uncertainties associated with a location-specific 
Final Report December 2005 

Page 9 

sampling and risk assessment approach. EPA also consulted with environmental health and science 

experts in the academic and research sector on the cleanup approach described above. Though there were 

many questions and a desire for more data collection, 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 approach 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 biased towards immediate action to protect the 

health and safety of the residents of Lower Manhattan, was consistent with federal disaster plan 

guidelines, and adhered to the applicable and 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 sequentially) along parallel tracks for the purpose of initiating 

residential cleanups as soon as possible. These components included: development of health-based 

benchmarks for indoor air and settled dust (EPA, 2003c); a site-specific 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 Cleanup Program EPA relied on existing data, the 

intergovernmental collaboration process, and discussions 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 provides a summary of efforts to develop health-based 

benchmarks, characterize background, and assess the efficacy 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 number of initiatives were undertaken concurrently to 

expedite the cleanup of residences in Lower 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 contaminants that might pose long-term health risks to 

local residents (EPA, 2003c). As part of this evaluation, a task force sub-committee was established 

(COPC Committee) to identify contaminants of potential 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 systematic risk-based approach was used to select 

COPC. The goal was to identify those contaminants most likely to be present within indoor environments 

at levels of health concern. The following chemicals were identified as COPC: Dioxin, PAHs, Lead, 

Asbestos, Fibrous Glass, and Crystalline Silica. 

Health-based benchmarks were developed to be protective of long-term habitability of residential 

dwellings. A hierarchical approach was employed for developing benchmark values, including relevant 

and appropriate environmental standards/regulations (HUD standard for lead in indoor dust); calculation 

of health-based benchmarks employing conventional environmental risk assessment paradigms and 

guidance (for asbestos, dioxin and PAHs); and adaptation of occupational standards with additional safety 

factors (fibrous glass and crystalline silica). 

Confirmation Cleaning Study

The second component was an effort to confirm that the cleaning methods recommended to the public 

were effective in reducing contaminants from dust generated 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 methods that were likely to be used by or were 

recommended to residents and professional cleaning companies 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 construction 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 method 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 addition 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 vacuums 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 professional 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-related 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 correlates with the level of contamination. 

• Concentrations of some contaminants in the WTC dust were elevated above health-based 

benchmarks. 

• The use of a standard cleaning method of vacuuming and wet wiping significantly reduced levels 

of WTC-related contamination with each cleaning event and was successful in reducing 

concentrations to levels below 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 whether 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 concentrations 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 information 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 concentrations of these compounds in urban residential 

indoor environments. 

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

materials and materials found in fire-related combustion byproducts, including asbestos, synthetic 

vitreous fiber (SVF), fibrous glass, crystalline silica (as alpha-quartz), calcite, gypsum, dioxin, and 

polycyclic aromatic hydrocarbons (PAHs). The results 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 benchmarks 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 78th 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 78th 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 collected from two residential units and from one common 

area, such as the lobby, hallway, stairwell, or shared laundry facility in each building. The comparison of 

the results from the background study to the data from the WTC site did not include formal tests to 

determine if the concentrations were statistically significant 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 USEPA 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 estimated background concentrations were consistent with 

other background studies and historical data, when comparison data were available; and, provide a source 

of data to help address data gaps in the scientific literature on background concentrations 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 residents about the Program. Components of this 

initiative included: distribution of pamphlets at residential buildings, subway stations and local 

businesses; meetings with community groups; operation of a registration hotline; establishment of a 

website for on-line registration; mailings; and, newspaper 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 confirmatory testing, or, in the event the residence was 

already professionally cleaned and/or not significantly 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 workers (the Cleanup Contractor) certified by New York 

State and New York City. Separate, third-party contractors, also licensed by New York State, oversaw 

the cleanup work and conducted all testing (the Project 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 attack. National Oceanic and Atmospheric Administration 

meteorological stations are indicated as: Newark (EWR), Teterboro (TEB), LaGuardia Airport (LGA), Central Park (NYC) and John 

F. Kennedy Airport (JFK). Numbers in red are the hourly average concentration of particulate matter ≤ 2.5 μm in size in μg/m3

. 

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 after the attack. National Oceanic and Atmospheric 

Administration meteorological stations are indicated as: Newark (EWR), Teterboro (TEB), LaGuardia Airport (LGA), Central 

Park (NYC) and John F. Kennedy Airport (JFK). Numbers in red are the hourly average concentration of particulate matter ≤ 2.5 

μm in size in μg/m3

. Plume direction is primarily towards the northeast. 
Final Report December 2005 

Page 16 

The Project Monitor contacted residents requesting assistance to confirm and schedule cleanup and 

testing. The Project Monitor had access to a translation service to assist with the process for those whose 

primary language is not English. There were three phases to the actual work: 1) Pre-cleaning inspection; 

2) Cleaning; and 3) Testing. 

Owners and managers of residential buildings and coop boards could request to have their building's 

common areas cleaned and HVAC system evaluated and cleaned, if necessary. After receiving the 

request, common areas such as the building lobby, hallways, 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 and testing options, determine any special concerns 

or needs, and answer questions. The Project Monitor 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 resident's options for post-cleanup testing. 

Damage to a building as a result of the WTC collapse may have resulted in the growth of mold in 

apartments. As part of the Cleaning Program, the 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 affected 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 potential 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 determined the appropriate cleanup approach. Most 

residences were addressed under EPA's "Scope A" cleanup. 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 minimize 
Final Report December 2005 

Page 17 

spreading of possible contamination while removing the dust/debris. Areas where localized 

accumulations of WTC dust were found in a residence 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 residence 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 cleanings, 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, including 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 prohibited. A detailed description of the minimum 

cleaning requirements is listed below (field experience may have resulted in the modification of these 

procedures): 

• Terraces, balconies, exterior window sills, window 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, continue 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. Fabric covered furniture will be vacuumed using a 

stiff brush attachment. 

• Carpets will be cleaned with a water extraction cleaner 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 will be HEPA vacuumed. 

• Electrical outlets will be vacuumed. 

• Window air conditioners will be vacuumed then removed from their position and vacuumed 

internally. Filters will be HEPA vacuumed and reinstalled. Air 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 register will be reinstalled and 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 will 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 (including 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 equipment, 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 remnants 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 event. 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 sampling for 23 metals plus dioxin. A description of 

the testing (sampling and analysis) protocol is summarized below. 

When cleaning was completed, the Project Monitor did a visual inspection. If dust was observed, the 

residence was re-cleaned as necessary. 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 hours and was completed within one day (24 hours) of 

the completion of cleanup work. Residents had a choice between two forms of airborne asbestos testing, 

modified-aggressive and aggressive. Modified-aggressive 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 before testing was begun. From that point on, the two 

tests are identical. Any air conditioners were turned on and 20-inch fans (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 located 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 microscopy (PCM) was conducted to obtain a count of 

non-asbestos fibrous material. 

Residents may have occupied their home during modified-aggressive testing but were cautioned to be 

prepared for noise and disruption. Occupants with known allergies, asthma or other health concerns were 

advised to consider contacting their health care provider to determine whether it was advisable to be 

present while cleaning and/or testing was underway. Residents were required to relocate during, and for 

48 hours after, aggressive testing. The Red Cross agreed to provide financial assistance to defray costs 

for relocation expenses. Information from the Red Cross was provided as needed. Occupants were 

required to remove or secure objects, including pictures and artwork that could be blown over or 

otherwise damaged. 

The Project Monitor conducted a post-cleaning inspection 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 determined whether cleaning/monitoring activities were 

completed and whether any property damage or loss had 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 dioxin, mercury and metals. Approximately 50 test only

residences received a single round of wipe sampling for 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 the 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 total non-asbestos fibers by PCM analysis. Residencespecific test results were not made public. Residences were re-cleaned and re-tested 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 analysis 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 requirement. 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 accurately count asbestos fibers, was the most common 

cause of inconclusive results. Other causes of inconclusive results included blown and/or damaged filters. 

Apartments with overloaded and/or blown/damaged filters 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 Cleanup 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 approximately 18 months after the release, could not 

reconstruct the collective exposure incurred since 9/11, it could serve to put into context the existing 
Final 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 procedures and methods presented in 

the Quality Assurance Project Plan (QAPP). Procedures for the collection 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 the 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 asbestos, 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 approximately 14% before cleanup, to 3% after cleanup, on a 

sample-basis. The exceedance rate for dust dioxin loading was less than 1% before and after cleanup. 

3.1 DATA SUMMARY

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 common areas that were cleaned and tested. Results by 

PCMe were compared to the health-based benchmark 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 μm) fiber counts. The use of a minimum fiber length of 5 μm 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 fiber analysis. Historically, most of the occupational 

studies available (and reviewed by IRIS) to estimate the cancer potency of asbestos, employed PCM 

analysis. Therefore, in cases where TEM is used for asbestos 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 μm (AHERA also stipulates a 

minimum 5:1 aspect ratio) were distinguished from total (i.e., >0.5 μm) fiber counts, although total fiber 

counts were also recorded. To maximize analytical 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 μm) 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 asbestos 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 Samples Common Area Samples 

Samples collected 22,497 6,205 

Number of samples 

>0.0009a

 (exceeds) 102 21 

Percent exceeds 0.45% 0.34% 

Maximum 

concentration 0.0204 0.0042 

Minimum 

concentration Not Detectedb Not Detected 

a

The health-based benchmark for asbestos is 0.0009 fibers/cubic centimeters. b

Detection limit ranged from 0.0004 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 detected in approximately 4% of the available TEM 

samples. Chrysotile asbestos was detected in approximately 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, tremolite, 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 lead 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 μg/ft2

 were set equal to the detection limit. Review of existing 

environmental standards/regulations identified an applicable and relevant standard to set a health-based 

benchmark for lead in interior dust. The Residential Lead-Based Paint Hazard Reduction Act (Title X) 

Final Rule (40 CFR, Part 745, 1/5/01) established uniform 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 detected; 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 presenta 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 μg/ft2

 for floors (including carpeted floors), and 250 μg/ft2 for interior 

window sills. To support the development of a dust standard, EPA performed an analysis of the 

Rochester Lead-in-Dust Study (HUD, 1995). At 40 μg/ft2

, a multimedia analysis shows a 5.3% 

probability that a child’s blood lead level would exceed 10 μg/dL. Thus, this standard meets the criteria 

established by EPA (i.e., 95% probability to be below 10 μg/dL) (EPA, 1994) for managing 

environmental lead hazards. However, an additional 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 μg/ft2

. 

Approximately 9% of all lead wipe samples (i.e., test only and clean and test) were above the HUD 

screening level of 25 μg/ft2 (Table 3-3); approximately 14% of the pre-cleanup samples exceeded the 

HUD screening level , while approximately 3% of the 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 μg/ft2

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. Basic statistics for the data are provided in Table 3-6. The 

dioxin results were modified using a toxicity equivalency 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. The TEQ value reported in the table represents the estimated maximum 

potential concentration (EMPC). The TEQ EMPC value used data that indicated the presence of a 

Table 3-3. Statistics for All Lead Wipe Data Combined (μg/ft2) 

This table provides summary statistics for residential dust lead loading 

data that was collected from residences that were cleaned and tested 

(both before and after cleanup), and tested only. 

Apartments sampleda 263 

Buildings sampled 157 

Number of samples 1540 

Nondetects 264 (17.1%) 

Exceedances @ 25 μg/ft2 a 136 (8.8%) 

Exceedances @ 40 μg/ft2 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 μg/ft2
Final Report December 2005 

Page 26 

congener above zero ng/m2

 even if all of the QA/QC reporting level criteria were met for that sample. 

This value represents the highest potential concentration 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. 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 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 μg/ft2 a 93 (13.7%) 21 (3.1%) 

Exceedances @ 40 μg/ft2 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 

CVc 8.07 14.70 

S-W Statisticd 0.07 0.03 

Prob Normale <0.0001 <0.0001 

a

Exceedance: lead wipe samples that exceeded the health-based screening level of 25 μg/ft2 

b

Exceedance: lead wipe samples that exceeded the HUD health-based benchmark of 40 μg/ft2

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 
Final 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-transformed 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 μg/ft2 a 92 (13.5) 20 (3.0)

Exceedances @ 40 μg/ft2 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

CVc 4.74 2.39

S-W Statisticd 0.15 0.21

Prob Normale <0.0001 <0.0001

a

Exceedance: lead wipe samples that exceeded the health-based screening level of 25 μg/ft2 

b

Exceedance: lead wipe samples that exceeded the HUD health-based benchmark of 40 μg/ft2

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/m2) 

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 sampleda 263 

Buildings sampled 157 

Number of samples 1535 

Nondetects 399 (26.0%) 

Exceedancesb 8 (0.52%) 

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: dioxin wipe samples that exceeded the health-based 

benchmark of 2 ng/m2 TEQ EMPC (ND = 1⁄2).
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Aluminum Number Percentages Aluminum Number Percentages Aluminum Samples 1544 Samples 1336 Samples Detects 1536 99.5% Detects 1329 99.5% Detects Nondetects 8 0.5% Nondetects 7 0.5% Nondetects Nondetects @ 200 7 87.5% Nondetects @ 200 7 100.0% Nondetects @ 200 Nondetects @ 1000 1 12.5% Nondetects @ 1000 0 0.0% Nondetects @ 1000 Max 319000 Max 296000 Max Min ND @ 200 Min ND @ 200 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 3258.97  Avg Post-Means 1093.05  Avg % Reduction 35.44 Antimony Number Percentages Antimony Number Percentages Antimony Samples 1544 Samples 1336 Samples Detects 13 0.8% Detects 7 0.5% Detects Nondetects 1531 99.2% Nondetects 1329 99.5% Nondetects Nondetects @ 80 1526 99.7% Nondetects @ 80 1329 100.0% Nondetects @ 80 Nondetects @ 400 5 0.3% Nondetects @ 400 0 0.0% Nondetects @ 400 Max 1180 Max 1180 Max Min ND @ 80 Min ND @ 80 Min Exceedances 2 0.1% Exceedances 2 0.1% Exceedances  Avg Pre-Means 84.38  Avg Post-Means 80.01  Avg % Reduction 1.47 Arsenic Number Percentages Arsenic Number Percentages Arsenic Samples 1544 Samples 1336 Samples Detects 34 2.2% Detects 30 2.2% Detects Nondetects 1510 97.8% Nondetects 1306 97.8% Nondetects Nondetects @ 20 1505 99.7% Nondetects @ 20 1306 100.0% Nondetects @ 20 Nondetects @ 100 5 0.3% Nondetects @ 100 0 0.0% Nondetects @ 100 Max 286 Max 268 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 21.21  Avg Post-Means 20.06  Avg % Reduction 2.09 Notes: 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 Sa

and post-cleanup samples collected from the same location.. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Barium Number Percentages Barium Number Percentages Barium Samples 1544 Samples 1336 Samples Detects 245 15.9% Detects 210 15.7% Detects Nondetects 1299 84.1% Nondetects 1126 84.3% Nondetects Nondetects @ 200 1294 99.6% Nondetects @ 200 1126 100.0% Nondetects @ 200 Nondetects @ 1000 5 0.4% Nondetects @ 1000 0 0.0% Nondetects @ 1000 Max 23400 Max 23400 Max Min ND @ 200 Min ND @ 200 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means: 367.84  Avg Post-Means: 215.12  Avg % Reduction: 13.92 Beryllium Number Percentages Beryllium Number Percentages Beryllium Samples 1544 Samples 1336 Samples Detects 0 0.0% Detects 0 0.0% Detects Nondetects 1544 100.0% Nondetects 1336 100.0% Nondetects Nondetects @ 20 1539 99.7% Nondetects @ 20 1336 100.0% Nondetects @ 20 Nondetects @ 100 5 0.3% Nondetects @ 100 0 0.0% Nondetects @ 100 Max ND @ 100 Max ND @ 20 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 20.00  Avg Post-Means 20.00  Avg % Reduction 0.00 Cadmium Number Percentages Cadmium Number Percentages Cadmium Samples 1544 Samples 1336 Samples Detects 68 4.4% Detects 50 3.7% Detects Nondetects 1476 95.6% Nondetects 1286 96.3% Nondetects Nondetects @ 20 1471 99.7% Nondetects @ 20 1286 100.0% Nondetects @ 20 Nondetects @ 100 5 0.3% Nondetects @ 100 0 0.0% Nondetects @ 100 Max 1180 Max 906 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 22.34  Avg Post-Means 20.28  Avg % Reduction 3.13 Notes: 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 S

and post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Calcium Number Percentages Calcium Number Percentages Calcium Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 4050000 Max 4050000 Max Min 1440 Min 1680 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 43239.15  Avg Post-Means 24571.60  Avg % Reduction 28.95 Chromium Number Percentages Chromium Number Percentages Chromium Samples 1544 Samples 1336 Samples Detects 855 55.4% Detects 723 54.2% Detects Nondetects 689 44.6% Nondetects 613 45.8% Nondetects Nondetects @ 20 684 99.3% Nondetects @ 20 613 100.0% Nondetects @ 20 Nondetects @ 100 5 0.7% Nondetects @ 100 0 0.0% Nondetects @ 100 Max 1900 Max 1050 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 48.01  Avg Post-Means 28.23  Avg % Reduction 21.45 Cobalt Number Percentages Cobalt Number Percentages Cobalt Samples 1544 Samples 1336 Samples Detects 4 0.3% Detects 2 0.1% Detects Nondetects 1540 99.7% Nondetects 1334 99.9% Nondetects Nondetects @ 200 1535 99.7% Nondetects @ 200 1334 100.0% Nondetects @ 200 Nondetects @ 1000 5 0.3% Nondetects @ 1000 0 0.0% Nondetects @ 1000 Max 1000 Max 654 Max Min ND @ 200 Min ND @ 200 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 200.92  Avg Post-Means 200.00  Avg % Reduction 0.29 Notes: 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 Sa

and post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Copper Number Percentages Copper Number Percentages Copper Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 14500 Max 14500 Max Min 36 Min 36 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 387.98  Avg Post-Means 226.82  Avg % Reduction 18.73 Iron Number Percentages Iron Number Percentages Iron Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 228000 Max 212000 Max Min 207 Min 462 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 5438.09  Avg Post-Means 1689.07  Avg % Reduction 34.77 Lead Number Percentages Lead Number Percentages Lead Samples 1544 Samples 1336 Samples Detects 1280 82.9% Detects 1090 81.6% Detects Nondetects 264 17.1% Nondetects 246 18.4% Nondetects Nondetects @ 1.86 260 98.5% Nondetects @ 1.86 246 100.0% Nondetects @ 1.86 Nondetects @ 9.29 4 1.5% Nondetects @ 9.29 0 0.0% Nondetects @ 9.29 Max 7250 Max 7250 Max Min ND @ 1.86 Min ND @ 1.86 Min Exceedances 136 8.8% Exceedances 112 8.4% Exceedances  Avg Pre-Means 24.40  Avg Post-Means 16.21  Avg % Reduction 8.19 Notes: 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 Sa

and post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Magnesium Number Percentages Magnesium Number Percentages Magnesium Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 1550000 Max 1550000 Max Min 2920 Min 4650 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 15852.43  Avg Post-Means 11540.41  Avg % Reduction 12.46 Manganese Number Percentages Manganese Number Percentages Manganese Samples 1544 Samples 1336 Samples Detects 1455 94.2% Detects 1263 94.5% Detects Nondetects 89 5.8% Nondetects 73 5.5% Nondetects Nondetects @ 20 85 95.5% Nondetects @ 20 73 100.0% Nondetects @ 20 Nondetects @ 100 4 4.5% Nondetects @ 100 0 0.0% Nondetects @ 100 Max 4410 Max 4410 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 117.82  Avg Post-Means 51.19  Avg % Reduction 30.18 Mercury Number Percentages Mercury Number Percentages Mercury Samples 1517 Samples 1298 Samples Detects 593 39.1% Detects 469 36.1% Detects Nondetects 924 60.9% Nondetects 829 63.9% Nondetects Nondetects @ 0.4 885 95.8% Nondetects @ 0.4 793 95.7% Nondetects @ 0.4 Nondetects @ 1.6 8 0.9% Nondetects @ 1.6 7 0.8% Nondetects @ 1.6 Nondetects @ 40 2 0.2% Nondetects @ 40 2 0.2% Nondetects @ 40 Nondetects @ 2 20 2.2% Nondetects @ 2 19 2.3% Nondetects @ 2 Nondetects @ 4 9 1.0% Nondetects @ 4 8 1.0% Nondetects @ 4 Max 248 Max 248 Max Min ND @ 0.4 Min ND @ 0.4 Min Exceedances 6 0.4% Exceedances 5 0.4% Exceedances  Avg Pre-Means 4.71  Avg Post-Means 2.24  Avg % Reduction 0.84 Notes: 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’ inc

cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7, B-7a or B-13 bec

all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Nickel Number Percentages Nickel Number Percentages Nickel Samples 1544 Samples 1336 Samples Detects 613 39.7% Detects 523 39.2% Detects Nondetects 931 60.3% Nondetects 813 60.8% Nondetects Nondetects @ 20 928 99.7% Nondetects @ 20 813 100.0% Nondetects @ 20 Nondetects @ 100 3 0.3% Nondetects @ 100 0 0.0% Nondetects @ 100 Max 3160 Max 3160 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 62.56  Avg Post-Means 27.13  Avg % Reduction 23.15 Potassium Number Percentages Postassium Number Percentages Postassium Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 239000 Max 239000 Max Min 1350 Min 1350 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 24749.34  Avg Post-Means 20235.38  Avg % Reduction 10.67 Selenium Number Percentages Selenium Number Percentages Selenium Samples 1544 Samples 1319 Samples Detects 1204 78.0% Detects 984 74.7% Detects Nondetects 340 22.0% Nondetects 334 25.3% Nondetects Nondetects @ 20 280 82.4% Nondetects @ 20 277 82.9% Nondetects @ 20 Nondetects @ 40 60 17.6% Nondetects @ 40 57 17.1% Nondetects @ 40 Max 590 Max 590 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 137.85  Avg Post-Means 240.49  Avg % Reduction -38.53 Notes: 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 Sa

and post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Silver Number Percentages Silver Number Percentages Silver Samples 1544 Samples 1336 Samples Detects 26 1.7% Detects 24 1.8% Detects Nondetects 1518 98.3% Nondetects 1312 98.2% Nondetects Nondetects @ 20 1512 99.6% Nondetects @ 20 1311 99.9% Nondetects @ 20 Nondetects @ 100 5 0.3% Nondetects @ 100 0 0.0% Nondetects @ 100 Nondetects @ 200 1 0.1% Nondetects @ 200 1 0.1% Nondetects @ 200 Max 1400 Max 1400 Max Min ND @ 20 Min ND @ 20 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 22.49  Avg Post-Means 650.77  Avg % Reduction -3151.96 Sodium Number Percentages Sodium Number Percentages Sodium Samples 1544 Samples 1336 Samples Detects 1538 99.6% Detects 1333 99.8% Detects Nondetects 6 0.4% Nondetects 3 0.2% Nondetects Nondetects @ 400 2 33.3% Nondetects @ 400 1 33.3% Nondetects @ 400 Nondetects @ 4000 2 33.3% Nondetects @ 4000 1 33.3% Nondetects @ 4000 Max 2 33.3% Max 1 33.3% Max Min 557000 Min 557000 Min Exceedances ND @ 400 Exceedances ND @ 400 Exceedances  Avg Pre-Means 63441.36  Avg Post-Means 51980.14  Avg % Reduction 11.33 Dioxin (TEQ ND=1/2) Number Percentages Dioxin (TEQ ND=1/2) Number Percentages Dioxin (TEQ ND=1/Samples 1538 Samples 1322 Samples Detects 1136 73.9% Detects 938 71.0% Detects Nondetects 402 26.1% Nondetects 384 29.0% Nondetects Max 75.3 Max 2.29 Max Min 0.265 Min 0.265 Min Exceedances 8 0.5% Exceedances 3 0.2% Exceedances  Avg Pre-Means 0.65  Avg Post-Means 0.64  Avg % Reduction 0.01 Notes: 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 Sa

and post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7

7a and B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13). 
Table 3-7. Dust Wipe Sample Data 

Total Samples Matched Pre- and Post-Cleaning Samples Tes

Thallium Number Percentages Thallium Number Percentages Thallium Samples 1544 Samples 938 Samples Detects 0 0.0% Detects 0 0.0% Detects Nondetects 1544 100.0% Nondetects 938 100.0% Nondetects Nondetects @ 200 1539 99.7% Nondetects @ 200 938 100.0% Nondetects @ 200 Nondetects @ 1000 5 0.3% Nondetects @ 1000 0 0.0% Nondetects @ 1000 Max ND @ 1000 Max ND @ 200 Max Min ND @ 200 Min ND @ 200 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 235.80  Avg Post-Means 195.51  Avg % Reduction 3.85 Vanadium Number Percentages Vanadium Number Percentages Vanadium Samples 1544 Samples 1336 Samples Detects 7 0.5% Detects 3 0.2% Detects Nondetects 1537 99.5% Nondetects 1333 99.8% Nondetects Nondetects @ 200 1532 99.7% Nondetects @ 200 1333 100.0% Nondetects @ 200 Nondetects @ 1000 5 0.3% Nondetects @ 1000 0 0.0% Nondetects @ 1000 Max 1000 Max 539 Max Min ND @ 200 Min ND @ 200 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 236.67  Avg Post-Means 641.60  Avg % Reduction -218.87 Zinc Number Percentages Zinc Number Percentages Zinc Samples 1544 Samples 1336 Samples Detects 1544 100.0% Detects 1336 100.0% Detects Nondetects 0 0.0% Nondetects 0 0.0% Nondetects Max 78900 Max 78900 Max Min 372 Min 539 Min Exceedances 0 0.0% Exceedances 0 0.0% Exceedances  Avg Pre-Means 2196.83  Avg Post-Means 1419.72  Avg % Reduction 16.89 

Notes: 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 Sam

post-cleanup samples collected from the same location. Similarly, the lead and dioxin sample numbers do not match the sample numbers shown in Tables B-7, B-7a 

B-13 inlcude all pre-and post-cleanup samples (i.e., residences with only pre- or post-cleanup samples are not excluded from Tables B-7, B-7a and B-13).
Final Report December 2005 

Page 37 

The database contained 1,517 results for mercury, and 1,544 results for all of the other metals. The rate 

of detection (based on all samples) varied 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 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 μg/m2

 for antimony was exceeded in 2 precleanup samples (0.1% of all samples); the maximum measured value was 1,180 μg/m2

. The screening 

value of 157 μg/m2

 for mercury was exceeded in 5 pre-cleanup 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 EFFICACY OF THE DUST CLEANUP PROGRAM

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 benchmark for asbestos, or 

one or more samples could not be analyzed in the laboratory due to excessive dust on the air filter (i.e., 

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

99% of the time 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 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 results suggest that the cleaning methods used were 

effective in reducing asbestos concentrations 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 adapted 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 analyzed 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 aggressive 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 

Leada 25 

Manganese 31358 

Mercury 157 

Nickel 31358 

Selenium 7839 

Silver 7839 

Thallium 110 

Vanadium 10975 

Zinc 470366 

Dioxina 2 

Table is based on in EPA, May 2003 COPC report. All 

benchmarks are μg/m2

, except for lead, which is in μg/ft2, and 

dioxin, which is ng/m2

. 

a

The health-based benchmark for lead is 40 μg/ft2; however, the 

more stringent screening HUD screening value of 25 μg/ft2

 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; the 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 exceedances 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 dynamic 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 certainly from one building to the next. In addition to 

WTC proximity, high variability is also likely due to the wide range of diversity in the housing stock, 

contents of the residences and common areas, and preexisting conditions, or previous activity, at these 

sites. 

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

that were collected before the 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 apartments, located in 145 buildings. 

The cleanup program reduced the average dust lead loading in residential units by approximately 16 μg/ft2

(20%) (Section E.4.1). 

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

25 μg/ft2

. Average post-cleanup dust lead loading in residences with average pre-cleanup loadings above 

the HUD screening level of 25 μg/ft2

 were approximately 85 μg/ft2 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 μg/ft2

 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 μg/ft2

. Average post-cleanup dust lead loading in residences with average pre-cleanup loadings above 

the HUD benchmark of 40 μg/ft2

 were approximately 120 μg/ft2 lower than average pre-cleanup loadings. 

The cleanup program reduced the average dust lead leading in 21 out of the 23 residences, a success rate 

of approximately 91%. 

Residences located on the third floor or lower tended to have higher pre-cleanup average loadings 

(39.52 μg/ft2

) than residences located on floors 4-10 (21.08 μg/ft2) and floors higher than 10 

(14.18 μg/ft2

). Reduction in average dust lead loading also varied by building floor level. On average, 

dust lead loadings were reduced by 33.1 μg/ft2

 (43.5%) for residences on floors 3 and lower, by 

11.1 μg/ft2

 (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 exceedances, corresponding to two different benchmarks for 

dust lead loading. The first set of bars corresponds to the WTC screening level of 25 μg/ft2

; the second 

set corresponds to the HUD health-based benchmark of 40 μg/ft2

. Regardless of the benchmark that is 

used, the reduction in the number of exceedances (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, which 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 buildings. 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/m2

; all residential average dust dioxin loadings were less than the healthbased benchmark of 2 ng/m2

. The cleanup program reduced the residential average dust dioxin loading 

by approximately 0.01 ng/m2 (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 this 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/m2

). 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

Antimony

Dioxin (ND = 1/2)

Dioxin (ND = 0)

Mercury

Exceedances 

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 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/m2). The number of exceedances is low regardless of the method that is used to 

treat the nondetects. Two sets of bars are also shown for dust lead loading exceedances, corresponding to 

two different benchmarks for dust lead loading. The first set of bars corresponds to the WTC screening 

level of 25 μg/ft2

; the second set corresponds to the HUD health-based benchmark of 40 μg/ft2. The 

reductions in the number of exceedances (pre-cleanup, post-cleanup) are as follows: antimony (2, 0); 

dioxin (ND=1/2) (4, 4); dioxin (ND=0) (2, 1); lead (>25 μg/ft2

) (115, 21); lead (≥ 40 μg/ft2) (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 reduced from 2 in the pre-cleanup samples to 0 in the post-cleanup 

samples; the number of dust mercury exceedances was reduced from 5 in the pre-cleanup samples to 1 in 

the post-cleanup samples. 

3.3 SUMMARY OF THE SPATIAL ANALYSIS OF PCME EXCEEDANCES

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) pattern 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 exceedance 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 statistical 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 positive spatial autocorrelation) (Section E.3.2.2). 

b. Comparison of the rates of PCMe exceedances across the SSAs indicated that 

SSAs with exceedance rates that were significantly 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) pattern of PCMe exceedances, using nearest 

neighbor methods, suggests the pattern is consistent with a spatially random process 

(Section E.3.2.3). 
Final Report December 2005 

Page 43 

4. Analysis of the building-level (local) pattern of PCMe exceedances, using Ripley’s K 

function, also failed to reject the hypothesis that the PCMe exceedances were generated 

by a spatially random process (Section E.3.2.3). 

5. Analysis of the site-level vertical distribution 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 distribution 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., ≤ 3rd floor) were approximately 2 times more likely to 

exceed the health-based benchmark for airborne 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 COMPARISON OF WTC INDOOR DUST PROGRAM AND EPA BACKGROUND STUDY

As described earlier, a background study was conducted in Upper Manhattan to determine indoor 

concentrations of selected analytes that were identified in WTC-related dust. Several of the analytes, 

specifically asbestos, lead, and dioxin, that were measured 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 two years after the collapse of the WTC were similar 

to those measured in Upper Manhattan. The evaluation consists of comparing the frequency of detection, 

the range of values reported (i.e., minimum and maximum), and the percentage of samples that were 

above the health-based benchmark for each analyte (Table 3-9). 

The most appropriate measurements for comparison are the frequency of detection and the percentage of 

samples that exceeded the health-based 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 two studies. As sample size increases there is a 
Final 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 other, the results from the studies were compared to 

values obtained from the literature. Studies were identified that reported concentrations from indoor 

environments for these three analytes using similar sampling and analytical methodologies. The 

minimum, maximum, mean, median, and 90th percentile values from the literature were compared with 

the values reported from the EPA studies. The literature 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 collected 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 identical, while the maximum detected concentration in 

Lower Manhattan was higher than the maximum detected concentration in Upper Manhattan. Although 

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

that exceeded the health-based criterion 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 Statistics 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 measured in in Upper Manhattan (‘background’). The most appropriate 

measurements for comparison are the frequency of detection and the percentage of samples that 

exceeded the health-based benchmark. Comparison 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

% 

aboveb n

% 

det.a min max

% 

aboveb

Asbestos 

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

Lead 

(μg/ft2

) 1812 78% <1.86 2530 7.6% 114 50% <0.5 49 0.9%

Dioxin 

(ng/m2

)

d 1549 74% 0.292 5.14 0.5% 114 77% 0.475 1.66 0%

s/cc: structures per cubic centimeters; :g/ft2

 = micrograms of lead per square foot of surface; ng/m2 = 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-based benchmark (health-based benchmarks: 

asbestos: 0.0009 f/cc; lead: 25 μg/ft2

; dioxin 2 ng/m2:). c Phase contrast microscopy equivalent (PCMe) results; see glossary for definition d International toxicity equivalent quotient (TEQ) see glossary for definition; all congeners that were not detected were set equal 

to 1⁄2 their detection limit. 

 

A summary paper by the Health Effects Institute presented asbestos results for several different types of 

buildings, including schools, residences, and public/commercial spaces (HEI-AR, 1991). The asbestos 

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

method that was utilized in the EPA studies. The values reported in the summary paper were leftcensored; a value of zero was substituted for samples 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 90th 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 only 
Final 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 detection 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 detected concentration, and the percentage of samples 

that exceeded the health-based criteria were higher in Lower Manhattan when compared to the results 

from Upper Manhattan1

. 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 from 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 90th percentile values were plotted beside the same values from the USEPA studies 

(Figure 3-3). The horizontal axis reports the results from the test-only data set from Lower Manhattan 

Asbestos 0.0037 0.0204

0

0.0005

0.001

0.0015

0.002

Concentration, 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 concentrations 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 this 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 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 

USEPA studies (after replacing non-detects with 0). This may be in part due to the high number of nondetect samples that were present in the USEPA studies. If the detection limit were substituted for the 

USEPA non-detect samples, the means from the USEPA studies would be near 0.0005 s/cc. Its likely that 

the true mean asbestos concentration in Manhattan, based on data from the USEPA studies, lies somewhere 

between 0 and 0.0005 s/cc An estimate that is in the 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 detected 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 just 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 studies, the means, medians, and 90th percentile values for the LM-Post, 

UM, and HUD were below the benchmark, and all but the 90th 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 Manhattan was slightly higher than Upper Manhattan2

. 

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

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

There was limited information in the literature for dioxin 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 methods as those used in the EPA studies. The data 

represents the seventh round of post-occupancy sampling, which occurred in 1999, 18 years after the 

building fire. This was the last round of sampling because the dioxin concentrations were very low 

throughout the building. The values presented in the paper were reported in Toxicity Equivalents 

Quotients (TEQs) where congeners that were below the detection limit were set to 1⁄2 of the detection 

limit. The minimum, maximum, median, mean, and 90th 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. There was a slightly higher value for the 90th percentile from the 

NYSDOH data set (NYSDOH, 2002). The minimum, median, mean, and 90th 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 

 

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 dioxin concentrations observed in the WTC Indoor Dust 

Program were similar to background, and similar to values reported in the literature. 

In summary, a comparison between analytical results from Lower Manhattan and Upper Manhattan show 

that the number of samples that exceed health-based criteria for three analytes one to two years after the 

collapse of the WTC are similar. Additionally, values reported in the literature for these analytes indicate 

that the mean, median, and 90th 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 2350 394 1845

0

10

20

30

40

50

60

Concentration, ug/ft2

Median

Mean

90th

Max

Min

LM-Pre LM-Post UM HUD

Benchmark (25 ug/m2)

Figure 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). Data for carpeted and uncarpeted 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 Program; Upper Manhattan data (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 below the detection limit were substituted with 1⁄2 of the 

detection limit. The maximum values detected 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 just 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 

studies, the means, medians, and 90th percentile values for the LM-Post, UM, and HUD were below the 

benchmark, and all but the 90th percentile for the LM-Pre data set were below the benchmark. 
Final Report December 2005 

Page 50 

Dioxin

0

1

2

3

4

5

6

Concentration, ng/m2

Median

Mean

90th

Max

Min

LM-Pre LM-Post UM Binghamton

Benchmark (2 ng/m2)

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 York State Department of 

Health (NYSDOH, 2002). The figure 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, 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 90th percentile from the NYSDOH data set (NYSDOH, 2002). The minimum, median, 

mean, and 90th 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 indicates that the dioxin concentrations 

observed in the WTC Indoor Dust Program were similar 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 Surface Dust in Residential Areas of Lower Manhattan, 

September. 

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

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

Clark, P.J, and F.C. Evans. 1954. Distance to Nearest Neighbor as a Measure of Spatial Relationships in 

Populations. Ecology (35)4. 

Cressie, N.A.C. 1993. Statistics for Spatial Data. 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 Particulate 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 Statistics Program for the Analysis of Crime Incident 

Locations. Ned Levine & Associates, Houston, TX, and 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 Urban Children. Final Report to U.S. HUD from the 
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University of Rochester School of Medicine (Rochester, NY) and the National Center for Lead Safe 

Housing (Columbia, MD). 

U.S. Department of Housing and Urban Development (HUD). 2001. National Survey of Lead and 

Allergens in Housing, Final Report, Volume I: Analysis 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 Register, 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 Disaster. 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 Potential 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 positive 

skewness 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%) 

exceedancesa 93 (13.7%) 21 (3.1%) 

mean 35.46 19.03

standard deviation 286.03 279.64

skewness 20.56 25.77

CVb 8.07 14.70

variance 81812.09 78199.03

maximum 6790 7250

99th percentile 470 83.2

95th percentile 76.30 16.00

90th percentile 37.30 11.00

75th percentile 12.50 8.40

median 7.32 6.38

25th percentile 3.23 2.47

10th percentile 1.86 1.86

5th percentile 1.86 1.86

1st percentile 1.86 1.86

minimum 1.86 1.86

S-W Statisticc 0.07 0.03

Prob Normald <0.0001 <0.0001

a

Exceedance: lead wipe (μg/ft2) samples that exceeded the HUD screening level of 

25 μg/ft2

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 test 
Final 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 positive 

skewness 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) 

exceedancesa 92 (13.5) 20 (3.0) 

mean 25.52 8.28 

standard deviation 121.00 19.79 

skewness 15.24 13.89 

CVb 4.74 2.39 

variance 14641.04 391.80 

maximum 2530 394 

99th percentile 366.00 83.20 

95th percentile 73.00 15.50 

90th percentile 37.25 10.90 

75th percentile 12.50 8.40 

median 7.32 6.37 

25th percentile 3.22 2.47 

10th percentile 1.86 1.86 

5th percentile 1.86 1.86 

1st percentile 1.86 1.86 

minimum 1.86 1.86 

S-W Statisticc 0.15 0.21 

Prob Normald <0.0001 <0.0001 

a

Exceedance: lead wipe (μg/ft2) samples that exceeded the HUD screening level of 

25 μg/ft2

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 test 
Final 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 

(μg/ft2

) measured by wipe samples are shown, and statistics for the reduction in 

the average dust lead loading. The statistics describe the distribution of precleanup and post-cleanup average lead loadings (μg/ft2

) that were measured in 

each residence. The average dust lead loadings and the reduction in the averages, 

continue to display substantial departures from normality; a log-transformation of 

the data fails to improve the fit of a normal 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

CVa 4.02 2.727 2.096

variance 4245.98 4401.40 292.29

maximum 708.21 748.95 241.67

99th percentile 289.03 294.33 39.25

95th percentile 81.65 92.44 16.08

90th percentile 31.37 44.73 11.77

75th percentile 9.50 18.44 8.44

median 1.77 8.66 6.79

25th percentile 0.00 4.94 3.01

10th percentile -1.31 2.34 1.86

5th percentile -3.35 1.86 1.86

1st percentile -21.50 1.86 1.86

minimum -163.27 1.86 1.86

S-W Statisticb 0.33 0.15 0.22

Prob Normalc <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 test 
Final Report December 2005 

Page A-4 

Table A-3. Statistics on Reduction in Average Lead Wipe Loadings (Pre- and PostCleanup) in Residences With Pre-cleanup Averages Greater Than the HUD 

Screening Level of 25 μg/ft2

The statistics describe the distribution of pre-cleanup and post-cleanup average lead 

loadings (μg/ft2

) that were measured in each residence. 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 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 

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

CVa 166.01 135.50 229.28

variance 19838.34 19147.47 1569.75

maximum 708.21 748.95 241.67

99th percentile 708.21 748.95 241.67

95th percentile 408.96 413.87 40.74

90th percentile 199.74 209.36 31.73

75th percentile 88.03 101.78 11.56

median 38.82 48.52 8.08

25th percentile 25.26 34.74 6.39

10th percentile 19.92 26.86 3.78

5th percentile 10.35 25.98 2.08

1st percentile -163.27 25.86 1.86

minimum -163.27 25.86 1.86

S-W Statisticb 0.64 0.56 0.32

Prob Normalc < 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 test 
Final 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 (μg/ft2) tend to decrease with increasing floor level, and variance tends 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 E.2.2 for details). 

Floor groupa 

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

CVb 2.60 2.20 2.68

variance 10549.74 2154.14 1442.33

maximum 748.95 413.87 294.33

99th percentile 748.95 413.87 294.33

95th percentile 175.15 78.40 39.48

90th percentile 73.45 38.62 21.90

75th percentile 27.23 18.44 9.54

median 9.20 10.03 7.25

25th percentile 5.46 5.27 3.80

10th percentile 2.17 2.32 2.56

5th percentile 1.86 1.96 1.98

1st percentile 1.86 1.86 1.86

minimum 1.86 1.86 1.86

S-W Statisticc 0.36 0.34 0.25

Prob Normald <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 test
Final 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/m2

), 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. Very few exceedances 

were observed for dioxin. 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=0.71/0.89, p<0.0001/p<0.0001). This table 

excludes one observation that has been 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 samplesa 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 

CVb 0.49 0.42 

variance 15.62 0.08 

maximum 75.4 4.34 

99th percentile 1.85 1.76 

95th percentile 1.11 1.17 

90th percentile 0.90 0.90 

75th percentile 0.68 0.68 

median 0.60 0.59 

25th percentile 0.56 0.54 

10th percentile 0.47 0.46 

5th percentile 0.43 0.43 

1st percentile 0.34 0.34 

minimum 0.27 0.27 

S-W Statisticc 0.03 0.62 

Prob Normald <0.0001 <0.0001 

a

Exceedance: lead wipe samples that exceeded the HUD screening level of 25 μg/ft2

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 test 
Final 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/m2), 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. Very few exceedances 

were observed for dioxin. 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=0.88/0.89, p<0.0001/p<0.0001). One precleanup observation with a value of 75.3 ng/m2 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 samplesa 673 668 

nondetects 0 0 

exceedances 2 4 

mean 0.66 0.65 

standard deviation 0.29 0.28 

skewness 6.79 5.27 

CVb 0.44 0.42 

variance 0.09 0.08 

maximum 5.14 4.34 

99th percentile 1.81 1.76 

95th percentile 1.10 1.17 

90th percentile 0.90 0.90 

75th percentile 0.68 0.68 

median 0.60 0.59 

25th percentile 0.56 0.54 

10th percentile 0.47 0.46 

5th percentile 0.43 0.43 

1st percentile 0.34 0.34 

minimum 0.27 0.27 

S-W Statisticc 0.57 0.62 

Prob Normald <0.01 <0.01 

a

Exceedance: lead wipe samples that exceeded the HUD screening level of 25 μg/ft2

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 test 
Final 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 into between the New York City Department of 

Environmental Protection (DEP) and the Monitoring Contractor must be in compliance with all applicable 

laws and regulations, including but not limited to regulations issued by the United States Environmental 

Protection Agency (EPA), the United States Department 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 direct 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 include various options, including cleaning within 

residences and common spaces by licensed asbestos contractors 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, coordinating 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 monitoring contractor shall supply all equipment and 

supplies necessary to perform the work specified in this contract. 
Final Report December 2005 

Page B-2 

B. Roles and Responsibilities

As provided in the instant contract, EPA’s World Trade 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 shall 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 disapprove 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 monitoring 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 available on-site during the performance of work and shall be presented 

upon request. 

Project Monitors

The Project Monitors shall be employees of the Monitoring Contractor. The Project Monitors must 

possess valid NYS Asbestos Project Monitor certificates. 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 inspections shall be conducted by qualified personnel. At a minimum 

such personnel shall have an understanding of HVAC system operation and experience in utilizing 

accepted indoor environmental sampling practices, current industry HVAC cleaning procedures, and 

applicable industry standards. The HVAC System Inspection contractor shall be a certified member of 

National Air Duct Cleaners Association (NADCA). 

Background Investigations

All contractors and subcontractors involved in the monitoring activities under this contract are responsible 

for completing a background check on their employees and for screening unacceptable candidates from 

the pool of on-site workers. Background checks shall 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. Specifications

This contract between DEP and the monitoring contractor 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 scheduled for 20 residences per day. Air sampling shall 

be scheduled and completed within 24 hours of successful 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 sampling only). Work shall be scheduled 7 days per 

week. A sufficient number of properly trained and 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 sufficient 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 capacities 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 for the kick off meeting shall include scope of work, sampling logistics 

and resource requirements and procedures for data acquisition 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 conference calls with EPA to coordinate field activities, 

as requested. 

The Monitoring Contractor shall adopt and follow the 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 provided 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. 38th 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 contract shall be provided to EPA. EPA will review 

submitted invoices to confirm that the work has been 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 agreed upon alternative electronic format approved by 

EPA and DEP. The Project Monitor shall also maintain 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 responsible for accessing a secure EPA Website (url, user name and 

password to be provided by EPA) to obtain requestor information (address, building identification 

numbers, etc.). In coordination with the EPA, the Project Monitor will access residential services 

requested from the Website, schedule pre-cleaning inspections and schedule cleaning activities 

and air sampling with the tenants/owners, asbestos abatement contractors and air monitoring 

technicians for the various residences. The Project 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 addresses 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 conduct pre-cleaning or pre-sampling inspections of 

the residences, common spaces and heating, ventilation and air conditioning 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 accordance with inspection checklists prepared by 

EPA. 

b. Residents will be provided with a fact sheet and information prepared by EPA regarding 

sampling options (aggressive and modified aggressive air sampling protocols) and the 

handling of valuable personal items (money, jewelry, heirlooms, etc.) and fragile objects 

during work. 

 

c. The Project Monitor will obtain written authorization for access for all work to be 

performed for this project including inspection, 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 commercial 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 Indoor 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-sampling inspection information in the EPA Indoor 

Air database or using an agreed upon electronic format. 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 all 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 has been requested, the 

Monitoring Contractor will notify EPA and fax a written request for assistance in evaluating 

potential health hazard to NYC DOH at 212-442-3378. 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 Remediation Guidelines. 

Deliverables 

 

The Project Monitor shall record if mold or flaking, 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 electronic 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, delaminated, etc. suspected ACM is observed, the 

monitoring contractor will notify EPA and fax written notification to the NYC DOH. Cleaning or 

air monitoring will not proceed in areas where such suspected ACM is observed until instructed 

otherwise. 

The Project Monitor shall record if damaged, deteriorated, delaminated, etc. suspected ACM is 

observed and notify EPA and fax written notification 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 have been impacted by dust 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 provided to EPA within 2 business days of the 

completion of the evaluation. If the whole system 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, the basis for the determining whether or not 

the system was impacted by the collapse of the 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 has 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 dust 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 other 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, Ventilating 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 spaces where cleaning has not been requested: 

a. Based on consultation with resident, aggressive 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 against walls, ceilings, floors and other 

surfaces. This shall continue for at least five minutes per 1,000 square feet of 

floor. When directing the exhaust, caution shall be taken to minimize 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 including 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 shall run for at least 15 minutes prior to the start 

of sampling. The fans shall operate continuously 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 shall 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 accordance 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 sampling cassettes need to be monitored to ensure 
Final 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. 38th St., New York, New York, 

212-290-0051. 

f. Wipe samples will be collected at 10 percent of the residences where sampling only has 

been requested, up to a maximum of 13 residences, as instructed by EPA. This sampling 

will consist of the collection of 3 wipe samples 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 will be collected in accordance with the 

procedures specified in ASTMD 6661-01. Wipe samples and copies of necessary 

documentation 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. 

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 document 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 sample 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 using the secure EPA website (url, user name and 

password to be provided by EPA). Information on sampling activities (date, sample ID) 

shall be provided to EPA, via the website, within 24 hours of completion of sampling. 

All data shall be maintained as confidential. 

The Project Monitor will keep a field notebook, document the size of the sampled area, sampling 

locations (including a field sketch) and equipment 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 laboratory 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-cleanup 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 samples 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 document all the necessary information regarding 

sampling activities; will ensure personnel complete 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 collecting 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 oversee 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 building 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 residential 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 information on the status of cleaning activities using the 

secure EPA website (url, user name and password to be provided by EPA). Cleaning tasks 

completed in accordance with the Cleaning Contract Scope of Work shall be documented on the 

Cleaning Checklist and reported to EPA via the website 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 the Monitoring Contractor in accordance with US 

DOL OSHA requirements. The person who conducts sampling shall possess a valid NYS DOL 
Final 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 sample 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 Asbestos 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 employees or their designated representatives for their 

review in accordance with 29 CFR 1910.1020 and 29 CFR 1926.1101. 

The Monitoring Contractor shall document 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 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 field package consisting of field sheets, field 

notes (copy), and chain-of-custody forms (copy), which 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 inspections 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 inspection 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 sampling will be conducted to verify attainment of 

the clean-up criteria. Based on consultation with 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 against walls, ceilings, floors and other 

surfaces. This shall continue for at least five minutes per 1,000 square feet of 

floor area. When directing the exhaust, caution shall be taken to minimize 
Final 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 including 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 shall run for at least 15 minutes prior to the start 

of sampling. The fans shall operate continuously 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 shall 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 accordance 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 sampling 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. 38th 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 using the modified aggressive air sampling 
Final Report December 2005 

Page B-13 

method. 

i. The Monitoring Contractor shall document 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 sample 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 using 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 data shall be maintained as confidential. 

The Project Monitor shall record all post-cleaning information in the EPA Indoor Air database or 

an agreed upon electronic format. This information shall include, but is not limited to sampling 

technique (aggressive or modified aggressive), visually 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/debris, 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 (start/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 Project 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 recleaned 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 necessary 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 shall 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 final visual inspection. The Project Monitor shall 

ensure that the Project Completion Form is completed for each residence cleaned or sampled. 

19. The Project Monitor will complete a daily report 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 data from the completed Project Completion Form 

into the EPA Indoor Air web database or an agreed upon alternative electronic format or website 

approved by EPA within 24 hours of activity. 

20. EPA will notify residents of the results of sampling 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 engaged 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 parties 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 provide guidance for determining the presence of WTCrelated dust in residential ventilation systems in Lower Manhattan. This document is solely concerned 

with determination of the presence of WTC-related dust in residential ventilation systems. Determining 

the exact nature of all hazardous air contaminants and 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, Historical 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 impacted by the WTC collapse. This document is not 

concerned with the following: 

• Mechanical operation of the systems 

• Environmental conditions, contaminants, or other conditions within the systems that are not related to 

the WTC collapse 

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

C.2.0 PURPOSE 

The purpose of this document is to provide procedures for inspecting and evaluating residential 

ventilation systems in Lower Manhattan to determine if such systems have been impacted by airborne 
Final Report December 2005 

Page C-2 

dust from the WTC collapse, and to provide guidelines for the qualifications of personnel accomplishing 

such inspection and evaluation. 

WTC-related dust is generally considered to have common, consistent, and readily observable 

characteristics visually and tactilely differentiating 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 wallboard, 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-related dust and environmental dust that was present in 

the ventilation system prior to or after the WTC collapse. 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 replacement panels, plates or access doors to cover such 

openings as required under Section C.5.5.1.2of this document. 

SMACNA HVAC Duct Construction Standards – Metal and Flexible, Sheet Metal and Air Conditioning 

Contractors’ National Association, Inc., 2nd Edition (1995). 

The SMACNA standard includes construction and material specifications 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., 6th Edition (1992). 

The SMACNA standard includes construction and material specifications 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 Air Heating and Air-Conditioning Systems, National Fire Protection 

Association (1999 Edition). 

The NFPA standards include construction and material specifications 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 

 INDIVIDUALS 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 ventilation 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 individuals performing the evaluations, as well as health 

and safety requirements pertinent to conducting the evaluations. 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 document references several key OSHA standards that are relevant to 

this work. 

C.4.1 Evaluation Team 

All evaluations shall be performed in teams consisting 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 Professional - 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 able to demonstrate competency and document 

experience in the following areas: air handling 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/tagout, fall protection, and personal protective 

equipment standards. 

 NOTE: If necessary, a licensed electrician shall be subcontracted to de-energize electrically 

 operated equipment 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 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 personal protective 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 responsible for implementing the use of such 

equipment; 

c. locating and identifying ventilation system components 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 Professional – 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 requirement is to be able to identify asbestos-containing 

materials and asbestos-related hazards in order to avoid 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 personal 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 type, 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 duct, exhaust fan, and rooftop or wall exhaust outlet devices. If 

information suggests that contamination of direct exhaust 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 equipment expected to have the greatest impact from WTCrelated dust include air intakes and intake ducts, intake air dampers, intake 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, transitions and elbows, and 

interior system components such as control devices, dampers, thermal coils, turning vanes, fans, etc. 

One factor that may be considered in performing 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 evaluation, the HVAC/Electrical Professional shall ensure that the ventilation 

system is cycled and that there are no obvious existing 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 necessarily 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 strip 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 coils 
Final 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 treatments (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 assessment of the accessibility of the various 

components of the system. For example, components may be enclosed within permanent sheet metal 

panels, or may be located above or behind 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 diffusers 
Final 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 accessing 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 shall assess the accessibility of each HVAC 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 needed 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 with NFPA, NADCA and SMACNA standards and NYC building 

 codes. 

The Environmental Professional shall repair or seal any 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 using 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 camera 
Final 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 stream 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 concrete and/or gypsum wallboard, 

and may contain asbestos fibers. The 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 double-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 surface 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 other available information from site occupants or 

building managers, such as the known status of system operating conditions at the time of the WTC 

collapse, ventilation system maintenance (i.e., cleanings, filter changes, or replacement since the WTC 

collapse). 
Final Report December 2005 

Page C-12 

NOTE: Based on these assessments, to the best of his/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 environmental 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 indicates otherwise, WTC-related dust shall be assumed to 

be an asbestos-containing material and cleaning procedures 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 shipped 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-related if: pH is 9 or above; 

fibrous glass content is between 30 and 40 % and crystalline silica content is 5% or greater. Laboratory 

data will be reported back to the 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 grams 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 the 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 Environmental 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 contamination 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 shall ensure that all interior ventilation system 

components that were subject to the cleaning procedures are visibly clean. An interior surface will be 

considered visibly clean when it is free from non-adhered substances 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 into between the New York City Department of 

Environmental Protection (DEP) and the cleaning contractor must be in compliance with all applicable 

laws and regulations, including but not limited to regulations issued by the United States Environmental 

Protection Agency (EPA), the United States Department 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 direct 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 include various options, including cleaning within 

residences and common spaces by licensed asbestos contractors 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 services of a NYS DOL licensed asbestos contractor with 

DEP and NYS DOL certified workers for the performance 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 Trade 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 shall include the following authority related to 

cleaning work conducted under this contract: 

 - The authority to stop work for health and safety reasons. 

 - The authority to stop work for 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 disapprove 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 cleaning operations. Thereafter, personal air samples will 

be taken randomly at a rate of one sample per day. 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 designated 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 cleaning 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 licensed 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 involved in the cleaning. A copy of these licenses and 

certificates must be available on-site during the performance 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 membership 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 cleaning activities under this contract are responsible 

for completing a background check on their employees and for screening unacceptable candidates from 

the pool of on-site workers. Background checks shall 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 contractor 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 inspection by the Monitoring Contractor. Copies of 

all invoices for work conducted under this contract shall be provided to EPA. EPA will review submitted 
Final Report December 2005 

Page D-4 

 

invoices to confirm that the work has been completed 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 accordance 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 equipped 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 shall 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 significant accumulations of dust and/or debris from the 

collapse of the WTC in residences or common 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, continue 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 extraction cleaner after consultation with and approval by 

 owner. After cleaning, red rosin construction paper will 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. 

 - Surfaces that are not cleaned by wet methods (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 approved by EPA. HVAC systems cleaning, if warranted, 

shall be completed prior to the initiation of the cleaning of common space or residences within an affected 

building. In the event that the 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 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 the 
Final Report December 2005 

Page D-6 

 

system where dust or other contaminants are present shall 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. Encapsulating 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 exterior 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, continue 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 extraction cleaner after consultation with and approval by 

 owner/resident. After cleaning, red rosin construction paper will 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 will be vacuumed using an appropriate 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 and housing will then be vacuumed. Filters will be vacuumed and reinstalled. 

 Air conditioners will 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 and 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 vacuumed. Exhaust fans will be vacuumed and wiped. 

 - Unobstructed closet floors will be vacuumed. 

 - Solid objects (electrical equipment, exercise equipment, 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 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 shall be present in each building 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 Monitoring Contractor will conduct a thorough visual inspection to verify 

 the absence of visible dust accumulations. 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 necessary by EPA. This clean-up criterion may be reevaluated and 

 revised, if determined necessary 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 during the cleaning shall be the responsibility of the 

 contractor. All waste generated shall be treated as asbestos-containing material (ACM). 

 Transportation and disposal of generated 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 Contractor. 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 airtight with duct tape or equivalent 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 common spaces, residences or portions thereof (such as 

windows, terraces or balconies).

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

in cases where the work area can be isolated by the erection of isolation 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 New 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 the decontamination enclosure system). 

 - Personal protective equipment including disposable 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 holding area with a lockable door for waste shall be located at the 

 site and shall comply with all applicable 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 enclosure system shall be considered when appropriate, i.e. inability 

 to comply with the provision due to space limitation or other 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 possible, 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 penetrations 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 double-bagged. Accumulation of water is prohibited. 

 Water misting shall be sufficient 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. After 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 carpets and fabric covered furniture 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 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. 

 - Disposal of all wastes generated during the cleaning shall be the responsibility of the 

 contractor. All waste generated shall be treated as asbestos-containing material (ACM). 

 Transportation and disposal of generated 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 Monitoring Contractor will conduct a thorough visual inspection to verify 

 the absence of visible dust accumulations. 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 necessary by EPA. This clean-up criterion may be reevaluated and 

 revised, if determined necessary based on field conditions and analytical limitations. 

 - After successful clearance air monitoring, isolation 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 other 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 Monitoring Contractor. 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 airtight with duct tape or equivalent 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 the 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 SUMMARY OF DATA COLLECTED

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 common areas that were cleaned and tested. Results by 

PCMe were compared to the health-based benchmark 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 μm 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 fiber analysis. Historically, most of the occupational 

studies available (and reviewed in IRIS), from which estimates 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 μm (AHERA also stipulates a 

minimum 5:1 aspect ratio) were distinguished from total (i.e., > 0.5 μm) fiber counts, although total fibers 

counts were also recorded. To maximize analytical 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 detected 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 approximately 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 dioxin dust wipe data that were collected from 

263 residences that were located in 157 buildings. Wipe data that were used to assess efficacy of the 

cleanup program are discussed in Section E.4.0 
Final Report December 2005 

Page E-3 

E.1.3.1.1 Lead Wipe Data 

The database contained 1,540 wipe samples for dust lead 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 μg/ft2

 were set equal to the detection limit. Review of existing 

environmental standards/regulations identified an applicable and relevant standard to set a health-based 

benchmark for lead in interior dust. The Residential Lead-Based Paint Hazard Reduction Act (Title X) 

Final Rule (40 CFR, Part 745, 1/5/01) established uniform 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 μg/ft2

 for floors (including carpeted floors), and 250 μg/ft2 for interior 

window sills. To support the development of a dust standard, EPA performed an analysis of the 

Rochester Lead-in-Dust Study (HUD, 1995). At 40 μg/ft2

, a multimedia analysis shows a 5.3% 

probability that a child’s blood lead level would exceed 10 μg/dL. Thus, this standard meets the criteria 

established by EPA (i.e., 95% probability to be below 10 μg/dL) (EPA, 1994) for managing 

environmental lead hazards. However, an additional 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 μg/ft2

. 

Approximately 9% of all lead wipe samples (i.e., test only and clean and test) were above the HUD 

screening level of 25 μg/ft2

 (Table 3-3); approximately 14% of the pre-cleanup samples exceeded the 

HUD screening level , while approximately 3% of the 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 μg/ft2

(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. Summary statistics for the data are provided in Table 3-6. The 

dioxin results were modified using a toxicity equivalency 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/m2

 even if the sample did not meet all of the QA/QC reporting level criteria. 

This value represents the highest potential concentration of dioxin that may be present. At least one of 

the 17 congeners were detected in 1,136 of the samples; the remaining 399 samples were reported as 
Final 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 health-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 for mercury, and 1,544 results for all 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 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 μg/m2

, for antimony, was exceeded in 

2 pre-cleanup samples (0.1% of all samples); the maximum measured value was 1,180 μg/m2

. The 

screening value of 157 μg/m2

 for mercury was exceeded in six samples (0.4% of all samples). 

E.2.0 EFFICACY OF THE DUST CLEANUP EFFORT

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 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. The 

cleanup effort was effective in achieving the clearance criteria for PCMe approximately 99% of the time 
Final 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 attempt 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 methods 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 adapted 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 analyzed 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 aggressive and modified aggressive sampling 

procedures, respectively; the 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 exceedances 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 complex and dynamic 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 certainly from one building to the next. In addition 
Final 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 preexisting conditions, or previous activity, at these 

sites. 

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

that were collected before the 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 apartments, located in 145 buildings. Samples that were below the 

detection limit of 1.86 μg/ft2

 were set equal to the detection limit. Table 3-4 provides statistics for the 

pre-cleanup and post-cleanup lead wipe data; a more 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 large coefficient of variation (CV). The high positive 

skewness indicates that a few lead wipe samples contained much higher levels of lead than the majority of 

the samples (Figure E-1). The large CV indicates the data are highly 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 presence of lead-based paint in older (i.e., pre 1950) 

housing. This factor is exemplified in the case of the two highest recorded lead wipe samples in the data 

set. These two samples were pre- (6,790 μg/ft2) and post-cleaning (7,250 μg/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 μg/ft2

, and the lead loading in the two other post-cleanup 

samples was 7.41 and 7.56 μg/ft2

. The extremely high lead loads in these two matched samples prompted 

additional investigation which determined that the chest surface was remarkable for flaking paint. A paint 

chip sample was analyzed and contained 14% (140,000 μg/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, their 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 between 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., skewness=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 data. Log-transformation of the data reduced the skewness to 1.17 and 

0.71 for the pre-cleanup and post-cleanup data, respectively, and the CV to 0.57 and 0.48, respectively. 

Likewise, tests for normality3

 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 (μg/ft2

), 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 25th %; 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 terminates 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 substantial 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 substantial departures from normality (p<0.0001 for both 

subsets). 

The cleanup program reduced the overall number of exceedances 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 cleanup 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 effectiveness 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 differences between pre-cleanup and post-cleanup loadings 

on a residence-by-residence basis is that it takes into 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 comparisons 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 loading4

. 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 samples from a standard normal distribution. 

The value of the sample is shown on the x-axis, the 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 distribution is a bell-shaped curve that is symmetric 

about the mean, which equals 0 for the standard normal 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-cleanup and post-cleanup dust lead loadings, on a 

residence-by-residence basis, and the reduction in the average dust lead loading for the 214 cleaned 

residences. A more complete set of statistics 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 improve 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 samples 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 distribution of a sample. As shown above, the boxplot for 

a normal distribution is symmetric about the mean/median. The median (indicated by horizontal line 

that is located within the box) and mean (indicated by black ‘+’) of a normal distribution are equal, and 

located at the center of the box. The 25th percentile of the distribution (indicated by the bottom of the 

box) and the 75th 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 approximately equidistant from the median/mean. The skewness of a normal 

distribution equals zero; a positively 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 average dust lead loading in each residence by 

approximately 16 μg/ft2

 (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 assumption, the significance of the reduction in dust 

lead loadings was also tested using the nonparametric sign test. The sign test assumes only that the 

differences between the pre-cleanup and post-cleanup means are independent random variables6

. The 

sign test considers only the direction of the difference (i.e., positive or negative), not the magnitude of the 

differences, which removes the effects of the extreme measurements (which produce the positive 

skewness) on the test results. The sign test also indicated 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-corrected 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 valid because random sampling methods were not used to select 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) (μg/ft2) 

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 dust lead loadings and the reduction in the 

averages, 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 averages: 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 

CVa 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 Statisticb 0.33 0.15 0.22 

Prob Normalc <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 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% CI3

, 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 μg/ft2

 to a post-cleanup loading of 

27.1 for a 22nd floor residence, and an increase from 4.4 to 25.9 μg/ft2

 for a 5th 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

pre post

x

x − x =100 ⋅ Equation E-13

Where, 

 x pre = average pre-cleanup dust lead loading

 x pre = average post-cleanup dust lead loading

Figure E-4. Boxplot for the reduction in residential average dust lead loading (μg/ft2). 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 μg/ft2

. 
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 reductions (i.e., not the magnitude of the reductions) and, 

therefore, provides a test for the statistical significance 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 residential dust lead loading is statistically significant 

(M=53.5, p<0.0001). 

E.2.2.1 Reductions in dust lead loadings in residences 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 μg/ft2 was assessed. Table E-2

provides statistics for dust lead loading for these residences; 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 data 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 improve normality (S-W statistic=0.71, p<0.0001). 

 

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

25 μg/ft2

. The cleanup program reduced the average dust lead loading in the residences with average precleanup loadings above the HUD screening level by approximately 85 μg/ft2 (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 μg/ft2

 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 μg/ft2; 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 μg/ft2

. The post-cleanup 

average increased from 78 to 242 μg/ft2

 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 average 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 μg/ft2

. Average post-cleanup dust lead loading in residences with average pre-cleanup loadings above 

the HUD benchmark of 40 μg/ft2

 were approximately 120 μg/ft2 lower than average pre-cleanup loadings. 

The cleanup program reduced the average dust lead leading 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) 

(μg/ft2) in Residences with Pre-cleanup Greater than 

the Health-based Benchmark of 25 ug/ft2

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 logtransformation of the averages slightly improves the fit of a normal distribution to the data (S-W 

statistic for reductions=0.71, p<0.0001; pre-cleanup 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

CVa 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 Statisticb 0.64 0.56 0.32

Prob Normalc <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 by Shapiro-Wilk test 
Final Report December 2005 

Page E-16 

E.2.2.2 Effect of Floor Level / Pre-Cleanup Average Dust Lead Loading 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 lead loadings. Lower floors tended to have higher 

pre-cleanup lead loadings7

 and, therefore, showed greater reduction 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 μg/ft2

, 17 of them were located on lower floors (i.e., ≤ 3rd floor), 14 on floors between the 4th and 10th

floors, and 5 at floors greater than the 10th (two at 11 and one at 12). Figure E-5 shows a plot of the logtransformed pre-cleanup means vs. floor number; higher 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-cleanup 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 average dust lead loadings for each 

floor group are shown in Table E-5; Table A-4 provides additional statistics for this data. The data show 

moderate to high variability, and large positive skewness; 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 μg/ft2

) is statistically significant (t-test with 

log-transformed data = 2.60, p=0.0104); the difference 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 contain 

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 (μg/ft2) 

The tendency for average dust lead loadings to decrease with increasing floor 

level is indicated by the statistics shown 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 Groupa 

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 

CVb 2.60 2.20 2.68 

S-W Statisticc 0.36 0.34 0.25 

Prob Normald <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 data are from a normal distribution by Shapiro-Wilk test 
Final Report December 2005 

Page E-18 

The difference between the means of the middle and upper floor groups (6.9 μg/ft2

) 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 significant by the Mann-Whitney test (W=2,067, p=0.0069). 

As expected, the reduction in dust lead loading in μg/ft2

, 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 groups, 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 the percent reduction in dust lead loadings between the 

different floor groups, two observations with extreme 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 middle 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 average dust lead loadings (vertical axis), by floor. 

The plot indicates that the pre-cleanup average residential dust lead loading decreases with increasing floor levels up 

to approximately floor level of 15. The mean and variability 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 μg/ft2

, or 

43.5 % (95% CI,3

 17.8, 78.9 μg/ft2; 29.71, 53.39%). Average residential dust lead loadings in the middle 

floors were reduced on average by 11.1 μg/ft2

, or 23.1 % (95% CI,3 4.40, 27.14 μg/ft2, 17.42, 39.55 %, 

respectively). The dust lead loading in floors higher than 10 were reduced on average by 6.9 μg/ft2

, or 

8.6% (95% CI,3

 1.44, 28.54 μg/ft2, -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, which limits the usefulness of the dioxin data to assess 

the efficacy of the dust cleanup program. For this reason, the analysis of the dioxin results is less 

extensive than the analysis 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 were 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/m2

, approximately 20 times higher than the next highest value (5.14 ng/m2). This sample was removed 

from the data set as an outlier. The pre- and post-cleanup data exhibit moderate 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 improve 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 (μg/ft2) 

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 Groupa 

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 data are from a normal distribution by Shapiro-Wilk test 
Final Report December 2005 

Page E-21 

Table E-5. Percent Reduction in Average Residential Dust Lead Loading by 

Floor Group (μg/ft2) 

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 skewness for each floor level is due to 

a few increases in average dust lead loading 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 μg/ft2

, 

respectively). 

Floor Groupa 

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 data are from a normal distribution by Shapiro-Wilk test 
Final 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 transformation 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/m2

 (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/m2) 

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 observed 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-Cleanupa 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%) 

Exceedancesb 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 

CVc 0.49 0.44 0.42 

S-W Statisticd 0.03 0.57 0.62 

Prob Normale <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/m2 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 from a normal distribution deviation/mean 
Final Report December 2005 

Page E-23 

Wilcoxon signed rank test will tend to be more powerful 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 than 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 which 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 residential 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/m2

 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/m2

, 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/m2) 

The reductions in average residential dust dioxin loadings are more modest than the 

reductions achieved for average dust lead loading, primarily due to the low average precleanup dust dioxin loadings. Analysis of the 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/m2

. 

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

CVb 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 Statisticc 0.85 0.83 0.78 

Prob Normald <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 data are from a normal distribution by Shapiro-Wilk test 
Final Report December 2005 

Page E-25 

Figure E-6. Boxplots for residential dust dioxin loading (μg/ft2), by sample type. The distributions of the preand post-cleanup average dust dioxin loadings also exhibit extreme positive skewness. No pre- or post-cleanup 

average dust dioxin loadings exceeded the health-based benchmark of 2 ng/m2. One observation, with a value of 

103 ng/mr

, was removed from the data as an outlier. 

Figure E-7. Boxplot for the reduction in residential average dioxin wipe reductions. The short length of 

the ‘box’ indicates the most of the reductions are close 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/m2

). One observation, with a value of 103 ng/mr, 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 exceedance is defined as a sample result that exceeded the 

heath-based benchmark of 0.0009 fibers/cc. Detection of spatial patterns in the exceedances could be 

used to identify possible sources of the exceedances, or lead to explanations 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 that 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 measure 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 where the individual exceedances occurred). However, 

aggregating the data tends to reduce variability, which 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 determine 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 geographic 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). Statistical 
Final Report December 2005 

Page E-27 

summary areas were based on census-block groups that 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 are 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 identifying 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 statistical regression analysis that considers the spatial 

autocorrelation exhibited by the data, if any. In the present context, (positive) spatial autocorrelation is 

the tendency for SSAs with similar rates of PCMe exceedances 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 portion of this assumption. Therefore, using classical 

regression methods with data that exhibits spatial autocorrelation 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 statistical analysis are developed mathematically based, in part, on the assumption that the data are 

independent and identically distributed. The assumption of independence requires that the probability 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 distributed 

assumption requires that all of the observations are members of the same population (i.e., the same distribution function). 
Final Report December 2005 

Page E-28 

The spatial analysis of the PCMe data focuses on the spatial distribution of PCMe exceedances, rather 

than on the spatial distribution of the PCMe concentration. When analyzed in this way, the PCMe data 

are converted into one of two values: one (concentrations > 0.0009 f/cc) or zero (concentrations < 0.0009 

f/cc). In this format, the data can be modeled with a binomial distribution (DeGroot, 1989): 

 ( ) ( ) ( ) x x n x

n f x n p p p − | , = 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 probability of observing an exceedance from any 

given test (p). The variable x therefore is limited to positive integers 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 time9

. 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:

 

E[ Equation E-2 X ] = np

and its variance of x is: 

 ) Var[X ] = np(1− p Equation E-3

Notice that the number of exceedances will tend to increase with sample size. The variance also increases 

with sample size. The relationship between variance 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 equation 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 shows the percent of the 

10,000 samples that have the value indicated on the x-axis. A Poisson distribution is commonly 

used to model the occurrence of rare events within a fixed period of time or space. The Poisson 

distribution with mean = 2 is positively 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 there 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 relationship was satisfied for each of the SSAs: 

 nSSA × exceedance rateSSA > 5 

where, nSSA is the number of samples in the SSA, and exceedance rateSSA is the number of exceedances 

located in the SSA divided by nSSA. 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 samples from a Poisson distribution with mean = 

2. The values of the randomly drawn samples are 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 f x

x λ λ−λ

= Equation E-4

The variable x is limited to positive integers between 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 exceedance 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 differences in the rate of PCMe exceedances between 

the SSAs. The local pattern of the exceedances was 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 Program 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-based benchmark of 0.0009 fibers/cc for each building 

(i.e., the number of exceedances). The term exceedance event is used to refer to buildings that contain at 

least one PCMe exceedance. Consistent with the approach 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 test buildings), and the 219 buildings that contain at least one residence 

that was tested only (test only buildings), respectively. Note that the two groups of buildings are not 

mutually exclusive: approximately 39% of the clean and 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 the 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 buildings. 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 the 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 their residences and test their indoor air for asbestos. Centrographic statistics are the two 

dimensional counterparts of common one-dimensional summary statistics; they describe global characteristics of the data. Polygons 

represent statistical summary areas (SSAs); hatching indicates SSAs where PCMe data was not collected. Two-dimensional locational 

statistics are indicated by stars (mean center, or average of X and Y coordinates) and diamonds (median 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 relative 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’ slightly towards the north. Comparison of the standard deviational ellipses, 

which illustrate the dispersion of events around their mean centers, indicates that the pattern of exceedances is more evenly distributed 

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 building locations are more dispersed in the north-south direction than they are 

in the east-west direction. The median number of samples collected from clean and test buildings that had at least 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. The four 

shades of green correspond to quartiles of the number of samples; the darkest green is assigned to SSAs with the largest number of 

samples (i.e., 4th quartile). There is a strong relationship between the sample size and the location 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 air tested for asbestos but declined to have their residences cleaned. The figure indicates 

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 eastwest 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 summary areas (SSAs) indicate the number of samples collected from 

each SSA. The four shades of green correspond to quartiles of the number of samples; the darkest green is assigned to SSAs with 

the largest number of samples (i.e., 4th 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 centers, 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 the X coordinates and the mean of the Y coordinates, 

respectively, of the building centroids. The coordinates 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 sampled 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 oriented line; the lone exceedance event located near the 

northern boundary of the site has a very large influence 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 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). 
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 samples, and 47 had fewer than 10. 

In contrast, the 8 test only buildings with one or more exceedance had an average 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 four shades of green correspond to quartiles of the number of samples; the darkest green is 

assigned to SSAs with the largest number of samples (i.e., 4th 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 exceedance 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 exceedances 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 the areas north of the WTC site. Again, the apparent 

pattern may be attributable, at least in part, to differences 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 variability in the number of samples that were collected 

between buildings. 

The possible differences in intensity of exceedance events across the site were further addressed using 

methods from spatial autoregression (Section E.3.2.3) and using additional methods from point pattern 
Final Report December 2005 

Page E-38 

analysis (Section E.2.2.3). The effect of sample size (i.e., number 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 rate 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 exceedances 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 rates (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 summary 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 autocorrelation). 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 residences 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 collected 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 exceedances fall in the upper two quartiles, 

indicating the rareness of the exceedance events. Statistical summary areas with exceedance rates in the upper 

quartile of the distribution of PCMe exceedances are located 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 reasonable statistical models for the PCMe exceedances 

(Section E.2.1.3). Binomial and Poisson distributions were fit to log-transformed rates10 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 between 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; the 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 measurements from the database. The binomial and 

Poisson models were refit to the data to assess the effect of these five buildings on the estimates of the 

model parameters, and their effect on the goodness-of-fit of the models to the data. The parameter 

estimates differed slightly, and both models continued 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 mathematically equivalent to fitting the Poisson model to the log-transformed 

rates; see Appendix H for further explanation. 

11 Violation of the constant variance assumption 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 error 

rate, α) will tend to be larger than the intended error rate (Griffith and Layne, 1999). 
Final Report December 2005 

Page E-42 

in the data. Based on these results, the spatial autocorrelation-filtered Poisson models with parameters -

5.94 and -6.06 (log-transformed rates of exceedances) 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 plausible 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 rendered 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 rarity of their occurrence and provides a better fit 

to the data, accounting for approximately twice the variance that is explained by the binomial 

model. 

Prior to Considering 

Spatial 

Autocorrelationa

With Spatial Filter Added to Modelsa 

Model Parameter 

Estimate 

EquidispersionParameter 

Estimate

Equidispersion % Variance 

Accounted 

for

Binomial -5.3183 NA -6.0572 NA 30%

Poisson -5.3232 NA -6.0625 NA 60%

Negative binomialb -5.0964 4.6066 -6.1506 0.4476 

a

Spatial autocorrelation is accounted for in the statistical models using an eigenfunction spatial filter (Griffith, 2002); 

see Appendix E for details. b

A negative binomial distribution was fit to the PCMe exceedances to assess the assumption of equidispersion (equal 

mean and variance), which is a feature 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 plausible 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. The apparent violation 

of the equidispersion assumption (i.e., equal mean 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 the same percent of the variance in the data. 

 Prior to Considering 

Spatial Autocorrelationa With Spatial Filter Added to Modelsa 

Model Parameter 

Estimate 

EquidispersionParameter 

Estimate

Equidispersion % Variance 

Accounted for

Binomial -5.4713 NA -5.9347 NA 40%

Poisson for rates -5.4756 NA -5.9383 NA 40%

Negative 

binomialb

 for rates 

-5.2098 2.8692 -c 

a

Spatial autocorrelation is accounted for in the statistical models using an eigenfunction spatial filter (Griffith, 2002); 

see Appendix E for details. b

A negative binomial distribution was fit to the PCMe exceedances to assess the assumption of equidispersion (equal 

mean and variance), which is a feature 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 based on the spatial autocorrelationfiltered Poisson models described above. These comparisons essentially consist of calculating the 

difference between the rates for two SSAs, and determining if the absolute value of the difference is 

statistically different from zero. In general, the differences in the exceedance rates will approach a 

normal distribution as the means for the rates increases. 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 confirmed 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 simulation analysis. The simulation experiments are 

described in Appendix H. 

Pairwise comparisons that were significant 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 are 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 indicate there are three SSAs with 

exceedance rates that are statistically 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 majority of the other SSAs are located east of the 

WTC. The number of exceedances 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 Poisson model provides a reasonable model for the PCMe 

exceedance rates after the model is modified to account 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 presence 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 distance. 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 grouped 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 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 

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 exhibited above is similar to the pattern of exceedance rates 

that is shown in Figure 3-13 however, 4 of the 7 SSAs with exceedance rates in the 4th 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 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 included 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 4th quartile (Figure 3-14) were found to be significantly different 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 distance between the exceedance events. The observed 

nearest neighbor distance NNd is the average distance between each exceedance events and its closest 

neighbor (i.e., another exceedance event). It is calculated by determining the distance between each event 

and its nearest neighbor, then taking the average of the distances. The observed NNd is compared to the 

average distance between nearest neighbors that would 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 E(NNd) = ; λ

ˆ = N / A 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 spatial 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 neighbor distance yields the nearest neighbor index 

(NNI): 

 NNI = ( ) NNd E[NNd ] Equation E-6 

Nearest neighbor indexes equal to one indicate complete spatial randomness (CSR; i.e., homogeneous 

Poisson process); NNIs less than one indicate spatial 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 lower (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) may 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): 

 

NNd SE

NNd E NNd Z − [ ] = Equation E-7
Final Report December 2005 

Page E-49 

Where NNd SE =standard error of the estimate of the mean nearest neighbor distance: 

 

 ( )

4 N

4 SE NNd π− π = Equation E-8

A shortcoming of the above test is that it assumes the data are a random sample from the population 

(Bailey and Gatrell, 1995; Dixon, 2001), which has already 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 case 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, isotropic 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 numerical 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 exceedance 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 pattern 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 removes 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 randomly 
Final 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 added 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 between 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 average distance between each PCMe 

exceedance and its second nearest neighbor( ) NND k=2 , and the expected NND for k=2 ( ) [ ] E NNd k=2 : 

 ( ) [ ] K N A

K K E NNd

K k 2 2

(2 !)

(2 )! = = Equation E-9

Evaluating the average nearest neighbor distance at orders 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 compared 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 and the NNI for the sampled buildings. Relative NNIs 

less than (greater than) one indicate clustering (dispersion) 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 reject the null hypothesis of first 

order spatial randomness (p=0.33). The p-value indicates that 33% of the simulated patterns of clean and 
Final 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 ‘orders’ 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 relative 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 method for assessing whether the exceedance events are 

clustered. While the NNd looks at the distance between 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 that were counted over all the events: 

 ( ) ∑ ∑ ( ) = =

=

N

j

ij

N

i

I d

N

A K r

1 1

2 Equation E-10
Final 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 (greater than) 1 indicate spatial clustering 

(dispersion) of PCMe exceedances. The NNIs 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 gathered using random sampling methods, or the 

that the entire population was measured; neither assumption is valid given the data 

were obtained by voluntary participation in the WTC dust cleanup program. 

Test Only Buildings Test Only Exceedances 

Order NNda E[NNd]b NNIc NNda E[NNd]b NNIc Rel-NNId 

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 NNda E[NNd]b NNIc NNda E[NNd]b NNIc Rel-NNId 

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, under assumption of complete spatial randomness (CSR) c

NNI: nearest neighbor index 

d

Rel-NNI: relative nearest neighbor index=NNI for exceedances/NNI for all buildings 
Final Report December 2005 

Page E-53 

Figure E-16. Nearest neighbor analysis for the PCMe asbestos data. The nearest neighbor index (NNI) is the ratio of the 

observed nearest neighbor distance (NNd; average distance between 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 indicate clustering, NNIs >1 indicate dispersion (e.g., spatial 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 locations 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 buildings in an urban landscape. The NNIs for the clean 

and test exceedances are very similar to the NNIs for the clean and test buildings;, up to order =5, indicating a lack of spatial 

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 events 

appear to be spaced further apart on average 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 the 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

Order

Nearest Neighbor Index

Test only bldgs

Test only exceedances

Clean & test bldgs

Clean & test exceedance

Unique test only bldgs
Final 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 events 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 separation distance r is the sum of all counts over all 

events (Equation 6, Section 3.2.2.2.3). Ripley’s K function is typically 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 distance 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, dij=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 performed by plotting a conversion of K(r), L(r), versus r: 

 ( ) ( ) r

K r L r = − 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 E K r = 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 conversion 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 for 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 determined. 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) from 
Final 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 distance. The significance of the estimated K-function at 

each distance r was made by comparing it to the simulation 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 sampled buildings. The exceedances also appear to be 

dispersed relative to the location of the sampled buildings, after the Ripley K function is adjusted to 

consider the number of samples that were collected from each building. At separation distances greater 

than approximately 400 feet, the curve for the exceedances falls at or below the curve that corresponds to 

the 5th percentile of the simulated Ripley K values, indicating that the pattern of test only exceedances 

may be more dispersed than expected based on chance 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 the exceedances are slightly 

dispersed relative to the location of the sampled buildings. 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 for the sampled buildings after it is adjusted to 

consider the number of samples that were collected from each building. However, the curve for 

exceedance events falls between the 5th and 95th percentile of the simulated Ripley K values, indicating 

that the pattern of exceedances does not differ significantly 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 exceedances was performed using contingency tables and by 

fitting Poisson regression models to the data. Floor levels 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 3 
Final Report December 2005 

Page E-57 

and lower), middle floors (floors between 4 and 9, inclusive), and upper floors (floors 10 and higher). 

The analysis was performed in two ways. The first approach was performed at the sample level (samplebasis); each sample result was used in the analyses (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 exceedance 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 building with at least one 

PCMe results > 0.0009 f/cc) to an expected number of neighbors based upon the null hypothesis that the 

events are randomly distributed across the geographic landscape according to a homogenous 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 repeated, each time increasing 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 the plot more linear (i.e., easier to 

interpret). Values of L(d) greater than 0 indicate clustering; values less than 0 indicate dispersion. Ripley’s 

K for the test only PCMe events is consistent with 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 5th and 95th 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 exceeds
Final 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 marginally significant (p=0.08, 2-sided). A higher 

exceedance rate was observed for the middle floor group (0.73%) then either the lower (0.11%) and upper 

(0.37%) floor groups. Additional tests were performed between the floor groups to determine which 

exceedance rates were significantly different, 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 with 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 5th and 95th percentile of the simulated 

Ripley K function at each distance interval (r), fails to support a conclusion that 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 exceeds
Final 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 the 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 873a 

99.89b 

1c 

0.11d 874

middle 1768 

99.27

13 

0.73 1781

upper 

1617 

99.63

6 

0.37 1623

Totals 4258 

99.53

20 

0.47e 4278e 

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 Clean and Test PCMe Exceedances, on a Sample-Basis

The observed exceedance rate increases with floor level. The p-value for Fisher’s exact test is 

0.02, indicating the differences between the floor groups is statistically significant at the 0.05 

level. 

Floor Group Not PCMe 

Exceedances PCMe Exceedances Totals

lower 4233a 

99.34b 

28c 

0.66d 4261

middle 8971 

99.56

40 

0.44 9011

upper 

10488 

99.68

34 

0.32 10522

Totals 23692 

99.57

102 

0.43e 23794e 

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 floor 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 147a 

100b 

0c 

0d 147

middle 303 

99.02

3 

0.98 306

upper 

292 

99.32

2 

0.68 294

Totals 742 

99.33

5 

0.67e 747e 

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 Clean and Test PCMe exceedances, on a Residence-Basis 

Very little difference in the exceedance rate is observed between floor groups. The p-value for 

Fisher’s exact test is 0.84, indicating the differences between the floor groups is statistically 

significant. 

Floor Group Not PCMe 

Exceedances PCMe Exceedances Totals

lower 534a 

99.07b 

5c 

0.93d 539

middle 1306 

99.24

10 

0.76 1316

upper 

1497 

99.27

11 

0.73 1508

Totals 3337 

99.23

26 

0.77e 3363e 

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 fiber 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 wider range of asbestos and other man-made vitreous 

fibers. Listed below is a summary of each fiber measurement 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 indoor air. This counting method employs transmission 

electron microcopy (TEM) for analysis but registers only asbestos fibers greater than 5 microns in length 

with a minimum length-to-width ratio of 5:1. The minimum length of 5 um represents the current 

scientific consensus that attributes cancer-causing potential to long (i.e. >5 microns) asbestos fibers. The 

health-based benchmark for the PCMe measurement is .0009 f/cc, which equates to an excess lifetime 

cancer risk of one-in-ten thousand based on the conservative assumption of 30 years of continuous 

exposure at the benchmark concentration. 

Phase Contrast Microscopy (PCM) was used for airborne asbestos analysis in the preponderance of 

occupational epidemiology studies which provide the robust dose-response data base on asbestos-related 

carcinogenesis. However, advancements 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 quantifying 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 obtained. In effect, PCM “equivalence” is an exercise 

to convert TEM counts to structures that would be visualized 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 conversion 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 were 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 fibers greater than 0.5 microns are counted. This is the asbestos fiber 

counting method used in the Asbestos Hazard Emergency Response Act (AHERA). All fibers greater 

than (>) 0.5 um are counted as per AHERA counting methods for school reentry. This is the method used 

under EPA standards to determine whether an asbestos 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 thinner 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 workers of 0.1 f/cc of air. Because it is not specific 

for asbestos, the PCM analysis also serves as a measure 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 the 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 and 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 where both pairs of measurements were detected. 

This greatly reduced the number of total pairs that could be included in the calculation, particularly the 

correlations between PCMe and total 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 asbestos 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 the 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 coefficient measures the nonlinear relationship between two 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 the 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 (structures/cubic centimeter) (vertical axis) and 

AHERA results (structures/cubic centimeter). The plot indicates 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 (structures/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 (structures/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 (structures/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 (structures/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 the AHERA method. 

Figure F-6. Scatter plot between PCMe results (structures/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 (structures/cubic centimeter) (vertical axis) 

and non-asbestos fibers results (fibers/cubic centimeter). Non-detects for both measurements 

were set = -0.1 to allow plotting. The non-asbestos fiber results varied from approximately 0 

to 0.24 (f/cc) when asbestos fibers 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 x y

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 minimizing 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

x x y y

x x y y x x y y x x y y

Arc

1

1/ 2 2

1

2

1 1

2 2

1

2

1

2

2

4

θ tan

 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

x x y y Sin S

1

2

2

cos 2 θ θ Equation 3

[ ] ( ) ( )

⎭

⎬

⎫

⎩

⎨

⎧

−

− − − = ∑=

N

i

i i

y N

x x y y S

1

2

2

sin cos 2 θ θ Equation 4
Final 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 may 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 for a Poisson random variable) is by replacing a Poisson 

random variable with a negative binomial random variable—which can be viewed as a gamma mixture of 

Poisson random variables. In doing so, the distribution of counts is viewed as either (1) having missing 

variables for the mean specification, or (2) being dependent (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 <

0.5 , 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 exceedances are constrained by the number of samples 

collected suggests that these data may be described by a binomial model. Recoding counts of 

exceedances to a binary (0-1) presence/absence measurement 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 violation of the equidispersion assumption. The following 

evidence supports this claim: 

= ≈ 0.58333

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 hindered by two features of the collected data. One is 

the rareness of exceedance. In order to further explore 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 region, 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. Right: 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 (E1, E8, E10, E22; these were selected using the stepwise options for PROC LOGISTIC in 

SAS, and SWPOIS in STATA) denoting consequential 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 relevant eigenvectors. Top left: E1. Top right: E8. Bottom left: E10. 

 Bottom right: E22. 
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 equidispersion 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 attributable to latent spatial autocorrelation (0.4476 << 

0.8571 0.58333

0.5 ≈ ). 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. Aggregate 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 E1 E8 E10 E22

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 -6.6709
Final Report December 2005 

Page H-6 

Although both the binomial and the Poisson regression models produce very similar descriptive results 

for the lower Manhattan asbestos data, the Poisson model seems to furnish a better model-based 

inferential basis. 

Consider the difference between two Poisson random variables with means μ1 and μ2 . 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 μ1 − μ2 . If these two Poisson random variables are 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, μ1 and μ2 , increase to infinity, the distribution of the difference of these two 

independent Poisson variables rapidly converges to normality. Convergence on a normal probability 

distribution is quick, with a very good approximation attained once μ1>4 and μ2 >4. But for small 

values of μ1 and/or μ2 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 overall 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 which 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 tested will turn up significant, merely due to chance 

fluctuation. In other words, there is a very good chance 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 # of tests

α . For the WTC study, and a two-tailed test, this 

becomes 231

0.005 for an overall α=0.01, 231

0.025 for an overall α=0.05, and 2310.05 for an overall α=0.10. 

As correlation between the samples increases, the denominator of this adjustment effectively decreases 
Final Report December 2005 

Page H-7 

toward 1. Uncorrelated variables require a full Bonferroni adjustment, perfectly correlated variables 

require no adjustment, and partially correlated variables required an adjustment between these two 

extremes. 

Differences of Exceedance Rates

The estimated spatially filtered Poisson model produces sample mean estimates for uncorrelated Poisson 

variables. These models include LN (# of cases) as an offset variable. Therefore, dividing both sides of 

the estimated equation for i μˆ (i.e., the mean rate for areal unit i) by the corresponding number of samples 

yields the set of estimated rates, assuming an underlying Poisson process, of 

i

i

n

μˆ , i=1, 2, ..., 22. The 

accompanying set of null hypotheses becomes 

Ho: 

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 difference of means replications (total=231×50,000) was 

conducted using the spatially filtered Poisson model estimation results. The simulated Poisson random 

variable, Y, then was used in a bivariate 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 , R2

 = 1.00, and 

2

j

j

2

i

i

n

ˆ

n

ˆ μ

+ μ = -0.00000 + 1.00031

n j

j y

ni

yi

s − + e , R2 = 1.00. 

In both cases, the intercept is not significantly different 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 difference of rates assessments are based upon a Hopetype nonparametric simulation analysis, involving 99,999 replications coupled with each observed 

difference. The simulated distribution is based on a pair of Poisson random variables, each with the same 

mean of 

1 2

1 2 2 1

2n n

n μ + n 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. Basically, roughly 37% of the extreme MCGB mean pairs tend 

to be significantly different. These differences arise 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 approximately 1.5 (MCBG 10015012). The fourth group is 

dominated by the relatively small mean of roughly 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-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 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 

exhibited above is similar to the pattern of exceedance rates that is shown in Figure 3-13 however, 4 of the 7 SSAs with 

exceedance rates in the 4th quartile (Figure 3-13) were found to be significantly different 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 the threshold level is 102. 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 6 SSAs. 

Initial Data Analysis

The rareness of exceedances suggests that these data may 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 for a Poisson random variable) is by replacing a Poisson 

random variable with a negative binomial random variable—which can be viewed as a gamma mixture of 

Poisson random variables. In doing so, the distribution of counts is viewed as either (1) having missing 

variables for the mean specification, or (2) being dependent (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 <

0.5 , 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 exceedances are constrained by the number of samples 

collected suggests that these data may be described by a binomial model. Recoding counts of 

exceedances to a binary (0-1) presence/absence measurement 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 violation of the equidispersion assumption. The following 

evidence supports this claim: 

= ≈ 2.68421

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 hindered by two features of the collected data. One is 

the rareness of exceedance. In order to further explore spatial dependency in this context, average 

measures of asbestos also were analyzed. Both the rates and the average measures were transformed, 

using a logarithmic (i.e., Box-Cox 0 power) transformation 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. Because 
Final Report December 2005 

Page H-14 

these areal units are dispersed across the study region, 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 corrupted 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. Right: 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 (E2, E3, E8, E17, E22; these were selected using the stepwise options for PROC LOGISTIC 

in SAS, and SWPOIS in STATA) denoting consequential positive spatial autocorrelation help describe 

the geographic distribution of exceedance rates for 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 Poisson model is 1.36, suggesting a lack of serious 

overdispersion. One eigenvector (E10) 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 equidispersion 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

E2 -4.2422 -4.2812 NA

E3 4.4056 4.4575 NA

E8 -5.4424 -5.5115 NA

E10 NA NA -4.4191

E17 -2.2640 -2.2961 NA

E22 4.2200 

Failed to be estimable 

4.2615 NA
Final Report December 2005 

Page H-16 

Figure H-6. Geographic distribution of relevant eigenvectors. Top left: E2. Top right: E3. Bottom left: E17. 

Pairwise Comparisons Between SSAs

Pairwise comparisons of SSA asbestos exceedance sampling results were made to assess whether or not 

statistically significant differences exist. Aggregate 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 produces sample mean estimates for uncorrelated Poisson 

variables. These models include LN(# of cases) as an offset variable. Therefore, dividing both sides of 

the estimated equation for i μˆ (i.e., the mean rate for areal unit i) by the corresponding number of samples 

yields the set of estimated rates, assuming an underlying Poisson process, of 

i

i

n

μˆ , i=1, 2, ..., 31. The 

accompanying set of null hypotheses becomes 

 Ho: 

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 difference of means replications (total=496×50,000) was 

conducted using the spatially filtered Poisson model estimation results. The simulated Poisson random 

variable, Y, then was used in a bivariate 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 , R2

 = 1.00, and 

2

j

j

2

i

i

n

ˆ

n

ˆ μ

+ μ = 0.00001 + 0.99873

n j

j y

ni

yi

s − + e , R2 = 1.00. 

In the second case, the intercept is significantly different from 0, and in both cases the slope is 

significantly different from 1. These may well be size effect results, since substantively 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 non-normal. Each simulated dataset was subjected to 
Final 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 difference of rates assessments are based upon a Hopetype nonparametric simulation analysis, involving 99,999 replications coupled with each observed 

difference. The simulated distribution is based on a pair of Poisson random variables, each with the same 

mean of 

1 2

1 2 2 1

2n n

n μ + n 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 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. Basically, 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 small 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 between 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 Ratesa 

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 included 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 4th 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 intensively 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 (SSAs) for lower Manhattan. One of these SSAs is the 

site that housed the WTC; the modified database contains no data for this plus an additional 7 SSAs. 

Initial Data Analysis

Simple estimation results for each of the four models (i.e., Poisson, negative 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 (E3, E8, and E17 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 (E2). One 

model difference now is that the binomial model links to eigenvector E22, whereas the Poisson model 

links to eigenvector E27. The negative binomial model yielded a dispersion parameter estimate of 0 here, 

making it indistinguishable from a Poisson model. As before, the same single eigenvector (E10) 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 roughly 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

E1 2.2292 2.2311 NA

E3 4.1741 4.2493 NA

E8 -3.4133 -3.4547 NA

E10 NA NA -5.3629

E17 -2.8619 -2.8848 NA

E22 NA NA NA

E27 -3.3557 

Failed to be estimable 

-3.4310 NA
 Final 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, including lead and other metals, and dioxin. Wide 

geographic coverage was sought by attempting to identify 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 information on the age of the buildings we sampled and matched 

this information to the sample results so that the possible relationship between building age and sample 

results could be assessed. A summary of the distribution 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 = probable; C = possible; D = no dust 

 N= number of samples, approximately 4 samples per apartment 
 Final Report December 

2005 

 Page I-3 

The public expressed continued concern that Mercury, 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 benchmarks established for the program 

Table I-2. Overview of Key Results for these Contaminants in Relation to the Health Based 

Cleaning Benchmarks Established 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 measurement 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 function 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 the 95% upper confidence limit on the building mean to 

be greater than the 25 ug/ft2 benchmark. An analysis was performed to determine 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 and 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 building age was then used to evaluate whether there was any association 

between the lead results and the building age. The New York City records 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

1200

Lead 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

1200

Lead Measurment (ug/ft2

)

Zone B: Lead vs Year Constructed
 Final 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

1200

Lead Measurements (

μg/ft2 )

Zone A Lead Measurements ≥ 25μg/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

1200

Lead Measurement (

μg/ft2)

Zone B Lead Measurements ≥ 25 μg/ft2 vs Year Constructed
 Final 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 Sample Results by Floor and EPIC Zone 

 Zone 

Floor Overall Mean (n) A A1 B C D 

Basement 113 (4) --- --- 113 --- --- 

1st 14 (29) 5 5 26 --- -- 

2nd 117 (124) 168 56 9 --- 9 

3rd 29 (127) 38 38 42 24 6 

4th 21 (114) 10 10 36 23 13 

5th 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 without two outliers 

Table I-6. The Means (ug/ft2) of Lead Sample Results by Floor and Building Age 

 Zone 

Floor Overall Mean (n) <1920 1921-50 >1950 No Year 

Basement 113 (4) 113 --- --- --- 

1st 14 (29) 19 --- 2 5 

2nd 117 (124) 204 (41)* 34 9 8 

3rd 29 (127) 53 15 5 16 

4th 21 (114) 35 24 7 10 

5th 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 outliers 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 results do not appear meaningfully different among the 

four zones, and among the five distance categories. There is a suggestion that higher concentrations are 

found on lower building floors, across zones although this may be confounded with the presence of older 

buildings across zones. The clearest relationship is between 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 concentrations 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 "confirmed" 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 (zone, floor and age) may be related to the observation 

of the highest measurements of lead in this sampling 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 renovation 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. (However, 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 = probable; 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 concentrations 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 percentage of buildings may meet the partial criteria 

for building cleanup of 95% UCL of mean being greater than health screening benchmark of 25 ug/ft2.