DRAFT—DO NOT CITE OR QUOTE EPA/635/R-11/002

www.epa.gov/iris

TOXICOLOGICAL REVIEW

OF

LIBBY AMPHIBOLE ASBESTOS

In Support of Summary Information on the

Integrated Risk Information System (IRIS)

May 2011

(Note: This document is an assessment of the noncancer and cancer health effects 

associated with the inhalation route of exposure only)

NOTICE

This document is an Interagency Science Consultation draft. This information is distributed 

solely for the purpose of pre-dissemination peer review under applicable information quality 

guidelines. It has not been formally disseminated by EPA. It does not represent and should not 

be construed to represent any Agency determination or policy. It is being circulated for review 

of its technical accuracy and science policy implications.

U.S. Environmental Protection Agency

Washington, DC
This document is a draft for review purposes only and does not constitute Agency policy.

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DISCLAIMER

This document is a preliminary draft for review purposes only. This document is 

distributed solely for the purpose of predissemination peer review under applicable information 

quality guidelines. It has not been formally disseminated by EPA. It does not represent and 

should not be construed to represent any Agency determination or policy. Mention of trade 

names or commercial products does not constitute endorsement or recommendation for use. 
This document is a draft for review purposes only and does not constitute Agency policy.

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CONTENTS—TOXICOLOGICAL REVIEW OF LIBBY AMPHIBOLE ASBESTOS 

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

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

LIST OF ABBREVIATIONS AND ACRONYMS ...................................................................xii

FOREWORD............................................................................................................................xv

AUTHORS, CONTRIBUTORS, AND REVIEWERS..............................................................xvi

1. INTRODUCTION............................................................................................................... 1-1

1.1. RELATED ASSESSMENTS...................................................................................... 1-3

1.1.1. IRIS Assessment for Asbestos.......................................................................... 1-3

1.1.2. EPA Health Assessment for Vermiculite .......................................................... 1-4

1.2. LIBBY AMPHIBOLE ASBESTOS-SPECIFIC CANCER ASSESSMENT................ 1-4

2. LIBBY AMPHIBOLE ASBESTOS: GEOLOGY, USE, AND EXPOSURE 

POTENTIAL...................................................................................................................... 2-1

2.1. HISTORICAL BACKGROUND .................................................................................. 2-1

2.2. GEOLOGY, MINERALOGY, AND FIBER MORPHOLOGY OF LIBBY 

AMPHIBOLE ASBESTOS........................................................................................... 2-3

2.2.1. Silicate Minerals.................................................................................................. 2-4

2.2.1.1. Mineralogy and Structure of Amphiboles ............................................... 2-6

2.2.1.2. Fiber Morphology .................................................................................. 2-9

2.2.2. Vermiculite ........................................................................................................2-10

2.2.3. The Mineralogy of Libby Amphibole Asbestos ..................................................2-11

2.2.3.1. Mineralogy............................................................................................2-11

2.2.3.2. Morphology of the Libby Amphibole Asbestos Fibers...........................2-13

2.2.3.3. Dimensional Characteristics of Libby Amphibole Asbestos...................2-13

2.3. ANALYTICAL TECHNIQUES FOR FIBER ANALYSIS ..........................................2-13

2.4. EXPOSURE POTENTIAL ..........................................................................................2-20

2.4.1. Libby Community ...............................................................................................2-21

2.4.2. Communities Near Vermiculite Expansion and Processing Plants .......................2-24

2.4.3. Exposures from Zonolite and Vermiculate for Homeowners, Contractors, and 

Other Populations................................................................................................2-24

3. FIBER TOXICOKINETICS................................................................................................ 3-1

3.1. DEPOSITION OF FIBERS IN THE RESPIRATORY TRACT .................................... 3-3

3.2. CLEARANCE .............................................................................................................. 3-7

3.2.1. Inhalation ............................................................................................................ 3-7

3.2.1.1. Respiratory Tract.................................................................................... 3-7

3.2.1.2. Pleural Cavity and Extrapulmonary Sites...............................................3-11

3.2.2. Ingestion ............................................................................................................3-12

3.2.3. Dermal ...............................................................................................................3-12

3.3. SUMMARY ................................................................................................................3-12
This document is a draft for review purposes only and does not constitute Agency policy.

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CONTENTS (continued)

4. HAZARD IDENTIFICATION OF LIBBY AMPHIBOLE ASBESTOS ............................. 4-1

4.1. STUDIES IN HUMANS—EPIDEMIOLOGY............................................................. 4-1

4.1.1. Studies of Libby, MT Vermiculite Mining Operation Workers........................... 4-3

4.1.1.1. Description of Mining and Milling Operations...................................... 4-3

4.1.1.2. Exposure Estimation............................................................................. 4-4

4.1.1.3. Cancer Mortality Risk .........................................................................4-13

4.1.2.Libby, MT Community Studies .........................................................................4-23

4.1.2.1. Geographic-Based Mortality Analysis .................................................4-24

4.1.2.2. Community Health Screening ..............................................................4-24

4.1.2.3. Other Reports of Asbestos-related Disease among Libby, MT 

Residents.............................................................................................4-29

4.1.3. Marysville, OH Vermiculite Processing Plant Worker Studies...........................4-31

4.1.3.1. Summary of Marysville, OH Vermiculite Processing Plant Worker 

Studies.................................................................................................4-34

4.1.4.Community Studies from Other Vermiculite Processing Plants..........................4-34

4.1.4.1. Summary of Community Studies from Other Vermiculite 

Processing Plants.................................................................................4-34

4.2. SUBCHRONIC AND CHRONIC STUDIES AND CANCER BIOASSAYS IN 

ANIMALS – ORAL, INHALATION AND OTHER ROUTES OF EXPOSURE4-41

4.2.1. ORAL.................................................................................

4.2.2.INHALATION.......................................................................

4.2.3.INTRATRACHEAL INSTILLATION..........................................

 4.2.4 INJECTION/IMPLANTATION.................................................

 4.3 OTHER DURATION OR ENDPOINT SPECIFIC STUDIES

4.3.1 Immunological

 4.4 MECHANISTIC DATA AND OTHER STUDIES IN THE SUPPORT OF THE MODE 

OF ACTION

4.4.1 Inflammation and Immune Response

4.4.2 Genotoxicity

4.4.3 Cytotoxicity and Cellular Proliferation

 4.5 SYNTHESIS OF MAJOR NONCANCER EFFECTS

4.5.1 Mode-of-Actoin Information

4.5.1.1 Chronic Inflammation

4.5.1.2 Cytotoxicity and Cellular Proliferation

4.6.EVALUATION OF CARCINOGENICITY.................................................................4-77

4.6.1. Summary of Overall Weight of Evidence ..........................................................4-77

4.3.1.1. Hazard Characterization for the Libby Amphibole Asbestos Fibers .....4-77

4.6.2. Mode–of-Action Information.............................................................................4-79

4.3.2.1. Description of the Mode-of-Action Information...................................4-79

4.3.2.2. Application of the Age-Dependent Adjustment Factors (ADAFs)........4-83

4.7. SUSCEPTIBLE POPULATIONS...............................................................................4-84

4.7.1.Influence of Different Lifestages on Susceptibility ............................................4-84

4.4.1.1. Early Lifestage Susceptibility ..............................................................4-85

4.7.2.Influence of Gender on Susceptibility................................................................4-88
This document is a draft for review purposes only and does not constitute Agency policy.

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CONTENTS (continued)

4.7.3.Influence of Race or Ethnicity on Susceptibility ................................................4-89

4.7.4.Influence of Genetic Polymorphisms on Susceptibility ......................................4-89

4.7.5.Influence of Health Status on Susceptibility.......................................................4-91

4.7.6.Influence of Lifestyle Factors on Susceptibility .................................................4-92

4.7.7. Susceptible Populations Summary.....................................................................4-92

5. EXPOSURE-RESPONSE ASSESSMENT.......................................................................... 5-1

5.1. ORAL REFERENCE DOSE (RfD)............................................................................... 5-1

5.2. INHALATION REFERENCE CONCENTRATION (RfC)........................................... 5-1

5.3. UNCERTAINTIES IN THE ORAL REFERENCE DOSE (RfD) AND 

INHALATION REFERENCE CONCENTRATION (RfC)........................................... 5-1

5.4. CANCER EXPOSURE-RESPONSE ASSESSMENT................................................... 5-1

5.4.1. Overview of Methodological Approach.............................................................. 5-1

5.4.2. Choice of Study/Data—with Rationale and Justification .................................... 5-3

5.4.2.1. Description of the Libby Worker Cohort ............................................... 5-4

5.4.2.2. Description of Cancer Endpoints........................................................... 5-6

5.4.2.3. Description of Libby Amphibole Asbestos Exposures........................... 5-7

5.4.2.4. Description of Libby Worker Cohort Work Histories ...........................5-14

5.4.2.5. Estimated Exposures Based on Job Exposure Matrix and Work 

Histories..............................................................................................5-14

5.4.3. Exposure-Response Modeling...........................................................................5-21

5.4.3.1. Modeling of Mesothelioma Exposure-Response in the Libby 

Worker Cohort ....................................................................................5-21

5.4.3.2. Modeling of Lung Cancer Exposure-Response in the Libby Worker 

Cohort .................................................................................................5-25

5.4.3.3. Summary of Analysis of Libby Worker Cohort ....................................5-28

5.4.3.4. Analysis of Sub-cohort of Employees Hired After 1959 .......................5-30

5.4.4. Exposure Adjustments and Extrapolation Methods............................................5-43

5.4.5. Inhalation Unit Risk (IUR) of Cancer Mortality ................................................5-43

5.4.5.1. Unit Risk Estimates for Mesothelioma Mortality..................................5-43

5.4.5.2. Unit Risk Estimates for Lung Cancer Mortality....................................5-47

5.4.5.3. IUR Derivation for Combined Mesothelioma and Lung Cancer 

Mortality .............................................................................................5-51

5.4.5.4. Applications of the Combined Mesothelioma and Lung Cancer 

Mortality IUR to Partial Lifetime Environmental Exposure 

Scenarios.............................................................................................5-58

5.4.6. Uncertainties in the Cancer Risk Values............................................................5-59

5.4.6.1. Sources of Uncertainty.........................................................................5-59

5.4.6.2. Summary .............................................................................................5-75

6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND 

EXPOSURE RESPONSE................................................................................................... 6-1

6.1. HUMAN HAZARD POTENTIAL............................................................................... 6-1

6.1.1. Exposure............................................................................................................ 6-1
This document is a draft for review purposes only and does not constitute Agency policy.

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CONTENTS (continued)

6.1.2. Fiber Toxicokinetics .......................................................................................... 6-3

6.1.3. Noncancer Health Effects: Pulmonary and Pleural Effects ................................. 6-4

6.1.4. Carcinogenicity in Humans and Laboratory Animals ......................................... 6-5

6.1.5. Susceptible Populations ..................................................................................... 6-5

6.1.6. Mode-of-Action Information.............................................................................. 6-6

6.1.7. Weight-of-Evidence Descriptor for Cancer Hazard ............................................ 6-7

6.2. EXPOSURE-RESPONSE............................................................................................ 6-7

6.2.1. Background and Methods................................................................................... 6-7

6.2.2. Modeling of Mesothelioma Exposure-Response................................................6-10

6.2.3. Unit Risk Estimates for Mesothelioma Mortality...............................................6-11

6.2.4. Modeling of Lung Cancer Exposure-Response..................................................6-12

6.2.5. Unit Risk Estimates for Lung Cancer Mortality.................................................6-13

6.2.6. IUR Derivation Based on Combined Mesothelioma and Lung Cancer 

Mortality from Exposure to Libby Amphibole Asbestos...................................6-15

6.2.6.1. Comparison with Other Published Studies of Libby Workers 

Cohort .................................................................................................6-15

6.2.7. Uncertainty in the Cancer Risk Values..............................................................6-17

6.3. APPLICATION OF THE LIBBY AMPHIBOLE ASBESTOS IUR ............................6-19

6.3.1. Sites and Materials............................................................................................6-19

6.3.2. Exposure Units for the IUR...............................................................................6-20

6.3.3. Applications to Early Lifetime and Partial Lifetime Environmental 

Exposure Scenarios ..........................................................................................6-21

7. REFERENCES................................................................................................................... 7-1

APPENDIX A: SUMMARY OF EXTERNAL PEER REVIEW AND PUBLIC 

COMMENTS AND DISPOSITION ...............................................................A-1

APPENDIX B : Particle Size Distribution Data for Libby Amphibole Structures 

Observed in Air at the Libby Asbestos Superfund Site

APPENDIX C: Characterization of Amphibole fibers from ore originating from Libby, Montana; 

Louisa County, Virginia; and Palabora, Republic of South Africa 

APPENDIX D: ANALYSIS OF SUBCHRONIC AND CHRONIC STUDIES AND CANCER 

BIOASSAYS IN ANIMALS AND MECHANISTIC STUDIES

APPENDIX E: Evaluation of Exposure-Response Data for Discrete Pleural Thickening in 

Workers from the Marysville Cohort 

APPENDIX F: Marysville Employee Exposure Reconstruction; Decision Points for 

Development of the Exposure Matrix

APPENDIX G: EXTRA RISK AND UNIT RISK CALCULATION
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF TABLES

1-1. Derivation of the current IRIS inhalation unit risk for asbestos from the lifetime 

risk tables in the AAHAU............................................................................................ 1-3

2-1. Properties of vermiculite.............................................................................................2-11

2-2. Air sampling results for disturbance of Libby amphibole asbestos-contaminated 

materials in Libby, MT ...............................................................................................2-23

2-3. Air sampling results for asbestos from Zonolite VAI in three homes...........................2-26

3-1. Factors influencing fiber deposition and clearance in the respiratory system................ 3-4

4-1. Exposure assessment methodologies used in evaluations of Libby, MT and 

Marysville, OH worker cohorts.................................................................................... 4-5

4-2. Source of primary samples for fiber measurements at the Libby mining and 

milling operations........................................................................................................ 4-6

4-3. Dimensional characteristic of fibers from air samples collected in the vermiculite 

mill and screening plant, Libby, MT ............................................................................ 4-9

4-4. Respiratory cancer mortality and lung cancer dose-response analyses based on 

studies of the vermiculite mine workers in Libby, MT ................................................4-11

4-5. Non-malignant respiratory mortality studies of the vermiculite mine workers in 

Libby, MT ..................................................................................................................4-18

4-6. Pulmonary radiographic studies of the Libby, MT vermiculite mine workers..............4-21

4-7. Cancer mortality and non-malignant respiratory disease mortality in the Libby, 

MT community...........................................................................................................4-25

4-8. Pulmonary radiographic, and other health studies in the Libby, MT community .........4-27

4-9. Pulmonary radiographic studies of the OH vermiculite processing plant workers........4-33

4-10. ATSDR health consultations of 28 sites with potential Libby Amphibole asbestos 

contamination by status of health data and location (state) of the site ..........................4-36

4-11. Description of study areas in ATSDR health consultations evaluating cancer 

incidence and mortality...............................................................................................4-37

4-12. Incidence and mortality results for potential asbestos-related cancers in 

communities with ATSDR health consultations evaluating potential Libby 

Amphibole asbestos contamination.............................................................................4-38
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF TABLES (continued)

4-13. Incidence and mortality results for breast cancer and prostate cancer cancers in 

communities with ATSDR health consultations evaluating potential Libby 

Amphibole asbestos contamination.............................................................................4-40

4-14. Pleural adhesions and tumors following intraperitoneal injection exposure in 

LVG:LAK hamsters ...................................................................................................4-42

4-15. Fiber characteristics and numbers of resulting tumors following intrapleural 

injection into Syrian hamsters of 10- or 25-mg fiber samples......................................4-43

4-16. Fiber characteristics for intratracheal instillation studies in mice.................................4-45

4-17. Size distribution of UICC crocidolite and Libby Amphibole asbestos used in 

Pietruska et al. (2010).................................................................................................4-51

4-18. Percent clastogenic micronuclei following exposure to Libby Amphibole asbestos 

or crocidolite ..............................................................................................................4-52

4-19. Gene expression changes following exposure to 26.4 μg/cm

2

amphibole asbestos 

for 24 hr .....................................................................................................................4-54

4-20. In vivo data following exposure to Libby Amphibole asbestos....................................4-56

4-21. In vitro data following exposure to Libby Amphibole asbestos...................................4-57

4-22. Fiber characteristics and distribution of fibers analyzed in feed studies in F344 

rats.............................................................................................................................4-59

4-23. Pulmonary fibrosis and irregular alveolar wall thickening produced by tremolite 

exposure .....................................................................................................................4-60

4-24. Tumors (benign and malignant) produced by tremolite exposure ................................4-60

4-25. Chrysotile and tremolite fiber characteristics of fibers used in inhalation exposure 

studies in rats..............................................................................................................4-61

4-26. Fiber characteristics of three tremolite samples analyzed by in vivo and in vitro 

methods......................................................................................................................4-63

4-27. Fiber characteristics in a 10-mg dose ..........................................................................4-64

4-28. Characteristics of tremolite fibers intraperitoneally injected into Wistar rats...............4-65

4-29. Fiber characteristics of five fibers examined in vitro for cytotoxic and 

proliferative effects.....................................................................................................4-68
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF TABLES (continued)

4-30. Micronuclei induction and chromosomal aberrations following exposure to 

tremolite for 24 hours.................................................................................................4-70

4-31. In vivo data following exposure to tremolite asbestos.................................................4-72

4-32. In vitro data following exposure to tremolite asbestos.................................................4-74

5-1. Demographic and exposure characteristics of the Libby, MT vermiculite worker 

cohort .......................................................................................................................... 5-5

5-2. Exposure intensity for each location operation from the beginning of operations 

through 1982 ............................................................................................................... 5-9

5-3. Demographic and exposure characteristics of the subset of the Libby, MT

vermiculite worker cohort hired after 1959 .................................................................5-13

5-4. Comparison of univariate model fit of various exposure metrics for mesothelioma 

mortality in the full Libby worker cohort ....................................................................5-24

5-5. Comparison of model fit of exposure metrics for mesothelioma mortality in the 

sub-cohort hired after 1959.........................................................................................5-31

5-6. Mesothelioma mortality exposure metrics fits, slopes, and credible intervals ..............5-33

5-7. Model fit comparison for different exposure metrics and lung cancer mortality 

associated with Libby Amphibole asbestos, controlling for age, gender, race, and 

date of birth ................................................................................................................5-34

5-8. Lung cancer mortality exposure metrics fits, slopes, and confidence intervals.............5-39

5-9. Relative fit of mesothelioma exposure metrics for full and abbreviated data sets.........5-40

5-10. Sensitivity analysis of model fit comparison for different exposure metrics and 

lung cancer mortality associated with Libby Amphibole Asbestos controlling for 

age, gender, race, and date of birth..............................................................................5-41

5-11. Mesothelioma mortality exposure metrics unit risks....................................................5-46

5-12. Unit risks for the sub-cohort hired after 1959 corresponding to the different 

metrics adjusted for underascertainment .....................................................................5-47

5-13. Subset of lung cancer models with lagged exposures that yielded statistically 

significant model fit and exposure metric fit to the epidemiologic data .......................5-50
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF TABLES (continued)

5-14. Reasonable upper bound and lowest information criteria estimates of central risks 

and unit risks, per fiber/cc, for mesothelioma mortality, lung cancer mortality, and 

the IUR for the combined mortality risk from mesothelioma and lung cancer .............5-54

5-15. Lung cancer regression results from different analyses of cumulative exposure in 

the cohort of workers in Libby, MT ............................................................................5-55

5-16 Mesothelioma regression results from different analyses of cumulative exposure 

in the cohort of workers in Libby, MT ........................................................................5-58

6-1. Reasonable upper bound and lowest information criteria estimates of central risks 

and unit risks, per fibers/cc, for mesothelioma mortality, lung cancer mortality, 

and the IUR for the combined mortality risk from mesothelioma and lung cancer.......6-15

6-2. Lung cancer regression results from different analyses of cumulative exposure in 

the cohort of workers in Libby, MT ............................................................................6-16

6-3. Mesothelioma regression results from different analyses of cumulative exposure 

in the cohort of workers in Libby, MT ........................................................................6-17
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF FIGURES

2-1. Vermiculite mining operation on Zonolite Mountain, Libby, Montana ......................... 2-2

2-2. Expanded vermiculite used as a soil conditioner or attic insulation .............................. 2-2

2-3. Nationwide Distribution of Libby Ore by County ........................................................ 2-3

2-4. Structure of the silicate minerals, illustrating silicate subclasses by the linking of 

the basic silicon tetrahedron into more complex structures.......................................... 2-5

2-5. Cross-section of amphibole fibers showing the silicon tetrahedrons that make up 

each double-chain plate ............................................................................................... 2-8

2-6. Vermiculite sample...................................................................................................... 2-9

2-7. Solution series linking tremolite, winchite, and richterite amphibole fibers .................2-12

2-8. Mineralogy of Libby Amphibole asbestos fibers from samples taken from the 

Zonolite Mountain site................................................................................................2-14

2-9. Scanning electron microscope image of Libby Amphibole asbestos fibers ..................2-15

2-10. Fiber morphology of Libby amphibole asbestos viewed under a transmission 

electron microscope....................................................................................................2-16

2-11. Particle Size, fibers in Libby ore and Libby air. ..........................................................2-17

3-1. General scheme for fiber deposition, clearance, and translocation of fibers from 

the lung and GI tract .................................................................................................... 3-1

4-1. Location of 28 sites included in the Phase 1 community evaluations conducted by 

ATSDR ......................................................................................................................4-35

5-1. Plot of the NIOSH job exposure matrix for different job categories over time .............. 5-8

5-2. Histogram showing the number of workers who experienced each incremental 

number of different jobs among the 880 workers hired after 1959...............................5-15
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF ABBREVIATIONS AND ACRONYMS

AAHAU Airborne Asbestos Health Assessment Update

AIC Akaike information criterion

APC antigen-presenting cells

ATSDR Agency for Toxic Substances and Disease Registry

BGL β-glucuronidase

BMI body mass index

cc cubic centimeter

CDF cumulative distribution frequency

CE cumulative exposure

CI confidence interval

COPD chronic obstructive pulmonary disease

CYP cytochrome P450

DHE dehydroergosterol

DIC deviance information criterion

DLCO single breath carbon monoxide diffusing capacity

DNA deoxyribonucleic acid

ECx effective concentration

ECSOD extracellular superoxide dismutase

EDS energy dispersive x-ray analysis

EPA U.S. Environmental Protection Agency

EPMA electron probe microanalysis

FVC force vital capacity

GSH glutathione

GST glutathione S-transferase

HKNM human pleural mesothelial cells

HO heme oxygenase

HPRT hypoxanthine-guanine phosphoribosyltransferase

HTE hamster tracheal epithelial

IARC International Agency for the Research of Cancer

ICD International Classification of Diseases

IFN interferon

IL interleukin

IRIS Integrated Risk Information System
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF ABBREVIATIONS AND ACRONYMS (continued)

IUR inhalation unit risk

JEM job exposure matrix

LECx lowest effective concentration

LDH lactate dehydrogenase

MCMC Markov chain Monte Carlo

MESA Mining Enforcement and Safety Administration

MIP-2 macrophage inflammatory protein-2

MnSOD manganese superoxide dismutase

MOA mode of action

mppcf million particles per cubic foot

MPPD multipath particle dosimetry

MSHA Mine Safety and Health Administration

NAT2 N-acetyl-transferase 2

NCHS National Center for Health Statistics

NDI National Death Index

Nf2 neurofibromatous 2

NIEHS National Institute of Environmental Health Sciences

NIOSH National Institute of Occupational Safety and Health

NMRD non-malignant respiratory disease

NTP National Toxicology Program

Ogg1 8-oxoguanine-DNA-glycosylase 1

OSHA Occupational Safety and Health Administration

PARP poly(ADP-ribose)polymerase

PBS phosphate buffered saline

PCM phase contrast microscopy

PCMe phase contrast microscopy equivalent

PM2.5 particulate matter 2.5 μ diameter or less

POD point of departure

RfC reference concentration

RfD reference dose

RNA ribonucleic acid

RONS reactive oxygen and nitrogen species

ROS reactive oxygen species
This document is a draft for review purposes only and does not constitute Agency policy.

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LIST OF ABBREVIATIONS AND ACRONYMS (continued)

RPM rat pleural mesothelial

RR relative risk

RT-PCR reverse transcription polymerase chain reaction

RTW residence time-weighted

SAS Statistical Analysis Software

SEER Surveillance, Epidemiology and End Results

SEM scanning electron microscopy

SH spontaneously hypertensive

SHE Syrian hamster embryo

SHHF spontaneously hypertensive-heart failure

SIR standard incidence ratio

SMR standardized mortality ratio

SOD superoxide dismutase

SPF specific pathogen free

SRR standardized rate ratio

SSA/Ro52 autoantibody marker for apoptosis

STEM scanning transmission electron microscopy

SV40 simian virus 40

TEM transmission electron microscopy

TLC total lung capacity

TWA time-weighted average

VAI vermiculite attic insulation

WHO World Health Organization

WKY Wistar Kyoto rat

XRCC X-ray repair cross complementing

XRD X-ray diffraction
This document is a draft for review purposes only and does not constitute Agency policy.

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FOREWORD

The purpose of this Toxicological Review is to provide scientific support and rationale 

for the hazard and dose-response assessment in IRIS pertaining to chronic inhalation exposure to

Libby Amphibole asbestos, a unique mixture of asbestos fibers originating from the vermiculite 

mine in Libby, Montana. It is not intended to be a comprehensive treatise on the agent or 

toxicological nature of Libby Amphibole asbestos. The purpose of this document is to establish 

a Libby Amphibole asbestos-specific reference concentration to address noncancer health effects 

and to characterize the carcinogenic potential and establish an inhalation unit risk for Libby 

Amphibole asbestos-related lung cancer and mesothelioma mortality.

The intent of Section 6, Major Conclusions in the Characterization of Hazard and 

Exposure Response, is to present the significant conclusions reached in the derivation of the 

reference dose, reference concentration, and cancer assessment where applicable, and to 

characterize the overall confidence in the quantitative and qualitative aspects of hazard and dose 

response by addressing the quality of data and related uncertainties. The discussion is intended 

to convey the limitations of the assessment and to aid and guide the risk assessor in the ensuing 

steps of the risk assessment process.

For other general information about this assessment or other questions relating to IRIS, 

the reader is referred to EPA’s IRIS Hotline at (202) 566-1676 (phone), (202) 566-1749 (fax), or 

hotline.iris@epa.gov (e-mail address).
This document is a draft for review purposes only and does not constitute Agency policy.

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AUTHORS, CONTRIBUTORS, AND REVIEWERS

CHEMICAL MANAGERS/AUTHORS

Thomas F. Bateson, ScD, MPH

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Robert Benson, PhD

Region 8

Office of Partnerships and Regulatory Assistance

U.S. Environmental Protection Agency

Denver, CO

Danielle DeVoney, PhD, DABT, PE

Captain in the U.S. Public Health ServiceNational Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

AUTHORS 

Krista Yorita Christensen, Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Glinda Cooper, PhD

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Rebecca Dzubow, MPH, MEM

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Maureen R. Gwinn, PhD, DABT

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Leonid Kopylev, Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC
This document is a draft for review purposes only and does not constitute Agency policy.

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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

CONTRIBUTING AUTHORS

David Berry, PhD

Region 8

U.S. Environmental Protection Agency

Denver, CO

Malcolm Field, PhD

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Patricia Sullivan, ScD

Division of Respiratory Disease Studies

National Institute for Occupational Safety and Health

Morgantown, WV

CONTRIBUTORS

David Bussard

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Samantha J. Jones, PhD

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Babasaheb Sonawane, PhD

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Paul White

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

CONTRACTOR SUPPORT 

William Brattin, PhD

Syracuse Research Corporation

Denver, CO
This document is a draft for review purposes only and does not constitute Agency policy.

xviii DRAFT—DO NOT CITE OR QUOTE

REVIEWERS

This document has been provided for review to EPA scientists. 
This document is a draft for review purposes only and does not constitute Agency policy.

1-1 DRAFT—DO NOT CITE OR QUOTE

1 1. INTRODUCTION

2 This document presents background information and justification for the Integrated Risk 

3 Information System (IRIS) Summary of the hazard and dose-response assessment of Libby 

4 Amphibole asbestos, a unique mixture of amphibole fibers found in the vermiculite mine in 

5 Libby, MT. IRIS Summaries may include oral reference dose (RfD) and inhalation reference 

6 concentration (RfC) values for chronic and other exposure durations, and a carcinogenicity 

7 assessment. 

8 The RfD and RfC, if derived, provide quantitative information for use in risk assessments 

9 for health effects known or assumed to be produced through a nonlinear (presumed threshold) 

10 mode of action. The RfD (expressed in units of mg/kg-day) is defined as an estimate (with 

11 uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human 

12 population (including sensitive subgroups) that is likely to be without an appreciable risk of 

deleterious effects during a lifetime. The inhalation RfC (expressed in units of mg/m313 ) is 

14 analogous to the oral RfD, but provides a continuous inhalation exposure estimate. The 

15 inhalation RfC considers toxic effects for both the respiratory system (portal-of-entry) and for 

16 effects peripheral to the respiratory system (extrarespiratory or systemic effects). Reference 

17 values are generally derived for chronic exposures (up to a lifetime), but may also be derived for 

18 acute (≤24 hours), short-term (>24 hours up to 30 days), and subchronic (>30 days up to 10% of 

19 lifetime) exposure durations, all of which are derived based on an assumption of continuous 

20 exposure throughout the duration specified. Unless specified otherwise, the RfD and RfC are 

21 derived for chronic exposure duration.

22 The carcinogenicity assessment provides information on the carcinogenic hazard 

23 potential of the substance in question, and quantitative estimates of risk from oral and inhalation 

24 exposures may be derived. The information includes a weight-of-evidence judgment of the 

25 likelihood that the agent is a human carcinogen and the conditions under which the carcinogenic 

26 effects may be expressed. Quantitative risk estimates may be derived from the application of a 

27 low-dose extrapolation procedure. If derived, the oral slope factor is a plausible upper bound on 

28 the estimate of risk per mg/kg-day of oral exposure. Similarly, an inhalation unit risk is a

plausible upper bound on the estimate of cancer risk per μg/m329 air breathed. For Libby 

30 Amphibole asbestos the RfC and IUR are expressed as fibers/cc and cumulative exposure is 

31 expressed as fibers-yr/cc.

32 The assessment of cancer health effects from exposure to Libby Amphibole asbestos does 

33 not estimate risks for non-cancer mortality from inhalation of Libby Amphibole asbestos. There 

34 are potential risks of mortality from noncancer disease (e.g. NMRD, potentially cardiovascular 
This document is a draft for review purposes only and does not constitute Agency policy.

1-2 DRAFT—DO NOT CITE OR QUOTE

1 disease). An inhalation RfC addresses noncancer health effects of inhalation exposure to Libby 

2 Amphibole asbestos. Libby Amphibole asbestos-specific data are not available to support RfD or 

3 cancer slope factor derivations for oral exposures. 

4 Development of these hazard identification and dose-response assessments for Libby 

5 Amphibole asbestos has followed the general guidelines for risk assessment as set forth by the 

6 National Research Council (1983). EPA Guidelines and Risk Assessment Forum technical panel 

7 reports that may have been used in the development of this assessment include the following: 

8 Guidelines for the Health Risk Assessment of Chemical Mixtures (U.S. EPA, 1986a), Guidelines 

9 for Mutagenicity Risk Assessment (U.S. EPA, 1986b), Recommendations for and Documentation 

10 of Biological Values for Use in Risk Assessment (U.S. EPA, 1988), Guidelines for 

11 Developmental Toxicity Risk Assessment (U.S. EPA, 1991), Interim Policy for Particle Size and 

12 Limit Concentration Issues in Inhalation Toxicity (U.S. EPA, 1994a), Methods for Derivation of 

13 Inhalation Reference Concentrations and Application of Inhalation Dosimetry (U.S. EPA, 

14 1994b), Use of the Benchmark Dose Approach in Health Risk Assessment (U.S. EPA, 1995), 

15 Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996), Guidelines for 

16 Neurotoxicity Risk Assessment (U.S. EPA, 1998), Science Policy Council Handbook: Risk 

17 Characterization (U.S. EPA, 2000a), Benchmark Dose Technical Guidance Document

18 (U.S. EPA, 2000b), Supplementary Guidance for Conducting Health Risk Assessment of 

19 Chemical Mixtures (U.S. EPA, 2000c), A Review of the Reference Dose and Reference 

20 Concentration Processes (U.S. EPA, 2002), Guidelines for Carcinogen Risk Assessment 

21 (U.S. EPA, 2005a), Supplemental Guidance for Assessing Susceptibility from Early-Life 

22 Exposure to Carcinogens (U.S. EPA, 2005b), Science Policy Council Handbook: Peer Review 

23 (U.S. EPA, 2006a), and A Framework for Assessing Health Risks of Environmental Exposures to 

24 Children (U.S. EPA, 2006b).

25 The literature search strategy employed for this compound was based on at least 

26 one common name. Any pertinent scientific information submitted by the public to the IRIS 

27 Submission Desk was also considered in the development of this document. Specifically, the 

28 literature search for this compound is based on EPA’s National Center for Environmental 

29 Assessment’s Health and Environmental Research Outline database tool (which includes

30 PubMed, MEDLINE, Web of Science, JSTOR, and other literature sources). The key search 

31 terms included the following: Libby Amphibole, tremolite, asbestos, richterite, winchite, 

32 amphibole, and Libby, MT. The relevant literature was reviewed through April 2011.

33

34
This document is a draft for review purposes only and does not constitute Agency policy.

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1 1.1. RELATED ASSESSMENTS

2 1.1.1. IRIS Assessment for Asbestos (U.S. EPA, 1988)

3 The IRIS assessment for asbestos was posted online in IRIS in 1988 and includes an IUR 

4 of 0.23 excess cancers per 1 fiber/cc (U.S. EPA, 1988). (This unit risk is given in units of the 

5 fibers as measured by phased contrast microscopy [PCM].) The IRIS IUR for asbestos is derived 

6 by estimation of excess cancers for a continuous lifetime exposure and is based on the central 

7 tendency, not the upper bound, of the risk estimates (U.S. EPA, 1988) and is applicable to 

8 exposures across a range of exposure environments and types of asbestos (CAS Number 1332-

9 21-4). Although other cancers have been associated with asbestos (e.g., laryngeal, stomach, 

10 ovarian) (Straif et al., 2009), the IRIS IUR for asbestos accounts for only lung cancer and 

11 mesothelioma. Additionally, pleural and pulmonary effects from asbestos exposure (e.g.,

12 asbestosis, pleural plaques, reduced lung function) are well documented, though, currently, there 

13 is no RfC for these noncancer health effects.

14 The derivation of the unit risk is based on the Airborne Asbestos Health Assessment 

15 Update (AAHAU) (U.S. EPA, 1986a). The AAHAU provides various potency factors and 

16 mathematical models of lung cancer and mesothelioma mortality based on synthesis of data from 

17 occupational studies and presents estimates of lifetime cancer risk for continuous environmental 

18 exposures (0.0001 fiber/cc and 0.01 fiber/cc) (U.S. EPA, 1986a, see Table 6-3). For both lung 

19 cancer and mesothelioma, lifetable analysis was used to generate risk estimates based on the 

20 number of years of exposure and the age of onset of exposure. Although various exposure 

21 scenarios were presented, the unit risk is based on a lifetime continuous exposure from birth. 

22 The final asbestos IUR is 0.23 excess cancer per 1 fiber/cc continuous exposure and was 

23 established by the EPA Carcinogen Risk Assessment Verification Endeavor workgroup and 

24 posted on the IRIS database in 1988 (see Table 1-1) (U.S. EPA, 1988).

25

26

27 Table 1-1. Derivation of the current IRIS inhalation unit risk for asbestos 

28 from the lifetime risk tables in the AAHAU

29

Gender

Deaths per 100,000a

Mesothelioma Lung cancer Total Risk Unit risk

Female 183 35 218.5 2.18 E-03

Male 129 114 242.2 2.42 E-03

All 156 74 230.3 2.30 E-03 0.23

a

30 Data are for exposure at 0.01 f/cc for a lifetime.

31 AAHAU = Airborne Asbestos Health Assessment Update.

32 Source: U.S. EPA (1988).
This document is a draft for review purposes only and does not constitute Agency policy.

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1 1.1.2. EPA Health Assessment for Vermiculite (1991)

2 An EPA health assessment for vermiculite reviewed available health data, including 

3 studies on workers in mines without significant amphibole fiber contamination. The cancer and 

4 noncancer health effects observed in the Libby, MT miner cohort were not seen in studies of 

5 workers exposed to vermiculite from mines with similar exposure to vermiculite, but much lower 

6 exposures to asbestos. Therefore, it was concluded that the health effects observed from the 

7 materials mined from Zonolite Mountain near Libby, MT, were most likely due to amphibole 

8 fibers, not the vermiculite itself (U.S. EPA, 1991). At the time, EPA recommended the 

9 application of the IRIS IUR for asbestos fibers (0.23 per fiber/cc) in addressing potential risk of 

10 the amphibole fibers entrained in vermiculite mined in Libby, MT.

11

12 1.2. LIBBY AMPHIBOLE ASBESTOS-SPECIFIC HUMAN HEALTH ASSESSMENT

13 Libby Amphibole asbestos is a complex mixture of amphibole fibers, both 

14 mineralogically and morphologically (see Section 2.2). The mixture primarily includes 

15 tremolite, winchite, and richterite fibers with trace amounts of magnesioriebeckite, edenite, and 

16 magnesio-arfvedsonite. These fibers exhibit a complete range of morphologies from prismatic 

17 crystals to asbestiform fibers (Meeker et al., 2003). Exposure to Libby Amphibole asbestos

18 results in the same types of adverse health effects as are seen with exposure to other amphibole 

19 mineral fibers. Epidemiologic studies of workers exposed to Libby Amphibole asbestos fibers 

20 indicate increased lung cancer and mesothelioma, as well as asbestosis, and other nonmalignant 

21 respiratory diseases (Lockey et al., 1984; McDonald et al., 1986a,b, 2004; Amandus and 

22 Wheeler, 1987; Amandus et al., 1987a,b; Peipins et al., 2003; Sullivan, 2007; Rohs et al., 2008;

23 Larson et al., 2010a,b, Moolgavkar et al., 2010).

24 The IRIS IUR for asbestos is based on a synthesis of 14 epidemiologic studies that

25 included occupational exposure to chrysotile, amosite, or mixed mineral exposures (chrysotile, 

26 amosite, crocidolite) (U.S. EPA, 1988, 1986a). There is some uncertainty in applying the 

27 resulting IUR to exposure environments and minerals different from those analyzed in the 

28 AAHAU (U.S. EPA, 1986a). There is currently no RfC derived for asbestos.
This document is a draft for review purposes only and does not constitute Agency policy.

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1 2. LIBBY AMPHIBOLE ASBESTOS: GEOLOGY, USE, AND EXPOSURE POTENTIAL

2 2.1. HISTORICAL BACKGROUND

3 The term Libby Amphibole asbestos refers to a mixture of amphibole asbestos found in 

4 the rocks and ore of Zonolite Mountain, 6 miles northeast of Libby, MT (Figure 2-1). Zonolite 

5 Mountain contains a large vermiculite deposit that has been mined since the early 1920s for 

6 various commercial uses. Vermiculite miners, mill workers, and those working in the processing

7 plants were exposed to these amphibole fibers, which remain within vermiculite ore and product. 

8 As amphibole asbestos is present in the geological deposit from which the vermiculite ore was 

9 being mined, workers were exposed to asbestos fibers during various activities such as extracting 

10 ore from the mine, transporting ore and waste rock, milling operations, and shipping the final 

11 product (Meeker et al., 2003; Amandus et al., 1987a; McDonald et al., 1986a). Mortality and 

12 morbidity studies on the mine and mill workers from Libby have reported adverse health effects 

13 in these workers including lung cancer, mesothelioma, nonmalignant respiratory disease 

14 (NRMD), asbestosis, pleural anomalies, interstitial fibrosis, and altered lung function (McDonald 

15 et al., 1986a, b, 2004; Amandus and Wheeler, 1987; Amandus et al., 1987a; Sullivan, 2007;

16 Larson et al., 2010; Moolgavkar et al., 2010). Pleural anomalies and signs of interstitial fibrosis 

17 have also been reported in workers exfoliating and processing expanded Libby vermiculite in 

18 other facilities (Lockey et al., 1984; Rohs et al., 2008). 

19 The primary commercial product from the Zonolite mining operation was vermiculite 

ore. When heated to approximately 150○20 C, the vermiculite mineral expands like popcorn into a 

21 light porous material. The unexpanded mineral exhibits a sheetlike structure that is seen in 

22 related minerals (e.g., mica) (Figure 2-2). This process of expanding the mineral ore is termed 

23 ―exfoliation‖ or ―popping‖ and occurs when the silicate sheets within the ore are rapidly 

24 dehydrolyzed by applying high heat. Since the vermiculite product was contaminated with fibers 

25 of Libby Amphibole asbestos, fibers were released during this energetic process potentially 

26 exposing workers and resulting in asbestos contamination of the finished product sold to 

27 consumers. The Libby mine produced approximately 80% of the world‘s supply of vermiculite

28 ore during its operation from 1923 to 1990. Expanded vermiculite from the Libby, MT site was 

29 used as attic insulation, packing material, and a soil conditioner, and in the production of gypsum 

30 wall board. 

31 The refined vermiculite ore concentrate (after milling and size selection) was then loaded 

32 onto railcars and transported across the country to expansion plants where it was exfoliated and

33 distributed locally (see Figure 2-3). ATSDR (2008) has surveyed 28 of these facilities, 

34 identifying potential community exposures both to amphibole asbestos fibers from the 
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1

2 Figure 2-1. Vermiculite mining operation on Zonolite Mountain, Libby, 

3 Montana.

4

5

6 (a) (b)

7

8 Figure 2-2. Expanded vermiculite used as a soil conditioner (a) or attic 

9 insulation (b).

10

11 Source: U.S. EPA (2010). 
This document is a draft for review purposes only and does not constitute Agency policy.

2-3 DRAFT—DO NOT CITE OR QUOTE

1

2 Figure 2-3. Nationwide Distribution of Libby Ore by County (in tons). Data 

3 on the distribution of ore are based on approximately 80,000 invoices that EPA 

4 obtained from W.R. Grace, which document shipments of vermiculite ore made 

5 from the Libby mine between 1964 to 1990. EPA tabulated this shipping 

6 information in a database. 

7

8 Source: GAO (2007).

9

10

11 vermiculite concentrate before exfoliation, during exfoliation and processing and waste rock 

12 from the processing plants (see Section 4.1.4 and Figure 4-1).

13

14 2.2. GEOLOGY, MINERALOGY, AND FIBER MORPHOLOGY OF LIBBY 

15 AMPHIBOLE ASBESTOS

16 A large vermiculite deposit is located on Zonolite Mountain, northeast of Libby, MT, 

17 within a geologic unit known as the Rainy Creek complex. Geologic processes within the Rainy 

18 Creek complex have resulted in the formation of fibrous amphiboles adjacent to igneous 

19 intrusions into the complex (veins and dikes of alkaline granite, pegmatite, and quartz) 

20 (Boettcher, 1996). The amphibole fibers identified fall within the tremolite-richterite-
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2-4 DRAFT—DO NOT CITE OR QUOTE

1 magnesioriebecktite field (e.g. winchite, richterite and tremolite) (Meeker et al., 2003). An 

2 appropriate understanding of the mineralogy and geology of these materials is helpful in defining 

3 the mineral fibers specific to Libby Amphibole asbestos. 

4 Geological terms provide fiber and mineral definitions based on habit of formation and 

5 fiber morphology. Conversely, the analytical methods that have been used to count fibers in air

6 samples, in both historical and current exposure environments, define microscopic fibers based

7 on dimensional characteristics and mineralogy (depending on the analytical method). Current

8 analytical methods do not have specific procedures for determining fiber morphology at the 

9 microscopic level. Because the human and experimental animal data on adverse health effects of 

10 asbestos rely on available analytical methods to document exposure, these definitions are 

11 relevant to determining what constitutes a fiber for this health assessment. Therefore, available 

12 data on the fiber morphology and fiber-size distribution of Libby Amphibole asbestos are 

13 presented in the following sections.

14

15 2.2.1. Silicate Minerals

16 Silicate minerals are basically made up of oxygen and silicon, two of the most abundant 

17 elements in the Earth‘s crust. Approximately 25% of known minerals and 40% of the common 

18 minerals are silicates. With a few minor exceptions, all igneous rock-forming minerals are 

19 silicates, which mean that silicates comprise more than 90% of the Earth‘s crust (Klein and 

20 Hurlbut, 1977).

21 Some silicates are very hard, infusible, and insoluble. Even in acid, they are not easily 

22 soluble. Specific gravity ranges from fairly light to intermediate, luster is commonly glassy, and 

23 most crush to a light powder even when the bulk specimen is black prior to crushing. Silicates

24 chiefly occur as components of rocks, segregations in rocks, or crystals lining cavities in rocks. 

25 Most hard silicates are primary minerals, not products of weathering. Secondary silicates 

26 typically contain water (Klein and Hurlbut, 1977).

The basic chemical unit of silicates is the [SiO4]

4−

27 tetrahedron-shaped anionic group. The 

28 basic unit consists of four oxygen molecules at the apices of a regular tetrahedron surrounding 

and coordinated with one silicon ion (Si4+ 29 ) at the center. The chemistry is such that the oxygen 

molecules can bond to another silicon ion and, therefore, link one [SiO4]

4−

30 tetrahedron to 

31 another, and then another, and so forth by the process of polymerization. The silicates can form 

32 as single units, double units, chains, sheets, rings, and framework structures (see Figure 2-4). 

33 Silicate minerals can be defined by chemical structure, crystal structure, trace minerals, and habit 

34 of formation.
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1 (a) Nesosilicates or single tetrahedron.

2 The single tetrahedron comprises four oxygen 

3 molecules covalently bound to the silicon, at 

the center of the [SiO4]

4–

4 -tetrahedron. 

5

6

7 (b) Inosilicates [ino (gr.) = thread] -Single8 chain silicates. Chain silicates are realized 

by linking [SiO4]

4–

9 -tetrahedrons in a way to 

10 form continuous chains. They can be 

represented by a composition of [SiO3]

2–

11 . A 

12 typical example is diopside CaMg[Si2O6], in 

13 which the ―endless‖ chains are also held 

together by Ca2+ and Mg2+ 14 ions.

15

16

17 (c) Inosilicates - Double-chain silicates.

18 Two silicate chains of the inosilicates are 

19 linked at the corners, forming double chains 

and yielding [Si4O11]

6–

20 ions, as realized in 

21 the tremolite-ferro-actinolite series 

22 Ca2(Mg,Fe)5Si8O22(OH)2. Double-chain 

23 silicates are commonly grouped with the 

24 single-chain inosilicates. 

25

26

27 (d) Phyllosilicates [phyllo (gr.) = sheet] or 

28 sheet silicates. These are formed if the

double-chain inosilicate [Si4O11]

6–

29 chains are 

30 linked to form continuous sheets with the 

chemical formula [Si2O5]

2–

31 . Examples of a 

32 sheet silicates include chrysotile 

33 Mg3Si2O5(OH)And vermiculite [(Mg, 

34 Fe,A)3(Al,Si)2O10(OH)2 ●4H2O].

35

36

37

38

39

40

41

42

43

44 Figure 2-4. Structure of the silicate minerals, illustrating silicate subclasses 

45 by the linking of the basic silicon tetrahedron (a) into more complex 

46 structures (b, c, or d).

47

48
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1 Silicates are divided by structure into the following subclasses:

2

3 Nesosilicates (single tetrahedrons)

4 Sorosilicates (double tetrahedrons)

5 Inosilicates (single and double chains)

6 Cyclosilicates (rings) 

7 Phyllosilicates (sheets)

8 Tectosilicates (frameworks)

9

10

11 Each subclass of silicates has many mineral members. Specific minerals are defined by 

12 the structure, chemistry, and morphology of the mineral. The minerals of interest in this 

13 assessment are vermiculite (a sheet silicate) and various forms of amphibole asbestos (which are 

14 within the class of inosilicates) (see Figure 2-5). The flat, layered sheets give vermiculite its 

15 perfect cleavage and its unique ability to expand when heated. Chrysotile is also a sheet silicate, 

16 but can present with an asbestiform morphology where the sheets role into fibrils, forming 

17 asbestiform fibers. The habit of formation depends on conditions (e.g., pressure, temperature, 

18 and cooling rate) that favor a particular form, such as fibrous versus crystalline (see Section 2.2.2 

19 on fiber morphology). 

20

21 2.2.1.1. Mineralogy and Structure of Amphiboles

22 Amphibole minerals are double-chain inosilicates, meaning the chemical building block 

23 for amphiboles is connected chains of the silicon tetrahedron (see Figure 2-4c). Amphiboles 

form when edge-shared octahedra link two of the double-chain [SiO4]

4− 24 plates (see Figure 2-4d). 

25 The specific cations between the two double-chain plates define the chemistry of the mineral, 

26 while the ratio of these cations in each location is used to classify amphiboles within a solution 

27 series. The cation sites are designated as A, B, and C in the equation below, which shows the 

28 general chemical formula for double-chain inosilicate amphiboles:

29

30

31 A0–1B2C5T8O22(OH, F, Cl)2

32

33 where:

34 A = Na, K

B = Na, Li, Ca, Mn, Fe2+ 35 , Mg

C = Mg, Fe2+, Mn, Al, Fe3+ 36 , Ti

37 T = Si, Al

38
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1 The mineral subgroup within amphiboles is determined by cationic chemistry (shown 

2 above). 

3

4

5 Tremolite subgroup (Ca amphiboles)

6 Anthophyllite subgroup (Fe-Mg-Li orthoamphiboles)

7 Richterite subgroup (Ca-Na amphiboles)

8 Cummingtonite (Fe-Mg-Li clinoamphiboles)

9

10

11 Cation sites between the double-chain silicate plates could host one or more cations as 

12 noted in the above formula. Sites A, B, and C in the crystal structure refer to cations that 

13 covalently link the amphibole plates at different locations in the crystalline structure (see 

14 Figure 2-5). Based on the above formula, Site A could contain Na or K or both in varied 

proportions; Site B could contain any of the cations listed (Na, Li, Ca, Mn, Fe2+ 15 , Mg ); and 

Site C could contain Mg, Fe2+, Mn, Al, Fe3+ 16 , or Ti. Minerals often have a mixture of ions 

17 between the plates at each location (site A, B or C) and these cations will be in varied

18 proportions depending on the specific geologic deposit. 

19 A solution series includes a continuum of minerals with different cation composition for 

20 each site. Solid solution series are defined by their end members, where mineral terminology 

21 can change as the proportion of cations changes within the crystalline structure. For example, a 

22 solution series for the cation Site A will have one end member with 100% sodium ions and one 

23 end member with 100% potassium ions. This series would include all intervening ratios. 

24 Because each cation site has multiple possibilities, the mineral content of the amphibole silicates 

25 can be quite complex. 

26 Cation changes observed in Libby Amphibole asbestos demonstrate how mineral content 

27 can change in a solution series (Figure 2-7) where the trace minerals at each site (A, B, or C) are 

28 used to define the mineral subgroup and solution series. 

29 It is the complexity of the amphiboles that historically has given rise to a proliferation of 

30 mineral names with no systematic basis (Hawthorne, 1981). Currently, amphiboles are identified 

31 by a clear classification scheme based on crystal chemistry that uses well-established names 

32 based on the basic mineralogy, with prefixes and adjective modifiers indicating the presence of 

33 substantial substitutions that are not essential constituents of the end members (Leake et al., 

34 1997). 

35
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1

2 Figure 2-5. Cross-section of amphibole fibers showing the silicon 

3 tetrahedrons (Δ) that make up each double-chain plate (shown along the 

4 fiber axis). Cations (shown as the darkened dots) occur between the plates 

5 forming the basic fiber. 

6

7 Source: Kirk-Othmer Encyclopedia of Chemical Technology (2010).

8

9
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1

2 Figure 2-6. Vermiculite sample. Brinton's Quarry, near West Chester, Chester 

3 County, Pennsylvania, USA.

4

5 Source: Micaceous vermiculite book (http://www.excaliburmineral.com/cdintro.htm)

6 © Jeff Weissman/ Photographic Guide to Mineral Species.

7

8

9 2.2.1.2. Fiber Morphology

10 Fiber morphology is a function of the structural form of the silicate and the geologic habit 

11 of formation. Silicates can occur in crystalline or massive form without the presence of distinct 

12 fibers. Some silicates can form asbestiform fibers in specific growth habits. The long, slender, 

13 hairlike fibers of commercial asbestiform minerals are visual to the naked eye; they have high 

14 tensile strength and in some cases, can be woven (e.g., chrysotile). The fibrils of asbestiform 

15 chrysotile are due in part to the structure and chemistry specific to chrysotile—the mineralogy, 

16 which allows the sheet silicate to roll, forming fibrils. These fibrils, however, are also a function 

17 of the temperature and pressure conditions during crystallization—habit of formation. 

18 Amphibole fiber morphology is more complex than serpentine asbestos fibers. As with 

19 chrysotile, habit of formation determines whether the amphibole mineral forms a crystalline 

20 structure (massive) or is fibrous. The double-chain form of the amphibole fibers, however, can 

21 result in a range of fiber morphologies, including not only the asbestiform, but also, fibrous, 
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1 acicular, and prismatic. Additionally, crystalline amphibole can fracture, breaking along the 

2 cleavage plane of the crystal, forming shards or cleavage fragments. 

3 The geologic terms for fiber morphology arise from classification of field samples based 

4 on the macroscopic appearance of the crystals and fibers (AGI, 1972):

5

6

7 Acicular: A crystal that is needlelike in form.

8 Fibrous: A mineral that is elongated with thin, needle-like grains or fibers (e.g., 

9 asbestos).

10 Asbestiform: A mineral that is fibrous–that is, like asbestos.

11

12

13 Fibers, crystals, and cleavage fragments can appear to be the same under transmission 

14 electron microscopes (TEM) (e.g., fibers with parallel sides) (see Figure 2-10). Therefore, the 

15 use of these definitions to describe individual fibers viewed by TEM can be problematic (Meeker 

16 et al., 2003). Important characteristics such as crystal structure and surface chemistry cannot be 

17 adequately categorized solely with visually determined definitions developed for classification of 

18 field samples. 

19

20 2.2.2. Vermiculite

21 Vermiculite is the mineralogical name given to hydrated laminar magnesium-aluminum22 ironsilicate, which resembles mica in appearance [Figure 2-6; (Mg, Fe,A)3(Al,Si)2O10(OH)2

23 ●4H2O] (AGI, 1972). Vermiculite is in the clay mineral group of the phyllosilicates, which also 

24 includes kaolinite and montmorillonite. Mica, talc, and serpentine (e.g., chrysotile asbestos) 

25 minerals are other well-known sheet silicates. These sheet-like structures are produced by rings 

26 of tetrahedrons that are linked to other rings by shared oxygen ions in a two-dimensional plane 

27 (see Figure 2-4d). The silicate sheet can extend broadly, and the layered appearance of the 

28 mineral reflects this sheet-like structure. The symmetry of these minerals is controlled primarily 

29 by the symmetry of the rings, which is usually altered to a lower symmetry by other ions and 

30 other layers. Typically, crystals of this subclass are flat, platy, and book-like, as in the mica 

31 group, and the sheets are then connected to each other by layers of cations. These cation layers 

32 are weakly bonded and often have water molecules and other neutral atoms or molecules trapped 

33 between the sheets. When subjected to heat, vermiculite has the unusual property of exfoliating 

34 or expanding into ―worm-like‖ pieces. The term vermiculite is derived from the Latin 

35 vermiculare, which means to breed worms (The Vermiculite Association, 

http://www.vermiculite.org). Vermiculite exfoliation occurs at approximately 150○36 C, producing 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 a lightweight and highly absorbent material (AGI, 1972). Additional properties of vermiculite 

2 are listed in Table 2-1. Vermiculite ore is shown in Figure 2-6.

3

4 Table 2-1. Properties of vermiculite

5

Mineral class/subclass Mineral silicates/phyllosilicate

Chemical formula (Mg, Fe,A)3(Al,Si)2O10(OH)2 ●4H2O

Crystal habit of formation Clay, scaly, aggregate

Hardness (Mohs scale) 203

Cleavage Perfect

Specific gravity 2.4–2.7

6

7

8 Vermiculite is mined across the world, including the United States (Virginia, South 

9 Carolina, and Montana); South Africa; Uganda; China; Brazil; Russia; India; and Australia 

10 (British Geological Survey, 2005). The specific mineralogy and geologic formation habit of 

11 vermiculite deposits vary, and although amphibole minerals are consistent with the ultramafic 

12 rock formations (composed chiefly of ferromagnesian igneous rock) that bear vermiculite, not all 

13 vermiculite deposits contain amphibole asbestos. 

14

15 2.2.3. The Mineralogy of Libby Amphibole Asbestos

16 2.2.3.1. Mineralogy

17 Various research groups have characterized the mineralogical composition and 

18 morphology of amphiboles from the Rainy Creek deposit near Libby, Montana (Gunter and 

19 Sanchez, 2009; Sanchez et al., 2008; Meeker et al., 2003; Wylie and Verkouteren, 2000; Ross, 

20 1993; and Moatamed et al., 1986). For the purposes of this document, the material from this 

21 mine will be called Libby Amphibole. Libby Amphibole asbestos is in the tremolite subgroup 

22 and historically has been reported as a sodium-rich tremolite (Larsen, 1942; Boettcher, 1966; 

23 Leake, 1978). However, a more exact analysis of Libby Amphibole asbestos, characterizing the 

24 cation composition of individual fibers to better define the amphiboles present, indicates 

25 winchite and richterite are also components as well as traces of related minerals (Meeker et al., 

26 2003). The minerals identified are classified by the number of cations present in specific sites 

27 within the amphibole crystalline structure (e.g. sites A, B C and T, see section 2.1.1.1) (Leake, 

28 1978; Leake et al., 1997). Figure 2-7 shows the compositional variations between the 

29 predominate minerals found in the Libby amphibole asbestos (winchite, richterite and tremolite). 
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1

2

3 Figure 2-7. Solution series linking tremolite, winchite, and richterite 

4 amphibole fibers. (See Section 2.2.1.1 for the general form of the equations 

5 shown for each mineral species.)

6

7 Source: Meeker et al. (2003).

8

9

10 Although these are reported as discrete mineral compositions they are in fact graded 

11 across the solid solution series shown in the figure. For example, tremolite is one endmember of 

12 the solid solution series. As calcium decreases and sodium increases, the fibers transition to 

13 richterite. Similarly, as fibers have decreased magnesium and calcium with respect to tremolite, 

14 they are defined as winchite. The sodium content that distinguishes these amphiboles has been 

15 redefined over time in the International Mineral Association‘s mineral classification system, 

16 most recently in 1997 (Leake, 1978; Leake et al., 1997). As a result, some amphibole fibers 

17 previously defined as tremolite prior to the new classification system are currently considered 
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1 winchite based on chemical composition (Leake et al., 1997). The following discussion uses 

2 only the most recent classification system.

3 Libby Amphibole asbestos is not only made up of the end members of these groupings, 

4 but the spectrum of fibers in between each end of the solution series shown, as well as traces of 

5 other minerals. Additionally, samples collected from different areas of the mine and 

6 representing different habits of formation can have a higher or lower proportion of any of the 

7 various minerals (Meeker et al., 2003). Figure 2-8 shows data from 30 samples across the mine. 

8 The cation composition identified for each fiber analyzed at specific sites within the crystalline 

9 structure, determines its mineral identity (Leaky et al., 1997). Here the USGS used two different 

10 techniques to identify the cation content of each fiber [Energy dispersive x-ray analysis (EDS)

11 and Electron Probe Microanalysis (EPMA)]. Regardless of analytical technique used, similar 

12 mineralogy was identified (Figure 2-8). Most Libby Amphibole fibers are classified as winchite 

13 (84%), with lesser amounts of richterite (11%) and tremolite (6%), based on the current 

14 mineralogical nomenclature (Meeker et al. (2003). There are also trace amounts of 

15 magnesioriebeckite, edenite, and magnesio-arfvedsonite present in Libby Amphibole asbestos

16 (Meeher et al., 2003.). All of these would be within the tremolite-richterite-magnesioriebecktite 

17 series of mineral. All of the amphiboles found at the mine site, with the possible exception of 

18 magnesioriebeckite, can occur in fibrous habit. These amphibole materials, even when originally 

19 present as massive material, can produce abundant, extremely fine fibers by gentle abrasion or 

20 crushing. 

21

22 2.2.3.2. Morphology of the Libby Amphibole Asbestos 

23 Rock samples taken from the mine include veins of asbestiform amphibole and various 

24 fiber morphologies in surrounding rock (Meeker et al., 2003). A sample viewed by scanning 

25 electron microscope from the Zonolite Mountain mine illustrates the broad range of fiber 

26 morphologies and fiber size in the Libby Amphibole asbestos material (see Figure 2-9). The 

27 U.S. Geological Survey has described asbestiform fibers, acicular particles, particles showing 

28 curvature, and cleavage fragments all within Libby Amphibole asbestos (Meeker et al., 2003). 

29 As individual fibers and fiber bundles are viewed under greater magnification under a 

30 transmission electron microscope, the range of fiber types can be more clearly seen (Figure 2-10) 

31

32 2.2.3.3. Dimensional Characteristics of Libby Amphibole Asbestos

33 Cumulative particle size distribution frequencies (CDF) were developed for Libby ore 

34 grade 3 and Libby ore grade 3 expanded by EPA Region 8 using the procedure described in 

35 detail in Appendix C. As shown in Figure 2-11 the particle size distribution frequency for the 
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1 Libby grade 3 ore and the Libby grade 3 ore expanded were similar to the particle size 

2 distribution frequency in the ambient air monitoring samples in Libby, MT. In the figure, CDF 

3 is cumulative distribution frequency. Data from the ambient air monitoring in Libby are 

4 presented in Appendix B. The data to construct the plot are described in Appendix B and 

5 Appendix C. There are slight shifts towards longer and thicker fibers in the ore samples 

6 compared to the air samples, with the aspect ratios being almost identical in the ore and air 

7 samples. However, all of these differences are minor and all of these fibers are respirable.

8 Mineralogical characterization of the fibers from the Libby ore grade 3 and the expanded 

9 product using energy dispersive x-ray analysis [EDX] and selected area electron diffraction 

10 [SAED] provided further confirmation of the similarity between the fibers from the Libby grade 

11 3 ore and Libby Amphibole (methodology described in Sec 2.3; Appendix B). EDX spectra 

12 yielded an elemental fingerprint with sodium and potassium peaks that were highly consistent 

13 with values reported for the winchite richterite solution series described for the Libby, MT ores 

14 (Meeker et. al., 2003).

15 Based on these data, it is reasonable to conclude that the fibers from the Libby grade 3 

16 ore and expanded ore are similar in physical and mineralogical characteristics to the Libby 

17 Amphibole fibers found in samples of air in Libby, MT. Therefore, the exposure and health 

18 effects information from the Marysville facility may be used to derive an RfC that can be applied 

19 in the Libby community and other sites that received vermiculite ore from Libby, Montana. 

20 The Marysville, Ohio facility also used vermiculate ore from Virginia, South Africa, and 

21 South Carolina. Dr. Lockey obtained samples of the Virginia and South Africa ores from the 

22 Marysville facility in 1980 and supplied these ores to EPA for analysis. The Virginia and South 

23 African ores were tested for the presence of fibers as described in Appendix F. As described in 

24 Appendix E, the Virginia and South African ores released only a small quantity of amphibole 

25 fibers.

26 EPA was unable to obtain an ore sample from South Carolina. However, vermiculite ore 

27 from the Enoree mine in South Carolina is known to contain fibers (Appendix F; U.S. EPA, 

28 2000d; McDonald et al., 1988).

29

30
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1

2 Figure 2-8. Mineralogy of Libby Amphibole asbestos fibers from samples 

3 taken from the Zonolite Mountain site. An evaluation of the textural 

4 characteristics shows the material to include a complete range of morphologies 

5 from prismatic crystals to fibers. Each data point represents the cation 

6 composition (number of occupied sites) for a single fiber. The x axis shows the 

7 number sites occupied by Na, and the Y axis shows the number of sites occupied 

8 by Na or K. The data shown are a composite of the analysis fibers taken from of 

9 30 different field samples from various locations within the mine. Notes: EDS is 

10 Energy dispersive x-ray analysis; EPMA is Electron Probe Microanalysis

11

12 Source: Meeker et al. (2003).
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1

2

3 Figure 2-9. Scanning electron microscope image of Libby Amphibole 

4 asbestos fibers. Acicular, bundles, prismatic crystals, and curved fibers are all 

5 present. 

6

7 Source: Meeker et al. (2003). 

8

9

10
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1

2

3 Figure 2-10 Fiber morphology of Libby amphibole asbestos viewed under a 

4 transmission electron microscope.

5

6 Source: Meeker et al. (2003).

7

8

9

10

11
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1

2 Figure 2-11. Particle Size (length, width, aspect ratio), fibers in Libby ore 

3 and Libby air.

4 CDF = cumulative distribution frequency; Source: U.S. EPA, 2010
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1

2 2.3. ANALYTICAL TECHNIQUES FOR FIBER ANALYSIS

3 Various techniques are available to measure particles and fibers in air samples. Fibers 

4 are considered elongated particles, often defined as having as aspect ratio (generally a ratio of

5 length to diameter) of 3:1 or greater. As inhalation exposure is of primary concern in 

6 determining adverse health effects of asbestos and other mineral fibers, an understanding of air 

7 sampling methods and analytic techniques is germane to a discussion of how to define mineral 

8 fibers. Historically, exposure measurements in industrial settings have been based on particle 

9 counts (midget impinger, thermal precipitator) and membrane filter counts. Particle counts (total 

10 dust) were measured as mass per unit air or as particles counted per unit of air. The midget 

11 impinger particle-counting method collects the particles in a liquid sample and particles are

12 visually counted under a light microscope. Midget impinger results are reported as million 

13 particles per cubic foot of air (mppcf). This method counts particles regardless of their shape, 

14 aspect ratio), or the composition of the particle. Thus total dust air sampling results may be 

15 useful to compare the relative dustiness of different working environments but do not 

16 specifically measure either fibers in general or asbestos.

17 The collection of fibers on an air filter, visually counted under a phase contrast 

microscope118 (PCM) was first described in 1934 by the Dutch physicist Frits Zernike. The 

19 specification of a fiber as >5 μm in length and length-to-diameter ratio (i.e., aspect ratio) of at 

20 least 3:1 resulted from this method. As a light microscope technique, the PCM method cannot 

21 distinguish between asbestos and nonasbestos fibers or determine fiber mineralogy. The U.S.

22 Public Health Service developed and tested a standard air sampling method based on PCM 

23 detection (NIOSH Method # 7400), which has been used in industrial settings and is the basis of 

24 the IRIS IUR for asbestos (U.S. EPA, 1988). The method specifies the analyst count fibers 

25 >5 μm in length with an aspect ratio of at least 3:1. Fibers viewed and counted by the PCM 

26 method are referred to as  ̳PCM fibers.‘ Since PCM cannot distinguish between asbestos and 

27 nonasbestos fibers, the NIOSH method provides for the examination of fibers by transmission 

28 electron microscope where use of x-ray diffraction techniques allows for the mineralogical 

29 identification of the fiber. 

30 This PCM metric was never intended to define biologically active fibers. The authors 

31 aptly point out in the AAHAU (U.S. EPA, 1986) that PCM fiber counts include nonrespirable 

32 fibers and that many smaller, respirable fibers are not quantified by this method. Dement and 

33 Harris (1979) report that approximately 98% of crocidolite, 95% of chrysotile, and 45% of 

34 amosite fibers were less than 5 μm long. These fiber-length profiles were calculated with a 

 

1

PCM is a contrast-enhancing optical technique that can be used to produce high-contrast images of transparent 

specimens. 
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1 lower length cut-off of 0.08 μm applied during fiber counting with transmission electron 

2 microscopy (Dement and Harris, 1979). The PCM metric measure, however, can be used as a 

3 surrogate for the material present. The proportion of fibers >5-μm long varies by mineral type, 

4 sample, and handling. 

5 Electron microscopy employs electrons rather than light to visualize the specimen. 

6 Furthermore, instead of using glass lenses to focus the light wavelengths, electromagnetic lenses 

7 are used to focus electrons on the sample. The analytical techniques included in electron 

8 microscopy for asbestos testing are transmission electron microscopy (TEM), scanning electron 

9 microscopy (SEM), and scanning transmission electron microscopy (STEM). TEM produces 

10 two-dimensional (2-D) images that generally use a magnification factor of about 500 to 

11 500,000 . SEM produces three-dimensional (3-D) images that generally result in about 10 to 

12 300,000 magnification. STEM can produce both 2-D and 3-D images that generally result in 

13 about 10 to 500,000 magnification. X-ray diffraction (XRD) may be used with the above 

14 techniques to differentiate crystalline structure of minerals in solid materials and provides 

15 information on the availability of the total mineral present. Thus, XRD can determine the 

16 mineral form of asbestos fibers and structures. Although SEM and STEM are available for from 

17 commercial laboratories and for research, standard analytical techniques for enumerating 

18 asbestos structures in air samples have been developed for TEM (ISO 10312).

19 TEM analytical techniques are more specific than PCM for visualizing asbestos fibers

20 and identifying types of asbestos. TEM analysis can detect fibers smaller than 0.006 μm in 

21 diameter and can determine the specific mineralogy by selected area electron diffraction and 

22 crystallography of asbestos fibers, thus distinguishing between asbestos fibers and nonasbestos 

23 fibers, as well as among different types of asbestos fibers by energy dispersive spectroscopy. 

24 TEM analysis is often used to determine asbestos in environmental and ambient air samples 

25 since asbestos structures can be identified. The International Standard method (ISO 10312) 

26 enumerates structures much smaller than the PCM fibers with a minimum length requirement of 

27 0.5 μm. Additionally, structures with an aspect ratio of at least 5:1 are considered fibers, rather 

28 than 3:1 as with PCM analysis. The ISO 10312 method also defines other structures (fiber 

29 bundles, clusters, and matrices) that are included in the structure count. Therefore, the term 

30 structure rather than fiber is used when presenting air sampling results from ISO 10312 where 

31 structures per cc of air (s/cc) are reported. Although TEM has the advantages of viewing much 

32 smaller structures, counting various types of structures and mineralogical identification; there are 

33 some limitations of the method. As TEM is generally used at much higher magnifications than 

34 PCM in order to view the smaller structures, larger fibers and structures may be undercounted. 
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1 Additionally, a much smaller portion of the air filter is viewed under the greater magnification,

2 impacting method detection limits. 

3 EPA often employs a modified version of the TEM technique when assessing 

4 environmental sites. The OSWER Framework for assessing asbestos-contaminated sites (U.S. 

5 EPA, 2008) recommends that air samples be analyzed by TEM and then it specifies that a subset 

6 of the fibers visible under TEM be counted such that the fiber counts are considered equivalent 

7 to the subset of fibers that would have been observable using PCM; it recommends this subset of 

8 TEM fibers be used for site risk assessment (U.S. EPA, 2008). These PCM equivalent fibers (or 

9 PCMe fibers) are defined as those fibers viewed on TEM that meet the PCM analytical 

10 requirements: ≥5 μm in length and an aspect ratio of at least 3:1. Although the PCM 

11 methodology does not specify a minimum fiber width, current PCM analytical methods reliably 

12 detect fibers of 0.25 μm in width (WHO, 1980), which EPA employs to define PCMe fibers 

13 (U.S. EPA, 2008). 

14

15 2.4. EXPOSURE POTENTIAL

16 Although the occurrence of Libby Amphibole asbestos is limited to a relatively small 

17 geographic area, the potential for exposure to it has been greatly enhanced by the historical 

18 mining, milling, and distribution of vermiculite operations in Libby, MT. Additionally, material 

19 was sent to processing plants across the nation where plant workers and community contacts may 

20 have been exposed. Lastly, consumer products containing vermiculite mined near Libby are 

21 contaminated with the Libby Amphibole asbestos, and consumers may have been exposed to 

22 Libby amphibole asbestos while using the products. For example, asbestos–contaminated 

23 vermiculite attic insulation from Libby remains in homes today across North America, where 

24 there is the potential for residential exposures. This section summarizes the potential for current 

25 exposures to the Libby amphibole asbestos in vermiculite in the Libby community, other 

26 communities potentially impacted by processing plants, and from in-place Libby vermiculite 

27 attic insulation. Historical exposures for the workers in Libby, MT, and other facilities are

28 discussed in Section 4.1, where data are available.

29 There are also lifestyle, activity and lifestage factors which may influence one‘s exposure 

30 potential to asbestos. For example, children spend more hours outside and may engage in 

31 activities which impact exposure level compared to adults (NRC, 1993; U.S. EPA 2006). In 

32 general, children inhale more air per unit body weight (U.S. EPA, 2008) and spend more time 

33 outdoors than adults (Bateson and Schwartz, 2008; NRC, 1993), which could have resulted in 

34 increased inhalation exposure to Libby Amphibole asbestos in children compared with adults. In 

35 contrast, some adult activity patterns may also result in increased exposures, such as gardening 
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1 and home repair, where Libby Amphibole asbestos may be present. Thus for the various 

2 environments where people may be exposed to Libby Amphibole asbestos, the potential 

3 activities and pathways of exposure are discussed below, and where available exposure 

4 measurements given for various exposure environment and activities. 

5

6 2.4.1. Libby Community

7 The Libby community (the towns of Libby, Troy, and surrounding residences) defines the 

8 community that may have been directly and indirectly impacted by mining/millings activities. 

9 Many individuals who worked in the mine lived in the surrounding areas. Facilities in the 

10 community may have residual contamination from past milling and transport activities. 

11 Additionally, expanded vermiculite, waste stoner rock (the waste material from exfoliation), and 

12 other materials all potentially containing Libby amphibole asbestos may have been transported 

13 off site to residences and recreational areas. Taken together, there are numerous potential 

14 exposure pathways for community residents, both historical and current.

15 During plant operations, individuals may have been exposed to materials inadvertently 

16 transported from the workplace to vehicles, homes, and other establishments, typically on the 

17 clothing, shoes, and hair of workers. This transport of material may result in ―take-home

18 exposure‖ for the workers, their families, and other co-residents. The magnitude of these 

19 exposures was not measured, so the levels to which individuals in the home might have been 

20 exposed are not known. Based on studies of other industrial take-home exposures, individuals 

21 doing laundry and cleaning house (often women) can be exposed to materials on workers‘

22 clothing. Also, children who play on the floor might be more exposed than adults to dust from 

23 take-home exposures (Kelly et al., 2006). The community health screening studies from Libby 

24 showed that men were more likely to have both occupational and non-occupational exposures, 

25 while women were more likely to have household contact with exposed workers (ATSDR, 2001; 

26 Peipins et al., 2003). There could also be gender differences in types of activities (e.g., 

27 household chores such as laundry and cleaning) or in intensity or duration of occupational and 

28 recreational activities (Peipins et al., 2003).

29 Expanded vermiculite, as a finished product, was used as a soil amender and for attic 

30 insulation. Community members may have been exposed and are possibly still exposed to these 

31 consumer products. In a survey of Libby residents conducted by ATSDR in 2000–2001, almost 

32 52% reported using vermiculite for gardening and 8.8% used vermiculite around the home and 

33 51% reported handling vermiculite attic insulation (Peipins et al., 2003). As vermiculite ore, 

34 waste rock, and product were present in the community, numerous activities may have resulted 

35 in exposure. Individuals also reported exposures from the following activities: participating in 
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1 recreational activities along Rainy Creek Road, the road leading to the mine (67%); playing at 

2 the ball field near the expansion plant (66%); playing in the vermiculite piles (34%); heating the 

3 vermiculite to make it expand/pop (38%); or other activities in which there was contact with 

4 vermiculite (31%) (Peipins et al., 2003). Memoranda from Christopher Weis (U.S. EPA) states

5 that asbestos mineral fibers were detected in outdoor sources (yard soil, garden soil, driveway 

6 material, and assorted mine-waste materials) and indoor sources (dust and vermiculite insulation) 

7 in Libby (U.S. EPA, 2001a, b). Prior to plant closure, there are no measurements of exposure

8 levels in the community for the materials and activities from various exposure pathways. 

9 EPA has conducted more recent exposure sampling in the Libby community. Limited 

10 data are available from sampling conducted in 2000/2001 to assess airborne asbestos levels from 

11 various activities (see Table 2-2). Air samples were taken in the community during activities

12 considered appropriate for various potential exposure scenarios. Personal air monitors were 

13 placed on the investigator conducting the activity, and a second air sample was taken from a 

14 fixed location (area sample). Asbestos fibers were collected on filters and counted by 

15 two different laboratory methods: 1) PCM and 2) TEM. Although TEM analysis can count 

16 smaller fibers, results are shown here for PCM size fibers used to estimate risk, called PCM 

17 equivalent fibers, or PCMe (See Section 2.2.3.1) 

18 EPA continues to conduct air monitoring in the Libby community to support clean-up and risk 

19 assessment activities. Ambient air monitoring conducted in 2006/2007 at 18 locations across the 

20 area indicated that low levels of asbestos fibers are occasionally detected in the air, even with no 

21 localized disturbance of asbestos-contaminated material (U.S. EPA, 2009b). Fibers were 

22 counted by TEM, and structures ≥0.5 μm in length and with an aspect ratio ≥3 were included 

23 (structures per cc of air, s/cc). Average ambient air levels for the various sampling locations

ranged from 8 x 10-6s/cc to 1.9 x 10-524 s/cc (U.S. EPA, 2009b). Both ambient and activity-based 

25 air monitoring have been completed in five community schools (U.S. EPA, 2010). Outdoor 

26 activities conducted that were considered relevant to children‘s exposures at the schools included 

27 playing sports, using playground equipment, and running/walking in outdoor areas. Outdoor 

28 activities to assess exposure of the school maintenance workers included digging/raking, power 

29 sweeping parking lots, and mowing/ and edging school lawns. Additionally, ambient air samples 

30 were taken in each school (i.e., classrooms, cafeteria, gymnasium, and hallways). Asbestos 

31 PCMe fibers were detected by TEM analysis in 5 of 63 outdoor activity-based samples, ranging 

32 from 0.0022 to 0.039 f/cc. No PCMe fibers were detected in indoor air samples. However, 2 of 

33 50 indoor area samples detected TEM asbestos structures not considered PCMe fibers 

(5.1 x 10-4s/cc and 5.9 x 10-434 s/cc), which is within the range of analytical sensitivity for the 
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1 indoor air samples (U.S. EPA, 2010). It should be noted that indoor air sampling did not include 

2 any activity-based sampling to assess student or employee exposures.
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1 Table 2-2. Air sampling results for disturbance of Libby amphibole asbestos2 contaminated materials in Libby, MT

3

Activity

Air sampling results

Type of air 

sample

PCM

(f/cc)1

TEM

(PCME-asb)

(f/cc)

Rototilling Vermiculitecontaminated soilArea sample 0.020 0.019

Personal air 

monitor

0.227 0.066

Active cleaning Household dust Area sample 0.0212

(0.007–0.068)

0.008

(0.007–0.010)

Personal air 

monitor

0.112

(0.014–1.017)

0.010

(0.004–0.013)

Routine activity Household dust Area sample 0.006

(0.002–0.012)

0.009

(0.0003–0.036)

Personal air 

monitor

0.007

(0.001–0.014

0.035

(0.023–0.048)

Simulated home 

remodeling

Vermiculite 

insulation

Area sample 0.142

(0.035–0.324)

0.309

(0.023–0.789)

Personal air 

monitor

0.568

(0.118–1.62)

0.309

(0.042–1.057)

4

5 Source: EPA (2001).

6 Notes: 1) Air sampling is reported in fibers per cc of air (f/cc). PCM fibers are greater than 5 μ in length with an 

7 aspect ratio of at least 3:1.

8 2) Where multiple measurements were taken, the mean air concentration is given followed by the range of 

9 detected air levels.

10

11

12 Although data are limited, there are reports that milling operations in the city of Libby 

13 resulted in increased ambient air levels measured as PCM fibers. In efforts to document 

14 historical air transport of asbestos fibers, researchers have examined tree bark. A study 

15 conducted in Libby determined that trees may act as reservoirs for Libby Amphibole asbestos

16 fibers, which may have become embedded in the bark by diffusion or impaction-type processes

17 (Ward et al., 2006; Webber et al., 2006). Therefore, loggers and others in the timber industry in 

18 the Libby area could have had additional exposure to Libby Amphibole asbestos. Because trees 

19 act as reservoirs for Libby Amphibole asbestos, inhalation exposures could have occurred where 

20 wood is burned to provide heat (Ward et al., 2009). 
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1

2 2.4.2. Communities near Vermiculite Expansion and Processing Plants

3 The ATSDR Summary Report on the 28 Libby vermiculite expansion and processing

4 facilities reported that household residents were exposed by contact with vermiculite from the 

5 workers‘ clothes, shoes, and hair. Workers‘ personal vehicles likely contained vermiculite dust 

6 from the facility emissions and from vermiculite that fell from their clothing and hair on the 

7 drive home after work. The O.M. Scott Company reported that company policy was to launder 

8 work clothes for their employees and to make showers available for use after work. Had these 

9 services been used, exposure via household contact could have been greatly reduced (ATSDR, 

10 2005). Whether other facilities made these services available or how frequently they might have 

11 been used is unknown. 

12 Communities near the expansion plants were exposed to many of the same exposure 

13 pathways as for the Libby community. The Summary Report from ATSDR (2008) observed that 

14 individuals in the community could have been exposed through multiple avenues, such as living 

15 near the plant and breathing emissions from the facility, disturbing waste-rock piles, having 

16 direct contact with waste-rock brought home, and living with indoor dust containing asbestos 

17 brought in from outdoor sources (ATSDR, 2008). 

18 For the O.M. Scott Company facility in Marysville, Ohio, about 185 people currently live 

19 within 1 mile of the facility, but only about 60% of the housing units within the 1-mile radius 

20 existed while the Libby vermiculite was being exfoliated. Community members living farther 

21 from the facility, however, might still have been exposed through ambient air because many 

22 individuals used the swimming pool at the company facility (ATSDR, 2005). More detailed 

23 information on the community exposures near this facility is not available.

24 Waste rock from an exfoliation facility in Minnesota was made available to residents of 

25 the community, and personal use was encouraged. Individuals used the material to landscape, to 

26 fill in driveways, and as a soil additive. The waste rock was dumped outside the plant, and plant 

27 employees and children reported playing in and around the piles. Substantial airborne emissions 

28 also were measured close to the plant (ATSDR, 2003).

29

30 2.4.3. Exposures from Zonolite and Vermiculite for Homeowners, Contractors, and Other 

31 Populations

32 It is estimated that 80% of the vermiculite used in the United States, and consumer 

33 products include Zonolite vermiculite attic insulation (VAI) and potting soil amended with

34 expanded vermiculite came from the mine in Libby, MT. EPA conducted a study to estimate the 

35 potential for exposure to asbestos in homes containing VAI. Air samples were taken to define 
This document is a draft for review purposes only and does not constitute Agency policy.

2-27 DRAFT—DO NOT CITE OR QUOTE

1 exposure levels in the homes under various conditions: no activity (e.g., ambient air), as well as 

2 during simulated remodeling activities and removal of the VAI (U.S. EPA, 2003). Samples were 

3 taken in the both the living space of the homes as well as attic space. 

4 Background air samples were collected in five occupied homes where Zonolite VAI was 

5 in place (asbestos detected from trace levels to 1.54% by bulk analysis); no fibers were detected

6 in the air samples above 0.0016 PCMe f/cc in these homes. However, the air samples were taken 

7 when the homes were empty, and there was no disturbance of the VAI or entry/exit into the attic

8 space. Therefore, EPA conducted a number of simulations under controlled conditions to 

9 estimate exposures when VAI is disturbed during normal activities (e.g., moving boxes in an 

10 attic), remodeling, and removal of the VAI. Structures were built within safe containment to 

11 simulate attic space above living space and VAI was installed in the simulated attics. 

12 Remodeling activities resulted in personal exposures ranging from 0.50 to 1.841 f/cc PCMe. 

13 Stationary samples of the attic air ranged from 0.008 to 0.203 f/cc PCMe. For those simulations 

14 that included sampling in the  ̳living space‘ below the attic, asbestos fibers ranged from 0.001 to 

15 0.25 f/cc PCMe during renovations and from 0.001 to 0.035 in the living space after renovations 

16 were complete (U.S. EPA, 2003). These data indicate that exposures to asbestos fibers may 

17 occur when disturbing Libby Amphibole asbestos-contaminated VAI in homes. 

18 A second study on potential exposures to Zonolite VAI was conducted by an 

19 environmental firm hired by attorneys representing individuals with VAI in their homes (Ewing 

20 et al., 2010). This study was conducted in three homes containing Zonolite VAI, and air 

21 sampling was taken, representing ambient conditions (no disturbance of VAI), remodeling, 

22 activity in the attic, and removal of the VAI by various methods (see Table 2-3). Disturbance of 

23 the asbestos-contaminated VAI resulted in airborne asbestos levels, both in the personal air 

24 monitors and area samples (Ewing et al., 2010).
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1 Table 2-3. Air sampling results for asbestos from Zonolite VAI in 

2 three homes 

3

Activity

Personal samples Area samples

PCM1

(f/cc)

TEM2

(PCME, s/cc)

TEM

(PCME, s/cc)

No activity NS3 NS <0.003

Cleaning items in 

the attic

1.54 <0.42 0.07

Cleaning storage 

area in the attic

2.87 2.58 0.47

Cutting a hole in the 

ceiling below the 

VAI

5.80 1.32 0.52

VAI removal 

(various methods) 2.9–12.54

0.98–10.3 0.53–1.47

4

5 Air sampling results reported as fibers analyzed by phased contrast microscopy (PCM).

6 Air sampling results reported as structures, PCM equivalent (PCME) as analyzed by transmission electron 

7 microscope (TEM).

8 NS—not sampled, personal samples were not taken for background levels.

9 Range of results for three different removal methods (shop vacuum, homeowner method, and manufacturer10 recommended method).

11

12 Source: Ewing et al. (2010).
This document is a draft for review purposes only and does not constitute Agency policy.

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1 3. FIBER TOXICOKINETICS

2 There are no published data on the toxicokinetics of Libby Amphibole asbestos. 

3 However, to help inform the reader as to the expected toxicokinetics of Libby Amphibole 

4 asbestos, this section contains a general summary description of toxicokinetics of fibers. A more 

5 detailed discussion of fiber toxicokinetics is beyond the scope of this document, but is reviewed 

6 elsewhere (NIOSH, 2011; ICRP, 1994). 

7 Understanding fiber toxicokinetics and retained fiber dose is a complex component of

8 analyzing asbestos toxicity. The principal components of fiber toxicokinetics in mammalian 

9 systems are (1) deposition at the lung epithelial surface, and (2) clearance from the lung due to 

10 physical and biological mechanisms including both translocation from the lung to other tissues 

11 (including the pleura), and elimination from the body (see Figure 3-1). 

12

13

14 Figure 3-1. General scheme for fiber deposition, clearance, and translocation 

15 of fibers from the lung and GI tract. General scheme for fiber deposition 

16 (heavy arrows), clearance (light dotted arrows), and translocation (light arrows). 

17 Diagram of Bignon et al. (1978) derived from ICRP lung model by the Task 

18 Group on Lung Dynamics (1966).

19

20 Source: ICRP (1994).
This document is a draft for review purposes only and does not constitute Agency policy.

3-2 DRAFT—DO NOT CITE OR QUOTE

1 The fiber characteristics of the sample to be studied define the toxicokinetics of that 

2 specific sample. These characteristics vary among fiber types, and in many cases, among

3 samples of the same fibers. Libby Amphibole asbestos includes fibers with a range of mineral 

4 compositions including amphibole fibers primarily identified as richterite, winchite, and 

5 tremolite (see Section 2.2). Although the fiber size varies somewhat from sample to sample, a 

6 large percentage (~45%) is less than 5-μm long in bulk samples examined from the Libby mine 

7 site (Meeker et al., 2003). Limited data from air samples taken in the workplace also document a 

large percentage of respirable18 fibers as well as fibers <5um-long (see Section 4.1.1.2 and 

9 Table 4-3). The importance of the size of fibers and how they deposit following inhalation is 

10 described below. Due to a lack of data specific to Libby Amphibole asbestos, these deposition 

11 steps are discussed for asbestos in general.

12 The main route of human exposure to fibers is through inhalation, although other routes 

13 of exposure play a role. Exposure of pulmonary tissue to fibers via the inhalation route depends

14 on the fiber concentration in the breathing zone, the physical (aerodynamic) characteristics of the 

15 fibers, and the anatomy and physiology of the respiratory tract. Ingestion is another pathway of 

16 human exposure and occurs mainly through the swallowing of material removed from the lungs 

17 via mucociliary clearance or drinking water contaminated with asbestos, or eating, drinking, or 

18 smoking in asbestos-contaminated work environments (Condie et al., 1983). Handling asbestos 

19 can result in heavy dermal contact and exposure. Asbestos fibers could become lodged in the 

20 skin, producing a callus or corn, but generally, no serious health effects (Lockey et al., 1984). 

21 Because few studies have examined the deposition and clearance of fibers following ingestion of 

22 or dermal exposure to fibers, the focus of this section is on the main route of exposure, 

23 inhalation. 

24 Studies useful for assessing the relationship between airborne fiber concentrations and 

25 respiratory disease must involve meaningful measurements of environmental exposure and an 

26 understanding of how to apply these measurements to the target tissue dose. Tissue dose is a 

27 more specific measure than external dose, and is determined both by fiber characteristics of the 

28 exposure environment and the exposed population. Dose to the lung is a function of airway 

29 anatomy, lung volume, ventilation rate, and clearance from the lung, as well as the fiber‘s 

30 physical and chemical characteristics (Oberdorster, 1991; U.S. EPA, 2004). Many studies have 

31 examined the role of these physical and chemical characteristics in asbestos-induced disease in 

32 the lung and are reviewed in more depth elsewhere (NIOSH, 2011; ATSDR, 2001; Myojo and 

33 Takaya, 2001; Witschi and Last, 1996; Lippmann 1990; Merchant 1990; Yu et al., 1986; Griffis 

 

1 Respirable fibers are those that can be inhaled into the lower lung where gas exchange occurs and are defined by 

their aerodynamic diameter (da ≤ 3μm, NIOSH). 
This document is a draft for review purposes only and does not constitute Agency policy.

3-3 DRAFT—DO NOT CITE OR QUOTE

1 et al., 1983; Harris and Fraser, 1976; Harris and Timbrell, 1975). Factors influencing dose to 

2 other tissues in the body (e.g., pleura, peritoneum, stomach, ovaries) are not as well known, but 

3 they are discussed below where data are available.

4

5 3.1. DEPOSITION OF FIBERS IN THE RESPIRATORY TRACT

6 The deposition of fibers in the respiratory tract is dependent on the aerodynamic 

7 properties of the fiber (length, width, and density) and the anatomy and physiology of the 

8 respiratory tract (NIOSH, 2008; ATSDR, 2001; Myojo and Takaya, 2001; Witschi and Last, 

9 1996; Yu et al., 1986; Griffis et al., 1983; Harris and Fraser, 1976; Harris and Timbrell, 1975). 

10 Thicker fibers are deposited in the upper airways; thinner fibers are carried deeper into the 

11 airways and alveolar regions. All fibers with aerodynamic diameters less than approximately 3 

12 μm meet the physical criteria necessary for deposition in the terminal bronchioles and beyond to 

13 the alveoli. The site of fiber deposition within the respiratory tract has implications related to 

14 lung retention and surface dose of fibers. The respiratory tract encompasses the upper 

15 respiratory tract (nose, nasal passages, and throat), respiratory airways (larynx, trachea, bronchi 

16 and bronchioles) and the lungs (respiratory bronchioles, alveolar ducts, alveolar sacs and 

17 alveoli). A full review of the anatomy and architecture of the respiratory tract is beyond the 

18 scope of this document but has been reviewed by ICRP (1995). 

19 Fiber deposition occurs by five mechanisms: impaction, interception, sedimentation, 

20 diffusion, and electrostatic precipitation (see Table 3-1). 

21

22 Impaction: The momentum of the fiber causes it to directly impact the airway surface as 

23 the airflow changes direction. This is the predominant method of deposition in 

24 the upper airways where airflow is swift and larger fibers/particles are present.

25 Interception: A special case of impaction where the edge of the fiber touches the airway 

26 surface and is prevented from continuing along the airway. This mechanism is 

27 important in the transmitting airways (trachea and bronchi), where the airflow is 

28 slower and laminar flow along the airway surface is conducive to interception.

29 Sedimentation: Gravitational forces and air resistance cause fibers/particles to settle out 

30 of the air column onto the airway surface. For sedimentation to occur, air flow 

31 velocities must be low, to allow the particle/fiber to settle, and this is a 

32 predominant mechanism to the smaller conductive airways.

33 Diffusion: This method of deposition is predominant in the alveolar region where air 

34 movement is negligible. Diffusion occurs from interactions of the fibers with the 

35 movement of air molecules; this Brownian motion increases with decreasing fiber 

36 size (<0.5 μm diameter). 

37 Electrostatic Precipitation: A special case of diffusion, where fiber motion towards the 

38 airway surface is a function of static charge between the fiber and airway surface. 
This document is a draft for review purposes only and does not constitute Agency policy.

3-4 DRAFT—DO NOT CITE OR QUOTE

1 As with classic diffusion, this primarily occurs in the alveolar region where 

2 airflow is negligible and the electrostatic forces can predominate.

3

4
This document is a draft for review purposes only and does not constitute Agency policy.

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—DO NOT CITE OR QUOTE

Table 3-1. Factors influencing fiber deposition and clearance in the respiratory system

Size of fiber 

(aerodynamic 

diameter)

Area of deposition 

in respiratory 

system

Predominant 

Method of 

deposition

Mechanisms for 

fiber retention Physical clearance Dissolution

Target tissue for 

translocation

5–30 μm Nasopharyngeal 

region (upper airway 

passages – nose and 

throat)

Impaction Epithelial cell uptake Mucous flow 

(mucociliary apparatus 

into gastrointestinal 

tract)

Macrophage: 

phagocytosis and 

transport

Not measured, 

although dissolution 

can occur, removal 

from mucous flow is 

fairly quick and 

likely predominant

Gastrointestinal tract

Nasal-associated 

lymphoid tissue, 

lymph system

1–5 μm Trachea, bronchial 

and bronchiolar 

region (windpipe and 

larger branches of the 

lungs)

Sedimentation, 

impaction, 

interception

Epithelial cell uptake Mucociliary apparatus

Macrophage: 

phagocytosis and 

transport

Mucous

Macrophage

Gastrointestinal tract

Mucosa-associated 

lymphoid tissue, 

lymph system

Pleura

1 μm or less Alveolar region 

(smaller branches of 

lung and the air 

exchange area)

Diffusion Epithelial cell uptake

Translocation to other 

target tissues

Macrophage: 

phagocytosis and 

transport

Lung surfactant

Macrophage

Asbestos bodies

Gastrointestinal tract

Mucosa-associated 

lymphoid tissue, 

lymph system

Pleura

Source: Adapted from Klaasen et al., 1986 in Casarett and Doull‘s Toxicology: The Basic Science of Poisons, 3rd edition, p. 343.
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Aerodynamic diameter (also called aerodynamic equivalent diameter) of fibers accounts 

2 for dimensional properties that influence the movement of the fiber‘s center of gravity through 

3 the airways, so aerodynamic diameter is important in all depositional mechanisms. The 

aerodynamic diameter is the diameter of a unit density (1g/cm34 ) sphere that has the same 

5 gravitational settling velocity as the particle of interest. Since the aerodynamic diameter informs 

6 the deposition patterns of fibers, it is used in modeling to determine the expected fiber deposition 

7 in the respiratory tract. Impaction and interception, however, are also heavily influenced by fiber 

8 length. Where the physical length of the fiber greatly exceeds the aerodynamic diameter, 

9 impaction and interception can be underpredicted by modeling the center of gravity of the fiber. 

10 Sedimentation is related to the mass of the fiber, as well as the aerodynamic diameter, but 

11 generally occurs at lower velocities in smaller airways. Diffusion occurs from interactions of the 

12 fibers with the movement of air molecules; this Brownian motion increases with decreasing fiber 

13 size (<0.5 μm diameter). Electrostatic precipitation occurs when fiber charges induce opposite 

14 charges on the airway surfaces and the fiber is drawn to the airway walls (Lippmann, 1990). 

15 For high aspect ratio fibers, like asbestos, the shape factor often approaches one and the 

16 equation reduces to the aerodynamic diameter approximately equal to the nominal fiber 

17 diameter. Therefore in employing the information from Table 3-1 to high aspect ratio fibers, one 

18 may get an idea of the depositional characteristic of fibers from the nominal diameter. By 

19 definition, fibers have a greater aspect ratio than particles and as discussed, high aspect ratio 

20 fibers may act significantly different than other particles with respect to some mechanisms of 

21 deposition (e.g., impaction, interception, and electrostatic precipitation). Therefore, the 

22 depositional characteristics of fibers are not characterized completely by aerodynamic diameter. 

23 No equivalent depositional model, however, is yet available for fibers in the dimensional range 

24 of asbestos, which take into consideration the increased sedimentation and impaction for high 

25 aspect ratio particles.

26 Fibers enter the respiratory tract along with airflow through the nasal and oral passages. 

27 The nasal passage, from the nostril to the pharynx, serves as a filter for some fibers with 

28 diameters 5–30 μm. Clumps of fibers also could deposit in these regions. Many animal species, 

29 including rats and mice, are obligate nose breathers, meaning that fibers pass only through the 

30 nasal passages, and, therefore, are always subject to nasopharyngeal filtering. Humans, 

31 monkeys, and dogs, among other species, breathe both orally and nasally (oronasal). Therefore, 

32 larger fibers and clumps of fibers can bypass the upper respiratory tract filtering and be inhaled 

33 directly into the larynx/trachea, especially during exertion (e.g., exercise or work), which may 

34 further alter deposition by increased turbulence in the airways. This distinction is important 

35 when reviewing inhalation studies between different species. 
This document is a draft for review purposes only and does not constitute Agency policy.

3-7 DRAFT—DO NOT CITE OR QUOTE

1 The conducting airways beyond the nasopharyngeal region include the trachea and 

2 bronchi, which have branched bifurcations with decreasing internal diameters. The aerodynamic 

3 diameter of fibers that can deposit in the tracheobronchial region is in the range of 1–5 μm. 

4 Fibers with aerodynamic diameter <1 μm can deposit in the bronchioles and then farther to the 

5 alveoli (ICRP, 1994). 

6 Generally, fibers with aerodynamic characteristics conducive to deposition at the primary 

7 or secondary bronchioles and alveoli can cause fibrogenesis and associated disease by either 

8 retention in the alveoli or penetration into the peribronchiolar space. All fibers (aerodynamic 

9 diameter less than approximately 2 μm), including Libby Amphibole asbestos, meet the physical 

10 criteria necessary for deposition in the deeper regions of the respiratory tract at the level of the 

11 terminal bronchioles or alveoli.

12 Deposition of fibers in the alveolar region of the lung is consistent with radiological 

13 findings in humans of fibrosis in the lower lung fields at early stages of disease. Deposition of 

14 fibers in the deep lung alveoli can become limited when fiber length approaches 40 μm (Morgan

15 et al., 1978). Deposition of fibers with high aspect ratios and length ranging from less than 1 μm 

16 to greater than 200 μm long, however, has been recorded (Morgan et al., 1978). In all 

17 documented observations of fibers collected from either healthy or diseased individuals, short 

18 fibers (<5 μm) were present in substantially greater numbers in lung tissue than were long fibers 

19 (>5 μm) (Churg, 1982; Churg and Warnock, 1980). Although information is limited on how 

20 fibers get to the pleura, fibers observed in tissue from mesothelioma cases are more likely to be 

21 short (<5 μm) (Suzuki et al., 2005). These observations could be due in part to the increased 

22 deposition of smaller fibers or the breakage of larger fibers over time (Bernstein et al., 1994;

23 Davis, 1994). 

24 The lung and nasal depositional differences are due in part to differences in airway 

25 structure and breathing patterns across lifestages (i.e., children, adults), changing the depositional 

26 pattern of different fiber sizes, possibly altering the site of action, and potentially resulting in 

27 differential clearance and health effects (see Section 4.7).

28 Modeling of fiber deposition has been examined for various fiber types (e.g., refractory 

29 ceramic fibers, chrysotile asbestos) (Sturm 2009; Zhou et al., 2007; Lentz et al., 2003; Dai and 

30 Yu, 1998; Yu et al., 1997; Coin et al., 1992), but not for Libby Amphibole asbestos. In general, 

31 fibers are expected to share patterns of deposition similar to particles which is well-studied 

32 (reviewed in ICRP 1994). For example, the multipath particle dose (MPPD) model (Jarabek 

33 et al., 2005; Brown et al., 2005) uses information on the physical properties of the particles 

34 (length and width [also called bivariate distribution] and density), the anatomy and architectural 

35 features of the airways, airflow patterns that influence the amount and the location of the 
This document is a draft for review purposes only and does not constitute Agency policy.

3-8 DRAFT—DO NOT CITE OR QUOTE

1 deposition of the particles, and dissolution and clearance mechanisms that are operative to

2 estimate the retained dose in the target tissue. However, although this model has not been fully 

3 developed for fiber deposition, due to similarities with particle deposition this can be used to 

4 inform deposition of fibers. 

5

6 3.2. CLEARANCE 

7 Complex relationships exist among different mechanisms that affect fiber accumulation: 

8 the amount of exposure, the duration of exposure, the physiological clearance from the lung, and 

9 the toxicological response observed in the body. In general, adverse health effects increase with 

10 an increase in exposure duration or dose (Bernstein et al., 2003, 2005; Cullen et al., 1997;

11 Ehrlich et al., 1992; Seidman et al., 1986). 

12 Clearance from one tissue may involve translocation to another tissue. For example, 

13 following fiber deposition in the respiratory tract fibers may then clear via translocation to 

14 extrapulmonary tissues like the pleura. The specific mechanism and translocation route depend 

15 both on fiber characteristics and the tissue of deposition. Whether or not fibers are translocated 

16 appears to depend on their physical-chemical characteristics, including two-dimensional size 

17 (length and width); durability; solubility; and reactivity. This translocation is aided by high 

18 biopersistence and inflammation-induced increase in permeability but is hindered by fibrosis. 

19 Deposition occurs in the respiratory tract as described above; translocation from the respiratory 

20 tract may in turn lead to fibers  ̳depositing‘ in extrapulmonary sites. Movement of fibers within 

21 the respiratory tract can also influence inflammatory reactions and cause tissue injury remote 

22 from the site of deposition. A general scheme for deposition and clearance of fibers is illustrated 

23 in Figure 3-1.

24

25 3.2.1. Inhalation

26 3.2.1.1. Respiratory Tract

27 Once fibers deposit on the surface of the respiratory tract, they may be removed (cleared) 

28 from the lungs in several ways, including physical clearance, dissolution, phagocytosis, or 

29 encapsulation. Some of these mechanisms, such as dissolution of the fibers, or removal via the 

30 mucociliary apparatus may result in the fibers being cleared from the body (see Figure 3-1). 

31 Other clearance mechanisms may remove fibers from the surface of the respiratory tract but 

32 result in transport of the fibers to other tissues by translocation. Translocation of fibers from the 

33 terminal bronchioles and alveoli into the transbroncheolar space, lymph nodes, and pleura of the 

34 lungs has been implicated in disease causation (e.g., pleural plaques, mesothelioma) (Dodson 

35 et al., 2001). In human studies, the translocation of asbestos fibers following inhalation has been 
This document is a draft for review purposes only and does not constitute Agency policy.

3-9 DRAFT—DO NOT CITE OR QUOTE

1 observed to varying degrees throughout the pulmonary and extrapulmonary tissues (Suzuki and 

2 Kohyama, 1991; Dodson et al., 2005; Kohyama and Suzuki, 1991; Dodson et al., 2001;

3 Sebastien et al., 1980), as well as other organs, including the brain, kidney, liver (Miserocchi 

4 et al., 2008), and ovaries (Langseth et al., 2007). In many cases, the type of fiber is not defined, 

5 and the individual exposure information is not available. Fibers that are not cleared may remain 

6 at the epithelial surface or enter the parenchymal tissue of the lung. 

7 Berry (1999) provided a review of the animal toxicity literature specifically for fiber 

8 clearance. There are limited data on clearance patterns based on autopsy studies in humans.

9 Two studies estimated increased clearance half-life for amphibole asbestos (~20 years) as 

10 compared to chrysotile asbestos (~10 years) (Churg and Vedal 1994; Finkelstein and Dufresne 

11 1999); in evaluating the data on lung fiber burden, Berry et al. (2009) estimated the range of the 

12 half-life for crocidolite to be between 5 and 10 years. Generally, studies have focused on 

13 determining the size and type of asbestos retained in specific tissues (Dodson et al., 1990; Gibbs 

14 et al., 1991; Suzuki et al., 2005; Dumortier et al., 1998; Suzuki and Yuen 2001; McDonald et al., 

15 2001) and did not discuss changes in fiber content since exposure. Sebastien et al. (1980) 

16 concluded that lung fiber burden could not be used as an accurate reflection of pleural fiber 

17 burden. 

18

19 3.2.1.1.1. Physical clearance of fibers

20 Large fibers deposited in the nasal passages can be removed by physical clearance. 

21 When breathing occurs through the nose, many fibers are filtered by the turbulent airflow in the 

22 nasal passages, impacting against the hairs and nasal turbinates, as well as becoming entrained in 

23 mucus in the upper respiratory tract where they can be subsequently removed by reflexive 

24 actions such as coughing or sneezing. Fibers can also translocate due to physical forces 

25 associated with respiration (Davis, 1989). Fibers with aerodynamic characteristics that cause 

26 them to impact at the nasopharyngeal or tracheobronchial regions of the respiratory tree are 

27 likely to be quickly removed (in minutes or hours) by the action of the mucociliary escalator. 

28 The mucociliary escalator removes fibers through ciliary movement of the sticky mucus lining 

29 (Churg et al., 1989; Wanner et al., 1996). Fibers removed from the pulmonary space through 

30 this mechanism are either coughed out, or subsequently swallowed and enter the digestive tract

31 where they may adversely affect the tissue, enter the body via the blood, or be excreted. 

32 Clearance of fibers via this mechanism is rapid and is usually complete within minutes or hours. 

33 However, the mucociliary escalator extends only to the level of the terminal bronchioles of the 

34 respiratory tree and not to the alveoli. Therefore, particles that reach these deeper regions cannot 

35 be cleared through this process.
This document is a draft for review purposes only and does not constitute Agency policy.

3-10 DRAFT—DO NOT CITE OR QUOTE

1 Some fibers are not cleared from the lung, leading to an accumulation with time (Case et 

2 al., 2000; Finkelstein and Dufresne, 1999; Jones et al., 1988). The fibers that remain in the lung 

3 may undergo a number of processes including translocation, dissolution, fragmentation, splitting 

4 along the longitudinal axis, or encapsulation with protein and iron. Available data indicate 

5 prolonged clearance from the lung of longer serpentine chrysotile fibers (>5 μm) or either long 

6 (>5 μm) or short amphibole fibers (Coin et al., 1994; Tossavainen et al., 1994). The prolonged 

7 clearance times for long amphibole fibers have led some investigators to conclude that long 

8 amphibole fibers are predominant in the cause of disease despite the relatively small numbers of 

9 these longer fibers in comparison to short fibers. However, Dodson et al. (2003) argue that 

10 fibers of all lengths induce pathological responses and urge caution in excluding any population 

11 of fibers based on their length, from contributing to the disease process. There are no data 

12 available with Libby Amphibole to resolve the issue about short or long fibers. The important 

13 feature illustrated by Figure 2-4 is that all of the fibers identified from the Libby ore samples are 

14 potentially respirable and can penetrate to the alveoli.

15 Apparent translocation of fibers throughout the respiratory tract is evident from 

16 experimental animal research done by several investigators following exposure by both 

17 intrapleural injection and inhalation (Bignon et al., 1979; Holt, 1982; Smith et al., 1974, 1979, 

18 1980; Miserocchi et al., 2008). Conflicting results from another study, however, indicate no 

19 evidence of fiber translocation from the central to peripheral compartments following inhalation 

20 exposure in rats, although this could be due to the short duration of the study (29 days post

21 exposure) (Coin et al., 1992). The data from most studies show that fibers can and do translocate 

22 among tissues and organs and move by both physiological and physical mechanisms (Cook and

23 Olson, 1979; Holt, 1982, 1983).

24

25 3.2.1.1.2. Dissolution of fibers

26 Dissolution, or the chemical breakdown of fibers, is another method of removal of fibers 

27 from the lung. This process varies, depending on the chemical composition of the fibers, as well 

28 as the physiological environment. Dissolution can occur in the lung‘s extracellular fluids or in 

29 the macrophage phagolysosome. Studies performed in vitro to determine dissolution rate of 

30 fibers attempt to mimic the extracellular lung fluids and macrophage-phagolysosome system to 

31 understand the length of time that fibers remain in the system (Rendall and du Toit, 1994). 

32 Studies have shown that dissolution occurs more rapidly for chrysotile fibers than for amphiboles 

33 (Coffin et al., 1983). Fibers can also be physically diminished through splitting or breakage. 

34 These smaller fragments are then more easily removed by phagocytosis or translocation.

35
This document is a draft for review purposes only and does not constitute Agency policy.

3-11 DRAFT—DO NOT CITE OR QUOTE

1 3.2.1.1.3. Removal of fibers through phagocytosis

2 The principal clearance pathway for insoluble fibers deposited in the alveoli is through 

3 phagocytosis by macrophages. Alveolar macrophages that have phagocytized insoluble fibers 

4 migrate to the bronchoalveolar junctions where they enter onto the mucociliary escalator for 

5 removal (Green, 1973). Alternatively, alveolar macrophages that have phagocytized insoluble 

6 fibers can also migrate through the epithelial wall into the interstitial space and enter the 

7 lymphatics (Green, 1973).

8 Alveolar macrophage cells engulf and transport invasive particles to the mucociliary 

9 escalator or through the bronchoalveolar epithelium to the interstitial tissues, where they are 

10 removed or translocated by the blood or lymphatics. Durable fiber impaction in these deeper 

11 regions also stimulates activation of alveolar macrophage cells. In vitro and in vivo studies 

12 clearly indicate that macrophage cells play a role in the translocation of fibers (Bignon et al., 

13 1979; Brody et al. 1981; Castranova et al., 1996; Dodson et al., 2000b). These studies have 

14 demonstrated the presence of asbestos fibers in cell cytoplasm where they can be transported in 

15 association with cytoskeletal elements to the proximity of the cell nucleus. Small chrysotile 

16 fibers can also penetrate the nuclear membrane (Malorni et al., 1990). 

17 A number of processes can disrupt the normal phagocytic function of the alveolar 

18 macrophages. These processes include death of phagocytes due to ingestion of highly reactive 

19 fibers, due to a high burden of deposited fibers (overload), or an attempt by the macrophages to 

20 engulf fibers that exceed the macrophage length (often termed ―frustrated phagocytosis‖) 

21 (NIOSH, 2008). All of these processes can induce inflammatory and fibrogenic responses.

22

23 3.2.1.1.4. Encapsulation of fibers

24 Fibers that are too large to be easily engulfed by the alveolar macrophage can stimulate 

25 the formation of asbestos bodies. Asbestos bodies are fibers that are coated with proteins, iron 

26 and calcium oxalate. The mechanisms that result in the formation of asbestos bodies are poorly 

27 understood, although most appear to be formed around amosite fibers (Dodson et al., 1996). The 

28 iron in the coating, however, is derived from the asbestos fiber, cells, or medium surrounding the 

29 fiber and can remain highly reactive (Ghio et al., 1992; Lund et al., 1994). These bodies are

30 sometimes referred to as ―ferruginous‖ bodies because of their affinity for iron-loving 

31 histological stains. Ferruginous bodies can remain in the lung throughout the lifetime of the 

32 exposed individual. Asbestos bodies comprise a minor portion of the overall fiber burden of the 

33 lung, and, after the fiber is fully coated, these fibers might or might not participate directly in 

34 asbestos disease. The presence of iron in the coating, however, could provide a source for 

35 catalysis of reactive oxygen species similar to that observed with fibers.
This document is a draft for review purposes only and does not constitute Agency policy.

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1 3.2.1.1.5. Translocation to extrapulmonary tissues

2 Translocation of fibers to extrapulmonary tissues has been studied in multiple studies, 

3 however, the mechanism is still unknown. This was more recently reviewed by Miserocchi et al. 

4 (2008). Fibers have been measured in extrapulmonary tissues including lung parenchyma, 

5 pleural plaques, and mesothelial tissue (i.e., pleural or peritoneal) in miners, brake workers, 

6 insulation workers, and shipyard workers (Dodson et al., 2000a; Roggli et al., 2002; Churg et al.,

7 1994; Kohyama and Suzuki, 1991). These studies found fibers at all locations analyzed, with 

8 increased levels of amphibole as compared to chrysotile in the parenchyma when subjects were 

9 exposed to a mixture of both fiber types. Amphibole fibers, however, were less prevalent in the 

10 pleura and mesothelial tissues (Sebastien et al., 1980, 1989; Bignon et al., 1979; Churg 1988; 

11 Kohyama and Suzuki, 1991). Few studies have examined the size distribution of fibers 

12 translocated to specific tissues. For example, one early study suggested that the longer 

13 amphibole fibers predominate in the lung while shorter chrysotile fibers are found in the pleura 

14 (Sebastien et al., 1980); others showed that the fiber-length distribution was the same by fiber 

15 type regardless of location (Kohyama and Suzuki, 1991; Bignon et al., 1979). 

16 Transplacental transfer of both asbestos (chrysotile, tremolite, actinolite, and 

17 anthophyllite) and nonasbestos fibers has been shown to occur in humans, as measured in the 

18 placenta and in the lungs of stillborn infants (Haque and Kanz, 1988; Haque et al., 1992, 1996, 

19 1998). It is hypothesized that maternal health might influence the translocation of fibers, as 

20 some of the mothers had preexisting health conditions (e.g., hypertension, diabetes, or asthma)

21 (Haque et al., 1992). This group also measured transplacental translocation in a mouse study and 

22 observed early translocation of crocidolite fibers through the placenta in animals exposed via 

23 tail-vein injection (Haque et al., 1998) These studies did not evaluate the source or levels of 

24 exposure, only the presence of fibers in the body during early lifestages in mice and humans. 

25 Sebastien et al. (1980) found chrysotile was the predominant fiber in parietal pleura of 

26 autopsy cases, while the amphibole fibers found in the lungs ranged from 0 to 100% (mean 

27 56%). Bignon et al. (1979) found similar distributions but also found increased amphibole fibers 

28 in the associated lymph nodes. In this study, chrysotile and amphibole fibers were found 

29 together in the lung parenchyma and alveolar spaces. Other studies show fewer amphibole fibers 

30 at the site of diseased tissue in the pleura and mesothelial tissue than chrysotile (Churg, 1988;

31 Kohyama and Suzuki, 1991). Sebastien et al. (1989) examined fiber types in lungs of chrysotile 

32 textile and mining workers from South Carolina and Quebec to better understand the unknown 

33 reason for differences in disease risk in each cohort. Both groups were exposed to similar

34 material, yet the South Carolina cohort had a much greater risk of respiratory cancer. This study 

35 examined only lungs, although some of those exposed had nonpulmonary cancers. Overall, the 
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3-13 DRAFT—DO NOT CITE OR QUOTE

1 number of tremolite fibers was higher than that of chrysotile fibers in both cohorts. Size 

2 distribution showed that most fibers measured were 5.8−8.0 μm long, although measurements 

3 were not made for anything smaller than this. Tremolite fibers had a greater mean diameter in 

4 both cohorts (0.35 μm) as compared to chrysotile (0.10 μm), while chrysotile had more

5 ―Stanton‖ fibers (25.2–31.8%) as compared to tremolite (5.9–6.3%). Stanton fibers are defined 

6 as >8 μm long and <0.25 μm in diameter (Stanton et al., 1981, reviewed in Appendix G).

7

8 3.2.1.2. Pleural Cavity and Extrapulmonary Sites

9 Studies have demonstrated fiber clearance from the respiratory tract may lead to 

10 translocation to the pleural cavity and extrapulmonary sites. For example, in a study comparing 

11 fiber burden in the lung, thoracic lymph nodes, and pleural plaques, Dodson et al. (1990) 

12 observed that the average-length fiber found in the lung (regardless of type) was longer than

13 those found in the lymph nodes or plaques. Most fibers at all three sites were short (<5 μm). A 

14 later study by this group (i.e., Dodson et al., 2000a) examined tissue from 20 individuals with 

15 mesotheliomas, most with known asbestos exposures. Seventeen of the cases (85%) had 

16 asbestos fibers in at least one other extrapulmonary site. The most prevalent type of asbestos in 

17 the mesentery was amosite, and the second most prevalent was chrysotile. Tremolite was also

18 found, to some degree, in the mesentery and omentum, and in the lung. Dodson et al. (2005)

19 examined parenchymal lung tissue from a cohort of 54 mesothelioma patients and determined 

20 the presence of asbestos in all patients analyzed. However, very little information is known 

21 about the specific mechanisms of fiber clearance and/or translocation from the pleural cavity and 

22 extrapulmonary sites, although many studies examining these tissues have observed fibers in 

23 multiple tissue sites. Following intrapleural injection of fibers in rats, Bignon et al. (1979) used 

24 transmission electron microscopic evaluation following serial sacrifice to monitor migration of 

25 fibers from the pleural cavity to the lung parenchyma. 

26

27 3.2.2. Ingestion

28 Although ingestion is a potential route of exposure, limited research has examined 

29 clearance (e.g., translocation) of fibers following ingestion, and no clearance studies are 

30 available specific to Libby Amphibole asbestos. An early study to examine the tissue response 

31 to asbestos fibers is not truly representative of a natural ingestion exposure, as the researchers 

32 directly injected a suspension of amosite fibers into the duodenal wall (Meek and Grasso, 1983). 

33 This study, however, also examined oral ingestion of amosite in healthy animals and those with 

34 gastrointestinal ulcers to determine if translocation of fibers occurs through ulcers. Following 

35 injection of amosite, granulomatous lesions were observed. Ingestion of the same material 
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3-14 DRAFT—DO NOT CITE OR QUOTE

1 resulted in no such lesions or in any other histopathological changes in either healthy or

2 compromised rats. Thus, no translocation was observed from either the healthy or the 

3 compromised rat gastrointestinal tracts in this study. A later International Agency for Research 

4 on Cancer study (Truhaut and Chouroulinkov, 1989) examined the effects of chrysotile and 

5 crocidolite ingestion in Wistar rats. No translocation was observed. No further studies have 

6 been found on clearance or translocation of fibers from the gastrointestinal tract. 

7

8 3.2.3. Dermal

9 No studies of dermal clearance or translocation have been reported in the published 

10 literature.

11

12 3.3. SUMMARY

13 Although oral or dermal exposure to fibers does occur, inhalation is considered the main 

14 route of human exposure to fibers, and therefore has been the focus of more fiber toxicokinetic 

15 analyses. Exposure to Libby Amphibole asbestos is proposed to be through all three routes of 

16 exposure, although information specific toxicity based on differential routes of exposure is not 

17 available. Generally, fiber deposition in the respiratory tract is fairly well defined based on fiber 

18 dimensions and density, although the same cannot be said for fiber translocation to 

19 extrapulmonary sites (e.g., pleura). The deposition location within the respiratory tract and 

20 extrapulmonary sites plays a role in the clearance of the fibers from the organism. 

21 Fiber clearance can occur through physical mechanisms like coughing and sneezing, or 

22 biological mechanisms including translocation. Limited mechanistic information is available on 

23 fiber clearance mechanisms in general, and no information specific to clearance of Libby 

24 Amphibole asbestos fibers is available. Fibers have been observed in various pulmonary and 

25 extrapulmonary tissues following exposure, suggesting translocation occurs to a variety of 

26 tissues. Studies have also demonstrated fibers may be cleared through physical mechanisms 

27 (coughing, sneezing) or through dissolution of fibers. 

28 Multiple fiber characteristics play a role in the toxicokinetics of fibers (e.g., dimensions, 

29 density, and durability). For this reason, careful attention has been paid to these fiber 

30 characteristics when analyzing research studies on Libby Amphibole asbestos and tremolite,

31 another amphibole fiber that comprises part of Libby Amphibole asbestos (see Appendix D). No 

32 toxicokinetic data is available specific to Libby Amphibole asbestos, tremolite, richterite or 

33 winchite. When available, this information is presented in the discussion of each paper in 

34 relation to the toxic endpoints described. 
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1 4. HAZARD IDENTIFICATION OF LIBBY AMPHIBOLE ASBESTOS

2 Several human studies are available that provide evidence for the hazard identification of 

3 Libby Amphibole asbestos. This discussion focuses primarily on data derived from studies of 

4 people exposed to Libby Amphibole asbestos—either at work or in the community. The adverse 

5 health effects in humans are supported by the available Libby Amphibole asbestos experimental 

6 animal and laboratory studies. Libby Amphibole asbestos contains winchite (84%), with lesser 

7 amounts of richterite (11%) and tremolite (6%) with trace amounts of magnesioriebeckite, 

8 edenite, and magnesio-arfvedsonite (Meeker et al., 2003) (see Section 2 for a more complete 

9 discussion). Adverse health effects from tremolite exposure have been reported in both human 

10 communities and laboratory animals; these effects are consistent with the human health effects 

11 reported for Libby Amphibole asbestos. The hazard identification discussion for Libby 

12 Amphibole asbestos (including tremolite) is not limited to cancer incidence and mortality data, 

13 the noncancer health effects—and mechanistic data related to these effects—are also described. 

14 Some pathogenic pathways might be shared between some noncancer health effects (e.g., 

15 inflammation, fibrosis) and cancer; thus, these effects can be considered relevant to the 

16 subsequent development of cancer. The presentation of noncancer and cancer health effects 

17 provides a comprehensive review of adverse health effects observed from exposures to the Libby 

18 Amphibole asbestos. 

19 Few data specific to Libby Amphibole asbestos are currently available on which to base a 

20 detailed understanding of mechanisms for its adverse effects. Similarly, specific questions about 

21 susceptible populations are not fully addressed by Libby Amphibole asbestos data alone. 

22 Therefore, the discussions of the mode(s) of action (Section 4.3.2) and susceptible populations 

23 (Section 4.4) also draw from the literature on mineral fiber toxicity but are not intended to be a 

24 comprehensive review of the broader subject.

25

26 4.1. STUDIES IN HUMANS—EPIDEMIOLOGY

27 The Libby Amphibole asbestos epidemiologic database includes studies conducted in 

28 occupational settings examining exposures to workers, and community-based studies, which can 

29 include exposures to workers, exposures to family members of workers, and exposures from 

30 environmental sources. Occupational epidemiology studies exist for two work sites where 

31 workers were exposed to Libby Amphibole asbestos. These are the mine and mill at the Zonolite 

32 Mountain operations near Libby, MT, and a vermiculite processing plant in Marysville, OH. 

33 Worker cohorts from each site and the study results are described in Section 4.1.1. 
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1 Whitehouse et al. (2008) described the occurrence of 11 cases of mesothelioma among 

2 individuals who lived in or around Libby, MT, who had not worked in the mining or milling 

3 operations. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted 

4 community health consultations for Libby, MT, including an evaluation of cancer mortality data, 

5 and a health screening of current and former area residents (including workers) that collected 

6 medical and exposure histories, chest x-rays, and pulmonary function tests (ATSDR, 2000, 

7 2001). Several published papers are available based on data from this health screening. 

8 ATSDR, in conjunction with state health departments, also conducted health consultations for 

9 28 other communities around vermiculite processing plants that were potentially exposed to 

10 Libby Amphibole asbestos (see Section 4.3). These health consultations consisted of analyses of 

11 cancer incidence or mortality data; results from nine of these studies are currently available. The 

12 other studies have not been completed at the time of this assessment. 

13 No occupational studies are available for exposure to tremolite, richterite, or winchite 

14 mineral fibers individually or as a mixture exposure, other than Libby Amphibole asbestos. 

15 Communities, however, have been exposed to tremolite and other mineral fibers from natural 

16 soils and outcroppings. Tremolite asbestos-containing soil has been used in whitewash in 

17 interior wall coatings in parts of Turkey and Greece. Studies in these areas published as early as 

18 1979 reported an increased risk of pleural and peritoneal malignant mesothelioma (Baris et al., 

19 1987; Langer et al., 1987; Baris et al., 1979; Sichletides et al., 1992). More recent studies of 

20 communities exposed to tremolite and chrysotile fibers report excess lung cancer and 

21 mesothelioma (1.3- and 6.9-fold, respectively) (Hasanoglu et al., 2006). Other studies reported 

22 pleural anomalies in residents exposed to naturally occurring asbestos, which includes actinolite, 

23 tremolite, and anthophyllite (Metintas et al., 2005; Zeren et al., 2000). Clinical observations 

24 include a bilateral increase in pleural calcification accompanied by restrictive lung function as 

25 the disease progresses, a condition known as Metsovo lung, named after a town in Greece 

26 (Comkrantopoulos et al., 1985). In one community, the prevalence of pleural calcification was 

27 46% (of 268 residents), increasing with age to 80% in residents over 70 (Langer et al., 1987). 

28 Both tremolite and chrysotile were identified in bronchoalveolar lavage fluid of 65 residents 

29 from different areas of Turkey who were environmentally exposed (Dumortier et al., 1998). The 

30 health effects observed in communities with environmental and residential exposure to tremolite 

31 are consistent with health effects documented for workers exposed to commercial forms of 

32 asbestos. 

33
This document is a draft for review purposes only and does not constitute Agency policy.

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1 4.1.1. Studies of Libby, MT Vermiculite Mining Operation Workers

2 Several studies of mortality from specific diseases among workers in the Libby, MT

3 mining operations have been conducted, beginning in the 1980s with the studies by McDonald et 

4 al. (1986a,b) and Amandus (1987a,b; Amandus and Wheeler, 1987). McDonald et al. (2002, 

5 2004) published an update with mortality data through 1999, and Sullivan (2007) updated the 

6 National Institute of Occupational Safety and Health (NIOSH) cohort (referred to in this 

7 document as the Libby worker cohort) with mortality data through 2001. Additionally, Larson et 

8 al. (2010a) reconstructed a worker cohort and analyzed mortality through 2006 in this same 

9 study population, while another study examined changes in lung abnormalities using x-rays 

10 taken between 1955 and 2004 of 88 workers (Larson et al., 2010b). 

11

12 4.1.1.1. Description of Mining and Milling Operations

13 The vermiculite mining and milling operations have been described in considerable detail 

14 (ATSDR, 2000). An open-pit vermiculite mine began limited operations in 1923, and production 

15 increased rapidly between 1940 and 1950. This mine is located on Zonolite Mountain, several 

16 miles east of the town of Libby (ATSDR, 2000). The Kootenai River runs between the town and 

17 the mine. The mining and milling operations continued until 1990 (ATSDR, 2000).

18 The drilling and blasting procedures used in the strip mining operations generated 

19 considerable dust exposures, although the mining operations had lower intensity exposures 

20 compared to the milling operations. Amandus et al. (1987a) noted that in 1970, a new drill with 

21 a dust-control bagging system aimed at limiting workplace exposure was introduced to the 

22 mining operations. 

23 Another aspect of the operations was the loading of ore for railroad shipment. From 

24 1935–1950, railroad box cars were loaded at a station in Libby. In 1950, the loading station was 

25 moved to a loading dock on the Kootenai River, 7 miles east of town. Tank cars were used from 

26 1950–1959 and then switched to enclosed hopper cars in 1960. 

27 The milling operations use a screening or sifting procedure to separate vermiculite flakes 

28 from other particles and increase the concentration of vermiculite from approximately 20% in the 

29 bulk ore to 80–95% in the resulting product. A dry mill began operating in 1935, and a wet mill 

30 began operating in the 1950s in the same building as the dry mill. One of the primary changes in 

31 conditions in the dry mill was the installation of a ventilation fan in 1964. Exposure to asbestos 

32 inside the mill was estimated to be 4.6 times higher preceding this installation (McDonald et al., 

33 1986a). This ventilation fan resulted in higher amphibole fiber exposures in the mill yard until 

34 1968, when the exhaust stack for the fan was moved away from the shop area around the mill. 

35 Other changes to the milling operations in the 1970s included replacement of hand bagging and 
This document is a draft for review purposes only and does not constitute Agency policy.

4-4 DRAFT—DO NOT CITE OR QUOTE

1 sewing with an automatic bagging machine (1972), pressurization of the skipper control room 

2 used for transferring the concentrate from the mill to a storage site (1972), and construction of a 

3 new wet mill (1974). Closing of the old dry and wet mills in 1976 had a substantial impact on 

4 exposures at the worksite. In 1974, a new screening plant used to size-sort the concentrate was 

5 constructed at the loading dock near the river. Two processing plants operated within the town 

6 of Libby (ATSDR, 2001). These expansion or exfoliation plants heated the ore concentrate, 

7 resulting in additional release of the Libby Amphibole asbestos fibers in the area. 

8 4.1.1.2. Exposure Estimation

9 In the early 1980s, two research groups conducted parallel studies of the mortality 

10 experienced by workers in the Libby mining and milling operations. One study was undertaken 

11 by NIOSH, and the other by researchers from McGill University. The exposure assessment 

12 procedures used by the two groups relied on the same exposure measurements and used similar 

13 assumptions in creating exposure estimates for specific job activities and time periods 

14 (Table 4-1). In brief, available air sampling data were used to construct a job exposure matrix 

15 assigning daily exposures (8-hour time-weighted average) for identified job codes based on 

16 sampling data for specific locations and activities. Varying job codes and air exposures were 

17 used for different time periods as appropriate to describe plant operations. Individual exposure 

18 metrics (e.g., lifetime cumulative exposure) were calculated using the work history of each 

19 individual in the study in conjunction with the plant job exposure matrix. The specific study 

20 details for the Libby, MT worker cohort are described in more detail below, with differences 

21 between the research groups highlighted.

22 Before 1970, exposure estimates were based on midget impinger samples taken primarily 

23 in the dry mill, by state and federal inspectors. Total dust samples were measured as million 

24 particles per cubic foot (mppcf) by the midget impinger method. Amandus et al. (1987a) 

25 describe the period during which most of the midget impinger measurements were made as 

26 1962–1967, and McDonald et al. (1986a) describe this period as 1962–1969, with a few 

additional measures in earlier years.127 The number of samples available before 1970 was 336 

28 (Amandus et al., 1987a). Membrane-filter air samples for fibers, taken at various locations 

29 within the operations, began in 1967, and data are available from company records as well as 

30 State and Federal Agencies (Table 4-2). Stationary and short-term (i.e., 20-minute to less than 

31 4-hour) measurements were primarily used prior to 1974. The number of membrane-filter 

32 samples available was 4,116. Air samples collected through membrane filters were analyzed by 

 

1

Amandus et al. (1987a) indicates that one sample was available from 1942, and additional samples were available 

after 1956; McDonald et al. (1987a) indicates that additional samples were available from 1944, 1956, and 1958.
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Table 4-1. Exposure assessment methodologies used in evaluations of Libby, 

2 MT and Marysville, OH worker cohorts

3

Operation and study cohort

Asbestos fiber quantification and job 

exposure classification Studies using methodology

Libby, MT mining and 

milling operations; NIOSH 

cohort

Exposure based on phase-contrast 

microscopy of fibers >5-μm long and 

aspect ratio >3:1 (1967-1982), and midget 

impinger data (1956-1969).

Samples assigned to 25 ―occupation 

locations‖ to estimate exposures for 

specific jobs and time periods 1945–1982. 

Membrane-filter measurement to impinger 

conversion ratio: 4.0 f/cc per mppcf.

Amandus et al., 1987a, b; Amandus 

and Wheeler, 1987

Libby, MT mining and 

milling operations; NIOSH 

cohort

Modification to Amandus et al. (1987a) job 

classification: laborers and ―unknown‖ 

jobs assigned weighted-average exposure 

for all unskilled jobs in work area (if 

known) during calendar time period, rather 

than lower mill yard exposure. 

Weights based on the number of workers 

assigned to unskilled jobs during same 

calendar time period.

Sullivan, 2007; Moolgavkar et al., 

2010

Libby, MT mining and 

milling operations; ATSDR 

cohort assembled from 

NIOSH records

Extension of Amandus et al. (1987a) 

exposure data, with additional application 

of exposure estimates to job titles from 

early 1980s through 1993

Larson et al., 2010a, b

Libby, MT mining and 

milling operations; McGill 

University cohort

Similar to Amandus (1987a), except with 

28 ―occupation locations,‖ and conversion 

ratio = 4.6 for dry mill pre- and post 1964.

McDonald et al., 2004, 1986a, b

Marysville, OH

fertilizer production facility 

using Libby, MT vermiculite

Plant operated since 1957; Libby, MT

vermiculite ore used in the plant 1963–

1980.a

Industrial hygiene monitoring began 1972 

(based on fibers >5-μm long, diameter 

<3 μm, ≥aspect ratio 3:1). Breathing zone 

samples used after 1976. Fiber analysis by 

PCM. 

Lockey et al., 1984; Rohs et al., 2008

4

a

5 Rohs et al. (2008) use 1963 as the beginning date of the use of Libby, MT vermiculite at the Marysville, OH plant,

6 citing the Agency for Toxic Substances and Disease Registry (ATSDR) Health Consultation document (ATSDR, 

7 2005). That document cites personal communication with company personnel as a source for a start date of 1967, 

8 however. Another ATSDR document, a review of 28 vermiculite exfoliation plants, reported the likely period of use 

9 was 1963–1980 (ATSDR, 2008). Lockey et al. (1984) used 1957 as the beginning date. Additional information was 

10 used to conclude that the beginning date for use of Libby vermiculite ore was 1959 (Appendix F)

11 NIOSH = National Institute for Occupational Safety and Health.

12

13

14
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Table 4-2. Source of primary samples for fiber measurements at the Libby 

2 mining and milling operations

3

Source Unit of measurement Years Number of samples

State of Montana mppcfa1956-1969b336

NIOSH f/ccc1967-1968 48

MESA/MSHA f/cc 1971-1981 789

Company records f/cc 1970-1982 3,279

4

a

5 Million particles per cubic foot of air, sampled by a midget impinger apparatus and examined by light 

6 microscopy.

b

7 MESA: U.S. Mining and Enforcement and Safety Administration (former name of MSHA).

c

8 Fibers per cc of air drawn through a filter and examined under a phased contrast light microscope. Objects 

9 >5 μ and with an aspect ratio >3 were reported as fibers (see Section 2 for details).

d

10 MSHA: U.S. Mining and Safety Administration.

11

12 Source: Amandus et al. (1987a).

13

14

15 phased contrast microscopy (PCM), which visually counts fibers greater than >5-μm long and 

16 having an aspect ratio >3:1 (Amandus et al., 1987a). PCM methods from the 1960s allowed 

17 reliable characterization of fibers with widths greater than approximately 0.4 μm (Skikne, 1980; 

18 Amandus et al., 1987a). Further standardization of the PCM method provides better 

19 visualization of thinner fibers, and 0.25-μm width is considered the resolution for fiber width 

20 (WHO, 1986).

21 The samples taken from specific work locations within the plant were used to estimate 

22 exposures in specific jobs and time periods, based on professional consideration of temporal 

23 changes in facilities, equipment, and job activities. The analysis by McDonald et al. (1986a) was 

24 based on 28 occupation locations, while the work of Amandus et al. (1987a) was based on 

25 25 occupation locations. For the years after 1968, data from filter samples were available for all 

26 locations, and NIOSH researchers used the average (arithmetic mean) exposure when more than 

27 one sample was available for a given location or job task and time period. McDonald et al. 

28 (1986a) used an alternative procedure described by Oldham (1965) to estimate the mean of 

29 log-normal distributions. 

30 For exposures occurring prior to 1968, both research groups estimated exposures for all 

location operations231 except the dry mill based on post-1968 PCM air sampling results and 

 

2 Location Operations were defined by the respective research groups to categorize tasks and locations across the 

mining milling and shipping operations. The intention of the location operations is to group like tasks, with respect 

to exposure potential, for evaluation. Both research groups established similar location operations for the Libby 

cohort.
This document is a draft for review purposes only and does not constitute Agency policy.

4-7 DRAFT—DO NOT CITE OR QUOTE

1 reasonable assumption regarding historical exposures. McDonald et al. (1986) estimated pre2 1968 exposure measurements for 26 LOs; assumptions were made and estimates based on data 

3 from later years or related operations, although these assumptions are not stated by the authors. 

4 McDonald et al. (1986) did recognize the uncertainty in these calculations, and for four areas, 

5 provided high and low estimates (drilling, ore loading, river dock, and bagging plant). Similarly, 

6 NIOSH researchers interviewed company employees, considered relative exposure levels 

7 between locations post 1968, employing best available judgment to estimate task specific 

8 exposure levels. Amandus et al. (1987a) expanded the procedures described in McDonald et al. 

9 (1986a, 1987a) and used various assumptions to estimate pre-1968 exposures for four location 

10 operations (drilling, ore loading, river dock, and bagging plant). ―Low‖ and ―high‖ estimates 

11 were generated using different assumptions; the detailed results for the various assumptions were 

12 not presented, but the differences between them were described by the authors as ―slight,‖ and 

13 the results presented were based on the high estimate of exposure. Their decisions and specific 

14 assumptions are detailed (Amandus et al., 1987a). The authors acknowledge there is uncertainty 

15 in exposure estimates prior to 1968 for many of these locations. They do note that variability in 

16 sample results for the midget impinger was low and that, in general, sample variability was low 

17 for fiber air sampling results for areas where the greatest numbers of employees worked (mill, 

18 service area, loading and bagging.)

19 To estimate dry mill exposures prior to 1967, when fiber counts from phase contrast 

20 microscopy air samples began to be used to measure exposures, Amandus et al. (1987a) 

21 established a conversion factor from total dust counts (mmpcf) to fiber counts (fibers/cc). The 

22 conversion ratio was based on a comparison of 336 impinger samples taken in 1965–1969 and 

23 81 filter samples taken in 1967–1971. Both sets of samples were taken in the dry mill. Using 

24 different subsets of the samples (i.e., different years) resulted in ratios that ranged from 

25 1.9 fibers/cc:1.0 mppcf to 11.5 fibers/cc:1.0 mppcf. The ratio based on the average fiber counts 

26 from air samples (1967-1971) to the average total dust measurements in sample years 1965-1969 

27 was 4.0 fibers/cc:1.0 mppcf. This was the ratio used in the analyses in the NIOSH studies 

28 (Amandus et al., 1987a, b; Amandus and Wheeler, 1987) because it allowed for the use of the 

29 greatest amount of data from overlapping time periods, while controlling for the reduced 

30 exposure levels after 1971 where fiber count based on phase contrast microscopy but not midget 

31 impinger data were available. This dust–to-fiber conversion factor was only used to estimate 

32 exposures in the dry mill. The resulting exposure concentrations of 168 f/cc in 1963 and all prior 

33 years and 35.9 fibers/cc in 1964-1967 were applied to dry mill exposures (Amandus et al., 

34 1987a).
This document is a draft for review purposes only and does not constitute Agency policy.

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1 McDonald et al. (1986a) used a different procedure, based on the estimated reduction in 

2 dust exposure with the installation of the ventilation system in 1964. Rather than develop a 

3 direct dust-to-fiber conversion factor, they observed that total dust levels dropped approximately 

4 4.6-fold after the installation of ventilation in the dry mill. Therefore, exposures in the dry mill 

5 prior to 1965 were calculated as 4.6 times the fiber exposures measured by PCM between 1970 

6 and 1974 (22.1 fibers/cc) resulting in estimated dry mill exposures of 101.5 fibers/cc prior to 

7 1965 (McDonald et al., 1986a). 

8 Exposure estimates for each location operation derived from sampling data and history of 

9 changes in control measures were used to develop a job exposure matrix that estimated exposure 

10 in fibers/cc for each job code during several calendar time periods. Jobs were mapped to 

11 operation/location based on estimated time spent in different job tasks, thus estimating an 8-hour 

12 time-weighted average exposure for each job during several calendar time periods. Job histories 

13 from date of first employment to 1982 were used with the job exposure matrix to develop 

14 cumulative exposure estimates for each worker. 

15

16 4.1.1.2.1. Characteristics of historical fiber exposures

17 The resulting exposure estimates presented by both research groups, and the job exposure 

18 matrices used in calculating cumulative exposure for the cohort are based on fiber counts by 

19 phase contrast microscopy analysis of air filters. As discussed in Section 2, phase contrast 

20 microscopy analysis does not distinguish between fiber mineralology or morphology and all 

21 fibers >5 μm in length with an aspect ratio of 3:1 or greater are included. Both researcher groups 

22 analyzed fibers available at the facility in order to identify the mineral fibers in the air samples. 

Transmission electron microscopy323 (TEM) analysis of airborne asbestos fibers indicated a 

24 range of fiber morphologies—including long fibers with parallel sides, needlelike fibers, and 

25 curved fibers (McDonald et al., 1986a). Of the fibers examined by TEM, >62% were >5 um in 

26 length and a wide range of dimensional characteristic were noted: length (1-70 um), width 

27 (0.1-2 um), and aspect ratios from 3-100. Energy dispersive spectroscopy was used to determine 

28 the mineral analysis indicated fibers that would be in the actinolite-tremolite solution series, but 

29 sodium rich (McDonald et al., 1986a). This analysis is consistent with the current understanding 

30 of amphibole asbestos found in the Libby mine (Section 2).

31 At the time of their study, when exposure concentrations were reduced to generally less 

32 than 1 f/cc, NIOSH researchers obtained eight air filters from area air samples collected in the 

 

3

Transmission electron microscopy (TEM) utilizes a high-energy electron beam to irradiate the sample. This allows 

visualization of structures much smaller than can been seen under light microscopy. TEM instruments may be fitted 

with two supplemental instruments that allow for a more complete characterization of structure than is possible 

under light microscopy: energy dispersive spectroscopy (EDS) and selected area electron diffraction (SAED).
This document is a draft for review purposes only and does not constitute Agency policy.

4-9 DRAFT—DO NOT CITE OR QUOTE

1 new wet mill and screening plant (provided by the mining company). These samples were 

2 analyzed by phased contrast microscopy using the appropriate analytical method for the time 

3 (NIOSH Physical and Chemical Analytical Method No. 239). From early method development 

4 through current PCM analytical techniques, the PHS, OSHA, and NIOSH methods have defined 

5 a fiber by PCM analysis as having an aspect ratio greater than 3:1 (Edwards and Lynch, 1967; 

6 NIOSH method 7400). NIOSH reported the dimensional characteristics of the fibers from these 

7 filters including aspect ratio, width, and length (Table 4-3) (Amandus et al.,1987a). Data for 

8 599 fibers from the 8 area air samples collected in the wet mill and screening plant are provided. 

9 These data are limited in one sense by the minimum diameter and length cutoffs (>4.98-μm long, 

10 >0.44-μm wide, aspect ratio >3.0). Even with these limitations, where thinner fibers are not 

11 viewed, 96% of the fibers had aspect ratios greater than 10:1, with 16% greater than 50:1 aspect 

12 ratio. Only 7% of the fibers had a width greater than 0.88 um, with one fiber reported of the 559 

13 with a width greater than 1.76. It should be noted that as NIOSH was examining PCM visible 

14 fibers, these data do not give the full fiber-size distribution of Libby amphibole asbestos fibers 

15 (Section 2.2.3.3.)

16

17 Table 4-3. Dimensional characteristic of fibers from air samples collected in 

18 the vermiculite mill and screening plant, Libby, MT. Fibers were viewed and 

19 counted by Phase Contract Microscopy (PCM)

20

Fiber length (μm) Fiber width (μm) Aspect ratio

Range

Total

counted

Percent 

(%) Range

Total

counted

Percent 

(%) Range

Total

counted

Percent 

(%)

4.98–7.04 54 9 0.44–0.62 406 68 5–10 24 4

7.04–9.96 109 18 0.62–0.88 151 25 10–20 176 29

9.96–14.08 107 18 0.88–1.24 27 5 20–50 305 51

14.08–19.91 111 19 1.24–1.76 14 2 50–100 84 14

19.91–28.16 90 15 1.76–2.49 0 0 >100 10 2

28.16–39.82 65 11 >2.49 1 0

39.82–66 46 8

66–88 10 2

>88 7 1

21

22 Source: Amandus et al. (1987a).

23

24

25
This document is a draft for review purposes only and does not constitute Agency policy.

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1 4.1.1.2.2. Descriptions of cohorts

2 The cohort studies conducted in the 1980s were similar in terms of exposure assessment 

3 (as described in the previous section, Table 4-1), and other aspects of the study design 

4 (Table 4-4). Both studies included workers who had worked for at least 1 year. Amandus and 

5 Wheeler (1987) included men hired before 1970 (n = 575), with follow-up through December 31, 

6 1981. McDonald et al. (1986a) included men hired before 1963 (n = 406) with follow-up 

7 through 1983. A later analysis (McDonald et al., 2004) extended this follow-up through 1999.

8 A more recent analysis of the Libby, MT workers expanded the cohort to include all workers 

9 hired prior to 1970, regardless of duration of employment. The total sample (n = 1,672 white 

10 men) included 808 workers who had worked for less than 1 year. These short-term workers had 

11 been excluded from the previous studies in Table 4-4. Analyses presented in the report were 

12 based on follow-up from 1960–2001. This beginning point was chosen because comparison 

13 rates for asbestosis, an outcome of interest, were not available before 1960 in the NIOSH Life 

14 Table Analysis System, the analytic software used in the analysis (Sullivan, 2007). Few deaths 

15 had occurred before 1960 (95 men dead or lost to follow-up before 1960 were excluded), so this 

16 exclusion criterion would not be expected to result in a substantial loss of outcomes. Because 

17 mesothelioma was not coded separately until 1999, the published mesothelioma analysis is based 

18 on data from 1999–2001. 

19 In the study by Sullivan (2007), comparison rates for standardized mortality ratio (SMR) 

20 analyses were calculated from U.S. population cause-specific mortality data, limited to white 

21 males, and adjusted for age and calendar year of follow-up (using 5-year groups). McDonald et 

22 al. (2002) also used comparison rates from the U.S. population and included additional analyses 

23 for the category of respiratory cancers using Montana population rates.

24 Larson et al. (2010b) reconstructed a worker cohort based on company records and 

25 analyzed mortality risks through 2006. This study included 1862 workers; inclusion and 

26 exclusion criteria are not stated, and, thus, it is not clear whether this analysis excluded females 

27 or specific ethnic groups. The exposure assessment methodology was based on the methods

28 described by Amandus et al. (1987a), without the modification used by Sullivan (2007). 

29 Multiple causes of death (i.e., from any mention on the death certificate) was used, rather than 

30 underlying cause of death. Because multiple causes of death are used, more than one cause of 

31 death can be coded for an individual. 

32 The studies of the Libby worker cohort by Amandus and Wheeler (1987), Sullivan 

33 (2007), and Larson et al. (2010a) defined lung cancer mortality based on a more limited range of 

34 diagnostic codes compared to the broader classification of ―all respiratory cancer‖ used by 

35 McDonald et al. (1986a, 2004). For example, the International Classification of Diseases (ICD)-
This document is a draft for review purposes only and does not constitute Agency policy.

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1 9 codes used for deaths occurring during the applicable years (1979 to 1999) in the NIOSH 

2 cohort in Sullivan (2007) were ICD-9 162 (trachea, bronchus, and lung) and 163 (pleura); ICD3 10 codes C33-C34 were used for deaths in 2000 or later. In the first McDonald et al. (1986a) 

4 analysis, ICD-9 codes 160–163 for respiratory cancer were used, which also included cancer of 

5 the larynx (ICD-9 code 161) and some types of ―other‖ respiratory cancers (ICD-9 code 160). 

6 The updated follow-up for 1999 included ICD-9 codes 160–165 for respiratory cancer, adding to 

7 the ―other‖ respiratory cancer group (ICD-9 codes 164 and 165). In the national Surveillance, 

8 Epidemiology, and End Results (SEER) cancer data from 2001–2005, the age-adjusted incidence 

9 rate for cancer of the larynx was 3.6, compared to 63.9 per 100,000 person-years for lung and 

10 bronchial cancer (NCI, 2009). Thus, these additional categories (larynx and ―other‖ respiratory 

11 cancers) represent a relatively small proportion of respiratory cancers, but could be a source of 

12 some misclassification of the outcome if these other cancers are not related to asbestos exposure. 

13 The classification of mesothelioma was more difficult because of the lack of a unique 

ICD code for mesothelioma prior to the 10th 14 revision, implemented in the United States in 1999. 

15 The updated NIOSH study by Sullivan (2007) identified 15 deaths for which mesothelioma was 

16 mentioned on the death certificate. Only two of these deaths occurred between 1999 and 2001; 

17 these were coded using the ICD-10 mesothelioma coding (C45). Larson et al. (2010) classified 

18 any death certificate listing mesothelioma as an immediate or underlying cause of death as 

19 ICD-10 code C45. The updated McGill study (McDonald et al., 2004) also noted that the 

20 classification of mesothelioma was based on a nosologist’s review of death certificates; only 5 of 

21 the 12 cases classified as mesothelioma had a cause of death listed as pleural cancer (ICD-9 

22 code 163).
This document is a draft for review purposes only and does not constitute Agency policy.

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Table 4-4. Respiratory cancer mortality and lung cancer dose-response analyses based on studies of the 

vermiculite mine workers in Libby, MTa

Reference(s) Inclusion criteria and design details

Standardized mortality 

ratio (SMR) (95% CI) Dose-response analyses – lung cancer

Amandus and 

Wheeler, 1987 

Men, hired before 1970, worked at least 

one year, follow-up through 1982 (n = 

575); 161 deaths (159 with death 

certificates).

Mean duration: 8.3 years (0 worked less 

than 1 year).

Mean fiber-years: 200.3.

12 female workers not included in this 

analysis.

No exclusions:

All cancer (n = 38) 

SMR: 1.3 (0.9, 1.8)

Lung (n = 20) 

SMR: 2.2 (1.4, 3.4)

20 or more years since 

first hire (latency):

Lung (n = 12)

SMR: 2.3 (p < 0.05)

No exclusions:

Cumulative Exposure n SMR (95% CI)b

0.0–49 fibers/cc-yrs 6 1.5 (not reported) 

50–99 fibers/cc-yrs 2 1.6 (not reported) 

100–399 fibers/cc-yrs 2 1.1 (not reported) 

≥400 fibers/cc-yrs 10 5.8 (not reported, but p < 0.01)

20 or more years since first hire (20-year latency)

Cumulative Exposure n SMR (95% CI)b

0.0–49 fibers/cc-yrs 2 0.85 (not reported)

50–99 fibers/cc-yrs 2 2.3 (not reported)

100–399 fibers/cc-yrs 1 1.1 (not reported) 

≥400 fibers/cc-yrs 7 6.7 (not reported, but p < 0.01)

In a linear regression analysis of data with at least 20 years latency, 

the results per fiber-year were: beta (standard error) = 0.60 (0.13) 

and 0.58 (0.08) for threshold and nonthreshold models. Using a 

survival (Cox) model, the corresponding estimate is 0.11 (0.04). All 

estimates are statistically significant (p < 0.05). 

McDonald et al. 

2004; McDonald 

et al., 1986a 

Men, hired before 1963, worked at least 

one year (n = 406); follow-up through –

1999 (McDonald et al., 2004); 165 

deaths before July 1983 (163 with death 

certificates); 120 deaths July 1983–

1999; cause of death from National 

Death Index.

Mean duration: 8.7 years (0 worked less 

than 1 year).

Mean fiber-years: 144.6.

Respiratory (n = 44)

SMR: 2.4 (1.7, 3.2)

Mesothelioma (n = 12) 

4.2% of deaths were 

from mesothelioma

Excluding first 10 years of follow-up:

Cumulative Exposure n RR (95% CI)d

0.0–11.6 fibers/cc-yrs 5 1.0 (referent)

11.7–25.1 fibers/cc-yrs 9 1.7 (0.58, 5.2)

25.2–113.7 fibers/cc-yrs 10 1.9 (0.63, 5.5)

≥113.8 fibers/cc-yrs 16 3.2 (1.2, 8.8)

per 100 fibers/cc-yrs – 0.36 (0.03, 1.2) (p = 0.02)

Similar patterns were reported for analyses of intensity and 

residence-weighted exposure, but results not presented in paper.
This document is a draft for review purposes only and does not constitute Agency policy.

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Table 4-4. Respiratory cancer mortality and lung cancer dose-response analyses based on studies of the 

vermiculite mine workers in Libby, MTa(continued)

Reference(s) Inclusion criteria and design details

Standardized mortality 

ratio (SMR) (95% CI) Dose-response analyses – lung cancer

Sullivan, 2007 White men, enumerated in 1982, alive 

in 1960 or hired after 1960, worked at 

least one day, follow-up 1960–2001 (n 

= 1,672); 767 deaths (95% with known 

cause of death).

Mean duration: 4.0 years (808, ~50%

worked less than 1 year).

Median fibers/cc-years: 8.7.

Underlying cause of death data from 

death certificates or National Death 

Index-Plus.

15 year exposure lag:

All cancer (n = 202)

SMR: 1.4 (1.2, 1.6)

Lung (n = 89) 

SMR: 1.7 (1.4, 2.1)

Mesothelioma (n = 2, 

1999–2001)

SMR: 15.1 (1.8, 54.4)

Pleural (n = 4)

SMR: 23.3 (6.3, 59.5)

15 year exposure lag:

Cumulative Exposure n SMR (95% CI)bSRR (95% CI)c

0.0–4.49 fibers/cc-yrs 19 1.5 (0.9, 2.3) 1.0 (referent)

4.5–22.9 fibers/cc-yrs 24 1.6 (1.1, 2.5) 1.1 (0.6, 2.0)

23.0–99.0 fibers/cc-yrs 23 1.8 (1.1, 2.7) 1.4 (0.7, 2.7)

≥100 fibers/cc-yrs 23 1.9 (1.2, 2.9) 1.5 (0.8, 2.8)

Duration n SMR (95% CI)bSRR (95% CI)c 

<1 year 41 1.6 (1.1, 2.1) 1.0 (referent)

1–9.9 years 34 1.7 (1.1, 2.3) 1.1 (0.7, 1.8)

≥10 years 14 2.5 (1.4, 4.3) 1.8 (0.9, 3.4)

Larson et al. 

(2010b)

Inclusion criteria not described (n = 

1,862); 952 deaths (80% with known 

cause of death). 

Median duration: 0.8 years; 

Median fibers/cc-yr = 4.3. 

Immediate and underlying cause of 

death data (i.e., multiple cause of death) 

from death certificates or National 

Death Index-Plus.

Lung (n=104)

 SMR:1.6 (1.3, 2.0)

20 year exposure lag:

Cumulative Exposure n SMR (95% CI)b RR (95% CI)e

0.0–<1.4 fibers/cc-yrs 19 (not reported) 1.0 (referent)

1.4 to < 8.6 fibers/cc-yrs 20 (not reported)) 1.1 (0.6, 2.1)

8.6 to < 44.0 fibers/cc-yrs 21 (not reported) 1.7 (1.0, 3.0)

≥ 44.0 fibers/cc-yrs 38 (not reported) 3.2 (1.8, 5.3)

a

Includes miners, millers, and processors; workers in the screening plant, loading docks, and expansion plants; and office workers.

b

SMR based on external referent group. 

c

In Sullivan (2007), the SRR is a ratio of sums of weighted rates in which the weight for each stratum-specific rate is the combined person-years for the observed 

cohort across all duration (or cumulative level of exposure) categories. The Life Table Analysis System provides the SRR for each duration (or cumulative 

level of exposure) group compared to the referent group. The cutoff points for the categories must be specified by the user (e.g., 0–100 ppm-years might define 

the referent group, 100–200 ppm-years might define the next group). Taylor-series-based confidence intervals are given for each specific SRR. 

d

In McDonald et al. (2004), the RR is based on Poisson analysis using an internal referent group.

e

In Larson et al. (2010b), the RR is based on Cox proportional hazards modeling using an internal referent group.

SMR = standardized mortality ratio, CI = confidence interval, SRR = standardized rate ratio, RR = relative risk.
This document is a draft for review purposes only and does not constitute Agency policy.

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1 4.1.1.3. Cancer Mortality Risk

2 4.1.1.3.1. Lung cancer

3 The results within and among the papers in these two sets of studies (Amandus and 

4 Wheeler, 1987; Sullivan, 2007; Larson et al. 2010a; McDonald et al., 1986a, 2004) show similar 

5 effects in terms of the increased risk seen for lung (or respiratory) cancer (Table 4-4). 

6 Exposure-response analyses from these studies also demonstrated clear patterns of increasing 

7 mortality with increasing exposure, using categorical and continuous measures of exposure, 

8 different lag periods, and different exposure metrics. Because of the congruence in results and 

9 overlapping of study participants among these studies, the most recent studies are discussed in 

10 detail below. 

11 The analysis of McDonald et al. (2004) is limited to 406 male workers who were hired 

12 before 1963 and who were employed for at least 1 year. The mean duration of work was 

13 8.7 years. Cause of death data were obtained from the National Death Index, with expected rates 

14 based on age-, race- and sex- specific rates. A total of 44 deaths due to respiratory cancers were 

15 observed, for an SMR = 2.4 (95% confidence interval [CI] = 1.7, 3.2). A pattern of increasing 

16 mortality with increasing cumulative exposure was seen, with relative risks of 1.0 (referent), 1.7, 

17 1.9, and 3.2 in categories of 0.0–11.6, 11.7–25, 25.2–113.7, and ≥113.8 fibers/cc-yrs, 

18 respectively (Table 4-4). The estimated linear increase in RR of respiratory cancer risk per 

19 100 fibers/cc-years cumulative exposure was 0.36 (95% CI = 0.03, 1.2) (p = 0.02). McDonald et 

20 al. (2004) reported that similar results were obtained with measures of exposure intensity and 

21 measures of residence-weighted exposure, but the data were not presented in the paper.

22 Sullivan (2007) included 1,672 white male workers who were alive in 1960 or hired after 

23 1960. There was no minimum duration of employment required for inclusion in this analysis, 

24 and approximately 50% of the cohort (n = 808) had worked less than 1 year. Mortality follow-up 

25 was conducted through 2001, with 767 identified deaths. The exposure assessment protocol was 

26 based on that described by Amandus et al. (1987a), with a modification to the estimated intensity 

27 of exposure to laborers and to those with ―unknown‖ jobs. Sullivan (2007) assigned 

28 weighted-average exposure for all unskilled jobs in work area (if known) during a calendar time 

29 period, rather than lower mill yard exposure used by Amandus et al. (1987a). The weights are 

30 based on the number of workers assigned to unskilled jobs during the same calendar time period. 

31 In the Sullivan (2007) follow-up, SMRs, using underlying cause-of-death data (based on death 

32 certificates) obtained through the National Death Index and from individual States, and expected 

33 mortality based on national age-, race-, and sex-specific rates, were calculated. Using a 15-year 

34 exposure lag, SMRs were increased for lung cancer (n = 89, SMR = 1.7, 95% CI = 1.4, 2.1) and 

35 for all cancer mortality (n = 202, SMR = 1.4, 95% CI = 1.2, 1.6) (Table 4-4). Additionally, an 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 internal referent group was used for analyses of risk in relation to cumulative exposure and 

2 duration. The results of these internal analyses are presented as standardized relative risks (SRR) 

3 for white men, controlling for age group. Increasing risks across categories of cumulative 

4 exposure and duration were observed with both types of analyses, indicating a positive 

5 exposure-response relationship. The SMR estimates for lung cancer mortality were 1.5, 1.6, 1.8, 

6 and 1.9 in the 1- to 4.49-, 4.5- to 22.9-, 23.0- to 99.0-, and ≥100 fibers/cc-year exposure 

7 categories, respectively. The SRR estimates were 1.0, 1.1., 1.4, and 1.5, respectively, across 

8 these same exposure categories (Table 4-4). For comparison to the earlier work by McDonald et 

9 al. (1986a), an SMR was provided for all respiratory cancer in those employed at least 1 year 

10 (SMR 2.0 {1.5-2.5}). For the full cohort employed at least 1 day, the SMR for all respiratory 

11 cancer was 1.7 {1.4-2.1} (Sullivan, 2007).

12 Amandus and Wheeler (1987) provide some information on the smoking history of a 

13 sample of 161 male workers employed during 1975–1982 with at least 5 years of employment in 

14 the Libby cohort study and comparison data based on surveys conducted in the United States 

15 from 1955–1978. Among the workers, 35% were current smokers, and 49% were former 

16 smokers. This smoking information was obtained from questionnaires the company 

17 administered to workers after 1975. Assuming the definitions are similar to those of the national 

18 surveys, however, the prevalence of current smokers is similar in the worker cohort compared to 

19 the U.S. white male population data (ranging from 37.5–41.9% current smokers between 1975 

20 and 1978). The only year in this range with data on former smokers in the national survey is 

21 1975, and, at that time, the prevalence of former smokers in the population data was 29.2%, 

22 about 20% lower than among the workers. Using an estimated RR of lung cancer of 14 among 

23 smokers, Amandus and Wheeler (1987) estimated that the difference in smoking rates between 

24 workers and the comparison population could have resulted in a 23% increase in the observed 

25 risk ratio and commented that the increased risk observed in the lower dose range 

26 (<50 fiber-years) could be the result of confounding by smoking status. 

27 Smoking patterns in the U.S. population changed considerably over the period 

28 corresponding to the data reported by Amandus and Wheeler (1987). In the National Health 

29 Interview Surveys conducted between 1974 and 1983, the prevalence of smoking in males 

30 age 20 and older decreased from 42.1 to 35.5% (DHHS, 1989, p.269). In addition, the 

31 prevalence of former smokers can depend on the definition used. Based on 1986 survey data, the 

32 percentage of adults age 17 and older classified as former smokers varied between 14.7 and 

33 25.8% using different definitions for time since last smoked (e.g., from quitting 5 or more years 

34 ago to quitting within the past 3 months) (DHHS, 1989). Thus given the lack of information 

35 pertaining to the period in which smoking information was collected and the specifics of the 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 questions that were used, EPA concludes there is considerable uncertainty regarding the 

2 evidence for differences in smoking rates between the workers and a comparison population.

3 Larson et al. (2010b) evaluated multiple causes of death, and, therefore, more than one 

4 cause of death can be coded for an individual. A total of 104 lung or bronchus cancer deaths 

5 were observed, for an SMR of 1.6 (95% CI 1.3, 2.0) using an external comparison of United 

6 States cause of death data from 1960 to 2002 (Larson et al., 2010b). A higher risk was seen in 

7 the higher cumulative exposure categories using Cox proportional hazards modeling with an 

8 internal referent group: RR 1.0, 1.1, 1.7 and 3.2, respectively, for <1.4 (referent), 1.4 to <8.6, 8.6 

9 to <44.0 and ≥44.0 fibers/cc-yrs. Larson et al. (2010b) used data from a health screening 

10 program conducted in Libby by ATSDR in 2000–2001 (described in Section 4.1.2.2) pertaining 

11 to smoking history to estimate that the proportion of smokers ranged from 50% to 66% in the 

12 unexposed group (defined as exposure < 8.6 f/cc-yrs) and between 66% and 85% among the 

13 exposed (defined as ≥ 8.6 f/cc-yrs). These estimates were used in a Monte Carlo simulation to 

14 estimate the potential bias in lung cancer risks that could have been introduced by differences in 

15 smoking patterns. The bias-adjustment factor (RRunadjusted/RRadjusted = 1.3) reduced the overall 

16 RR estimate for lung cancer from 2.4 to 2.0. 

17

18 4.1.1.3.2. Mesothelioma

19 McDonald et al. (2004) presented dose-response modeling of mesothelioma risk based on 

20 12 cases. Using Poisson regression, the mesothelioma mortality rate across increasing categories 

21 of exposure was compared to the rate in the lowest exposure category. Note that the referent 

22 group was also at excess risk of dying from mesothelioma; that is, one to three cases of 

23 mesothelioma were observed in the referent group, depending on the exposure index. Three 

24 exposure indices were used in analysis: average intensity over the first 5 years of employment, 

25 cumulative exposure, and residence-weighted cumulative exposure. Because of the requirement 

26 for 5 years of employment data, 199 individuals (including 3 mesothelioma cases) were excluded 

27 from the analysis of average intensity. The residence-weighted cumulative exposure was based 

28 on the summation of exposure by year, weighted by years since the exposure. This metric gives 

29 greater weight to exposures that occurred a longer time ago. Although all exposure groups were 

30 clearly at excess risk of dying from mesothelioma, there was little evidence of increasing RR 

31 with increasing average intensity or cumulative exposure. For the cumulative exposure metric, 

32 the RR estimates were 1.0 (referent), 3.72, 3.42, and 3.68 based on 1, 4, 3, and 4, cases, 

33 respectively. The mean exposure levels in these four quartiles were 8.6, 16.7, 53.2, and 

34 393.8 fibers/cc-years, respectively. Evidence of an exposure-response relationship was greater 

35 for residence-weighted cumulative exposure, with an RR of 1.57 reported among those with 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 500.1–1826.8 fibers/cc-yrs exposure, and an RR of 1.95 among workers with higher 

2 residence-weighted cumulative exposure. 

3 Sullivan (2007) identified 15 deaths from mesothelioma through a manual review of 

4 death certificates, with 14 classified as ―pleural or unspecified,‖ and 1 classified as ―peritoneal.‖ 

5 Only two of these deaths occurred between 1999 and 2001, the period for which comparison data 

6 using the ICD-10 classification criteria were available. Based on these two mesothelioma 

7 deaths, the SMR was 14.1 (95% CI = 1.8, 54.4). 

8 Larson et al. (2010b) identified 19 mesothelioma deaths (coding any mention of 

9 mesothelioma on the death certificate as the ICD-10 classification of C45). Comparison data 

10 were based on multiple causes of death data (1960 to 2002). The SMR for mesothelioma was 

11 94.8 (95% CI 57.0, 148.0). However, since the ICD-10 coding scheme was not used before 

12 2000, reference rates based on data from 2000 to 2006, used with observations of mesothelioma 

13 cases from 1960 to 2006, could result in an upwardly biased SMR if the true but unobserved 

14 reference rates for mesothelioma during the same time period as the deaths (1960-2006) were 

15 higher than from 2000-2006. An increase was seen across the quartiles of exposure in the 

16 analysis using an internal referent group and a 20-yr lag period, with RR of 1.0 (referent) in the 

17 <1.4 fibers/cc-yr group, 1.9 (95% CI: 0.3, 13.6) in the 1.4 to <8.6 fibers/cc-yr group, 4.5 (95% CI 

18 0.8, 24.6) in the 8.6 to < 44.0 group, and 17.1 (95% CI 3.7, 78.1) in the ≥44.0 fibers/cc-yr group.

19

20 4.1.1.3.3. Other cancers

21 Larson et al. (2010b) presented data on cancers other than respiratory tract and 

22 mesothelioma. The category of malignant neoplasms of digestive organs and peritoneum 

23 included 39 observed deaths, for an SMR of 0.8 (95% CI: 0.6, 1.1). No risk in relation to 

24 asbestos exposure was seen with a 20-year lag. The potential for underascertainment of specific 

25 causes of death should be noted, however, given the 10% loss to follow-up and missing cause of 

26 death data for 20% of the identified deaths.

27

28 4.1.1.3.4. Summary of cancer mortality risk in Libby, MT vermiculite mining operation 

29 workers

30 The studies conducted in the 1980s (Amandus and Wheeler, 1987; McDonald et al., 

31 1986a) as well as the extended follow-up studies published in more recent years (Sullivan et al., 

32 2007; McDonald et al., 2004; Larson et al., 2010) provide clear evidence of an increased risk of 

33 lung cancer mortality and of mesothelioma mortality among the workers in the Libby vermiculite 

34 mining and processing operations. An increasing risk with increasing exposure is seen. A 

35 plateauing of the increased risk may be seen, depending on the cut-point used to examine risk in 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 the highest exposure groups (Sullivan, 2007; Larson et al., 2010). This type of pattern is 

2 commonly seen in occupational cohort mortality studies (Stayner et al., 2003).

3

4 4.1.1.4. Noncancer Effects

5 4.1.1.4.1. Asbestosis and other non-malignant respiratory disease mortality

6 The studies described previously also reported noncancer mortality data, with a specific focus on 

7 respiratory diseases (Table 4-5). In Sullivan (2007), the SMR for asbestosis (ICD-9 code 501) 

8 was 166 (based on n = 22, underlying cause of death compared to a U.S. white male referent 

9 group). In Larson et al. (2010a), the SMR (based on 69 observed asbestosis-related deaths using 

10 multiple-causes-of-death data) was 143 (95% CI 111, 181). Increasing cumulative exposure was 

11 observed to increase the risk for asbestosis mortality in both of these analyses (Table 4-5). A 

12 two- to three-fold increase was also seen for other categories of nonmalignant respiratory disease 

13 in Larson et al. (2010), with an SMR of 2.4 (95% CI 2.2, 2.6) for all non-malignant respiratory 

14 disease, and SMR = 2.8 (95% CI 2.3, 3.4) for diseases other than asbestosis, chronic obstructive 

15 pulmonary disease, and silicosis. These results are similar to the nonmalignant respiratory 

16 disease mortality data from studies of this cohort using underlying cause-of-death data. A 

17 markedly higher risk of nonmalignant respiratory disease mortality was also observed in the 

18 cumulative exposure category of ≥300 or ≥400 fibers/cc-years in Sullivan (2007) (Table 4-5). 

19 Larson et al. (2010) used a Monte Carlo simulation to estimate the potential bias in non20 malignant respiratory disease risk that could have been introduced by differences in smoking 

21 patterns between exposed and unexposed workers in the cohort. The bias-adjustment factor 

22 (RRunadjusted/RRadjusted = 1.2) reduced the overall RR estimate from 2.1 to 1.8. 

23 Table 4-5. Non-malignant respiratory mortality studies of the vermiculite 

mine workers in Libby, MTa24

Reference(s)

Respiratory disease

(SMR, 95% CI)

Dose-response analyses:

Nonmalignant respiratory diseases and asbestosis

Amandus and 

Wheeler, 

1987

(NIOSH)

No exclusions:

Non-malignant respiratory 

diseases (n = 20)

SMR: 2.4 (1.5, 3.8)

20 year latency: 

Non-malignant respiratory 

diseases (n = 12)

SMR: 2.5 (p < 0.05)

No exclusions: Non-malignant respiratory diseases

Cumulative Exposure n SMR (95%CI)b

0.0–49 fibers/cc-yrs 8 2.2 (not reported)

50–99 fibers/cc-yrs 2 1.7 (not reported)

100–399 fibers/cc-yrs 3 1.8 (not reported)

≥400 fibers/cc-yrs 10 4.0 (not reported, but p < 0.01)

20 or more years since first hire (latency): Non-malignant respiratory 

diseases

Cumulative Exposure n SMR (95%CI)b

0.0–49 fibers/cc-yrs 7 3.3 (not reported, but p < 0.05)

50–99 fibers/cc-yrs 2 2.8 (not reported) 

100–399 fibers/cc-yrs 0 0 (not reported) 
This document is a draft for review purposes only and does not constitute Agency policy.

4-19 DRAFT—DO NOT CITE OR QUOTE

Reference(s)

Respiratory disease

(SMR, 95% CI)

Dose-response analyses:

Nonmalignant respiratory diseases and asbestosis

≥400 fibers/cc-yrs 3 2.8 (not reported)

McDonald et 

al. 2004; 

McDonald et 

al., 1986a 

(McGill)

Non-malignant respiratory 

diseases (n = 51) 

SMR: 3.1 (2.3, 4.1)

Excluding first 10 years of follow-up: Non-malignant respiratory 

diseases

Cumulative Exposure n RR (95%CI)d

0.0–11.6 fibers/cc-yrs 5 1.0 (referent)

11.7–25.1 fibers/cc-yrs 13 2.5 (0.88, 7.2) 

25.2–113.7 fibers/cc-yrs 14 2.6 (0.93, 7.3)

≥113.8 fibers/cc-yrs 19 3.1 (1.2, 8.4)

per 100 fibers/cc-yrs – 0.38 (0.12, 0.96) (p = 0.0001)

Sullivan, 

2007

(NIOSH)

15 year exposure lag:

Asbestosis (n = 22)

SMR: 166 (104, 251)

Non-malignant respiratory 

diseases (n = 111)

SMR: 2.4 (2.0, 2.9)

Chronic obstructive 

pulmonary disease (n = 

53)

SMR: 2.2 (1.7, 2.9)

Other non-malignant 

respiratory diseases (n = 

19)

 SMR: 2.7 (1.6, 4.2)

15 year exposure lag: Asbestosis

Cumulative Exposure n SMR (95% CI)bSRR (95% CI)c

0.0–49.9 fibers/cc-yrs 3 37 (7.5, 122) 1.0 (referent)

50.0–249.9 fibers/cc-yrs 8 213 (91.6, 433) 7.3 (1.9, 28.5) 

≥250 fibers/cc-yrs 11 749 (373, 1368) 25.3 (6.6, 96.3) 

15 year exposure lag: Non-malignant respiratory diseases

Cumulative Exposure n SMR (95%CI)bSRR (95% CI)c

0.0–4.49 fibers/cc-yrs 18 1.8 (1.1, 2.8) 1.0 (referent)

4.5–19.9 fibers/cc-yrs 24 2.0 (1.3, 3.0) 1.2 (0.6, 2.3)

20.0–84.9 fibers/cc-yrs 26 2.2 (1.5, 3.3) 1.5 (0.8, 2.9)

85.0–299.9 fibers/cc-yrs 20 2.6 (1.6, 4.0) 1.4 (0.7, 2.7)

≥300 fibers/cc-yrs 23 4.8 (3.1, 7.3) 2.8 (1.3, 5.7)

Larson et al., 

2010a

Asbestosis (n = 69)

SMR: 143 (111, 181)

Non-malignant respiratory 

diseases (n = 425)

SMR: 2.4 (2.2, 2.6)

Chronic obstructive 

pulmonary disease (n = 

152)

SMR: 2.2 (1.9, 2.6)

Other non-malignant 

respiratory (n = 120)

 SMR: 2.8 (2.3 3.4)

20 year exposure lag: Asbestosis

Cumulative Exposure n SMR (95% CI)b RR (95% CI)e

< 1.4 fibers/cc-yrs 4 (not reported) 1.0 (referent)

1.4–< 8.6 fibers/cc-yrs 8 (not reported) 2.8 (1.0, 7.6) 

86–<44.0 fibers/cc-yrs 25 (not reported) 8.0 (3.2, 19.5)

≥44.0 fibers/cc-yrs 32 (not reported) 11.8 (4.9, 28.7) 

20 year exposure lag: Non-malignant respiratory diseases

Cumulative Exposure n SMR (95% CI)b RR (95% CI)e

< 1.4 fibers/cc-yrs 43 (not reported) 1.0 (referent)

1.4–< 8.6 fibers/cc-yrs 46 (not reported) 1.4 (0.9, 2.1) 

86–<44.0 fibers/cc-yrs 56 (not reported) 1.8 (1.3, 2.7)

≥44.0 fibers/cc-yrs 58 (not reported) 2.5 (1.7, 3.6) 

a

1 Includes miners, millers, and processors; workers in the screening plant, loading docks, and expansion plants; and 

2 office workers.

b

3 SMR based on external referent group. 

c

4 In Sullivan (2007), the SRR is a ratio of sums of weighted rates in which the weight for each stratum-specific rate is 

5 the combined person-years for the observed cohort across all duration (or cumulative level of exposure) categories. 

6 The Life Table Analysis System provides the SRR for each duration (or cumulative level of exposure) group 

7 compared to the referent group. The cutoff points for the categories must be specified by the user (e.g., 0–100 ppm8 years might define the referent group, 100–200 ppm-years might define the next group). Taylor-series-based 

9 confidence intervals (Rothman, 1986) are given for each specific SRR. 

d

10 In McDonald et al. (2004), the RR is based on Poisson analysis using internal referent group.

e

11 In Larson et al. (2010), the RR is based on Cox proportional hazards modeling using an internal referent group.

12

13 SMR = standardized mortality ratio, CI = confidence interval, SRR = standardized rate ratio, RR = relative 

14 risk.
This document is a draft for review purposes only and does not constitute Agency policy.

4-20 DRAFT—DO NOT CITE OR QUOTE

1 4.1.1.4.2. Cardiovascular-related mortality

2 Larson et al. (2010) presents data on mortality due to cardiovascular diseases, with SMRs 

3 of 0.9 (95% CI 0.9, 1.0) seen for heart disease (n=552) and 1.4 (95% CI 1.2, 1.6) seen for 

4 circulatory system diseases (n=258). Deaths due to heart diseases were further categorized into 

5 ischemic heart disease (n=247) and other heart disease (n=120, for pericarditis, endocarditis, 

6 heart failure, and ill-defined descriptions and complications of heart disease), with SMRs of 0.7 

7 (95% CI: 0.6, 0.8) and 1.5 (95% 1.2, 1.8), respectively. Circulatory diseases included 

8 hypertension without heart disease (n=42), with an SMR of 1.7 (95% CI 1.2, 2.4) and diseases of 

9 arteries, veins, or lymphatic vessels (n=136), SMR = 1.6 (95% CI 1.4, 2.0). The combined 

10 category of cardiovascular-related mortality resulted in modestly increased risks across quartiles 

11 of exposure, with RR of 1.0 (referent), 1.3 (95% CI: 1.0, 1.6), 1.3 (95% CI: 1.0, 1.6), and 

12 1.5 (95% CI: 1.1, 2.0) with exposure groups of <1.4, 1.4 to <8.6, 8.6 to <44.0, and 

13 ≥44.0 fibers/cc-yrs, respectively. Larson et al. (2010) used a Monte Carlo simulation to estimate 

14 the potential bias in cardiovascular disease risk that could have been introduced by differences in 

15 smoking patterns between exposed and unexposed workers in the cohort. The bias-adjustment 

16 factor (RRunadjusted/RRadjusted = 1.1) reduced the overall RR estimate from 1.6 to 1.5. Because 

17 Larson et al. (2010) analyzed multiple causes of death, the observed association between 

18 exposure and cardiovascular disease-related mortality may reflect, at least in part, a consequence 

19 of an underlying respiratory disease. 

20

21 4.1.1.4.3. Radiographic anomalies

22 Respiratory disease risk is also evidenced by chest radiographs showing pleural thickening and 

23 anomalies in the Libby, MT worker cohorts (Table 4-6). Two of these studies were conducted in 

24 the 1980s and were based on x-rays of a subset of workers taken for either an annual workplace 

25 screening or as part of a study examination. The subset McDonald et al. (1986b) selected 

26 included 164 workers currently employed at the Libby facility, 80 former employees, and 47 area 

27 residents without known dust exposure. The subset selected by Amandus et al. (1987b) included 

28 workers with at least 5 years tenure who had worked at Libby at some time during 1975–1982. 

29 The most recent x-ray film for each worker, which NIOSH obtained from the Libby hospital that 

30 performed the screening, was read by three qualified readers using the International Labor 

31 Organization 1981 classification system. Consensus readings were used for pleural findings, 

32 while the median reading was used to determine the profusion category of small opacities. In the 

33 McDonald et al. (1986b) study, there was 90% agreement among readers that the chest x-rays 

34 contained evidence of pleural calcification and pleural thickening, and 80% agreement that there 

35 was evidence of small opacities, pleural plaques, and diffuse thickening. Amandus et al. (1987b) 
This document is a draft for review purposes only and does not constitute Agency policy.

4-21 DRAFT—DO NOT CITE OR QUOTE

1 provided a more detailed breakdown of the correspondence between categories for the rating of 

2 small opacities, and reported that the overall difference of 6% in the prevalence of opacities was 

3 similar to that seen in other studies. Other design details are described in Table 4-6. 

4

5 Table 4-6. Pulmonary radiographic studies of the Libby, MT vermiculite 

6 mine workers

7

Reference(s) Inclusion criteria and design details Results

McDonald et al. (1986b) Men employed on July 1, 1983 (n = 164).

Former male employees living within 200 miles; 

hired before 1963 (n = 80), worked at least one 

year (80 participants from 110 eligible); 43 had a 

previous x-ray.

Men without known dust exposure (n = 47); 

x-rays taken for other reasons (mostly 

employment related) at same place during study 

period; 24 had a previous x-ray.

Data from 9 women employed on July 1, 1983 

not included in this report.

Pleural thickening observed in 15.9% 

of current employees and 52.5% of 

past employees.

Small opacities (≥1/0) observed in 

9.1% of current employees and 37.5% 

of past employees.

Both abnormalities increased with age.

Age-adjusted and age-stratified 

(>60 years old) analyses showed 

increasing risk of both abnormalities 

with increasing cumulative exposure.

Amandus et al. (1987b) Men, employed during 1975–1982 with at least 

5 years tenure (n = 191); 184 with previous chest 

x-rays; 121 with smoking questionnaires.

Annual radiographs taken since 1964; most 

recent radiograph evaluated.

Mean employment duration: 14 years.

Mean fiber-years: 123 (all workers), 119 

(workers with radiographs).

Pleural thickening observed in 13% 

Small opacities (≥1/0) observed in 

10%.

Both abnormalities increased with 

increasing cumulative exposure.

Whitehouse (2004) n = 123 (86 former employees of W.R. Grace, 

27 family members of employees, and 10 Libby 

residents with only environmental exposures). 

Average age 66 years; 80% males. 56 patients 

had interstitial changes at profusion category 0/1 

or 1/0. Chest x-rays and/or HRCT scans; 

pulmonary function tests (FVC, TLC, and 

DLCO). 

Average yearly loss (n = 123):

 FVC 2.2% 

 TLC 2.3% 

 DLCO 3.0% 

Average yearly loss (n = 94 with 

worsening lung function):

 FVC 3.2% 

 TLC 2.3% 

 DLCO 3.3% 

Larson et al. (2010b) Men with 2 or more x-rays spanning a period of 

4 or more years, with consensus reached by 

panel of 3 NIOSH B raters (n=84).

Latency (time from hire to observed 

change), median (25th, 75th percentile) 

yrs:

Circumscribed pleural plaque

 8.6 (1.4, 14.7)

Any pleural calcification

 17.5 (8.1, 24.2)

Diffuse pleural thickening

 27.0 (10.7, 29.8)

8

9 DLCO = single breath carbon monoxide diffusing capacity ; FVC = forced vital capacity; TLC = total lung capacity.

10

11
This document is a draft for review purposes only and does not constitute Agency policy.

4-22 DRAFT—DO NOT CITE OR QUOTE

1 Amandus et al. (1987b) reported pleural thickening in 13% and small opacities (≥1/0) in 

2 9.1% of current employees. Similar data were reported by and McDonald et al. (1986b), with 

3 15.9% and 10% with pleural thickening and small opacities, respectively. In both studies, 

4 prevalence of these anomalies increased with increasing cumulative exposure, as shown in 

5 Table 4-6. McDonald et al. (1986b) also included 80 former employees in their study. The 

6 prevalence of pleural thickening (52.5%) and small opacities (37.5%) was higher in these 

7 workers compared with current workers. These groups differed by age, however, with only one 

8 of the 80 former workers < age 40 years compared with 80 of 164 current workers. Within the 

9 age category 40 to 59 years, the prevalence of pleural thickening was 20.3 and 40.0% in current 

10 and former employees, respectively, and, in the ≥ 60 years age group, the prevalence was 40.0 

11 and 61.2%, respectively. The authors attribute these differences in prevalence rates in current

12 compared with former employees to differences in cumulative exposure. Among the 47 area 

13 residents without known dust exposure in an occupational setting in the study by McDonald et al. 

14 (1986b), the prevalence of pleural thickening was 8.5% (n = 4), and the prevalence of small 

15 opacities was 2.1% (n = 1). 

16 Both Amandus et al. (1987b) and McDonald (1986b) provided categorical 

17 exposure-response data as well as logistic models for various endpoints (e.g., small opacities, 

18 pleural calcification, pleural thickening). In McDonald et al. (1986b), the regression coefficient 

19 for cumulative exposure was 0.0024 per unit increase in cumulative exposure for the log odds of 

20 the presence of pleural thickening, adjusting for age and smoking. In the analysis of small 

21 opacities, the beta was 0.0035 per unit increase in cumulative exposure. Amandus et al. (1987b) 

22 reported similar results to those of McDonald et al. (1986b). 

23 Whitehouse (2004) examined changes in pulmonary function measures in 123 patients 

24 seen in a pulmonary disease practice serving the Libby, MT area, with a mean follow-up time of 

25 35 months. This study population included 86 former employees of W.R. Grace, 27 family 

26 members of employees, and 10 Libby residents with only environmental (i.e., non-occupational, 

27 non-family-related) exposures. The average age at the time of the first pulmonary study was 66 

28 years, and 80% were male. Chest x-rays or high resolution computed tomography (HRCT) scans 

29 revealed no evidence of interstitial changes in 67 (55%) of the 123 patients, and 56 patients 

30 (45%) were found to have interstitial changes at profusion category 0/1 or 1/0. Pulmonary 

31 function tests included forced vital capacity (FVC), total lung capacity (TLC), and the single 

32 breath carbon monoxide diffusing capacity (DLCO). The average yearly loss was 2.2% for 

33 FVC, 2.3% for TLC, and 3.0% for DLCO. The subset of 94 patients who experienced a loss of 

34 FVC was characterized as the group with worsening lung function. Among this group, the 
This document is a draft for review purposes only and does not constitute Agency policy.

4-23 DRAFT—DO NOT CITE OR QUOTE

1 average yearly loss was 3.2% for FVC, 2.3% for TLC, and 3.3% for DLCO. This decline was 

2 noted by the author to be greater than seen in other studies of asbestos-exposed patients. 

3 Larson et al. (2010b) analyzed data from a subset of workers for whom pleural and/or 

4 parenchymal abnormalities were seen on the most recently available x-ray and who had one or 

5 more previous x-rays covering a span of at least 4 years available for comparison. Three 

6 NIOSH B readers reviewed each of the available x-rays in reverse chronological order to 

7 determine the latency (i.e., length of time between first exposure, as measured by date of hire and 

8 observed abnormality), and the degree of progression by type of lesion. Stored x-rays were 

9 found for 184 workers and 84 were included in the analysis. Exclusions were based on the 

10 following: 76 did not have at least one x-ray over the span of at least 4 years, 20 declined to 

11 participate, consensus classification was not reached for three, and one worker did not have any 

12 detectable abnormality. Circumscribed pleural plaque was seen in 83 of these 84 workers who 

13 were known to have had pleural and/or parenchymal abnormalities at a median latency of 8.6 

14 years. Any pleural calcification was seen in 37 workers, with a median latency of 17.5 years, 

15 and diffuse pleural thickening was seen in 12 workers (median latency: 27.0 years). The latency 

16 period increased with increasing profusion categories, from a median of 18.9 years for ≥1/0, 33.3 

17 years for progression to ≥2/1, and 36.9 years for progression to ≥3/2. 

18

19 4.1.1.4.4. Summary of noncancer risk in Libby, MT vermiculite mining operation 

20 workers

21 The risk of mortality related to asbestosis and other forms of non-malignant respiratory 

22 disease is elevated in the Libby vermiculite mining and processing operations, with increasing 

23 risk seen with increasing exposure in studies conducted in the 1980s (Amandus and Wheeler, 

24 1987; McDonald et al., 1986a) and in the extended follow-up studies published in more recent 

25 years (Sullivan et al., 2007; McDonald et al., 2004; Larson et al., 2010). Radiographic evidence 

26 of pleural thickening and pleural plaques has also been shown in studies of Libby workers 

27 (McDonald et al., 1986b; Amandus et al., 1987b; Whitehouse, 2004; Larson et al., 2010b). 

28

29 4.1.2. Libby, MT Community Studies

30 In addition to worker exposures, the operations of the Zonolite Mountain mine are 

31 believed to have resulted in both home exposures and community exposures. Potential pathways 

32 of exposure (discussed below) range from release of airborne fibers into the community, 

33 take-home exposure from mine workers (e.g., clothing), and recreational activities including 

34 gardening and childhood play activities. Due to a potential for a broader community concern, 
This document is a draft for review purposes only and does not constitute Agency policy.

4-24 DRAFT—DO NOT CITE OR QUOTE

1 ATSDR conducted several studies and health actions responding to potential asbestos 

2 contamination in the Libby, MT area.

3 4.1.2.1. Geographic-Based Mortality Analysis

4 ATSDR conducted a location-specific analysis of mortality risks and a community health 

5 screening for asbestos in the Libby area (Table 4-7). The mortality analysis was based on death 

6 certificate data from 1979–1998, with geocoding of current residence at time of death. The six 

7 geographic areas used in the analysis were defined as the Libby city limits (1.1 square miles 

8 around the downtown); the extended boundary of Libby (2.2 square miles around the 

9 downtown); the boundary based on air modeling (16 square miles, based on computer modeling 

10 of asbestos fiber distribution); the medical screening boundary (25 square miles, including the 

11 town of Libby and areas along the Kootenai River); the Libby valley (65 square miles); and 

12 central Lincoln County (314 square miles, based on a 10-mile radius around downtown Libby) 

13 (ATSDR, 2000). 

14 The 1990 population estimates were 2,531, 3,694, 4,300, 6,072, 8,617, and 9,512, 

15 respectively, for these six areas. Age-standardized SMRs were calculated using underlying 

16 cause-of-death information obtained from death certificates issued during the study period for 

17 413 of 419 identified decedents, and U.S. and Montana populations were used as reference 

18 groups. Increased SMRs were observed for both asbestosis and pulmonary circulation diseases 

19 (Table 4-7). The SMR for lung cancer ranged from 0.9–1.1 and 0.8–1.0 in the analyses for each 

20 of the six geographic boundaries using Montana and U.S. reference rates, respectively. In 

21 addition, four deaths due to mesothelioma were observed during the study period. These 

22 analyses did not distinguish between deaths among workers and deaths among other community 

23 members.

24

25 4.1.2.2. Community Health Screening

26 The ATSDR community health screening was conducted from July–November 2000 and 

27 July–September 2001 with 7,307 total participants (6,149 in the first phase) (ATSDR, 2001; 

28 (Table 4-8). Eligibility was based on residence, work, or other presence in Libby for at least 

29 6 months before 1991. The total eligible population was not explicitly determined, but the 

30 participation rate was approximately 70% of 10,250 persons, which was the estimated population 

31 of Libby, MT, in 2000. In addition to a standardized interview regarding medical history, 

32 symptoms, work history, and other potential exposures, clinical tests included spirometry (forced 

33 expiratory volume in one second, FEV1, and forced vital capacity, FVC) and chest x-rays (for 

34 participants aged 18 years and older). Moderate to severe restriction (defined as FVC <70% 
This document is a draft for review purposes only and does not constitute Agency policy.

4-25 DRAFT—DO NOT CITE OR QUOTE

1 predicted value) was observed in 2.2% of the men and 1.6% of women but was not observed in 

2 individuals less than age 18. 

3

4 Table 4-7. Cancer mortality and non-malignant respiratory disease 

5 mortality in the Libby, MT community

6

Reference(s) Inclusion criteria and design details Results

ATSDR, 2000 1979–1998, underlying cause of death 

from death certificates; geocoding of 

street locations (residence at time of 

death) within 6 geographic boundaries 

(ranging from 2,532 residents in Libby 

city limits to 9,521 in central Lincoln 

County in 1990). Inquiries to 

postmaster were required because of 

P.O. Box address for 8% (n = 32); 

information on 47 of 91 residents of 

elderly care facilities resulted in 

reclassification of 16 of 47 (34%) to 

non-residents of Libby. 

U.S. Census data corresponding to the 

same 6 geographic boundaries of 

Libby, MT.

419 decedents identified, 418 death 

certificates obtained, 413 with 

geocoding.

Age-standardized SMRs based on 

Montana and U.S. comparison rates. 

Asbestosis SMRs were somewhat 

higher using the United States referent 

group, but choice of referent group had 

little difference on SMRs for most 

diseases.

4 deaths from mesothelioma observed 

in the study area. 

Lung cancer (n = 82), SMR (95% CI)

 Comparison area (Montana reference rates):

 Libby city limits 1.1 (0.8, 1.5)

 Extended Libby boundary 1.1 (0.8, 1.5)

 Air modeling 1.0 (0.8, 1.4)

 Medical screening 0.9 (0.7, 1.2)

 Libby valley 0.9 (0.7, 1.2)

 Central Lincoln county 0.9 (0.7, 1.1)

Pancreatic cancer (n = 10) 

 Comparison area (Montana reference rates):

 Libby city limits 1.0 (0.5, 2.1)

 Extended Libby boundary 0.9 (0.4, 1.7)

 Air modeling 0.7 (0.3, 1.4)

 Medical screening 0.7 (0.3, 1.2)

 Libby valley 0.6 (0.3, 1.0)

 Central Lincoln county 0.5 (0.3, 1.0)

Prostate cancer (n = 14) 

 Comparison area (Montana reference rates):

 Libby city limits 0.8 (0.4, 1.5)

 Extended Libby boundary 0.8 (0.4, 1.5)

 Air modeling 0.7 (0.4, 1.2)

 Medical screening 0.6 (0.3, 1.1)

 Libby valley 0.6 (0.3, 1.0)

 Central Lincoln county 0.5 (0.3, 0.9)

Asbestosis (n = 11) 

 Comparison area (Montana reference rates):

 Libby city limits 40.8 (13.2, 95.3)

 Extended Libby boundary 47.3 (18.9, 97.5)

 Air modeling 44.3 (19.1, 87.2)

 Medical screening 40.6 (18.5, 77.1)

 Libby valley 38.7 (19.3, 69.2)

 Central Lincoln county 36.3 (18.1, 64.9)

 Comparison area (United States reference rates):

 Libby city limits 63.5 (20.5, 148)

 Extended Libby boundary 74.9 (30.0, 154)

 Air modeling 71.0 (30.6, 140)

 Medical screening 66.1 (30.2, 125)

 Libby valley 63.7 (31.7, 114)

 Central Lincoln county 59.8 (29.8, 107)

Pulmonary circulation (n = 14) 

 Comparison area (Montana reference rates):

 Libby city limits 2.3 (1.1, 4.4)

 Extended Libby boundary 1.9 (0.9, 3.7)

 Air modeling 1.8 (0.9, 3.3)

 Medical screening 1.6 (0.8, 2.9)
This document is a draft for review purposes only and does not constitute Agency policy.

4-26 DRAFT—DO NOT CITE OR QUOTE

Reference(s) Inclusion criteria and design details Results

 Libby valley 1.6 (0.9, 2.7)

 Central Lincoln county 1.5 (0.8, 2.5)

1

2 Two board-certified radiologists (B readers) examined each radiograph, and a third reader 

3 was used in cases of disagreement. Readers were aware that the radiographs were from 

4 participants in the Libby, MT health screening but were not made aware of exposure histories 

5 and other characteristics (Peipins et al., 2003; Price, 2004; Peipins, 2004). The radiographs 

6 revealed pleural abnormalities in 17.9% of participants, with prevalence increasing with 

7 increasing number of ―exposure pathways‖ (defined on the basis of potential work and 

8 residential exposure to asbestos within Libby and from other sources) (Table 4-8). Detailed 

9 results of an analysis excluding the former Libby workers cohort were not presented, but the 

10 authors noted that the relationship between number of exposure pathways and increasing 

11 prevalence of pleural anomalies was somewhat attenuated with this exclusion. The prevalence of 

12 pleural anomalies decreased from approximately 35% to 30% in individuals with 12 or more 

13 exposure pathways when these workers were excluded from the analysis. Among individuals 

14 with no definable exposure pathways, the prevalence of pleural anomalies was 6.7%, which is 

15 higher than reported in other population studies (Price, 2004; Peipins, 2004). No information is 

16 provided regarding analyses excluding all potential work-related asbestos exposures. 

17

18

19

20 Table 4-8. Pulmonary radiographic, and other health studies in the Libby, 

21 MT community

22

Reference(s) Inclusion criteria and design details Results

Health screening and pulmonary radiographic changes

Peipins et al., 2003; 

ATSDR, 2001 

Resided, worked, attended school, or participated in other 

activities in Libby for at least 6 months before 1991 

(including mine employees and contractors). 

Health screening between July and November 2000.

Conducted interviews (n = 6,149, 60% of Libby residents 

based on 2000 Census data) and chest x-rays (n = 5,590, 

18 years and older), and determined spirometry—forced 

expiratory volume in one second (FEV1), forced vital 

capacity (FVC1), and ratio (FEV1/FVC <70% = moderate 

to severe restriction).

19 ―exposure pathways‖ included Libby mining company 

work, contractor work, dust exposure at other jobs, 

vermiculite exposure at other jobs, potential asbestos 

exposure at other jobs or in the military, cohabitation with 

Libby mining company worker, and residential and 

Peipins (2003) and ATSDR 

(2001): Pleural abnormalities seen 

in 17.9% of participants; 

increasing prevalence with 

increasing number of exposure 

pathways (6.7% with no specific 

pathways, 34.6% with 12 or more 

pathways).

ATSDR (2001):

Moderate to severe FVC1 

restriction (FVC <70% predicted): 

2.2% men >17 years old; 1.6% 

women >17 years old; 0.0% men 

or women <18 years old.
This document is a draft for review purposes only and does not constitute Agency policy.

4-27 DRAFT—DO NOT CITE OR QUOTE

Reference(s) Inclusion criteria and design details Results

recreational use of vermiculite.

Peipins et al. (2003) similar to ATSDR, 2001 except longer 

screening period (July–November 2000 and July–

September 2001). Conducted interviews (n = 7,307) and 

chest x-rays (n = 6,668).

Also includes data on selfreported lung diseases and 

symptoms.

Vinikoor et al. 

(2010)

Same as above (ATSDR, 2001 and Peipins et al., 2003). 

Analysis limited to n=1,003 ages 10-29 years at time of 

health screening (≤ age 18 in 1990 when the 

mining/milling operations closed). Excluded if worked for 

W.R. Grace, or for a contractor of W.R. Grace, exposed to 

dust at other jobs, or expose to vermiculite at other jobs. 

Exposure characterized by 6 activities (never, sometimes, 

or frequently participated in 1-2 or ≥ 3 activities). Analysis 

of history of respiratory symptoms (usually have cough, 

coughed up bloody phlegm in past year, shortness of 

breath) and spirometry data (obstructive, restrictive or 

mixed).

Little difference across exposure 

levels in prevalence of physiciandiagnosed lung disease or 

abnormal spirometry. Association 

(OR, 95% CI) seen between ≥ 3 

activities and:

Usual cough 2.93 (0.93, 9.25)

Shortness of 

breath

1.32 (0.51, 3.42)

Bloody 

phlegm

1.49 (0.41, 5.43)

1 Vinikoor et al. (2010) used the 2000–2001 health screening data to examine respiratory 

2 symptoms and spirometry results among 1,224 adolescents and young adults who were ≤ age 18 

3 in 1990 when the mining/milling operations closed. At the time of the health screening, the ages 

4 in this group ranged from 10 to 29 years. Exclusion criteria for this analysis included previous 

5 work for W.R. Grace, work for a contractor of W.R. Grace, exposure to dust at other jobs, or 

6 exposure to vermiculite at other jobs. The total number of exclusions was 221, leaving 1,003 in 

7 the analysis. The potential for vermiculite exposure was classified based on responses to 

8 questions about six activities (handling vermiculite insulation, participation in recreational 

9 activities along the vermiculite-contaminated gravel road leading to the mine, playing at the ball 

10 fields near the expansion plant with large piles of vermiculite, playing in or around the 

11 vermiculite piles, heating the vermiculite, and other activities involving vermiculite). The 

12 medical history questionnaire included information on three respiratory symptoms and one 

13 diagnosis: usually have a cough (n = 108, 10.8%), are troubled by shortness of breath when 

14 walking up a slight hill or when hurrying on level ground (n = 145, 14.5%), have coughed up 

15 phlegm that was bloody in the past year (n = 59, 5.9%), and have a history of physician16 diagnosed lung disease (n = 51, 5.1%). The spirometry results were classified as normal in 896 

17 (90.5%), obstructive in 62 (6.3%), restrictive in 30 (3.0%), and mixed in 2 (0.2%). Information 

18 on smoking history was also collected in the questionnaire: 15.8% and 7.3% were classified as 

19 current and former smokers, respectively. Approximately half of the participants lived with 

20 someone who smoked. The analyses adjusted for age, sex, personal smoking history and living 

21 with a smoker. For usually having a cough, the ORs were 1.0 (referent), 1.88 (95% CI: 0.71, 

22 5.00), 2.00 (95% CI: 0.76, 5.28) and 2.93 (95% CI 0.93, 9.25) for never, sometimes, frequently 
This document is a draft for review purposes only and does not constitute Agency policy.

4-28 DRAFT—DO NOT CITE OR QUOTE

1 participated in 1-2 activities, and frequently participated in ≥3 activities, respectively. For 

2 shortness of breath, the corresponding ORs across those exposure categories were 1.0 (referent), 

3 1.16 (95% CI: 0.55, 2.44), 1.27 (95% CI 0.61, 2.63) and 1.32 (95% CI 0.51, 3.42), and for 

4 presence of bloody phlegm in the past year the ORs were 1.0 (referent), 0.85 (95% CI: 0.31, 

5 2.38), 1.09 (0.41, 2.98), and 1.49 (95% CI: 0.41, 5.43). For history of physician-diagnosed lung 

6 disease and abnormal spirometry results, there was little difference in the association seen across 

7 the exposure categories: for lung disease, the ORs were 1.0 (referent), 1.95 (95% CI: 0.57, 6.71), 

8 1.51 (95% CI: 0.43, 5.24) and 1.72 (95% CI: 0.36, 8.32) for the categories of never, sometimes, 

9 frequently participated in 1-2 activities, and frequently participated in ≥ 3 activities, respectively. 

10 For abnormal spirometry (i.e., obstructive, restrictive, or mixed, n = 94 cases), the ORs were 1.0 

11 (referent), 1.34 (95% CI: 0.60, 2.96), 1.20 (95% CI: 0.53, 2.70) and 1.33 (95% CI: 0.42, 4.19) 

12 across these exposure groups.

13 Two other studies examining autoimmune disease and autoantibodies in residents of 

14 Libby, Montana are described in Section 4.3. 

15

16 4.1.2.3. Other Reports of Asbestos-related Disease among Libby, MT Residents

17 Whitehouse et al. (2008) recently reviewed 11 cases of mesothelioma diagnosed between 

18 1993 and 2006 in residents in or around Libby, MT (n = 9) and in family members of workers in 

19 the mining operations (n = 2). Three cases were men who might have had occupational asbestos 

20 exposure through construction work (Case 1), working in the U.S. Coast Guard and as a 

21 carpenter (Case 5), or through railroad work involving sealing railcars in Libby (Case 7). One 

22 case was a woman whose father had worked at the mine for 2 years; although the family lived 

23 100 miles east of Libby, her exposure may have come through her work doing the family laundry 

24 which included her father’s work clothes. The other seven cases (four women, three men) had 

25 lived or worked in Libby for 6–54 years, and had no known occupational exposure to asbestos. 

26 Pathology reports were obtained for 10 of the 11 patients; for the other patient, diagnosis was 

27 based on medical records obtained from the patient’s physician. Given the relatively small 

28 population in the study area (approximately 9,500 for central Lincoln County), Whitehouse et al. 

29 (2008) note that the occurrence of 11 cases of mesothelioma during the approximate 15 years (or 

30 150,000 person-years) covered by this analysis is considerably higher than the estimated 

31 background incidence of 1 case per 1,000,000 person-years. Whitehouse et al. (2008) used 

32 information from a W.R. Grace unpublished report of measures taken in 1975 to estimate that 

33 exposure levels of 1.1 fibers/cc were found in Libby, and 1.5 fibers/cc were measured near the 

34 mill and railroad facilities. Because the mining and milling operations continued past 1990, and 
This document is a draft for review purposes only and does not constitute Agency policy.

4-29 DRAFT—DO NOT CITE OR QUOTE

1 because of the expected latency period for mesothelioma, Whitehouse et al. (2008) suggests that 

2 additional cases can be expected to occur within this population.

3

4 4.1.2.4. Summary of health effects in Libby, MT community studies

5 The geographic-based mortality analysis of 1997-1998 mortality data indicates that 

6 asbestosis-related mortality is substantially increased in Libby, MT, and the surrounding area, 

7 with rates 40 times higher compared with Montana rates and 60-70% higher compared with 

8 United States rates (ATSDR, 2000). However, because this analysis did not distinguish between 

9 deaths among workers and deaths among other community members, it is not possible based on 

10 these data to estimate the risk of asbestos-related mortality experienced by residents who were 

11 not employed at the mining or milling operations. The community health screening studies 

12 provide more detailed information regarding exposure pathways in addition to occupation 

13 (ATSDR, 2001). Data from this study indicate that the prevalence of pleural abnormalities, 

14 identified by radiographic examination, increases with the number of exposure pathways, from 

15 6.7% with no specific pathways to 34.6% with 12 or more pathways (Peipins et al., 2003). In 

16 addition, the prevalence of some self-reported respiratory symptoms among 10-29-year old 

17 adolescents and young adults was associated with certain exposure pathways. These participants 

18 were ≤ age 18 in 1990 when the mining/milling operations closed (Vinikoor et al., 2010). A 

19 better understanding of the community health effects and the examination of the potential 

20 progression of adverse health effect in this community would benefit from additional research to 

21 establish the clinical significance of these findings. The observation by Whitehouse et al. (2008) 

22 of cases of mesothelioma among individuals with no direct occupational exposure to the mining 

23 and milling operations indicates the need for continued surveillance for this relatively rare 

24 cancer.

25

26 4.1.3. Marysville, OH Vermiculite Processing Plant Worker Studies

27 Libby vermiculite was used in the production of numerous commercial products, 

28 including as a potting soil amender and a carrier for pesticides and herbicides. A Marysville, OH

29 plant that used Libby vermiculite in the production of fertilizer from approximately 

30 1963–1980 is the location of the two studies described in this section. Although vermiculite 

31 operation may have begun in the late 1950s (1957 or 1959), use of vermiculite from Libby, MT, 

32 is not documented until 1963.

33 The processing facility had eight main departments, employing approximately 

34 530 workers, with 232 employed in production and packaging of the fertilizer and 99 in 

35 maintenance; other divisions included research, the front office, and the polyform plant (Lockey, 
This document is a draft for review purposes only and does not constitute Agency policy.

4-30 DRAFT—DO NOT CITE OR QUOTE

1 1985). Six departments were located at the main facility (trionizing, packaging, warehouse, 

2 plant maintenance, central maintenance, and front offices). Research and development and a 

3 polyform fertilizer plant were located separately, approximately one-quarter mile from the main 

4 facility. In the trionizing section of the plant, the vermiculite ore was delivered by rail or truck, 

5 unloaded into a hopper, and transported to the expansion furnaces. After expansion, the 

6 vermiculite was blended with other materials (e.g., urea, formaldehyde, potash, herbicides), 

7 packaged, and stored. Changes to the expander type and dust-control measures began in 1967, 

8 with substantial improvement in dust control in 1974. 

9 Information about exposure assessment at the Marysville, OH plant is summarized in the 

10 final row of Table 4-1. Industrial hygiene monitoring at the plant began in 1972, and limited 

11 industrial hygiene sampling data from 1972–1974 are available. Lockey (1985) and Lockey et 

12 al. (1984) noted that the limited availability of data that would allow for extrapolation of 

13 exposures for earlier time periods possibly resulted in the underestimation of exposures before 

14 1974. Task-level samples were conducted, and measurements were determined using polarized 

15 light microscopy and transmission electron microscopy (based on particles >5-μm long, <3-μm 

16 diameter, and ≥3:1 aspect ratio).

17 Based on measurements and knowledge of plant operations, three categories of exposure 

18 levels were defined. Group I (considered to be the non-exposed group) consisted of the chemical 

19 processing, research, and front office workers. The chemical process plant was about 1 quarter 

20 mile from the main vermiculite facility, but the same chemicals were used in both locations. The 

21 8-hour time-weighted average for vermiculite exposure in this group, both before and after 1974, 

22 was estimated as 0.049 fiber/cc (based on a single stationary sample taken outside the main 

23 facility), which was characterized as similar to the background levels in the community. 

24 Group II was the ―low exposure‖ category and included central maintenance, packing, and 

25 warehouse workers. The 8-hour time-weighted averages for vermiculite exposures in this group 

26 were estimated as approximately 0.1–0.4 fibers/cc before 1974 and 0.03–0.13 fibers/cc in and 

27 after 1974. Group III was the ―highest exposure‖ category, and included vermiculite expanders, 

28 plant maintenance, and pilot plant workers. The 8-hour time-weighted averages for vermiculite 

29 exposures in this group were approximately 1.2–1.5 fibers/cc before 1974 and 0.2–0.43 fibers/cc

30 in and after 1974. Cumulative fiber exposure indexes, expressed as fibers-yr/cc, were derived 

31 for each worker from available industrial hygiene data and individual work histories (Lockey, 

32 1985) and those with less than 1 fiber/cc-year were assumed to be equivalent to a community 

33 population (in terms of exposure) and were used as the comparison group. The estimated 

34 cumulative exposure for the work force ranged from 0.01 to 39.9 fibers-yr/cc using an 8-hour 
This document is a draft for review purposes only and does not constitute Agency policy.

4-31 DRAFT—DO NOT CITE OR QUOTE

1 workday and an assumed 365 days of exposure per year. Exposure was assumed to occur from 

2 1957 to 1980 in this study. Exposure after work hours was assumed to be zero.

3 The first study of pulmonary effects in the Ohio plant workers was conducted in 1980 

4 and involved 512 workers (97% of the 530 workers previously identified with past vermiculite 

5 exposure) (Table 4-9; Lockey et al., 1984). The distribution by exposure group was 112 in 

6 Group I, 206 in Group II, and 194 in Group III. Physical examination (for detection of 

7 pulmonary rales and nail clubbing), spirometry, and chest-x-rays were performed, and 

8 information pertaining to smoking history, work history at the plant, and other relevant work 

9 exposures was collected using a trained interviewer. Radiographs were read by two board10 certified radiologists (B readers), with a consensus reading by a third reader when needed. The 

11 number of workers within each exposure group was 112, 206, and 194 in Groups I, II, and III, 

12 respectively. Approximately 44% were current smokers, 20% former smokers, and 35% lifetime 

13 nonsmokers, but smoking history (i.e., smoking status, pack-years) did not differ by exposure 

14 group. Mean cumulative fiber estimates were 0.45, 1.13, and 6.16 fibers/cc in Groups I, II, and 

III, respectively.415

16 A follow-up study of this cohort was conducted in 2004–2005 (Rohs et al., 2008) 

17 (Table 4-9). This study included 298 workers, of which 280 completed the study interview and 

18 pulmonary x-ray. Details of the reasons for non-participation rates are described in Table 4-9. 

19 The study interview included information about smoking history and asbestos exposure at the 

20 Ohio plant and other work sites. 

21

 

4

Calculated from data presented in Table 2 of Lockey et al. (1984).
This document is a draft for review purposes only and does not constitute Agency policy.

4-32 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-9. Pulmonary radiographic studies of the OH vermiculite processing 

2 plant workers

3

Reference(s) Inclusion criteria and design details Results

Lockey et al., 1984; 

Lockey, 1985

1980, n = 512 (from 530 identified employees with 

past vermiculite exposure; non-participants 

included 9 refusals and 9 unavailable due to illness 

or vacation).

Smoking history, work history at the plant, and 

other asbestos and fiber mineral work history data 

were collected. 

Chest exam (rales), nail clubbing, spirometry, 

forced vital capacity, forced expiratory volume, 

single-breath carbon monoxide diffusing capacity, 

and chest x-rays (available for 502 

participants)were analyzed. 

Mean employment duration: 10.2 yearsa

Cumulative fiber exposure related to 

history of pleuritic chest pain and 

shortness of breath.

No relation between cumulative 

exposure and spirometry results, forced 

vital capacity and expiratory volume 

tests, or carbon monoxide diffusing 

capacity.

Pleural thickening in 10 workers (2%); 

bilateral, small opacities in 1 (0.2%).

Abnormality increased with increasing 

cumulative exposure.

Rohs et al., 2008 2004–2005, interviews and chest x-rays conducted, 

n = 298; 280 with interviews and readable chest 

x-rays (from 431 workers in the 1980 study group 

of 513b who were alive in 2004; 151 living 

non-participants included 49 refusals, 76 located 

but did not respond, 8 not located but presumed 

alive, and 18 missing either x-ray or interview).

Age, smoking, asbestos exposure measure (at this 

plant), and other asbestos exposure data used to 

compare participants and non-participants.

Libby, MT vermiculite ore used in the plant from 

1963–1980.

Pleural changes in 80 workers (28.9%).

Small opacities (≥1/0) in 8 workers 

(2.9%).

Increasing risk of pleural changes with 

increasing cumulative exposure: odds 

ratios (adjusting for date of hire, body 

mass index) by quartile of cumulative 

fibers were 1.0 (referent), 2.7, 3.5, and 

6.9.

4

a

5 Calculated based on stratified data presented in Table 2 of Lockey et al. (1984).

b

6 Rohs et al. (2008) identified one additional eligible worker from the original 530 employees identified with past 

7 vermiculite exposure.

8

9

10 The evaluation of each worker included an interview to determine work and health 

11 history, spirometry, pulmonary examination, and chest X-ray. Exposure was estimated using the 

12 procedure previously described (Lockey, 1985) using the data on fiber levels. Exposure was 

13 assumed to occur from 1963 to 1980 in this study, assuming an 8-hour workday and 365 days of 

14 exposure per year (J. Lockey, University of Cincinnati, personal communication to R. Benson). 

15 Each worker supplied a detailed work history (start and end date for each area within the 

16 facility). The exposure reconstruction resulted in a cumulative exposure estimate for each 

17 individual. The estimated cumulative exposure for this follow-up study ranged from 0.01 to 

18 19.03 fibers-yr/cc (mean = 2.48). The time from first exposure ranged from 23 to 47 years. 

19 Twenty-eight workers reported previous occupational exposure to asbestos. Exposure after work 

20 hours was assumed to be zero. 
This document is a draft for review purposes only and does not constitute Agency policy.

4-33 DRAFT—DO NOT CITE OR QUOTE

1 Three board-certified radiologists independently classified the radiographs using ILO 

2 criteria (ILO, 2000). Radiologists were blinded to all identifiers. Pleural changes that were 

3 considered were localized (pleural plaques) and/or diffuse pleural thickening. Localized 

4 (discrete) pleural thickening was defined as thickening with or without calcification, excluding 

5 solitary costophrenic angle blunting. Diffuse pleural thickening was pleural thickening, 

6 including costophrenic angle blunting, with or without calcification. Interstitial changes were 

7 defined as irregular opacities, profusion of 1/0 or greater. A radiographic reading was defined as 

8 positive when the median classification from the three independent readings was consistent with 

9 pleural change and/or interstitial changes. Radiographs classified as unreadable were not used. 

10 Radiographic changes found in the study population are summarized in the Tables 4-10 and 4-

11 11.

12 Pleural changes were observed in 80 workers (28.9%), and small opacities (≥1/0) were 

13 observed in 8 (2.9%). Increasing risk of costophrenic angle blunting (an indicator of pleural 

14 effusions) (n = 11), pleural and parenchymal changes (n = 11), or any of these changes (n = 22) 

15 were observed with increasing cumulative exposure when assessed by category of workers or by 

16 cumulative exposure across all workers. The prevalence of any radiographic change was 2.8% in 

17 Group I, 3.9% in Group II, and 5.8% in Group III. Using the cumulative fiber metric, the 

18 prevalence of any radiographic change was 2.4% in the <1 fiber/cc-year, 5.0% in 1–

19 10 fibers/cc-year, and 12.5% in the >10 fibers/cc-year groups. The prevalence of pleural 

20 changes increased across exposure quartiles from 7.1% in the first quartile to 24.6%, 29.4%, and 

21 54.3% in the second, third, and fourth quartiles, respectively. The range of exposures was 

22 estimated as 0.01–0.28, 0.29–0.85, 0.86–2.20, and 2.21–19.03 fiber/cc-years in the first, second, 

23 third, and fourth quartiles, respectively. Adjusting for age, date of hire, and body mass index 

24 resulted in odds ratios of 2.7, 3.5, and 6.9 for the second, third, and fourth quartiles, respectively

25 (Rohs et al, 2008).
This document is a draft for review purposes only and does not constitute Agency policy.

4-34 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-10. Prevalence of pleural radiographic changes according to 

2 quartiles of cumulative fiber exposure in 280 participants

3

Exposure 

quartile

Exposure, fiberyr/cc, and (mean)

Number of 

workers

Number of 

workers with 

pleural change 

(%)b

Crude OR

(95% CI)

Number of 

workers with 

parenchymal 

change (%)

First 0.01 – 0.28 (0.12) 70 5 (7.1) Reference 0 (0)

Second 0.29 – 0.85 (0.56) 72a17 (24.6) 4.02

(1.39 – 11.60)

0 (0)

Third 0.86 – 2.20 (1.33) 68a20c(29.4) 5.42

(1.90 –15.46)

1 (1.5)

Fourth 2.21 – 19.03 (7.93) 70 38 (54.3) 15.44

(5.55 – 42.98)

7 (10)

Total (2.48) 280 80 (28.6) 8 (2.9)

4 The 80 workers with pleural radiographic changes include 68 with discrete pleural thickening (85%) and 12 with 

5 diffuse pleural thickening (15%)

a Two observations in the second quartile and two in the third quartile had exact exposure values at the 50th 6

7 percentile cutoff point. Rounding put these four observations in the second quartile.

b

8 Significant trend, p < 0.001

c

9 Typographical error in publication corrected.

10

11 Source: Rohs et al. (2008), Table 3 

12

13 Pleural changes were significantly associated with hire on or before 1973 and age at time 

14 of interview (p<0.001). Body mass index (BMI), smoking, and sex were not associated with 

15 pleural changes (Table 4-11). Body mass index is a potentially important confounder because fat 

16 pads can sometime be misclassified as discrete pleural thickening. Hire on or before 1973 and 

17 ages at time of interview are each highly correlated with cumulative exposure to fibers. The 

18 small number of females (16) in the cohort limits the analysis of the association with sex.

19

20
This document is a draft for review purposes only and does not constitute Agency policy.

4-35 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-11. Prevalence of pleural radiographic changes in 280 participants 

2 according to various cofactors

3

Variable Number 

of 

workers

Number with 

pleural change 

(%)

Crude 

OR

95% CI P Value

Hired on or 

before 1973

Hired after 

1973

186

 94

70 (37.6)

10 (10.6)

5.07

Reference

2.47 – 10.41 <0.001

Body Mass 

Indexa, kg/m2

≤24.9

25 – 29.9

≥30

 28

101

110

 8 (28.6)

31 (30.7)

27 (24.5)

Reference

1.11

0.81

0.44 – 2.79

0.32 – 2.06

0.52

0.43

Ever smokedb

Yes

No

184

 96

55 (29.9)

25 (26.04)

1.21

Reference

0.70 – 2.11 0.50

Age at time of 

interview

40 – 49

50 – 59

≥ 60

 55

116

109

 5 (9.1)

28 (24.1)

47 (43.1)

Reference

3.18

7.58

1.16 – 8.76

2.80 – 20.49

0.03

<0.001

Female

Male

 16

264

 1 (6.3)

79 (29.9)

Reference

6.40 0.83 – 49.32 0.07

a

4 n = 239 for Body Mass Index due to 38 persons undergoing phone interview and 3 persons with 

5 onsite interviews who were not measured for height and weight.

b

6 Smoking history as recorded in 2004 questionnaire. Of these 280 participants, 20 persons 

7 reported never smoking in the 1980 questionnaire but subsequently reported a history of smoking 

8 in the 2004 questionnaire (either current or ex-smoker)

9

10 Source: Rohs et al. (2008)

11

12 Modeling of odds ratios with cumulative fiber exposure and including various cofactors 

13 (age, hired before 1973, or BMI) with the first exposure quartile as the reference was also 

14 conducted. Each model demonstrated the same trend: increasing pleural change with increasing 

15 cumulative exposure to fibers. Lack of multicolinearity was demonstrated by the stable results 
This document is a draft for review purposes only and does not constitute Agency policy.

4-36 DRAFT—DO NOT CITE OR QUOTE

1 of the modeling with the exposure-response relationship remaining the same when different 

2 models were used with a single cofactor or all cofactors together. There was no evidence of 

3 significant interactions using this modeling.

4 There was potential co-exposure to a number of herbicides, pesticides, and other 

5 chemicals in the facility (personal communication to Robert Benson, EPA Region 8, from Ivan 

6 Smith, The Scotts Company, June 7, 2007). The herbicides and pesticides used during the time 

7 when Libby ore was used included atrazine, benomyl, bensulide, chloroneb, chlorothalonyl, 

8 chlorpyrifos, 2,4-D, dacthal, diazinon, dicamba, dephenamid, disodium methanearsonate, 

9 dyrene, ethoprop, linuron, MCPP, monuron, neburon, oxadiazon, terrachlor, pentachlorophenol, 

10 phenylmercuric acetate, siduron, terrazole, thiophannate-methyl, thiram. Other chemicals used 

11 included ammonium hydroxide, brilliant green crystals, caustic soda, corncobs, ferrous 

12 ammonium sulfate, ferrous sulfate, florex RVM, frit-504, frit-505, hi sil, lime, magnesium 

13 sulfate, mon-a-mon, potash, potassium sulfate, sudan orange, sudan red, sulfur, sulfuric acid, 

14 UFC, urea, and Victoria green liquid dye. No quantitative information on exposure to these 

15 chemicals is available. However, the addition of the other chemicals to the vermiculite carrier 

16 occurred in a different part of the facility after expansion of the vermiculite ore. Industrial 

17 hygiene monitoring in these areas showed very low levels of fibers in the air. In addition, none 

18 of these other chemicals is volatile. Thus, it is unlikely that workers would be co-exposed by 

19 inhalation to these other chemicals. EPA has no information indicating that exposure to these 

20 chemicals (when administered as a single entity) causes pleural or parenchymal changes typical 

21 of those found in workers employed in the Marysville facility.

22 This study demonstrates that exposure to Libby Amphibole asbestos can cause 

23 radiographic evidence of pleural and parenchymal changes in exposed workers. The prevalence 

24 of radiographic changes involving the pleura increased from 2% in 1984 (10/501) to 28.9% in 

25 2004 (80/280). The prevalence of any radiographic change (discrete pleural thickening, diffuse 

26 pleural thickening, and/or parenchymal change) was 29.3% (82/280). The increase in prevalence 

27 in the follow-up study is most likely due to the additional time between the two studies giving 

28 additional time for the changes to become apparent in conventional x-rays. The follow-up study 

29 also shows a clear exposure-response relationship for discrete pleural thickening and more 

30 extensive radiographic changes (that is, diffuse pleural thickening and parenchymal change) with 

31 increasing cumulative exposure to Libby Amphibole asbestos.

32
This document is a draft for review purposes only and does not constitute Agency policy.

4-37 DRAFT—DO NOT CITE OR QUOTE

1 4.1.3.1. Summary of Marysville, OH Vermiculite Processing Plant Worker Studies

2 The studies conducted in the 1980s (Lockey et al., 1984; Lockey 1985) and the follow-up 

3 of the cohort (Rohs et al., 2008) indicate that pleural changes can be seen among workers in this 

4 plant, with increasing prevalence of changes seen with increasing cumulative exposure. 

5

6 4.1.4. Community Studies from Other Vermiculite Processing Plants

7 ATSDR is conducting community evaluations of 28 sites, in addition to Libby, 

8 surrounding exfoliation plants that require further action by EPA because of current 

9 contamination or evidence (based on a database of invoices) that the plant processed more than 

10 100,000 tons of vermiculite from the Libby, MT mine (Figure 4-1; Table 4-12). These 

11 community-level evaluations do not address individual exposures or residential histories; 

12 therefore, the evidence in these evaluations pertaining to disease risk is somewhat limited. 

13 Nine of these evaluations included analyses conducted in conjunction with state health 

14 departments using death certificate data (Table 4-13). The lung cancer standardized incidence 

15 ratios for these evaluations range from 0.74–1.07, and the SMRs range from 0.74–1.1, indicating 

16 little evidence of an increased risk of lung cancer among these studies (Table 4-14). As expected 

17 from the small number of observations, the standardized incidence ratios for mesothelioma or the 

18 category of cancer of the peritoneum, retroperitoneum, and pleura (excluding mesothelioma, but 

19 which could reflect some misdiagnoses) are more variable, ranging from approximately 0.5–2.5. 

20 Breast and prostate cancer were selected as negative controls (i.e., cancers that have not 

21 previously been associated with asbestos exposure) in these evaluations. The patterns observed 

22 for these two cancer sites (Table 4-15) were similar to those of lung cancer (Table 4-14). In 

23 summary, these studies do not provide evidence of an increased risk of asbestos-related cancers 

24 in the communities surrounding plants that processed vermiculite contaminated with Libby 

25 Amphibole asbestos. A limitation of these studies, however, is the use of a very broad exposure 

26 classification, which would be expected to result in considerable misclassification. As a result, 

27 these studies might provide useful information to communities, but more refined study designs 

28 are needed to evaluate risk to individuals.

29

30 4.1.4.1. Summary of Community Studies from Other Vermiculite Processing Plants

31 The community-based mortality studies around the 28 exfoliation plants that processed 

32 vermiculite contaminated with Libby Amphibole asbestos provide little evidence of an increased 

33 risk of asbestos-related cancers in the surrounding communities. These studies are quite limited, 

34 however, by the broad exposure classification and the inability to limit the analysis to individuals 

35 who had resided in the specific areas during the relevant exposure periods. Additional studies 
This document is a draft for review purposes only and does not constitute Agency policy.

4-38 DRAFT—DO NOT CITE OR QUOTE

1 would be needed to more fully examine the potential risks associated with residential exposures 

2 from these sources.

3

4

5

6 Figure 4-1. Location of 28 sites included in the Phase 1 community 

7 evaluations conducted by ATSDR

8

9 Source: ATSDR (2008) http://www.atsdr.cdc.gov/asbestos/sites/national_map/.

10
This document is a draft for review purposes only and does not constitute Agency policy.

4-39 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-10. ATSDR health consultations of 28 sites with potential Libby 

2 Amphibole asbestos contamination by status of health data and location 

3 (state) of the site

4

Health data status Site Year of report

Health data not included in report, but might be 

available in the future

Glendale, Arizona not reported

Phoenix, Arizona not reported

Denver, Colorado 2003

Tampa, Florida 2005

Honolulu, Hawaii 2005

Wilder, Kentucky 2005

New Orleans, Louisiana 2005

Beltsville, Maryland 2003

Easthampton, Massachusetts 2006

Minneapolis, Minnesota 2003

Omaha, Nebraska not reported

Weesport (Cayuga County), New Yorkc2004d

Portland, Oregonanot reported

Portland, Oregona2005

Ellwood City, Pennsylvania 2006

New Castle, Pennsylvania 2005

Dallas, Texas 2005

Spokane, Washington 2004d

Health studies completed Los Angeles, California 2007

Newark, California 2005

Santa Ana, California 2003

West Chicago, Illinois 2003

Dearborn, Michigan 2005

St. Louis, Missouri 2006

Trenton (Hamilton), New Jerseyb2005d

Edgewater, New Jerseyb2007

Marysville, OH 2005

5

a

6 Two separate studies of two different sites in Portland were conducted. 

b

7 Health data included in a separate report available through the New Jersey Department of Health and Senior Services.

c

8 Health data were mentioned, but they were not included in the report.

d

9 Publication date obtained from a separate source.

10

11 Source: ATSDR (2008).
This document is a draft for review purposes only and does not constitute Agency policy.

4-40 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-11. Description of study areas in ATSDR health consultations 

evaluating cancer incidence and mortalitya2

3

Site, exposure period Study area (n from 1990 census) Year of report

Los Angeles, California, 1950–1977 Incidence: census tract (n = 21,945)

Mortality: zip code (n = 57,615)

2007

Newark, California, 1967–1992 Incidence: census tract (n = 7,785)

Mortality: zip code (n = 37,861)

2005

Santa Ana, California, 1972–1993 Census tract (35,000) 2003

West Chicago, Illinois, 1974–1996 Mortality: zip code (n = 14,796) 2003

Dearborn, Michigan, early 1950s–1989 City limits (n = 89,015) 2005

St. Louis, Missouri, 1956–1988 Census tracts (n = 20,112) 2006

Trenton, New Jersey, 1920s–1990 Census tracts and areas (n = 26,762) 2005

Edgewater, New Jersey, not reported Not reported 2005

Marysville, OH, 1963–1980b City limits (n = 9,656) 2005

4

a

5 All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as comparison group except 

6 New Jersey, which used New Jersey state rates. All mortality studies used U.S. rates from the National Center for 

7 Health Statistics.

b

8 The Agency for Toxic Substances and Disease Registry (ATSDR, 2008) report presented incidence data from 

9 1979–2000, but the 1986–1995 incidence data and the mortality data were obtained from the report of the New 

10 Jersey Department of Health and Social Services.

c

11 The start date for the use of the Libby, MT vermiculite was variously described as 1957 and 1967 in the ATSDR 

12 health consultation report (ATSDR, 2008); the studies by Lockey et al. (1984) and Rohs et al. (2008) used 1963 as 

13 the start date.
This document is a draft for review purposes only and does not constitute Agency policy.

4-41 DRAFT—DO NOT CITE OR QUOTE

1 Table 4-14. Incidence and mortality results for potential asbestos-related 

2 cancers (by cancer site) in communities with ATSDR health consultations 

3 evaluating potential Libby Amphibole asbestos contamination

4

Study areac

Incidencea Mortalityb

Observed ExpectedcSIR (95% CI) Observed ExpectedcSMR (95% CI)

Lung and bronchus

Los Angeles, CAd100 117.4 0.85 (0.69, 1.04) 210 285.0 0.74 (0.64, 0.84)

Newark, CAd29 27.2 1.07 (0.71, 1.53) 125 124.3 1.01 (0.84, 1.2)

Santa Ana, CAd79 95.4 0.83 (0.66, 1.03) – – – –

West Chicago, IL – – – – 95 98.6 0.96 (0.78, 1.18)

Dearborn, MI 757 764.4 0.99 (0.92, 1.06) 1,133 1,261.3 0.90 (0.85, 0.95)

St. Louis, MO – – – – 319 286.6 1.1 (1.0, 1.2)

Trenton, NJ 496 671.0 0.74 (0.68, 0.81) 976 1,100.3 0.89 (0.83, 0.94)

Edgewater, NJ 35 30.7 1.14 (0.80, 1.59) 51 50 1.02 (0.76, 1.34)

Marysville, OH – – – – 106 98.1 1.1 (0.9, 1.3)

Mesothelioma

Los Angeles, CAd1 1.9 0.53 (0.01, 2.96) – – – –

Newark, CAd1 0.4 2.49 (0.03, 13.9) – – – –

Santa Ana, CAd4 1.5 2.68 (0.72, 6.87) – – – –

West Chicago, IL – – – – – – – –

Dearborn, MI 8 12.3 0.65 (0.28, 1.28) – – – –

St. Louis, MO – – – – – – – –

Trenton, NJ 6 10.6 0.57 (0.21, 1.24) – – – –

Edgewater, NJ 1 0.5 2.11 (0.03, 11.7) – – – –

Marysville, OH – – – – – – – –

Peritoneum, retroperitoneum, and pleura (excluding 

mesothelioma)

(including mesothelioma)

Los Angeles, CAd1 3.1 0.32 (0.00, 1.78) 0 2.1 0.0 –

Newark, CAd3 0.7 4.06 (0.82, 11.9) 0 0.9 0.0 (0, 4.10)

Santa Ana, CAd6 2.7 2.24 (0.82, 4.87) – – – –

West Chicago, IL – – – – 1 0.8 1.28 (0.02, 7.12)

Dearborn, MI 16 19.1 0.84 (0.48, 1.36) 9 9.6 0.93 (0.43, 1.77)

St. Louis, MO – – – – 3 2.3 1.g26

3

(0.3, 3.8)

Trenton, NJ 10 16.7 0.60 (0.29, 1.10) 18 8.3 2.17 (1.29, 3.43)

Edgewater, NJ 1 0.8 1.28 (0.02, 7.13) 0 0.2 0.0 –
This document is a draft for review purposes only and does not constitute Agency policy.

4-42 DRAFT—DO NOT CITE OR QUOTE

Study areac

Incidencea Mortalityb

Observed ExpectedcSIR (95% CI) Observed ExpectedcSMR (95% CI)

Marysville, OH – – – – 0 0.8 0.0 –

1

Table 4-14. Incidence and mortality results for potential asbestos-related 

cancers (by cancer site) in communities with ATSDR health consultations 

evaluating potential Libby Amphibole asbestos contamination (continued)

Study areac

Incidencea Mortalityb

Observed ExpectedcSIR (95% CI) Observed ExpectedcSMR (95% CI)

Respiratory system and intrathoracic organs

Los Angeles, CAd111 128.2 0.87 (0.71, 1.04) 217 294.4 0.74 (0.64, 0.84)

Newark, CAd32 30.1 1.06 (0.73, 1.50) 126 128.7 0.98 (0.82, 1.17)

Santa Ana, CAd86 105.0 0.82 (0.66, 1.01) – – – –

West Chicago, IL – – – – 100 102.2 0.98 (0.80, 1.19)

Dearborn, MI 831 832.7 1.00 (0.93, 1.07) 1,173 1, 305.1 0.90 (0.85, 0.95)

St. Louis, MO – – – – 334 296.5 1.1 (1.0, 1.2)

Trenton, NJ 536 732.8 0.73 (0.67, 0.80) 1,008 1,139.1 0.88 (0.83, 0.94)

Edgewater, NJ 42 33.6 1.25 (0.90, 1.69) 53 51.9 1.02 (0.77, 1.34)

Marysville, OH – – – – 107 101.6 1.0 (0.9, 1.3)

Selected digestive organs

Los Angeles, CAd101 139.5 0.72 (0.59, 0.88) 155 170.0 0.91 (0.77, 1.07)

Newark, CAd25 27.0 0.92 (0.60, 1.36) 74 63.4 1.17 (0.92, 1.47)

Santa Ana, CAd79 104.0 0.76 (0.60, 0.95) – – – –

West Chicago, IL – – – – 70 62.7 1.12 (0.87, 1.41)

Dearborn, MI 899 843.2 1.07 (1.00, 1.14) 819 785.1 1.04 (0.97, 1.12)

St. Louis, MO – – – – 172 196.7 0.9 (0.7, 1.0)

Trenton, NJ 626 718.1 0.87 (0.80, 0.94) 752 659.8 1.14 (1.06, 1.22)

Edgewater, NJ 51 32.6 1.57 (1.17, 2.06) 27 29.7 0.91 (0.60, 1.32)

Marysville, OH – – – – 67 66.8 1.0 (0.8, 1.3)

a

2 All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as the comparison group 

3 except New Jersey, which used New Jersey state rates; incidence period in all analyses was 1986–1995. An 

4 additional analysis compared the Hamilton, New Jersey mesothelioma rates to SEER rates; standard incidence ratio 

5 (SIR) wsa reported to be ―increased slightly but remained under 1.0.‖ Incidence data, ICD-10 (International 

6 Classification of Diseases) codes: lung and bronchus, C340:C349; mesothelioma, M-9050:9053; peritoneum, 

7 retroperitoneum, and pleura, C480:C488, C384; respiratory system and intrathoracic organs, C320:C399-excluding 

8 mesothelioma; selective digestive organs, C150:C218, C260-C269-excluding mesothelioma. 

b

9 All mortality studies used U.S. rates from the National Center for Health Statistics. Mortality period was 1989–

10 1998 in the Los Angeles and Newark, California analyses, and was 1979–1998 in all analyses. Mortality data, 

11 ICD-9 codes: lung and bronchus, 162.2–162.9; peritoneum, retroperitoneum, and pleura, 158, 163; respiratory 

12 system and intrathoracic organs, 161–165; selective digestive organs, 150–154, 159. 
This document is a draft for review purposes only and does not constitute Agency policy.

4-43 DRAFT—DO NOT CITE OR QUOTE

c

1 Expected values have been rounded. 

d

2 Similar results were observed in the California analyses using alternative methods to calculate standardized risk 

3 ratios for incidence and mortality.

4 CI = confidence interval.

5 Source: ATSDR (2008).

6 Table 4-15. Incidence and mortality results for breast cancer and prostate 

7 cancer cancers in communities with ATSDR health consultations evaluating 

8 potential Libby Amphibole asbestos contamination

9

Study areac

Incidencea Mortalityb

Observed ExpectedcSIR (95% CI) Observed ExpectedcSMR (95% CI)

Breast cancer (female)

Los Angeles, CAd104 130.6 0.80 (0.65, 0.97) 109 89.5 1.22 (1.00, 1.47)

Newark, CAd43 34.3 1.25 (0.91, 1.69) 46 38.8 1.18 (0.87, 1.58)

Santa Ana, CAd101 110.2 0.92 (0.75, 1.11) – – – –

West Chicago, IL – – – – 44 33.5 1.31 (0.95, 1.76)

Dearborn, MI 764 736.1 1.04 (0.97, 1.11) 401 370.8 1.08 (0.98, 1.19)

St. Louis, MO – – – – 116 93.1 1.2 (1.0, 1.5)

Trenton, NJ 497 682.2 0.73 (0.67, 0.80) 349 334.4 1.04 (0.94, 1.16)

Edgewater, NJ 30 32.6 0.92 (0.62, 1.31) 24 15.5 1.55 (0.99, 2.30)

Marysville, OH – – – – 39 34.6 1.1 (0.8, 1.5)

Prostate cancer (male)

Los Angeles, CAd79 118.1 0.67 (0.53, 0.83) 47 64.5 0.73 (0.54, 0.97)

Newark, CAd24 22.4 1.07 (0.69, 1.59) 17 20.4 0.83 (0.49, 1.34)

Santa Ana, CAd77 88.0 0.87 (0.69, 1.09) – – – –

West Chicago, IL – – – – 24 18.14 1.32 (0.85, 1.97)

Dearborn, MI 899 810.3 1.11 (1.04, 1.18) 266 292.9 0.91 (0.80, 1.02)

St. Louis, MO – – – – 65 66.9 1.0 (0.7, 1.2)

Trenton, NJ 391 674.8 0.58 (0.52, 0.64) 208 225.4 0.92 (0.80, 1.06)

Edgewater, NJ 27 31.7 0.86 (0.56, 1.24) 6 10.4 0.57 (0.21, 1.25)

Marysville, OH – – – – 28 24.1 1.2 (0.8, 1.7)

10

a

11 All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as the comparison group 

12 except New Jersey, which used New Jersey state rates; incidence period in all analyses was 1986–1995. Incidence 

13 data, ICD-10 (International Classification of Diseases) codes: breast cancer, C500:C509 (excluding mesothelioma); 

14 prostate cancer, C619 (excluding mesothelioma).

b

15 All mortality studies used U.S. rates from the National Center for Health Statistics. Mortality period was 1989–

16 1998 in the Los Angeles and Newark, California analyses, and was 1979–1998 in all analyses. Mortality data, 

17 ICD-9 codes: breast cancer, 174; prostate cancer, 185.

c

18 Expected values have been rounded.

d

19 Similar results were observed in the California analyses using alternative methods to calculate standardized risk 

20 ratios for incidence and mortality.
This document is a draft for review purposes only and does not constitute Agency policy.

4-44 DRAFT—DO NOT CITE OR QUOTE

1 CI = confidence interval.

2

3

4

5

6 4.1.5. Case reports

7 Progressive disease from exposure to Libby Amphibole was noted in a case report of fatal 

8 asbestosis in an individual 50 years after working at a vermiculite processing plant for a few 

9 months at about age 17 (Wright et al., 2002). In another case report, exposures that stemmed 

10 from playing for a few years as a child in contaminated vermiculite waste materials around a 

11 former Libby vermiculite processing facility was reportedly associated with the development of 

12 asbestosis and fatal lung cancer (Srebro and Roggli, 1994).

13

14 4.2. SUBCHRONIC AND CHONIC STUDIES AND CANCER BIOASSAYS IN 

15 ANIMALS – ORAL, INHALATION AND OTHER ROUTES OF EXPOSURE

16 Laboratory animal studies with exposure to Libby Amphibole or tremolite asbestos show 

17 effects similar to those observed in occupationally exposed human populations including pleural 

18 pathology, mesothelioma and lung cancer. Tremolite is an amphibole asbestos fiber that is a 

19 component of Libby Amphibole asbestos (~6%). Also, in early studies Libby Amphibole 

20 asbestos was defined as tremolite. Therefore, laboratory animal studies examining the effect of 

21 tremolite exposure have been reviewed and are summarized below to potentially increase 

22 understanding of the effects and mechanisms of Libby Amphibole asbestos. Detailed study 

23 summaries can be found in Appendix D and summarized in Table 4-16 and 4-17. No inhalation 

24 studies have been performed for Libby Amphibole asbestos, but chronic intrapleural injection 

25 studies in hamsters demonstrate carcinogenicity following exposure. The chronic inhalation and 

26 intrapleural injection laboratory animal studies with tremolite asbestos demonstrated pleural 

27 pathology and carcinogenicity in rats. These studies support the epidemiology studies of Libby 

28 Amphibole asbestos exposure (Section 4.1), and aid in informing the mechanisms of Libby 

29 Amphibole asbestos-induced disease. 

30

31 4.2.1. Oral

32 No studies in laboratory animals with oral exposure to Libby Amphibole were found in 

33 the literature. However, one chronic cancer bioassay was performed following oral exposure to 

34 tremolite. McConnell et al. (1983a) describe part of a National Toxicology Program study (NTP, 

35 1990a) performed to evaluate the toxicity and carcinogenicity of ingestion of several minerals, 

36 including tremolite. The tremolite (Governeur Talc Co, Governeur, New York) used was not 

37 fibrous. No significant tumor induction was observed in the animals with oral exposure to 
This document is a draft for review purposes only and does not constitute Agency policy.

4-45 DRAFT—DO NOT CITE OR QUOTE

1 tremolite animals. Although non-neoplastic lesions were observed in many of the aging rats, 

2 these were mostly in the stomach and occurred in both controls and exposed animals. The 

3 observed lesions included chronic inflammation, ulceration, and necrosis of the stomach 

4 (McConnell et al., 1983a). McConnell et al. (1983a) suggested that nonfibrous tremolite could 

5 account for the lack of toxicity following exposure in this group of animals. Also, oral studies of 

6 asbestos, in general, show decreased toxicity and carcinogenicity as compared to inhalation and

7 implantation/injection studies (Condie, 1983). 

8 4.2.2. Inhalation

9 There are no laboratory animal studies following inhalation exposure to Libby 

10 Amphibole asbestos; however two studies have examined the effect of inhalation exposure to 

11 tremolite in Wistar rats (Bernstein et al., 2005; 2003; Davis et al. 1985). Davis et al., (1985)

12 performed a chronic inhalation study examining response in male Wistar rats exposed in a 

chamber to 10 mg/m313 (~1,600 fibers/mL, >5 μm) of commercially mined tremolite over a 12-

month period. Bernstein et al. (2003; 2005) exposed Wistar rats to tremolite (100 fibers/cm314 ) and 

15 chrysotile for 13 consecutive weeks (6 hours per day, 5 days per week) with 1-year follow-up. 

16 The results of these inhalation studies produced pronounced inflammation and very high levels 

17 of pulmonary fibrosis. Davis et al (1985) also demonstrated an increase in carcinomas and 

18 mesotheliomas following exposure to tremolite, with no pulmonary tumors observed in the 

19 controls. These results show that Wistar rats exposed to tremolite exhibited increased numbers 

20 of pulmonary lesions and possibly tumors. 

21

22 4.2.3. Intratracheal Instillation Studies

23 Intratracheal instillation has been used to examine the effect of exposure to Libby 

24 Amphibole (Putnam et al., 2008; Smartt et al., 2009; Shannahan et al., 2011a) and tremolite 

25 asbestos (Sahu et al., 1975; Blake et al., 2008; Pfau et al., 2008). These studies exposed either 

26 C57Bl/6 mice (100 μg/mouse) or WKY rats (0.25 or 1mg/rat) once to Libby Amphibole asbestos 

27 and analyzed the results up to 3 mos post-exposure. Putnam et al. (2008) observed non28 statistically significant increases in collagen following exposure to Libby Amphibole asbestos, as 

29 well as gene expression alterations related to membrane transport, signal transduction, epidermal 

30 growth factor signaling, and calcium regulation. Smartt et al. (2009) followed up this study by 

31 analyzing specific genes by quantitative RT-PCR for genes involved in collagen accumulation 

32 and scar formation (Col1A1, Col1A2, Col3A1). Libby Amphibole asbestos exposure led to 

33 increased gene expression of Col1A2 at 1 week post-instillation and Col3A1 at 1 month post 

34 exposure. Both studies observed increased inflammation, however, Libby Amphibole asbestos 
This document is a draft for review purposes only and does not constitute Agency policy.

4-46 DRAFT—DO NOT CITE OR QUOTE

1 exposure demonstrated minimal inflammation that did not progress in the time points examined. 

2 These studies demonstrate that exposure to Libby Amphibole asbestos may lead to inflammation 

3 and fibrosis. Shannahan et al. (2011) exposed two rat models of human cardiovascular disease to 

4 Libby Amphibole asbestos to determine if the pre-existing cardiovascular disease in these 

5 models would impact the lung injury and inflammation following exposure. Healthy Wistar 

6 Kyoto (WKY) rats were compared to spontaneously hypertensive (SH) and spontaneously 

7 hypertensive heart failure (SHHF) rats following exposure. All rats (male only) were exposed to 

8 0, 0.25 or 1.0 mg/rat via intratracheal instillation and were examined at 1 d, 1 wk and 1 mo post9 exposure. No changes were observed histopathologically, however, changes were observed in 

10 markers of homeostasis, inflammation and oxidative stress. While inflammation and cell injury 

11 were observed in all strains, no strain-related differences were observed following exposure to 

12 Libby Amphibole asbestos (Shannahan et al., 2011). In conclusion, this study showed the 

13 potential for population variability related to cardiac disease in response to exposure to Libby 

14 Amphibole asbestos, including markers of cellular injury, iron homeostasis and inflammation. 

15 Laboratory animal studies of tremolite intratracheal instillation exposure have been 

16 performed in mice in doses ranging from 60 μg to 5 mg. Male Swiss albino mice exposed to 

17 tremolite (5mg) via intratracheal instillation demonstrated histological changes (Sahu et al., 

18 1975). Microscopic results following exposure to tremolite showed acute inflammation of the 

19 lungs at 7 days post exposure, including macrophage proliferation and phagocytosis similar to 

20 that observed with amosite and anthophyllite. Limited progression of fibrotic response was 

21 observed at 60 and 90 days post exposure, with no further progression of fibrotic response. 

22 Blake et al. (2008) and Pfau et al. (2008) examined the role of asbestos in autoimmunity. Blake 

23 et al. (2008) performed in vitro assays with Libby Amphibole asbestos (Section 4.4), and both 

24 studies performed the in vivo assays with tremolite. C57BL/6 mice were instilled intratracheally 

25 for a total of two doses each of 60-μg saline and wollastonite or Korean tremolite sonicated in 

26 sterile PBS, given 1 week apart in the first 2 weeks of a 7-month experiment. Sera from mice 

27 exposed to tremolite showed antibody binding colocalized with SSA/Ro52 on the surface of 

28 apoptotic blebs (Blake et al., 2008). In Pfau et al. (2008), by 26 weeks, the tremolite-exposed 

29 animals had a significantly higher frequency of positive anti-nuclear antibody tests compared to 

30 wollastinate and saline. Most of the tests were positive for dsDNA and SSA/Ro52. Serum 

31 isotyping showed no major changes in immunoglobulin subclasses (IgG, IgA, IgM), but serum 

32 IgG in tremolite-exposed mice decreased overall. Further, IgG immune complex deposition in 

33 the kidneys increased, with abnormalities suggestive of glomerulonephritis. No increased 

34 proteinuria was observed during the course of the study. Local immunologic response was 

35 further studied on the cervical lymph nodes. Although total cell numbers and lymph-node size 
This document is a draft for review purposes only and does not constitute Agency policy.

4-47 DRAFT—DO NOT CITE OR QUOTE

1 were significantly increased following exposure to tremolite, percentages of T- and B-cells did 

2 not significantly change. 

3

4 4.2.4. Injection/ImplantationStudies

5 There are no laboratory animal studies examining intraperitoneal injection or 

6 implantation of Libby Amphibole asbestos. Biological effects following exposure to tremolite 

7 have been examined in five intraperitoneal injection studies (Smith 1978; Smith et al., 1979; 

8 Wagner et al., 1982; Davis et al., 1991; Roller et al., 1996; 1997) and one implantation study 

9 (Stanton et al., 1981).

10 Studies by Smith and colleagues (1978; Smith et al. 1979), Wagner et al. (1982), Davis et 

11 al. (1991) and Roller et al. (1996; 1997) demonstrated that intrapleural injections of tremolite

asbestos512 is associated with an increase in pleural fibrosis and mesothelioma in hamsters and rats 

13 compared to controls or animals injected with less fibrous materials. Doses ranged from 10 – 25

14 mg/animal for each study, and although carcinogenesis was observed in these studies there was a 

15 variable level of response to the different tremolite forms examined. Although these studies 

16 clearly show the carcinogenic potential of Libby Amphibole or tremolite asbestos fibers, 

17 intrapleural injections bypass the clearance and dissolution of fibers from the lung after 

18 inhalation exposures. Further, limited information was provided confirming the presence or 

19 absence of particles or fibers less than 5 μm in length in these studies, limiting the interpretation 

20 of results. 

21 There is one laboratory animal study which examined the effect of tremolite exposure 

22 following implantation of fibers in the pleural cavity. Stanton et al. (1981) also examined 

23 tremolite and describe a series of studies on various forms of asbestos. Fibers, embedded in 

24 hardened gelatin, were placed against the lung pleura. As an intrapleural exposure, results might 

25 not be comparable to inhalation exposures, as the dynamics of fiber deposition and pulmonary 

26 clearance mechanisms are not accounted for in the study design. Studies using two tremolite 

27 asbestos samples from the same lot were described as being in the optimal size range for 

28 carcinogenesis; the fibers were distinctly smaller in diameter than the tremolite fibers Smith et al. 

29 (1979) used. These samples both had a high number of fibers in the size range (>8-μm long and 

30 <0.25-μm diameter; i.e., ―Stanton fibers‖). Exposure to both tremolite samples led to 

31 mesotheliomas in 21 and 22 of 28 rats exposed. The Stanton et al. (1981) study also used talc 

32 that did not lead to mesothelioma production. 

 

5

Smith (1978) used tremolite from Libby, MT; Smith et al. (1979) may also have used tremolite from Libby, MT 

(i.e., Libby Amphibole asbestos).
This document is a draft for review purposes only and does not constitute Agency policy.

4-48 DRAFT—DO NOT CITE OR QUOTE

1 There are no studies currently available in laboratory animals exposed to Libby 

2 Amphibole asbestos by inhalation. However, the chronic intraperitoneal injection study in 

3 hamsters (Smith 1978; Smith et al., 1979) demonstrated tumor formation following exposure to 

4 tremolite obtained from the Libby, MT mine. No other chronic studies of Libby Amphibole 

5 asbestos are available. A recent study in rats examining the impact of pre-existing 

6 cardiovascular disease on pulmonary inflammation demonstrated an increase in inflammatory 

7 markers following exposure to Libby Amphibole asbestos via intratracheal instillation in SH rats 

8 as compared to normal healthy controls exposed to the same dose (Shannahan et al., 2011). More 

9 recent studies examined gene expression changes (Putnam et al., 2008; Hillegass et al., 2010)

10 and early protein markers of fibrosis (Smartt et al., 2009) in mice exposed to Libby Amphibole 

11 asbestos via intraperitoneal injection. These studies demonstrated an increase in gene and 

12 protein expression related to fibrosis following exposure to Libby Amphibole asbestos. 

13 Tremolite fibers, although obtained from different locations throughout the world, consistently 

14 led to pulmonary lesions and/or tumor formation with various routes of exposure (inhalation, 

15 injection, instillation) and in multiple species (rats, hamsters, and mice) (Bernstein et al., 2003; 

16 2005; Davis et al., 1985; Wagner et al., 1982; Roller et al., 1996; 1997; Stanton et al., 1981). 

17 Although comparing potency of the various forms of tremolite is difficult given the limited 

18 information on fiber characteristics and study limitations (e.g., length of follow-up post19 exposure), these results show potential increased risk for cancer (lung and mesothelioma) 

20 following exposure to tremolite asbestos.

21 The results of the studies described above show the fibrogenic and carcinogenic potential 

22 of Libby Amphibole and tremolite asbestos. Further, the more recent studies by Blake et al. 

23 (2008) and Pfau et al. (2008) support human studies demonstrating potential autoimmune effects 

24 of asbestos exposure. 
This document is a draft for review purposes only and does not constitute Agency policy.

49 DRAFT―DO NOT CITE OR QUOTE

Table 4-16. In vivo data following exposure to Libby Amphibole asbestos

Species (sex) Exposure route Fiber type Effects* Reference

LVG:LAK 

Hamsters (M)

(n ~ 60/group)

Intraperitoneal 

injection (once)

25 mg/0.5 mL 0.9% 

NaCl solution

Tremolite (sample 60) and 

tremolite + vermiculite 

(sample 63)

Pleural adhesions (fibrosis): examined10 

animals/group at ~3 mo post exposure:

Sample 60: 10/10; Sample 63: 10/10; Control: 

0/10

Mesothelioma:

Sample 60: 5 /66; Sample 63: 5/64; Control: 0/60

Smith, 1978 (W.R. Grace 

study)

C57Bl/6 mice 

(M, F)

(n = 7/group)

Intratracheal instillation 

(once)

1 wk, 1 mo, 3 mo

100 μg of sample in 30 

μL saline

Libby Amphibole asbestos 

(Six Mix) and crocidolite

Altered gene expression in mice exposed to both 

samples; increase in collagen in exposed animals 

Putnam et al., 2008

C57Bl/6 mice 

(M, F)

(n = 7/group)

Intratracheal instillation 

(once)

1 wk, 1 mo, 3 mo

100 μg of sample in 30 

μL saline

Libby Amphibole asbestos 

(Six Mix) and crocidolite

Collagen gene expression and protein levels 

increased following exposure to both forms of 

asbestos (~ 1 mo post exposure).

Smartt et al., 2009

Wistar Kyoto rats 

(M)

(n = 12/group)

Spontaneously 

Hypertensive (SH)

(n = 6/group)

SH Heart Failure 

(SHHF)

(n = 6/group)

Intratracheal instillation 

(once)

1 d, 1 wk, 1 mo

0.25 or 1.0 mg/rat

Libby Amphibole asbestos 

(Six Mix)

Strain-related differences observed in biomarkers 

of inflammation following exposure to Libby

Amphibole asbestos. 

No differences were observed in histopathology.

Shannahan et al., 2011

*When available, results are shown as number of animals with tumors/total number of animals examined.
This document is a draft for review purposes only and does not constitute Agency policy.

50 DRAFT―DO NOT CITE OR QUOTE

Table 4-17. In vivo data following exposure to tremolite asbestos

Species (sex) Exposure route Fiber type Effects* Reference

F344 rats (M, F)

(n = 100 to 

250/group)

Oral

1% bw in feed pellets; 

lifetime exposure 

starting in dam

Tremolite-nonfibrous 

(Governeur Talc Co., 

Governeur, NY)

Offspring from exposed mothers were smaller at 

weaning and throughout life; 

No toxicity or increase in neoplasia in tremolite 

rats as compared to controls.

McConnell et al., 1983a

Wistar rats (M)

(n = 48)

Inhalation

10 mg/m3(7 h each 

day, 5 days per week, 

total of 224 days)

South Korean tremolite and 

brucite

Increased fibrosis (19/39) and carcinogenesis

(18/39).

Davis et al., 1985

AF/Han rats

(n = 33 –

36/group)

Intraperitoneal injection

10 mg/2 mL PBS; 

single exposure

Tremolite (Six samples) All six fibers could induce mesothelioma:

California: 36/36*

Swansea: 35/36*

Korea: 32/36*

Italy: 24/36

Carr Brae: 4/33

Shinness: 2/36

* asbestiform types led to mesothelioma in most if 

not all exposed animals in this study

Davis et al., 1991

Hamsters

(n ≤ 35/group)

Intrapleural injection

10 or 25 mg

Four types of tremolite 

(Sample FD-14; 275; 31; 72)

Sample FD-14: 0/35

Sample 275: 0/34 (10 mg); 0/31 (25 mg)Samples 

31: 3/41 (10 mg); 12/28 (25 mg)

Sample 72: 4/13 (10 mg); 13/20 (25 mg)

Smith et al., 1979

Sprague-Dawley 

and Wistar rats

(n = 32 Wistar rats 

(Sample A); 48 

Sprague-Dawley 

rats (Samples B 

and C))

Intrapleural injection

20 mg/rat

Tremolite (Three samples) No tumors following exposure to Sample A and B; 

Sample C: 14/47 

Wagner et al., 1982

Osborne-Mendel 

rats

(n = 28/group)

Hardened gelatin 

technique

40 mg

Tremolite (Two samples) Sample 1: 21/28 pleural sarcomas

Sample 2: 22/28 pleural sarcomas

Stanton et al., 1981
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51 DRAFT―DO NOT CITE OR QUOTE

Table 4-17. In vivo data following exposure to tremolite asbestos

Species (sex) Exposure route Fiber type Effects* Reference

Wistar rats (F)

(n = 40/group)

Intraperitoneal injection

1 × 3.3 and 1 × 15 mg, 

lifetime observation

Tremolite Limited details in text. Increase in mesothelioma

following exposure to tremolite: 3.3 mg sample: 

9/29; 15 mg sample: 30/37

Roller et al., 1996, 1997

Wistar rats (M)

(n = 56)

Inhalation (flow-past 

nose only)

100 fibers/cm3longer 

than 20 μm, 5 days, 

follow-up 1 yr later

Tremolite Tremolite had a pronounced inflammatory 

response with rapid granuloma development (1day 

post exposure); 

Slight interstitial fibrosis observed at 90 and 180 

days post-exposure.

Bernstein et al., 2003, 

2005

C57Bl/6 mice (F)

(n = 10/group)

Intratracheal instillation 

Two doses of 60 μg 

each given 1 wk apart 

in the first and second 

week of a 7-mo 

experiment

Tremolite and wollastonite Tremolite-exposed mice demonstrated increased 

IgG immune complex deposition in the kidneys, 

increased size of local lymph nodes, and increased 

total cell count. 

Pfau et al., 2008

*When available, results are shown as number of animals with tumors/total number of animals examined.
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1 4.2.5. Summary of Animal Studies for Libby Amphibole and Tremolite Asbestos

2 Tables 4-16 and 4-17 summarize the studies described in this section, with full study 

3 details available in Appendix F. Limited in vivo studies have been performed exposing 

4 laboratory animals to Libby Amphibole asbestos. One intrapleural injection study using 

5 tremolite from the Libby, MT area is included in this section under Libby Amphibole asbestos 

6 since earlier terminology for Libby Amphibole asbestos was often tremolite (Smith, 1978). 

7 Hamsters in this study exposed to Libby Amphibole asbestos developed fibrosis and 

8 mesothelioma following exposure. Subchronic studies in mice (Putnam et al., 2008; Smartt et al., 

9 2008) demonstrated gene and protein expression changes related to fibrosis production following 

10 exposure to Libby Amphibole asbestos. Finally, a short-term study in a rat model for human 

11 cardiovascular disease demonstrated an increase in inflammatory markers following exposure to 

12 Libby Amphibole asbestos (Shannahan et al., 2011). 

13 Since tremolite is part of Libby Amphibole asbestos, results from tremolite studies were 

14 also described. In general, fibrous tremolite has been shown to cause pulmonary inflammation,

15 fibrosis and/or mesothelioma or lung cancer in rats (Bernstein et al., 2003, 2005; Davis et al., 

16 1985, 1991; Wagner et al., 1982) and hamsters (Smith et al., 1979). The single short-term study 

17 on mice showed limited response to tremolite (Sahu et al., 1975). The one chronic oral study 

18 (McConnell et al., 1983a) did not show increased toxicity or carcinogenicity; this study, 

19 however, used only nonfibrous tremolite, which later studies showed to be less toxic and 

20 carcinogenic than fibrous tremolite (Davis et al., 1991). 

21 The results of the studies described above show the fibrogenic and carcinogenic potential 

22 of Libby Amphibole and tremolite asbestos. Further, the more recent studies by Blake et al. 

23 (2008) and Pfau et al. (2008) support human studies demonstrating potential autoimmune effects 

24 of asbestos exposure. 

25

26 4.3. OTHER DURATION OR ENDPOINT-SPECIFIC STUDIES

27 4.3.1. Immunological

28 Two epidemiology studies have examined the potential role of Libby Amphibole asbestos 

29 and autoimmunityNoonan et al. (2006) used the data from the community health screening to 

30 examine self-reported history of autoimmune diseases (rheumatoid arthritis, scleroderma, or 

31 lupus) in relation to the asbestos exposure pathways described in the pulmonary analyses 

32 (Section 4.1, Table 4-8). To provide more specificity in the self-reported history of these 

33 diseases, a follow-up questionnaire was mailed to participants to confirm the initial report and 

34 obtain clarifying information regarding the type of disease, whether the condition had been 

35 diagnosed by a physician, and whether the participant was currently taking medication for the 
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1 disease. Responses were obtained from 208 (42%) of those individuals who had reported these 

2 conditions. Of these 208 responses, 129 repeated the initial report of the diagnosis of rheumatoid 

3 arthritis, and 161 repeated the initial report of the diagnosis of rheumatoid arthritis, scleroderma, 

4 or lupus. Among people aged 65 and over (n = 34 rheumatoid arthritis cases, determined using 

5 responses from the follow-up questionnaire), a two- to three-fold increase in risk was observed in 

6 association with past work in mining and milling operations, asbestos exposure in the military, 

7 use of vermiculite in gardening, and frequent playing on vermiculite piles when young. 

8 Furthermore, this increase was observed in individuals exhibiting two measures of pulmonary 

9 effects—restricted forced vital capacity and presence of parenchymal abnormalities. Restricted 

10 forced vital capacity, playing on vermiculite piles, and other dust or vermiculite exposures were 

11 also associated with rheumatoid arthritis in the group younger than 65 (n = 95 cases). The 

12 definition of restricted forced vital capacity used in the analysis by Noonan et al. (2006). (FVC 

13 <80% of predicted values and a ratio of FEV1 to FVC that is ≥70% predicted, differs from the 

14 definition used in the ATSDR report of the community health screening (ATSDR, 2001). In the 

15 ATSDR report, a moderate-to-severe restricted forced vital capacity is defined as an FVC <70%. 

16 For all participants, an increased risk of rheumatoid arthritis was observed with increasing 

17 number of exposure pathways. RRs of 1.0, 1.02, 1.79, 2.51, and 3.98 were observed for 0 

18 (referent), 1, 2–3, 4–5, and 6 or more pathways, respectively (trend p < 0.001, adjusting for 

19 restrictive spirometry, parenchymal abnormalities, and smoking history). Although the 

20 information gathered in the follow-up questionnaire and repeated reports of certain diagnoses 

21 decreased the false-positive reports of disease, considerable misclassification (over-reporting and 

22 under-reporting) is likely, given the relatively low confirmation rate in other studies—

23 particularly for rheumatoid arthritis (Rasch, 2003; Karlson, 2003; Ling, 2000). 

24 Another study examined serological measures of autoantibodies in 50 residents of Libby, 

25 MT, and a comparison group of residents of Missoula, Montana (Pfau et al., 2005; Table 4-18). 

26 The Libby residents were recruited for a study of genetic susceptibility to asbestos-related lung 

27 disease, and the Missoula residents were participants in a study of immune function. The Libby 

28 sample exhibited an increased prevalence (22%) of high-titer (≥1:320) antinuclear antibodies 

29 when compared to the Missoula sample (6%), and similar increases were seen in the Libby 

30 sample for rheumatoid factor, anti-RNP, anti-Scl-60, anti-Sm, anti-Ro (SSA), and anti-La (SSB) 

31 antibodies. Although neither sample was randomly selected from the community residents, an 

32 individual’s interest in participating in a gene and lung disease study likely would not be 

33 influenced by the presence of autoimmune disease or autoantibodies in that individual. 

34

35 Table 4-18. Pulmonary radiographic, and other health studies in the Libby, 

36 MT community
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1

Reference(s) Inclusion criteria and design details Results

Autoimmune Disease

Noonan et al., 2006 Nested case-control study among 7,307 participants in 

2000–2001 community health screening. Conducted 

interviews, gathered self-reported history of rheumatoid 

arthritis, scleroderma, or lupus.

Follow-up questionnaire mailed to participants concerning 

self-report of ―physician-diagnosis‖ of these diseases and 

medication use.

Association with work in Libby 

mining/milling operations (ages 

65 and older):

Rheumatoid arthritis 

OR: 3.2 (95% CI = 1.3, 8.0)

Rheumatoid arthritis, lupus, 

scleroderma OR: 2.1 (95% 

CI = 0.90, 4.1)

Risk increased with increasing 

number of asbestos exposure 

pathways.

Pfau et al., 2005 Libby residents (n = 50) recruited for study of genetic 

susceptibility to asbestos-related lung disease.

Missoula, Montana comparison group (n = 50), recruited 

for study of immune function; age and sex-matched to 

Libby participants.

Serum samples obtained; IgA levels, prevalence of 

antinuclear, anti-dsDNA antibodies, anti-RF antibodies, 

and anti-Sm, RNP, SS-A, SS-B, and Scl-70 antibodies 

determined.

Increased prevalence of high titer 

(≥1:320) antinuclear antibodies in 

Libby sample (22%) compared to 

Missoula sample (6%).

Similar increases for rheumatoid 

factor, anti-RNP, anti-Scl-60, antiSm, anti-Ro (SSA) and anti-La 

(SSB) antibodies observed in 

Libby sample.

2
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1 Hamilton et al. (2004), Blake et al. (2008) and Pfau et al. (2008) examined the role of 

2 asbestos in autoimmunity in laboratory animal or in vitro studies. Blake et al. (2008) performed 

3 in vitro assays with Libby Amphibole asbestos (Section 4.4), and both studies performed the in 

4 vivo assays with tremolite. C57BL/6 mice were instilled intratracheally for a total of two doses 

5 each of 60-μg saline and wollastonite or Korean tremolite sonicated in sterile PBS, given 1 week 

6 apart in the first 2 weeks of a 7-month experiment. Sera from mice exposed to tremolite showed 

7 antibody binding colocalized with SSA/Ro52 on the surface of apoptotic blebs (Blake et al., 

8 2008). In Pfau et al. (2008), by 26 weeks, the tremolite-exposed animals had a significantly 

9 higher frequency of positive anti-nuclear antibody tests compared to wollastinate and saline. 

10 Most of the tests were positive for dsDNA and SSA/Ro52. Serum isotyping showed no major 

11 changes in immunoglobulin subclasses (IgG, IgA, IgM), but serum IgG in tremolite-exposed 

12 mice decreased overall. Further, IgG immune complex deposition in the kidneys increased, with 

13 abnormalities suggestive of glomerulonephritis. No increased proteinuria was observed during 

14 the course of the study. Local immunologic response was further studied on the cervical lymph 

15 nodes. Although total cell numbers and lymph-node size were significantly increased following 

16 exposure to tremolite, percentages of T- and B-cells did not significantly change. Hamilton et al. 

17 (2004) investigated the ability of Libby Amphibole, crocidolite, and PM2.5 (collected over a 6 

18 month period in Houston, TX, from EPA site 48-201-1035) to alter the antigen-presenting cell 

19 (APC) function was altered in cultured human alveolar macrophages. Asbestos exposure 

20 (regardless of type) and PM2.5 up-regulated a TH1 lymphocyte derived cytokine, interferon 

21 gamma (IFNγ), and the TH2 lymphocyte-derived cytokines interleukin-4 (IL-4) and interleukin22 13 (IL-13). There was, however, extreme variation among subjects in the amount of response. 

23 In addition, there was no correlation between an individual’s cells’ response to asbestos versus 

24 PM, suggesting that more than one possible mechanism exists for a particle-induced APC effect 

25 and individual differential sensitivities to inhaled bioactive particles.

26 Although limited number of studies, these results suggest a possible effect on 

27 autoimmunity following exposure to Libby Amphibole asbestos. Further studies are needed to 

28 increase understanding of this potential effect. 

29

30 4.4. MECHANISTIC DATA AND OTHER STUDIES IN SUPPORT OF THE MODE OF 

31 ACTION

32 In vitro analysis of fibers depends on the characteristics of the fibers and cell types used 

33 for the studies. Therefore, in reviewing the literature it is important to pay attention to cell types 

34 used, particularly related to the ability to internalize fibers and produce an oxidative stress 

35 response. Results from in vitro studies have demonstrated potential biological mechanisms of 

36 oxidative stress and inflammation in response to exposure to Libby Amphibole and tremolite 
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1 asbestos. These studies are summarized below and in Tables 4-19 and 4-20, with detailed study 

2 descriptions available in Appendix D.

3 Limited in vitro studies have been conducted with Libby Amphibole asbestos from the 

4 Zonolite Mountain mine. These studies demonstrated an effect of Libby Amphibole asbestos on 

5 inflammation and immune function (Blake et al., 2007; 2008; Hamilton et al., 2004; Duncan et 

6 al., 2010), oxidative stress (Hillegass et al., 2010), and genotoxicity (Pietruska et al., 2010). 

7 Similar endpoints have been examined in vitro following exposure to tremolite asbestos (Wagner 

8 et al., 1982; Athanasiou et al., 1992; Suzuki and Hei 1996; Wylie et al., 1997; Okayasu et al., 

9 1999). 

10

11 4.4.1. Inflammation and Immune Function

12 Hamilton et al., (2004) showed an increase in TH1 and TH2 cytokines following 

13 exposure to both asbestos and particulate matter, suggesting a similar effect of exposure to both

14 materials on immune function. Analysis of these results is limited, as the use of primary cells in 

15 culture that led to an extremely variable response. Two studies by Blake et al. (2007, 2008) 

16 further examined the effect of Libby Amphibole asbestos on immune response in murine 

17 macrophages. These studies demonstrated that Libby Amphibole asbestos was internalized, and 

18 this internalization resulted in an increase in ROS. These studies also showed a variable 

19 cytotoxic response, as Libby Amphibole asbestos exposure did not result in a statistically 

20 significant increase in cytotoxicity, while crocidolite did. DNA damage also was increased in 

21 crocidolite-exposed cells, but not in Libby Amphibole asbestos exposed-cells. An increase 

22 (relative to controls) in autoantibody formation following exposure to Libby Amphibole asbestos 

23 also was observed. Studies that examined cellular response to tremolite also found that fiber 

24 characteristics (length and width) play a role in determining ROS production, toxicity, and 

25 mutagenicity (Wagner et al., 1982; Okayasu et al, 1999). 

26 Mechanisms of oxidative stress following exposure to Libby Amphibole asbestos were 

27 also studied in human mesothelial cells (Hillegass et al. 2010). Gene expression changes 

following exposure to 15×10

6

μm

2

/cm2Libby Amphibole asbestos628 as compared to the nonpathogenic control (75×106

μm2/cm229 glass beads) in the human mesothelial cell line LP9/TERT30 1 for 8 and 24 hrs. Gene ontology of these results demonstrated alterations in genes related to 

31 signal transduction, immune response, apoptosis, cellular proliferation, extracellular matrix, cell 

32 adhesion and motility and only one gene related to reactive oxygen species processing. Oxidative 

 

6

Libby Amphibole asbestos samples were characterized for this study with analysis of chemical composition and 

mean surface area (Meeker et al., 2003). Doses were measured in surface area and described based on viability 

assays as either the non-toxic (15×106μm2/cm2) or the toxic dose (75×106μm2/cm2). 
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1 stress was observed as both dose- and time-dependent in cells exposed to Libby Amphibole 

2 asbestos but was increased following exposure to the higher dose of Libby Amphibole asbestos 

3 (statistical analysis not possible). GSH levels were transiently depleted following 2–8 hrs 

4 exposure to the higher dose of Libby Amphibole asbestos, with a gradual recovery up to 48hrs in 

5 LP9/TERT-1 cells (HKNM-2 not analyzed). These studies demonstrate that Libby Amphibole 

6 asbestos exposure leads to increases in oxidative stress as measured by ROS production, gene 

7 expression, protein and functional changes in oxidative stress proteins (SOD), and GSH level 

8 alterations in human mesothelial cells. 

9 Gene expression alterations of interleukin-8 (IL-8), cyclooxygenase-2 (COX-2), heme 

10 oxygenase (HO)-1 as well as other stress-responsive genes as compared to amosite (Research 

11 Triangle Institute) was observed in primary human airway epithelial cells (HAEC) following 

12 exposure to Libby Amphibole asbestos. Comparisons were made with both fractionated 

13 (aerodynamic diameter ≤2.5μm) and unfractionated fiber samples (Duncan et al., 2010). 

14 Crocidolite (CRO) fibers (UICC) were also included in some portions of this study for 

comparison. Primary HAECs were exposed to 0, 2.64, 13.2, and 26.4 μg/cm215 of crocidolite, 

16 amosite (AM), AM2.5 (fractionated), Libby Amphibole asbestos (LA), or LA 2.5 (fractionated) 

17 for 2 or 24 hours in cell culture. Cytotoxicity was determined by measurement of lactate 

dehydrogenase (LDH) from the maximum dose (26.4 μg/cm218 ) of both amosite and Libby 

19 Amphibole asbestos samples, with less than 10% LDH present following exposure to all four 

20 samples. Minimal increases in gene expression of IL-8, COX-2 or HO-1 were observed at 2 h 

21 post-exposure to all five fiber types; at 24h post-exposure, however, a dose response was 

22 observed following exposure to all fiber types with the results showing a pro-inflammatory gene 

23 expression response (Duncan et al., 2010). These results support a limited cytotoxicity of both 

24 amosite and Libby Amphibole asbestos under these concentrations and time frames.

25

26 4.4.2. Genotoxicity

27 ROS production and genotoxicity (micronuclei induction) following exposure to Libby 

28 Amphibole asbestos has been demonstrated in XRCC1-deficient human lung epithelial H460 

29 cells (Pietruska et al., 2010). XRCC1 is involved in the repair mechanisms for oxidative DNA 

30 damage, particularly single strand breaks. Micronuclei induction was measured following 

treatment of cells by controls (positive, hydrogen peroxide; negative, paclitaxel) and by 5μg/cm231

32 fibers or TiO2 particles for 24 hrs. Spontaneous micronuclei induction was increased in XRCC1-

33 deficient cells in a dose-dependent manner following exposure to crocidolite and Libby 

34 Amphibole asbestos as compared to control. These results support a potential genotoxic effect of 

35 exposure to both crocidolite and Libby Amphibole asbestos.
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1 Athanasiou et al. (1992) performed a series of experiments to measure genotoxicity 

2 following exposure to tremolite, including the Ames mutagenicity assay, micronuclei induction, 

3 chromosomal aberrations, and gap-junction intercellular communication. Although a useful test 

4 system for mutagenicity screening for many agents, the Ames assay is not the most effective test 

5 to detect mutations induced by mineral fibers. Mineral fibers can cause mutation through 

6 generation of ROS or direct disruption of the spindle apparatus during chromatid segregation. 

7 Fibers do not induce ROS in the Ames system, however, and the Salmonella typhimurium strains 

8 do not endocytose the fibers. Only one study was found in the published literature that used the 

9 Ames assay to measure mutagenicity of tremolite. Metsovo tremolite asbestos has been shown 

10 to be the causative agent of endemic pleural calcification and an increased level of malignant 

11 pleural mesothelioma (Section 4.1). To measure the mutagenicity of Metsovo tremolite, 

12 S. typhimurium strains (TA98, TA100, and TA102) were exposed to 0–500 μg/plate of asbestos 

13 (Athanasiou et al., 1992). Metsovo tremolite did not yield a significant increase in revertants in 

14 the Ames assay, including in the TA102 Salmonella strain, which is generally sensitive to 

15 oxidative damage. This study demonstrated clastogenic effects of tremolite, including 

16 chromosomal aberrations and micronuclei induction. Tremolite exposure in Syrian hamster 

17 embryo (SHE) cells did lead to a dose-dependent increase in chromosome aberrations that was 

statistically significant at the highest doses tested (1.0–3.0 μg/cm218 ) (p < 0.01) (Athanasiou et al., 

19 1992). A statistically significant dose-dependent increase in levels of micronuclei was 

demonstrated following tremolite exposure at concentrations as low as 0.5 μg/cm220 (p < 0.01) in 

21 BPNi cells after 24-hour exposure. Literatures searches did not find tremolite tested for 

22 clastogenicity in other cell types, but the results of this study suggest interference with the 

23 spindle apparatus by these fibers. No analysis was performed to determine if fiber interference 

24 of the spindle apparatus could be observed, which would have supported these results.No effect 

25 on the gap-junctional intercellular communication following tremolite exposure was observed in 

26 both Chinese hamster lung fibroblasts (V79) and Syrian hamster embryo BPNi cells, which are 

27 sensitive to transformation ( Athanasiou et al. 1992). 

28 Okayasu et al. (1999) analyzed the mutagenicity of Metsovo tremolite, erionite, and the 

29 man-made ceramic (RCF-1) fiber. Human-hamster hybrid A(L) cells contain a full set of 

30 hamster chromosomes and a single copy of human chromosome 11. Mutagenesis of the CD59 

31 locus on this chromosome is quantifiable by antibody complement-mediated cytotoxicity assay. 

32 The authors state that this is a highly sensitive mutagenicity assay, and previous studies have 

33 demonstrated mutagenicity of both crocidolite and chrysotile (Hei et al., 1992). The cytotoxicity 

analysis for mutagenicity was performed by exposing 1 × 10534 A(L) cells to a range of 

concentrations of fibers as measured by weight (0–400 μg/mL or 0–80 μg/cm235 ) for 24 hours at 
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1 37°C. CD59 mutant induction showed a dose-dependent increase in mutation induction for 

2 erionite and tremolite, but RCF-1 did not.

3

4 4.4.3. Cytotoxicity and Cellular Proliferation

5 Wagner et al. (1982) examined the in vitro cytotoxicity of three forms of tremolite used 

6 in their in vivo studies. LDH and BGL were measured in the medium following incubation of 

7 unactivated primary murine macrophages to 50, 100, and 150 μg/mL of each sample for 18 

8 hours. The Korean tremolite (Sample C) produced results similar to the positive control: 

9 increased toxicity of primary murine macrophages, increased cytoxicity of Chinese hamster 

10 ovary (CHO) cells, and increased formation of giant cells from the A549 cell line. The tremolite 

11 sample from Greenland (Sample B) did result in increased toxicity over controls; although to a 

12 lesser degree (statistics are not given). Although differential toxicity of these samples was noted 

13 on a mass basis, data were not normalized for fiber content or size. The inference is that 

14 differential results may be due, at least in part, to differential fiber counts.

15 Wylie et al. (1997) examined the mineralogical features associated with cytotoxic and 

16 proliferative effects of asbestos in hamster tracheal epithelial (HTE) and rat pleural mesothelial 

17 (RPM) cells with a colony-forming efficiency assay. HTE cells are used because they give rise 

18 to tracheobronchial carcinoma, while RPM cells give rise to mesotheliomas. The results of the 

analysis with fiber exposure by mass (μg/cm219 ) show elevated colonies in HTE cells following 

20 exposures to both asbestos fibers (p < 0.05) at the lowest concentrations, while significant 

decreases were observed for both asbestos fibers at the higher concentrations (0.5 μg/cm221 , 

22 p < 0.05) (Wylie et al., 1997). No proliferation was observed for either chrysotile or crocidolite 

23 asbestos fibers in RPM cells, but cytotoxicity was observed at concentrations greater than 0.05 

μg/cm224 (p < 0.05). All talc samples were less cytotoxic in both cell types. Analyzing the data 

25 for cytotoxicity and proliferation based on the exposure measurement demonstrated differences 

26 in response depending solely on how the fibers were measured: by mass, number, or surface 

27 area. These results show variability in interpreting the results of the same assay based on the 

28 defined unit of exposure. Most early studies used mass as the measurement for exposure, which 

29 can impact how the results are interpreted. When possible, further analysis of fiber number and 

30 surface area would help elucidate the role of these metrics, particularly for in vivo studies. 

31

32 Summary

33 The review of these studies clearly highlights the need for more controlled studies 

34 examining Libby Amphibole asbestos in comparison to other forms of asbestos and for 

35 examining multiple endpoints, including ROS production, DNA damage, and pro-inflammatory
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1 gene expression alterations, to improve understanding of mechanisms involved in cancer and 

2 other health effects. These results suggest that Libby Amphibole asbestos may act through 

3 similar mechanisms as other forms of asbestos, but data gaps still remain to determine specific 

4 mechanisms involved in Libby Amphibole asbestos-induced disease. Studies that examined 

5 cellular response to tremolite also found that tremolite exposure may lead to increased ROS 

6 production, toxicity, and genotoxicity (Wagner et al., 1982; Okayasu et al, 1999). As with the in 

7 vivo studies, the definition of fibers and how the exposures were measured varies among studies. 
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Table 4-19. In vitro data following exposure to Libby Amphibole asbestos

Test system Fiber type Dose/exposure duration Effects Reference

Primary human alveolar 

macrophages and 

lymphocytes

Libby Amphibole asbestos or 

crocidolite

0, 25, 50 μg/mL

24 h

Upregulated TH1 and TH2 cytokines 

(IFNgamma, IL-4, IL-13) 

Hamilton et al., 

2004

Murine macrophages 

(primary and 

RAW264.7)a

Libby Amphibole asbestos

and crocidolite

Internalization: 

0, 5, 62.5 μg/cm2

3–24 h

Internalized Libby Amphibole asbestos fibers 

were mostly less than 2 μm in length.

Blake et al., 2007

Oxidative stress:

0, 6.25, 32.5, 62.5 μg/cm2

3, 7, 12, and 24 h

Increased ROS over control (wollastonite) and 

crocidolite 

Decreased GSH

Cell viability:

0, 6.25, 32.5, 62.5 μg/cm2

3, 7, 12, and 24 h

No effect was observed on cell viability 

DNA damage:

0, 6.25, 32.5, 62.5 μg/cm2

3, 7, 12, and 24 hNo increase in DNA damage and adduct 

formation.

Murine macrophages 

(primary and 

RAW264.7)

Libby Amphibole asbestos or 

crocidolite

0, 62.5 μg/cm2

0–72 h

Time-course dose response for apoptosis; 

Redistribution of autoantigen on cell surface

Blake et al., 2008

Human lung epithelial 

cells (wild-type and 

XRCC1-deficient)

Libby Amphibole asbestos or 

crocidolite

5 μg/cm2

24 h

Dose-dependent increase in micronuclei in both 

cell types, but increased in the XRCC1-

deficient cells as compared to wild-type

Pietruska et al., 

2010

Human mesothelial cells 

(LP9/TERT-1 and 

HKNM-2)

Libby amphibole asbestos or 

crocidolite

0, 15×106μm

2

/cm2(non-toxic) 

and 75×106μm

2

/ cm2(toxic) 

for 8 or 24 h

Alterations in genes related to oxidative stress, 

particularly SOD2

Hillegass et al., 

2010

Primary human airway 

epithelial cells (HAECs)

Libby Amphibole asbestos 

(fractionated and 

unfractionated), amosite 

(fractionated and 

unfractionated) , crocidolite

0, 2.64, 13.2 or 26.4 μg/cm2

2, 4 or 24 h

Increases in pro-inflammatory gene expression 

and ROS production 

Duncan et al., 

2010

aAll results for RAW264.7. Data not shown for primary cells though authors state similar response to RAW264.7.
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PBS = phosphate buffer saline, ROS = reactive oxygen species, GSH = glutathione, DNA = deoxyribonucleic acid, LDH = lactic dehydrogenase, 

BGL = β-glucuronidase, SHE = Syrian hamster ovary, HTE = hamster tracheal epithelial, RPM = rat pleural mesothelial, NIEHS = National Institute of 

Environmental Health Sciences, HPRT = hypoxanthine-guanine phosphoribosyltransferase.
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Table 4-12. In vitro data following exposure to tremolite asbestos

Test system/species Fiber type Dose/exposure duration Effects Reference

Primary murine 

macrophages

Sample A (flake-like from 

California talc deposits); 

Sample B (medium-sized 

fibrous from Greenland); 

Sample C (fine-fiber material 

from S. Korea); Positive 

Control (crocidolite)

0, 50, 100, and 150 μg/mL

18 h

LDH and BGL levels increased following 

exposure to Sample C (longer, thinner fibers) 

and crocidolite (positive control). 

Sample C led to the greatest increases in giant 

cell formation and cytotoxicity of samples 

tested. 

Sample B also led to some increased 

cytotoxicity. 

Wagner et al., 

1982

TA98, TA100, TA102 S. 

typhimurium

Metsovo tremolite TA98, TA100, and TA102:

0–500 μg/per plate

2 days

No significant revertants were observed in any 

of the three Salmonella strains tested.

Athanasiou et al., 

1992

V79 and BPNi cells V79 and BPNi: 

0–4 μg/cm2

6, 24, and 48 h

No affect was observed on gap-junctional 

intercellular communication. 

BPNi cells BPNi:

0–2 μg/cm2

24 h

Tremolite led to a dose-dependent increase in 

micronuclei induction.

SHE cells SHE:

0–3 μg/cm2

24 h

Tremolite exposure led to increased 

chromosomal aberrations but not in a dosedependent fashion. 

A[L] cells (hamster 

hybrid cells containing 

human chromosome 11)

UICC chrysotile, crocidolite, 

Metsovo tremolite, erionite

0, 2.5–40 μg/mL

24 h

Relative increase in heme oxygenase as 

compared to control. 

Suzuki and Hei, 

1996

HTE and RPM cell lines NIEHS chrysotile, NIEHS 

crocidolite, FD14, S157, CPS 

183 (talc fibers containing 

tremolite)

Varied (based on weight, fiber 

length, and surface area).

Fibrous talc exposure led to limited 

proliferation of cells

Wylie et al., 1997
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A[L] cells (hamster 

hybrid cells containing 

human chromosome 11)

Tremolite, erionite, RCF-1 0–400 μg/mL

24 h

No significant increase in HPRT mutations for 

these three fibers; 

Dose-dependent induction of mutations in 

CD59 did occur for erionite and tremolite. 

Okayasu et al., 

1999

PBS = phosphate buffer saline, ROS = reactive oxygen species, GSH = glutathione, DNA = deoxyribonucleic acid, LDH = lactic dehydrogenase, 

BGL = β-glucuronidase, SHE = Syrian hamster ovary, HTE = hamster tracheal epithelial, RPM = rat pleural mesothelial, NIEHS = National Institute of 

Environmental Health Sciences, HPRT = hypoxanthine-guanine phosphoribosyltransferase.

1
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1

2 4.5. SYNTHESIS OF MAJOR NONCANCER EFFECTS

3 The non-cancer health effects observed following inhalation exposure to Libby 

4 Amphibole asbestos include pleural thickening and asbestosis. Recent studies have also 

5 examined non-cancer health effects following exposure to Libby Amphibole asbestos in other 

6 systems, including autoimmune effects and cardiovascular disease.

7

8 4.5.1. Pleural Thickening

9 Pleural thickening includes two distinct biological lesions, discrete pleural plaques in the 

10 parietal pleura and diffuse pleural thickening of the visceral pleura. Although the visceral and 

11 parietal pleura provide a continuous lining for the interior of the thoracic cage, wrapping around 

12 the lobes of the lung, they are distinct (Jones, 2002; Broaddus et al., 2011). Differences lie 

13 primarily in the submesothelial layers, the existence of stomata in the parietal pleura and greater 

14 innervations of the parietal pleura (Jones, 2002.) Although discrete pleural plaques may extend 

15 to impact a large surface, these deposits lie between the pleural membrane and the thoracic 

16 cavity, and fusing of discrete pleural plaques do not constitute a diagnosis of diffuse pleural 

17 thickening, since this diagnosis specifically refers to thickening of the visceral pleura (ATS 

18 2004, ILO B-Reader standards). There are reports of discrete plaques in the visceral pleura, 

19 which may be localized thickening of this tissue. It should also be noted that extensive 

20 thickening of the visceral pleura may be accompanied by fusing between the parietal and visceral 

21 surfaces of the pleural membrane where there are discrete plaques. Thickening of the visceral 

22 pleura is more often localized to lobes of the lung with pronounced parenchymal changes and it 

23 has been hypothesized that the inflammatory and fibrogenic processes within the lung 

24 parenchyma may influence the fibrogenic process in the visceral pleura. 

25 In clinical settings there are difficulties in distinguishing between these two lesions in 

26 part due to lack of radiographic film diagnostic specificity as well as the ILO definitions for 

27 diffuse pleural thickening of the visceral pleura which may result in some variability in reporting 

28 of discrete pleural plaques. There are two classifications for pleural thickening in current 

29 guidelines (ILO, 2000). Diffuse pleural thickening is currently only determined where there is 

30 thickening along significant extent of the chest wall, and this thickening is ―in the presence of 

and in continuity with, an obliterated costophrenic angle 731 ‖ (ILO, 2000). Although previous 

 

7 Obliteration of the costophrenic angle (CA): The costophrenic angle is measured as the angle between the 

ribcage and the diaphragm on a posterior anterior viewed radiograph (the costophrenic recess, Figure 5-1). When 

CA blunting or obliteration is noted on a radiograph it is recorded as present or absent (ILO, 2000). Obliteration of 

the CA may occur in the absence of other radiographic signs. However, where it occurs in conjunction with pleural 

thickening, it may be used to define the type of pleural thickening.
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1 classification schemes attempted to distinguish between thickening of the parietal pleura and 

2 diffuse thickening of the visceral pleura, the ILO states‖ reading standard x-rays cannot always 

3 distinguish between thickening of the visceral or parietal pleura‖ (ILO, 2000). Thus, all other 

4 pleural thickening is now defined as localized pleural thickening (i.e. where there is no 

5 costophrenic angle obliteration). Localized pleural thickening on the chest wall, and diffuse 

6 pleural thickening may be graded indicating thickness, and the extent of the chest wall impacted, 

7 however these data are not always provided in study reports, therefore regardless of extent of the 

8 pleural thickening, only presence or absence may be noted. 

9

10 4.5.2. Asbestosis

11 Asbestosis is the most severe nonmalignant disease associated with exposure to asbestos 

12 fibers. Asbestosis is the interstitial pneumonitis and fibrosis caused by inhalation of asbestos 

13 fibers and becomes evident only after an appreciable latency period. Asbestosis is characterized 

14 by a diffuse increase of collagen in the alveolar walls (fibrosis) and the presence of asbestos 

15 fibers, either free or coated with a proteinaceous material and iron (asbestos bodies). The 

16 mildest form of asbestosis involves the alveolated walls of respiratory bronchioles and the 

17 alveolar ducts. More severe grades involve greater proportions of the acinus until the whole 

18 acinar structure is involved and some alveoli are completely obliterated (ATS, 2004). A 

19 profusion of irregular opacities at the level of 1/0 is used as the boundary between normal and 

20 abnormal. Asbestosis is associated with dyspnea, bibasilar rales, and changes in pulmonary 

21 function: a restrictive pattern, mixed restrictive-obstructive pattern, and/or decreased diffusing 

22 capacity (ATS, 2004). In one of the earliest studies conducted, about 50% of the asbestos 

23 workers presented with a FVC below 80% of predicted (ATS, 2004).

24 Radiographic evidence of small opacities in the lung is direct evidence of scarring of the 

25 lung tissue and as the fibrotic scaring of lung tissue consistent with mineral dust and mineral 

26 fiber toxicity. The scarring of the parenchymal tissue of the lung contributes to measured 

27 changes in pulmonary function, including obstructive pulmonary deficits from narrowing 

28 airways, restrictive pulmonary deficits from impacting the elasticity of the lung as well as 

29 decrements in gas exchange. However, although data across the mineral fiber literature strongly 

30 support a finding of functional deficits where small opacities are visible on radiographs, the data 

31 also indicate that deficits in pulmonary function (consistent with interstitial fibrosis) are seen 

32 before these changes are detected by radiographic examination. Thus changes in lung function 

33 may occur before the fibrotic lesions can be detected on standard radiographs (ATS, 2004 and 

34 Brodikin et al., 1994). For example, decreased CO2 diffusion is a sign of reduced gas exchange 

35 in the pulmonary region of the lung and is observed in workers exposed to other types of 

36 asbestos even when small opacities are absent on radiographs. Similarly, obstructive deficits in 
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1 lung function may be observed without radiographic signs for fibrotic lesions. As decreased 

2 diffusion and obstructive deficits are mechanistically linked to changes in the parenchymal tissue 

3 these data suggest radiographs may not be sensitive enough to protect against adverse effects 

4 from parenchymal effects of asbestos exposure. 

5

6 4.5.3. Other noncancer health effects (cardiovascular toxicity, autoimmune effects)

7 There is limited available research available on noncancer health effects occurring 

8 outside the respiratory system. Two such effects (cardiovascular toxicity and autoimmunity) 

9 have been examined following Libby Amphibole asbestos exposures and thus, are discussed 

10 briefly here. Recent studies have explored the role of asbestos exposure in cardiovascular 

11 disease (Larson et al., 2010). Mechanistic studies have examined the potential role of iron and 

12 the associated inflammation for both the respiratory and cardiovascular disease (Shannahan et 

13 al., 2011). Limited studies have examined a causal association between asbestos exposure and 

14 autoimmune disease (reviewed in Bunderson-Schelvan et al., 2011), but limitations in the scope 

15 and number of these studies makes it difficult to reach conclusions as to the role of asbestos 

16 exposure in autoimmunity. 

17

18 4.5.4. Libby Amphibole asbestos summary of non-cancer health effects

19 The studies in humans summarized in Section 4.1 have documented an increase in 

20 mortality from non-malignant respiratory disease and in radiographic changes in the pleura and 

21 parenchyma among employees of the Libby vermiculite mining operations (Amandus et al., 

22 1987a, b, c; McDonald et al., 2004, 1986a, b; Sullivan 2007; Larson et al. 2010a). Additional 

23 studies (Lockey et al., 1984; Rohs et al., 2008) have documented an increase in radiographic 

24 changes in the pleura and parenchyma among employees of a manufacturing facility in 

25 Marysville, Ohio that used Libby vermiculite ore contaminated with Libby Amphibole asbestos. 

26 These studies used ILO diagnostic criteria and demonstrated that the increases in prevalence of 

27 adverse effects in the lung and pleural cavity are positively associated with increased cumulative 

28 exposure to Libby Amphibole asbestos. Finally, additional studies have documented an increase 

29 in pleural abnormalities (Peipins et al., 2003; Muravov et al., 2005) and progressive loss of lung 

30 function (Whitehouse, 2004) among residents of Libby, Montana. Two studies (Pfau et al, 2005; 

31 Noonan et al., 2006) conducted among residents of Libby, Montana, support the hypothesis that 

32 auto-immunity may also be associated with exposure to Libby Amphibole asbestos. Larson et al. 

33 (2010a) also showed that Libby Amphibole may increase mortality from cardiovascular toxicity 

34 in residents of Libby.
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1 Although experimental data in animals and data on toxicity mechanisms are limited for 

2 Libby Amphibole asbestos, the existing data are consistent with the non-cancer health effects 

3 observed in both Libby workers and community members. Pleural fibrosis was increased in 

4 hamsters after intrapleural injections of Libby Amphibole asbestos (Smith, 1978). More recent 

5 studies have demonstrated increased collagen deposition consistent with fibrosis following 

6 intratracheal instillation of Libby Amphibole asbestos fibers in mice (Putnam et al., 2008; Smartt 

7 et al., 2009). Pulmonary fibrosis, inflammation, and granulomas were observed after tremolite 

8 inhalation exposure in Wistar rats (Bernstein et al., 2003, 2005) and intratracheal instillation in 

9 albino Swiss mice (Sahu et al., 1975). Davis et al. (1985) also reported pulmonary effects after 

10 inhalation exposure in Wistar rats including increases in peribronchiolar fibrosis, alveolar wall 

11 thickening, and interstitial fibrosis. A recent study by Shannahan et al. (2011) examined the 

12 effect of exposure to Libby Amphibole asbestos in rats with pre-existing cardiovascular disease, 

13 and found strain-related differences for biomarkers of inflammation following exposure to Libby 

14 Amphibole asbestos. This short-term study only examined animals for 1 month post-exposure, 

15 so the long-term effect of altered inflammation is unknown. Limited in vitro studies have 

16 demonstrated oxidative stress following Libby Amphibole asbestos exposures in various cell 

17 types (Blake et al., 2007; Pietruska et al., 2010; Hillegass et al., 2010; Duncan et al. 2010). Libby 

18 Amphibole asbestos fibers increased intracellular ROS in both murine macrophages and human 

19 epithelial cells (Blake et al., 2007; Duncan et al., 2010). Surface iron, inflammatory marker gene 

20 expression were increased following exposure to Libby Amphibole asbestos in human epithelial 

21 cells (Duncan et al., 2010; Pietruska et al., 2010; Shannahan et al., 2011). Tremolite studies 

22 demonstrate cytotoxic in various cell culture systems. 

23

24 4.5.5. Mode-of-Action Information (non-cancer)

25 The precise mechanisms causing toxic injury from inhalation exposure to asbestos or 

26 other mineral fibers have not been established. However, nearly all-durable mineral fibers with 

27 dimensional characteristics that allow penetration to the terminal bronchioles and alveoli of the 

28 lung have the capacity to induce pathologic response in the lung and pleural cavity (ATSDR, 

29 2001; Witschi and Last, 1996). The physical-chemical attributes of mineral fibers are important 

30 in determining the type of toxicity observed. Fiber dimension (width and length), density and 

31 other characteristics such as chemical composition, surface area, solubility in physiological 

32 fluids, and durability all play important roles in both the type of toxicity observed and the 

33 biologically significant dose. Fibrosis results from a sequence of events following lung injury 

34 which includes inflammatory cell migration, edema, cellular proliferation and accumulation of 

35 collagen. There is currently insufficient evidence to determine the non-cancer mode of action for 
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1 Libby Amphibole asbestos. However, current support for mechanisms (i.e., chronic 

2 inflammation, cytotoxicity and cellular proliferation) involved in potential non-cancer health 

3 effects following exposure to Libby Amphibole asbestos are described below.

4

5 4.5.5.1. Chronic Inflammation

6 Chronic pulmonary inflammation has been observed following inhalation exposure to 

7 fibers, which is often followed by fibrosis at the site of inflammation if the fibers persist 

8 (Oberdorster, 1994). Macrophages phagocytose fibers and particulate matter and are activated to 

9 trigger the release of inflammatory cytokines, ROS, and growth factors. These responses lead to 

10 a sustained inflammatory response that can result in fibrosis at the site of fiber deposition. As 

11 described in Section 3, fibers that are deposited in the lung may be taken up by alveolar 

12 macrophages. Once engulfed by the macrophage, small fibers may remain in place or be 

13 translocated across the bronchiole or alveolar membrane to the interstitial and pleural space. 

14 Longer fibers that are not easily engulfed by the macrophages may remain in the alveoli for an 

15 extended period of time. Further, longer fibers partially engulfed by macrophages may lead to 

16 increased release of inflammatory mediators (i.e., frustrated phagocytosis) (Gwinn and 

17 Vallyathan 2006). It is believed that these macrophages release mediators (lymphokines and 

18 growth factors) that attract immunocompetent cells or stimulate collagen production. Thus the 

19 mediators of the disease may be an on-going inflammatory response as a result of the prolonged 

20 ―residence time‖ of the fibers within the lung parenchyma. This increased inflammatory response 

21 may be involved in the pathogenesis of pleural fibrosis and asbestosis.

22 Laboratory animal studies of general asbestos exposure generally demonstrate 

23 inflammatory changes in the lung and pleura followed by cellular proliferation; the level of this 

24 effect is believed to be dependent on various fiber characteristics (McConnell et al., 1999). The 

25 inflammatory response to fibers has been studied in depth for other types of asbestos (crocidolite, 

26 chrysotile) but not for Libby Amphibole asbestos or tremolite (reviewed in Mossman et al., 

27 2007). Although limited, the data described here for Libby Amphibole asbestos, and to a greater 

28 extent for tremolite, suggest a similar response in the lung and pleura. In vivo exposure to 

29 tremolite led to an increase in inflammation for all studies where it was measured. This increase 

30 appeared in some cases to depend on fiber size and morphology (Smith et al., 1979; Davis et al., 

31 1991). Bernstein et al. (2003, 2005) observed that exposure to tremolite led to pronounced 

32 inflammation as soon as 1 day after inhalation exposure in male Wistar rats. Inflammation also 

33 occurred in male albino Swiss mice in an acute-duration study that did not lead to fibrosis or 

34 carcinogenesis, possibly due to the short study duration (150 days) (Sahu et al., 1975). In vitro 

35 analysis of Libby Amphibole asbestos showed increases in inflammatory cytokines (Hamilton et 
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1 al., 2004) and in pro-inflammatory gene expression (Duncan et al., 2010). 

2 Inflammation is observed in animal studies for Libby Amphibole asbestos and tremolite 

3 and is relevant to humans based on similar responses in other asbestos-exposed cohorts analyzed 

4 (Musk et al., 2008; Hein et al., 2007; Levin et al., 1998). Therefore, the evidence described 

5 above suggests chronic inflammation is observed following Libby Amphibole and tremolite 

6 asbestos exposure; however, the role of inflammation in the non-cancer health effects (e.g., 

7 pleural thickening) following exposure to Libby Amphibole asbestos is unknown. 

8

9 4.5.5.2. Cytotoxicity and Cellular Proliferation

10 The initial stages of any fibrotic response involve cellular proliferation, which may be 

11 compensatory for cell death due to cytotoxicity. Analysis of cellular proliferation has 

12 demonstrated both increases and decreases following exposure to asbestos fibers in vitro and in 

13 vivo depending on the specific fiber or cell type (Mossman et al., 1985; Topping and Nettesheim, 

14 1980). Other studies have focused on the activation of cell-signaling pathways that lead to 

15 cellular proliferation following exposure to asbestos (Zanella et al., 1996; Scapoli et al., 2004; 

16 Shukla et al., 2003; Ding et al., 1999). 

17 Although slightly increased compared to controls, cytotoxicity in murine macrophage 

18 cells exposed to Libby Amphibole asbestos was decreased compared to other fiber types (Blake 

19 et al., 2008). Cytotoxicity was slightly, but statistically significantly, increased compared to an 

20 unexposed control at 24 hours post exposure to Libby Amphibole asbestos, while crocidolite 

21 exposure resulted in even higher levels of cytotoxicity. No other in vitro study examined 

22 cytotoxicity following exposure to Libby Amphibole asbestos, although an increase in apoptosis 

23 was demonstrated in this same cell system (Blake et al., 2008). Recent studies in mice exposed 

24 to Libby Amphibole asbestos demonstrated increased collagen deposition and collagen gene 

25 expression, markers of fibrosis (Putnam et al., 2008; Smartt et al., 2009). Tremolite and Libby 

26 Amphibole asbestos exposure led to increases in both fibrosis in all but one animal study, 

27 supporting a role for proliferation in response to these fibers. Taken together with studies on 

28 other asbestos fibers, these data suggest that a cytotoxicity and cell proliferation may play a role 

29 in the non-cancer health effects following exposure to Libby Amphibole asbestos. 

30

31

32 4.6. EVALUATION OF CARCINOGENICITY

33 4.6.1. Summary of Overall Weight of Evidence

34 Under the U.S. EPA Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) 

35 Libby Amphibole asbestos is ―carcinogenic to humans‖ following inhalation exposure based on
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1 epidemiologic evidence that shows a convincing association between exposure to Libby 

2 Amphibole asbestos fibers and an increased incidence of lung cancer and mesothelioma and 

3 subsequent mortality (McDonald et al., 1986a, 2004; Amandus and Wheeler, 1987; Sullivan, 

4 2007, Larson et al., 2010b, Moolgavkar et al., 2010). These results are further supported by 

5 animal studies that demonstrate the carcinogenic potential of Libby Amphibole asbestos fibers 

6 and tremolite fibers in rodent bioassays. As a durable mineral fiber of respirable size, this 

7 conclusion is consistent with the extensive published literature that documents the 

8 carcinogenicity of amphibole fibers. 

9

10 4.6.1.1. Synthesis of Human, Animal, and Other Supporting Evidence

11 Libby, MT workers have been the subject of multiple mortality studies demonstrating an 

12 increased cancer mortality in relation to estimated fiber exposure. Occupational studies 

13 conducted in the 1980s (Amandus and Wheeler, 1987; McDonald et al., 1986a) as well as the 

14 extended follow-up studies published in more recent years (Sullivan et al., 2007; McDonald et 

15 al., 2004; Larson et al., 2010b) and additional analyses of the extended follow-up (Moolgavkar et 

16 al., 2010) provide evidence of an increased risk of lung cancer mortality and of mesothelioma 

17 mortality among the workers exposed to Libby amphibole asbestos in the Libby vermiculite 

18 mining and processing operations. Positive exposure-response relationships are shown in each 

19 study. Although a plateauing of the increased risk of lung cancer (relative risks of 1.4 and 1.5) in 

20 the two highest exposure categories (23.0 99 and ≥ 100 fibers/cc-yrs) was seen in Sullivan 

21 (2007). This type of pattern is commonly seen in occupational cohort mortality studies (Stayner 

22 et al., 2003) and does not diminish the weight of these results in supporting the determination of 

23 the carcinogenicity of Libby Amphibole asbestos. This association is also supported by the case 

24 series of 11 mesothelioma patients among residents in or around Libby, MT and among family 

25 members of workers in the mining operations (Whitehouse et al., 2008). 

26 Although experimental data in animals and data on toxicity mechanisms are limited for 

27 Libby Amphibole asbestos, tumors were observed in tissues similar to those in humans 

28 (mesotheliomas, lung cancer) indicating the existing data are consistent with the cancer effects 

29 observed in humans exposed to Libby amphibole asbestos. Smith (1978) reported increased 

30 incidence of mesotheliomas in hamsters after intrapleural injections of Libby Amphibole 

31 asbestos. Additionally, studies in laboratory animals (rats and hamsters) exposed to tremolite via 

32 inhalation (Bernstein et al., 2005, 2003; Davis et al., 1985), intrapleural injection (Smith et al., 

33 1979; Wagner et al., 1982; Davis et al., 1991, Roller et al., 1997, 1996) or implantation (Stanton 

34 et al., 1981) have shown increases in mesotheliomas and lung cancers. Tremolite from various 

35 sources was used and varied in fiber content and in potency (Section 4.2.1.2). Although 
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1 McConnell et al. (1983a) observed no increase in carcinogenicity following oral exposure to 

2 nonfibrous tremolite, the ability of this study to inform the carcinogenic potential of fibrous 

3 tremolite through inhalation is unclear, and these study results contribute little weight to the 

4 evaluation of the carcinogenicity of fibrous Libby Amphibole asbestos.

5 The available mechanistic information suggests Libby Amphibole asbestos induces 

6 effects that may play a role in carcinogenicity (Section 4.3.2). Several in vitro studies have 

7 demonstrated oxidative stress and genotoxicity following Libby Amphibole asbestos exposures 

8 in various cell types (Blake et al., 2007; Pietruska et al., 2010; Hillegass et al., 2010; Duncan et 

9 al. 2010). Libby Amphibole asbestos increased intracellular ROS in both murine macrophages 

10 and human epithelial cells (Blake et al., 2007; Duncan et al., 2010). Additionally, surface iron, 

11 inflammatory marker gene expression and aneugenic micronuclei were increased following 

12 exposure to Libby Amphibole asbestos in human epithelial cells (Duncan et al., 2010; Pietruska 

13 et al., 2010). Tremolite studies demonstrate cytotoxic and clastogenic effects (micronucleus 

14 induction and chromosomal aberrations) of the fibers in various cell culture systems. 

15 In summary, the epidemiologic data demonstrate an association between exposure to 

16 Libby Amphibole asbestos and increased cancer incidence. Supporting evidence of 

17 carcinogenicity was observed in laboratory animal studies exposed to Libby Amphibole asbestos 

18 or tremolite following multiple routes of exposure in multiple species (see Table 4-20, 4-31). 

19 Overall, the available evidence supports the conclusion that Libby Amphibole asbestos is

20 carcinogenic to humans.

21 

22 4.6.2. Mode–of-Action Information

23 4.6.2.1. Description of the Mode-of-Action Information

24 EPA guidance provides a framework for analyzing the potential mode(s) of action by 

25 which physical, chemical, and biological information is evaluated to identify key events in an 

26 agent’s carcinogenicity (U.S. EPA, 2005). Agents can work through more than one mode of 

27 action (MOA), and MOA can differ for various endpoints (e.g., lung cancer versus 

28 mesothelioma). Reasonably, the analysis of a MOA would start with some knowledge of an 

29 agent’s biological activity that leads to cellular transformation resulting in carcinogenicity. 

30 Although early steps in the process often can be identified, carcinogenicity is a complex process 

31 resulting from multiple changes in cell function. Due to the limited data available specific to 

32 Libby Amphibole asbestos, the mode of action of Libby Amphibole asbestos for lung cancer and 

33 mesothelioma following inhalation exposure is unknown. 

34 Research on various types of mineral fibers supports the role of multiple potential events

35 following exposure to asbestos in general (i.e., chronic inflammation, generation of reactive 
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1 oxygen species (ROS), direct genotoxicity, and cytotoxicity and cellular proliferation) in the 

2 carcinogenic response to mineral fibers. It is possible that these events are relevant to both the 

3 non-cancer and cancer effects following exposure to Libby Amphibole asbestos. However, 

4 mechanistic information of these effects following exposure to Libby Amphibole asbestos and/or 

5 tremolite is restricted to a limited number of laboratory animal and in vitro studies. These effects 

6 are described below, including discussion of the data limitations for reaching a conclusion 

7 regarding the mode of action for Libby Amphibole asbestos. Multiple key events for one 

8 particular MOA have not been identified; therefore the mode of action for Libby Amphibole 

9 asbestos carcinogenicity cannot be established.

10

11 4.6.2.1.1. Chronic inflammation

12 As described in Section 4.5.1.1, chronic pulmonary inflammation has been observed 

13 following inhalation exposure to fibers, which is often followed by fibrosis at the site of 

14 inflammation if the fibers persist (Oberdorster, 1994). Chronic inflammation is hypothesized to 

15 lead to a carcinogenic response through the production of reactive oxygen species and increased 

16 cellular proliferation (Hannahan and Weinberg 2011). Although limited, the data described in 

17 Section 4.5.1.1 suggest an increase in inflammatory response following exposure to Libby 

18 Amphibole and tremolite asbestos similar to that observed for other durable mineral fibers 

19 (reviewed in Mossman et al., 2007). Whether this inflammatory response then leads to cancer is 

20 unknown. Studies examining other types of asbestos (crocidolite, chrysotile, and amosite) have 

21 demonstrated an increase in chronic inflammation as well as respiratory cancer related to 

22 exposure (reviewed in Kamp and Weitzman, 1999). Chronic inflammation has also been linked 

23 to genotoxicity and mutagenicity following exposure to some particles and fibers (Driscoll et al., 

24 1995, 1996, 1997). The evidence described above suggests chronic inflammation is observed 

25 following Libby Amphibole and tremolite asbestos exposure; however, the role of inflammation 

26 and whether it leads to lung cancer or mesothelioma following exposure to Libby Amphibole 

27 asbestos is unknown. 

28

29 4.6.2.1.2. Reactive oxygen and nitrogen species production

30 Fiber exposure has been shown to lead to increases in ROS (reviewed in Kamp et al., 

31 1992). Fibers can directly lead to the production of ROS by iron-catalyzed generation through 

32 the Fenton reaction. ROS are also produced following phagocytosis of fibers. ROS production 

33 following exposure to asbestos has been shown to be associated with DNA damage, chronic 

34 inflammation, and lipid peroxidation. As described in Section 4.3.2.1.1, chronic inflammation 

35 may lead to increased proliferation and DNA damage, which in turn may lead to tumor 
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1 formation. The hydroxyl radical produced as part of the ROS production following fiber 

2 exposure has been shown to directly interact with DNA (Leanderson et al., 1988). 

3 ROS production has been measured in response to both Libby Amphibole asbestos and 

4 tremolite exposure. The study of Libby Amphibole asbestos (Blake et al., 2007) demonstrated 

5 an increase in superoxide anion. Blake et al. (2007) also demonstrated that total superoxide 

6 dismutase was inhibited following exposure to Libby Amphibole asbestos, along with a decrease 

7 in intracellular glutathione, both of which are associated with increased levels of ROS. These 

8 results are supported by a recent study in human mesothelial cells (Hillegass et al., 2010). 

9 Increased ROS production was also observed in human airway epithelial cells following 

10 exposure to Libby Amphibole asbestos (Duncan et al., 2010). This increase in ROS and decrease 

11 in glutathione are a common effect following exposure to asbestos fibers and particulate matter. 

12 Limited studies, however, have examined the specific type of ROS produced following exposure 

13 to each type of asbestos. Although ROS production is relevant to humans, based on similar 

14 human responses as compared to animals, information on the specifics of ROS production 

15 following exposure to Libby Amphibole asbestos is limited to the available data described here. 

16 Therefore, the role of ROS production in lung cancer and mesothelioma following exposure to 

17 Libby Amphibole asbestos is unknown. 

18

19 4.6.2.1.3. Genotoxicity/mutagenicity

20 Genotoxicity and, more specifically, mutagenicity, are associated with tumor formation 

21 through alterations in genetic material. Mutagenicity refers to a permanent effect on the 

22 structure and/or amount of genetic material that can lead to heritable changes in function, while 

23 genotoxicity is a broader term including all adverse effects on the genetic information (Eastmond 

24 et al., 2009). Results of standard mutation assays like the Ames test, which analyze for point 

25 mutations, have found asbestos and other mineral fibers to be negative or only marginally 

26 positive (Walker et al., 1992). Several other studies, however, have shown that asbestos 

27 exposure can result in a variety of chromosomal alterations, which are briefly discussed below. 

28 Genotoxicity following exposure to asbestos fibers has been described as the result of two 

29 distinct mechanisms, either ROS production leading to direct DNA damage, or physical 

30 interference of mitosis by the fibers. For both DNA damage and mitotic interference, the fibers 

31 must first enter the cell. Some studies have shown that a direct interaction between fibers and 

32 cellular receptors might also lead to increased ROS production. ROS production is likely to be a 

33 key event in fiber-induced direct DNA damage, as observed following exposure to other forms 

34 of asbestos, while the indirect DNA damage requires fiber interaction with cellular components 

35 (e.g., mitotic spindle, chromosomes).
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1 One in vitro study examined genotoxicity of Libby Amphibole asbestos by measuring 

2 DNA adduct formation following exposure via murine macrophages (primary and immortalized) 

3 (Blake et al., 2007). The data showed no increase in adduct formation as compared to unexposed 

4 controls. A second study observed increases in micronuclei induction in both normal human 

5 lung epithelial cells and XRCC1-deficient cells for both Libby Amphibole and crocidolite 

6 asbestos (Pietruska et al. 2010). Two studies of tremolite examined genotoxicity. The first 

7 found no significant increase in revertants in the Ames assay (Athanasiou et al., 1992), which is 

8 similar to results obtained for other forms of asbestos. This study did find, however, that 

9 tremolite exposure led to a dose-dependent increase in chromosome number and micronuclei 

10 formation, which has also been described for other asbestos fibers (as reviewed in Hei et al., 

11 2007; Jaurand, 1999). Hei and colleagues (Okayasu et al., 1999) performed mutation analysis 

12 with tremolite and found a dose-dependent increase in mutations in CD59 in hamster hybrid 

13 cells. Genotoxicity analysis in humans, following exposure to Libby Amphibole asbestos or 

14 tremolite, has not been measured, although other types of asbestos fibers have led to increases in 

15 genotoxicity in primary cultures and lymphocytes (Dopp et al., 2005; Poser et al., 2004). In 

16 general, these studies have examined genotoxicity with a focus on ROS production as a key 

17 event. Although Libby Amphibole asbestos- and tremolite-specific data are limited to in vitro 

18 studies, given the similarities in response to other forms of asbestos, there is some evidence to 

19 suggest genotoxicity following exposure to Libby Amphibole and tremolite asbestos. However, 

20 the potential role of this genotoxicity in lung cancer or mesothelioma following exposure to 

21 Libby Amphibole asbestos is unknown.

22

23 4.6.2.1.4. Cytotoxicity and cellular proliferation

24 The initial stages of tumorigenicity may be an increased cellular proliferation at the site 

25 of fiber deposition, which can increase the chance of cancer by increasing the population of 

26 spontaneous mutations affording genotoxic effects an opportunity to multiply. Increased cell 

27 proliferative regeneration is also a hallmark of tumor clonal expansion and generally occurs in

28 response to increased apoptosis. As described in Section 4.5.1.2, increased cytotoxicity and 

29 cellular proliferation has been observed in in vitro studies following exposure to Libby 

30 Amphibole asbestos and tremolite. Tremolite and Libby Amphibole asbestos exposure led to 

31 increases in both fibrosis and tumorigenicity in all but one animal study, supporting a possible 

32 role for proliferation in response to these fibers. Taken together with studies on other asbestos 

33 fibers, these data suggest that a cytotoxicity and cell proliferation may play a role in tumor 

34 formation. However, there is limited data to demonstrate that increased cytotoxicity and cellular 
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1 proliferation following exposure to Libby Amphibole asbestos leads to lung cancer or 

2 mesothelioma.

3

4 Summary. Data for Libby Amphibole asbestos and tremolite provide limited evidence that 

5 suggest chronic inflammation, ROS, genotoxicity, and cytotoxicity and cellular proliferation may 

6 play a role in the carcinogenicity of Libby Amphibole asbestos. However, data are inadequate to 

7 establish how these effects may lead to lung cancer or mesothelioma following exposure to 

8 Libby Amphibole asbestos. 

9

10 4.6.2.2. Application of the Age-Dependent Adjustment Factors (ADAFs)

11 As described above, the mode of action for Libby Amphibole asbestos is unknown. The 

12 weight of evidence does not support a mutagenic mode of action for Libby Amphibole asbestos 

13 carcinogenicity. Therefore, according to EPA’s Supplemental Guidance for Assessing 

14 Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005b), the application of 

15 the Age-Dependent Adjustment Factors (ADAFs) is not recommended.

16

17 4.7. SUSCEPTIBLE POPULATIONS

18 Certain populations may be more susceptible to adverse health effects from exposure to 

19 Libby Amphibole asbestos. Because the adverse health effects resulting from exposure to Libby 

20 Amphibole asbestos have been, for the most part, studied in occupational cohorts of adult white 

21 men (see Sections 4.1.1 and 4.1.3), there is limited information on the effects to a broader 

22 population. A few studies, however, have examined health effects resulting from 

23 non-occupational exposure in other age groups, in other genders (i.e., females), and in different 

24 race or ethnicity groups. The data from these studies could inform whether any differential risk 

25 exists for these groups (see Sections 4.1.2 and 4.1.4). However, it should be noted that the 

26 ability to distinguish true differences from chance variation in effect estimates is related to the 

27 sample size and statistical power, which, in most cases, is quite limited in these studies. In 

28 addition, genetic polymorphisms, preexisting health conditions, and differences in nutritional 

29 status may alter an individual’s response to Libby Amphibole asbestos. Finally, coexposures to 

30 other substances (e.g., tobacco smoke or particulate matter) may increase an individual’s risk of 

31 adverse health effects from exposure to Libby Amphibole asbestos. Where data are available, 

32 each of these factors is discussed below with respect to increased susceptibility to non-cancer 

33 effects and cancer from exposure to Libby Amphibole asbestos, and where information specific 

34 to Libby Amphibole asbestos is not available, the general literature on the toxicity of mineral 

35 fibers is briefly referenced. 
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1 There are also factors which may influence one’s exposure potential to asbestos based on 

2 lifestage or other defined population. For example, children spend more hours outside and may 

3 engage in activities which impact exposure level compared to adults (NRC, 1993; US EPA 

4 2006.) Since lifestage and activity patterns which increase exposure potential may increase the 

5 potential for health effects from exposure, these factors define those who may be more 

6 susceptible to health effects due to greater exposure. Section 2.3 discusses this exposure 

7 potential, including how children workers, household contacts and residents may be exposed to 

8 Libby amphibole asbestos. 

9

10 4.7.1. Influence of Different Lifestages on Susceptibility

11 Individuals at different lifestages differ from one another physiologically, anatomically, 

12 and biochemically. Individuals in early and later lifestages differ markedly from adulthood in 

13 terms of body composition, organ function, and many other physiological parameters, which can 

14 influence the toxicokinetics and toxicodynamics of chemicals and their metabolites in the body 

15 (ILSI, 1992). This also holds true for mineral fibers, including asbestos fibers (see Section 3). 

16 This section presents and evaluates the literature on how individuals in early or later lifestages 

17 might respond differently and thus potentially be more susceptible to adverse healtheffects of 

18 Libby Amphibole asbestos exposure. 

19

20 4.7.1.1. Lifestage Susceptibility

21 Humans in early lifestages (i.e., conception through adolescence) can have unique 

22 susceptibilities compared to those in later lifestages because they undergo rapid physiological 

23 changes during critical periods of development (Selevan et al., 2000). Furthermore, they are 

24 often exposed to xenobiotics via unique exposure pathways (i.e., transplacental transfer and 

25 breast milk ingestion) (NRC, 1993; U.S. EPA, 2006, 2008). Although no data exist for Libby 

26 amphibole asbestos, limited observations in stillborn infants, however, indicate transplacental 

27 transfer of tremolite (Haque et al., 1996, 1998) and other asbestos and nonasbestos fibers (Haque 

28 et al., 1991, 1992, 1996, 1998). Transplacental transfer of asbestos also has been demonstrated 

29 in animals following maternal exposure by gavage (Haque et al., 2001) or injection 

30 (Cunningham and Pontefract, 1974; Haque and Vrazel, 1998) (see Section 3). These studies did 

31 not evaluate sources or levels of exposure. Haque et al. (1992) hypothesized that maternal health 

32 conditions might influence the translocation of fibers, as some of the mothers had pre-existing 

33 health conditions. Based on these studies, Libby Amphibole asbestos fibers may be transferred 

34 through the placenta, resulting in prenatal exposure at any stage of fetal development. 
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1 Increased lung deposition of fibers in children compared with adults has been observed 

2 (Asgharian et al., 2004; Bennett et al., 2008; Isaacs and Martonen, 2005; Oldham et al., 1997; 

3 Phalen and Oldham, 2001; Phalen et al., 1985; Schiller-Scotland et al., 1994). Nasal deposition 

4 of particles was shown to be lower in children compared to adults—particularly during exercise 

5 (Becquemin et al., 1991) The lung and nasal depositional differences are due in part to structural 

6 differences across lifestages, which can change the depositional pattern of different fiber sizes 

7 and possibly alter the site of action and result in differential clearance and subsequent health 

8 effects. It is unclear, however, whether the lung surface, body weight, inhalation volume, or 

9 exposure patterns are most determinative of dose. One study reported that the ratio of lung 

10 surface area to body weight does not differ considerably for a 10-month-old, a 9-year-old, and an 

11 adult (Short, 1952). Another study suggested that deposition of fine particles (2 μm MMAD, 

12 which is in the size range of those for Libby Amphibole asbestos reported in Table 2-2) in the 

lung is increased for overweight (≥95th 13 percentile body mass index [BMI]) children who breathe 

more at rest compared to underweight children (<25th 14 percentile BMI) (Bennett and Zeman, 

15 2004).

16 There are limited studies analyzing non-cancer outcomes in children exposed to Libby 

17 amphibole in the published literature. A Libby medical screening program collected data on 

18 7,307 participants, including 600 children aged 10-17 years old, representing 8.2% of the cohort 

19 (Peipins et al., 2003). Pulmonary function tests showed that none of these children had moderate 

20 or severely restricted lung function (ATSDR, 2001; 2002). This study also studied chest 

21 radiographs for those 18 years old or older (ATSDR, 2001; Noonan et al., 2006; Peipins et al., 

22 2003), but were not given to children. In addition, the prevalence of some self-reported 

23 respiratory symptoms among 10-29-year old adolescents and young adults was associated with 

24 certain exposure pathways. These participants were ≤ age 18 in 1990 when the mining/milling 

25 operations closed (Vinikoor et al., 2010). A better understanding of the community health 

26 effects and the examination of the potential progression of adverse health effect in this 

27 community would benefit from additional research to establish the clinical significance of these 

28 findings. Aside from these studies, no other studies of non-cancer outcomes in early lifestages of 

29 humans or experimental animals exposed to Libby amphibole have been reported in the 

30 published literature. 

31 For exposure to other types of asbestos, studies have reported non-cancer outcomes in 

32 early lifestages. Those in the very young include reports of stillbirth (Haque et al., 1996, 1998)

33 and death among infants (age 1-27 months) due to sudden infant death syndrome and 

34 bronchopulmonary dysplasia (Haque and Kanz, 1988). These studies found higher levels of

35 asbestos in the lungs of those who died compared to controls. In the infant study, the authors 
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1 speculate that either there was a pre-existing abnormal lung physiology in these children that 

2 may contribute to a reduced ability to clear fibers from the lung, or that the children could have 

3 an increased exposure to asbestos (Haque and Kanz, 1988). Those in older children include 

4 reports of pleural and diaphragmatic calcifications (Epler et al., 1980) and altered immune and 

5 respiratory conditions (Shtol et al., 2000). 

6 In experimental animals, offspring of rats exposed to tremolite had decreased body 

7 weight gain at weaning and 8-weeks-old compared to controls (McConnell et al., 1983a; NTP, 

8 1990b). This was also observed in some similar studies of other forms of asbestos (McConnell

9 et al., 1983a; NTP, 1985, 1988, 1990a, 1990b) but not in others (McConnell et al., 1983b; NTP, 

10 1983). Embryonic toxicity was observed in a few experimental animal studies. Crocidolite 

11 injected into pregnant mice resulted in altered limb differentiation in cultured embryos (Krowke 

12 et al., 1983, abstract), and chrysotile in drinking water given to pregnant mice resulted in 

13 decreased post-implantation survival in cultured embryos (Schneider and Maurer, 1977); 

14 however, pregnant mice exposed to chrysotile in drinking water did not affect in vivo embryonic 

15 survival (Schneider and Maurer, 1977).

16 It is possible that early lifestage exposure may increase the risk of non-cancer outcomes 

17 in adulthood compared to adult exposure. For instance, one man exposed to Libby amphibole 

18 when he was 18-19 years old was diagnosed with asbestosis at 65 years old (Wright et al., 2002). 

19 After tremolite exposure during childhood, there is one study of altered immunity in adulthood 

20 (Zerva et al., 1989) and a case report of asbestosis in adulthood (Voisin et al., 1994). Another 

21 study also reported an increased risk of asbestosis in after childhood exposure to asbestos from 

22 parental occupational exposure to asbestos (Kilburn et al., 1985).To address the potential for 

23 increased susceptibility to cancer from early lifetime exposures, one needs to consider if there is 

24 evidence of differential health effects such as increased potency from early lifetime exposure; 

25 decreased latency based on the age of exposure or cancers observed with early lifetime exposures 

26 not seen with adult exposures. There are no published reports that can directly answer these 

27 questions for exposure to Libby amphibole asbestos. 

28 While cancers in adults have been documented following exposure to Libby Amphibole 

29 asbestos, similar reports describing childhood cancers resulting from this exposure have not been 

30 identified. Few cancers occurring in children have been documented in children exposed to any 

31 form of asbestos. Examples of cases include a 17-year-old exposed to chrysotile and tremolite 

32 (Andrion et al., 1994) and a 3-year-old exposed to chrysotile (Lieben and Pistawka, 1967), both 

33 of whom developed mesothelioma. However, childhood mesothelioma, in particular, may have 

34 an etiology that is different from that of the disease that is seen in adults (Cooper et al., 1989). 
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1 No cancer bioassays have been performed in juvenile animals exposed to Libby Amphibole 

2 asbestos. 

3 There are several case reports of individuals who were exposed to Libby amphibole 

4 asbestos as children who were later diagnosed with mesothelioma (Whitehouse et al., 2008). Of 

5 the 11 cases described by Whitehouse et al. (2008), 2 reported potential exposure scenarios that 

6 were limited to childhood, and both of these were diagnosed at a relatively young age at 

7 diagnosis (48, compared with 52 to 82 years of age for the other 9 cases). Although these case 

8 studies support the link between exposure to Libby amphibole asbestos and mesothelioma, it is 

9 unclear if children are more susceptible than adults.

10 Case reports of exposure to tremolite during childhood, and subsequent diagnosis of 

11 mesothelioma in adulthood (Magee et al., 1986; Rey et al., 1993; Sakellariou et al., 1996; 

12 Schneider et al., 1998; Senyigit et al., 2000), support the limited data summarized above for 

13 Libby Amphibole asbestos. Additional case studies of mesothelioma after childhood exposure to 

14 other types of asbestos are available (Anderson et al., 1976; Ascoli et al., 2003; Cazzadori et al., 

15 1992; Inase et al., 1991; Kane et al., 1990; Li et al., 1978, 1989; Magnani et al., 2001; 

16 Martensson et al., 1984; Roguin et al., 1994; Rom et al., 2001; Schneider et al, 1995, 1996a, 

17 1996b; Wagner et al., 1960; Wassermann et al., 1980; Yano et al., 2009). These studies, 

18 however, do not clarify whether exposure during childhood yields different adverse health 

19 effects compared with exposure during adulthood.

20 In experimental studies, the offspring of rats orally exposed to non-fibrous tremolite did 

21 not demonstrate an increase in tumors compared to controls (McConnell et al., 1983a; NTP, 

22 1990b). Similar studies of other forms of asbestos did report an increase of various neoplasms in 

23 the offspring (McConnell et al., 1983a, 1983b; NTP, 1985, 1988, 1990a), but another study

24 reported none (NTP, 1983).

25 Studies of exposure to other types of asbestos have attempted to determine if exposure to 

26 asbestos in early life results in an increased risk of developing cancer. An early study in the 

27 United Kingdom described occupational exposure to chrysotile, crocidolite, and amosite for a 

28 group of 900 women. First exposure from ages 15–24 years led to a higher relative mortality 

29 risk for lung and pleural cancer compared with women who were first exposed at older ages 

30 (SMR 30 based on 12 observed and 0.4 expected, SMR 8 based on 4 observed and 0.5 expected, 

31 and SMR 6.7 based on 6 observed and 0.9 expected in the first exposure at ages 15-24, 25-34, 

32 and ≥35 years, respectively) (Newhouse et al., 1972). A study by Hansen et al. (1998) in 

33 Wittenoom, Western Australia examined 27 individuals diagnosed with mesothelioma who had 

34 been environmentally exposed to crocidolite (i.e., residents of the town but not directly employed 

35 in the area’s crocidolite mining and milling industry); 11 of these subjects were children 
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1 <15 years old at the time of exposure. One-third of all the subjects were less than 40 years old 

2 when diagnosed, but the authors found no increase in mesothelioma mortality rates when 

3 analyzed by age at first exposure. However, risk was significantly increased based on time from 

4 first exposure, duration of exposure, and cumulative exposure (Hansen et al., 1998). Additional 

5 studies of this cohort found that the mesothelioma mortality rate was lower for those first 

6 exposed (based on age residence in the area began) to crocidolite at ages <15 years (n = 24; 

7 mesothelioma mortality rate 47 per 100,000 person-years) compared with those first exposed at 

8 ages ≥15 years (n = 43; mesothelioma mortality rate 112 per 100,000 person-years) (Reid et al., 

9 2007). The hazard ratio for age at first residential exposure of ≥15 years compared with 

10 <15 years was 3.83 (95% CI 2.19, 6.71), adjusting for cumulative exposure, gender, and an 

11 interaction term for gender and cumulative exposure. 

12 Based on these very limited and inconclusive studies on other forms of asbestos, no 

13 conclusions can be drawn about differential risk of adverse health effects after early lifestage 

14 exposure to Libby Amphibole asbestos compared to exposure during adulthood. It is unknown 

15 whether early lifestage exposure compared to adult exposure increases susceptibility for adult 

16 cancers, as measured by increased incidence, severity or disease progression, or decreased 

17 latency. 

18 Later lifestage is generally defined as ≥65 years old. Because pulmonary function 

19 (volume and rate of breathing) decreases with age, increased deposition of fibers in the lung from 

20 exposures in later lifestages is unlikely. Clearance of fibers from the lung might be reduced, 

21 however, as older adults have a less effective cough reflex and strength and the cilia are less able 

22 to move mucus up and out of the airway (U.S. EPA, 2005). Older adults could be more 

23 susceptible to the effects of Libby Amphibole asbestos due to the gradual age-related decline in 

24 physiological processes. Additionally, decreased immune function, increased genetic damage, 

25 and decreased DNA repair capacity can result in increased susceptibility with age (U.S. EPA, 

26 2005). These age-associated alterations could decrease fiber-induced DNA damage repair but 

27 might also reduce the incidence of fiber-induced DNA damage due to decreased phagocytosis or 

28 inflammation. Specific data pertaining to age-varying effects of Libby amphibole asbestos on 

29 these processes are not available.

30 Due to the natural increase of non-cancer health effects among older adults, it is likely 

31 that older individuals exposed to Libby amphibole at some point in their lives will have elevated 

32 rates of disease. Radiographic tests among those exposed to Libby amphibole show that older 

33 age is one of the factors most associated with pleural or interstitial abnormalities (Amandus et 

34 al., 1987b; ATSDR, 2001; Horton et al., 2006; Lockey et al., 1984; McDonald et al., 1986b;

35 Muravov et al., 2005; Peipins et al., 2003; Rohs et al., 2008). However, abnormal radiographs 
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1 are more common with age (Pinsky et al., 2006). In workers 65-years-old and over at the time of 

2 screening, an increased risk of systemic autoimmune disease and rheumatoid arthritis was 

3 observed among mine workers, as well as those who were exposed to asbestos in the military 

4 (Noonan et al., 2006). However, systemic autoimmune disease, rheumatoid arthritis, and 

5 systemic lupus erythematosus are more common in those aged 45-64 compared to those aged 17-

6 44 years or those 65 years old and older (Noonan et al., 2006). 

7 While these results show more non-cancer effects among older individuals exposed to 

8 Libby amphibole, the available studies do not assess the timing of the exposure in relation to 

9 these outcomes. This data is necessary to evaluate if older individuals are more susceptible than 

10 younger individuals. Therefore, no conclusions can be drawn about differential risk of non11 cancer after later lifestage exposure to Libby amphibole compared to exposure earlier in life.

12 No studies assessing the carcinogenic effect of exposures occurring in older age groups 

13 are available for Libby Amphibole asbestos. It should be noted that observed health effects 

14 among individuals exposed to Libby Amphibole asbestos are likely to increase with increasing 

15 age due to the long latency period for the exposure-response for asbestos and lung cancer and 

16 other chronic diseases. However this type of observation would not directly address the question 

17 of whether exposures at older ages have a stronger or weaker effect compared with exposures at 

18 younger ages. 

19 4.7.2. Influence of Gender on Susceptibility

20 A discussion of gender-related differences in risk from asbestos exposure raises several 

21 important issues, such as gender-related differences in exposure patterns, physiology, and 

22 dose-response (Smith, 2002). For example, nasal breathing filters out particles, and men tend to 

23 breathe less through their nose during exercise than women do (Bennett et al., 2003). Bennett et 

24 al. (1996) showed a gender difference in fractional deposition (defined as the ratio of particles 

25 not exhaled to total particles inhaled) of particles 2 μm in mass median aerodynamic diameter. 

26 This particle diameter is within the range of Libby Amphibole asbestos particles reported in 

27 Table 2-2. This study found that, in general, women had a greater retention of particles 

28 compared to men because men had higher ventilation rates compared to women; however, the 

29 overall deposition rate was higher in the men (Bennett et al., 1996). 

30 Most occupational studies for Libby Amphibole asbestos have examined the effects of 

31 exposure only in men (Amandus and Wheeler, 1987; Amandus et al., 1987a, 1988; McDonald et 

32 al., 1986a, 1986b, 2004; Sullivan, 2007; Moolkavkar et al., 2010). There is limited information 

33 specifically on women exposed to Libby Amphibole asbestos. In the Libby, MT community 

34 studies, no gender-related trends in mortality due to lung or digestive cancer were observed 
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1 (ATSDR, 2000). These limited data do not provide a basis for drawing conclusions regarding 

2 gender-related differences in adverse health effects from Libby Amphibole asbestos. 

3

4 4.7.3. Influence of Race or Ethnicity on Susceptibility

5 Race and ethnicity often are used in medical and epidemiological studies to define 

6 various groups of the population. These categories could be surrogates for differences in 

7 exposure (e.g., occupation, socioeconomics, behavior) or biology (e.g., physiology, genetics), in 

8 which case these factors may play a role in susceptibility as well. Nasal structure and lung 

9 architecture can influence the depositional patterns for both particles and fibers. One study of 

10 18 Caucasians (ages 8 to 30 years) and 14 African Americans (ages 8 to 25 years) reported 

11 increased ventilation rates during exercise in the African Americans (matched on sex, age, 

12 height, and weight) (Cerny, 1987). Another study (11 Caucasians and 11 African Americans, 

13 ages 18 to 31 years) reported decreased nasal deposition efficiency (for particle sizes of 1–2 μm, 

14 which is in the range of those for Libby Amphibole asbestos reported in Table 2-2) in African 

15 Americans compared to Caucasians (Bennett and Zeman, 2005). Furthermore, nasal breathing 

16 during exercise occurred less in Caucasians compared to African Americans in this study 

17 (Bennett et al., 2003). 

18 Of the occupational and residential studies for Libby Amphibole asbestos, the vast 

19 majority of subjects with known race were white, precluding the ability to conduct an analysis of 

20 racial and ethnicity-related differences in the mortality risks within the Libby worker cohort. In 

21 a study of occupational exposure to chrysotile asbestos in a textile factor, lung cancer mortality 

22 risk in relation to exposure was lower in non-white males (0.84, 95% CI = 0.52–1.27) compared 

23 to white males (2.34, 95% CI = 1.94–2.79), although a statistically significant increase in SMR 

24 was observed for non-white males at high exposure levels (≥120 fiber-years/mL) (Hein et al., 

25 2007). This observed difference could be due to a lower prevalence of smoking among 

26 non-white compared with white males (Hein et al., 2007).

27

28 4.7.4. Influence of Genetic Polymorphisms on Susceptibility

29 XRCC1 is a DNA damage repair gene. A recent study demonstrated that 

30 XRCC1-deficient cells exposed to Libby Amphibole or crocidolite asbestos demonstrated 

31 increased levels of micronuclei induction (Pietruska et al., 2010). Two other studies examined 

32 XRCC1 polymorphisms in relation to disease risk with other types of asbestos exposure. Zhao et 

33 al. (2005) found no association between XRCC1 polymorphisms and asbestosis in

34 asbestos-exposed workers. A study, by Dianzani et al. (2006), however, did find an association 

35 between XRCC1 and asbestos-induced lung disease in a population exposed to asbestos 
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1 pollution. Further work is necessary, with clear definitions of patient populations and their 

2 exposure levels, so that these studies and others can be compared to determine if XRCC1 

3 polymorphisms increase susceptibility to adverse health effects following exposure to Libby 

4 amphibole asbestos. 

5 SODs are free radical scavengers that dismutate superoxide anion to oxygen and 

6 hydrogen peroxide. SODs are expressed in most cell types exposed to oxygen. Several common 

7 forms of SODs occur and are named by the protein cofactor: copper/zinc, manganese, iron, or 

8 nickel. A recent study observed no significant alterations in levels of intracellular SOD 

9 following a 3 hr exposure to Libby Amphibole asbestos in mice (Blake et al., 2007). Other 

10 studies in humans and mice have examined SOD expression in relation to other types of asbestos 

11 exposure. Manganese superoxide dismutase (MnSOD) activity was elevated in biopsies of 

12 human asbestos-associated malignant mesothelioma, although no genotypic differences were 

13 found to be related to this change in activity (Hirvonen et al., 2002). Other studies have focused 

14 on the role of extracellular superoxide dismutase (EcSOD) and asbestos-induced pulmonary 

15 disease (Fattman et al., 2006; Gao et al., 2008; Kliment et al., 2008; Tan et al., 2004). These 

16 studies have suggested a protective effect of ECSOD, with mice that lack this form of SOD 

17 having increased sensitivity to asbestos-induced lung injury (Fattman et al., 2006). 

18 No studies that examine the role of cell-cycle control genes were found. Additionally, no 

19 information on other genetic polymorphisms in relation to disease risk among those exposed to 

20 Libby Amphibole asbestos was identified in the available literature. 

21 Familial studies showing unusually high incidence of mesothelioma suggest that genetic 

22 factors might play a role in the etiology of mesothelioma (Huncharek, 2002; Roushdy-Hammady 

23 et al., 2001; Ugolini et al., 2007). Whether a genetic factor or a common environmental element 

24 leads to the similar responses in these families is difficult to determine. Increased interest in the 

25 role of genetic factors in asbestos-related health outcomes has led to several analytical studies on 

26 specific genetic polymorphisms. A review of 24 published reports (19 studies) discusses the 

27 current state of knowledge regarding genetic susceptibility associated with asbestos-related 

28 diseases (in particular, malignant pleural mesothelioma). Results from several studies 

29 demonstrated an association between asbestosis-related diseases and GSTM1-null 

30 polymorphism, whereas results for other polymorphisms were conflicting (Neri et al., 2008). 

31 Some polymorphisms discussed in Neri et al. (2008) are in genes for N-acetyl-transferase 2 

32 (NAT2); glutathione-s-transferases (GSTs); SOD; CYP1A1, CYP2D6; neurofibromatous 2 

33 (Nf2); p53; and XRCC1. Although occupational asbestos exposure was assessed, the type of 

34 asbestos is generally unknown in these studies. 
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1 Limited animal studies have examined the role of genetic variations related to asbestos 

2 exposure, including specific signaling pathways (Shukla et al., 2007), DNA damage repair (Lin 

3 et al., 2000; Ni et al., 2000), and tumor suppressor genes (Kleymenova et al., 1997; Vaslet et al., 

4 2002; Marsella et al., 1997). Genetic alterations of particular interest for mesothelioma include 

5 those involved in tumor suppression (p53, Nf2) and oxidative stress (SOD, GSTs). Nf2 and p53 

6 are frequently altered in mesotheliomas, but no consistent mutations have been found (Bianchi et 

7 al., 1995; Cheng et al., 1999; Mayall et al., 1999). Alterations in expression of antioxidant 

8 enzymes like SOD and GST in mesothelioma can yield cells more resistant to oxidative stress as 

9 compared to normal cells due to increased antioxidant activity (Ramos-Nino et al., 2002; 

10 Rahman et al., 1999). 

11

12 4.7.5. Influence of Health Status on Susceptibility

13 Preexisting health conditions could potentially alter the biological response to asbestos 

14 exposure. Mesothelioma risk has been hypothesized to be related to immune impairment 

15 (Bianchi and Bianchi, 2008) and simian virus 40 exposure in humans (Bocchetta et al., 2000; 

16 Carbone et al., 2007; Cristaudo et al., 2005; Foddis et al., 2002; Mayall et al., 1999; Kroczynska 

17 et al., 2006). Co-exposure to asbestos and SV40 has been associated with p53-related effects in 

18 vitro (Mayall et al., 1999; Bocchetta et al., 2000; Foddis et al. 2002), and cell signaling 

19 aberrations in vivo (Kroczynska et al., 2006; Cristaudo et al. 2005). However, the influence on 

20 cancer risk is unknown, as these lines of research are not fully developed and have not been 

21 applied specifically to Libby Amphibole asbestos. 

22 Obesity can compromise inhalation exposure, as increased particle deposition in the lungs 

23 of overweight children (Bennett and Zeman, 2004) and adults (Graham et al., 1990) has been 

24 observed. Individuals with respiratory diseases could have compromised lung function that 

25 alters inhalation exposure to Libby Amphibole asbestos. For example, individuals with chronic 

26 obstructive pulmonary disease have increased inhalation volume (Phalen et al., 2006) and 

27 increased fine particle deposition (Bennett et al., 1997; Kim and Kang, 1997; Phalen et al., 2006) 

28 and retention (Regnis et al., 2000). Similarly, studies have reported an increase in coarse particle 

29 (aerodynamic diameter >5 μm) deposition in individuals with cystic fibrosis (Brown et al., 2001; 

30 Brown and Bennett, 2004). For people exposed to Libby Amphibole asbestos, an increased risk 

31 for interstitial lung abnormalities was observed for those with a history of pneumonia (Peipins et 

32 al., 2003). In another study, bronchial asthma was examined as a potential confounding variable 

33 for asbestos-related effects on pulmonary function, although no confounding was observed 

34 (Whitehouse, 2004). 

35
This document is a draft for review purposes only and does not constitute Agency policy.

4-86 DRAFT—DO NOT CITE OR QUOTE

1 4.7.6. Influence of Lifestyle Factors on Susceptibility

2 No studies were identified that examined lifestyle factors specifically with respect to 

3 Libby Amphibole asbestos. Lifestyle factors such as exercise, nutritional status, and smoking 

4 habits could affect the biological effects of asbestos exposure through various mechanisms. For 

5 example, those with more physically demanding jobs or those who regularly engage in vigorous 

6 exercise might experience increased lung deposition from fine particles or fibers compared to 

7 those with a more sedentary lifestyle (Phalen et al., 2006; Becquemin et al., 1991). Randomized 

8 controlled trials of vitamin supplementation (beta-carotene and retinol) have been conducted for 

9 asbestos-related lung cancer, but results do not support a protective effect (Cullen et al., 2005) 

10 The Libby occupational exposure studies (Amandus et al., 1987a, Amandus and Wheeler, 

11 1987; Larson et al., 2010; McDonald et al., 2004; 2002; 1986a,b; Sullivan, 2007) did not 

12 examine the potential interaction between smoking, Libby Amphibole asbestos exposure, and 

13 lung cancer risk. However, epidemiological (Lee, 2001; Savastano et al., 2004; Reif and Heeren, 

14 1999) and laboratory studies (Jaurand et al., 1983; Jung et al., 2000; Valavanidis et al., 1996, 

15 2009; Lohani et al., 2002) have examined the potential synergistic effect of asbestos and 

16 cigarette smoke co-exposure in lung cancer. A synergistic interaction between the ROS from 

17 cigarette smoke and asbestos fibers might contribute to increases in lung cancer in asbestos mine 

18 workers (Kamp et al., 1992; Jackson et al., 1987). Research has suggested that asbestos fibers 

19 might also enhance the delivery of multiple carcinogens in cigarette smoke, and that cigarette 

20 smoking decreases the clearance mechanisms in the lungs and could, therefore, lead to an 

21 increase in fiber presence in the lungs (Nelson and Kelsey, 2002). For lung cancer, a synergistic 

22 relationship between cigarette smoking and asbestos exposure has been demonstrated (Hammond 

23 et al., 1979; Selikoff and Hammond, 1979, Wraith and Mengersen, 2008). Smoking likely 

24 causes genetic alterations associated with lung cancer (Landi et al., 2008) that might increase the 

25 carcinogenic risk from exposure to asbestos. Benzo(a)pyrene, a component of tobacco, also has 

26 been observed to enhance the carcinogenic effects of asbestos (DiPaolo et al., 1983; Kimizuka et 

27 al., 1987; Loli et al., 2004; Mossman et al., 1983, 1984; Reiss et al., 1983).

28

29 4.7.7. Susceptible Populations Summary

30 A very limited amount of information is available on exposure to Libby Amphibole 

31 asbestos early in life that could lead to increased risk of asbestos-induced disease later in life. 

32 Due to the long latency period of some diseases in relation to asbestos exposure in general, 

33 however, adverse effects may be more likely to be observed with an increase in age. This 

34 assumption requires further investigation. The number of women who have been occupationally 

35 exposed to Libby Amphibole asbestos is very small, and health risks have not been evaluated 
This document is a draft for review purposes only and does not constitute Agency policy.

4-87 DRAFT—DO NOT CITE OR QUOTE

1 specifically for this group. Differences between men and women in residential sources and types 

2 of exposure (e.g., types of activities done in the household) also preclude the possibility of 

3 drawing conclusions regarding the relative susceptibility of women compared with men to health 

4 effects of exposure to Libby Amphibole asbestos. Similarly, sufficient data are not available to 

5 draw conclusions regarding racial or ethnic variation in susceptibility to diseases caused by 

6 exposure to Libby Amphibole asbestos. In addition, the potential modifying effects of genetic 

7 polymorphisms, pre-existing health conditions, nutritional status, and other lifestyle factors have 

8 not been studied, specifically as related to exposure of Libby amphibole asbestos and health 

9 outcomes. 
5-1 

5. EXPOSURE-RESPONSE ASSESSMENT 

5.1. ORAL REFERENCE DOSE (RfD) 

 Data are unavailable to characterize the toxic effects of Libby Amphibole asbestos 

following oral exposure. Thus, an oral reference dose is not derived. 

5.2. INHALATION REFERENCE CONCENTRATION (RfC) 

5.2.1. Choice of Principal Study and Critical Effect 

 Studies in humans have shown several health effects on the lung and pleura (a thin tissue 

surrounding the lung and lining the chest cavity) such as pleural thickening and fibrosis of the 

lung and pleura as well as potential autoimmune or cardiovascular disease following inhalation 

exposure to Libby Amphibole asbestos (see Section 4.5). There are no studies in laboratory 

animals by the inhalation route of exposure suitable for derivation of an RfC since the available 

animal studies lack adequate dose-response information and are of a short-term duration. 

Table 5-1. Summary of rationale for identifying candidate principal studies on 

Libby Amphibole asbestos for RfC development. 

Attribute 

Preferred Characteristics for Candidate Principal Studies for the Libby Amphibole 

Asbestos RfC 

Relevance of exposure 

paradigm 

Subchronic or chronic studies over studies of acute exposure duration as most 

relevant environmental exposure scenarios are expected to address chronic exposure 

scenarios (potentially including both continuous exposure from ambient conditions 

and episodic activity-related exposures).

Measures of cumulative exposure rather than mean or peak exposure; cumulative 

exposure is a widely used metric to address asbestos risk. It is consistent with the 

expectation that toxic responses will reflect accumulative effect of asbestos inhaled 

and deposited in tissues over time as opposed to shorter term patterns of exposure to 

asbestos concentrations. 

Data on exposure concentrations and durations are preferred to address associations 

with time since first exposure (TSFE), peak exposure, or average intensity.

Relatively lower exposure intensities which may represent conditions more similar to 

environmental exposures.

Study design characteristics Sufficient follow-up time for outcomes to develop (which can depend on the health 

outcome being addressed). 

Study size and participation rates that are adequate to detect and quantify health 

outcomes being studied; with no indications of bias in study population selection. 

Use of a study design or analytic approach which adequately addresses the relevant 

sources of potential confounding, including age, sex, smoking, and exposure to other 

risk factors (such as non-Libby asbestos).
5-2 

Measurement of exposure Exposure estimates should supported by appropriate measurement techniques. 

Exposure estimates supported by methods that measure fiber exposures for Libby 

Amphibole asbestos; fiber concentration data based upon phase contrast microscopy 

(PCM). Stronger studies will often be based upon knowledge of individual work 

histories (job titles/tasks with consideration of changes over time), however 

appropriate group based exposure estimates may also be relevant. 

Measurement of effect(s) Emphasis is placed on the more sensitive health outcome endpoints that are available 

(e.g, generally morbidity findings will be more sensitive than mortality findings). An 

RfC is intended to be a level at which no category of adverse health outcome would 

occur.

Pleural and parenchymal changes assessed using good quality radiographs, evaluated 

by ≥1 certified B reader according to International Labour Organization (ILO) 

standards. 

Evaluation of radiographs should not be influenced by knowledge of exposure status 

Quantitatively, study characteristics preferred for RfC derivation include adequate 

exposure-response information, ideally with analyses based on estimates including assignment of 

quantitative exposure estimates to distinguish exposure levels in the study subjects. The 

evidence for effects on the lung and pleura in humans includes both cohort studies of exposed 

workers as well as population studies drawn from the Libby community (including former 

workers). Pleural and lung anomalies have been reported in population studies of individuals 

exposed in and around Libby, MT (Weill et al., 2010; Peipins et al., 2003; Peipins et al., 2004; 

Whitehouse 2004; Muravov et al., 2005). However, these morbidity studies based on 

community/worker populations were not considered as candidate principal studies because 

quantitative exposure estimates are not available and the studies cannot support derivation of an 

RfC. Increased respiratory symptoms and bloody phlegm have also been associated with the 

number of potential pathways of exposure to Libby Amphibole asbestos in the community 

among individuals under 18 years of age (Vinikoor et al., 2010). Additionally, two studies 

examined autoimmune effects in community members living in Libby, MT; Noonan et al. (2006) 

included former vermiculite facility workers, while Pfau et al. (2005) do not specify if former 

workers were included. These studies (Vinikoor et al. 2010; Noonan et al. 2006; Pfau et al. 

2005) do not quantify exposure, and therefore cannot be used for RfC derivation. 

Five cohort mortality studies of Libby miners identified increased risk of mortality from 

non-cancer causes (McDonald et al., 1986a; Amandus et al., 1987b; McDonald et al., 2004; 

Sullivan, 2007; Larson et al., 2010a). These studies were not considered because mortality is not 

a preferred endpoint for deriving a reference value. Studies with more sensitive endpoints (i.e. 

morbidity studies) are preferred to mortality studies. Several morbidity studies examined the 

quantitative association between exposure to Libby Amphibole asbestos and lesions in the lung 

or surrounding pleura in exposed human populations; two are studies in Libby miners (Amandus 
5-3 

et al., 1987b; McDonald et al., 1986b) and two are studies in workers from the Marysville 

facility (Lockey et al., 1984; Rohs et al., 2008). Rohs et al. (2008) was a follow-up study to 

Lockey et al. (1984) in of the same cohort and reported a higher prevalence of adverse effects 

following the longer time from first exposure. These four studies, all of which demonstrate an 

association between Libby Amphibole asbestos exposure and increased risk of effects on the 

lung and pleura were considered for selection as the principal study to serve as the basis for the 

derivation of the RfC. 

All four candidate principal studies (Rohs et al., 2008; Amandus et al., 1987b; McDonald 

et al., 1986b; Lockey et al., 1984) have adequate reporting of the studied populations, methods of 

analysis, statistical analyses, and results. Each of the four candidate studies have shown nonmalignant respiratory effects, specifically pleural thickening (localized and/or diffuse) and 

asbestosis (parenchymal changes and pulmonary fibrosis) in exposed individuals based on 

guidelines from the International Labour Organization (ILO) for classification of radiographs 

(ILO, 1971; ILO, 1980; ILO, 2000). Characteristics of these four studies are summarized in 

Table 5-2. See Sections 4.1.2.2 and 4.1.3 for detailed information for each study. 

These candidate studies were then evaluated in terms of quality attributes that would 

support their use as a principal study in derivation of an RfC. In selecting among potential 

candidate principal studies, there were several factors, summarized in Table 5-1, that were 

considered. 
5-4 

Table 5-2. Summary of candidate principal studies on Libby Amphibole asbestos for RfC derivation. 

Cohort and 

Reference Study Population Outcome assessment Radiographic endpoints evaluated Exposure assessment E

Libby Worker Cohort McDonald et al., 1986b

244 employees, comprising 164 “current” workers (as of July 1, 1983) and 80 “past” workersAge at exam (years): “current” “past”<39 80 140-59 69 30>60 15 49No job tenure information; (10.7 as reported by Armstrong et al., 1988)

Radiographs taken at time of cohort assembly (1983)Films independently read by 3 experienced readers using 1980 ILO standardsFilm quality:Good: 56%Fair: 36%Poor: 7% Unreadable: 0.4% 

1) Parenchymal changes (small opacities ≥ 1/0)2) Pleural changes (Pleural thickening of chest wall, Pleural calcification)

Individual work histories and exposure levels for specific work locations were used to estimate cumulative exposures for cohort members.1935-1967: Exposure estimated based on professional judgment. For mill locations only (1950-1967) exposure estimated using dust to fiber conversion and interviews with plant employees1968-1982: Air samples analyzed for fibers by PCM analysis 

M““pE <121>

Amandus et al., 1987b

184 men employed 1975-1982, with at least 5 years job tenureMean (SD), years:Age at exam: 44 (12) Job tenure: 14 (8) 

Company radiographs Source year:1981-82 (72.8%)

1976-1980 (26.6%)<1975 (1 worker)

Films independently read by 3 certified B-readers using 1980 ILO standards Film quality (by reader):

Acceptable: 60.9, 60.9, 29.3%Poor: 16.3, 14.7, 22.8%

Unreadable: None

1) Parenchymal changes (small opacities ≥ 1/0)

2) Pleural changes (Pleural thickening at any site withoug costophrenic angle obliteration, pleural calcification, pleural thickening of chest wall only)

Individual work histories and exposure levels for specific work locations were used to estimate cumulative exposures for cohort members.1935-1967: Exposure estimated based on professional judgment. For mill locations only (1950-1967), exposure estimated using dust to fiber conversion and interviews with plant employees.

E 013>
5-5 

1968-1982: Air samples analyzed for fibers by PCM analysis 

OM Scott plant Cohort, Marysville, OH*  Lockey et al., 1984

512 plant employees Mean (range), years:

Age at exam: 37.5 (19-66) 

Mean (SE), years:Job tenure by exposure group and smoking status (NS=non-smoker, EX=former smoker, CS=current smoker)

Low, NS: 6.6 (1.1)Low, EX: 11.3 (1.6)Low, CS: 10.5 (1.2)

Medium, NS: 8.4 (1.0) 

Medium, EX: 13.3 (8.9) 

Medium, CS: 8.9 (0.7)High, NS: 12.2 (0.9)High, EX:13.0 (1.1)

High, CS: 10.7 (0.9)

Posterior-anterior chest radiographs taken in 1980Films independently read by 2 certified B-readers using modification of 1971 ILO standards

1) Parenchymal changes (only one small opacity recorded (grade 1/1), unclear if opacities graded 1/0 or 0/1 would have been reported)2) Pleural changes (Discrete pleural plaque, Pleural thickening, Pleural calcification)3) costophrenic angle blunting only

Individual work histories and exposure levels for specific work locations were used to estimate cumulative exposures for cohort members.1957-1971: Exposure estimated based on interviews with plant employees and post-1972 air measurements. Some workplace exposure control measures were taken prior to 1972. 1972-1980: Air samples analyzed for fibers by PCM analysis. Some jobs do not have measurements associated with them especiallyprior to industrial hygiene measures.

E 11

Rohs et al., 2008 280 plant employees 

(follow-up of cohort described in Lockey et al., 1984)Mean (SD), range (years):

Age: 59.1 (10.5), 44-87Mean (SD), median (years):Years since first exposure

No pleural changes (n=200): 32.1 (5.5), 31.0

Posterior-anterior chest radiographs taken 2002- 2005Films independently read by 3 certified B-readers using 2000 ILO standards7 employees had unreadable films and are not included in the 

1) Parenchymal changes (small opacities, profusion score >1/0)2) Pleural changes (Localized pleural thickening {thickening excluding costophrenic angle blunting}, Diffuse pleural thickening {thickening with costophrenic angle 

Exposure assessment from Lockey et al. (1984) with change in start date to 1963.

E 0001
5-6 

Pleural changes present (n=80): 36.8 (4.9), 37.9

cohort of 280 participants

blunting}, Pleural calcification)

*In addition to the exposure information used by Lockey et al. (1984) and Rohs et al. (2008), the University of Cincinnati augmented aestimates using additional exposure data, which included industrial hygiene measurements not previously available and estimates of ex
5-7 

The two radiographic studies of workers from Libby, MT (Amandus et al., 1987b; 

McDonald et al., 1986b) were conducted by different research groups, and focused on different 

subsets of the cohort of vermiculite facility workers. Since both groups utilized the same 

exposure measurements and employment records, similar but not identical job exposure matrices 

(JEMs) and subsequent exposure estimates, were developed. For example, different numbers of 

occupation locations were used, and different approaches for estimating exposure where more 

than one sample was available for a given location, task or time period. McDonald et al. (1986b) 

examined radiographic changes among men and women employed by the company at the time of 

the study, and among former male employees still residing in the area. Logistic regression 

analysis was used to examine the relationship between cumulative Libby Amphibole asbestos 

exposure and radiographic abnormalities, controlling for age and smoking. Positive exposure 

response relationships were found with cumulative exposure for asbestosis and pleural 

anomalies. The study by Amandus et al. (1987b) was conducted in the same base population, 

but consisted of current employees only. In contrast to McDonald et al. (1986b), where the xrays used were performed specifically for the study, Amandus et al. (1987) used company 

radiographs. Among the 184 participants, prevalence of radiographic abnormalities was 4% for 

pleural calcification, 10% for small opacities and 13% for pleural thickening. Logistic 

regression analysis was used to examine the relationship between cumulative Libby Amphibole 

asbestos exposure and radiographic abnormalities, controlling for age; a separate model was 

constructed restricting to current and former smokers for comparison. A positive exposure 

response relationship was found for cumulative exposure and asbestosis. Multivariate analyses 

did not find a significant relationship between cumulative exposure and pleural anomalies. 

The two remaining candidate studies are in workers from the Marysville plant; the study 

by Rohs et al. (2008) is a follow-up of the earlier study by Lockey et al. (1984). As with the 

previously described studies, there was variability in the quality of exposure assessment over 

time for the cohort (Rohs et al., 2008; Lockey, 1985; Lockey et al., 1984). The first 

measurements of in-plant exposure began in 1972, and earlier exposures were estimated 

assuming the mean fiber value for a given department, for the years in which fiber measurements 

became available, as late as 1980 for some tasks (Lockey, 1980). To examine the association 

between cumulative Libby Amphibole asbestos exposure and radiographic abnormalities, each 

participant with radiographic abnormality was pair-matched by age with a participant without 

radiographic abnormalities. The follow-up study by Rohs et al. (2008) examined radiographic 

abnormality in a subset of the cohort defined by Lockey et al. (1984). Data collection took place 

in 2002-2005. Some of the original participants were deceased (16%), while others were not 

included due to refusal (12.8%), non-response (16.2%), or because they were not located (1.9%). 

Participants provided a new chest radiograph, which was read according to 2000 ILO standards. 
5-8 

The association between quartiles of cumulative Libby Amphibole asbestos exposure and pleural 

changes was assessed using logistic regression; several covariates were investigated, including 

age at exam, smoking history, body mass index (BMI), sex and year of hire (pre-1973 versus 

1973 and later). 

5.2.1.1. Evaluation of Exposure in Candidate Studies 

Each of the studies provided estimates of cumulative Libby Amphibole asbestos exposure 

(in fibers-yr/cc), rather than mean or peak exposure. However, there were differences in 

exposure intensity. In contrast to vermiculite facility workers in Libby, MT, the workers at the 

OM Scott Plant in Marysville, OH, were generally exposed at lower levels (see Table 5-2), and 

were primarily exposed in the workplace. Because of showering and changing into civilian 

clothes at the end of the work shift for most employees, non-occupational exposure in the 

Marysville workers was minimal. Despite the uncertainty in the magnitude of pre-1972 

exposures (discussed below), the available data indicate worker exposures in the Marysville 

plant did not generally include the high intensity exposures observed for the Libby worker 

cohort, with Rohs et al. (2008) reporting a mean exposure of 2.48 fiber/cc-year. The lower 

intensity exposures for the Marysville cohort, and corresponding lower cumulative exposures, 

are an advantage of this study considering there are uncertainties inherent in exposure-response 

data and extrapolating from the high intensity occupation exposures to lower level exposures 

often seen in community and environmental exposures. 

5.2.1.2. Evaluation of Study Populations in Candidate Studies 

The candidate principal studies differed in the study populations, in terms of follow-up 

time, study participation, and available information. In comparing the studies of Libby miners, 

an advantage of the McDonald et al. (1986b) study is the inclusion of both current and former 

workers—these former workers likely have longer time since first exposure and may have higher 

exposure intensities and durations compared to current workers. However, there is a potential 

for differential participation. All current employees were included in the McDonald et al. 

(1986b) study, while 80 of 110 eligible former employees agreed to provide chest radiographs. 

Although exact participation rates are not given, Amandus et al. (1987b) report that radiographs 

were unavailable for >50% of workers who were employed <5 years, or who ceased employment 

prior to 1975. Among Marysville workers, there were very few employees who declined to 

participate in the earlier study by Lockey et al. (1984), where 512 out of 530 employees were 

included, but there is potential for selection bias in the follow-up by Rohs et al. (2008), where 

only 280 employees out of the original cohort were evaluated. If non-participants from either 

cohort had different incidence rates compared to participants, the prevalence of radiographic 
5-9 

changes may be under- or overestimated. Rohs et al. (2008) state that employees hired in 1973 

or earlier (when exposure estimates were more uncertain) were more likely to participate 

compared to employees hired after 1973, and while the range of cumulative Libby Amphibole 

asbestos exposure was similar between participants and non-participants, participants did have 

higher mean cumulative exposure estimates. 

The studies by McDonald et al. (1986b) and Rohs et al. (2008) had the longest follow-up 

times—the first included former workers as well as current workers, while the second was a 

follow-up conducted 24 years after the original study by Lockey et al. (1984). In terms of 

information available for statistical analysis, both studies of Libby miners and the follow-up 

study by Rohs et al. (2008), used age rather than time since first exposure (TSFE) in logistic 

regression models. Lockey et al. (1984) matched cases and non-cases on age. TSFE is 

preferable, since it gives more information about latency between exposure and outcome 

development; while age and TSFE are related, they are not perfectly correlated, in that there is a 

range of ages for a given date of hire. Both studies of Libby miners also evaluated smoking as a 

potential confounder of the association between Libby Amphibole asbestos exposure and 

radiographic changes. Lockey et al. (1984) only matched on age, but the follow-up examination 

by Rohs et al. (2008) included information on several important covariates including gender, 

prior exposure to asbestos, body mass index (BMI) and smoking history. 

5.2.1.3. Evaluation of Exposure Assessment in Candidate Studies 

The two studies in Libby miners (McDonald et al., 1986b; Amandus et al., 1987b) used 

similar exposure estimation, based on the same fiber measurements and work records. However, 

there is considerable uncertainty in exposure estimates in earlier years. Exposure during this 

time was estimated using dust to fiber conversion and information from employee interviews. 

McDonald et al. (1986b) used a factor of 4.6 for dust to fiber conversion, while Amandus et al. 

(1987a) used a factor of 4. However, Table V in Amandus et al. (1987a) showed that different 

comparisons of dust versus fiber levels in the Libby workplace showed potential values of that 

factor in the range of 1.2 to 11.5. Additionally, only duration of employment—not job 

category—was known for 69% of the whole cohort of vermiculite facility workers hired from 

1935 to 1959. Without knowledge of the job category, the same exposure concentration was 

assigned to most of these workers, resulting in particularly large measurement error for workers 

hired prior to 1960. Another source of uncertainty in exposure estimates for this cohort is 

possible community/non-occupational exposures. Members of the Libby worker cohort may 

have lived in Libby prior to/after employment, and resided in Libby and surrounding areas 

during employment. In both cases there may have been community exposures to Libby 

Amphibole asbestos which are not captured in occupational-based cumulative exposure metrics. 
5-10 

This is not likely to be a major source of concern for the workers in the study since estimated 

occupational exposures were at high levels, but is a source of uncertainty in estimating the 

exposure-response relationship. 

Exposure assessment also changed over time in the Marysville cohort (Lockey, 1985; 

Rohs et al., 2008). Fiber concentration measurements were not available prior to 1972, and since 

industrial hygiene measurements were implemented in the Marysville plant in the 1970’s, 

exposure levels during prior years are unknown. EPA collaborated with a research team at the 

University of Cincinnati to update the exposure reconstruction for all workers who participated 

in the Lockey et al. (1984) and Rohs et al. (2008) studies, taking into account additional 

industrial hygiene data that were not previously available. As discussed in detail in Appendix F, 

exposure estimates for each worker in the Marysville plant were developed based on available 

industrial hygiene data from the plant. Figure 5-1 shows the measured or estimated average 

concentration of Libby Amphibole (PCM f/cc) in air of each department from 1957 to 2000. 

FIGURE 5-1. Estimated and measured exposure concentrations in Marysville facility

Trionizing is a term used in the Marysville facility and includes unloading of rail cars containing vermiculite ore 

(track), using conveyers to move the vermiculite ore into the expander furnaces, separation of the expanded 

vermiculite from sand, blending in of lawn care chemicals, and drying and packaging of the final product. 

Using available data on the year of hire and the departments in which each person 

worked, the authors estimated the cumulative exposure (CE, f-yrs/cc) for each worker for each 

year since the date of hire. Information obtained and recorded for each worker included: 

• Gender 

• Body mass index (2000s only) 

• Smoking history 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Concentration (PCM f/cc)

Trionizing

Plant Maintanance

Central Maintainance

Bkg

Estimated Measured
5-11 

• Date and age at time of health examination (1980 and/or 2000s) 

• Occurrence of adverse effects at the time of examination, including presence or absence 

of a) localized pleural thickening, b) diffuse pleural thickening, c) parenchymal change. 

Pleural and parenchymal changes were detected by conventional radiographs 

• Date of start and end of employment 

• Exposure to Libby Amphibole asbestos in the Marysville facility by year 

• Presence or absence of known exposure to asbestos at other locations 

The updated exposure reconstruction utilized 914 industrial hygiene measurements for 

fibers, which represented a substantial increase from the 180 industrial hygiene measurements 

for fibers available in Lockey et al. (1984) and Rohs et al. (2008). Additionally, the 

reconstruction included exposure after 1980, as the additional industrial hygiene data showed 

that fibers were still present in the facility after 1980 in contrast to the previous assumption of 

zero fibers present after 1980 (Lockey et al., 1984; Rohs et al, 2008), and assigned the beginning 

of exposure to Libby Amphibole asbestos as 1959 in contrast to 1957 (Lockey et al., 1984) or 

1963 (Rohs et al., 2008). The assignment of the start of exposure was based on interviews with 

plant personnel. The exposure reconstruction procedure took into account each worker’s job 

history, including extensive overtime for some workers, and is described in detail in Appendix F. 

5.2.1.4. Evaluation of Outcome Assessment in Candidate Studies 

In all four candidate studies, outcomes were assessed using chest radiographs 

independently evaluated by ≥1 reader. However, there were differences in the standards used for 

evaluation of radiographic changes, as well as timing and quality of the radiographs. The two 

studies in Libby miners (McDonald et al., 1986b; Amandus et al., 1987b) used similar outcome 

assessment procedures, with radiographs evaluated by 3 readers according to 1980 ILO 

standards. Two different sets of standards were used to evaluate radiographs in the Marysville 

cohort. The first study used modified 1971 ILO standards (modifications not stipulated) (Lockey 

et al., 1984), while the follow-up study used the updated 2000 ILO standards (Rohs et al., 2008). 

Radiograph quality may also impact outcome assessment. In McDonald et al. (1986b), 

which used radiographs taken in 1983 specifically for the study, 7% of films were classed as 

poor quality (some technical defect impairing the pneumoconiosis classification) and 0.4% as 

unreadable. Amandus et al. (1987b), which used company radiographs taken over a wide time 

period (<1975 to 1982) report that the proportion of films rated as poor ranged from 14.7% to 

22.8% depending on the reader. In the Marysville cohort, radiographic quality was better—

Lockey et al. (1984) state that “radiographs that could not be interpreted because of poor quality 
5-12 

were repeated” while Rohs et al. (2008) note that only 7 out of 298 radiographs taken were 

considered unreadable. 

5.2.1.5. Selection of Principal Study 

Of the four candidate principal studies, the two studies of pleural and parenchymal 

changes among Libby miners (Amandus et al., 1987b; McDonald et al., 1986b) were considered, 

but compared to the studies among Marysville workers (Rohs et al., 2008; Lockey et al., 1984), 

were not preferred due to the higher levels of exposure (particularly for earlier years, when only 

dust level measurements were available and when exposure predictions have greater 

uncertainty), and limitations in the quality of the radiographs used for outcome assessment. 

The Marysville cohort (as defined in Rohs et al., 2008 and Lockey et al., 1984) was 

selected as the principal study for the derivation of the RfC based on increased risk of pleural 

and parenchymal effects, at lower cumulative exposure levels (compared to the Libby worker 

cohort), the examination of longer time from first exposure (considering time from first exposure 

is an important factor in the development of the health effect), information on more covariates, 

use of more recent 2000 ILO diagnostic criteria, and the availability of detailed exposure and 

health outcome information for each worker. 

5.2.1.6. Selection of Critical Effect 

The pleural and parenchymal effects observed in exposed individuals in the Marysville 

cohort (Rohs et al., 2008; Lockey et al., 1984) included increased prevalence of pleural 

thickening (characterized as either discrete pleural thickening or diffuse pleural thickening) and 

parenchymal changes (small opacities). These effects were determined using conventional 

radiographs (Rohs et al., 2008). In clinical settings there are difficulties in distinguishing 

between these two lesions in part due to lack of radiographic film diagnostic specificity as well 

as the ILO definitions for diffuse pleural thickening of the visceral pleura which may result in 

some variability in reporting of discrete pleural plaques. There are two classifications for pleural 

thickening in current guidelines (ILO, 2000). Diffuse pleural thickening is currently only 

determined where there is thickening along significant extent of the chest wall, and this 

thickening is “in the presence of and in continuity with, an obliterated costophrenic angle” (ILO, 

2000). Although previous classification schemes attempted to distinguish between thickening of 

the parietal pleura and diffuse thickening of the visceral pleura, the ILO states” reading standard 

x-rays cannot always distinguish between thickening of the visceral or parietal pleura” (ILO, 

2000). Thus, all other pleural thickening is now defined as localized pleural thickening (i.e. 

where there is no costophrenic angle obliteration). Localized pleural thickening on the chest 

wall, and diffuse pleural thickening may be graded indicating thickness, and the extent of the 
5-13 

chest wall impacted, however these data are not always provided in study reports, therefore 

regardless of extent of the pleural thickening, only presence or absence may be noted. 

The radiographic changes occur in different anatomical locations. Localized pleural 

thickening may include plaques in the parietal pleura, or thickening of the visceral pleura, where 

this thickening does not impact the costophrenic angle (angle between the diaphragm and chest 

wall as viewed on a radiograph). Diffuse pleural thickening is designated when thickening of the 

visceral pleura extends to blunt/obliterate the costophrenic angle. Pleural thickening does not 

progress to parenchymal changes. The data on the radiographic changes are presented in Table 

5-3, Figure 5-2 and Figure 5-3. For the purposes of illustration, the data are presented in groups 

of 50 individuals each, representing approximate quintiles after exclusion of workers with other 

previous asbestos exposure. Figures 5-2 and 5-3 present the cumulative prevalence of 

radiographic changes; Figure 5-2 shows the mean cumulative human equivalent exposure 

expressed on a normal scale, while in Figure 5-3 exposure is expressed on a log scale to provide 

a better visual image at the low end of the distribution. 

Table 5-3. Mean cumulative human equivalent exposures and prevalence of health 

outcomes found in Marysville workers (Rohs et al., 2008) 

Mean Cumulative Human 

Equivalent Exposure, fibersyr/cc (range) 

Localized 

Pleural 

Thickening 

Diffuse Pleural 

Thickening 

Parenchymal 

Change 

0.061 

(0.001-0.121) 

3/50 0/50 0/50 

0.156 

(0.123-0.197) 

8/50 0/50 0/50 

0.282 

(0.198-0.420) 

12/50 2/50 1/51 

1.037 

(0.421-2.460) 

17/50 1/50 1/50 

13.140 

(2.475-34.165) 

19/52* 7/52 5/52 

Total 59/252 10/252 7/252 

* Includes 3 workers with both discrete pleural and parenchymal changes (small opacities viewed on the

radiograph). 
5-14 

Figure 5-2. Prevalence of radiographic change versus mean cumulative human equivalent 

exposure 

Figure 5-3. Prevalence of radiographic change versus mean cumulative human equivalent 

exposure 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 2 4 6 8 10 12 14

Prevalence

Mean Cumulative Human Equivalent Exposure (fibers-yr/cc)

Discrete Pleural 

Diffuse Pleural

Parencyhmal

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.01 0.1 1 10 100

Prevalence

Log Mean Cumulative Human Equivalent Exposure (fibersyr/cc)

Discrete Pleural 

Diffuse Pleural

Parencyhmal
5-15 

EPA selected localized pleural thickening (characterized by Lockey et al., 1984 and Rohs 

et al., 2008 as discrete pleural thickening) as the critical effect due to its higher prevalence 

relative to the other outcomes, minimal adversity (compared with other effects), and specificity 

for durable mineral fiber exposure. EPA considers localized pleural thickening to be an adverse 

effect and is attributed to exposure to Libby Amphibole asbestos. 

Pleural thickening in general is associated with decreased pulmonary function (Miller et 

al., 1994, Wang et al., 2001 and Petrovic et al., 2004) and this association is strengthened as the 

severity of the pleural thickening increases (Lilis et al., 1991) (see Section 4.5). Thickening of 

the parietal pleura (all sites) is an accepted marker of mineral fiber exposures, and the most 

sensitive of the endpoints detected by standard radiographs, both in terms of exposure levels and 

time. The available information is largely in agreement that where multiple and bilateral parietal 

plaques are observed that this is a distinctive marker of exposure to asbestos and other durable 

mineral fibers (e.g., erionite) (ATS 2004, Broaddus et al., 2011). Therefore, localized pleural 

thickening is a sensitive and fairly specific biological effect of mineral fiber exposure. 

The radiographic classification of localized pleural thickening may include both parietal 

plaques and diffuse visceral thickening (without costophrenic angle involvement) (ILO, 2000). 

These lesions are distinct and may contribute independently to observed health effects. Parietal 

plaques are known to induce chronic constricting chest pain which increases in severity as the 

extent of the plaques increases. Pleural thickening in general is associated with reduced lung 

function parameters with increased effect correlating with increased severity of the pleural 

thickening (Lilis et al., 1991; Miller et al., 1994; Wang et al., 2001; Petrovic et al., 2004). 

Specifically, lung function impairment has been demonstrated in several studies where pleural 

thickening without costophrenic angle involvement has been studied (Broderick et al., 1992; 

Kilburn and Warshaw, 1991; Garcia-Closas et al., 1995). Although there is some evidence from 

HRTC studies that parietal plaques alone may not directly impair lung function, the studies were 

small, and may not have included severe plaques. There is clear evidence from HRTC studies 

that the presence and extent of visceral thickening does impair lung function (Swartz et al., 1993; 

Copley et al., 2001). Thus the radiographic classification of localized pleural thickening (ILO, 

2000) defines radiographic anomalies which are associated with chronic chest pain, decreased 

lung volume and decreased measures of lung function. 
5-16 

Figure 5-4. HRCT Scan of an individual with localized pleural thickening (arrows), 

Source: Dr. Brad Black, Card Clinic, Libby, MT 

5.2.2. Methods of Analysis 

5.2.2.1. Exposure data and choice of exposure metric 

In brief, the starting point for exposure reconstruction was the measured or estimated 

concentration of Libby Amphibole asbestos in air (fibers/cc) of each department from 1957-

2000. The distribution of exposure by department is summarized in Figure 5-1. Using available 

data on the year of hire and the departments in which each person worked, the cumulative 

exposure (fibers-yrs/cc) for each worker for each year since the date of hire was estimated. Each 

worker’s cumulative exposure was then adjusted to a cumulative human equivalent exposure for 

continuous exposure (CHEEC; fibers-yr/cc) to represent exposure 24 hours/day and 365 

days/year (assuming that any exposure off site was zero). Adjustments for different inhalation 

rates in working versus nonworking time periods were incorporated in this analysis. These 

calculations are somewhat more involved than the usual conversions to equivalent continuous 

exposure concentrations that EPA makes in the analysis of occupational studies. Conversions for 

non-cancer effects are usually made using an adjustment factor of 240 days/365 days x 10 m3

/20 
5-17 

m

3

 (U.S. EPA 1994). However, the adjustment factor in this assessment takes into account the 

extensive seasonal overtime for some job classes at the Marysville facility, as well as other 

annual periods when work hours were reduced (Appendix F). The estimated CHEEC was used 

to represent Libby Amphibole asbestos exposure in all subsequent analyses, as it combines 

aspects of both intensity of exposure and duration of exposure. (Note: the University of 

Cincinnati coined the term CHEEC, in its report. However, the calculated value is similar to 

what EPA usually calls continuous human equivalent exposure (HEC, U.S. EPA, 1994b). For 

Libby Amphibole asbestos the exposure metric is calculated as cumulative exposure (fibersyr/cc). 

It is anticipated that there is some biological latency period between exposure to Libby 

Amphibole asbestos and development of localized pleural thickening—that is, exposure in the 

most recent past may not contribute to thickening. Because localized pleural thickening does not 

generally occur immediately after exposure, and requires some time to develop to the state that it 

can be detected on a conventional chest x-ray, exposures that occur close to the time of x-ray 

may not contribute to the occurrence of observable disease, and may tend to obscure the 

exposure-response relationship. Accordingly, a lagged exposure (i.e. cumulative exposure 

discounting the most recent time period) may be the most appropriate measure to use. 

Therefore, exposure estimates with various lags were investigated (lags of 0, 5, 10, 15 and 20 

years). For example, a CE value based on lag of 5 years excludes all exposures that occurred 

within 5 years of the date of x-ray. The values are designated as CE0, CE5, CE10, CE15, and 

CE20. Looking at prevalence of the outcome for various categories of time elapsed since first 

exposure the first localized pleural thickening was detected ~10 years after the first exposure. 

5.2.2.2. Datasets for Modeling Analyses 

The individual health outcome data for all workers who participated in the Lockey et al. 

(1984) and the follow-up by Rohs et al. (2008) studies was used for exposure-response modeling. 

To avoid any bias from previous occupational exposure to asbestos, only the data from those 

who did not report any previous occupational exposure to asbestos were used. The data from 

Lockey et al. (1984) and Rohs et al. (2008) were combined for the full cohort to provide a greater 

range in time from first exposure. Outcome assessments, i.e. chest x-rays, were performed at 

two different time points, 1980 and 2002-2005. While the evaluation approaches were generally 

similar (independent readings by 3 certified B-readers), it is important to note that x-ray readings 

were performed by different individuals, under a different reading protocol in 1980 (modified 

1971 ILO standards) compared to 2000s (2000 ILO standards), leading to some uncertainty in 

statistical analyses that combine these datasets. An additional consideration is body 

composition—in some cases, difficulty in distinguishing fat pads from true pleural thickening 
5-18 

may lead to misclassification of the outcome. BMI measurements are available for the latter 

study but not for the 1980 evaluation; the effect of BMI was investigated and is discussed below.

The consensus read of the x-rays from three B-readers was used in both studies. Because 

the ILO criteria were updated in 2000, the reader forms from Lockey et al. (1984) showing 

pleural changes were evaluated for consistency with the ILO 2000 criteria. No change in 

diagnosis was found. In addition, no change in x-ray or film quality was found that would 

reduce confidence in the Lockey et al. (1984) data.

The full data set of the exposure-response relationship for localized pleural thickening 

was as follows: 

1. The data from Lockey et al., (1984), n = 513 and Rohs et al. (2008), n = 280 were 

combined, n = 793 

2. All workers who reported exposure to asbestos at other locations were excluded (n = 793 

– 105 = 688). 

3. For workers who were x-rayed in both Lockey et al. (1984) and Rohs et al. (2008), one of 

the observations was excluded so that there were no repeat observations for individual 

workers. For workers who were negative for localized pleural thickening in Lockey et 

al., the 1984 study data were excluded and the Rohs et al. (2008) data were retained. For 

workers who were positive for localized pleural thickening in Lockey et al. and also in 

Rohs et al., the 1984 study data were retained. One worker was positive in 1984 and 

negative in 2008. The 2008 study data were retained for this worker. (n = 688 – 252 = 

436). 

4. Two workers from Lockey et al. (1984) were excluded because the start day and the x-ray 

date were the same. (n = 436 – 2 = 434). 

The Marysville cohort data comprise 434 workers who were not previously exposed to 

asbestos and had at least one valid x-ray observation. Because the concentration of Libby 

Amphibole in workplace air was estimated rather than measured for all years prior to 1972, this 

data set was stratified into two sub-sets: 1) workers hired in 1972 or after (for whom all 

exposure values are measured), and 2) workers hired before 1972 (for whom some of the 

exposure values are estimated). Distribution of cases and time since first exposure (T) at each 

outcome assessment are shown in Table 5-4. Due to the differences in exposure assessment 

(estimated prior to 1972, measured thereafter), the cohort is stratified by date of first exposure. 
5-19 

TABLE 5-4. DISTRIBUTION OF CASES AND TIME FROM FIRST EXPOSURE (T) 

FOR MARYSVILLE WORKERS 

 All participants* First exposed before 1972 First exposed 1972 or later 

Cases/Total Range of T Cases/Total Range of T Cases/Total Range of T

Examined 

1980 

(Lockey 

et al., 

1984) 

5/434 0.42-23.43 4/236 8.76-23.43 1/198 0.42-8.42

Examined 

2002-2005 

(Rohs et 

al., 2008) 

59/252 23.15-47.38 47/133 31.09-47.38 12/119 23.15-32.65

*The 252 individuals examined in 2002-2005 were also examined in 1980. Note that there were originally 513 

individuals in the Lockey et al. (1984) cohort; of these, 77 had previous asbestos exposure and were excluded 

(n=436). Two individuals were excluded because their x-ray date was the same as their employment start date 

(n=434). These exclusions are also reflected in the Roh et al. (2008) cohort. 

Source: Rohs et al. (2008) and Lockey et al. (1984)

The best measured exposure data are considered to be those from 1972 and later. Due to 

the longer follow-up time and additional covariate information, the most informative outcome 

data come from the 2002-2005 examination. Based on these considerations, the sub-cohort 

which includes data from workers from in the 2002-2005 examination, and who began work in 

1972 or later (12/119 cases) (Rohs et al., 2008), was chosen as the preferred analysis to develop a 

point of departure (POD) for localized pleural thickening to serve as the basis for the RfC. 

Additionally, sample POD estimates based on statistical analyses of results from the full cohort 

(Lockey et al., 1984 and Rohs et al., 2008 combined) were included for comparison. 

5.2.2.3. Statistical Modeling of the Sub-cohort 

EPA performed analyses of study results for the sub-cohort whose exposures began on or 

after 1/1/1972 when workplace PCM measurements were available, reducing uncertainties 

associated with exposure assessment. The most sensitive endpoint for exposure to Libby 

Amphibole asbestos was identified as localized pleural thickening. Epidemiologic methods were 

used to analyze the exposure-response data and benchmark dose (BMD) methodology was used 

to estimate PODs. In this approach, the available data are fit to a set of mathematical exposureresponse models to determine an appropriate empirical representation of the data. A 

recommended model form is then determined; commonly this is the model with the best fit as 
5-20 

measured by Akaike’s Information Criterion (AIC) value among these model forms judged to 

provide an appropriate and statistically adequate representation of the data. For inhalation data 

the benchmark concentration (BMC) is defined as the exposure level, calculated from the best-fit 

model, which results in a specified benchmark response (BMR). The RfC is derived from the 

lower 95% confidence limit of the BMC, referred to as the BMCL, which accounts for statistical 

uncertainty in the model fit to the data. All analyses were performed using SAS® statistical 

software v. 9.1. BMCLs were obtained by the profile likelihood method as recommended by 

Crump and Howe (1985) using the NLMIXED procedure in SAS (Wheeler, 2005) (See 

Appendix E for details). 

For models where a background parameter is included, a 1% risk of localized pleural 

thickening was assumed. Previous studies have reported a range of estimates of the prevalence 

of localized pleural thickening. In two studies of persons not previously exposed to asbestos, 

Anderson et al (1979) and Castellan et al (1985) report estimated prevalence estimates of 1.2% 

(4/326) and 0.2% (3/1422), respectively. In cross-sectional studies, which may include persons 

with occupational exposure to asbestos, Rogan reported localized pleural thickening prevalence 

estimates of 1.2% in the NHANES I (1971-1975; Rogan, 1987) and 3.9% in the NHANES II 

(Rogan, 2000). Among military populations, two studies have reported an estimated prevalence 

of 2.3% (Miller, 1996; Bohnker, 2005). Based on these reports, the 1% background rate was 

chosen as representing the prevalence among persons without occupational exposure to asbestos. 

EPA selected a BMR of 10% extra risk for the prevalence of localized pleural thickening. 

In the absence of information to indicate otherwise, this response level was considered 

appropriate for derivation of the RfC under the assumption that this level of localized pleural 

thickening is minimally adverse (U.S. EPA, 2000). In this particular case, only workers who did 

not report any previous occupational exposure to asbestos were included in the analyses and the 

study by Rohs et al. (2008) can readily detect a 10% increase in prevalence of localized pleural 

thickening, a highly specific response to exposure to asbestos and other mineral fibers. 

 For the Marysville cohort, however, individual exposure and health outcome results are 

available and a more comprehensive analysis is possible. This type of analysis has more 

statistical power since it benefits from individual information on each study subject and allows 

for consideration of potential confounders. 

5.2.2.3.1. Statistical model evaluation and selection 

Dichotomous statistical models describing the probability of individual response as a 

function of cumulative exposure (represented by CHEEC in units of fibers-yr/cc) were used. In 

order to investigate the key explanatory variables for analysis, a forward selection process was 

used to evaluate the association of each of the potential covariates with risk of localized pleural 
5-21 

thickening, controlling for Libby Amphibole asbestos exposure. Covariates considered for 

inclusion in the model were: time since first exposure (T), age at x-ray, gender, smoking history 

and BMI. This initial modeling was done using a standard logistic regression model, as is 

commonly applied in analysis of epidemiological data. The base model was a logistic regression 

model with cumulative Libby Amphibole asbestos exposure (natural log transformed) as the 

independent variable. This model provided an adequate fit to the data (Hosmer-Lemeshow pvalue of 0.7073), and the exposure variable was statistically significantly associated with the 

outcome (beta [SE] = 0.5398 [0.2374] increase in log odds for every unit increase in CHEEC, pvalue=0.0230). Covariates were evaluated according to whether inclusion of the covariate 

improved model fit as assessed by the AIC, and statistical significance of the covariate. When 

controlling for Libby Amphibole asbestos exposure, inclusion of each of the covariates increased 

the AIC for the model, and none were associated with odds of localized pleural thickening: T: pvalue=0.9350; age at x-ray: p-value=0.6822; gender: p-value=0.7734; smoking: p-value=0.1905; 

BMI: p-value=0.3206. Therefore, only cumulative Libby Amphibole asbestos exposure 

(CHEEC) was included in further analyses.

The candidate models (see Table 5-5 for model forms) were: logistic (with CHEEC 

considered as continuous and continuous with a natural logarithm transformation), probit (with 

CHEEC considered as continuous and continuous with a natural logarithm transformation), 3-

parameter log-logistic, dichotomous Hill, and dichotomous Michaelis-Menten models (with only 

CHEEC for the latter three models). These are statistical models used to evaluate dichotomous 

data, and were considered appropriate here given the supralinear nature of the observed 

relationship between Libby Amphibole asbestos exposure and prevalence of localized pleural 

thickening. Further details of these analyses are included in Appendix E. All of the candidate 

models had adequate fit as assessed by the Hosmer-Lemeshow test (a form of the Pearson chisquared goodness-of-fit statistic). Models were compared using the AIC—values were quite 

similar among the candidate models, ranging from 75.7 to 78.3 (Table 5-5). The model with the 

lowest AIC was the 3-parameter log-logistic model (AIC=75.7). For models from the set of 

candidate models with AICs within 2 units of the lowest AIC, BMC10 and BMCL10 estimates 

were similar (within a factor of 3 of the values from the lowest-AIC model); thus based on the 

AIC the 3-parameter log-logistic model was selected. 

Different exposure lags (0, 5, 10, 15 and 20 years) were then investigated for this model. 

The AIC values did not vary much for lags of 0 to 15 years (AIC ranging from 75.1 to 75.7), but 

the 10-year lagged exposure provided the lowest AIC and was selected as the preferred exposure 

metric. There was a larger decrease in model fit when using the 20-year lagged exposure (AIC 

of 77.00). In addition, there were cases of localized pleural thickening in the full cohort with 
5-22 

fewer than 20 years since first exposure; therefore, using such a long lag (which necessitates the 

assumption that these are background cases) was not judged to be appropriate. 

The 3-parameter log-logistic model using the 10-year lagged exposure had a p-value for 

fit of 0.74 and an AIC value of 75.1, and the exposure variable was statistically significantly 

associated with odds of localized pleural thickening (beta [SE]=0.6524 [0.2741] per fibers-yr/cc, 

p-value=0.0189) (Figure 5-5). This model yielded a BMC10 of 0.2543 fibers-yr/cc and a 

corresponding BMCL10 of 0.0757 fibers-yr/cc for a 10% increase in prevalence of localized 

pleural thickening. This BMCL10 of 0.0757 fibers-yr/cc is the preferred POD estimate to support 

development of an RfC for Libby Amphibole asbestos.
5-23 

Table 5-5. Candidate models for association between cumulative Libby Amphibole asbestos exposure in th

cohort and localized pleural thickening. 

Model Exposure Metric Form AIC HosmerLemeshow 

GOF p-value 

BMD 

Logistic CHEEC P(CE)=1/[1+exp(-a-b*CE)] 78.3 0.7442 0.5715 Logistic ln(CHEEC) P(CE)=1/[1+exp(-a-b*ln(CE))] 76.2 0.7073 0.2490 Probit model CHEEC P(CE)=Φ(a+b*CE) 77.9 0.7726 1.5267 Probit model ln(CHEEC) P(CE)=Φ(a+b*ln(CE)) 76.6 0.6650 0.2276 3-parameter log-logistic 

ln(CHEEC P(CE)=bkg+(1 – bkg) / [1 + exp(-a – 

b*ln(CE))] 

75.7 0.7339 0.3447 

 CE, lag 5 75.3 0.5519 0.2980  CE, lag 10* 75.1 0.6833 0.2543  CE, lag 15 75.4 0.4226 0.1993  CE, lag 20 7777.0 0.6363 0.1258 Dichotomous Hill† 

ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE^b / 

[exp(-a)+CE^b] 

77.7 0.6724 0.3295 

MichaelisMenten± ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE / [exp(-a)+CE] 75.8 0.5413 0.3330 
5-24 

1 

2 

3 

4 Figure 5-5. Graph of observed prevalence of localized pleural thickening and estimated 

5 probability of localized pleural thickening calculated using the 3-parameter log-logistic 

6 model with 10-year lagged exposure. 

7 

8 5.2.3. RfC Derivation—Including Application of Uncertainty Factors (UFs) 

9 The derivation of the RfC from the morbidity studies of the Marysville cohort (Rohs et 

10 al., 2008; Lockey et al., 1984) was calculated from a POD (BMCL10) of 0.076 fibers-yr/cc for 

11 localized pleural thickening by adjusting to 70 years of exposure, lagged by 10 years (non12 occupational, lifetime exposure), and dividing by a total uncertainty factor of 100. The RfC is 1 

x 10-5 13 fibers/cc for continuous exposure (rounded to one significant digit) was derived as 

14 follows: 

15 

16 RfC = POD ÷ UF 

17 RfC = PODADJ ÷ UF 

18 RfC = [0.076 fibers-yr/cc (1/(70-10 years)] ÷ 100 

19 RfC = 1.2 x 10-5 fibers/cc, rounded to 1 x 10-5 fibers/cc 

20 

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6

Risk of localized pleural thickening

CHEEC (fiber-yr/cc)

Prevalence by quartiles of 

unlagged exposure among 

cases

Estimated risk by 10-year 

lagged exposure
5-25 

1 A total uncertainty factor of 100 was applied in the derivation of the RfC. The rationale 

2 for the individual uncertainty factors are below. 

3 An UF value of 1 was applied for extrapolation from animals to humans (interspecies 

4 extrapolation, UFA) because the critical effect used as the basis for the RfC was observed in 

5 humans. 

6 An UF value of 10 was applied for interindividual variability to account for human-to7 human variability in susceptibility (intraspecies extrapolation, UFH) in the absence of 

8 quantitative information to assess the toxicokinetics and toxicodynamics of Libby Amphibole 

9 asbestos in humans. Only adults sufficiently healthy for full time employment were included in 

10 the principal study; and the study population was primarily male. 

11 An UF value of 1 was applied for LOAEL-to-NOAEL extrapolation (UFL) because the 

12 current approach is to address this factor as one of the considerations in selecting a BMR for 

13 modeling. In this case, a BMR of 10% increase in the incidence of localized pleural thickening 

14 was selected under an assumption that it represents a minimally biologically significant change. 

15 An UF value of 1 was applied for extrapolation to chronic exposure (subchronic to 

16 chronic exposure extrapolation, UFS) as the exposure in the facility was long term (from 

17 approximately 13 weeks to more than 44 years, with more than 90% of the workers having a 

18 duration of exposure of more than 6 years). 

19 An UF value of 10 was applied to account for deficiencies in the database (UFD) based on 

20 lack of data on effects other than in the respiratory system, limitations of the principal study 

21 design, other observed effects (cardiovascular disease and autoimmune effects) that have not 

22 been well-studied, and uncertainties in time from first exposure. 

23 Lack of data on effects other than in the respiratory system: There are no data in 

24 laboratory animals or humans on general systemic effects, developmental and reproductive 

25 effects, neurotoxicity, or developmental neurotoxicity for Libby Amphibole asbestos. The lack 

26 of studies could reflect reasonable judgments made by some (ATSDR, 2001; ATS, 2004) that 

27 other effects are not expected at exposures that would be protective of effects in the respiratory 

28 tract or that general systemic effects would not be expected. However, it is known that inhaled 

29 asbestos fibers migrate out of the lung and into other tissues lending uncertainty to any 

30 assumptions that other effects would not be expected. 

31 Limitations of principal study design: The human study used to derive the POD was 

32 conducted in a relatively small population of workers with no previous occupational exposure to 

33 asbestos with a large range in duration of exposure (from approximately 13 weeks to more than 

34 44 years); although, the study is sufficient to detect a 10% increase in the prevalence of an 

35 adverse effect. The study was conducted more than 20 years after the facility stopped using ore 
5-26 

1 from Libby. However, the study used a cross-sectional design and may be negatively biased 

2 since individuals with more severe disease could have left employment or may have died and not 

3 been included in the follow-up study resulting in an underestimation of toxicity. Additionally, it 

4 is known that high-resolution computer tomography (HRCT) can identify mineral fiber-related 

5 lesions in the respiratory tract, which cannot be identified by standard radiographs. Thus the 

6 technology employed for determining the prevalence of radiographic changes in the Marysville 

7 cohort may underestimate the actual prevalence of localized pleural thickening. 

8 Other observed effects that have not been well-studied but for which there is evidence of 

9 association with exposure to Libby Amphibole asbestos: Two studies have found a possible 

10 increased prevalence of autoimmune disease and biological markers for autoimmune disease in 

11 Libby residents (Pfau et al., 2005 Noonan et al., 2006) although these studies do not indicate 

12 whether the autoimmune effects would be observed at exposures lower than that observed for 

13 localized pleural thickening. Subsequent animal studies have indicated that exposure to Libby 

14 amphibole asbestos does induce autoantibodies in mice (Blake et al., 2008). It is unknown if 

15 autoimmune effects are secondary to the chronic inflammatory response expected from exposure 

16 to mineral fibers. However, one study of individuals in a community exposed to tremolite found 

17 changes in immune parameters in exposed individuals without localized pleural thickening and 

18 that additional immune markers, including autoantibodies, increased in individuals with localized 

19 pleural thickening (Zerva et al., 1989). A mortality analysis for the Libby worker cohort also 

20 found associations between occupational exposures to Libby Amphibole asbestos and mortality 

21 due to cardiovascular disease (Larson et al., 2010a). 

22 Time from first exposure: The data for the sub-cohort of workers exposed post 1972 

23 allowed for assessing prevalence of localized pleural thickening approximately 30 years after 

24 first exposure. However, there is evidence to indicate that the prevalence of localized pleural 

25 thickening is likely to continue to increase more than 30 years after first exposure (Paris, 2009, 

26 2008; Jakobsson et al., 1995; Hillerdal 1994; Ehrlich et al., 1992; Järvholm, B, 1992; Lilis et al. 

27 1991; Merchant, 1990; McDonald et al 1986b). Therefore, a POD established at 30 years time 

28 from first exposure may underestimate the actual prevalence of localized pleural thickening 70 

29 years from first exposure. Since the full-cohort model includes time from first exposure as a 

30 variable, one approach to address this uncertainty is to use this model to extrapolate to later time 

31 points. This modeling shows a non-linear increase in prevalence of localized pleural thickening 

32 with time from first exposure. However, this approach is problematic because there are no data 

33 available with time from first exposure greater than 47 years in the cohort. Therefore, while the 

34 likelihood of increased prevalence of localized pleural thickening beyond 30 years is supported 

35 by statistical analysis of the full cohort, this modeling does not allow accurate estimation of the 
5-27 

1 size of such an effect. Thus, the use of a POD established 30 years from first exposure does 

2 represent uncertainty with respect to assessing the potential effects for a lifetime of exposure. 

3 The RfC was developed based on fiber counts employing PCM following NIOSH 

4 counting rules where the minimum detectable fiber diameter is 0.25 μm, fiber length is > 5 μm 

5 and the aspect ratio is ≥ 3. For use at other sites, the fiber dimensions at the site can be attained 

6 using PCM or TEM where they are reported as phase contrast microscope equivalents [PCMe]. 

7 The RfC is for continuous lifetime exposure (24 hours/day, 365 days/year, with exposure 

8 beginning at birth and continuing for 70 years). See Table 5-6 for guidance on using the RfC for 

9 other exposure scenarios—the RfCs in this table are estimated using the POD divided by the 

10 total uncertainty factor of 100, pro-rated over a range of exposure durations. The RfC can be 

11 applied at other sites where vermiculite ore from Libby, MT was used.

12 

13 TABLE 5-6. Exposure concentrations (RfCs) for various exposure durations* 

Age at 

First 

Exposure 

Age at 

Examination 

For Health 

Effect 

1 yr 2 yrs 5 yrs 10 yrs 20 yrs 30 yrs 40 yrs 50 yrs 60 yrs 

0 70 7.6E-04 

3.8E04 1.5E04 7.6E05 3.8E05 2.5E05 1.9E05 1.5E05 1.3E05 

10 70 7.6E-04 

3.8E04 1.5E04 7.6E05 3.8E05 2.5E05 1.9E05 1.5E05 

20 70 7.6E-04 

3.8E04 1.5E04 7.6E05 3.8E05 2.5E05 1.9E05 

 

30 70 7.6E-04 

3.8E04 1.5E04 7.6E05 3.8E05 2.5E05 

 

40 70 7.6E-04 

3.8E04 1.5E04 7.6E05 3.8E05 

 

50 70 7.6E-04 

3.8E04 1.5E04 7.6E05 

 

14 *Because the presence of localized pleural thickening is not dependent on time from first exposure in the model 

15 used, the RfC is a linear function of duration of exposure. The equation is RfCD = POD/(Duration x UF). The POD 

16 is 0.076 fibers-cc/yr, duration is years and excludes any exposure after age 60, and UF is 100. 

17 

18 

19 5.2.4. Alternative Analyses of the Full Marysville Cohort 

20 Modeling of the full cohort was also conducted utilizing the full dataset for localized 

21 pleural thickening from the Marysville cohort. Since the full cohort includes data combined 

22 from Lockey et al. (1984) and Rohs et al. (2008), there were individuals who had more than one 

23 observation. As noted in Section 5.2.2.2, for those workers x-rayed in both 1980 (Lockey et al., 
5-28 

1 1984) and 20042-2005 (Rohs et al., 2008), one of the observations was excluded so that there are 

2 no repeat observations for individual workers. 

3 Time from first exposure to x-ray (T) is an important variable in understanding the full 

4 Marysville dataset, as can be seen by the much higher prevalence of localized pleural thickening 

5 in the 2000s compared to the 1980 assessment, an increase which cannot be fully explained by 

6 the increases in cumulative exposure occurring with continued exposure. Consequently, in 

7 looking at the full cohort, T is a strong predictor of localized pleural thickening. In contrast, for

8 the sub-cohort analysis above, T is not predictive of localized pleural thickening risk when 

9 CHEEC is in the model; one reason is that in the sub-cohort the range of T is much smaller and 

10 all the values of T well exceed the minimal time since first exposure until the observation of 

11 thickening of about 10 years suggested by the data (23.15-32.62 years); in addition, there is less 

12 measurement error in the exposure for this group. One consideration in interpreting these results 

13 is that T does not represent time from first exposure to an event (or a detectable event), rather it 

14 is time from first exposure to the detection of an event. As such, it cannot be construed as an 

15 indicator of biological latency. Secondly, there is an element of time encompassed in the 

16 cumulative exposure metric—CHEEC reflects duration of exposure as well as intensity. 

17 Therefore, in some sense adding the T variable is ‘double-counting’ time. Finally, given the 

18 occurrence of higher exposures in earlier years (i.e., for individuals having large values of T), 

19 there is confounding between inference about the quantitative effect of time since first exposure 

20 and estimated cumulative exposure.

21 A similar approach as described in Section 5.2.2.3 was used to evaluate candidate models 

22 for the full cohort. Details are provided in Appendix E. However, since time since first 

23 exposure (T) was an important covariate for these analyses, further efforts were needed to 

24 develop a model incorporating T along with exposure. The logistic and probit models including 

25 CHEEC as a continuous exposure had inadequate model fit as evaluated using the Hosmer26 Lemeshow test (p-values of 0.003 for both) and so were not considered for further analysis. The 

27 remaining candidate models (logistic and probit with the natural logarithm of CHEEC, 3-

28 parameter log-logistic, dichotomous Hill, and dichotomous Michaelis-Menten) had adequate fit. 

29 Among these models, the AIC values ranged from 327.9 (Michaelis-Menten) to 346.8 (logistic 

30 with the natural logarithm of CHEEC). Based on these results, the Michaelis-Menten model was 

31 selected for further evaluation and different approaches were investigated to represent T along 

32 with cumulative Libby Amphibole asbestos exposure using this model form. 

33 The approach taken to incorporate T was through modification of the plateau term in the 

34 Michaelis-Menten model to allow the plateau for the exposure-response relationship to change 

35 for different values of T. After investigating various forms for the plateau (described in 
5-29 

1 Appendix E), the lowest AIC resulted when the plateau term took the form: Plateau = 

2 Background + (1-background)* Φ(T|m,s), where Φ(T|m,s) represents the cumulative normal 

3 probability distribution function. Different exposure lags were then investigated for this 

4 model—as seen for the sub-cohort, the AIC values were quite similar for lags of 0-15 years 

5 (AICs ranging from 277.72 to 278.04. However, the 20-year lagged exposure had an increased 

6 AIC of 280.60 and was not judged an appropriate choice. The 10-year lagged exposure resulted 

7 in the lowest AIC, and was thus chosen as the preferred exposure metric. In order to estimate a 

8 BMC10 and corresponding BMCL10 for this model form, a fixed value of T must be specified. 

9 A first analysis need for this model is to provide results based on the full cohort for 

10 comparison with the estimated BMC10 and BMCL10 values developed above for the sub-cohort 

11 with exposure data starting in 1972 (Section 5.1.2.3.1). For this need, a value of T=30 years was 

12 selected in order to provide a suitable comparison to modeling results for the sub-cohort, where 

13 the mean time since first exposure was 28 years. The BMC10 was 0.1384 fibers-yr/cc, with a 

14 corresponding BMCL10 of 0.0541 fibers-yr/cc. These values, and particularly the BMCL10, are 

15 in the same range as those estimated using the preferred analysis with the sub-cohort (BMC10

16 and BMCL10 of 0.2543 and 0.0757 fibers-yr/cc, respectively). This analysis thus provides 

17 confirmation of the reasonableness of the modeling results developed using the sub-cohort 

18 database. 

19 One alternative analysis using the full cohort model, with a time since first exposure 

20 value of T=40 years was conducted. A BMCL10 of 0.0134 fibers-yr/cc was calculated with the 

21 Cumulative Normal Michaelis-Menten model. The BMCL10 with T = 40 years is used because it 

22 is near the upper end of the range of T values available in the data set (Tmax = 47.375 years). 

23 This POD combined with a lag time of 5 years (used because Larson et al. (2010b) showed that 

24 discrete pleural thickening could be observed much earlier than previously thought) and a total 

uncertainty factor of 100 was used to derive an alternative RfC of 3.8 x 10-6 25 fibers/cc or rounding 

to one significant digit, 4 x 10-6 26 fibers/cc. See Appendix E for details. This alternative RfC is an 

27 order of magnitude lower compared to both the preferred sub-cohort analysis and the full cohort 

28 analysis with a fixed T of 30 years. 

29 Another alternative analysis based on projection of risks using the full cohort model for a 

30 “lifetime” time since first exposure of T = 70 years. Note that none of the workers had a T>50 

31 years; therefore this modeling represents a statistical extrapolation beyond available data. A 

32 BMCL10 of 0.0041 fibers-yr/cc was calculated with the Cumulative Normal Michaelis-Menten 

33 model. This POD combined with a lag time of 5 years and a total uncertainty factor of 30 was 

used to derive an alternative RfC of 2.1 x 10-6 34 fibers/cc or rounding to one significant digit, 2 x 

10-6 35 fibers/cc. See Appendix E for details. 
5-30 

1 Each of the candidate PODs (analyses from both the sub-cohort and full cohort) has 

2 strengths and weaknesses. A major strength of the preferred analysis (Marysville sub-cohort) is 

3 that by limiting the data set to those individuals hired in 1972 or later, the exposure 

4 reconstruction relies only on data supported by industrial hygiene measurements in the facility. 

5 The exposures were also lower after 1972 as compared to previous years. However, this 

6 approach reduces the number of individuals in the data set from 434 to 119 and reduces the 

7 number of cases from 61 to 12. In addition this approach narrows the range in the time from first 

8 exposure to 23.15 to 32.65 years. The analyses of the full cohort have the strength of using all of 

9 the data available on the Marysville cohort and of using a model that incorporates both 

10 cumulative exposure and time from first exposure. One weakness of the full cohort analyses is 

11 that the exposure reconstruction relies on estimates of the exposure conditions in the Marysville 

12 facility before industrial hygiene data were available in 1972. 

13 Because of the greater certainty in the exposure reconstruction in the sub-cohort based on 

14 the data set where industrial hygiene data were collected in the facility (that is, workers hired in 

1972 and later and examined for health effects in 2004), the RfC of 1 x 10-5 15 fibers/cc for Libby 

16 Amphibole asbestos is based on the preferred sub-cohort analysis. 

17 

18 5.2.5. Previous RfC Derivation 

19 There is no previous RfC derivation for Libby Amphibole asbestos. 

20 

21 5.3. UNCERTAINTIES IN THE INHALATION REFERENCE CONCENTRATION 

22 (RfC) 

23 Uncertainty in the exposure reconstruction 

24 As in all epidemiological studies there are uncertainties in the exposure reconstruction. 

25 In this case there is some uncertainty in the employment history and some individuals had 

26 extensive overtime work. Employment history was self reported during interviews with each 

27 individual for the original study (Lockey et al., 1984) and errors in this process could affect 

28 assigned Libby Amphibole asbestos exposure estimates. As stated previously, exposure 

29 measurements started in the Marysville plant in 1972; exposures prior to this time were estimated 

30 by University of Cincinnati scientists, based on focus group interviews with 15 long-term former 

31 workers. Exposure estimates for the period prior to 1972 thus can be considered as semi32 quantitative rather than directly based on industrial hygiene data. The University of Cincinnati 

33 analysis assumed that early exposure levels in the plant are twice those measured in 1972 

34 (Appendix F). The greater uncertainty of the pre-1972 exposure estimates led to EPA’s decision 

35 to focus the analysis on the post-1972 group of workers rather than the full cohort. Although it is 

36 generally true that the use of more data is an advantage for statistical analyses because it allows 
5-31 

1 for the computation of more statistically precise effect estimates, this increased precision may be 

2 offset by a negative impact on the accuracy of the effect estimate if an increase in sample size is 

3 accompanied by greater exposure misclassification or other biases. 

4 While the uncertainties related to a lack of quantitative measurements are not relevant to 

5 the sub-cohort analysis, it is important to recognize that exposure assessment post-1972 also has 

6 some limitations. The main source of uncertainty is the lack of exposure measurements for 

7 certain occupations/tasks (including polyform, warehouse, blender, feeder, resin, screen/mill, and 

8 track jobs) before industrial hygiene improvements that started in 1974 (Appendix F; Lockey, 

9 1980). For some of these occupations, post-1974 measurements are quite high, suggesting that 

10 industrial hygiene measures were not immediately implemented. 

11 There is uncertainty when the Libby ore was first used in the facility. Company records 

12 indicated that the date was between 1957 and 1960, and the University of Cincinnati used the 

13 best available information from focus group interviews to assign the first usage of Libby ore in 

14 1959 (see Appendix C). There is also uncertainty in the post-1972 data regarding asbestos 

15 content in other ore sources (Virginia, South Carolina and South Africa). Libby Amphibole 

16 asbestos was not used in the facility after 1980. However, industrial hygiene measurements 

17 collected after 1980 showed low levels of fibers in the facility. PCM analysis does not determine 

18 the mineral/chemical make-up of the fiber, and thus cannot distinguish between different kinds 

19 of asbestos. Analysis of the bulk ores from Virginia and South Africa showed the presence of 

20 only a few or no amphibole fibers. No information is available to evaluate the nature of the 

21 fibers in these post 1980 samples. Lockey (1980, Figure 2, Tables 8-9) shows contradictory 

22 results on asbestos content in ore from Africa and asbestos content in South Carolina material 

23 comparable to Libby; recent retesting by the University of Cincinnati indicates that levels of 

24 these other ores were present at levels just above the limit of detection (Appendix C). University 

25 of Cincinnati was not able to obtain South Carolina material, but this material was used only for 

26 a relatively short time. 

27 Accordingly, post-1980 exposure was included in the calculation of cumulative exposure 

28 to fibers and assumes that these fibers cause the same health effect. However, because the 

29 concentration of fibers in the workplace was near background after 1980, this exposure makes 

30 only a small contribution to an individual’s cumulative exposure estimate.

31 There was potential co-exposure to other chemicals in the Marysville facility. These 

32 other chemicals were used after expansion of vermiculite ore in another area of the facility. 

33 Industrial hygiene data showed very low levels of fibers in these areas. In addition, none of 

34 these chemicals are volatile. The most likely route of exposure to these chemicals is through 
5-32 

1 dermal contact. It is unlikely that any co-exposure to these particular chemicals would alter the 

2 exposure-response relationship of Libby Amphibole asbestos in the respiratory system. 

3 The University of Cincinnati Research Team assumed that there was no exposure to 

4 Libby Amphibole outside of the workplace. The interviews with the Marysville workers 

5 revealed that only about 10% of the workers reported bringing raw vermiculite home. These 

6 interviews also revealed that change to street clothes from work-supplied coveralls was standard 

7 practice at the end of the shift and approximately 64% of the workers showered before leaving 

8 the work place. For these workers it is likely that additional exposure outside the workplace was 

9 minimal. However, for the remainder of the workers it is reasonable to assume that additional 

10 exposure could have occurred at home. Additional data collected by the University of Cincinnati 

11 Research Team document that no increased prevalence of pleural or parenchymal change 

12 consistent with asbestos exposure has been observed in household contacts of the workers from 

13 the Marysville facility (J. Lockey, University of Cincinnati, personal communication to Robert 

14 Benson, EPA). 

15 

16 Uncertainty in the radiographic assessment of localized pleural thickening 

17 There is some uncertainty associated with ascertainment of the health outcome. 

18 Conventional radiographs were used to determine the health outcome. Discrete pleural 

19 thickening may be difficult to detect, particularly if the affected areas are small in size, and fat 

20 deposits or other anatomical features (musculature, fractures) may be mistaken for pleural 

21 plaques (Gilmartin 1979). Thus, outcome misclassification may be present in the Marysville 

22 data. For example, one of the workers had a positive X-ray at 1980 scan, but a negative X-ray at 

23 2000s scan (excluding this worker from the analysis did not change results). However, 

24 uncertainty in the prevalence of localized pleural thickening in each individual is considered 

25 minimal due to the use of a team of highly qualified chest radiologists evaluating the 

26 radiographic films and the use of consensus diagnosis. 

27 Body mass index (BMI) was investigated as a potential explanatory variable because fat 

28 pads can sometimes be misdiagnosed as pleural thickening. BMI was not measured in the 1980 

29 examination, but was available for most participants of the 2000s examination. To address 

30 whether fat deposits may affect outcome classification, EPA considered the effect of adding BMI 

31 as a covariate in the model. However, BMI did not display an association with odds of localized 

32 pleural thickening in this population (See Appendix E). While these covariates were not 

33 associated with risk of localized pleural thickening in the sub-cohort after adjusting for exposure, 

34 it was not possible to evaluate this relationship in the full cohort. In general, smoking rates have 
5-33 

1 declined between 1980 and 2000s and BMIs have probably increased, so one can’t necessarily 

2 assume the relationships will be the same for the two examination periods. 

3 In terms of the technology used for outcome assessment, high-resolution computed 

4 tomography (HRCT) can detect mineral fiber-related lesions in the respiratory tract, which 

5 cannot be identified by standard radiographs. Thus the technology employed for determining the 

6 prevalence of radiographic changes in the Marysville cohort may underestimate the actual 

7 prevalence of localized pleural thickening.

8 

9 Uncertainty due to time from first exposure 

10 There is some uncertainty associated with the length of follow-up of the Marysville 

11 cohort. The observed range of time since first exposure to x-ray in the full cohort is 0.4-47 

12 years, and 23.2-32.7 years in the recommended sub-cohort. It is anticipated that the prevalence 

13 of localized pleural thickening in the study population – and in the post 1972 exposure cohort – 

14 may continue to show some increase with passage of time. In this case, the modeling approach 

15 may not accurately reflect the exposure-response relationship that would be seen with a longer 

16 follow-up time. However, a recent study by Larson et al. (2010b) examined serial radiographs 

17 conducted on a group of Libby vermiculite workers with pleural or parenchymal changes. They 

18 found that among those workers with localized pleural thickening, all cases were identified 

19 within 30 years, and that the median time from hire to the first detection of localized pleural 

20 thickening was 8.6 years. Although the workers included in the Marysville sub-cohort had 

21 generally higher exposures and earlier start dates (mostly prior to 1972) compared to the full 

22 worker population, these findings indicate that the range of follow-up time in the sub-cohort is 

23 likely sufficient to support the dose response modeling developed in this assessment. Note that 

24 the likelihood that prevalence of localized pleural thickening may further beyond 30 years after 

25 first exposure is a principal rationale cited for the selection of a database UF of 10 in this 

26 assessment.

27 Modeling of the full cohort (see Appendix E) showed that time from first exposure was 

28 the most significant explanatory variable. However, modeling of the sub-cohort hired in 1972 or 

29 later showed that time from first exposure was not a significant explanatory variable. The range 

30 of time from first exposure in the sub-cohort is much shorter (23 – 32.65 years versus 0.42 – 

31 47.38 years). Therefore, it is likely that the prevalence of localized pleural thickening would 

32 increase if the individuals hired in 1972 or later were examined in the future. 

33 

34 Uncertainty in background rate of localized pleural thickening
5-34 

1 In the derivation of the RfC, a background rate of 1% for localized pleural thickening was 

2 used. However, there is a range of estimates (0.02% to 3.9%) of prevalence of localized pleural 

3 thickening in studies looking at the background rate in populations not known to be 

4 occupationally exposed to asbestos. In populations where efforts were made to exclude 

5 individuals with prior exposure to asbestos, rates of 1.2% (4/326) and 0.2% (3/1422) were 

6 reported by Anderson et al. (1979) and by Castellan et al. (1985). Higher rates of localized 

7 pleural thickening are reported from cross sectional studies of the general population and 

8 individuals with military service. Using chest x-rays collected in NHANES I (1971-1975), 

9 Rogan et al. (1987) reported a background rate of 1.2%; a similar assessment using chest x-rays 

10 collected in NHANES II (1976-1980) found a background rate of 3.9% (Rogan et al. 2000). 

11 Miller et al. (1996) reported a background rate of 2.3% in inpatients and outpatients at a 

12 Veteran’s Administration medical center, while Bohnker et al. (2005) reported a background rate 

13 of 2.3% based on data collected from the U.S. Navy Asbestos Medical Surveillance Program. 

14 One reason estimated background rates vary in the published literature is due to differing age 

15 structure of the study populations, as localized pleural thickening prevalence in influenced by 

16 age. The mean age of the Marysville sub-cohort at the time of x-ray is 52 years (median is 50 

17 years). However, in statistical modeling of the Marysville sub-cohort, uncertainty in the 

18 background rate of localized pleural thickening is very low. Both the fixed and estimated values 

19 are in the range of estimates above and the difference in the POD when the background rate is 

20 fixed at 1% versus when it is estimated (estimated background rate of 3.76%) is less than 3%. 

21 As discussed in Section 4.1, there is a range of estimates (0.2% to 3.9%) in the background rate 

22 of localized pleural thickening. In the modeling, the background rate was set at 1% and not 

23 estimated. This is expected to have an insignificant effect on the value of the RfC. 

24 

25 Uncertainty in Model Functional Form and Lagged Exposure 

26 A number of model forms were explored in the initial stages of analysis (Appendix E), 

27 before selecting the log-logistic model. The log-logistic model is widely used for epidemiologic 

28 data, and is easily interpreted. In this application the ratio of the BMC to the BMCL 

29 (0.2543/0.0757 = 3.4) was reasonable given the size of the available dataset, indicating 

30 acceptable statistical precision in the BMC estimate. In addition, BMCs and BMCLs estimated 

31 from other candidate models for the post 1972 exposure sub-cohort were in a similar range to the 

32 selected model. Finally, the complementary analysis with the full cohort (utilizing a 30 year 

33 time since first exposure value which was selected to be consistent with time since first exposure 

34 values within the sub-cohort) provided similar results to the sub-cohort analysis. A second 

35 model-based uncertainty is the choice of lag for cumulative exposure. The RfC derivation is 
5-35 

1 based on the exposure lagged by 10 years, since this lag yielded the lowest AIC. However, if 

2 other lags (with similar AICs) are used, the difference in POD may fluctuate to be approximately 

3 8% higher or approximately 40% lower. However, as the RfC is rounded to one significant digit, 

4 the choice of lag does not affect the estimated RfC.

5 

6 5.4. CANCER EXPOSURE-RESPONSE ASSESSMENT 

7 5.4.1. Overview of Methodological Approach 

8 The objective of this human health assessment is to derive a cancer estimate for 

9 inhalation exposure to Libby Amphibole asbestos. The inhalation unit risk (IUR) is defined as 

10 an upper-bound excess lifetime cancer risk estimated to result from continuous exposure to an 

agent at a concentration of 1 μg/L in water, or 1 μg/m311 in air. However, current health standards 

12 for asbestos are given in fibers/cc of air as counted by phase contrast microscopy (PCM), since 

13 they are based on health effects observed in occupational cohorts and this is the standard for 

14 measuring fibers in an occupational environment (U.S. EPA, 1988; OSHA, 2008). Similarly, 

15 when examining the available health effects data for Libby Amphibole asbestos, the best 

16 available exposure metric available at this time is fibers/cc counted by PCM (Section 4.1.1.2). 

17 Therefore, for Libby Amphibole asbestos the IUR represents the lifetime risk of mortality from 

18 either mesothelioma or lung cancer in the general U.S. population from chronic inhalation 

19 exposure to Libby Amphibole asbestos at a concentration of 1 fiber/cc of air. 

20 IURs are based on human data when appropriate epidemiologic studies are available. 

21 The general approach to developing an IUR from human epidemiologic data is to quantitatively 

22 evaluate the exposure-response relationship for that agent to derive a specific estimate of its 

23 cancer potency in the studied population. For this assessment, the first step was to identify the 

24 most appropriate data set available, which in this case can be used to quantitatively estimate the 

25 effects of Libby Amphibole asbestos exposure on cancer mortality. Once the relevant data 

26 describing a well-defined group of individuals along with their exposures and health outcomes 

27 was collected, an appropriate statistical model was selected that adequately fit the data, and then 

28 individual-level exposures were modeled using a variety of possible exposure metrics (Section 

29 5.4.2). The available epidemiologic data allowed for modeling of the effects of estimated 

30 ambient occupational exposures to Libby Amphibole asbestos on the observed mortality risk in 

31 workers. Exposure-response modeling was conducted for each cancer mortality endpoint 

32 individually, and in some cases, the statistical model and the specific metric of exposure used for 

33 each cancer endpoint may have been different. For example, the exposure metric that best 

34 describes the exposure-response relationship for mortality from mesothelioma attributable to 

35 occupational exposure to Libby Amphibole asbestos was found to be different from the exposure 
5-36 

1 metric that best describes mortality from lung cancer (Section 5.4.3). Potential covariates that 

2 may also be important predictors of cancer mortality are included in the statistical models. These 

3 models were then statistically evaluated to determine which exposure metric representing 

4 estimated ambient occupational exposures provided the best statistical fit to the epidemiologic 

5 data. 

6 This cancer potency estimate derived from the epidemiologic data is then applied to the 

7 general U.S. population to determine the exposure level that would be expected to result in 1% 

8 extra cancer mortality risk over a lifetime of continuous exposure. For epidemiologic studies, 

9 which may be based on larger numbers of individual observations, smaller response levels that 

10 are closer to the background response levels are considered appropriate. Extra risk is defined as 

11 equaling (Rx - Ro) / (1 - Ro), where Rx is the lifetime cancer mortality risk in the exposed 

12 population, and Ro is the lifetime cancer mortality risk in an unexposed population (i.e., the 

13 background risk). For example, if the expected lifetime risk of lung cancer mortality in the 

14 unexposed general U.S. population is 5%, then this human health assessment seeks to estimate 

15 the level of exposure to Libby Amphibole asbestos that would be expected to result in a lifetime 

16 risk of lung cancer mortality of 5.95%; this lifetime risk of mortality is equivalent to a 1% extra 

17 risk (0.0595 − 0.05) / (1 − 0.05) = 0.01. For mesothelioma mortality, an absolute risk was 

18 considered, rather than extra risk, because mesothelioma is very rare in the general population 

19 (Hillerdal, 1983). 

20 A life-table analysis (see Appendix G for details) was used to compute the 95% lower 

21 boundon the lifetime exposure to Libby Amphibole asbestos that corresponds to a 1% extra risk 

22 of cancer mortality in the general U.S. population using age-specific mortality statistics and the 

23 exposure-response relationships for each cancer endpoint as estimated in the studied population. 

24 This lower bound on the level of exposure serves as the point of departure (POD) for 

25 extrapolation to lower exposures and for deriving the unit risk. Details of this analysis are 

26 presented in Section 5.4.4. A cancer-specific unit risk was obtained by dividing the extra risk 

27 (1%) by the POD. The cancer-specific unit risk estimates for mortality from either 

28 mesothelioma or lung cancer were then statistically combined to derive the final IUR (Section 

29 5.4.5). Uncertainties in this cancer assessment are described in detail in Section 5.4.6. 

30 5.4.2. Choice of Study/Data—with Rationale and Justification 

31 This human health assessment is specific to Libby Amphibole asbestos. This assessment 

32 does not seek to evaluate quantitative exposure-response data on cancer risks from studies of 

33 asbestos that did not originate in Libby, MT. 

34 The available sources of data included the cohort of workers employed at the vermiculite 

35 mining and milling operation in and around Libby, MT. This cohort has been the subject of 
5-37 

1 several epidemiologic analyses (Amandus and Wheeler, 1987; Amandus et al., 1987a, 1987b; 

2 McDonald et al., 1986; Sullivan, 2007; Larson et al., 2010a; Moolgavkar et al., 2010; and 

3 described in detail in Section 4.1). There have also been published reports on cases of 

4 mesothelioma in the Libby, MT area (Whitehouse et al., 2008) and mortality data published by 

5 the Agency for Toxic Substances and Disease Registry (ATSDR) (2000). However, published 

6 mortality data on Libby, MT residents (Whitehouse et al., 2008; ATSDR, 2000) could not be 

7 used in exposure-response modeling due to lack of quantitative exposure data. 

8 The other available cohort of workers exposed to Libby Amphibole asbestos was from an 

9 Ohio vermiculite processing plant (Section 4.1.3) (Lockey et al., 1984; Rohs et al., 2008). 

10 Pleural changes were evaluated; however, no data were available pertaining to cancer incidence 

11 or mortality in the Ohio cohort. No other worker cohorts exposed to Libby Amphibole asbestos 

12 with cancer incidence or mortality data were available. 

13 The most appropriate available dataset with quantitative exposure data for deriving 

14 quantitative cancer mortality risk estimates based on Libby Amphibole asbestos exposure in 

15 humans is the cohort of workers employed at the vermiculite mining and milling operation in and 

16 around Libby, MT (hereafter referred to as the Libby worker cohort). These data are considered 

17 the most appropriate to inform this human health assessment for several reasons: (1) these 

18 workers were directly exposed to Libby Amphibole asbestos, (2) detailed work histories and job19 specific exposure estimates are available to reconstruct estimates of each individual’s 

20 occupational exposure experience, (3) the cohort is sufficiently large and has been followed for a 

21 sufficiently long period of time for cancer to develop (i.e., cancer incidence) and result in 

22 mortality, and (4) the broad range of exposure experiences in this cohort provided an 

23 information-rich dataset which allowed evaluation of several different metrics of exposure. 

24 Uncertainties in these data are discussed in Section 5.4.6. 

25 

26 5.4.2.1. Description of the Libby Worker Cohort 

27 The Libby worker cohort has been extensively studied. McDonald et al. published three 

28 studies on a subset of the cohort (1986, 2002, 2004). Scientists from the National Institute for 

29 Occupational Safety and Health (NIOSH) conducted two epidemiologic investigations, resulting 

30 in several published reports on different subsets of the cohort (Amandus and Wheeler, 1987; 

31 Amandus et al., 1987a, b, 1988; Sullivan, 2007). Larson et al. (2010a) analyzed an ATSDR 

32 reconstruction of the Libby worker cohort from company records with exposure estimates 

33 obtained from NIOSH with mortality follow-up through 2006. Moolgavkar et al. (2010) re34 analyzed the Sullivan (2007) data with mortality follow-up through 2001 using a different 

35 statistical approach. 
5-38 

1 According to Sullivan (2007), nearly all of these study subjects were workers at the 

2 Libby, MT vermiculite mine, mill, and processing plant. Although the mine was several miles 

3 from Libby, MT, some of the study subjects worked in the town (see Section 4.1.1.1). Workers 

4 may have also been assigned jobs as truck drivers, or jobs working in the screening plant, 

5 railroad loading dock, expansion plants, or an office. Individuals’ demographic and work history 

6 data were abstracted from company personnel and pay records. A database created by NIOSH in 

7 the 1980s contained demographic data and work history starting from September 1935, and vital 

8 status at the end of 1981 for 1,881 workers. NIOSH compared these data with company records 

9 on microfilm, and work history data were re-abstracted to ensure data quality. One person was 

10 removed from the cohort because company records stated that he was hired, but never worked 

11 (Sullivan 2007). Nine workers with Social Security numbers listed in company records were 

12 excluded because demographic and work history data were not available, leaving 1,871 workers 

13 in the cohort available for epidemiologic analysis. Table 5-7 shows the demographic and 

14 exposure characteristics of this cohort. 

15 For the purposes of this assessment, vital status follow-up was completed by NIOSH 

16 through 2006 using the National Death Index (NDI-Plus; Bilgrad, 1995). Workers known to be 

17 alive on or after January 1, 1979 (the date NDI began tracking deaths nationwide), but not found 

18 in the NDI search, were assumed to have been alive on December 31, 2006 (Sullivan, 2007). 

19 Nearly 54% of workers in the cohort (n = 1,009) had died by December 31, 2006. NIOSH 

20 researchers obtained death certificates from the states (while exposure occurred in and around 

21 Libby, deaths could have occurred elsewhere) for deaths prior to 1979 and coded to the ICD 

22 revision in effect at the time of death by a single National Center for Health Statistics-trained 

23 nosologist. After 1979, ICD codes were obtained from the NDI-Plus. For workers known to be 

24 deceased, the underlying cause of death was determined from death certificates and coded to the 

25 ICD codes using the rubrics of the ICD revision in effect at the time of death (ICD-5 [WHO, 

26 1938], ICD-6 [WHO, 1948], ICD-7 [WHO, 1957], ICD-8 [WHO, 1967]; ICD-9 [WHO, 1977]; 

27 or ICD-10 [WHO, 1992]). 

28 

29 
5-39 

1 Table 5-7. Demographic and exposure characteristics of the Libby worker 

2 cohort 

3 

Characteristic All workers

Number of workers 1,871 

Number of deaths from all causes 1,009 

Number of deaths from mesothelioma 18 

Number of deaths from lung cancer 111 

Mean year of birth 1,929

Mean year of hire 1,959

Mean age at hire (years) 30.2

Mean person-years of follow-up (no lag) 35.9 

Total person-years of follow-up (no lag) 67,101 

Mean employment duration (years) 3.7

Mean cumulative exposure (fibers/cc-years) 96.0

Median cumulative exposure (fibers/cc-years) 9.8 

4 

5 

6 Basic demographic information on the occupational cohort members was largely 

7 complete. However, when data were missing, they were imputed by NIOSH based on the 

8 following assumptions regarding gender, race, and date of birth. Seven workers with unknown 

9 gender were assumed to be male because 96% of the workforce was male and NIOSH review of 

10 names did not challenge that assumption (Sullivan, 2007). Workers of unknown race (n = 935) 

11 were assumed to be white because workers at this facility were known to be primarily white, and 

12 U.S. Census Bureau data indicate that 90-95% of the local population identify themselves as 

13 white (Sullivan, 2007). For four workers with unknown birth dates, date of birth was estimated 

14 by subtracting the mean age at hire for the cohort from the worker’s hire date. The potential 

15 impact of this imputation procedure on the analytic results is discussed in Section 5.4.6. 

16 

17 5.4.2.2. Description of Cancer Endpoints 

18 This human health assessment of Libby Amphibole asbestos focuses on two cancer 

19 endpoints: mesothelioma and lung cancer. The endpoint for both mesothelioma and lung cancer 

20 was mortality, not incidence. Incidence data are not available for the Libby worker cohort. 

21 However, there is evidence that other cancer endpoints may also be associated with exposure to 
5-40 

1 asbestos. The International Agency for Research on Cancer (IARC) concluded that there was 

2 sufficient evidence in humans that other types of asbestos (chrysotile, crocidolite, amosite, 

3 tremolite, actinolite, and anthophyllite) were causally associated with mesothelioma and lung 

4 cancer, as well as cancer of the larynx and the ovary (Straif et al., 2009). Among the entire 

5 Libby worker cohort, only 2 deaths were found to be due to laryngeal cancer, and there were no 

6 deaths from ovarian cancer among the 84 female workers. The EPA did not evaluate these other 

7 outcomes as part of this assessment. The limited number of female workers in this cohort is 

8 discussed later as a source of uncertainty in the derived estimates (Section 5.4.6). 

Mesothelioma did not have a distinct ICD code prior to introduction of the 10th 9 revision 

10 (ICD-10) which was not implemented until 1999. Therefore, for deaths in the Libby worker 

11 cohort occurring from 1979 to 1998, death certificates were obtained if the NDI identified the 

12 death as being from one of the possible mesothelioma codes identified by Marsh et al. (2001), or 

13 from respiratory cancer, nonmalignant respiratory disease, digestive cancer, or unspecified 

14 cancer. Death certificates (1940-1998) were reviewed by the NIOSH principal investigator 

15 (Sullivan, 2007) to identify any mention of mesothelioma on the death certificate, as is the 

16 standard procedure for assessing mesothelioma mortality and as has been used in other analyses 

17 of Libby worker cohort mesothelioma mortality (McDonald et al., 2004; Larson et al., 2010a). 

18 In total, 18 mesothelioma deaths occurring from 1979 to 2006 were identified by NIOSH using 

19 these methods (which serves as the basis for this assessment); 19 mesothelioma deaths were 

20 identified by Larson et al. (2010a) for the same cohort from death certificates for all causes of 

21 death rather than the more targeted set of causes indentified by Marsh et al. (2001) or Sullivan 

22 (2007). 

23 Whitehouse et al. (2008) identified four mesothelioma cases among workers that were 

24 not included in Sullivan (2007) with mortality follow-up in her paper through 2001; no other 

25 information was provided. It is most likely that three mesothelioma cases from these four were 

26 accounted for during the update of NIOSH cohort to 2006 (which serves as the basis for this 

27 assessment). Whitehouse et al. (2008) also provided detailed information on 11 residential cases, 

28 but this information could not be used in exposure-response analyses for this assessment, as there 

29 is no quantitative exposure information for these cases and no information defining or 

30 enumerating the population from which these cases arose. 

31 Mortality records (and death certificates) may not always reflect the true cause of death 

32 for various reasons (e.g., misdiagnosis, improper recording on the death certificate, or miscoding 

33 of the cause of death). For mesothelioma, the undercounting of cases (underascertainment) is a 

34 particular concern given the limitations of the ICD classification systems used prior to 1999 

35 (detection rates varied from 12% from ICD-9 codes alone to 83% from manual inspection of 
5-41 

1 death certificates [Davis et al., 1992]); recent studies demonstrated that ICD-10 coding has 

2 detection rates similar to the rates above (Pinhiero et al., 2004; Camidge et al., 2006). The 

3 appropriate procedure for pre-ICD-10 codes is not to use ICD codes alone but manually inspect 

4 death certificates, as was done by Sullivan (2007). There is also evidence that the detection rate 

5 of peritoneal mesothelioma is much lower than pleural mesothelioma (Selikoff and Seidman, 

6 1992). This assessment has accounted for the impact of this underascertainment on the final IUR 

7 (Section 5.4.5.1.1). 

8 Lung cancer mortality was based on the underlying cause of death identified by the ICD 

9 code on death certificates according to the ICD version in use at the time of death. Based on 

10 these different ICD codes, lung cancer mortality included malignant neoplasms of the trachea, 

11 bronchus, and lung, and was identified by the following codes: ICD-5 code ‘047’, ICD-6 and 

12 ICD-7 codes ‘162’ or ‘163’, ICD-8 and ICD-9 code ‘162’, and ICD-10 codes ‘C33’ or ‘C34’. In 

13 all, there were 111 deaths with an underlying cause of death attributed to lung cancer. All deaths 

14 after 1960 were coded as bronchus or lung as the ICD versions in use as that time distinguished 

15 malignant neoplasms of the trachea as distinct from bronchus and lung. Other investigators of 

16 this cohort have used different definitions of lung cancer or used different follow-up periods, as 

17 described in Section 4.1.1.2 (Description of Study Cohorts). 

18 

19 5.4.2.3. Description of Libby Amphibole Asbestos Exposures 

20 The mining, milling, and processing operations at the mine and in and around Libby, 

21 conditions of exposure, and job-specific estimates of exposure intensity have been thoroughly 

22 described in Section 4.1 (Amandus and Wheeler, 1987; Amandus et al., 1987a, 1987b; 

23 McDonald et al., 1986; Sullivan, 2007). Briefly, miners extracted vermiculite ore from an open24 pit mine that operated on Zonolite Mountain outside the town of Libby, MT. The ore was 

25 processed in a dry mill (1935–1974) and/or two wet mills (1950–1974 and 1974–1990). The 

26 resulting concentrate was transported by railroad to processing plants around the United States 

27 where the vermiculite was expanded for use in loose-fill attic insulation, gardening and other 

28 products (Section 2.1). 
5-42 

1 EPA adopted the job-exposure matrix (JEM) developed and used by Sullivan (2007) 

2 (Figure 5-6), which was, in turn, based on that used in the earlier NIOSH study for jobs through 

3 1982 (Amandus and Wheeler, 1987). As discussed in more detail in Section 4.1, Amandus et al. 

4 (1987a) defined 25 location operations to which they assigned exposure intensity based on 

5 available information (Table 5-8). A job category may have involved more than one location 

6 operation, and the 8-hour time-weighted average exposure (8-hr TWA) for each job category in 

7 the JEM was calculated from the exposure intensity and time spent at each location operation 

8 (Amandus et al., 1987a). 

9 

10 

11 Figure 5-6. Plot of the NIOSH job exposure matrix for different job 

12 categories over time. The height of each bar represents the intensity of exposure 

13 as an 8-hr TWA (fibers/cc) for a job in a particular year. Each row for “Selected 

14 Jobs” represents a specific job category.
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5-43 DRAFT—DO NO

Table 5-8. Exposure intensity (fibers/cc) for each location operation from the beginning of operatio

1982 (Amandus et al., 1987a) 

Location operation 

Year 

<50 50-59 60-63 64-67 68-70 71 72-74 75-76 

Downtown office building 0 0 0 0 0 0 0 0 Bus ride 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0 Mine office 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.5 Mine misc. 1.6 1.6 1.6 1.6 1.6 1.6 1.6 0.8 Mine—nondrilling 2.6 2.6 2.6 2.6 2.6 2.6 2.6 0.6 Transfer point 2.2 2.2 2.2 2.2 2.2 2.2 2.2 0.6 Quality control lab 13.1 13.1 13.1 2.6 2.6 2.6 2.6 0.6 Service area by mill 1.9 1.9 1.9 3.8 1.9 1.9 1.9 0.2 Dry mill 168.4 168.4 168.4 33.2 33.2 33.2 16.6 -- Dry mill sweeping 182.1 182.1 182.1 35.9 35.9 35.9 19 -- Old and New wet mill—millwright -- 7.0 7.0 7.0 7.0 7.0 7.0 0.6 Old wet mill—nonmillwright -- 3.7 3.7 3.7 3.7 3.7 3.7 -- New wet mill—nonmillwright -- -- -- -- -- -- 3.2 2.0 Skip area 88.3 88.3 88.3 17.4 17.4 17.4 4.8 0.6 Concentrate hauling 5.5 5.5 5.5 5.5 5.5 5.5 5.5 0.4 River station binside 21.2 21.2 21.2 21.2 21.2 21.2 21.2 0.7 River conveyor tunnel 112.5 112.5 112.5 112.5 112.5 112.5 112.5 0.3 River office binside 10.6 10.6 10.6 10.6 10.6 10.6 10.6 0.2 Verxite plant 22.6 22.6 2.8 2.8 2.8 -- -- -- Bagging plant 12.9 12.9 12.9 12.9 12.9 12.9 4.3 1.2 Tails belt 7.3 7.3 7.3 7.3 7.3 7.3 7.3 0.7 

1 
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5-44 DRAFT—DO NO

Table 5-8. Exposure intensity (fibers/cc) for each location operation from the beginning of operatio(continued) 

Location operation 

Year 

<50 50-59 60-63 64-67 68-70 71 72-74 75-76 

Screen plant -- -- -- -- -- -- -- 0.5 Drilling High 23 23 23 23 9.2 9.2 9.2 0.6 

Low 6.7 6.7 6.7 6.7 6.7 9.2 9.2 0.6 

Ore Loading High 82.5 27.7 10.7 10.7 3.2 3.2 3.2 0.2 

Low 24 15 9 9 3.2 3.2 3.2 0.2 

River dock High 116.9 42.5 17 17 17 5.1 5.1 0.5 

Low 38 19 6.4 6.4 5.1 5.1 5.1 0.5 

Bagging plant High 12.9 12.9 12.9 12.9 12.9 12.9 4.3 1.2 

Low 4.6 4.6 4.6 4.6 4.6 4.6 4.3 1.2 
This document is a draft for review purposes only and does not constitute Agency policy.

5-45 DRAFT—DO NOT CITE OR QUOTE 

1 From 1967 through 1982, over 4,000 air samples analyzed for fibers by PCM analysis 

2 were available to inform the exposure intensity for the 25 location operations (Table 5-8). 

3 Therefore, the JEM for 1968-1982 is based on direct analytic measurements in air for each 

4 location operation (Amandus et al., 1987a). With the exception of the dry mill, no air samples 

5 were available for other location operations at the mine and processing facilities prior to 1967. 

6 In order to estimate exposures which occurred before that time, the NIOSH researchers 

7 interviewed plant employees, and based estimates of exposure intensities on known changes in 

8 operations over the years and professional judgments regarding the relative intensity of exposure; 

9 exposure intensity for 23 of the pre-1967 location operations was extrapolated from post-1967 

10 measurements based on reasoned assumptions for each location operation (Amandus et al., 

11 1987a). 

12 However, the amount and quality of measurement data in the facility in earlier years was 

13 much more limited (Amandus et al. 1987a). A total of 40 dust samples were taken, exclusively 

14 in the dry mill, over the years 1950−1964. Using these measurements, much higher exposures 

15 were inferred to occur prior to 1964 than those measured in later years. Although air sampling 

16 for fibers by PCM was available beginning in 1967, average fiber concentrations (dry mill) 

17 differed rather widely between limited data sets from different investigators up through the early 

18 1970s: 1967-68, NIOSH data, 65 f/cc (n = 14); 1970, company data, 11 f/cc (n = 15); 1971, 

19 MSHA data, 31 f/cc (n = 52); 1972, MSHA and company data, 15 f/cc (n = 45). Thus, estimated 

20 exposure levels continue to be uncertain during the period when fiber concentration 

21 measurements by PCM became available in 1967. 

22 Air samples collected by the State of Montana were available for the dry mill from 

23 1956-1969, but these were analyzed for total dust, not asbestos fibers. Total dust samples 

24 (collected by a midget impinger) were examined by light microscopy, but no distinction was 

25 made between mineral dusts, debris, and asbestos fibers. All objects were counted and reported 

26 in the units of million particles per cubic foot of air (mppcf). Amandus et al. (1987a) developed 

27 a relationship between total dust and asbestos fiber counts based on comparison of 

28 contemporaneous air sampling in the dry mill (see Section 4.1.1.2). The conversion ratio of 

29 4.0 fibers/cc per mppcf was used to estimate exposure intensity for two location operations in the 

30 dry mill for the years prior to 1967. 

31 The exposure intensity (fibers/cc) for each of the location operations (Table 5-8) was 

32 used to calculate an estimate of daily occupational exposure for each job category in the JEM 

33 (Figure 5-6). For each job, the time spent at each location operation and the exposure intensity 

34 for each location operation were averaged to derive an estimate of the 8-hour TWA. The 

35 resulting JEM available for this assessment and previous epidemiologic studies of the Libby 
This document is a draft for review purposes only and does not constitute Agency policy.

5-46 DRAFT—DO NOT CITE OR QUOTE 

1 worker cohort is based on the air concentration of fibers as enumerated by PCM which measures 

2 fibers longer than 5 μm with an aspect ratio >3:1 (i.e., the fiber size regulated under the OSHA 

3 standard [U.S. Department of Labor, 2006]). Additionally, only fibers that are wide enough to 

4 be viewed on PCM can be detected with this method. Amandus et al. (1987a) considered fibers 

5 >0.44 μm in diameter to be visible by PCM in the historical filter analysis. More recent 

6 techniques have refined the PCM method and fibers greater than 0.25 μm in diameter are now 

7 considered PCM fibers (WHO, 1980). 

8 There was one important limitation of the NIOSH JEM. In the earlier study (Amandus 

9 and Wheeler, 1987), workers with “common laborer” job assignments and some workers with 

10 unknown job assignments hired between 1935 and 1959 were assigned the relatively low 

11 exposure levels estimated for the mill yard. In the more recent study by Sullivan (2007), these 

12 workers (686 from 991 hired before 1960) were assigned the same average estimated exposure 

13 intensity for all unskilled jobs during the relevant calendar time period. The lack of information 

14 on specific job assignments for these workers in the JEM resulted in the misclassification of the 

15 exposure and made exposure metrics, representative of duration of employment alone, describe 

16 this large part of the cohort. For this reason and because there was little measured fiber exposure 

17 data during the earlier period, difficulties were experienced identifying an adequate exposure18 response model fit. These difficulties are described in detail in Section 5.4.3.3. As a result, the 

19 IUR analyses were based on the subset of workers hired after 1959 (i.e., on or after January 1, 

20 1960) and consisted of 880 workers. Table 5-9 shows the demographic and exposure 

21 characteristics of this sub-cohort. 

22 In addition, re-abstracting work histories for the more recent study (Sullivan, 2007) 

23 identified several job assignments not mentioned in the earlier publications. Sullivan (2007) re24 abstracted work histories by extrapolating exposure estimates for the additional job and calendar 

25 time period-specific combinations based on professional experience and review of exposure 

26 records from earlier studies of the Libby worker cohort (Amandus and Wheeler, 1987; Amandus 

27 et al., 1987a, b; McDonald et al., 1986). While the Sullivan (2007) study was limited to the 

28 white male workers, EPA’s analysis includes all workers regardless of race or gender. 

29 Figure 5-6 shows a three-dimensional representation of the job-exposure matrix used by Sullivan 

30 (2007) and in this assessment. Not all jobs were included; thus, the figure is not comprehensive 

31 but rather illustrative. The three axes show the intensity of fiber exposure as an 8-hour TWA 

32 (fibers/cc, vertical axis) for selected job categories over time (horizontal axes). For several jobs,

33 the estimated 8 hr TWA was greater than 100 fibers/cc for the decades prior to 1963. Figure 5-6 

34 shows the variability in exposures across jobs and over time. From 1967-1982, all exposure 

35 measurements that inform the JEM are based on location-specific air samples analyzed for fibers 
This document is a draft for review purposes only and does not constitute Agency policy.

5-47 DRAFT—DO NOT CITE OR QUOTE 

1 by PCM. As stated above, pre-1968 exposures in the dry mill were based on measurement of 

2 dust levels from 1956-1967 that were converted to PCM by Amandus et al. (1987a) and 

3 extrapolated backwards in time. Pre-1968 exposures for all other locations within the JEM were 

4 extrapolated from post-1967 fiber levels based on reasoned assumptions (Amandus et al., 

5 1987a). 

6 

7 
This document is a draft for review purposes only and does not constitute Agency policy.

5-48 DRAFT—DO NOT CITE OR QUOTE 

1 Table 5-9. Demographic and exposure characteristics of the subset of the 

2 Libbyworker cohort hired after 1959 

3 

Characteristic Sub-cohort hired after 1959

Number of workers 880 

Number of deaths from all causes 230 

Number of deaths from mesothelioma 7 

Number of deaths from lung cancer 32 

Mean year of birth 1942 

Mean year of hire 1971 

Mean age at hire (years) 28.6 

Mean person-years of follow-up (no lag) 32.2 

Total person-years of follow-up (no lag) 28,354 

Mean employment duration (years) 3.3 

Mean cumulative exposure (fibers/cc-years) 19.2 

Median cumulative exposure (fibers/cc-years) 3.4 

4 

5 

6 Amandus and coworkers (1987a) recognized the uncertainty in the pre-1968 exposures 

7 assigned to the cohort. Although there is some uncertainty in the dust–to-fiber conversion, this 

8 conversion (4.0 fibers/cc per mppcf) was based on contemporaneously collected dust and fiber 

9 data collected in the dry mill and only applied to the dry mill environment. Amandus and 

10 coworkers considered a range of possible conversion factors (1.2-11.5 fibers/cc per mppcf). 

11 Greater uncertainty may lie with the reasoned assumptions used to extrapolate exposures to the 

12 early decades for all location operations considered. For example, there were four location 

13 operations for which Amandus and coworkers estimated a range of possible exposure intensities: 

14 drilling location site, ore loading location site, river dock, and the bagging plant, where intensity 

15 of exposure may vary as much as 3-fold between the low and high estimates (Table 5-8). 

16 Finally, some workers were employed after 1982 through 1993 in plant close-out and clean-up 

17 operations, which have not been evaluated (Sullivan, 2007). However, none of the members of 

18 the post-1959 cohort (n = 880) remained employed by 1982 according to employment records. 

19 

20 5.4.2.4. Description of Libby Worker Cohort Work Histories 

21 NIOSH staff and contractors abstracted demographic data and work history data from 

22 company personnel records, including W-2 federal tax forms. An individual’s work history was 
This document is a draft for review purposes only and does not constitute Agency policy.

5-49 DRAFT—DO NOT CITE OR QUOTE 

1 determined from job change slips, which recorded new job assignment, date of change, and 

2 change in hourly pay rate (which differed by the job assignment). Work history records span the 

3 time period from September 1935 to May 1982. Dates of termination were unknown for 58 of 

4 640 workers (9%), who left employment before September 1953. EPA adopted the assumption 

5 used by NIOSH (Sullivan, 2007) that these people worked for 384 days, based on the mean 

6 duration of employment among all workers with known termination dates before September 

7 1953. The majority of workers in this cohort as a whole and among those hired on or after 

8 January 1, 1960 worked at multiple jobs; many of the workers switched jobs repeatedly. Of the 

9 880 hired in 1960 or afterwards, the mean number of job changes was 5, the median was 2, and 

10 the maximum number of job changes was 32 (Figure 5-7).

11 

12 

13 

14 Figure 5-7. Histogram showing the number of workers who experienced 

15 each incremental number of different jobs among the 880 workers hired 

16 after 1959. 

N

u

m

b

e

r 

o

f 

W

o

r

k

e

r

s

Number of Jobs Held by Worker 
This document is a draft for review purposes only and does not constitute Agency policy.

5-50 DRAFT—DO NOT CITE OR QUOTE 

1 5.4.2.5. Estimated Exposures Based on Job Exposure Matrix and Work Histories 

2 Exposure-response modeling of epidemiologic data is based on several considerations as 

3 summarized by Finkelstein (1985): 

4 

5 After identification of an occupational hazard one of the goals of occupational 

6 epidemiology is to quantify the risks by determining the dose-response relations for the 

7 toxic agent. In many circumstances little is known about the dose received by target 

8 tissues; the data available usually pertain only to exposure to various concentrations of 

9 the toxic material in the workplace. The calculation of dose requires additional 

10 physiological and chemical information relating to absorption, distribution, biochemical 

11 reactions, retention, and clearance. In asbestos epidemiology the usual measure of 

12 exposure is the product of the concentration of asbestos dust in the air (fibres or particles 

13 per ml) and the duration of exposure to each concentration summed over the entire 

14 duration of exposure (years); this measure is the cumulative exposure. 

15 

16 Cumulative exposure has been the traditional method of measuring exposure in 

17 epidemiologic analyses of many different occupational and environmental exposures and was the 

18 exposure metric applied to the risk of lung cancer mortality in the IRIS assessment for general 

19 asbestos (U.S. EPA, 1988). Two alternative approaches to developing exposure metrics to 

20 describe the effects of concentrations of asbestos dust in the air on the risks of mortality have 

21 also been proposed. The first alternative was proposed by Jahr (1974) who studied silica22 induced pneumoconiosis and suggested that exposures to occupational dusts could be weighted 

23 by the time since exposure. This yields an exposure metric which gives greater weight to earlier 

24 exposures. Berry et al. (1979) subsequently suggested the application of exposure metrics that 

25 allowed for the clearance of dust or fibers by using a decay term on exposures. For the 

26 evaluation of mortality risk from mesothelioma, U.S. EPA (1988) used a different exposure 

27 metric than was used for lung cancer mortality which factored in the time since first exposure. 

28 As observed in U.S. EPA (1988), it is important to note that different characterizations of 

29 estimated ambient exposures may be reasonably expected to be associated with different 

30 endpoints. 

31 Most studies of asbestos-related mortality have evaluated either cumulative exposure, 

32 exposure concentration, or the duration of employment as exposure metrics. Many studies have 

33 been limited in the availability of detailed exposure data—especially at the individual level. In 

34 the Libby worker cohort data developed by NIOSH and used in this assessment, detailed work 
This document is a draft for review purposes only and does not constitute Agency policy.

5-51 DRAFT—DO NOT CITE OR QUOTE 

1 histories, together with job-specific exposure estimates, allowed for the reconstruction of each 

2 individual’s occupational exposure experience over time to define multiple exposure metrics. 

3 From this information-rich, individual-level dataset from NIOSH, EPA constructed a 

4 suite of the different metrics of occupational exposure which had been proposed in the asbestos 

5 literature or used in the IRIS asbestos assessment (U.S. EPA, 1988). This suite of models was 

6 defined a priori to encompass a reasonable set of proposed exposure metrics to allow sufficient 

7 flexibility in model fit to these data. These exposure metrics were evaluated in analytic8 regression models to test which exposure metrics were the best empirical predictors of observed 

9 cancer mortality and the better fitting models were advanced for consideration as the basis of the 

10 exposure-response relationship for the IUR. The types of exposure metrics evaluated were 

11 intended to allow for variations of the classic metric of cumulative exposure, allowing for more 

12 or less weight to be placed on earlier or later exposures. These simulated exposure metrics were 

13 derived mathematically to approximate underlying processes that are not well understood (see 

14 Section 5.4.6). Thus the fit of exposure metrics is evaluated on the basis of maximizing the 

15 likelihood for the Libby worker cohort and the estimated parameters do not necessarily have 

16 biological interpretations. 

17 The first exposure metric—cumulative exposure (CE)—is a simple addition of each day 

18 of exposure across time (Eq. 5-1). CE has been widely used in modeling risk of cancer in 

19 occupational epidemiology and has been used for modeling lung cancer (McDonald et al., 2004, 

20 Sullivan, 2007, Larson et al., 2010a, Moolgavkar et al., 2010) and mesothelioma (McDonald et 

21 al., 2004) in the Libby worker cohort. When using this exposure metric in the risk model, all 

22 exposures have equal weight regardless of when they occurred and lead to the same estimated 

23 cancer risk whether exposure happened early or later in life. 

24 EPA calculated each individual’s occupational CE to Libby Amphibole asbestos over 

25 time from their date of hire until the date they ceased to be employed in the Libby operations. 

26 Workers were assumed to remain at their final occupational CE level until death or the end of the 

27 follow-up period on December 31, 2006. Each worker’s CE at any time point (daily increment) 

28 since their date of hire was computed as the sum of their exposure intensity (fibers/cc) on each 

29 specific occupational day (xt) from day 1 through day k. Mathematically, this was defined as 

30 

31 

32 Cumulative exposure at time tk = (Eq. 5-1) 

33 

34 where 

∑=

k

j

t

j

x

1
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5-52 DRAFT—DO NOT CITE OR QUOTE 

xt

j

 = the estimated job-specific exposure intensity for the day tj 1 , and 

2 tk = the day on which the exposure is estimated. 

3 

4 

5 A second exposure metric—residence time-weighted (RTW) exposure—gives additional 

6 weight to early exposures. By doing so, the RTW exposure metric allows the possibility that 

7 early exposures are more influential on cancer mortality predictions in the model. Unlike many 

8 chemicals that are rapidly metabolized in the body and excreted, asbestos fibers are durable and 

9 some may remain in the body for years. Fibers that remain in the lung may continue to damage 

10 lung cells and tissue until they are removed or cleared (Section 3.2). Similarly, fibers that 

11 translocate to the pleura may damage cells as long as they remain in this tissue. Therefore, a 

12 fiber exposure may not only damage tissue during the exposure, but fibers may remain in these 

13 tissues, with cellular and tissue damage accumulating over time. 

14 The RTW exposure metric in this assessment is sometimes called the cumulative burden, 

15 or the area under the curve. A type of RTW metric was proposed for modeling of mesothelioma 

16 mortality by Newhouse and Berry (1976) based on a general understanding of the relationship 

17 between tumor incidence rate and time to cancer (Cook et al., 1969) as well as animal models of 

18 mesothelioma (Berry and Wagner, 1969). Similar types of RTW metrics were applied to the 

19 insulators asbestos cohort by Peto et al. (1982), discussed by Finkelstein (1985), and applied in 

20 the derivation of the IUR in the IRIS assessment for asbestos (U.S. EPA, 1988). McDonald et al. 

21 (2004) and Moolgavkar et al. (2010) used RTW-type metrics for modeling mesothelioma in the 

22 Libby worker cohort, and McDonald et al. (2004) applied an RTW metric for modeling lung 

23 cancer mortality in the Libby worker cohort. 

24 In calculating RTW, each day’s exposure is multiplied by the time since the exposure 

25 occurred (Eq. 5-2). Residence time-weighted CE was calculated as a cumulative function of 

26 each time-interval’s CE such that earlier exposures contribute greater weight. 

27 

28 

Residence time-weighted CE at time tk = ∑∑= =

k

j

j

i

t

i

x

1 1

29 (Eq. 5-2) 

30 

31 where 

xt

i

 = the estimated job-specific exposure intensity for the day ti 32 , and 

33 tk = the day on which the exposure is estimated. 

34 
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5-53 DRAFT—DO NOT CITE OR QUOTE 

1 

2 The CE and RTW exposure metrics result in increasing or sustained metrics of exposure 

3 across time. However, it is known that some cellular and genetic damage may be repaired over 

4 time, which could decrease cancer risk from exposure over time. Additionally, asbestos fibers 

5 are cleared (removed) from the lung through natural processes and translocated to other tissues 

6 (Section 3.2.1.1). Therefore, when considering lung cancer, it is possible that removal of 

7 asbestos fibers from the lung would reduce lung cancer risk over time. Although less is known 

8 about removal of asbestos from the pleura, there may be clearance mechanisms operative in that 

9 tissue as well (Section 3.2.1.2). As noted earlier, Berry et al. (1979) proposed the use of 

10 exposure metrics based on occupational exposures which addressed the issue of clearance using 

11 a mathematical decay term to modify measured ambient exposures. For mesothelioma, modeling 

12 a decay term on exposure has been proposed by Berry (1999). Based on this proposal, several 

13 recent papers applied a decay term to modeling mesothelioma mortality (Reid et al., 2009; Berry 

14 et al., 2009; Barone-Adese et al., 2008; Gasparrini et al., 2008; Clemens et al., 2007; Hodgson et 

15 al., 2005; Berry, 2004). Similarly, recent publications indicate that the relative risk of lung 

16 cancer due to asbestos exposure declines 15-20 years after the cessation of exposure to asbestos 

17 (Magnani et al., 2008; Hauptmann et al., 2002). 

18 Mathematically allowing for the magnitude of earlier exposures to diminish with 

19 advancing time was considered to be a method of giving less weight in the analyses to earlier 

20 exposures compared to the previous two exposure metrics. Therefore, two additional exposure 

21 metrics were considered, where a decay rate was applied to the CE and RTW exposure metrics 

22 (Eqs. 5-3 and 5-4). 

23 For each exposure metric, the application of a half-life was calculated by depreciating 

24 each time-interval’s (tj-1;tj) exposure according to a model of exponential decay with various 

25 half-lives (T1/2 ) of 5, 10, 15, and 20 years. Note that the particular fiber kinetics of Libby 

26 Amphibole asbestos fibers are not fully understood and the relevance of these particular half27 lives was determined from the statistical fit of these exposure metrics to the risk of cancer 

28 mortality, rather than the biological half-life of the fibers. For a very large half-life, decay is 

29 very slow, and these metrics would be very similar to the CE and RTW metrics. 

30 

31 

CE with half-life at time tk = 

( ) ( ) ∑=









































∗ −

∗

k

j

k j

t

T

t t

x

j

1 2

1

ln 5.0

exp 32 (Eq. 5-3) 

33 
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RTW with half-life at time tk = 

( ) ( ) ∑∑= =









































∗ −

∗

j

i

k i

t

k

j

T

t t

x

i

1 2

1

1

ln 5.0

exp 1 (Eq. 5-4) 

2 

3 where 

xt

j

 = the estimated job-specific exposure intensity for the day tj 4 , and 

5 tk = the day on which the exposure is estimated. 

6 

7 

8 In addition to the exposure metrics used in the lung cancer mortality analysis, modeling 

9 of mesothelioma mortality (Section 5.4.3) included the exposure model used in the IRIS 

10 assessment for asbestos (U.S., 1988), originally proposed in Peto et al. (1982): 

11 

12 

13 Im = C·Q·KM (Eq. 5-5) 

14 

15 where 

16 Im = the observed deaths from mesothelioma/person-years, 

17 C = the average concentration of asbestos in the air, 

18 KM = an estimated slope describing the relationship between Libby amphibole 

19 exposure and mesothelioma mortality, and 

20 Q = the function of the time since first exposure (t) and the duration of employment 

21 (d): 

22 For t ≤ 10, Q = 0 

For 10 < t ≤ d + 10, Q = (t – 10)323 

For t > d + 10, Q = (t – 10)3 – (t – 10 – d)324 . 

25 

26 

27 The asbestos IUR (U.S., EPA, 1988) metric was originally fit to aggregate cohort data 

28 and was based on a function of average cohort exposure, time since first exposure, and duration 

29 of employment. The analysis here of individual-data for Libby Amphibole asbestos is, therefore, 

30 a different application of this exposure metric, and it’s fit to the mesothelioma mortality of the 

31 Libby worker cohort is evaluated in this assessment. 

32 In addition to the use of these methods of describing exposure metrics representing 

33 estimated ambient exposure to Libby Amphibole asbestos dust for use in predicting the risk of 

34 mortality there is the important issue of potential modifying the exposure metrics to account for 
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1 cancer latency. Without knowledge of the specific timing of etiologically relevant exposure 

2 which may initiate and promote cancers ultimately resulting in mortality, any exposure metric 

3 may include exposures during some time period which do not have bearing on the risk of 

4 mortality. In the absence of such information on the specific cancer latency associated with a 

5 specific exposure, Rothman (1981) suggested that the most relevant exposure period could be 

6 identified by comparing the fit of exposure metrics across multiple lag periods to allow for the 

7 identification of the optimal latency period as an expression of a lag time between exposure and 

8 mortality. This has since become a standard practice in occupational and environmental 

9 epidemiology. Accordingly, exposure estimates for all exposure metrics were adjusted to 

10 account for the time period between the onset of cancer and mortality. The lag period defines an 

11 interval before death, or end of follow-up, during which, any exposure is excluded from the 

12 calculation of the exposure metric. Cohort members who died or were lost within the initial 

13 years of follow-up were assigned lagged exposure values of zero if they had not been followed 

14 for longer than the lag time. The various exposure metrics were lagged at 10, 15, and 20 years to 

15 account for different potential cancer latencies within the limitations of the available data. While 

16 mesothelioma is known to have an average latency as long as 55 years among selected 

17 occupations (e.g., Bianchi and Bianchi, 2009), several mesothelioma deaths in the cohort 

18 occurred within 30 years from the start of the exposure, suggesting a shorter latency period in 

19 this population. Metrics without a lag were fit for comparison purposes but were not considered 

20 to be biologically reasonable, given that the outcome under analysis is cancer mortality 

21 (specifically, mesothelioma and lung cancer), for which latency periods of 10 years or more have 

22 been established for asbestos (U.S. EPA, 1988). Consequently, metrics that were not adjusted by 

23 lagging exposure in the final years before mortality (or the end of follow-up) were not 

24 considered further in the development of an IUR for Libby Amphibole asbestos. 

25 

26 5.4.3. Exposure-Response Modeling 

27 Sufficient biological information to select models for the epidemiology data on the basis 

28 of biological mechanism (Section 3) is not available. In this situation, EPA’s practice is to 

29 investigate a range of model forms to determine how to best empirically model the exposure 

30 response relationship in the range of the observed data. For Libby Amphibole asbestos, possible 

31 exposure metrics were explored for model fit to the chosen models. The exposure metric options 

32 were selected to provide a range of shapes that was sufficiently flexible to allow for a variety of 

33 ways that time and duration might relate to cancer risk in the data being modeled. EPA then 

34 evaluated how well the models and exposure metric combinations fit the data being modeled. 

35 Metrics that did not fit the data well were rejected. For purposes of calculating a reasonable 
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1 upper-bound on the risk per exposure EPA accounted for uncertainty in the choice of exposure 

2 metrics by using the exposure metric (among those of reasonable fit) that estimated the highest 

3 risk. This is explained in more detail below and in Sections 5.4.4-5.4.5. 

4 The risk estimates are based on epidemiological analysis of the primary NIOSH data 

5 (Libby worker cohort). The rationale for selection of the Libby worker cohort is presented in the 

6 previous section (Section 5.4.2). Analysis of this primary epidemiologic database allows the 

7 comparison of multiple metrics of exposure to quantify the exposure-response relationship. This 

8 approach is intended to support the empirical representation of the exposure-response 

9 relationship of estimated ambient occupational exposure to Libby Amphibole asbestos with 

10 observed cancer mortality risk. The exposure-response modeling may be influenced by 

11 uncertainties in the magnitude and time course of the exposure estimates and, therefore, may not 

12 necessarily reflect the biologic disposition of inhaled fibers, but simply, the empirical 

13 observation of estimated exposure to Libby Amphibole asbestos, and subsequent mortality as the 

14 mode of action, is not well understood (Section 5.4.6). 

15 

16 5.4.3.1. Modeling of Mesothelioma Exposure-Response in the Libby Worker Cohort 

17 The background incidence of mesothelioma is extremely rare (Hillerdal, 1983). Since 

18 there is a very low background risk, the exposure-response model applied here examines the 

19 relationship of the absolute risk of mesothelioma mortality that is attributable to Libby 

20 Amphibole asbestos exposure because there is not a true background risk of mesothelioma 

21 mortality among people who were truly unexposed to Libby Amphibole asbestos (as opposed to 

22 the relative risk model, which is used for lung cancer mortality; see Section 5.4.3.2). Poisson 

23 regression models are employed here for estimating the absolute risk of mesothelioma, as the 

24 Poisson distribution is an appropriate model for use with data that are counts of a relatively rare 

25 outcome, such as observed mesothelioma deaths in the Libby worker cohort. Other analyses of 

26 mesothelioma mortality in the Libby worker cohort have also used the Poisson model 

27 (McDonald et al., 2004; Moolgavkar et al., 2010). In the Poisson model, probability of k events 

28 is specified as 

29 

30 

 



	


!

31 (Eq. 5-6) 

32 

33 

34 where 
 is parameterized with the exposure metric (defined in Section 5.4.2.5). Then, life table 

35 analysis is used to estimate risks in the general U.S. population for derivation of the unit risk of 

36 mesothelioma mortality (Section 5.4.5.1). 
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1 Estimation of the exposure-response relationship for mesothelioma mortality using the 

2 Poisson model was performed using a Monte Carlo Markov Chain (MCMC) Bayesian approach 

3 with an uninformative or diffuse prior (WinBUGS Version 1.4 [Spiegelhalter et al., 2003]). Use 

4 of diffuse priors is a standard procedure in Bayesian analysis, in situations like this one, when 

5 there is no prior knowledge about the toxicity of Libby Amphibole asbestos under a particular 

6 model. Since this analysis focuses only on the Libby worker cohort and does not try to factor in 

7 data from other sources in estimating potency, use of a diffuse prior is considered appropriate for 

8 this analysis. 

9 The benefit of using the WinBUGS software is its computational ease and that it provides 

10 a posterior distribution of β (the mesothelioma slope factor) rather than just a point estimate. A 

11 diffuse (high variance) Gaussian distribution, truncated to exclude negative parameter values, is 

12 used as a diffuse prior. With such a prior, results of MCMC analysis are expected to be similar 

13 to maximum likelihood estimation in a non-Bayesian analysis. Standard practices of MCMC 

14 analysis were followed for verifying convergence and sensitivity to the choice of initial values. 

15 The posterior distribution is based on three chains with a burn-in of 10,000 (i.e., the first 10,000 

16 simulations are dropped so that remaining samples are drawn from a distribution close enough to 

17 the true stationary distribution to be usable for estimation and inference) and thinning rate of 10 

(i.e., only each 10th 18 simulation is used - thus reducing autocorrelation) such that 3,000 total 

19 simulations constitute the posterior distribution of β. The mean of the posterior distribution 

served as a central estimate, the 90% credible interval1 defined the 5th percentile and the 95th 20 

percentile of the distribution, which served as bounds for the 95th 21 lower and upper one-sided 

22 confidence intervals, respectively. 

23 Multiple metrics of exposure (Section 5.4.2.5) as well as exposure intensity, duration of 

24 employment, age at death or loss to follow-up, and time since first exposure were compared 

25 using the Deviance Information Criterion (DIC). DIC (Spiegelhalter et al., 2002) is used in 

26 Bayesian analysis and is an analogue of the Akaike Information Criterion (AIC), with smaller 

27 values indicating a better statistical fit to the data. Use of the DIC and AIC is standard practice 

28 in comparing the fit of non-nested models to the same dataset with the same dependent outcome 

29 variable, but different independent covariates. According to Burnham and Anderson (2002), 

30 ‘These methods allow the data-based selection of a “best” fitting model and a ranking and 

31 weighting of the remaining models in a pre-defined set.’ Because of the small number of deaths 

32 from mesotheliomas in absolute terms, only uni- and bi-variate models (with age or time since 

33 first exposure as the second covariate) were considered. Sex and race were not used as 

34 covariates since all mesotheliomas were observed in men assumed to be white (Sullivan, 2007). 

 

1

 A credible interval is the Bayesian analogue of a confidence interval. 
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1 Each exposure metric was lagged by 0, 10, 15, or 20 years. The use of a lag period aims to 

2 account for the latency period between the onset of mesothelioma (which occurs some time 

3 before clinical diagnosis) and mesothelioma mortality. 

4 

5 Results of mesothelioma mortality analysis 

6 For the full Libby worker cohort (n = 1,871), the duration of employment provided a 

7 considerably better univariate model fit than the other possible exposure metrics, indicating that 

8 this exposure metric was the best single predictor of mesothelioma mortality in the full Libby 

9 worker cohort. The bivariate model which included duration of employment and age at death or 

10 censoring provided the overall best fit (DIC = 196). The inclusion of information on the 

11 concentration of exposure beyond the duration of employment resulted in a degradation in model 

12 fit (Table 5-10). The metric used in the IUR for asbestos (U.S. EPA, 1988) (Eq. 5-5) had a 

13 higher DIC of 233.7 in the analysis here. It is likely that the poorer fit seen when using 

14 information on exposure concentration is the result of the fact that duration of employment is 

15 measured with comparatively little error, while derivation of specific exposure concentrations 

16 may be subject to a sizable measurement error. Moreover, as described in Section 5.4.2.3, for 

17 686 of 991 (69%) workers hired from 1935 to 1959, only the duration of employment was 

18 known, but not the job category, and thus the same exposure concentration had been assigned to 

19 653 of these workers (Sullivan, 2007). It is likely that because of the potential for particularly 

20 large exposure measurement error among more than two thirds of the workers hired prior to 1960 

21 who were assigned the same exposure intensity, this resulted in the duration of employment 

22 being the best predictor of mesothelioma mortality. Additionally, estimates of exposure intensity 

23 prior to 1968 have greater uncertainty associated with them than more recent exposure 

24 measurements which are based on fiber counts in air samples analyzed by PCM. For the 

25 majority of job locations (23 of 25), no exposure measurements were available prior to 1968, and 

26 exposures were estimated based on employee interviews (in 1982) and what was known about 

27 major changes in operations between 1935 and 1967. For two exposure locations, the dust–to28 fiber conversion ratio is based on measurements taken in the late 1960s, so extrapolations from 

29 the mid-1960s to the early 1960s is likely to be more certain than extrapolation further back in 

30 time. The fact that the metric using only duration of employment fit best and the additional 

31 incorporation of exposure intensity information worsened the fit indicates that it is unlikely that 

32 IUR estimates can be developed using the full cohort data because exposure values were not 

33 predictive of mesothelioma mortality. 

34 
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1 Table 5-10. Comparison of univariate model fit of various exposure metrics 

for mesothelioma mortality in the full Libby worker cohort (n = 1,871)a,b 2 

3 

Variable DIC 

Duration of employment 202.9 

Age at death or censoring 209.2 

CE lagged 15 yr 209.5 

CE lagged 10 yr 209.9 

RTW lagged 10 yr with 5yr 1⁄2 life 210.4 

CE lagged 10 yr with 20yr 1⁄2 life 210.6 

RTW with 5 yr 1⁄2 life 210.7 

RTW with 10 yr 1⁄2 life 211.0 

CE 211.4 

Time since first exposure 211.4 

4 

a

5 Since one of the mesothelioma deaths occurred less than 20 years from start of the exposure, lag 20 metrics 

6 assigned no exposure to this case which resulted in the very poor fit of exposure metrics lagged 20 years. 

b

7 Lower DIC values represent better fits. Models with DIC within 10 units of the DIC of the model with the lowest 

8 DIC are shown. 

9 DIC = Deviance Information Criterion. 

10 

11 

12 The DIC values for models that included lag and/or half-life adjustments to the exposure 

13 metrics were not penalized in the regression analyses for including these extra parameters 

14 because those factors were not represented as covariates but rather were embedded in the 

15 exposure metrics. While these results were obtained using each instance with lag and/or half-life 

16 as a separate model fit, it may be appropriate to penalize the DIC values from these results for 

17 inclusion of these parameters. Note that if the DIC values from the lag and/or half-life models 

18 were penalized, this would serve to improve the relative fit of the model using only duration as a 

19 parameter in comparison with the lag and/or half-life models because the DICs for the penalized 

20 models would increase while the DIC for the un-penalized models would be unchanged. 

21 

22 5.4.3.2. Modeling of Lung Cancer Exposure-Response in the Libby Worker Cohort 

23 To develop an exposure-response relationship for lung cancer, the lung cancer mortality 

24 data were modeled as a function of the historical exposure data for the Libby worker cohort. The 

25 mesothelioma mortality data were modeled to estimate the absolute risk because it is very rare in 

26 the general population (Hillerdal, 1983).. Lung cancer mortality does have a known background 
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1 risk and thus, modeling of lung cancer mortality is based on the relative risk rather than the 

2 absolute risk. As such, there are different analytic methods available which can use information 

3 on time-varying exposures. The NIOSH-developed individual-level exposure data for the Libby 

4 worker cohort are very detailed, with start and stop dates for each of the workers’ jobs and 

5 estimated fiber exposures for 25 specific location-operations (Amandus et al., 1987). It is, 

6 therefore, important to find a model that makes efficient and effective use of these time7 dependent data. 

8 The Cox proportional hazards model (Cox, 1972) is one of the most commonly used 

9 statistical models used for the epidemiologic analysis of survival and mortality in cohort studies 

10 with extensive follow-up (Larson et al., 2010a; Moolgavkar et al., 2010). In the Cox model, the 

11 conditional hazard function, given the covariate Z, is assumed to have the form 

12 

13 

( | ) ( ) exp( ) t Z 0t Z

T

14 λ = λ β (Eq. 5-7) 

15 

16 

17 where β is the vector of regression coefficients, λ0(t) denotes the baseline hazard function, and T 

18 denotes transposition. One of the strengths of this model is that knowledge of the baseline risk 

19 function is not necessary and no particular shape is assumed for the baseline hazard; rather, it is 

20 estimated nonparametrically. The contributions of covariates to the hazard are multiplicative. 

When Z represents exposure and β

T

21 Z is small, the Cox proportional hazards model is consistent 

22 with linearity of dose response for low doses. 

23 When the proportional hazards assumption holds, it is possible to estimate the hazard 

24 ratio of exposure (relative risk) without estimating the hazard function in the unexposed (or in 

25 the lowest exposures seen within the study group) since this baseline hazard function drops out 

26 of the calculations. The Cox proportional hazards model assumes that a function of covariates 

27 (i.e., exposures) result in risks that are a constant multiple of the baseline hazard in unexposed 

28 individuals over some time scale, typically calendar time or age. This proportionality is assumed 

29 to be constant across the range of observed exposures, given the set of modeled covariates, and 

30 can be evaluated across time. 

31 The Cox proportional hazards model was chosen to model the lung cancer mortality data 

32 for several reasons. Of primary importance is that it takes statistical advantage of the extensive 

33 exposure data collected for the cohort on time-varying exposures to Libby Amphibole asbestos. 

34 There are no other standard model formulations that allow for the analysis of time-varying 

35 exposures in the manner achieved by the Cox proportional hazards models. The exposure36 response relationship (proportional hazards ratio) determined in this model intrinsically takes 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 into account the effects of other causes of mortality that are unrelated to exposure (i.e., 

2 independent censoring). Further, all comparisons are made within the cohort by comparing the 

3 mortality experience of people with different exposures within the same cohort population. The 

4 issue of competing risks that are dependent on exposure (e.g., asbestosis or non-malignant 

5 respiratory disease) is an acknowledged uncertainty for this type of analysis (see Section 5.4.6). 

6 Other methods common to occupational epidemiology, such as the use of standardized 

7 mortality ratios (SMRs), typically rely upon comparisons of the mortality experience in an 

8 exposed population group compared to that in the general population. However, the comparison 

9 population may not always be appropriate due to differences in general health status (e.g., the 

10 healthy worker effect) and differences in exposure to other risk factors for a specific disease 

11 (e.g., smoking history). The lack of comparability between the study population and the 

12 comparison population can lead to confounding by other measured or unmeasured characteristics 

13 which may be statistically associated with both the exposure of interest and the endpoint. The 

14 Cox proportional hazards model controls for such potentially confounding characteristics by 

15 using a comparison group from within the study population (i.e., internal controls). Internal 

16 controls are a statistically appropriate comparison group because they are expected to be more 

17 similar in potentially confounding characteristics to the remainder of the cohort, thereby 

18 controlling for both measured and unmeasured confounding and helping ensure that comparisons 

19 are more statistically valid. 

20 

21 Results of lung cancer mortality analysis 

22 As described in the previous section, quantitative exposure-response relationships for 

23 lung cancer mortality were evaluated using the Cox proportional hazards model. Cox models of 

24 this type require the specification of a time scale. Age is typically the time-related variable, with

25 the strongest relationship to cancer mortality, and was used as the timescale in these analyses. 

26 Use of age as the time scale in a time-varying Cox proportional hazards model controls for age 

27 as a risk factor by design rather than by parametric modeling and effectively rules out age as a 

28 potential confounder. Individual covariates available to EPA in the complete analytic dataset 

29 compiled from the NIOSH data were evaluated for their ability to explain the lung cancer 

30 mortality. These included sex, race, birth year, age at hire, and various exposure-related 

31 variables including TWA workplace intensity of exposure in fibers/cc, job type, and the start and 

32 stop date of each different job. These data allowed for the computation of cumulative exposure, 

33 cumulative exposure with application of a half-life, and residence time-weighted cumulative 

34 exposure (with and without application of a half-life). Each exposure metric was also lagged by 

35 0, 10, 15, or 20 years. The use of a lag period aims to account for the latency period between the 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 onset of lung cancer (which occurs some time before clinical diagnosis) and lung cancer 

2 mortality. 

3 All lung cancer mortality analyses were conducted using SAS software version 9.1 (SAS, 

4 Cary, NC). There are two forms of the Cox model considered in this evaluation: the standard 

5 model, which allows only time-independent covariates, and the extended Cox model, which 

6 allows for time-dependent variables. Both forms of the Cox model rely on the assumption that 

7 the hazards are proportional.

8 Standard Cox proportional hazard models assess the risk of exposures at one particular 

9 point in time (e.g., death or loss to follow-up). While an individual’s exposures may have 

10 changed over time, the only exposure value included in the analysis is the value of the exposure 

11 metric at the time of the event (i.e., death) or censoring at the end of the mortality follow-up 

12 period on December 31, 2006. Since Cox proportional hazard models rely on the assumption 

13 that the hazard rate among the exposed is proportional to the hazard rate among the unexposed, it 

14 is important to test the model against this assumption. Therefore, analyses of standard Cox 

15 models (based on a single summary exposure value for each person) tested this assumption using 

16 a Wald test on the model interaction term between the Libby Amphibole asbestos exposure 

17 metric and the time scale (i.e., age). As a general rule, a nonzero slope that is either increasing or 

18 decreasing indicates a violation of the proportional hazards assumption. Wald tests for the 

19 complete cohort showed that the interaction term was a statistically significant predictor of lung 

20 cancer mortality (p < 0.05) and was interpreted as evidence that the hazards did not remain 

21 proportional over time. The cause of the lack of proportionality is unknown, but several likely 

22 explanations are discussed in Section 5.4.3.3. 

23 Having observed that the proportional hazards assumption was violated when using the 

24 time-independent exposure data, the EPA fit a variation of the extended Cox proportional 

25 hazards model (Kleinbaum, 1996; Tableman and Kim, 2004), which included both time26 independent factors such as sex, race, and date of birth, as well as time-dependent measures of 

27 Libby Amphibole asbestos exposure over the entire time course of each individuals’ lifetime 

28 from their date of hire until death or loss to follow-up. This method also allows for control of 

29 potential confounding by age by design rather than through multivariate covariate modeling. 

30 EPA’s analyses of time-dependent exposure data also checked the proportionality 

31 assumption for the Libby worker cohort and again found that it failed to remain proportional 

32 across the time scale based on age given the modeled covariates. While this methodology of 

33 using the extended Cox proportional hazards model does not have as strong a dependence on the 

34 assumption of truly proportional hazards (Kleinbaum, 1996; Tableman and Kim, 2004) it is still 

35 preferable to have the proportionality assumption hold. 
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1 

2 5.4.3.3. Summary of Analysis of Libby Worker Cohort 

3 Several possible explanations exist for the finding that duration of employment was the 

4 best fitting exposure metric for mesothelioma mortality, as well as the finding of the lack of 

5 proportionality of hazards in the lung cancer mortality modeling. 

6 

7 1) Duration of employment, but not job category, was known for 686 of 991 (69%) workers 

8 hired from 1935 to 1959. Without knowledge of the job category, the same exposure 

9 concentration had been assigned to 653 of these workers, likely resulting in a particularly 

10 large measurement error for exposure in approximately one third of the total cohort of 

11 1,871 workers. This is a very likely explanation for the superior fit for duration of 

12 employment in modeling of mesothelioma mortality relative to the other exposure 

13 metrics based on measured exposures. Assigning the same exposure concentration to so 

14 many of the workers hired before 1960, regardless of job, likely resulted in significant 

15 exposure misclassification. Random error in exposure measurements generally attenuates 

16 the strength of epidemiologic associations between exposure and observed effect, 

17 weakening the predictive ability of any of the exposure-based metrics compared to 

18 duration of employment, which was more accurately determined for all workers in the 

19 cohort. 

20 2) Even where the job category was identified, few exposure data exist prior to 1968. For 

21 the majority of job locations (23 of 25), no exposure measurements were available prior 

22 to 1967, and so exposures were estimated based on employee interviews (conducted in 

23 1982) to determine what was known about major changes in operations between 1935 

24 and 1967. For two job locations, dust–to--PCM extrapolations are based on 

25 measurements taken in the late 1960s, so extrapolating from the mid-1960s to the early 

26 1960s is likely to be more certain than extrapolating further back in time. Random error 

27 in these exposure measurements would also generally attenuate the strength of 

28 association between exposure and observed effect during the earlier years of mine 

29 operation and, thus, a greater degree of measurement error in the earlier years could have 

30 resulted in the lack in proportionality of the hazard ratios for lung cancer over time. A 

31 greater degree of measurement error in the earlier years could also provide an explanation 

32 for the worse fit of the mesothelioma models that incorporated these exposure measures. 

33 3) Another explanation for the lack of proportional hazards in modeling lung cancer 

34 mortality may be that this cohort has an anomalous age structure due to the hiring of 

35 much older individuals during the time of the Second World War. Among those workers 

36 in the cohort hired prior to 1960, 9% were older than 50 years, and 22% were older than 

37 40 years. Among those workers hired in 1960 or afterwards, only 4% were older than 50 

38 years, and 14% were older than 40 years. Older workers differ from younger workers in 

39 several potentially important ways that could alter their response to exposures. Older 

40 workers were born in a different era, with different nutritional and public health standards 

41 which may influence mortality patterns. 
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1 4) The lack of proportional hazards in modeling lung cancer mortality may also be a 

2 reflection of confounding or effect modification, which can change in magnitude over 

3 time. The most likely candidate for confounding or effect modification is smoking. 

4 NIOSH records show that of the 1,871 workers in the full Libby workers cohort, 1,121 

5 workers (60%) were missing smoking status data, while 750 (40%) had data with values 

6 ‘S’ (Smoker), ‘Q’ (Former Smoker), or ‘N’ (Non-smoker). Given this high percentage of 

7 missing values, EPA did not consider these smoking data to be adequate for use in the 

8 evaluation of confounding or effect modification. 

9 Smoking rates, over time, among the sub-cohort of workers hired after 1959 are likely to 

10 have been more similar since smoking rates change more slowly over shorter periods of 

11 time than over longer ones. This restriction in time period of hiring would also result in 

12 less variation by birth year cohort, which is strongly related to smoking patterns as people 

13 of different generations developed different smoking rates. Thus, this restriction in the 

14 time period of hiring may make the cohort members more similar to each other thereby 

15 reducing the potential impact of any smoking-related confounding. Further discussion of 

16 the relevance of smoking can be found in the section on uncertainties (Section 5.4.6). 

17 

18 When the assumption of proportionality is not met, the potential influence of 

19 confounding factors in the full-cohort analysis is of concern. Additionally, the lack of job 

20 category information for 69% of the workers hired prior to 1960 and greater measurement error 

21 in early exposures may result in significant random exposure measurement error, which may bias 

22 the observed exposure-response relationships towards the null. 

23 Although duration of employment was the best exposure metric for modeling 

24 mesothelioma mortality in the full cohort, it made quantitatively estimating an exposure-response 

25 relationship difficult. In addition, violation of the underlying statistical assumptions adversely 

26 impacted modeling of lung cancer mortality in the full cohort. Therefore, EPA chose to 

27 undertake a sub-cohort analysis. 

28 In particular, because uncertainty in retrospective assessment of workplace exposures is 

29 reduced in the later years, EPA decided to analyze a sub-cohort of all the workers with as late a 

30 starting employment date as possible, while still maintaining a sufficient number of lung cancer 

31 and, especially, mesothelioma mortalities. The last of the 686 workers with only duration of 

32 employment available as an exposure metric was hired in 1959, and so EPA developed a sub33 cohort analysis by dividing the total cohort into those hired prior to 1960 (n = 991) and those 

34 hired after 1959 (n = 880). This cut point roughly divided the cohort in half. For the sub-cohort 

35 of those workers hired after 1959, there were sufficient numbers of both mesothelioma and lung 

36 cancer mortalities to apply the Poisson and Cox proportional hazards model, correspondingly. 

37 EPA initially examined the fit of these models using several exposure metrics to predict 

38 mortality from mesothelioma and found that in this sub-cohort, the exposure metrics that 
This document is a draft for review purposes only and does not constitute Agency policy.

5-65 DRAFT—DO NOT CITE OR QUOTE 

1 included information on exposure concentration provided superior statistical fits to the exposure 

2 metrics based only on employment duration. In this same sub-cohort, the assumptions of the 

3 Cox proportional hazards model were also satisfied for the modeling of time-varying exposure. 

4 While it is generally true that the use of more data is an advantage in statistical analyses 

5 because it allows for the computation of more statistically precise effect estimates, this advantage 

6 in precision may be offset by a negative impact on the accuracy of the effect estimate if an 

7 increase in sample size is accompanied by greater exposure misclassification or other biases. 

8 EPA’s use of the sub-cohort analysis is based on the belief that it is important to accurately 

9 estimate the true underlying exposure-response relationships by relying on the most accurate 

10 exposure data (see also Section 5.4.6 on uncertainties). 

11 

12 5.4.3.4. Analysis of Sub-cohort of Employees Hired After 1959 

13 5.4.3.4.1. Results of analysis of mesothelioma mortality in the sub-cohort 

14 Of the 880 workers hired after 1959, 230 (26%) had died by December 31, 2006. The 

15 number of mesothelioma deaths in the sub-cohort is seven (2 deaths coded in ICD-10 and 5 

16 deaths coded in ICD-9), and the mesothelioma death rate of 24.7 per 100,000 person-years for 

17 the sub-cohort is similar to the mesothelioma death rate of 26.8 per 100,000 person-years for the 

18 full cohort (18 mesothelioma deaths), with a difference of less than 10%. 

19 Table 5-11 shows the relative fit of various exposure metrics for mesothelioma mortality 

20 in the sub-cohort hired after 1959, including only those exposure metrics whose information 

21 weight was greater than 0.01. Information weights are computed from the reported DICs 

22 (Burnham and Anderson, 2002). As discussed below, metrics with higher DICs and lower 

23 information weights are unlikely to provide a good fit and are thus not included in Table 5-11. 

24 Information weights are commonly used in Bayesian analyses. Information weights can be 

25 computed by first assessing the differences between the best DIC and each of the others (∆ DICi) 

26 

27 

1

1 1

exp exp

2 2

R

i i i

r

DIC w DIC DIC

=

    = − ∆ − ∆         28 ∑ (Eq. 5-8) 

29 

30 Table 5-11. Comparison of model fit of exposure metrics for mesothelioma 

mortality in the sub-cohort hired after 1959a,b 31 . Only the model fits with 

32 information weights greater than 0.010 are shown

33 

Exposure metric Lag(yr) DIC Information Weight 

CE with 5yr 1⁄2 life 15 70.6 0.428 
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CE with 5yr 1⁄2 life 10 72.8 0.143 

CE with 10yr 1⁄2 life 10 73.9 0.082 

CE with 10yr 1⁄2 life 15 74.0 0.078 

CE with 10 yr 1⁄2 life 0 74.5 0.061 

CE with 5 yr 1⁄2 life 0 75.0 0.047 

CE with 15 yr 1⁄2 life 10 75.7 0.033 

CE with 15 yr 1⁄2 life 0 76.0 0.029 

CE with 15 yr 1⁄2 life 15 76.1 0.028 

CE with 20 yr 1⁄2 life 10 76.7 0.020 

CE with 20 yr 1⁄2 life 0 77.0 0.017 

CE with 20 yr 1⁄2 life 15 77.2 0.016 

1 

a

2 Lower DIC values represent better fits. 

b

3 Since one of mesothelioma deaths occurred in less than 20 years from start of the exposure, lag 20 metrics assigned 

4 no exposure to this case and the very poor fit of lag 20 metrics is a result.

5 DIC = Deviance Information Criterion. 

6 

7 

8 The other exposure metrics that were fit included those metrics used in the full cohort 

9 analysis (duration of employment, time since first exposure, age at death or censoring, RTW 

10 metrics, CE with lag metrics and IRIS IUR (1988) metric), but all of them fit worse than any of 

11 the metrics in Table 5-11, irrespective of possible penalization for extra parameters as discussed 

12 in the analysis of the full cohort. The two metrics with cumulative exposure lagged 15 and 10 

13 years, both with 5-year half-life, providing the two best fits as indicated by their lower DIC 

14 values and higher information weights (Table 5-11). Cumulative exposures lagged 10 or 15 

15 years, both with 10-year half-life, provided the next two best fits according to DIC values, but 

16 models including each of these metrics exhibited noticeably lower information weights than the 

17 best metric. All metrics in Table 5-11 contain a decay term and have the same number of 

18 parameters in their corresponding model, allowing for a direct comparison of the DIC values 

19 (DICs are similar to AICs in what is considered an important difference) and information 

20 weights. 

21 It is important to note that the suite of exposure metrics which were applied in this 

22 assessment to modeling mesothelioma mortality encompass the range of choices described in 

23 the asbestos literature including CE, RTW and decay metrics as well as the IRIS IUR(1988) 

24 metric. 
This document is a draft for review purposes only and does not constitute Agency policy.

5-67 DRAFT—DO NOT CITE OR QUOTE 

1 In the sub-cohort hired after 1959, the DIC value for mesothelioma using the IRIS IUR 

2 (1988) metric (Eq. 5-5) is substantially higher (DIC=98.4) than for any of the metrics in 

3 Table 5-11. This indicates that the IRIS IUR (1988) metric does not provide as good a fit for the 

4 Libby Amphibole asbestos worker cohort, using the estimated historical exposure levels, as the 

5 other metrics in Table 5-11. Setting the exponents in the IRIS IUR (1988) metric to the values of 

6 2 and 4, as suggested by Nicholson et al. (1980), did not improve the fit of the metric to the 

7 Libby Amphibole asbestos worker cohort data (results not shown). A substantial difference of 

8 this analysis from the IRIS IUR (1988) modeling is that this analysis is based on individual-level 

9 data, whereas IRIS IUR (1988) application was to aggregate data. Also, cohorts used in IRIS 

10 IUR (1988) did not include cohorts exposed to Libby amphibole asbestos and Libby Amphibole 

11 asbestos may be different from other types of asbestos. Alternately, the relative fit of this model 

12 may have been affected by uncertainties in the estimated exposure described in detail in 

13 Section 5.4.6. 

14 Next, EPA considered which covariates should be added to the model with the exposure 

15 metric that provided the best fit. The addition of covariates ‘age at death or censoring’ and ‘time 

16 since first exposure’ did not improve the fit, as measured by DIC (results not shown). 

17 As described in Section 5.4.1.5, only metrics with non-zero lag were retained for 

18 derivation of unit risks. Table 5-12 shows slopes and credible intervals for all retained metrics 

19 from Table 5-11. The units of the slopes are fibers/cc-year. These slopes and credible intervals 

20 represent calendar year continuous environmental exposure as described above and define the 

21 ‘Exposed Hazard Rate’ in the life-table procedure when multiplied by the exposure level (see 

22 Appendix G for details).

23 Based on the results from the exposure metric with the lowest DIC (cumulative exposure 

24 with a 5-year half-life for decay and a 15-year lag for cancer mortality latency), the slope was 

2.06 x 10-4 25 per fiber/cc-year based on a 365-day calendar year and the 95% upper bound on the 

slope was 3.43 x 10-4 26 per fiber/cc-year. This point estimate and 95% upper bound represent the 

27 relative risk (including statistical uncertainty within the exposure metric) of mesothelioma 

28 mortality observed from exposure to Libby Amphibole asbestos fibers in the worker cohort for 

29 this exposure metric. Issues related to uncertainty in the choice of exposure metric are described 

30 further in the section on the derivation of the combined IUR of mesothelioma and lung cancer 

31 (Section 5.4.5.3). 
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1 Table 5-12. Mesothelioma mortality exposure metrics fits, slopes, and 

2 credible intervals 

3 

Exposure metric Lag years DIC Slope x10-590% CI for slope x10-5

CE – 5 yr 1⁄2 life 15 70.6 20.6 (10.2, 34.3) 

CE – 5 yr 1⁄2 life 10 72.8 31.1 (15.2, 50.8) 

CE – 10 yr 1⁄2 life 10 73.9 9.93 (5.00, 16.3) 

CE – 10 yr 1⁄2 life 15 74.0 7.78 (3.72, 12.9) 

CE – 15 yr 1⁄2 life 10 75.7 6.17 (3.04, 10.1) 

CE – 15 yr 1⁄2 life 15 76.1 5.30 (2.63, 8.69)

CE – 20 yr 1⁄2 life 10 76.7 4.71 (2.34, 7.71)

CE – 20 yr 1⁄2 life 15 77.2 4.27 (2.12, 6.98) 

4 

5 

6 5.4.3.4.2. Results of the analysis of the lung cancer mortality in the sub-cohort 

7 EPA based its final analyses for lung cancer mortality on the subset of workers hired after 

1959. Thus, this analysis is based on 32 deaths from lung cancer2

8 (ICD-8: two deaths with the 

9 code 162.1; ICD-9: one death with the code 162.2, 20 deaths with the code 162.9; ICD-10: nine 

10 deaths with the code C349) out of 230 deaths that occurred in sub-cohort of 880 workers. 

11 All multivariate Cox proportional hazards models with time-varying exposures were 

12 initially fit, using one exposure metric at a time, to the sub-cohort hired after 1959 employing, 

13 with covariates for sex, race, and date of birth. Lung cancer mortality was modeled using 

14 cumulative exposure and residence-time-weighted exposure, where each metric was potentially 

15 modified by four different half-lives (5, 10, 15, or 20 years). Each of these exposure metrics was 

16 also evaluated with four different lag period to allow for cancer latencies of 0, 10, 15, or 20 

17 years. The lag period is defined as immediately prior to observed cancer death, where exposure 

18 is not considered to be causally related to mortality. In all, 40 exposure-response multivariate 

19 models were evaluated for the adequacy of the exposure metric to fit the epidemiologic data. 

20 Each exposure metric and the comparative model fit statistics are presented in Table 5-13. 

21 The assumptions of the Cox proportional hazards models were re-evaluated for the sub22 cohort. Restricting the cohort addressed each of the previously listed potential explanations for 

 

2

 Note that in the full cohort, it was unclear whether there were cases of tracheal cancer included in the definition of 

lung cancer as many of the recorded ICD codes on death certificates did not provided sufficient detail to distinguish 

tracheal cancer cases from lung cancer cases. However, among the sub-cohort of workers hired after 1959, all the 

deaths from the broader category of cancers of the lung, bronchus, and trachea did provide sufficient detail to show 

that there were no deaths from tracheal cancer. 
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5-69 DRAFT—DO NOT CITE OR QUOTE 

1 Table 5-13. Model fit comparison for different exposure metrics and lung 

2 cancer mortality associated with Libby Amphibole asbestos, controlling for 

3 age, gender, race, and date of birth 

4 

Ordered by exposure metric Ordered by model fit 

Exposure metric

Lag 

(yr) AIC Exposure metric

Lag 

(yr) AIC 

Multivariate 

model p-value

Exposure 

p-value 

CE 0 361.610 CE 10yr 1⁄2 life 10 358.400 0.0071 0.0009 

CE 10 361.073 CE 5yr 1⁄2 life 10 358.502 0.0075 0.0010 

CE 15 363.124 CE 15yr 1⁄2 life 10 358.777 0.0084 0.0015 

CE 20 364.964 CE 20yr 1⁄2 life 10 359.122 0.0098 0.0022 

CE 20yr 1⁄2 life 0 361.123 CE 5yr 1⁄2 life 15 359.910 0.0138 0.0032 

CE 20yr 1⁄2 life 10 359.122 CE 10yr 1⁄2 life 15 360.543 0.0181 0.0079 

CE 20yr 1⁄2 life 15 361.533 CE 10 361.073 0.0227 0.0188 

CE 20yr 1⁄2 life 20 364.703 CE 20yr 1⁄2 life 0 361.123 0.0232 0.0155 

CE 15yr 1⁄2 life 0 361.382 CE 15yr 1⁄2 life 15 361.129 0.0232 0.0162 

CE 15yr 1⁄2 life 10 358.777 CE 15yr 1⁄2 life 0 361.382 0.0258 0.0184 

CE 15yr 1⁄2 life 15 361.129 CE 20yr 1⁄2 life 15 361.533 0.0276 0.0254 

CE 15yr 1⁄2 life 20 364.588 RTW 5yr 1⁄2 life 0 361.593 0.0283 0.0309 

CE 10yr 1⁄2 life 0 362.169 CE 0 361.610 0.0285 0.0307

CE 10yr 1⁄2 life 10 358.400 CE 10yr 1⁄2 life 0 362.169 0.0360 0.0358 

CE 10yr 1⁄2 life 15 360.543 RTW 10yr 1⁄2 life 0 362.283 0.0378 0.0588 

CE 10yr 1⁄2 life 20 364.342 RTW 15yr 1⁄2 life 0 362.714 0.0452 0.0863 

CE 5yr 1⁄2 life 0 364.225 RTW 20yr 1⁄2 life 0 362.973 0.0503 0.1084 

CE 5yr 1⁄2 life 10 358.502 CE 15 363.124 0.0535 0.1215 

CE 5yr 1⁄2 life 15 359.910 RTW 5yr 1⁄2 life 10 363.224 0.0558 0.1343 

CE 5yr 1⁄2 life 20 363.644 CE 5yr 1⁄2 life 20 363.644 0.0662 0.1751 

RTW 0 363.869 RTW 0 363.869 0.0726 0.2397 

RTW 10 364.835 RTW 10yr 1⁄2 life 10 364.041 0.0778 0.2810 

RTW 15 364.990 CE 5yr 1⁄2 life 0 364.225 0.0838 0.2908 

RTW 20 364.502 RTW 15yr 1⁄2 life 10 364.336 0.0876 0.3733 

RTW 20yr 1⁄2 life 0 362.973 CE 10yr 1⁄2 life 20 364.342 0.0878 0.3661 

RTW 20yr 1⁄2 life 10 364.477 RTW 20yr 1⁄2 life 10 364.477 0.0927 0.4314 

RTW 20yr 1⁄2 life 15 365.011 RTW 20 364.502 0.0936 0.5307 

RTW 20yr 1⁄2 life 20 364.628 CE 15yr 1⁄2 life 20 364.588 0.0969 0.4815 

RTW 15yr 1⁄2 life 0 362.714 RTW 20yr 1⁄2 life 20 364.628 0.0985 0.5763 

5 
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Table 5-13. Model fit comparison for different exposure metrics and lung 

cancer mortality associated with Libby Amphibole asbestos, controlling for 

age, gender, race, and date of birth (continued) 

Ordered by exposure metric Ordered by model fit 

Exposure metric

Lag 

(yr) AIC Exposure metric

Lag 

(yr) AIC 

Multivariate 

model p-value

Exposure 

p-value 

RTW 15yr 1⁄2 life 10 364.336 RTW 15yr 1⁄2 life 20 364.662 0.0998 0.5909 

RTW 15yr 1⁄2 life 15 365.001 CE 20yr 1⁄2 life 20 364.703 0.1014 0.5530 

RTW 15yr 1⁄2 life 20 364.662 RTW 10yr 1⁄2 life 20 364.719 0.1021 0.6188 

RTW 10yr 1⁄2 life 0 362.283 RTW 5yr 1⁄2 life 15 364.768 0.1041 0.6021 

RTW 10yr 1⁄2 life 10 364.041 RTW 5yr 1⁄2 life 20 364.831 0.1067 0.6884 

RTW 10yr 1⁄2 life 15 364.962 RTW 10 364.835 0.1069 0.6586 

RTW 10yr 1⁄2 life 20 364.719 RTW 10yr 1⁄2 life 15 364.962 0.1124 0.8173 

RTW 5yr 1⁄2 life 0 361.593 CE 20 364.964 0.1125 0.8204 

RTW 5yr 1⁄2 life 10 363.224 RTW 15 364.990 0.1136 0.8809 

RTW 5yr 1⁄2 life 15 364.768 RTW 15yr 1⁄2 life 15 365.001 0.1141 0.9100 

RTW 5yr 1⁄2 life 20 364.831 RTW 20yr 1⁄2 life 15 365.011 0.1146 0.9599 

1 

2 CE: Cumulative exposure with or without exponential decay modeled with different half-lives. RTW: Residence3 time weighted exposure with or without exponential decay with different half-lives. AIC: Akaike Information 

4 Criterion. 

5 

6 

7 the lack of hazard proportionality (see Section 5.4.3.3). First, measurement error for exposures 

8 is likely to have been smaller after 1959 for several reasons. One reason is that the 686 workers 

9 for which job category information was missing during part or all of their employment were 

10 removed from the analysis. Also, beginning in 1968, fiber concentrations by PCM analysis of 

11 site-specific air samples were available for all location operations to inform the JEM. Prior to 

12 1968, the exposure intensity for 23 of 25 location operations was estimated based on reasoned 

13 assumptions informed by employee interviews in the early 1980s. It is likely the uncertainty of 

14 these reasoned assumptions increased over time, making the earliest exposure estimates (1940s 

15 and 1950) less certain than those only a few years before fiber count data were available. 

16 Finally, between 1956 and 1967, dust-to-PCM extrapolation data were used to estimate 

17 exposures in the dry mill based on measurements taken in the late 1960s. Although there is some 

18 uncertainty in the conversion ratio selected by Amandus and coworkers (1987a), conversions 

19 from dust to fiber counts is likely to be less uncertain than extrapolations further backwards in 

20 time to the 1950s and 1940s, where only one air sample for dust was available in 1944. Thus, 
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1 the potential attenuation effect of non-differential measurement error is likely to be reduced by 

2 examining the post-1959 cohort alone compared to the entire cohort. 

3 In addition, by focusing on the more homogeneous age distribution of workers hired after 

4 1959, concerns about differential cancer mortality latency were diminished. Third, smoking 

5 rates among this more narrowly defined sub-cohort are likely to have been more homogeneous, 

6 and thus, restricting analysis to this sub-cohort would help to limit any potential confounding due 

7 to smoking. Finally, when the extended Cox proportional hazards model was applied to the sub8 cohort hired post-1959, there was no evidence to reject the hypothesis of proportionality, and the 

9 exposure models demonstrated adequate fits to the data, with statistically significant effect 

10 estimates. In each of the Cox proportional hazards model analyses with time-varying 

11 exposures—across all the exposure metrics and across all the lag lengths—no violations of the 

12 assumption of proportionality of hazards were found. 

13 As the exposure-response models cannot strictly be considered to be nested, a standard 

14 measure of fit called the Akaike Information Criterion (AIC [Burnham and Anderson, 2002]) 

15 was used for comparison of goodness of fit across models based on the same data set. In their 

16 text on model selection, Claeskens and Hjort (2008) state that ‘for selecting a model among a list 

17 of candidates, Akaike’s information criterion (AIC) is among the most popular and versatile 

18 strategies.’ Smaller AIC values generally indicate a better fitting model relative to larger AIC 

19 values. While large differences in AIC values can reveal important differences in model fit, 

20 small differences are less conclusive. Nonetheless the model yielding the smallest AIC ‘is 

21 judged the best one’ (Claeskens and Hjort, 2008) and it is a common practice in environmental 

22 epidemiology to select the single model with the best statistical fit among the models that were 

23 evaluated. However, models differing in AIC by 2 units or less may not always be meaningfully 

24 differentiated from each other on the basis of AIC (Burnham and Anderson, 2002). 

25 Table 5-13 shows the models and exposure metrics ordered by fit. Of interest is whether 

26 there are models with distinct exposure metrics that adequately fit these data (as measured by 

27 statistical significance of the model p-value) and then, a measure of relative weight among these 

28 adequately fitting models. Of the 40 exposure-response metrics, 14 demonstrated an adequate fit 

29 to the data as measured by the overall model fit, with the likelihood ratio test being statistically 

30 significant (p < 0.05), as well as having statistically significant exposure metrics (p < 0.05). 

31 However, note that only the nine models that demonstrated adequate model and exposure metric 

32 fit and incorporated a lag period to account for lung cancer mortality latency were advanced for 

33 potential use in developing a unit risk. While metrics that did not include an adjustment for lag 

34 on the exposure metric to account for cancer mortality latency were fit to these data for the sake 

35 of completeness, they were dropped from further consideration because they implicitly assume 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 no passage of time between the initiation of cancer, subsequent promotion of that cancer, and 

2 mortality. 

3 Several general patterns were discernable with respect to which exposure metrics best 

4 predicted lung cancer mortality when comparing AICs for relative model fit. The data show that 

5 lagging exposure by 10 years best predicts lung cancer mortality compared to other lags. This 

6 trend is seen across both the cumulative exposure without decay and the various half-life 

7 cumulative exposure metrics where a 10-year lag of exposure best predicts lung cancer mortality 

8 for all cumulative exposure metrics compared to other lags; metrics with 15-year lags were 

9 generally the next best in terms of fit. Another clear conclusion is that the models that included 

10 residence time-weighted exposure metrics, regardless of half-life or lag, were less suitable than 

11 the models that employed cumulative exposure and its variants. 

12 Among the 40 exposure metric models that were evaluated, the exposure model with the 

13 lowest AIC value was for cumulative exposure with a 10-year half-life for decay and a 10-year 

14 lag for cancer mortality latency and had a model p-value of 0.0071 (Table 5-13). This 

15 multivariate model controlled for age, gender, race, and date of birth. The extended Cox model 

with time-varying exposures estimated a slope (beta) of 1.26 x 10-2 16 per fiber/cc-year based on a 

365-day calendar year3 and the 95th percentile upper bound on this parameter was 1.88 x 10-2 17 per 

18 fiber/cc-year. The p-value for the Libby Amphibole asbestos regression coefficient (slope) was 

19 <0.001 indicating that this parameter was statistically significantly greater than zero. 

20 According to the model results presented in Table 5-13, there were other exposure 

21 metrics that predicted lung cancer mortality and exhibited statistically significant effect 

22 estimates. Several other metrics were considered to fit nearly as well as the model with the 

23 smallest AIC since their AIC values were within two units of the exposure model with the lowest 

24 AIC, a proximity that can be considered to be a range that cannot clearly differentiate between 

25 models (Burnham and Anderson, 2002). As each of the other exposure metrics was based on a 

26 different reorganization of the same exposure data, the different slopes are not directly 

27 comparable, but all adequately fitting lagged models also produce statistically significant slopes 

28 for the exposure-response relationship (p < 0.05). Of particular note are the results of the 

29 cumulative exposure model, with a 10-year lag for latency, but without a decay function, since it 

30 showed the lowest AIC among non-decay models. 

31 The AIC values for models that included lag and/or half-life adjustments to the exposure 

32 metrics were not penalized in the regression analyses for using these extra parameters because 

33 these factors were not represented as covariates but rather were embedded in the computation. 

 

3

 The two-sided 90% confidence interval is (6.00 x 10

-3, 1.88 x 10-2); the two-sided 95% confidence interval is (5.12 

 x 10-3, 2.00 x 10-2). 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 While these results were obtained using each instance of lag and/or half-life terms in separate 

2 model fit, it may be appropriate to mathematically penalize the AICs for inclusion of these 

3 additional parameters. AIC values, as typically computed by regression software, include the 

4 addition of a penalty for model complexity as measured by the number of parameters that are fit 

5 in the regression model (thereby increasing the AIC). In the AIC calculations presented in Table 

6 5-13 the models are treated as having the same number of parameters since each model 

7 represents the same exposures in a different way but with a single exposure parameter in the 

8 regression models and are, therefore, equally penalized in software’s AIC calculation. However, 

9 an argument can be made that exposure metrics, which do not include a decay function with their 

10 half-life term, are implicitly more parsimonious (simpler) because they do not include decay. 

11 Statistical adjustment for this type of model complexity is not straightforward, as an exposure 

12 metric without decay can also be thought of as an exposure metric with a sufficiently long decay 

13 (since human life is finite). In this sense, if the models without decay are indistinguishable from 

14 models with a sufficiently long half-life, then there is no difference in the inherent complexity. 

15 If the decay model fits were penalized for the inclusion of the decay function in the computation 

16 of the exposure metric then with such an adjustment, the relative fit of the CE models would 

17 improve as compared to the values in Table 5-13 (AICs are generally penalized 2 units for each 

18 additional parameter). However, the difference between the lowest AIC and the AIC of the CE 

19 model without decay exceeds any reasonable penalty, and thus the model results for cumulative 

20 exposure with a 10-year half-life for decay and a 10-year lag for cancer mortality latency would 

21 still have the lowest AIC regardless of whether a penalty was applied. 

22 Table 5-14 displays the lagged exposure-response models and metrics with adequate 

23 model fit (p < 0.05) to the epidemiologic data that were further considered. Table 5-14 shows 

24 slopes and confidence intervals for all retained metrics from Table 5-13. The units of the slopes 

25 are fibers/cc-year. These slopes and confidence intervals represent calendar year continuous 

26 environmental exposure as described above and define the ‘Exposed Hazard Rate’ in the life27 table procedure when multiplied by the exposure level (see Appendix G for details). 

28 

29 5.4.3.4.3. Summary of results of the analysis of the lung cancer mortality in the sub-cohort 

30 As presented in Table 5-14 the CE model with 10 year half-life and lag provided an 

31 adequate fit to the data (p < 0.05) and had the lowest AIC value. The cumulative exposure 

32 model with a 10-year lag also yielded a statistically adequate fit to these data (p < 0.05), as did 

33 several decay models with a 15-year lag. These results demonstrate reasonable uncertainty in the 

34 metric of exposure such that no single exposure model can be definitively selected based on 
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5-74 DRAFT—DO NOT CITE OR QUOTE 

1 goodness of fit alone. Uncertainty in the metric of exposure is addressed in Section 5.4.5.3 as 

2 IUR is based on the plausible upper bound of the effect estimate. Based on the results from the

3 Table 5-14. Lung cancer mortality exposure metrics fits, slopes, and 

4 confidence intervals 

5 

Exposure 

metric 

Lag 

years AIC 

Slope 

(Beta) SE 

Exposure

p-value 90% CI for the slope 

CE 10yr 1⁄2 life 10 358.400 0.0126 0.0038 0.0009 (0.0063, 0.0189) 

CE 5yr 1⁄2 life 10 358.502 0.0179 0.0055 0.0010 (0.0089, 0.0269) 

CE 15yr 1⁄2 life 10 358.777 0.0106 0.0033 0.0015 (0.0052, 0.0160) 

CE 20yr 1⁄2 life 10 359.122 0.0095 0.0031 0.0022 (0.0044, 0.0146) 

CE 5yr 1⁄2 life 15 359.910 0.0155 0.0052 0.0032 (0.0069, 0.0241) 

CE 10yr 1⁄2 life 15 360.543 0.0115 0.0043 0.0079 (0.0044, 0.0186) 

CE 10 361.073 0.0058 0.0025 0.0188 (0.0017, 0.0099)

CE 15yr 1⁄2 life 15 361.129 0.0097 0.0040 0.0162 (0.0031, 0.0163) 

CE 20yr 1⁄2 life 15 361.533 0.0087 0.0039 0.0254 (0.0023, 0.0151) 

6 

7 

8 lowest AIC multivariate model (i.e., cumulative exposure with a 10-year half-life for decay and 

a 10-year lag for cancer mortality latency), the slope was 1.26 x 10-2 9 per fiber/cc-year based on a 

365-day calendar year and the 95% upper bound on the slope was 1.88 x 10-2 10 per fiber/cc-year. 

11 This point estimate and 95% upper bound represent the relative risk (including statistical 

12 uncertainty within exposure metric) of lung cancer mortality observed from exposure to Libby 

13 Amphibole asbestos fibers in the worker cohort for this exposure metric. Issues related to 

14 uncertainty in the choice of exposure metric are described further in the section on the derivation 

15 of the combined IUR of mesothelioma and lung cancer (Section 5.4.5.3).

16 

17 5.4.3.4.4. Sensitivity analysis of the influence of high exposures in early 1960s on the model 

18 fit 

19 As discussed in Section 5.4.2.5, the comparison of model fit between various exposure 

20 metrics is an empirical process and does not reflect either a specific biological or other factor as 

21 an underlying cause for model fit. Although data do not exist to evaluate biological bases for 

22 model fit, other potential factors can be explored where data allow. For example, because of 

23 concerns that very high (>100 fibers/cc) early (1960-1963) 8-hour time-Libby Amphibole 

24 asbestos TWA exposures may have influenced the relative fit of the various exposure metrics, 

25 EPA conducted a sensitivity analysis of the impact of removing the 59 workers with such 
This document is a draft for review purposes only and does not constitute Agency policy.

5-75 DRAFT—DO NOT CITE OR QUOTE 

1 exposures on the relative model fit. It is important to note that excluding these workers left only 

2 5 mesothelioma deaths and 28 lung cancer deaths among 821 workers. The follow-up time for 

3 the 880 member sub-cohort was 28,354 person-years (mean 32.2), while excluding the 59 highly 

4 exposed workers resulted in 26,009 person-years (mean 31.7). The reduction in cancer-specific 

5 deaths and the abbreviation in the range of exposures leads to a diminished ability to differentiate 

6 the relative fit (i.e. smaller AIC/DIC differences) between the various exposure metrics due to 

7 reduced statistical power. 

8 For modeling mesothelioma mortality, the absolute and relative differences in fit 

9 diminished with the exclusion of these highly exposed workers, but the observation that exposure 

10 metrics including decay fit better than exposure metrics without decay was unchanged. 

11 Moreover, the relative fit of the metrics remained almost unchanged (Table 5-15). Among this 

12 abbreviated data set, the relative fit of the best of cumulative exposure metrics including decay 

13 was at least three units lower DIC than the best of cumulative exposure metrics not involving 

14 decay (Table 5-15). Abbreviated dataset DIC for model used in IRIS IUR (1988) was 69.4. 

15 

16 Table 5-15. Relative fit of mesothelioma exposure metrics for full and 

17 abbreviated data sets. Only the best of non-decay metrics is shown. DIC 

18 cannot be compared across columns as they are based on different numbers 

19 of observations. 

20 

Exposure

metric 

Halflife Lag

All workers higher after 1959 

(n = 880); based on 7 

mesothelioma deaths 

DIC 

(As shown in Table 5-11) 

Excluding highly exposed workers 

(n = 821); based on 5 

mesothelioma deaths 

DIC 

CE 5 15 70.6 56.5 

CE 5 10 72.8 57 

CE 10 10 73.9 57 

CE 10 15 74 57.3 

CE 15 10 75.7 57.7 

CE 15 15 76.1 58.3 

CE 20 10 76.7 58.1 

CE 20 15 77.2 58.2 

CE 10 80.8 59.7 

21 

22 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 For modeling lung cancer mortality, among this abbreviated data set, based on AIC 

2 comparisons, there was no clear distinction between the fit of the same exposure models that fit 

3 the sub-cohort of workers hired after 1959 and included highly exposed workers during 1960-

4 1963 (Table 5-16). 

5 

6 Table 5-16. Sensitivity analysis of model fit comparison for different 

7 exposure metrics and lung cancer mortality associated with Libby 

8 Amphibole Asbestos controlling for age, gender, race, and date of birth. 

9 Excludes n = 59 workers who had job codes with exposure intensity greater than 

10 100 f/cc during 1960-63. Lung cancer models presented include those with 

11 statistically significant multivariate model p-value and non-zero lag in exposure. 

12 AIC cannot be compared across columns as they are based on different numbers 

13 of observations

14 

Exposure 

Metric

Lag

(yr)

All workers higher after 1959 

(n = 880)

Based on 32 deaths from lung cancer

(As shown in Table 5-13)

Excluding highly exposed workers 

(n = 821)

Based on 28 deaths from lung cancer

AIC

Multi-variate 

Model

p-value

Exposure

p-value AIC

Multi-variate 

Model

p-value

Exposure

p-value

CE 10yr 1⁄2 life 10 358.400 0.0071 0.0009 313.236 0.1697 0.4007

CE 5yr 1⁄2 life 10 358.502 0.0075 0.0010 313.684 0.2010 0.6996

CE 15yr 1⁄2 life 10 358.777 0.0084 0.0015 313.047 0.1578 0.3293

CE 20yr 1⁄2 life 10 359.122 0.0098 0.0022 312.975 0.1535 0.3045

CE 5yr 1⁄2 life 15 359.910 0.0138 0.0032 312.378 0.1217 0.1694

CE 10yr 1⁄2 life 15 360.543 0.0181 0.0079 311.660 0.1049 0.1141

CE 10 361.073 0.0227 0.0188 312.994 0.1546 0.2909

CE 15yr 1⁄2 life 15 361.129 0.0232 0.0162 312.044 0.1067 0.1113

CE 20yr 1⁄2 life 15 361.533 0.0276 0.0254 312.125 0.1102 0.1157

15 

16 CE: Cumulative Exposure with or without exponential decay modeled with different 1⁄2 lives. 

17 AIC: Akaike Information Criterion. 

18 

19 

20 

21 5.4.3.4.5. Additional analysis of the potential for confounding of lung cancer results by 

22 smoking in the sub-cohort of workers hired after 1959 

23 In the full cohort analysis, the proportional hazard assumption was not found to hold and 

24 it was possible that one of the reasons for this failure was the presence of confounding by 
This document is a draft for review purposes only and does not constitute Agency policy.

5-77 DRAFT—DO NOT CITE OR QUOTE 

1 smoking which altered the proportionality of the hazard rate in the exposed workers compared to 

2 the baseline hazard rate over time. By restricting the dates of hire in the sub-cohort, those 

3 workers in the sub-cohort may be made more similar to each other in ways that would reduce the 

4 potential for confounding by smoking and, in this sub-cohort, the proportional hazards 

5 assumption was found to hold thus reducing concern regarding confounding by smoking. 

6 As an additional check on the potential for confounding, a new method was evaluated to 

7 test for confounding by smoking in occupational cohorts which do not have data on smoking. 

8 Confounding, which can bias observed results when there is an uncontrolled variable which is 

9 correlated with both explanatory variable and outcome variable, is a distinct concept from effect 

10 measure modification (i.e. synergy) which might reflect different observed effects of exposure to 

11 Libby amphibole asbestos among smokers as compared to non-smokers. The extent of effect 

12 measure modification cannot be assessed without adequate data on smoking; however, the issue 

13 is discussed in Section 5.4.6. 

14 A method has been described by Richardson (2010) to determine if an identified 

15 exposure relationship with lung cancer is confounded by unmeasured smoking in an occupational 

16 cohort study. Richardson (2010) demonstrated that an exposure of interest (i.e., Libby 

17 Amphibole asbestos) can be used to predict an outcome other than lung cancer such as chronic 

18 obstructive pulmonary disease (COPD), which is known to be caused by smoking, but not 

thought to be related to the exposure of concern.419 If a positive relationship is identified where no

20 causal association is suspected, this would suggest that smoking and the exposure metric (Libby 

21 Amphibole asbestos) were positively correlated and that the identified exposure-response 

22 relationship was, in fact, confounded by smoking. EPA implemented this methodology to model 

23 the potential effects of Libby Amphibole asbestos on the risk of COPD mortality on the sub24 cohort of workers hired after 1959. Using the exposure metric defined as cumulative exposure 

25 with a 10-year lag, the extended Cox model with time-varying exposures estimated a slope (beta) 

26 for COPD of -0.056 per fiber/cc-year based on a 365-day calendar year. The p-value for the 

27 coefficient (slope) was 0.102 indicating that this parameter was not statistically significantly 

28 different from zero. Using the exposure metric defined as cumulative exposure with a 10-year 

29 half-life for decay and a 10-year lag for cancer latency, the extended Cox model with time30 varying exposures estimated a slope (beta) of -0.135 per fiber/cc-year based on a 365-day 

31 calendar year. The p-value for the coefficient (slope) was 0.116 indicating that this parameter 

32 was not statistically significantly different from zero. 

 

4

 Richardson (2010) cited articles by Rushton (2007a, b) with possible associations between asbestos and COPD 

which, if true, would have explained a positive association among the Libby workers cohort but should not detract 

from the use of the Richardson method as applied to these Libby workers, where negative association is found. 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Summarizing these findings, EPA used the method described by Richardson (2010) to 

2 evaluate whether exposures to Libby Amphibole asbestos predicted mortality from COPD as an 

3 indication of potential confounding by smoking and found a non-significant negative 

4 relationship, which was inconsistent with confounding by smoking in the sub-cohort of workers 

5 hired after 1959. 

6 

7 5.4.4. Exposure Adjustments and Extrapolation Methods 

8 The estimated exposures based on job exposure matrix and work histories are discussed 

9 in Section 5.4.2.5. Note that all slopes presented with units of fibers/cc-years are for calendar 

10 year and not for occupational year. 

11 

12 5.4.5. Inhalation Unit Risk (IUR) of Cancer Mortality 

13 The derivation of the unit risk estimates, defined as the lifetime risk of mortality from 

14 either mesothelioma or lung cancer from chronic inhalation of Libby Amphibole asbestos at a 

15 concentration of 1 fiber/cc of air, is presented in the following subsections. Note that all slopes 

16 are presented as per fiber/cc-year for a 365-day calendar year rather than for an occupational 

17 year. Also, note that while the slopes are not adjusted for differences in breathing rates and the 

18 number of hours of exposure in an occupational (eight-hour) day as compared to a whole 

19 (24-hour) day, the central risk and unit risk estimates do incorporate this adjustment. 

20 

21 5.4.5.1. Unit Risk Estimates for Mesothelioma Mortality 

22 The increased risk of mesothelioma mortality attributable to continuous fiber exposure 

23 was estimated using a life-table procedure based on the general U.S. population. The life-table 

24 procedure involved the application of the estimated Libby Amphibole asbestos-specific toxicity 

25 to a structured representation of the general U.S. population in such a manner as to yield 

26 age-specific risk estimates for mesothelioma mortality in the absence of exposure to Libby 

27 Amphibole asbestos and in the presence of exposure to Libby Amphibole asbestos. Baseline all28 cause mortality rates were included in the life-table in such a way as to enable computation of 

29 the specific absolute risk of mesothelioma mortality while accounting for other competing causes 

30 of mortality (Appendix G). For each age-interval in the life-table, the effect estimates of the 

31 Poisson model analysis (the absolute risk) was used to estimate mesothelioma mortality at a 

32 particular exposure level. These age-specific absolute risks can then be summed over a lifetime. 

33 Different exposure levels are evaluated to ascertain what magnitude of exposure would be 

34 expected to produce 1% absolute risk of mesothelioma mortality. By this method, the 

35 exposure-response relationship determined in the Libby worker cohort is used to estimate 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 mesothelioma mortality in the general U.S. population that would be expected from continuous 

2 lifetime environmental exposure to various concentrations of Libby Amphibole asbestos. Details 

3 of this life-table methodology are shown in Appendix G. 

4 Assuming no background risk for mesothelioma, extra risk is the same as absolute risk. 

5 Absolute risk estimates were calculated using the effect estimates derived from the modeling of 

6 the mesothelioma mortality risk and a life-table analysis program that accounts for competing 

causes of death.57 The unit risk of mesothelioma is computed using the 95% upper bound to 

8 estimate an upper bound for extra risk of mesothelioma due to Libby Amphibole asbestos 

9 exposure. The upper bound calculation is specific to the exposure metric parameters; the effect 

10 of metric uncertainty in these values is discussed below in Section 5.4.5.3. As this human health 

11 assessment derived a combined IUR for both mesothelioma and lung cancer mortality, an interim 

12 value based on the central effect estimate (rather than the upper bound) is also computed to avoid 

13 statistical concerns regarding the combination of upper bounds. Details are shown in 

14 Section 5.4.5.3. This assessment does not directly apply life-table calculations to estimate partial 

15 lifetime risk scenarios; the use of the IUR for partial lifetime extrapolations is discussed in 

16 Section 5.4.5.4. 

17 U.S. age-specific all-cause mortality rates from the 2010 National Vital Statistics Report 

18 for deaths in 2007 among all race and gender groups combined (NCHS, 2010) were used to 

19 specify the all-cause background mortality rates (Ro) in the life-table analysis. The risk with 

exposure (Rx) was computed up to age 85 years,620 assuming continuous environmental exposure 

21 to Libby Amphibole asbestos. Conversions between occupational Libby Amphibole asbestos 

22 exposures and continuous environmental asbestos exposures were made to account only for 

23 differences in the amount of air inhaled per day during a higher effort occupational shift 

(8 hours; 10 m3

) compared to a standard 24-hour (20 m324 ) day (U.S. EPA, 1994), as results were 

25 already based on a 365-day calendar year. The computation of the unit risk involved three steps. 

26 As the estimated absolute risk of mesothelioma mortality was based on the exposure experiences 

27 of an occupational cohort and most directly applicable to adult exposures, the first step was to 

28 compute the unit risk for adults. This was achieved by initiating exposure at age 16 years and 

29 maintaining continuous exposure throughout the remainder of life while allowing for the 

 

5

 This program is an adaptation of the approach previously used by the Committee on the Biological Effects of 

Ionizing Radiation (BEIR, 1988). Compared to life-table methods based on full life exposures from birth, the 

method used here yielded unit risk differences between full life exposure to scaled adult-only exposure between -3% 

to -2% for the mesothelioma mortality unit risks for the two mesothelioma models in Appendix G. A spreadsheet 

containing the extra risk calculation for the derivation of the LEC01 for mesothelioma mortality is presented in 

Appendix G. 

6

 Note that 85 years is not employed here as an average lifespan but, rather, as a cut-off point for the life-table 

analysis, which uses actual age-specific mortality rates. 
This document is a draft for review purposes only and does not constitute Agency policy.

5-80 DRAFT—DO NOT CITE OR QUOTE 

incremental mathematical decay of previously accumulated exposure.71 An adjustment was also 

2 made in the life-table for the lag period, so that the age-specific risk calculations began at 16 + 

3 (the length of the lag period) years of age. The standard assumption used by EPA is that the 

4 average lifetime spans 70 years. Since the adult-only unit risk excluded the first 16 years, the 

5 adult-only unit risk based on 54 years was then rescaled for an entire life of continuous exposure 

6 by multiplying the interim value for adult-only exposures by 70/54 to cover the childhood years 

7 (<16 years) to compute the “adult-based” unit risk. After re-scaling, the resulting “adult-based” 

8 lifetime unit risk estimate (in contrast to the unscaled “adult-only-exposure” unit risk estimate 

9 obtained from the life-table calculations) may be prorated for less-than-lifetime exposure 

10 scenarios in the same manner as would be used for an “adult-based” unit risk estimate derived 

11 from a rodent bioassay (Section 5.4.5.4). 

12 Consistent with the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), the 

13 same data and methodology were also used to estimate the exposure level (ECx; “effective 

14 concentration”) and the associated 95% lower confidence limit of that exposure level (LECx) 

15 corresponding to an absolute risk of 1% (x = 0.01). A 1%-risk level is commonly used for the 

16 determination of the point of departure (POD) for low-dose extrapolation from epidemiological 

17 data, and the LEC value corresponding to that risk level was used as the actual POD. 

18 The modeling analysis presented above showed that metrics including lag and half-life 

19 parameters provided the best empirical fit to the Libby worker sub-cohort data. Although there 

20 is uncertainty in applying these models for occupational mortality to estimation of risks for 

21 different exposure levels and time patterns (Section 5.4.6), following the recommendations of the 

22 Guidelines for Carcinogen Risk Assessment, (U.S. EPA, 2005a), a linear low-dose extrapolation

23 below the POD was used because the mode of action for Libby Amphibole asbestos for 

24 mesothelioma is largely unknown. Using the results of the cumulative exposure model with 

25 best-fitting lag and decay parameters, the LEC01 for the adult-only exposures was determined to 

26 be 0.245 fibers/cc, which yielded an adult-based unit risk of mesothelioma mortality of 0.041 

27 (POD of 1% divided by the LEC01), which when scaled by 70/54 to encompass the whole 

28 lifespan, yielded a lifetime unit risk of 0.053 per fiber/cc. The value of the risk corresponding to 

29 the measure of central tendency involves EC01 rather than LEC01. The EC01 for the adult-only 

30 exposures was determined to be 0.406 per fiber/cc, which when divided into a POD of 1% 

31 yielded an adult-based central estimate for mesothelioma mortality of 0.025, which when scaled 

32 by 70/54 to encompass the whole lifespan, yielded a lifetime central estimate of 0.032 per 

33 fiber/cc. 

 

7

 Exposures in the life tables were computed at the mid-point of each age interval and appropriately lagged. 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 The mesothelioma unit risks for model results presented in Table 5-12 and discussed in 

2 Section 5.4.3.4.1, are presented in Table 5-17. All of the metrics in Table 5-17 are cumulative 

3 exposure (CE) metrics lagged 10-15 years (the fit of 20-year lag models was much worse since 

4 one of seven mesothelioma deaths occurred before 20 years; lags longer than 15 years are 

5 possible, and this is an uncertainty described in Section 5.4.6). Issues related to uncertainty in 

6 the choice of exposure metric are described further in the section on the derivation of the 

7 combined IUR of mesothelioma and lung cancer (Section 5.4.5.3). 

8 

9 Table 5-217. Mesothelioma mortality exposure metrics unit risks 

10 

Exposure metric

Lag 

years DIC

Information 

weight

Central risk 

Estimate Unit risk

CE - 5yr 1⁄2 life 15 70.6 0.428 0.032 0.053 

CE - 5yr 1⁄2 life 10 72.8 0.143 0.054 0.088 

CE - 10yr 1⁄2 life 10 73.9 0.082 0.028 0.047 

CE - 10yr 1⁄2 life 15 74.0 0.078 0.020 0.032 

CE - 15yr 1⁄2 life 10 75.7 0.033 0.022 0.036 

CE - 15yr 1⁄2 life 15 76.1 0.028 0.017 0.027 

CE - 20yr 1⁄2 life 10 76.7 0.020 0.020 0.032 

CE - 20yr 1⁄2 life 15 77.2 0.016 0.015 0.025 

11 

12 

13 5.4.5.1.1 Adjustment for mesothelioma underascertainment

14 For mesothelioma, the undercounting of cases (underascertainment) is a particular 

15 concern given the limitations of the ICD classification systems used prior to 1999. In practical 

16 terms, this means that some true occurrences of mortality due to mesothelioma are missed on 

17 death certificates and in almost all administrative databases such as the National Death Index. 

18 Even after introduction of special ICD code for mesothelioma with introduction of ICD-10 in 

19 1999, detection rates are still imperfect (Pinhiero et al., 2004; Camidge et al., 2006) and the 

20 reported numbers of cases typically reflect an undercount of the true number. Kopylev et al. 

21 (2011) reviewed the literature on this underascertainment and developed methods to account for 

22 the likely numbers of undocumented mesothelioma deaths. 

23 Because the analysis of mesothelioma mortality was based on absolute risk, it was 

24 possible to compensate for mesothelioma underascertainment in Libby worker cohort. The 

25 mesothelioma mortality unit risk was adjusted, following the analysis of Kopylev et al. (2011). 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 With the adjustment factor of 1.39 (see Table 3 in Kopylev et al., 2011), the adjusted 

2 mesothelioma central risk (based on the EC01), corresponding to the best-fit metric, was 0.044 

3 per fiber/cc, and adjusted mesothelioma mortality unit risk was 0.074 per fiber/cc. This 

4 adjustment factor can be used because the number of peritoneal mesotheliomas is partly known 

5 and, therefore, the approach following Selikoff and Seidman (1992) provides the most 

6 appropriate estimate of the number of mesothelioma mortality cases. 

7 Mesothelioma mortality-adjusted unit risks are listed in Table 5-18 along with their 

8 information weights. 

9 

10 Table 5-18. Adjusted for underascertainment unit risks for the sub-cohort 

11 hired after 1959 corresponding to the different metrics 

12 

Exposure metric Lag years

Information 

weight

Adjusted central 

risk estimate

Adjusted unit 

risk

CE - 5yr 1⁄2 life 15 0.428 0.044 0.074 

CE - 5yr 1⁄2 life 10 0.143 0.075 0.122 

CE - 10yr 1⁄2 life 10 0.082 0.039 0.065 

CE - 10yr 1⁄2 life 15 0.078 0.028 0.044 

CE - 15yr 1⁄2 life 10 0.033 0.031 0.050 

CE - 15yr 1⁄2 life 15 0.028 0.024 0.038 

CE - 20yr 1⁄2 life 10 0.020 0.028 0.044 

CE - 20yr 1⁄2 life 15 0.016 0.022 0.035 

13 

14 

15 5.4.5.2. Unit Risk Estimates for Lung Cancer Mortality 

16 As with mesothelioma mortality, the increased risk of lung cancer mortality attributable 

17 to continuous fiber exposure was estimated using a life-table procedure based on the general U.S. 

18 population. The life-table procedure involved the application of the estimated Libby Amphibole 

19 asbestos-specific toxicity to a structured representation of the general U.S. population in such a 

20 manner as to yield age-specific risk estimated for lung cancer mortality in the absence of 

21 exposure to Libby Amphibole asbestos and in the presence of exposure to Libby Amphibole 

22 asbestos. Baseline all-cause mortality and lung cancer-specific mortality rates were included in 

23 the life-table in such a way as to enable computation of the specific extra risk of lung cancer 

24 mortality while accounting for other competing causes of mortality (Appendix G). For each 

25 age-interval in the life-table, the effect estimates of the Cox proportional hazards model analysis 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 (the relative risk) were used to estimate extra lung cancer mortality at a particular exposure level, 

2 given the background risk. These age-specific extra risks can then be summed over a lifetime. 

3 Different exposure levels are evaluated to ascertain what magnitude of exposure would be 

4 expected to produce 1% extra risk of lung cancer mortality. By this method, the exposure5 response relationship estimated in the worker cohort is used to estimate lung cancer mortality in 

6 the general U.S. population that would be expected from continuous lifetime environmental 

7 exposure to various concentrations of Libby Amphibole asbestos under the assumption of a 

8 10-year latency for cancer mortality and a 10-year half-life decay of the exposure metric. Details 

9 of the life-table methodology are shown in Appendix G. 

10 Extra risk is defined as equaling (Rx-Ro)/(1-Ro), where, Rx is the lifetime lung cancer 

11 mortality risk in the exposed population and Ro is the lifetime lung cancer mortality risk in an 

12 unexposed population (i.e., the background risk). Extra risk estimates were calculated using the 

13 effect estimates derived from the multivariate modeling of the lung cancer mortality risk and a 

life-table analysis program that accounts for competing causes of death.814 The unit risk of lung 

15 cancer mortality is computed using the 95% upper bound to estimate an upper bound for extra 

16 risk of lung cancer mortality due to Libby Amphibole asbestos exposure. As this human health 

17 assessment derived a combined IUR of mesothelioma and lung cancer mortality, an interim value 

18 based on the central effect estimate (rather than the upper bound) is also computed to avoid 

19 statistical concerns regarding the combination of upper bounds. Details are shown in 

20 Section 5.4.5.3. 

21 U.S. age-specific all-cause mortality rates from the 2010 National Vital Statistics Report 

22 NVSR 58(19) 2010 for deaths in 2007 among all race and gender groups combined (NCHS, 

23 2010) were used to specify the all-cause background mortality rates (Ro) in the life-table 

24 analysis. Cause-specific background mortality rates for cancers of the lung, trachea, and 

25 bronchus were obtained from a Surveillance, Epidemiology, and End Results (SEER) report on 

26 mortality during 2003-2007 (SEER Table 15.10, age-specific U.S. death rates). 

 The risk with exposure (Rx) was computed up to age 85 years927 assuming continuous 

28 environmental exposure to Libby Amphibole asbestos using the life-table analysis (see 

29 Tables G-3 and G-4 in Appendix G for details). Conversions between occupational exposures 

 

8

 This program is an adaptation of the approach previously used by the Committee on the Biological Effects of 

Ionizing Radiation (BEIR, 1988). Compared to life table methods based on full life exposures from birth, the 

method used here yielded unit risk differences between full life exposure to adult-only exposure between -18% to 

+6% for the lung cancer mortality unit risks for the two lung cancer models in Appendix G. A spreadsheet 

illustrating the extra risk calculation for the derivation of the LEC01 for lung cancer mortality is presented in 

Appendix G.

9

 Rates above age 85 years are not included because cause-specific disease rates are less stable for those ages. Note 

that 85 years is not employed here as an average lifespan but, rather, as a cut-off point for the life-table analysis, 

which uses actual age-specific all cause and cause-specific mortality rates. 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 and continuous environmental exposures to Libby Amphibole asbestos were made to account 

2 only for differences in the amount of air inhaled per day during a higher effort occupational shift 

(8 hours; 10 m3) compared to a standard 24-hour (20-m33 ) day (U.S. EPA, 1994), as results were 

4 already based on a 365-day calendar year. The computation of the unit risk involved three steps. 

5 As the estimated RR of lung cancer mortality was based on the exposure experiences of an 

6 occupational cohort and most directly applicable to adult exposures, the first step was to compute 

7 the unit risk for adults. This was achieved by initiating exposure at age 16 years and maintaining 

8 continuous exposure throughout the remainder of life while allowing for the incremental 

9 mathematical decay of previously accumulated exposure depending on the particular exposure 

metric.10 10 An adjustment was also made in the life-table for the lag period, so that the age11 specific risk calculations began at (16 + lag) years of age. The standard assumption used by EPA 

12 is that the average lifetime spans 70 years. Since the “adult-only-exposure” unit risk excluded 

13 the first 16 years, the “adult-only-exposure” unit risk based on 54 years was then rescaled for an 

14 entire life of continuous exposure by multiplying the interim value for adult-only exposures by 

15 70/54 to cover the childhood years (<16 years) to compute the “adult-based” unit risk. After re16 scaling, the resulting “adult-based” lifetime unit risk estimate (in contrast to the unscaled “adult17 only-exposure” unit risk estimate obtained from the life-table calculations) may be prorated for 

18 less-than-lifetime exposure scenarios in the same manner as would be used for an “adult-based” 

19 unit risk estimate derived from a rodent bioassay. 

20 Although there is uncertainty in applying these models for occupational mortality to the 

21 estimation of risks for different exposure levels and time patterns (Section 5.4.6), following the 

22 recommendations of the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), a linear 

23 low-dose extrapolation below the POD was used because the mode of action for Libby 

24 Amphibole asbestos for lung cancer is undetermined. The nine exposure-response models 

25 retained from Table 5-13 all had reasonably similar goodness of fits. No single model stands out 

26 as clearly statistically superior; however, there is a range of quality of fit within the set that could 

27 be considered adequate. The lung cancer mortality unit risks are shown in Table 5-19. 

28 Using the results of the exposure model with the lowest AIC value (i.e., cumulative 

29 exposure with a 10-year half-life for decay and a 10-year lag for cancer latency) alone, the LEC01 

30 Table 5-39. Subset of lung cancer models with lagged exposures that yielded 

31 statistically significant model fit (p < 0.05) and exposure metric fit (p < 0.05) 

32 to the epidemiologic data 

33 

Exposure metric Lag AIC

Exposure

p-value

Central risk estimate

(based on EC01)

Unit risk

(based on LEC01)

 

10 Exposures in the life tables were computed at the mid-point of each age interval and appropriately lagged. 
This document is a draft for review purposes only and does not constitute Agency policy.

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CE 10yr 1⁄2 life 10 358.400 0.0009 0.0260 0.0389

CE 5yr 1⁄2 life 10 358.502 0.0010 0.0195 0.0293

CE 15yr 1⁄2 life 10 358.777 0.0015 0.0300 0.0455

CE 20yr 1⁄2 life 10 359.122 0.0022 0.0326 0.0501

CE 5yr 1⁄2 life 15 359.910 0.0032 0.0167 0.0260

CE 10yr 1⁄2 life 15 360.543 0.0079 0.0231 0.0375

CE 10 361.073 0.0188 0.0399 0.0679

CE 15yr 1⁄2 life 15 361.129 0.0162 0.0258 0.0434

CE 20yr 1⁄2 life 15 361.533 0.0254 0.0280 0.0486

1 

2 

3 for the adult-only exposures was determined to be 0.333 fibers/cc which yielded an adult-based 

4 unit risk of lung cancer mortality of 0.0300 (POD of 1% divided by the LEC01) which when 

5 scaled by 70/54 to encompass the whole lifespan yielded a lifetime unit risk of 0.0389 per 

6 fiber/cc. The value of the risk that would correspond to the measure of central tendency involves 

7 EC01 rather than LEC01. The EC01 for the adult only exposures was determined to be 0.499 per 

8 fiber/cc which when divided into a POD of 1% yielded an adult-based central estimate for lung 

9 cancer mortality of 0.0200 which when scaled by 70/54 to encompass the whole lifespan yielded 

10 a lifetime central estimate of 0.0260 per fiber/cc.

11 Using the results of the exposure model based on cumulative exposure with a 10-year lag 

12 for cancer latency, the LEC01 for the adult-only exposures was determined to be 0.191 fibers/cc 

13 which yielded an adult-based unit risk of lung cancer mortality of 0.0524 (POD of 1% divided by 

14 the LEC01) which when scaled by 70/54 to encompass the whole lifespan yielded a lifetime unit 

15 risk of 0.0679 per fiber/cc. The EC01 for the adult only exposures was determined to be 0.325 

16 per fiber/cc which when divided into a POD of 1% yielded an adult-based central estimate for 

17 lung cancer mortality of 0.0308 which when scaled by 70/54 to encompass the whole lifespan 

18 yielded a lifetime central estimate of 0.0399 per fiber/cc. 

19 The resulting unit risks in Table 5-19 ranged from 0.0260 to 0.0679 fibers/cc. This 

20 shows that the unit risk (i.e., 0.0389 per fiber/cc) based on the exposure metric with the lowest 

21 AIC value (i.e., cumulative exposure with a 10-year half-life for decay and a 10-year lag for 

22 cancer latency) is in the center of this range and is thus statistically robust. However, because 

23 this estimate is in the middle of the range it does not capture the uncertainty across metrics with 

24 similar goodness of fit. As noted (Section 5.4.2.2), an argument can be made that the CE metric 

25 with a 10 year lag and no half-life is implicitly more parsimonious (simpler) because it was not 

26 explicitly adjusted to include decay, although this metric is mathematically equivalent to CE 
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1 metric with 10 year lag and an infinitely long decay half-life. Conceptually, the AIC values are 

2 penalized for increased model complexity (thereby increasing the AIC). The AIC for the CE 

3 models may reasonably be thought to be somewhat lower than through the standard calculation 

4 of AIC. However, such a hypothesized adjustment would not alter the identification of the 

5 exposure model with the lowest AIC value which remains the CE model with a 10 year lag and a 

6 10 year half-life. Nonetheless, the CE metric with a 10 year lag does fit these data, is a simpler 

7 and more straightforward metric and has an extensive tradition of use in the epidemiologic 

8 literature and in the practice of risk assessment. 

9 Issues related to uncertainty in the choice of exposure metric are described in the section 

10 on the derivation of the combined IUR of mesothelioma and lung cancer below. 

11 

12 5.4.5.3. IUR Derivation for Combined Mesothelioma and Lung Cancer Mortality 

13 Before risks can be combined, it is important to understand several concepts that are 

14 pertinent to the evaluation and comparison of the cancer-specific mortality unit risks that will be 

15 combined. First, there is statistical uncertainty in the potency estimate within the exposure 

16 response model defined by each exposure metric. This within metric uncertainty is accounted 

for by the 95th 17 CI around the potency estimates (see Tables 5-6 and 5-8). Next, there is 

18 uncertainty in the choice of metrics for developing IUR (called cross-metric uncertainty, 

19 described below). Finally, when unit risks corresponding to metrics are chosen accounting for 

20 uncertainty, these are statistically combined into IUR. Details are provided below. 

21 For this assessment, EPA obtained the best available demographic, exposure, and vital 

22 status data from NIOSH. Subsequently, the best-fitting statistical models were identified, which 

23 were then applied to derive central estimates of the lifetime mesothelioma and lung cancer 

24 mortality risk in the general population exposed to a continuous concentration of 1 fiber/cc of 

25 Libby Amphibole asbestos. Then, the individual exposure metric-specific risks were calculated 

26 as the statistical (95%) upper confidence bounds on these central estimates. Use of the upper 

27 confidence bound accounts for uncertainty in the effect estimate for each metric—otherwise 

28 referred to as the within-metric uncertainty. 

29 Another source of uncertainty is the choice of the appropriate exposure metric among a 

30 set of results that appear to fit the data similarly well. This uncertainty is referred to as the 

31 between-metric or cross-metric uncertainty. For the Libby worker cohort data, the best-fit 

32 (lowest information criterion values) metrics lead to estimates of risks that are more like mid33 range estimates among the other metrics (Tables 5-11 and 5-13) with sufficiently close 

34 information criterion values, rather than upper bound estimates. While the lung cancer unit risk 

35 computed from the model with the lowest AIC appears to be robust, Table 5-19 shows that there 
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1 is a range of possible unit risk values from the set of models with adequate fit (as measured by a 

2 statistically significant p-value for the exposure metric term) and similar goodness of fit. 

3 Likewise, for mesothelioma mortality, among the models with adequate fit shown in Table 5-17, 

4 there is a range of possible unit risk values. 

5 The IUR should be a reasonable upper bound on the extra risk. As is clear from 

6 Tables 5-11 and 5-13 in the preceding sections, the unit risks based on the metrics with the 

7 lowest information criterion values provide a lower estimate of cancer mortality risk than some 

8 other similarly fitting metrics. While the models with the lowest information criterion values 

9 have the greatest statistical support, other models that yield higher unit risks are also statistically 

10 plausible. This assessment selected the upper bound unit risk among the plausible exposure 

11 metrics (regardless of the small residual differences in quality of fit) to account for cross-metric 

12 uncertainty. Because there were few metrics with unit risks higher than the best fitting metric’s 

13 unit risk for each cancer mortality endpoint, this method effectively selects the highest unit risk 

14 among those considered for each cancer mortality endpoint. 

15 Once the cancer-specific mortality unit risks are selected, the two are then combined. 

16 Because each of the unit risks is itself an upper bound estimate, summing such upper bound 

17 estimates across mesothelioma and lung cancer mortality is likely to overstate the overall risk. 

18 Therefore, following the recommendations of the Guidelines for Carcinogen Risk Assessment

19 (U.S. EPA, 2005a), a statistically appropriate upper bound on combined risk was derived in order 

20 to gain an understanding of the overall risk of mortality resulting from mesothelioma and from 

21 lung cancers. It is important to note that this estimate of overall potency describes the risk of 

22 mortality from cancer at either of the considered sites and is not just the risk of both cancers 

23 simultaneously. 

24 Because the estimated risk for both mesothelioma and lung cancer mortality were derived 

25 using Poisson and Cox proportional hazards models, correspondingly, it follows from statistical 

26 theory that each of these estimates of risk is approximately normally distributed. For 

27 independent normal random variables, a standard deviation for a sum is easily derived from 

28 individual standard deviations, which are estimated from confidence intervals: standard 

29 deviation = (unit risk – central risk) / Z0.95, where Z0.95 is a standard normal quantile equal to 

30 1.645. For normal random variables, the standard deviation of a sum is the square root of the 

31 sum of the squares of individual standard deviations. 

32 The upper bound among the mesothelioma mortality unit risks was 0.122 per fiber/cc. 

33 The upper bound among the computed lung cancer mortality unit risks was 0.0680 per fiber/cc. 

34 The central estimate of risk was 0.075 for mesothelioma mortality per fiber/cc and 0.0399 per 

35 fiber/cc for lung cancer mortality (Tables 5-7 and 5-9). 
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1 In order to combine the unit risks, one first obtains an estimate of standard deviation of 

2 the sum of the individual unit risks as 

3 

4 

5 Sqrt { [[(0.122 - 0.075) / 1.645]^2 +(0.068 - 0.0399) / 1.645 ]^2 } = 0.033 per fiber/cc (Eq. 5-9) 

6 

7 

8 Then, the combined central estimate of risk of mortality from either mesothelioma or lung cancer 

9 is 0.0399 + 0.075 = 0.115 per fiber/cc, and the combined IUR is 0.115 + 0.033*1.645 = 0.169 

10 per fiber/cc. 

11 Selecting the upper bound unit risk estimates for use in combining unit risks accounts for 

12 many potential uncertainties. It accounts for uncertainty in the effect estimate (i.e., the within13 metric uncertainty) and the uncertainty attributable to the choice of exposure metric (i.e., the 

14 cross-metric uncertainty). The combined IUR from the best fitting mesothelioma and lung 

15 cancer mortality models (using two different model selection criteria) can be computed for 

16 comparison with Tables 5-7 and 5-9, respectively, by the same steps as above, and the results are 

17 shown in Table 5-20.

18 Compared to the combined IUR from the best fitting exposure models, the EPA’s 

19 selected combined IUR of mesothelioma and lung cancer mortality accounts for both the 

20 demonstrated cross-metric uncertainty as well as several additional potential uncertainties, which 

21 could have resulted in underestimates of the mesothelioma and lung cancer mortality risks from 

22 the epidemiologic data. These additional uncertainties are discussed in Section 5.4.6. The IUR 

23 value of 0.169 per fiber/cc accounts for important quantitative uncertainties in the selection of 

24 the specific exposure metric that may have remained in an IUR that might have been based on 

25 the best fitting exposure models alone. 

26 

27 5.4.5.3.1. Comparison with other published studies of Libby Workers Cohort 

28 For lung cancer, two alternative analytic approaches to the use of EPA’s extended Cox 

29 proportional hazards models could have been used for the calculation of a unit risk of lung 

30 cancer mortality. All of the choices are based on different analyses of the Libby worker cohort; 

31 however, inclusion criteria differ among the analyses as does the length of mortality follow-up. 

32 Each of the two approaches has two options to estimate the slope of the exposure-response 

33 relationship in place of the regression slope estimated from the Cox proportional hazards model 

34 and follow through with the same life-table procedure to calculate the unit risk of lung cancer 

35 mortality. 

36 
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1 Table 5-20. Reasonable upper bound and lowest information criteria 

2 estimates of central risks and unit risks, per fiber/cc, for mesothelioma 

3 mortality, lung cancer mortality, and the IUR for the combined mortality 

4 risk from mesothelioma and lung cancer 

5 

Model

Mesothelioma Lung cancer

Combined mesothelioma 

and lung cancer

Central 

estimate Unit risk

Central

estimate

Unit 

risk

Central

estimate IUR

Reasonable upper bounda0.075 0.122 0.040 0.068 0.115 0.169

Lowest information criteriab0.044 0.074 0.026 0.040 0.070 0.103

6 

a

7 For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay half8 life of 5 years and a 15-year lag. For lung cancer, the selected model parameterized exposure as cumulative 

9 exposure without decay and a 10-year lag. 

b

10 For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay 

11 half-life of 5 years and a 10 -year lag. For lung cancer, the selected model parameterized exposure as cumulative 

12 exposure with exponential decay half-life of 10 years and a 10-year lag. 

13 

14 

15 The first approach would be to use the published categorical results based on Sullivan 

16 (2007). The first option in this approach was for EPA to estimate a slope to those categorical 

17 data. The second option was to use the slope estimated in a published re-analysis of categorical 

18 data of Sullivan (2007) cohort by Berman and Crump (2008). The second approach would be to 

19 use the published regression results of other researchers who modeled the underlying continuous 

20 data. The first option in this approach was to use the slope estimated by Larson et al. (2010a). 

21 The second option was to use the slope estimated by Moolgavkar et al. (2010). 

22 For comparison purposes, the lung cancer unit risk from these alternatives are computed, 

23 however, as all analyses are based upon different subsets of the Libby workers cohort and used 

24 different analytic methods, the results are not necessarily interchangeable. Table 5-21 

25 summarizes lung cancer risks derived from these studies. 

26 The first alternative analytic approach to estimating the extra risk from a linear regression 

27 of individual mortality data was to use a standard technique used in EPA cancer risk assessments 

28 (U.S. EPA, 2005a) when individual-level data are not available. This approach used a weighted 

29 linear regression of standardized rate ratio (SRR) estimators for lung cancer mortality in white 

30 males, as calculated in the NIOSH cohort analysis (Sullivan, 2007), with categorical cumulative 

31 exposure and a 15-year lag. The Sullivan (2007) analysis was based only on those who have not 

32 died or been lost to follow-up before January 1, 1960 (in contrast to employment beginning after 

33 January 1, 1960) because the NIOSH software program (LTAS) used for this analysis only has 

34 statistics on external comparison rates for asbestosis (one of the primary outcomes of interest in 
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1 the Sullivan [2007] analysis) beginning in 1960. The SRR analysis involves internal 

2 comparisons of lung cancer mortality rates in the lowest exposure category to the lung cancer 

3 mortality rates in the higher exposure categories. The weights used for the SRRs were the 

4 inverses of the variances. Midpoints of the exposure intervals were used, and for the unbounded 

5 interval, the midpoint was assumed to be twice the starting point of that interval.

6 

7 Table 5-24. Lung cancer regression results from different analyses of 

8 cumulative exposure in the cohort of workers in Libby, MT. All analyses 

9 used NIOSH-collected exposure data, but used different cohort definitions, 

10 lengths of follow-up, and lengths of exposure lags to account for cancer 

11 latency. 

12 

Lung cancer analysis Cohort definition 

Lung 

cancer cases/N

Slope per 

fibers/cc-years x10-3

(calendar year) 

Risk based on UCL 

on the slope 

(per fiber/cc) 

This assessment Hired post-1959 32/880 5.8 0.068 

Sullivan, 2007 Full cohort; still 

employed post-1959 

White males 

99/1672 4.2 0.037 

Moolgavkar et al., 2010a Full cohort, 

White males 

95/1662 1.69 0.011 

Berman and Crump, 2008a Full cohort, 

White males 

93/1672 3.96 0.079 

Larson et al., 2010 Full cohort 98/1862 1.61 0.010 

13 

a

14 Re-analysis of Sullivan (2007) 

15 

16 

Using this approach, a regression coefficient of 4.2 x 10-3 17 per fiber/cc-year (standard 

error [SE] = 7.7 x 10-4 18 per fiber/cc-year, p = 0.03) was obtained from the weighted linear 

19 regression of the categorical SRR results. Because the data from Sullivan (2007) were already 

20 adjusted for the length of an occupational year (240 days) to the length of a calendar year 

21 (365 days), only the standard adjustment for inhaled air volume was performed. The 

22 concentration estimate obtained using this regression modeling and the life-table analysis 

23 procedure was LEC01 = 0.272 fibers/cc resulting in the lung cancer unit risk of 0.0368 per 

24 fiber/cc.

25 Berman and Crump (2008) re-analyzed Sullivan (2007) data except they used lag of 10 

26 years. They fit IRIS IUR (1988) lung cancer model to aggregate data using extra multiplicative 

27 parameter α. In this model, the relative risk at zero exposure is α rather than 1. With α = 1, their 

28 model does not fit and with α estimated, the fit was satisfactory. Berman and Crump (2008) 
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1 chose central estimate of slope from the fit with α estimated, but constructed “informal” 90% 

2 confidence interval by the union of two confidence intervals (this upper bound is shown in 

3 Table 5-21). This was done to address uncertainty in estimated parameter α, similar to what is 

4 done in this assessment with estimated lag and decay. Note also, that Berman and Crump (2008) 

5 also provide an uncertainty factor to adjust for several sources of uncertainty in exposures, 

6 resulting in upper bound risk of 0.3162. 

7 The second alternative analytic approach to estimating the extra risk of lung cancer from 

8 a Cox regression with time-dependent covariates of individual mortality data was to use the 

9 results published by Larson et al. (2010a), with cumulative exposure and a 20-year lag. This 

10 analysis of lung cancer mortality was based on the full cohort of 1,862 workers updated until 

11 2006 and using the same model form as the current EPA analysis (the extended Cox proportional 

hazards model). Larson et al. (2010a) reported a regression coefficient of 1.06 x 10-3 12 per 

fiber/cc-year (SE = 3.1 x 10-4 per fiber/cc-year, p = 0.0006).11 13 EPA assumed that the cumulative 

14 exposures reported by Larson et al. (2010) were based on years of occupational exposure 

15 (240 days per year) during a 365-day calendar year. In order to account for exposure on every 

16 day of the year for a calculation of unit risk, adjustment for exposures during the length of an 

17 occupational year (240 days) to the length of an calendar year (365 days) and the adjustment for 

18 the volume of inhaled air were performed to match EPA’s analyses. The concentration estimate 

19 obtained using the Larson et al. (2010) regression modeling and the life-table analysis procedure 

20 was LEC01 = 1.26 fibers/cc resulting in a lung cancer unit risk of 0.0103 per fiber/cc. 

21 Moolgavkar et al. (2010) also used the Cox model with time-dependent covariates for 

22 analysis of the Sullivan (2007) cohort with a 15-year lag. The parameter in this study estimates 

(1.11 x 10-3 per fiber/cc-year (SE = 2.5 x 10-4 23 per fiber/cc-year) are very close to Larson et al.

24 (2010a), and, therefore, the lung cancer unit risk based on their analysis would be very close to 

25 Larson et al. (2010a). Comparison with McDonald et al. (2004) is difficult, since their definition 

26 of lung cancer (ICD-9 160-165) is much more expansive than other researchers’ definitions; 

27 however, the parameter estimate resulting from the Poisson analysis by McDonald et al. (2004) 

was 3.6 x 10-3 28 . 

29 EPA based their analyses on the exposures that occurred after 1959, while the Sullivan 

30 (2007), Larson et al. (2010a), and Moolgavkar et al. (2010) analyses were based on the cohort 

31 including those hired before 1960, and McDonald et al. (2004) included only workers hired 

32 before 1964. As explained in detail in the discussion (Section 5.4.6) on uncertainty in the 

33 exposure assessment, there were only several measurements from the 1950s and one from 1942, 

 

11 Note that EPA results based on the sub-cohort hired after 1959 were from the same model form but based on the 

cumulative exposure with a 10-year lag and had a slope of 5.81 x 10-3 per fiber/cc-year (SE = 2.48 x 10-3 per 

fiber-cc/year, p = 0.018). 
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1 and most of the exposure estimation for the early years of the cohort’s experience were based on 

2 estimates of the ratio of dust to fibers estimated in the late 1960s and extrapolated backwards in 

3 time for several decades. Moreover, 686 workers hired before 1960 (not necessarily short-term) 

4 did not have exposure measurement assigned to them at all, leading to much larger measurement 

5 error. These limitations in the underlying exposure assessment for the years prior to 1968 likely 

6 resulted in exposure measurement error that could have attenuated the analytic regression results, 

7 thereby yielding a smaller effect estimate for the whole cohort compared to the sub-cohort hired 

8 after 1959. It appears the differences in results are mostly attributable to the time periods of 

9 analysis and corresponding to the time period measurement errors rather than the analytic 

10 approach. The small discrepancy between observed lung cancer deaths between this assessment 

11 and Larson et al. (2010a), described in Section 4.1.1.1, is unlikely to play a role in the difference 

12 between risk estimates. Moreover, for the sub-cohort hired after 1959, all deaths are included in 

13 the Larson et al. (2010a) lung cancer-counting rules. 

14 Neither of the approaches used by McDonald et al. (2004), Sullivan (2007), nor Larson et 

15 al. (2010a) could have been appropriately used for the unit risk of mesothelioma as they are not 

16 based on absolute risk metrics of association, and the current assessment considered the relevant 

17 metric of association to be the absolute risk. Berman and Crump (2008) did not evaluate risk of 

18 mesothelioma. Moolgavkar et al. (2010) used an absolute risk model for mesothelioma. These 

19 results are summarized in Table 5-22. The upper bound results for the full cohort presented by 

20 Moolgavkar et al. (2010) are about 80% of the IRIS IUR (1988) estimate of mesothelioma slope 

21 factor in a similar RTW-type metric, leading to approximately 80% estimate of the mesothelioma 

22 unit risk, as dependence is linear in the mesothelioma slope factor (Eq. 5-5). This is very close 

23 to this assessment’s estimate based on sub-cohort, which is also about 80% of the IRIS IUR 

24 (1988) estimate of mesothelioma risk. Duration of employment is the best metric for the full 

25 cohort, and it does not support dose-response estimation. 

26 
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1 Table 5-225 Mesothelioma regression results from different analyses of 

2 cumulative exposure in the cohort of workers in Libby, MT. All analyses 

3 used NIOSH-collected exposure data, but used different cohort definitions, 

4 lengths of follow-up, and lengths of exposure lags to account for cancer 

5 latency. 

6 

Mesothelioma analysis Cohort definition 

Mesothelioma

cases/N 

Mesothelioma risk 

(absolute risk model) 

(per fiber/cc) 

This assessment Hired post-1959 7 /880 Upper Bound = 0.12 

Central = 0.08 

Sullivan. 2007 Full cohort; still employed post-1959; 

White males 

15/1672 No estimates of absolute risk 

Moolgavkar et al.. 2010a Full cohort; White males 15/1662 Upper Bound ≈ 0.13 

Central ≈ 0.08 

Larson et al., 2010 Full Cohort 19/1862 No estimates of absolute risk 

Berman and Crump, 2008a No estimates provided 

7 

a

8 Re-analysis of Sullivan (2007) 

9 

10 

11 5.4.5.4. Applications of the Combined Mesothelioma and Lung Cancer Mortality IUR to 

12 Partial Lifetime Environmental Exposure Scenarios 

13 In the application of the IUR, scenarios other than lifetime environmental exposure are 

14 often of interest to risk assessors. The life-table analysis in the (general) IRIS IUR for asbestos 

15 (U.S. EPA, 1988) predicts risk increases as the age of the first exposure decreases. The authors 

16 of that analysis recommended the life-tables in that analysis be consulted when assessing partial 

17 lifetime exposures (U.S. EPA, 1986). In 2009, EPA (OSWER) provided guidance for 

18 calculating risk estimates for less-than-lifetime exposures based on the source life-table analysis 

19 (U.S. EPA, 2009). The age-at-onset of exposure and duration-dependent unit risks reflect the 

20 influence of the time cubed function in the mesothelioma model (eq. 5-5) (U.S. EPA, 1986, 

21 2009) used in the 1986 assessment. Because the time-cubed model, or parameterization of 

22 exposure metrics, did not fit the data for mesothelioma mortality from exposure to the Libby 

23 Amphibole asbestos, the approach to estimating risk of partial life exposure recommended by 

24 EPA when applying the general IRIS IUR for asbestos (U.S. EPA, 1988) is not appropriate when 

25 applying the Libby amphibole asbestos-specific IUR.

26 Thus this assessment recommends that estimates of the risks of less-than-lifetime 

27 exposures be computed by simple calculations of average lifetime exposure concentration 

28 multiplied by IUR. This recommendation is consistent with standard Superfund guidance (U.S. 
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1 EPA, 1986b), where exposures are estimated, averaged across a lifetime exposure, and the IUR 

2 simply applied to calculate excess cancer risk (U.S. EPA, 2005). 

3 

4 5.4.6. Uncertainties in the Cancer Risk Values 

5 It is important to consider the discussion of uncertainties in the derivation of the 

6 mesothelioma and lung cancer mortality risks in this assessment in the context of uncertainties in 

7 animal-based health assessments. This assessment does not involve extrapolation from high 

8 dose in animals to low dose in humans. The current assessment is based on a well-documented 

9 and well-studied cohort of workers with adequate years of follow-up to evaluate mesothelioma 

10 and lung cancer mortality risks with PODs within the range of the data. The discussions below 

11 explore uncertainty in the derivation of the IUR in order to provide a comprehensive and 

12 transparent context for the resulting cancer mortality risk estimates. 

13 

14 5.4.6.1. Sources of Uncertainty 

15 Sources of uncertainty in this assessment include: 

16 

17 1) Uncertainty in low-dose extrapolation,

18 2) Uncertainty in exposure assessment, including analytical measurements uncertainty, 

19 3) Uncertainty in model form,

20 4) Uncertainty in selection of exposure metric,

21 5) Uncertainty in assessing mortality corresponding to the cancer endpoints,

22 6) Uncertainty in control of potential confounding in modeling lung cancer mortality,

23 7) Uncertainty due to potential effect modification,

24 8) Uncertainty due to length of follow-up,

25 9) Uncertainty in use of life-tables to calculate cancer mortality unit risks,

26 10) Uncertainty in combining of mortality risks to derive a composite cancer mortality IUR, 

27 11) Uncertainty due to extrapolation of findings in adults to children. 

28 

29 1) Uncertainty in low-dose extrapolation

30 A major source of uncertainty in quantitative cancer risk assessments generally derives 

31 from extrapolating from high doses in animals to low doses in humans. Compared to 

32 assessments based on animal data, the uncertainty from low-dose extrapolation in this 

33 assessment employing occupational epidemiology data is considered to be somewhat reduced for 

34 the following reasons. The NIOSH worker cohort developed by Sullivan (2007) includes 410 

35 workers employed less than 1 year among the 880 workers hired on or after January 1, 1960. 
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1 Although short-term workers, on average, experience a mean exposure intensity per day worked 

2 greater than workers employed more than a year (Sullivan, 2007), the cohort nevertheless 

3 includes many short-term workers with relatively low cumulative occupational exposures. 

4 Further, inclusion of salaried workers in the NIOSH cohort (Sullivan, 2007) adds many workers 

5 with lower workplace exposure. Thus, while occupational exposure concentrations may be 

6 generally higher than typical ongoing environmental concentrations, the exposures in this 

7 occupational database may be representative of non-occupational exposures. 

8 While many occupational epidemiology studies are based on relatively high exposure 

9 levels that are beyond the range of common environmental exposures, many in the Libby 

10 workers cohort experienced exposures that were near or below the PODs derived from the 

11 life-table analysis. The POD for the selected lung cancer mortality exposure metric was 

12 0.191 fibers/cc. The POD for the selected mesothelioma mortality exposure metric was 

13 0.106 fibers/cc. Among the workers hired after 1959 who had at least 1 year of occupational 

14 exposure (n = 470), there were 19 (4%) with exposures to average occupational concentrations of 

15 less than 0.3 fibers/cc, including one out of 20 lung cancer deaths (5%) in these workers. 

16 Although data might have been modeled all the way down to a very low level, the 

17 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) recommends defining a POD for 

18 low-exposure extrapolation in order to increase the stability of the IUR estimate at lower 

19 exposures, where fewer cancers might be expected. Thus, the uncertainty associated with 

20 low-dose extrapolation is somewhat mitigated since the linear extrapolations from the dose 

21 associated with the POD from the life-table analyses of each cancer endpoint was encompassed 

22 within the observed data range. Nonetheless, some uncertainty remains in the extrapolation from 

23 occupational exposures to lower environmental exposures when using a POD. 

24 

25 2) Uncertainty in exposure assessment 

26 Accurate exposure assessment is generally considered to be a major challenge for 

27 occupational epidemiologic studies and is a challenge that is well recognized by the NIOSH 

28 investigators (Amandus et al., 1987a). As stated previously in Section 5.4.3.3, while it is 

29 generally true that the use of more data is an advantage in statistical analyses because it allows 

30 for the computation of more statistically precise effect estimates, this advantage in precision may 

31 be offset by a negative impact on the accuracy of the effect estimate if an increase in sample size 

32 is accompanied by greater exposure misclassification or other biases. Therefore, EPA decided to 

33 base this Libby Amphibole asbestos-specific human health risk assessment upon the mortality 

34 experience of workers hired on or after January 1, 1960. EPA’s use of the sub-cohort analysis is 

35 based on the belief that it is important to accurately estimate the true underlying 
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1 exposure-response relationships by relying on the most accurate exposure data. The use of this 

2 sub-cohort greatly reduces the uncertainty in exposure error compared to evaluations based on 

3 the entire cohort. More specifically, 

4 

5 a) Job assignment was unknown for 686 of the 991 workers employed from 1935 to 

6 1959 (69% of the cohort for this time period). Almost all of these workers were 

7 assigned the same exposure concentration for all years without job category 

8 information. Examination of the post-1959 cohort removes this significant source of 

9 exposure misclassification. 

10 b) Using the more recently hired cohort minimizes the uncertainty in estimated worker 

11 exposures based on the job exposure matrix, which was informed by air sampling 

12 data available in 1956 and later years. Although there are still uncertainties in the 

13 task-specific exposure estimates from 1960-1967, uncertainty in the assessment of 

14 earlier exposure levels is considerably greater. 

15 c) Exposure measurements were collected from the area samples and represented 

16 exposures for all the workers with the same job code. Statistically, this causes 

17 Berkson-type measurement error effect which is described later in this section. 

18 

19 As the EPA exposure-response modeling for mesothelioma and lung cancer mortality is 

20 based on the post-1959 sub-cohort, the remaining discussion of uncertainty in exposure 

21 measurement will address these data. 

22 

23 Sources of uncertainty in job history information 

24 Worker exposures for the EPA exposure-response modeling were calculated based on job 

25 histories and the JEM from 1960 through 1982 (Figure 5-6). Overall, there is little uncertainty in 

26 the job history information. Regarding exposure estimation for the occupational cohort, the 

27 NIOSH investigators (Amandus et al., 1987a) conducted a detailed retrospective exposure 

28 assessment to estimate the individual worker exposures. NIOSH used extensive occupational 

29 exposure data to construct the time-specific job exposure matrix, spanning decades (Amandus, 

30 1987a). These data were re-abstracted from the workers’ employment records for quality 

31 assurance (Sullivan, 2007). NIOSH records on work histories and job-specific exposure 

32 extended from the 1930s through May 1982. But, the vermiculite mining and milling operation 

33 continued on for several years, and some workers were retained through 1993 for plant close-out 

34 activities. However, none of the members of the post-1959 cohort (n = 880) were employed as 

35 of the 1982 employment records. Of the employees still working in 1982 (n = 167), all were 

36 hired prior to 1960. 
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1 Sources of uncertainty in exposure intensity for the identified location operations 

2 The available exposure data that inform the JEM include over 4,000 air samples, the 

3 majority of which were collected after 1967 (Table 4-1). All of the job location exposure 

4 estimates (Table 5-8) from 1968-1982 were directly informed from air samples collected on 

5 membrane filters and analyzed for fibers by PCM. The availability of site- and task-specific air 

6 samples for these years provides a good basis for the exposure estimates. However, there are 

7 some uncertainties in estimating asbestos exposures using air samples analyzed by PCM. 

8 

9 

10 1) PCM analysis does not determine the mineral or chemical make-up of the fiber: The 

11 PCM method defines and counts fibers based on the size (aspect ratio and length) of the 

12 particle without regard for the material that makes up the fiber being viewed. The PCM 

13 method was developed for use in occupational environments where asbestos was present, 

14 and the nature of the fibers should be further evaluated to confirm the fibers viewed 

15 under PCM are asbestos. McGill University researchers evaluated the fibers collected on 

16 membrane filters in the early 1980s and confirmed the presence of asbestos fibers in the 

17 tremolite-actinolite solution series consistent with the Libby Amphibole asbestos 

18 (McDonald et al., 1986). NIOSH researchers confirmed the presence of tremolite 

19 asbestos in bulk dust samples but not in air samples from the facility (Amandus et al. 

20 (1987a). Although less specific to fibers, 60-80% of the airborne dust in the mills in 

21 1968 was tremolite, further supporting the presence of asbestos in the air (based on State 

22 of Montana air sampling, and x-ray diffraction analysis by the Public Health Service) 

23 [PHS correspondence, October 17, 1968]. However, although the presence of mineral 

24 fibers in the actinolite-tremolite series was confirmed in the work environment, it is 

25 possible that there were also fibers counted by PCM from other materials (such as textiles 

26 from clothes and packaging materials). Therefore, it is unknown from these data what 

27 proportion of the counted PCM fibers were mineralogically asbestos, or other materials 

28 present in the workplace. 

29 2) PCM defines fibers as particles with an aspect ratio greater than 3:1: There is an 

30 ongoing debate in the literature on asbestos toxicity regarding the influence of aspect 

31 ratio on relative toxicity. Specifically, in mining environments, it has been speculated 

32 that a larger proportion of low aspect ratio fibers from mineral dusts may significantly 

33 impact the apparent cancer potency of the measured PCM fibers in those environments 

34 (IRIS IUR, 1988, Berman, 2010). There are few data available to understand fiber 

35 morphology and fiber aspect ratios in the Libby cohort working environment. 

36 Considering the post-1959 cohort, PCM fiber size distribution and aspect ratio data only 

37 exist for a set of eight air samples (599 fibers) collected from the wet mill and screening 

38 operations and analyzed by the NIOSH researchers (Amandus et al. 1986a). For these air 

39 samples, over 96% of the fibers viewed by PCM had an aspect ratio greater than 10:1 

40 (Table 4-2, Amandus et al., 1987a). However, because these samples were provided by 

41 the company in the early 1980s, they do not represent conditions in the old wet mill or 

42 dry mill operations, which were significantly dustier environments (Amandus et al., 

43 1987a). It is possible that prior to industrial hygiene (IH) modifications in 1974, the dry 
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1 and old wet mills generated proportionally more mineral dusts than screening and new 

2 wet mill operations after IH modifications. No data are available for the mining 

3 environment, which would also be expected to generate a range of mineral dusts. 

4 Therefore, there is a significant uncertainty about the size and aspect ratio of fibers 

5 included in PCM fiber counts for the majority of the post-1960 workers cohort. 

6 3) The resolution of visible PCM fibers: Current analytical instruments used for PCM 

7 analysis have resulted in a standardization of minimum fiber width considered visible by 

8 PCM between 0.2 and 0.25 μm. Historical PCM analysis (1960s and early 1970s) 

9 generally had less resolution, and fibers with minimum widths of 0.4 or 0.44 μm were 

10 considered visible by PCM (Skinke, 1980; Amandus et al., 1987a). McDonald et al. 

11 (1986) compared fibers viewed by PCM and TEM and estimated that approximately 1/3 

12 of the total fibers could be viewed by the optical microscope. Because 38% of the fibers 

13 were <5 μm in length, this implies approximately 30% were not viewable by optical 

14 microscopy for other reasons, such as width. However, it is unknown what proportion of 

15 that 30% would be viewed with the minimum width resolution of 0.25 μm for later 

16 optical microscopy. It is likely that early PCM counts were underestimated relative to the 

17 later data for the cohort, but by less than a factor of 2. 

18 

19 Prior to 1968, no air sampling data were available for 23 of the 25 job location operations 

20 (Table 4-2), and the exposure estimates were extrapolated from later air sampling data. 

21 Amandus et al. (1987) recognized there is significant uncertainty in the extrapolation of available 

22 air sampling data to previous time periods. The researchers took into account major changes in 

23 operations and interviewed employees in the early 1980s regarding previous years of operation. 

24 The assumptions used to make these extrapolations are clearly stated for each of the plant 

25 operations. For four operations, high and low estimates of pre-1968 exposures were provided 

26 based on different sets of exposure assumptions (Table 5-8). For ore loading, there were 

27 negligible differences in the exposure estimates for the period from 1960-1967 (10.7 versus 

28 9 fibers/cc). For drilling, the river dock, and the bagging plant, there were 3.4-, 2.6-, and 2.8-

29 fold differences, respectively, between the high and low estimates of exposure between 1960 and 

30 1968. 

31 Dry mill exposures between 1960 and 1968 were informed by air sampling for total dust 

32 collected in the dry mill facility from 1956-1969 (where total dust was collected by midget 

33 impingers). Amandus et al. (1987a) derived a conversion factor of 4.0 fibers/cc per mppcf to 

34 apply to the two location operations in the dry mill during these years. There was a range of 

35 conversion factors considered for the dry mill depending on how the dust and fiber air samples 

36 (PCM) were grouped and averaged (1.2 to 11.5 fibers/cc per mppcf). A subset of dust and fiber 

37 samples available over the same time period (1967-1968) resulted in a ratio 8.0 fibers/cc per 

38 mppcf. In contrast, a ratio of 1.9 fibers/cc resulted when total dust samples from 1969 were 
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1 compared with fiber samples from 1970. However, both of these subsets had limited numbers of 

2 samples available. Therefore, the conversion factor of 4.0 fibers/cc per mppcf was selected 

3 based on using the maximum samples available over a time period when the dry mill exposures 

4 were considered similar: dust samples (1965-1969) and fiber samples (1967-1971). This 

5 conversion factor is within a factor of two of the similar datasets considered. 

6 

7 Sources of uncertainty in the calculation of the job exposure matrix (JEM) 

8 The exposures in the job exposure matrix (Figure 5-6) were calculated from the exposure 

9 intensities of the various task-specific exposure intensities shown by job location operation 

10 (Table 5-8). The uncertainties in the exposure intensity for the job location operations will 

11 impact the JEM. Additionally, for each of the job categories in the JEM, NIOSH researchers 

12 defined which tasks (job location operations) were conducted and for what proportion of the 

13 work day. A TWA exposure for each job category across time was calculated based upon these 

14 assumptions and the task-specific exposure estimates. There is a measure of uncertainty in these 

15 assumptions for each job category. Additionally, there is inter-individual variation within the job 

16 categories. These uncertainties are common to exposure reconstruction for epidemiological 

17 cohorts. 

18 

19 Uncertainty in the exposure metric 

20 The PCM measurement is the available exposure metric for analysis of Libby worker 

21 cohort at this time. Currently, there is no optimal choice of the best dose metric for asbestos in 

22 general and in particular for Libby amphibole asbestos, even if TEM-based dose-response JEM 

23 were available. Uncertainties related to PCM analytical method are discussed in Section 2.3. 

24 Briefly, PCM cannot distinguish between asbestos and non-asbestos material, or differentiate 

25 between specific types of asbestos. Further, due to limitations of this methodology, PCM does 

26 not take into account fibers smaller than 5 μm in length. 

27 

28 Evaluation of the effects of uncertainties in exposure measurement 

29 An understanding of the effects of exposure measurement error on the risks estimated 

30 from epidemiologic analyses is important to place these possible exposure measurement errors in 

31 context. The effect of exposure measurement error on estimates of the risk of mesothelioma or 

32 lung cancer mortality attributable to exposure depends upon the degree to which that error may 

33 be related to the likelihood of mesothelioma or lung cancer mortality. Exposure measurement 

34 error that is similar in pattern among workers who died of lung cancer to exposure measurement 

35 error in people who did not die of lung cancer is a non-differential exposure measurement error. 
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1 Differential exposure measurement error that is associated with the outcome can cause bias in an 

2 effect estimate towards or away from the null, while non-differential exposure error typically 

3 results in bias towards the null (Rothman, 1998). From the above evaluation of uncertainties, 

4 there is no indication that the uncertainties in job history information, exposure estimates for 

5 specific tasks, or calculation of the JEM would be differential based on the cancer health 

6 outcome data. Therefore, these uncertainties are considered non-differential, and the general 

7 result is likely to be an attenuation in risk estimates towards the null (that is, the addition of 

8 random noise to a clear signal tends to reduce the clarity of the observed signal and the 

9 avoidance of random noise – here from poor quality exposure measurements, results are a 

10 stronger observed signal). 

11 Generally speaking, if the exposure concentrations estimated by NIOSH were 

12 systematically too high, then the associated risks of exposure estimated in the regression analysis 

13 would be low since the same actual risk would be spread across a larger magnitude of exposure. 

14 Similarly, if the exposure concentrations estimated by NIOSH were systematically too low, then 

15 the associated risks of exposure estimated in the regression analysis would be too high. From the 

16 above evaluation, the majority of the sources of uncertainty are not systematic. There are a few 

17 areas of uncertainty that may be classified as biased: 

18 

19 

20 1) High- and low-exposure estimates for four job location operations were provided 

21 between 1960 and 1967. Amandus et al. (1987a) choose the high estimates of 

22 exposure for these job location operations when calculating the JEM. Therefore, 

23 there will be a bias towards the high end for the job categories informed by these 

24 data. There was a 1.1- to 3.4-fold difference between the high and low estimates. 

25 This difference will be less pronounced where these exposure concentrations are 

26 averaged with other job location operations in the JEM and across multiple jobs for 

27 the majority of the workers (Figure 5-7). 

28 2) Current PCM analysis would count more fibers relative to early PCM methods based 

29 on minimum fiber width resolution. For example, Amandus et al. (1987a) used a 

30 minimum width cutoff of 0.44 in their review of PCM fibers in the 1980s, which may 

31 have resulted in as much as a 2-fold underestimate compared to current PCM 

32 methods with a width resolution of 0.25 μm. Additionally, as PCM methodology has 

33 developed over time, it is unknown when PCM results from company records would 

34 be considered relatively standard to a minimum width resolution between 0.2 and 

35 0.25 μm. Also, prior to standardization of PCM to 0.25-μm minimum width, there 

36 was inter-laboratory variability as well. Therefore, the size distribution of PCM 

37 fibers (e.g., minimum width) reported in the JEM may have changed over time. 

38 Although theoretically a systematic bias, given the years for which PCM data are 

39 available, this is likely an insignificant effect. 
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1 3) Asbestos was a contaminant of vermiculite that was the primary object of production. 

2 Mine, old dry mill, and wet mill ambient air may have contained material other than 

3 asbestos that could have contributed to PCM fiber count. The exposures in the old 

4 dry and wet mills and mine location may have included a greater proportion of dust to 

5 fibers than tasks using the ore and refined vermiculite after the new wet mill became 

6 operational. It is possible there is a systematic over-count of fibers in the dusty 

7 environment due to interference from mineral fragments. This likely impacts the 

8 exposure intensity for 23 of 25 job location operations within the mine and old dry 

9 mill. Estimated exposures from job categories that include these operations may be 

10 biased upwards. 

11 

12 Non-differential measurement error in a continuous exposure can be of the classical or 

13 Berkson type and typically arises in environmental and occupational settings as a mixture of the 

14 two forms (Zeger et al., 2000). Classical error occurs when true exposures are measured with 

15 additive error (Carroll et al., 2006) and the average of many replicate measurements, conditional 

16 on the true value, equals the true exposure (Armstrong, 1998). This error is statistically 

17 independent of the true exposure that is being measured and attenuates true linear effects of 

18 exposure, resulting in effect estimates in epidemiologic studies that are biased towards the null 

19 (Zeger et al., 2000; Armstrong, 1998; Heid et al., 2004). Such errors occur when the mean 

20 values of multiple local air samples are used. 

21 Berkson measurement error is independent of the surrogate measure of exposure (Heid et 

22 al., 2004; Berkson, 1950) and is present when the average of individuals’ true exposures, 

23 conditional on the assigned measurement, equals the assigned measurement. Berkson 

24 measurement error can arise from the use of local area mean sampled exposures to represent the 

25 individual exposures of people in that area—even when the estimated area mean is equal to the 

26 true underlying mean (i.e., no classical error). Examples of random variability in personal 

27 behavior that may produce Berkson-type error in personal exposure estimates include the volume 

28 of air breathed per day among the workers and the effectiveness of individuals’ nasal filtration at 

29 removing contaminants. In general, Berkson error is not thought to bias effect estimates but 

30 rather increases the standard errors of effect estimates (Zeger et al., 2000). However, some 

31 epidemiologic studies have suggested that Berkson error can produce a quantitatively small bias 

32 towards the null in some analyses (Burr, 1988; Reeves et al., 1998; Kim et al., 2006; Bateson and 

33 Wright, 2010). 

34 
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1 Exposure to other kinds of asbestos and residential exposure 

2 Another source of uncertainty in the estimation of exposures in the Libby workers cohort 

3 is the potential contribution of non-occupational or residential exposures as well as exposures to 

4 other kinds of asbestos in employment before or after working in Libby. 

5 Many of the workers resided in Libby, MT, before and/or after their employment at the 

6 mining and milling facilities ended. The vermiculite from the mine had been used at numerous 

7 sites around the town, including baseball fields around the expansion plant and as filler in 

8 gardens (U.S. EPA, 2001, 2010). Exposure to asbestos could have occurred among individuals 

9 outside of the workplace, particularly through activities with the potential of stirring up dirt or 

10 other materials that had been mixed with the vermiculite (Weis, 2001). The results of 

11 community sampling indicated that even 10 years after mill operations ceased during some 

12 activities, asbestos fiber concentrations in the air could exceed OSHA standards established for 

13 the protection of workers (Weis, 2001). 

14 Therefore, the workers’ actual personal exposures as the sum of occupational and 

15 non-occupational exposures are likely to have been underestimated by the use of estimated 

16 Libby-related occupational exposure alone. The difficulty stems from the lack of data on 

17 residential exposures and lack of information on pre- and post-employment residence of the 

18 Libby workers. Non-occupational exposures were likely to have been smaller in magnitude than 

19 the occupational exposures, but workers may have lived in and around Libby, MT, for many 

20 more years than they were exposed occupationally. The impact of residential exposure could be 

21 more prominent for workers with lower occupational exposure who resided in Libby for a long 

22 time. Whitehouse et al. (2008) has reported several cases of mesothelioma among residents of 

23 the Libby, MT region who were not occupationally exposed. However, since the report by 

24 Whitehouse et al. (2008) details only the cases and does not define or enumerate the population 

25 from which those cases were derived, computed relative risks from non-occupational exposures 

26 were not available. ATSDR (2000) reported higher relative risks of mesothelioma among the 

27 population of Libby, MT, including former workers residing in Libby, but did not provide 

28 relative risk for non-occupational exposure. Instead, the ATSDR report on mortality (2000) 

29 grouped cases among the former workers with non-occupationally exposed cases. Therefore, it 

30 is not clear what the magnitude of the contribution of workers’ non-occupational exposures were 

31 to their overall risk. 

32 Some of the occupational workers with lower exposures, such as short-term workers, may 

33 have either been high school or college students working during the summer or may have been 

34 transient workers who may not have stayed for a long time in Libby. Sullivan (2007) analyzed 

35 differences between short and long term workers and reported little difference between the 
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1 groups except for age at hire. As the shorter term workers were younger on average, this 

2 supported the suggestions that some of the short term workers may have been college students 

3 working during the summer. It is not clear that this population of short-term workers is well 

4 defined; however, it is possible that transient workers could potentially have been exposed to 

5 other kinds of asbestos or other lung carcinogens in their non-Libby occupational career, which 

6 might have affected their pre- and post-Libby risk profile for asbestos exposure. While their 

7 occupational histories are unknown, it is very unlikely that they include exposures of the 

8 magnitude that were encountered in the Libby mine and mill. This type of underestimation of 

9 exposure related to unknown occupational histories outside of Libby could potentially lead to 

10 overestimation of regression slopes for occupational exposures in Libby and, hence, somewhat 

11 overestimated risks, although the magnitude of this overestimation is unquantifiable. 

12 Counterbalancing any upward bias on the estimated effects of occupational exposures due to 

13 non-occupational exposures is the downward bias from random exposure measurement error 

14 with lower occupational exposure affected disproportionately. 

15 

16 Conclusion regarding uncertainty in exposure assessment 

17 Overall, there were likely to be multiple sources of uncertainty attributable to exposure 

18 measurement error. It is possible that systematic error may have been introduced into the 

19 exposure intensities assigned to several of the job location operations discussed above. In each 

20 case, these errors in estimating exposures were overestimates. The magnitude of the potential 

21 overestimates of drilling and dry and old wet mill exposures is uncertain. The dust-to-fiber 

22 conversion ratio applied to the dry mill during 1960-1967 could be an over or underestimate by 

23 as much as 2-fold. Random error in the measurement of dust or fibers would likely have 

24 produced an underestimation of risk. There is no known bias in the assumptions to extrapolate 

25 exposure to pre-1968 location operations outside of the dry mill, and random bias would also 

26 likely have produced an underestimation of risk. 

27 

28 3) Uncertainty in model form 

29 For mesothelioma mortality, the Poisson model is commonly used for rare outcomes and 

30 has been applied by McDonald et al. (2004) and Moolgavkar et al. (2010) to model 

31 mesothelioma risk in the Libby worker cohort. For lung cancer mortality, the Cox proportional 

32 hazards model is a well-established method that is commonly used in cohort studies, including 

33 by Larson et al. (2010a) and Moolgavkar et al. (2010) for the Libby worker cohort, because this 

34 type of survival analysis takes into account differences in follow-up time among the cohort. 

35 Larson et al. (2010a) conducted Poisson regression analyses and reported that their lung cancer 
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1 results using this different model form were similar to those from their extended Cox 

2 proportional models, but those results were not shown. 

3 Both of these model forms allow for the evaluation and control of important potential 

4 confounding factors such as age, sex, and race, and allow for the modeling of exposure as a 

5 continuous variable. Both model forms yielded exposure-response results with good fit to the 

6 occupational exposure data. The default assumption of the extended Cox proportional hazards 

7 model as well as the Poisson model is that all censoring (due to death or loss to follow-up) is 

8 assumed to be independent of exposure to the Libby Amphibole asbestos (e.g., death in an 

9 automobile accident or moved to Canada). However, exposure to Libby Amphibole asbestos 

10 may be causing deaths from other causes such as asbestosis or non-malignant respiratory disease 

11 (Larson et al., 2010a), which is referred to as dependent censoring. The concern is that the 

12 observation of lung cancer mortality may be precluded by mortality from other causes. 

13 In the cohort of 880 workers hired after 1959, 32 died of lung cancer, while 10 died of 

14 asbestosis and 21 died of non-malignant respiratory disease. The mean length of follow-up from 

15 the date of hire until death for the workers who died of lung cancer was 24.9 years. However, 

16 the mean length of follow-up for the workers who died of asbestosis or non-malignant 

17 respiratory disease was 30.4 years, so it does not appear that early deaths from other causes 

18 associated with exposure to the Libby Amphibole asbestos (Larson et al., 2010a) would have 

19 precluded many cases of lung cancer. This implies that any potential bias in the lung cancer risk 

20 estimates due to dependent competing risks is small. 

21 With respect to mesothelioma mortality, it should be noted that the exposure-response 

22 modeling is limited by the number of deaths. However, dependent censoring, as described 

23 above, is not accounted for in the Poisson model and likely causes a downward bias in the 

24 estimation of risk. The mean length of follow-up for the workers who died of mesothelioma was 

25 30.1 years, and there is some evidence that early deaths from other exposure-related causes 

26 precluded an individual’s risk of death from mesothelioma; only lung cancer exhibited a shorter 

27 average follow-up time compared to mesothelioma, and in 419 cases of mesothelioma, 

28 mesothelioma and lung cancer were never co-identified (Roggli and Vollmer, 2008). 

29 

30 4) Uncertainty in selection of exposure metric 

31 There is uncertainty about what metric should be used for modeling exposure to Libby 

32 Amphibole asbestos. The previous IRIS IUR assessment for asbestos (1988) found that 

33 cumulative exposure with a 10-year lag was the best metric for lung cancer mortality, and a more 

34 complicated model (Eq. 5-5) based on average cohort exposure intensity, average cohort time 

35 since first exposure, and average duration of employment was the best metric for mesothelioma 
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1 mortality. This assessment evaluated these models, but also models that include unlagged and 

2 lagged cumulative exposure with and without a half-life of various lengths, and residence-time 

3 weighted exposure with and without a half-life. In the analysis of comparative model fit, lagged 

4 cumulative exposure with a half-life provided the best fits for both mesothelioma and lung 

5 cancer mortality associated with Libby Amphibole asbestos. However, evaluation of 20-year lag 

6 and longer lag times for mesothelioma was not possible, as the earliest mesothelioma death 

7 happened less than 20 years from the start of the exposure, and, hence, exposure was zeroed out, 

8 and the fit of any model with 20-year lag was very poor. Latency time for mesothelioma may be 

9 as long as 60-70 years (e.g., Bianchi and Bianchi, 2009), so the precise lag time is uncertain. 

10 In evaluating the data on lung fiber burden, Berry et al. (2009) estimated the range of the 

11 half-life for crocidolite to be between 5 and 10 years. That range is consistent with the finding of 

12 a 5-10 year half-life with 10-15 years lag that provided the best fit to the Libby workers cohort 

13 mesothelioma mortality data. Similarly, recent publications indicate that the relative risk of lung 

14 cancer due to asbestos exposure declines 15-20 years after the cessation of exposure to asbestos 

15 (Hauptmann et al., 2002; Magnani et al., 2008). The marginally best fit for the Libby workers 

16 cohort lung cancer mortality data was for CE models with a 5-20-year half-life and 10-year lag. 

17 However, the precise lag and half-life times are somewhat uncertain. Sensitivity analysis that 

18 excluded people with high exposure during 1960-1963 (Section 5.4.5.3) provides further 

19 evidence that distinguishing between various lag and decays may be difficult with these data. A 

20 limitation of this sensitivity analysis is the decrease in the number of cases, especially for 

21 mesothelioma. Resolving this uncertainty will require longer follow-up time that would allow 

22 for a sub-cohort analysis of workers hired in 1967 or afterwards (when exposure estimates began 

23 to be based on PCM measurements) until a sufficient number of cases would be available for 

24 additional analysis. 

25 These simulated decay models were derived mathematically to approximate underlying 

26 biological processes that are not well understood, and their better fit is based on maximizing the 

27 likelihood for the workers cohort and may not necessarily apply to the environmental exposure 

28 patterns. Nonetheless, while the mode of action for carcinogenicity is unknown, the models 

29 incorporating a half-life in the exposure metric were clearly preferable for mesothelioma 

30 mortality, and the goal of the regression modeling effort was to identify the best fitting exposure 

31 model for the Libby worker cohort. 

32 The selection of the exposure metric is a source of cross-metric variability discussed in 

33 Section 5.4.5.3, and the IUR incorporates this variability. The difference between this value and 

34 the value derived from the best fitting exposure model describes the quantitative uncertainty, 

35 which is less than 2-fold. 
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1 5) Uncertainty in assessing of mortality corresponding to the cancer-specific endpoints 

2 As well established in the literature, mortality rates calculated from death certificates are 

3 lower than the true rate of death due to both lung cancer, and to a larger degree, mesothelioma 

4 (lung cancer sensitivity: ranging from 86% in an asbestos cohort [Selikoff and Seidman, 1992] to 

5 95% in general [Percy et al., 1981]; mesothelioma sensitivity: ranging from 40% for ICD-9 

6 [Selikoff and Seidman, 1992] to about 80% for ICD-10 [Camidge et al., 2006; Pinhiero et al., 

7 2004]). This underestimation of the true rate will result in a lower estimated risk compared with 

8 that which would be estimated based on the true rate. The underestimation of risk is much more 

9 pronounced for the absolute risk model (mesothelioma) than for the relative risk model (lung 

10 cancer). Misdiagnosis rates would need to be quite disparate in the cohort and the comparison 

11 population to impact relative risks, and this is unlikely for internal controls that were used in the 

12 lung cancer analysis using the Cox model. Therefore, EPA considered use of a procedure to 

13 adjust risks for mesothelioma, but not for lung cancer—underascertainment (Section 5.4.5.1.1). 

14 This procedure makes certain assumptions, in particular, that an adjustment factor derived for the 

15 full cohort applies to the sub-cohort hired after 1959, and that the rate of misdiagnosis of 

16 peritoneal mesotheliomas has not improved recently and that the proportion of peritoneal 

17 mesotheliomas in the cohort is estimated from the available information on the type of 

18 mesothelioma in one-third of mesothelioma cases. However, overall uncertainty in this 

19 adjustment is low, and the application of the adjustment reduces the bias associated with the 

20 diagnostic underascertainment. 

21 The endpoint for both mesothelioma and lung cancer was mortality, not incidence. The 

22 latter is generally desirable, but median survival with lung cancer and, especially, mesothelioma 

23 is not very long, so uncertainty related to the endpoint being death and not incidence is low. 

24 There is evidence that other cancer endpoints may also be associated with exposure to the 

25 commercial forms of asbestos. IARC concluded that there was sufficient evidence in humans 

26 that commercial asbestos (chrysotile, crocidolite, amosite, tremolite, actinolite, and 

27 anthophyllite) was causally associated with lung cancer and mesothelioma, as well as cancer of 

28 the larynx and the ovary (Straif et al., 2009). Among the entire Libby workers cohort, only 

29 2 deaths were found to be due to laryngeal cancer, and there were no deaths from ovarian cancer 

30 among the 24 deaths of 84 female workers. The lack of sufficient number of workers to estimate 

31 risk of ovarian cancer is an uncertainty in an overall cancer health assessment. 

32 The remaining uncertainties attributed to assessing mortality corresponding to the cancer 

33 endpoints are considered to be low. 

34 
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1 6) Uncertainty in control of potential confounding in modeling lung cancer 

2 It is well known that smoking is a strong independent risk factor for lung cancer and may 

3 have a synergistic effect with asbestos exposure (Wraith and Mengersen, 2007). In contrast, 

4 smoking is not considered a risk factor for mesothelioma (Mossman et al., 1990; Selikoff and 

5 Lee, 1978). 

6 As an important potential confounder of the lung cancer mortality analysis, the possible 

7 effect of smoking on the estimated risk of lung cancer mortality associated with exposure to 

8 Libby Amphibole asbestos needs to be evaluated to the fullest extent possible. This 

9 consideration was discussed in Amandus and Wheeler (1987) and in Section 4.1.1.3 . 

10 Additionally, WR Grace instituted a smoking ban on the property in 1979 (Peacock, 

11 2003). However, it is unclear how the ban on smoking at work relates to smoking patterns 

12 outside of the work environment. Moreover, since only about 30% of the sub-cohort was still 

13 employed in 1979 and all of the post-1959 cohort had been terminated by May of 1982, the 

14 impact of a workplace smoking ban on cohort smoking history is unlikely to be a significant 

15 factor but may explain the higher proportion of former smokers in the Amandus and Wheeler 

16 (1987) data. 

17 Without high-quality individual-level data on smoking that could be used to control for 

18 potential confounding, it is still possible to comment upon the likelihood and potential magnitude 

19 of confounding and the impact any confounding would be expected to have on the lung cancer 

20 mortality risk estimates. Confounding can be controlled for in a number of ways including by 

21 modeling and by restriction. Restriction of the study population can reduce any potential 

22 confounding by making the resulting population more similar. For instance, there can be no 

23 confounding by gender when a study population is restricted to only men. This assessment 

24 restricted the study population to those workers hired after 1959. Smoking habits have changed 

25 over time, and it can reasonably be assumed that the range of smoking habits among those hired 

26 after 1959 is less variable than that among the whole cohort, particularly because of the narrower 

27 range of birth cohorts represented in this sub-cohort. This should have the effect of reducing 

28 some of the potential for confounding. 

29 Additionally, the extended Cox proportional hazards models controlled for date of birth, 

30 which effectively controls for any secular trends in confounders over time (Tableman and Kim, 

31 2004). Amandus and Wheeler (1987) cite data from the U.S. Public Health Service (1979) 

32 showing a steady decrease in the prevalence of current smoking from 52.9% in 1964 when the 

33 U.S. Surgeon General’s report on smoking was released to 42.3% in 1970 and 37.5% in 1978 

34 (U.S. Surgeon General, 1990). If current smoking were a meaningful confounder, such a 

35 reduction in smoking rates over time should have produced a noticeable distortion in the 
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1 proportionality of the hazards as the magnitude of confounding by smoking changes with 

2 smoking prevalence. No violation of the proportional hazards assumption was observed in the 

3 context of the Cox model; hence, there is no evidence of confounding by smoking in the analyses 

4 of workers hired after 1959. 

5 Lastly, a method has been described by Richardson (2010) to determine if an identified 

6 exposure relationship with lung cancer is confounded by unmeasured smoking in an occupational 

7 cohort study. EPA implemented this methodology to model the potential effects of Libby 

8 Amphibole asbestos on the risk of COPD mortality on the sub-cohort of workers hired after 1959 

9 (see Section 5.4.3.4.4). Summarizing these findings, EPA used the method described by 

10 Richardson (2010) to evaluate whether exposures to Libby Amphibole asbestos predicted 

11 mortality from COPD as an indication of potential confounding by smoking and found a non12 significant negative relationship, which was inconsistent with confounding by smoking. 

13 

14 7) Uncertainty due to potential effect modification 

15 Among the 32 deaths from lung cancer in workers hired after 1959 that were used to 

16 estimate the unit risk of lung cancer mortality (see Section 5.4.5.2), data on smoking listed 16 as 

17 smokers, 4 as former smokers and 12 of the 32 had missing data. Thus, data to support an 

18 estimate of the risk of Libby Amphibole asbestos among known non-smokers were not available. 

19 It is theoretically possible that the risk of lung cancer mortality estimated in this 

20 assessment is a reflection of a positive synergy between smoking and asbestos, and that the 

21 adverse effect of Libby Amphibole asbestos among the potentially non-smoking workers has 

22 been overestimated. The unit risk of the lung cancer estimate herein and the combined 

23 mesothelioma and lung cancer mortality IUR would then be health protective for any population 

24 that had a lower prevalence of smoking than that of the Libby worker cohort. 

25 

26 8) Uncertainty due to length of follow-up 

27 There is some potential uncertainty regarding the length of follow-up for cancer 

28 mortality, even more so with the restriction of the cohort to those workers hired after 1959. The 

29 hire dates among this subset of the cohort ranged from January 1960 to November 1981 (the 

30 mean date of hire was May 1971). Follow-up continued until the date of death or 

31 December 31, 2006, whichever occurred first. Therefore the range of follow-up was from 25 to 

32 46 years, with a mean of more than 35 years. 

33 However, for mesothelioma mortality, the length of the latency period is considerably 

34 longer. Suzuki (2001) reviewed 1517 mesothelioma cases from 1975 through 2000 and was able 

35 to estimate the latency for 800. Suzuki (2001) reported 17% of cases had a latency of less than 
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1 30 years with 52% of cases with a latency of less than 40 years. Bianchi and Bianchi (2009) 

2 estimated the mesothelioma latency in 552 cases and reported mean latency periods of 35 years 

3 among insulators, 46 years among various industries, and 49 years among shipyard workers. 

4 The effect of insufficient length of follow-up for mesothelioma mortality would be to 

5 underestimate the risk of exposure since there would be workers who may eventually die of 

6 mesothelioma that are not counted in this assessment. As the risk of mesothelioma mortality is 

7 evaluated as an absolute risk, the unit risk of mesothelioma mortality may reasonably be 

8 expected to rise with time moderated by the increase in person-years of follow-up. According to 

9 the results of Suzuki (2001) and of Bianchi and Bianchi (2009), a mean length of follow-up of 

10 35 years may only have captured half of all eventual mesothelioma mortality cases among the 

11 Libby workers hired after 1959. If this were so, then the unit risk of mesothelioma mortality 

12 could be larger than was estimated from existing data, depending on the relationship between the 

13 number of additional deaths and increase in person-years. 

14 

15 9) Uncertainty in use of life-tables to calculate cancer mortality IUR 

16 The life-table procedure computes the extra risk of death from birth up to 85 years of age, 

17 in part, because this is how national cancer incidence and mortality rate data that are one basis of 

18 the life-tables are made available (see 2003-2007 SEER Table 15.10, age-specific U.S. death 

19 rates). Because the prevalence of cancer mortality is a function of increasing age, this cut-off at 

20 age 85 ignores a small additional risk of lung cancer mortality among a small percentage of 

21 people who have the higher background risk. This has the effect of slightly underestimating the 

22 IUR that would be derived if the life-table were extended for an additional period of time, 

23 accounting for longer life spans. Extension of the life-table analysis to people over the age of 85 

24 requires an additional assumption. Assuming that having attained the age of 85 years, the 

25 additional life expectancy is 5 years, then the lung cancer mortality unit risk based on the LEC01 

26 would be somewhat larger—on the order of 5-10%—slightly more than the additional 

27 mesothelioma mortality risk if the life-tables were extended. 

28 

29 10) Uncertainty in combining of risk for composite cancer IUR 

30 For the purpose of combining risks, it is assumed that the unit risks of mesothelioma and 

31 lung cancer mortality are normally distributed. Since risks were derived from a large 

32 epidemiological cohort, this is a reasonable assumption supported by the statistical theory, and 

33 uncertainty related to it is low. 

34 
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1 11) Uncertainty in extrapolation of findings in adults to children 

2 The analysis of lung cancer mortality specifically tested and confirmed the assumption 

3 that the relative risk of exposure is independent of age within the age range of the occupational 

4 sub-cohort hired after 1959. However, no comparable data are available to estimate the lifetime 

5 risk from early life exposures. The life-table procedure is conducted so as to initiate exposure at 

6 age 16 to represent adult exposures. Then, the adult-only-exposure IUR estimates derived from 

7 the life-table analysis need to be re-scaled to a 70-year lifespan in order to yield the standard 

8 lifetime IUR, allowing risk estimate calculations involving less-than-lifetime exposure scenarios, 

9 in the standard manner. After re-scaling, the resulting “adult-based” IUR estimate (in contrast to 

10 the unscaled “adult-only-exposure” IUR estimate obtained from the life-table calculations) can 

11 be used seamlessly by the end-user in the same manner as for an adult-based IUR estimate 

12 derived from a rodent bioassay. Lack of published information on risks associated with Libby 

13 Amphibole asbestos-specific exposure during childhood is the uncertainty associated with the 

14 proposed extrapolation. If such information is subsequently published, the extrapolation 

15 procedure can be updated. As per the EPA Cancer Guidelines (U.S. EPA, 2005b), when the 

16 information regarding early-life susceptibility to cancer is not available, the linear low-dose 

17 extrapolation approach will be used. 

18 

19 5.4.6.2. Summary 

20 In the discussion of the overall uncertainty in the IUR, it is important to distinguish 

21 between uncertainty that encompasses both the direction and the magnitude from uncertainty 

22 with known directional effects on the IUR but of unknown magnitude. In this summary, only the 

23 latter uncertainties, which may result in underestimated or overestimated risk, are listed below. 

24 Uncertainties that are not thought to alter the estimated magnitude of the risk in a systematic 

25 direction are not included in this summary. 

26 The sources of uncertainty that could lead to a likely underestimation of the cancer risk 

27 value include the following: 

28 

29 

30 • Use of historical PCM exposure measurements. As asbestos was a contaminant of 

31 vermiculite that was the primary object of production, mine and dry and old wet mill 

32 ambient air may have contained material other than asbestos that could have contributed 

33 to fibers counted by PCM. Therefore, it is possible that exposure estimates for some, or 

34 possibly a large portion of the cohort, are overestimated, and, therefore, the resulting IUR 

35 may be underestimated.

36 • Measurement error in exposure assessment and assignment. This assessment showed 

37 that unit risk results from analysis of the lung cancer mortality in the full cohort 
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1 (Sullivan, 2007; Larson et al., 2010a) compared to the sub-cohort hired after 1959 may 

2 have been attenuated as much as 2-6 times (see earlier section on statistical uncertainty). 

3 By excluding those cohort members hired before 1960 on whom there was insufficient 

4 work history information to estimate their exposures, the unit risk for lung cancer was 

5 less attenuated due to exposure measurement error. However, exposure measurements 

6 from the 1960s are also imperfect and include a lesser degree of exposure measurement 

7 error, which could have led to underestimated risk even in the sub-cohort hired after 

8 1959. 

9 • Limited length of follow-up. Absolute risk is used for mesothelioma; therefore, the unit 

10 risk of mesothelioma mortality could be larger than was estimated from existing data, 

11 depending on the relationship between the number of additional deaths and an increase in 

12 person-years. 

13 • Use of life-tables to calculate the IUR based on cancer mortality. The lung cancer 

14 mortality unit risk based on the LEC01 would be somewhat larger, about 5-10%, and the 

15 mesothelioma unit risk would be slightly less (about 3%) than that if the life-tables were 

16 extended from 85 to 90 years to account for longer life spans. 

17 • Small number of women and ovarian cancer. While asbestos is causally associated with 

18 increased risks of ovarian cancer (Straif et al., 2009), there were only 84 women in the 

19 whole cohort, and there were no deaths from ovarian cancer among 24 total deaths. To 

20 the extent that there was an increased risk of ovarian cancer in the Libby workers cohort 

21 due to inhalation exposures that was unobserved, then the IUR would be somewhat 

22 underestimated. However, it was not possible to estimate the magnitude of this 

23 underestimation on the total cancer risk. 

24 • Dependent competing risks. Competing risk of mortality from other diseases related to 

25 exposure may have resulted in underestimates of the risk of mortality from either 

26 mesothelioma or lung cancer. The mean length of follow-up for the Libby workers who 

27 died of mesothelioma was to 30.1 years, and evidence exists (Suzuki, 2001; Bianchi and 

28 Bianchi, 2009) that early deaths from other exposure-related causes could have precluded 

29 an individual’s risks of death from mesothelioma. However, it was not possible to 

30 estimate the magnitude of this effect on the total cancer risk. 

31 

32 The sources of uncertainty that could lead to a likely overestimation of the cancer risk 

33 value include the following: 

34 

35 • Potential residual confounding and effect modification. The unit risk of lung cancer 

36 mortality estimated herein, and the combined mesothelioma and lung cancer mortality 

37 IUR, would over-estimate the risk in any population that had a lower prevalence of 

38 smoking than that of the Libby worker cohort. Since the Libby worker cohort had a large 

39 prevalence of smokers and no known non-smokers developed lung cancer, it is also 

40 possible that estimated risk for lung cancer is actually risk for an interaction of lung 
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1 cancer and smoking and effects of smoking and asbestos are known to be between 

2 additive and multiplicative (Section 4). 

3 • The resolution of visible PCM fibers in 1960s and 1970s and use of TEM-based PCMe. 

4 It is likely that early PCM counts were underestimated relative to the later PCM 

5 measurements. Since PCMe counts fibers that correspond to modern PCM, using PCMe 

6 for risk derived partially in old PCM fibers could lead to overestimation of mortality risk. 
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1 6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND 

2 EXPOSURE RESPONSE

3 Libby Amphibole asbestos, a contaminant of vermiculite mined near Libby, MT, is a 

4 complex mixture of amphibole fibers—both mineralogically and morphologically (Section 2.2). 

5 The mixture primarily includes tremolite, winchite, and richterite fibers. These fibers exhibit a 

6 range of morphologies from asbestiform to cleavage fragments (Meeker et al., 2003). Given the 

7 exposure potential to Libby Amphibole asbestos—and its unique mineral composition—a hazard 

8 characterization and cancer dose-response assessment is presented. 

9 As discussed in Section 1, there is currently no RfC for asbestos and the U.S. EPA IRIS

10 inhalation unit risk (IUR) for asbestos is based on a synthesis of 14 epidemiologic studies that

11 included occupational exposure to chrysotile, amosite, or mixed mineral fibers (chrysotile, 

12 amosite, and crocidolite) (U.S. EPA, 1986). There is uncertainty in applying the resulting IUR to 

13 environments and minerals that are not included in the studies considered for the asbestos IUR 

14 derivation (U.S. EPA, 1988). Published mortality studies on the Libby, MT worker cohort have 

15 become available since the derivation of the IRIS asbestos IUR (i.e., McDonald et al., 1986a, 

16 2004; Amandus and Wheeler, 1987; Sullivan, 2007; Larson et al., 2010a; Moolgavkar et al., 

17 2010). This assessment documents non-cancer and cancer health effects from inhalation 

18 exposure to Libby Amphibole asbestos. Data are not available to support derivation of either an 

19 RfD or a cancer slope factor following oral exposures to Libby Amphibole asbestos.

20

21 6.1. HUMAN HAZARD POTENTIAL

22 6.1.1. Exposure

23 Libby Amphibole asbestos was not mined for industrial or commercial use, but it was a 

24 contaminant of vermiculite mined near Libby, MT (Section 2). Vermiculite from the Libby, MT

25 mine was used commercially from the 1920s to 1990, and it comprised approximately 80% of 

26 the total vermiculite sold in this country until that point. Vermiculite was most notably used as 

27 attic insulation, as a soil amender for gardening, and in the manufacturing of gypsum wallboard. 

28 The exposure potential to Libby Amphibole asbestos includes historical exposures (both 

29 occupational and community); as well as the potential for ongoing exposures to waste materials, 

30 contaminated soils and vegetation, and consumer products (e.g., vermiculite attic insulation).

31 There are many ways in which workers and residents in Libby, MT, and the surrounding 

32 communities may have been exposed while the mining and milling operations were active. 

33 Historical routes of exposure include (1) occupational exposure; (2) take-home exposure for 

34 household contacts of the workers (including children); (3) dust/fiber emissions to the 

35 community from the milling and exfoliating facilities; (4) distribution of waste material into the 
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1 community as fill (including yards and recreational areas); (5) use of vermiculite attic insulation

2 in homes; (6) use of vermiculite in gardening/horticulture; and (7) children playing in the waste 

3 rock piles (stoner rock) (ATSDR, 2001). Other than documentation of dust and fiber exposure 

4 levels for mine and mill workers, there are few data to inform the levels of exposure to 

5 household contacts and community members during mine and mill operations. Although no 

6 historical exposure measurements are available from the homes of the workers, the EPA has 

7 conducted some sampling to determine exposure levels from vermiculite and waste materials that

8 remain in the community (U.S. EPA, 2001; Appendix B). These data provide information useful 

9 to understand what historical exposures might have been for similar activities. 

10 Outside of Libby, MT, milled vermiculite ore was shipped to 271 domestic sites that

11 served as processing facilities (GAO-08-71). These sites included exfoliation plants (e.g. for the 

12 production of vermiculite insulation) as well as non-exfoliation facilities (e.g., production of 

13 gypsum wallboard). The vermiculite ore was exfoliated by heat-induced expansion resulting in 

14 vermiculite produced for commercial purposes. Both the commercial vermiculite and the waste 

15 rock (i.e. residual waste rock from exfoliation) were contaminated with Libby Amphibole 

16 asbestos fibers. Potential exposure routes in these communities located around the country

17 parallel the exposures in Libby, MT, including occupational exposures, take-home exposures 

18 from workers, and children playing in the piles of waste rock near the facility (ATSDR, 2008).

19 Waste materials (expanded vermiculite and waste rock) from some of these facilities were also 

20 used for fill in local communities, potentially creating additional exposure pathways based on an 

21 ATSDR review of 28 facilities, and a survey of the Western Minerals Plant, MN (ATSDR, 2001, 

22 2008). Few historical samples that could be used to quantify the exposure potential for workers 

23 or for the surrounding communities are available from these facilities. Air modeling conducted 

24 for one exfoliating facility in Minnesota does provide support for the potential of dust/fiber 

25 emissions from exfoliating plants to impact ambient air quality in the vicinity of the plant 

26 (ATSDR, 2005). 

27 While the mine was active, there was exposure potential to commercial products 

28 containing vermiculite from Libby, MT, especially in gardening soils and vermiculite attic 

29 insulation. No studies have evaluated the potential for consumer exposure when vermiculite

30 from Libby, MT, was employed as a soil amender, but air sampling at one facility where this was 

31 produced (O.M. Scott facility in Marysville, OH) demonstrated that workers handling this 

32 material during manufacture were exposed to asbestos fibers (Lockey et al., 1984). There is 

33 potential for exposure for homes that contain vermiculite attic insulation from Libby, MT, as 

34 residents and workers enter attics for various uses, repairs, and renovations and for using 

35 firewood from Libby, MT area (Section 2.3.3). 

36
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1 6.1.2. Fiber Toxicokinetics

2 There is no specific information available at the time of this assessment on the fiber

3 toxicokinetics of Libby Amphibole asbestos. However, as a mineral fiber, the characteristics that 

4 define the deposition, clearance, and translocation of other amphibole fibers might apply to 

5 Libby Amphibole asbestos. As discussed in Section 3, the specific fiber dimensions and density

6 of Libby Amphibole asbestos will determine the probable pattern of deposition in the respiratory 

7 tract and other tissues (e.g., pleura, peritoneum). Based on the fiber-size profile of airborne

8 Libby Amphibole asbestos fibers, deposition is expected throughout the respiratory tract 

9 including the alveolar regions. Less is known about mineral fiber translocation to other target 

10 tissues in general, and, to date, no studies have specifically examined translocation following 

11 exposure to Libby Amphibole asbestos. 

12 As with other mineral fibers, clearance is likely to occur via the mucociliary apparatus in

13 the upper respiratory tract and the mucociliary escalator for those fibers deposited in the trachea 

14 and bronchioles. This clearance is enhanced by macrophage action, which may transport some 

15 of the fibers from the alveolar sac to the mucociliary system. Fibers may also be dissolved in 

16 lung fluids or through the more aggressive action of alveolar macrophages. In general, 

17 amphibole asbestos is considered more persistent and less likely to dissolve than other natural 

18 fibers. However, no data are available for Libby Amphibole asbestos specifically, and it is 

19 unknown if Libby Amphibole asbestos fibers would split or break in the pulmonary compartment 

20 as has been shown with some amphibole fibers (e.g., ferroactinolite) (Coffin et al., 1983). 

21 Any fibers deposited in the respiratory tract and not cleared via the mucociliary system,

22 or which are not dissolved, can remain in the lung or can be transported to other tissues. 

23 Although data specific to Libby Amphibole asbestos are not yet available, other asbestos fiber 

24 types may translocate from the lung via macrophage action and transport through the lymph 

25 system, or direct migration may occur through tissues from the mechanical action of the lung. 

26 Pleural and peritoneal effects documented in Libby Amphibole asbestos-exposed individuals 

27 support the potential for translocation of Libby Amphibole asbestos into the pleura.

28

29 6.1.3. Noncancer Health Effects in Humans and Laboratory Animals

30 Noncancer health effects identified in humans include effects on the respiratory system 

31 (e.g., lung function, pleural and parenchymal effects) following inhalation exposure to Libby 

32 Amphibole asbestos, as well as increased mortality from non-cancer causes. Five cohort 

33 mortality studies of Libby miners identified increased risk of mortality from non-cancer causes, 

34 including nonmalignant respiratory disease (McDonald et al., 1986a; Amandus and Wheeler

35 1987; McDonald et al., 2004; Sullivan, 2007; Larson et al., 2010a) and cardiovascular disease 

36 (Larson et. al., 2010a). Studies of respiratory system effects comprise six studies in community 
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1 members living in Libby, MT (Weill et al., 2010; Peipins et al., 2003; Peipins et al., 2004;

2 Whitehouse 2004; Muravov et al., 2005; Vinikoor et al., 2010), two studies among Libby miners 

3 (McDonald et al., 1986b; Amandus et al., 1987b), and two studies among Marysville workers 

4 (Lockey et al., 1984; Rohs et al., 2008). Among the studies in community members, all but one 

5 (Vinikoor et al., 2010) included former vermiculite facility workers, and none of the six provided 

6 quantitative exposure information. The four studies conducted in two occupational cohorts 

7 (McDonald et al., 1986b; Amandus et al., 1987b; Lockey et al., 1984; Rohs et al., 2008) did 

8 provide quantitative exposure estimates, and were considered suitable for exposure-response 

9 analysis to support RfC derivation. Each of the four studies demonstrated an increased risk of 

10 pleural anomalies in workers.

11 Although laboratory animal data and experimental data on toxicity mechanisms are 

12 limited for Libby Amphibole asbestos, the existing data are consistent with the health effects 

13 observed in both Libby workers and community members. Animal studies have demonstrated 

14 increased collagen deposition consistent with fibrosis following intratracheal instillation of Libby 

15 Amphibole asbestos fibers in C57Bl6 mice (sex unspecified, Putnam et al., 2008; male and 

16 female, Smartt et al., 2009) and increased markers of pulmonary inflammation in a rat model for 

17 human cardiovascular disease (Shannahan et al., 2011). Pulmonary fibrosis, inflammation, and 

18 granulomas were observed after tremolite inhalation exposure in SPF male Wistar rats 

19 (Bernstein et al., 2003, 2005) and intratracheal instillation in male albino Swiss mice (Sahu et al., 

20 1975). Davis et al. (1985) also reported pulmonary effects after inhalation exposure to tremolite

21 in SPF male Wistar rats including increases in peribronchiolar fibrosis, alveolar wall thickening, 

22 and interstitial fibrosis. 

23

24 6.1.4. Carcinogenicity in Humans and Laboratory Animals

25 Libby Amphibole asbestos fibers are associated with lung cancer and mesothelioma in 

26 workers from the Libby vermiculite mining and milling operations (Larson et al., 2010a; 

27 Moolgavkar et al., 2010; Sullivan, 2007; McDonald et al., 1986a; 2004; Amandus et al., 1988; 

28 Amandus and Wheeler, 1987; NIOSH, 1986). No other occupational cohort with exposures to 

29 Libby Amphibole asbestos has been studied with respect to mortality risks. Whitehouse et al. 

30 (2008) documented 11 mesothelioma cases in non-workers exposed to Libby Amphibole in 

31 Libby, MT. Increased lung cancer and mesothelioma deaths are also reported for worker cohorts 

32 exposed to other forms of amphibole fibers (amosite and crocidolite) (de Klerk, 1989; 

33 Seidman et al., 1986; Henderson and Enterline, 1979). These findings are consistent with the 

34 increased cancers reported for communities exposed to various rocks and soils containing 

35 tremolite fibers, which are a component of Libby Amphibole asbestos (Baris, 1987; Yaziciglu, 

36 1976; Yaziciglu et al., 1973; Langer et al., 1987; Baris et al., 1979; Sichletides et al., 1992; 
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1 Hasanoglu et al., 2006). Although potency, fiber dimension, and mineralogy differ between 

2 amphiboles, these studies are supportive of the hazard identification of Libby Amphibole 

3 asbestos fibers described in this document.

4 Although there is a limited laboratory animal database, the studies that are available 

5 support the carcinogenicity of Libby Amphibole asbestos fibers. Smith (1978) demonstrated 

6 mesotheliomas in hamsters given a single intrapleural injection of Libby Amphibole asbestos

7 material (Table 4-16). Tremolite, which comprises approximately 6% of the fiber mixture in 

8 Libby Amphibole asbestos, is also carcinogenic in studies in rats, hamsters, and mice, resulting 

9 in pleural mesothelioma, peritoneal mesothelioma, and lung cancer depending on the route of 

10 exposure (Table 4-17) (Davis et al., 1985; Davis et al., 1991; Stanton, 1981; Roller et al., 1996; 

11 Bernstein et al., 2003, 2005). Although comparing the potency of the tremolite used in these 

12 studies is difficult given the lack of information on fiber characteristics and other study 

13 limitations, these results show an increased risk for lung cancer and mesothelioma following 

14 exposure to tremolite asbestos.

15

16 6.1.5. Susceptible Populations

17 Certain populations could be more susceptible than the general population to adverse 

18 health effects from exposure to Libby Amphibole asbestos. In general, factors that may 

19 contribute to increased susceptibility from environmental exposures include lifestage, gender, 

20 race/ethnicity, genetic polymorphisms, health status, and lifestyle. However, little data exist to 

21 address the potential of increased susceptibility to cancer from exposure to the Libby Amphibole 

22 asbestos. 

23 Most occupational studies of workers exposed to Libby Amphibole asbestos have 

24 examined the effects only in men because this group represents the vast majority of workers in 

25 these settings (Amandus and Wheeler, 1987; Amandus et al., 1987c, 1988; McDonald et al., 

26 1986a, 1986b, 2004; Sullivan, 2007; Moolgavkar et al., 2010). The analysis presented here 

27 includes all workers, but there were few women and, consequently, no determination can be 

28 made regarding increased susceptibility to lung cancer or mesothelioma. Gender-related 

29 differences in exposure patterns, physiology, and dose-response are some of the factors that may 

30 contribute to gender-related differences in risk from asbestos exposure (Smith, 2002). The 

31 limited data available from community-based studies (ATSDR, 2000) do not provide a basis for 

32 drawing conclusions regarding gender-related differences in carcinogenic effects from Libby 

33 Amphibole asbestos. Racial diversity among workers exposed to Libby Amphibole asbestos is 

34 also limited, and data on ethnic groups absent, precluding the ability to examine racial and 

35 ethnicity-related differences in the mortality risks within the Libby worker cohort. Finally, the 

36 potential modifying effects of genetic polymorphisms, pre-existing health conditions, nutritional 
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1 status, and other lifestyle factors have not been studied sufficiently to determine their possible 

2 contribution to variation in risk in the population.

3

4 6.1.6. Mode-of-Action Information

5 Limited information is available on the biological effects that may contribute to a specific 

6 mode of action. Laboratory animal studies of mice (Putnam et al., 2008; Smartt et al., 2009),

7 hamsters (Smith, 1978) or rats (Shannahan et al., 2011) exposed to Libby Amphibole asbestos 

8 suggest an inflammatory response similar to that observed with other mineral fibers; however, no 

9 inhalation studies have been performed. In vivo studies in rats, hamsters, or mice exposed to 

10 tremolite (McConnell et al., 1983a; Davis et al., 1991; Smith et al., 1979; Wagner et al., 1982; 

11 Stanton et al., 1981; Roller et al., 1996, 1997) show results similar to other amphibole asbestos 

12 fibers including lung cancer and mesothelioma, although again with limited inhalation studies 

13 (Davis et al., 1985; Bernstein et al., 2003; 2005). In vitro studies demonstrate that the uptake of 

14 Libby Amphibole asbestos fibers by macrophage, mesothelial, and lung epithelial cell lines may 

15 lead to an increase in oxidative stress as measured by reactive oxygen species production, gene 

16 expression changes (Blake et al., 2007; Hillegass et al., 2010), or genotoxicity (Pietruska et al., 

17 2010). Thus the available data indicate that Libby Amphibole asbestos induces biological 

18 responses similar to other forms of asbestos such as oxidative stress, chronic inflammation, 

19 genotoxicity, and increased cell proliferation. These mechanisms are potentially related to the 

20 adverse health effects of asbestos fibers and one or more of these activities may be relevant to 

21 the carcinogenicity of Libby Amphibole asbestos fibers. However, data are not sufficient to 

22 establish a mode of action for Libby Amphibole asbestos.

23

24 6.1.7. Weight-of-Evidence Descriptor for Cancer Hazard

25 Libby Amphibole asbestos is carcinogenic to humans through inhalation exposure based 

26 on the framework provided in the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005). 

27 Epidemiologic evidence provides an association between exposure to Libby Amphibole asbestos

28 fibers in the workplace and the mortality from lung cancer and mesothelioma (Larson et al., 

29 2010a; Sullivan, 2007; McDonald et al., 1986a, 2002, 2004; Amandus et al., 1988; Amandus and 

30 Wheeler, 1987; NIOSH, 1986). Although limited, the available chronic laboratory animal study 

31 involving exposure to Libby Amphibole asbestos supports these findings (Smith, 1978). 

32 As specified in the Guidelines for Carcinogen Risk Assessment, the descriptor 

33 ―carcinogenic to humans‖ is appropriate when there is convincing epidemiologic evidence of a 

34 causal association between human exposure and cancer (U.S. EPA, 2005). As described in 

35 Section 4.1, the evidence of both lung cancer and mesothelioma in the Libby worker cohort is

36 consistent over time. Additionally, this association and a convincing exposure-response 
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1 relationship are observed when examining both workers with a minimum tenure (McDonald et 

2 al., 2004; Amandus and Wheeler, 1987), as well as when including workers regardless of 

3 duration of employment (Sullivan, 2007). 

4

5 6.2. EXPOSURE-RESPONSE

6 This assessment contains a derivation of a RfC for noncancer effects and an IUR for cancer 

7 based on epidemiologic data. It does not contain an RfD or oral cancer slope factor.

8

9 6.2.1. Noncancer/Inhalation

10 Libby Amphibole asbestos has been shown to cause localized pleural thickening, diffuse 

11 pleural thickening, and parenchymal changes in exposed populations. The study of Marysville 

12 workers (Lockey et al., 1984 and the follow-up by Rohs et al., 2008) was selected as the 

13 principal study due to the availability of quantitative exposure information and information on 

14 important covariates, increased risk of pleural and parenchymal effects, at lower cumulative 

15 exposure levels (compared to the Libby worker cohort—McDonald et al., 1986b; Amandus et 

16 al., 1987), the length of follow-up, use of more recent 2000 ILO diagnostic criteria, and the 

17 availability of detailed exposure and health outcome information for each worker. The outcomes 

18 examined in this cohort included localized and diffuse pleural thickening, as well as parenchymal 

19 changes. Localized pleural thickening was selected as the critical effect due to its higher 

20 prevalence relative to the other outcomes, minimal adversity (compared with other effects), and 

21 specificity for durable mineral fiber exposure. For RfC derivation, analyses focused on the 

22 subcohort of Marysville workers described by Rohs et al. (2008) who started employment after 

23 1972, due to the greater certainty in exposure assessment in this group. 

24 Benchmark dose modeling, with a BMR of 10% extra risk, was used to derive the POD. 

25 A log-logistic regression model was used to estimate the exposure-response relationship for 

26 Libby Amphibole asbestos and localized pleural thickening. Cumulative exposure with a lag of 

27 10 years was selected as the exposure metric, based on evidence for biological latency and model 

28 fit considerations. A background rate of localized pleural thickening of 1% was assumed. The 

29 resulting BMD under these modeling assumptions was 0.2543 fibers-yr/cc; the corresponding 

30 BMDL of 0.076 fibers-yr/cc is the POD for RfC derivation. The RfC is obtained by first 

31 dividing the POD by a total uncertainty factor of 100 (10 for interspecies variability and 10 for 

32 database deficiencies): 0.076 fibers-yr/cc x 1/100 = 0.00076 fibers-yr/cc. The RfC is for 

33 continuous exposure (24 hours/day, 365 days/year, with exposure beginning at birth and 

34 continuing for 70 years), adjusting for the 10-year lagged exposure, is 0.00076 fibers-yr/cc x 

1/(70-10) years = 1.27 x 10-5or rounded to one significant digit, 1 x 10-535 fibers/cc. Table 5-6 

36 shows estimated RfCs for less than lifetime exposure scenarios. Modeling was also conducted in 
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1 the full cohort of workers described in Lockey at al. (1984) and Rohs et al. (2008). These 

2 analyses used a different modeling approach, due to the wider range of exposures and time since 

3 first exposure (T). A modified Michaelis-Menten model provided the best fit to the full cohort 

4 data, which incorporated T via the plateau term for the model. For a T of 30 years, the BMD and 

5 BMDL corresponding to a 10% extra risk of localized pleural thickening were 0.1384 and 0.0541 

6 fibers-yr/cc, respectively. These are quite similar to the values obtained in the analysis for the 

7 RfC, and provide important support for the selected modeling approach. 

8 Confidence in the principal study is considered medium- to-high. The data used are 

9 human, epidemiological data rather than animal bioassays, and the principal study is conducted 

10 in a population of occupationally exposed workers with long-term, relatively low intensity 

11 exposures. Confidence in the database is low-to-medium. The database contains long-term 

12 mortality and morbidity studies in humans exposed via inhalation to Libby Amphibole asbestos, 

13 but there is a lack of data on effects other than in the respiratory system and uncertainties in time 

14 from first exposure. Therefore, confidence in the RfC is medium reflecting medium-to-high 

15 confidence in the principal study and low-to-medium confidence in the database. 

16 It is important to consider the sources of uncertainties in the derivation of the RfC for 

17 Libby Amphibole asbestos. These include: 

18 Measurement error in exposure assessment and assignment. The estimated exposure for 

19 each individual relied on self-reported employment history, which may be subject to recall error. 

20 Only data from post-1972 were used for RfC derivation, based on lack of measured exposure 

21 prior to this date; however, for certain occupations/tasks, no measurements were available until 

22 1974 (Lockey, 1980). There is also uncertainty in the post-1972 data regarding asbestos content 

23 in other ore sources (Virginia, South Carolina and South Africa). Although Libby Amphibole 

24 asbestos was not used in the facility after 1980, industrial hygiene measurements collected after 

25 1980 showed low levels of fibers in the facility. However, because the concentration of fibers in 

26 the workplace was near background after 1980, this exposure makes only a small contribution to 

27 an individual’s cumulative exposure estimate. Similarly, any exposure to Libby Amphibole 

28 asbestos outside of the workplace is not likely to contribute significantly to cumulative 

29 exposure—only ~10% of workers reported bringing raw vermiculite home, and the majority 

30 showered and changed clothes before leaving the work place. 

31 Radiographic assessment of localized pleural thickening. Conventional radiographs, 

32 rather than the more sensitive high-resolution computed tomography, were used to determine the 

33 health outcome. Discrete pleural thickening may be difficult to detect, leading to the potential 

34 for outcome misclassification. However, uncertainty in the prevalence of localized pleural 

35 thickening in each individual is considered minimal due to the use of a team of highly qualified 

36 chest radiologists evaluating the radiographic films and the use of consensus diagnosis. 
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1 Length of follow-up. Time from first exposure to x-ray was 23.2-32.7 years in the 

2 recommended sub-cohort. However, it is likely that the prevalence of localized pleural 

3 thickening may continue to show some increase with increased follow-up. In this case, the 

4 modeling approach may not accurately reflect the exposure-response relationship that would be 

5 seen with a longer follow-up time. Note that the likelihood that prevalence of localized pleural 

6 thickening may further beyond 30 years after first exposure is a principal rationale cited for the 

7 selection of a database UF of 10 for RfC derivation.

8 Background rate of localized pleural thickening. In the derivation of the RfC, a 

9 background rate of 1% for localized pleural thickening was used. Previous studies have reported 

10 a range of prevalence estimates (0.02% to 3.9%) in populations not known to be occupationally 

11 exposed to asbestos. However, in statistical modeling of the Marysville sub-cohort, uncertainty 

12 in the background rate of localized pleural thickening is very low. Both the fixed and estimated 

13 values are in the range of estimates above and the difference in the POD when the background 

14 rate is fixed at 1% versus when it is estimated (estimated background rate of 3.76%) is less than 

15 3%. 

16 Model Form. A number of model forms were explored in the initial stages of analysis 

17 (Appendix E), before selecting the log-logistic model. The log-logistic model is appropriate and 

18 widely used for epidemiologic data, and is easily interpreted. Further, BMCs and BMCLs 

19 estimated from other candidate models for the sub-cohort, as well as those obtained in modeling 

20 from the full cohort, were in a similar range to the selected model. A second model-based 

21 uncertainty is the choice of lag for cumulative exposure. The RfC derivation is based on the 

22 exposure lagged by 10 years, since this lag yielded the lowest AIC. However, if other lags (with 

23 similar AICs) are used, the difference in POD may fluctuate to be approximately 8% higher or 

24 approximately 40% lower. However, as the RfC is rounded to one significant digit, the choice of 

25 lag does not affect the estimated RfC.

26 

27 6.2.2. Cancer/Inhalation

28 6.2.2.1. Background and Methods

29 The most appropriate data set for deriving quantitative cancer risk estimates based on 

30 Libby Amphibole asbestos exposure in humans is the cohort of workers employed at the 

31 vermiculite mining and milling operation near Libby, MT (see Section 4.1). No data were 

32 available pertaining to cancer incidence or mortality in the Marysville, OH cohort, and mortality 

33 and exposure data for other populations exposed to Libby Amphibole asbestos are very limited. 

34 Whitehouse et. al. (2008) provided detailed information on 11 mesothelioma cases among non35 workers, but this information could not be used in exposure-response analyses for this risk 
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1 assessment, as there is no quantitative exposure information for these cases and no information 

2 on the population from which these cases arose.

3 The Libby worker cohort has been the focus of two epidemiologic investigations by 

4 NIOSH scientists. A database created by NIOSH in the 1980s contains demographic data, work 

5 history, and vital status at the end of 1981 for 1,881 workers at the vermiculite mine, mill, and 

6 processing plant in Libby, MT (see Section 4.1.1.1). Vital status follow-up was completed by 

7 NIOSH through 2006 using the National Death Index (NDI-Plus; Bilgrad, 1995). Nearly 54% of 

8 workers in the cohort (n = 1,009) had died by December 31, 2006. 

9 EPA does not have sufficient biological information to select models for the 

10 epidemiology data on the basis of biological mechanism (Section 5). In this situation, EPA’s 

11 practice is to investigate a range of model forms to determine how to best empirically model the 

12 exposure response relationship in the range of the observed data. In this case, possible exposure 

13 metrics were explored for model fit to the chosen models. The exposure metric options were 

14 selected to provide a range of shapes that was sufficiently flexible to allow for a variety of ways 

15 that time and duration might relate to cancer risk in the data being modeled. EPA then evaluated 

16 how well the models and exposure metric combinations fit the data being modeled. Metrics that 

17 did not fit the data well were rejected. For purposes of calculating a reasonable upper-bound on 

18 the risk per exposure EPA accounted for uncertainty in the choice of exposure metrics by using 

19 the exposure metric (among those of reasonable fit) that estimated the highest risk. This is 

20 explained in more detail below and in Sections 5.4.3-5.4.5. However, there are other 

21 uncertainties in the modeling of the epidemiological data that may impact the IUR and these are 

22 described in detail in Section 5.4.6.

23 Finkelstein (1985) provides an excellent summary of exposure-response modeling of 

24 epidemiologic data: ―After identification of an occupational hazard one of the goals of 

25 occupational epidemiology is to quantify the risks by determining the dose-response relations for 

26 the toxic agent. In many circumstances little is known about the dose received by target tissues; 

27 the data available usually pertain only to exposure to various concentrations of the toxic material 

28 in the workplace. The calculation of dose requires additional physiological and chemical 

29 information relating to absorption, distribution, biochemical reactions, retention, and clearance. 

30 In asbestos epidemiology, the usual measure of exposure is the product of the concentration of 

31 asbestos dust in the air (fibers or particles per mL) and the duration of exposure to each 

32 concentration summed over the entire duration of exposure (years); this measure is the 

33 cumulative exposure.‖

34 Cumulative exposure has been the traditional method of measuring exposure in 

35 epidemiologic analyses of many different occupational and environmental exposures and was the 

36 exposure metric applied for to the risk of lung cancer mortality in the general asbestos evaluation 
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1 in the 1988 U.S. EPA. Two alternative approaches to developing exposure metrics to describe 

2 the effects of air concentrations of asbestos dust in the air on the risks of mortality have also been 

3 proposed. The first alternative was proposed by Jahr (1974) who studied silica-induced 

4 pneumoconiosis. He also suggested that exposures to occupational dusts could be weighted by 

5 the time since exposure yielding an exposure metric which gives greater weight to earlier 

6 exposures. Berry et al. (1979) subsequently suggested the application of exposure metrics that 

7 allowed for the clearance of dust or fibers by using a decay term on exposures. For the 

8 evaluation of mortality risk from mesothelioma, U.S. EPA (1988) used a different exposure 

9 metric than was used for lung cancer mortality which factored in the time since first exposure. It 

10 is important to note that different characterizations of ambient exposures may be reasonably 

11 expected to be associated with different endpoints (i.e. lung cancer or mesothelioma).

12 Most studies of asbestos-related mortality have evaluated either cumulative exposure, 

13 exposure concentration, or the duration of employment as exposure metrics. Many studies have 

14 been limited in the availability of detailed exposure data—especially at the individual level. In 

15 the Libby worker cohort data developed by NIOSH and used by the EPA in this assessment, 

16 detailed work histories, together with job-specific exposure estimates, allowed for the 

17 reconstruction of each individual’s occupational exposure experience over time to define 

18 multiple exposure metrics. From this information-rich individual-level dataset from NIOSH, the 

19 EPA constructed a suite of the different metrics of occupational exposure which had been 

20 proposed in the asbestos literature or used in the U.S. EPA health assessment on general asbestos 

21 exposures (U.S. EPA, 1988). This suite of models was defined a priori to encompass a 

22 reasonable set of proposed exposure metrics to allow sufficient flexibility in model fit to these 

23 data. These exposure metrics were evaluated in analytic-regression models to test which 

24 exposure metrics were the best empirical predictors of observed cancer mortality and the better 

25 fitting models were advanced for consideration as the basis of the exposure-response relationship 

26 for the IUR. The types of exposure metrics evaluated were intended to allow for variations of 

27 the classic metric of cumulative exposure, allowing for more or less weight to be placed on

28 earlier or later exposures. These simulated exposure metrics were derived mathematically to 

29 approximate underlying processes that are not well understood, and their fit is evaluated on the 

30 basis of maximizing the likelihood for the workers cohort and estimated parameters does not 

31 necessarily have biological interpretation (see Section 5.4.2.5 for details).

32 Exposure estimates for all exposure metrics were adjusted to account for the time period 

33 between the onset of cancer and mortality. The lag period defines an interval before death, or 

34 end of follow-up, during which, any exposure is excluded from the calculation of the exposure 

35 metric. Modeling of mesothelioma mortality included two additional exposure metrics: duration

36 of exposure and the exposure metric including a cubic function of time (see Eq. 5-5), originally 
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1 proposed in Peto et al. (1982) and employed in derivation of the IUR for asbestos (U.S. EPA, 

2 1988). 

3 Analyses of mesothelioma mortality were conducted using a Poisson model with a 

4 Markov chain Monte Carlo (MCMC) Bayesian approach, whereas analyses of lung cancer 

5 mortality were conducted using the Cox proportional hazards model with time-varying 

6 exposures. There was one important limitation of the NIOSH JEM. 686 workers with ―common 

7 laborer‖ job assignments and some workers with unknown job assignments hired between 1935 

8 and 1959 were assigned the same average estimated exposure intensity for all unskilled jobs 

9 during the relevant calendar time period. That aspect of the JEM resulted in the misclassification 

10 of the exposure and made cumulative exposure metrics represent duration for the large part of 

11 the cohort. For this reason and because there was little measured fiber exposure data during the 

12 earlier period, identifying an adequate exposure-response model fit was unsuccessful. The two 

13 biggest problems were that the duration of employment was the best-fitting metric for modeling 

14 mesothelioma and that the Cox model assumptions were violated in modeling lung cancer 

15 mortality (Section 5.4.3.3). As a result, EPA developed a sub-cohort analysis by dividing the 

16 whole cohort into two groups: those hired prior to 1960 and those hired after 1959. This 

17 removed all 686 cohort members with missing job category information and lessened the effect 

18 of estimates of early exposures where no air sampling data were available. For the sub-cohort of 

19 those hired after 1959, those two biggest problems were resolved: the assumptions of Cox 

20 models were satisfied, and a lagged cumulative exposure with a decay (rather than duration of 

21 exposure, as for the full cohort) was the best-fitting metric for mesothelioma. 

22 Of the 880 workers hired after 1959, 230 (26%) had died by December 31, 2006. The 

23 number of mesothelioma deaths in the sub-cohort is relatively small (n = 7, 2 deaths coded in 

24 ICD-10 and 5 deaths coded in ICD-9), but it was very similar rate of 24.7 per 100,000 person25 years vs. 26.8 per 100,000 person-years for the full cohort (18 mesothelioma deaths), a 

26 difference of less than 10%. 

27

28 6.2.3. Modeling of Mesothelioma Exposure-Response

29 A Poisson model is employed for estimating the absolute risk of mesothelioma following 

30 exposure to Libby Amphibole asbestos, as the Poisson distribution is an appropriate model for 

31 use with data that are counts of a relatively rare outcome, such as observed mesothelioma deaths 

32 in the Libby worker cohort. Estimation of the exposure-response relationship for mesothelioma 

33 using the Poisson model was performed in WinBUGS software by a MCMC Bayesian approach 

34 with an uninformative prior. The model was run to fit the mortality data to exposure data for 

35 various exposure metrics described above. To comparatively evaluate how much better one 

36 model fits than another, Deviance Information Criterion (DIC) was used. DIC is used in 
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1 Bayesian analysis and is an analogue of AIC (Akaike Information Criterion [Burnham and 

2 Anderson, 2002]). Use of the DIC and AIC is standard practice in comparing the fit of 

3 non-nested models to the same dataset with the same dependent outcome variable but different 

4 independent covariates. 

5 Two cumulative exposure metrics with decay provided two best model fits. Both metrics 

6 had a common 5-year half life, with lag times of either 10 or 15 years. In the sub-cohort hired 

7 after 1959, the DIC value for mesothelioma using the IRIS IUR (U.S. EPA, 1988) metric

8 (Eq. 5-5) is substantially higher (DIC = 98.4) than for any of the metrics in Table 5-11, where the 

9 lowest DIC is 70.6. This difference of over 20 DIC units, is an indication that the model used for 

10 mesothelioma in the EPA (1988) IUR derivation (eq. 5-5), does not fit these data from the Libby 

11 work cohort, compared to other exposure metrics presented (Table 5-11). It should be noted that 

12 the data modeled here are very different from the data on which the IRIS assessment for asbestos 

13 (U.S EPA, 1988) is based—and one does not necessarily expect the same model to fit different 

14 datasets—this is why EPA goes through a process to determine the best fitting model in each 

15 case. One difference with IRIS IUR (U.S. EPA, 1988) modeling is that this analysis is based on 

16 individual-level data, whereas IRIS IUR (U.S. EPA, 1988) application was to aggregate data. 

17 Also, cohorts used in IRIS IUR (U.S. EPA, 1988) did not include cohorts exposed to Libby 

18 amphibole asbestos and Libby amphibole asbestos may be different from other types of asbestos. 

19 Alternately, the relative fit of this model may have been affected by uncertainties in the 

20 estimated exposure described in detail in Section 5.4.6.

21 As it is less likely that exposure during the last few years before death were contributory 

22 to the development of the cancer, the zero lag metrics were dropped from further consideration.

23 All eight models retained for derivation of IUR include a decay half-life in the exposure metric. 

24 For the sub-cohort hired after 1959, the best fitting exposure metric was cumulative exposure 

25 with a 5 year half-life and a 15 year lag time with central estimate for the β of 2.07E-4 with 95% 

26 UCL 3.42E-4. 

27

28 6.2.4. Unit Risk Estimates for Mesothelioma Mortality

29 The increased risk of mesothelioma mortality attributable to continuous fiber exposure 

30 was estimated using a life-table procedure based on the general U.S. population. The life-table 

31 procedure involved the application of the estimated Libby Amphibole asbestos toxicity to a 

32 structured representation of the general U.S. population in such a manner as to yield age-specific 

33 risk estimates for cancer mortality in the presence or absence of exposure to Libby Amphibole 

34 asbestos (Section 5.4.5; Appendix G).

35 A default linear low-dose extrapolation below the point of departure (POD) was used 

36 because the mode of action by which Libby Amphibole asbestos causes mesothelioma is largely 
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1 unknown. The lower limit on the effective concentration (LEC01) was determined to be 

2 0.245 fibers/cc, which yielded a unit risk for mesothelioma mortality of 0.053 per fiber/cc (POD 

3 of 1% divided by the LEC01). 

4 The value of the effective concentration that would correspond to the measure of central 

5 tendency is the EC01. This value is used in the derivation of a combined risk of mesothelioma 

6 and of lung cancer. The EC01 was determined to be 0.406 per fiber/cc, which when divided into 

7 a POD of 1% and scaled (by 70/54) to encompass the whole lifespan, gives a lifetime central 

8 estimate value of 0.032 per fiber/cc.

9 For mesothelioma, the undercounting of cases (underascertainment) is a particular 

10 concern given the limitations of the ICD classification systems used prior to 1999. In practical 

11 terms, this means that some true occurrences of mortality due to mesothelioma are missed on 

12 death certificates and in almost all administrative databases such as the National Death Index. 

13 Even after introduction of special ICD code for mesothelioma with introduction of ICD-10 in 

14 1999, detection rates are still imperfect (Pinhiero et al., 2004; Camidge et al., 2006) and the 

15 reported numbers of cases typically reflect an undercount of the true number. Kopylev et al. 

16 (2011) reviewed the literature on this underascertainment and developed methods to account for 

17 the likely numbers of undocumented mesothelioma deaths.

18 To compensate for mesothelioma underascertainment attributable to ICD coding, the 

19 mesothelioma mortality unit risk was further adjusted following the analysis of Kopylev et al. 

20 (2011). The adjusted mesothelioma central (MLE) risk, corresponding to the best-fit metric, was 

21 0.044 per fiber/cc, and the adjusted mesothelioma mortality unit risk was 0.074 per fiber/cc. The 

22 adjusted mesothelioma mortality unit risks from all eight exposure parameterization models with 

23 adequate fit produced a range of unit risk values (Table 5-14) from 0.044 to 0.122. The second 

24 best fit metric, cumulative exposure with 10 year lag and 5-year decay, corresponded to the 

25 highest unit risk (details are in Section 5.4.5.1.1). 

26

27 6.2.5. Modeling of Lung Cancer Exposure-Response 

28 All multivariate extended Cox models were fit to the sub-cohort hired after 1959 with 

29 covariates for sex, race, and date of birth, and exposure. Exposure for each of the 40 exposure 

30 parameterizations was calculated independently and fit of these exposure metrics was evaluated 

31 one at a time. As the exposure-response models cannot strictly be considered to be nested, a 

32 standard measure of fit, the AIC (Burnham and Anderson, 2002), was used for comparison of 

33 model fit with smaller values of AIC, indicating better goodness of fit. Of the 40 exposure34 response metrics, 14 demonstrated an adequate fit to the data as measured by the overall model 

35 fit with the likelihood ratio test (p < 0.05) as well as having statistically significant exposure 

36 metrics (p < 0.05). However, only the nine models that demonstrated adequate model and 
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1 exposure metric fit and incorporated a lag period to account for cancer latency were considered 

2 further in the development of the IUR (Table 5-19).

3 Lagging exposure by 10 years was a better predictor of lung cancer mortality compared 

4 to other lags. As it is less likely that exposure during the last few years before death were 

5 contributory to the development of the cancer, the zero lag metrics were dropped from further 

6 consideration. The residence time-weighted cumulative exposure, both with and without decay 

7 of the exposure metric, did not fit these lung cancer mortality data well compared to the other 

8 models (Table 5-13); this form of exposure metric does not demonstrate evidence of an empirical 

9 fit to these epidemiologic data.

10 The model with the smallest AIC was for cumulative exposure with a 10-year half-life for 

11 decay and a 10-year lag for cancer latency. This multivariate model controlled for age, gender, 

12 race, and date of birth within the occupational cohort. The extended Cox model with 

time-varying exposures estimated a slope (beta) of 1.26 × 10-213 per fiber/cc-year based on a 

365-day year, and the 95th percentile upper bound was 1.88 × 10-214 per fiber/cc-year. The p-value 

15 for the Libby Amphibole asbestos regression coefficient (slope) was <0.001. The slopes and

16 confidence interval for the other exposure metrics, which had similar fits to these data are 

17 reported in Table 5-14. Uncertainty in the choice of the exposure metric (cross-metric 

18 uncertainty) is considered in the derivation of the final unit risk (see details in Section 5.4.5.3), 

19 representing the range of unit risks that are derived from these similarly fitting metrics. The 

20 model results which were ultimately selected to reflect the upper-bound among the range of 

21 results were based on the cumulative exposure with a 10-year lag exposure metric (CE10). The 

extended Cox model with time-varying exposures estimated a slope (beta) of 5.28 × 10-322 per 

fiber/cc-year based on a 365-day year, and the 95th percentile upper bound was 1.00 × 10-223 per 

24 fiber/cc-year. 

25

26 6.2.6. Unit Risk Estimates for Lung Cancer Mortality

27 The increased risk of lung cancer mortality attributable to continuous fiber exposure was 

28 estimated using a life-table procedure based on the general U.S. population. The life-table 

29 procedure involved the application of the estimated Libby Amphibole asbestos-specific toxicity 

30 to a structured representation of the general U.S. population in such a manner as to yield age31 specific risk estimated for cancer mortality in the presence or absence of exposure to Libby 

32 Amphibole asbestos (Section 5.4.5; Appendix G).

33 The nine exposure-response models retained in Table 5-18 all had reasonably similar 

34 goodness of fits. No single model stands out as clearly statistically superior; however, there is a 

35 range of quality of fit within the set that could be considered to have adequate fit. The lung 

36 cancer mortality unit risks are shown in Table 5-19. 
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1 Using the results of the exposure model with the lowest AIC value (i.e., cumulative 

2 exposure with a 10-year half-life for decay and a 10-year lag for cancer latency) alone, the LEC01 

3 for the adult-only exposures was determined to be 0.333 fibers/cc. This yields an adult-based 

4 unit risk of lung cancer mortality of 0.0300 (POD of 1% divided by the LEC01). This estimate 

5 was then scaled by 70/54 to encompass the whole lifespan; it yielded a lifetime unit risk of 

6 0.0389 per fiber/cc. The value of the concentration that would correspond to the measure of 

7 central tendency was based on the EC01 rather than LEC01. The EC01 for the adult-only 

8 exposures was determined to be 0.499 per fiber/cc, which, when divided into a POD of 1%,

9 yielded an adult-based central estimate for lung cancer mortality of 0.0200. This estimate was 

10 then scaled by 70/54 to encompass the whole lifespan to, yielded a lifetime central estimate of 

11 0.0260 per fiber/cc.

12 Using the results of the exposure model based on cumulative exposure with a 10-year lag 

13 for cancer latency, the LEC01 for the adult-only exposures was determined to be 0.191 fibers/cc, 

14 yielding an adult-based unit risk of lung cancer mortality of 0.0524 (POD of 1% divided by the 

15 LEC01). When scaled by 70/54 to encompass the whole lifespan, it yielded a lifetime unit risk of 

16 0.0679 per fiber/cc. The value of the risk that would correspond to the measure of central 

17 tendency involves the EC01 rather than the LEC01. The EC01 for the adult-only exposures was 

18 determined to be 0.325 per fiber/cc, which, when divided into a POD of 1%, yielded an 

19 adult-based central estimate for lung cancer mortality of 0.0308. This estimate was then scaled 

20 by 70/54 to encompass the whole lifespan to, yielded a lifetime central estimate of 0.0399 per 

21 fiber/cc.

22 The resulting unit risks in Table 5-19 ranged from 0.0260 to 0.0679 fibers/cc. This 

23 shows that the unit risk (i.e., 0.0389 per fiber/cc) based on the exposure metric with the lowest 

24 AIC value (i.e., cumulative exposure with a 10-year half-life for decay and a 10-year lag for 

25 cancer latency) is in the center of this range and is thus statistically robust. However, because 

26 this estimate is in the middle of the range it does not capture the uncertainty across metrics with 

27 similar goodness of fit. The model results which were ultimately selected to reflect the upper28 bound among the range of results were based on the CE10 exposure metric. The CE metric with 

29 a 10 year lag does fit these data, is a simpler and more straightforward metric and has an 

30 extensive tradition of use in the epidemiologic literature and in the practice of risk assessment.

31

32 6.2.7. IUR Derivation Based on Combined Mesothelioma and Lung Cancer Mortality from 

33 Exposure to Libby Amphibole Asbestos

34 When risks are combined, it is important to understand several concepts that are pertinent 

35 to the evaluation and comparison of the cancer-specific mortality unit risks that will be 

36 combined. First, there is statistical uncertainty in the potency estimate within the exposure 
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1 response model defined by each exposure metric. This within metric uncertainty is accounted 

2 for in the confidence interval on slope. Next, there is an uncertainty in the choice of metrics for 

3 developing IUR (cross-metric uncertainty). Finally, when unit risks corresponding to metrics are 

4 chosen accounting for uncertainty, these are statistically combined into IUR. Details are 

5 provided in Section 5.4.5.3. 

6 Table 6-1 shows cancer-specific unit risks as well as combined risk of mesothelioma and 

7 lunch cancer. The IUR value of 0.169 per fiber/cc accounts for important quantitative 

8 uncertainties in the selection of the specific exposure metric that may have remained in an IUR 

9 that might have been based on the best fitting exposure models alone. Additional uncertainties 

10 are discussed in detail in Section 5.4.6.

11

12 Table 6-1. Reasonable upper bound and lowest information criteria 

13 estimates of central risks and unit risks, per fibers/cc, for mesothelioma 

14 mortality, lung cancer mortality, and the IUR for the combined mortality 

15 risk from mesothelioma and lung cancer

16

Model

Mesothelioma Lung cancer

Combined mesothelioma 

and lung cancer

Central 

estimate Unit risk

Central

estimate

Unit 

risk

Central

estimate IUR

Reasonable upper bounda0.075 0.122 0.040 0.068 0.115 0.169

Lowest information criteriab0.044 0.074 0.026 0.040 0.070 0.103

17

a

18 For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay half19 life of 5 years and a 15-year lag. For lung cancer, the selected model parameterized exposure as cumulative 

20 exposure without decay and a 10 year lag.

b

21 For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay 

22 half-life of 5 years and a 10-year lag. For lung cancer, the selected model parameterized exposure as cumulative 

23 exposure with exponential decay half-life of 10 years and a 10-year lag.

24

25

26 6.2.7.1. Comparison with Other Published Studies of Libby Workers Cohort

27 For lung cancer, two alternative analytic approaches to the use of EPA’s extended Cox 

28 proportional hazards models could have been used for the calculation of a unit risk of lung 

29 cancer mortality. All of the choices are based on different analyses of the Libby worker cohort; 

30 however, inclusion criteria differ among the analyses as does the length of mortality follow-up. 

31 Each of the two approaches has two options to estimate the slope of the exposure-response 

32 relationship in place of the regression slope estimated from the Cox proportional hazards model 

33 and follow through with the same life-table procedure to calculate the unit risk of lung cancer 

34 mortality.
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1 The first approach would be to use the published categorical results based on Sullivan 

2 (2007). The first option in this approach was for EPA to estimate a slope to those categorical 

3 data. The second option was to use the slope estimated in a published re-analysis of categorical 

4 data of Sullivan (2007) cohort by Berman and Crump (2008). The second approach would be to 

5 use the published regression results of other researchers who modeled the underlying continuous 

6 data. The first option in this approach was to use the slope estimated by Larson et al. (2010a). 

7 The second option was to use the slope estimated by Moolgavkar et al. (2010).

8 For comparison purposes, the lung cancer unit risk from these alternatives are computed, 

9 however, as all analyses are based upon different subsets of the Libby workers cohort and used 

10 different analytic methods, the results are not necessarily interchangeable. Table 6-2 summarizes 

11 lung cancer risks derived from these studies.

12

13 Table 6-2. Lung cancer regression results from different analyses of 

14 cumulative exposure in the cohort of workers in Libby, MT. All analyses 

15 used NIOSH-collected exposure data, but used different cohort definitions, 

16 lengths of follow-up, and lengths of exposure lags to account for cancer 

17 latency

18

Lung cancer analysis Cohort definition

Lung

cancer cases/N

Slope per

fibers/cc-years ×10-3

(calendar year)

Risk based on UCL 

on the slope

(per fiber/cc)

This assessment Hired post-1959 32/880 5.8 0.068

Sullivan, 2007 Full cohort; still 

employed post-1959

White males

99/1672 4.2 0.037

Moolgavkar et al., 2010aFull cohort,

White males 

95/1662 1.69 0.011

Berman and Crump, 2008aFull cohort,

White males

93/1672 3.96 0.079b

Larson et al., 2010 Full cohort 98/1862 1.61 0.010

19

a

20 Reanalysis of Sullivan (2007) 

b

21 Obtained from confidence interval that also addresses model uncertainty (details in Section 5.4.5.3.1).

22

23 For mesothelioma, neither of the approaches used by McDonald et al. (2004), Sullivan 

24 (2007), nor Larson et al. (2010a) could have been appropriately used for the unit risk of 

25 mesothelioma as they are not based on absolute risk metrics of association, and the current 

26 assessment considered the relevant metric of association to be the absolute risk. Berman and 

27 Crump (2008) did not evaluate risk of mesothelioma. Moolgavkar et al. (2010) did use an 

28 absolute risk model for mesothelioma. These results are summarized in Table 6-3. The upper 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 bound results for the full cohort presented by Moolgavkar et al. (2010) are about 80% of the EPA 

2 IRIS (1988) estimate of mesothelioma slope factor in a similar RTW-type metric, leading to 

3 approximately 80% estimate of the mesothelioma unit risk, as dependence is linear in the 

4 mesothelioma slope factor (Eq. 5-5). This is very close to this assessment’s estimate based on 

5 the analyzed sub-cohort, which is also about 80% of the EPA IRIS (1988) estimate of 

6 mesothelioma risk. Duration of employment is the best metric for the full cohort, and it does not 

7 support dose-response estimation.

8

9 Table 6-3. Mesothelioma regression results from different analyses of 

10 cumulative exposure in the cohort of workers in Libby, MT. All analyses 

11 used NIOSH-collected exposure data, but used different cohort definitions, 

12 lengths of follow-up, and lengths of exposure lags to account for cancer 

13 latency

14

Mesothelioma analysis Cohort definition

Mesothelioma

cases/N

Mesothelioma risk

(absolute risk model)

(per fiber/cc)

This assessment Hired post-1959 7/880 Upper Bound = 0.12

Central = 0.08

Sullivan, 2007 Full cohort; still employed post-1959; 

White males

15/1672 No estimates of absolute 

risk

Moolgavkar et al., 2010aFull cohort; White males 15/1662 Upper Bound ≈ 0.13

Central ≈ 0.08

Larson et al., 2010 Full Cohort 19/1862 No estimates of absolute 

risk

Berman and Crump, 2008a No estimates provided

15

a

16 Reanalysis of Sullivan (2007).

17

18

19 6.2.8. Uncertainty in the Cancer Risk Values

20 It is important to consider the uncertainties in the derivation of the mesothelioma and 

21 lung cancer mortality risks in this assessment in the context of uncertainties in animal-based 

22 health assessments. This assessment does not involve extrapolation from high dose in animals to 

23 low dose in humans. The current assessment is based on a well-documented and well-studied 

24 cohort of workers with adequate years of follow-up to evaluate mesothelioma and lung cancer

25 mortality risks with PODs within the range of the data. The discussions in Section 5.4.6 explore 

26 uncertainty in the derivation of the IUR in order to provide a comprehensive and transparent 

27 context for the resulting cancer mortality risk estimates.
This document is a draft for review purposes only and does not constitute Agency policy.

6-20 DRAFT—DO NOT CITE OR QUOTE

1 The summary below includes likely one–sided uncertainties (biases) associated with the

2 derivation of the IUR in order to provide a context for the resulting cancer risk estimates.

3 The sources of uncertainty that could lead to a likely underestimation of the cancer risk 

4 value include the following:

5

6

7 Use of historical PCM exposure measurements. As asbestos was a contaminant of 

8 vermiculite that was the primary object of production, mine and dry and old wet mill 

9 ambient air may have contained material other than asbestos that could have contributed 

10 to fibers counted by PCM. Therefore, it is possible that exposure estimates for some or 

11 possibly a large portion of the cohort are overestimated, and, therefore, the resulting IUR 

12 may be underestimated.

13 Measurement error in exposure assessment and assignment. Results from the analyses of 

14 the lung cancer mortality in the full cohort compared to the sub-cohort hired after the 

15 1959 sub-cohort were attenuated as much as 2-6 times. Job assignments were unknown 

16 for 686 of the 991 workers employed from 1935 to 1959 (69% of the cohort for this time 

17 period). The majority of these workers were assigned the same exposure concentration

18 for all years without job category information. Examination of the post-1959 cohort 

19 removes this significant source of exposure misclassification.

20 Limited length of follow-up. The IUR for mesothelioma mortality could be larger than 

21 was estimated from existing data, since latency of mesothelioma can be as long as 

22 60 years. The maximum length of follow-up was 46 years in this cohort. The magnitude 

23 of underestimation would depend on the relationship between the number of additional 

24 deaths and the increase in person-years.

25 Use of life-tables to calculate the IUR based on cancer mortality. The life-table 

26 procedure computes the extra risk of death from birth up to 85 years of age. This cut-off 

27 at age 85 ignores a small additional risk of lung cancer mortality among a small 

28 percentage of people who have a higher background risk because of the increase in lung 

29 cancer risk that is seen with increasing age. The lung-cancer mortality unit risk based on 

30 the LEC01 would be somewhat larger, on the order of 5-10%. On the other hand, the

31 additional mesothelioma mortality risk, if the life-tables were extended to account for 

32 longer life spans, would be about 3%

33 Small number of women and ovarian cancer. While asbestos is causally associated with 

34 increased risks of ovarian cancer (Straif et al., 2009), there were only 84 women in the 

35 whole cohort, and there were no deaths from ovarian cancer among 24 total deaths. The 

36 lack of observed ovarian cancer in this cohort may be a function of the limited number of 

37 female deaths in the cohort allowing for the possibility that exposure to LAA could result 

38 in increased risk of ovarian cancer. However, it was not possible to estimate the 

39 magnitude of this underestimation on the total cancer risk.

40 Dependent competing risks. Competing risk of mortality from other diseases related to 

41 exposure may have resulted in underestimates of the risk of mortality from either 
This document is a draft for review purposes only and does not constitute Agency policy.

6-21 DRAFT—DO NOT CITE OR QUOTE

1 mesothelioma or lung cancer. The mean length of follow-up for the Libby workers who 

2 died of mesothelioma was to 30.1 years, and evidence exists (Suzuki, 2001; Bianchi and 

3 Bianchi, 2009) that early deaths from other exposure-related causes could have precluded 

4 an individual’s risks of death from mesothelioma. However, it was not possible to 

5 estimate the magnitude of this effect on the total cancer risk.

6

7 The source of uncertainty that could lead to a likely overestimation of the cancer risk 

8 value:

9

10 Effect modification. It is theoretically possible that the risk of lung cancer mortality 

11 estimated in this assessment is a reflection of a positive synergy between smoking and 

12 asbestos, and that the adverse effect of Libby Amphibole asbestos among the potentially 

13 nonsmoker workers has been overestimated. The IUR for lung cancer mortality 

14 estimated herein, and the combined lung cancer and mesothelioma IUR would then be 

15 health protective for any population that had a lower prevalence of smoking than that of 

16 the Libby worker cohort. 

17

18 6.3. APPLICATION OF THE LIBBY AMPHIBOLE ASBESTOS RFC AND IUR

19 6.3.1. Sites and Materials

20 This Libby Amphibole asbestos specific assessment is based on the evaluation of worker 

21 cohorts, exposed to asbestos from a single mine in Libby, MT, and is intended to allow for 

22 estimates of the risk due to exposure to the asbestos fibers from that mine, or exposures to 

23 asbestos fibers that arise from the management or use of the vermiculite ore and exfoliated 

24 vermiculite from this mine. Therefore, it is appropriate to apply the Libby Amphibole asbestos25 specific RfC and/or IUR to sites which are believed to have been contaminated by these 

26 materials when assessing risk from the amphibole fibers present from this contamination. This 

27 may include sites where the ore was shipped or handled, where the vermiculite was exfoliated 

28 and further processed, facilities which in other ways shipped or handled the exfoliated 

29 vermiculite, where products containing the raw or exfoliated vermiculite were present, the 

30 consumer products themselves (e.g., VAI) and any waste streams from the above processes 

31 which contain vermiculite and the related LAA-fibers. The assessment was derived from PCM 

32 measurements taken at the Libby occupational sites and the mixture of minerals found in those 

33 measurements. It does not estimate the risk attributable to specific subsets of those fibers 

34 whether based on size, shape, or mineral composition other than the limitations on size and shape 

35 reflected in the PCM methodology and counting rules. As detailed in Section 2, the amphibole 

36 asbestos present in the mine, ore and expanded vermiculite, do not fit cleanly into a single 

37 category of nomenclature for amphibole minerals. Most Libby Amphibole fibers are classified 
This document is a draft for review purposes only and does not constitute Agency policy.

6-22 DRAFT—DO NOT CITE OR QUOTE

1 as winchite (84%), with lesser amounts of richterite (11%) and tremolite (6%), based on the 

2 nomenclature proposed by Leake et al. (1997) (Meeker et al., 2003). There are also trace 

3 amounts of magnesioriebeckite, edenite, and magnesio-arfvedsonite present in Libby Amphibole 

4 asbestos (Meeker et al., 2003). Within the 30 samples taken from the mine the proportion of 

5 these minerals differed between samples (Meeker et al., 2003) and the relative proportions of 

6 these species may have varied over time (as ore from different locations were processed). This 

7 assessment estimates the risk of exposure to the varying range of mineral fiber mixtures that 

8 result from material originating from the geological deposit, recognizing there is variation and 

9 uncertainty as to variations in the exposure to the underlying cohort and complex variation in 

10 settings to which these estimates will be applied. 

11

12 6.3.2. Exposure Units for Libby Amphibole Asbestos

13 As with the IRIS assessment for asbestos (U.S. EPA, 1988), the RfC and IUR specific to 

14 Libby Amphibole asbestos are presented here as fibers-yr/cc exposure, where exposure 

15 measurements are based on analysis of air filters by Phase Contrast Microscopy (PCM). For 

16 example, analysis of mortality data presented in Section 5 for both mesothelioma and lung 

17 cancer used the available measurements of worker exposure for the facilities in Libby, MT. 

18 From 1968 forward, exposure measurements were available for all work environments as fiber 

19 counts by PCM analysis. Between 1960 and 1968, fiber counts were estimated, and a dust-to20 fiber conversion ratio was applied to exposures in the dry mill to generate estimated fiber 

21 exposures in PCM units. Therefore, the IUR is given in units based on PCM fiber analysis and is 

22 intended to estimate the risk per exposure to PCM-countable Libby amphibole asbestos fibers.

23 Early PCM analytical techniques did not have the same resolution as current analytical 

24 methods, and it is understood that PCM data for the majority of the exposures characterized for 

25 the Libby workers would likely have a width resolution of 0.4-0.44 μm (Skinke, 1980; Amandus 

26 et al., 1987a; WHO, 1980). Therefore, as with the IRIS assessment for asbestos (U.S. EPA, 

27 1988), the dimensions of the PCM fibers for the Libby Amphibole asbestos unit risk are defined 

28 as fibers ≥5 μm in length with an aspect ratio of 3:1 or greater and a width >0.4 μm.

29 Environmental air sampling for asbestos is now often analyzed by transmission electron 

30 microscopes (TEM) to confirm that the fibers viewed are asbestos, and often it is used to identify 

31 the mineralogy of the fiber. Although some historical data do exist providing TEM analysis of 

32 airborne fibers from the Libby, MT mill operation (McDonald et al., 1986a; Langer et al., 1974), 

33 these data are not sufficient to provide an alternative set of exposure measurements in TEM units

34 for the Libby worker cohort, or provide a PCM to TEM conversion across the various work 

35 environments. 
This document is a draft for review purposes only and does not constitute Agency policy.

6-23 DRAFT—DO NOT CITE OR QUOTE

1 Different sampling environments and varied site conditions may pose the potential for 

2 airborne fibers from various materials. Because of that, it is expected that for many 

3 environmental risk assessments conducted now and in the near future, measures of exposure may 

4 be done with methods such as TEM and then adjusted through fiber-counting rules to estimate 

5 the number of PCM-countable asbestos fibers. Site-specific environmental conditions should be 

6 considered in determining how to best identify PCM-countable asbestos fibers in relevant air 

7 samples for exposure assessments used in conjunction with this health assessment to yield 

8 estimates of risk.

9

10 6.3.3. Applications to Early Lifetime and Partial Lifetime Environmental Exposure 

11 Scenarios for IUR

12 The life-table analysis in the IRIS assessment for asbestos (U.S. EPA, 1988) predicts risk 

13 increases as the age of first exposure decreases. It was recommended that this life-table analysis

14 be consulted when assessing partial lifetime exposures (U.S. EPA, 1988). In 2009, EPA 

15 provided guidance for calculating risk estimates for less-than-lifetime exposures based on the 

16 1988 life-table analysis (U.S. EPA, 2009). The age-at-onset of exposure and duration-dependent 

17 unit risks reflect the influence of the time cubed function in the mesothelioma model (eq. 5-5)

18 (U.S. EPA, 1986; 1988; 2009) used in the 1988 assessment. Because the time-cubed model, or 

19 parameterization of exposure metrics, did not fit the data for mesothelioma mortality from 

20 exposure to Libby Amphibole asbestos, the approach to estimating risk of partial life exposure 

21 recommended by EPA when applying the IRIS IUR for asbestos (U.S. EPA, 1988) is not 

22 appropriate when applying the Libby Amphibole asbestos-specific IUR.

23 The Libby Amphibole asbestos-specific unit risk derived in this assessment is a combined 

24 risk of lung cancer and mesothelioma, each with its own adjustment for uncertainty in metrics.

25 The life table analyses for Libby Amphibole asbestos do not predict greater risk from early-life 

26 exposures. Thus this assessment recommends that estimates of the risks of less-than-lifetime 

27 exposures be computed by simple calculations of average lifetime exposure concentration 

28 multiplied by IUR. This recommendation is consistent with standard Superfund guidance 

29 (U.S. EPA, 1986b), where exposures are estimated, averaged across a lifetime exposure, and the 

30 IUR simply applied to calculate excess cancer risk (U.S. EPA, 2005). The weight of evidence 

31 does not support a mutagenic mode of action for Libby Amphibole asbestos carcinogenicity. 

32 Therefore, according to EPA’s Supplemental Guidance for Assessing Susceptibility from Early33 Life Exposure to Carcinogens (U.S. EPA, 2005b), the application of the Age-Dependent 

34 Adjustment Factors (ADAFs) is not recommended. 
This document is a draft for review purposes only and does not constitute Agency policy.

7-1 DRAFT—DO NOT CITE OR QUOTE

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7-31 DRAFT—DO NOT CITE OR QUOTE

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implications for public policy. Am Ind Hyg Assoc J 54:239-252.

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This document is a draft for review purposes only and does not constitute Agency policy.

A-1 DRAFT—DO NOT CITE OR QUOTE

APPENDIX A. SUMMARY OF EXTERNAL PEER REVIEW AND PUBLIC 

COMMENTS AND DISPOSITION
This document is a draft for review purposes only and does not constitute Agency policy.

B-1 DRAFT—DO NOT CITE OR QUOTE

APPENDIX B. PARTICLE SIZE DISTRIBUTION DATA FOR LIBBY AMPHIBOLE 

STRUCTURES OBSERVED IN AIR AT THE LIBBY ASBESTOS SUPERFUND SITE
PARTICLE SIZE DISTRIBUTION DATA FOR 

LIBBY AMPHIBOLE STRUCTURES OBSERVED IN AIR 

AT THE LIBBY ASBESTOS SUPERFUND SITE 

July 14, 2010 

Prepared by: 

U.S. Environmental Protection Agency 

Region 8 

Denver, CO 

With Technical Assistance from: 

SRC, Inc. 

Denver, CO 

1 

PARTICLE SIZE DISTRIBUTION DATA FOR 

LIBBY AMPHIBOLE STRUCTURES OBSERVED IN AIR 

AT THE LIBBY ASBESTOS SUPERFUND SITE 

1.0 INTRODUCTION 

Libby is a community in northwestern Montana that is located near a large open-pit vermiculite 

mine. Vermiculite from this mine contains varying levels of a form of asbestos referred to as 

Libby Amphibole (LA). In 1999, EPA Region 8 initiated environmental investigations in the 

town of Libby and in February, 2002, EPA listed the Libby Asbestos Site (the Site) on the 

National Priorities List. The Site includes the former vermiculate mine and residential homes, 

commercial businesses, schools and parks that may have become contaminated with asbestos 

fibers as a result of vermiculite mining and processing conducted in and around Libby as well as 

other areas in the vicinity that may have been impacted by mining-related releases of asbestos. 

Historic mining, milling, and processing operations at the Site, as well as bulk transfer of 

mining-related materials, tailings, and waste to locations throughout Libby Valley, are known to 

have resulted in releases of vermiculite and LA to the environment. 

As part of the response actions taken pursuant to the Comprehensive Environmental Response, 

Compensation and Liability Act, EPA has performed a number of investigations to characterize 

the nature and extent of LA contamination of air, soil, dust and other media in and around the 

community of Libby. Because available information suggests that the toxicity of asbestos is at 

least partially influenced by the size of the inhaled asbestos particles, these investigations have 

included the measurement of the dimensions (length and width) of LA particles observed in 

samples collected from the Libby site. 

The purpose of this report is to summarize size distribution data for LA particles that have been 

observed in air samples collected at the site, and to utilize these data to make comparisons 

between various subsets of the data to determine if any important differences in particles size 

distributions can be recognized. 

2.0 METHODS 

2.1 Data Overview 

EPA has been collecting samples of air since 2001 at the Libby site. Table 1 provides an 

overview of the sampling programs that have generated these data. The raw data for the air 

samples included in this assessment are provided in Appendix A. 

Most of the samples that have been collected have been analyzed for asbestos by transmission 

electron microscopy (TEM) using either ISO 10312 (ISO 1995) or AHERA (AHERA 1986) 
2 

counting rules, as modified by site-specific modifications as described in modifications forms 

LB-000016 and LB-000031 (provided in Appendix B). In all cases, the data that are recorded 

during the analysis of a sample include the length, width and aspect ratio (length/width) of all 

particles that meet the counting rules specified for the analysis. 

2.2 Data Presentation 

One convenient method for comparing the size distributions of two different sets of LA particles 

is through a graph that plots the cumulative distribution function (CDF) for each particle set. 

This graphical format shows the fraction of all particles that have a dimension less than some 

specified value. This format is used in this document to present the distributions of length, width 

and aspect ratio. 

There are a number of statistical tests that can be used to compare two distributions in order to 

support a statistical statement about whether the distributions are “same” or “different”. Such 

comparisons are complicated by the fact that the distributions may be similar over some intervals 

and dissimilar over other intervals. However, at present, data are not sufficient to know which 

parts of the distribution are most important from a toxicological perspective. Therefore, this 

document relies upon simple visual inspection to assess the degree of difference between various 

regions of differing distributions. 

3.0 RESULTS 

3.1 Data Validation 

The Libby2 database and Libby OU3 database have a number of built-in quality control checks 

to identify unexpected or unallowable data values during upload into the database. Any issues 

identified by these automatic upload checks were resolved by consultation with the analytical 

laboratory before entry of the data into the database. After entry of the data into the database, 

several additional data verification steps were taken to ensure the data were recorded and entered 

correctly. A total of 29,504 LA structures are included in Table 1. Of these structures, 25% 

have undergone data validation in accord with standard site-wide operating procedures (USEPA 

2008b) to ensure that data for length, width, particle type, and mineral class are correct. Of the 

structures that have undergone validation, only 39 of 7,464 (0.5%) structures had errors in 

length, width, or mineral class. These errors were corrected and the database updated as 

appropriate. 

3.2 Consolidated Data Set 

Originally, most samples of air at Libby were analyzed using a counting rule based on a fiber 

aspect ratio of 5:1. More recently, most air samples are counted using an aspect ratio rule of 3:1. 

Because this rule has varied over time, Libby-specific laboratory modifications LB-000016 and 
3 

LB-000031 (see Attachment B) were created to document the historic modifications and 

instructions that laboratories have followed throughout the Libby program. 

Figure 3-1 presents the particle size distributions for 29,504 LA particles observed to date1

 in air 

samples collected at the Libby Asbestos Superfund site that have an aspect ratio of 5:1 or more, 

along with the distributions for 11,451 particles that were counted using an aspect ratio rule of 

3:1. As seen, the distributions are very similar. This is because the number LA particles that 

have an aspect ratio > 3:1 and < 5:1 is a relatively small fraction of the total (7%). 

For simplicity, all remaining analyses focus on the set of particles with an aspect ratio of 5:1 or 

more. 

3.3 Frequency of Complex Structures 

Asbestos particles occur not only as fibers but also in more complex structures including 

bundles, clusters, and matrix complexes. The frequency of these structure types in air samples 

from Libby are summarized below: 

Type2 Number Frequency 

Fiber 23,933 81% 

Bundle 2,366 8% 

Matrix 3,150 11% 

Cluster 54 0.2% 

Total 29,504 100% 

As shown, most (81%) of the enumerated structures are fibers, with less than 20 % complex 

structures. 

3.4 Comparisons of Stratified Data Sets 

The data sets shown in Figure 3-1 are based on air samples that were collected at a number of 

different locations around the site, and which were analyzed by several different methods. In 

order to investigate whether there are any important differences in size distributions between 

operable units, sampling locations (indoor, outdoor), activity (e.g., active or passive), and /or 

analytical method, the consolidated data set was partitioned into a number of subsets, as follows: 

 

1

 Based on a query of the Libby2 database on 12/08/09 and the Libby OU3 database on 2/9/10. 

2

 In some cases, the structure type assignment provided by the laboratory was not a valid choice according to the 

recording rules for the specified analysis method. Table A-1 in Appendix A presents the types of invalid structure 

types and the structure class assumption that was made in order to include the structure in this report. 
4 

Figure Comparison 

3-2 LA particles observed in air stratified by structure type 

3-3 LA particles observed in air stratified by Operable Unit 

3-4 LA particles observed in air stratified by sample type (ambient, indoor, outdoor ABS) 

3-5 LA particles observed in air stratified by preparation method (direct vs indirect) 

3-6 LA particles observed in air stratified by analysis method (ISO vs AHERA) 

Figure 3-2 is a comparison of different structure types (fiber, bundles, and matrices). Clusters 

were not included because there were too few for a distribution to be meaningful. As seen, the 

length distribution for matrix particles is somewhat left-shifted compared to fibers. This is 

perhaps expected because some portion of the fiber length in matrix fibers is obscured by the 

matrix particle. In contrast, the length and thickness distributions for bundles are right-shifted 

compared to fibers. This is expected because a bundle is several fibers lying in parallel. 

Figure 3-3 compares the size distributions of LA at different operable units (OUs) at the site. As 

seen, there appears to be little difference in structures from the different OUs. 

Figure 3-4 shows the distribution of structure sizes for different types of air samples. Samples 

have been placed into three groups: ambient air, indoor ABS, and outdoor ABS. As shown, the 

length and width distributions for indoor and outdoor ABS samples are relatively similar, while 

the length and width distribution for ambient air samples appear to be right shifted. However, 

this observation should be considered to be relatively uncertain because of the small number 

(136) of particles that constitute the ambient air data set. 

Figure 3-5 compares the size distributions for samples using direct and indirect preparation 

methods. As shown, there is little difference in the distributions or either length of width, 

suggesting that preparation method does not have a significant impact on particle size. 

Figure 3-6 compares the particle size distributions as a function of analytical counting rules. As 

shown, the length and width distributions for particles analyzed using AHERA rules tend to be 

somewhat right-shifted relative to the distributions for particles analyzed using ISO 10312 rules. 

This apparent difference might be related either to differences in counting rules between 

methods, or possibly to differences in the nature of samples analyzed by each method. In either 

event, the difference between methods appears to be relatively small. 

4.0 SUMMARY 

Particle size data are available for nearly 30,000 LA structures that have been observed in air 

samples collected at the Libby Asbestos Superfund site. Most (about 80%) LA particles are 

fibers, with less than 20% complex structures (bundles, clusters, or matrices). LA particle 
5 

lengths typically range from a little less than 1 um up to 20-30 um, and occasionally higher. The 

average length is about 7 um. Thicknesses typically range from about 0.1 um up to about 2 um, 

with an average of about 0.5 um. Although some variations occur, particle size distributions are 

generally similar between different locations and between different types of samples. 

5.0 REFERENCES 

AHERA. 1986. Asbestos Hazardous Emergency Response Act . Title 20, Chapter 52, Sec. 

4011. Public Law 99-519. 

ISO. 1995. International Organization for Standardization (ISO). Ambient Air – Determination 

of Asbestos Fibres – Direct-Transfer Transmission Electron Microscopy Method. ISO 

10312:1995(E). 

USEPA. 2000. Sampling and Quality Assurance Project Plan Revision 1 for Libby, Montana. 

Environmental Monitoring for Asbestos. Baseline Monitoring for Source Area and Residential 

Exposure to Tremolite-Actinolite Asbestos Fibers. Report prepared by U.S. Environmental 

Protection Agency Region. January 4, 2000. 

USEPA. 2001. Phase 2 Sampling and Quality Assurance Project Plan (Revision 0) for 

Libby, Montana. Environmental Monitoring for Asbestos. Evaluation of Exposure to 

 Airborne Asbestos Fibers During Routine and Special Activities. Report prepared by U.S. 

Environmental Protection Agency Region 8. March 2001. 

USEPA. 2002. Final Sampling and Analysis Plan, Remedial Investigation, Contaminant 

Screening Study, Libby Asbestos Site, Operable Unit 4. Report prepared by U.S. Environmental 

Protection Agency Region 8, with technical support from CDM. April 30, 2002. 

USEPA. 2003. Final Sampling and Analysis Plan Addendum, Post Clean-up Evaluation 

Sampling, Contaminant Screening Study, Libby Asbestos Site, Operable Unit 4. Report 

prepared by U.S. Environmental Protection Agency Region 8, with technical support from CDM 

and Syracuse Research Corporation. December 1, 2003. 

USEPA. 2005. Supplemental Remedial Investigation Quality Assurance Project Plan for Libby, 

Montana. Revision 1. U.S. Environmental Protection Agency Region 8. August 5, 2005. 

USEPA. 2006. Sampling and Analysis Plan for Outdoor Ambient Air Monitoring at the Libby 

Asbestos Site. Revision 1. Report prepared by U.S. Environmental Protection Agency Region 

8, with technical support from CDM and Syracuse Research Corporation. December 7, 2006. 
6 

USEPA. 2007a. Sampling and Analysis Plan for Outdoor Ambient Air Monitoring – Operable 

Units 1, 2, 5, and 6. Final Addendum prepared by U.S. Environmental Protection Agency 

Region 8, with technical support from CDM and Syracuse Research Corporation. July 3, 2007 

USEPA. 2007b. Sampling and Analysis Plan for Activity-Based Outdoor Air Exposures, 

Operable Unit 4, Libby, Montana, Superfund Site. Final. U.S. Environmental Protection 

Agency, Region 8. July 6, 2007. 

USEPA. 2007c. Sampling and Analysis Plan for Activity-Based Indoor Air Exposures, 

Operable Unit 4, Libby, Montana, Superfund Site. Final. U.S. Environmental Protection 

Agency, Region 8. July 6, 2007. 

USEPA 2008a. Request for Modification to Laboratory Activities LB-000031A. Requested by 

Lynn Woodbury of Syracuse Research Corporation. January 18, 2008. 

USEPA. 2008b. Standard Operating Procedure for TEM Data Review and Data Entry 

Verification. SOP No. EPA-LIBBY-09 (rev 1). Prepared by U.S. Environmental Protection 

Agency, Region 8, with technical support from SRC, Inc. March 5, 2008. 

USEPA. 2008c. Phase II Sampling and Analysis Plan for Operable Unit 3 Libby Asbestos 

Superfund Site. Part B: Ambient Air and Groundwater. Prepared by U.S. Environmental 

Protection Agency Region 8, with technical support from Syracuse Research Corporation and 

NewFields Boulder LLC. July 2, 2008. 

USEPA. 2008d. Final Sampling and Analysis Plan Libby Public Schools – Stationary Air 

Sample Collection Libby Asbestos Site Libby, Montana. Prepared by U.S. Dept. of 

Transportation and CDM Federal Programs Corp. with technical support from Syracuse 

Research Corporation. December 5, 2008. 

USEPA. 2009a. Remedial Investigation for Operable Unit 3 Libby Asbestos Superfund Site. 

Phase III Sampling and Analysis Plan. Prepared by U.S. Environmental Protection Agency 

Region 8, with technical support from Syracuse Research Corporation and NewFields Boulder 

LLC. May 26, 2009. 

USEPA. 2009b. Final Sampling and Analysis Plan for Activity-Based Outdoor Air Exposures at 

Libby Public Schools Libby Asbestos Site Libby, Montana. Prepared by U.S. Dept. of 

Transportation and CDM Federal Programs Corp. with technical support from SRC, Inc. July 

17, 2009. 
Program Program Date Range Sampling and Analysis Plan (s) 

Phase 1

Table 1. Air Sample Collection Programs

Initial investigation sampling to assess nature and extent of potential contamination. Includes source areas (e.g., screening plant, export plant), commercial buildings, and 

Program Description

Dec 1999 - present USEPA 2000 

Phase 1R Jun 2000 - present USEPA 2000 Phase 2 Mar - Nov 2001 USEPA 2001 Phase 2R Apr 2008 - Nov 2009 

CSS A 2003 O 2006 S A 2002 

residential properties.Monitoring and confirmation sampling as part of clean-up activities.Activity-based sampling (ABS) included four scenarios: 1) routine indoor activities, 2) active cleaning, 3) simulated remodeling disturbances, 4) garden rototilling.Monitoring and confirmation sampling as part of Phase 2C i S i S d f ibb i d i d f di i CSS Apr 2003 - Oct 2006 USEPA 2002 

SQAPP

Jun 2005 - Oct 2006 USEPA 2005 

Ambient Air (AA) Oct 2006 – Jun 2008 USEPA 2006, USEPA 2007a 

Contaminant Screening Study of Libby properties to determine need for remediation.Sampling to address risk assessment data gaps. Included indoor ABS (routine activities) and outdoor ABS (raking, mowing, playing), as well as clean-up evaluation samples.Ambient air monitoring program for 14 stations in OU4, 2 stations in OU2, 2 stations in OU6. Samples represent long-term (continuous 5-day) collection periods.

OU4 Indoor/ Outdoor ABS Jul 2007– Jun 2008

USEPA 2007b, 

USEPA 2007c 

Indoor Schools Dec 2008 USEPA 2008d Outdoor Schools Jul - Sept 2009 USEPA 2009b Phase 2 

Sampling to assess exposures during indoor ABS (passive & active activities) and outdoor ABS (raking, mowing, playing) in OU4.Stationary air sample collection from within Libby public schoolsOutdoor ABS sampling from Libby public schools simulating exposures to students and maintenance staff.Ambient air sampling. Samples represent long-term (continuous 5-day) collection 

(OU3) July - Oct 2008 USEPA 2008c 

Phase 3 

(OU3) Aug - Nov 2009 USEPA 2009a 

Clean-up Evaluation Nov 2003 - Feb 2004 USEPA 2003 Other Aug 2001 - present various 

Sampling to monitor air and dust levels after completion of clean-up activities at 31 properties.Includes various site-specific sampling investigations (e.g., Stimson Lumber, Flyway, BNSF) and smaller-scale sampling programs.

p g p p g ( y)

periods.ABS air sampling of ATV riding, hiking, camp fire construction

LA structure counts are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

Program LA Structures Description

 (a) Restricted to LA structures recorded in accordance with a 5:1 aspect ratio rule.

Other

1A 9 AIRS Site (418 Mineral Ave)

BN 17 BNSF

CR 3 Cumulative Risk Study

DM 1 Demolition Sampling from 2006 only

E1 1 BNSF Rail Yard Exclusion Zones

EP 104 Export PlantFC 184 Flower Creek

FL 146 WR Grace (Flyway site) WR G (Fl it )

SL 266 Stimson Lumber
Figure 3-1. Particle Size Distributions of LA Particles in Libby Air Samples

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Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

All Air Samples

Number of Structures (29,504)

Type Number Frequency

F 23,933 81%

B 2,366 8%

M 3,150 11%

C 54 0.2%

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TEM Results_12-8-09_V4.xls, ALL51
Figure 3-2. Particle Size Distributions of LA Particles in Libby Air Samples by Structure Type

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Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

Structure 

Type N Structures

F 23,933

B 2,366

M 3,150

Clusters have not been included in this figure because N = 54 and this in not believed to be a suffficient number of structures.

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TEM Results_12-8-09_V4.xls, ALL_StrucType
Figure 3-3. Particle Size Distributions of LA Particles in Libby Air Samples by Operable Unit (OU)

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Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

OU N Structures

1 447

2 7,421

3 4,382

4 13,005

5 335

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TEM Results_12-8-09_V4.xls, OU
Figure 3-4. Particle Size Distributions of LA Particles in Libby Air Samplesby Air Type

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Outdoor Ambient Air

Outdoor ABS

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Outdoor Ambient Air

Outdoor ABS

Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

Samples Source N Structures

Ambient Air 136

Indoor ABS 891

Outdoor ABS 5,953

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Outdoor Ambient Air

Outdoor ABS

TEM Results_12-8-09_V4.xls, AA_ABS
Figure 3-5. Particle Size Distributions of LA Particles in Libby Air Samples by Preparation Method

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Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

Preparation N Structures

Direct 17,578

Indirect 11,926

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TEM Results_12-8-09_V4.xls, Prep
Figure 3-6. Particle Size Distributions of LA Particles in Libby Air Samples by Analysis Method

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AHERA

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Data are based on a download of Libby 2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.

Analysis Method N Structures

ISO 12,657

AHERA 16,847

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TEM Results_12-8-09_V4.xls, Method
APPENDIX A 

RAW DATA: LA STRUCTURE DATA FROM THE LIBBY 2 DATABASE AND THE 

LIBBY OU3 DATABASE 

Libby2DB based on a download date of 12/8/09 

Libby OU3 DB based on a download date of 2/9/10 

 See attached file
Counting Rule Structure 

Type

Structure 

Class (a) Fiber? Notes

B B

C C

F FY

M (F) Y

MD (F) Y **recorded using ISO structure type, assumed 

to be disperse matrix

MD11 (F) Y **recorded using ISO structure type, assumed 

to be disperse matrix

MF (F) Y **recorded using ISO structure type, assumed 

to be matrix w/fiber protrusion

B B

C C **recorded using AHERA structure type, 

assumed to be compact cluster

CB B

CF F Y

DM10 F Y **assumed to be fiber within a matrix (MF)

F FY

FM F Y **assumed to be fiber within a matrix (MF)

M M **recorded using AHERA structure type, 

assumed to be compact matrix

MB B

MB10 B **assumed to be bundle within a matrix (MB)

Table A-1. Structure Type Assignments

AHERA/ASTM

ISO 10312 MC M **assumed to be a compact matrix

MC+0 M

MC0 M **assumed to be a compact matrix

MC10 M **assumed to be a compact matrix

MD F Y **assumed to be fiber within a matrix (MF)

MD10 F Y **assumed to be fiber within a matrix (MF)

MD11 F Y **assumed to be fiber within a matrix (MF)

MF F Y

MF1 F Y **assumed to be fiber within a matrix (MF)

MF10 F Y **assumed to be fiber within a matrix (MF)

MF2 F Y **assumed to be fiber within a matrix (MF)

ND10 F Y **typo MD10; assumed to be fiber within a 

matrix (MF)

** Structure Type is not valid

(a) For matrices recorded by AHERA counting rules, assumed that all matrices had fiber protrusions.

ISO 10312
APPENDIX B 

LIBBY-SPECIFIC LABORATORY MODIFICATION FORMS 

LB-000016 

LB-000031 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Lynn Woodbury Title: Technical Consultant 

Company: Syracuse Research Corporation Date: April 10, 2008 

Description of Modification: 

Permanent modifications and clarifications to the Transmission Electron Microscopy analysis of air and dust samples using 

ISO 10312. The purpose of the attached is to document historic modifications & clarifications, and provide additional 

permanent clarifications. 

Reason for Modification: 

To optimize the efficiency of air and dust sample analysis and to provide consistency in analytical procedures and data 

recording in the project laboratories. 

Potential Implications of this Modification: 

Modifications reflect changes necessary to clarify ISO requirements in relation to project-specific issues. Negative 

implications - comparisons of the Total # of LA structures between historical results and current results may be biased (high 

or low) due to differences in recording rules with regard to aspect ratio criteria. Positive implications - consistency in 

procedures between and within project laboratories and documentation of those procedures. 

Laboratory Applicability (circle one): All Individual(s) 

This laboratory modification is (circle one): NEW APPENDS to ___________ SUPERCEDES LB-000016 

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

See attached sheets for the description of the TEM-ISO clarifications/modifications. 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016A 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

ISO 10312 MODIFICATIONS AND CLARIFICATIONS 

1. Overloading Modification: 

The ISO method requirement states that if the specimen grid exhibits more than approximately 10% obscuration on 

the majority of the grid openings, the specimen shall be designated as overloaded. A rejection criteria of >25% 

obscuration and <50% intact grid openings will be used for this project. The 25% overload criteria resulted from 

various communications that took place 29 December 1999 between EPA Region 8, Camp Dresser McKee, Volpe 

Center, and Reservoirs. 

2. Indirect Preparation of Air Samples Modification: 

ISO 10312 is a direct preparation method. If the sample is visibly overloaded or contains loose debris, it will be 

prepared indirectly according to procedures provided in SOP EPA-Libby-08. Secondary filters will be analyzed 

according to the ISO counting rules for this project. Calculations will be adjusted to contain a dilution factor. This 

indirect preparation procedure will enable the capture of data from samples that otherwise would be rejected. 

3. Stopping Rule Clarification: 

Stopping rules for ISO analyses are completion of the grid opening on which the 100th asbestos structure has been 

recorded, or a minimum of four grid openings. For this project, a maximum of ten grid openings will be read unless 

specifically instructed otherwise. 

4. Abundant Chrysotile Modification: 

If abundant chrysotile is present, the chrysotile count may be terminated in accordance with the counting rules 

specified in LB-000039. 

5. Structure Counting and Recording Modifications and Clarifications: 

a. Non-asbestos material (NAM) structures are not being recorded, unless identified as a “close call” (see LB000066 for details). This project-specific modification stems from the need only to quantify levels of 

contaminants of concern (i.e., asbestos) at a given sample location. 

b. Recording rules will be as described in the ISO method except that the aspect ratio requirement will depend 

upon the classification of the sample as “investigative” or “non-investigative”, as specified in LB-000053. If 

samples are classified as investigative, the aspect ratio requirement will be 3:1, rather than 5:1, unless programspecific sampling and analysis plans (SAPs) specify otherwise or specifically requested otherwise. Thus, fibers 

(either individual fibers or fibers within disperse matrices or clusters) shall only be recorded if the length is 

greater than or equal to 0.5 um and the aspect ratio is greater than or equal to the appropriate criterion. Bundles 

shall only be recorded if they contain individual constituent fibers with an aspect ratio greater than or equal to 

the appropriate criterion. The aspect ratio criterion does not apply to compact clusters, compact matrices, or 

residuals. The overall aspect ratio of a bundle, compact cluster, compact matrix, or residual may have any 

value. 

c. The definition of a PCM equivalent (PCME) structure is as follows: Any fiber, bundle, matrix, or cluster with 

an aspect ratio of 3:1 or greater, length longer than 5 um, and width greater than or equal to 0.25 um. 

d. The overall dimensions of disperse clusters (CD) and disperse matrices (MD) will not be recorded in two 

perpendicular directions. The matrix type and individual sub-structures associated with the matrix or cluster 

will be recorded as described in the ISO method. 

e. Structures that intersect a non-countable grid bar (i.e., top and left grid bars) will be recorded on the count sheet 

but excluded from the structure density and concentration calculations. These non-countable structures will be 

denoted with a zero in the Total column. 

f. If a structure originates in one grid opening and extends into an adjacent grid opening, providing that it does not 

intersect a non-counting grid bar, the entire length of the fiber is recorded. 
Lab Modification Form Revision 10 (9-11-07) 

g. If a structure intersects both a countable and a non-countable grid bar, the observed length of the structure will 

be recorded. 

h. See Attachment A for detailed examples of how to record specific structure types that may be encountered in 

Libby samples. 

These modifications and clarifications in structure counting and recording are to provide consistency in analytical 

procedures and data recording in the project laboratories.

OVERVIEW OF HISTORICAL RECORDING CRITERIA 

At the beginning of the Libby project, analytical laboratories (primarily EMSL and RESI) were following the ISO method 

with regard to structure recording (i.e., recording only those structures meeting an aspect ratio of greater than or equal to 

5:1). 

Approximately the time of the Phase 2 Investigation (late Spring 2001), project laboratories were instructed by Chris Weis 

(EPA, Region 8) to record all structures regardless of minimum length or aspect ratio. This recording rule change enabled 

data users to gain a better understanding of the dimension attributes for structures at the Libby site and allowed for the 

calculation of PCM equivalent (PCME) structures. In the ISO report generated by the TEM EDD spreadsheet, structures 

with an aspect ratio less than 5:1 were counted in Bin A and structures with a length less than 0.5 um were counted in Bin 

B. Also at this time, the TEM EDD spreadsheet was modified to allow for the capture of the raw structure data, as entered 

from the laboratory bench sheet, into the Libby site database. 

Although it is uncertain exactly when the recording rules changed after the Phase 2 Investigation, based on analyst 

interviews, project laboratories reverted back to following the ISO method (i.e., recording only those structures meeting an 

aspect ratio of greater than or equal to 5:1) beginning approximately December 2001, unless specifically requested 

otherwise in project-specific SAPs and/or QAPPs (e.g., the Supplemental Remedial Investigation samples collected under 

the SQAPP specified an aspect ratio criterion of greater than or equal to 3:1). 

Laboratory modifications LB-000016B through 16F (provided as Attachment B) document the historical laboratory and 

analyst-specific deviations in recording/counting rules for ISO based on analyst interviews conducted in August and 

September 2006. 

Beginning August 29, 2006, all project laboratories began utilizing an aspect ratio criterion of 3:1, unless specifically 

requested otherwise. 

Preparation techniques and recording rules were further refined as part of LB-000053 (effective date: December 21, 2006), 

whereby all Libby samples were classified as “investigative” and “non-investigative”. Samples classified as “investigative” 

were to utilize an aspect ratio criterion of 3:1, and samples classified as non-investigative were to utilize an aspect ratio 

criterion of 5:1, unless program-specific sampling and analysis plans (SAPs) specify otherwise or specifically requested 

otherwise. 

Because of the differences in recording rules for ISO analyses across time, data users should be cautious when making 

comparisons across samples based on the total number of LA structures. The binned metric of total number of LA 

structures may differ depending upon the recording rule in place at the time. 
Lab Modification Form Revision 10 (9-11-07) 

ATTACHMENT A 

STRUCTURE-SPECIFIC EXAMPLES OF DATA RECORDING 

Figure 1 

Figure 2 

Figure 3 

Count as three fibers (F). The large structure is excluded 

because it crosses a non-countable grid bar (left grid bar). 

Count as one fiber (F). Record the length as that observed 

without doubling. 

Count as disperse matrix, consisting of one fiber longer 

than 5 um. 

Record as MD11, followed by one fiber (MF). When 

recording the MF, do not double the length. 
Lab Modification Form Revision 10 (9-11-07) 

Figure 4 

Figure 5 

Figure 6 

Count as disperse matrix, consisting of one fiber longer 

than 5 um. 

Record as MD11, followed by one fiber (MF). When 

recording the MF, double the length of the observed fiber.

Count as one compact cluster containing more than 9 

fibers, which includes one fiber that is longer than 5 um. 

Record as CC+1. When recording the CC, record the 

length of the cluster as double the length of the observed 

fiber longer than 5 um. 

Count as disperse cluster, consisting of one fiber which is 

longer than 5 um and one compact cluster residual 

containing more than 9 fibers. 

Record as CD+1, followed by one CF and one CR+0. 

When recording the CF intersecting grid bar, double the 

length. 
Lab Modification Form Revision 10 (9-11-07) 

Figure 7 

Figure 8 

Figure 9 

Count as one fiber (F). Record the actual length, including 

protrusion into adjacent grid opening. 

Count as disperse cluster, consisting of four fibers each 

longer than 5 um. 

Record as CD44, followed by four CFs. 

Count as disperse cluster, consisting of four fibers each 

longer than 5 um. 

Record as CD44, followed by four CFs. 
Lab Modification Form Revision 10 (9-11-07) 

ATTACHMENT B 

LABORATORY AND ANALYST-SPECIFIC DEVIATIONS 

IN ISO 10312 RECORDING AND COUNTING RULES PRIOR TO AUGUST 2006 

(LB-000016B through 16F) 

LB-000016B - Batta 

LB-000016C - EMSL 

LB-000016D - Hygeia 

LB-000016E - MAS 

LB-000016F - RESI 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Bo Li Title: Manager of Microscopy Services 

Company: Batta Laboratories, Inc. Date: September 26, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using ISO 

10312, as modified by LB-000016. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using ISO 10312, as 

modified by LB-000016, and provide a historical timeline of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Batta Laboratories, Inc. 

This laboratory modification is (circle one): NEW APPENDS to LB-000016A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016B 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Beginning May 2002 (when Batta Laboratories began analyzing Libby samples), all analysts recorded structures 

based on ISO definitions (i.e., aspect ratio criterion of ≥ 5:1). However, when PCM-equivalent structures were 

encountered (length > 5 μm and an aspect ratio ≥ 3:1), they were recorded and marked as countable. The same 

recording rule was applied to clusters, bundles and matrices that contained PCME structures. 

Starting September 11, 2006 (beginning with Lab Job # QC-27, Index ID SQ-00208), recording rules were changed to 

utilize a length criterion of > 0.5 μm and an aspect ratio criterion of ≥ 3:1 in accordance with the email direction from 

Anni Autio (CDM). 

DEVIATIONS FROM LAB MODIFICATION LB-000016

Section 4A – Recording of NAM Structures

Analysts were instructed in May 2007 not to record NAMs. However, analysts may occasionally record as NAM if the 

analyst thought there would be a chance that such a structure might likely be mistaken by a second analyst as a 

possible asbestos structure. In such a case, “0” was assigned to both primary and total structure columns. 

Section 4B – Recording Dimensions for Disperse Clusters (CD) and Matrices (MD)

Dimensions for disperse clusters (CD) and matrices (MD) were measured at two perpendicular directions across the 

structure. The longest length was defined first, and then the width perpendicular to the length. 

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

Before March 2006, structures intersecting the adjacent grid bars on the lower left corner were counted and those 

intersecting the adjacent bars on the upper right were rejected. After March 2006, this practice was corrected to 

comply with ISO rules: only those fibers interesting or touching the lower right corner grid bars were counted and 

others were rejected. 

Section 4D – Recording Structures Crossing Multiple Grid Openings

ISO rules on structures touching grid bars were followed. 

Section 4E – Recording Structures Crossing Non-Countable and Countable Grid Bars

Refer to Section 4C above. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Ed Cahill Title: National Director 

Company: EMSL Analytical, Inc. Date: September 27, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using ISO 

10312, as modified by LB-000016. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using ISO 10312, as 

modified by LB-000016, and provide a historical timeline for each Libby laboratory of potential changes in aspect ratio 

recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) EMSL 

This laboratory modification is (circle one): NEW APPENDS to LB-000016A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016C 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

Analysts Interviewed Ex Employees (not interviewed) 

Jodie Bourgerie Duane Salinas 

Ed Cahill Thomas Ferrante 

Robyn Denton Richard White 

Bob Georgens Anant Samudra 

Richard Harding Adrian Arav 

Ken Klutts 

Brett Macey 

Ron Mahoney 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Beginning in approximately late 1999 (when EMSL Analytical began analyzing Libby samples), all analysts 

interviewed applied a 5:1 aspect ratio and the counting rules outlined in the ISO method. Sometime during the spring 

of 2001, verbal directions were given to record all structures, regardless of aspect ratio. Before, during and after this 

time, if any other aspect ratio than > 5:1 was recorded, it was due to a written or verbal project specific directive (i.e. 

SQAPP and/or Phase 2 project samples). 

DEVIATIONS FROM LAB MODIFICATION LB-000016

All analysts followed ISO method recording rules, as well as the guidance in Laboratory Mod LB-000016 except as 

noted below: 

Section 4A – Recording of NAM Structures

All analysts in compliance. 

Section 4B – Recording Dimensions for Disperse Clusters (CD) and Matrices (MD)

All analysts in compliance. 

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

Robyn Denton: Has not been adhering to Section 4C which called for recording asbestos structures even when they 

originated from non-countable grid bars. 

Section 4D – Recording Structures Crossing Multiple Grid Openings

All analysts in compliance. 

Section 4E – Recording Structures Crossing Non-Countable and Countable Grid Bars

All analysts in compliance. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Kyeong Corbin Title: TEM Laboratory Supervisor 

Company: Hygeia Laboratories Inc. Date: September 22, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using ISO 

10312, as modified by LB-000016. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using ISO 10312, as 

modified by LB-000016, and provide a historical timeline for each Libby laboratory of potential changes in aspect ratio 

recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Hygeia Laboratories Inc. 

This laboratory modification is (circle one): NEW APPENDS to LB-000016A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016D 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Hygeia Laboratories Inc. received the first batch of ISO 10312 samples on May 30, 2002. Two analysts, Kyeong 

Corbin and Quynh Trieu, were involved in analyzing all Libby project samples. All ISO samples were counted and 

recorded for asbestos structures utilizing an aspect ratio criterion of 5:1 or greater, unless the project-specific 

requirements stated otherwise. For example, all SQAPP samples submitted (first job submitted in June 20, 2005) and 

all settled dust samples (SRC-Libby-07 method; first job submitted in June 23, 2005) for ISO 10312 method were 

analyzed for aspect ratio 3:1 or greater. 

DEVIATIONS FROM LAB MODIFICATION LB-000016

Both analysts agree on the counting rules mentioned in LB-000016 and clarifications stated below: 

1. Hygeia recorded no asbestos detected grid opening as “NSD” instead of “ND” as requested during the 

period of 6/1/02 to 11/30/02. The Lab Mod LB-000023 was filed. 

2. Project-specific stopping rules or target analytical sensitivity superseded the ISO 10312 method stopping 

rules. 

Section 4A – Recording of NAM Structures

NAM structures were not recorded as of August 6, 2002. At times, NAM structures were still recorded if the analysts 

thought it was necessary and marked as such on the data sheet and on the EDD. (If recorded, NAM structures were 

noted as non-countable on the data sheet and EDD.) 

Section 4B – Recording Dimensions for Disperse Clusters (CD) and Matrices (MD)

All analysts in compliance. 

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

All structures intersecting countable grid bars (bottom/south and right/east) were counted and the lengths were 

doubled. Doubled length was indicated as “X” on the data sheet and indicated as “doubled length” on the comment 

field of EDD. In general, structures intersecting non-countable grid bars were recorded and indicated as noncountable on the data sheet and EDD. Intersecting grid bar was also indicated on the EDD comment field as crossing 

E, W, S, N grid bar, i.e. “Non-countable; CWGB,” or “CSGB; Doubled length.” 

Section 4D – Recording Structures Crossing Multiple Grid Openings

All analysts in compliance (see text in Section 4C above). 

Section 4E – Recording Structures Crossing Non-Countable and Countable Grid Bars

All analysts in compliance (see text in Section 4C above). 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Michael D. Mount Title: EM Manager 

Company: MAS, LLC Date: December 7, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using ISO 

10312, as modified by LB-000016. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using ISO 10312, as 

modified by LB-000016, and provide a historical timeline for each Libby laboratory of potential changes in aspect ratio 

recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) MAS 

This laboratory modification is (circle one): NEW APPENDS to LB-000016A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016E 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

MAS currently has four analysts (Mike Mount, Kevin Simpson, Mehrdad Motamedi and Will Stark) performing ISO 

10312 analyses of Libby samples. The first set of Libby ISO samples (dust samples) were received on 04/03/03. At 

this time analyst performing analysis were Will Stark, Ding Qian and Jayme Callan. All ISO samples were counted 

and recorded for asbestos structures utilizing an aspect ratio criterion of 5:1 or greater, unless the project-specific 

requirements stated otherwise. SQAPP samples and settled dust samples for ISO 10312 submitted since that time 

(generally around June 2005) methods were analyzed for aspect ratio 3:1 or greater. 

DEVIATIONS FROM LAB MODIFICATION LB-000016

All analysts followed ISO method recording rules, as well as the guidance in Laboratory Mod LB-000016 except as 

noted below: 

Section 4A – Recording of NAM Structures

On some occasions one analyst (KS) has recorded non-asbestos structures (NAM) that he deemed significant. 

However, when NAM structures were recorded, they were always noted as non-countable structures (i.e., placed a “0” 

in the Total column). 

Section 4B – Recording Dimensions for Disperse Clusters (CD) and Matrices (MD)

MAS analysts (MDM and others) may have recorded the overall dimensions of the matrix or cluster rather than the 

dimensions of the sub-structures. 

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

All analysts in compliance. MAS records asbestos structures that are deemed non-countable due to an intersection of 

the north and west grid bars and places a “0” in the structure count column. 

Section 4D – Recording Structures Crossing Multiple Grid Openings

All analysts in compliance. MAS records the entire length of a structure that extends into an adjacent grid opening. 

Section 4E – Recording Structures Crossing Non-Countable and Countable Grid Bars

All analysts in compliance. MAS records the entire length of a structure that crosses a non-countable grid bar and a 

countable grid bar. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Jeanne Orr Title: President 

Company: RESI Date: December 12, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using ISO 

10312, as modified by LB-000016. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using ISO 10312, as 

modified by LB-000016, and provide a historical timeline for each Libby laboratory of potential changes in aspect ratio 

recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) RESI 

This laboratory modification is (circle one): NEW APPENDS to LB-000016A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000016F 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Reservoirs Environmental began analyzing samples for the Libby Project in November, 1999. Analysts applied an 

aspect ratio of 5:1 to the countable structures unless project specific requirements stated otherwise. The samples 

with a prefix of “SQ-“ or “2-“ were analyzed with an aspect ratio of 3:1 as requested. Current aspect ratio rules are 

outlined in LB-000053 and are delineated by the classification of “investigative” or “non-investigative”. 

DEVIATIONS FROM LAB MODIFICATION LB-000016

All analysts followed ISO method recording rules to the best of their ability, as well as the guidance in Laboratory Mod 

LB-000016. Samples counted in 1999 and 2000 on historical laboratory bench sheets are identified in LB-000001 

through LB-000014. 

Section 4A – Recording of NAM Structures

Reservoirs has recorded NAM structures if the analyst determined the structure had morphology similar to an 

asbestos structure. NAM structures have always been marked as non-countable on the data sheet. The requirement 

to record non close-call NAM structures such as gypsum or glass was removed after August, 2006. 

Section 4B – Recording Dimensions for Disperse Clusters (CD) and Matrices (MD)

All analysts in compliance. 

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

All analysts in compliance. 

Section 4D – Recording Structures Crossing Multiple Grid Openings

All analysts in compliance. 

Section 4E – Recording Structures Crossing Non-Countable and Countable Grid Bars

All analysts in compliance. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Lynn Woodbury Title: Technical Consultant 

Company: Syracuse Research Corporation Date: January 18, 2008 

Description of Modification: 

Permanent modifications and clarifications to the Transmission Electron Microscopy analysis of air samples using AHERA

and dust samples using ASTM. The purpose of the attached is to document historic modifications & clarifications, and 

provide additional permanent clarifications. 

Reason for Modification: 

To optimize the efficiency of air and dust sample analysis and to provide consistency in analytical procedures and data 

recording in the project laboratories. 

Potential Implications of this Modification: 

Modifications reflect changes necessary to clarify ISO requirements in relation to project-specific issues. Negative 

implications - comparisons of the Total # of LA structures between historical results and current results may be biased (high 

or low) due to differences in recording rules with regard to aspect ratio criteria. Positive implications - consistency in 

procedures between and within project laboratories and documentation of those procedures. 

Laboratory Applicability (circle one): All Individual(s) 

This laboratory modification is (circle one): NEW APPENDS to _________ SUPERCEDES LB-000017, LB000017A, LB-000031

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

Request for Modification

to 

Laboratory Activities 

LB-000031A 
Lab Modification Form Revision 10 (9-11-07) 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

AHERA/ASTM MODIFICATIONS AND CLARIFICATIONS 

1. Overloading Modification: 

A rejection criteria of >25% obscuration will be used for this project. The 25% overload criteria resulted from various 

communications that took place 29 December 1999 between EPA Region 8, Camp Dresser McKee (CDM), Volpe Center, 

and Reservoirs (RESI). [The AHERA method grid rejection of 10% has been established and clarified by NVLAP Lab 

Bulletin 7-2002 – July 12, 2002]. 

2. Indirect Preparation Of Air Samples Modification: 

If the sample is visibly overloaded or contains loose debris, it will be prepared indirectly according to procedures provided 

in SOP EPA-Libby-08. Secondary filters will be analyzed according to the AHERA/ASTM counting rules for this project. 

Calculations will be adjusted to contain a dilution factor. This indirect preparation procedure will enable the capture of data 

from samples that otherwise would be rejected. 

3. Stopping Rule Clarification: 

For this project, a maximum of ten grid openings will be read unless specifically instructed otherwise. 

4. Abundant Chrysotile Modification: 

If abundant chrysotile is present, the chrysotile count may be terminated in accordance with the counting rules specified in 

LB-000039. 

5. Cassette Modification: 

Cassettes with a 0.8 μm pore size and no 5.0 μm diffuser filter (PCM cassettes) are primarily used for AHERA/ASTM 

sample collection. 

6. Structure Counting and Recording Modifications and Clarifications: 

a. All structures will be recorded with the following “Structure Type” designations on the EPA Region 8 spreadsheet: 

Fiber F 

Bundle B 

Cluster C 

Matrix M 

If the analyst determines that additional information is needed to describe a structure, comments pertaining to the 

structure in additional to a sketch will be recorded in the structure comments column. 

b. Non-asbestos material (NAM) structures are not being recorded, unless identified as a “close call” (see LB-000066 

for details). This project-specific modification stems from the need only to quantify levels of contaminants of 

concern (i.e., asbestos) at a given sample location. 

c. For the Libby project, a designation of “ND” (none detected) will be used to document when no structures are 

detected for the grid opening. 

d. Recording rules will be as described in the AHERA method except that the aspect ratio requirement will depend 

upon the classification of the sample as “investigative” or “non-investigative”, as specified in LB-000053. If 

samples are classified as investigative, the aspect ratio requirement will be 3:1, rather than 5:1, unless programspecific sampling and analysis plans (SAPs) specify otherwise or specifically requested otherwise. Thus, fibers 

shall only be recorded if the aspect ratio is greater than or equal to the appropriate criterion. Bundles, clusters, and 

matrices shall only be recorded if they contain individual constituent fibers with an aspect ratio greater than or equal 

to the appropriate criterion. The overall aspect ratio of a bundle, cluster, or matrix may have any value. 

e. Structures that are non-countable (e.g., aspect ratio does not meet the appropriate criterion, matrices without an 
Lab Modification Form Revision 10 (9-11-07) 

exposed termination) should be recorded for informational purposes, but identified as non-countable, to ensure they 

are excluded from structure density and concentration calculations. These non-countable structures will be denoted 

with a zero in the Total column. 

f. If a structure originates in one grid opening and extends into an adjacent grid opening, the entire length of the fiber 

is recorded. 

g. The AHERA method requires the analyst to “record the length category and structure type classification non the 

count sheet after the field number and fiber number”. As a clarification to this, the actual length and width of 

individual fibers, bundles, compact clusters, and compact matrices will be recorded. For disperse clusters and 

matrices, the length of only the longest protruding structure will be recorded. It is not appropriate to record all substructures as in the ISO 10312 method. Structure dimensions may be recorded in microns (μm) or screen units 

provided that the scaling factors are recorded. See Attachment A for detailed examples of how to record specific 

structure types that may be encountered in Libby samples. 

h. In the AHERA method, one of the provided illustrations for bundles is unclear (i.e., the bundle appears as three 

fibers). The AHERA written definition for bundles will be utilized. 

These modifications and clarifications in structure counting and recording are to provide consistency in analytical 

procedures and data recording in the project laboratories.

OVERVIEW OF HISTORICAL RECORDING CRITERIA 

At the beginning of the Libby project, analytical laboratories (primarily EMSL and RESI) were following the AHERA 

method with regard to structure recording (i.e., recording only those structures meeting an aspect ratio of greater than or 

equal to 5:1). 

Approximately the time of the Phase 2 Investigation (late Spring 2001), project laboratories were instructed by Chris Weis 

(EPA, Region 8) to record all structures regardless of minimum length or aspect ratio. This recording rule change enabled 

data users to gain a better understanding of the dimension attributes for structures at the Libby site and allowed for the 

calculation of PCM equivalent (PCME) structures. 

Although it is uncertain exactly when the recording rules changed after the Phase 2 Investigation, based on analyst 

interviews, project laboratories reverted back to following the AHERA method (i.e., recording only those structures meeting 

an aspect ratio of greater than or equal to 5:1) beginning approximately in late 2001 to early 2002. 

Laboratory modifications LB-000031B through 31F (provided as Attachment B) document the historical laboratory and 

analyst-specific deviations in recording/counting rules for AHERA/ASTM based on analyst interviews conducted in August 

and September 2006. Beginning August 29, 2006, all project laboratories were instructed to utilize an aspect ratio criterion 

of 5:1 for AHERA/ASTM analyses, unless specifically requested otherwise. 

Beginning April 24, 2007, all project laboratories were instructed to utilize an aspect ratio criterion of 3:1 for 

AHERA/ASTM analyses of “investigative” samples. Instruction regarding classification of samples as “investigative” and 

“non-investigative” was provided as part of LB-000053 (effective date: December 21, 2006. Samples classified as “noninvestigative” were to utilize an aspect ratio criterion of 5:1, unless program-specific sampling and analysis plans (SAPs) 

specify otherwise or specifically requested otherwise. 

Because of the differences in recording rules for AHERA/ASTM analyses across time, data users should be cautious when 

making comparisons across samples based on the total number of LA structures. The binned metric of total number of LA 

structures may differ depending upon the recording rule in place at the time. 
Lab Modification Form Revision 10 (9-11-07) 

ATTACHMENT A 

STRUCTURE-SPECIFIC EXAMPLES OF DATA RECORDING 

Structure 1: 

Structure 2: 

Structure 3: 

Simple fiber – Record length and width. Structure must meet 

AHERA length and aspect ratio criteria. 

Stepped fiber – Record length. Record width as a best estimate 

of the average width. Structure must meet AHERA length and 

aspect ratio criteria. 

Bundle – Record length and width of bundle. The aspect ratio 

of the overall structure is not a factor. At least three individual 

sub-structures in parallel arrangement separated by less than 

one sub-structure diameter, adequate to meet AHERA bundle 

definition, must meet AHERA length and aspect ratio criteria. 
Lab Modification Form Revision 10 (9-11-07) 

Structure 4: 

Structure 5: 

Structure 6: 

Stepped bundle – Record longest length of bundle. Record 

width as a best estimate of the average width. The aspect ratio 

of the overall structure is not a factor. At least three individual 

sub-structures in parallel arrangement separated by less than 

one sub-structure diameter, adequate to meet AHERA bundle 

definition, must meet AHERA length and aspect ratio criteria. 

Matrix – Record length of longest exposed structure and its 

width. Structure must meet AHERA length and aspect ratio 

criteria. 

[Do not record the dimensions of the matrix, only the longest 

protruding structure.] 

Fiber with adhering matrix material - This structure does not 

fall into the matrix category as defined in that both ends are 

exposed (definition 14, AHERA) - Record length and width of 

the fiber. Structure must meet AHERA length and aspect ratio 

criteria. 
Lab Modification Form Revision 10 (9-11-07) 

Structure 7: 

Structure 8: 

Structure with protrusions < aspect ratio criterion but an overall 

aspect ratio meeting criterion. Provided that the structure can 

be observed to be continuous through the adhering material, 

count as a fiber. Structure must meet AHERA length and aspect 

ratio criteria. If the structure cannot be observed to be 

continuous through the adhering material, do not count. 

Cluster – Record the length of the longest observable substructure. Record width as a best estimate of the average width 

of the overall structure, not the individual sub-structures. The 

aspect ratio of the overall structure is not a factor. There must 

be at least three intersections comprised of individual substructures that meet AHERA length and aspect ratio criteria to 

meet cluster definition. 
Lab Modification Form Revision 10 (9-11-07) 

ATTACHMENT B 

LABORATORY AND ANALYST-SPECIFIC DEVIATIONS 

IN AHERA/ASTM RECORDING AND COUNTING RULES PRIOR TO AUGUST 2006 

(LB-000031B through 31F) 

LB-000031B - Batta 

LB-000031C - EMSL 

LB-000031D - Hygeia 

LB-000031E - MAS 

LB-000031F - RESI 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Bo Li Title: Manager of Microscopy Services 

Company: Batta Laboratories, Inc. Date: September 26, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031, and provide a historical timeline for each 

Libby laboratory of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Batta Laboratories, Inc. 

This laboratory modification is (circle one): NEW APPENDS to LB-000031A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000031X 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Beginning May 2002 (when Batta Laboratories began analyzing Libby samples), all analysts used fiber definition of ≥

5:1 aspect ratio and ≥ 0.5 μm length for all AHERA analyses, and any project-specific requirements regarding the 

aspect ratio were incorporated into AHREA/ASTM (dust) analyses as available through various lab modifications. 

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

Prior to LB-000066, NAM structures were recorded by all analysts when deemed necessary by the analysts. 

Inconsistencies regarding the measurement and recording of clusters and matrices began as early as May, 2003 

when compared with the guidelines outlined in LB-000031). See Attachments #1 through #4 for clarifications and 

illustrations. Figure numbers in the following attachments are corresponding to structure examples provided in LB000031. 

Batta Laboratories, Inc. began to comply with LB-000031 among analysts beginning August 23, 2006, with sample 

batch CDM-91 (EPA Job# L10972).
Lab Modification Form Revision 10 (9-11-07) 

Attachment #1 

 

F1

F2

F1

Figure 5: Matrix

Batta recorded as one matrix:

width: one analyst took the average

of F1 and F2 (if both are within the

fiber definition), and the other took

the overall width of the entire structure

as illustrated.

Batta recorded as one matrix:

width: refer to above.
Lab Modification Form Revision 10 (9-11-07) 

Attachment #2 

 

Figure 6

Particle

Batta recorded as either as one fiber or

one matrix:

1) Matrix: particle size is equal or greater than

0.5 micron.

2) Fiber: particle size is less than 0.5 micron.

Figure 7

Batta recorded as either a fiber or a matrix:

1) Fiber: both particles are less than 0.5 micron

2) Matrix: any one of the particles is equal or greater than 0.5 micron

3) Not a structure: both exposed fiber ends do not meet the fiber definition

and both particles are equal or greater than 0.5 micron

Particle
Lab Modification Form Revision 10 (9-11-07) 

Attachment #3 

 

Figure 8: Cluster

Batta has internal discrepancies in recording

the dimension of the this structure:

One analyst recorded as illustrated; the other

recorded as the following:

length = the longest fiber within the cluster

width = average width of all fibers within the

cluster
Lab Modification Form Revision 10 (9-11-07) 

Attachment #4 

Particulate >= 0.5 micron

Criteria Used by Batta for Matrix Determination

in Compliance with AHERA Definition:

As long as the fiber is embedded or hidden by a

particulate that is equal or longer than 0.5 micron,

regardless whether the particulate is opaque,

translucent, or transparent as illustrated.

Particulate >= 0.5 micron

Particulate >= 0.5 micron
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Ed Cahill Title: National Director 

Company: EMSL Analytical Date: September 26, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031, and provide a historical timeline for each 

Libby laboratory of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) EMSL Analytical 

This laboratory modification is (circle one): NEW APPENDS to LB-000031A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000031C 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

Analysts Interviewed

Steve Bennett 

Jodie Bourgerie (August 2001 in Libby and 5 times after) 

Ed Cahill 

Joe Centifonti 

Robyn Denton (late 2002 and 2003) 

Bob Georgens 

Richard Harding 

Ken Klutts 

Brett Macey 

Ron Mahoney 

Anant Samudra 

Paul Senne (in Libby 2003, 2004, 2005) 

Ex Employees (not interviewed)

Adrian Arav (whereabouts unknown) 

Duane Salinas (whereabouts unknown) 

Tom Ferrante (deceased) 

Richard White (whereabouts unknown) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Beginning in late 1999, the aspect ratio applied by EMSL was greater than or equal to 5:1. Sometime in the spring of 

2001, analysts received verbal direction to record structures regardless of aspect ratio. In late 2001 or early 2002, 

project analysts reverted back to 5:1. EMSL analyst interviews were conducted in August and September 2006. On 

August 29, 2006, all laboratories were instructed to use 5:1 for AHERA/ASTM analyses. 

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

All analysts followed AHERA/ASTM method recording rules, as well as the guidance in the above laboratory 

modifications, except as noted below. Figure numbers in the following attachments are corresponding to structure 

examples provided in LB-000031. 

structure Steve Bennett 

2 record longest L and maximum width 

3 as stated in Mod 31 

4 record longest L and maximum width 

8 record longest sub-structure L and maximum width perpendicular to that sub-structure 

 

structure Joe Centifonti 

2 Maximum width 

3 As stated in Mod 31, BUT AR of whole bundle is used as criterium 

4 Maximum width 

5 Add ALL fiber Lengths and average fiber width 

6 As stated in Mod 31, BUT Matrix 

7 As stated in Mod 31, BUT Matrix 

8 L=longest fiber BUT W=average width 

 

structure Robyn Denton (late 2002 and 2003) 

5 

Pre 2005 L=add up all sub structure L. W=average W of sub structure fibers. From 2005 

on, overall L and W was recorded. 

6 

Matrix. Pre 2005 L=add up all sub structure L W=average W of sub structure fibers. From 

2005 on overall L and W was recorded. 

7 would call Matrix 

 

structure Bob Georgens 

2 record longest L and maximum width 

4 record longest L and maximum width 
Lab Modification Form Revision 10 (9-11-07) 

6 matrix not fiber. Record total L and max width 

7 matrix not fiber. Record total L and max width 

8 record longest sub-structure L and widest sub-structure W 

 

structure Ken Klutts 

3 

As stated in Mod 31, UNLESS AR of bundle <5 the record average width of component 

fibers 

4 

As stated in Mod 31, UNLESS AR of bundle <5 the record average width of component 

fibers 

5 Add ALL AHERA countable fiber Lengths and average fiber width 

8 L = longest fiber W = average fiber width 

 

structure Brett Macey 

3 as stated in Mod 31 BUT AR of whole bundle is used as criterion 

5 Add ALL fiber Lengths and average fiber width 

6 as stated in Mod 31 BUT Matrix 

7 as stated in Mod 31 BUT Matrix 

8 L=longest fiber BUT W=maximum width 

 

structure Anant Samudra 

8 record longest sub-structure L and maximum width perpendicular to that sub-structure 

**At times, recorded AHERA analyses in ISO 10312 format 

structure Paul Senne (in Libby 2003, 2004, 2005) 

6 Matrix if matrix material is >0.5 microns 

 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Kyeong Corbin Title: TEM Laboratory Supervisor 

Company: Hygeia Laboratories Inc. Date: September 20, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031, and provide a historical timeline for each 

Libby laboratory of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Hygeia Laboratories Inc. 

This laboratory modification is (circle one): NEW APPENDS to LB-000031A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000031D 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Hygeia Laboratories Inc. received the first batch of AHERA samples on September 16, 2002. Two analysts, Kyeong 

Corbin and Quynh Trieu, were involved in analyzing all Libby project samples. All AHERA samples were analyzed 

according to the AHERA counting rules (aspect ratio 5:1 or greater) and any project-specific requirements were 

incorporated as available. At times, asbestos structures with an aspect ratio of at least 3:1 were recorded by the 

analyst. These were indicated as such on the data sheet and on the EDD, and were recorded as non-countable (i.e., 

total column populated with a zero). 

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

Both analysts agree on the counting rules mentioned in LB-000017, LB-000017a, and LB-000031 and clarifications 

stated below: 

1. Hygeia recorded no asbestos detected grid opening as “NSD” as expressed in the AHERA instead of 

“ND” as requested by project modification during the period of 6/1/02 to 11/30/02. The Lab Mod LB000023 was filed. 

2. As results of August 6, 2002 teleconference, the following instructions were given to the analysts: 

• Do not count NAM structures. At times, NAM structures were still recorded if the analyst thought it 

was necessary and marked as such on the data sheet and on the EDD, and were recorded as 

non-countable (i.e., total column populated with a zero). 

• Record fiber, bundle, cluster, and matrix as F, B, C, M as indicated by AHERA. (Do not record 

matrix as MF, MB, MC, F/M, B/M, or C/M.) 

 

3. As results of August 13, 2002 teleconference, the following instructions were given to the analysts: 

• Measure only visible portion of the fiber or bundle sticking out of the matrix. (Prior to this date, any 

matrix components that were embedded in opaque matrix were measured by doubling length or to 

the end of matrix depending on the matrix size.) 

• It is a non-countable structure if both ends were embedded in the matrix. (In general, noncountable structures were recorded and indicated as such on the data sheet and on the EDD.) 

• Intersection is defined as stated on the AHERA, i.e. Non-parallel toughing or crossing of fibers, 

with the projection having an aspect ratio 5:1 or greater. (If <5:1 projection, it was considered no 

intersection.) 

 

4. As results of September 9 and September 13, 2003 teleconferences, the following clarifications were 

given to the analysts as of October 2003: 

• No doubling of length measurement when crossing grid bars (different from ISO, i.e. ISO requires 

length to be doubled); 

• Count all grid bars; 

• Measure visible portion only; 

• For C or M, record the dominant/longest sub-structure’s dimension; 

• Do not use F/m, B/m, or C/m classification; just record it as M, matrix. 

5. Stopping rules for chrysotile structures were changed from 100 structures to 50 structures in October 

2003. The laboratory was instructed to file an individual lab mod for these cases. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Michael D. Mount Title: EM Manager 

Company: Materials Analytical Services Date: September 25, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031, and provide a historical timeline for each 

Libby laboratory of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Materials Analytical Services 

This laboratory modification is (circle one): NEW APPENDS to LB-000031A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000031E 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

MAS currently has four analysts (Mike Mount, Kevin Simpson, Mehrdad Motamedi, and Will Stark) performing ISO 

10312 analyses of Libby samples. The first set of Libby AHERA samples (dust samples) were received on 10/08/04. 

At this time, analyst performing TEM analysis were Will Stark, Ding Qian and Kevin Simpson. AHERA/ASTM method 

dust samples were counted and recorded for asbestos structures utilizing an aspect ratio criterion of 5:1 or greater, 

unless the project-specific requirements stated otherwise. 

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

All analysts followed AHERA/ASTM method recording rules, as well as the guidance in the above laboratory 

modifications, except as noted below: 

• Between 1/1/06 to 8/31/06, TEM analysts Mike Mount and Mehrdad Motamedi, recorded asbestos fibers and 

bundles with visible ends as a matrix structure instead of a fiber or bundle. 
Lab Modification Form Revision 10 (9-11-07) 

Instructions to Requester: E-mail form to contacts at bottom of form for review and approval. 

File approved copy with Data Manager (CDM). Data Manager distributes approved forms as follows: 

All Labs Applicable forms – copies to: EPA, Volpe, CDM, All project labs 

Individual Labs Applicable forms – copies to: EPA, Volpe, CDM, Initiating Lab 

Method (circle one/those applicable): TEM-AHERA TEM-ISO 10312 PCM-NIOSH 7400 NIOSH 9002 

EPA/600/R-93/116 ASTM D5755 EPA/540/2-90/005a SRC-LIBBY-03 

Other: 

Requester: Jeanne Orr Title: President 

Company: Reservoirs Environmental, Inc. Date: September 25, 2006 

Description of Modification: 

Laboratory-specific clarification of potential inconsistencies among analysts when recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031. 

Reason for Modification: 

To document potential past differences between analysts and laboratories in recording structures using 

AHERA/ASTM, as modified by LB-000017, LB-000017A, and LB-000031, and provide a historical timeline for each 

Libby laboratory of potential changes in aspect ratio recording rules. 

Potential Implications of this Modification: 

None. 

Laboratory Applicability (circle one): All Individual(s) Reservoirs Environmental, Inc. 

This laboratory modification is (circle one): NEW APPENDS to LB-000031A SUPERCEDES ____________

Duration of Modification (circle one): 

Temporary Date(s): 

Analytical Batch ID: 

Temporary Modification Forms – Attach legible copies of approved form w/ all associated raw data packages 

 

Permanent (Complete Proposed Modification Section) Effective Date: HISTORIC 

Permanent Modification Forms – Maintain legible copies of approved form in a binder that can be accessed by analysts. 

Data Quality Indicator (circle one) – Please reference definitions on reverse side for direction on selecting data quality indicators: 

Not Applicable Reject Low Bias Estimate High Bias No Bias 

Proposed Modification to Method (attach additional sheets if necessary; state section and page numbers of Method 

when applicable): 

 

Technical Review: Date: 

(Laboratory Manager or designate) 

Project Review and Approval: Date: 

(Volpe: Project Technical Lead or designate) 

Approved By: Date: (USEPA: Project Chemist or designate) 

Request for Modification

to 

Laboratory Activities 

LB-000031F 
Lab Modification Form Revision 10 (9-11-07) 

DATA QUALITY INDICATOR DEFINITIONS 

 

Reject - Samples associated with this modification form are not useable. The conditions outlined in the modification 

form adversely effect the associated sample to such a degree that the data are not reliable. 

Low Bias - Samples associated with this modification form are useable, but results are likely to be biased low. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated low. 

Estimate - Samples associated with this modification form are useable, but results should be considered 

approximations. The conditions outlined in the modification form suggest that associated sample data are reliable, but 

estimates. 

High Bias - Samples associated with this modification form are useable, but results are likely to be biased high. The 

conditions outlined in the modification form suggest that associated sample data are reliable, but estimated high. 

No Bias - Samples associated with this modification form are useable as reported. The conditions outlined in the 

modification form suggest that associated sample data are reliable as reported. 
Lab Modification Form Revision 10 (9-11-07) 

HISTORIC INFORMATION ON ASPECT RATIO RECORDING RULES 

Reservoirs Environmental began analyzing samples for the Libby Project in November, 1999. Analysts applied an 

aspect ratio of 5:1 to the countable structures unless project specific requirements stated otherwise. 

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

All analysts followed AHERA/ASTM method recording rules, as well as the guidance in the above laboratory 

modifications, except as noted below: 

 Analysts at Reservoirs have historically characterized a fiber with both ends visible and matrix in the middle as a 

matrix, whereas LB-000031 designates this type of structure as a fiber. 

 Samples counted in 1999 and 2000 were recorded on historical laboratory bench sheets and are identified in LB000001 through LB-000014. 

 In August, 2002 the project no longer required that NAM structures be recorded. On occasion, analysts recorded 

NAM structures when the analyst found the structure notable. The NAM structure was noted as non-countable by 

recording a zero in the total column. 

 Excluded structures, such as a fiber with both ends obscured by matrix or a fiber with less than the required 

aspect ratio, were recorded and designated as non-countable by recording a zero in the total column. 
C-1

Appendix C. Characterization of Amphibole Fibers from ore originating from Libby, 

Montana; Louisa County, Virginia; and Palabora, Republic of South Africa.

By:

David L. Berry, Ph.D.

U.S. EPA Region 8

Ecosystems Protection and Remediation

1595 Wynkoop Street [8EPR-PS]

Denver, CO 80202-1129 
C-2

The O. M. Scott plant in Marysville, Ohio manufactured a number of products including 

fertilizers, dyes, and pesticides that were bound to a vermiculite carrier as a delivery vehicle. 

The plant received ore from Enoree, South Carolina; Louisa County, Virginia; Libby, Montana;

and Palabora, Republic of South Africa which was processed in an exfoliation furnace to 

produce vermiculite used in the manufacture of their commercial products. Only ore from South 

Carolina was used in 1957 and 1958. From 1959 to 1971, ores from South Carolina and Libby 

were used. From 1972 to 1980, ores from Libby, South Africa, and Virginia were used. No ore 

from Libby was used after 1980. Only ore from South Africa and Virginia were used after 1980 

[See appendix F]. 

EPA Region 8 obtained samples of ore from Libby, South Africa, and Virginia from Dr. 

James Lockey, University of Cincinnati, and analyzed the samples to determine mineralogy and 

particle size distribution (length, width, and aspect ratio) using transmission electron microscopy 

and energy dispersive spectroscopy to identify the nature of the amphibole fibers. Dr. Lockey 

obtained the South African and Virginia ore samples from the Marysville facility in 1980 and the 

Libby ore [Libby #3ore] from an expansion plant in Salt Lake City, Utah, in 1981. Region 8 was 

unable to obtain vermiculite or ore from the Enoree, South Carolina mine complex. 

The ore from the Rainey Creek complex [Vermiculite Mountain Mine, Libby, Montana] 

resides in large ultramafic intrusive bodies that are rich in biotite, pyroxenite, and biotitite, a rock 

comprised of almost pure biotite. The ultramafic intrusions are cut by deposits of syenite and 

carbonatite and much of the biotite has been hydrothermally altered to hydrobiotite and 

vermiculite [Frank and Edmond, 2001, Meeker, et. al. 2003]. The pyroxenite has been altered to 

fibrous soda-rich amphiboles and contacts with pyroxenite surrounding the biotitite contain the 

vermiculite ore zone containing diopside, hydrobiotite and apatite. Fibrous and non-fibrous 

amphiboles are located in both veins and disseminated throughout the intrusive rock along 

cleavage planes of pyroxene. Amphiboles from Vermiculite Mountain had been referred to as 

soda tremolite, richterite, soda-rich tremolite, tremolite asbestos, and richterite asbestos by a 

number of investigators. In 2000, Wylie and Verkouteren [2000] identified winchite as the 

principle amphibole in the Vermiculite Mountain deposit based on chemical investigation 

referencing the classification system of Leake et al. [1997] and optical properties. Meeker et al. 

[2003] investigated amphibole types from the mine complex using electron probe microanalysis 

and X-ray diffraction analysis and reported the presence of winchite, richterite, tremolite, and 

magnesioriebeckite. Magnesio-arfvedsonite and edenite were detected in low abundance. The 

amphibole composition of the Libby Amphiboles is roughly winchite, richterite, tremolite, 

magnesio-riebeckite, magnesio-arfvedsonite, and edenite [84:11:6:<1:<1:<1]. The O. M. Scott 
C-3

facility received ore from the Vermiculite Mountain Mine complex, Libby, Montana from 1959 

through 1980. 

The Palabora Igneous Complex located near Phalaborwa, Republic of South Africa is the 

location of the Palabora mine. The Palabora ore deposit shares many features with the 

Vermiculite Mountain mine complex including zoned deposits with ultramafic rocks 

[pyroxenite] and intrusion by alkalic rock primarily syenite. The primary mica at Palabora is 

phlogopite rather than biotite and the primary alteration product that forms vermiculite ore is 

hydrophlogopite rather than hydrobiotite [Shoeman, 1989]. 

The Palabora ore is reported to contain little or no asbestiform fibers based on polarized 

light microscopy by the Institute of Occupational Medicine in Edinburgh [IOM. 2008]. Crude 

vermiculite from the Palabora complex was also reported to be free of asbestiform fibers by 

polarized light microscopy [IOM, 2006]. In both reports, the analysis by polarized light 

microscopy were conducted with a detection limit of 1 ppm and since no chrysotile or 

amphibole structures were detected, no further analysis by electron microscopy and x-ray 

diffraction were conducted. 

The ore from the Virginia Vermiculite mine in Louisa County, Virginia is described as 

mafic rock intruded by a series of small pegmatites [Gooch, 1957]. Meisinger [1979] classified 

the deposits as Type 3, similar to the ores from Enoree, South Carolina. The formations consist 

of potassic ultramafic bodies primarily biotite. The vermiculite ores are found primarily in 

hydrobiotite portions of the biotite intrusions. The hydrobiotite deposits are preferentially mined 

because of better commercial properties compared to vermiculite. 

There is limited information on the asbestos content of the ores from the Louisa deposit. 

Rohl and Langer [1977] reported both chrysotile and amphibole fibers in six ore samples from 

the Louisa deposit. The chrysotile was reported as fibers and bundles while the amphiboles were

described as widely composed with most of the fibers classified as actinolite. Moatamed et. al. 

[1986] analyzed a Virginia ore sample collected at a processing plant in Salt Lake City, Utah and 

reported traces of fibrous amphibole asbestos identified as actionlite in the form of cleavage 

fragments having low aspect ratios. Amphibole content for both unexfoliated and exfoliated ores 

ranged up to 1.3 % amphibole asbestos. 

Ores from the Enoree, South Carolina deposits are primarily hydrobiotite and biotite in 

origin. Fluroapatite is a common mineral collocated with the hydrobiotite. Zircon is also widely 
C-4

dispersed throughout the plutons along with minor accessory minerals including talc, chlorite, 

chromite, rutile, titanite, corundum, anatase, and amphibole asbestos [Hunter, 1950]. The 

amphibole asbestos identified in the vermiculite deposit at Enoree has been classified as 

tremolite [Libby, 1955]. 

As previously noted, EPA Region 8 obtained samples of ore from Libby, South Africa, 

and Virginia from Dr. James Lockey, University of Cincinnati, and analyzed the samples to 

determine the particle size distribution (length, width, and aspect ratio) using transmission 

electron microscopy and energy dispersive spectroscopy to identify the mineral composition of 

the amphibole fibers. Region 8 was unable to acquire a sample of ore from the South Carolina 

Enoree mine complex for analysis. Region 8 conducted analysis of the ore and exfoliated 

materials to connect the exposures of workers to mineral fibers in Marysville, OH to the ore 

originating in Libby, MT. The connection is based on fiber morphology, mineralogy, and fiber 

size similarities. 

In order to analyze the fibers from the ore and vermiculite bulk material, the fibers must 

be loaded onto filters and prepared for analysis by transmission electron microscopy [TEM]. 

Three potential methods were considered for transferring the fibers from the bulk material to 

filters: water elutriation, glove-box transfer, and the fluidized bed asbestos segregator [FBAS]. 

Of these three methods, the glove-box and FBAS involved physical disturbance of the bulk 

material to elutriate fibers into the air that might be similar to handling and processing of ore in 

the Marysville plant. Due to the limited quantity of test material available for analysis, Region 8 

employed the FBAS as an analytical instrument to load the mineral fibers onto filters for TEM 

analysis

Briefly, samples of ore and vermiculite were prepared following the procedure outlined 

by Bern et al. [2002]. Samples were dried, ground with a Wylie mill and mortar and pestle and 

sieved through a 230 μm [60 mesh] sieve. Samples [exactly 2.0 gms] were mixed with 18 gms 

of analytical silica sand and placed in a fluidized bed asbestos segregator vessel to load 25mm 

MCE air sampling filters [0.8 μ pore size]. The fluidized bed asbestos segregator was run for 3 

minutes to load the filter cassettes with sufficient fibers for analysis by transmission electron 

microscopy. Five filters were loaded for each of the ore and vermiculite samples. After loading, 

the filters were prepared for TEM analysis by mounting on copper girds, carbon coating, and 

subjected to TEM analysis [TEM-ISO 10312 method]. 
C-5

The laboratory followed fiber counting rules detailed in the Quality Assurance Project

Plan for the specific study using Libby-specific laboratory modifications. Total amphibole fibers 

and Phase Contrast Microscopy equivalent [PCMe] fibers were counted for each of the 

ore/vermiculite samples as described in Appendix B. A total of 1.0 mm2area or a total of 200 

asbestos structures were counted to achieve the desired analytical sensitivity [1/g; 1.5E+4]. 

Energy dispersive spectroscopy [EDS] was performed on selected samples from each of the 

vermiculite/ore samples to provide mineral characterization of individual fibers. Fiber counts 

were recorded on NADES data sheets for further analysis. Only the Libby vermiculite and Libby 

ore samples had sufficient fibers detected to construct a fiber size distribution. 

Fiber counts were determined by counting fiber numbers for a specific area of the filter 

grid or a specific number of grid openings [which ever was achieved first] to determine total 

fibers present. As shown in Table C-1, the number of fibers for the test materials varied greatly 

depending on the source and the grid area measurement was exceeded prior to the fiber count 

metric [167 grid openings~1.0 mm2]. 

Table C-1 Fiber Detected in Ore and Expanded Product

Structures 

Counted Concentration [s/g]

Sample Type Grid Openings LA OA C LA OA C

Virginia Ore 167 0 0 0 0 0 0

Virginia Expanded 167 1 0 0 13,008 0 0

South Africa Ore 167 2 0 2 26,403 0 26,403

South Africa Expanded 167 0 0 0 0 0 0

Libby # 3 Ore 167 320 0 0 1,393,873 0 0

Libby Expanded 167 100 0 0 468,213 0 0

LA = Libby amphibole. OA = Other amphibole. C = Chrysotile Note: the designation of fibers as LA in this 

instance reflects only a qualitative morphological comparison to amphiboles of the Libby series.

The Libby #3 ore and the Libby #3 expanded material contained the greatest number of fibers 

both in fiber counts on the filters and in calculated structures per gram of bulk material. Virginia 

expanded and South African ore contain amphibole structures represented by low fiber counts. 

South African ore also contained chrysotile fibers as determined by morphology and EDS

analysis. The absence of fibers detected in the Virginia ore and the South African expanded 

materials probably represents actual low fiber content of the ore and is a function of the detection 

limit for the structure analysis. The estimation of structures per gram of material indicated that 

there were 13- to 26 thousand fibers per gram of bulk material which was approximately 18 

times lower than the Libby ore samples. The decrease in fibers found in the Marysville facility 
C-6

after 1980 when only ore from Virginia, Palabora, and South Carolina was used (Appendix F) is 

consistent with the findings of low fiber counts for the Virginia and Palabora materials. In 

addition, numerous non asbestiform minerals were also detected including biotite, micas, and 

pyroxenes in the bulk materials from Virginia and South Africa. 

Amphiboles are a complex group of minerals characterized by double chains of silicate 

tetrahedral and the generic chemical formula of: A0-1B2C5T8O22[OH]2 where A, B, C, and T 

represent the various cations. The modern classification system of amphiboles is described in: 

Leake et al. [1997]. To classify the mineral species of the amphibole, it is not sufficient to 

determine its composition; the various cations must be assigned to the specific A, B, C, and T 

sites. The cutoffs of the compositional ranges allowed for each amphibole mineral species are 

based on the number of the cations in the various sites. The methodology to classify an 

amphibole is to first determine its elemental compositions [e.g., as expressed as weight percent 

oxide for each element or as atomic percent for each element]. Then a normalized routine is 

applied to the raw elemental measurements to calculate the number of each of the cations 

contained in one formula unit. [This is a simple arithmetic calculation since the cation percents 

have been measured and the stoichiometry must balance the charges of the cations and anions.] 

Generally, one formula unit is assumed to contain 23 oxygens. Next the sites are filled up by 

assigning cations to them subsequently, specifically:

T: Si4+, Al3+, and Ti4+

C: Al3+ and Ti4+ [only after the T sites are filled first] and then Mg2+, Fe2+ ,Fe3+, 

and then Mn2+.

B: Any remaining Mg2+, Fe2+, and Mn2+ [after the C sites are filled], all Ca2+, then 

Na+if there is any room left.

A: Na+and K+only

Once the cations are assigned to their sites, it is a simple matter to classify the minerals 

based on the cutoffs of the compositions field allowed for each mineral. 

The Libby amphibole group of minerals is a complex group of amphiboles consisting of 6

minerals:

Winchite, CaNa[Mg, Fe2+]4[Al, Fe3+]Si8O22 [OH]2

Richterite, NaCaNa [Mg, Fe2+, Mn, Fe3+]5Si8O22[OH]2

Tremolite, Ca2Mg5Si8O22[OH]2

Magnesio-riebeckite Na2[Mg3, Fe3+

2]Si8O22[OH]2

Magnesio-arfvedsonite NaNa2[Mg4,Fe3+]Si8O22[OH]2
C-7

Edenite NaCa2Mg5Si7AlO22[OH]2 

Libby amphibole is characterized by a low amount of Al in the T site [and a 

correspondingly high Si content] so according to Leake’s classification, if the Si [expressed as 

atoms per formula unit, apfu] is at least 7.5 and Al content in the T site is <0.5, all 6 Libby 

amphibole types can be plotted on a graph of Na content of the B site versus the [Na + K] 

content in the A site. This approach was described by Meeker et al. [2003] for the Rainy Creek 

complex. 

EDS spectra [TEM/EDS] were collected from all amphibole fibers found in the South 

Africa and Virginia samples, and 6 randomly-selected LA fibers in each of the Libby ore and 

Libby expanded samples. Two bundles of asbestiform serpentine [chrysotile] were found in the 

South Africa ore sample. EDS spectra were collected for one of the bundles. The chemical 

formula of serpentine is Mg3Si2O5[OH]4. The EDS software package collected and summarized 

each spectrum to determine the atomic percent of each element of interest.

Several assumptions were made in the treatment of the EDS data:

1) Numbers of cations per formula unit are calculated on the basis of 23 oxygens. This may 

or may not be correct, since an [OH] site in the amphibole crystal can be occupied by 

either OH-, F-, Cl-

, or O2-

. The calculated cation numbers will be affected if a significant 

quantity of O2-is in the OH site. 

2) A persistent problem with amphiboles is that they can contain both ferric [3+] and ferrous 

[2+] iron in the same crystal. For the purposes of this report all Fe was assumed to be 

Fe2+. A routine for calculating the ratio of Fe2+ to Fe3+ is described in Leake et al. [1997]

but it is very complex, applies to polished sections, and was not attempted for this report. 

3) For the purposes of this report, the T sites were assumed to be filled completely full to 8 

apfu and the C sites were assumed to be completely full to 5 apfu. All Ca and any Mg, 

Fe, and Mn remaining after the C site was full were then assigned to the B site. Next, Na 

was assigned to the B site until it was full [2 apfu], then any remaining Na and all K was 

assigned to the A site. 

Applying these assumptions to the TEM/EDS data produce a useable graph of the Na and 

K content of the amphibole fibers. As shown in Figure C-1, Libby #3 ore and Libby #3 

Expanded amphiboles were characteristic of winchite and tremolite. Virginia Expanded 

and South African ore both contained amphibole fibers characteristic of non-Libby [Na 

and K negative] in the tremolite series. 
C-8

Following all assumptions described above and the approach of plotting Na in the B-site 

versus Na + K in the A-site as described by Meeker et al. [2003], the mineral species of the 

Marysville fibers can be described as:

The single Virginia amphibole asbestos fiber is an actinolite

Both of the South African amphibole fibers are tremolite

8 of the LA fibers from Libby are winchite

4 of the LA fibers from Libby are Tremolite

Figure C-1. Cation values for Na in the B site and the Na + K in the A site from individual 

amphibole fibers.

Fiber size distributions for amphibole fibers from the Libby #3 ore and Libby #3 Expanded 

sources were conducted on the fibers counted during the TEM analysis of the filter grids. Due to 

the low fiber count detected in the Virginia and South Africa sources, it was not possible to 

develop a fiber size distribution for these fibers. The LA fiber size data were plotted as a 

cumulative distribution frequency for fiber length, fiber width, and aspect ratio. These data were 

compared to LA fibers collected in Libby as part of EPA’s ongoing ambient air monitoring 

program and the Libby Asbestos Superfund site [Appendix B]. The Libby ore and expanded 
C-9

material showed an increased frequency of longer and wider fibers than the fibers from the Libby 

ambient air sampling program. Aspect ratios were nearly identical. The differences between the 

length and width frequency were not outside of the expected range for LA fibers and were 

consistent with fiber size distributions for soil activity-based-sampling data from Libby. 

Based on the TEM morphological analysis of filter grids, TEM/EDS analysis for the fiber 

mineralogy, and the fiber size distribution data, it can be concluded that the amphibole fibers 

detected in the Libby # 3 ore samples from the Salt Lake Expansion facility are consistent with 

data from authentic Libby amphibole fibers [Meeker et. al., 2003] found in Libby, Montana [see 

also Appendix B]. Further, ore samples from Virginia and South Africa contained amphibole 

and chrysotile fibers but at a much lower frequency of detection than the Libby amphibole ore as 

reported in Appendix F.
C-10

Figure C-2. Fiber size Distribution of LA amphiboles
C-11

References

Bern, A.G., Brownfield, I. and Meeker, G.P. 2002. Preparation and analysis of soil samples for 

amphibole asbestos content by scanning electron microscopy and energy dispersive 

spectroscopy. USGS Open File Report. December 18, 2002. pp. 7. 

Bern, A.G., Meeker, G.P. and Brownfield, I. 2002. Guide to analysis of soil samples from 

Libby, Montana for asbestos content by scanning electron microscopy and energy 

dispersive spectroscopy. USGS Open File Report. October 17, 2002. pp. 3.

Frank, D. and Edmund, L. 2001. Feasibility for identifying mineralogical and geochemical 

traces from vermiculite ore deposits. U.S. EPA Region 10, Seattle, WA. EPA 910-R-01-

002. 

Gooch, E.O. 1957. Vermiculite. Virginia Minerals 3[1]:1-5. 

 

Gunter, M.E. & Sanchez, M.S. 2009. Amphibole forensics: Using the composition of amphibole 

to determine their source, the Libby Montana example. Amer. Mineralogist 94:837-840. 

Hunter, C.E. 1950. Vermiculite of the southeastern states. In: Snyder, F.G., ed. Aymposiun on 

Mineral Resources of the Southeastern United State. Knoxville, TN., University of 

Tennessee Press. Pgs 120-127. 

 

International Organization for Standardization [ISO]. 1995. Ambient air: Determination of 

asbestos fibers. Direct-transfer transmission electrom microscopy method. ISO Method

10312. Reference number: ISO 10312:1995[E]. 

 

IOM Consulting. 2006. Sampling and analysis of crude vermiculite samples for possible 

asbestiform fibre and quartz content. M. Darling, Palabora Mining Co, Palabora Europe 

Ltd., Guildford Surrey GU@ 8XG. Report #609-01591. pp.6. 

IOM Consulting. 2008. Sampling and analysis of crude vermiculite samples for possible 

asbestiform fibre and quartz content. M. Darling, Palabora Mining Co, Palabora Europe 

Ltd., Guildford Surrey GU@ 8XG. Report #609-02386. pp.28. 

Leake, B. et al. 1997. “Nomenclature of Amphiboles: Report of the Subcommittee on 

Amphiboles of the International Mineralogical Association, Commission on New 

Minerals and Mineral Names.” The Canadian Mineralogist 35:219-246.

Langer, W.H., Van Gosen, B.S., Meeker, G.P., Adams, D.T. & Hoefen, T.M. 2010. The 

dispersion of fibrous amphiboles by glacial processes in the area surrounding Libby, 

Montana, USA. Environ. Earth Sci. 10:1-23. 
C-12

Libby, S.C. 1975. The origin of potassic ultramafic rocks in the Enoree “Vermiculite” District, 

South Carolina. University Park, Pennsylvania, Pennsylvania State University, thesis, 

pp.116. 

Meeker, G.P., Bern, A.M., Brownfield, I.K., Lowers, H.A., Sutley, S.J., Hoefen, T.M. and 

Vance, J.S. 2003. The composition and morphology of amphiboles for the Rainy Creek 

complex, near Libby, Montana. Amer. Mineralogist 88:1955-1969. 

 

Meisinger, A.C. 1979. Vermiculite. In: Minerals Yearbook 1978-1979, Metals and Minerals. 

Bureau of Mines. 1:977-980. 

Moatamed, F., Lockey, J.E., & Parry, W.T. 1986. Fiber contamination of vermiculite: A 

potential occupational and environmental health hazard. Environ. Res. 41:207-218. 

 

Rohl, A.N. and Langer, A.M. 1977. Mineral analysis of core samples from the Green Springs 

area. Virginia vermiculite deposit: Unpublished letter report from Mt. Sinai School of 

Medicine, pp. 10. 

Schoeman, J.J. 1989. Mica and vermiculite in South Africa. J. South African Institute of Mining 

and Mineralogy 89:1-12. 

U.S. EPA. 2001. Feasibility for identifying mineralogical and geochemical tracers for 

vermiculite ore deposits. Frank, D. and Edmond, L. USEPA Region 10, Office of 

Environmental Assessment. pp 43. EPA 910-R-01-002. 

USEPA. 2008. Geochemical signatures as a tool for vermiculite provenance determination. 

Wright, K.E. & Palmer, C.D. USEPA Region 10 and Idaho National Laboratory. pp.157. 

INL/EXT-0801428. 

Wylie, A.G. & Verkouteren, J.R. 2000. Amphibole asbestos from Libby Montana: Aspects of 

nomenclature. Amer. Mineralogist 85:1540-1542. 
APPENDIX D: Analysis of

Subchronic and Chronic Studies and Cancer Bioassays in Animals 

and Mechanistic Studies

D.1 Subchronic and Chronic Studies and Cancer Bioassays

D.1.1 Oral

McConnell et al. (1983a) describe part of a National Toxicology Program study (NTP, 

1990a) performed to evaluate the toxicity and carcinogenicity of ingestion of several minerals. 

This study examined chrysotile and amosite in both hamsters and rats, and crocidolite and 

tremolite only in rats. This chronic bioassay was designed to encompass the lifetime of the 

animal, including exposure of the dams from which the test animals were derived. Although the 

study examined chrysotile, amosite, crocidolite, and tremolite, for the purposes of this document, 

the focus is on the results from exposure to tremolite. The tremolite (Governeur Talc Co, 

Governeur, New York) used was not fibrous. Instead, the material was crystalline, as this form 

was a common contaminant in talc at the time of these studies (McConnell et al., 1983a) 

(Table D-1). Citing the Stanton (1981) paper, McConnell et al. (1983a) stated that crystalline 

tremolite can become fibrous upon grinding. Tremolite was incorporated by 1% weight into 

NIH-31 feed and given to 250 male and female F344 rats from birth until death (118 male and 

female controls). 

Table D-1. Fiber characteristics and distribution of fibers analyzed in feed 

studies in F344 rats

Characteristic

Length intervala

<3 μm ≥3 μm , <5 μm ≥5 μm, <10 μm ≥10 μm

Mean width 0.77 1.78 2.87 5.22

Tremolite particles 120 61 17 49

% of Tremolite particles 19.4 9.85 3 8

aAverage groups, more detailed in primary paper.

Source: McConnell et al. (1983a).

No significant tumor induction was observed in the animals with oral exposure to 

tremolite animals. Although non-neoplastic lesions were observed in many of the aging rats, 
these were mostly in the stomach and occurred in both controls and exposed animals. The 

lesions included chronic inflammation, ulceration, and necrosis of the stomach (McConnell et al., 

1983a). McConnell et al. (1983a) suggested that nonfibrous tremolite could account for the lack 

of toxicity following exposure in this group of animals. Also, oral studies of asbestos, in general, 

show decreased toxicity and carcinogenicity as compared to inhalation and 

implantation/injection studies.

D.1.2 Inhalation

Davis et al. (1985) performed a chronic inhalation study examining response to tremolite 

asbestos. Groups of 48 specific-pathogen-free (SPF) male Wistar rats were exposed in a 

chamber to 10 mg/m3(~1,600 fibers/mL, >5 μm) of commercially mined tremolite (South 

Korea) for a total of 224 days (7 hours per day, 5 days per week) over a 12-month period. The 

tremolite sample contained approximately 50% fibers 10–100 μm long, using a fiber definition 

of length >5 μm, diameter <3 μm, and aspect ratio >3:1. The results of the inhalation study 

produced very high levels of pulmonary fibrosis, as well as 16 carcinomas and 2 mesotheliomas, 

among the 39 tremolite-exposed animals (Tables D-2 and D-3). No pulmonary tumors were 

observed in the controls.

Table D-2. Pulmonary fibrosis and irregular alveolar wall thickening 

produced by tremolite exposure

Time after start of exposure

(number of rats examined)

12 mo

(n = 3)

18 mo

(n = 4)

27–29 mo

(n = 12)

Peribronchiolar fibrosis (SD)a23.0 (21.4–24.2) 13.4 (9.7–18.9) –

Irregular alveolar wall thickening (SD)b35.2 (27.7–41.0) 27.7 (20.8–35.4) –

Interstitial fibrosis (SD)b0 3.0 (0–5.6) 14.5 (3.8–26.9)

a

Percentage of 100 squares counted in lung tissue area.

b

Percentage of total lung tissue area.

SD = standard deviation

Source: Adapted from Davis et al. (1985).

Table D-3. Tumors (benign and malignant) produced by tremolite exposure

Tumor site Control (n = 36) Tremolite (n = 39)

Pulmonary 

Adenomas 0 2

Adenocarcinomas 0 8

Squamous carcinomas 0 8
Mesotheliomas 0 2

Other organ systems

Digestive/peritoneal 5 3

Urinogenital 3 1

Endocrine 3 5

Musculoskeletal, integumentary 5 5

Reticuloendothelial/vascular 20 15

Source: Adapted from Davis et al. (1985).

Although Davis et al. (1985) did not describe the data, the difference between tremolite 

and chrysotile was stated to be statistically significant, with tremolite exposure inducing more 

fibrotic and carcinogenic lesions (Table D-2). These results show that rats exposed to tremolite 

exhibited increased numbers of pulmonary lesions and tumors. Tumors observed in other organ 

systems are also listed in Table 4-24 and appear to be unrelated to exposure. Although a method 

for an injection study is described in Davis (1985), only the inhalation results are presented. This 

same tremolite was used in later intraperitoneal injection experiments (Davis et al., 1991) and 

might be what the authors are referring to in this article. 

Wistar rats were exposed for 13 consecutive weeks (6 hours per day, 5 days per week) to 

either Calidria chrysotile asbestos or tremolite asbestos in a flow-past, nose-only inhalation study

(Bernstein et al., 2003) (Table D-4). The long-term effects from the same exposure were 

described in Bernstein et al. (2005) (6 hours per day, 5 days per week). This study describes the 

full results through 1 year after cessation of tremolite exposure in Wistar rats (n = 56). The 

tremolite samples were chosen to have 100 fibers/mL of fibers longer than 20 μm present in the 

exposure aerosol. Fibers were defined as any object with an aspect ratio >3:1, length ≥5 μm, and 

diameter ≤3 μm, and all other objects were considered nonfibrous particles. Counting was 

stopped when nonfibrous particle counts reached 30, and fiber counting was stopped at 500 with 

length ≥5 μm, diameter ≤3 μm, or a total of 1,000 fibers and nonfibrous particles were recorded 

(Bernstein et al., 2003). Lung tissue and associated lymph nodes were examined by 

histopathology following tissue digestion. Associated lymph nodes showed erythrophagocytosis 

(minimal severity) in one animal at all time points, compared to chrysotile and control, which 

showed erythrophagocytosis (minimal severity) only at 180 days.

Table D-4. Chrysotile and tremolite fiber characteristics of fibers used in 

inhalation exposure studies in rats

Fiber type

Mean no. 

fibers 

evaluated

Mean no. 

total 

fibers/mL

Mean % total 

fibers,

>20 μm length

Mean 

diameter 

(μm) ± SD

Mean length

(μm) ± SD

Diameter 

range (μm)

Length 

range (μm)
Chrysotile 2,016 48,343.2 0.4 0.08 ± 0.07 3.61 ± 7.37 0.02–0.7 0.07–37.6

Tremolite 1,627 3,128.1 3.4 0.32 ± 3.52 5.49 ± 13.97 0.1–3.7 0.9–75

Source: Bernstein et al. (2003).

Table D-4 shows the comparison of number, concentration, and mean size distribution of 

fibers used in this study. Note that the mean tremolite fiber diameter and length are much greater 

than those of chrysotile, but the size ranges do overlap somewhat (Bernstein et al., 2003). The 

long tremolite fibers, once deposited in the lung, remain throughout the rat‘s lifetime. Even the 

shorter fibers, following early clearance, remain with no dissolution or additional removal. At 

365 days post exposure, the mean lung burden was 0.5 million tremolite fibers >20-μm long and 

7 million fibers 5–20-μm long with a total mean lung burden of 19.6 million tremolite fibers. 

The tremolite-exposed rats showed a pronounced inflammatory response in the lung as early as 

1 day post exposure, with the rapid development of granulomas (1 day post exposure) followed 

by the development of pulmonary fibrosis characterized by collagen deposition within the 

granulomas. Increases in alveolar macrophages and granulomas were observed at all time points 

(1, 2, 14, 90, and 180 days) measured except 365 days. Pulmonary fibrosis increased starting at 

14 days and continued to be observed for up to 365 days. Slight interstitial fibrosis also was 

observed, but only at 90 and 180 days post exposure. This study demonstrates that tremolite 

exposure leads to pronounced inflammation and fibrosis (Bernstein et al., 2006). Tumors were 

not observed in this study, which is a consistent observation with the time frame observed in 

other studies (i.e., 1 year post exposure) (Smith, 1978).

D.1. 3 Intratracheal Instillation

A recent study by Putnam et al. (2008) was designed to explore gene-environment 

interactions in the development of asbestos-related diseases. C57Bl/6 mice were exposed once 

to either Libby Amphibole asbestos (Six Mix) (100 μg via intratracheal instillation); crocidolite 

(100 μg via intratracheal instillation); or saline (30 μL via intratracheal instillation). 

Characteristics of fibers are described in Table D-5. Animals were sacrificed, and the lungs 

harvested 6 months post-instillation. The left lung was used for ribonucleic acid (RNA) 

isolation, and the right lung was used for histology (personal communication, e-mail from E. 

Putnam [University of Montana] to M. Gwinn [U.S. EPA] 02/26/09). Histology on mouse lungs 

from each treatment group demonstrated an increase in fibrosis, as viewed by Gomori‘s 

trichrome staining, following exposure to crocidolite and, to a lesser extent, Libby Amphibole 

asbestos. Histologic tissue was also exposed to Lucifer Yellow stain to further analyze 
Table D-5. Fiber characteristics for intratracheal instillation studies in mice.

Material Diameter Length Aspect Ratio

Libby Amphibole asbestos (Six Mix) 0.61 ± 1.22 μm 7.21 ± 7.01 μm 22.52 ± 22.87

Crocidolite 0.16 ± 0.09 μm 4.59 ± 4.22 μm 34.05 ± 43.29

Source: Blake et al. (2007, 2008); Putnam et al. (2008); Smartt et al. (2009).

variability in collagen following exposure. Lucifer Yellow staining revealed an increase in 

collagen following exposure to both crocidolite and Libby Amphibole asbestos, but only 

crocidolite exposure led to a statistically significant increase (p < 0.05). RNA was isolated from 

homogenized lungs and purified for use in microarray analysis. Pooled RNA samples from mice 

in each exposure group were analyzed on a 0K element mouse oligonucleotide array (MWG 

Biotech), and expression was compared to a mouse reference standard RNA. Gene expression 

results were analyzed by GO Miner, and genes exhibiting at least 1.25-fold up- or downregulation in treated lungs were described. These included genes involved in membrane 

transport, signal transduction, epidermal growth factor signaling, and calcium regulation for both 

crocidolite and Libby Amphibole asbestos exposures, which supports the increase in collagen 

observed above. Some limitations to this study are the use of a standard reference for gene 

expression comparisons (as opposed to the saline controls), the practice of describing genes only 

if a greater than two-fold difference in expression is observed, and the use of pooled samples of 

homogenized whole lung that in some cases could dilute variability between different areas of 

exposed lung (different lobes, fibrotic versus nonfibrotic).

A follow-up paper to Putnam et al. (2008), prepared by Smartt et al. (2009), examined the 

increase of collagen in C57Bl/6 mouse lung following exposure to crocidolite or Libby 

Amphibole asbestos and also examined a few specific gene alterations by quantitative reverse 

transcription polymerase chain reaction (RT-PCR). Animals (n = 3 to 6 mice per group) were 

dosed with the same samples (Table D-5) as described above (Putnam et al., 2008) but were 

euthanized at 1 week, 1 month, and 3 months post-instillation. Treated mice were then divided 

into two groups, with the left lung from the first group used for RNA isolation and the right lung 

used for histology. The lungs from the second group were used for protein isolation and 

hydroxyproline assay (personal communication, e-mail from E. Putnam [University of Montana] 

to M. Gwinn [U.S. EPA] 02/26/09). Similar to results from Putnam et al. (2008), Gomori‘s 

staining demonstrated increased collagen and inflammation at the airways in lungs of mice 

exposed to either Libby Amphibole asbestos or crocidolite. These results were similar following 

exposure to both amphiboles, with crocidolite effects appearing more severe at all time points 

examined. No changes at the pleura of the lungs that were indicative of potential mesothelioma 
were observed; such changes, however, would not be expected in such a short time-frame. This 

study also examined severity of inflammation and found that, on average, crocidolite-exposed 

animals demonstrated minimal inflammation at 1 week post-instillation, which then 

progressively worsened at 1 and 3 months post-instillation. Although both asbestos exposures 

led to increased inflammation, Libby Amphibole asbestos exposure demonstrated minimal 

inflammation that did not progress in the time points examined. Gene expression alterations 

were measured by quantitative RT-PCR for genes involved in collagen accumulation and scar 

formation (Col1A1, Col1A2, Col3A1). Although exposure to both forms of asbestos at 1 week 

and 1 month post-instillation led to increased Col gene expression, the levels and subtypes 

altered varied. Libby Amphibole asbestos exposure led to increased gene expression of Col1A2 

at 1 week post-instillation and Col3A1 at 1 month post exposure, while crocidolite led to no 

significant alterations in the expression of these genes. Both crocidolite and Libby Amphibole 

asbestos exposure led to increased Col1A1 gene expression as compared to saline control at 1 

week and 1 month post exposure. Due to these differences in expression, the authors also 

examined the collagen protein levels in the lungs to compare to the gene expression changes. 

Total collagen content was determined by measuring the hydroxyproline content in the caudal 

aspect of the left lung. As compared to saline-exposed mice, a significant increase in 

hydroxyproline was observed at 1 week and 1 month following exposure to both crocidolite and 

Libby Amphibole asbestos; however, only lungs from crocidolite-exposed animals demonstrated 

a significant increase at 3 months post exposure. These studies demonstrate that exposure to 

Libby Amphibole asbestos lead to inflammation and fibrosis, although with differences in the 

time and level of response.

Shannahan et al. (2011) exposed two rat models of human cardiovascular disease to 

Libby Amphibole asbestos1to determine if the pre-existing cardiovascular disease in these 

models would impact the lung injury and inflammation following exposure. Healthy Wistar 

Kyoto (WKY) rats were compared to spontaneously hypertensive (SH) and spontaneously 

hypertensive heart failure (SHHF) rats following exposure. These rat models demonstrate 

pulmonary iron homeostasis dysregulation (Shannahan et al., 2010). All rats (male only) were 

exposed to 0, 0.25 or 1.0 mg/rat via intratracheal instillation and were examined at 1 d, 1 wk and 

1 mo post-exposure. No changes were observed histopathologically, however, changes were 

observed in markers of homeostasis, inflammation and oxidative stress. Bronchoalveolar lavage 

fluid (BALF) protein was significantly increased in both the SH and SHHF rat models as 

compared to controls as early as 1 wk post-exposure. GGT activity was increased in a 

concentration-dependent manner with exposure to Libby Amphibole asbestos at the earliest 

 

1 Median fiber dimensions as determined by TEM: length = 3.59 μm; width = 0.23 μm; aspect ratio ≥5.
timepoint measured (1 d), and was more pronounced in WKY rats as compared to SH and SHHF 

rats. LDH activity was also elevated in all strains, but was more pronounced in the SHHF rat 

model. Neutrophil increases were observed following exposure in all strains, peaking at 1 d 

post-exposure in all strains and persisting in the SH and SHHF rats until 1 mo post-exposure. 

Macrophages showed similar results but persisted only in the SH rat model until 1 mo postexposure. In order to determine any impact of exposure on iron homeostasis, BALF ferritin and 

transferrin levels were measured in the lung. Increases in ferritin and transferrin were observed 

in both SH and SHHF rats as compared to WKY controls. Non-heme iron was also observed to 

be increased in only the SH rats at 1 d and 1 wk post-exposure. Markers of inflammation 

(MIP-2) and oxidative stress (heme oxygenase-1, HO-1) were elevated in both SH and SHHF as 

compared to WKY rats at baseline, but limited exposure-related differences were observed. 

Limited changes were also observed in ascorbate and glutathione levels in BALF and lung tissue. 

While inflammation and cell injury were observed in all strains, no strain-related differences 

were observed following exposure to Libby Amphibole asbestos (Shannahan et al., 2011). In 

conclusion, this study showed the potential for population variability related to cardiac disease in 

response to exposure to Libby Amphibole asbestos, including markers of cellular injury, iron 

homeostasis and inflammation. 

In an early study, Sahu et al. (1975) described histological changes in the lungs of mice 

exposed individually to amosite, anthophyllite, and tremolite. Fibers were described only as 

<30-μm long. Groups of 20 male albino Swiss mice were exposed to amosite, anthophyllite, and 

tremolite at a single dose of 5 mg, and two animals from each group were sacrificed at 1, 2, 7, 

15, 30, 60, 90, 120, and 150 days post exposure. Microscopic results following exposure to

tremolite showed acute inflammation of the lungs at 7 days post exposure, including macrophage 

proliferation and phagocytosis similar to that observed with amosite and anthophyllite. Limited

progression of fibrotic response was observed at 60 and 90 days post exposure, with no further 

progression of fibrotic response. 

Blake et al. (2008) and Pfau et al. (2008) examined the role of asbestos in autoimmunity. 

Blake et al. (2008) performed in vitro assays with Libby Amphibole asbestos, and both studies 

performed the in vivo assays with tremolite. C57BL/6 mice were instilled intratracheally for a 

total of two doses each of 60-μg saline and wollastonite or Korean tremolite sonicated in sterile 

PBS, given 1 week apart in the first 2 weeks of a 7-month experiment. Detailed fiber 

characteristics were described in Blake et al. (2007) for wollastonite and Libby Amphibole 

asbestos, but not for Korean tremolite (Table D-5; wollastonite not shown). 

Blake et al. (2008) described autoantibody production, monitored biweekly with blood 

samples from saphenous vein bleeds and then by cardiac puncture following euthanization. 

Specific autoantibodies were identified by immunoblotting with known nuclear antigens. These 
autoantibodies were then incubated with murine macrophage cells previously exposed to Libby 

Amphibole asbestos, wollastonite, or vehicle. Only sera from mice exposed to tremolite showed 

antibody binding colocalized with SSA/Ro52 on the surface of apoptotic blebs (Blake et al., 

2008). 

In Pfau et al. (2008), collected serum samples and urine were checked for protein 

bi-weekly for 7 months. By 26 weeks, the tremolite-exposed animals had a significantly higher 

frequency of positive anti-nuclear antibody tests compared to wollastinate and saline. Most of 

the tests were positive for dsDNA and SSA/Ro52. Serum isotyping showed no major changes in 

immunoglobulin subclasses (IgG, IgA, IgM), but serum IgG in tremolite-exposed mice decreased 

overall. Further, IgG immune complex deposition in the kidneys increased, with abnormalities 

suggestive of glomerulonephritis. No increased proteinuria was observed during the course of 

the study. Local immunologic response was further studied on the cervical lymph nodes. 

Although total cell numbers and lymph-node size were significantly increased following 

exposure to tremolite, percentages of T- and B-cells did not significantly change. Because 

tremolite is part of the makeup of Libby Amphibole asbestos (6%), using tremolite-exposed mice 

might yield a similar response to Libby Amphibole asbestos-exposed mice. This same effect has 

been demonstrated following exposure to ultraviolet radiation in skin cells, suggesting a similar 

mechanism (Saegusa et al., 2002).

D.1.4 Injection/Implantation

LVG:LAK hamsters were intrapleurally injected with tremolite obtained from the Libby, 

MT mine in an unpublished study by Smith (1978) prepared for W.R. Grace & Company. These 

samples were identified as tremolite (22260p5; Sample 60) and 50% tremolite + 50% vermiculite 

(22263p2, Sample 63). Both fiber samples were measured by optical phase microscopy, and 

fibers were described as amorphous, irregularly shaped particles of about 5–15 μm diameter, 

with Sample 60 (tremolite) also containing the occasional fiber up to 30 μm long. Fiber size for 

Sample 60 (tremolite) also was measured by scanning electron microscopy (SEM) and was 

determined to have a geometric mean length of 2.07 μm, geometric mean diameter of 0.2 μm, 

and average aspect ratio of 10.36. Twenty-five milligrams of each of the two samples were 

individually injected intraperitoneally into the pleural cavity of LVG:LAK hamsters. Pathology 

was examined at approximately 3 months post exposure in 10 animals from each group, with the 

remaining animals observed until death, or 600 days post exposure, depending on the health of 

the animal. Average survivorship was 410, 445, and 421 days in groups exposed to Sample 60, 

Sample 63, and saline, respectively (Table D-6). Pleural fibrosis was observed 3 months post 
exposure, and mesothelioma was observed in both treatment groups between 350 and 600 days 

post exposure, with no mesotheliomas in control groups. 

Table D-6. Pleural adhesions and tumors following intraperitoneal injection 

exposure in LVG:LAK hamsters (25 mg)

Endpoint Control Sample 60 (tremolite)

Sample 63 (tremolite 

and vermiculite)

Average adhesion ratinga,b 0 (n = 10) 3.3 (n = 10) 3.6 (n = 10)

Total tumors/animalsc8/59 8/58 16/61

Benign 3/59 2/58 5/61

Malignant 5/59 6/58 9/61

Mesothelioma 0/59 5/58 5/61

aAs analyzed in first group sacrificed (between 41 and 92 days post exposure).

bRating for pleural adhesions: 0 = no adhesions; 1 = minimal adhesions; 4 = extensive adhesions.

cThese include adrenal adenomas, adrenal adenocarcinomas, lymphoma, pulmonary adenocarcinoma, adrenal 

and salivary carcinoma, mesothelioma, rhabdomyosarcoma, hepatoma, thyroid carcinoma, subcutaneous 

carcinoma, and malignant melanoma.

Source: Smith (1978).

The Smith et al. (1979) study was designed to determine whether mesothelioma is a 

non-specific result of mesothelial cells trapped in fibrous pleural adhesions, occurring regardless 

of fiber type. Earlier studies by this group suggested that fibrosis and tumors resulting from fiber 

exposure (chrysotile or glass) were related to fiber dimensions (>20-μm long, >0.75-μm 

diameter) (Smith, 1974). Injected fibrous talc (FD-14) was used as a negative control in earlier 

studies and led to limited fibrosis and no tumor formation. The characteristics of the FD-14 

sample are described in the proceedings of Smith (1974). No further information could be found 

on the characteristics of the samples used in this study.2 Because the talc contained 

50% tremolite, 35% talc, 10% antigorite, and 5% chlorite, it was considered a tremolite sample 

by Smith (1978). When the sample was later analyzed independently by Wylie et al. (1993), 

only 64 (12.8%) of 500 tremolite particles measured met the NIOSH definition of a fiber (≥3:1 

aspect ratio). Wylie et al. (1993) note, however, that very long fibers of the mineral talc, with 

narrow widths and fibrillar structure, occur in this sample. A second tremolite sample (sample 

275) used by Smith et al.(1979) was described as similar to FD-14, although no details were 

given. The last two samples were prepared from a deposit of tremolitic talc from the western 

 

2

This fiber is also analyzed in Wylie et al. (1993) and Stanton et al. (1981).
United States (sample 31) and from a specimen of asbestiform tremolite (sample 72),3

respectively. 

Each of the four samples was examined microscopically, although the data were not 

reported in the paper by Smith et al. (1979). The average fibers in sample 72 were long, thin, 

crystalline fibers (>20-μm long, 0.4-μm diameter). Sample 31 appeared to have fewer long, thin 

fibers than sample 72, and many of the fibers in this sample were acicular. The characteristics of 

the FD-14 sample were determined by phase microscopy (Smith, 1974), but no characterization 

method was reported for the other three samples in this study. Other samples used by this group 

have been analyzed by both optical and electron microscopy (Smith, 1974; Smith, 1978). The 

limited information on the fiber characteristics of the samples used in these studies is provided in 

Table D-7. Note that no information was provided confirming the presence or absence of 

particles or fibers less than 5 μm in length in any of the three papers by Smith (1974) or Smith et 

al. (1978, 1979). These data deficiencies limit the interpretation of results from this study. 

Table D-7. Fiber characteristics and numbers of resulting tumors following 

intrapleural injection into Syrian hamsters of 10- or 25-mg fiber samples

Sample

Average 

lengtha(μm)

Average 

diametera(μm)

Tumors/survivors at 10 mgb Tumors/survivors at 25 mgb

350 

days 500 days

600 

days 350 days 500 days 600 days

FD-14 5.7 1.6 N/D N/D N/D 0/35 0/26 0/20

275 N/D N/D 0/34 0/14 0/6 0/31 0/15 0/3

31 >20 <0.4 1/41 1/19 1/11 2/28 4/9 6/5

72 >20 <0.4 0/13 1/6 3/2 3/20 5/6 5/1

aAlthough average length and diameter are reported, what range of fibers was counted is unclear. Smith, 1978 

(unpublished) states that only fibers greater than 5 μm long are included. No other information is provided for 

these samples.

bNumerator = cumulative number of animals with tumors; denominator = number of survivors.

N/D = not described

Source: Smith et al. (1979); Smith (1974).

Following analysis of Syrian hamsters intrapleurally injected with 10 or 25 mg of each of 

the four samples of tremolite, Smith (1978) reported tumors at 350 days post exposure (25 mg) 

or 600 days postexposure (10 mg) for Samples 31 and 72 (Table D-7). Although number of 

animals was not provided by Smith et al. (1979), previous studies by these authors reported using 

50 animals per exposure group (Smith et al., 1978; Smith, 1974). The results in Table D-7

present the cumulative number of tumors (numerator) at each time point analyzed over the 

 

3 Although the source of this material is not reported, these studies parallel those in the unpublished studies 

performed by Smith et al. for W.R. Grace that used material from Libby, MT. Whether Sample 72 is material from 

Libby, MT, or another location is unknown.
remaining survivors (denominator). The survival rate without tumor presentation was decreased 

for animals exposed to samples 72, 31, and 275. Smith et al. (1979) concluded that the FD-14 

and 275 samples were noncarcinogenic, and sample 31 was less carcinogenic than sample 72. 

Hamsters exposed to sample 72 had extensive pleural fibrosis, which was observed to a lesser 

degree in hamsters exposed to the other samples (sample 72 > sample 31 > sample 

275 = FD-14). No statistical information was reported for these results, and because the number 

of background tumors in control animals was not provided, no statistical analysis can be 

performed.

Both studies demonstrate that intrapleural injections of Libby Amphibole asbestos4leads 

to an increase in pleural fibrosis and mesothelioma in hamsters compared to controls or animals 

injected with less fibrous materials. The use of doses of equal mass for both studies makes it 

difficult to compare potency between samples, as each sample could have vastly different fiber 

number and total surface area. Although these studies clearly show the carcinogenic potential of 

Libby Amphibole asbestos fibers, intrapleural injections bypass the clearance and dissolution of 

fibers from the lung after inhalation exposures. 

Stanton et al. (1981) also examined tremolite and describe a series of studies on various 

forms of asbestos. Fibers, embedded in hardened gelatin, were placed against the lung pleura. 

As an intrapleural exposure, results might not be comparable to inhalation exposures, as the 

dynamics of fiber deposition and pulmonary clearance mechanisms are not accounted for in the 

study design. Studies using two tremolite asbestos samples from the same lot were described as 

being in the optimal size range for carcinogenesis; the fibers were distinctly smaller in diameter 

than the tremolite fibers Smith et al. (1979) used. These samples both had a high number of 

fibers in the Stanton et al. (1981) size range (>8-μm long and <0.25-μm diameter). Exposure to 

both tremolite samples led to mesotheliomas in 21 and 22 of 28 rats exposed. The Stanton et al. 

(1981) study also used talc that did not lead to mesothelioma production. This talc was found to 

be the same as that used by Smith et al. (1979) and later by Wylie et al. (1993). Wylie et al. 

(1993) stated that, although the two tremolites were consistent by size with commercial 

amphibole asbestos, the talc used contained fibers that were much thinner and shorter, which is 

not typical of prismatic tremolite fibers. 

Wagner et al. (1982) examined three types of tremolite (California talc, Greenland, and 

Korea) using SPF Sprague-Dawley (n = 48) and Wistar (n = 32) rats, then followed up with a 

range of in vitro tests using the same fiber samples. Rats were injected intrapleurally (20 mg 

tremolite) at 8–10 weeks of age and allowed to live out their lives. Median survival times after 

injections were 644 days (California talc), 549 days (Greenland tremolite) and 557 days (Korean 

 

4

Assuming Smith et al. (1979) used Libby Amphibole asbestos.
tremolite). Positive controls had a decreased survival time due to an infection, which limits the 

interpretation of these data. Also, this study was performed separately using different rat strains 

for the three tremolite samples. The authors state that, although the decreased control survival 

time and use of different rat strains limit the usefulness of the study for quantitative analysis, the 

results can be described qualitatively. Of the three tremolites, only the Korean tremolite (Table

D-9) showed carcinogenic activity producing mesothelioma (14/47 rats, 30%). Analysis of the 

fiber characteristics showed the Korean sample had fibers that were longer than 8 μm and a 

diameter less than 1.5 μm. The California talc and Greenland tremolite had little to no fibers in 

this size range (Table D-8). Follow-up in vitro assays in the sample publication (Wagner et al. 

1982) confirmed the in vivo results, with the exposure to Korean tremolite resulting in increased 

lactic dehydrogenase (LDH) and β-glucuronidase (BGL) release, cytotoxicity, and giant-cell 

stimulation. 

Table D-8. Fiber characteristics of three tremolite samples analyzed by in 

vivo and in vitro methods (TEM measurements)

Sample Location Fiber type Length Diameter

No. of nonfibrous particles 

(×104 )

Total no. of 

fibers 

(×104 )

No. of fibers >8 μm 

long (×103 )

<1.5 μm diameter

A California Flake-like 

material

<6 μm <0.8 μm 6.9 5.1 1.7

B Greenland Medium-sized 

fibrous mineral

<3 μm <1.2 μm 20.7 4.8 0

C Korea Fine-fiber 

material

>8 μm <1.5 μm 3.3 15.5 56.1

TEM = transmission electron microscopy.

Source: Wagner et al. (1982).

Davis et al. (1991) examined six tremolites with differing morphologies through 

intraperitoneal injections with male SPF Wistar rats. Four of the tremolites were from 

Jamestown, California; Korea; Wales; and Italy; and two were from Scotland. Of these, the three 

from California, Korea, and Wales were asbestiform, and the other three were fiber bundles or 

prismatic (Table D-9). Rats were exposed (n = 33 or 36) with one intraperitoneal injection with

samples that were 10-mg/2 mL sterile phosphate buffered saline (PBS). Animals were allowed 

to live out their full life spans or until signs of debility or tumor formation developed. Although 

exposure was performed based on sample weight, each sample was analyzed to determine the

number of expected fibers per milligram, and, therefore, per exposure. These samples also were 

characterized further by counting fibers versus particles. Data were collected for all fibers 
(aspect ratio >3:1) and particles (aspect ratio <3:1) of total fibers. A fiber was defined as any 

component ≥8-μm long and <0.25-μm diameter as measured by SEM (i.e., Stanton fibers).

Table D-9. Fiber characteristics in a 10-mg dose (as numbers of fibers)

Sample

No. of 

animals

No. of 

mesothelioma

s

No. of fibers in 

1 mg of injected 

dust (× 105)

No. of fibers ≥8 

μm long, <0.25 

μm diametera(× 

105)

No. of particles 

in 1 mg injected 

dust (× 105) Morphology

California 36 36 13,430 121 18,375 Asbestiform

Swansea 36 35 2,104 8 4,292 Asbestiform

Korea 36 32 7,791 48 13,435 Asbestiform

Italy 36 24 1,293 1 20,137 Fiber bundles

Carr Brae 33 4 8,99 0 9,490 Fiber bundles

Shinness 36 2 3,83 0 5,901 Prismatic

a

Stanton fibers. 

Source: Davis et al. (1991).

The authors‘ overall conclusions were that all materials studied could cause 

mesothelioma by this method of exposure and the number of Stanton fibers was not sufficient to 

explain the differences in response. Mesothelioma incidence was not correlated to Stanton 

fibers, total particles, or mass of dust. The best predictor of mesothelioma incidence was total 

fibers (Table D-9). Although three samples were considered asbestiform (California, Swansea, 

Korea), all samples had <1% of counted fibers defined as Stanton fibers. The highest 

mesothelioma incidence was observed for the California sample, which contained the most 

Stanton fibers (121 fibers per mg dust). The tremolite from Swansea, resulted in 97% 

mesothelioma incidence yet contained only 8 Stanton fibers per milligram (more than 90% less 

than in the California sample). In contrast, the Italy tremolite, although containing only 

0.08% Stanton fibers, resulted in 67% mesothelioma incidence. Little is known, however, about 

the characteristics of particles or fibers <5-μm long. This study highlights two issues associated 

with all fiber studies: the limits of analytical techniques and the variability in response based on 

the metric used to measure exposure. This study also supports the premise that asbestos samples 

containing fibers that are not long and thin can be carcinogenic.

The Roller et al. (1996) study was designed to provide data on the dose-response of 

various fiber types in relation to their fiber dimensions (as measured by SEM). Fibers were

defined in this study as having an aspect ratio of >5:1 for all lengths and widths. Female Wistar 

rats (n = 40) were given either one intraperitoneal injection of 3.3 mg or 15 mg of tremolite. 
Rats were examined for tumors in the abdominal cavity following a lifetime (up to 30 months) of 

observation. This paper described the fiber dimensions in depth (Table D-10), while limited 

discussion is focused on the exposure results. This table shows the characteristics of the fibers 

Table D-10. Characteristics of tremolite fibers intraperitoneally injected into 

Wistar rats

Fiber number per ng dust and mass fraction (%)

Aspect Ratio (L/D) >5/1; D <2 μm (Roller study) Aspect Ratio (L/D) <3/1; D <3 μm (WHO, 1985)

Length: >5 μm >10 μm >20 μm Diameter: >5 μm >10 μm >20 μm

No. % 

Mass

No. % 

Mass

No. % 

Mass

No. % 

Mass

No. % 

Mass

No. % 

Mass

17.4 32 6.9 27 1.9 18 18.4 43 7.0 35 2.0 26

Fiber size distribution for aspect ratio (L/D) >3/1 (all lengths, all diameters; SEM)

% Total 

fibers L 

>5 μm Length (μm) Diameter (μm)

10% < 50% < 90% < 99% < 10% < 50% < 90% < 99% <

22% 0.8 2.4 9.2 29.4 0.14 0.27 0.67 1.49

SEM = scanning transmission microscopy

Source: Roller et al. (1996).

sorted first by aspect ratio and diameter, and the fiber size distribution binned by the length and 

diameter for those fibers with a length >5 um. Results were described in this study in a table as 

―positive rats‖ being those with histologically confirmed mesothelioma or macroscopically 

supposed mesothelioma. No information was provided on how these determinations were made. 

Exposure to 3.3-mg and 15-mg tremolite resulted in 9 mesotheliomas in 29 animals (64 weeks 

post exposure) and 30 mesotheliomas in 37 animals (42 weeks post exposure), respectively. This 

study demonstrates that intraperitoneal injection of tremolite led to mesothelioma in Wistar rats. 

Analysis of other tissues was not described.

D.2. Mechanistic Data and Other Studies in Support of the Mode of Action

D.2.1 In Vitro Studies – Libby Amphibole Asbestos

Hamilton et al. (2004) examined the potential for fibers, including Libby Amphibole 

asbestos, to modify the function of antigen-presenting cells (APC). Analysis was performed for 

24 hours with two forms of asbestos (crocidolite [25 or 50 μg/mL] and Libby Amphibole 
asbestos obtained from Site No. 30, Libby, MT [25 or 50 μg/mL]) and ultrafine particulate 

matter (PM2.5 [particulate matter 2.5 microns diameter or less] [50 or 100 μg/mL]). Limited 

information is provided by Hamilton et al. (2004) on fiber characteristics. Samples from Site 

No. 30, however, are described as predominantly richterite and winchite by Meeker et al. (2003). 

Primary human alveolar macrophages were incubated for 24 hours with Libby Amphibole 

asbestos (25 or 50 μg/mL), crocidolite (25 or 50 μg/mL), or ultrafine particulate matter (50 or 

100 μg/mL). Following incubation, cells were isolated from remaining particles and nonviable 

cells, after which 0.25 106 macrophages were co-cultured with autologous lymphocytes 

(1 106cells) in an 11-day APC assay. This assay analyzes the antigen-presenting function of 

the pre-treated macrophages by stimulating the lymphocytes using tetanus toxoid as the antigen. 

The supernatant was assayed for cytokines at day 11, and Hamilton et al. (2004) found that 

pretreatment with either asbestos or PM2.5 significantly upregulated both TH1 and TH2 cytokines 

(interferon gamma [IFNγ]; interleukin-4 [IL-4]; and interleukin-13 [IL-13]) (p < 0.05). 

Therefore, preexposure to either fibers or particles increased APC function, as reflected in 

increased cytokine release after tetanus challenge. No significant differences, however, were 

discernable between asbestos and PM2.5 pretreatment. The authors speculated that the variability 

in response between samples assayed—presumably due to the use of primary cells—obscures 

statistical significance. Although this study supports a role for fibers and PM2.5 in potentiating 

immune response, the implications of these findings to human health are unclear because many 

agents can activate macrophages prior to antigen challenge.

Recent studies (Blake et al., 2007, 2008) compared the response of murine macrophages 

(primary and cell line RAW264.7) to Libby Amphibole asbestos fibers and crocidolite asbestos 

fibers. The Libby Amphibole asbestos fibers (7.21 ± 7.01 μm long, 0.61 ± 1.22 μm diameter) 

used in these studies were obtained from the U.S. Geological Survey and were chemically 

representative of the Libby, MT mine (Meeker et al., 2003). The crocidolite fibers 

(4.59 ± 4.22 μm long, 0.16 ± 0.09 μm diameter) used in these studies were provided by Research 

Triangle Institute, North Carolina, and the noncytotoxic control fiber 

(wollastonite, 4.46 ± 5.53 μm long, 0.75 ± 1.02 μm diameter) was provided by NYCO Minerals, 

New York. Cells were exposed for 24 hours to fiber samples measured by relative mass 

(5 μg/cm2), after which the cells were analyzed by transmission electron microscopy to measure 

internalization. The results of the first study (Blake et al., 2007) indicate that Libby Amphibole 

asbestos fibers can both attach to the plasma membrane and be internalized by macrophages, 

similar to the crocidolite fibers. These internalized fibers were primarily less than 2-μm long 

and were found localized in the cytoplasm, in cytoplasmic vacuoles, and near the nucleus 

following 3-hour exposure, 62.5 μg/cm2. This same concentration (62.5 μg/cm2) was selected 

for the remaining studies because cell viability was not decreased at this concentration for the 
Libby Amphibole asbestos (92%); cell viability was decreased for crocidolite (62%), however, at 

this concentration. As a result, the remaining assays would be expected to have decreased 

viability following exposure to crocidolite, which may impact the levels of various responses. 

For example, the reactive oxygen species (ROS) measurement would increase with increased cell 

number; therefore, some of the quantitative results would be difficult to compare between fiber 

types unless normalized to cell number.

Oxidative stress was measured by the induction of ROS and the reduction in glutathione 

(GSH) levels. These two measurements generally complement each other, as GSH is used in 

cells to maintain intracellular redox balance in cells in response to increased ROS levels. Both 

Libby Amphibole asbestos and crocidolite fiber internalization generated a significant increase 

(p < 0.05) in intracellular ROS as quantified by the oxidation of 2,7-dichlorodihydrofluorescien 

(DCFH) to dichlorofluorescein with hourly readings on a fluorescent plate reader. Libby 

Amphibole asbestos exposure significantly increased ROS in a dose-dependent manner (6.25, 

32.5, and 62.5 μg/cm2), as early as 1 hour post exposure at the highest dose (p < 0.05), as 

compared to a no-treatment group. Only the highest concentration of crocidolite was tested. The 

lower concentrations of Libby Amphibole asbestos were not compared to crocidolite and 

wollastonite, but a comparison of the highest exposure concentrations (62.5 μg/cm2) of Libby 

Amphibole asbestos, crocidolite, and wollastonite revealed greater ROS production following 

Libby Amphibole asbestos exposure (1 hour, p < 0.05). Blake et al. (2007) stated that similar 

results were seen in the primary cell line but did not report the data. To differentiate the type of 

ROS produced, dehydroergosterol (DHE) fluorescence intensity levels were used, revealing that 

superoxide anion was significantly increased following exposure to Libby Amphibole asbestos 

as compared to controls. This observation was further confirmed with use of a free radical 

scavenger (PEG-SOD [polyethylene glycol-superoxide dismutase]) specific to superoxide anion. 

This co-exposure of Libby Amphibole asbestos and PEG-SOD led to a significant decrease in 

ROS as compared to cells exposed only to Libby Amphibole asbestos (p < 0.05). Total 

intracellular superoxide dismutase (SOD) activity also was measured following exposure to 

Libby Amphibole asbestos and showed a decrease in activity at 3 hours post exposure as 

compared to controls (p < 0.05). Crocidolite appears to increase intracellular SOD activity at 

24 hours post exposure. These three assays demonstrate that Libby Amphibole asbestos 

exposure leads to increased superoxide anion in macrophages, most likely by suppressing 

activity of intracellular SOD.

GSH levels were found to be decreased in response to Libby Amphibole asbestos and 

crocidolite exposure in the macrophage cell line as compared to unexposed cells (p < 0.05). The 

decreased GSH levels were more prominent following crocidolite exposure as compared to 

Libby Amphibole asbestos. Crocidolite exposure has been shown in other studies to lead to 
increased hydrogen peroxide, but not superoxide anion (Kamp et al., 1992; Kamp and Weitzman, 

1999). The increased hydrogen peroxide from crocidolite exposure can then lead to increased 

hydroxyl radical production (through interactions with endogenous iron), and potentially 

deoxyribonucleic acid (DNA) adduct formation. DNA adduct formation (8-hydroxy2‘deoxyguanosine, 8-OHdG), 8-oxoguanine-DNA-glycosylase 1 (Ogg1) levels and DNA 

damage (comet assay) also were measured. A significant increase in DNA damage in exposed 

macrophages, as measured by increases in both 8-OHdG formation and expression of Ogg1, a 

DNA repair enzyme that excises 8-OHdG from DNA following oxidative stress, was observed 

following exposure to crocidolite, but not Libby Amphibole asbestos. Increased superoxide 

anion following Libby Amphibole asbestos exposure does not appear to yield oxidative damage 

similar to crocidolite. These results suggest a chemical-specific response to each type of 

amphibole that yields varied cellular responses. Therefore, the mechanism of action following 

response to Libby Amphibole asbestos might be different than that of crocidolite, also an 

amphibole fiber. 

To determine if the ROS production was related to fiber number for both Libby 

Amphibole asbestos and crocidolite, cell-fiber interactions and fiber internalization were 

measured following exposure to equal concentrations of crocidolite, Libby Amphibole asbestos, 

and wollastonite (62.5 μg/cm2, 3 hours). With phase contrast light microscopy, the number of 

cells interacting with one or more fibers was counted (one hundred cells counted for each 

treatment). All murine macrophages bound or internalized at least one fiber from the Libby 

Amphibole asbestos sample (mean ± SD, 4.38 ± 1.06 internalized) or the crocidolite sample 

(3.28 ± 1.58 internalized), but not the wollastonite sample (Blake et al., 2007). No significant 

differences were observed in the responses to Libby Amphibole asbestos or crocidolite samples, 

suggesting that the differences in measured ROS were not related to cell number. Fiber sizes 

varied between the two samples, with the crocidolite sample containing a more homogeneous 

mixture of long fibers (exact size not given), while the Libby Amphibole asbestos sample 

contained a mixture of sizes and widths. These characteristics were not analyzed to determine 

what, if any, role they might play in the varied response.

The second study by Blake et al. (2008) reports the effects of in vitro exposure to Libby 

Amphibole asbestos on apoptosis by exploring autoimmune response following asbestos 

exposure. Although Libby Amphibole asbestos was not directly used in the autoimmune studies, 

the autoantibody (SSA/Ro52) is a known marker of apoptosis, and the in vitro studies included 

treatment with Libby Amphibole asbestos. RAW264.7 cells exposed to Libby Amphibole 

asbestos induced apoptosis over 72 hours, as measured by induction of poly (ADP-ribose) 

polymerase (PARP) cleavage and increased Annexin V staining. Redistribution of SSA/Ro52 in 

apoptotic blebs was demonstrated in Libby Amphibole asbestos-exposed RAW cells but not in 
the unexposed controls and wollastonite-exposed RAW264.7 murine macrophages, further 

confirming apoptosis.

The role of reactive oxygen species in chromosomal damage from asbestos was examined 

in a recent study of Libby Amphibole asbestos and UICC crocidolite in XRCC1-deficient human 

lung epithelial H460 cells (Pietruska et al., 2010). XRCC1 is involved in the repair mechanisms 

for oxidative DNA damage, particularly single strand breaks. This study examined the effect of 

XRCC1 deficiency (induced in cells by shRNA knockdown) following exposure to genotoxic 

(crocidolite and Libby Amphibole asbestos) and nongenotoxic compounds (wollastonite, 

titanium dioxide) on micronucleus formation. Cells were exposed to chemicals with known 

oxidants hydrogen peroxide (0–60μM) or bleomycin (0-10μg/ml) for 1 and 3 hrs, or the 

non-oxidant paclitaxel (0-5nM, 24hrs) to confirm the clonogenic survival of the knockout cells, 

and as positive and negative controls. Fiber size distribution for crocidolite and Libby 

Amphibole asbestos is shown in Table D-11. Micronuclei induction was measured following 

treatment of cells by controls as described above, and by 5μg/cm2fibers or TiO2 particles for 

24 hrs. Following treatment, cells were fixed, permeabilized, and blocked before exposed to 

anti-centromere antibodies, and micronuclei were counted and scored as centromere negative 

arising from DNA breaks (clastogenic) or centromere positive arising from chromosomal loss 

(aneugenic). Spontaneous micronuclei induction was increased in XRCC1-deficient cells as 

compared to control. Wollastonite and titanium dioxide did not induce micronuclei in either cell 

type. Crocidolite and Libby Amphibole asbestos induced dose-dependent increases in 

micronuclei formation in both cell types but with an increase in proportion of micronuclei in the 

XRCC1-deficient cells (Table D-12). Libby Amphibole asbestos exposure led to a decreased 

amount of micronuclei as compared to crocidolite. Specifically examining clastogenic versus 

aneugenic micronuclei, crocidolite exposure led to mainly clastogenic micronuclei while Libby 

Amphibole asbestos exposure led to a mixture of aneugenic and clastogenic micronuclei. 

Nuclear bud formation was also observed, but only with exposure to crocidolite and bleomycin. 

Western blot analysis was performed to analyze protein expression related to DNA damage 

repair (XRCC1) and cell cycle progression (p53, p21) (data not shown in publication). The 

differences observed between crocidolite and Libby Amphibole asbestos are most likely related 

to their physicochemical differences, particularly related to their iron content. However, these 

results support a genotoxic effect of exposure to both crocidolite and Libby Amphibole asbestos.
Table D-11. Size distribution of UICC crocidolite and Libby Amphibole 

asbestos used in Pietruska et al. (2010)a

Length (μm)

% fibers in size range

Crocidolite Libby Amphibole Asbestos

0.1-1.0 46.4 12.6

1.1-5.0 44.8 38.5

5.1-8.0 3.8 23.1

8.1-10.0 0.9 10.4

10.1-20.0 2.4 11.6

≥20.1 1.7 3.6

a

Distribution by diameter also given in original manuscript.

Source: Adapted from Supplemental Material of Pietruska et al. (2010).

Table D-12. Percent clastogenic micronuclei following exposure to Libby 

Amphibole asbestos or crocidolite

H460 cells XRCC1-deficient

Libby Amphibole Asbestos 

(5 μg/cm2)

71.5 ± 3.4% 86.0 ± 1.2%a

Crocidolite (5 μg/cm2) 57.2 ± 2.2% 65.1 ± 2.2%a

a p < 0.05 as compared to control cells.

Source: Pietruska et al. (2010).

Mechanisms of oxidative stress following exposure to Libby Amphibole asbestos were 

also studied in human mesothelial cells (Hillegass et al. 2010). Gene expression changes were 

measured with Affymetrix U133A microarrays (analysis with GeneSifter) following exposure to 

15×106μm

2

/cm2Libby Amphibole asbestos5as compared to the non-pathogenic control 

(75×106μm

2

/cm2glass beads) in the human mesothelial cell line LP9/TERT-1 for 8 and 24 hrs. 

Gene expression of only one gene (manganese superoxide dismutase) was altered following 

exposure to Libby Amphibole asbestos for 8 hours, while 111 genes had an altered gene 

expression following exposure to Libby Amphibole asbestos at 24 hours (altered by at least 

2-fold as compared to control). 

 

5

Libby Amphibole asbestos samples were characterized for this study with analysis of chemical composition and 

mean surface area (Meeker et al., 2003). Doses were measured in surface area and described based on viability 

assays as either the non-toxic (15×106μm2/cm2) or the toxic dose (75×106μm2/cm2). 
The gene for manganese superoxide dismutase (MnSOD; SOD2) was observed to be 

significantly upregulated at both time points (p < 0.05) as compared to non-pathogenic controls. 

This gene was confirmed in normal human pleural mesothelial cells (HKNM-2) by quantitative 

RT-PCR at 24 hours following exposure to the non-toxic dose of Libby Amphibole asbestos. 

Upregulation of three genes from this and previous studies by these authors were confirmed by 

quantitative RT-PCR (SOD2, ATF, and IL8) in HKNM-2 cells exposed to both Libby Amphibole 

and crocidolite asbestos. Gene ontology of these results demonstrated alterations in genes 

related to signal transduction, immune response, apoptosis, cellular proliferation, extracellular 

matrix, cell adhesion and motility and only one gene related to reactive oxygen species 

processing. Follow-up studies at both the  ̳non-toxic dose‘ (15×106μm

2

/cm2) and the  ̳toxic 

dose‘ (75×106μm

2

/cm2) exposure levels in LP9/TERT-1 cells examined SOD protein and 

activity, reactive oxygen species production and glutathione (GSH) levels. At 24 hours, SOD2 

protein levels were increased following exposure to the toxic dose of Libby Amphibole asbestos 

(p < 0.05) but not at 8 hours. Cells exposed to all doses of Libby Amphibole and crocidolite 

asbestos also resulted in increases in copper-zinc superoxide dismutase (Cu/ZnSOD; SOD1) 

protein at 24 hours (p < 0.05) but not at 8 hours. Although total SOD activity remained 

unchanged, a dose-related SOD2 activity was observed following exposure to both doses of 

Libby Amphibole asbestos for 24 hours, but this appeared to be minimal and was not statistically 

significant (8 hours was not examined). Oxidative stress was measured by 

dichlorodihydrofluorescein diacetate (DCFDA) fluorescence staining detected by flow cytometry 

and was observed as both dose- and time-dependent in cells exposed to Libby Amphibole 

asbestos but was increased following exposure to the toxic dose of Libby Amphibole asbestos 

(statistical analysis not possible). Oxidative stress was further supported by analysis of gene 

expression of heme oxygenase 1 (HO-1) following exposure to Libby Amphibole asbestos in 

both LP9/TERT-1 and HKNM-2 cells for 8 and 24 hrs. HO-1 was significantly increased 

following exposure to the toxic dose of Libby Amphibole asbestos in both cell lines (p-value not 

given). GSH levels were transiently depleted following 2–8 hrs exposure to 75×106μm

2

/cm2 

levels of Libby Amphibole asbestos, with a gradual recovery up to 48hrs in LP9/TERT-1 cells 

(HKNM-2 not analyzed). Exposure to crocidolite asbestos at the toxic dose led to a significant 

GSH decrease at all times points up to 24 hrs exposure (p < 0.05). These studies demonstrate 

that Libby Amphibole asbestos exposure leads to increases in oxidative stress as measured by 

ROS production, gene expression, protein and functional changes in oxidative stress proteins 

(SOD), and GSH level alterations in human mesothelial cells. 

The relative toxicity of Libby Amphibole asbestos was measured by gene expression 

changes of interleukin-8 (IL-8), cyclooxygenase-2 (COX-2), heme oxygenase (HO)-1 as well as 

other stress-responsive genes as compared to amosite (Research Triangle Institute) in primary 
human airway epithelial cells (HAEC) in vitro. Comparisons were made with both fractionated 

(aerodynamic diameter ≤2.5μm) and unfractionated fiber samples (Duncan et al., 2010). 

Crocidolite (CRO) fibers (UICC) were also included in some portions of this study for 

comparison. Fractionation was performed using the water elutriation method (Webber et al., 

2008) and characterized as described in Lowers and Bern (2009). Primary HAECs were exposed 

to 0, 2.64, 13.2, and 26.4 μg/cm2of crocidolite, amosite (AM), AM2.5 (fractionated), Libby 

Amphibole asbestos (LA), or LA 2.5 (fractionated) for 2 or 24 hours in cell culture. Confocal 

microscopy was used to determine fiber content in cells exposed for 4 or 24 hours to 26.4 μg/cm2

AM2.5 or LA2.5 only. At 4h post-exposure, fibers were mainly localized on the periphery of the 

cell with some fibers internalized. By 24 h post-exposure, most fibers appeared to be 

internalized and localized by the nucleus. Cytotoxicity was determined by measurement of 

lactate dehydrogenase (LDH) from the maximum dose (26.4 μg/cm2) of both amosite and Libby 

Amphibole asbestos samples, with less than 10% LDH present following exposure to all four 

samples. Cytotoxicity was also determined for just the fractionated samples of amosite and 

Libby Amphibole asbestos by measuring intracellular calcein fluorescence emitted by live cells 

and showed 95% and 99% viability for AM2.5 and Libby Amphibole asbestos 2.5, respectively. 

These results support a limited cytotoxicity of both amosite and Libby Amphibole asbestos under 

these concentrations and time frames. 

Gene expression changes in specific inflammatory markers (IL-8, COX-2, HO-1) were 

analyzed by quantitative RT-PCR for AM, AM2.5, LA, LA2.5, and CRO at both 2 and 24h 

post-exposure (all doses). Minimal increases in gene expression of IL-8, COX-2 or HO-1 were 

observed at 2 h post-exposure to all five fiber types; at 24h post-exposure, however, a dose 

response was observed following exposure to all fiber types. The smaller size fractions resulted 

in differences in magnitude of gene expression changes between AM2.5 and LA2.5, with AM2.5 

leading to greater induction of IL-8 and COX-2 as compared to LA2.5. HO-1 levels were 

comparable between the two samples (Table D-13). Gene expression of transforming growth 

factor (TGF)-B1 was also quantified, but only following exposure to AM2.5 and LA2.5 (all 

doses; data not shown in publication). Levels of IL-8 protein were also measured following 24 h 

exposure to AM2.5 and LA2.5 (all doses) and were statistically significant at the two highest 

exposures (13.2 and 26.4 μg/cm2). Gene expression changes were also examined for 84 genes 

involved in cellular stress and toxicity using a 96-well RT-PCR array format following 24 h 

exposure to 13.2 μg/cm2 AM, LA, AM2.5 or LA2.5 or to 26.4 μg/cm2LA2.5 only. The results 

show a pro-inflammatory gene expression response. Gene expression profiles were similar 

between AM and LA, but differences were observed between AM2.5 and LA2.5. 
Table D-13. Gene expression changes following exposure to 26.4 μg/cm2

amphibole asbestos for 24 hra

Amosite (AM)

Amosite, 

fractionated 

(AM2.5)

Libby 

Amphibole 

(LA)

Libby 

Amphibole, 

fractionated 

(LA2.5)

IL-8 50 ± 7.5 120 ± 25 46 ± 8.3 37 ± 7.8

COX-2 5.4 ± 0.5 16 ± 2.8 9.0 ± 1.7 1.6 ± 0.3

HO-1 2.9 ± 0.2 4.5 ± 0.3 2.5 ± 0.2 5.1 ± 0.6

aAll results in fold change as compared to untreated control cells.

Source: Duncan et al. (2010).

To determine if surface iron on the fibers played a role in the inflammatory response, 

Duncan et al. (2010) also examined surface iron concentrations by two methodologies: 

inductively coupled plasma optical emission spectroscopy (ICP-OES) and citrate-bicarbonatedithionite (CBD). Both assays determined AM2.5 appeared to have the greatest amount of 

surface iron, with both LA samples containing much lower levels of iron. Similar patterns of 

reactive oxygen species production were observed as measured by TBA-reactive product 

formation following exposure to AM, AM2.5, LA, and LA2.5. Both AM samples were found to 

generate the greatest amount of hydroxyl radicals compared to the two LA samples, with the 

fractionated AM2.5 and LA2.5 exhibiting small increases in ROS produced compared to the 

unfractionated samples.

D.2.2 In Vitro Studies - Tremolite

The studies described are summarized in tables at the end of this section. In general, all 

fibrous tremolite samples were shown to be carcinogenic, with those containing more of the 

longer, thinner fibers (>10-μm length, <1-μm diameter) being more potent carcinogens. Most 

studies described here used weight as the measurement of fibers for exposure, with the doses 

ranging from 0 to 40 mg/animal. One set of studies did expose animals with fibers measured by 

number (100 fibers/cm3) (Bernstein et al., 2005, 2006). 

Cytotoxicity

Wagner et al. (1982) examined the in vitro cytotoxicity of three forms of tremolite (see

Table D-8) used in their in vivo studies. LDH and BGL were measured in the medium following 

incubation of unactivated primary murine macrophages to 50, 100, and 150 μg/mL of each 

sample for 18 hours. Cytotoxicity of Chinese hamster lung fibroblasts V79-4 was measured by 
methylene blue staining (fiber concentrations not given). Giant-cell formation in A549 human 

basal alveolar epithelial cell cultures was measured, using 100 and 200 μg/mL of each sample for 

5 days. Crocidolite fibers were used as the positive control. 

In all three assay systems, the Korean tremolite produced results similar to the positive 

control: increased toxicity of primary murine macrophages, increased cytoxicity of Chinese 

hamster ovary (CHO) cells, and increased formation of giant cells from the A549 cell line. The 

tremolite sample from Greenland (Sample B) did result in increased toxicity over controls, 

although to a lesser degree (statistics are not given). The authors speculate that the iron content 

in Sample B might have contributed to these results. Although differential toxicity of these 

samples was noted on a mass basis, data were not normalized for fiber content or size. The 

inference is that differential results are due, at least in part, to differential fiber counts.

In a study to further elucidate the role of ROS following exposure to asbestos, Suzuki and 

Hei (1996) examined the role of heme oxygenase (HO) in response to asbestos. HO is induced 

in response to oxidative stress and functions to degrade heme; it might, therefore, prevent 

iron-mediated hydroxyl radical production. All fibers tested led to an increase in HO, though 

chrysotile (UICC) and crocidolite (UICC) led to a greater increase than tremolite (Metsovo, 

Greece) and erionite (Rome, Oregon). No statistics, however, are described for these results. 

This study focused on response to 20 and 40 μg/mL of chrysotile and then used doses that 

yielded 0.5 and 0.3 relative survival fractions for all other fibers (crocidolite, 20 and 40 μg/mL; 

tremolite, 150 and 300 μg/mL; erionite, 200 and 400 μg/mL). Fibers were not characterized in 

this paper. When normalized by survival fraction, the induction of HO above control was 3.89-, 

3.86-, 2.75-, and 2.78-fold above background for chrysotile, crocidolite, tremolite, and erionite, 

respectively. Limited information is provided on the results of tremolite exposures beyond an 

increase in HO following an 8-hour exposure. This increased HO following exposure to 

tremolite demonstrates a response similar to that observed for crocidolite and chrysotile in this 

study. Crocidolite is further analyzed, with exposures to the antioxidants, superoxide dismutase 

and catalase, leading to a dose-dependent decrease in HO induction, which supports the role of 

HO in oxidative stress.

Wylie et al. (1997) examined the mineralogical features associated with cytotoxic and 

proliferative effects of asbestos in hamster tracheal epithelial (HTE) and rat pleural mesothelial 

(RPM) cells with a colony-forming efficiency assay. HTE cells are used because they give rise 

to tracheobronchial carcinoma, while RPM cells give rise to mesotheliomas. Cells were exposed 

to fibers by weight, number, and surface area (Table D-14). 
Table D-14. Fiber characteristics of five fibers examined in vitro for 

cytotoxic (HTE cells) and proliferative effects (RPM cells)

Sample Description (% of sample) Surface area (mm2/g) Fibers/μg Fibers ≥5μm/μg

FD14 Talc (37), tremolite (35), serpentine 

(15), other (<2), unknown (12)

6.2 ± 0.2 2.5 × 1030.8 × 103

SI57 Talc (60), tremolite (12), unknown 

(21), other (4), anthophyllite (3), 

quartz (1)

4.9 ± 0.2 1.1 × 1044.8 × 103

CPS183 Talc (50), quartz (12), unknown 

(28), tremolite (4), other (4), 

anthophyllite (3)

4.9 ± 0.4 1.1 × 1049.2 × 103

NIEHS crocidolite Riebeckite (100) 10.3 ± 1.3 5.3 × 1053.8 × 105

NIEHS chrysotile Chrysotile (100) 25.4 ± 0.5 5.3 × 1043.4 × 104

NIEHS = National Institute of Environmental Health Sciences

Source: Wylie et al. (1997).

Colony-forming efficiency assay results are expressed as the number of colonies in 

exposed cultures divided by the control colonies times 100. Increases in colony numbers 

indicate increased cell proliferation or survival in response to the exposure. Decreases in colony 

numbers indicate toxicity or growth inhibition in response to the exposure. The results of the 

analysis with fiber exposure by mass (μg/cm2) show elevated colonies in HTE cells following 

exposures to both asbestos fibers (p < 0.05) at the lowest concentrations, while significant 

decreases were observed for both asbestos fibers at the higher concentrations (0.5 μg/cm2, 

p < 0.05) (Wylie et al., 1997).

No proliferation was observed for either chrysotile or crocidolite asbestos fibers in RPM 

cells, but cytotoxicity was observed at concentrations greater than 0.05 μg/cm2(p < 0.05). All 

talc samples were less cytotoxic in both cell types. Comparing results of these samples when 

exposure is measured by fiber number, the same number of crocidolite asbestos fibers >5-μm 

long leads to proliferation in HTE cells, but proliferation did not occur for FD14 fibers. The 

other two talc samples showed both insignificant cytotoxicity (SI57) and significant cytotoxicity 

(CPS183, p < 0.05). Therefore, when measured by fiber number, the results show differential 

responses for the fibers analyzed, suggesting the mineralogy of the fibers is more important in 

determining the biological response to fibers. In the RPM cells, however, similar responses were 

seen for all fibers analyzed, except for the slight cytotoxicity of FD14 at 2.6 fibers/cm2. This 

suggests that fiber number does play a role in biological response in this cell type. 

Data analysis by surface area of these samples is shown in Table 4-29. The results of 

these samples in both cell lines demonstrated that the cellular responses seemed unrelated to the 
surface area, which demonstrates the impact of the dose metric on data. Analyzing the data for 

cytotoxicity and proliferation based on the exposure measurement demonstrated differences in 

response depending solely on how the fibers were measured: by mass, number, or surface area. 

These results show variability in interpreting the results of the same assay based on the defined 

unit of exposure. Most early studies used mass as the measurement for exposure, which can 

impact how the results are interpreted. When possible, further analysis of fiber number and 

surface area would help elucidate the role of these metrics, particularly for in vivo studies. 

Genotoxicity

Athanasiou et al. (1992) performed a series of experiments to measure genotoxicity 

following exposure to tremolite, including the Ames mutagenicity assay, micronuclei induction, 

chromosomal aberrations, and gap-junction intercellular communication. Although a useful test 

system for mutagenicity screening for many agents, the Ames assay is not the most effective test 

to detect mutations induced by mineral fibers. Mineral fibers can cause mutation through 

generation of ROS or direct disruption of the spindle apparatus during chromatid segregation. 

Fibers do not induce ROS in the Ames system, however, and the Salmonella typhimurium strains 

do not endocytose the fibers. Only one study was found in the published literature that used the 

Ames assay to measure mutagenicity of tremolite. Metsovo tremolite asbestos has been shown 

to be the causative agent of endemic pleural calcification and an increased level of malignant 

pleural mesothelioma (see Section 4.1). To measure the mutagenicity of Metsovo tremolite, 

S. typhimurium strains (TA98, TA100, and TA102) were exposed to 0–500 μg/plate of asbestos 

(Athanasiou et al., 1992). This assay demonstrated that, like most asbestos fiber types tested in 

earlier studies, Metsovo tremolite did not yield a significant increase in revertants in the Ames 

assay, including in the TA102 Salmonella strain, which is generally sensitive to oxidative 

damage. Although these strains can detect ROS mutations, they would not be able to produce 

ROS from fibers alone or through necessary signaling pathways, and they do not endocytose 

fibers. Thus, negative results in the Ames assay do not inform the cytotoxicity of Metsovo 

tremolite.

Furthermore, this study demonstrated the clastogenic effects of tremolite, including 

chromosomal aberrations and micronuclei induction. Tremolite exposure (0–3.0 μg/cm2) in 

Syrian hamster embryo (SHE) cells resulted in a statistically significant increase in chromosomal 

aberrations (p < 0.02) when all treatment groups were combined and then compared to controls; 

however, no clear dose-response relationship was evident (Athanasiou et al., 1992). Tremolite 

exposure in SHE cells did lead to a dose-dependent increase in chromosome aberrations that was 

statistically significant at the highest doses tested (1.0–3.0 μg/cm2) (p < 0.01) (Table D-15). 
Table D-15. Micronuclei induction (BPNi cells) and chromosomal 

aberrations (SHE cells) following exposure to tremolite for 24 hours

Asbestos dose (μg/cm2)

Micronuclei incidence/1,000 

cells

Chromosomal aberrations (including 

chromatid gaps, breaks, isochromatid 

breaks, and chromosome type)

0 17 3

0.5 31a4

1.0 70b12c

2.0 205b9

a

3.0 Not tested 13c

a

Significantly different from control (p < 0.05).

b

Significantly different from control (p < 0.01).

c

Significantly different from control (p < 0.02).

Source: Athanasiou et al. 1992

Micronuclei induction was measured in BPNi cells after 24-hour exposure to 

0–2.0 μg/cm2tremolite. A statistically significant dose-dependent increase in levels of 

micronuclei was demonstrated following tremolite exposure at concentrations as low as 

0.5 μg/cm2(p < 0.01). Literatures searches did not find tremolite tested for clastogenicity in 

other cell types, but the results of this study suggest interference with the spindle apparatus by 

these fibers. No analysis was performed to determine if fiber interference of the spindle 

apparatus could be observed, which would have supported these results.

To determine if tremolite has some tumor promoter characteristics, Athanasiou et al. 

(1992) further examined intercellular communication following exposure to 0–4.0 μg/cm2

tremolite in both Chinese hamster lung fibroblasts (V79) and Syrian hamster embryo BPNi cells, 

which are sensitive to transformation. Inhibition of gap-junctional intercellular communication 

has been proposed to detect tumor-promoting activity of carcinogens (Trosko et al., 1982). No 

effect on the gap-junctional intercellular communication following tremolite exposure was 

observed.

Okayasu et al. (1999) analyzed the mutagenicity of Metsovo tremolite, erionite, and the 

man-made ceramic (RCF-1) fiber. Whether this tremolite is the same as that used in previous 

studies from this group is unclear. Tremolite from Metsovo, Greece, used in this study was 

characterized as 2.4 ± 3.1 μm long and 0.175 ± 0.13 μm diameter (arithmetic mean) with the 

number of fibers per microgram of sample equal to 1.05 × 105. Human-hamster hybrid A(L) 

cells contain a full set of hamster chromosomes and a single copy of human chromosome 11. 
Mutagenesis of the CD59 locus on this chromosome is quantifiable by antibody 

complement-mediated cytotoxicity assay. The authors state that this is a highly sensitive 

mutagenicity assay, and previous studies have demonstrated mutagenicity of both crocidolite and 

chrysotile (Hei et al., 1992). The cytotoxicity analysis for mutagenicity was performed by 

exposing 1 × 105 A(L) cells to a range of concentrations of fibers as measured by weight 

(0–400 μg/mL or 0–80 μg/cm2) for 24 hours at 37°C. CD59 mutant induction showed a 

dose-dependent increase in mutation induction for erionite and tremolite, but RCF-1 did not.

Summary:

In vitro studies have been conducted with Libby Amphibole asbestos from the Zonolite 

Mountain mine. These studies demonstrated an effect of Libby Amphibole asbestos on 

inflammation and immune function (Blake et al., 2007; 2008; Hamilton et al., 2004; Duncan et 

al., 2010), oxidative stress (Hillegass et al., 2010), and genotoxicity (Pietruska et al., 2010). 

These results suggest that Libby Amphibole asbestos may act through similar mechanisms as 

other forms of asbestos, but data gaps still remain to determine specific mechanisms involved in 

Libby Amphibole asbestos-induced disease. 

Studies that examined cellular response to tremolite also found that fiber characteristics 

(length and width) play a role in determining ROS production, toxicity, and mutagenicity 

(Wagner et al., 1982; Okayasu et al, 1999). As with the in vivo studies, the definition of fibers 

and how the exposures were measured varies among studies. 
1

Appendix E. Evaluation of Exposure-Response Data for Discrete Pleural Thickening in 

Workers from the Marysville Cohort

1. STATISTICAL ANALYSIS OF THE 2004 POST 1972 DATA SET

All analyses were performed using SAS® statistical software v. 9.1. BMDLs were obtained by 

the profile likelihood method as recommended by Crump and Howe (1985) using the NLMIXED 

procedure in SAS (Wheeler, 2005).

Investigation of Explanatory Variables

Dichotomous statistical models describing the probability of individual response as a function of 

cumulative exposure as measured by CHEEC in the units of fibers-yr/cc. In order to investigate 

the key explanatory variables for analysis, a forward selection process was used to evaluate the 

association of each of the potential covariates with odds of discrete pleural thickening, 

controlling for CHEEC. Covariates considered for inclusion in the model were time since first 

exposure, age at x-ray, gender, smoking history and BMI. This initial modeling was done using 

a standard logistic regression model as commonly applied in analysis of epidemiological data. 

The base model was a logistic regression model with cumulative exposure (natural log 

transformed) as the independent variable. This model provided an adequate fit to the data 

(Hosmer-Lemeshow p-value of 0.7073), and the exposure variable was statistically significantly 

associated with the outcome (beta [SE] = 0.5398 [0.2374], p-value=0.0230). Covariates were 

evaluated according to whether inclusion of the covariate improved model fit as assessed by the 

AIC, and statistical significance of the covariate. When controlling for cumulative exposure, 

inclusion of each of the covariates increased the AIC for the model, and none were associated 

with odds of discrete pleural thickening: time since first exposure -- p-value=0.9350; age at x-ray 

-- p-value=0.6822; gender – p-value=0.7734; smoking -- p-value=0.1905; BMI -- pvalue=0.3206. Therefore, only exposure (CHEEC) was included in further analyses. See Table 

E-1.
2

TABLE E-1 Evaluation of covariates for the 2004 post 1972 set

*Note that only 98 observations used, due to missing values (AIC not comparable)

Investigation of Candidate Models

The candidate models were: logistic (with CHEEC considered as continuous and continuous with 

a natural logarithm transformation), probit (with CHEEC considered as continuous and 

continuous with a natural logarithm transformation), 3-parameter log-logistic, dichotomous Hill, 

and dichotomous Michaelis-Menten models. These are statistical models used to evaluate 

dichotomous data, and were considered appropriate given the supralinear nature of the observed 

relationship between Libby Amphibole exposure and prevalence of discrete pleural thickening. 

All of the candidate models had adequate fit as assessed by the Hosmer-Lemeshow test (a form 

of the Pearson chi-squared goodness of fit statistic) (Hosmer and Lameshow, 2000). BMDs and 

corresponding BMDLs were estimated for each of the candidate models. For the BMD 

estimates, a 10% BMR was used (see Section 5.2.2). Models were compared using the AIC--

values were quite similar among the candidate models, ranging from 75.7 to 78.1. The model 

with the lowest AIC was the 3-parameter log-logistic model (AIC=75.7). Since BMD and 

BMDL estimates were similar from the set of candidate models, the 3-parameter log-logistic 

model was selected. See Table E-2.

Different exposure lags (0, 5, 10, 15 and 20 years) were then investigated for this model. The 

AIC values did not vary much for lags of 0 to 15 years (AIC ranging from 75.1 to 75.7); the 10-

year lagged exposure provided the lowest AIC, and was selected as the preferred exposure 

metric. There was a notable decrease in model fit when using the 20-year lagged exposure (AIC 

of 77.0). In addition, there were cases of discrete pleural thickening in the full cohort with fewer 

than 20 years since first exposure; therefore, using such a long lag (which necessitates the 

assumption that these are background cases) was not judged to be appropriate.

The various model forms were compared using Akaike’s Information Criterion (AIC), and 

general model fit was evaluated with the Hosmer-Lemeshow test. This is a goodness-of-fit test 

which compares observed and expected events. Observations are sorted in increasing order of 

estimated probability of the event occurring and then divided into ~10 groups. The GOF statistic 

is calculated as the Pearson chi-square statistic of observed and expected frequencies in these 

groups. The BMD was estimated for each candidate model using a BMR of 10% and assuming a 

Covariate Wald p-value for 

beta coefficient 

corresponding to 

covariate

Wald p-value for 

beta coefficient 

corresponding to 

exposure

AIC

Base model (only 

ln(CHEEC)) 

-- 0.0230 76.2

Time since first exposure 0.9350 0.0375 78.2

Age at x-ray 0.6822 0.0219 78.0

Gender 0.7734 0.0236 78.1

Smoking history 0.1905 0.0298 76.3

BMI* 0.3206 0.0110 57.4
3

background rate of 1%. BMDLs were obtained by the profile likelihood method as 

recommended by Crump and Howe (Crump and Howe, 1985), using the NLMIXED procedure 

in SAS (Wheeler, 2005).

The 3-parameter log-logistic model using the 10-year lagged exposure had a p-value for fit of 

0.74, an AIC value of 75.1, and the exposure variable was statistically significantly associated 

with odds of discrete pleural thickening (beta [SE]=0.6524 [0.2741], p-value=0.0189). This 

model yielded a BMD of 0.2543 fibers-yr/cc, and corresponding BMDL of 0.0757 fibers-yr/cc 

for a 10% increase in prevalence of discrete pleural thickening. See Table E-2 and Figure E-1.

TABLE E-2 Evaluation of different model forms for the 2004 post 1972 set

*Parameter estimates for the best fitting model are as follows: intercept = -1.3040 (SE=0.4378), 

b=0.6524 (SE=0.2741, p-value=0.0189). 

†For statistical modeling, the equivalent model form was used: P(CHEEC) = bkg + (Plateau –

bkg) / [1 + exp(-a – b*ln(CE))]

± For statistical modeling, the equivalent model form was used: P(CE) = bkg + (Plateau – bkg) / 

[1 + exp(-a – ln(CHEEC))]

Model Exposure Metric Form AIC HosmerLemeshow 

GOF p-value

BMD BMDL

Logistic CHEEC P(CE)=1/[1+exp(-a-b*CE)] 78.3 0.7442 0.5715 0.2547

Logistic ln(CHEEC) P(CE)=1/[1+exp(-a-b*ln(CE))] 76.2 0.7073 0.2490 0.0558

Probit model CHEEC P(CE)=Φ(a+b*CE) 77.9 0.7726 1.5267 0.9185

Probit model ln(CHEEC) P(CE)=Φ(a+b*ln(CE)) 76.6 0.6650 0.2276 0.0472

3-parameter 

log-logistic

ln(CHEEC P(CE)=bkg+(1 – bkg) / [1 + exp(-a –

b*ln(CE))]

75.7 0.7339 0.3447 0.0956

 CE, lag 5 75.3 0.5519 0.2980 0.0887

 CE, lag 10* 75.1 0.6833 0.2543 0.0757

 CE, lag 15 75.4 0.4226 0.1993 0.0536

 CE, lag 20 77.0 0.6363 0.1258 0.0196

Dichotomous 

Hill†

ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE^b / 

[exp(-a)+CE^b]

77.7 0.6724 0.3295 0.0961

MichaelisMenten±ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE / 

[exp(-a)+CE]

75.8 0.5413 0.3330 0.1342
4

Figure E-1. Observed prevalence of discrete pleural thickening and estimated probability 

of discrete pleural thickening

DERIVATION OF THE CANDIDATE POD AND RFC FOR DISCRETE PLEURAL

THICKENING USING THE 3-PARAMETER LOG-LOGISTIC MODEL

The candidate POD is 0.0757 fibers-yr/cc, the BMDL10 for this data set. RfC is derived from the 

POD using the duration of exposure of 70 years, lagged by 10 years, and a total uncertainty 

factor of 100. See Section 5.1.3.2. This is the first approach referred to in Section 5.2.3.1.

RfC = 0.0757 fibers-yr/cc x 1/ (70 – 10) years x 1/100 = 1E-05 fibers/cc (rounded to 1 

significant digit)

2. STATISTICAL ANALYSIS OF THE FULL DATA SET

Identification of Key Explanatory Variables

In order to begin modeling the data, key explanatory variables were identified using logistic 

regression to analyze the data of Rohs et al. (2008). Logistic regression was performed using the 

R statistical software version 2.11.1. All fitting was performed using individual data, without 

any grouping. The dependent variable was discrete pleural thickening (n = 59) noted on chest xrays of former workers in the Marysville facility (n = 252) and no reported history of exposure to 

commercial asbestos at other locations. The available potential explanatory variables included 

CE (cumulative exposure at the time of x-ray, f-yrs/cc; equivalent to CHEEC used in the 

University of Cincinnati report), T (time between first exposure and date of x-ray, yrs), age at 

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6

Prevalence of discrete pleural thickening

CHEEC (fiber-yr/cc)

Prevalence by quartiles 

among cases of unlagged 

exposure

Estimated prevalence by 

exposure lagged by 10 

years
5

time of x-ray, gender, smoking status (ever, never), and body mass index (BMI). The BMI 

variable was missing for 34 individuals.

Initial analysis showed that CE was a significant explanatory variable using both CE and 

log(CE). The strategy used to determine what other explanatory variables were influential 

consisted of including CE and then adding one additional explanatory variable at a time. 

Explanatory variables having p > 0.2 were dropped from further consideration. Explanatory 

variables having p < 0.2 were given further consideration.

Body mass index (BMI) was investigated as a potential explanatory variable because fat pads can 

sometimes be misdiagnosed as pleural thickening. Thus, there might be a positive relation 

between BMI and pleural thickening. Analysis of a model with CE or log (CE) plus BMI (n = 

218) showed that BMI was not a significant explanatory variable. Two subsequent models using 

BMI cutoffs of 25 and 30 also showed that BMI was not a significant explanatory variable. 

Analysis of a model with CE or log (CE) plus smoking indicated smoking was not a significant 

explanatory variable.

Analysis of a model of CE plus gender indicated gender was a potential contributing explanatory 

variable (p = 0.18). However, it should be noted that the worker cohort was highly imbalanced 

with 236 males and 16 females. Only 3 females have a cumulative human equivalent exposure 

greater than 0.15 f-yr/cc. These considerations indicated that the potential relevance of gender as 

an explanatory variable should be viewed with caution. Analysis of log (CE) plus gender 

showed that gender was not a significant explanatory variable. Accordingly, gender was 

eliminated as an explanatory variable.

The importance of T (the time from first exposure until the time of examination) is clearly 

illustrated by comparing the results of Lockey et al. (1984) with the results of Rohs et al. (2008). 

These two studies were conducted in the same occupational cohort 24 years apart. In the initial 

study (Lockey et al., 1984), only 2% of the individuals showed pleural changes; in the follow-up 

study (Rohs et al., 2008), 28% of the individuals showed pleural changes. Logistic fitting of a 

model including CE or log (CE) plus T showed that T was a highly significant explanatory 

variable with p < 0.0005. This result is consistent with findings in other occupational cohorts 

exposed to various forms of asbestos fibers that the time from first exposure is a significant 

explanatory variable, even in the absence of continued exposure (Ehrlich et al., 1992; Järvholm, 

1992). T was retained as an explanatory variable. However, an important point of clarification 

is that the T variable is not the same as time of event. The discrete pleural thickening could have 

formed at any time before the x-ray was taken (for example, discrete pleural thickening detected 

in 2004 could have been present in 1990).

Analysis of a model of CE plus age at x-ray indicated that age was a significant explanatory 

variable with p = 0.032. Analysis of a model of log (CE) plus age at x-ray showed that age at xray was a potentially significant explanatory variable with p = 0.14. It should be noted that this 
6

result does not mean that age is an independent risk factor for the development of discrete pleural 

thickening. In fact there is no biological evidence that age is an independent predictor of the 

development of discrete pleural thickening without a history of previous exposure to durable 

mineral fibers such as amphibole fibers. Rather, it is likely that the association between age and 

prevalence is because age at x-ray is related to time from first exposure (T), which is clearly one 

of the key explanatory variables. Therefore, age at x-ray was not included as an explanatory 

variable.

Selection of Model Form

Figure E-1 (Panel A) presents a plot of prevalence of discrete pleural thickening as a function of 

time from first exposure (T), stratified by cumulative exposure (CE). As seen, the prevalence 

appears to be low (close to zero) until about 15-20 years after first exposure and then appears to 

rise in a non-linear fashion. Figure E-1 (Panel B) presents a plot of prevalence as a function of 

cumulative exposure, stratified according to time from first exposure. As seen, prevalence 

appears to rise rapidly with increasing cumulative exposure, but then tends to flatten out 

(plateau).
7

FIGURE E-1 Raw data plots

Panel A. Prevalence vs Time Since First Exposure (Grouped by CE)

CE Bins

Bin No. Min Max Mean N Cases Prev

CE1 0 0.05 0.021 67 2 3.0%

CE2 0.05 0.1 0.071 44 1 2.3%

CE3 0.1 0.2 0.145 108 10 9.3%

CE4 0.2 1 0.452 101 20 19.8%

CE5 1 35 9.728 114 28 24.6%

Panel B. Prevalence vs CE (Grouped by Time Since First Exposure)

T Bins

Index Min Max Mean N Cases Prev

T1 0 10 4.39 87 1 1.1%

T2 10 20 12.69 53 0 0.0%

T3 20 30 25.41 123 8 6.5%

T4 30 40 34.50 118 27 22.9%

T5 40 50 45.76 53 25 47.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50

Time Since First Exposure (T, years) Prevalence

CE 1

CE 2

CE 3

CE 4

CE 5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12

CE (f-yrs/cc) Prevalence

T1

T2

T3

T4

T5
8

Based on these attributes of the base data set, the objective was to select a model that included a 

plateau term whose value depended on T. Several alternative model forms were investigated, 

using the Dichotomous Hill model as the starting point:

p(CE) = bkg + (Plateau – bkg) / [1 + exp{-a – b*ln(CE)}]

In the Dichotomous Hill model, the plateau term is a constant, with a value bounded between 

background and 1.0. In order to be consistent with the data, this model was modified so that the 

plateau term was a function of T. Several different non-linear equations for the plateau function 

were tested, including the following:

- Plateau = MIN[1, bkg +(1-bkg)*k1*T]

- Plateau = MIN[1, bkg +(1-bkg)*k1*T2]

- Plateau = MIN[1, bkg +(1-bkg)*k1*T3]

- Plateau = bkg + (1-bkg)* Φ(T|m,s), where Φ(T|m,s) = cumulative normal probability 

function

- Plateau = bkg + (1-bkg)* G(T|α,β), where G(T|α,β), = cumulative gamma probability 

function

- Plateau = bkg + (1-bkg)* W(T|α,β), where W(T|α,β) = cumulative Weibull probability 

function

Based on Akaike’s Information Criterion (AIC), the cumulative normal equation was found to fit 

the data best. Combining this equation for the plateau term with the basic probability model 

yields:

p (CE,T) = bkg + (1 – bkg) * Φ(T|m,s) / [1 + exp{-a – b*ln(CE)}]

Further testing indicated that the lowest AIC was achieved when the b term was set to 1.0, 

resulting in a modified version of the discrete Michaelis-Menten equation:

p (CE,T) = bkg + (1 – bkg) * Φ(T|m,s) / [1 + exp{-a –ln(CE)}]

This equation can also be written as:

p (CE,T) = bkg + (1 – bkg) * Φ(T|m,s) * {CE/[CE + exp(-a)]}

This equation was selected as the preferred model for fitting to the data. In this model, T (years) 

and CE (f-yrs/cc) are explanatory variables. Fitting parameters are m, s, and a. Background is a 

constant (0.01).

Parameterization

Fitting of the model to selected data sets was performed using the method of maximum 

likelihood (MLE), using individual data without binning. The BMD for any specified value of T 

is calculated from the MLE parameters and the specified value of T as follows:

BMDT = exp [-a – ln{Q*Φ(T|m,s)-1}]

Where:

Q = (1-bkg) / (BMR – bkg)

For a BMR of 10% extra risk, the value Q is 0.10.
9

Model Fitting Results 

Table E-3 provides the model fitting results for each of the three data sets evaluated for each of 5 

lags of CE and for each of 5 values of T. In all cases, the Benchmark Response (BMR) is 10% 

extra risk. Based on a background rate of 0.01, this BMR corresponds to a probability of 0.109.
10

TABLE E-3. MODEL FITTING RESULTS FOR THE FULL DATA SET

Study

Year 

of 

Hire

N Cases CE

Lag

MLE Parameters T = 30 T = 35 T = 40 T = 50 T = 70

m s a AIC BMD BMDL BMD BMDL BMD BMDL BMD BMDL BMD BMDL

1980+

2004 All 434 61

0 42.38 13.30 1.977 278.02 0.1822 0.0709 0.0731 0.0260 0.0421 0.0138 0.0224 0.0067 0.0157 0.0042

5 42.44 13.54 2.000 277.87 0.1711 0.0666 0.0707 0.0253 0.0412 0.0136 0.0221 0.0066 0.0154 0.0042

10 42.58 14.10 2.061 277.61 0.1477 0.0580 0.0651 0.0235 0.0389 0.0129 0.0212 0.0064 0.0146 0.0040

15 42.86 15.16 2.167 277.67 0.1166 0.0486 0.0567 0.0219 0.0352 0.0124 0.0197 0.0062 0.0133 0.0038

20 43.28 16.06 2.395 279.11 0.0876 0.0349 0.0449 0.0159 0.0286 0.0091 0.0162 0.0045 0.0107 0.0028

1980+

2004

≥ 

1972 198 13

0 31.41 10.47 -0.015 88.85 0.2930 0.1023 0.1900 0.0399 0.1462 0.0227 0.1177 0.0136 0.1128 0.0109

5 31.58 11.81 0.095 88.43 0.2623 0.0956 0.1770 0.0399 0.1374 0.0232 0.1082 0.0142 0.1011 0.0112

10 3.5E+05 3.0E+06 0.162 87.81 0.2402 0.0905 0.2402 0.0432 0.2402 0.0262 0.2402 0.0162 0.2402 0.0123

15 1.4E+06 5.4E+06 6.5E-01 88.29 0.1766 0.0643 0.1766 0.0315 0.1766 0.0185 0.1766 0.0107 0.1766 0.0075

20 2.1E+06 4.2E+06 1.5E+00 91.23 0.1036 0.0220 0.1036 0.0059 0.1036 0.0029 0.1036 0.0013 0.1036 0.0007

1980+

2004

< 

1972 236 48

0 43.15 13.33 2.259 192.77 0.1689 0.0227 0.0613 0.0071 0.0341 0.0037 0.0175 0.0017 0.0119 0.0010

5 43.22 13.46 2.331 192.86 0.1540 0.0190 0.0569 0.0059 0.0318 0.0031 0.0164 0.0014 0.0111 0.0008

10 43.44 13.88 2.472 193.04 0.1270 0.0092 0.0492 0.0028 0.0279 0.0015 0.0145 0.0007 0.0097 0.0004

15 43.71 14.83 2.625 193.34 0.0934 -- 0.0406 -- 0.0241 -- 0.0128 -- 0.0084 --

20 44.18 15.84 2.903 194.27 0.0642 -- 0.0303 -- 0.0185 -- 0.0101 -- 0.0065 --
11

The BMD for any specified value of T is calculated from the model parameter estimates and the specified value of T as follows:

BMD = exp[-a – ln{(1-bkg) / (BMR – bkg)*Φ(T|m,s)-1}]

The BMDL is estimated by re-writing the model so that BMD appears as an explicit term in the model, for a specified T of interest:

BMR = bkg + (1-bkg)*Φ(T|m,s) / (1 + exp(-a – ln(BMD)]

Solving for a yields: -a = ln[Q*Φ(70|m,s)-1] + ln(BMD70) 

Substituting yields: p(CE,T) = bkg + (1-bkg)*Φ(T|m,s) / [1+ exp(z’)]

Where: z’ = ln(Q*Φ(70|m,s)-1) + ln(BMD70) – ln(CE)

Simplifying yields: p(CE,T) = bkg + (1-bkg)*Φ(T|m,s) / [1+ Q*Φ(70|m,s)-1)* BMD70/ CE]

Using this equation, a trial value of the BMD is selected and treated as a constant, and the equation is re-fit to the data to find the MLE values of the remaining 

parameters (m, s). After optimization, the value of the log-likelihood is recorded for the specified trial value of the BMD, and the process is repeated for other 

trial values of the BMD. The BMDL is the trial value of the BMD where the log-likelihood decreases from the MLE log-likelihood value by an amount equal to 

CHIDIST (2α, 1)/2. For α = 0.05, the decrease is 1.3528.
12

Inspection of this table reveals that, for each of the three data sets evaluated, there is relatively 

little effect of CE lag over the interval 0-15 years. For the full data set and the post-1972 data 

set, the lowest AIC is achieved for a lag of 10 years. However, the difference in AIC values 

between lags of zero, 5, and 10 years is sufficiently small that the difference is not considered to 

be significant. 

Figure E-2 presents a graph comparing the observed data to the predicted values from the model 

(no lag) for the full data set. As above, this requires grouping the observed data into bins, even 

though fitting was performed using the individual data. Because the choice of bins is arbitrary, 

the appearance of the graphs would likely be changed somewhat if different bins were chosen. 

Nevertheless, it seems apparent that the model predictions are in good accord with the data.
13

FIGURE E-2 Observed versus predicted for base-case data set

Panel A. Observed vs Predicted Prevalence as a Function of Time Since First Exposure (Grouped by CE)

CE Bins

Index Min Max Mean N Cases Prev

CE1 0 0.05 0.021 67 2 3.0%

CE2 0.05 0.1 0.071 44 1 2.3%

CE3 0.1 0.2 0.145 108 10 9.3%

CE4 0.2 1 0.452 101 20 19.8%

CE5 1 35 9.728 114 28 24.6%

Panel B. Observed vs Predicted Prevalence as a Function of CE (Grouped by Time Since First Exposure)

T Bins

Index Min Max Mean N Cases Prev

T 1 0 10 4.39 87 1 1.1%

T 2 10 20 12.69 53 0 0.0%

T 3 20 30 25.41 123 8 6.5%

T 4 30 40 34.50 118 27 22.9%

T 5 40 50 45.76 53 25 47.2%

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 2 4 6 8 10 12

CE (f-yrs/cc) Prevalence

T 1

T 2

T 3

T 4

T 5

symbols = data

lines = predicted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50

Time Since First Exposure (yrs) Prevalence

CE1

CE2

CE3

CE4

CE5

symbols = data

lines = predicted
14

Derivation of the POD and RfC for Discrete Pleural Thickening Using the Cumulative

Normal Michaelis-Menten Model

A candidate POD and RfC is derived for discrete pleural thickening from the combined 1980 + 

2004 data set as this data set provides the widest distribution of T values. A lag period of five 

years is used because Larson et al. (2010b) showed that discrete pleural thickening could be 

observed much earlier than previously thought.

Because the RfC is intended to provide protection for a lifetime of exposure (exposure begins at 

birth and continues to age 70), the point of departure is the BMDL10 with T = 70 years of 0.0041 

fibers-yr/cc calculated with the CumNorm Michaelis-Menten model. The POD is divided by 

duration of exposure of 70 years, lagged by 5 years, and then divided by an uncertainty factor. 

In this case as the model accounts for the full lifetime of exposure of 70 years, the uncertainty 

factor of 100 is reduced to 30. 

RfC = 0.0041 fibers-yr/cc x 1/ (70 -5 years) x 1/30 = 2E-06 fibers/cc (rounded to one significant 

digit)

To provide a frame of reference, the calculation above was repeated with the data set restricted to 

those hired in 1972 or later when industrial hygiene data were collected in the facility.

RfC = 0.0141 fibers-yr/cc x 1/ (70 – 5) years x 1/30 = 7E-06 fibers/cc (rounded to one significant 

digit)

The reasonably good correlation in the calculated RfCs with the two different data sets provides 

some confidence in the exposure reconstruction pre 1972.

An alternative candidate POD is the BMDL10 with T = 40 years of 0.0134 fibers-yr/cc calculated 

with the Cumulative Normal Michaelis-Menten model. The BMDL10 with T = 40 years is used 

because it is near the upper end of the range of T values available in the data set (Tmax = 47.375 

years). A lag time of 5 years and a total uncertainty factor of 100 are used. See Section 5.1.3.3. 

This is the third approach referred to in Section 5.2.3.1.

RfC = 0.0134 fibers-yr/cc x 1/ (40 – 5) years x 1/100 = 4E-06 fibers/cc (rounded to one 

significant digit).

Sensitivity Analysis

For purposes of comparison, RfC values were calculated using alternative modeled values of T 

for the full data set (all workers) and those hired in 1972 or later. These results are summarized 

in Table E-4. The calculations for the RfC based on T = 30, T = 35, T = 40, and T = 50 used a 

lag of 5 years and a total uncertainty factor of 100. The calculations for the RfC based on T = 70 

used a lag of 5 years and a total uncertainty factor of 30, as the model accounts for the full 

exposure of 70 years.
15

TABLE E-4 Sensitivity analysis of RfC based on alternative modeled values of T 

The University of Cincinnati increased the exposure metric by a factor of 2 between 1972 and 

1967 to account for conditions in the facility before engineering controls were added. For the 

purposes of comparison, the cumulative exposure was also calculated without this doubling. 

Plots of prevalence of discrete pleural thickening with these two different exposure metrics are 

virtually identical (not shown).

One worker in the 1980 study was exposed only 5 months before x-ray and showed discrete 

pleural thickening. Excluding this worker from the analysis did not change the result.

Figure E-4 shows a plot of the PODs (fibers-yr/cc) versus time from first exposure (years) 

calculated from each of the models. Because the 3-parameter loglogistic model is independent 

of time from first exposure, the mean value of T for the data set is used. As there are few 

individuals with long T and low cumulative exposure, it is not clear whether the apparent plateau 

with the Cumulative Normal Michaelis-Menten model is a reflection of the limitation of the data 

or an expression of the underlying biology.

Basis All 

Workers

Hired in 1972 or 

Later

T = 30 2.6E-05 3.8E-05

T = 35 8.3E-06 1.3E-05

T = 40 3.8E-06 6.8E-06

T = 50 1.4E-06 3.6E-06

T = 70 2.1E-06 7.2E-06
16

Figure E-3 PODs (fibers-yr/cc) versus time from first exposure (years)

0.0666

0.0253

0.0136

0.0066

0.0042

0.0757

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 20 40 60 80

Fibers-yr/cc

Time Since First Exposure (years) 

CumNorm MM Model All 

Data

LogLogistic Model, 2004 

Post 1972
1

Appendix F: Marysville Worker Occupational Exposure Reconstruction

The Development of a Cumulative Human Equivalent Exposure Concentration 

BY:

James E. Lockey, MD, MS

Carol Rice, Ph.D.

Eric Borton, BS

Timothy Hilbert, MS

Grace LeMasters, Ph.D.

University of Cincinnati

Department of Environmental Health

3223 Eden Ave., ML 0056

Cincinnati, OH 45267
2

Introduction

This project builds on the previous work of Dr. James Lockey et al. investigating possible effects 

of exposures to dust containing Libby amphiboles at a plant in Marysville, Ohio1,2. The data used 

in the original exposure reconstruction and as reported in the published manuscripts, was based 

on the exposures measurements available at that time1. This exposure reconstruction is based on

approximately five times additional occupational fiber exposure data than was previously 

utilized in 1980. These exposure measurements were recently obtained by the US Environmental 

Protection Agency (EPA) from the company and through trial transcripts from the United States 

of America vs. WR Grace, et al., as well as the archived data used in the 1980 exposure 

reconstruction. Four steps were undertaken to construct an exposure matrix describing exposure 

over each year from 1957 to 2000. In a final fifth step, this matrix was used to calculate an 

exposure metric for workers. 

1. Data searches, requests, and document selection

2. Document evaluation, data entry, cleaning, editing and standardization

3. Completeness and trends in measurements

4. Decisions relevant to the exposure matrix

5. Development of a cumulative human equivalent exposure concentration 

1. Data searches, requests, and document selection

Three sources of paper records were identified. First, sampling reports from OM Scott 

that included measurements at the facility pre- and post-1980 were received via the EPA. These 

reports contained both measurement results and information about the plant. OM Scott was also 

contacted with a request for available maps of the plant layout prior to 1980. Secondly, archived 

files from the Lockey et al, (1984) study were identified. Lastly, as a result of the recent WR 

Grace trial, there was additional discovery of material relevant to the OM Scott plant. The 

Department of Justice (DOJ) was contacted for the release of these data. There were seven 4” 

binders available for review and every page (approximately 3,150 pages) was scanned visually to 

identify pages relevant to the current project. Aspects of particular interest included the 

manufacturing process, usage and source of raw materials, engineering and design changes in the 

plant, work practices and exposure assessment methodology. Approval was received from the 

DOJ to utilize the relevant data for this project. 

2. Document evaluation, data entry (qualitative and quantitative), cleaning, editing and 

standardization

All of the records--both the qualitative and quantitative--were reviewed in this second phase. 

2.1. Qualitative information:
3

Written reports, letters, memos and notes contained background information on plant operations. 

A total of 1,489 pages were read for potentially useful and pertinent information regarding OM 

Scott and abstracted into a data file. From these records, we obtained:

Plant layout, including changes over time. This allowed us to associate the descriptions 

used on air sampling data forms/reports with jobs or departments within the plant. A 

limited number of aerial images were available to identify major structures.

Process descriptions were derived including workers per shift, workers per department, 

sources of raw materials, and raw material volume in number of railroad cars received, 

tonnage of railroad cars from Libby and South Carolina, and tonnage of unexpanded 

vermiculite received. 

For each department a list of job titles and tasks 

Gaps in understanding were filled-in with information gathered from the focus groups, 

specifically regarding: 

Plant lay-out and changes over time, including engineering controls

Historical pattern of job rotations within department from 1957 to 1980 

Time spent in work locations at the plant site

Overtime associated with departments and season

Use/non-use of respirators.

2.2 Quantitative data:

Air sampling reports include quantitative measurement of airborne dust and fiber 

concentration associated with a department job. These records were computerized following the 

data entry scheme provided on June 1, 2009 and approved. Records were double entered and 

verified.

Two identical Microsoft Access databases were created for initial and duplicate entry of 

the quantitative data. Each individual performing data entry had a unique and separate database 

to avoid possible data entry confusion. Variables to be entered have been previously provided. 

A random 10% check of entered data was conducted throughout the data entry process to 

maintain quality of data, to address data entry questions and to resolve potential database issues. 

Data entry differences were below 5% throughout the entry process. 

Each record was assigned a document and record identification (ID) number. The 

document ID variable was based on data source. For example, if the data were provided by the 

EPA from OM Scott then the EPA document ID was used. Data hardcopies from the EPA, 

Department of Justice and 1980 UC data were each numbered starting from 1. The document ID 

variable states EPA, DOJ or UC followed by the document number. Record IDs were generated 
4

by using a unique identifier like a sample number for each document. If a unique identifier was 

unable to be discerned then the entry personnel was instructed to consecutively number each 

sample per document starting from 1.

A final verification of data entry used SAS version 9.2 PROC COMPARE to import the 

initial and duplicate Access tables. Discrepancies were below 5% as a result of the 10% random 

checks throughout the entry process. All discrepancies were addressed by reviewing the original 

document. The initial and duplicate Access databases were archived. A copy of the initial 

database was converted to Microsoft Excel format for ease of standardization and analyses.

2.3 Process of standardization

The standardization process included categorizing entered data into appropriate variable 

fields, spell checking, identifying duplicate record entry from duplicate documents, merging 

records for the same sample or measurement, evaluating data for completeness and categorizing 

groups of data based on type of sample or measurement.

Data were reviewed and edited to ensure the information was entered into the appropriate 

data field. A frequency of the data fields using SAS 9.2 PROC FREQ identified spelling 

differences and patterns to ensure correct labeling of the data. Additional data variables were 

created depending on recognized need to distinguish important pieces of data. A new 

variable called group ID was created to identify, track and consolidate partial and/or complete 

duplicate data into one unique sample. Partial data were identified on a combination of sample 

date, sample record ID, sample result, volume, sampling time and/or document patterns. A 

document pattern would include instances where only a group of sample results were available in 

one document and another document(s) would match the exact sequence of sample results.

Data were further categorized based on the type of sample. Categories include dust 

samples, bulk samples, personal and area fiber samples, limit of detection (LOD) or 

quantification (LOQ) samples, off-site locations, and time weighted average samples. Some 

samples were collected with a direct reading fibrous aerosol monitor, but these were not used as 

there was no calibration information included in the records. Thus, only the fiber count data 

collected with a sampling pump were used. In addition, group IDs lacking a sample result, 

sample year or department were excluded.

Personal and area samples were plotted by year and department and found to be visually 

similar. In addition the range, means, and standard deviations were approximately equal. 

Therefore, personal and area sample datasets were merged and both utilized for the development 

of the Exposure Matrix. Group IDs with only LOD or LOQ values were grouped by year and 

categorized as trionize or background. In order to assign an estimate for the LOD or LOQ the 

median value of each group was divided by two and assigned to all samples in that group. Given 

the small number of LOD and LOQ samples (n=35), it is unlikely any detectable bias was 
5

introduced using this method. Time weighted average (TWA) values were not utilized when the

individual measurements that comprised the TWA were already available.

Sample analysis did not specify the type of fibers identified in the fiber counts. Counting 

rules used included any fiber with the proper dimensions and not specifically Libby amphibole 

fibers. Attempts in other studies to convert from total dust to fiber count have relied on 

similarities in equipment or process where side-by-side samples were collected. We did not 

identify any „pairs‟ of dust/fiber data from this plant. Moreover, fibers are a minor component of 

the dust exposure, limiting an ability to find a relationship over time. Therefore, total dust 

measurements were not converted to fiber counts and were not used as part of the fiber exposure 

estimation. 

3. Completeness and trends in measurements

From the paper records, we concluded that additional information would be helpful from 

workers in order to obtain descriptions of work organization and practices. Focus groups 

discussions were conducted with long-term OM Scott workers (n=15) in 2010. These focus 

groups provided valuable qualitative data in order to fill gaps regarding work plant operations, 

especially during the earlier years. 

As described earlier, the data used for exposure reconstruction was obtained from three 

sources: UC archived records (reported previously by Lockey et al.), information obtained by the

EPA from the company, and from the DOJ documents. Table 1 shows that a total of 914 IH 

fiber measurements were available for this analysis. Of this total, only 180 (19.6%) of the IH 

fiber measurements were available from the UC archived records. The yearly number of samples 

collected was not uniform. As shown in table 2, the first fiber count measurements were 

available in 1972 and the last in 1994. About 26% of the samples were collected in 1978. Focus 

group participants reported working in the summer. Summer activities, however, involved fewer 

work hours and included clean-up and repair activities in addition to production. Since less than 

6% of the fiber samples were collected during the summer months, no seasonal trend analysis 

was possible. 

4. Decisions relevant to the exposure matrix: 

4.1 General issues: 

A graphical display of fiber count results indicated that all samples in various trionizing 

jobs generally followed the same pattern: higher in the early years of IH sampling, and declining 

gradually over time. Further, from the focus groups, we learned that no one, single engineering 

change resulted in a dramatic reduction in the perception of dustiness in the plant. Thus, the 
6

workers‟ recollections supported the findings from the IH data demonstrating a gradual decline 

in levels of exposure rather than a dramatic step-wise drop due to any one engineering change. 

Changes in work practices such as the use of compressed air and brooms for clean-up 

versus the use of wet vacuuming may result in a marked decreases in exposure We discussed 

work practices in the focus groups, and no remarkable changes were documented. Participants 

did note that during some years, sampling practices included leaving pumps in control rooms 

during high-dust activities. High-dust activities included the use of compressed air to remove 

particulate from surface areas. We did not find any documentation that high exposure work was 

excluded from the sampling effort in the IH reports. In fact, in the early years, some activities 

recorded in the sampling record included reference to compressed air “blow down”, one of the 

activities associated with potentially high exposures. Consequently, no adjustment was made for 

any potentially un-sampled periods from 1972 through 1994 when IH measurements were 

available. 

Per the focus groups, workers reported very sporadic usage of respirators due to heat and 

discomfort. Because of the heat, the workers preferred paper masks, and reported reusing them 

from day to day. There was no documentation of fit-testing of the paper masks. Paper masks 

may provide some protection against the larger particles, but likely provided little reduction in 

respirable particles, particularly when reused. Therefore, no adjustment was made to lower the 

exposure estimates due to respirator use. 

4.2 Vermiculite raw material sources:

Libby vermiculite usage ended in 1980 per shipping records obtained from B. Benson 

and an Agency for Toxic Substances and Disease Registry (ATSDR) report3,4. Post 1980 usage 

included African/Virginia/South Carolina vermiculite until 2000. In 2000, corn cobs were 

introduced as an inert carrier of lawn care chemicals, and vermiculite usage ended. There were 

two primary sources of information regarding vermiculite sources:

An internal UC document from the 1980 study with estimates of railroad car loads 

delivered to the plant per year. Documents indicate railroad cars from Libby were 100 ton 

cars and from South Carolina 70 ton cars.

The Chamberlain memo provides information regarding vermiculite sources for 1964-

1972 in railroad car loads per year.

Per the UC document, 100% South Carolina vermiculite was estimated to be used from 

1957-1960. Per the Chamberlain memo, Libby vermiculite began arriving in 1960. Focus 

groups placed it earlier, in 1958 or 1959. We believe there is sufficient evidence to support a 

1959 start date for Libby vermiculite with 1957 and 1958 assumed to be 100% South Carolina 

vermiculite. 
7

Documentation was found from the original 1980 UC documents indicating an estimated 

Libby tonnage contribution of 32% from 1959-1963. These percentages for 1959-1963 were 

adopted for use in this project. After adjusting for the difference in rail car sizes, the 

Chamberlain memo indicates that Libby tonnage usage increased from 57% in 1964 to 73% in 

1965 to 92% in 1966. Table 3 illustrates the distribution of unexpanded vermiculite sources 

received at the plant between 1957 and 1971. From 1959 until 1971 fiber level adjustments were 

made based on the percent Libby versus South Carolina vermiculite tonnage received at the 

plant. The estimates were derived from 1972 when the earliest IH samples were available and 

93% of the vermiculite was Libby. 

To develop the relationship of fiber levels between South Carolina and Libby 

vermiculite, samples that recorded a 100% of either source for vermiculite were identified. Two 

jobs with a higher number of samples from the same year from each source were used to 

establish the relationship: track-unload for 1977 and expander for 1978. The samples used 

included 22 Libby track-unload, 8 Libby expander, 17 South Carolina track-unload, and 7 South 

Carolina expander. A weighted average of these samples generated a 10:1 fiber count ratio for 

Libby:South Carolina vermiculite. This ratio was used for estimating the proportion of Libby 

versus South Carolina fiber exposure levels from 1959 to 1971. From 1972 and beyond, IH 

measurements were available and no adjustment in the IH data was made based on vermiculite 

source. Tonnage records demonstrate that Libby was the primary source of vermiculite from 

1972 until 1979, supplemented by African vermiculite, and that Libby vermiculite usage ended 

in 1980. 

The 100% Libby samples were compared to samples labeled as 50% Libby. The 

resultant measurements were accordingly lower, demonstrating internal consistency within the 

data. 

Assessment of exposure in 1977 during application of the final, expanded product that 

included a mix of South African and Libby vermiculite showed no fibers. Therefore, fiber 

exposure estimation was restricted to jobs in the plant areas where expanding was conducted. 

4.3 Exposure estimates by time period for the trionizing department:

For this project, exposures of interest were from 1957 through 2000. Exposure 

measurements in the plant where vermiculite was used were initiated in 1972. For prior years, it 

was necessary to estimate exposure from the measurements collected in 1972 and later and with 

supporting qualitative information. Important changes occurred in production due to increasing 

use of engineering controls to reduce airborne particulate. In addition, the source of vermiculite 

changed over the years. Therefore, the exposure estimation process was divided into two efforts: 

1972 and later when IH measurements were available; 1957 to 1971, when no IH measurements 

were available. The exposure estimation process is described below, first for Trionizing where 
8

vermiculite was expanded and then for other departments where either no or expanded 

vermiculite was used. 

Trionizing department exposure estimation > 1972- 2000: 

For the years with exposure measurements, fiber exposure level was estimated from the 

measurement data. This was done by department. 

Trionizing Department – The trionizing department included jobs from the entry of 

vermiculite into the plant, through final product. These were: track at raw material entry and 

production jobs of screen/mill, dryer, expander, blender, resin, and clean-up, Workers rotated 

through the various jobs within the department. Overall rotation among jobs reported in the 

1980 Lockey et al. study was verified by the focus groups. 

Plots of the measurements over time were made for individual trionizing jobs. Based on 

these plots, it was determined that all IH sample results from the various trionizing production 

jobs (screen/mill through clean-up) followed the same general distribution and should be 

combined. The track job included two very different work activities: unloading rail cars 

containing vermiculite (track unload) and general track work such as bringing in the rail cars, 

and monitoring discharge (track other). The two track job activities (unload and other) had a 

substantially larger range of sampling results and were treated separately. 

The following steps were followed: 

1. The data were log-transformed.

2. For all exposure values for the combined trionizing jobs from 1972-1979, a curve was 

drawn connecting the mean values of years having at least 40 exposure measurements (1973, 

1976, and 1978). This criteria was chosen to assure that stable means were used to define the 

curve over this time period. For each year, the annual exposure estimate was determined by 

exponentiation of the value from the curve. The sharp decline seen in exposures throughout this 

time period parallels the addition of engineering controls including dust collection, enclosing 

vibrating conveyors, adding ventilators, erecting a wall between track and trionizing, and sealing 

leaks in the system. As values for 1980-1994 were similar and near the level of detection, the 

mean value for all the samples was used and then extended until 2000. 

3. The measurement results for track unload and track other were plotted and a straight 

line produced to best fit the data points. An estimate of exposure at each year was determined by 

exponentiation of the value on the line for that year. 

4. For the trionizing department, it was estimated that 11% of work time was spent in 

track and 89% in all other jobs. This is consistent with the previous weights used in the 1980 

Lockey study and confirmed by the focus group. 
9

5. The Focus groups reported that when working track, track unload required about 25% 

of the time and track other comprised about 75% of the track job time. Therefore, a weighted 

average for exposure at track within the trionizing department was derived. This 25% time 

estimate for track unload is higher than that previously published.1

Figure 1 illustrates on a log scale a fitted line of all usable IH measurements across all jobs 

(except track) within the trionizing department.

Trionizing department exposure estimation 1957-1971: 

There are no IH measurements available prior to 1972. Engineering changes did not 

result in “step-function” decreases in exposures based on focus group reports. Rather a more 

gradual decline in exposure occurred beginning with improvements in 1968, when two dust 

collectors were added. Focus group workers report that dust exposures in trionizing were at 

least two times higher in the 1960‟s. Track jobs, however, were outdoors and likely unaffected 

by plant engineering controls. Hence, estimates for fiber exposure levels for track duties were 

adjusted by type of vermiculite only.

For trionizing employees, excluding outdoor track duties, the estimate from the focus 

group of „twice as high‟ was generated beginning from 1972 and increasing until 1967. The year 

1972 was used as the start of the “gradual” retrospective increase in exposure back to 1967 as 

1972 was the first year when IH measurements were available, and the percent Libby vermiculite 

utilized was 93%. The year 1967 was selected as this was the year preceding engineering 

controls. A line was drawn to connect these two points and then the adjustment was made for 

the percent yearly Libby and South Carolina vermiculite utilized from 1967 through 1971. Prior 

to 1967, exposure was extended backward in time, assuming no change from the 1967 value 

except for a yearly adjustment for percent Libby and South Carolina usage. As described above 

and shown in figure 1, after 1980 when Libby vermiculite was no longer used and major 

environmental controls had been implemented, fiber exposure levels remained near the level of 

detection (0.01) through the last available IH information in 1994. The levels were estimated to 

be the same from 1994 forward until 2000 when vermiculite was no longer used. 

4.4 Exposure estimates for non-trionizing departments:

Departments using only expanded vermiculite or no vermiculite were defined as having 

“plant background” exposure. These included the departments of polyform, plant maintenance, 

office, research, pilot plant, warehouse, central maintenance, and packaging. This decision was 

based on plots of available sampling data showing similar levels, and qualitative reports 

documenting that there were not fibers in the finished product. Plant background prior to 1972 

was calculated using similar methodology as for trionizing. Although the background level was 

not affected by engineering control as in trionizing, exposures would be affected by the percent 

of Libby vermiculite used. Therefore, for the years prior to 1972, the measured plant 

background rate in 1972 of 0.02 was adjusted by the yearly percent Libby vermiculite utilized. 
10

The two years prior to Libby vermiculite usage, 1956 and 1957, were assigned level of detection 

(0.01). This is in line with IH measurements post Libby vermiculite usage through 1994. 

Polyform began in 1969, and no unexpanded vermiculite was used there. The background 

exposure level was used for any time in Polyform.

Plant Maintenance – Although there were some differences of opinion in the focus group 

regarding where plant maintenance spent their time, the consensus reached was to assign 

approximately 50% of time in trionizing and 50% in areas defined as plant background for their 

work in shop and other departments. 

Office – Assigned plant background.

Research – Assigned plant background.

Pilot plant – Per the focus group participants, the pilot plant did not have its own expander, and 

used only expanded vermiculite in test and run simulations. Plant background levels were thus 

assigned to the pilot plant.

Warehouse – Only expanded vermiculite was in this area. Although bags did break, the exposure 

was to final product, not unexpanded vermiculite. 

Central Maintenance – According to the focus group, these employees worked outside of 

trionizing for about 90% time (background) and 10% (trionizing) for installation of new 

equipment/parts. Around 1982 central maintenance department was discontinued, and the work 

was contracted to outside personnel. 

Packaging – Assigned plant background. 

Table 4 illustrates the fiber exposure matrix from 1957 to 2000 using this methodology.

4.5. Decisions related to break periods and hours worked:

Cumulative exposure is the product over time of the level of exposure and duration. 

Level of exposure is derived from the exposure matrix and duration from the work history. 

However, in this workforce, work time is complicated by breaks where exposure is at a lower 

level and seasonal changes resulting in extra hours worked beyond the usual 40 hour week. Each 

of these factors is described below: 

According to the focus group data there was approximately a 30 minute break for lunch 

and two fifteen minute breaks during the day. Therefore, every worker was considered to have at 

least one hour of background exposure daily. There was no documentation that a 3rd fifteen 

minute break was provided when working longer than eight hours in a day.
11

Employees in some departments frequently worked extra hours each day, and weekends 

as well, depending on the production needs and season. Decisions regarding this work 

organization are summarized below: 

1) Extra hours – Were defined as hours worked in excess of 8 hours per day,

2) Four departments worked no extra hours – office, pilot plant, research, central maintenance

3) According to focus group data, the only departments that worked extra hours outside of their 

own department were trionizing and polyform. Thus, a decision was needed as to how to 

appropriate the amount of overtime spent outside trionizing and polyform.

Extra hours for polyform workers - According to the focus groups, polyform workers first 

worked in their own department, and went to trionizing to work extra hours. According to 

workers, about 75 percent of the daily overtime was in their own department. Therefore, for each 

four hours worked beyond the normal eight hour day, it is estimated that they spent three hours 

in polyform and one in trionizing. This rule was not applied to 8-hour weekend days worked. 

Extra hours for trionizing workers – As for polyform workers, above, it is estimated that 

trionizing workers spent three hours in trionizing and one hour in polyform as a daily average.

4) Schedules by season differed due to production rate:

For trionizing, plant maintenance, polyform, warehouse, and packaging the spring schedule was 

from January through May – 7 days @ 12 hrs

For trionizing, plant maintenance, polyform, warehouse, and packaging the summer schedule 

was from June – August – 5 days @ 8 hrs. Due to the difficulty that heat and humidity brought 

to the process, polyform was shut down during summer. During the summer, polyform workers 

did outside jobs. As these jobs have the same exposure level as polyform (background rate), no 

adjustment was made for the summer polyform shutdowns. The trionizing department more 

typically slowed down production in the summer, and this is reflected in the number of hours 

worked from June through August. 

For trionizing, plant maintenance, polyform, warehouse, and packaging the fall schedule was 

from September through December – 5 days @12 hrs and 2 weekend days @ 8 hrs. 

In light of these extra hours, exposure values by department and season were modified for use in 

the cumulative equivalent human equivalent exposure concentration estimations.

5. Development of a cumulative human equivalent exposure concentration 

An EPA adjustment of cumulative occupational exposure to fibers to continuous human

exposure to fibers (24 hrs/day; 7 days/week) was provided by B. Benson. This adjustment was 
12

accepted as provided for the development of a cumulative human equivalent exposure 

concentration (CHEEC) for the Marysville occupational cohort.

5.1 Seasonal schedule correction factor: 

For this project the Correction Factor was adjusted for the specific information on work 

schedules related to the seasonal changes to meet production demands as described above in 

section 4.4. UC applied these correction factors supplied by the EPA (B. Benson) to the work 

history data obtained by UC during 1980 and updated in 2004. 

5.2 Decision rules to address department changes occurring within seasons:

Decision rules were implemented to systematically standardize each worker‟s 

occupational history to a format that corresponded directly with the seasonal changes that 

occurred at the plant. Previous decisions related to department exposure levels and seasonal 

work resulted in six unique exposure categories: trionizing, plant maintenance, central 

maintenance, polyform, background (office, research, pilot plant), and background with extra 

time (warehouse, packaging). The date of any job change by a worker between these six 

categories was adjusted so the change occurred at the starting month for the nearest season. 

5.3 Development of CHEEC

In preparation for creating the CHEEC, the exposure matrix was converted to a seasonal 

(spring, summer, fall) exposure value. This value is the estimate of the amount of exposure 

occurring by department for each season of each year. With the worker‟s occupational histories 

standardized to the same seasons, the CHEEC for each worker was then calculated as the sum of 

exposure values for all seasons worked between1957-2000. The correction factors used in 

derivation of the CHEEC are outlined below. 

General Procedure

(Cumulative Fibers)OCCUP x Correction Factor = (Cumulative Fibers)HEC

OCCUP = Occupational Exposure

HEC = Human Equivalent Concentration for exposure of 24 hrs/day, 7 days/week

The Correction Factor usually used with an occupational study is 5 days/7 days x 10 

m

3

/20 m3

UC Procedure

CHEEC= (Exposure Est year-dept-season 1 x Correction Factorseason 1 x Seasonal Duration Factor) + 

(Exposure Est year-dept-season 2 x Correction Factorseason 2 x Seasonal Duration Factor) + ... 

(Exposure Est year-dept-season x x Correction Factorseason x Seasonal Duration Factor)
13

Where the Seasonal Duration Factor for the Spring is 5/12 year; the Summer is 3/12 year; the 

Fall is 4/12 year.

Detailed calculations follow.

Work schedule for trionizing, plant maintenance, polyform, warehouse, and packaging

Spring

January 1 to May 31: 7 days/week, 12 hrs/day, with New Years‟ Day off, and accounting 

for leap years

151.25 -1 = 150.25 days

Breathing rate, working = 1.25 m3/hr x 12 hrs = 15 m3

Breathing rate, not working = 0.625 m3/hr x 12 hrs = 7.5 m3

Total breathing rate = 15 + 7.5 = 22.5 m3/day

Correction Factor Spring = 150.25/151.25 x 15/22.5 = 0.662259

Summer

June 1 to August 31: 5 days/week, 8 hrs/day, 2 week summer vacation

(92 – 14) x 5/7 = 55.714286 days

Breathing rate, working = 1.25 m3/hr x 8 hrs = 10 m3

Breathing rate, not working = 0.625 m3/hr x 16 hrs = 10 m3

Total breathing rate = 10 + 10 = 20 m3/day

Correction Factor Summer = 55.714286/92 x 10/20 = 0.302795

Fall

September 1 to December 31: 5 days/week, 12 hrs/day and 2 days/week, 8 hrs/day, with 

Christmas Day off

122 – 1 = 121 days

Breathing rate, working, 12 hr day = 1.25 m3/hr x 12 hrs = 15 m3

Breathing rate, working, 8 hr day = 1.25 m3/hr x 8 hrs = 10 m3
14

Breathing rate, not working = 0.625 m3/hr x 16 hrs = 10 m3

Total breathing rate, 12 hour work day = 15 + 7.5 = 22.5 m3/day

Total breathing rate, 8 hr work day = 10 + 10 = 20 m3/day

Correction Factor Fall = 121/122 x (86.42857 x 15/22.5 + 34.57143 x 10/20)/121 = 

0.613973

Work schedule for office, pilot plant, research, and central maintenance 

No extra days or extra hours

Spring

January 1 to May 31: 5 days/week, 8 hrs/day, with New Years‟ Day off, and accounting 

for leap years

(151.25 -1) x 5 days/7 days = 107.321429

Breathing rate, working = 1.25 m3/hr x 8 hrs = 10 m3

Breathing rate, not working = 0.625 m3/hr x 16 hrs = 10 m3

Total breathing rate = 10 + 10 = 20 m3/day

Correction Factor Spring = 107.321429/151.25 x 10/20 = 0.354782

Summer

June 1 to August 31: 5 days/week, 8 hrs/day, 2 week summer vacation

(92 – 14) x 5/7 = 55.714286 days

Breathing rate, working = 1.25 m3/hr x 8 hrs = 10 m3

Breathing rate, not working = 0.625 m3/hr x 16 hrs = 10 m3

Total breathing rate = 10 + 10 = 20 m

3

/day

Correction Factor Summer = 55.714286/92 x 10/20 = 0.302795

Fall

September 1 to December 31: 5 days/week, 8 hrs/day, with Christmas Day off

(122 – 1) x 5/7 = 86.428571 days
15

Breathing rate, working, 8 hr day = 1.25 m3/hr x 8 hrs = 10 m3

Breathing rate, not working = 0.625 m3/hr x 16 hrs = 10 m3

Total breathing rate = 10 + 10 = 20 m3/day

Correction Factor Fall = 86.428571/122 x 10/20 = 0.354215

5.4 Results of the Cumulative human equivalent exposure concentration (CHEEC)

To verify the accuracy of the CHEEC calculations, several quality control checks were 

conducted. The distribution was evaluated by reviewing the mean, median, standard deviation, 

highest 10 values, and lowest 10 values. Several workers were also randomly selected and their 

values hand-calculated to ensure all programming was correct. Tables 5-7 provide a list of all 

280 subjects participating in the 2004 Marysville health update. 2These tables describe each 

subject‟s identification number, job start and stop date, date of radiograph, age, gender, body 

mass index, smoking history, asbestos exposures, health outcomes, and the cumulative human 

equivalent exposure concentration (CHEEC) for all departmental exposures they reported while 

employed at the OM Scott Marysville, Ohio plant.

6.0 Strengths and Limitations:

There are major strengths in this exposure reconstruction project.

1. Data were gathered from court records, federal sources and archived files, totaling over 3000 

pages. These data were reviewed and both qualitative and quantitative data were abstracted to 

aid in this reconstruction.

2. Approximately five times more fiber measurements became available than had been used in 

the original studies. 

3. Two focus groups were conducted in 2010 with long term workers who provided input 

regarding exposure and production process changes.

4. There were sufficient data available to examine exposure intensity over time for jobs within 

the trionizing department as well as for other departments. These data enhanced exposure 

estimates for all departments from 1972 to 1994.

5. IH data were available allowing for comparisons of fiber counts when 100% Libby or 100% 

South Carolina vermiculite was used in order to calculate a ratio of fibers in each. 

6. There were data available from archived records, Scott memos, and worker information that 

allowed for exposure estimates to be adjusted for type of vermiculite used from 1957 until 1971 

when no IH data were available.
16

7. Worker report data were available that provided documentation for increased dustiness before 

IH data were available, compared with years when measurements were available. 

8. Based on past and current data gathered in the focus group, exposures were adjusted to 

account for seasonal work schedules by departments.

9. All decisions based on level of exposure by year were data driven.

The limitations for this project are also recognized.

1. The exposure metric used (fibers/cc) results from an analytical method that is a count of fibers 

(defined as any viewed elongated particle in excess of 5 um in length and with a length to width 

ratio of 3:1) collected on a filter and viewed at 400x with light microscopy. The composition of 

the fiber is not known. Also, a fiber with diameter less than a limit of resolution of 0.2 um 

cannot be viewed with this method.

2. It is unknown if other sampling results exist. If any are found in the future, these can be 

incorporated into a future exposure assessment. 

3. Some dusty activities may not have been sampled or rarely sampled e.g., summer cleanup. 

We have no way of estimating the effect of these activities on overall exposure estimates. 

4. We did not reduce exposure estimates due to possible use of respiratory protection. 

Substantially more documentation regarding enforced usage, fit testing and cleaning/storage 

protocols would be needed for meaningful reduction in exposure estimates. 

5. By combining all individual trionizing job duties into one department exposure, the nonexpander trionizing exposure estimates may have been overestimated as there were more 

expander measurements, and these were somewhat higher than for other job duties.

6. From 1980 forward, Libby vermiculite was not used. Thus for any individual year during this 

period, exposure from a qualitative and quantitative perspective does not reflect Libby 

amphibole exposure.

7. Seasonal work schedule adjustments were based on recall of focus group participants and may 

over or under estimate true durations and location of additional work hours. 
17

Literature cited.

1. Lockey JE, Brooks SM, Jarabek AM, Khoury PR, McKay RT, Carson A, Morrison JA, Wiot 

JF, Spitz HB. 1984. Pulmonary changes after exposure to vermiculite contaminated with fibrous 

tremolite. Am Rev Respir Dis. 129:952-958.

2. Rohs AM, Lockey JE, Dunning JE, Shukla R, Fan H, Hilbert T, Borton E, Wiot J, Meyer C, 

Shipley RT, LeMasters GK, Kapil V. 2008. Low-level fiber-induced radiographic changes 

caused by Libby vermiculite, a 25-year follow-up study. Am J Respir Crit Care Med. 177:630-

637.

3. Benson R. Use of Libby amphibole in Marysville, Ohio. Ref 8P-W, US EPA Region 

8,December 7, 2009:1-2.

4. U.S. Department of Health and Human Services, Agency for Toxic Substances and Disease 

Registry (ATSDR). Health consultation: The Scotts Company, LLC. EPA Facility ID: 

OHD990834483. September 22, 2005.
18

Log of fiber count (fiber/cc)

Figure F-1. Illustrates on a log scale a fitted line of all usable IH measurements across all 

jobs (except track) within the trionizing department.
19

Table 1. Industrial Hygiene Fiber Measurements by Document 

Source

Document Source Trionize Background Total (%)

DOJ 38 0 38 (4.16)

EPA 398 122 520 (56.89)

UC 135 45 180(19.69)

COMBINED 172 4 176(19.26)

Total (%) 743 (81.29) 171 (18.71) 914
20

Table 2. Industrial Hygiene Fiber Measurements by Department 

and Year

Dept 197

2

197

3

197

5

1976 197

7

1978 197

9

198

0

198

1

198

2

198

3

198

4

198

5

198

6

198

7

198

8

199

3

199

4

Total 

(Dept 

%)

BACKGRO

UND 3 0 2 0 10 54 2 0 12 7 3 11 5 23 13 16 0 10

171 

(18.7

1)

TRIONIZE 9 40 20 115 68 183 26 23 38 24 8 27 14 52 33 31 3 29

743 

(81.2

9)

Total 12 40 22 115 78 237 28 23 50 31 11 38 19 75 46 47 3 39 914

(Year %) (1.3

1)

(4.3

8)

(2.4

1)

(12.5

8)

(8.5

3)

(25.9

3)

(3.0

6)

(2.5

2)

(5.4

7)

(3.3

9)

(1.2

0)

(4.1

6)

(2.0

8)

(8.2

1)

(5.0

3)

(5.1

4)

(0.3

3)

(4.2

7)

(100.

00)
21

Table 3. Tonnage by year and vermiculite source

Year % Tonnage Libby % Tonnage SC Comment

1957 100 No confirmation of Libby usage 

1958 100 No confirmation of Libby usage 

1959 32 68 Libby usage began per focus groups; Chamberlain says 1960

1960 32 68 Chamberlain memo and 1980 chart

1961 32 68 Chamberlain memo and 1980 chart

1962 32 68 Chamberlain memo and 1980 chart

1963 32 68 Chamberlain memo and 1980 chart

1964 57 43 Chamberlain memo

1965 73 27 Chamberlain memo

1966 92 8 Chamberlain memo

1967 87 13 Chamberlain memo

1968 79 21 Chamberlain memo

1969 82 18 Chamberlain memo

1970 90 10 Chamberlain memo

1971 95 5 Chamberlain memo
22

Table 4. Exposure Matrix Assuming Doubling of Fiber Levels from 1972 to 1967 but with Adjustment for Vermiculite Source from 1957-1971

Department 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971

Trionizing 0.729 0.729 2.825 2.825 2.825 2.825 2.825 4.462 5.510 6.755 6.427 5.542 5.279 4.923 4.316

Plant maint (50/50) 0.369 0.369 1.416 1.416 1.416 1.416 1.416 2.237 2.763 3.387 3.222 2.779 2.648 2.470 2.168

Central maint (90/10) 0.082 0.082 0.289 0.289 0.289 0.289 0.289 0.457 0.565 0.692 0.659 0.569 0.543 0.509 0.449

Background* 0.010 0.010 0.008 0.008 0.008 0.008 0.008 0.012 0.015 0.019 0.018 0.016 0.017 0.018 0.019

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986-2000

Trionizing 3.674 3.007 2.464 2.019 1.391 0.150 0.086 0.077 0.063 0.063 0.060 0.060 0.055 0.055 0.052

Plant maint (50/50) 1.847 1.513 1.242 1.020 0.705 0.090 0.053 0.044 0.036 0.036 0.035 0.035 0.032 0.032 0.031

Central maint (90/10) 0.385 0.319 0.264 0.220 0.157 0.030 0.027 0.017 0.015 0.015 0.015 0.015

Background* 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010

*Background applies to Pilot Plant, Research, Polyform, Office, Packaging, 

Warehouse
23
This document is a draft for review purposes only and does not constitute Agency policy.

G-1 DRAFT—DO NOT CITE OR QUOTE

1 APPENDIX G. EXTRA RISK AND UNIT RISK CALCULATION

2 The following tables illustrate the computational details of the unit risks for 

3 mesothelioma mortality (Tables G-1 and G-2) and for lung cancer mortality (Tables G-3 and 

4 G-4). Section 5.4.5.1 details the application of the life-table methodology for mesothelioma. 

5 The results of Tables G-1 and G-2 are shown in Table 5-17 and are not adjusted for the 

6 underascertainment of mesothelioma described in the Section 5.4.5.1.1. The unit risks adjusted 

7 for underascertainment are shown in Table 5-18.

8

9 Column Definitions for Tables G-1 and G-2

10

11 Column A: Age interval up to age 85.

Column B: All-cause mortality rate for interval i (× 10

5

12 /year) (2007 data NVSR 58(19) 2010).

13 Column C: All-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of 

14 years in age interval).

15 Column D: Probability of surviving interval i (qi) (= exp(−h*i)).

16 Column E: Probability of surviving up to interval i (Si) (S1 = 1; Si = Si−1 × qi−1, for i>1).

17 Column F: Lagged exposure at mid-interval (x dose).

18 Column G: Mesothelioma mortality hazard rate in exposed people for interval. To estimate the 

19 LEC01, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 

20 1%, the 95% upper bound on the regression coefficient is used.

21 Column H: All-cause hazard rate in exposed people for interval i (h*xi) (= h*i + (hxi − hi)).

22 Column I: Probability of surviving interval i without dying from mesothelioma for exposed 

23 people (qxi) (= exp(−h*xi)).

24 Column J: Probability of surviving up to interval i without dying from mesothelioma for 

25 exposed people (Sxi) (Sx1 = 1; Sxi = Sxi−1 × qxi-1, for i>1).

26 Column K: Conditional probability of dying from mesothelioma in interval i for exposed people 

27 (= (hxi/h*xi) × Sxi × (1−qxi)) (Rx, the lifetime probability of dying from 

28 mesothelioma for exposed people = the sum of the conditional probabilities across 

29 the intervals).

30

31

32 Note that the life tables for mesothelioma mortality estimate the extra risk as the absolute 

33 risk since there is no assumption of a background risk in the absence of exposure. In each of the 

34 life tables, inhalation exposure commences at age 16 years and continues at the same exposure 

35 concentration for the duration of the life table. This allows for the computation of an 

36 “adult-only” occupational lifetime unit risk, which is then scaled by a ratio of 70:54 to account 
This document is a draft for review purposes only and does not constitute Agency policy.

G-2 DRAFT—DO NOT CITE OR QUOTE

1 for risk over the standard 70-year lifetime. While exposure is initiated in the life table at age 

2 16 years, this exposure is lagged to match the corresponding exposure-response models, which 

3 provide the hazard rates per unit of exposure. For example, in Table G-1, Column F shows 

4 exposure lagged by 10 years so that no lagged exposure appears in the table until age 26 years. 

5 In Table G-2, Column F shows exposure lagged by 15 years so that no lagged exposure appears 

6 in the table until age 31 years. 

7
This document is a draft for review purposes only and does not constitute Agency policy.

G-3 DRAFT—DO NOT CITE OR QUOTE

1 Table G-1. Mesothelioma extra risk calculation for environmental exposure 

2 to 0.1479 fibers/cc Libby Amphibole asbestos using the metric of cumulative 

3 exposure with a 10-year exposure lag and a 5-year half-life of exposure, as 

4 described in Section 5.4.5.3 as the reasonable upper bound

5

A B C D E F G H I J K

Age

int.

All cause

mortality

(xE5/yr)

All cause

hazard

rate (h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving

up to

interval

(S)

Lagged

exp. 

mid. int. 

(Xdose)

Exposed

meso.

hazard

rate (hx)

Exposed

all cause 

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. 

prob. of 

meso. in 

interval

(Rx)

<1 684.5 0.0068 0.9932 1.0000 0.000 0.0000 0.0068 0.9932 1.0000 0.0000

1-2 28.6 0.0003 0.9997 0.9932 0.000 0.0000 0.0003 0.9997 0.9932 0.0000

2-3 28.6 0.0003 0.9997 0.9929 0.000 0.0000 0.0003 0.9997 0.9929 0.0000

3-4 28.6 0.0003 0.9997 0.9926 0.000 0.0000 0.0003 0.9997 0.9926 0.0000

4-5 29.9 0.0003 0.9997 0.9923 0.000 0.0000 0.0003 0.9997 0.9923 0.0000

5-6 13.7 0.0001 0.9999 0.9920 0.000 0.0000 0.0001 0.9999 0.9920 0.0000

6-7 13.7 0.0001 0.9999 0.9919 0.000 0.0000 0.0001 0.9999 0.9919 0.0000

7-8 13.7 0.0001 0.9999 0.9918 0.000 0.0000 0.0001 0.9999 0.9918 0.0000

8-9 13.7 0.0001 0.9999 0.9916 0.000 0.0000 0.0001 0.9999 0.9916 0.0000

9-10 13.7 0.0001 0.9999 0.9915 0.000 0.0000 0.0001 0.9999 0.9915 0.0000

10-11 18.7 0.0002 0.9998 0.9914 0.000 0.0000 0.0002 0.9998 0.9914 0.0000

11-12 18.7 0.0002 0.9998 0.9912 0.000 0.0000 0.0002 0.9998 0.9912 0.0000

12-13 18.7 0.0002 0.9998 0.9910 0.000 0.0000 0.0002 0.9998 0.9910 0.0000

13-14 18.7 0.0002 0.9998 0.9908 0.000 0.0000 0.0002 0.9998 0.9908 0.0000

14-15 18.7 0.0002 0.9998 0.9906 0.000 0.0000 0.0002 0.9998 0.9906 0.0000

15-16 61.9 0.0006 0.9994 0.9904 0.000 0.0000 0.0006 0.9994 0.9904 0.0000

16-17 61.9 0.0006 0.9994 0.9898 0.000 0.0000 0.0006 0.9994 0.9898 0.0000

17-18 61.9 0.0006 0.9994 0.9892 0.000 0.0000 0.0006 0.9994 0.9892 0.0000

18-19 61.9 0.0006 0.9994 0.9886 0.000 0.0000 0.0006 0.9994 0.9886 0.0000

19-20 61.9 0.0006 0.9994 0.9880 0.000 0.0000 0.0006 0.9994 0.9880 0.0000

20-21 98.3 0.0010 0.9990 0.9874 0.000 0.0000 0.0010 0.9990 0.9874 0.0000

21-22 98.3 0.0010 0.9990 0.9864 0.000 0.0000 0.0010 0.9990 0.9864 0.0000

22-23 98.3 0.0010 0.9990 0.9854 0.000 0.0000 0.0010 0.9990 0.9854 0.0000

23-24 98.3 0.0010 0.9990 0.9845 0.000 0.0000 0.0010 0.9990 0.9845 0.0000

24-25 98.3 0.0010 0.9990 0.9835 0.000 0.0000 0.0010 0.9990 0.9835 0.0000

6
This document is a draft for review purposes only and does not constitute Agency policy.

G-4 DRAFT—DO NOT CITE OR QUOTE

Table G-1. Mesothelioma extra risk calculation for environmental exposure 

to 0.1479 fibers/cc Libby Amphibole asbestos using the metric of cumulative 

exposure with a 10-year exposure lag and a 5-year half-life of exposure, as 

described in Section 5.4.5.3 as the reasonable upper bound (continued)

A B C D E F G H I J K

Age

int.

All cause

mortality

(xE5/yr)

All cause

hazard

rate (h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving

up to

interval

(S)

Lagged

exp. 

mid. int. 

(Xdose)

Exposed

meso.

hazard

rate (hx)

Exposed

all cause 

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. 

prob. of 

meso. in 

interval

(Rx)

25-26 99.4 0.0010 0.9990 0.9825 0.000 0.0000 0.0010 0.9990 0.9825 0.0000

26-27 99.4 0.0010 0.9990 0.9815 0.144 0.0001 0.0011 0.9989 0.9815 0.0001

27-28 99.4 0.0010 0.9990 0.9806 0.401 0.0002 0.0012 0.9988 0.9805 0.0002

28-29 99.4 0.0010 0.9990 0.9796 0.626 0.0003 0.0013 0.9987 0.9793 0.0003

29-30 99.4 0.0010 0.9990 0.9786 0.821 0.0004 0.0014 0.9986 0.9780 0.0004

30-34 110.8 0.0055 0.9945 0.9777 1.268 0.0006 0.0062 0.9938 0.9767 0.0006

35-39 145.8 0.0073 0.9927 0.9723 1.701 0.0009 0.0082 0.9919 0.9706 0.0008

40-44 221.6 0.0111 0.9890 0.9652 1.918 0.0010 0.0121 0.9880 0.9628 0.0009

45-49 340.0 0.0170 0.9831 0.9546 2.026 0.0010 0.0180 0.9821 0.9512 0.0010

50-54 509.0 0.0255 0.9749 0.9385 2.080 0.0011 0.0265 0.9738 0.9342 0.0010

55-59 726.3 0.0363 0.9643 0.9149 2.107 0.0011 0.0374 0.9633 0.9098 0.0010

60-64 1,068.3 0.0534 0.9480 0.8823 2.121 0.0011 0.0545 0.9470 0.8764 0.0009

65-69 1,627.5 0.0814 0.9218 0.8364 2.127 0.0011 0.0825 0.9209 0.8299 0.0009

70-74 2,491.3 0.1246 0.8829 0.7710 2.131 0.0011 0.1256 0.8819 0.7642 0.0008

75-79 3,945.9 0.1973 0.8209 0.6807 2.132 0.0011 0.1984 0.8201 0.6740 0.0007

80-84 6,381.4 0.3191 0.7268 0.5588 2.133 0.0011 0.3202 0.7260 0.5527 0.0005

Absolute Rx = 0.0100

1

2 Absolute risk = 0.01000, exp. level = 0.1479; Occupational lifetime unit risk = 0.0676 (Based on occupational 

3 exposures beginning at age 16 years); Scaled occupational lifetime unit risk = 0.0876 (Scaled by ratio of 70:54 to 

4 account for risk over 70 year lifetime).

5
This document is a draft for review purposes only and does not constitute Agency policy.

G-5 DRAFT—DO NOT CITE OR QUOTE

1 Table G-2. Mesothelioma extra risk calculation for environmental exposure 

2 to 0.2446 fibers/cc Libby Amphibole asbestos using the metric of cumulative 

3 exposure with a 15-year exposure lag and a 5-year half-life of exposure, as 

4 described in Section 5.4.5.3 as the lowest information criterion

5

A B C D E F G H I J K

Age int.

All cause

mortality

(xE5/yr)

All cause

hazard

rate (h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving 

up to

interval

(S)

Lagged

exp. 

mid. 

int.

(Xdose)

Exposed

meso.

hazard

rate (hx)

Exposed

all cause

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. 

prob. of 

meso in 

interval

(Rx)

<1 684.5 0.0068 0.9932 1.0000 0.000 0.0000 0.0068 0.9932 1.0000 0.0000

1-2 28.6 0.0003 0.9997 0.9932 0.000 0.0000 0.0003 0.9997 0.9932 0.0000

2-3 28.6 0.0003 0.9997 0.9929 0.000 0.0000 0.0003 0.9997 0.9929 0.0000

3-4 28.6 0.0003 0.9997 0.9926 0.000 0.0000 0.0003 0.9997 0.9926 0.0000

4-5 29.9 0.0003 0.9997 0.9923 0.000 0.0000 0.0003 0.9997 0.9923 0.0000

5-6 13.7 0.0001 0.9999 0.9920 0.000 0.0000 0.0001 0.9999 0.9920 0.0000

6-7 13.7 0.0001 0.9999 0.9919 0.000 0.0000 0.0001 0.9999 0.9919 0.0000

7-8 13.7 0.0001 0.9999 0.9918 0.000 0.0000 0.0001 0.9999 0.9918 0.0000

8-9 13.7 0.0001 0.9999 0.9916 0.000 0.0000 0.0001 0.9999 0.9916 0.0000

9-10 13.7 0.0001 0.9999 0.9915 0.000 0.0000 0.0001 0.9999 0.9915 0.0000

10-11 18.7 0.0002 0.9998 0.9914 0.000 0.0000 0.0002 0.9998 0.9914 0.0000

11-12 18.7 0.0002 0.9998 0.9912 0.000 0.0000 0.0002 0.9998 0.9912 0.0000

12-13 18.7 0.0002 0.9998 0.9910 0.000 0.0000 0.0002 0.9998 0.9910 0.0000

13-14 18.7 0.0002 0.9998 0.9908 0.000 0.0000 0.0002 0.9998 0.9908 0.0000

14-15 18.7 0.0002 0.9998 0.9906 0.000 0.0000 0.0002 0.9998 0.9906 0.0000

15-16 61.9 0.0006 0.9994 0.9904 0.000 0.0000 0.0006 0.9994 0.9904 0.0000

16-17 61.9 0.0006 0.9994 0.9898 0.000 0.0000 0.0006 0.9994 0.9898 0.0000

17-18 61.9 0.0006 0.9994 0.9892 0.000 0.0000 0.0006 0.9994 0.9892 0.0000

18-19 61.9 0.0006 0.9994 0.9886 0.000 0.0000 0.0006 0.9994 0.9886 0.0000

19-20 61.9 0.0006 0.9994 0.9880 0.000 0.0000 0.0006 0.9994 0.9880 0.0000

20-21 98.3 0.0010 0.9990 0.9874 0.000 0.0000 0.0010 0.9990 0.9874 0.0000

21-22 98.3 0.0010 0.9990 0.9864 0.000 0.0000 0.0010 0.9990 0.9864 0.0000

22-23 98.3 0.0010 0.9990 0.9854 0.000 0.0000 0.0010 0.9990 0.9854 0.0000

23-24 98.3 0.0010 0.9990 0.9845 0.000 0.0000 0.0010 0.9990 0.9845 0.0000

24-25 98.3 0.0010 0.9990 0.9835 0.000 0.0000 0.0010 0.9990 0.9835 0.0000

6
This document is a draft for review purposes only and does not constitute Agency policy.

G-6 DRAFT—DO NOT CITE OR QUOTE

Table G-2. Mesothelioma extra risk calculation for environmental exposure 

to 0.2446 fibers/cc Libby Amphibole asbestos using the metric of cumulative 

exposure with a 15-year exposure lag and a 5-year half-life of exposure, as 

described in Section 5.4.5.3 as the lowest information criterion (continued)

A B C D E F G H I J K

Age int.

All cause

mortality

(xE5/yr)

All cause

hazard

rate (h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving 

up to

interval

(S)

Lagged

exp. 

mid. 

int.

(Xdose)

Exposed

meso.

hazard

rate (hx)

Exposed

all cause

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. 

prob. of 

meso in 

interval

(Rx)

25-26 99.4 0.0010 0.9990 0.9825 0.000 0.0000 0.0010 0.9990 0.9825 0.0000

26-27 99.4 0.0010 0.9990 0.9815 0.000 0.0000 0.0010 0.9990 0.9815 0.0000

27-28 99.4 0.0010 0.9990 0.9806 0.000 0.0000 0.0010 0.9990 0.9806 0.0000

28-29 99.4 0.0010 0.9990 0.9796 0.000 0.0000 0.0010 0.9990 0.9796 0.0000

29-30 99.4 0.0010 0.9990 0.9786 0.000 0.0000 0.0010 0.9990 0.9786 0.0000

30-31 110.8 0.0055 0.9945 0.9777 0.000 0.0000 0.0011 0.9989 0.9777 0.0000

31-32 110.8 0.0055 0.9945 0.9777 0.238 0.0001 0.0012 0.9988 0.9766 0.0001

32-33 110.8 0.0055 0.9945 0.9777 0.664 0.0002 0.0013 0.9987 0.9754 0.0002

33-34 110.8 0.0055 0.9945 0.9777 1.035 0.0004 0.0015 0.9985 0.9741 0.0003

34-35 110.8 0.0055 0.9945 0.9777 1.357 0.0005 0.0016 0.9984 0.9727 0.0005

35-39 145.8 0.0073 0.9927 0.9723 2.097 0.0007 0.0080 0.9920 0.9712 0.0007

40-44 221.6 0.0111 0.9890 0.9652 2.813 0.0010 0.0120 0.9880 0.9634 0.0009

45-49 340.0 0.0170 0.9831 0.9546 3.171 0.0011 0.0181 0.9821 0.9519 0.0010

50-54 509.0 0.0255 0.9749 0.9385 3.350 0.0011 0.0266 0.9738 0.9348 0.0011

55-59 726.3 0.0363 0.9643 0.9149 3.440 0.0012 0.0375 0.9632 0.9103 0.0011

60-64 1,068.3 0.0534 0.9480 0.8823 3.485 0.0012 0.0546 0.9469 0.8768 0.0010

65-69 1,627.5 0.0814 0.9218 0.8364 3.507 0.0012 0.0826 0.9207 0.8302 0.0010

70-74 2,491.3 0.1246 0.8829 0.7710 3.518 0.0012 0.1258 0.8818 0.7644 0.0009

75-79 3,945.9 0.1973 0.8209 0.6807 3.524 0.0012 0.1985 0.8200 0.6740 0.0007

80-84 6,381.4 0.3191 0.7268 0.5588 3.527 0.0012 0.3203 0.7259 0.5527 0.0006

Absolute Rx = 0.0100

1

2 Absolute risk = 0.01000; exp level = 0.2446; Occupational lifetime unit risk = 0.0409 (Based on occupational 

3 exposures beginning at age 16 years); Scaled occupational lifetime unit risk = 0.0530 (Scaled by ratio of 70:54 to 

4 account for risk over 70 year lifetime).

5
This document is a draft for review purposes only and does not constitute Agency policy.

G-7 DRAFT—DO NOT CITE OR QUOTE

1 The following tables show details of the computations of the unit risks for lung cancer 

2 mortality (Tables G-3 and G-4). Section 5.4.5.2 details the application of the life-table 

3 methodology for lung cancer. The results of Tables G-3 and G-4 are shown in Table 5-19.

4

5 Column Definitions for Tables G-3 and G-4

6

7 Column A: Age interval up to age 85.

Column B: All-cause mortality rate for interval i (× 1058 /year) (2007 data NVSR 58(19) 2010).

Column C: Lung cancer mortality rate for interval i (× 1059 /year) (2003-2007 SEER Table 

10 15.10, age-specific U.S. death rates).

11 Column D: All-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of 

12 years in age interval).

13 Column E: Probability of surviving interval i (qi) (= exp(−h*i)).

14 Column F: Probability of surviving up to interval i (Si) (S1 = 1; Si = Si−1 × qi−1, for i>1).

15 Column G: Lung cancer mortality hazard rate for interval i (hi) (= lung cancer mortality rate × 

16 number of years in interval).

17 Column H: Conditional probability of dying from lung cancer in interval i (= (hi/h*i) × Si × 

18 (1−qi)), i.e., conditional upon surviving up to interval i (Ro, the background lifetime 

19 probability of dying from lung cancer = the sum of the conditional probabilities 

20 across the intervals).

21 Column I: Lagged exposure at mid-interval (x dose).

22 Column J: Lung cancer mortality hazard rate in exposed people for interval. To estimate the 

23 LEC01, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 

24 1%, the 95% upper bound on the regression coefficient is used, i.e., 

25 MLE + 1.645 × SE.

26 Column K: All-cause hazard rate in exposed people for interval i (h*xi) (= h*i + (hxi − hi)).

27 Column L: Probability of surviving interval i without dying from lung cancer for exposed 

28 people (qxi) (= exp(−h*xi)).

29 Column M: Probability of surviving up to interval i without dying from lung cancer for exposed 

30 people (Sxi) (Sx1 = 1; Sxi = Sxi−1 × qxi-1, for i>1).

31 Column N: Conditional probability of dying from lung cancer in interval i for exposed people 

32 (= (hxi/h*xi) × Sxi × (1−qxi)) (Rx, the lifetime probability of dying from lung 

33 cancer for exposed people = the sum of the conditional probabilities across the 

34 intervals).

35

36

37 Note that the life tables for lung cancer mortality estimate the extra risk as (Rx - Ro) / 

38 (1 - Ro), where Rx is the lifetime cancer mortality risk in the exposed population and Ro is the 
This document is a draft for review purposes only and does not constitute Agency policy.

G-8 DRAFT—DO NOT CITE OR QUOTE

1 lifetime lung cancer mortality risk in an unexposed population (i.e., the background risk). In 

2 each of the life tables, inhalation exposure commences at age 16 years and continues at the same 

3 exposure concentration for the duration of the life table. This allows for the computation of an 

4 “adult-only” occupational lifetime unit risk, which is then scaled by a ratio of 70:54 to account 

5 for risk over the standard 70-year lifetime. While exposure is initiated at age 16 years, this 

6 exposure is lagged to match the corresponding exposure-response models, which provide the 

7 hazard rates per unit of exposure. For example, in Tables G-3 and G-4, Column I shows 

8 exposure lagged by 10 years so that no lagged exposure appears until age 26 years. 
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-9 DRAFT: DO NOT CITE O

Table G-3. Lung cancer extra risk calculation for environmental exposure to 0.191 fibers/cc Libby Amphibole 

asbestos using a linear exposure-response model based on the metric of cumulative exposure with a 10-year 

exposure lag, as described in Section 5.4.5.3 as the reasonable upper bound

A B C D E F G H I J K L M N

Age

Int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All

cause

hazard

rate 

(h*)

Prob. of

surviving

interval 

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. prob.

of lung CA

mortality

in interval

(Ro)

Lagged

exp. mid. 

int. 

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

Cond. prob.

of lung CA

in interval

(Rx)

<1 684.5 0 0.0068 0.9932 1.0000 0.0000 0.0000 0.00 0.0000 0.0068 0.9932 1.0000 0.0000

1-2 28.6 0 0.0003 0.9997 0.9932 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9932 0.0000

2-3 28.6 0 0.0003 0.9997 0.9929 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9929 0.0000

3-4 28.6 0 0.0003 0.9997 0.9926 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9926 0.0000

4-5 29.9 0 0.0003 0.9997 0.9923 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9923 0.0000

5-6 13.7 0 0.0001 0.9999 0.9920 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9920 0.0000

6-7 13.7 0 0.0001 0.9999 0.9919 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9919 0.0000

7-8 13.7 0 0.0001 0.9999 0.9918 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9918 0.0000

8-9 13.7 0 0.0001 0.9999 0.9916 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9916 0.0000

9-10 13.7 0 0.0001 0.9999 0.9915 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9915 0.0000

10-11 18.7 0 0.0002 0.9998 0.9914 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9914 0.0000

11-12 18.7 0 0.0002 0.9998 0.9912 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9912 0.0000

12-13 18.7 0 0.0002 0.9998 0.9910 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9910 0.0000

13-14 18.7 0 0.0002 0.9998 0.9908 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9908 0.0000

14-15 18.7 0 0.0002 0.9998 0.9906 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9906 0.0000

15-16 61.9 0 0.0006 0.9994 0.9904 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9904 0.0000
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-10 DRAFT: DO NOT CITE O

Table G-3. Lung cancer extra risk calculation for environmental exposure to 0.191 fibers/cc Libby Amphibole 

asbestos using a linear exposure-response model based on the metric of cumulative exposure with a 10-year 

exposure lag, as described in Section 5.4.5.3 as the reasonable upper bound (continued)

A B C D E F G H I J K L M N

Age

Int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All

cause

hazard

rate 

(h*)

Prob. of

surviving

interval 

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. prob.

of lung CA

mortality

in interval

(Ro)

Lagged

exp. mid. 

int. 

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

Cond. prob.

of lung CA

in interval

(Rx)

16-17 61.9 0 0.0006 0.9994 0.9898 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9898 0.0000

17-18 61.9 0 0.0006 0.9994 0.9892 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9892 0.0000

18-19 61.9 0 0.0006 0.9994 0.9886 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9886 0.0000

19-20 61.9 0 0.0006 0.9994 0.9880 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9880 0.0000

20-21 98.3 0.1 0.0010 0.9990 0.9874 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9874 0.0000

21-22 98.3 0.1 0.0010 0.9990 0.9864 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9864 0.0000

22-23 98.3 0.1 0.0010 0.9990 0.9854 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9854 0.0000

23-24 98.3 0.1 0.0010 0.9990 0.9845 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9845 0.0000

24-25 98.3 0.1 0.0010 0.9990 0.9835 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9835 0.0000

25-26 99.4 0.2 0.0010 0.9990 0.9825 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9825 0.0000

26-27 99.4 0.2 0.0010 0.9990 0.9815 0.0000 0.0000 0.10 0.0000 0.0010 0.9990 0.9815 0.0000

27-28 99.4 0.2 0.0010 0.9990 0.9806 0.0000 0.0000 0.29 0.0000 0.0010 0.9990 0.9806 0.0000

28-29 99.4 0.2 0.0010 0.9990 0.9796 0.0000 0.0000 0.48 0.0000 0.0010 0.9990 0.9796 0.0000

29-30 99.4 0.2 0.0010 0.9990 0.9786 0.0000 0.0000 0.67 0.0000 0.0010 0.9990 0.9786 0.0000

30-34 110.8 0.5 0.0055 0.9945 0.9777 0.0000 0.0000 1.24 0.0000 0.0055 0.9945 0.9777 0.0000

35-39 145.8 2.1 0.0073 0.9927 0.9723 0.0001 0.0001 2.20 0.0001 0.0073 0.9927 0.9722 0.0001

40-44 221.6 7.9 0.0111 0.9890 0.9652 0.0004 0.0004 3.15 0.0004 0.0111 0.9890 0.9652 0.0004

45-49 340.0 20.2 0.0170 0.9831 0.9546 0.0010 0.0010 4.11 0.0011 0.0171 0.9831 0.9545 0.0010
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-11 DRAFT: DO NOT CITE O

Table G-3. Lung cancer extra risk calculation for environmental exposure to 0.191 fibers/cc Libby Amphibole 

asbestos using a linear exposure-response model based on the metric of cumulative exposure with a 10-year 

exposure lag, as described in Section 5.4.5.3 as the reasonable upper bound (continued)

A B C D E F G H I J K L M N

Age

Int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All

cause

hazard

rate 

(h*)

Prob. of

surviving

interval 

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. prob.

of lung CA

mortality

in interval

(Ro)

Lagged

exp. mid. 

int. 

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

haz. rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

Cond. prob.

of lung CA

in interval

(Rx)

50-54 509.0 39.8 0.0255 0.9749 0.9385 0.0020 0.0018 5.06 0.0022 0.0257 0.9747 0.9384 0.0020

55-59 726.3 74.7 0.0363 0.9643 0.9149 0.0037 0.0034 6.02 0.0042 0.0368 0.9639 0.9146 0.0038

60-64 1,068.3 139.8 0.0534 0.9480 0.8823 0.0070 0.0060 6.97 0.0080 0.0544 0.9470 0.8815 0.0069

65-69 1,627.5 220.9 0.0814 0.9218 0.8364 0.0110 0.0089 7.93 0.0129 0.0832 0.9201 0.8348 0.0103

70-74 2,491.3 304.3 0.1246 0.8829 0.7710 0.0152 0.0110 8.88 0.0181 0.1275 0.8803 0.7682 0.0131

75-79 3,945.9 369.5 0.1973 0.8209 0.6807 0.0185 0.0114 9.84 0.0224 0.2013 0.8177 0.6762 0.0137

80-84 6,381.4 379.4 0.3191 0.7268 0.5588 0.0190 0.0091 10.79 0.0235 0.3236 0.7236 0.5529 0.0111

Ro = 0.0531 Rx = 0.0625

Extra risk = 0.01001; exp level = 0.191; Occupational lifetime unit = 0.0524 (Based on occupational exposures beginning at age 16 years); Scaled occupational 

lifetime unit = 0.0679 (Scaled by ratio of 70:54 to account for risk over 70-year lifetime).
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-12 DRAFT: DO NOT CITE O

Table G-4. Lung cancer extra risk calculation for environmental exposure to 0.333 fibers/cc Libby Amphibole 

asbestos using the metric of cumulative exposure with a 10-year exposure lag and a 10-year half-life of exposure, 

as described in Section 5.4.5.3 as the lowest information criterion

A B C D E F G H I J K L M N

Age

int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All cause

hazard

rate

(h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. 

prob. of 

lung CA

mortality

in interval

(Ro)

Lagged

exp. mid.

int.

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

hazard 

rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. prob. 

of lung CA

in interval

(Rx)

<1 684.5 0 0.0068 0.9932 1.0000 0.0000 0.0000 0.00 0.0000 0.0068 0.9932 1.0000 0.0000

1-2 28.6 0 0.0003 0.9997 0.9932 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9932 0.0000

2-3 28.6 0 0.0003 0.9997 0.9929 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9929 0.0000

3-4 28.6 0 0.0003 0.9997 0.9926 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9926 0.0000

4-5 29.9 0 0.0003 0.9997 0.9923 0.0000 0.0000 0.00 0.0000 0.0003 0.9997 0.9923 0.0000

5-6 13.7 0 0.0001 0.9999 0.9920 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9920 0.0000

6-7 13.7 0 0.0001 0.9999 0.9919 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9919 0.0000

7-8 13.7 0 0.0001 0.9999 0.9918 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9918 0.0000

8-9 13.7 0 0.0001 0.9999 0.9916 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9916 0.0000

9-10 13.7 0 0.0001 0.9999 0.9915 0.0000 0.0000 0.00 0.0000 0.0001 0.9999 0.9915 0.0000

10-11 18.7 0 0.0002 0.9998 0.9914 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9914 0.0000

11-12 18.7 0 0.0002 0.9998 0.9912 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9912 0.0000

12-13 18.7 0 0.0002 0.9998 0.9910 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9910 0.0000

13-14 18.7 0 0.0002 0.9998 0.9908 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9908 0.0000

14-15 18.7 0 0.0002 0.9998 0.9906 0.0000 0.0000 0.00 0.0000 0.0002 0.9998 0.9906 0.0000

15-16 61.9 0 0.0006 0.9994 0.9904 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9904 0.0000
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-13 DRAFT: DO NOT CITE O

Table G-4. Lung cancer extra risk calculation for environmental exposure to 0.333 fibers/cc Libby Amphibole 

asbestos using the metric of cumulative exposure with a 10-year exposure lag and a 10-year half-life of exposure, 

as described in Section 5.4.5.3 as the lowest information criterion (continued)

A B C D E F G H I J K L M N

Age

int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All cause

hazard

rate

(h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. 

prob. of 

lung CA

mortality

in interval

(Ro)

Lagged

exp. mid.

int.

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

hazard 

rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. prob. 

of lung CA

in interval

(Rx)

16-17 61.9 0 0.0006 0.9994 0.9898 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9898 0.0000

17-18 61.9 0 0.0006 0.9994 0.9892 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9892 0.0000

18-19 61.9 0 0.0006 0.9994 0.9886 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9886 0.0000

19-20 61.9 0 0.0006 0.9994 0.9880 0.0000 0.0000 0.00 0.0000 0.0006 0.9994 0.9880 0.0000

20-21 98.3 0.1 0.0010 0.9990 0.9874 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9874 0.0000

21-22 98.3 0.1 0.0010 0.9990 0.9864 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9864 0.0000

22-23 98.3 0.1 0.0010 0.9990 0.9854 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9854 0.0000

23-24 98.3 0.1 0.0010 0.9990 0.9845 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9845 0.0000

24-25 98.3 0.1 0.0010 0.9990 0.9835 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9835 0.0000

25-26 99.4 0.2 0.0010 0.9990 0.9825 0.0000 0.0000 0.00 0.0000 0.0010 0.9990 0.9825 0.0000

26-27 99.4 0.2 0.0010 0.9990 0.9815 0.0000 0.0000 0.16 0.0000 0.0010 0.9990 0.9815 0.0000

27-28 99.4 0.2 0.0010 0.9990 0.9806 0.0000 0.0000 0.48 0.0000 0.0010 0.9990 0.9806 0.0000

28-29 99.4 0.2 0.0010 0.9990 0.9796 0.0000 0.0000 0.77 0.0000 0.0010 0.9990 0.9796 0.0000

29-30 99.4 0.2 0.0010 0.9990 0.9786 0.0000 0.0000 1.04 0.0000 0.0010 0.9990 0.9786 0.0000

30-34 110.8 0.5 0.0055 0.9945 0.9777 0.0000 0.0000 1.74 0.0000 0.0055 0.9945 0.9777 0.0000

35-39 145.8 2.1 0.0073 0.9927 0.9723 0.0001 0.0001 2.64 0.0001 0.0073 0.9927 0.9722 0.0001

40-44 221.6 7.9 0.0111 0.9890 0.9652 0.0004 0.0004 3.27 0.0004 0.0111 0.9889 0.9652 0.0004
This document is a draft for 

review purposes only and does 

not constitute Agency policy.

G-14 DRAFT: DO NOT CITE O

Table G-4. Lung cancer extra risk calculation for environmental exposure to 0.333 fibers/cc Libby Amphibole 

asbestos using the metric of cumulative exposure with a 10-year exposure lag and a 10-year half-life of exposure, 

as described in Section 5.4.5.3 as the lowest information criterion (continued)

A B C D E F G H I J K L M N

Age

int.

All

cause

mortality

(xE5/yr)

Lung CA

mortality

(xE5/yr)

All cause

hazard

rate

(h*)

Prob. of

surviving

interval

(q)

Prob. of

surviving

up to

interval

(S)

Lung

CA

hazard

rate (h)

Cond. 

prob. of 

lung CA

mortality

in interval

(Ro)

Lagged

exp. mid.

int.

(Xdose)

Exposed

lung CA

hazard

rate (hx)

Exposed

all cause

hazard 

rate

(h*x)

Exposed

prob. of

surviving

interval

(qx)

Exposed

prob. of

surviving

up to int.

(Sx)

Exposed

cond. prob. 

of lung CA

in interval

(Rx)

45-49 340.0 20.2 0.0170 0.9831 0.9546 0.0010 0.0010 3.72 0.0012 0.0172 0.9830 0.9545 0.0011

50-54 509.0 39.8 0.0255 0.9749 0.9385 0.0020 0.0018 4.04 0.0023 0.0258 0.9746 0.9383 0.0021

55-59 726.3 74.7 0.0363 0.9643 0.9149 0.0037 0.0034 4.26 0.0044 0.0370 0.9637 0.9144 0.0039

60-64 1,068.3 139.8 0.0534 0.9480 0.8823 0.0070 0.0060 4.42 0.0083 0.0547 0.9468 0.8812 0.0071

65-69 1,627.5 220.9 0.0814 0.9218 0.8364 0.0110 0.0089 4.53 0.0131 0.0834 0.9200 0.8343 0.0105

70-74 2,491.3 304.3 0.1246 0.8829 0.7710 0.0152 0.0110 4.61 0.0181 0.1274 0.8803 0.7675 0.0130

75-79 3,945.9 369.5 0.1973 0.8209 0.6807 0.0185 0.0114 4.67 0.0220 0.2008 0.8180 0.6757 0.0135

80-84 6,381.4 379.4 0.3191 0.7268 0.5588 0.0190 0.0091 4.71 0.0226 0.3227 0.7242 0.5527 0.0107

Ro = 0.0531 Rx = 0.0626

Extra risk = 0.01001; exp level = 0.333; Occupational lifetime unit risk = 0.0300 (Based on occupational exposures beginning at age 16 years); Scaled 

occupational lifetime unit = 0.0389 (Scaled by ratio of 70:54 to account for risk over 70-year lifetime).