DRAFT

— DO NOT CITE OR QUOTE EPA/635/R

- 11/002 www.epa.gov/iris



# TOXICOLOGICAL REVIEW

OF

LIBBY AMPHIBOLE ASBE STOS

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 a n Inter a gency 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 shou ld 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

, DCThis document is a draft for review purposes only and does not constitute Agency policy. ii DRAFT

— DO NOT CITE OR QUOTE DISCLAIMER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. iii DRAFT

— DO NOT CITE OR QUOTE CONTENTS

— TOXICOLOGICAL REVIEW OF LIBBY AMPHIBOLE ASBESTOS

LIST OF TABLES

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

................................ ................................ ................................ ................... xi LIST OF ABBREVIA TIONS AND ACRONYMS

................................ ................................ ... xii FOREWORD

................................ ................................ ................................ ............................ xv AUTHORS, CONTRIBUTORS, AND REVIEWERS

................................ .............................. xvi

1. INTRODUCTION

................................ ................................ ................................ ............... 1

- 1 1.1. RELATED ASSESSM ENTS

................................ ................................ ...................... 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 ASSE SSMENT

................ 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 MORPHOLO GY 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

- 12This document is a draft for review purposes only and does not constitute Agency policy. iv DRAFT

— DO NOT CITE OR QUOTE 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 Communit y 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 EXPOSURE 4

- 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 CARCINOGENI CITY

................................ ................................ . 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

- 8 8This document is a draft for review purposes only and does not constitute Agency policy.

v DRAFT

— DO NOT CITE OR QUOTE 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 REF ERENCE 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. Expo sure

- 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 Scena rios

................................ ................................ ............................. 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

- 1This document is a draft for review purposes only and does not constitute Agency policy. vi DRAFT

— DO NOT CITE OR QUOTE 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 an d 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 a t 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 SUBCHRON IC AND CHRONIC STUDI ES AND CANCER

BIOASSAYS IN ANIMALS AND MECHANISTIC STUD IES 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 CALCULATIONThis document is a draft for review purposes only and does not constitute Agency policy. vii DRAFT

— DO NOT CITE OR QUOTE 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

- 2 3

2

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

........................... 2

- 26

3

- 1. Factors influencing fiber deposition and clearance in th e 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. Dimension al 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 o f 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. Incidenc e and mortality results for potential asbestos

- related cancers in communities with ATSDR health consultations evaluating potential Libby Amphibole asbestos contamination

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

- 38This document is a draft for review purposes only and does not constitute Agency policy. viii DRAFT

— DO NOT CITE OR QUOTE 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 intratrache al 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/ c m 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 fe ed 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 characterist ics 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 fi bers intraperitoneally injected into Wistar rats

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

- 65

4

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

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

- 68This document is a draft for review purposes only and does not constitute Agency policy. ix DRAFT

— DO NOT CITE OR QUOTE 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 an d 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 Li bby worker cohort

................................ ................................ .... 5

- 24

5

- 5. Comparison of model fit of exposure metrics for mesothelioma morta lity in th e 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 asbes tos, 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. Se nsitivity 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 risk s

................................ .................... 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 exposu re metric fit to the epidemiologic data

....................... 5

- 50This document is a draft for review purposes only and does not constitute Agency policy.

x DRAFT

— DO NOT CITE OR QUOTE 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 mesothelio ma 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. M esothelioma regression results from different analyses of cumulative exposure in the cohort of workers in Libby, MT

................................ ................................ ........ 6

- 17This document is a draft for review purposes only and does not constitute Agency policy. xi DRAFT

— DO NOT CITE OR QUOTE LIST OF FIGURES

2

- 1. Vermiculite mining operation on Zo nolite Mountain, Libby, Montana

......................... 2

- 2

2

- 2. Expanded vermiculite used as a soi l 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 Lib by 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 t ransmission 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 wo rkers hired after 1959

............................... 5

- 1 5This document is a draft for review purposes only and does not constitute Agency policy. xii DRAFT

— DO NOT CITE OR QUOTE 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 c hronic obstructive pulmonary disease
CYP cytochrome P450
DHE dehydroergosterol
DIC d eviance information criterion
DLCO single brea th carbon monoxide diffusing capacity
DNA deoxyribonucleic acid
EC x 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 SystemThis document is a draft for review purposes only and does not constitute Agency policy. xiii DRAFT

— DO NOT CITE OR QUOTE LIST OF ABBREVIATIONS AND ACRONYMS (continued)

IUR inhalation unit risk
JEM job exposure matrix
LEC x lowest effective concen tration
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 multi path 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 Scie nces 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)polymeras e PBS phosphate buffered saline
PCM phase contrast microscopy
PCMe phase contrast microscopy equivalent
PM 2.5 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 speciesThis document is a draft for review purposes only and does not constitute Agency policy. xiv DRAFT

— DO NOT CITE OR QUOTE 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
S T EM scanning transmission electron microscopy
SV40 simian virus 40
TEM transmission electron micr oscopy 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 diffractionThis document is a draft for review purposes only and does not constitute Agency policy.

xv DRAFT

— DO NOT CITE OR QUOTE 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 a sbestos , a unique mixture of asbestos fibers originating from the vermiculite
mine in Libby, M ontana

. 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 noncan cer 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 Hazar d 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 th e limitations of the assessment and to aid and guide the risk assessor in the ensuing
steps o f 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 Hotlin e 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. xvi DRAFT

— DO NOT CITE OR QUOTE AUTHORS, CONTRIBUTOR S, AND REVIEWERS

CHEMICAL MANAGER S/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. Gwin n , 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, DCThis document is a draft for review purposes only and does not constitute Agency policy. xvii DRAFT

— DO NOT CITE OR QUOTE 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 Divisio n 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 Envir onmental Assessment U.S. Environmental Protection Agency Washington, DC

CONTRACT OR SUPPORT William Brattin, PhD Syracuse Research Corporation Denver, COThis 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. INTRODUCTION 1

This document presents background information and justification for the Integrated Risk 2 Information System (IRIS) Summary of the hazard and dose

- response assessment of Libby 3 Amphibole asbestos

, a unique mixture of amphibole fibers found in the vermiculite mine in 4 Libby, M T

. IRIS Summaries may include oral reference dose (RfD) and inhalation reference 5 concentration (RfC) values for chronic and other exposure durations, and a c arcinogenicity 6

assessment. 7
The RfD and RfC , if derived, provide quantitative information for use in risk assessments 8 for health effects known or assumed to be produced through a nonlinear ( presumed threshold) 9

m ode of a ction. The RfD (expressed in units of mg/kg

- day) is defined as an estimate (with 10 uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human 11 population (including sensitive subgroups) that is likely to be without an appreciable risk of 12 deleterious effects during a l ifetime. The inhalation RfC (expressed in units of mg/m 3 ) is 13 analogous to the oral RfD, but provides a continuous inhalation exposure estimate. The 14 inhalation RfC considers toxic effects for both the respiratory system (portal

- of

- entry) and for 15 effects p eripheral to the respiratory system (extrarespiratory or systemic effects). Reference 16
values are generally derived for chronic exposures (up to a lifetime), but may also be derived for 17

acute ( ≤ 24 hours), short

- term ( >24 hours up to 30 days), and subchroni c ( >30 days up to 10% of 18 lifetime) exposure durations, all of which are derived based on an assumption of continuous 19 exposure throughout the duration specified. Unless specified otherwise, the RfD and RfC are 20 derived for chronic exposure duration. 21
The car cinogenicity assessment provides information on the carcinogenic hazard 22 potential of the substance in question

, and quantitative estimates of risk from oral and inhalation 23

exposure s may be derived

. The information includes a weight

- of

- evidence judgment of the 24 likelihood that the agent is a human carcinogen and the conditions under which the carcinogenic 25 effects may be expressed. Quantitative risk estimates may be derived from the application of a 26
low

- dose extrapolation procedure . If derived, t he oral slo pe factor is a plausible upper bound on 27 the estimate of risk per mg/kg

- day of oral exposure. Similarly, a n inhalation unit risk is a 28 plausible upper bound on the estimate of cancer risk per μg/m 3 air breathed. For Libby 29 Amphibole asbestos the RfC and IUR are expressed as fibers/cc and cumulative exposure is 30 expressed as fibers

- yr/cc. 31 The assessment of cancer health effects from exposure to Libby Amphibole asbestos does 32
not estimate risks for non

- cancer mortality from inhalation of Libby Amphibole asbesto s. There 33 are potential risks of mortality from noncancer disease (e.g. NMRD, potentially cardiovascular 34This document is a draft for review purposes only and does not constitute Agency policy. 1

- 2 DRAFT

— DO NOT CITE OR QUOTE disease). An inhalation RfC addresses noncancer health effects of inhalation exposure to Libby 1 Amphibole asbestos. Libby Amphibole asbestos

- specific d a ta are not available to support RfD or 2 cancer slope factor derivation s for oral exposures. 3 Development of these hazard identification and dose

- response assessments for Libby 4 Amphibole asbestos has followed the general guidelines for risk assessment as se t forth by the 5 National Research Council (1983). EPA Guidelines and Risk Assessment Forum technical panel 6 reports that may have been used in the development of this assessment include the following: 7 Guidelines for the Health Risk Assessment of Chemical Mi xtures (U.S. EPA, 1986a), Guidelines 8 for Mutagenicity Risk Assessment (U.S. EPA, 1986b), Recommendations for and Documentation 9 of Biological Values for Use in Risk Assessment (U.S. EPA, 1988), Guidelines for 10 Developmental Toxicity Risk Assessment (U.S. EPA, 1991), Interim Policy for Particle Size and 11 Limit Concentration Issues in Inhalation Toxicity (U.S. EPA, 1994a), Methods for Derivation of 12 Inhalation Reference Concentrations and Application of Inhalation Dosimetry (U.S. EPA, 13 1994b), Use of the Benchmark Dose Approach in Health Risk Assessment (U.S. EPA, 1995), 14 Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996), Guidelines for 15 Neurotoxicity Risk Assessment (U.S. EPA, 1998), Science Policy Council Handbook : Risk 16 Characterizatio n (U.S. EPA, 2000 a ), Benchmark Dose Technical Guidance Document 17 (U.S. EPA, 2000 b ), Supplementary Guidance for Conducting Health Risk Assessment of 18
Chemical Mixtures (U.S. EPA, 2000 c ), A Review of the Reference Dose and Reference 19
Concentration Processes (U. S. EPA, 2002)

, Guidelines for Carcinogen Risk Assessment 20 (U.S. EPA, 2005a) , Supplemental Guidance for Assessing Susceptibility from Early

- Life 21 Exposure to Carcinogens (U.S. EPA, 2005b)

, Science Policy Council Handbook: Peer Review 22 (U.S. EPA, 2006a ), and A Framework for Assessing Health Risks of Environmental Exposures to 23
Children (U.S. EPA, 2006b). 24 The literature search strategy employed for this compound was based on at least 25

one common name. Any pertinent scientific information submitted by the public to the IRIS 26 Submission Desk was also considered in the development of this document. Specifically, t he 27 literature search for this compound is based on EPA’s National Center for Environmental 28
Assessment’ s Health and Environmental Res earch Outline database tool ( which includ es 29 PubMed, MEDLINE , Web of Science, JSTOR

, and other literature sources )

. The key search 30
terms includ ed the following : Libby Amphibole, tremolite, asbestos, r ichterite, winchite, 31 amphibole

, and Libby, M T . The relevant literature was reviewed through April 2011

. 32
33
34This document is a draft for review purposes only and does not constitute Agency policy. 1

- 3 DRAFT

— DO NOT CITE OR QUOTE 1.1. RELATED ASSESSMENTS 1

1.1.1. IRIS Assessment for Asbestos ( U.S

. EPA , 1988) 2

The IRIS assessment for asbestos was posted online in IRIS in 1988 and includes an IUR 3
of 0.23 excess cancer s per 1 f iber / cc ( U.S. EPA, 1988 ). ( This unit risk is given in units of the 4 fibers as measured by phased contrast microscopy [PCM].) The IRIS IUR for asbestos is derived 5 by estimat ion of excess cancers for a conti nuous lifetime exposure and is based on the central 6
tend e ncy , not the upper bound

, of the risk estimates ( U.S. EPA, 198 8 ) and is applicable to 7 exposures across a range of expo s ure environments and types of asbestos (CAS Number 1332

- 8
21

- 4) . Although other cancers have been associated with asbestos (e.g.

, laryngeal, stomach

, 9
ovarian ) ( Straif et al.

, 2009 ) , the IRIS IUR for asbestos accounts for only lung cancer and 10 mesothelioma. Additionally, pleural and pulmonary effects from asbestos exposure (e.g.

, 11
asbes tosis, pleural plaques, reduced lung function) are well documented

, though

, currently

, there 12
is no RfC for these noncancer health effects. 13
The derivation of the unit risk is based on the Airborne Asbestos Health Assessment 14 Update (AAHAU) (U.S. EPA, 1986a) . The AAHAU provides various potency factors and 15
mathematical models of l ung cancer and mesothelioma mortality based on synthesis of data from 16 occupational studie s and presents estimates of lifetime cancer risk for continuous environmental 17

exposure s (0.0001 f iber / cc and 0.01 f

iber / cc ) ( U.S. EPA, 1986 a , see Table 6

- 3). For both lung 18
cancer and mesothelioma

, lifetable analysis was used to generate risk estimates based on the 19 number of years of exposure and the age of onset of exposure. Although vario us exposure 20 scenarios were presented, the unit risk is based on a lifetime continuous exposure from birth. 21 The final asbestos IUR is 0.23 excess cancer per 1 f iber / cc continuous exposure and was 22 established by the EPA Carcinogen Risk Assessment Verificati on Endeavor workgroup and 23 posted on the IRIS database in 1988 ( see Table 1

- 1) (U.S. EPA, 198 8 )

. 24 25 26 Table 1

- 1 . Derivation of the current IRIS inhalation unit risk for asbestos 27 from the lifetime risk tables in the AAHAU 28 29

Gender Deaths per 100,000 a
Risk Unit risk Mesothelioma Lung cancer Total
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 Data are for exposure at 0.01 f/cc for a lifetime. 30 AAHAU = Airborne Asbestos Health Assessment Update

. 31 Source: U.S. EPA (1988). 32This document is a draft for review purposes only and does not constitute Agency policy. 1

- 4 DRAFT

— DO NOT CITE OR QUOTE 1.1.2. EPA Health Assessment for Vermiculite (199 1 ) 1

A n EPA health assessment for vermiculite reviewed available health data, including 2 studies on workers in mines without significant amphibole fiber contamination

. The cancer and 3

nonca nc er health effects observed in the Libby, MT miner cohort were not seen in studies of 4 workers exposed to vermiculite from mines with similar exposure to vermiculite, but much lower 5 exposures to asbestos. Therefore, it was concluded that the health effects observed from the 6
materials mined from Zonolite Mountain near Libby, MT

, were most likely due to amphibole 7
fibers , not the vermiculite itself ( U.S. EPA, 199 1 ). At the time, EPA recommended the 8 application of the IRIS IUR for asbestos fibers (0.23 per f iber /cc) in addressing potential risk of 9 the amphibole fibers entrained in v ermiculite mined in Libby, MT

. 10
11 1.2. LIBBY AMPHIBOLE A SBESTOS

- SPECIFIC HUMAN HEALTH ASSESSMENT 12 Libby Amphibole asbestos is a complex mixture of amphibole fibers, both 13 mineralogically and morphologically ( see Section 2.2 ). T he mixture primarily inc ludes 14 tremolite, winchite

, and richt e rite fibers with trace amounts of magnesioriebeckite, edenite, and 15 magnesio

- arfvedsonite . These fibers exhibit a complete range of morphologies from prismatic 16 crystals to asbestiform fibers (Meeker et al

. , 2003). Exposure t o Libby A mphibole asbestos 17
results in the same types of adverse health effects as are seen with exposure to other amphibole 18
mineral fibers. Epidemiologic studies of workers exposed to L ibby Amphibole asbestos fibers 19 indicate increased lung cancer and meso thelioma, as well as asbestosis

, and other nonmalignant 20 respiratory disease s ( Lockey et al., 1984; McDonald et al., 1986a,b, 2004; Amandu s and 21 Wheeler, 1987; Amandus et al., 1987 a,b ; Peipins et al., 2003; Sullivan, 2007 ; Rohs et al., 2008 ; 22 Larson et al., 2010 a,b , Moolgavkar et al., 2010 )

. 23
T he IRIS IUR for asbestos is based on a synthesis of 14 epidemiologic studies that 24 included occupational exposure to chrysotile, amosite

, or mixed mineral exposures (chrysotile, 25 amosite, crocidolite) ( U.S. EPA

, 198 8

, 1986a ). Th ere is some uncertainty in applying the 26 resulting IUR to exposure environments and minerals different from those analyzed in the 27
AAHAU ( U.S. EPA

, 1986 a )

. There is currently no RfC derived for asbestos. 28This document is a draft for review purposes only and does not constitute Agency policy. 2

- 1 DRAFT

— DO NOT CITE OR QUOTE 2. LIBBY AMPHIBOLE ASBESTOS : GEOLOGY, USE , AND EXPOSURE P OTENTIAL 1

2.1. HISTORICAL BACKGROUN D 2 The term Libby A mphibole asbestos refers to a mixture of amphibole asbestos found in 3
the rocks and ore of Zonolite Mountain , 6 miles northeast of Libby, MT (Figure 2

- 1) . Zonolite 4 Mountain contains a large vermiculite deposit that has been mined since the early 1920s for 5 various commercial uses. Vermiculite miners, mill workers

, and those working in the pr ocessing 6 plants were exposed to these amphibole fibers, which remain within vermiculite ore and product

. 7 As amphibole asbestos is present in the geological deposit from which the vermiculite ore was 8 being mined, workers were exposed to asbestos fibers dur ing various activities such as ex t racting 9 ore from the mine, transporting ore and waste rock, milling operations, and shipping the final 10 product (Meeker et al., 2003; Amandus et al., 1987a ; McDonald et al., 1986a )

. Mortality and 11 morbidity studies on the mine and mill workers from Libby have reported adverse health effects 12 in these workers including lung cancer, mesothelioma, nonmalignant respiratory disease 13 ( NRMD ) , asbestosis, pleural anomalies , interstitial fibrosis

, and altered lung function (McDonald 14
et al.

, 1986a

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

, 2010 ; Moolgavkar et al.

, 2010 ). P leural anomalies and signs of interstitial fibrosis 16
have also been reported in workers exfoliating and processing expanded Libby vermiculite in 17 other facilities (Lockey et al., 1984 ; Rohs et al.

, 2008). 18 The primary commercial product from the Zonolite mining operation was vermiculite 19 ore. When heated to approximat ely 150 ○ C, the vermiculite mineral expands like popcorn into a 20 light porous material. The un expanded mineral exhibits a sheetlike structure that is seen in 21 related minerals (e.g., mica) (Figure 2

- 2). This process of expanding the mineral ore is termed 22
―exfoliati on‖ or ―popping‖ and occurs when the silicate sheets within the ore are rapidly 23 dehydrolyzed by applying high heat. Since the vermiculite product was contaminated with fibers 24 of Libby Amphibole asbestos, fibers were released during this energetic process potentially 25 exposing workers and resulting in asbestos contamination of the finished product sold to 26

consumers. The Libby mine produced approximately 80% of the world‘s supply of vermiculite 27 ore during its operation from 1923 to 1990. Expanded vermiculit e from the Libby , MT site was 28 used as attic insulation, packing material, and a soil conditioner, and in the production of gypsum 29
wall board. 30
The refined vermiculi te ore concentrate (after milling and size selection) was then loaded 31
onto railcars and tra nsported across the country to expansion plants where it was exfoliated and 32 distributed locally (see Figure 2

- 3). ATSDR (2008) has surveyed 28 of these facilities, 33 identifying potential community exposures both to amphibole asbestos fibers from the 34This document is a draft for review purposes only and does not constitute Agency policy. 2

- 2 DRAFT

— DO NOT CITE OR QUOTE 1 Fig ure 2

- 1 . Vermiculite mining operation on Zonolite Mountain, Libby, 2 Montana. 3 4 5

(a) (b) 6 7 Figure 2

- 2 . Expanded vermiculite used as a soil conditioner (a) or attic 8 insulation (b). 9 10 Source: U.S. EPA (2010). 11This document is a draft for review purposes only and does not constitute Agency policy. 2

- 3 DRAFT

— DO NOT CITE OR QUOTE 1 Figure 2

- 3 . Nationwide Distribution of Libby Ore by County (in tons). Data 2 on the distribution of ore are based on approximately 80,000 invoices that EPA 3 obtained from W.R. Grace

, which document shipments of vermiculite ore made 4 from the Libby mine between 1964 to 1990. EPA tabulated this shipping 5 information in a database. 6 7 Source: GAO (2007). 8 9 10 vermiculite concentrate before exfoliation, during exfolia tion and processing and waste rock 11 from the processing plants (see Section 4.1.4 and Figure 4

- 1). 12
13
2.2. GEOLOGY, MINERALOGY, AND FIBER MORPHOLOGY OF LIBBY 14 AMPHIBOLE ASBESTOS 15 A large vermiculite deposit is located on Zonolite Mountain, northeast of Libby, M T

, 16
within a geologic unit known as the Rainy Creek complex. Geologic processes within the Rainy 17 Creek complex have resulted in the formation of fibrous amphiboles adjacent to igneous 18 intrusions into the complex (veins and dikes of alkaline granite, pegmatite, and quartz) 19 (Boettcher, 1996). The amphibole fibers identified fall within the tremolite

- richterite

- 20This document is a draft for review purposes only and does not constitute Agency policy. 2

- 4 DRAFT

— DO NOT CITE OR QUOTE magnesioriebecktite field ( e.g. winchite

, richterite and tremolite ) (Meeker et al., 2003). An 1 appropriate understanding of the mineralogy and geology of t hese materials is helpful in defining 2 the mineral fibers specific to Libby Amphibole asbestos. 3 Geological terms provide fiber and mineral definitions based on habit of formation and 4 fiber morphology. Conversely, the analytical methods that have been use d to count fibers in air 5 samples, in both historical and current exposure environments, define microscopic fibers based 6 on dimensional characteristics and mineralogy (depending on the analytical method). Current 7 analytical methods do not have specific pro cedures for determining fiber morphology at the 8 microscopic level

. Bec a use the human and experimental animal data on adverse health effects of 9 asbestos rely on available analytical methods to document exposure, these definitions are 10
relevant to determining what constitutes a fiber for this health assessment. Therefore

, available 11 data on the fiber morphology and fiber

- size distribution of Libby Amphibole asbestos are 12 presented in the following sections

. 13
14
2.2.1. Silicate Minerals 15

S ilicate minerals are basically made up of oxygen and silicon, two of the most abundant 16
elements in the Earth‘s crust. Approximately 25% of known minerals and 40% of the common 17
minerals are silicates

. W ith a few minor exceptions, all igneous rock

- forming minerals are 18
silicate s, which mean that silicates comprise more than 90% of the Earth‘s crust ( Klein and 19 Hurlbut, 1977). 20
Some silicates are very hard

, infusible

, and insoluble. Even in acid, they are not easily 21
soluble. S pecific gravity ranges from fairly light to intermedia te , luster is commonly glassy , and 22

m ost crush to a light powder even when the bulk specimen is black prior to crushing

. Silicates 23 chiefly occur as components of rocks, segregations in rocks, or crystals lining cavities in rocks. 24
Most hard silicates are primary minerals, not products of weathering. Secondary silicates 25 typically contain water (Klein and Hurlbut, 1977)

. 26
The basic chemical unit of silicates is the [ SiO 4 ] 4

− tetrahedron

- shaped anionic group. The 27
basic unit consists of fou r oxygen molecules at the apices of a regular tetrahedron surrounding 28
and coordinated with one silicon ion ( Si 4+ ) at the center. The chemistry is such that the oxygen 29
molecules can bond to another silicon ion and

, therefore

, link one [ SiO 4 ] 4

− tetrahedron to 30
another

, and then another

, and so forth by the process of polymerization. The silicates can form 31 as single units, double units, chains, sheets, rings, and framework structures ( see Figure 2

- 4 )

. 32
S ilicate minerals can be defined by chemical structure, crystal structure, trace mineral s , and habit 33
of formation. 34This document is a draft for review purposes only and does not constitute Agency policy. 2

- 5 DRAFT

— DO NOT CITE OR QUOTE (a) Nesosilicates or single tetrahedron. 1 The single tetrahedron comprises four oxygen 2 molecules covalently bound to the silicon, at 3 the center of the [SiO 4 ] 4

–

- tetrahedron. 4 5 6
(b ) Inosilicates [ ino ( gr. ) = thread]

- Single

7 chain silicates. Chain silicates are realized 8 by linking [SiO 4 ] 4

–

- tetrahedrons in a way to 9 form continuous chains. They can be 10 represented by a composition of [SiO 3 ] 2

– . A 11 typical example is diopside CaMg[Si 2 O 6 ], in 12 which the ―endless‖ chains are also held 13 together by Ca 2+ and Mg 2+ ions. 14 15 16 (c) Inosilicates

- Double

- chain silicates. 17 Two silicate chains of the inosilicates are 18 linked at the corners, forming double chains 19 and yielding [Si 4 O 11 ] 6

– ions, as realized in 20 the tremolite

- ferro

- actinolite series 21 Ca 2 (Mg,Fe) 5 Si 8 O 22 (OH) 2 . Double

- chain 22 silicates are commonly grouped with the 23 single

- chain inosilicates. 24 25

26 (d ) Phyllosilicates [ phyllo ( gr. ) = sheet] or 27 sheet silicates. These are formed if the 28 double

- chain inosilicate [Si 4 O 11 ] 6

– chains are 29 linked to form continuous sheets with the 30 chemical formula [Si 2 O 5 ] 2

– . Examples of a 31 sheet silicates include chrysotile 32 Mg 3 Si 2 O 5 (OH)And vermiculite [(Mg, 33 Fe,A) 3 (Al,Si) 2 O 10 (OH) 2 ●4H 2 O]

. 34 35 36 37 38 39 40 41 42 43 Figure 2

- 4 . Structure of the silicate minerals, illustrating silicate subclasses 44 by the linking of the basic silicon tetrahedron (a) into more complex 45 structures (b, c, or d). 46 47 48This document is a draft for review purposes only and does not constitute Agency policy. 2

- 6 DRAFT

— DO NOT CITE OR QUOTE Silicates are divided by structure i nto the following subclasses: 1
2 Nesosilicates (single tetrahedrons) 3 Sorosilicates (double tetrahedrons) 4 Inosilicates (single and double chains) 5 Cyclosilicates (rings) 6 Phyllosilicates (sheets) 7 Tectosilicates (frameworks) 8

9 10 Each subclass of silicates has many mineral members. Specific minerals are defined by 11 the structure, chemistry, and morphology of the mineral. The minerals of interest in this 12 assessment are vermiculite (a sheet silicate) and various forms of amphibole a sbestos (which are 13 within the class of inosilicates) (see Figure 2

- 5 ). The flat, layered sheets give vermiculite its 14 perfect cleavage and its unique ability to expand when heated. Chrysotile is also a sheet silicate, 15 but can present with an asbestiform m orphology where the sheets role into fibrils, forming 16
asbestiform fibers

. The habit of formation depends on conditions (e.g., pressure, temperature, 17 and cooling rate) that favor a particular form, such as fibrous versus crystalline (see Section 2.2.2 18 on fiber morphology). 19
20
2.2.1.1. Mineralogy and Structure of Amphiboles 21 Amphibole minerals are double

- chain inosilicates, meaning the chemical building block 22 for amphiboles is connected chains of the silicon tetrahedron (see Figure 2

- 4c). Amphiboles 23
form whe n edge

- shared octahedra link two of the double

- chain [SiO 4 ] 4− plates (see Figure 2

- 4d). 24
The specific cations between the two double

- chain plates define the chemistry of the mineral

, 25 while the ratio of these cations in each location is used to classify amp hiboles within a solution 26
series . The cation sites are designated as A, B, and C in the equation below, which shows the 27 general chemical formula for double

- chain inosilicate amphiboles: 28
29 30 A 0

– 1 B 2 C 5 T 8 O 22 (OH, F, Cl) 2 31 32 where: 33 A = Na, K 34 B = Na, Li, Ca, Mn, Fe 2+ , Mg 35 C = Mg, Fe 2+ , Mn, Al, Fe 3+ , Ti 36 T = Si, Al 37
38This document is a draft for review purposes only and does not constitute Agency policy. 2

- 7 DRAFT

— DO NOT CITE OR QUOTE The mineral subgroup within amphiboles is determined by cationic chemistry (shown 1 above). 2
3 4 Tremolite subgroup (Ca amphiboles) 5 Anthophyllite subgroup (Fe

- Mg

- Li orthoamphiboles) 6 Richterite subgroup (Ca

- Na amphiboles) 7 Cummingtonite (Fe

- Mg

- Li clinoamphiboles) 8 9 10 Cation sites between the double

- chain silicate plates could host one or more cations as 11
noted in the abo ve formula. Sites A, B, and C in the crystal structure refer to cations that 12 covalently l ink the amphibole plates at different locations in the cryst alline structure (see 13 Figure 2

- 5 ). Based on the above formula, Site A could contain Na or K or both in varied 14 proportions; Site B could contain any of the cations listed (Na, Li, Ca, Mn, Fe 2+ , Mg ); and 15
Site C could contain Mg, Fe 2+ , Mn, Al, Fe 3+ , or Ti. Minerals often have a mixture of ions 16 between the plates at each location (site A, B or C) and these cations will be in varied 17 proportions depending on the specific geologic deposit

. 18
A solution series includes a continuum of minerals with different cation composition for 19
each site

. Sol id sol ution series are defined by their end members, where mineral terminology 20

can change as the proportion of cations changes within the crystalline structur e. For example, a 21
solution series for the cation Site A will have one end member with 100% sodium ions and one 22 end member with 100% potassium ions. This series would include all intervening ratios. 23 Because each cation site has multiple possibilities, the mi neral content of the amphibole silicates 24 can be quite complex. 25 Cation changes observed in Libby Amphibole asbestos demonstrate how mineral content 26 can change in a solution series ( Figure 2

- 7 ) where the trace minerals at each site (A, B, or C) are 27
used to define the mineral sub group and solution series

. 28 It is the complexity of the amphiboles that historically has given rise to a proliferation of 29 mineral names with no systematic basis (Hawthorne, 1981). Currently, amphiboles are identified 30 by a clear cla ssification scheme based on crystal chemistry that uses well

- established names 31 based on the basic mineralogy, with prefixes and adjective modifiers indicating the presence of 32
substantial substitutions that are not essential constituents of the end members (Leake et al., 33 1997). 34
35This document is a draft for review purposes only and does not constitute Agency policy. 2

- 8 DRAFT

— DO NOT CITE OR QUOTE 1 Figure 2

- 5 . Cross

- section of amphibole fibers showing the silicon 2 tetrahedrons (Δ) that make up each double

- chain plate (shown along the 3 fiber axis). Cations (shown as the darkene d dots) occur between the plates 4 forming the basic fiber. 5 6 Source: Kirk

- Othmer Encyclopedia of Chemical Technology (2010). 7 8 9This document is a draft for review purposes only and does not constitute Agency policy. 2

- 9 DRAFT

— DO NOT CITE OR QUOTE 1 Figure 2

- 6 . Vermiculite sample. Brinton's Quarry, near West Chester, Chester 2 County, Pennsylvania, USA. 3 4 Source: Micaceous vermiculite book (http://www.excaliburmineral.com/cdintro.htm) 5 © Jeff Weissman/ Photographic Guide to Mineral Species. 6
7 8 2.2.1.2. Fiber M

orphology 9 Fiber morphology is a function of the structural form of the silicate and the geologic habit 10 of formation. Silicates can occur in crystalline or massive form without the presence of distinct 11
fibers. Some silicates can form asbestiform fi bers in specific growth habits

. The long, slender, 12
hairlike fibers of commerc ial asbestiform minerals are visual to the naked eye; they have high 13 tensile strength and in some cases, can be woven (e.g., chrysotile). The fibrils of asbestiform 14 chrysotile are due in part to the structure and chemistry specific to chrysotile

— the miner alogy

, 15 which allows the sheet silicate to roll, forming fibrils. These fibrils, however, are also a function 16 of the temperature and pressure conditions during crystallization

— habit of formation

. 17 Amphibole fiber morphology is more complex than serpentine asbestos fibers. As with 18 chrysotile, habit of formation determines whether the amphibole mineral forms a crystalline 19

structure (massive) or is fibrous . The double

- chain form of the amphibole fibers, however, can 20 result in a range of fiber morphologies, includin g not only the asbestiform, but also, fibrous, 21This document is a draft for review purposes only and does not constitute Agency policy. 2

- 10 DRAFT

— DO NOT CITE OR QUOTE acicular, and prismatic. Additionally, crystalline amphibole can fracture, breaking along the 1 cleavage plane of the crystal, forming shards or cleavage fragments. 2 The geologic terms for fiber morpho logy arise from classification of field samples based 3 on the macroscopic appearance of the crystals and fibers (AGI, 1972): 4
5 6 Acicular: A crystal that is needlelike in form. 7

Fibrous: A mineral that is elongated with thin, needle

- like grains or fibers (e .g., 8 asbestos). 9

Asbestiform: A mineral that is fibrous

– that is, like asbestos. 10 11 12 Fibers, crystals, and cleavage fragments can appear to b e the same under transmission 13 electron microscopes (TEM) (e.g., fibers with parallel sides) (see Figure 2

- 10). Therefore, the 14

use of these definitions to describe individual fibers viewed by TE M can be problematic (Meeker 15 et al., 2003). Important characteristics such as crystal structure and surface chemistry cannot be 16 adequately categorized solely with visually d etermined definitions developed for classification of 17 field samples. 18
19
2.2.2. Vermiculite 20 Vermiculite is the mineralogical name given to hydrated laminar magnesium

- aluminum

21 ironsilicate, which resembles mica in appearance [ Figure 2

- 6; (Mg, Fe,A) 3 (Al,Si) 2 O 10 (OH) 2 22
●4H 2 O] (AGI, 1972). Vermiculite is in the clay mineral group of the phyllosilicates, which also 23 includes kaolinite and montmorillonite. Mica, talc, and serpentine (e.g., chrysotile asbestos ) 24
minerals are other well

- known sheet silicates. These sheet

- li ke structures are produced by rings 25 of tetrahedrons that are linked to other rings by shared oxygen ions in a two

- dimensional plane 26 (see Figure 2

- 4d). The silicate sheet can extend broadly, and the layered appearance of the 27
mineral reflects this sheet

- lik e structure. The symmetry of these minerals is controlled primarily 28 by the symmetry of the rings, which is usually altered to a lower symmetry by other ions and 29 other layers. Typically, crystals of this subclass are flat, platy, and book

- like, as in the mica 30 group, and the sheets are then connected to each other by layers of cations. These cation layers 31 are weakly bonded and often have water molecules and other neutral atoms or molecules trapped 32 between the sheets. When subjected to heat, vermiculite ha s the unusual property of exfoliating 33 or expanding into ―worm

- like‖ pieces. The term vermiculite is derived from the Latin 34
vermiculare , which means to breed worms (The Vermiculite Association

, 35 http://www.vermiculit e.org ). Vermiculite exfoliation occurs at approximately 150 ○ C, producing 36This document is a draft for review purposes only and does not constitute Agency policy. 2

- 11 DRAFT

— DO NOT CITE OR QUOTE a lightweight and highly absorb ent material (AGI, 1972). Additional properties of vermiculite 1
are listed in Table 2

- 1. Vermicu lite ore is shown in Figure 2

- 6

. 2
3 Table 2

- 1 . Properties of vermiculite 4 5
Mineral class/subclass Mineral silicates/ p hyllosilicate

Chemical formula (Mg, Fe,A) 3 (Al,Si) 2 O 10 (OH) 2 ●4H 2 O Crystal habit of formation Clay, scaly, aggregate

Hardness (Mohs scale) 203 Cleavage Perfect Specific gravity 2.4

– 2.7

6 7 Vermiculite is mined across the world, including the United States (Virginia, South 8 Carolina, and Montana); South Africa; Uganda; China; Brazil; Russia; India; and Australia 9 (British Geological Survey, 2005). The specific mineralogy and geologic formation habit of 10 vermiculite deposits vary, and although amphibole minerals are consistent with the ultramafic 11
rock formations (composed chiefly of ferromagnesian igneous rock) that bear vermiculite, not all 12 vermiculite deposits contain amphibole asbestos. 13
14
2.2.3. The Mineralogy of Libby Amphibole A sbestos 15

2.2.3.1. Mineralogy 16 Various research groups have characterized the mineralogical composition and 17 morphology of amphiboles from the Rainy Creek deposit near Libby, Montana (Gunter and 18 Sanchez, 2009; Sanchez et al., 2008; Meeke r et al., 2003; Wylie and Verkouteren, 2000; Ross, 19 1993; and Moatamed et al., 1986). For the purposes of this document, the material from this 20 mine will be called Libby Amphibole. Libby Amphibole asbestos is in the tremolite subgroup 21 and historically has been reported as a sodium

- rich tremolite (Larsen

, 1942 ; Boettcher

, 1966 ; 22 Leake, 197 8 )

. However, a m ore exact analysis of Libby Amphibole asbestos, characterizing the 23
cation composition of individual fibers to better define the amphiboles present, indicates 24
winchite and richterite are also components as well as traces of related minerals ( Meeker et al

. , 25 2003). The minerals identified are classified by the number of cations present in specific sites 26 within the amphibole c rystalline structure (e.g. s ites A, B C and T, see section 2.1.1.1) (Leake, 27 1978; Leake et al., 1997). Figure 2

- 7 shows the compositional variations between the 28 predominate minerals found in the Libby amphibole asbestos (winchite, richterite and tremolite). 29This document is a draft for review purposes only and does not constitute Agency policy. 2

- 12 DRAFT

— DO NOT CITE OR QUOTE 1
2 Figure 2

- 7 . Solution series linking tremolite, winchite, and richterite 3 amphibole fibers

. (See Section 2.2.1.1 for the general form of the equations 4 shown for each mineral species.) 5 6 Source: Meeker et al. (2003). 7 8 9 Although these are reported as discrete mineral compositions they are in fact graded 10
across the solid solution series shown in th e figure. For example, tremolite is one endmember of 11
the solid solution series. As calcium decreases and sodium increases, the fibers transition to 12 richterite. Similarly, as fibers have decreased magnesium and calcium with respect to tremolite, 13 they are defined as winchite. The sodium content that distinguishes these amphiboles has been 14
redefined over time in the Internation al Mineral Association‘s mineral classification system, 15 most recently in 1997 (Leake, 1978; Leake et al., 1997). As a result, some amphibole fibers 16 previously defined as tremolite prior to the new classification system are currently considered 17This document is a draft for review purposes only and does not constitute Agency policy. 2

- 13 DRAFT

— DO NOT CITE OR QUOTE winchite ba sed on chemical composition ( Leake et al., 1997 )

. The following discussion uses 1 only the most recent classification system. 2 Libby Amphibole asbestos is not only made up of the end members of these groupings, 3 but the spectrum of fibers in between each end of the solution series shown , as well as traces of 4
other minerals . Additionally, samples collected from different areas of the mine and 5 representing different habits of formation can have a higher or lower proportion of any of the 6 various minerals (Meeker et al., 2 003). Figure 2

- 8 shows data from 30 samples across the mine. 7
The cation composition identified for each fiber analyzed at specific sites within the crystalline 8
structure, determines its mineral identity (Le aky et al., 19 97 )

. Here the USGS used two different 9 techniques to identify the catio n content of each fiber [ Energy dispersive x

- ray analysis ( EDS ) 10 and Electron Probe Microanalysis ( EPMA )] . Regardless of analytical technique used, similar 11 mineralogy was identified (Figure 2

- 8). Most Libby Am phibole fibers are classified as winchite 12 (84%), with lesser amounts of richterite (11%) and tremolite (6%), based on the current 13 mineralogical nomenclature ( Meeker et al. (2003). There are also trace amounts of 14 magnesioriebeckite, edenite, and magnesio

- a rfvedsonite present in Libby Amphibole asbestos 15 (Meeher et al., 2003.)

. All of these would be within the tremolite

- richterite

- magnesioriebecktite 16
series of mineral. All of the amphiboles found at the mine site, with the possible exception of 17 magnesiorieb eckite, can occur in fibrous habit. These amphibole materials, even when originally 18 present as massive material, can produce abundant, extremely fine fibers by gentle abrasion or 19 crushing. 20
21
2.2.3.2. Morphology of the Libby Amphibole Asbestos 22 Rock samples taken from the mine include veins of asbestiform amphibole and various 23 fiber morphologies in surrounding rock (Meeker et al., 2003). A sample viewed by scanning 24 electron microscope from the Zonolite Mountain mine illustrates the broad range of fiber 25 morphologie s and fiber size in the Libby Amphibole asbestos material (see Figure 2

- 9). The 26 U.S. Geological Survey has described asbestiform fibers, acicular particles, particles showing 27 curvature, and cleavage fragments all within Libby Amphibole asbestos (Meeker et al., 2003). 28 As individual fibers and fiber bundles are viewed under greater magnification under a 29 transmission electron microscope, the range of fiber types can be more clearly seen ( F igure 2

- 10 ) 30
31
2.2.3.3. Dimensional Characteristics of Libby A mphibole A sbestos 32

Cumul ative particle size distribution frequencies (CDF) were developed for Libby ore 33 grade 3 and Libby ore grade 3 expanded by EPA Region 8 using the procedure de scribed in 34 detail in Appendix C . As shown in Figure 2

- 11 the particle size distribution freq uency for the 35This document is a draft for review purposes only and does not constitute Agency policy. 2

- 14 DRAFT

— DO NOT CITE OR QUOTE Libby grade 3 ore and the Libby grade 3 ore expanded were similar to the particle size 1 distribution frequency in the ambient air monitoring samples in Libby, MT. In the figure, CDF 2 is cumulative distribution frequency. Data from the ambient air monitoring in Libby are 3 presented in Appendix B . The data to construct the plot are described in Appendix B and 4 Appendix C

. There are slight shifts towards longer and thicker fibers in the ore samples 5 compared to the air samples, with the aspect rat ios being almost identical in the ore and air 6 samples. However, all of these differences are minor and all of these fibers are respirable. 7 Mineralogical characterization of the fibers from the Libby ore grade 3 and the expanded 8 product using energy dispe rsive x

- ray analysis [EDX] and selected area electron diffraction 9 [SAED] provided further confirmation of the similarity between the fibers from the Libby grade 10 3 ore and Libby Amphibole (methodology described in Sec 2.3; Appendix B ). EDX spectra 11 yielded an elemental fingerprint with sodium and potassium peaks that were highly consistent 12 with values reported for the winchite richterite solution series described for the Libby, MT ores 13 (Meeker et. al., 2003). 14 Based on these data, it is reasonable to conclude that the fibers from the Libby grade 3 15 ore and expanded ore are similar in physical and mineralogical characteristics to the Libby 16 Amphibole fibers found in samples of air in Libby, MT. Therefore, the expo sure and health 17 effects information from the Marysville facility may be used to derive an RfC that can be applied 18 in the Libby community and other sites that received vermiculite ore from Libby, Montana. 19 The Marysville, Ohio facility also used vermiculat e ore from Virginia, South Africa, and 20 South Carolina. Dr. Lockey obtained samples of the Virginia and South Africa ores from the 21 Marysville facility in 1980 and supplied these ores to EPA for analysis. The Virginia and South 22
African ores were tested for the presence of fibers as described in Appendix F. As described in 23
A ppendix E, the Virginia and South African ores released only a small quantity of amphibole 24
fibers. 25 EPA was unable to obtain an ore sample from South Carolina. However, vermiculite ore 26 from the Enoree mine in South Carolina is known to contain fibers (Appendix F ; U.S. EPA, 27
2000 d ; McDonald et al., 1988). 28
29
30This document is a draft for review purposes only and does not constitute Agency policy. 2

- 15 DRAFT

— DO NOT CITE OR QUOTE 1 Figure 2

- 8

. Mineralogy of Libby Amphibole asbestos fibers from samples 2 taken from the Zonolite Mountain site. An evaluation of the textural 3 characteristics shows the material to include a complete range of morphologies 4 from prismatic crystals to fibers. Each data point represents the cation 5 composition (number of occupied sites) for a single fib er. The x axis shows the 6 number sites occupied by Na, and the Y axis shows the number of sites occupied 7 by Na or K. The data shown are a composite of the analysis fibers taken from of 8 30 different field samples from various locations within the mine. Notes: EDS is 9 Energy dispersive x

- ray analysis ; EPMA is Electron Probe Microanalysis 10 11 Source: Meeker et al. (2003). 12This document is a draft for review purposes only and does not constitute Agency policy. 2

- 16 DRAFT

— DO NOT CITE OR QUOTE 1

2 Figure 2

- 9

. Scanning electron microscope image of Libby Amphibole 3 asbestos fibers. Acicular, bundles, prismatic crystals , and curved fibers are all 4 present. 5 6 Source: Meeker et al. (2003). 7 8 9 10This document is a draft for review purposes only and does not constitute Agency policy. 2

- 17 DRAFT

— DO NOT CITE OR QUOTE 1 2 Figure 2

- 10 Fiber morphology of Libby amphibole asbestos viewed under a 3 transmission electron microscope. 4 5 Source: Meeker et al. (2003). 6 7 8 9
10
11This document is a draft for review purposes only and does not constitute Agency policy. 2

- 18 DRAFT

— DO NOT CITE OR QUOTE 1 Figure 2

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

an d Libby air. 3 CDF = cumulative distribution frequency ; Source: U.S. EPA, 2010 4This document is a draft for review purposes only and does not constitute Agency policy. 2

- 19 DRAFT

— DO NOT CITE OR QUOTE 1 2.3. ANALYTICAL TECHNIQUE S FOR FIBER ANALYSIS 2 Various techniques are available to measure particles and fibers in air samples. Fibers 3 are considered elongated particles, often defined as having as aspect ratio (generally a ratio of 4 length to diameter ) of 3:1 or greater

. As inhalation exposure is of primary concern in 5
d etermining adverse health effects of asbestos and other mineral fibers, an understanding of air 6 sampling method s and analytic technique s is germane to a discussion of how to define mineral 7 fibers. Historically

, exposure measurements in industrial setting s have been based on particle 8 counts (midget impinger, thermal precipitato r) and membrane filter counts. Partic le counts (total 9 dust) were measured as mass per unit air or as particles counted per unit of air. The midget 10 impinger particle

- counting method collects the particles in a liquid sample and particles are 11 visually counted under a light microscope. Midget impinger results are reported as million 12 particles per cubic foot of air (mppcf). This method counts particles regardless of their shape, 13 aspect ratio )

, or the composition of the particle . T hus t otal dust air sampling results may be 14 useful to compare the relative dustiness of different working environments but do not 15 specifically measure either fibers in general or asbestos. 16 The collection of fibers on an air filter, visually counted under a phase contrast 17 microscope 1 (PCM) was first described in 1934 by the Dutch physicist Frits Zernike. The 18 specification of a fiber as >5 μm in length and length

- to

- diameter ratio ( i.e., aspect ratio) of at 19
least 3:1 resulted from th is method. As a light microscope technique, t he PCM method ca nnot 20 distinguish between asbestos and nonasbestos fibers or determine fiber mineralogy . The U

. S

. 21 Public Health Service developed and tested a standard air sampling method based on PCM 22
detection (NIOSH Method # 7400 )

, which has been used in industrial sett ings and is the basis of 23
the IRIS IUR for asbestos ( U.S. EPA

, 198 8 ). The method specifies the analyst count fibers 24
>5 μm in length with a n aspect ratio of at least 3:1. Fibers viewed and counted by the PCM 25
method are referred to as  ̳PCM fibers.‘ Since PCM cannot distinguish between asbestos and 26 nonasbestos fibers, the NIOSH method provides for the examination of fibers by transmission 27 electron microscope where use of x

- ray diffraction techniques allows for the mineralogical 28
identification of the f iber. 29 This PCM metric was never intended to define biologically active fibers

. The authors 30 aptly point out in the AAHAU ( U.S. EPA, 1986 ) that PCM fiber counts include nonrespirable 31
fibers and that many smaller

, respirable fibers are not quantified by th is method

. Dement and 32 Harris (1979) report that approximately 98% of crocidolite, 95% of chrysotile

, and 45% of 33

a mosite fibers were less than 5 μm long . These fiber

- length profiles were calculated with a 34

1 PCM is a contrast

- enhancing optical technique that can be used to produce high

- contrast images of transparent specimens.This document is a draft for review purposes only and does not constitute Agency policy. 2

- 20 DRAFT

— DO NOT CITE OR QUOTE lower length cut

- off of 0.08 μm applied during fib er counting with transmission electron 1 microscopy (Dement and Harris

, 1979). Th e PCM metric measure , however, can be used as a 2 surrogate for the material present. The proportion of fibers > 5

- μm long varies by mineral type

, 3 sample

, and handling

. 4 Electron microscopy employs electrons rather than light to visualize the specimen. 5 Furthermore, instead of using glass lenses to focus the light wavelengths, electromagnetic lenses 6 are used to focus electrons on the sample. The analytical techniques incl uded in electron 7 microscopy for asbestos testing are transmission electron microscopy (TEM), scanning electron 8 microscopy (SEM), and scanning transmission electron microscopy (STEM). TEM produces 9

two

- dimensional (2

- D) images that generally use a magnifica tion factor of about 500 to 10 500,000 . SEM produces three

- dimensional (3

- D) images that generally result in about 10 to 11 300,000 magnification. STEM can produce both 2

- D and 3

- D images that generally result in 12 about 10 to 500,000 magnification. X

- ray di ffraction ( XRD ) may be used with the above 13 techniques to differentiate crystalline structure of minerals in solid materials and provides 14
information on the availability of the total mineral present. Th us , XRD can determine the 15
mineral form of asbestos fib ers and structures

. Although SEM and STEM are available for from 16
commercial laboratories and for research, standard analytical techniques for enumerating 17 asbestos structures in air samples have been developed for TEM ( ISO 10312 )

. 18 TEM analytical techniques are more specific than PCM for visualizing asbestos fibers 19 and identifying type s of asbestos . TEM analysis can detect fibers sma ller than 0.006 μm in 20
diameter and can determin e the specific mineralogy by selected area electron diffraction and 21 crystallogr aphy of asbestos fibers, thus distinguishing between asbestos fibers and nonasbestos 22
fibers

, as well as among different types of asbestos fibers by energy dispersive spectroscopy. 23
TEM analysis is often used to determine asbestos in environmental and ambient air samples 24
since asbestos structures can be identified. The International S tandard method (ISO 10312) 25 enumerates structures much smaller than the PCM fibers with a minimum length requireme nt of 26
0.5 μm. Additionally

, structure s with an aspect ratio of at least 5:1 are considered fibers, rather 27 than 3:1 as with PCM analysis. The ISO 10312 method also defines other structures (fi ber 28 bundles, clusters

, and matrices ) that are included in the s tructure count. Therefore

, the term 29

structure rather than fiber is used when presenting air sampling results from ISO 10312 where 30 structures per cc of air (s/cc) are reported. Although TEM has the advantages of viewing much 31 smaller structures, counting v arious types of structures and mineralogical identification ; there are 32
some limitations of the method. As TEM is generally used at much higher magnifications than 33 PCM in order to view the smaller structures, larger fibers and structure s may be undercounte d. 34This document is a draft for review purposes only and does not constitute Agency policy. 2

- 21 DRAFT

— DO NOT CITE OR QUOTE Additionally

, a much smaller portion of the air filter is viewed under the greater magnification

, 1 impacting method detection limits

. 2 EPA often employs a modified version of the TEM technique when assessing 3
environmental sites. The OSWER Framework fo r assessing asbestos

- contaminated sites (U.S. 4 EPA, 200 8 ) recommends that air samples be analyzed by TEM and then it specifies that a subset 5
of the fibers visible under TEM be counted such that the fiber counts are considered equivalent 6

to the subset of fib ers that would have been observable using PCM ; it recommends this subset of 7
TEM fibers be used for site risk assessment ( U.S. EPA, 200 8 ). These PCM equivalent fibers (or 8 PCMe fibers) are defined as those fibers viewed on TEM that meet the PCM analytical 9

r equirements: ≥5 μm in length and an aspect ratio of at least 3:1. Although the PCM 10 methodology does not specify a minimum fiber width, current PCM analytical methods reliably 11 detect fibers of 0.25 μm in width (WHO, 1980), which EPA employs to define PCMe fibers 12 ( U.S. EPA, 200 8 ). 13
14
2.4. EXPOSURE P OTENTIAL 15 Although the occurrence of Libby Amphibole asbestos is limited to a relatively small 16 geographic area, the potential for exposure to it has been greatly enhanced by the historical 17 mining

, milling

, and distribution of vermiculite operations in Libby

, M T

. Additional ly, material 18 was sent to processing plants across the nation w h ere plant workers and community contacts may 19 have been exposed. Lastly

, consumer products containing vermiculite mined near Libby are 20 contaminated with the Libby A mphibole asbestos

, and consumers may have been exposed to 21 Libby amphibole asbestos while using the products. For example, asbestos

– contaminated 22
vermiculite attic insulation from Libby remain s in homes today across N orth America, where 23 there is the potential for residential exposures

. This section summarizes the potential for current 24 exposures to the Libby amphibole asbestos in vermiculite in the Libby community, other 25 communities potentially impacted by processing p lants

, and from in

- place Libby vermiculite 26
attic insulation. Historical exposures for the workers in Libby, MT

, and other facilities are 27 discussed in Section 4.1, where data are available. 28 There are also lifestyle, activity and lifestage factors which may influence one‘s exposure 29 potential to asbestos. For example, children spend more hours outside and may engage in 30 activities which impact exposure level compared to adults (NRC, 1993; U

. S

. EPA 2006)

. In 31 general, children inhale more air per unit body wei ght (U.S. EPA, 2008) and spend more time 32 outdoors than adults (Bateson and Schwartz, 2008; NRC, 1993), which could have resulted in 33 increased inhalation exposure to Libby Amphibole asbestos in children compared with adults. In 34 contrast, some adult activit y patterns may also result in increased exposures, such as gardening 35This document is a draft for review purposes only and does not constitute Agency policy. 2

- 22 DRAFT

— DO NOT CITE OR QUOTE and home repair, where Libby A mphibole asbestos may be present. Thus for the various 1
environments where people may be exposed to L ibby A mphibole asbestos, the potential 2 activities and pa thways of exposure are discussed below , and where available exposure 3 measurements given for various exposure environment and activities

. 4
5
2.4.1. Libby Community 6 The Libby c ommunity (the towns of Libby, Troy

, and surrounding residences) defines the 7 community tha t may have been directly and indirectly impacted by mining/millings activities. 8 Many individuals who worked in the mine lived in the surrounding areas. Facilities in the 9 community may have residual contamination from past milling and transport activities

. 10 Additionally, expanded vermiculite, waste stoner rock (the waste material from exfoliation)

, and 11
other materials all potentially containing Libby amphibole asbestos may have been transported 12 off site to residences and recreational areas. Taken together

, there are numerous potential 13

exposure pathways for community residents

, both historical and current. 14 During plant operations, i ndividuals may have been exposed to materials inadvertently 15 transported from the workplace to vehicles, homes

, and other establ ishments, typically on the 16
cloth ing , shoes, and hair of workers. This transport of material may result in ―take

- home 17 exposure‖ for the workers, their families

, and other co

- residents. The magnitude of these 18 exposures was not measured

, so the levels to which individuals in the home might have been 19 exposed are not known . Based on studies of other industrial take

- home exposures, individuals 20 doing laundry and cleaning house (often women) can be exposed to materials on worker s ‘ 21 clothing. A lso , children who play on the floor might be more exposed than adults to dust from 22
take

- home exposures (Kelly et al.

, 2006)

. The community health screening studies from Libby 23 showed that men were more likely to have both occupational and non

- occupational exposures, 24
whi le women were more likely to have household contact with exposed workers (ATSDR, 2001; 25 Peipins et al., 2003). There could also be gender differences in types of activities (e.g., 26 household chores such as laundry and cleaning) or in intensity or duration o f occupational and 27 recreational activities (Peipins et al., 2003). 28 Expanded vermiculite, a s a finished product

, was used as a soil amender and for attic 29
insulation . C ommunity members may have been exposed and are possibly still exposed to these 30

consumer p roducts. In a survey of Libby residents conducted by ATSDR in 2000

– 2001, almost 31 52% reported using vermiculite for gardening and 8.8% used vermiculite around the home and 32 51% reported handling vermiculite attic insulation (Peipins et al.

, 2003). As vermi culite ore, 33
waste rock

, and product were present in the community

, numerous activities may have resulted 34 in exposure. Individuals also reported exposures from the following activities: participating in 35This document is a draft for review purposes only and does not constitute Agency policy. 2

- 23 DRAFT

— DO NOT CITE OR QUOTE recreational activities along Rainy Creek Road, the road leading to the mine (67%) ; playing at 1 the ball field near the expansion plant (66%) ; playing in the vermiculite piles (34%) ; heating the 2 vermiculite to make it expand/pop (38%) ; or other activities in which there was contact with 3 vermiculite (31%) (Pe ipins et al.

, 2003). Memoranda from Christopher Weis (U

. S

. EPA) state s 4 that asbestos mineral fibers were detected in outdoor sources (yard soil, garden soil, driveway 5 material, and assorted mine

- waste materials) and indoor sources (dust and vermiculite in sulation) 6 in Libby ( U.S. EPA

, 2001a, b)

. Prior to plant closure, t here are no measurements of exposure 7
levels in the community f or the materials and activities from various exposure pathways

. 8
EPA has conducted more recent exposure sampling in the Libby community. Limited 9
data are available from sam pling conducted in 2000/2001 to assess airborne asbestos levels from 10
various activities ( see Table 2

- 2). Air samples were taken in the community during activities 11 considered appropriate for various potential exposure scenarios . Personal air monitors were 12 placed on the investigator con ducting the activity

, and a second air sample was taken from a 13 fixed location (area sample). Asbestos fibers were collected on filters and counted by 14

two different laboratory me thods: 1) PCM and 2) TEM. Although TEM analysis can c o unt 15 smaller fibers, results are shown here for PCM size fibers used to estimate risk

, called PCM 16 equivalent fibers

, or PCM e (See Section 2.2.3.1) 17 EPA continues to conduct air monitoring in the Libby community to support clean

- up and risk 18 assessment activities. Ambient air monitoring conducted in 2006/2007 at 18 locations across the 19 area indicated that low levels of asbestos fibers are occasionally detected in the air, even with no 20
localized disturban ce of asbestos

- contaminated material ( U.S. EPA, 2009b). Fibers were 21 counted by TEM, and structures ≥0.5 μm in length and with an aspect ratio ≥3 were included 22 (structures per cc of air, s/cc). Average ambient air levels for the various sampling locations 23 ranged from 8 x 10 - 6 s/cc to 1.9 x 10 - 5 s/cc ( U.S. EPA, 2009b)

. Both ambient and activity

- based 24 air monitoring have been completed in five community schools ( U.S. EPA, 2010). Outdoor 25
activities conducted that were considered relevant to children‘s exposures at the schools included 26 playing sports, using playground equipment, and running/walking in outdoor areas. Outdoor 27 activities to assess exposure of the school maintenance workers included digging/raking, power 28 sweeping parking lots, and mowing / a nd edging school lawns. Additionally, ambient air samples 29 were taken in each school (i.e.

, classrooms, cafeteria, gymnasium, and hallways). Asbestos 30
PCM e fibers were detected by TEM analysis in 5 of 63 outdoor activity

- based samples, ranging 31
from 0.0022 to 0.039 f/cc. No PCM e fibers were detected in indoor air samples. However, 2 of 32
50 indoor area samples detected TEM asbestos structures not considered PCMe fibers 33 (5.1 x 10 - 4 s/cc and 5.9 x 10 - 4 s/cc), which is within the range of analytical sensitivit y for the 34This document is a draft for review purposes only and does not constitute Agency policy. 2

- 24 DRAFT

— DO NOT CITE OR QUOTE indoor air samples ( U.S. EPA, 2010). It should be noted that indoor air sampling did not include 1 any activity

- based sampling to assess student or employee exposures. 2This document is a draft for review purposes only and does not constitute Agency policy. 2

- 25 DRAFT

— DO NOT CITE OR QUOTE Table 2

- 2

. Air sampling results for disturbance of Libby amphibole asbestos

1 contaminated materials in Libby, MT 2 3

Activity Air sampling results

Type of air sample PCM

(f/cc) 1 TEM

(PCME

- asb) (f/cc) Rototilling Vermiculite

contaminated soil Area sample 0.020 0.019

Personal air monitor 0.227 0.066

Active cleaning Household dust Area sample 0.021 2 (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 Source: EPA ( 2001 ). 5 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 6 aspect ratio of at least 3:1. 7 2) Where multiple measurements were taken, the mean air concentration is given followed by the range of 8 detected air levels. 9 10 11 Although data are limited, there are reports that milling operations in the city of Libby 12
resulted in increased ambient air levels measured as PCM fibers. In efforts to document 13 historical air transport of asbestos fibers, researchers have examined tree bark. A study 14 conducted in Libby determined that trees may act as reservoirs for Libby Amphibole asbestos 15
fibers

, which may have become embedded in the bark by diffusion or impaction

- type processes 16 (Ward et al.

, 2006 ; Webber et al.

, 2006). Therefore, loggers and others in the timber industry in 17 the Libby area could have had additional exposure to Libby Amphibole asbestos

. Because trees 18

act as reservoirs for Libby Amphibole asbestos, inhalation exposures could have occurred where 19
wood is burned to provide heat (Ward et al., 2009)

. 20This document is a draft for review purposes only and does not constitute Agency policy. 2

- 26 DRAFT

— DO NOT CITE OR QUOTE 1
2.4.2. Communities near Vermiculite Expansion and Processing Plants 2

The ATSDR Summary Report on the 28 Libby vermiculite e xpansion and processing 3 facilities reported that household residents were exposed by contact with vermiculite from the 4 workers‘ clothes, shoes, and hair. W orkers‘ personal vehicles likely contained vermiculite d ust 5 from the facility emissions and from vermiculite that fell from their clothing and hair on the 6 drive home after work. The O.M. Scott Company reported that company policy was to launder 7 work clothes for their employees and to make showers available for use after work. Had these 8
services been used , exposure via household contact c ould have been greatly reduced (ATSDR, 9 2005). Whether other facilities made these services available or how frequently they might have 10
been used is unknown

. 11
Communities near the expansion plants were exposed to many of the same exposure 12 pathways as for the Libby community. The Summary Report from ATSDR (2008) observed that 13 individuals in the community could have been exposed through multiple avenues, such as living 14
near the plant and breathing emissions from the facility, disturbing waste

- rock piles, having 15
direct contact with waste

- rock brought home, and living with indoor dust containing asbestos 16 brought in from outdoor sources (ATSDR

, 2008). 17
For the O.M. Scott Company facility in Marysville, Ohio, about 185 people currently live 18
within 1 mile of the facility

, but only about 60% of the housing units within the 1

- mile radius 19 existed while the Libby vermiculite was being exfoliated. C ommunity member s living f a rther 20 from the facility , however, might still have been exposed through ambient air because many 21 individuals used the swimming pool at the company facility (ATSDR, 2005). More detailed 22 information on the community exposures near this facility i s not available. 23
Waste rock from an exfoliation facility in Minnesota was made available to residents of 24 the community

, and personal use was encouraged. Individuals used the material to landscape, to 25
fill in driveways, and as a soil additive. The waste r ock was dumped outside the plant

, and plant 26 employees and children reported playing in and around the piles. S ubstantial airborne emissions 27
also were measured close to the plant ( ATSDR, 2003). 28
29
2.4.3. Exposures from Zonolite and V ermiculi te for H omeowners, C ontr actors , and O ther 30 P opulations 31
It is estimated that 80% of the vermiculite used in the United S tates , and consumer 32 products includ e Zonolite vermiculite attic insulation (VAI) and potting soil amended with 33 expanded vermiculite came from the mine in Libby, MT . EPA conducted a study to estimate the 34 potential for exposure to asbestos in homes containing VAI . Air samples were taken to define 35This document is a draft for review purposes only and does not constitute Agency policy. 2

- 27 DRAFT

— DO NOT CITE OR QUOTE exposure levels in the homes u n der various conditions: no activity (e.g.

, ambient air )

, as well as 1 during simulated remodeling activities and removal of the VAI ( U.S. EPA, 2003). Samples were 2 taken in the both the living space of the homes as well as attic space. 3
Ba ckground air samples were collected in five occupied homes where Zonolite V AI was 4 in place (asbestos detected from trace levels to 1.54% by bulk analysis) ; no fibers were detected 5 in the air samples above 0.0016 PCM e f/cc in these homes. However, the air samples were taken 6 when the homes were empty

, and there was no disturbance of the VAI or entry/exit into the attic 7 space. Ther e fore

, EPA conducted a number of simulations under controlled conditions to 8 estimate exposures when VAI is disturbed during normal activities ( e.g. , moving boxes in an 9
attic ), remodeling

, and removal of t he VAI. Structures were built within safe containment to 10 simulate attic space above living space and VAI was installed in the simulated attics. 11 Remodeling activities resulted in personal exposures ranging from 0.50 to 1.841 f/cc PCMe. 12 Stationary samples of the attic air ranged from 0.008 to 0.203 f/cc PCMe . For those simulations 13
that included sampling in the  ̳living space‘ below the attic, asbestos fibers ranged from 0.001 to 14
0.25 f/cc PCMe during renovations and from 0.001 to 0.035 in the living space after renovations 15 were complete ( U.S. EPA, 2003). These data indicate that exposures to asbestos fibers may 16
occur when disturbing Libby A mphibole asbestos

- contaminated VAI in homes. 17 A second study on potential exposur es to Zonolite VAI was conducted by an 18 environmental firm hired by attorneys representing individuals with VAI in their homes (Ewing 19 et al., 2010). This study was conducted in three homes containing Zonolite VAI

, and air 20 sampling was taken

, representing a mbient conditions (no distur bance of VAI), remodeling, 21 activity in the attic

, and removal of the VAI by various methods ( see Table 2

- 3)

. Disturbance of 22
the asbestos

- contaminated VAI resulted in airborn e asbestos levels, both in the personal air 23
monitors a nd area samples (Ewing et al., 2010)

. 24This document is a draft for review purposes only and does not constitute Agency policy. 2

- 28 DRAFT

— DO NOT CITE OR QUOTE Table 2

- 3

. Air sampling results for asbe stos from Zonolite VAI in 1 three homes 2 3

Activity Personal samples Area s amples

PCM 1 (f/cc) TEM 2

(PCME, s/cc) TEM (PCME, s/cc) No activity NS 3 NS <0.003 Cleaning items in the a t tic 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

– 12.5 4 0.98

– 10.3 0.53

– 1.47

4 Air sampling results reported as fibers analyzed by phased contrast microscopy (PCM). 5 Air sampling results reported as structures, PCM equivalent (PCME) as analyzed by transmission electron 6 microscope (TEM). 7 NS

— not sampled, personal samples were not taken for background levels. 8 Range of results for three different removal methods (shop vacuum, homeowner method, and manufacturer

9 recommended method). 10 11 Source: Ewing et al. ( 2010 ). 12This document is a draft for review purposes only and does n ot constitute Agency policy. 3

- 1 DRAFT

— DO NOT CITE OR QUOTE 3. FIBER TOXICOKINETICS 1

There are no published data on the toxicokinetics of Libby Amphibole asbestos. 2 However, to help inform the reader as to the expected toxicokinetics of Libby Amphibole 3 asbestos, this section contains a general summary description of toxicokinetics of fibers. A more 4 detailed discussion of fiber toxicokinetics is beyond the scope of this document, but is reviewed 5 elsewhere (NIOSH

, 2011; ICRP

, 1994). 6 Understanding fiber t oxicokinetics and retained fiber dose is a complex component of 7 analyzing asbestos toxicity . The principal components of fiber toxicokinetics in mammalian 8
systems are (1) deposition at the lung epitheli al surface , and (2) clear ance from the lung due to 9 ph ysical and biological mechanisms including both translocation from the lung to other tissues 10 (including the pleura), and elimination from the body (see Figure 3

- 1). 11
12

13 Figure 3

- 1

. General scheme for fiber deposition, clearance, and translocation 14 of fibers from the lung and GI tract

. General scheme for fiber deposition 15 (heavy arrows), clearance (light dotted arrows)

, and translocation (light arrows). 16 Diagram of Bignon et al. (1978) derived from ICRP lung model by the Task 17 Group on Lung Dynamics (1966). 18 19 Source: ICRP (1994)

. 20This document is a draft for review purposes only and does n ot constitute Agency policy. 3

- 2 DRAFT

— DO NOT CITE OR QUOTE The fiber characteristics of the sample to be studied define the toxicokinetics of that 1 specif

ic sample. Th ese characteristics vary among fiber types, and in many cases

, among 2 samples of the same fibers. Libby Amphibole asbestos includes fibers with a range of mineral 3 compositions including amphibole fibers primarily identified as richterite, win chite

, and 4 tremolite ( see Sec tion 2. 2 ). Although the fiber size varies somewhat from sample to sample, a 5 large percentage (~45%) is less than 5

- μm long in bulk samples examined from the Libby mine 6
site (Meeker et al.

, 2003). Limited data from air samples taken in the workplace also document a 7 large percentage of respirable 1 fibers as well as fibers <5um

- long (see Section 4.1.1.2 and 8
Table 4

- 3). The importance of the size of fibers and how they deposit following inhalation is 9
described below. Due to a la ck of data specific to Libby Amphibole asbestos, t hese deposition 10 steps are discussed for asbestos in general. 11
The main route of human exposure to fibers is through inhalation , although other routes 12 of exposure play a role

. Exposure of pulmonary tissue to fibers via the inhalation route depend s 13 on the fiber concentration in the breathing zone, the physical (aerodynamic) characteristics of the 14 fibers, and the anatomy and physiology of the respiratory tract. Ingestion is a nother pathway of 15
human exposure and occurs mainly through the swallowing of material removed from the lungs 16 via mucociliary clearance or drinking water contaminated with asbestos

, or eating, drinking, or 17 smoking in asbestos

- contaminated work environments ( C ondie et al., 1983). Handling asbestos 18 can result in heavy dermal contact and exposure. Asbestos fibers could become lodged in the 19 skin, producing a callus or corn, but generally

, no serious health effects (Lockey et al., 1984). 20
Because few studies have examin ed the depositi on and clearance of fibers following ingestion of 21 or dermal exposure to fiber s, the focus of this section is on the main route of exposure, 22
inhalation. 23 Studies useful for assessing the relationship between airborne fiber concentrations and 24 respiratory di sease must involve meaningful measurements of environmental exposure and an 25 understanding of how to apply these measurements to the target tissue dose. Tissue dose is a 26 more specific measure than external dose, and is determined both by fiber characterist ics of the 27 exposure environment and the exposed population. Dose to the lung is a function of airway 28 anatomy, lung volume, ventilation rate, and clearance from the lung

, as well as the fiber‘s 29 physical and chemical characteristics (Oberdorster

, 1991; U.S. EPA, 2004 ) . Many studies have 30 examined the role of these physical and chemical characteristics in asbestos

- induced disease in 31 the lung and are reviewed in more depth elsewhere (NIOSH, 2011; ATSDR, 2001; Myojo and 32 Takaya, 2001; Witschi and Last, 1996; Lip pmann 1990; Merchant 1990; Yu et al., 1986; Griffis 33

1 Respirable fibers are those that can be inhaled into the lower lung where gas exchange occurs and are defined by their aerodynamic diameter (d a ≤ 3μm, NIOSH).This document is a draft for review purposes only and does n ot constitute Agency policy. 3

- 3 DRAFT

— DO NOT CITE OR QUOTE e t al., 1983; Harris and Fraser, 1976; Harris and Timbrell, 1975) . Factors influencing dose to 1 other tissues in the body (e.g., pleura, peritoneum, stomach, ovaries) are not as well known, but 2 they are d iscussed below where data are available. 3
4
3.1. DEPOSITION OF FIBERS IN THE RESPIRATORY T RACT 5 The deposition of fibers in the respiratory tract is dependent on the aerodynamic 6 properties of the fiber (length, width, and density) and the anatomy and physiology of the 7 respiratory tract (NIOSH, 2008; ATSDR, 2001; Myojo and Takaya, 2001; Witschi and Last, 8 1996; Yu et al., 1986; Griffis et al., 1983; Harris and Fraser, 1976; Harris and Timbrell, 1975). 9 Thicker fibers are deposited in the upper airways; thinner fibers are carried deeper into the 10 airways and alveolar regions. All fibers with aerodynamic diameters less than approximately 3 11 μm meet the physical criteria necessary for deposition in the terminal bronchioles and beyond to 12
the alveoli. The site of fiber depo sition within the respiratory tract has implications related to 13 lung retention and surface dose of fibers. The respiratory tract encompasses the upper 14 respiratory tract (nose, nasal passages, and throat), respiratory airways (larynx, trachea, bronchi 15
and bronchioles) and the lungs (respiratory bronchioles, alveolar ducts, alveolar sacs and 16 alveoli). A full review of the anatomy and architecture of the respiratory tract is beyond the 17 scope of this document but has been reviewed by ICRP (1995). 18
Fiber dep osition occurs by five mechanisms: impaction , interception, sedimentation, 19
diffusion , and electrostatic precipitation ( see Table 3

- 1). 20
21 Impaction: The momentum of the fiber causes it to directly impact the airway surface as 22 the airflow changes direction. This is the predominant method of deposition in 23 the upper airways where airflow is swift and larger fibers/particles are present. 24 Interception: A special case of impaction where the edge of the fiber touches the airway 25 surface and is prevented from continuing along the airway. This mechanism is 26 important in the transmitting airways (trachea and bronchi), where the airflow is 27 slower and laminar fl ow along the airway surface is conducive to interception. 28

Sedimentation: G ravitational forces and air resistance cause fibers/particles to settle out 29 of the air column on to the airway surface . For sedimentation to occur, air flow 30 velocities must be low, to allow the particle/fiber to settle, and this is a 31 predominant mechanism to the smaller conductive airways. 32

Diffusion: This method of deposition is predominant in the alveolar region where air 33 movement is negligible. Diffusion occur s from interactions of the fibers with the 34 movement of air molecules ; this Brownian motion increases with decreasing fiber 35 size (<0.5 μm diameter)

. 36 Electrostatic Precipitation: A special case of diffusion, where fiber motion towards the 37 airway surface is a function of stati c charge between the fiber and airway surface. 38This document is a draft for review purposes only and does n ot constitute Agency policy. 3

- 4 DRAFT

— DO NOT CITE OR QUOTE As with classic diffusion, this primarily occurs in the alveolar region where 1 airflow is negligible and the electrostatic forces can predominate. 2 3

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

3
- 5 DRAFT

— 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. 3

- 6 DRAFT

— DO NOT CITE OR QUOTE Aerodynamic diameter (also called aerodynamic equivalent diameter) of fibers accounts 1 for dimensional properties that influence the movement of the fiber‘s center of gravity through 2 the airways, so a erodynamic diamete r is important in all depositional mechanisms. The 3 aerodynamic diameter is the diameter of a unit density (1g/cm 3 ) sphere that has the same 4 gravitational settling velocity as the particle of interest. Since the aerodynamic diameter informs 5 the deposition patterns of fibers, it is used in modeling to determine the expected fiber deposition 6 in the respiratory tract. I mpaction and interception , however, are also heavily influenced by fiber 7 length . W here the physical length of the fiber greatly exceeds the a erodynamic diameter, 8 impaction and interception can be underpredicted by modeling the center of gravity of the fiber. 9 Sedimentation is related to the mass of the fiber, as well as the aerodynamic diameter, but 10 generally occurs at lower velocities in smal ler airways. Diffusion occur s from interactions of the 11
fibers with the movement of air molecules ; this Brownian motion increases with decreasing fiber 12 size (<0.5 μm diameter)

. Electrostatic precipitation occurs when fiber charges induce opposite 13 charges on the airway surfaces and the fiber is drawn to the airway walls (Lippmann , 1990). 14 For high aspect ratio fibers, like asbestos, the shape factor often approaches one and the 15 equation reduces to the aerodynamic diam e ter approximately equal to the nominal fiber 16
diameter. Therefore in employing the information from Table 3

- 1 to high aspect ratio fibers, one 17 may get an idea of the depositional characteristic of fibers from the nominal diameter. By 18 definition, fibers have a greater aspect ratio than particle s and as discussed, high aspect ratio 19 fibers may act significantly different than other particles with respect to some mechanisms of 20 deposition (e.g.

, impaction, interception

, and electrostatic precipitation) . Therefore, the 21 depositional characteristics o f fibers are not characterized completely by aerodynamic diameter

. 22
N o equivalent depositional model , however, is yet available for fibers in the dimensional range 23
of asbestos , which take into consideration the increased sedimentation and impaction for hig h 24 aspect ratio particles. 25 Fibers enter the respiratory tract along with airflow through the nasal and oral passages. 26 The nasal passage, from the nostril to the pharynx, serves as a filter for some fibers with 27
diameters 5

– 30 μm. C lumps of fibers also coul d deposit in these regions. Many animal species, 28 including rats and mice, are obligate nose breathers , meaning that fibers pass only through the 29 nasal passages

, and

, therefore

, are always subject to nasopharyngeal filtering. Humans, 30 monkeys

, and dogs, am ong other species, breathe both orally and nasally (oronasal). Therefore, 31 larger fibers and clumps of fibers can bypass the upper respiratory tract filtering and be inhaled 32 directly into the larynx/trachea, especially during exertion (e.g.

, exercise or wo rk) , which may 33 further alter deposition by increased turbulence in the airways

. This distinction is important 34 when reviewing inhalation studies between different species. 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 7 DRAFT

— DO NOT CITE OR QUOTE The conducting airways beyond the nasopharyngeal region include the trachea and 1
bronchi

, which have branched bifurcations with decreasing internal diameters. The aerodynamic 2 diameter of fibers that can deposit in the tracheobronchial region is in the range of 1

– 5 μm. 3 Fibers with aerodynamic diameter <1 μm can deposit in the bronchio les and then f a rther to the 4 alveoli ( ICRP, 1994 ). 5 Generally, fibers with aerodynamic characteristics conducive to deposition at the primary 6 or secondary bronchioles and alveoli can cause fibrogenesis and associated disease by either 7
retention in the alve oli or penetration into the peribronchiolar space. All fibers (aerodynamic 8 diameter less than approximately 2 μm), including Libby Amphibole asbestos , meet the physical 9 criteria necessary for deposition in the deeper regions of the respiratory tract at th e level of the 10
terminal bronchioles or alveoli. 11 Deposition of fibers in the alveolar region of the lung is consistent with radiological 12 findings in humans of fibrosis in the lower lung fields at early stages of disease. Deposition of 13 fibers in the deep lu ng alveoli can become limited when fiber length approach es 40 μm (Morgan 14 et al., 1978). D eposition of fibers with high aspect ratios and length ranging from less than 1 μm 15 to greater than 200 μm long, however, has been recorded (Morgan et al., 1978). In all 16 documented observations of fibers collected from either healthy or diseased individuals, short 17 fibers (<5 μm) were present in substantially greater numbers in lung tissue than were long fibers 18 (>5 μm) (Churg

, 1982; Churg and Warnock

, 1980). Although i nformation is limited on how 19 fibers get to the pleura, fibers observed in tissue from mesothelioma cases are more likely to be 20 short (<5 μm) (Suzuki et al., 200 5 ). Th ese observations could be due in part to the increased 21 deposition of smaller fibers or th e breakage of larger fibers over time (Bernstein et al

., 1994 ; 22
Davis

, 1994). 23 The lung and nasal depositional differences are due in part to differences in airway 24 structure and breathing patterns across lifestages (i.e., children, adults) , changing the depositional 25 pattern of different fiber sizes, possibly altering the site of action, and potentially resulting in 26
differential clearance and health effects (see Section 4.7 )

. 27 Modeling of fiber deposition has been examined for various fiber types (e.g., refractory 28 ceramic fibers, chrysotile asbestos) (Sturm 2009; Zhou et al., 2007; Lentz et al., 2003; Dai and 29 Yu, 1998; Yu et al., 1997; Coin et al., 1992), but not for Libby Amphibole asbestos. In general, 30 fibers are expected to share patterns of depositio n similar to particles which is well

- studied 31 (reviewed in ICRP 1994). For example, the multipath particle dose (MPPD) model (Jarabek 32

et al., 2005; Brown et al., 2005) uses information on the physical properties of the particles 33 (length and width [also ca lled bivariate distribution] and density), the anatomy and architectural 34 features of the airways, airflow patterns that influence the amount and the location of the 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 8 DRAFT

— DO NOT CITE OR QUOTE deposition of the particles, and dissolution and clearance mechanisms that are operative to 1 estimate the retained dose in the target tissue. However, although this model has not been fully 2 developed for fiber deposition, due to similarities with particle deposition this can be used to 3 inform deposition of fibers. 4
5
3.2. CLEARANCE 6 Complex relations hips exist among different mechanisms that affect fiber accumulation: 7 the amount of exposure, the duration of exposure, the physiological clearance from the lung, and 8 the toxicological response observed in the body . In general, adverse health effects incr ease with 9 an increase in exposure duration or dose (Bernstein et al., 2003, 2005; Cullen et al.

, 1997; 10
Ehrlich et al.

, 1992; Seidman et al., 1986). 11 Clearance from one tissue may involve translocation to another tissue. For example, 12 following fiber deposi tion in the respiratory tract fibers may then clear via translocation to 13 extrapulmonary tissues like the pleura. The specific mechanism and translocation route depend 14
both on fiber characteristics and the tissue of deposition. Whether or not fibers are t ranslocated 15 appears to depend on their physical

- chemical characteristics, including two

- dimensional size 16 (length and width) ; durability ; solubility ; and reactivity. This translocation is aided by high 17 biopersistence and inflammation

- induced increase in pe rmeability but is hindered by fibrosis. 18 Deposition occurs in the respiratory tract as described above; translocation from the respiratory 19 tract may in turn lead to fibers  ̳depositing‘ in extrapulmonary sites. Movement of fibers within 20 the respiratory tra ct can also influence inflammatory reactions and cause tissue injury remote 21 from the site of deposition. A general scheme for deposition and clearance of fibers is illustrated 22
in Figure 3

- 1. 23
24
3.2.1. Inhalation 25

3.2.1.1. Respiratory Tract 26 Once fibers deposit on the surface of the respiratory tract, they may be removed (cleared) 27 from the lungs in several ways, including physical clearance, dissolution, phagocytosis, or 28 encapsulation. Some of these mechanisms, such as dissolution of the fibers, or removal via the 29 mucociliary apparatus may result in the fibers being cleared from the body (see Figure 3

- 1). 30 Other clearance mechanisms may remove fibers from the surface of the respiratory tract but 31 result in transport of the fibers to other tissues by translocation. Translocatio n of fibers from the 32 terminal bronchioles and alveoli into the transbroncheolar space, lymph nodes, and pleura of the 33 lungs has been implicated in disease causation (e.g., pleural plaques, mesothelioma) (Dodson 34

et al.

, 2001). In human studies, the translo cation of asbestos fibers following inhalation has been 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 9 DRAFT

— DO NOT CITE OR QUOTE observed to varying degrees throughout the pulmonary and extrapulmonary tissues (Suzuki and 1 Kohyama

, 1991 ; Dodson et al

., 2005 ; Kohyama and Suzuki

, 1991 ; Dodson et al.

, 2001 ; 2
Sebastien et al.

, 1980)

, as well as other organs, including the brain, kidney, liver (Miserocchi 3

et al

., 2008)

, and ovaries (Langseth et al.

, 2007). In many cases, the type of fiber is not defined

, 4 and the individual exposure information is not available

. Fibers that are not cl eared may remain 5 at the epithelial surface or enter the parenchymal tissue of the lung. 6 Berry (1999) provided a review of the animal toxicity literature specifically for fiber 7 clearance. There are limited data on clearance patterns based on autopsy studi es in humans. 8
Two studies estimated increased clearance half

- l ife for amphibole asbestos (~20 years) as 9 compared to chrysotile asbestos (~10 years) (Churg and Vedal 1994; Finkelstein and Dufresne 10 1999) ; i n evaluating the data on lung fiber burden, Berry e t al. (2009) estimated the range of the 11
half

- life for crocidolite to be between 5 and 10 years. Generally, studies have focused on 12 determining the size and type of asbestos retained in specific tissues (Dodson et al., 1990; Gibbs 13 et al., 1991; Suzuki et a l., 2005; Dumortier et al., 1998; Su zuki and Yuen 2001; McDonald et al., 14 2001) and did not discuss changes in fiber content since exposure. Sebastien et al. (1980) 15 concluded that lung fiber burden could not be used as an accurate reflection of pleural fib er 16
burden. 17
18
3.2.1.1.1. Physical clearance of fibers 19

L arge fibers deposited in the nasal passages can be removed by physical clearance

. 20 When breathing occurs through the nose, many fibers are filtered by the turbulent airflow in the 21 nasal passages, impacting against the hairs and nasal turbinates, as well as becoming entrained in 22 mucus in the upper respiratory tract where they can be subsequently removed by reflexive 23 actions such as coughing or sneezing. F ibers can also translocate due to physical forces 24
associated with respiration (Davis

, 1989). F ibers with aerodynamic characteristics that cause 25 them to impact at the nasopharyngeal or tracheobronchial regions of the respiratory tree are 26 likely to be quickly removed (in minutes or hours) by the action of the mucocil iary escalator. 27 The mucociliary escalator remov es fibers through ciliary movement of the sticky mucus lining 28 (Churg et al.

, 1989; Wanner et al., 1996). Fibers removed from the pulmonary space through 29
this mechanism are either coughed out, or subsequently swallowed and enter the digestive tract 30 where they may adversely affect the tissue, enter the body via the blood, or be excreted. 31
C learance of fibers via this mechanism is rapid and is usually complete within minutes or hours. 32 However, the mucociliary e scalator extends only to the level of the terminal bronchioles of the 33 respiratory tree and not to the alveoli. Therefore, p articles that reach these deeper regions cannot 34 be cleared through this process. 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 10 DRAFT

— DO NOT CITE OR QUOTE Some fibers are not cleared from the lung, leading to an accumulation with time (Case et 1 al., 2000; Finkelstein and Dufresne, 1999; Jones et al., 1988). The fibers that remain in the lung 2 may undergo a number of processes including translocation, dissolution, fragmentation, splitting 3 along the longitudinal axis, or encapsulation with protein and iron. Available data indicate 4 prolonged clearance from the lung of longer serpentine chrysotile fibers (>5 μm) or either long 5 (>5 μm) or short amphibole fibers (Coin et al., 1994; Tossavainen et al., 1994). The pr olonged 6 clearance times for long amphibole fibers have led some investigators to conclude that long 7 amphibole fibers are predominant in the cause of disease despite the relatively small numbers of 8 these longer fibers in comparison to short fibers. However , Dodson et al. (2003) argue that 9 fibers of all lengths induce pathological responses and urge caution in excluding any population 10 of fibers based on their length, from contributing to the disease process. There are no data 11 available with Libby Amphibole to resolve the issue about short or long fibers. The important 12 feature illustrated by Figure 2

- 4 is that all of the fibers identified from the Libby ore samples are 13 potentially respirable and can penetrate to the alveoli. 14 Apparent translocation of fibers throughout the respiratory tract is evident from 15 experimental animal research done by several investigators following exposure by both 16 intrapleural injection and inhalation (Bignon et al., 1979; Holt

, 1982 ; Smith et al

., 1974, 1979, 17
1980 ; Miserocchi et al., 2008). C onflicting results from another study , however, indicate no 18 evidence of fiber translocation from the central to peripheral compartments following inhalation 19 exposure in rats, al

though this could be due to the short duration of the study (29 days post 20 exposure) (Coin et al., 1992). The data from most studies show that fibers can and do translocate 21 among tissues and organs and move by both physiological and physical mechanisms (Cook and 22
Olson

, 1979; Holt

, 1982, 1983). 23
24
3.2.1.1.2. Dissolutio n of fibers 25

Dissolution

, or the chemical breakdown of fibers

, is another method of removal of fibers 26 from the lung

. This process varies

, depending on the chemical composition of the fibers, as well 27
as the physiological environment. Dissolution can occur in the lung ‘s extracellular fluids or in 28 the macrophage phagolysosome. Studies performed in vitro to determine dissolution rate of 29 fibers attempt to mimic the extracellular lung fluids and macrophage

- phagolysosome system to 30 understand the length of time t hat fibers remain in the system (Rendall and du Toit, 199 4 ). 31 Studies have shown that dissolution occurs more rapidly for chrysotile fibers than for amphiboles 32 (Coffin et al

., 1983). Fibers can also be physically diminished through splitting or breakage. 33 These smaller fragments are then more easily removed by phagocytosis or translocation. 34
35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 11 DRAFT

— DO NOT CITE OR QUOTE 3.2.1.1.3. Removal of fibers through phagocytosis 1 The principal clearance pathway for insoluble fibers deposited in the alveoli is through 2 phagocytosis by macrophages. Alveolar macrophages that have phagocytized insoluble fibers 3 migrate to the bronchoalveolar junctions where they enter onto the mucociliary escalator for 4 removal (Green, 1973). Alternatively, alveolar macrophages that have phagocytized insoluble 5
fibers can also mi grate through the epithelial wall into the interstitial space and enter the 6 lymphatics (Green, 1973). 7 Alveolar macrophage cells engulf and transport invasive particles to the mucociliary 8 escalator or through the bronchoalveolar epithelium to the interstiti al tissues, where they are 9
removed or translocated by the blood or lymphatics. Durable fiber impaction in these deeper 10 regions also stimulates activation of alveolar macrophage cells. In vitro and in vivo studies 11 clearly indicate that macrophage cells play a role in the translocation of fibers (Bignon et al., 12 1979; Brody et al. 1981; Castranova et al., 1996; Dodson et al., 2000b). These s tudies have 13 demonstrated the presence of asbestos fibers in cell cyt oplasm where they can be transported in 14 association with cytoskeletal elements to the proximity of the cell nucleus. Small chrysotile 15
fibers can also penetrate the nuclear membrane (Malorni et al.

, 1990). 16 A number of processes can disrupt the normal phag ocytic function of the alveolar 17 macrophages. These processes include death of phagocytes due to ingestion of highly reactive 18 fibers, due to a high burden of deposited fibers (overload), or an attempt by the macrophages to 19 engulf fibers that exceed the mac rophage length (often termed ―frustrated phagocytosis‖) 20 (NIOSH, 2008). All of these processes can induce inflammatory and fibrogenic responses. 21
22
3.2.1.1.4. Encapsulation of fibers 23 Fibers that are too large to be easily engulfed by the alveolar macrophage can stimula te 24
the formation of asbestos bodies. Asbestos bodies are fibers that are coated with proteins, iron 25
and calcium oxalate. The mechanisms that result in the formation of asbestos bodies are poorly 26 understood, although most appear to be formed around amosit e fibers (Dodson et al.

, 1996). T he 27 iron in the coating , however, is derived from the asbestos fiber, cells, or medium surrounding the 28 fiber and can remain highly reactive (Ghio et al.

, 1992; Lund et al.

, 1994). These bodies are 29
sometimes referred to as ―ferruginous‖ bodies because of their affinity for iron

- loving 30 histological stains . Ferruginous bodies can remain in the lung throughout the lifetime of the 31 exposed individual. Asbestos bodies comprise a minor portion of the overall fiber burden of the 32
l ung, and

, after the fiber is fully coated, these fibers might or might not participate directly in 33
asbestos disease. The presence of iron in the coating, however, could provide a source for 34 catalysis of reactive oxygen species similar to that observed wit h fibers

. 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 12 DRAFT

— DO NOT CITE OR QUOTE 3.2.1.1.5. Translocation to extrapulmonary tissues 1 Translocation of fibers to extrapulmonary tissues has been studied in multiple studies, 2 however, the mechanism is still unknown

. This was more rec ently reviewed by Miserocchi et al. 3 (2008). Fibers have be en measured in extrapulmonary tissues including lung parenchyma, 4 pleural plaques

, and mesothelial tissue (i.e., pleural or peritoneal) in miners, brake workers, 5
insulation workers

, and shipyard workers (Dodson et al

. , 2000 a ; Roggli et al

., 2002 ; Churg et al

., 6
1994 ; Kohyama and Suzuki

, 1991). These studies found fibers at all locations analyzed, with 7 increased levels of amphibole as compared to chrysotile in the parenchyma when subjects were 8 exposed to a mixture of both fiber types. A mphibole fibers , howe ver, were less prevalent in the 9 pleura and mesothelial tissues (Sebastien et al., 1980, 1989; Bignon et al., 1979 ; Churg 1988; 10 Kohyama and Suzuki, 1991)

. Few studies have examined the size distribution of fibers 11 translocated to specific tissues. For exam ple, one early study suggested that the longer 12 amphibole fibers predominate in the lung while shorter chrysotile fibers are found in the pleura 13 (Sebastien et al

. , 1980) ; o thers showed that the fiber

- length distribution was the same by fiber 14 type regardless of location (Kohyama and Suzuki

, 1991 ; Bignon et al. , 1979). 15 Transplacental transfer of both asbestos (chrysotile, tremolite, actinolite

, and 16 anthophyllite) and nonasbestos fibers has been shown to occur in humans, as measured in the 17 placenta and in the lungs of stillborn infants (Haque and Kanz, 1988; Haque et al., 1992, 1996, 18 1998). It is hypothesized that maternal health might influence the translocation of fibers, as 19 some of the mothers had preexisting health conditions (e.g., hypertension, diabetes, or asthma) 20 (Haque et al., 1992). This group also measured transplacental translocation in a mouse study and 21 observed early translocation of crocidolite fibers through the placenta in animals exposed via 22
tail

- vein injection (Haque et al.

, 1998) These stu dies did not evaluate the source or levels of 23 exposure, only the presence of fibers in the body during early lifestages in mice and humans

. 24 Sebastien et al. (1980) found chrysotile was the predominant fiber in parietal pleura of 25 autopsy cases, while the amphibole fibers found in the lungs ranged from 0 to 100% (mean 26 56%). Bignon et al. (1979) found similar distributions but also found increased amphibole fibers 27 in the associated lymph nodes. In this study, chrysotile and amphibole fibers were found 28
toge ther in the lung parenchyma and alveolar spaces. Other studies show fewer amphibole fibers 29 at the site of diseased tissue in the pleura and mesothelial tissue than chrysotile (Churg

, 1988 ; 30 Kohyama and Suzuki

, 1991). Sebastien et al. (1989) examined fiber types in lungs of chrysotile 31 textile and mining workers from South Carolina and Quebec to better understand the unknown 32
reason for differences in disease risk in each cohort . Both groups were exposed to similar 33 material, yet the South Carolina cohort ha d a much greater risk of respiratory cancer. This study 34
examined only lungs, although some of those exposed had nonpulmonary cancers. Overall, the 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 13 DRAFT

— DO NOT CITE OR QUOTE number of tremolite fibers was higher than that of chrysotile fibers in both cohorts . Size 1
distribution sho wed that most fibers measured were 5.8

− 8.0 μ m long , although measurements 2 were not made for anything smaller than this . Tremolite fibers had a greater mean diameter in 3 both cohorts (0.35 μ m) as compared to chrysotile (0.10 μ m), while chrysotile had more 4 ―Stanton‖ fibers (25.2

– 31.8%) as compared to tremolite (5.9

– 6.3%). Stanton fibers are defined 5

as >8 μ m long and <0.25 μ m in diameter (Stanton et al., 1981, reviewed in Appendix G )

. 6
7
3.2.1.2. Pleural Cavity and Extrapulmonary Sites 8

Studies have demonstrated fiber clearance from the respiratory tract may lead to 9 translocation to the pleural cavity and extrapulmonary sites. For example, i n a study comparing 10 fiber burden in the lung, thoracic lymph nodes

, and pleural plaques, Dodson et al

. (1990) 11
observed that the av erage

- length fiber found in the lung (regardless of type) was longer than 12 those found in the lymph nodes or plaques. Most fibers at all three sites were short (<5 μ m). A 13 later study by this group ( i.e., Dodson et al.

, 2000 a ) examined tissue from 20 indiv iduals with 14 mesotheliomas, most with known asbestos exposures. Seventeen of the cases (85%) had 15 asbestos fibers in at least one other extrapulmonary site. The most prevalent type of asbestos in 16 the mesentery was amosite, and the second most prevalent was chrysotile. Tremolite was also 17
found

, to some degree

, in the mesentery and omentum, and in the lung. Dodson et al . (2005) 18 examined parenchymal lung tissue from a cohort of 54 mesothelioma patients and determined 19 the presence of asbestos in all patients analyzed. However, v ery little information is known 20 about the specific mechanisms of fiber clearance and/or translocation from the pleural cavity and 21 extrapulmonary sites , although many studies examining these tissues have observed fibers in 22 multiple tiss ue sites

. Following intrapleural injection of fibers in rats, Bignon et al. (1979) used 23
transmission electron microscopic evaluation following serial sacrifice to monitor migration of 24 fibers from the pleural cavity to the lung parenchyma. 25
26
3.2.2. Ingestion 27

Alt hough ingestion is a potential route of exposure, l imited research has examined 28 clearance (e.g., translocation ) of fibers following ingestion , and no clearance studies are 29 available specific to Libby Amphibole asbestos . An early study to examine the tissue response 30 to asbestos fibers is not truly representative of a natural ingestion exposure, as the researchers 31 directly injected a suspension of amosite fibers into the duodenal wall (Meek and Grasso

, 1983). 32
T his study , h owever, also examined oral ing estion of amosite in healthy animals and those with 33 gastrointestinal ulcers to determine if translocation of fibers occurs through ulcers. Following 34 injection of amosite, granulo ma tous lesions were observed. Ingestion of the same material 35This document is a draft for review purposes only and does not constitute Agency policy. 3

- 14 DRAFT

— DO NOT CITE OR QUOTE resulted in no such lesions or in any other histopathological changes in either healthy or 1 compromised rats. Th us , no translocation was observed from either the healthy or the 2 compromised rat gastrointestinal tract s in this study. A later International Agency for Resea rch 3
on Cancer study (Truhaut and Chouroulinkov

, 1989) examined the effects of chrysotile and 4 crocidolite ingestion in Wistar rats. No translocation was observed. No further studies have 5
been found on clearance or translocation of fibers from the gastroin testinal tract. 6
7
3.2.3. Dermal 8

No studies of dermal clearance or translocation have been reported in the published 9
literature. 10
11
3.3. S UMMARY 12 Although oral or dermal exposure to fibers does occur, inhalation is considered the main 13 route of human exposure to fibers, and therefore has been the focus of more fiber toxicokinetic 14 analyses. Exposure to Libby Amphibole asbestos is proposed to be throu gh all three routes of 15 exposure, although information specific toxicity based on differential routes of exposure is not 16 available. Generally, f iber deposition in the respiratory tract is fairly well defined based on fiber 17 dimensions and density , although t he same cannot be said for fiber translocation to 18 extrapulmonary sites (e.g., pleura) . The deposition location within the respiratory tract and 19 extrapulmonary sites plays a role in the clearance of the fibers from the organism. 20
Fiber clearance can occur through physical mechanisms like coughing and sneezing, or 21 biological mechanisms including translocation. Limited mechanistic information is available on 22
fiber clearance mechanisms in general, and no information specific to clearance of Libby 23 Amphibole as bestos fibers is available . Fibers have been observed in various pulmonary and 24 extrapulmonary tissues following exposure, suggesting translocation occurs to a variety of 25 tissues. Studies have also demonstrated fibers may be cleared through physical mechan isms 26 (coughing, sneezing) or through dissolution of fibers. 27 Multiple fiber characteristics play a role in the toxicokinetics of fibers (e.g., dimensions, 28 density, and durability ) . For this reason, careful attention has been paid to these fiber 29
characteri stics when analyzing research studies on Libby Amphibole asbestos and tremolite

, 30 another amphibole fiber that comprises part of Libby Amphibole asbestos ( see Appendix D ). No 31 toxicokinetic data is available specific to Libby Amphibole asbestos, tremolite, richterite or 32
winchite. When available, this information is presented in the discussion of each paper in 33 relation to the toxic endpoints described. 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 1 DRAFT

— DO NOT CITE OR QUOTE 4. HAZARD IDENTIFICATIO N OF LIBBY AMPHIBOLE ASBESTOS 1

Several human studies are available that provide evidence for the hazard identification of 2 Libby Amphibole asbestos. This discussion focuses primarily on data derived from studies of 3 people exposed to Libby Amphibole asbestos

— either at work or in the community. The adverse 4 health effects in humans are supported by the available Libby Amphibole asbestos experimental 5
animal and laborator y studies. Libby Amphibole asbestos contains winchite (84%), with lesser 6 amounts of richterite (11%) and tremolite (6%) with trace amounts of magnesioriebeckite, 7 edenite, and magnesio

- arfvedsonite (Meeker et al., 2003) (see Section 2 for a more complete 8
d iscussion). Adverse health effects from tremolite exposure have been reported in both human 9 communities and laboratory animals; these effects are consistent with the human health effects 10 reported for Libby Amphibole asbestos. The hazard identification di scussion for Libby 11 Amphibole asbestos (including tremolite) is not limited to cancer incidence and mortality data

, 12
the noncancer health effects

— and mechanistic data related to these effects

— are also described. 13 Some pathogenic pathways might be shared betw een some noncancer health effects (e.g., 14 inflammation, fibrosis) and cancer; thus, these effects can be considered relevant to the 15 subsequent development of cancer. The presentation of noncancer and cancer health effects 16 provides a comprehensive review of adverse health effects observed from exposures to the Libby 17 Amphibole asbestos. 18 Few data specific to Libby Amphibole asbestos are currently available on which to base a 19 detailed understanding of mechanisms for its adverse effects. Similarly, specific qu estions about 20 susceptible populations are not fully addressed by Libby Amphibole asbestos data alone. 21 Therefore, the discussions of the mode(s) of action (Section 4.3.2) and susceptible populations 22 (Section 4 .4) also draw from the literature on mineral fi ber toxicity but are not intended to be a 23 comprehensive review of the broader subject. 24
25
4.1. STUDIES IN HUMANS

— EPIDEMIOLOGY 26 The Libby Amphibole asbestos epidemiologic database includes studies conducted in 27 occupational settings examining exposures to workers, a nd community

- based studies, which can 28 include exposures to workers, exposures to family members of workers, and exposures from 29 environmental sources. Occupational epidemiology studies exist for two work sites where 30 workers were exposed to Libby Amphibole asbestos. These are the mine and mill at the Zonolite 31 Mountain operations near Libby, MT , and a vermiculite processing plant in Marysville, OH

. 32 Worker cohorts from each site and the study results are described in Section 4.1.1. 33This document is a draft for review purposes only and does not constitute Agency policy. 4

- 2 DRAFT

— DO NOT CITE OR QUOTE Whitehouse et al. (2008 ) described the occurrence of 11 cases of mesothelioma among 1
individuals who lived in or around Libby, MT , who had not worked in the mining or milling 2 operations. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted 3 community health consultations for Libby, MT , including an evaluation of cancer mortality data, 4 and a health screening of current and former area residents (including workers) that collected 5 medical and exposure histories, chest x

- rays, and pulmonary function tests (ATSDR, 2000, 6 2001). Several published papers are available based on data from this health screening. 7 ATSDR, in conjunction with state health departments, also conducted health consultations for 8
28 other communities around vermiculite processing plants that wer e potentially exposed to 9 Libby Amphibole asbestos (see Section 4.3). These health consultations consisted of analyses of 10 cancer incidence or mortality data; results from nine of these studies are currently available. The 11 other studies have not been compl eted at the time of this assessment. 12 No occupational studies are available for exposure to tremolite, richterite, or winchite 13 mineral fibers individually or as a mixture exposure, other than Libby Amphibole asbestos. 14 Communities, however, have been expo sed to tremolite and other mineral fibers from natural 15 soils and outcroppings. Tremolite asbestos

- containing soil has been used in whitewash in 16 interior wall coatings in parts of Turkey and Greece. Studies in these areas published as early as 17 1979 report ed an increased risk of pleural and peritoneal malignant mesothelioma (Baris et al., 18 1987; Langer et al., 1987; Baris et al., 1979; Sichletides et al., 1992). More recent studies of 19 communities exposed to tremolite and chrysotile fibers report excess lung cancer and 20 mesothelioma (1.3

- and 6.9

- fold, respectively) (Hasanoglu et al., 2006). Other studies reported 21 pleural anomalies in residents exposed to naturally occurring asbestos, which includes actinolite, 22 tremolite, and anthophyllite (Metintas et al., 2 005; Zeren et al., 2000). Clinical observations 23 include a bilateral increase in pleural calcification accompanied by restrictive lung function as 24 the disease progresses, a condition known as Metsovo lung, named after a town in Greece 25 (Comkrantopoulos et a l., 1985). In one community, the prevalence of pleural calcification was 26 46% (of 268 residents), increasing with age to 80% in residents over 70 (Langer et al., 1987). 27 Both tremolite and chrysotile were identified in bronchoa l veolar lavage fluid of 65 re sidents 28 from different areas of Turkey who were environmentally exposed (Dumortier et al., 1998). The 29 health effects observed in communities with environmental and residential exposure to tremolite 30
are consistent with health effects documented for workers exposed to commercial forms of 31
asbestos. 32
33This document is a draft for review purposes only and does not constitute Agency policy. 4

- 3 DRAFT

— DO NOT CITE OR QUOTE 4.1.1. Studies of Libby, MT Vermiculite Mining Operation Workers 1
Several studies of mortality from specific diseases among workers in the Libby, MT 2 mining operations have been conducted, beginning in the 1980s with the studies by McDonald et 3 al. (1986a,b) and Amandus (1987a,b; Amandus and Wheeler, 1987). McDonald et al. (2002, 4 2004) published an update with mortality data through 1999, and Sullivan (2007) updated the 5 National Institute of Occupational Safety and Health (NIOSH) cohort (referred to in this 6 document as the Libby worker cohort) with mortality data through 2001. Additionally, Larson et 7 al. (2010a) reconstructed a worker cohort and analyzed mortality through 2006 in this same 8 study population, while another study examined changes in lung abnormalities using x

- rays 9 taken between 1955 and 2004 of 88 workers (Larson et al., 2010b). 10
11
4.1.1.1. Description of Mining and Milling Operations 12
The vermiculite mining and milling operations have been described in considerable de tail 13 (ATSDR, 2000). An open

- pit vermiculite mine began limited operations in 1923, and production 14 increased rapidly between 1940 and 1950. This mine is located on Zonolite Mountain, several 15 miles east of the town of Libby (ATSDR, 2000). The Kootenai Riv er runs between the town and 16 the mine. The mining and milling operations continued until 1990 (ATSDR, 2000). 17 The drilling and blasting procedures used in the strip mining operations generated 18 considerable dust exposures, although the mining operations had lower intensity exposures 19 compared to the milling operations. Amandus et al. (1987a) noted that in 1970, a new drill with 20
a dust

- control bagging system aimed at limiting workplace exposure was introduced to the 21 mining operations. 22 Another aspect of the operations was the loading of ore for railroad shipment. From 23
1935

– 1950, railroad box cars were loaded at a station in Libby. In 1950, the loading station was 24 moved to a loading dock on the Kootenai River, 7 miles east of town. Tank cars were used from 25
1950

– 1959 and then switched to enclosed hopper cars in 1960. 26 The milling operations use a screening or sifting procedure to separate vermiculite flakes 27 from other particles and increase the concentration of vermiculite from approximately 20% in the 28
bulk ore to 80

– 95% in the resulting product. A dry mill began operating in 1935, and a wet mill 29 began operating in the 1950s in the same building as the dry mill. One of the primary changes in 30 conditions in the dry mill was the installation of a ventilation f an in 1964. Exposure to asbestos 31 inside the mill was estimated to be 4.6 times higher preceding this installation (McDonald et al., 32 1986a). This ventilation fan resulted in higher amphibole fiber exposures in the mill yard until 33 1968, when the exhaust st ack for the fan was moved away from the shop area around the mill. 34 Other changes to the milling operations in the 1970s included replacement of hand bagging and 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 4 DRAFT

— DO NOT CITE OR QUOTE sewing with an automatic bagging machine (1972), pressurization of the skipper control room 1

us ed for transferring the concentrate from the mill to a storage site (1972), and construction of a 2 new wet mill (1974). Closing of the old dry and wet mills in 1976 had a substantial impact on 3 exposures at the worksite. In 1974, a new screening plant used to size

- sort the concentrate was 4 constructed at the loading dock near the river. Two processing plants operated within the town 5 of Libby (ATSDR, 2001). These expansion or exfoliation plants heated the ore concentrate, 6 resulting in additional release of the Libby Amphibole asbestos fibers in the area. 7

4.1.1.2. Exposure Estimation 8
In the early 1980s, two research groups conducted parallel studies of the mortality 9 experienced by workers in the Libby mining and milling operations. One study was undertaken 10 by NIOSH , and the other by researchers from McGill University. The exposure assessment 11 procedure s used by the two groups relied on the same exposure measurements and used similar 12 assumptions in creating exposure estimates for specific job activities and time peri ods 13 ( Table 4

- 1). In brief, available air sampling data were used to construct a job exposure matrix 14 assigning daily exposures (8

- hour time

- weighted average) for identified job codes based on 15 sampling data for specific locations and activities. Varying jo b codes and air exposures were 16
used f

or different time periods as appropriate to describe plant operations. Individual exposure 17 metrics (e.g., lifetime cumulative exposure) were calculated using the work history of each 18 individual in the study in conjunct ion with the plant job exposure matrix. The specific study 19
details for the Libby, MT worker cohort are described in more detail below, with differences 20 between the research groups highlighted. 21 Before 1970, exposure estimates were based on midget impinger samples taken primarily 22 in the dry mill, by state and federal inspectors. Total dust samples were measured as million 23 particles per cubic foot (mppcf) by the midget impinger method. Amandus et al. (1987a) 24 describe the period during which most of the midg et impinger measurements were made as 25
1962

– 1967, and McDonald et al. (1986a) describe this period as 1962

– 1969, with a few 26 additional measures in earlier years. 1 The number of samples available before 1970 was 336 27 (Amandus et al., 1987a)

. Membrane

- filter air samples for fibers, taken at various locations 28 within the operations, began in 1967, and data are available from company records as well as 29 State and Federal Agencies ( Table 4

- 2). Stationary and short

- term (i.e., 20

- minute to less than 30
4

- hour) measur ements were primar ily used prior to 1974. The number of membrane

- filter 31 samples available was 4,116. Air samples collected through membrane filters were analyzed by 32

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. 4

- 5 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 1 . Exposure assessment methodologies used in evaluations of Libby, 1 MT and Marysville, OH worker cohorts 2 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. Mem brane

- 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 t o 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, 1986 a , 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 Rohs et al. (2008) use 1963 as the beginning date of the use of Libby, MT vermiculite at the Marysville, OH plant, 5 citing the Agency for Toxic Substances and Disease Registry (ATSDR) Health Consultation document (ATSDR, 6 2005). That document cites personal communication with company personnel as a source for a start date of 196 7, 7 however. Another ATSDR document, a review of 28 vermiculite exfoliation plants, reported the likely period of use 8 was 1963

– 1980 (ATSDR, 2008). Lockey et al. (1984) used 1957 as the beginning date. Additional information was 9 used to conclude that the beginning date for use of Libby vermiculite ore was 1959 (Appendix F) 10 NIOSH = National Institute for Occupational Safety and Health

. 11 12 13 14This document is a draft for review purposes only and does not constitute Agency policy. 4

- 6 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 2 . Source of primary samples for fiber measurements at the Libb y 1 mining and milling operations 2 3
Source Unit of measurement Years Number of samples

State of Montana mppcf a 1956

- 1969 b 336

NIOSH f/cc c 1967

- 1968 48

MESA/MSHA f/cc 1971

- 1981 789 Company records f/cc 1970

- 1982 3

, 279
4

a Million particles per cubic foot of air, sampled by a midget impinger apparatus and examined by light 5 microscopy. 6

b MESA: U.S. Mining and Enforcement and Safety Administration (former name of MSHA). 7

c Fibers per cc of air drawn through a filter and examined under a phased contrast light micro scope. Objects 8 >5 μ and with an aspect ratio >3 were reported as fibers (see Section 2 for details). 9

d MSHA: U.S. Mining and Safety Administration

. 10 11 Source : Amandus et al. (1987a). 12 13 14 phased contrast microscopy (PCM), which visually counts fibers greater than >5

- μm long and 15 having an aspect ratio >3:1 (Amandus et al., 1987a). PCM methods from the 1960s allowed 16 reliable characterization of fibers with widths greater than approximately 0 .4 μm (Skikne, 1980; 17 Amandus et al., 1987a). Further standardization of the PCM method provides better 18 visualization of thinner fibers, and 0.25

- μm width is considered the resolution for fiber width 19 (WHO, 1986). 20 The samples taken from specific work locati ons within the plant were used to estimate 21 exposures in specific jobs and time periods, based on professional consideration of temporal 22 changes in facilities, equipment, and job activities. The analysis by McDonald et al. (1986a) was 23 based on 28 occupatio n locations, while the work of Amandus et al. (1987a) was based on 24
25 occupation locations. For the years after 1968, data from filter samples were available for all 25 locations, and NIOSH researchers used the average (arithmetic mean) exposure when more th an 26 one sample was available for a given location or job task and time period. McDonald et al. 27 (1986a) used an alternative procedure described by Oldham (1965) to estimate the mean of 28 log

- normal distributions. 29 For exposures occurring prior to 1968, both research groups estimated exposures for all 30 location operations 2 except the dry mill based on post

- 1968 PCM air sampling results and 31

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, f

or 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 reasonable assumption regarding historical exposures. McDonald et al. (1986) estimated pre

1 1968 exposure measurements for 26 LOs; assumptions were made and estimates based on data 2 from later years or related operations, although these assumptions are not stated by the authors. 3 McDonald et al. (1986) did recognize the uncertainty in these calculations, and for four areas, 4

pro vided high and low estimates (drilling, ore loading, river dock, and bagging plant). Similarly, 5 NIOSH researchers interviewed company employees, considered relative exposure levels 6 between locations post 1968, employing best available judgment to estimate task specific 7 exposure levels. Amandus et al. (1987a) expanded the procedures described in McDonald et al. 8 (1986a, 1987a) and used various assumptions to estimate pre

- 1968 exposures for four location 9 operations (drilling, ore loading, river dock, and bag ging plant). ―Low‖ and ―high‖ estimates 10 were generated using different assumptions; the detailed results for the various assumptions were 11 not presented, but the differences between them were described by the authors as ―slight,‖ and 12 the results presented were based on the high estimate of exposure. Their decisions and specific 13 assumptions are detailed (Amandus et al., 1987a). The authors acknowledge there is uncertainty 14 in exposure estimates prior to 1968 for many of these locations. They do note that v ariability in 15 sample results for the midget impinger was low and that, in general, sample variability was low 16 for fiber air sampling results for areas where the greatest numbers of employees worked (mill, 17 service area, loading and bagging.) 18 To estimate dry mill exposures prior to 1967, when fiber counts from phase contrast 19 microscopy air samples began to be used to measure exposures, Amandus et al. (1987a) 20 established a conversion factor from total dust counts (mmpcf) to fiber counts (fibers/cc). The 21

conve rsion ratio was based on a comparison of 336 impinger samples taken in 1965

– 1969 and 22
81 filter samples taken in 1967

– 1971. Both sets of samples were taken in the dry mill. Using 23 different subsets of the samples (i.e., different years) resulted in ratios that ranged from 24
1.9 fibers/cc:1.0 mppcf to 11.5 fibers/cc:1.0 mppcf. The ratio based on the average fiber counts 25 from air samples (1967

- 1971) to the average total dust measurements in sample years 1965

- 1969 26 was 4.0 fibers/cc:1.0 mppcf. This was the rati o used in the analyses in the NIOSH studies 27 (Amandus et al.

, 1987a, b; Amandus and Wheeler, 1987) because it allowed for the use of the 28 greatest amount of data from overlapping time periods, while controlling for the reduced 29 exposure levels after 1971 wher e fiber count based on phase contrast microscopy but not midget 30 impinger data were available. This dust

– to

- fiber conversion factor was only used to estimate 31 exposures in the dry mill. The resulting exposure concentrations of 168 f/cc in 1963 and all prio r 32 years and 35.9 fibers/cc in 1964

- 1967 were applied to dry mill exposures (Amandus et al., 33 1987a). 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 8 DRAFT

— DO NOT CITE OR QUOTE McDonald et al. (1986a) used a different procedure, based on the estimated reduction in 1 dust exposure with the installation of the ventilation system in 196 4. Rather than develop a 2
direct dust

- to

- fiber conversion factor, they observed that total dust levels dropped approximately 3
4.6

- fold after the installation of ventilation in the dry mill. Therefore, exposures in the dry mill 4 prior to 1965 were calculated as 4.6 times the fiber exposures measured by PCM between 1970 5 and 1974 (22.1 fibers/cc) resulting in estimated dry mill exposures of 101.5 fibers/cc prior to 6 1965 (McDonald et al., 1986a). 7 Exposure estimates for each location operation derived from samp ling data and history of 8 changes in control measures were used to develop a job exposure matrix that estimated exposure 9 in fibers/cc for each job code during several calendar time periods. Jobs were mapped to 10 operation/location based on estimated time spe nt in different job tasks, thus estimating an 8

- hour 11
time

- weighted average exposure for each job during several calendar time periods. Job histories 12 from date of first employment to 1982 were used with the job exposure matrix to develop 13 cumulative exposur e estimates for each worker. 14
15
4.1.1.2.1. Characteristics of historical fiber exposures 16
The resulting exposure estimates presented by both research groups, and the job exposure 17 matrices used in calculating cumulative exposure for the cohort are based on fiber counts by 18 phase contrast microscopy analysis of air filters. As discussed in Sectio n 2, phase contrast 19 microscopy analysis does not distinguish between fiber mineralology or morphology and all 20 fibers >5 μm in length with an aspect ratio of 3:1 or greater are included. Both researcher groups 21 analyzed fibers available at the facility in o rder to identify the mineral fibers in the air samples. 22 Transmission electron microscopy 3 (TEM) analysis of airborne asbestos fibers indicated a 23 range of fiber morphologies

— including long fibers with parallel sides, needlelike fibers, and 24 curved fibers ( McDonald et al., 1986a). Of the fibers examined by TEM, >62% were >5 um in 25 length and a wide range of dimensional characteristic were noted: length (1

- 70 um), width 26 (0.1

- 2 um), and aspect ratios from 3

- 100. Energy dispersive spectroscopy was used to dete rmine 27 the mineral analysis indicated fibers that would be in the actinolite

- tremolite solution series, but 28 sodium rich (McDonald et al., 1986a). This analysis is consistent with the current understanding 29 of amphibole asbestos found in the Libby mine (Sect ion 2). 30 At the time of their study, when exposure concentrations were reduced to generally less 31 than 1 f/cc, NIOSH researchers obtained eight air filters from area air samples collected in the 32

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 selec ted 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 new wet mill and screening plant (provided by the mining compan y). These samples were 1 analyzed by phased contrast microscopy using the appropriate analytical method for the time 2 (NIOSH Physical and Chemical Analytical Method No. 239). From early method development 3 through current PCM analytical techniques, the PHS, OSHA, and NIOSH methods have defined 4 a fiber by PCM analysis as having an aspect r atio greater than 3:1 (Edwards and Lynch, 1967; 5 NIOSH method 7400). NIOSH reported the dimensional characteristics of the fibers from these 6 filters including aspect ratio, w idth, and length ( Table 4

- 3) (Amandus et al.,1987a). Data for 7
599 fibers from the 8 area air samples collected in the wet mill and screening plant are provided. 8 These data are limited in one sense by the minimum diameter and length cutoffs (>4.98

- μm long

, 9
>0.44

- μm wide, aspect ratio >3.0). Even with these limitations, where thinner fibers are not 10 viewed, 96% of the fibers had aspect ratios greater than 10:1, with 16% greater than 50:1 aspect 11 ratio. Only 7% of the fibers had a width greater than 0.88 um, with one fiber reported of the 559 12 with a width greater than 1.76. It should be noted that as NIOSH was examining PCM visible 13 fibers, these data do not give the full fiber

- size distribution of L ibby amphibole asbestos fibers 14 (Section 2.2.3.3.) 15
16
Table 4

- 3 . Dimensional characteristic of fibers from air samples collected in 17 the vermiculite mill and screening plant, Libby, MT. Fibers were viewed and 18 counted by Phase Contract Microscopy (PCM) 19 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 Source: Amandus et al. ( 1987a ). 22 23 24
25This document is a draft for review purposes only and does not constitute Agency policy. 4

- 10 DRAFT

— DO NOT CITE OR QUOTE 4.1.1.2.2. Descriptions of cohorts 1

The cohort studies conducted in the 1980s were similar in terms of exposure assessment 2 (as descri bed in the previous section, Table 4

- 1), and other aspects of the study design 3 ( Table 4

- 4). Both studies included workers who had worked for at least 1 year. Ama ndus and 4
Wheeler (1987) included men hired before 1970 (n = 575), with follow

- up through December 31, 5 1981. McDonald et al. (1986a) included men hired before 1963 (n = 406) with follow

- up 6 through 1983. A later analysis (McDonald et al., 2004) extended th is follow

- up through 1999. 7 A more recent analysis of the Libby, MT workers expanded the cohort to include all workers 8 hired prior to 1970, regardless of duration of employment. The total sample (n = 1,672 white 9 men) included 808 workers who had worked for less than 1 year. These short

- term workers had 10 been excluded from the previous studies in Table 4

- 4. Analyses presented in the report were 11
based on follow

- up from 1960

– 2001. This beginning point was chosen because comparison 12 rates for asbestosis, an ou tcome of interest, were not available before 1960 in the NIOSH Life 13
Table Analysis System, the analytic software used in the analysis (Sullivan, 2007). Few deaths 14 had occurred before 1960 (95 men dead or lost to follow

- up before 1960 were excluded), so th is 15 exclusion criterion would not be expected to result in a substantial loss of outcomes. Because 16 mesothelioma was not coded separately until 1999, the published mesothelioma analysis is based 17
on data from 1999

– 2001. 18 In the study by Sullivan (2007), com parison rates for standardized mortality ratio (SMR) 19 analyses were calculated from U.S. population cause

- specific mortality data, limited to white 20 males, and adjusted for age and calendar year of follow

- up (using 5

- year groups). McDonald et 21 al. (2002) als o used comparison rates from the U.S. population and included additional analyses 22 for the category of respiratory cancers using Montana population rates. 23 Larson et al. (2010b) reconstructed a worker cohort based on company records and 24 analyzed mortality ri sks through 2006. This study included 1862 workers; inclusion and 25 exclusion criteria are not stated, and, thus, it is not clear whether this analysis excluded females 26 or specific ethnic groups. The exposure assessment methodology was based on the methods 27 described by Amandus et al. (1987a), without the modification used by Sullivan (2007). 28 Multiple causes of death (i.e., from any mention on the death certificate) was used, rather than 29 underlying cause of death. Because multiple causes of death are used, more than one cause of 30
death can be coded for an individual. 31
The studies of the Libby worker cohort by Amandus and Wheeler (1987), Sullivan 32 (2007), and Larson et al. (2010a) defined lung cancer mortality based on a more limited range of 33 diagnostic codes compared to the broader classification of ―all respiratory cancer‖ used by 34 McDonald et al. (1986a, 2004). For example, the International Classification of Diseases (ICD)

- 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 11 DRAFT

— DO NOT CITE OR QUOTE 9 codes used for deaths occurring during the applicable years (1979 to 1999) in the NIOSH 1 cohort in Sullivan (2007) were ICD

- 9 162 (trachea, bronchus, and lung) and 163 (pleura); ICD

2
10 codes C33

- C34 were used for deaths in 2000 or later. In the first McDonald et al. (1986a) 3 analysis, ICD

- 9 codes 160

– 163 for respiratory cancer were used, which also included cancer of 4 the larynx (ICD

- 9 code 161) and some types of ―other‖ respiratory cancers (ICD

- 9 code 160). 5 The updated follow

- up for 1999 included ICD

- 9 codes 160

– 165 for respiratory cancer, adding to 6 the ―other‖ respiratory cancer group ( ICD

- 9 codes 164 and 165). In the national Surveillance, 7 Epidemiology, and End Results (SEER) cancer data from 2001

– 2005, the age

- adjusted incidence 8 rate for cancer of the larynx was 3.6, compared to 63.9 per 100,000 person

- years for lung and 9
bronchial can cer (NCI, 2009). Thus, these additional categories (larynx and ―other‖ respiratory 10 cancers) represent a relatively small proportion of respiratory cancers, but could be a source of 11
some misclassification of the outcome if these other cancers are not relat ed to asbestos exposure. 12 The classification of mesothelioma was more difficult because of the lack of a unique 13 ICD code for mesothelioma prior to the 10 th revision, implemented in the United States in 1999. 14 The updated NIOSH study by Sullivan (2007) ide ntified 15 deaths for which mesothelioma was 15 mentioned on the death certificate. Only two of these deaths occurred between 1999 and 2001; 16 these were coded using the ICD

- 10 mesothelioma coding (C45). Larson et al. (2010) classified 17 any death certificate l isting mesothelioma as an immediate or underlying cause of death as 18
ICD

- 10 code C45. The updated McGill study (McDonald et al., 2004) also noted that the 19 classification of mesothelioma was based on a nosologist’s review of death certificates; only 5 of 20
th e 12 cases classified as mesothelioma had a cause of death listed as pleural cancer (ICD

- 9 21
code 163). 22This document is a dr

aft for review purposes only and does not constitute Agency policy.

4
- 12 DRAFT―DO NOT CITE OR QUOTE Tab le 4

- 4 . Respiratory cancer mortality and lung cancer dose

- response analyses based on studies of the

vermiculite mine workers in Libby, MT a

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, bu t 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/c c

- 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 dr

aft for review purposes only and does not constitute Agency policy.

4
- 13 DRAFT―DO NOT CITE OR QUOTE Table 4

- 4. Respiratory cancer mortality and lung cancer dose

- response analyses based on studies of the

vermiculite mine workers in Libby, MT a (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) b SRR (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) b SRR (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 eac h 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 compar ed 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 ea ch 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 Ag ency policy. 4

- 14 DRAFT

— DO NOT CITE OR QUOTE 4.1.1.3. Cancer Mortality Risk 1

4.1.1.3.1. Lung c ancer 2
The results within and among the papers in these two sets of studies (Amandus and 3 Wheeler, 1987; Sullivan, 2007; Larson et al. 2010a; McDonald et al., 1986a, 2004) show similar 4
effects in terms of the increased risk seen for lung (or respiratory) cancer ( Table 4

- 4). 5 Exposure

- response analyses from these studies also demonstrated clear patterns of increasing 6 mortality with increasing exposure, using categorical and continuous measures of exposure, 7 different lag period s, and different exposure metrics. Because of the congruence in results and 8 overlapping of study participants among these studies, the most recent studies are discussed in 9
detail below. 10 The analysis of McDonald et al. (2004) is limited to 406 male worke rs who were hired 11 before 1963 and who were employed for at least 1 year. The mean duration of work was 12
8.7 years. Cause of death data were obtained from the National Death Index, with expected rates 13 based on age

- , race

- and sex

- specific rates. A total of 44 deaths due to respiratory cancers were 14 observed, for an SMR = 2.4 (95% confidence interval [CI] = 1.7, 3.2). A pattern of increasing 15 mortality with increasing cumulative exposure was seen, with relative risks of 1.0 (referent), 1.7, 16 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, 17 respectively ( Table 4

- 4). The estimated linear increase in RR of respiratory cancer risk per 18
100 fibers/cc

- years cumulative exposure was 0.36 (95% CI = 0.03, 1.2) ( p = 0.02). McDonald et 19 al. (2004) reported that similar results were obtained with measures of exposure intensity and 20
measures of residence

- weighted exposure, but the data were not presented in the paper. 21 Sullivan (2007) included 1,672 white male workers who were alive in 19 60 or hired after 22 1960. There was no minimum duration of employment required for inclusion in this analysis, 23 and approximately 50% of the cohort (n = 808) had worked less than 1 year. Mortality follow

- up 24 was conducted through 2001, with 767 identified de aths. The exposure assessment protocol was 25 based on that described by Amandus et al. (1987a), with a modification to the estimated intensity 26 of exposure to laborers and to those with ―unknown‖ jobs. Sullivan (2007) assigned 27 weighted

- average exposure for all unskilled jobs in work area (if known) during a calendar time 28 period, rather than lower mill yard exposure used by Amandus et al. (1987a). The weights are 29 based on the number of workers assigned to unskilled jobs during the same calendar time period. 30 In the Sullivan (2007) follow

- up, SMRs, using underlying cause

- of

- death data (based on death 31 certificates) obtained through the National Death Index and from individual States, and expected 32 mortality based on national age

- , race

- , and sex

- specific rates, were calculated. Using a 15

- year 33 exposure lag, SMRs were increased for lung cancer (n = 89, SMR = 1.7, 95% CI = 1.4, 2.1) and 34 for all cancer mortality (n = 202, SMR = 1.4, 95% CI = 1.2, 1.6) ( Table 4

- 4). Additionally, an 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 15 DRAFT

— DO NOT CITE OR QUOTE internal referent group was used for analyses of risk in relation to cumulative exposure and 1 duration. The results of these internal analyses are presented as standardized relative risks (SRR) 2 for white men, controlling for age group. Increasing risks across categories of cumulative 3

exp osure and duration were observed with both types of analyses, indicating a positive 4

exposure

- response relationship. The SMR estimates for lung cancer mortality were 1.5, 1.6, 1.8, 5
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 6 categories, respectively. The SRR estimates were 1.0, 1.1., 1.4, and 1.5, respectively, across 7 these same exposure categories ( Table 4

- 4). For comparison to the earlier work by McDonald et 8 al. (1986a), an SMR was provided for all respira tory cancer in those employed at least 1 year 9 (SMR 2.0 {1.5

- 2.5}). For the full cohort employed at least 1 day, the SMR for all respiratory 10 cancer was 1.7 {1.4

- 2.1} (Sullivan, 2007). 11 Amandus and Wheeler (1987) provide some information on the smoking hist ory of a 12 sample of 161 male workers employed during 1975

– 1982 with at least 5 years of employment in 13 the Libby cohort study and comparison data based on surveys conducted in the United States 14
from 1955

– 1978. Among the workers, 35% were current smokers, an d 49% were former 15 smokers. This smoking information was obtained from questionnaires the company 16 administered to workers after 1975. Assuming the definitions are similar to those of the national 17 surveys, however, the prevalence of current smokers is simi lar in the worker cohort compared to 18 the U.S. white male population data (ranging from 37.5

– 41.9% current smokers between 1975 19 and 1978). The only year in this range with data on former smokers in the national survey is 20 1975, and, at that time, the preval ence of former smokers in the population data was 29.2%, 21 about 20% lower than among the workers. Using an estimated RR of lung cancer of 14 among 22 smokers, Amandus and Wheeler (1987) estimated that the difference in smoking rates between 23
workers and the co mparison population could have resulted in a 23% increase in the observed 24 risk ratio and commented that the increased risk observed in the lower dose range 25 (<50 fiber

- years) could be the result of confounding by smoking status. 26 Smoking patterns in the U. S. population changed considerably over the period 27 corresponding to the data reported by Amandus and Wheeler (1987). In the National Health 28 Interview Surveys conducted between 1974 and 1983, the prevalence of smoking in males 29

age 20 and older decreased fr om 42.1 to 35.5% (DHHS, 1989, p.269). In addition, the 30 prevalence of former smokers can depend on the definition used. Based on 1986 survey data, the 31 percentage of adults age 17 and older classified as former smokers varied between 14.7 and 32 25.8% using d ifferent definitions for time since last smoked (e.g., from quitting 5 or more years 33 ago to quitting within the past 3 months) (DHHS, 1989). Thus given the lack of information 34 pertaining to the period in which smoking information was collected and the spe cifics of the 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 16 DRAFT

— DO NOT CITE OR QUOTE questions that were used, EPA concludes there is considerable uncertainty regarding the 1 evidence for differences in smoking rates between the workers and a comparison population. 2 Larson et al. (2010b) evaluated multiple causes of death, and, therefore, more than one 3 cause of death can be coded for an individual. A total of 104 lung or bronchus cancer deaths 4 were observed, for an SMR of 1.6 (95% CI 1.3, 2.0) using an external compa rison of United 5 States cause of death data from 1960 to 2002 (Larson et al., 2010b). A higher risk was seen in 6 the higher cumulative exposure categories using Cox proportional hazards modeling with an 7 internal referent group: RR 1.0, 1.1, 1.7 and 3.2, res pectively, for <1.4 (referent), 1.4 to <8.6, 8.6 8 to <44.0 and ≥44.0 fibers/cc

- yrs. Larson et al. (2010b) used data from a health screening 9 program conducted in Libby by ATSDR in 2000

– 2001 (described in Section 4.1.2.2) pertaining 10 to smoking history to esti mate that the proportion of smokers ranged from 50% to 66% in the 11 unexposed group (defined as exposure < 8.6 f/cc

- yrs) and between 66% and 85% among the 12 exposed (defined as ≥ 8.6 f/cc

- yrs). These estimates were used in a Monte Carlo simulation to 13
estimate the potential bias in lung cancer risks that could have been introduced by differences in 14 smoking patterns. The bias

- adjustment factor (RR unadjusted /RR adjusted = 1.3) reduced the overall 15 RR estimate for lung cancer from 2.4 to 2.0. 16
17
4.1.1.3.2. Mesothelioma 18

McDona ld et al. (2004) presented dose

- response modeling of mesothelioma risk based on 19
12 cases. Using Poisson regression, the mesothelioma mortality rate across increasing categories 20 of exposure was compared to the rate in the lowest exposure category. Note th at the referent 21 group was also at excess risk of dying from mesothelioma; that is, one to three cases of 22 mesothelioma were observed in the referent group, depending on the exposure index. Three 23 exposure indices were used in analysis: average intensity ov er the first 5 years of employment, 24 cumulative exposure, and residence

- weighted cumulative exposure. Because of the requirement 25 for 5 years of employment data, 199 individuals (including 3 mesothelioma cases) were excluded 26 from the analysis of average int ensity. The residence

- weighted cumulative exposure was based 27 on the summation of exposure by year, weighted by years since the exposure. This metric gives 28 greater weight to exposures that occurred a longer time ago. Although all exposure groups were 29
cle arly at excess risk of dying from mesothelioma, there was little evidence of increasing RR 30 with increasing average intensity or cumulative exposure. For the cumulative exposure metric, 31 the RR estimates were 1.0 (referent), 3.72, 3.42, and 3.68 based on 1, 4, 3, and 4, cases, 32 respectively. The mean exposure levels in these four quartiles were 8.6, 16.7, 53.2, and 33
393.8 fibers/cc

- years, respectively. Evidence of an exposure

- response relationship was greater 34
for residence

- weighted cumulative exposure, with an RR of 1.57 reported among those with 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 17 DRAFT

— DO NOT CITE OR QUOTE 500.1

– 1826.8 fibers/cc

- yrs exposure, and an RR of 1.95 among workers with higher 1
residence

- weighted cumulative exposure. 2 Sullivan (2007) identified 15 deaths from mesothelioma through a manual review of 3
death cert ificates, with 14 classified as ―pleural or unspecified,‖ and 1 classified as ―peritoneal.‖ 4 Only two of these deaths occurred between 1999 and 2001, the period for which comparison data 5 using the ICD

- 10 classification criteria were available. Based on th ese two mesothelioma 6 deaths, the SMR was 14.1 (95% CI = 1.8, 54.4). 7 Larson et al. (2010b) identified 19 mesothelioma deaths (coding any mention of 8
mesothelioma on the death certificate as the ICD

- 10 classification of C45). Comparison data 9
were based on multiple causes of death data (1960 to 2002). The SMR for mesothelioma was 10 94.8 (95% CI 57.0, 148.0). However, since the ICD

- 10 coding scheme was not used before 11 2000, reference rates based on data from 2000 to 2006, used with observations of mesotheliom a 12 cases from 1960 to 2006, could result in an upwardly biased SMR if the true but unobserved 13 reference rates for mesothelioma during the same time period as the deaths (1960

- 2006) were 14 higher than from 2000

- 2006. An increase was seen across the quartiles of exposure in the 15 analysis using an internal referent group and a 20

- yr lag period, with RR of 1.0 (referent) in the 16
<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 17 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. 18
19
4.1.1.3.3. Other c ancers 20
Larson et al. (2010b) presented data on cancers other than respiratory tract and 21 mesothelioma. The category of malignant neoplasms of digestive organs and peritoneum 22
included 39 observed deaths, for an SMR of 0.8 (95% CI: 0.6, 1.1). No risk in relation to 23 asbestos exposure was seen with a 20

- year lag. The potential for underascertainment of specific 24 causes of death should be noted, however, given the 10% loss to follow

- up and mi ssing cause of 25
death data for 20% of the identified deaths. 26
27
4.1.1.3.4. Summary of cancer mortality risk in Libby, MT vermiculite mining operation 28
workers 29

The studies conducted in the 1980s (Amandus and Wheeler, 1987; McDonald et al., 30 1986a) as well as the extended f ollow

- up studies published in more recent years (Sullivan et al., 31 2007; McDonald et al., 2004; Larson et al., 2010) provide clear evidence of an increased risk of 32 lung cancer mortality and of mesothelioma mortality among the workers in the Libby vermiculit e 33 mining and processing operations. An increasing risk with increasing exposure is seen. A 34 plateauing of the increased risk may be seen, depending on the cut

- point used to examine risk in 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 18 DRAFT

— DO NOT CITE OR QUOTE the highest exposure groups (Sullivan, 2007; Larson et al., 2010). This type of pattern is 1 commonly seen in occupational cohort mortality studies (Stayner et al., 2003). 2
3
4.1.1.4. Noncancer Effects 4

4.1.1.4.1. Asbestosis and other non

- malignant respiratory disease mortality 5

The studies described previously also reported noncancer mortality data, with a specific focus on 6 respiratory diseases ( Table 4

- 5). In Sullivan (2007), the SMR for asbestosis (ICD

- 9 code 501) 7 was 166 (based on n = 22, underlying cause of death compared to a U.S. white male referent 8 group). In Larson et al. (2010a), the SMR (based on 69 observed asbestosis

- related deaths using 9 multiple

- causes

- of

- death data) was 143 (95% CI 111, 181). Increasing cumulative exposure was 10 observed to increase the risk for asbestosis mortality in both of these analyses ( Table 4

- 5). A 11

two

- t o three

- fold increase was also seen for other categories of nonmalignant respiratory disease 12 in Larson et al. (2010), with an SMR of 2.4 (95% CI 2.2, 2.6) for all non

- malignant respiratory 13 disease, and SMR = 2.8 (95% CI 2.3, 3.4) for diseases other than as bestosis, chronic obstructive 14 pulmonary disease, and silicosis. These results are similar to the nonmalignant respiratory 15 disease mortality data from studies of this cohort using underlying cause

- of

- death data. A 16 markedly higher risk of nonmalignant resp iratory disease mortality was also observed in the 17 cumulative exposure category of ≥300 or ≥400 fibers/cc

- years in Sullivan (2007) ( Table 4

- 5). 18 Larson et al. (2010) used a Monte Carlo simulation to estimate the potential bias in non

19 malignant respiratory disease risk that could have been introduced by differences in smoking 20 patterns between exposed and unexposed workers in the cohort. The bias

- adjustment factor 21 (RR unadjusted

/RR adjusted = 1.2) reduced the overall RR estimate from 2.1 to 1.8. 22

Table 4

- 5 . Non

- malignant respiratory mortality studies of the vermiculite 23 mine workers in Libby, MT a 24

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 Ag ency 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 repor ted)

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) b SRR (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) b SRR (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 re spiratory 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 Includes miners, millers, and processors; workers in the screening plant, loading docks, and expansion plants; and 1 office workers. 2

b SMR based on external referent group. 3

c In Sullivan (2007), the SRR is a ratio of sums of weighted rates in which the weight for each stratum

- specific rate is 4 the combined person

- years for the observed cohort across all duration (or cumulative level of exposure) categories. 5 The Life Table Analy sis System provides the SRR for each duration (or cumulative level of exposure) group 6 compared to the referent group. The cutoff points for the categories must be specified by the user (e.g., 0

– 100 ppm

7 years might define the referent group, 100

– 200 ppm

- ye ars might define the next group). Taylor

- series

- based 8 confidence intervals (Rothman, 1986) are given for each specific SRR. 9

d In McDonald et al. (2004), the RR is based on Poisson analysis using internal referent group. 10

e In Larson et al. (2010), the RR i s based on Cox proportional hazards modeling using an internal referent group. 11 12 SMR = standardized mortality ratio, CI = confidence interval, SRR = standardized rate ratio, RR = relative 13 risk

. 14This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 20 DRAFT

— DO NOT CITE OR QUOTE 4.1.1.4.2. Cardiovascular

- related mortality 1
Larson et al. (2010) presents data on mortality due to cardiovascular diseases, with SMRs 2 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 3 circulatory system diseases (n=258). Deaths due to heart diseases were fur ther categorized into 4 ischemic heart disease (n=247) and other heart disease (n=120, for pericarditis, endocarditis, 5 heart failure, and ill

- defined descriptions and complications of heart disease), with SMRs of 0.7 6 (95% CI: 0.6, 0.8) and 1.5 (95% 1.2, 1.8) , respectively. Circulatory diseases included 7 hypertension without heart disease (n=42), with an SMR of 1.7 (95% CI 1.2, 2.4) and diseases of 8 arteries, veins, or lymphatic vessels (n=136), SMR = 1.6 (95% CI 1.4, 2.0). The combined 9 category of cardiovascu lar

- related mortality resulted in modestly increased risks across quartiles 10 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 11
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 12 ≥44. 0 fibers/cc

- yrs, respectively. Larson et al. (2010) used a Monte Carlo simulation to estimate 13 the potential bias in cardiovascular disease risk that could have been introduced by differences in 14 smoking patterns between exposed and unexposed workers in the cohort. The bias

- adjustment 15 factor (RR unadjusted

/RR adjusted = 1.1) reduced the overall RR estimate from 1.6 to 1.5. Because 16 Larson et al. (2010) analyzed multiple causes of death, the observed association between 17 exposure and cardiovascular disease

- rela ted mortality may reflect, at least in part, a consequence 18 of an underlying respiratory disease. 19
20
4.1.1.4.3. Radiographic anomalies 21
Respiratory disease risk is also evidenced by chest radiographs s howing pleural thickening and 22
anomalies in the Libby, MT worker coho rts ( Table 4

- 6). Two of these studies were conducted in 23
the 1980s and were based on x

- rays of a subs et of workers taken for either an annual workplace 24 screening or as part of a study examination. The subset McDonald et al. (1986b) selected 25
included 164 w orkers currently employed at the Libby facility, 80 former employees, and 47 area 26 residents without known dust exposure. The subset selected by Amandus et al. (1987b) included 27 workers with at least 5 years tenure who had worked at Libby at some time durin g 1975

– 1982. 28
The most recent x

- ray film for each worker, which NIOSH obtained from the Libby hospital that 29 performed the screening, was read by three qualified readers using the International Labor 30 Organization 1981 classification system. Consensus readi ngs were used for pleural findings, 31 while the median reading was used to determine the profusion category of small opacities. In the 32 McDonald et al. (1986b) study, there was 90% agreement among readers that the chest x

- rays 33 contained evidence of pleural c alcification and pleural thickening, and 80% agreement that there 34 was evidence of small opacities, pleural plaques, and diffuse thickening. Amandus et al. (1987b) 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 21 DRAFT

— DO NOT CITE OR QUOTE provided a more detailed breakdown of the correspondence between categories for the rating o f 1 small opacities, and reported that the overall difference of 6% in the prevalence of opacities was 2 similar to that seen in other studies. Other design details are described in Table 4

- 6. 3
4
Table 4

- 6 . Pulm onary radiographic studies of the Libby, MT vermiculite 5 mine workers 6 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%

Lar son 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 (25 th , 75 th 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 DLCO = single breath carbon monoxide diffusing capacity ; FVC = forced vital capacity; TLC = total lung capacity. 9 10 11This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 22 DRAFT

— DO NOT CITE OR QUOTE Amandus et al. (1987 b) reported pleural thickening in 13% and small opacities (≥1/0) in 1 9.1% of current employees. Similar data were reported by and McDonald et al. (1986b), with 2 15.9% and 10% with pleural thickening and small opacities, respectively. In both studies, 3

preva lence of these anomalies increased with increasing cumulative exposure, as shown in 4
Table 4

- 6. McDonald et al. (1986b) also included 80 former employees in their study. The 5 prevalence of pleural thickening (52.5%) and small opacities (37.5%) was higher i n these 6 workers compared with current workers. These groups differed by age, however, with only one 7 of the 80 former workers < age 40 years compared with 80 of 164 current workers. Within the 8 age category 40 to 59 years, the prevalence of pleural thicken ing was 20.3 and 40.0% in current 9 and former employees, respectively, and, in the ≥ 60 years age group, the prevalence was 40.0 10 and 61.2%, respectively. The authors attribute these differences in prevalence rates in current 11 compared with former employees to differences in cumulative exposure. Among the 47 area 12 residents without known dust exposure in an occupational setting in the study by McDonald et al. 13 (1986b), the prevalence of pleural thickening was 8.5% (n = 4), and the prevalence of small 14 opacities was 2.1% (n = 1). 15 Both Amandus et al. (1987b) and McDonald (1986b) provided categorical 16

exposure

- response data as well as logistic models for various endpoints (e.g., small opacities, 17 pleural calcification, pleural thickening). In McDonald et al. (1986b), the regression coefficient 18 for cumulative exposure was 0.0024 per unit increase in cumulative ex posure for the log odds of 19 the presence of pleural thickening, adjusting for age and smoking. In the analysis of small 20 opacities, the beta was 0.0035 per unit increase in cumulative exposure. Amandus et al. (1987b) 21 reported similar results to those of Mc Donald et al. (1986b). 22 Whitehouse (2004) examined changes in pulmonary function measures in 123 patients 23 seen in a pulmonary disease practice serving the Libby, MT area, with a mean follow

- up time of 24 35 months. This study population included 86 former e mployees of W.R. Grace, 27 family 25 members of employees, and 10 Libby residents with only environmental (i.e., non

- occupational, 26

non

- family

- related) exposures. The average age at the time of the first pulmonary study was 66 27 years, and 80% were male. Chest x

- rays or high resolution computed tomography (HRCT) scans 28 revealed no evidence of interstitial changes in 67 (55%) of the 123 patients, and 56 patients 29 (45%) were found to have interstitial changes at profusion category 0/1 or 1/0. Pulmonary 30
function te sts included forced vital capacity (FVC), total lung capacity (TLC), and the single 31 breath carbon monoxide diffusing capacity (DLCO). The average yearly loss was 2.2% for 32 FVC, 2.3% for TLC, and 3.0% for DLCO. The subset of 94 patients who experienced a l oss of 33 FVC was characterized as the group with worsening lung function. Among this group, the 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 23 DRAFT

— DO NOT CITE OR QUOTE average yearly loss was 3.2% for FVC, 2.3% for TLC, and 3.3% for DLCO. This decline was 1 noted by the author to be greater than seen in other studies of asbestos

- exposed patients. 2 Larson et al. (2010b) analyzed data from a subset of workers for whom pleural and/or 3 parenchymal abnormalities were seen on the most recently available x

- ray and who had one or 4 more previous x

- rays covering a span of at least 4 years av ailable for comparison. Three 5
NIOSH B readers reviewed each of the available x

- rays in reverse chronological order to 6 determine the latency (i.e., length of time between first exposure, as measured by date of hire and 7 observed abnormality), and the degree of progression by type of lesion. Stored x

- rays were 8 found for 184 workers and 84 were included in the analysis. Exclusions were based on the 9 following: 76 did not have at least one x

- ray over the span of at least 4 years, 20 declined to 10 participate, co nsensus classification was not reached for three, and one worker did not have any 11
detec table abnormality. Circumscribed pleural plaque was seen in 83 of these 84 workers who 12 were known to have had pleural and/or parenchymal abnormalities at a median laten cy of 8.6 13 years. Any pleural calcification was seen in 37 workers, with a median latency of 17.5 years, 14 and diffuse pleural thickening was seen in 12 workers (median latency: 27.0 years). The latency 15 period increased with increasing profusion categories, from a median of 18.9 years for ≥1/0, 33.3 16 years for progression to ≥2/1, and 36.9 years for progression to ≥3/2. 17
18 4.1.1.4.4. Summary of noncancer risk in Libby, MT vermiculite mining operation 19
workers 20

The risk of mortality related to asbestosis and other forms of non

- malignant respiratory 21 disease is elevated in the Libby vermiculite mining and processing operations, with increasing 22 risk seen with increasing exposure in studies conducted in the 1980s (Amandus and Wheeler, 23 1987; McDonald et al., 1986a) and in the ex tended follow

- up studies published in more recent 24 years (Sullivan et al., 2007; McDonald et al., 2004; Larson et al., 2010). Radiographic evidence 25 of pleural thickening and pleural plaques has also been shown in studies of Libby workers 26 (McDonald et al., 1986b; Amandus et al., 1987b; Whitehouse, 2004; Larson et al., 2010b). 27
28
4.1.2. Libby, MT Community Studies 29
In addition to worker exposures, the operations of the Zonolite Mountain mine are 30 believed to have resulted in both home exposures and community exposures . Potential pathways 31 of exposure (discussed below) range from release of airborne fibers into the community, 32
take

- home exposure from mine workers (e.g., clothing), and recreational activities including 33 gardening and childhood play activities. Due to a po tential for a broader community concern, 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 24 DRAFT

— DO NOT CITE OR QUOTE ATSDR conducted several studies and health actions responding to potential asbestos 1
contamination in the Libby, MT area. 2

4.1.2.1. Geographic

- Based Mortality Analysis 3

ATSDR conducted a location

- specific analysis of mortality risks and a community health 4 screening for asbestos in the Libby area ( Table 4

- 7). The mortality analysis was based on death 5
certificate data from 1979

– 1998, with geocoding of current residence at time of death. The six 6 geographic areas used in the analy sis were defined as the Libby city limits (1.1 square miles 7 around the downtown); the extended boundary of Libby (2.2 square miles around the 8 downtown); the boundary based on air modeling (16 square miles, based on computer modeling 9
of asbestos fiber distr ibution); the medical screening boundary (25 square miles, including the 10 town of Libby and areas along the Kootenai River); the Libby valley (65 square miles); and 11 central Lincoln County (314 square miles, based on a 10

- mile radius around downtown Libby) 12 ( ATSDR, 2000). 13 The 1990 population estimates were 2,531, 3,694, 4,300, 6,072, 8,617, and 9,512, 14 respectively, for these six areas. Age

- standardized SMRs were calculated using underlying 15

cause

- of

- death information obtained from death certificates issued d uring the study period for 16 413 of 419 identified decedents, and U.S. and Montana populations were used as reference 17 groups. Increased SMRs were observed for both asbestosis and pulmonary circulation diseases 18 ( Table 4

- 7). The SMR for lung cancer ranged fr om 0.9

– 1.1 and 0.8

– 1.0 in the analyses for each 19 of the six geographic boundaries using Montana and U.S. reference rates, respectively. In 20 addition, four deaths due to mesothelioma were observed during the study period. These 21 analyses did not distinguish between deaths among workers and deaths among other community 22
members. 23
24
4.1.2.2. Community Health Screening 25
The ATSDR community health screening was conducted from July

– November 2000 and 26 July

– September 2001 with 7,307 total participants (6,149 in the first phase) ( ATSDR, 2001; 27 ( Table 4

- 8). Eligibility was based on residence, work, or other presence in Libby for at least 28
6 mont hs before 1991. The total eligible population was not explicitly determined, but the 29 participation rate was approximately 70% of 10,250 pers ons, which was the estimated population 30
of Libby, MT , in 2000. In addition to a standardized interview regarding medical history, 31 symptoms, work history, and other potential exposures, clinical tests included spirometry (forced 32 expiratory volume in one se cond, FEV1, and forced vital capacity, FVC) and chest x

- rays (for 33 participants aged 18 years and older). Moderate to severe restriction (defined as FVC <70% 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 25 DRAFT

— DO NOT CITE OR QUOTE predicted value) was observed in 2.2% of the men and 1.6% of women but was not observed in 1
individ uals less than age 18. 2
3
Table 4

- 7 . Cancer mortality and non

- malignant respiratory disease 4 mortality in the Libby, MT community 5 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). Inquirie s 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 L incoln 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 mod eling 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 refer ence 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 Ag ency 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 Two board

- certified radiologists (B readers) examined each radiograph, and a third reader 2 was used in cases of disagreement. Readers were aware that the radiographs were from 3 participants in the Libby, MT health screening but were not made aware of exposure histories 4 and other characteristics (Peipins et al., 2003; Price, 2004; Peipins, 2004). The radiographs 5 revealed pleural abnormalities in 17.9% of participants, with prevalence increasing with 6 increasing number of ―exposure pathways‖ (d efined on the basis of potential work and 7 residential exposure to asbestos within Libby and from other sources) ( Table 4

- 8). Detailed 8 results of an analysis excluding the former Libby workers cohort were not presented, but the 9
authors noted that the relat ionship between number of exposure pathways and increasing 10 prevalence of pleural anomalies was somewhat attenuated with this exclusion. The prevalence of 11 pleural anomalies decreased from approximately 35% to 30% in individuals with 12 or more 12
exposure pat hways when these workers were excluded from the analysis. Among individuals 13 with no definable exposure pathways, the prevalence of pleural anomalies was 6.7%, which is 14 higher than reported in other population studies (Price, 2004; Peipins, 2004). No info rmation is 15 provided regarding analyses excluding all potential work

- related asbestos exposures. 16
17
18
19
Table 4

- 8 . Pulmonary radiographic, and other health studies in the Libby, 20 MT community 21 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 re sidents 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 ―expo sure 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 residentia l 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% predicte d): 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 Ag ency 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 self

reported 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. Gr ace, 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,

c oughed up bloody phlegm in past year, shortness of breath) and spirometry data (obstructive, restrictive or mixed). Little difference across exposure levels in prevalence of physician

diagnosed 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)

Vinikoor et al. (2010) used the 2000

– 2001 health screening data to examine respiratory 1 symptoms and spirometry results among 1,224 adolescents and young adults who were ≤ age 18 2 in 1990 when the mining/milling operations closed. At the time of the health screening, the ages 3 in this group ranged from 10 to 29 years. Exclusion criteria for this analysis included pr evious 4 work for W.R. Grace, work for a contractor of W.R. Grace, exposure to dust at other jobs, or 5 exposure to vermiculite at other jobs. The total number of exclusions was 221, leaving 1,003 in 6 the analysis. The potential for vermiculite exposure was c lassified based on responses to 7 questions about six activities (handling vermiculite insulation, participation in recreational 8 activities along the vermiculite

- contaminated gravel road leading to the mine, playing at the ball 9 fields near the expansion plan t with large piles of vermiculite, playing in or around the 10 vermiculite piles, heating the vermiculite, and other activities involving vermiculite). The 11 medical history questionnaire included information on three respiratory symptoms and one 12 diagnosis: us ually have a cough (n = 108, 10.8%), are troubled by shortness of breath when 13 walking up a slight hill or when hurrying on level ground (n = 145, 14.5%), have coughed up 14 phlegm that was bloody in the past year (n = 59, 5.9%), and have a history of physicia n

15 diagnosed lung disease (n = 51, 5.1%). The spirometry results were classified as normal in 896 16 (90.5%), obstructive in 62 (6.3%), restrictive in 30 (3.0%), and mixed in 2 (0.2%). Information 17 on smoking history was also collected in the questionnaire: 1 5.8% and 7.3% were classified as 18 current and former smokers, respectively. Approximately half of the participants lived with 19 someone who smoked. The analyses adjusted for age, sex, personal smoking history and living 20 with a smoker. For usually having a cough, the ORs were 1.0 (referent), 1.88 (95% CI: 0.71, 21 5.00), 2.00 (95% CI: 0.76, 5.28) and 2.93 (95% CI 0.93, 9.25) for never, sometimes, frequently 22This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 28 DRAFT

— DO NOT CITE OR QUOTE participated in 1

- 2 activities, and frequently participated in ≥3 activities, respectively. For 1
shortnes s of breath, the corresponding ORs across those exposure categories were 1.0 (referent), 2 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 3 presence of bloody phlegm in the past year the ORs were 1.0 (referent), 0.85 (95% CI: 0.31, 4 2.38), 1.09 (0.41, 2.98), and 1.49 (95% CI: 0.41, 5.43). For history of physician

- diagnosed lung 5 disease and abnormal spirometry results, there was little difference in the association seen across 6 the exposure categories: for lung disease, the ORs were 1.0 (referent), 1.95 (95% CI: 0.57, 6.71), 7 1.51 (95% CI: 0.43, 5.24) and 1.72 (95% CI: 0.36, 8.32) for the categories of never, sometimes, 8 frequently participated in 1

- 2 activities, and frequently participated in ≥ 3 activities, respectively. 9 For abnormal spirometry (i.e., obstructive, restrictive, or mixed, n = 94 cases), the ORs were 1.0 10 (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) 11 across these exposure groups. 12 Two other studies examining aut oimmune disease and autoantibodies in residents of 13 Libby, Montana are described in Section 4.3. 14
15
4.1.2.3. Other Reports of Asbestos

- related Disease among Libby, MT Residents 16
Whitehouse et al. (2008) recently reviewed 11 cases of mesothelioma diagnosed between 17
1993 and 2006 in residents in or around Libby, MT (n = 9) and in family members of workers in 18 the mining operations (n = 2). Three cases were men who might have had occupational asbestos 19 exposure through construction work (Case 1), working in the U.S. Coast G uard and as a 20 carpenter (Case 5), or through railroad work involving sealing railcars in Libby (Case 7). One 21 case was a woman whose father had worked at the mine for 2 years; although the family lived 22 100 miles east of Libby, her exposure may have come th rough her work doing the family laundry 23 which included her father’s work clothes. The other seven cases (four women, three men) had 24 lived or worked in Libby for 6

– 54 years, and had no known occupational exposure to asbestos. 25 Pathology reports were obtain ed for 10 of the 11 patients; for the other patient, diagnosis was 26 based on medical records obtained from the patient’s physician. Given the relatively small 27 population in the study area (approximately 9,500 for central Lincoln County), Whitehouse et al. 28 (2008) note that the occurrence of 11 cases of mesothelioma during the approximate 15 years (or 29 150,000 person

- years) covered by this analysis is considerably higher than the estimated 30 background incidence of 1 case per 1,000,000 person

- years. Whitehouse et al. (2008) used 31 information from a W.R. Grace unpublished report of measures taken in 1975 to estimate that 32 exposure levels of 1.1 fibers/cc were found in Libby, and 1.5 fibers/cc were measured near the 33 mill and railroad facilities. Because the mining and milling operations continued past 1990, and 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 29 DRAFT

— DO NOT CITE OR QUOTE because of the expected latency period for mesothelioma, Whitehouse et al. (2008) suggests that 1 additional cases can be expected to occur within this population. 2
3
4.1.2.4. Summary of health effects in Libby, MT commun ity studies 4
The geographic

- based mortality analysis of 1997

- 1998 mortality data indicates that 5
asbestosis

- related mortality is substantially increased in Libby, MT , and the surrounding area, 6 with rates 40 times higher compared with Montana rates and 60

- 70% higher compared with 7 United States rates (ATSDR, 2000). However, because this analysis did not distinguish between 8 deaths among workers and deaths among other community members, it is not possible based on 9
these data to estimate the risk of asbestos

- rela ted mortality experienced by residents who were 10 not employed at the mining or milling operations. The community health screening studies 11 provide more detailed information regarding exposure pathways in addition to occupation 12 (ATSDR, 2001). Data from this study indicate that the prevalence of pleural abnormalities, 13 identified by radiographic examination, increases with the number of exposure pathways, from 14 6.7% with no specific pathways to 34.6% with 12 or more pathways (Peipins et al., 2003). In 15
additio n, the prevalence of some self

- reported respiratory symptoms among 10

- 29

- year old 16 adolescents and young adults was associated with certain exposure pathways. These participants 17 were ≤ age 18 in 1990 when the mining/milling operations closed (Vinikoor et a l., 2010). A 18 better understanding of the community health effects and the examination of the potential 19 progression of adverse health effect in this community would benefit from additional research to 20 establish the clinical significance of these findings. The observation by Whitehouse et al. (2008) 21 of cases of mesothelioma among individuals with no direct occupational exposure to the mining 22 and milling operations indicates the need for continued surveillance for this relatively rare 23

cancer. 24
25
4.1.3. Marysville, OH Vermiculite Processing Plant Worker Studies 26
Libby vermiculite was used in the production of numerous commercial products, 27 including as a potting soil amender and a carrier for pesticides and herbicides. A Marysville, OH 28 plant that used Libby vermiculite in the production of fertilizer from approximately 29
1963

– 1980 is the location of the two studies described in t his section . Although vermiculite 30 operation may have begun in the late 1950s (1957 or 1959), use of vermiculite from Libby, MT

, 31
is not documente d until 1963. 32 The processing facility had eight main departments, employing approximately 33
530 workers, with 232 employed in production and packaging of the fertilizer and 99 in 34 maintenance; other divisions included research, the front office, and the polyf orm plant (Lockey, 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 30 DRAFT

— DO NOT CITE OR QUOTE 1985). Six departments were located at the main facility (trionizing, packaging, warehouse, 1 plant maintenance, central maintenance, and front offices). Research and development and a 2 polyform fertilizer plant were located separately, a pproximately one

- quarter mile from the main 3 facility. In the trionizing section of the plant, the vermiculite ore was delivered by rail or truck, 4 unloaded into a hopper, and transported to the expansion furnaces. After expansion, the 5
vermiculite was blen ded with other materials (e.g., urea, formaldehyde, potash, herbicides), 6 packaged, and stored. Changes to the expander type and dust

- control measures began in 1967, 7 with substantial improvement in dust control in 1974. 8 Information about exposure assessm ent at the Marysville, OH plant is summarized in the 9
final row of Table 4

- 1. Industrial hygiene monitoring at the plant began in 1972, and limited 10 industrial hygiene sampling data from 1972

– 1974 are available. Lockey (1985) and Lockey et 11 al. (1984) noted that the limited availability of data that would allow for extrapolation of 12 exposures for earlier time periods possibly resulted in the underestimation of exposures before 13
1974. Task

- level samples were conducted, and measurements were determined using po larized 14 light microscopy and transmission electron microscopy (based on particles >5

- μm long, <3

- μm 15 diameter, and ≥3:1 aspect ratio). 16 Based on measurements and knowledge of plant operations, three categories of exposure 17 levels were defined. Group I (consi dered to be the non

- exposed group) consisted of the chemical 18 processing, research, and front office workers. The chemical process plant was about 1 quarter 19 mile from the main vermiculite facility, but the same chemicals were used in both locations. The 20
8

- hour time

- weighted average for vermiculite exposure in this group, both before and after 1974, 21 was estimated as 0.049 fiber/cc (based on a single stationary sample taken outside the main 22 facility), which was characterized as similar to the background leve ls in the community. 23 Group II was the ―low exposure‖ category and included central maintenance, packing, and 24
warehouse workers. The 8

- hour time

- weighted averages for vermiculite exposures in this group 25 were estimated as approximately 0.1

– 0.4 fibers/cc be fore 1974 and 0.03

– 0.13 fibers/cc in and 26 after 1974. Group III was the ―highest exposure‖ category, and included vermiculite expanders, 27 plant maintenance, and pilot plant workers. The 8

- hour time

- weighted averages for vermiculite 28 exposures in this group were approximately 1.2

– 1.5 fibers /cc before 1974 and 0.2

– 0.43 fibers/cc 29
in and after 1974. Cumulative fiber exposure indexes, expressed as fibers

- yr/cc, were derived 30 for each worker from available industrial hygiene data and individual work histories (Lockey, 31 1985) and those with less th an 1 fiber/cc

- year were assumed to be equivalent to a community 32 population (in terms of exposure) and were used as the comparison group . The estimated 33 cumulative exposure for the work force ranged from 0.01 to 39.9 fibers

- yr/cc using an 8

- hour 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 31 DRAFT

— DO NOT CITE OR QUOTE workday and an assumed 365 days of exposure per year. Exposure was assumed to occur from 1 1957 to 1980 in this study. Exposure after work hours was assumed to be zero. 2
The first study of pulmonary effects in the Ohio plant workers was conducted in 1980 3
and involved 5 12 workers (97% of the 530 workers previously identified with past vermiculite 4 exposure) ( Table 4

- 9 ; Lockey et al., 1984 ). The distribution by exposure group was 112 in 5 Group I, 206 in Group II, and 194 in Gr oup III. Physical examination (for detection o f 6 pulmonary rales and nail clubbing), spirometry, and chest

- x

- rays were performed, and 7 information pertaining to smoking history, work history at the plant, and other relevant work 8 exposures was collected using a trained interviewer. Radiographs were read by two board

9 certified radiologists (B readers), with a consensus reading by a third reader when needed. The 10 number of workers within each exposure group was 112, 206, and 194 in Groups I, II, and III, 11 respectively. Approximately 44% were current smoker s, 20% former smokers, and 35% lifetime 12 nonsmokers, but smoking history (i.e., smoking status, pack

- years) did not differ by exposure 13 group. Mean cumulative fiber estimates were 0.45, 1.13, and 6.16 fibers/cc in Groups I, II, and 14 III, respectively. 4 15
A fol low

- up study of this cohort was conducted in 2004

– 2005 (Rohs et al., 2008) 16 ( Table 4

- 9). This study included 298 workers, of which 280 completed the study interview and 17 pulmonary x

- ray. Details of the reasons for non

- participation rates are described in T able 4

- 9. 18 The study interview included information about smoking history and asbestos exposure at the 19 Ohio plant and other work sites. 20
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 Ag ency policy. 4

- 32 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 9 . Pulmonary radiographic studies of the OH vermiculite proce ssing 1 plant workers 2 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 ref

usals 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 years a 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 513 b 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 m easure (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%). Incre asing 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 Calculated based on stratified data presented in Table 2 of Lockey et al. ( 1984 )

. 5

b Rohs et al. (2008) identified one additional eligible worker from the original 530 employees identified with past 6 vermiculite exposure. 7 8 9 The evaluation of each worker included an interview to determine work and health 10 history , spirometry, pulmonary examination, and chest X

- ray. Exposure was estimated using the 11 procedure previously described (Lockey, 1985) using the data on fiber levels. Exposure was 12 assumed to occur from 1963 to 1980 in this study, assuming an 8

- hour workday and 365 days of 13 exposure per year (J. Lockey, University of Cincinnati, personal communication to R. Benson). 14 Each worker supplied a detailed work history (start and end date for each area within the 15 facility). The exposure reconstruction resulted in a cumulative exposure estimate for each 16 individual. The estimated cumulative exposure for this follow

- up study ranged from 0.01 to 17
19.03 fibers

- yr/cc (mean = 2.48). The time from first exposure ranged from 23 to 47 years. 18 Twenty

- eight workers reported pre vious occupational exposure to asbestos. Exposure after work 19
hours was assumed to be zero. 20This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 33 DRAFT

— DO NOT CITE OR QUOTE Three board

- certified radiologists independently classified the radiographs using ILO 1 criteria (ILO, 2000). Radiologists were blinded to all identifiers. Pleural changes that were 2 considered were localized (pleural plaques) and/or diffuse pleural thickening. Localized 3 (discrete) pleural thickening was defined as thickening with or without calcification, excluding 4 solitary costophrenic angle blunting. Diffuse ple ural thickening was pleural thickening, 5 including costophrenic angle blunting, with or without calcification. Interstitial changes were 6 defined as irregular opacities, profusion of 1/0 or greater. A radiographic reading was defined as 7 positive when the m edian classification from the three independent readings was consistent with 8 pleural change and/or interstitial changes. Radiographs classified as unreadable were not used. 9 Radiographic changes found in the study population are summarized in the Tables 4

- 10 and 4

- 10
11. 11 Pleural changes were observed in 80 workers (28.9%), and small opacities (≥1/0) were 12 observed in 8 (2.9%). Increasing risk of costophrenic angle blunting (an indicator of pleural 13 effusions) (n = 11), pleural and parenchymal changes (n = 11), or any of these changes (n = 22) 14 were observed with increasing cumulative exposure when assessed by category of workers or by 15 cumulative exposure across all workers. The prevalence of any radiographic change was 2.8% in 16 Group I, 3.9% in Group II, and 5.8 % in Group III. Using the cumulative fiber metric, the 17 prevalence of any radiographic change was 2.4% in the <1 fiber/cc

- year, 5.0% in 1

– 18
10 fibers/cc

- year, and 12.5% in the >10 fibers/cc

- year groups. The prevalence of pleural 19 changes increased across exp osure quartiles from 7.1% in the first quartile to 24.6%, 29.4%, and 20 54.3% in the second, third, and fourth quartiles, respectively. The range of exposures was 21
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, secon d, 22 third, and fourth quartiles, respectively. Adjusting for age, date of hire, and body mass index 23 resulted in odds ratios of 2.7, 3.5, and 6.9 for the second, third, and fourth quartiles, respectively 24 (Rohs et al, 2008)

. 25This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 34 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 10. Prevalence of pleura l radiographic changes according to 1 quartiles of cumulative fiber exposure in 280 participants 2 3

Exposure quartile Exposure, fiber

yr/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) 72 a 17 (24.6) 4.02 (1.39

– 11.60) 0 (0)

Third 0.86

– 2.20 (1.33) 68 a 20 c (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) The 80 workers with pleural radiographic changes include 68 with discrete pleural thickening (85%) and 12 with 4 diffuse pleural thickening (15%) 5

a Two observations in the second quartile and two in the third quartile had exact exposure values at the 50 th 6 percentile cutoff point. Rounding put these four observations in the second quartile. 7

b Significant trend, p < 0.001 8

c Typographical error in publ ication corrected. 9 10 Source: Rohs et al. (2008), Table 3 11 12 Pleural changes were significantly associated with hire on or before 1973 and age at time 13 of interview (p<0.001). Body mass index (BMI), smoking, and sex were not associated with 14 pleural changes (Table 4

- 11 ) . Body mass index is a potentially important confounder because fat 15 pads can sometime be misclassified as discrete pleural thickening. Hire on or before 1973 and 16 ages at time of interview are each highly correlated with cumulative exposure t o fibers. The 17 small number of females (16) in the cohort limits the analysis of the association with sex. 18

19

20This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 35 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 11. Prevalence of pleural radiographic changes in 280 participants 1 according to various cofactors 2 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
Index a , kg/m

2
≤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 smoked b

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 n = 239 for Body Mass Index due to 38 persons undergoing phone interview and 3 persons with 4 onsite interviews who were not measured for height and weight. 5
b Smoking history as recorded in 2004 questionnaire. Of these 280 participants, 20 persons 6 reported never smoking in the 1980 questionnaire but subsequently reported a history of smoking 7 in the 2004 questionnaire (either current or ex

- smoker) 8 9 Source: Rohs et al. (2008) 10 11 Modeling of odds ratios with cumulative fiber exposure and including various cofac tors 12 (age, hired before 1973, or BMI) with the first exposure quartile as the reference was also 13 conducted. Each model demonstrated the same trend: increasing pleural change with increasing 14 cumulative exposure to fibers. Lack of multicolinearity was demo nstrated by the stable results 15This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 36 DRAFT

— DO NOT CITE OR QUOTE of the modeling with the exposure

- response relationship remaining the same when different 1 models were used with a single cofactor or all cofactors together. There was no evidence of 2 significant interactions using this modeli ng. 3 There was potential co

- exposure to a number of herbicides, pesticides, and other 4 chemicals in the facility (personal communication to Robert Benson, EPA Region 8, from Ivan 5 Smith, The Scotts Company, June 7, 2007). The herbicides and pesticides used during the time 6 when Libby ore was used included atrazine, benomyl, bensulide, chloroneb, chlorothalonyl, 7 chlorpyrifos, 2,4

- D, dacthal, diazinon, dicamba, dephenamid, disodium methanearsonate, 8 dyrene, ethoprop, linuron, MCPP, monuron, neburon, oxadiazon, terrachlor, pentachlorophenol, 9 phenylmercuric acetate, siduron, terrazole, thiophannate

- methyl, thiram. Other chemicals used 10 included ammonium hydroxide, brilliant green crystals, caustic soda, corncobs, ferrous 11 ammonium sulfate, ferrous sulfate, florex R VM, frit

- 504, frit

- 505, hi sil, lime, magnesium 12 sulfate, mon

- a

- mon, potash, potassium sulfate, sudan orange, sudan red, sulfur, sulfuric acid, 13 UFC, urea, and Victoria green liquid dye. No quantitative information on exposure to these 14
chemicals is availabl e. However, the addition of the other chemicals to the vermiculite carrier 15 occurred in a different part of the facility after expansion of the vermiculite ore. Industrial 16 hygiene monitoring in these areas showed very low levels of fibers in the air. In addition, none 17 of these other chemicals is volatile. Thus, it is unlikely that workers would be co

- exposed by 18 inhalation to these other chemicals. EPA has no information indicating that exposure to these 19 chemicals (when administered as a single entity) c auses pleural or parenchymal changes typical 20 of those found in workers employed in the Marysville facility. 21 This study demonstrates that exposure to Libby Amphibole asbestos can cause 22 radiographic evidence of pleural and parenchymal changes in exposed work ers. The prevalence 23 of radiographic changes involving the pleura increased from 2% in 1984 (10/501) to 28. 9 % in 24 2004 (80/280). The prevalence of any radiographic change (discrete pleural thickening, diffuse 25 pleural thickening, and/or parenchymal change) was 29.3% (82/280). The increase in prevalence 26
in the follow

- up study is most likely due to the additional time between the two studies giving 27 additional time for the changes to become apparent in conventional x

- rays. The follow

- up study 28
also shows a cle ar exposure

- response relationship for discrete pleural thickening and more 29 extensive radiographic changes (that is, diffuse pleural thickening and parenchymal change) with 30 increasing cumulat ive exposure to Libby Amphibole asbestos. 31
32This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 37 DRAFT

— DO NOT CITE OR QUOTE 4.1.3.1. Summary of Marysville, O H Vermiculite Processing Plant Worker Studies 1

The studies conducted in the 1980s (Lockey et al., 1984; Lockey 1985) and the follow

- up 2 of the cohort (Rohs et al., 2008) indicate that pleural changes can be seen among workers in this 3 plant, with increasing prevalence of changes seen with increasing cumulative exposure. 4
5
4.1.4. Community Studies from Other Vermiculite Processing Plants 6
ATSDR is conducting community evaluations of 28 sites, in addition to Libby, 7 surrounding exfoliation plants that require further action by EPA because of current 8 contamination or evidence (based on a database of invoices) that the plant processed more than 9 100,000 tons of vermiculite from the Libby, MT mine ( Figure 4

- 1; Table 4

- 1 2 ). These 10 community

- level evaluations do not address individual exposures or residential histories; 11 therefore, the evidence in these evaluations pertaining to disease risk is somewhat limited. 12 Nine of these evaluations included analyses conducted in conj unction with state health 13 departments using death certificate data ( Table 4

- 1 3 ). The lung cancer standardized incidence 14 ratios for these evaluations range from 0.74

– 1.07, and the SMRs range from 0.74

– 1.1, indicating 15
little evidence of an increased risk of lung cancer among these studies ( Table 4

- 1 4 ). As expected 16 from the small number of observations, the standardized incidence ratios for mesothelioma or the 17 category of cancer of the peritoneum, retroperitoneum, and pleura (excluding mesothelioma, but 18
whic h could reflect some misdiagnoses) are more variable, ranging from approximately 0.5

– 2.5. 19 Breast and prostate cancer were selected as negative controls (i.e., cancers that have not 20 previously been associated with asbestos exposure) in these evaluations. The patterns observed 21 for these two cancer sites ( Table 4

- 1 5 ) were similar to those of lung cancer ( Table 4

- 14 ). In 22 summary, these studies do not provide evidence of an increased risk of asbestos

- related cancers 23 in the communities surrounding plants that processed vermiculite contaminated with Libby 24 Amphibole asbestos. A limitation of these studies, however, is the use of a very broad exposure 25 classification, which would be expected to result in considerable misclassification. As a result, 26
these studies might provide useful information to communities, but more refined study designs 27
are needed to evaluate risk to individuals. 28 29 4.1.4.1. Summary of Community Studies from Other Vermiculite Processing Plants 30
The community

- based mortality studies around the 28 exfoliati on plants that processed 31 vermiculite contaminated with Libby Amphibole asbestos provide little evidence of an increased 32
risk of asbestos

- related cancers in the surrounding communities. These studies are quite limited, 33 however, by the broad exposure classi fication and the inability to limit the analysis to individuals 34 who had resided in the specific areas during the relevant exposure periods. Additional studies 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 38 DRAFT

— DO NOT CITE OR QUOTE would be needed to more fully examine the potential risks associated with residential exposures 1
from these sources. 2
3
4

5 Figure 4

- 1 . Location of 28 sites included in the Phase 1 community 6 evaluations conducted by ATSDR 7 8 Source: ATSDR ( 2008 ) http://www.atsdr.cdc.gov/asbestos/sites/national_map/

. 9 10This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 39 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 10 . ATSDR health consultations of 28 sites with potential Libby 1 Amphibole asbestos contamination by status of health data and location 2 (state) of the site 3 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 York c 2004 d
Portland, Oregon a not reported Portland, Oregon a 2005 Ellwood City, Pennsylvania 2006 New Castle, Pennsylvania 2005
Dallas, Texas 2005 Spokane, Washington 2004 d
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 Jersey b 2005 d
Edgewater, New Jersey b 2007 Marysville, OH 2005 5

a Two separate studies of two different sites in Portland were conducted. 6

b Health data included in a separate report available through the New Jersey Department of Health and Senior Services. 7

c Health data were mentioned, but they were not included in the report. 8

d Publication date obtained from a separate source. 9 10 Source: ATSDR ( 2008 ). 11This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 40 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 11 . Description of study areas in ATSDR health consultations 1 evaluating cancer incidence and mortality a 2 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

– 1980 b City limits (n = 9,656) 2005 4

a All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as comparison group except 5 New Jersey, which used New Jersey state rates. All mortality studies used U.S. rates from the National Center for 6 Health Statistics. 7

b The Agency for Toxic Substances and Disease Registry (ATSDR, 2008) report presented incidence data from 8 1979

– 2000, but the 1986

– 1995 incidence data and the mortality data were obtained from the report of the New 9 Jersey Department of Health and Social Services. 10

c The start date for the use of the Libby, MT vermiculite was variously described as 1957 and 1967 in th e ATSDR 11 health consultation report (ATSDR, 2008); the studies by Lockey et al. (1984) and Rohs et al. (2008) used 1963 as 12 the start date. 13This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 41 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 14 . Incidence and mortality results for potential asbestos

- related 1 cancers (by cancer site) in communities with ATSDR health consultations 2 evaluating potential Libby Amphibole asbestos contamination 3 4

Study area c Incidence a Mortality b

Observed Expected c SIR (95% CI) Observed Expected c SMR (95% CI)
Lung and bronchus Los Angeles, CA d 100 117.4 0.85 (0.69, 1.04) 210 285.0 0.74 (0.64, 0.84)
Newark, CA d 29 27.2 1.07 (0.71, 1.53) 125 124.3 1.01 (0.84, 1.2)
Santa Ana, CA d 79 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, CA d 1 1.9 0.53 (0.01, 2.96)

– – – – Newark, CA d 1 0.4 2.49 (0.03, 13.9)

– – – – Santa Ana, CA d 4 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, CA d 1 3.1 0.32 (0.00, 1.78) 0 2.1 0.0

– Newark, CA d 3 0.7 4.06 (0.82, 11.9) 0 0.9 0.0 (0, 4.10)
Santa Ana, CA d 6 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 Ag ency policy. 4

- 42 DRAFT

— DO NOT CITE OR QUOTE Study area c Incidence a Mortality b

Observed Expected c SIR (95% CI) Observed Expected c SMR (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 area c Incidence a Mortality b

Observed Expected c SIR (95% CI) Observed Expected c SMR (95% CI)
Respiratory system and intrathoracic organs Los Angeles, CA d 111 128.2 0.87 (0.71, 1.04) 217 294.4 0.74 (0.64, 0.84)
Newark, CA d 32 30.1 1.06 (0.73, 1.50) 126 128.7 0.98 (0.82, 1.17)
Santa Ana, CA d 86 105.0 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, CA d 101 139.5 0.72 (0.59, 0.88) 155 170.0 0.91 (0.77, 1.07)
Newark, CA d 25 27.0 0.92 (0.60, 1.36) 74 63.4 1.17 (0.92, 1.47)
Santa Ana, CA d 79 104.0 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 All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as the comparison group 2 except New Jersey, which used New Jersey state rates; incidence period in all analyses was 1986

– 1995. An 3 additional analysis compared the Hamilton, New Jersey mesothelioma rates to SEER rates; standard incidence ratio 4 (SIR) wsa reported to be ―increased slightly but remained under 1.0.‖ Incidence data, ICD

- 10 (International 5 Classification of Diseases) codes: lung and bronchus, C340:C349; mesothelioma , M

- 9050:9053; peritoneum, 6 retroperitoneum, and pleura, C480:C488, C384; respiratory system and intrathoracic organs, C320:C399

- excluding 7 mesothelioma; selective digestive organs, C150:C218, C260

- C269

- excluding mesothelioma. 8

b All mortality studies used U .S. rates from the National Center for Health Statistics. Mortality period was 1989

– 9 1998 in the Los Angeles and Newark, California analyses, and was 1979

– 1998 in all analyses. Mortality data, 10 ICD

- 9 codes: lung and bronchus, 162.2

– 162.9; peritoneum, retro peritoneum, and pleura, 158, 163; respiratory 11 system and intrathoracic organs, 161

– 165; selective digestive organs, 150

– 154, 159. 12This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 43 DRAFT

— DO NOT CITE OR QUOTE c Expected values have been rounded. 1

d Similar results were observed in the California analyses using alternative methods to calculate standardized risk 2 ratios for incidence and mortality. 3 CI = confidence interval

. 4 Source: ATSDR ( 2008 ). 5 Table 4

- 15 . Incidence and mortality results for breast cancer and prostate 6 cancer cancers in communities with ATSDR health consultations evaluating 7 potential Libby Amphibole asbestos contamination 8 9

Study area c Incidence a Mortality b

Observed Expected c SIR (95% CI) Observed Expected c SMR (95% CI)
Breast cancer (female) Los Angeles, CA d 104 130.6 0.80 (0.65, 0.97) 109 89.5 1.22 (1.00, 1.47)
Newark, CA d 43 34.3 1.25 (0.91, 1.69) 46 38.8 1.18 (0.87, 1.58)
Santa Ana, CA d 101 110.2 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, CA d 79 118.1 0.67 (0.53, 0.83) 47 64.5 0.73 (0.54, 0.97)
Newark, CA d 24 22.4 1.07 (0.69, 1.59) 17 20.4 0.83 (0.49, 1.34)
Santa Ana, CA d 77 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 All incidence studies used Surveillance, Epidemiology, and End Results (SEER) data as the comparison group 11 except New Jersey, which used New Jersey state rates; incidence period in all analyses was 1986

– 1995. Incidence 12 data, ICD

- 10 (International Classification of Diseases) codes: breast cancer, C500:C509 (excluding mesothelioma); 13 prostate cancer, C619 (excluding mesothelioma). 14

b All mortality studies used U.S. rates from the National Center for Health Statistics. Mortality pe riod was 1989

– 15 1998 in the Los Angeles and Newark, California analyses, and was 1979

– 1998 in all analyses. Mortality data, 16 ICD

- 9 codes: breast cancer, 174; prostate cancer, 185. 17

c Expected values have been rounded. 18

d Similar results were observed in the California analyses using alternative methods to calculate standardized risk 19 ratios for incidence and mortality. 20This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 44 DRAFT

— DO NOT CITE OR QUOTE CI = confidence interval

. 1 2 3 4 5 4.1.5. Case reports 6
Progressive disease from exposure to Libby Amphibole was noted in a case report of fatal 7
asbestosis in an individual 50 years after working at a vermiculite processing plant for a few 8 months at about age 17 (Wright et al., 2002). In another case report, exposures that stemmed 9 from playing for a few years as a child in contaminated vermiculite waste mate rials around a 10 former Libby vermiculite processing facility was reportedly associated with the development of 11 asbestosis and fatal lung cancer (Srebro and Roggli, 1994). 12
13
4.2. SUBCHRONIC AND CHONI C STUDIES AND CANCER BIOASSAYS IN 14 ANIMALS

– ORAL , INHALATION AND OTHER ROUTES OF EXPO SURE 15 Laboratory animal studies with exposure to Libby Amphibole or tremolite asbestos show 16 effects similar to those observed in occupationally exposed human populations including pleural 17 pathology, mesothelioma and lung cancer. Tremoli te is an amphibole asbestos fiber that is a 18 component of Libby Amphibole asbestos (~6%). Also, i n earl y studies Libby Amphibole 19
asbestos was defined as tremolite. Therefore, laboratory animal studies examining the effect of 20 tremolite exposure have been r eviewed and are summarized below to potentially increase 21 understanding of the effects and mechanisms of Libby Amphibole asbestos. Detailed study 22 summaries can be found in Appendix D and summarized in Table 4

- 16 and 4

- 17

. No inhalation 23 studies have been per formed for Libby Amphibole asbestos, but chronic intrapleural injection 24 studies in hamsters demonstrate carcinogenicity following exposure. The chronic inhalation and 25 intrapleural injection laboratory animal studies with tremolite asbestos demonstrated pl eural 26 pathology and carcinogenicity in rats. These studies support the epidemiology studies of Libby 27
A mphibole asbestos exposure (Sec tion 4.1), and aid in informing the mechanisms of Libby 28 Amphibole asbestos

- induced disease. 29
30 4.2.1. Oral 31

No studies in laborator y animals with oral exposure to Libby Amphibole were found in 32 the literature. However, one chronic cancer bioassay was performed following oral exposure to 33 tremolite. McConnell et al. (1983a) describe part of a National Toxicology Program study (NTP, 34
1990a ) performed to evaluate the toxicity and carcinogenicity of ingestion of several minerals, 35 including tremolite. The tremolite (Governeur Talc Co, Gov erneur, New York) used was not 36
fibrous. N o significant tumor induction was observed in the animals with or al exposure to 37This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 45 DRAFT

— DO NOT CITE OR QUOTE tremolite animals. Although non

- neoplastic lesions were observed in many of the aging rats, 1 these were mostly in the stomach and occurred in both controls and exposed animals. The 2
observed lesions included chronic inflammation, ulceration, and necrosis of the stomach 3 (McConnell et al., 1983a). McConnell et al. (1983a) suggested that nonfibrous tremolite could 4 account for the lack of toxicity following exposure in this group of animals. Also, oral studies of 5 asbestos, in general, show decr eased toxicity and carcinogenicity as compared to inhalation and 6 implantation/injection studies (Condie, 1983)

. 7

4.2.2. I nhalation 8
There are no laboratory animal studies following inhalation exposure to Li bby 9 Amphibole asbestos; however t wo studies have examined the effect of inhalation exposure to 10
tremolite in Wistar rats (Bernstein et al., 2005; 2003; Davis et al. 1985) . Davis et al., (1985) 11 performed a chronic inhalation study examining response in male Wistar rats exposed in a 12 chamber to 10 mg/m 3 (~1,600 fibers/mL, >5 μm) of commercially mined tremolite over a 12

- 13 month period

. Bernstein et al. (2003; 2005) exposed Wistar rats to tremolite (100 fibers/cm 3 ) and 14 chrysotile for 13 consecutive weeks (6 hours per day, 5 days per week) with 1

- year follow

- up

. 15
The results of the se inhalation stud ies produced pronounced inflammation and very high levels 16 of pulmonary fibrosis . Davis et al (1985) also demonstrated an increase in carcinomas and 17
mesotheliomas following exposure to tremolite, with n o pulmonary tumors observed in the 18
controls. These results show that Wistar rats exposed to tremolite exhibited increased numbers 19 of pulmonary lesions and possibly tumors. 20
21
4.2.3. Intratracheal Instillation Studies 22

Intratracheal instillation has been used to examine the effect of exposure to Libby 23 Amphibole (Putnam et al., 2008; Smartt et al., 2009; Shannahan et al., 2011a) and tremolite 24 asbestos ( Sahu et al., 1975; Blake et al., 2008; Pfau et al., 2008). These studies exposed either 25
C57Bl/6 mice (100 μg /mouse) or WKY ra ts (0.25 or 1mg/rat) once to Libby Amphibole asbestos 26 and analyzed the results up to 3 mos post

- exposure. Putnam et al. (2008) observed non

27 statistically significant increases in collagen following exposure to Libby Amphibole asbestos, as 28 well as gene expr ession alterations related to membrane transport, signal transduction, epidermal 29 growth factor signaling, and calcium regulation. Smartt et al. (2009) followed up this study by 30 analyzing specific genes by quantitative RT

- PCR for genes involved in collagen accumulation 31 and scar formation (Col1A1, Col1A2, Col3A1). Libby Amphibole asbestos exposure led to 32 increased gene expression of Col1A2 at 1 week post

- instillation and Col3A1 at 1 month post 33 exposure. Both studies observed increased inflammation, however, Libby Amphibole asbestos 34This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 46 DRAFT

— DO NOT CITE OR QUOTE exposure demonstrated minimal inflammation that did not progress in the time points examined. 1 These studies demonstrate that exposure to Libby Amphibole asbestos may lead to inflammation 2
and fibrosis. Shannahan et al. (2011) expo sed two rat models of human cardiovascular disease to 3 Libby Amphibole asbestos to determine if the pre

- existing cardiovascular disease in these 4 models would impact the lung injury and inflammation following exposure. Healthy Wistar 5 Kyoto (WKY) rats were c ompared to spontaneously hypertensive (SH) and spontaneously 6 hypertensive heart failure (SHHF) rats following exposure. All rats (male only) were exposed to 7 0, 0.25 or 1.0 mg/rat via intratracheal instillation and were examined at 1 d, 1 wk and 1 mo post

8 exposure. No changes were observed histopathologically, however, changes were observed in 9 markers of homeostasis, inflammation and oxidative stress. While inflammation and cell injury 10 were observed in all strains, no strain

- related differences were obser ved following exposure to 11 Libby Amphibole asbestos (Shannahan et al., 2011). In conclusion, this study showed the 12 potential for population variability related to cardiac disease in response to exposure to Libby 13 Amphibole asbestos, including markers of cel lular injury, iron homeostasis and inflammation. 14 Laboratory animal studies of tremolite intratracheal instillation exposure have been 15 performed in mice in doses ranging from 60 μg to 5 mg. Male Swiss albino mice exposed to 16 tremolite (5mg) via intratrach eal instillation demonstrated histological changes (Sahu et al., 17 1975). Microscopic results following exposure to tremolite showed acute inflammation of the 18 lungs at 7 days post exposure, including macrophage proliferation and phagocytosis similar to 19
that observed with amosite and anthophyllite. Limited progression of fibrotic response was 20 observed at 60 and 90 days post exposure, with no further progression of fibrotic response. 21 Blake et al. (2008) and Pfau et al. (2008) examined the role of asbestos in autoimmunity. Blake 22 et al. (2008) performed in vitro assays with Libby Amphibole asbestos (Section 4. 4 ) , and both 23 studies performed the in vivo assays with tremolite. C57BL/6 mice were instilled intratracheally 24
for a total of two doses each of 60

- μg saline and wollastonite or Korean tremolite sonicated in 25 sterile PBS, given 1 week apart in the first 2 week s of a 7

- month experiment. S era from mice 26 exposed to tremolite showed antibody binding colocalized with SSA/Ro52 on the surface of 27 apoptotic blebs (Blake et al., 2008). In Pfau et al. (2008), b y 26 weeks, the tremolite

- exposed 28 animals had a significantly higher frequency of positive anti

- nuclear antibody tests compared to 29 wollastinate and saline. Most of the tests were positive for dsDNA and SSA/Ro52. Serum 30 isotyping showed no major changes in immunoglobulin subclasses (IgG, IgA, IgM), but serum 31 IgG in tremolite

- exposed mice decreased overall. Further, IgG immune complex deposition in 32 the kidneys increased, with abnormalities suggestive of glomerulonephritis. No increased 33 proteinuria was observed during the course of the study. Local immunologic respo nse was 34 further studied on the cervical lymph nodes. Although total cell numbers and lymph

- node size 35This document is a draft for review purposes only and does not constitute Ag ency policy. 4

- 47 DRAFT

— DO NOT CITE OR QUOTE were significantly increased following exposure to tremolite, percentages of T

- and B

- cells did 1 not significantly change. 2 3
4.2.4. Injection/ImplantationStudies 4
There are no laboratory animal studies examining intraperitoneal injection or 5 implantation of Libby Amphibole asbestos. Biological effects following exposure to tremolite 6 have been examined in five intraperitoneal injection studies (Smith 1978; Smith et al., 1979; 7 Wagner et al., 1982; Davis et al., 1991; Roller et al., 1996; 1997) and one implantation study 8 (Stanton et al., 1981). 9
S tudies by Smith and colleagues (1978; Smith et al. 1979) , Wagner et al. (1982), Davis et 10 al. (1991) and Roller et al. (1996; 1997) demonstrate d that intrapleural injections of tremolite 11
asbestos 5 is associated with an increase in pleural fibrosis and mesothelioma in hamsters and rats 12 compared to controls or animals injected with less fibrous materials. Doses ranged from 10

– 25 13 mg/animal for each study, and although carcinogenesis was observed in these studies there was a 14 variable level of response to the different tremolite forms examined. Although these studies 15 clearly show the carcinogenic potential of Libby Amphibole or tre molite asbestos fibers, 16 intrapleural injections bypass the clearance and dissolution of fibers from the lung after 17 inhalation exposures. Further, limited information was provided confirming the presence or 18 absence of particles or fibers less than 5 μm in length in these studies, limit ing the interpretati on 19
of results

. 20 There is one laboratory animal study which examined the effect of tremolite exposure 21 following implantation of fibers in the pleural cavity. Stanton et al. (1981) also examined 22
tremolite a nd describe a series of studies on various forms of asbestos. Fibers, embedded in 23 hardened gelatin, were placed against the lung pleura. As an intrapleural exposure, results might 24 not be comparable to inhalation exposures, as the dynamics of fiber deposi tion and pulmonary 25 clearance mechanisms are not accounted for in the study design. Studies using two tremolite 26 asbestos samples from the same lot were described as being in the optimal size range for 27 carcinogenesis; the fibers were distinctly smaller in d iameter than the tremolite fibers Smith et al. 28 (1979) used. These samples both had a high number of fibers in the size range (>8

- μm long and 29
<0.25

- μm diameter ; i.e., ―Stanton fibers‖ ). Exposure to both tremolite samples led to 30
mesotheliomas in 21 and 22 of 28 rats exposed. The Stanton et al. (1981) study also used talc 31 that did not lead to mesothelioma production. 32

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 Ag ency policy. 4

- 48 DRAFT

— DO NOT CITE OR QUOTE T here are no studies currently available in laboratory animals exposed to Libby 1 Amphibole asbestos by inhalation. However, the chronic intr aperitoneal injection study in 2 hamsters (Smith 1978; Smith et al., 1979) demonstrated tumor formation following exposure to 3
tremolite obtained from the Libby, MT mine. No other chronic studies of Libby Amphibole 4
asbestos are available. A recent study in rats examining the impact of pre

- existing 5 cardiovascular disease on pulmonary inflammation demonstrated an increase in inflammatory 6 markers following exposure to Libby Amphibole asbestos via intratracheal instillation in SH rats 7

as compar ed to normal healthy controls exposed to the same dose (Shannahan et al., 2011). More 8 recent studies examined gene expression changes (Putnam et al., 2008; Hillegass et al., 2010) 9
and early protein markers of fibrosis (Smartt et al., 2009) in mice exposed to Libby Amphibole 10
asbestos via intraperitoneal injection. These studies demonstrated an increase in gene and 11 protein expression related to fibrosis following exposure to Libby Amphibole asbestos. 12 Tremolite fibers, although obtained from different locatio ns throughout the world, consistently 13 led to pulmonary lesions and /or tumor formation with various routes of exposure (inhalation, 14 injection, instillation) and in multiple species (rats, hamsters, and mice) (Bernstein et al., 2003; 15 2005; Davis et al., 1985 ; Wagner et al., 1982; Roller et al., 1996; 1997; Stanton et al., 1981)

. 16 Although comparing potency of the various forms of tremolite is difficult given the limited 17 information on fiber characteristics and study limitations (e.g., length of follow

- up post

18 exposure) , these results show potential increased risk for cancer (lung and mesothelioma) 19 following exposure to tremolite asbestos. 20 The results of the studies described above show the fibrogenic and carcinogenic potential 21
of Libby Amphibole and tremolite asbestos. Further, the more recent studies by Blake et al. 22 (2008) and Pfau et al. (2008) support human studies demonstrating potential autoimmune effects 23 of asbestos exposure. 24This 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 ~ 6 0/group) Intraperitoneal

injection (once)

25 mg/0.5 mL 0.9% NaCl solution Tremolite (sample 60) and tremolite + vermiculite (sample 63) Pleural adhesions (fibrosis) : examined 10 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)

C57 B l /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

C57 B l /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; N o toxicity or increase in neoplasia in tremolite rats as compared to controls. McConnell et al., 1983a

Wistar rats (M) (n = 48) Inhalation

10 mg/m 3 (7 h each day, 5 days per week, total of 224 days) South Korean tremolite and brucite Increased fibrosis (1 9 / 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 T remolite (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 animal s 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) S ample FD

- 14 : 0/35 Sample 275 : 0/34 (10 mg); 0/31 (25 mg)S amples 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 T remolite (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., 1981This document is a draft for review purposes only and does not constitute Agency policy.

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/cm 3 longer than 20 μm, 5 days, follow

- up 1 yr later Tremolite Tremolite had a pronounced inflammatory response with rapid granuloma d evelopment (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 increase d total cell count. Pfau et al., 2008

*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. 4

- 52 DRAFT

— DO NOT CITE OR QUOTE 4.2.5. Summary of Animal Studies for Libby Amphibole and Tremolite Asbestos 1

Table s 4

- 16 and 4

- 17 summarize the studies described in this section , with full study 2 details available in Appendix F . Limited in vivo studies have been performed exposing 3 laboratory animals to Libby Amphibole asbestos. One intrapleural injection study using 4 tremolite from the Libby, MT area is included in this section under Libby Amphibole asbestos 5
since earlier termi nology for Libby Amphibole asbestos was often tremolite (Smith, 1978). 6 Hamsters in this study exposed to Libby Amphibole asbestos developed fibrosis and 7 mesothelioma following exposure. Subchronic studies in mice (Putnam et al., 2008; Smartt et al., 8 2008) demonstrated gene and protein expression changes related to fibrosis production following 9 exposure to Libby Amphibole asbestos. Finally, a short

- term study in a rat model for human 10 cardiovascular disease demonstrated an increase in inflammatory markers f ollowing exposure to 11 Libby Amphibole asbestos (Shannahan et al., 2011). 12 Since tremolite is part of Libby Amphibole asbestos, results from tremolite studies were 13 also described. In general, fibrous tremolite has been shown to cause pulmonary inflammation, 14
fibrosis and/or mesothelioma or lung cancer in rats (Bernstein et al., 2003, 2005; Davis et al., 15 1985, 1991; Wagner et al., 1982) and hamsters (Smith et al., 1979). The single short

- term study 16 on mice showed limited response to tremolite (Sahu et al., 19 75). The one chronic oral study 17 (McConnell et al., 1983a) did not show increased toxicity or carcinogenicity; this study, 18 however, used only nonfibrous tremolite, which later studies showed to be less toxic and 19 carcinogenic than fibrous tremolite (Davis e t al., 1991). 20 The results of the studies described above show the fibrogenic and carcinogenic potential 21
of Libby Amphibole and tremolite asbestos. Further, the more recent studies by Blake et al. 22 (2008) and Pfau et al. (2008) support human studies demon strating potential autoimmune effects 23 of asbestos exposure. 24
25
4.3. OTHER DURATION OR ENDPOINT

- SPECIFIC STUDIES 26

4.3.1. Immunological 27
Two epidemiology studies have examined the potential role of Libby Amphibole asbestos 28 and autoimmunity Noonan et al. (2006) used the da ta from the community health screening to 29
examine self

- reported history of autoimmune diseases (rheumatoid arthritis, scleroderma, or 30 lupus) in relation to the asbestos exposure pathways described in the pulmonary analyses 31 ( Section 4.1, Table 4

- 8). To pro vide more specificity in the self

- reported history of these 32 diseases, a follow

- up questionnaire was mailed to participants to confirm the initial report and 33 obtain clarifying information regarding the type of disease, whether the condition had been 34 diagnos ed by a physician, and whether the participant was currently taking medication for the 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 53 DRAFT

— DO NOT CITE OR QUOTE disease. Responses were obtained from 208 (42%) of those individuals who had reported these 1 conditions. Of these 208 responses, 129 repeated the initial report of the diagnosis of rheumatoid 2 arthritis, and 161 repeated the initial report of the diagnosis of rheumatoid arthritis, scleroderma, 3 or lupus. Among people aged 65 and over (n = 34 rheumatoid arthritis cases, determined using 4 responses from the follow

- up questio nnaire), a two

- to three

- fold increase in risk was observed in 5 association with past work in mining and milling operations, asbestos exposure in the military, 6 use of vermiculite in gardening, and frequent playing on vermiculite piles when young. 7
Furthermo re, this increase was observed in individuals exhibiting two measures of pulmonary 8
effects

—

restricted forced vital capacity and presence of parenchymal abnormalities. Restricted 9 forced vital capacity, playing on vermiculite piles, and other dust or vermic ulite exposures were 10 also associated with rheumatoid arthritis in the group younger than 65 (n = 95 cases). The 11 definition of restricted forced vital capacity used in the analysis by Noonan et al. (2006). (FVC 12 <80% of predicted values and a ratio of FEV1 to FVC that is ≥70% predicted, differs from the 13 definition used in the ATSDR report of the community health screening (ATSDR, 2001). In the 14 ATSDR report, a moderate

- to

- severe restricted forced vital capacity is defined as an FVC <70%. 15 For all participan ts, an increased risk of rheumatoid arthritis was observed with increasing 16 number of exposure pathways. RRs of 1.0, 1.02, 1.79, 2.51, and 3.98 were observed for 0 17 (referent), 1, 2

– 3, 4

– 5, and 6 or more pathways, respectively (trend p < 0.001, adjusting fo r 18 restrictive spirometry, parenchymal abnormalities, and smoking history). Although the 19 information gathered in the follow

- up questionnaire and repeated reports of certain diagnoses 20
decreased the false

- positive reports of disease, considerable misclassifi cation (over

- reporting and 21
under

- reporting) is likely, given the relatively low confirmation rate in other studies

— 22 particularly for rheumatoid arthritis (Rasch, 2003; Karlson, 2003; Ling, 2000). 23 Another study examined serological measures of autoantibodi es in 50 residents of Libby, 24 MT, and a comparison group of residents of Missoula, Montana ( Pfau et al., 2005; Table 4

- 1 8). 25 The Libby residents were recruited for a study of genetic susceptibility to asbestos

- related lung 26 disease, and the Missoula resident s were participants in a study of immune function. The Libby 27 sample exhibited an increased prevalence (22%) of high

- titer (≥1:320) antinuclear antibodies 28 when compared to the Missoula sample (6%), and similar increases were seen in the Libby 29 sample for rh eumatoid factor, anti

- RNP, anti

- Scl

- 60, anti

- Sm, anti

- Ro (SSA), and anti

- La (SSB) 30 antibodies. Although neither sample was randomly selected from the community residents, an 31 individual’s interest in participating in a gene and lung disease study likely wou ld not be 32 influenced by the presence of autoimmune disease or autoantibodies in that individual. 33
34
Table 4

- 18 . Pulmonary radiographic, and other health studies in the Libby, 35 MT community 36This document is a draft for review purposes only and does not constitute Agency policy. 4

- 54 DRAFT

— DO NOT CITE OR QUOTE 1 Reference(s) Inclusion criteria and design detai ls 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) Rheu matoid 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 disea se. 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, anti

Sm, anti

- Ro (SSA) and anti

- La (SSB) antibodies observed in Libby sample. 2This document is a draft for review purposes only and does not constitute Agency policy. 4

- 55 DRAFT

— DO NOT CITE OR QUOTE Hamilton et al. (2004), Blake et al. (2008) and Pfau et al. (2008) examined the role of 1 asbestos in autoimmunity in laboratory animal or in vitro studies . Blake et al. (2008) performed 2
in vitro ass ays with Libby Amphibole asbestos (Section 4. 4 ), and both studies performed the in 3 vivo assays with tremolite. C57BL/6 mice were instilled intratracheally for a total of two doses 4
each of 60

- μg saline and wollastonite or Korean tremolite sonicated in ster ile PBS, given 1 week 5 apart in the first 2 weeks of a 7

- month experiment. Sera from mice exposed to tremolite showed 6 antibody binding colocalized with SSA/Ro52 on the surface of apoptotic blebs (Blake et al., 7 2008). In Pfau et al. (2008), by 26 weeks, th e tremolite

- exposed animals had a significantly 8 higher frequency of positive anti

- nuclear antibody tests compared to wollastinate and saline. 9 Most of the tests were positive for dsDNA and SSA/Ro52. Serum isotyping showed no major 10 changes in immunoglobuli n subclasses (IgG, IgA, IgM), but serum IgG in tremolite

- exposed 11 mice decreased overall. Further, IgG immune complex deposition in the kidneys increased, with 12 abnormalities suggestive of glomerulonephritis. No increased proteinuria was observed during 13
th e course of the study. Local immunologic response was further studied on the cervical lymph 14 nodes. Although total cell numbers and lymph

- node size were significantly increased following 15 exposure to tremolite, percentages of T

- and B

- cells did not signifi cantly change. Hamilton et al. 16 (2004) investigated the ability of Libby Amphibole, crocidolite, and PM 2.5 (collected over a 6 17 month period in Houston, TX, from EPA site 48

- 201

- 1035) to alter the antigen

- presenting cell 18 (APC) function was altered in culture d human alveolar macrophages. Asbestos exposure 19 (regardless of type) and PM 2.5 up

- regulated a T H1 lymphocyte derived cytokine, interferon 20 gamma (IFNγ), and the T H2 lymphocyte

- derived cytokines interleukin

- 4 (IL

- 4) and interleukin

21 13 (IL

- 13). There was, h owever, extreme variation among subjects in the amount of response. 22 In addition, there was no correlation between an individual’s cells’ response to asbestos versus 23 PM, suggesting that more than one possible mechanism exists for a particle

- induced APC eff ect 24 and individual differential sensitivities to inhaled bioactive particles. 25 Although limited number of studies, these results suggest a possible effect on 26 autoimmunity following exposure to Libby Amphibole asbestos. Further studies are needed to 27
increas e understanding of this potential effect. 28
29
4.4. MECHANISTIC DATA AND OTHER STUDIES IN SUP PORT OF THE MODE OF 30 ACTION 31

In vitro analysis of fibers depends on the characteristics of the fibers and cell type s used 32 for the studies. Therefore, in reviewing the literature it is important to pay attention to cell types 33 used, particularly related to the ability to internaliz e fibers and produce an oxidative stress 34

response

. Results from in vitro studies have demonstrated potential biological mecha nisms of 35 oxidative stress and inflammation in response to exposure to Libby Amphibole and tremolite 36This document is a draft for review purposes only and does not constitute Agency policy. 4

- 56 DRAFT

— DO NOT CITE OR QUOTE asbestos. These studies are summarized below and in Table s 4

- 19 and 4

- 20 , with detailed study 1 descriptions available in Appendix D

. 2
Limited in vitro studies have been conducted with Libby Amphibole asbestos from the 3
Zonolite Mountain mine. These studies demonstrated an effect of Libby Amphibole asbestos on 4 inflammation and immune function (Blake et al., 2007; 2008; Hamilton et al., 2004; Duncan et 5 al., 2010), oxidative stress (Hillegass et al., 2010), and genotoxicity (Pietruska et al., 2010). 6 Similar endpoints have been examined in vitro following exposure to tremolite asbestos (Wagner 7 et al., 1982; Athanasiou et al., 1992; Suzuki and Hei 1996 ; Wylie et al., 1997; Okayasu et al., 8 1999). 9
10
4.4.1. Inflammation and Immune Function 11

Hamilton et al., ( 2004 ) showed an increase in TH1 and TH2 cytokines following 12 exposure to both asbestos and particulate matter, suggesting a similar effect of exposure to both 13
materials on immune function. Analysis of these results is limited, as the use of primary cells in 14 culture that led to an extremely variable response

. T wo studies by Blake et al. (2007, 2008) 15 further examined the effect of Libby Amphibole asbestos on imm une response in murine 16 macrophages. These studies demonstrated that Libby Amphibole asbestos was internalized, and 17
this internalization resulted in an increase in ROS. These studies also showed a variable 18 cytotoxic response, as Libby Amphibole asbestos e xposure did not result in a statistically 19 significant increase in cytotoxicity, while crocidolite did. DNA damage also was increased in 20
crocidolite

- exposed cells, but not in Libby Amphibole asbestos exposed

- cells. An increase 21 (relative to controls) in au toantibody formation following exposure to Libby Amphibole asbestos 22
also was observed. Studies that examined cellular response to tremolite also found that fiber 23 characteristics (length and width) play a role in determining ROS production, toxicity, and 24

mu tagenicity (Wagner et al., 1982; Okayasu et al, 1999). 25 Mechanisms of oxidative stress following exposure to Libby Amphibole asbestos were 26 also studied in human mesothelial cells (Hillegass et al. 2010). Gene expression changes 27 following exposure to 15×1 0 6 μm 2 /cm 2 Libby Amphibole asbestos 6 as compared to the non

28 pathogenic control (75×10 6 μm 2 /cm 2 glass beads) in the human mesothelial cell line LP9/TERT

29
1 for 8 and 24 hrs. Gene ontology of these results demonstrated alterations in genes related to 30 signal transduction, immune response, apoptosis, cellular proliferation, extracellular matrix, cell 31 adhesion and motility and only one gene related to reactive oxygen species processing. Oxidative 32

6 Lib by 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 (15×10 6 μm 2 /cm 2 ) or the toxic dose (75×10 6 μm 2 /cm 2 ).This document is a draft for review purposes only and does not constitute Agency policy. 4

- 57 DRAFT

— DO NOT CITE OR QUOTE stress was observed as both dose

- and time

- dependent in cells expo sed to Libby Amphibole 1 asbestos but was increased following exposure to the higher dose of Libby Amphibole asbestos 2 (statistical analysis not possible). GSH levels were transiently depleted following 2

– 8 hrs 3 exposure to the higher dose of Libby Amphibole asbestos, with a gradual recovery up to 48hrs in 4
LP9/TERT

- 1 cells (HKNM

- 2 not analyzed). These studies demonstrate that Libby Amphibole 5 asbestos exposure leads to increases in oxidative stress as measured by ROS production, gene 6 expression, protein and fu nctional changes in oxidative stress proteins (SOD), and GSH level 7
alterations in human mesothelial cells. 8 Gene expression alterations of interleukin

- 8 (IL

- 8), cyclooxygenase

- 2 (COX

- 2), heme 9 oxygenase (HO)

- 1 as well as other stress

- responsive genes as co mpared to amosite (Research 10 Triangle Institute) was observed in primary human airway epithelial cells (HAEC) following 11 exposure to Libby Amphibole asbestos. Comparisons were made with both fractionated 12 (aerodynamic diameter ≤2.5μm) and unfractionated fibe r samples (Duncan et al., 2010). 13 Crocidolite (CRO) fibers (UICC) were also included in some portions of this study for 14 comparison. Primary HAECs were exposed to 0, 2.64, 13.2, and 26.4 μg/cm 2 of crocidolite, 15 amosite (AM), AM2.5 (fractionated), Libby Amph ibole asbestos (LA), or LA 2.5 (fractionated) 16 for 2 or 24 hours in cell culture. Cytotoxicity was determined by measurement of lactate 17 dehydrogenase (LDH) from the maximum dose (26.4 μg/cm 2 ) of both amosite and Libby 18 Amphibole asbestos samples, with less than 10% LDH present following exposure to all four 19 samples. Minimal increases in gene expression of IL

- 8, COX

- 2 or HO

- 1 were observed at 2 h 20
post

- exposure to all five fiber types; at 24h post

- exposure, however, a dose response was 21 observed following expo sure to all fiber types with the results showing a pro

- inflammatory gene 22 expression response (Duncan et al., 2010). These results support a limited cytotoxicity of both 23 amosite and Libby Amphibole asbestos under these concentrations and time frames. 24
25
4.4.2. Geno toxicity 26
ROS production and genotoxicity (micronuclei induction) following exposure to Libby 27 Amphibole asbestos has been demonstrated in XRCC1

- deficient human lung epithelial H460 28 cells (Pietruska et al., 2010). XRCC1 is involved in the repair mechanisms for oxidative DNA 29 damage, particularly single strand breaks. Micronuclei induction was measured following 30 treatment of cells by controls (positive, hydrogen peroxide; negative, paclitaxel) and by 5μg/cm 2 31
fibers or TiO 2 particles for 24 hrs. Spontaneous m icronuclei induction was increased in XRCC1

- 32
deficient cells in a dose

- dependent manner following exposure to crocidolite and Libby 33 Amphibole asbestos as compared to control. These results support a potential genotoxic effect of 34 exposure to both crocidolite and Libby Amphibole asbestos. 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 58 DRAFT

— DO NOT CITE OR QUOTE Athanasiou et al. (1992) performed a series of experiments to measure genotoxicity 1 following exposure to tremolite, including the Ames mutagenicity assay, micronuclei induction, 2 chromosomal aberrations, and gap

- jun ction intercellular communication. Although a useful test 3 system for mutagenicity screening for many agents, the Ames assay is not the most effective test 4 to detect mutations induced by mineral fibers. Mineral fibers can cause mutation through 5 generation of ROS or direct disruption of the spindle apparatus during chromatid segregation. 6 Fibers do not induce ROS in the Ames system, however, and the Salmonella typhimurium strains 7 do not endocytose the fibers. Only one study was found in the published liter ature that used the 8 Ames assay to measure mutagenicity of tremolite. Metsovo tremolite asbestos has been shown 9 to be the causative agent of endemic pleural calcification and an increased level of malignant 10 pleural mesothelioma (Section 4.1). To measure t he mutagenicity of Metsovo tremolite, 11
S. typhimurium strains (TA98, TA100, and TA102) were exposed to 0

– 500 μg/plate of asbestos 12 (Athanasiou et al., 1992). Metsovo tremolite did not yield a significant increase in revertants in 13 the Ames assay, including i n the TA102 Salmonella strain, which is generally sensitive to 14 oxidative damage. This study demonstrated clastogenic effects of tremolite, including 15
chromosomal aberrations and micronuclei induction. Tremolite exposure in Syrian hamster 16 embryo (SHE) cell s did lead to a dose

- dependent increase in chromosome aberrations that was 17 statistically significant at the highest doses tested (1.0

– 3.0 μg/cm 2 ) ( p < 0.01) (Athanasiou et al., 18
1992 ). A statistically significant dose

- dependent increase in levels of micr onuclei was 19 demonstrated following tremolite exposure at concentrations as low as 0.5 μg/cm 2 ( p < 0.01) in 20
BPNi cells after 24

- hour exposure. Literatures searches did not find tremolite tested for 21 clastogenicity in other cell types, but the results of thi s study suggest interference with the 22 spindle apparatus by these fibers. No analysis was performed to determine if fiber interference 23 of the spindle apparatus could be observed, which would have supported these results.No effect 24 on the gap

- junctional inte rcellular communication following tremolite exposure was observed in 25 both Chinese hamster lung fibroblasts (V79) and Syrian hamster embryo BPNi cells, which are 26 sensitive to transformation ( Athanasiou et al. 1992). 27 Okayasu et al. (1999) analyzed the muta genicity of Metsovo tremolite, erionite, and the 28

man

- made ceramic (RCF

- 1) fiber. Human

- hamster hybrid A(L) cells contain a full set of 29 hamster chromosomes and a single copy of human chromosome 11. Mutagenesis of the CD59 30 locus on this chromosome is quant ifiable by antibody complement

- mediated cytotoxicity assay. 31 The authors state that this is a highly sensitive mutagenicity assay, and previous studies have 32 demonstrated mutagenicity of both crocidolite and chrysotile (Hei et al., 1992). The cytotoxicity 33 analysis for mutagenicity was performed by exposing 1 × 10 5 A(L) cells to a range of 34
concentrations o f fibers as measured by weight (0

– 400 μg/mL or 0

– 80 μg/cm 2 ) for 24 hours at 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 59 DRAFT

— DO NOT CITE OR QUOTE 37°C. CD59 mutant induction showed a dose

- dependent increase in mutation induc tion for 1 erionite and tremolite, but RCF

- 1 did not. 2
3
4.4.3. Cytotoxicity and Cellular Proliferation 4
Wagner et al. (1982) examined the in vitro cytotoxicity of three forms of tremolite used 5
in their in vivo studies. LDH and BGL were measured in the medium followi ng incubation of 6 unactivated primary murine macrophages to 50, 100, and 150 μg/mL of each sample for 18 7 hours. The Korean tremolite (Sample C) produced results similar to the positive control: 8 increased toxicity of primary murine macrophages, increased c ytoxicity of Chinese hamster 9 ovary (CHO) cells, and increased formation of giant cells from the A549 cell line. The tremolite 10 sample from Greenland (Sample B) did result in increased toxicity over controls; although to a 11 lesser degree (statistics are not given). Although differential toxicity of these samples was noted 12 on a mass basis, data were not normalized for fiber content or size. The inference is that 13 differential results may be due, at least in part, to differential fiber counts. 14 Wylie et al. (19 97) examined the mineralogical features associated with cytotoxic and 15 proliferative effects of asbestos in hamster tracheal epi thelial (HTE) and rat pleural mesothelial 16 (RPM) cells with a colony

- forming efficiency assay. HTE cells are used because they gi ve rise 17 to tracheobronchial carcinoma, while RPM cells give rise to mesotheliomas. The results of the 18 analysis with fiber exposure by mass (μg/cm 2 ) show elevated colonies in HTE cells following 19 exposures to both asbestos fibers ( p < 0.05) at the lowest co ncentrations, while significant 20 decreases were observed for both asbestos fibers at the higher concentrations (0.5 μg/cm 2

, 21

p < 0.05) (Wylie et al., 1997). No proliferation was observed for either chrysotile or crocidolite 22 asbestos fibers in RPM cells, but cytotoxicity was observed at concentrations greater than 0.05 23 μg/cm

2 ( p < 0.05). All talc samples were less cytotoxic in both cell types. Analyzing the data 24 for cytotoxicity and proliferation based on the exposure measurement demonstrated differences 25
in response depending solely on how the fibers were measured: by mass, number, or surface 26 area. These results show variability in interpreting the results of the same assay based on the 27 defined unit of exposure. Most early studies used mass as the measurem ent for exposure, which 28 can impact how the results are interpreted. When possible, further analysis of fiber number and 29 surface area would help elucidate the role of these metrics, particularly for in vivo studies. 30
31 Summary 32 The review of these studies clearly highlights the need for more controlled studies 33 examining Libby Amphibole asbestos in comparison to other forms of asbestos and for 34 examining multiple endpoints, including ROS production, DNA damage, and pro

- inflammatory 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 60 DRAFT

— DO NOT CITE OR QUOTE gene expression alterations , to improve understanding of mechanisms involved in cancer and 1
other health effects

. These results suggest that Libby Amphibole asbestos may act through 2 similar mechanisms as other forms of asbestos, but data gaps still remain to determine specific 3 mechanisms involved in Libby Amphibole asbestos

- induced disease. Studies that examined 4 cellular response to tremolite also found that tremolite exposure may lead to increased ROS 5 production, toxicity, and genotox icity (Wagner et al., 1982; Okayasu et al, 1999). As with the in 6 vivo studies, the definition of fibers and how the exposures were measured varies among studies. 7This document is a draft for review purposes only and does not constitute Agency policy. 4

- 61 DRAFT

— DO NOT CITE OR QUOTE Table 4

- 19

. In vitro data following expos ure 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 μ g/mL 24 h U pregulated 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/cm 2 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/cm 2 3, 7, 12, and 24 h I ncreased ROS over control (wollastonite) and crocidolite D ecreased GSH Cell viability: 0, 6.25, 32.5, 62.5 μg/cm 2 3, 7, 12, and 24 h N o effect was observed on cell viability

DNA damage: 0, 6.25, 32.5, 62.5 μg/cm 2 3, 7, 12, and 24 h

No increase in DNA damage and adduct formation.

Murine macrophages (primary and RAW264.7) Libby Amphibole asbestos or crocidolite 0, 62.5 μg/cm 2 0

– 72 h T ime

- course dose response for apoptosis; R edistribution 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/cm 2 24 h D ose

- 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 × 10 6 μ m 2 / cm 2 (non

- toxic) and 75 × 10 6 μ m 2 / cm 2 (toxic) for 8 or 24 h A lterations in genes related to oxidat ive stress, particularly SOD2 Hillegass et al., 2010

Primary human airway epithelial cells (HAECs) Libby Amphibole asbest os (fractionated and unfractionated), amosite (fractionated and unfractionated) , crocidolite 0, 2.64, 13.2 or 26.4 μ g/cm 2 2, 4 or 24 h I ncreases in pro

- inflammatory gene expression and ROS production Duncan et al., 2010

a All results for RAW264.7. Data not shown for primary cells though authors state similar response to RAW264.7.This document is a draft for review purposes only and does not constitute Agency policy. 4

- 62 DRAFT

— DO NOT CITE OR QUOTE PBS = phosphate buffer saline, ROS = reactive oxygen species, GSH = glutathione, DNA = deoxyribonucleic acid, LDH = lactic de hydrogenase, BGL = β

- glucuronidase, SHE = Syrian h amster ovary, HTE = hamster tracheal epithelial, RPM = rat pleural mesothelial, NIEHS = National Institute of Environmental Health Sciences, HPRT = hypoxanthine

- guanine phosphoribosyltransferase

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

- 63 DRAFT

— DO NOT CITE OR QUOTE 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 (positiv e 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 μ g/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/cm 2 6, 24, and 48 h No affect was observed on gap

- junctional intercellular communication.

BPNi cells BPNi: 0

– 2 μg/cm 2 24 h Tremolite led to a dose

- dependent increase in micronuclei induction.

SHE cells SHE: 0

– 3 μg/cm 2 24 h Tremolite exposure led to increased chromosomal aberrations but not in a dose

dependent 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 R elative 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., 1997This document is a draft for review purposes only and does not constitute Agency policy. 4

- 64 DRAFT

— DO NOT CITE OR QUOTE 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; D ose

- 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 de hydrogenase, 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

. 1This document is a draft for review purposes only and does not constitute Agency policy. 4

- 65 DRAFT

— DO NOT CITE OR QUOTE 1
4.5. SYNTHESI S OF MAJOR NONCANCER E FFECTS 2

The n on

- cancer health effects observed following inhalation exposure to Libby 3 Amphibole asbes tos include pleural thickening and asbestosis. Recent studies have also 4
examined non

- cancer health effects following exposure to Libb y Amphibole asbestos in other 5
systems , including autoimmune effects and cardiovascular disease. 6
7
4.5.1. Pleural Thickening 8 Pleural thickening includes two distinct biological lesions, discrete pleural plaques in the 9 parietal pleura and diffuse pleural thickening of the visceral pleura. Although the visceral and 10 parietal pleura provide a continuous lining for the interior of the thoracic cage, wrapping around 11 the lobes of the lung, they are distinct (Jones, 2002; Broaddus et al., 2011). Differences lie 12 primarily in the submesothelial layers, the existence of stomata in the parietal pleura and greater 13 innervations of the parietal pleura (Jones, 2002.) Although discrete pleural plaques may extend 14 to impact a large surface, these deposits lie between the pleural me mbrane and the thoracic 15 cavity, and fusing of discrete pleural plaques do not constitute a diagnosis of diffuse pleural 16 thickening, since this diagnosis specifically refers to thickening of the visceral pleura (ATS 17 2004, ILO B

- Reader standards). There ar e reports of discrete plaques in the visceral pleura, 18 which may be localized thickening of this tissue. It should also be noted that extensive 19 thickening of the visceral pleura may be accompanied by fusing between the parietal and visceral 20
surfaces of the pleural membrane where there are discrete plaques. Thickening of the visceral 21 pleura is more often localized to lobes of the lung with pronounced parenchymal changes and it 22 has been hypothesized that the inflammatory and fibrogenic processes within the l ung 23 parenchyma may influence the fibrogenic process in the visceral pleura. 24 In clinical settings t here are difficulties in distinguishing between these two l esions in 25 part due to lack of radiographic film diagnostic specificity as well as the ILO definitions for 26 diffuse pleural thickening of the visceral pleura which may result in some variability in reporting 27 of discrete pleural plaques. There are two classifications for pleural thickening in current 28 guidelines (ILO

, 2000). Diffuse pleur al thickening is currently only determined where there is 29 thickening along significant extent of the chest wall, and this thickening is ―in the presence of 30 and in continuity with, an obliterated costophrenic angle 7 ‖ (ILO, 2000). Although previous 31

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

- 66 DRAFT

— DO NOT CITE OR QUOTE classi fication schemes attempted to distinguish between thickening of the parietal pleura and 1 diffuse thickening of the visceral pleura, the ILO states‖ reading standard x

- rays cannot always 2 distinguish between thickening of the visceral or parietal pleura‖ (ILO , 2000). Thus, all other 3 pleural thickening is now defined as localized pleural thickening (i.e. where there is no 4 costophrenic angle obliteration). Localized pleural thickening on the chest wall, and diffuse 5 pleural thickening may be graded indicating t hickness, and the extent of the chest wall impacted, 6
however these data are not always provided in study reports , therefore regardless of extent of the 7 pleural thickening, only presence or absence may be noted. 8
9 4.5.2. Asbestosis 10
Asbestosis is the most severe nonmalignant disease associated with exposure to asbestos 11 fibers. Asbestosis is the interstitial pneumonitis and fibrosis caused by inhalation of asbestos 12 fibers and becomes evident only after an appreciable latency period. Asbestosis is characterized 13 by a diffuse increase of collagen in the alveolar walls (fibrosis) and the presence of asbestos 14 fibers, either free or coated with a proteinaceous material and iron (asbestos bodies). The 15 mildest form of asbestosis involves the alveolated walls of respirato ry bronchioles and the 16 alveolar ducts. More severe grades involve greater proportions of the acinus until the whole 17 acinar structure is involved and some alveoli are completely obliterated (ATS, 2004). A 18 profusion of irregular opacities at the level of 1 /0 is used as the boundary between normal and 19 abnormal. Asbestosis is associated with dyspnea, bibasilar rales, and changes in pulmonary 20 function: a restrictive pattern, mixed restrictive

- obstructive pattern, and/or decreased diffusing 21 capacity (ATS, 2004 ). In one of the earliest studies conducted, about 50% of the asbestos 22 workers presented with a FVC below 80% of predicted (ATS, 2004). 23 Radiographic evidence of small opacities in the lung is direct evidence of scarring of the 24 lung tissue and as the fibro tic scaring of lung tissue consistent with mineral dust and mineral 25 fiber toxicity. The scar r ing of the parenchymal tissue of the lung contributes to measured 26 changes in pulmonary function, including obstructive pulmonary deficits from narrowing 27 airways, restrictive pulmonary deficits from impacting the elasticity of the lung as well as 28 decrements in gas exchange. However, although data across the mineral fiber literature strongly 29 support a finding of functional deficits where small opacities are visible on radiographs, the data 30 also indicate that deficits in pulmonary function (consistent with interstitial fibrosis) are seen 31 before these changes are detected by radiographic examination. Thus changes in lung function 32 may occur before the fibrotic lesions can be detected on standard radiographs (ATS, 2004 and 33 Brodikin et al., 1994). For example, decreased CO 2 diffusion is a sign of reduced gas exchange 34 in the pulmonary region of the lung and is observed in workers exposed to other types of 35
asbestos even wh en small opacities are absent on radiographs. Similarly, obstructive deficits in 36This document is a draft for review purposes only and does not constitute Agency policy. 4

- 67 DRAFT

— DO NOT CITE OR QUOTE lung function may be observed without radiographic signs for fibrotic lesions. As decreased 1 diffusion and obstructive deficits are mechanistically linked to changes in the p arenchymal tissue 2 these data suggest radiographs may not be sensitive enough to protect against adverse effects 3 from parenchymal effects of asbestos exposure. 4
5
4.5.3. Other noncancer health effects (cardiovascular toxicity, autoimmune effects) 6

There is limit ed available research available on nonc ancer health effects occurring 7 outside the respiratory system. Two such effects (cardiovascular toxicity and autoimmunity) 8 have been examined following Libby Amphibole asbestos exposures and thus, are discussed 9
brief ly here. Recent studies have explored the role of asbestos exposure in c ardiovascula r 10
disease (Larson et al., 2010)

. Mechanistic studies have examined the p otential role of iron and 11 the associated inflammation for both the respiratory and cardiovascular disease (Shannahan et 12 al., 2011 ). Limited studies have examined a causal association between asbestos exposure and 13 autoimmune disease (reviewed in Bunderson

- Schelvan et al., 2011), but limitations in the scope 14
and number of these studies makes it difficul t to reach conclusions as to the role of asbestos 15 exposure in autoimmunity. 16
17
4.5.4. Libby Amphibole asbestos summary of non

- cancer health effects 18

The studies in humans summarized in Section 4.1 have documented an increase in 19 mortality from non

- malignant respiratory disease and in radiographic changes in the pleura and 20 parenchyma among employees of the Libby vermiculite mining operations (Amandus et al., 21 1987a, b, c; McDonald et al., 2004, 1986a, b; Sullivan 2007; Larson et al. 2010a). Additional 22
studies (Lockey et al., 1984; Rohs et al., 2008) have documented an increase in radiographic 23 changes in the pleura and parenchyma among employees of a manufacturing facility in 24 Marysville, Ohio that used Libby vermiculite ore contaminated with Libby Amphibole asbe stos. 25 These studies used ILO diagnostic criteria and demonstrated that the increases in prevalence of 26 adverse effects in the lung and pleural cavity are positively associated with increased cumulative 27 exposure to Libby Amphibole asbestos. Finally, additi onal studies have documented an increase 28 in pleural abnormalities (Peipins et al., 2003; Muravov et al., 2005) and progressive loss of lung 29 function (Whitehouse, 2004) among residents of Libby, Montana. Two studies (Pfau et al, 2005; 30 Noonan et al., 2006) conducted among residents of Libby, Montana, support the hypothesis that 31

auto

- immunity may also be associated with exposure to Libby Amphibole asbestos. Larson et al. 32 (2010a) also showed that Libby Amphibole may increase mortality from cardiovascular toxi city 33 in residents of Libby. 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 68 DRAFT

— DO NOT CITE OR QUOTE Although experimental data in animals and data on toxicity mechanisms are limited for 1 Libby Amphibole asbestos, the existing data are consistent with the non

- cancer health effects 2 observed in both Libby workers and community mem bers. P leural fibrosis was increased in 3 hamsters after intrapleural injections of Libby Amphibole asbestos (Smith

, 1978). More recent 4 studies have demonstrated increased collagen deposition consistent with fibrosis following 5
intratracheal instillation of Libby Amphibole asbestos fibers in mice (Putnam et al., 2008; Smartt 6 et al., 2009). Pulmonary fibrosis, inflammation, and granulomas were observed after tremolite 7 inhalation exposure in Wistar rats (Bernstein et al., 2003, 2005) and intratracheal instill ation in 8 albino Swiss mice (Sahu et al., 1975). Davis et al. (1985) also reported pulmonary effects after 9 inhalation exposure in Wistar rats including increases in peribronchiolar fibrosis, alveolar wall 10 thickening, and interstitial fibrosis

. A recent stu dy by Shannahan et al. (2011) examined the 11 effect of exposure to Libby Amphibole asbestos in rats with pre

- existing cardiovascular disease, 12
and found strain

- related differences for biomarkers of inflammation following exposure to Libby 13 Amphibole asbestos. This short

- term study only examined animals for 1 month post

- exposure, 14 so the long

- term effect of altered inflammation is unknown. Limited in vitro studies have 15 demonstrated oxidative stress following Libby Amphibole asbestos exposures in various cell 16
typ es (Blake et al., 2007; Pietruska et al., 2010; Hillegass et al., 2010; Duncan et al. 2010). Libby 17 Amphibole asbestos fibers increased intracellular ROS in both murine macrophages and human 18 epithelial cells (Blake et al., 2007; Duncan et al., 2010). Surfa ce iron, inflammatory marker gene 19 expression were increased following exposure to Libby Amphibole asbestos in human epithelial 20 cells (Duncan et al., 2010; Pietruska et al., 2010; Shannahan et al., 2011). Tremolite studies 21 demonstrate cytotoxic in various cell culture systems. 22
23 4.5.5. Mode

- of

- Action Information (non

- cancer) 24
The precise mechanisms causing toxic injury from inhalation exposure to asbestos or 25 other mineral fibers have not been established. However, nearly all

- durable mineral fibers with 26
dimensional characteristics that allow penetration to the terminal bronchioles and alveoli of the 27 lung have the capacity to induce pathologic response in the lung and pleural cavity (ATSDR, 28 2001; Witschi and Last, 1996). The physical

- chemical attributes of mineral fi bers are important 29 in determining the type of toxicity observed. Fiber dimension (width and length), density and 30 other characteristics such as chemical composition, surface area, solubility in physiological 31 fluids, and durability all play important roles in both the type of toxicity observed and the 32 biologically significant dose

. Fibrosis results from a sequence of events following lung injury 33 which includes inflammatory cell migration, edema, cellular proliferation and accumulation of 34 collagen. There is c urrently insufficient evidence to determine the non

- cancer mode of action for 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 69 DRAFT

— DO NOT CITE OR QUOTE Libby Amphibole asbestos. However, current support for mechanisms (i.e., chronic 1 inflammation, cytotoxicity and cellular proliferation) involved in potential non

- cancer health 2
ef fects following exposure to Libby Amphibole asbestos are described below. 3
4
4.5.5.1. Chronic Inflammation 5
Chronic pulmonary inflammation has been observed following inhalation exposure to 6 fibers, which is often followed by fibrosis at the site of inflammation if the fibers persist 7 (Oberdorster, 1994). Macrophages phagocytose fibers and particulate matter and are activated to 8 trigger the release of inflammatory cytokines, ROS, and growth factors. These responses lead to 9 a sustained inflammatory response that can res ult in fibrosis at the site of fiber deposition. As 10 described in Section 3, f ibers that are deposited in the lung may be taken up by alveolar 11 macrophages. Once engulfed by the macrophage, small fibers may remain in place or be 12
translocated across the bro nchiole or alveolar membrane to the interstitial and pleural space. 13 Longer fibers that are not easily engulfed by the macrophages may remain in the alveoli for an 14 extended period of time. Further, longer fibers partially engulfed by macrophages may lead t o 15 increased release of inflammatory mediators (i.e., frustrated phagocytosis) (Gwinn and 16 Vallyathan 2006). It is believed that these macrophages release mediators (lymphokines and 17 growth factors) that attract immunocompetent cells or stimulate collagen pr oduction. Thus the 18 mediators of the disease may be an on

- going inflammatory response as a result of the prolonged 19 ―residence time‖ of the fibers within the lung parenchyma. This increased inflammatory response 20 may be involved in the pathogenesis of pleura l fibrosis and asbestosis. 21 Laboratory animal studies of general asbestos exposure generally demonstrate 22 inflammatory changes in the lung and pleura followed by cellular proliferation ; the level of this 23
effect is believed to be dependent on various fiber ch aracteristics ( McConnell et al., 1999 ). The 24 inflammatory response to fibers has been studied in depth for other types of asbestos (crocidolite, 25 chrysotile) but not for Libby Amphibole asbestos or tremolite (reviewed in Mossman et al., 26 2007). Although lim ited, the data described here for Libby Amphibole asbestos, and to a greater 27 extent for tremolite, suggest a similar response in the lung and pleura. In vivo exposure to 28
tremolite led to an increase in inflammation for all studies where it was measured. This increase 29 appeared in some cases to depend on fiber size and morphology (Smith et al., 1979; Davis et al., 30 1991). Bernstein et al. (2003, 2005) observed that exposure to tremolite led to pronounced 31 inflammation as soon as 1 day after inhalation exposu re in male Wistar rats. Inflammation also 32
occurred in male albino Swiss mice in an acute

- duration study that did not lead to fibrosis or 33 carcinogenesis, possibly due to the short study duration (150 days) (Sahu et al., 1975). In vitro 34 analysis of Libby A mphibole asbestos showed increases in inflammatory cytokines (Hamilton et 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 70 DRAFT

— DO NOT CITE OR QUOTE al., 2004) and in pro

- inflammatory gene expression (Duncan et al., 2010). 1 Inflammation is observed in animal studies for Libby Amphibole asbestos and tremolite 2
and is relevant to h umans based on similar responses in other asbestos

- exposed cohorts analyzed 3 (Musk et al., 2008; Hein et al., 2007; Levin et al., 1998). Therefore, the evidence described 4 above suggests chronic inflammation is observed following Libby Amphibole and tremoli te 5 asbestos exposure; however, the role of inflammation in the non

- cancer health effects (e.g., 6 pleural thickening) following exposure to Libby Amphibole asbestos is unknown. 7
8
4.5.5.2. Cytotoxicity and Cellular Proliferation 9
The initial stages of any fibrotic response involve cellular proliferation, which may be 10 compensatory for cell death due to cytotoxicity. Analysis of cellular proliferation has 11 demonstrated both increases and decreases following exposure to asbestos fibers in vitro and in 12 vivo depending on the specific fiber or cell type (Mossman et al., 1985; Topping and Nettesheim, 13 1980). Other studies have focused on the activation of cell

- signaling pathways that lead to 14 cellular proliferation following exposure to asbestos (Zanella et al., 1996; Scapol i et al., 2004; 15 Shukla et al., 2003; Ding et al., 1999). 16 Although slightly increased compared to controls, cytotoxicity in murine macrophage 17 cells exposed to Libby Amphibole asbestos was decreased compared to other fiber types (Blake 18 et al., 2008). Cytot oxicity was slightly, but statistically significantly, increased compared to an 19 unexposed control at 24 hours post exposure to Libby Amphibole asbestos, while crocidolite 20 exposure resulted in even higher levels of cytotoxicity. No other in vitro study exa mined 21 cytotoxicity following exposure to Libby Amphibole asbestos, although an increase in apoptosis 22 was demonstrated in this same cell system (Blake et al., 2008). Recent studies in mice exposed 23 to Libby Amphibole asbestos demonstrated increased collagen deposition and collagen gene 24 expression, markers of fibrosis (Putnam et al., 2008; Smartt et al., 2009). Tremolite and Libby 25 Amphibole asbestos exposure led to increases in both fibrosis in all but one animal study, 26 supporting a role for proliferation in response to these fibers. Taken together with studies on 27 other asbestos fibers, these data suggest that a cytotoxicity and cell proliferation may play a role 28
in the non

- cancer health effects following exposure to Libby Amphibole asbestos

. 29
30 31 4.6. EVALUATION OF CARCINOGENICITY 32

4.6.1. Summary of Overall Weight of Evidence 33
Under the U.S. EPA Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) 34 Libby Amphibole asbestos is ―carcinogenic to humans‖ following inhalation exposure based on 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 71 DRAFT

— DO NOT CITE OR QUOTE epidemiologic evidence that shows a convincing association between exposure to Libby 1 Amphibole asbestos fibers and an increased incidence of lung cancer and mesothelioma and 2 subsequent mortality (McDonald et al., 1986a , 2004; Amandus and Wheeler, 1987; Sullivan, 3 2007, Larson et al., 2010b, Moolgavkar et al., 2010). These results are further supported by 4 animal studies that demonstrate the carcinogenic potential of Libby Amphibole asbestos fibers 5 and tremolite fibers in rodent bioassays. As a durable mineral fiber of respirable size, this 6 conclusion is consistent with the extensive published literature that documents the 7 carcinogenicity of amphibole fibers. 8
9
4.6.1.1. Synthesis of Human, Animal, and Other Supporting Evidence 10
Libby, MT workers have been the subject of multiple mortality studies demonstrating an 11 increased cancer mortality in relation to estimated fiber exposure. Occupational studies 12 conducted in the 1980s (Amandus and Wheeler, 1987; McDonald et al., 1986a) as well as the 13
extended follow

- up studies published in more recent years (Sullivan et al., 2007; McDonald et 14 al., 2004; Larson et al., 2010b ) and additional analyses of the extended follow

- up ( Moolgavkar et 15 al., 2010) provide evidence of an increased risk of lung cancer mortality and of mesothelioma 16 mortality among the w orkers exposed to Libby amphibole asbestos in the Libby vermiculite 17 mining and processing operations. Positive exposure

- response relationships are shown in each 18 study. Although a plateauing of the increased risk of lung cancer (relative risks of 1.4 and 1 .5) in 19 the two highest exposure categories (23.0 99 and ≥ 100 fibers/cc

- yrs) was seen in Sullivan 20 (2007). This type of pattern is commonly seen in occupational cohort mortality studies (Stayner 21 et al., 2003) and does not diminish the weight of th ese resul ts in supporting the determination of 22 the carcinogenicity of Libby Amphibole asbestos

. This association is also supported by the case 23 series of 11 mesothelioma patients among residents in or around Libby, M T and among family 24
members of workers in the mini ng operations (Whitehouse et al., 2008). 25 Although experimental data in animals and data on toxicity mechanisms are limited for 26 Libby Amphibole asbestos, t umors were observed in tissues similar to those in humans 27 (mesotheliomas, lung cancer) indicating th e existing data are consistent with the cancer effects 28
observed in humans exposed to Libby amphibole asbestos

. Smith (1978) reported increased 29
incidence of mesothelioma s in hamsters after intrapleural injections of Libby Amphibole 30
asbestos . Addition ally, studies in laboratory animals (rats and hamsters) exposed to tremolite via 31 inhalation ( Bernstein et al., 2005, 2003; Davis et al., 1985) , intrapleural injection ( Smith et al., 32 1979; Wagner et al., 1982; Davis et al., 1991, Roller et al., 1997, 1996) or im plantation (Stanton 33 et al., 1981) hav e shown increases in mesotheliomas and lung cancers. Tremolite from various 34 sources was used and varied in fiber content and in potency (Section 4.2.1.2)

. Although 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 72 DRAFT

— DO NOT CITE OR QUOTE McConnell et al. (1983a) observed no increase in carc inogenicity following oral exposure to 1 nonfibrous tremolite, the ability of this s tudy to inform the carcinogenic potential of fibrous 2 tremolite through inhalation is unclear, and these study results contribute little weight to the 3
evaluation of the carcin ogenicity of fibrous Libby Amphibole asbestos. 4 The available mechanistic information suggests Libby Amphibole asbestos induces 5 effects that may play a role in carcinogenicity (Section 4.3.2). Several in vitro studies have 6
demonstrated oxidative stress and genotoxicity following Libby Amphibole asbestos exposures 7 in various cell types (Blake et al., 2007; Pietruska et al., 2010; Hillegass et al., 2010; Duncan et 8 al. 2010). Libby Amphibole asbestos increased intracellular ROS in both murine macrophages 9
and h uman epithelial cells (Blake et al., 2007; Duncan et al., 2010). Additionally, s urface iron, 10 inflammatory marker gene expression and aneugenic micronuclei were increased following 11 exposure to Libby Amphibole asbestos in human epithelial cells (Duncan et a l., 2010; Pietruska 12 et al., 2010). Tremolite studies demonstrate cytotoxic and clastogenic effects (micronucleus 13 induction and chromosomal aberrations) of the fibers in various cell culture systems. 14 In summary, t h e epidemiologic data demonstrate an assoc iation between exposure to 15 Libby Amphibole asbestos and increased cancer incidence . Supporting evidence of 16 carcinogenicity was observed in laboratory animal studies exposed to Libby Amphibole asbestos 17

or tremolite follow ing multiple routes of exposure in multiple species (see Table 4

- 20

, 4

- 31 ). 18 Overall, the available evidence support s the conclusion that Libby Amphibole asbestos is 19 carcinogenic to humans. 20
21
4.6.2. Mode

– of

- Action Information 22

4.6.2.1. Description of the Mode

- of

- Action Information 23
EPA guidance provides a f ramework for analyzing the potential mode(s) of action by 24 which physical, chemical, and biological information is evaluated to identify key events in an 25 agent’s carcinogenicity (U.S. EPA, 2005). Agents can work through more than one mode of 26 action (MOA), and MOA can differ for various endpoints (e.g., lung cancer versus 27 mesothelioma). Reasonably, the analysis of a MOA would start with some knowledge of an 28 agent’s biological activity that leads to cellular transformation resulting in carcinogenicity. 29
Alth ough early steps in the process often can be identified, carcinogenicity is a complex process 30 resulting from multiple changes in cell function. Due to the limited data available specific to 31 Libby Amphibole asbestos, the mode of action of Libby Amphibole a sbestos for lung cancer and 32 mesothelioma following inhalation exposure is unknown

. 33 Research on various types of mineral fibers supports the role of multiple potential e ven ts 34 following exposure to asbestos in general (i.e., chronic inflammation, generation of reactive 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 73 DRAFT

— DO NOT CITE OR QUOTE oxygen species ( ROS ) , direct genotoxicity , and cytotoxicity and cellular proliferation ) in the 1 carcinogenic response to mineral fibers

. It is possible that these events are relevant to both the 2

non

- cancer and cancer effects following exposure to Libby Amphibole asbestos. However, 3 mechanistic information of these effects following exposure to Libby Amphibole asbestos and/or 4 tremolite is restricted to a limited number of laboratory animal and in vitro studies. These effects 5 are described below, including discussion of the data limitations for reaching a conclusion 6 regarding the mode of action for Libby Amphibole asbestos. Multiple key events for one 7 particular MOA have not been identified; therefore the mode of action for Libby Amphibole 8 asbestos carcinogenicity cannot be established. 9
10
4.6.2.1.1. Chronic inflammation 11

As described in Section 4.5.1.1, c hronic pulmonary inflammation has been observed 12 following inhalation exposure to fibers , which is often followed by fibrosis at the site of 13 inflammation if the fibers persist (Oberdorster, 1994). Chronic inflammation is hypothesized to 14 lead to a carcinogenic response through the production of reactive oxygen species and increased 15 cellular proliferation (Hannahan and Weinberg 2 011)

. Although limited, the data described in 16
Section 4.5.1.1 suggest a n increase in inflammatory response following exposure to Libby 17 Amphibole and tremolite asbestos similar to that observed for other durable mineral fibers 18 (reviewed in Mossman et al., 2 007). Whether this inflammatory response then leads to cancer is 19
unknown. Studies examining other types of asbestos (crocidolite, chrysotile, and amosite) have 20
demonstrated an increase in chronic inflammation as well as respiratory cancer related to 21

exposu re (reviewed in Kamp and Weitzman, 1999). Chronic inflammation has also been linked 22 to genotoxicity and mutagenicity following exposure to some particles and fibers (Driscoll et al., 23 1995, 1996, 1997). The evidence described above suggests chronic inflam mation is observed 24 following Libby Amphibole and tremolite asbestos exposure; however, the role of inflammation 25 and whether it leads to lung cancer or mesothelioma following exposure to Libby Amphibole 26
asbestos is unknown. 27
28
4.6.2.1.2. Reactive oxygen and nitrogen sp ecies production 29
Fiber exposure has been shown to lead to increases in ROS (reviewed in Kamp et al., 30 1992). Fibers can directly lead to the production of ROS by iron

- catalyzed generation through 31 the Fenton reaction. ROS are also produced following phagoc ytosis of fibers. ROS production 32 following exposure to asbestos has been shown to be associated with DNA damage, chronic 33 inflammation, and lipid peroxidation. As described in Section 4.3.2.1.1 , chronic inflammation 34 may lead to increased proliferation and DNA damage, which in turn may lead to tumor 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 74 DRAFT

— DO NOT CITE OR QUOTE formation. The hydroxyl radical produced as part of the ROS production following fiber 1

exposure has been shown to directly interact with DNA (Leanderson et al., 1988). 2 ROS production has been measured in res ponse to both Libby Amphibole asbestos and 3 tremolite exposure. The study of Libby Amphibole asbestos (Blake et al

., 2007) demonstrated 4 an increase in superoxide anion. Blake et al. (2007) also demonstrated that total superoxide 5
dismutase was inhibited fo llowing exposure to Libby Amphibole asbestos, along with a decrease 6 in intracellular glutathione , both of which are associated with increased levels of ROS . These 7 results are supported by a recent study in human mesothelial cells (Hillegass et al.

, 2010). 8
I ncreased ROS production was also observed in human airway epithelial cells following 9 exposure to Libby Amphibole asbestos (Duncan et al., 2010). This increase in ROS and decrease 10 in glutathione are a common effect following exposure to asbestos fibers and particulate matter. 11 Limited studies, however, have examined the specific type of ROS produced following exposure 12 to each type of asbestos. Although ROS production is relevant to humans, based on similar 13 human responses as compared to animals, informa tion on the specifics of ROS production 14 following exposure to Libby Amphibole asbestos is limited to the available data described here. 15 Therefore, the role of ROS production in lung cancer and mesothelioma following exposure to 16 Libby Amphibole asbestos is unknown. 17
18
4.6.2.1.3. Genotoxicity/mutagenicity 19
Genotoxicity and, more specifically, mutagenicity, are associated with tumor formation 20 through alterations in genetic material. Mutagenicity refers to a permanent effect on the 21 structure and/or amount of genetic mater ial that can lead to heri table changes i n function, while 22 genotoxicity is a broader term including all adverse effects on the genetic information (Eastmond 23 et al., 2009). Results of standard mutation assays like the Ames test, which analyze for point 24 mutations, have found asbestos and other mine ral fibers to be negative or only marginally 25 positive (Walker et al., 1992). Several other studies, however, have shown that asbestos 26 exposure can result in a variety of chromosomal alterations, which are briefly discussed below. 27 Genotoxicity following e xposure to asbestos fibers has been described as the result of two 28 distinct mechanisms, either ROS production leading to direct DNA damage, or physical 29 interference of mitosis by the fibers. For both DNA damage and mitotic interference, the fibers 30
must fi rst enter the cell. Some studies have shown that a direct interaction between fibers and 31 cellular receptors might also lead to increased ROS production. ROS production is likely to be a 32 key event in fiber

- induced direct DNA damage, as observed following exposure to other forms 33 of asbestos, while the indirect DNA damage requires fiber interaction with cellular components 34 (e.g., mitotic spindle, chromosomes). 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 75 DRAFT

— DO NOT CITE OR QUOTE One in vitro study examined genotoxicity of Libby Amphibole asbestos by measuring 1
DNA adduct format ion following exposure via murine macrophages (primary and immortalized) 2 (Blake et al., 2007). The data showed no increase in adduct formation as compared to unexposed 3 controls. A second study observed increases in micronuclei induction in both normal hu man 4 lung epithelial cells and XRCC1

- deficient cells for both Libby Amphibole and crocidolite 5 asbestos (Pietruska et al. 2010). Two studies of tremolite examined genotoxicity. The first 6 found no significant increase in revertants in the Ames assay (Athana siou et al., 1992), which is 7 similar to results obtained for other forms of asbestos. This study did find, however, that 8 tremolite exposure led to a dose

- dependent increase in chromosome number and micronuclei 9 formation, which has also been described for other asbestos fibers (as reviewed in Hei et al., 10 2007; Jaurand, 1999). Hei and colleagues (Okayasu et al., 1999) performed mutation analysis 11
with tremolite and found a dose

- dependent increase in mutations in CD59 in hamster hybrid 12 cells. Genotoxicity an alysis in humans, following exposure to Libby Amphibole asbestos or 13 tremolite, has not been measured , although other types of asbestos fibers have led to increases in 14 genotoxicity in primary cultures and lymphocytes (Dopp et al., 2005; Poser et al., 2004). In 15 general, these studies have examined genotoxicity with a focus on ROS production as a key 16 event. Although Libby Amphibole asbestos

- and tremolite

- specific data are limited to in vitro 17 studies, given the similarities in response to other forms of asbe stos, there is some evidence to 18 suggest genotoxicity following exposure to Libby Amphibole and tremolite asbestos. However, 19 the potential role of this genotoxicity in lung cancer or mesothelioma following exposure to 20 Libby Amphibole asbestos is unknown. 21
22
4.6.2.1.4. C ytotoxicity and cellular proliferation 23
The initial stages of tumorigenicity may be an increased cell ular proliferation at the site 24 of fiber deposition, which can increase the chance of cancer by increasing the population of 25 spontaneous mutations affording genotoxic effects an opportunity to multiply. Increased cell 26 proliferative regeneration is also a hallmark of tumor clonal expansion and generally occurs in 27 response to increased apoptosis. As described in Section 4.5.1.2, increased cytotoxicity and 28 cellular proliferation has been observed in in vitro studies following exposure to Libby 29 Amphibole asbestos and tremolite. Tremolite and Libby Amphibole asbesto s exposure led to 30 increases in both fibrosis and tumorigenicity in all but one animal study, supporting a possible 31 role for proliferation in response to these fibers. Taken together with studies on other asbestos 32 fibers, these data suggest that a cytotoxi city and cell proliferation may play a role in tumor 33
formation. Ho wever, there is limited data to demonstrate that increased cytotoxicity and cellular 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 76 DRAFT

— DO NOT CITE OR QUOTE proliferation following exposure to Libby Amphibole asbestos leads to lung cancer or 1
mesothelioma. 2
3
Summ ary. Data for Libby Amphibole asbestos and tremolite provide limited evidence that 4 suggest chronic inflammation, ROS, genotoxicity, and cytotoxicity and cellular proliferation may 5 play a role in the carcinogenicity of Libby Amphibole asbestos. However, da ta are inadequate to 6 establish how these effects may lead to lung cancer or mesothelioma following exposure to 7 Libby Amphibole asbestos. 8
9
4.6.2.2. Application of the Age

- Dependent Adjustment Factors (ADAFs) 10

As described above, the mode of action fo r Libby Amphibol e asbestos is unknown. T he 11 weight of evidence does not support a mutagenic mode of action for Libby Amphibole asbestos 12 carcinogenicity. Therefore, according to EPA’s Supplemental Guidance for Assessing 13 Susceptibility from Early

- Life Exposure to Carcinoge ns (U.S. EPA, 2005b), the application of 14 the Age

- Dependent Adjustment Factors (ADAFs) is not recommended. 15
16
4.7. SUSCEPTIBLE POPULATI ONS 17 Certain populations may be more susceptible to adverse health effects from exposure to 18 Libby Amphibole asbestos. Because the adverse health effects resulting from exposure to Libby 19 Amphibole asbestos have been, for the most part, studied in occupational c ohorts of adult white 20 men (see Section s 4.1.1 and 4.1.3), there is limited information on the effects to a broader 21 population. A few studies, however, have examined health effects resulting from 22

non

- occupational exposure in other age groups, in other gend ers (i.e., females), and in different 23 race or ethnicity groups. The data from these studies could inform whether any differential risk 24 exists for these groups (see Section s 4.1.2 and 4.1.4). However, it should be noted that the 25 ability to distinguish tru e differences from chance variation in effect estimates is related to the 26 sample size and statistical power, which, in most cases, is quite limited in these studies. In 27 addition, genetic polymorphisms, preexisting health conditions, and differences in nut ritional 28 status may alter an individual’s response to Libby Amphibole asbestos. Finally, coexposures to 29 other substances (e.g., tobacco smoke or particulate matter) may increase an individual’s risk of 30 adverse health effects from exposure to Libby Amphibo le asbestos. Where data are available, 31 each of these factors is discussed below with respect to increased susceptibility to non

- cancer 32
effects and cancer from exposure to Libby Amphibole asbestos, and where information specific 33 to Libby Amphibole asbestos is not available, the general literature on the toxicity of mineral 34 fibers is briefly referenced. 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 77 DRAFT

— DO NOT CITE OR QUOTE There are also factors which may influence one’s exposure potential to asbestos based on 1 lifestage or other defined population. For example, children spend more hours outside and may 2 engage in activities which impact exposure level compared to adults (NRC, 1993; US EPA 3 2006.) Since lifestage and activity patterns which increase exposure potential m a y increase the 4 potential for health effects from exposure, these factors define those who may be more 5 susceptible to health effects due to greater exposure. Section 2.3 discusses this exposure 6 potential, including how children workers, household contacts and residents may be exposed to 7 Libby amphibole asbestos. 8
9
4.7.1. Influence of Different Lifestages on Susceptibility 10
Individuals at different lifestages differ from one another physiologically, anatomically, 11 and biochemically. Individuals in early and later lifestages differ markedly from adulthood in 12 terms of body composition, organ function, and many other physiological parameters, which can 13 influence the toxicokinetics and toxicodynamics of chemicals and their metabolites in the body 14 (ILSI, 1992). This also holds true for mineral fibers, including asbestos fibers (see Section 3). 15 This section presents and evaluates the literature on how individuals in early or later lifestages 16 might respond differently and thus potentially be more susceptible to adverse health effects of 17 Libby Amphibole asbestos exposure. 18
19
4.7.1.1. Lifest age Susceptibility 20
Humans in early lifestages (i.e., conception through adolescence) can have unique 21 susceptibilities compared to those in later lifestages because they undergo rapid physiological 22 changes during critical periods of development (Selevan et al., 2000). Furthermore, they are 23 often exposed to xenobiotics via unique exposure pathways (i.e., transplacental tran sfer and 24 breast milk ingestion) (NRC, 1993; U.S. EPA, 2006, 2008). Although no data exist for Libby 25 amphibole asbestos, limited observat ions in stillborn infants, however, indicate transplacental 26 transfer of tremolite (Haque et al., 1996, 1998) and other asbestos and nonasbestos fibers (Haque 27 et al., 1991, 1992, 1996, 1998). Transplacental transfer of asbestos also has been demonstrated 28
i n animals following maternal exposure by gavage (Haque et al., 2001) or injection 29 (Cunningham and Pontefract, 1974; Haque and Vrazel, 1998) (see Section 3). These studies did 30 not evaluate sources or levels of exposure. Haque et al. (1992) hypothesized th at maternal health 31 conditions might influence the translocation of fibers, as some of the mothers had pre

- existing 32 health conditions. Based on these studies, Libby Amphibole asbestos fibers may be transferred 33 through the placenta, resulting in prenatal ex posure at any stage of fetal development. 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 78 DRAFT

— DO NOT CITE OR QUOTE Increased lung deposition of fibers in children compared with adults has been observed 1 (Asgharian et al., 2004; Bennett et al., 2008; Isaacs and Martonen, 2005; Oldham et al., 1997; 2 Phalen and Oldham, 2001; Phalen et al., 1985; Schiller

- Scotland et al., 1994). Nasal deposition 3 of particles was shown to be lower in children compared to adults

— particularly during exercise 4 (Becquemin et al., 1991) The lung and nasal depositional differences are due in part to structu ral 5 differences across lifestages, which can change the depositional pattern of different fiber sizes 6 and possibly alter the site of action and result in differential clearance and subsequent health 7 effects. It is unclear, however, whether the lung surfac e, body weight, inhalation volume, or 8 exposure patterns are most determinative of dose. One study reported that the ratio of lung 9 surface area to body weight does not differ considerably for a 10

- month

- old, a 9

- year

- old, and an 10 adult (Short, 1952). Anoth er study suggested that deposition of fine particles (2 μm MMAD, 11 which is in the size range of those for Libby Amphibole asbestos reported in Table 2

- 2) in the 12 lung is increased for overweight (≥95 th percentile body mass index [BMI]) children who breathe 13

m ore at rest compared to underweight children (<25 th percentile BMI) (Bennett and Zeman, 14 2004)

. 15
There are limited studies analyzing non

- cancer outcomes in children exposed to Libby 16 amphibole in the published literature. A Libby medical screening program c ollected data on 17 7,307 participants, including 600 children aged 10

- 17 years old, representing 8.2% of th e cohort 18 (Peipins et al., 2003). Pulmonary function tests showed that none of these children had moderate 19 or severely restricted lung function (ATSDR, 2001; 2002). This study also studied chest 20 radiographs for those 18 years old or older (ATSDR, 2001; Noonan et al., 2006; Peipins et al., 21 2003) , but were not given to children

. In addition, the prevalence of some self

- reported 22 respiratory symptoms among 10

- 29

- year old adolescents and young adults was associated with 23 certain exposure pathways. These participants were ≤ age 18 in 1990 when the mining/milling 24 operations closed (Vinikoor et al., 2010). A better understanding of the community health 25
effects and the examination of the potential progression of adverse health effect in this 26 community would benefit from additional research to establish the clinical significance of these 27 findings. Aside from these studies , no other studies of non

- cancer outcomes in early lifestages of 28 humans or experimental animals exposed to Libby amphibole have been reported in the 29 published literature. 30 For exposure to other types of asbestos, studies have reported non

- cancer outcomes in 31 early lifestages. Those in the very yo ung include reports of stillbirth (Haque et al., 1996, 1998) 32
and death among infant s (age 1

- 27 months) due to sudden infant death syndrome and 33 bronchopulmonary dysplasia (Haque and Kanz, 1988). These studies found higher levels of 34
asbestos in the lungs of those who died compared to controls. In the infant study, the authors 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 79 DRAFT

— DO NOT CITE OR QUOTE speculate that either there was a pre

- existing abnormal lung physiology in these children that 1 may contribute to a reduced ability to clear fibers from the lung, or that the children could have 2 an increased exposure to asbestos (Haque and Kanz, 1988)

. Those in older children include 3 reports of pleural and diaphragmatic calcifications (Epler et al., 1980) and altered immune and 4 respiratory conditions (Shtol et al., 2000). 5 In experime ntal animals, offspring of rats exposed to tremolite had decreased body 6 weight gain at weaning and 8

- weeks

- old compared to controls (McConnell et al., 1983a; NTP, 7
1990b ). This was also observed in some similar studies of other forms of asbestos (McConnell 8 et al., 1983a; NTP, 1985, 1988, 1990a, 1990b ) but not in others (McConnell et al., 1983b; NTP, 9
1983 ). Embryonic toxicity was observed in a few experimental animal studies. C rocidolite 10 injected into pregnant mice resulted in altered limb differentiation i n cultured embryos (Krowke 11 et al., 1983, abstract), and chrysotile in drinking water given to pregnant mice resulted in 12 decreased post

- implantation survival in cultured embryos (Schneider and Maurer, 1977); 13 however, pregnant mice exposed to chrysotile in d rinking water did not affect in vivo embryonic 14 survival (Schneider and Maurer, 1977). 15
I t is possible that early lifestage exposure may increase the risk of non

- cancer outcomes 16
in adulthood compared to adult exposure. For instance, o ne man exposed to Libby amphibole 17
when he was 18

- 19 years old was diagnosed with asbestosis at 65 years old (Wright et al., 2002). 18
After tremolite exposure during childhood , t here is one study of altered immunity in adulthood 19 (Zerva et al., 1989) and a ca se report of asbestosis i n adulthood (Voisin et al., 1994). Another 20 study also reported an increased risk of asbestosis in after childhood exposure to asbestos from 21 parental occupational exposure to asbestos (Kilburn et al., 1985). To address the potential for 22 increased susceptibility to cancer from early lifetime exposures, one needs to consider if there is 23 evidence of differential health effects such as increased potency from early lifetime exposure; 24 decreased latency based on the age of exposure or cance rs observed with early lifetime exposures 25 not seen with adult exposures. There are no published reports that can directly answer these 26 questions for exposure to Libby amphibole asbestos. 27 While cancers in adults have been documented following exposure to Libby Amphibole 28 asbestos, similar reports describing childhood cancers resulting from this exposure have not been 29 identified. Few cancers occurring in children have been documented in children exposed to any 30 form of asbestos. Examples of cases include a 17

- year

- old exposed to chrysotile and tremolite 31 (Andrion et al., 1994) and a 3

- year

- old exposed to chrysotile (Lieben and Pistawka, 1967), both 32 of whom developed mesothelioma. However, childhood mesothelioma, in particular, may have 33 an etiology that is d ifferent from that of the disease that is seen in adults (Cooper et al., 1989). 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 80 DRAFT

— DO NOT CITE OR QUOTE No cancer bioassays have been performed in juvenile animals exposed to Libby Amphibole 1
asbestos. 2 There are several case reports of individuals who were exposed to Libby amph ibole 3 asbestos as children who were later diagnosed with mesothelioma (Whitehouse et al., 2008). Of 4 the 11 cases described by Whitehouse et al. (2008), 2 reported potential exposure scenarios that 5 were limited to childhood, and both of these were diagnose d at a relatively young age at 6 diagnosis (48, compared with 52 to 82 years of age for the other 9 cases). Although these case 7 studies support the link between exposure to Libby amphibole asbestos and mesothelioma, it is 8 unclear if children are more suscep tible than adults. 9 Case reports of exposure to tremolite during childhood, and subsequent diagnosis of 10 mesothelioma in adulthood (Magee et al., 1986; Rey et al., 1993; Sakellariou et al., 1996; 11 Schneider et al., 1998; Senyigit et al., 2000), support the li mited data summarized above for 12 Libby Amphibole asbestos. Additional case studies of mesothelioma after childhood exposure to 13 other types of asbestos are available (Anderson et al., 1976; Ascoli et al., 2003; Cazzadori et al., 14 1992; Inase et al., 1991; Ka ne et al., 1990; Li et al., 1978, 1989; Magnani et al., 2001; 15 Martensson et al., 1984; Roguin et al., 1994; Rom et al., 2001; Schneider et al, 1995, 1996a, 16 1996b; Wagner et al., 1960; Wassermann et al., 1980; Yano et al., 2009). These studies, 17 however, do not clarify whether exposure during childhood yields different adverse health 18 effects compared with exposure during adulthood. 19 In experimental studies, the offspring of rats orally exposed to non

- fibrous tremolite did 20
not demonstrate an increase in tumors compared to controls (McConnell et al., 1983a; NTP, 21 1990b). Similar studies of other forms of asbestos did report an increase of various neoplasms in 22 the offspring (McConnell et al., 1983a, 1983b; NTP, 1985, 1988, 1990a), but an other study 23 reported none (NTP, 1983). 24 Studies of exposure to other types of asbestos have attempted to determine if exposure to 25 asbestos in early life results in an increased risk of developing cancer. An early study in the 26 United Kingdom described occupational exposure to chryso tile, crocidolite, and amosite for a 27 group of 900 women. First exposure from ages 15

– 24 years led to a higher relative mortality 28 risk for lung and pleural cancer compared with women who were first exposed at older ages 29 (SMR 30 based on 12 observed and 0.4 expected, SMR 8 based on 4 observed and 0.5 expected, 30 and SMR 6.7 based on 6 observed and 0.9 expected in the first exposure at ages 15

- 24, 25

- 34, 31 and ≥35 years, respectively) (Newhouse et al., 1972). A study by Hansen et al. (1998) in 32 Wittenoom, Western Australia examined 27 individuals diagnosed with mesothelioma who had 33 been environmentally exposed to crocidolite (i.e., residents of the town but not directly employed 34 in the area’s crocidolite mining and milling industry); 11 of these subjects were chil dren 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 81 DRAFT

— DO NOT CITE OR QUOTE <15 years old at the time of exposure. One

- third of all the subjects were less than 40 years old 1 when diagnosed, but the authors found no increase in mesothelioma mortality rates when 2 analyzed by age at first exposure. However, risk was significantl y increased based on time from 3 first exposure, duration of exposure, and cumulative exposure (Hansen et al., 1998). Additional 4 studies of this cohort found that the mesothelioma mortality rate was lower for those first 5 exposed (based on age residence in t he area began) to crocidolite at ages <15 years (n = 24; 6 mesothelioma mortality rate 47 per 100,000 person

- years) compared with those first exposed at 7 ages ≥15 years (n = 43; mesothelioma mortality rate 112 per 100,000 person

- years) (Reid et al., 8 2007). T he hazard ratio for age at first residential exposure of ≥15 years compared with 9
<15 years was 3.83 (95% CI 2.19, 6.71), adjusting for cumulative exposure, gender, and an 10 interaction term for gender and cumulative exposure. 11 Based on these very limited an d inconclusive studies on other forms of asbestos, no 12
conclusions can be drawn about differential risk of adverse health effects after early lifestage 13 exposure to Libby Amphibole asbestos compared to exposure during adulthood. It is unknown 14 whether early lifestage exposure compared to adult exposure increases susceptibility for adult 15 cancers, as measured by increased incidence, severity or disease progression, or decreased 16 latency. 17 Later lifestage is generally defined as ≥65 years old. Because pulmonary function 18 (volume and rate of breathing) decreases with age, increased deposition of fibers in the lung from 19 exposures in later lifestages is unlikely. Clearance of fibers from the lung might be reduced, 20 however, as older adults have a less effective coug h reflex and strength and the cilia are less able 21 to move mucus up and out of the airway (U.S. EPA, 2005). Older adults could be more 22 susceptible to the effects of Libby Amphibole asbestos due to the gradual age

- related decline in 23 physiological processes. Additionally, decreased immune function, increased genetic damage, 24 and decreased DNA repair capacity can result in increased susceptibility with age (U.S. EPA, 25 2005). These age

- associated alterations could decrease fiber

- induced DNA damage repair but 26
mi ght also reduce the incidence of fiber

- induced DNA damage due to decreased phagocytosis or 27 inflammation. Specific data pertaining to age

- varying effects of Libby amphibole asbestos on 28 these processes are not available. 29
Due to the natural increase of non

- c ancer health effects among older adults, it is likely 30 that older individuals exposed to Libby amphibole at some point in their lives will have elevated 31 rates of disease. Radiographic tests among those exposed to Libby amphibole show that o lder 32

age is one of the factors most associated with pleural or interstitial abnormalities ( Amandus et 33 al., 1987b; ATSDR, 2001; Horton et al., 2006; Lockey et al., 1984; McDonald et al., 1986b ; 34 Muravov et al., 2005; Peipins et al., 2003; Rohs et al., 2008 ). However, abnormal radiographs 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 82 DRAFT

— DO NOT CITE OR QUOTE are more common with age (Pinsky et al., 2006). In workers 65

- years

- old and over at the time of 1 screening, an increased risk of systemic autoimmune disease and rheumatoid arthritis was 2 observed among mine workers, as well as those who were exposed to asbestos in the military 3 (Noonan et al., 2006). However, systemic autoimmune disease, rheumatoid arthritis, and 4 systemic lupus erythematosus are more common in those aged 45

- 64 compared to those aged 17

- 5 44 years or those 65 years old and older (Noonan et al., 2006). 6
While these results show more non

- cancer effects among older individuals exposed to 7 Libby amphibole, the available studies do not assess the timing of the exposure in relation to 8 these outcomes. This data is necessary to eva luate if older individuals are more susceptible than 9 younger individuals. Therefore, no conclusions can be drawn about differential risk of non

10 cancer after later lifestage exposure to Libby amphibole compared to exposure earlier in life. 11
No studies asses sing the carcinogenic effect of exposures occurring in older age groups 12 are available for Libby Amphibole asbestos. It should be noted that observed health effects 13 among individuals exposed to Libby Amphibole asbestos are likely to increase with increasin g 14 age due to the long latency period for the exposure

- response for asbestos and lung cancer and 15
other chronic diseases. However t his type of observation would not directly address the question 16 of whether exposures at older ages have a stronger or weaker e ffect compared with exposures at 17

younger ages. 18
4.7.2. Influence of Gender on Susceptibility 19
A discussion of gender

- related differences in risk from asbestos exposure raises several 20 important issues, such as gender

- related differences in exposure patterns, phys iology, and 21
dose

- response (Smith, 2002). For example, nasal breathing filters out particles, and men tend to 22 breathe less through their nose during exercise than women do (Bennett et al., 2003). Bennett et 23 al. (1996) showed a gender difference in fractio nal deposition ( defined as the ratio of particles 24 not exhaled to total particles inhaled ) of particles 2 μm in mass median aerodynamic diameter. 25 This particle diameter is within the range of Libby Amphibole asbestos particles reported in 26
Table 2

- 2. This study found that, in general, women had a greater retention of particles 27 compared to men because men had higher ventilation rates compared to women; however, the 28 overall deposition rate was higher in the men (Bennett et al., 1996). 29 Most occupational stud ies for Libby Amphibole asbestos have examined the effects of 30 exposure only in men (Amandus and Wheeler, 1987; Amandus et al., 1987a, 1988; McDonald et 31 al., 1986a, 1986b, 2004; Sullivan, 2007; Moolkavkar et al., 2010 ). There is limited information 32 specifi cally on women exposed to Libby Amphibole asbestos. In the Libby, MT community 33 studies, no gender

- related trends in mortality due to lung or digestive cancer were observed 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 83 DRAFT

— DO NOT CITE OR QUOTE (ATSDR, 2000). These limited data do not provide a basis for drawing conclusions r egarding 1 gender

- related differences in adverse health effects from Libby Amphibole asbestos. 2
3
4.7.3. Influence of Race or Ethnicity on Susceptibility 4
Race and ethnicity often are used in medical and epidemiological studies to define 5 various groups of the populat ion. These categories could be surrogates for differences in 6 exposure (e.g., occupation, socioeconomics, behavior) or biology (e.g., physiology, genetics), in 7 which case these factors may play a role in susceptibility as well. Nasal structure and lung 8

ar chitecture can influence the depositional patterns for both particles and fibers. One study of 9
18 Caucasians (ages 8 to 30 years) and 14 African Americans (ages 8 to 25 years) reported 10 increased ventilation rates during exercise in the African Americans ( matched on sex, age, 11 height, and weight) (Cerny, 1987). Another study (11 Caucasians and 11 African Americans, 12 ages 18 to 31 years) reported decreased nasal deposition efficiency (for particle sizes of 1

– 2 μm, 13 which is in the range of those for Libby Amph ibole asbestos reported in Table 2

- 2) in African 14 Americans compared to Caucasians (Bennett and Zeman, 2005). Furthermore, nasal breathing 15 during exercise occurred less in Caucasians compared to African Americans in this study 16 (Bennett et al., 2003). 17
Of the occupational and residential studies for Libby Amphibole asbestos, the vast 18 majority of subjects with known race were white, precluding the ability to conduct an analysis of 19 racial and ethnicity

- related differences in the mortality risks within the Lib by worker cohort. In 20 a study of occupational exposure to chrysotile asbestos in a textile factor, lung cancer mortality 21 risk in relation to exposure was lower in non

- white males (0.84, 95% CI = 0.52

– 1.27) compared 22

to white males (2.34, 95% CI = 1.94

– 2.79) , although a statistically significant increase in SMR 23
was observed for non

- white males at high exposure levels (≥120 fiber

- years/mL) (Hein et al., 24 2007). This observed difference could be due to a lower prevalence of smoking among 25

non

- white compared with white males (Hein et al., 2007). 26
27
4.7.4. Influence of Genetic Polymorphisms on Susceptibility 28
XRCC1 is a DNA damage repair gene. A recent study demonstrated that 29
XRCC1

- deficient cells exposed to Libby Amphibole or crocidolite asbestos demonstrated 30
increased lev els of micronuclei induction (Pietruska et al., 2010). Two other studies examined 31 XRCC1 polymorphisms in relation to disease risk with other types of asbestos exposure. Zhao et 32 al. (2005) found no association between XRCC1 polymorphisms and asbestosis in 33
asbestos

- exposed workers. A study, by Dianzani et al. (2006), however, did find an association 34
between XRCC1 and asbestos

- induced lung disease in a population exposed to asbestos 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 84 DRAFT

— DO NOT CITE OR QUOTE pollution. Further work is necessary, with clear definitions of patient po pulations and their 1 exposure levels, so that these studies and others can be compared to determine if XRCC1 2 polymorphisms increase susceptibility to adverse health effects following exposure to Libby 3 amphibole asbestos. 4 SODs are free radical scavengers t hat dismutate superoxide anion to oxygen and 5 hydrogen peroxide. SODs are expressed in most cell types exposed to oxygen. Several common 6 forms of SODs occur and are named by the protein cofactor: copper/zinc, manganese, iron, or 7 nickel. A recent study ob served no significant alterations in levels of intracellular SOD 8 following a 3 hr exposure to Libby Amphibole asbestos in mice (Blake et al., 2007). Other 9 studies in humans and mice have examined SOD expression in relation to other types of asbestos 10

expos ure. Manganese superoxide dismutase (MnSOD) activity was elevated in biopsies of 11
human asbestos

- associated malignant mesothelioma, although no genotypic differences were 12 found to be related to this change in activity (Hirvonen et al., 2002). Other studie s have focused 13 on the role of extracellular superoxide dismutase (EcSOD) and asbestos

- induced pulmonary 14 disease (Fattman et al., 2006; Gao et al., 2008; Kliment et al., 2008; Tan et al., 2004). These 15 studies have suggested a protective effect of ECSOD, wi th mice that lack this form of SOD 16 having increased sensitivity to asbestos

- induced lung injury (Fattman et al., 2006). 17
No studies that examine the role of cell

- cycle control genes were found. Additionally, no 18 information on other genetic polymorphisms in relation to disease risk among those exposed to 19 Libby Amphibole asbestos was identified in the available literature. 20 Familial studies showing unusually high incidence of mesothelioma suggest that genetic 21 factors might play a role in the etiology of mesothelioma (Huncharek, 2002; Roushdy

- Hammady 22 et al., 2001; Ugolini et al., 2007). Whether a genetic factor or a common en vironmental element 23 leads to the similar responses in these families is difficult to determine. Increased interest in the 24 role of genetic factors in asbestos

- related health outcomes has led to several analytical studies on 25 specific genetic polymorphisms. A review of 24 published reports (19 studies) discusses the 26 current state of knowledge regarding genetic susceptibility associated with asbestos

- related 27 diseases (in particular, malignant pleural mesothelioma). Results from several studies 28
demonstrated a n association between asbestosis

- related diseases and GSTM1

- null 29 polymorphism, whereas results for other polymorphisms were conflicting (Neri et al., 2008). 30 Some polymorphisms discussed in Neri et al. (2008) are in genes for N

- acetyl

- transferase 2 31 (NAT2); glutathione

- s

- transferases (GSTs); SOD; CYP1A1, CYP2D6; neurofibromatous 2 32 (Nf2); p53; and XRCC1. Although occupational asbestos exposure was assessed, the type of 33 asbestos is generally unknown in these studies. 34This document is a draft for review purposes only and does not constitute Agency policy. 4

- 85 DRAFT

— DO NOT CITE OR QUOTE Limited animal studies have examined the role of genetic variations related to asbestos 1 exposure, including specific signaling pathways (Shukla et al., 2007), DNA damage repair (Lin 2 et al., 2000; Ni et al., 2000), and tumor suppressor genes (Kleymenova et al., 1997; Vaslet et al., 3 2002; Marsella et al., 1997). Genetic alterations of particular interest for mesothelioma include 4 those involved in tumor suppression (p53, Nf2) and oxidative stress (SOD, GSTs). Nf2 and p53 5 are frequently altered in mesotheliomas, but no consistent mutations have been found (Bianchi et 6 al., 1995; Cheng et al., 1999; Mayall et al., 1999). Alterations in expression of antioxidant 7 enzymes like SOD and GST in mesothelioma can yield cells more resistant to oxidative stress as 8 compared to normal cells due to increased antio xidant activity (Ramos

- Nino et al., 2002; 9 Rahman et al., 1999). 10
11
4.7.5. Influence of Health Status on Susceptibility 12
Preexisting health conditions could potentially alter the biological response to asbestos 13 exposure. Mesothelioma risk has been hypothesized to be related to immune impairment 14 (Bianchi and Bianchi, 2008) and simian virus 40 exposure in humans (Bocchetta et al., 2000; 15 Carbone et al., 2007; Cristaudo et al., 2005; Foddis et al., 2002; Mayall et al., 1999; Kroczynska 16 et al., 2006). Co

- exposure to as bestos and SV40 has been associated with p53

- related effects in 17 vitro (Mayall et al., 1999; Bocchetta et al., 2000; Foddis et al. 2002), and cell signaling 18 aberrations in vivo (Kroczynska et al., 2006; Cristaudo et al. 2005 ). However, the influence on 19

can cer risk is unknown, as these lines of research are not fully developed and have not been 20 applied specifically to Libby Amphibole asbestos. 21 Obesity can compromise inhalation exposure, as increased particle deposition in the lungs 22 of overweight children ( Bennett and Zeman, 2004) and adults (Graham et al., 1990) has been 23 observed. Individuals with respiratory diseases could have compromised lung function that 24 alters inhalation exposure to Libby Amphibole asbestos. For example, individuals with chronic 25
obs tructive pulmonary disease have increased inhalation volume (Phalen et al., 2006) and 26 increased fine particle deposition (Bennett et al., 1997; Kim and Kang, 1997; Phalen et al., 2006) 27 and retention (Regnis et al., 2000). Similarly, studies have reported an increase in coarse particle 28 (aerodynamic diameter >5 μm) deposition in individuals with cystic fibrosis (Brown et al., 2001; 29 Brown and Bennett, 2004). For people exposed to Libby Amphibole asbestos, an increased risk 30 for interstitial lung abnormalities was observed for those with a history of pneumonia (Peipins et 31 al., 2003). In another study, bronchial asthma was examined as a potential confounding variable 32
for asbestos

- related effects on pulmonary function, although no confounding was observed 33 (White house, 2004). 34
35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 86 DRAFT

— DO NOT CITE OR QUOTE 4.7.6. Influence of Lifestyle Factors on Susceptibility 1
No studies were identified that examined lifestyle factors specifically with respect to 2 Libby Amphibole asbestos. Lifestyle factors such as exercise, nutritional status, and smoking 3
habits could affect the biological effects of asbestos exposure through various mechanisms. For 4 example, those with more physically demanding jobs or those who regularly engage in vigorous 5 exercise might experience increased lung deposition from fine particles o r fibers compared to 6 those with a more sedentary lifestyle (Phalen et al., 2006; Becquemin et al., 1991). Randomized 7 controlled trials of vitamin supplementation (beta

- carotene and retinol) have been conducted for 8
asbestos

- related lung cancer, but results do not support a protective effect (Cullen et al., 2005) 9 The Libby occupational exposure studies (Amandus et al., 1987a, Amandus and Wheeler, 10 1987; Larson et al., 2010; McDonald et al., 2004; 2002; 1986a,b; Sullivan, 2007) did not 11 examine the potential i nteraction between smoking, Libby Amphibole asbestos exposure, and 12 lung cancer risk. However, epidemiological (Lee, 2001; Savastano et al., 2004; Reif and Heeren, 13 1999) and laboratory studies (Jaurand et al., 1983; Jung et al., 2000; Valavanidis et al., 1 996, 14 2009; Lohani et al., 2002) have examined the potential synergistic effect of asbestos and 15 cigarette smoke co

- exposure in lung cancer. A synergistic interaction between the ROS from 16 cigarette smoke and asbestos fibers might contribute to increases in lung cancer in asbestos mine 17 workers (Kamp et al., 1992; Jackson et al., 1987). Research has suggested that asbestos fibers 18 might also enhance the delivery of multiple carcinogens in cigarette smoke, and that cigarette 19 smoking decreases the clearance mech anisms in the lungs and could, therefore, lead to an 20 increase in fiber presence in the lungs (Nelson and Kelsey, 2002). For lung cancer, a synergistic 21 relationship between cigarette smoking and asbestos exposure has been demonstrated (Hammond 22 et al., 1979 ; Selikoff and Hammond, 1979, Wraith and Mengersen, 2008). Smoking likely 23 causes genetic alterations associated with lung cancer (Landi et al., 2008) that might increase the 24 carcinogenic risk from exposure to asbestos. Benzo(a)pyrene, a component of toba cco, also has 25 been observed to enhance the carcinogenic effects of asbestos (DiPaolo et al., 1983; Kimizuka et 26 al., 1987; Loli et al., 2004; Mossman et al., 1983, 1984; Reiss et al., 1983). 27
28
4.7.7. Susceptible Populations Summary 29
A very limited amount of informat ion is available on exposure to Libby Amphibole 30 asbestos early in life that could lead to increased risk of asbestos

- induced disease later in life. 31 Due to the long latency period of some diseases in relation to asbestos exposure in general, 32 however, adver se effects may be more likely to be observed with an increase in age. This 33 assumption requires further investigation. The number of women who have been occupationally 34 exposed to Libby Amphibole asbestos is very small, and health risks have not been evalu ated 35This document is a draft for review purposes only and does not constitute Agency policy. 4

- 87 DRAFT

— DO NOT CITE OR QUOTE specifically for this group. Differences between men and women in residential sources and types 1 of exposure (e.g., types of activities done in the household) also preclude the possibility of 2 drawing conclusions regarding the relative susceptibility o f women compared with men to health 3 effects of exposure to Libby Amphibole asbestos. Similarly, sufficient data are not available to 4 draw conclusions regarding racial or ethnic variation in susceptibility to diseases caused by 5 exposure to Libby Amphibole asbestos. In addition, the potential modifying effects of genetic 6 polymorphisms, pre

- existing health conditions, nutritional status, and other lifestyle factors have 7 not been studied, specifically as related to exposure of Libby amphibole asbestos and hea lth 8

outcomes. 95-1 5. EXPOSURE-RESPONSE ASSESSMENT

5.1. ORAL REFERENCE DOSE (RfD)

Data are unavailable to characterize the toxic eff ects of Libby Amphibole asbestos following oral exposure. Thus, an oral reference d ose is not derived.

5.2. INHALATION REFERENCE CONCENTRATION (RfC) 5.2.1. Choice of Principal Study and Critical Effe ct
Studies in humans have shown several health effect s on the lung and pleura (a thin tissue surrounding the lung and lining the chest cavity) s uch 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 suitabl e for derivation of an RfC since the available animal studies lack adequate dose-response informat ion and are of a short-term duration.

Table 5-1. Summary of rationale for identifying can didate principal studies on Libby Amphibole asbestos for RfC development.

Attribute Preferred Characteristics for Candidate Principal S tudies 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 expec ted to address chronic exposure scenarios (potentially including both continuous ex posure 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 asbesto s risk. It is consistent with the expectation that toxic responses will reflect accum ulative effect of asbestos inhaled and deposited in tissues over time as opposed to sh orter term patterns of exposure to asbestos concentrations.

Data on exposure concentrations and durations are p referred to address associations with time since first exposure (TSFE), peak exposur e, or average intensity.

Relatively lower exposure intensities which may rep resent 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 adequat e 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 ad equately addresses the relevant sources of potential confounding, including age, se x, 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 measur e 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 c hanges over time), however appropriate group based exposure estimates may also be relevant.
Measurement of effect(s) Emphasis is placed on the more sensitive health out come endpoints that are available (e.g, generally morbidity findings will be more sen sitive 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 Lab our 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 analyse s based on estimates including assignment of quantitative exposure estimates to distinguish expo sure levels in the study subjects. The evidence for effects on the lung and pleura in huma ns includes both cohort studies of exposed workers as well as population studies drawn from th e Libby community (including former workers). Pleural and lung anomalies have been rep orted 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, t hese morbidity studies based on community/worker populations were not considered as candidate principal studies because quantitative exposure estimates are not available a nd the studies cannot support derivation of an RfC. Increased respiratory symptoms and bloody ph legm have also been associated with the number of potential pathways of exposure to Libby A mphibole asbestos in the community among individuals under 18 years of age (Vinikoor e t al., 2010). Additionally, two studies examined autoimmune effects in community members li ving 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 canno t be used for RfC derivation. Five cohort mortality studies of Libby miners ident ified 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 studi es 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 studi es. 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 (Amandus5-3

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 repo rted a higher prevalence of adverse effects following the longer time from first exposure. The se four studies, all of which demonstrate an association between Libby Amphibole asbestos exposu re and increased risk of effects on the lung and pleura were considered for selection as th e 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 r eporting 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 fibro sis) in exposed individuals based on guidelines from the International Labour Organizati on (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 deta iled information for each study.
These candidate studies were then evaluated in term s of quality attributes that would support their use as a principal study in derivatio n of an RfC. In selecting among potential candidate principal studies, there were several fac tors, summarized in Table 5-1, that were
considered.5-4Table 5-2. Summary of candidate principal studieson Libby Amphibole asbestos for RfC derivation.Cohort andReferenceStudy PopulationOutcome assessmentRadiographic endpointsevaluatedExposure assessmentExposure characteristicsLibby Worker CohortMcDonald et al.,1986b244 employees, comprising164 “current” workers (as ofJuly 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 byArmstrong et al., 1988)Radiographs taken attime of cohort assembly(1983)Films independently readby 3 experienced readersusing 1980 ILOstandardsFilm quality:Good: 56%Fair: 36%Poor: 7%Unreadable: 0.4%1) Parenchymal changes(small opacities≥1/0)2) Pleural changes (Pleuralthickening of chest wall,Pleural calcification)Individual work histories andexposure levels for specificwork locations were used toestimate cumulativeexposures for cohortmembers.1935-1967: Exposureestimated based onprofessional judgment. Formill locations only (1950-1967) exposure estimatedusing dust to fiberconversion and interviewswith plant employees1968-1982: Air samplesanalyzed for fibers by PCManalysisMean cumulative exposure“current” 40.1 f/cc-yr“past “ 118.9 f/cc-yrExposure Categories:<10 f/cc-yrs (n=92)10-20f/cc-yrs (n=64)20-100 f/cc-yrs (n=53)100-200 f/cc-yrs (n=16)>200 f/cc-yrs (n=19)Amandus et al.,1987b184 men employed 1975-1982, with at least 5 yearsjob tenureMean (SD), years:Age at exam: 44 (12)Job tenure: 14 (8)Company radiographsSource year:1981-82 (72.8%)1976-1980 (26.6%)<1975 (1 worker)Films independently readby 3 certified B-readersusing 1980 ILOstandardsFilm quality (by reader):Acceptable: 60.9, 60.9,29.3%Poor: 16.3, 14.7, 22.8%Unreadable: None1) Parenchymal changes(small opacities≥1/0)2) Pleural changes (Pleuralthickening at any sitewithoug costophrenic angleobliteration, pleuralcalcification, pleuralthickening of chest wallonly)Individual work histories andexposure levels for specificwork locations were used toestimate cumulativeexposures for cohortmembers.1935-1967: Exposureestimated based onprofessional judgment. Formill locations only (1950-1967), exposure estimatedusing dust to fiberconversion and interviewswith plant employees.Exposure Categories:0-15 f/cc-yrs (n=63)16-30 f/cc-yrs (n=29)31-85 f/cc-yrs (n=44)>86 f/cc-yrs (n=48)5-51968-1982: Air samplesanalyzed for fibers by PCManalysisOM Scott plant Cohort, Marysville, OH*Lockey et al.,1984512 plant employeesMean (range), years:Age at exam: 37.5 (19-66)Mean (SE), years:Job tenure by exposuregroup 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 chestradiographs taken in1980Films independently readby 2 certified B-readersusing modification of1971 ILO standards1) Parenchymal changes(only one small opacityrecorded (grade 1/1),unclear if opacities graded1/0 or 0/1 would have beenreported)2) Pleural changes(Discrete pleural plaque,Pleural thickening, Pleuralcalcification)3) costophrenic angleblunting onlyIndividual work histories andexposure levels for specificwork locations were used toestimate cumulativeexposures for cohortmembers.1957-1971: Exposureestimated based oninterviews with plantemployees and post-1972 airmeasurements. Someworkplace exposure controlmeasures were taken prior to1972.1972-1980: Air samplesanalyzed for fibers by PCManalysis. Some jobs do nothave measurementsassociated with themespeciallyprior to industrialhygiene measures.Exposure Categories:< 1 f/cc-yrs (n=247)1-10 f/cc-yrs (n=190)10-20f/cc-yrs (n=42)Rohs et al., 2008280 plant employees(follow-up of cohortdescribed in Lockey et al.,1984)Mean (SD), range (years):Age: 59.1 (10.5), 44-87Mean (SD), median (years):Years since first exposureNo pleural changes (n=200):32.1 (5.5), 31.0Posterior-anterior chestradiographs taken 2002-2005Films independently readby 3 certified B-readersusing 2000 ILOstandards7 employees hadunreadable films and arenot included in the1) Parenchymal changes(small opacities, profusionscore >1/0)2) Pleural changes(Localized pleuralthickening {thickeningexcluding costophrenicangle blunting}, Diffusepleural thickening{thickening withcostophrenic angleExposure assessment fromLockey et al. (1984) withchange in start date to 1963.Exposure Categories:0.005-0.24 f/cc-yrs (n=70)0.25-0.74 f/cc-yrs (n=72)0.75-1.91 f/cc-yrs (n=68)1.92-19.03 f/cc-yrs (n=70)5-6Pleural changes present(n=80): 36.8 (4.9), 37.9cohort of 280participantsblunting}, Pleuralcalcification)*In addition to the exposure information used by Lockey et al. (1984) and Rohs et al. (2008), the University of Cincinnati augmented and refined these exposureestimates using additional exposure data, which included industrial hygiene measurements not previously available and estimates of exposure after 2000.5-7

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 worke rs. Since both groups utilized the same exposure measurements and employment records, simil ar but not identical job exposure matrices (JEMs) and subsequent exposure estimates, were deve loped. For example, different numbers of occupation locations were used, and different appro aches 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 e mployed by the company at the time of the study, and among former male employees still re siding in the area. Logistic regression analysis was used to examine the relationship betwe en cumulative Libby Amphibole asbestos exposure and radiographic abnormalities, controllin g for age and smoking. Positive exposure response relationships were found with cumulative e xposure 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 contra st 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, prevalenc e 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 relatio nship between cumulative Libby Amphibole asbestos exposure and radiographic abnormalities, c ontrolling for age; a separate model was constructed restricting to current and former smoke rs for comparison. A positive exposure response relationship was found for cumulative expo sure and asbestosis. Multivariate analyses did not find a significant relationship between cum ulative 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, 198 5; Lockey et al., 1984). The first measurements of in-plant exposure began in 1972, an d earlier exposures were estimated assuming the mean fiber value for a given departmen t, for the years in which fiber measurements became available, as late as 1980 for some tasks (L ockey, 1980). To examine the association between cumulative Libby Amphibole asbestos exposur e 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 Lo ckey et al. (1984). Data collection took place in 2002-2005. Some of the original participants we re 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 Lib by Amphibole asbestos exposure and pleural changes was assessed using logistic regression; sev eral 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 cumulativ e Libby Amphibole asbestos exposure (in fibers-yr/cc), rather than mean or peak exposur e. However, there were differences in exposure intensity. In contrast to vermiculite fac ility workers in Libby, MT, the workers at the OM Scott Plant in Marysville, OH, were generally ex posed at lower levels (see Table 5-2), and were primarily exposed in the workplace. Because o f showering and changing into civilian clothes at the end of the work shift for most emplo yees, non-occupational exposure in the Marysville workers was minimal. Despite the uncerta inty in the magnitude of pre-1972 exposures (discussed below), the available data ind icate 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 ex posure 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 ar e uncertainties inherent in exposure-response data and extrapolating from the high intensity occu pation exposures to lower level exposures often seen in community and environmental exposures

.

5.2.1.2. Evaluation of Study Populations in Candidate Studi es The candidate principal studies differed in the stu dy populations, in terms of follow-up time, study participation, and available informatio n. In comparing the studies of Libby miners, an advantage of the McDonald et al. (1986b) study i s the inclusion of both current and former workers—these former workers likely have longer tim e since first exposure and may have higher exposure intensities and durations compared to curr ent workers. However, there is a potential for differential participation. All current employ ees were included in the McDonald et al. (1986b) study, while 80 of 110 eligible former empl oyees agreed to provide chest radiographs. Although exact participation rates are not given, A mandus et al. (1987b) report that radiographs were unavailable for >50% of workers who were emplo yed <5 years, or who ceased employment prior to 1975. Among Marysville workers, there wer e 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 pa rticipants, the prevalence of radiographic5-9

changes may be under- or overestimated. Rohs et al . (2008) state that employees hired in 1973 or earlier (when exposure estimates were more uncer tain) were more likely to participate compared to employees hired after 1973, and while t he 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 stu dy by Lockey et al. (1984). In terms of information available for statistical analysis, bot h studies of Libby miners and the follow-up study by Rohs et al. (2008), used age rather than t ime since first exposure (TSFE) in logistic regression models. Lockey et al. (1984) matched ca ses and non-cases on age. TSFE is preferable, since it gives more information about l atency between exposure and outcome development; while age and TSFE are related, they a re not perfectly correlated, in that there is a range of ages for a given date of hire. Both studi es of Libby miners also evaluated smoking as a potential confounder of the association between Lib by Amphibole asbestos exposure and radiographic changes. Lockey et al. (1984) only ma tched on age, but the follow-up examination by Rohs et al. (2008) included information on sever al important covariates including gender, prior exposure to asbestos, body mass index (BMI) a nd smoking history.

5.2.1.3. Evaluation of Exposure Assessment in Candidate Stud ies The two studies in Libby miners (McDonald et al., 1 986b; Amandus et al., 1987b) used similar exposure estimation, based on the same fibe r measurements and work records. However, there is considerable uncertainty in exposure estim ates in earlier years. Exposure during this time was estimated using dust to fiber conversion a nd information from employee interviews. McDonald et al. (1986b) used a factor of 4.6 for du st to fiber conversion, while Amandus et al. (1987a) used a factor of 4. However, Table V in Am andus et al. (1987a) showed that different comparisons of dust versus fiber levels in the Libb y 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 v ermiculite facility workers hired from 1935 to 1959. Without knowledge of the job categor y, the same exposure concentration was assigned to most of these workers, resulting in par ticularly 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. Mem bers 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 be en community exposures to Libby Amphibole asbestos which are not captured in occupa tional-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 M arysville cohort (Lockey, 1985; Rohs et al., 2008). Fiber concentration measuremen ts 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. EP A collaborated with a research team at the University of Cincinnati to update the exposure rec onstruction 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 av ailable. As discussed in detail in Appendix F, exposure estimates for each worker in the Marysvill e 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 concent rations in Marysville facility Trionizing is a term used in the Marysville facilit y and includes unloading of rail cars containing ve rmiculite ore (track), using conveyers to move the vermiculite or e into the expander furnaces, separation of the exp anded vermiculite from sand, blending in of lawn care che micals, and drying and packaging of the final produ ct.

Using available data on the year of hire and the de partments in which each person worked, the authors estimated the cumulative exposu re (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 YearConcentration (PCM f/cc)

Trionizing

Plant Maintanance
Central Maintainance Bkg Measured Estimated5-11

• Date and age at time of health examination (1980 an d/or 2000s)

• Occurrence of adverse effects at the time of examin ation, including presence or absence of a) localized pleural thickening, b) diffuse pleu ral thickening, c) parenchymal change. Pleural and parenchymal changes were detected by co nventional radiographs

• Date of start and end of employment

• Exposure to Libby Amphibole asbestos in the Marysvi lle facility by year

• Presence or absence of known exposure to asbestos a t other locations

The updated exposure reconstruction utilized 914 in dustrial hygiene measurements for fibers, which represented a substantial increase fr om the 180 industrial hygiene measurements for fibers available in Lockey et al. (1984) and Ro hs 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 afte r 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 st art of exposure was based on interviews with plant personnel. The exposure reconstruction proce dure took into account each worker’s job history, including extensive overtime for some work ers, and is described in detail in Appendix F.

5.2.1.4. Evaluation of Outcome Assessment in Candidate Studi es In all four candidate studies, outcomes were assess ed using chest radiographs independently evaluated by ≥ 1 reader. However, there were differences in the s tandards used for evaluation of radiographic changes, as well as timi ng and quality of the radiographs. The two studies in Libby miners (McDonald et al., 1986b; Am andus et al., 1987b) used similar outcome assessment procedures, with radiographs evaluated b y 3 readers according to 1980 ILO
standards. Two different sets of standards were us ed to evaluate radiographs in the Marysville cohort. The first study used modified 1971 ILO sta ndards (modifications not stipulated) (Lockey et al., 1984), while the follow-up study used the u pdated 2000 ILO standards (Rohs et al., 2008). Radiograph quality may also impact outcome assessme nt. In McDonald et al. (1986b), which used radiographs taken in 1983 specifically f or the study, 7% of films were classed as poor quality (some technical defect impairing the p neumoconiosis classification) and 0.4% as unreadable. Amandus et al. (1987b), which used com pany radiographs taken over a wide time period (<1975 to 1982) report that the proportion o f films rated as poor ranged from 14.7% to 22.8% depending on the reader. In the Marysville c ohort, radiographic quality was better— Lockey et al. (1984) state that “radiographs that c ould not be interpreted because of poor quality5-12

were repeated” while Rohs et al. (2008) note that o nly 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 st udies 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 worker s (Rohs et al., 2008; Lockey et al., 1984), were not preferred due to the higher levels of expo sure (particularly for earlier years, when only dust level measurements were available and when exp osure 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., 2 008 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 exposu re 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 he alth effect), information on more covariates, use of more recent 2000 ILO diagnostic criteria, an d 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 exp osed individuals in the Marysville cohort (Rohs et al., 2008; Lockey et al., 1984) inc luded increased prevalence of pleural thickening (characterized as either discrete pleura l thickening or diffuse pleural thickening) and parenchymal changes (small opacities). These effec ts were determined using conventional radiographs (Rohs et al., 2008). In clinical setti ngs there are difficulties in distinguishing between these two lesions in part due to lack of ra diographic film diagnostic specificity as well as the ILO definitions for diffuse pleural thickeni ng of the visceral pleura which may result in some variability in reporting of discrete pleural p laques. There are two classifications for pleural thickening in current guidelines (ILO, 2000). Diff use pleural thickening is currently only determined where there is thickening along signific ant 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 at tempted to distinguish between thickening of the parietal pleura and diffuse thickening of the v isceral 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 d efined 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 the5-13

chest wall impacted, however these data are not alw ays provided in study reports, therefore regardless of extent of the pleural thickening, onl y presence or absence may be noted. The radiographic changes occur in different anatomi cal locations. Localized pleural thickening may include plaques in the parietal pleu ra, or thickening of the visceral pleura, where this thickening does not impact the costophrenic an gle (angle between the diaphragm and chest wall as viewed on a radiograph). Diffuse pleural t hickening is designated when thickening of the visceral pleura extends to blunt/obliterate the cos tophrenic angle. Pleural thickening does not progress to parenchymal changes. The data on the r adiographic changes are presented in Table 5-3, Figure 5-2 and Figure 5-3. For the purposes o f illustration, the data are presented in groups of 50 individuals each, representing approximate qu intiles after exclusion of workers with other previous asbestos exposure. Figures 5-2 and 5-3 pr esent the cumulative prevalence of radiographic changes; Figure 5-2 shows the mean cum ulative human equivalent exposure expressed on a normal scale, while in Figure 5-3 ex posure is expressed on a log scale to provide a better visual image at the low end of the distrib ution.

Table 5-3. Mean cumulative human equivalent exposu res and prevalence of health outcomes found in Marysville workers (Rohs et al., 2008)
Mean Cumulative HumanEquivalent 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 vers us mean cumulative human equivalent

exposure

Figure 5-3. Prevalence of radiographic change vers us 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 14Prevalence

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 100Prevalence

Log Mean Cumulative Human Equivalent Exposure (fibersyr/cc) Discrete Pleural

Diffuse Pleural Parencyhmal5-15

EPA selected localized pleural thickening (characte rized 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 Amphi bole asbestos. Pleural thickening in general is associated with de creased pulmonary function (Miller et al., 1994, Wang et al., 2001 and Petrovic et al., 2 004) 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 mark er of mineral fiber exposures, and the most sensitive of the endpoints detected by standard rad iographs, both in terms of exposure levels and time. The available information is largely in agre ement that where multiple and bilateral parietal plaques are observed that this is a distinctive mar ker 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 biolo gical effect of mineral fiber exposure. The radiographic classification of localized pleura l thickening may include both parietal plaques and diffuse visceral thickening (without co stophrenic angle involvement) (ILO, 2000). These lesions are distinct and may contribute indep endently to observed health effects. Parietal plaques are known to induce chronic constricting ch est pain which increases in severity as the extent of the plaques increases. Pleural thickenin g in general is associated with reduced lung function parameters with increased effect correlati ng 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 dem onstrated in several studies where pleural thickening without costophrenic angle involvement h as been studied (Broderick et al., 1992; Kilburn and Warshaw, 1991; Garcia-Closas et al., 19 95). Although there is some evidence from HRTC studies that parietal plaques alone may not di rectly impair lung function, the studies were small, and may not have included severe plaques. T here 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 classi fication of localized pleural thickening (ILO, 2000) defines radiographic anomalies which are asso ciated with chronic chest pain, decreased lung volume and decreased measures of lung function

.5-16

Figure 5-4. HRCT Scan of an individual with localiz ed 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 reconstru ction was the measured or estimated concentration of Libby Amphibole asbestos in air (f ibers/cc) of each department from 1957- 2000. The distribution of exposure by department i s summarized in Figure 5-1. Using available data on the year of hire and the departments in whi ch each person worked, the cumulative exposure (fibers-yrs/cc) for each worker for each y ear 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 repres ent 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 wer e incorporated in this analysis. These
calculations are somewhat more involved than the us ual conversions to equivalent continuous exposure concentrations that EPA makes in the analy sis of occupational studies. Conversions for non-cancer effects are usually made using an adjust ment factor of 240 days/365 days x 10 m 3 /205-17

m 3 (U.S. EPA 1994). However, the adjustment factor i n 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 (Append ix F). The estimated CHEEC was used to represent Libby Amphibole asbestos exposure in a ll 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. H owever, 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 cal culated as cumulative exposure (fibersyr/cc). It is anticipated that there is some biological lat ency period between exposure to Libby Amphibole asbestos and development of localized ple ural 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 req uires some time to develop to the state that it can be detected on a conventional chest x-ray, expo sures 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 lag ged 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 wer e 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 ar e designated as CE0, CE5, CE10, CE15, and CE20. Looking at prevalence of the outcome for var ious 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) stud ies was used for exposure-response modeling. To avoid any bias from previous occupational exposu re to asbestos, only the data from those who did not report any previous occupational exposu re to asbestos were used. The data from Lockey et al. (1984) and Rohs et al. (2008) were co mbined for the full cohort to provide a greater range in time from first exposure. Outcome assessm ents, i.e. chest x-rays, were performed at two different time points, 1980 and 2002-2005. Whi le the evaluation approaches were generally similar (independent readings by 3 certified B-read ers), it is important to note that x-ray readings were performed by different individuals, under a di fferent reading protocol in 1980 (modified 1971 ILO standards) compared to 2000s (2000 ILO sta ndards), leading to some uncertainty in statistical analyses that combine these datasets. An additional consideration is body composition—in some cases, difficulty in distinguis hing fat pads from true pleural thickening5-18

may lead to misclassification of the outcome. BMI measurements are available for the latter study but not for the 1980 evaluation; the effect o f BMI was investigated and is discussed below. The consensus read of the x-rays from three B-reade rs was used in both studies. Because the ILO criteria were updated in 2000, the reader f orms 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-r ay or film quality was found that would reduce confidence in the Lockey et al. (1984) data. The full data set of the exposure-response relation ship for localized pleural thickening
was as follows:
1. The data from Lockey et al., (1984), n = 513 and Ro hs et al. (2008), n = 280 were combined, n = 793
2. All workers who reported exposure to asbestos at ot her 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 localiz ed 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 thi ckening in Lockey et al. and also in Rohs et al., the 1984 study data were retained. On e worker was positive in 1984 and negative in 2008. The 2008 study data were retaine d 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 observati on. Because the concentration of Libby Amphibole in workplace air was estimated rather tha n measured for all years prior to 1972, this data set was stratified into two sub-sets: 1) work ers 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 ca ses 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 FIRS T 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 als o examined in 1980. Note that there were originall y 513 individuals in the Lockey et al. (1984) cohort; of these, 77 had previous asbestos exposure and were e xcluded (n=436). Two individuals were excluded because the ir x-ray date was the same as their employment star t date (n=434). These exclusions are also reflected in th e Roh et al. (2008) cohort.

Source: Rohs et al. (2008) and Lockey et al. (1984)

The best measured exposure data are considered to b e 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 2002-2 005 examination, and who began work in 1972 or later (12/119 cases) (Rohs et al., 2008), w as chosen as the preferred analysis to develop a point of departure (POD) for localized pleural thic kening to serve as the basis for the RfC. Additionally, sample POD estimates based on statist ical 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 sens itive endpoint for exposure to Libby Amphibole asbestos was identified as localized pleu ral thickening. Epidemiologic methods were used to analyze the exposure-response data and benc hmark 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 empiric al representation of the data. A recommended model form is then determined; commonly this is the model with the best fit as5-20

measured by Akaike’s Information Criterion (AIC) va lue among these model forms judged to provide an appropriate and statistically adequate r epresentation 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 respo nse (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 anal yses were performed using SAS® statistical software v. 9.1. BMCLs were obtained by the profil e likelihood method as recommended by Crump and Howe (1985) using the NLMIXED procedure i n 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 repo rted 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) re port 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 repor ted 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 stu dies have reported an estimated prevalence of 2.3% (Miller, 1996; Bohnker, 2005). Based on th ese 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 preval ence 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 ass umption 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 as bestos 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 ex posure and health outcome results are available and a more comprehensive analysis is poss ible. 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 select ion Dichotomous statistical models describing the proba bility of individual response as a function of cumulative exposure (represented by CHE EC 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 pot ential covariates with risk of localized pleural5-21

thickening, controlling for Libby Amphibole asbesto s exposure. Covariates considered for inclusion in the model were: time since first expos ure (T), age at x-ray, gender, smoking history and BMI. This initial modeling was done using a st andard logistic regression model, as is commonly applied in analysis of epidemiological dat a. The base model was a logistic regression model with cumulative Libby Amphibole asbestos expo sure (natural log transformed) as the independent variable. This model provided an adequ ate fit to the data (Hosmer-Lemeshow pvalue of 0.7073), and the exposure variable was sta tistically significantly associated with the outcome (beta [SE] = 0.5398 [0.2374] increase in lo g 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 stat istical 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 wit h 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 Li bby 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 natu ral logarithm transformation), probit (with
CHEEC considered as continuous and continuous with a natural logarithm transformation), 3- parameter log-logistic, dichotomous Hill, and dicho tomous Michaelis-Menten models (with only CHEEC for the latter three models). These are stat istical models used to evaluate dichotomous data, and were considered appropriate here given th e supralinear nature of the observed relationship between Libby Amphibole asbestos expos ure 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-L emeshow test (a form of the Pearson chisquared goodness-of-fit statistic). Models were co mpared 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 lo west AIC, BMC 10 and BMCL 10 estimates were similar (within a factor of 3 of the values fr om 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 1 5 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 w hen using the 20-year lagged exposure (AIC of 77.00). In addition, there were cases of locali zed pleural thickening in the full cohort with5-22

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-yea r lagged exposure had a p-value for fit of 0.74 and an AIC value of 75.1, and the expos ure variable was statistically significantly associated with odds of localized pleural thickenin g (beta [SE]=0.6524 [0.2741] per fibers-yr/cc, p-value=0.0189) (Figure 5-5). This model yielded a BMC 10 of 0.2543 fibers-yr/cc and a corresponding BMCL 10 of 0.0757 fibers-yr/cc for a 10% increase in preva lence of localized pleural thickening. This BMCL 10 of 0.0757 fibers-yr/cc is the preferred POD estima te to support development of an RfC for Libby Amphibole asbestos.5-23Table 5-5. Candidate models for association betweencumulative Libby Amphibole asbestos exposure in the Marysville subcohort and localized pleural thickening.Model Exposure Metric Form AIC HosmerLemeshowGOF p-valueBMD BMDLLogistic CHEEC P(CE)=1/[1+exp(-a-b*CE)] 78.3 0.74420.5715 0.2547Logistic ln(CHEEC) P(CE)=1/[1+exp(-a-b*ln(CE))] 76.2 0.7073 0.2490 0.0558Probit model CHEEC P(CE)=Φ(a+b*CE) 77.9 0.7726 1.5267 00.9185Probit model ln(CHEEC) P(CE)=Φ(a+b*ln(CE)) 76.6 0.6650 0.2276 0.04723-parameterlog-logisticln(CHEEC P(CE)=bkg+(1 – bkg) / [1 + exp(-a –b*ln(CE))]75.7 0.7339 0.3447 0.0956CE, lag 575.3 0.5519 0.2980 0.0887CE, lag 10*75.1 0.6833 0.2543 0.0757CE, lag 1575.4 0.4226 0.1993 0.0536CE, lag 207777.0 0.6363 0.1258 0.0196DichotomousHill†ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE^b /[exp(-a)+CE^b]77.7 0.6724 0.3295 0.0961MichaelisMenten±ln(CHEEC) P(CE) = bkg + (Plateau – bkg)*CE /[exp(-a)+CE]75.8 0.5413 0.3330 0.13425-24

1

2 3 Figure 5-5. Graph of observed prevalence of localized pleural t hickening and estimated 4 probability of localized pleural thickening calcula ted using the 3-parameter log-logistic 5 model with 10-year lagged exposure. 6
7 5.2.3. RfC Derivation—Including Application of Unc ertainty Factors (UFs) 8 The derivation of the RfC from the morbidity studie s of the Marysville cohort (Rohs et 9 al., 2008; Lockey et al., 1984) was calculated from a POD (BMCL 10 ) of 0.076 fibers-yr/cc for 10 localized pleural thickening by adjusting to 70 yea rs of exposure, lagged by 10 years (non11 occupational, lifetime exposure), and dividing by a total uncertainty factor of 100. The RfC is 1 12
x 10 -5 fibers/cc for continuous exposure (rounded to one significant digit) was derived as 13
follows: 14
15 RfC = POD ÷ UF 16 RfC = POD ADJ ÷ UF 17 RfC = [0.076 fibers-yr/cc (1/(70-10 years)] ÷ 100 18 RfC = 1.2 x 10-5 fibers/cc, rounded to 1 x 10-5 fi bers/cc 19
20 0

0.1 0.2 0.3 0.4 0.5 0.6

0 1 2 3 4 5 6Risk of localized pleural thickening

CHEEC (fiber-yr/cc) Prevalence by quartiles of unlagged exposure among

cases Estimated risk by 10-year lagged exposure5-25

A total uncertainty factor of 100 was applied in th e derivation of the RfC. The rationale 1 for the individual uncertainty factors are below. 2 An UF value of 1 was applied for extrapolation from animals to humans (interspecies 3 extrapolation, UF A ) because the critical effect used as the basis for the RfC was observed in 4
humans. 5 An UF value of 10 was applied for interindividual v ariability to account for human-to6 human variability in susceptibility (intraspecies e xtrapolation, UF H ) in the absence of 7 quantitative information to assess the toxicokineti cs and toxicodynamics of Libby Amphibole 8 asbestos in humans. Only adults sufficiently healt hy for full time employment were included in 9 the principal study; and the study population was p rimarily male. 10 An UF value of 1 was applied for LOAEL-to-NOAEL ext rapolation (UF L ) because the 11 current approach is to address this factor as one o f the considerations in selecting a BMR for 12 modeling. In this case, a BMR of 10% increase in t he incidence of localized pleural thickening 13 was selected under an assumption that it represents a minimally biologically significant change. 14 An UF value of 1 was applied for extrapolation to chronic exposure (subchronic to 15 chronic exposure extrapolation, UF S ) as the exposure in the facility was long term (fr om 16 approximately 13 weeks to more than 44 years, with more than 90% of the workers having a 17 duration of exposure of more than 6 years). 18 An UF value of 10 was applied to account for defic iencies in the database (UF D ) based on 19 lack of data on effects other than in the respirato ry system, limitations of the principal study 20 design, other observed effects (cardiovascular dise ase and autoimmune effects) that have not 21 been well-studied, and uncertainties in time from f irst exposure. 22 Lack of data on effects other than in the respirato ry system : There are no data in 23 laboratory animals or humans on general systemic ef fects, developmental and reproductive 24 effects, neurotoxicity, or developmental neurotoxic ity for Libby Amphibole asbestos. The lack 25 of studies could reflect reasonable judgments made by some (ATSDR, 2001; ATS, 2004) that 26 other effects are not expected at exposures that wo uld be protective of effects in the respiratory 27 tract or that general systemic effects would not be expected. However, it is known that inhaled 28 asbestos fibers migrate out of the lung and into ot her tissues lending uncertainty to any 29 assumptions that other effects would not be expecte d. 30 Limitations of principal study design : The human study used to derive the POD was 31 conducted in a relatively small population of worke rs with no previous occupational exposure to 32 asbestos with a large range in duration of exposure (from approximately 13 weeks to more than 33 44 years); although, the study is sufficient to det ect a 10% increase in the prevalence of an 34 adverse effect. The study was conducted more than 20 years after the facility stopped using ore 355-26

from Libby. However, the study used a cross-sectio nal design and may be negatively biased 1
since individuals with more severe disease could ha ve left employment or may have died and not 2 been included in the follow-up study resulting in a n underestimation of toxicity. Additionally, it 3 is known that high-resolution computer tomography ( HRCT) can identify mineral fiber-related 4 lesions in the respiratory tract, which cannot be i dentified by standard radiographs. Thus the 5 technology employed for determining the prevalence of radiographic changes in the Marysville 6 cohort may underestimate the actual prevalence of l ocalized pleural thickening. 7 Other observed effects that have not been well-stud ied but for which there is evidence of 8 association with exposure to Libby Amphibole asbest os: Two studies have found a possible 9 increased prevalence of autoimmune disease and biol ogical markers for autoimmune disease in 10 Libby residents (Pfau et al., 2005 Noonan et al., 2 006) although these studies do not indicate 11
whether the autoimmune effects would be observed at exposures lower than that observed for 12 localized pleural thickening. Subsequent animal st udies have indicated that exposure to Libby 13 amphibole asbestos does induce autoantibodies in mi ce (Blake et al., 2008). It is unknown if 14 autoimmune effects are secondary to the chronic inf lammatory response expected from exposure 15 to mineral fibers. However, one study of individua ls in a community exposed to tremolite found 16 changes in immune parameters in exposed individuals without localized pleural thickening and 17 that additional immune markers, including autoantib odies, increased in individuals with localized 18 pleural thickening (Zerva et al., 1989). A mortali ty analysis for the Libby worker cohort also 19 found associations between occupational exposures t o Libby Amphibole asbestos and mortality 20 due to cardiovascular disease (Larson et al., 2010a ). 21 Time from first exposure : The data for the sub-cohort of workers exposed post 1972 22 allowed for assessing prevalence of localized pleur al thickening approximately 30 years after 23 first exposure. However, there is evidence to indi cate that the prevalence of localized pleural 24 thickening is likely to continue to increase more t han 30 years after first exposure (Paris, 2009, 25 2008; Jakobsson et al., 1995; Hillerdal 1994; Ehrli ch et al., 1992; Järvholm, B, 1992; Lilis et al. 26 1991; Merchant, 1990; McDonald et al 1986b). There fore, a POD established at 30 years time 27 from first exposure may underestimate the actual pr evalence of localized pleural thickening 70 28 years from first exposure. Since the full-cohort m odel includes time from first exposure as a 29 variable, one approach to address this uncertainty is to use this model to extrapolate to later time 30 points. This modeling shows a non-linear increase in prevalence of localized pleural thickening 31 with time from first exposure. However, this appro ach is problematic because there are no data 32 available with time from first exposure greater tha n 47 years in the cohort. Therefore, while the 33 likelihood of increased prevalence of localized ple ural thickening beyond 30 years is supported 34 by statistical analysis of the full cohort, this mo deling does not allow accurate estimation of the 355-27

size of such an effect. Thus, the use of a POD est ablished 30 years from first exposure does 1 represent uncertainty with respect to assessing the potential effects for a lifetime of exposure. 2 The RfC was developed based on fiber counts employi ng PCM following NIOSH 3 counting rules where the minimum detectable fiber d iameter is 0.25 μm, fiber length is > 5 μm 4 and the aspect ratio is ≥ 3. For use at other sites, the fiber dimensions a t the site can be attained 5 using PCM or TEM where they are reported as phase c ontrast microscope equivalents [PCMe]. 6 The RfC is for continuous lifetime exposure (24 hou rs/day, 365 days/year, with exposure 7 beginning at birth and continuing for 70 years). S ee Table 5-6 for guidance on using the RfC for 8 other exposure scenarios—the RfCs in this table are estimated using the POD divided by the 9 total uncertainty factor of 100, pro-rated over a r ange of exposure durations. The RfC can be 10 applied at other sites where vermiculite ore from L ibby, MT was used. 11
12 TABLE 5-6. Exposure concentrations (RfCs) for vari ous exposure durations* 13

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 *Because the presence of localized pleural thickeni ng is not dependent on time from first exposure in the model 14 used, the RfC is a linear function of duration of e xposure. The equation is RfC D = POD/(Duration x UF). The POD 15 is 0.076 fibers-cc/yr, duration is years and exclud es any exposure after age 60, and UF is 100. 16 17 18 5.2.4. Alternative Analyses of the Full Marysville Cohort 19 Modeling of the full cohort was also conducted util izing the full dataset for localized 20 pleural thickening from the Marysville cohort. Sin ce the full cohort includes data combined 21 from Lockey et al. (1984) and Rohs et al. (2008), t here were individuals who had more than one 22 observation. As noted in Section 5.2.2.2, for thos e workers x-rayed in both 1980 (Lockey et al., 235-28

1984) and 20042-2005 (Rohs et al., 2008), one of th e observations was excluded so that there are 1 no repeat observations for individual workers. 2 Time from first exposure to x-ray (T) is an importa nt variable in understanding the full 3 Marysville dataset, as can be seen by the much high er prevalence of localized pleural thickening 4 in the 2000s compared to the 1980 assessment, an in crease which cannot be fully explained by 5 the increases in cumulative exposure occurring with continued exposure. Consequently, in 6 looking at the full cohort, T is a strong predictor of localized pleural thickening. In contrast, for 7 the sub-cohort analysis above, T is not predictive of localized pleural thickening risk when 8 CHEEC is in the model; one reason is that in the su b-cohort the range of T is much smaller and 9
all the values of T well exceed the minimal time si nce first exposure until the observation of 10 thickening of about 10 years suggested by the data (23.15-32.62 years); in addition, there is less 11 measurement error in the exposure for this group. One consideration in interpreting these results 12 is that T does not represent time from first exposu re to an event (or a detectable event), rather it 13 is time from first exposure to the detection of an event. As such, it cannot be construed as an 14 indicator of biological latency. Secondly, there i s an element of time encompassed in the 15 cumulative exposure metric—CHEEC reflects duration of exposure as well as intensity. 16 Therefore, in some sense adding the T variable is ‘ double-counting’ time. Finally, given the 17 occurrence of higher exposures in earlier years (i. e., for individuals having large values of T), 18 there is confounding between inference about the qu antitative effect of time since first exposure 19 and estimated cumulative exposure. 20 A similar approach as described in Section 5.2.2.3 was used to evaluate candidate models 21 for the full cohort. Details are provided in Appen dix E. However, since time since first 22 exposure (T) was an important covariate for these a nalyses, further efforts were needed to 23 develop a model incorporating T along with exposure . The logistic and probit models including 24 CHEEC as a continuous exposure had inadequate model fit as evaluated using the Hosmer25 Lemeshow test (p-values of 0.003 for both) and so w ere not considered for further analysis. The 26 remaining candidate models (logistic and probit wit h the natural logarithm of CHEEC, 3- 27 parameter log-logistic, dichotomous Hill, and dicho tomous Michaelis-Menten) had adequate fit. 28 Among these models, the AIC values ranged from 327. 9 (Michaelis-Menten) to 346.8 (logistic 29 with the natural logarithm of CHEEC). Based on the se results, the Michaelis-Menten model was 30 selected for further evaluation and different appro aches were investigated to represent T along 31 with cumulative Libby Amphibole asbestos exposure u sing this model form. 32 The approach taken to incorporate T was through mod ification of the plateau term in the 33 Michaelis-Menten model to allow the plateau for the exposure-response relationship to change 34 for different values of T. After investigating var ious forms for the plateau (described in 355-29

Appendix E), the lowest AIC resulted when the plate au term took the form: Plateau = 1 Background + (1-background)* Φ (T|m,s), where Φ (T|m,s) represents the cumulative normal 2 probability distribution function. Different expos ure lags were then investigated for this 3 model—as seen for the sub-cohort, the AIC values we re quite similar for lags of 0-15 years 4 (AICs ranging from 277.72 to 278.04. However, the 20-year lagged exposure had an increased 5 AIC of 280.60 and was not judged an appropriate cho ice. The 10-year lagged exposure resulted 6 in the lowest AIC, and was thus chosen as the prefe rred exposure metric. In order to estimate a 7
BMC 10 and corresponding BMCL 10 for this model form, a fixed value of T must be sp ecified. 8 A first analysis need for this model is to provide results based on the full cohort for 9 comparison with the estimated BMC 10 and BMCL 10 values developed above for the sub-cohort 10 with exposure data starting in 1972 (Section 5.1.2. 3.1). For this need, a value of T=30 years was 11 selected in order to provide a suitable comparison to modeling results for the sub-cohort, where 12 the mean time since first exposure was 28 years. T he BMC 10 was 0.1384 fibers-yr/cc, with a 13 corresponding BMCL 10 of 0.0541 fibers-yr/cc. These values, and particu larly the BMCL 10 , are 14 in the same range as those estimated using the pref erred analysis with the sub-cohort (BMC 10 15
and BMCL 10 of 0.2543 and 0.0757 fibers-yr/cc, respectively). This analysis thus provides 16 confirmation of the reasonableness of the modeling results developed using the sub-cohort 17
database. 18 One alternative analysis using the full cohort mode l, with a time since first exposure 19 value of T=40 years was conducted. A BMCL 10 of 0.0134 fibers-yr/cc was calculated with the 20
Cumulative Normal Michaelis-Menten model. The BMCL 10 with T = 40 years is used because it 21 is near the upper end of the range of T values avai lable in the data set (Tmax = 47.375 years). 22 This POD combined with a lag time of 5 years (used because Larson et al. (2010b) showed that 23 discrete pleural thickening could be observed much earlier than previously thought) and a total 24 uncertainty factor of 100 was used to derive an alt ernative RfC of 3.8 x 10 -6 fibers/cc or rounding 25 to one significant digit, 4 x 10 -6 fibers/cc. See Appendix E for details. This alte rnative RfC is an 26 order of magnitude lower compared to both the prefe rred sub-cohort analysis and the full cohort 27 analysis with a fixed T of 30 years. 28 Another alternative analysis based on projection o f risks using the full cohort model for a 29 “lifetime” time since first exposure of T = 70 year s. Note that none of the workers had a T>50 30 years; therefore this modeling represents a statist ical extrapolation beyond available data. A 31
BMCL 10 of 0.0041 fibers-yr/cc was calculated with the Cum ulative Normal Michaelis-Menten 32 model. This POD combined with a lag time of 5 year s and a total uncertainty factor of 30 was 33
used to derive an alternative RfC of 2.1 x 10 -6 fibers/cc or rounding to one significant digit, 2 x 34
10 -6 fibers/cc. See Appendix E for details. 355-30

Each of the candidate PODs (analyses from both the sub-cohort and full cohort) has 1 strengths and weaknesses. A major strength of the preferred analysis (Marysville sub-cohort) is 2 that by limiting the data set to those individuals hired in 1972 or later, the exposure 3 reconstruction relies only on data supported by ind ustrial hygiene measurements in the facility. 4 The exposures were also lower after 1972 as compare d to previous years. However, this 5 approach reduces the number of individuals in the d ata set from 434 to 119 and reduces the 6 number of cases from 61 to 12. In addition this ap proach narrows the range in the time from first 7 exposure to 23.15 to 32.65 years. The analyses of the full cohort have the strength of using all of 8 the data available on the Marysville cohort and of using a model that incorporates both 9 cumulative exposure and time from first exposure. One weakness of the full cohort analyses is 10 that the exposure reconstruction relies on estimate s of the exposure conditions in the Marysville 11 facility before industrial hygiene data were availa ble in 1972. 12 Because of the greater certainty in the exposure re construction in the sub-cohort based on 13 the data set where industrial hygiene data were col lected in the facility (that is, workers hired in 14
1972 and later and examined for health effects in 2 004), the RfC of 1 x 10 -5 fibers/cc for Libby 15 Amphibole asbestos is based on the preferred sub-co hort analysis. 16 17 5.2.5. Previous RfC Derivation 18 There is no previous RfC derivation for Libby Amph ibole asbestos. 19
20
5.3. UNCERTAINTIES IN THE INHALATION REFERENCE CON CENTRATION 21 (RfC) 22 Uncertainty in the exposure reconstruction 23 As in all epidemiological studies there are uncert ainties in the exposure reconstruction. 24 In this case there is some uncertainty in the emplo yment history and some individuals had 25 extensive overtime work. Employment history was se lf reported during interviews with each 26 individual for the original study (Lockey et al., 1 984) and errors in this process could affect 27 assigned Libby Amphibole asbestos exposure estimate s. As stated previously, exposure 28 measurements started in the Marysville plant in 197 2; exposures prior to this time were estimated 29 by University of Cincinnati scientists, based on fo cus group interviews with 15 long-term former 30 workers. Exposure estimates for the period prior t o 1972 thus can be considered as semi31 quantitative rather than directly based on industri al hygiene data. The University of Cincinnati 32 analysis assumed that early exposure levels in the plant are twice those measured in 1972 33 (Appendix F). The greater uncertainty of the pre-1 972 exposure estimates led to EPA’s decision 34 to focus the analysis on the post-1972 group of wor kers rather than the full cohort. Although it is 35 generally true that the use of more data is an adva ntage for statistical analyses because it allows 365-31

for the computation of more statistically precise e ffect estimates, this increased precision may be 1 offset by a negative impact on the accuracy of the effect estimate if an increase in sample size is 2 accompanied by greater exposure misclassification o r other biases. 3 While the uncertainties related to a lack of quanti tative measurements are not relevant to 4 the sub-cohort analysis, it is important to recogni ze that exposure assessment post-1972 also has 5 some limitations. The main source of uncertainty i s the lack of exposure measurements for 6 certain occupations/tasks (including polyform, ware house, blender, feeder, resin, screen/mill, and 7 track jobs) before industrial hygiene improvements that started in 1974 (Appendix F; Lockey, 8 1980). For some of these occupations, post-1974 me asurements are quite high, suggesting that 9 industrial hygiene measures were not immediately im plemented. 10 There is uncertainty when the Libby ore was first u sed in the facility. Company records 11 indicated that the date was between 1957 and 1960, and the University of Cincinnati used the 12 best available information from focus group intervi ews to assign the first usage of Libby ore in 13 1959 (see Appendix C). There is also uncertainty i n the post-1972 data regarding asbestos 14 content in other ore sources (Virginia, South Carol ina and South Africa). Libby Amphibole 15 asbestos was not used in the facility after 1980. However, industrial hygiene measurements 16
collected after 1980 showed low levels of fibers in the facility. PCM analysis does not determine 17 the mineral/chemical make-up of the fiber, and thus cannot distinguish between different kinds 18 of asbestos. Analysis of the bulk ores from Virgin ia and South Africa showed the presence of 19 only a few or no amphibole fibers. No information is available to evaluate the nature of the 20 fibers in these post 1980 samples. Lockey (1980, F igure 2, Tables 8-9) shows contradictory 21
results on asbestos content in ore from Africa and asbestos content in South Carolina material 22 comparable to Libby; recent retesting by the Univer sity of Cincinnati indicates that levels of 23 these other ores were present at levels just above the limit of detection (Appendix C). University 24
of Cincinnati was not able to obtain South Carolina material, but this material was used only for 25 a relatively short time. 26 Accordingly, post-1980 exposure was included in the calculation of cumulative exposure 27
to fibers and assumes that these fibers cause the s ame health effect. However, because the 28 concentration of fibers in the workplace was near b ackground after 1980, this exposure makes 29 only a small contribution to an individual’s cumula tive exposure estimate. 30 There was potential co-exposure to other chemicals in the Marysville facility. These 31 other chemicals were used after expansion of vermic ulite ore in another area of the facility. 32 Industrial hygiene data showed very low levels of f ibers in these areas. In addition, none of 33 these chemicals are volatile. The most likely rout e of exposure to these chemicals is through 345-32

dermal contact. It is unlikely that any co-exposur e to these particular chemicals would alter the 1 exposure-response relationship of Libby Amphibole a sbestos in the respiratory system. 2 The University of Cincinnati Research Team assumed that there was no exposure to 3 Libby Amphibole outside of the workplace. The inte rviews with the Marysville workers 4 revealed that only about 10% of the workers reporte d bringing raw vermiculite home. These 5 interviews also revealed that change to street clot hes from work-supplied coveralls was standard 6 practice at the end of the shift and approximately 64% of the workers showered before leaving 7 the work place. For these workers it is likely tha t additional exposure outside the workplace was 8 minimal. However, for the remainder of the workers it is reasonable to assume that additional 9 exposure could have occurred at home. Additional d ata collected by the University of Cincinnati 10 Research Team document that no increased prevalence of pleural or parenchymal change 11 consistent with asbestos exposure has been observed in household contacts of the workers from 12 the Marysville facility (J. Lockey, University of C incinnati, personal communication to Robert 13 Benson, EPA). 14
15 Uncertainty in the radiographic assessment of local ized pleural thickening 16 There is some uncertainty associated with ascertain ment of the health outcome. 17 Conventional radiographs were used to determine the health outcome. Discrete pleural 18 thickening may be difficult to detect, particularly if the affected areas are small in size, and fat 19 deposits or other anatomical features (musculature, fractures) may be mistaken for pleural 20 plaques (Gilmartin 1979). Thus, outcome misclassif ication may be present in the Marysville 21 data. For example, one of the workers had a positi ve X-ray at 1980 scan, but a negative X-ray at 22 2000s scan (excluding this worker from the analysis did not change results). However, 23 uncertainty in the prevalence of localized pleural thickening in each individual is considered 24 minimal due to the use of a team of highly qualifie d chest radiologists evaluating the 25 radiographic films and the use of consensus diagnos is. 26 Body mass index (BMI) was investigated as a potenti al explanatory variable because fat 27 pads can sometimes be misdiagnosed as pleural thick ening. BMI was not measured in the 1980 28 examination, but was available for most participant s of the 2000s examination. To address 29 whether fat deposits may affect outcome classificat ion, EPA considered the effect of adding BMI 30 as a covariate in the model. However, BMI did not display an association with odds of localized 31 pleural thickening in this population (See Appendix E). While these covariates were not 32 associated with risk of localized pleural thickenin g in the sub-cohort after adjusting for exposure, 33 it was not possible to evaluate this relationship i n the full cohort. In general, smoking rates have 345-33

declined between 1980 and 2000s and BMIs have proba bly increased, so one can’t necessarily 1 assume the relationships will be the same for the t wo examination periods. 2 In terms of the technology used for outcome assessm ent, high-resolution computed 3 tomography (HRCT) can detect mineral fiber-related lesions in the respiratory tract, which 4 cannot be identified by standard radiographs. Thus the technology employed for determining the 5 prevalence of radiographic changes in the Marysvill e cohort may underestimate the actual 6 prevalence of localized pleural thickening. 7
8 Uncertainty due to time from first exposure 9 There is some uncertainty associated with the lengt h of follow-up of the Marysville 10 cohort. The observed range of time since first exp osure to x-ray in the full cohort is 0.4-47 11 years, and 23.2-32.7 years in the recommended sub-c ohort. It is anticipated that the prevalence 12 of localized pleural thickening in the study popula tion – and in the post 1972 exposure cohort – 13 may continue to show some increase with passage of time. In this case, the modeling approach 14 may not accurately reflect the exposure-response re lationship that would be seen with a longer 15 follow-up time. However, a recent study by Larson et al. (2010b) examined serial radiographs 16 conducted on a group of Libby vermiculite workers w ith pleural or parenchymal changes. They 17 found that among those workers with localized pleur al thickening, all cases were identified 18 within 30 years, and that the median time from hire to the first detection of localized pleural 19 thickening was 8.6 years. Although the workers inc luded in the Marysville sub-cohort had 20 generally higher exposures and earlier start dates (mostly prior to 1972) compared to the full 21 worker population, these findings indicate that the range of follow-up time in the sub-cohort is 22 likely sufficient to support the dose response mode ling developed in this assessment. Note that 23 the likelihood that prevalence of localized pleural thickening may further beyond 30 years after 24 first exposure is a principal rationale cited for t he selection of a database UF of 10 in this 25

assessment. 26 Modeling of the full cohort (see Appendix E) showe d that time from first exposure was 27 the most significant explanatory variable. However , modeling of the sub-cohort hired in 1972 or 28 later showed that time from first exposure was not a significant explanatory variable. The range 29 of time from first exposure in the sub-cohort is mu ch shorter (23 – 32.65 years versus 0.42 – 30 47.38 years). Therefore, it is likely that the pre valence of localized pleural thickening would 31
increase if the individuals hired in 1972 or later were examined in the future. 32
33 Uncertainty in background rate of localized pleural thickening 345-34

In the derivation of the RfC, a background rate of 1% for localized pleural thickening was 1 used. However, there is a range of estimates (0.02 % to 3.9%) of prevalence of localized pleural 2 thickening in studies looking at the background rat e in populations not known to be 3 occupationally exposed to asbestos. In populations where efforts were made to exclude 4 individuals with prior exposure to asbestos, rates of 1.2% (4/326) and 0.2% (3/1422) were 5 reported by Anderson et al. (1979) and by Castellan et al. (1985). Higher rates of localized 6 pleural thickening are reported from cross sectiona l studies of the general population and 7 individuals with military service. Using chest x-r ays collected in NHANES I (1971-1975), 8 Rogan et al. (1987) reported a background rate of 1 .2%; a similar assessment using chest x-rays 9 collected in NHANES II (1976-1980) found a backgrou nd rate of 3.9% (Rogan et al. 2000). 10 Miller et al. (1996) reported a background rate of 2.3% in inpatients and outpatients at a 11 Veteran’s Administration medical center, while Bohn ker et al. (2005) reported a background rate 12 of 2.3% based on data collected from the U.S. Navy Asbestos Medical Surveillance Program. 13 One reason estimated background rates vary in the p ublished literature is due to differing age 14 structure of the study populations, as localized pl eural thickening prevalence in influenced by 15 age. The mean age of the Marysville sub-cohort at the time of x-ray is 52 years (median is 50 16 years). However, in statistical modeling of the Ma rysville sub-cohort, uncertainty in the 17 background rate of localized pleural thickening is very low. Both the fixed and estimated values 18 are in the range of estimates above and the differe nce in the POD when the background rate is 19 fixed at 1% versus when it is estimated (estimated background rate of 3.76%) is less than 3%. 20 As discussed in Section 4.1, there is a range of es timates (0.2% to 3.9%) in the background rate 21 of localized pleural thickening. In the modeling, the background rate was set at 1% and not 22 estimated. This is expected to have an insignifica nt effect on the value of the RfC. 23
24 Uncertainty in Model Functional Form and Lagged Exp osure 25 A number of model forms were explored in the initia l stages of analysis (Appendix E), 26 before selecting the log-logistic model. The log-l ogistic model is widely used for epidemiologic 27 data, and is easily interpreted. In this applicati on the ratio of the BMC to the BMCL 28 (0.2543/0.0757 = 3.4) was reasonable given the size of the available dataset, indicating 29 acceptable statistical precision in the BMC estimat e. In addition, BMCs and BMCLs estimated 30 from other candidate models for the post 1972 expos ure sub-cohort were in a similar range to the 31 selected model. Finally, the complementary analys is with the full cohort (utilizing a 30 year 32 time since first exposure value which was selected to be consistent with time since first exposure 33 values within the sub-cohort) provided similar resu lts to the sub-cohort analysis. A second 34 model-based uncertainty is the choice of lag for cu mulative exposure. The RfC derivation is 355-35

based on the exposure lagged by 10 years, since thi s lag yielded the lowest AIC. However, if 1 other lags (with similar AICs) are used, the differ ence in POD may fluctuate to be approximately 2 8% higher or approximately 40% lower. However, as the RfC is rounded to one significant digit, 3 the choice of lag does not affect the estimated RfC

. 4
5
5.4. CANCER EXPOSURE-RESPONSE ASSESSMENT 6 5.4.1. Overview of Methodological Approach 7 The objective of this human health assessment is to derive a cancer estimate for 8 inhalation exposure to Libby Amphibole asbestos. T he inhalation unit risk (IUR) is defined as 9 an upper-bound excess lifetime cancer risk estimate d to result from continuous exposure to an 10
agent at a concentration of 1 μg/L in water, or 1 μg/m 3 in air . However, current health standards 11 for asbestos are given in fibers/cc of air as count ed by phase contrast microscopy (PCM), since 12 they are based on health effects observed in occupa tional cohorts and this is the standard for 13 measuring fibers in an occupational environment (U. S. EPA, 1988; OSHA, 2008). Similarly, 14 when examining the available health effects data fo r Libby Amphibole asbestos, the best 15 available exposure metric available at this time is fibers/cc counted by PCM (Section 4.1.1.2). 16 Therefore, for Libby Amphibole asbestos the IUR rep resents the lifetime risk of mortality from 17 either mesothelioma or lung cancer in the general U .S. population from chronic inhalation 18 exposure to Libby Amphibole asbestos at a concentra tion of 1 fiber/cc of air. 19 IURs are based on human data when appropriate epide miologic studies are available. 20 The general approach to developing an IUR from huma n epidemiologic data is to quantitatively 21 evaluate the exposure-response relationship for tha t agent to derive a specific estimate of its 22 cancer potency in the studied population. For this assessment, the first step was to identify the 23 most appropriate data set available, which in this case can be used to quantitatively estimate the 24 effects of Libby Amphibole asbestos exposure on can cer mortality. Once the relevant data 25 describing a well-defined group of individuals alon g with their exposures and health outcomes 26 was collected, an appropriate statistical model was selected that adequately fit the data, and then 27 individual-level exposures were modeled using a var iety of possible exposure metrics (Section 28 5.4.2). The available epidemiologic data allowed f or modeling of the effects of estimated 29 ambient occupational exposures to Libby Amphibole a sbestos on the observed mortality risk in 30 workers. Exposure-response modeling was conducted for each cancer mortality endpoint 31 individually, and in some cases, the statistical mo del and the specific metric of exposure used for 32 each cancer endpoint may have been different. For example, the exposure metric that best 33 describes the exposure-response relationship for mo rtality from mesothelioma attributable to 34 occupational exposure to Libby Amphibole asbestos w as found to be different from the exposure 355-36

metric that best describes mortality from lung canc er (Section 5.4.3). Potential covariates that 1 may also be important predictors of cancer mortalit y are included in the statistical models. These 2 models were then statistically evaluated to determi ne which exposure metric representing 3 estimated ambient occupational exposures provided t he best statistical fit to the epidemiologic 4
data. 5 This cancer potency estimate derived from the epide miologic data is then applied to the 6 general U.S. population to determine the exposure l evel that would be expected to result in 1% 7 extra cancer mortality risk over a lifetime of cont inuous exposure. For epidemiologic studies, 8 which may be based on larger numbers of individual observations, smaller response levels that 9 are closer to the background response levels are co nsidered appropriate. Extra risk is defined as 10 equaling (R x - R o ) / (1 - R o ), where R x is the lifetime cancer mortality risk in the expos ed 11 population, and R o is the lifetime cancer mortality risk in an unexpo sed population (i.e., the 12 background risk). For example, if the expected lif etime risk of lung cancer mortality in the 13 unexposed general U.S. population is 5%, then this human health assessment seeks to estimate 14 the level of exposure to Libby Amphibole asbestos t hat would be expected to result in a lifetime 15 risk of lung cancer mortality of 5.95%; this lifeti me risk of mortality is equivalent to a 1% extra 16 risk (0.0595 − 0.05) / (1 − 0.05) = 0.01. For mesot helioma mortality, an absolute risk was 17 considered, rather than extra risk, because mesothe lioma is very rare in the general population 18 (Hillerdal, 1983). 19 A life-table analysis (see Appendix G for details) was used to compute the 95% lower 20 boundon the lifetime exposure to Libby Amphibole as bestos that corresponds to a 1% extra risk 21 of cancer mortality in the general U.S. population using age-specific mortality statistics and the 22 exposure-response relationships for each cancer end point as estimated in the studied population. 23 This lower bound on the level of exposure serves as the point of departure (POD) for 24 extrapolation to lower exposures and for deriving t he unit risk. Details of this analysis are 25 presented in Section 5.4.4. A cancer-specific unit risk was obtained by dividing the extra risk 26 (1%) by the POD. The cancer-specific unit risk est imates for mortality from either 27 mesothelioma or lung cancer were then statistically combined to derive the final IUR (Section 28 5.4.5). Uncertainties in this cancer assessment ar e described in detail in Section 5.4.6. 29

5.4.2. Choice of Study/Data—with Rationale and Jus tification 30 This human health assessment is specific to Libby A mphibole asbestos. This assessment 31 does not seek to evaluate quantitative exposure-res ponse data on cancer risks from studies of 32 asbestos that did not originate in Libby, MT. 33
The available sources of data included the cohort o f workers employed at the vermiculite 34 mining and milling operation in and around Libby, M T. This cohort has been the subject of 355-37

several epidemiologic analyses (Amandus and Wheeler , 1987; Amandus et al., 1987a, 1987b; 1 McDonald et al., 1986; Sullivan, 2007; Larson et al ., 2010a; Moolgavkar et al., 2010; and 2 described in detail in Section 4.1). There have al so been published reports on cases of 3 mesothelioma in the Libby, MT area (Whitehouse et a l., 2008) and mortality data published by 4 the Agency for Toxic Substances and Disease Registr y (ATSDR) (2000). However, published 5 mortality data on Libby, MT residents (Whitehouse e t al., 2008; ATSDR, 2000) could not be 6 used in exposure-response modeling due to lack of q uantitative exposure data. 7 The other available cohort of workers exposed to Li bby Amphibole asbestos was from an 8 Ohio vermiculite processing plant (Section 4.1.3) ( Lockey et al., 1984; Rohs et al., 2008). 9 Pleural changes were evaluated; however, no data we re available pertaining to cancer incidence 10 or mortality in the Ohio cohort. No other worker c ohorts exposed to Libby Amphibole asbestos 11 with cancer incidence or mortality data were availa ble. 12 The most appropriate available dataset with quantit ative exposure data for deriving 13 quantitative cancer mortality risk estimates based on Libby Amphibole asbestos exposure in 14 humans is the cohort of workers employed at the ver miculite mining and milling operation in and 15 around Libby, MT (hereafter referred to as the Libb y worker cohort). These data are considered 16 the most appropriate to inform this human health as sessment for several reasons: (1) these 17 workers were directly exposed to Libby Amphibole as bestos, (2) detailed work histories and job18 specific exposure estimates are available to recons truct estimates of each individual’s 19 occupational exposure experience, (3) the cohort is sufficiently large and has been followed for a 20 sufficiently long period of time for cancer to deve lop (i.e., cancer incidence) and result in 21 mortality, and (4) the broad range of exposure expe riences in this cohort provided an 22
information-rich dataset which allowed evaluation o f several different metrics of exposure. 23
Uncertainties in these data are discussed in Sectio n 5.4.6. 24
25
5.4.2.1. Description of the Libby Worker Cohort 26 The Libby worker cohort has been extensively studie d. McDonald et al. published three 27 studies on a subset of the cohort (1986, 2002, 2004 ). Scientists from the National Institute for 28 Occupational Safety and Health (NIOSH) conducted tw o epidemiologic investigations, resulting 29 in several published reports on different subsets o f the cohort (Amandus and Wheeler, 1987; 30 Amandus et al., 1987a, b, 1988; Sullivan, 2007). L arson et al. (2010a) analyzed an ATSDR 31 reconstruction of the Libby worker cohort from comp any records with exposure estimates 32 obtained from NIOSH with mortality follow-up throug h 2006. Moolgavkar et al. (2010) re33 analyzed the Sullivan (2007) data with mortality fo llow-up through 2001 using a different 34 statistical approach. 355-38

According to Sullivan (2007), nearly all of these s tudy subjects were workers at the 1 Libby, MT vermiculite mine, mill, and processing pl ant. Although the mine was several miles 2 from Libby, MT, some of the study subjects worked i n the town (see Section 4.1.1.1). Workers 3 may have also been assigned jobs as truck drivers, or jobs working in the screening plant, 4 railroad loading dock, expansion plants, or an offi ce. Individuals’ demographic and work history 5 data were abstracted from company personnel and pay records. A database created by NIOSH in 6 the 1980s contained demographic data and work histo ry starting from September 1935, and vital 7 status at the end of 1981 for 1,881 workers. NIOSH compared these data with company records 8 on microfilm, and work history data were re-abstrac ted to ensure data quality. One person was 9 removed from the cohort because company records sta ted that he was hired, but never worked 10 (Sullivan 2007). Nine workers with Social Security numbers listed in company records were 11 excluded because demographic and work history data were not available, leaving 1,871 workers 12 in the cohort available for epidemiologic analysis. Table 5-7 shows the demographic and 13 exposure characteristics of this cohort. 14 For the purposes of this assessment, vital status f ollow-up was completed by NIOSH 15 through 2006 using the National Death Index (NDI-Pl us; Bilgrad, 1995). Workers known to be 16 alive on or after January 1, 1979 (the date NDI beg an tracking deaths nationwide), but not found 17 in the NDI search, were assumed to have been alive on December 31, 2006 (Sullivan, 2007). 18 Nearly 54% of workers in the cohort (n = 1,009) had died by December 31, 2006. NIOSH 19
researchers obtained death certificates from the st ates (while exposure occurred in and around 20 Libby, deaths could have occurred elsewhere) for de aths prior to 1979 and coded to the ICD 21 revision in effect at the time of death by a single National Center for Health Statistics-trained 22 nosologist. After 1979, ICD codes were obtained fr om the NDI-Plus. For workers known to be 23 deceased, the underlying cause of death was determi ned from death certificates and coded to the 24 ICD codes using the rubrics of the ICD revision in effect at the time of death (ICD-5 [WHO, 25 1938], ICD-6 [WHO, 1948], ICD-7 [WHO, 1957], ICD-8 [WHO, 1967]; ICD-9 [WHO, 1977]; 26 or ICD-10 [WHO, 1992]). 27
28
295-39

Table 5-7. Demographic and exposure characteristics of the Li bby worker 1 cohort 2 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 Basic demographic information on the occupational c ohort members was largely 6 complete. However, when data were missing, they we re imputed by NIOSH based on the 7 following assumptions regarding gender, race, and d ate of birth. Seven workers with unknown 8 gender were assumed to be male because 96% of the w orkforce was male and NIOSH review of 9 names did not challenge that assumption (Sullivan, 2007). Workers of unknown race (n = 935) 10
were assumed to be white because workers at this fa cility were known to be primarily white, and 11
U.S. Census Bureau data indicate that 90-95% of the local population identify themselves as 12 white (Sullivan, 2007). For four workers with unkn own birth dates, date of birth was estimated 13 by subtracting the mean age at hire for the cohort from the worker’s hire date. The potential 14 impact of this imputation procedure on the analytic results is discussed in Section 5.4.6. 15
16
5.4.2.2. Description of Cancer Endpoints 17 This human health assessment of Libby Amphibole asb estos focuses on two cancer 18 endpoints: mesothelioma and lung cancer. The endpo int for both mesothelioma and lung cancer 19 was mortality, not incidence. Incidence data are n ot available for the Libby worker cohort. 20 However, there is evidence that other cancer endpoi nts may also be associated with exposure to 215-40

asbestos. The International Agency for Research on Cancer (IARC) concluded that there was 1 sufficient evidence in humans that other types of a sbestos (chrysotile, crocidolite, amosite, 2 tremolite, actinolite, and anthophyllite) were caus ally associated with mesothelioma and lung 3 cancer, as well as cancer of the larynx and the ova ry (Straif et al., 2009). Among the entire 4 Libby worker cohort, only 2 deaths were found to be due to laryngeal cancer, and there were no 5 deaths from ovarian cancer among the 84 female work ers. The EPA did not evaluate these other 6 outcomes as part of this assessment. The limited n umber of female workers in this cohort is 7 discussed later as a source of uncertainty in the d erived estimates (Section 5.4.6). 8 Mesothelioma did not have a distinct ICD code prior to introduction of the 10 th revision 9 (ICD-10) which was not implemented until 1999. The refore, for deaths in the Libby worker 10 cohort occurring from 1979 to 1998, death certifica tes were obtained if the NDI identified the 11 death as being from one of the possible mesotheliom a codes identified by Marsh et al. (2001), or 12 from respiratory cancer, nonmalignant respiratory d isease, digestive cancer, or unspecified 13 cancer. Death certificates (1940-1998) were review ed by the NIOSH principal investigator 14 (Sullivan, 2007) to identify any mention of mesothe lioma on the death certificate, as is the 15 standard procedure for assessing mesothelioma morta lity and as has been used in other analyses 16 of Libby worker cohort mesothelioma mortality (McDo nald et al., 2004; Larson et al., 2010a). 17 In total, 18 mesothelioma deaths occurring from 197 9 to 2006 were identified by NIOSH using 18 these methods (which serves as the basis for this a ssessment); 19 mesothelioma deaths were 19 identified by Larson et al. (2010a) for the same co hort from death certificates for all causes of 20 death rather than the more targeted set of causes i ndentified by Marsh et al. (2001) or Sullivan 21 (2007). 22 Whitehouse et al. (2008) identified four mesothelio ma cases among workers that were 23 not included in Sullivan (2007) with mortality foll ow-up in her paper through 2001; no other 24 information was provided. It is most likely that th ree mesothelioma cases from these four were 25 accounted for during the update of NIOSH cohort to 2006 (which serves as the basis for this 26 assessment). Whitehouse et al. (2008) also provided detailed information on 11 residential cases, 27 but this information could not be used in exposure- response analyses for this assessment, as there 28 is no quantitative exposure information for these c ases and no information defining or 29 enumerating the population from which these cases a rose. 30 Mortality records (and death certificates) may not always reflect the true cause of death 31 for various reasons (e.g., misdiagnosis, improper r ecording on the death certificate, or miscoding 32 of the cause of death). For mesothelioma, the unde rcounting of cases (underascertainment) is a 33 particular concern given the limitations of the ICD classification systems used prior to 1999 34 (detection rates varied from 12% from ICD-9 codes a lone to 83% from manual inspection of 355-41

death certificates [Davis et al., 1992]); recent st udies demonstrated that ICD-10 coding has 1 detection rates similar to the rates above (Pinhier o et al., 2004; Camidge et al., 2006). The 2 appropriate procedure for pre-ICD-10 codes is not t o use ICD codes alone but manually inspect 3 death certificates, as was done by Sullivan (2007). There is also evidence that the detection rate 4 of peritoneal mesothelioma is much lower than pleur al mesothelioma (Selikoff and Seidman, 5 1992). This assessment has accounted for the impac t of this underascertainment on the final IUR 6 (Section 5.4.5.1.1). 7 Lung cancer mortality was based on the underlying c ause of death identified by the ICD 8 code on death certificates according to the ICD ver sion in use at the time of death. Based on 9 these different ICD codes, lung cancer mortality in cluded malignant neoplasms of the trachea, 10 bronchus, and lung, and was identified by the follo wing codes: ICD-5 code ‘047’, ICD-6 and 11 ICD-7 codes ‘162’ or ‘163’, ICD-8 and ICD-9 code ‘1 62’, and ICD-10 codes ‘C33’ or ‘C34’. In 12 all, there were 111 deaths with an underlying cause of death attributed to lung cancer. All deaths 13 after 1960 were coded as bronchus or lung as the IC D versions in use as that time distinguished 14 malignant neoplasms of the trachea as distinct from bronchus and lung. Other investigators of 15 this cohort have used different definitions of lung cancer or used different follow-up periods, as 16 described in Section 4.1.1.2 (Description of Study Cohorts). 17
18
5.4.2.3. Description of Libby Amphibole Asbestos Exposures 19 The mining, milling, and processing operations at t he mine and in and around Libby, 20 conditions of exposure, and job-specific estimates of exposure intensity have been thoroughly 21 described in Section 4.1 (Amandus and Wheeler, 1987 ; Amandus et al., 1987a, 1987b; 22 McDonald et al., 1986; Sullivan, 2007). Briefly, m iners extracted vermiculite ore from an open23 pit mine that operated on Zonolite Mountain outside the town of Libby, MT. The ore was 24 processed in a dry mill (1935–1974) and/or two wet mills (1950–1974 and 1974–1990). The 25 resulting concentrate was transported by railroad t o processing plants around the United States 26 where the vermiculite was expanded for use in loose -fill attic insulation, gardening and other 27 products (Section 2.1). 285-42

EPA adopted the job-exposure matrix (JEM) developed and used by Sullivan (2007) 1 (Figure 5-6), which was, in turn, based on that use d in the earlier NIOSH study for jobs through 2 1982 (Amandus and Wheeler, 1987). As discussed in more detail in Section 4.1, Amandus et al. 3 (1987a) defined 25 location operations to which the y assigned exposure intensity based on 4 available information (Table 5-8). A job category may have involved more than one location 5 operation, and the 8-hour time-weighted average exp osure (8-hr TWA) for each job category in 6 the JEM was calculated from the exposure intensity and time spent at each location operation 7 (Amandus et al., 1987a). 8
9

10 Figure 5-6

. Plot of the NIOSH job exposure matrix for differe nt job 11 categories over time. The height of each bar represents the intensity of exposure 12 as an 8-hr TWA (fibers/cc) for a job in a particula r year. Each row for “Selected 13 Jobs” represents a specific job category. 14This document is a draft for review purposes only and does not constitute Agency policy.DRAFT—DO NOT CITE OR QUOTE5-43Table 5-8.Exposure intensity (fibers/cc) for each locationoperation from the beginning of operations through1982 (Amandus et al., 1987a)Location operationYear<50 50-59 60-63 64-67 68-70 71 72-74 75-76 77-79 80-82Downtown office building0 0 0 00 0 0 0 0 0Bus ride1.2 1.2 1.2 1.21.2 1.2 1.2 0 0 0Mine office1.0 1.0 1.0 1.01.0 1.0 1.0 0.5 0.5 0.5Mine misc.1.6 1.6 1.6 1.61.6 1.6 1.6 0.8 0.8 0.8Mine—nondrilling2.6 2.6 2.6 2.62.6 2.6 2.6 0.6 0.6 0.6Transfer point2.2 2.2 2.2 2.22.2 2.2 2.2 0.6 0.6 0.6Quality control lab13.1 13.1 13.1 2.62.6 2.6 2.6 0.6 0.6 0.6Service area by mill1.9 1.9 1.9 3.81.9 1.9 1.9 0.2 0.2 0.2Dry mill168.4 168.4 168.4 33.233.2 33.2 16.6 -- -- --Dry mill sweeping182.1 182.1 182.1 35.935.9 35.9 19 -- -- --Old and New wet mill—millwright-- 7.0 7.0 7.07.0 7.0 7.0 0.6 0.6 0.6Old wet mill—nonmillwright-- 3.7 3.7 3.73.7 3.7 3.7 -- -- --New wet mill—nonmillwright-- -- -- ---- -- 3.2 2.0 0.8 0.8Skip area88.3 88.3 88.3 17.417.4 17.4 4.8 0.6 0.6 0.6Concentrate hauling5.5 5.5 5.5 5.55.5 5.5 5.5 0.4 0.4 0.4River station binside21.2 21.2 21.2 21.221.2 21.2 21.2 0.7 0.7 0.7River conveyor tunnel112.5 112.5 112.5 112.5112.5 112.5 112.5 0.3 0.3 0.3River office binside10.6 10.6 10.6 10.610.6 10.6 10.6 0.2 0.2 0.2Verxite plant22.6 22.6 2.8 2.82.8 -- -- -- -- --Bagging plant12.9 12.9 12.9 12.912.9 12.9 4.3 1.2 1.2 1.2Tails belt7.3 7.3 7.3 7.37.3 7.3 7.3 0.7 0.7 0.71This document is a draft for review purposes only and does not constitute Agency policy.DRAFT—DO NOT CITE OR QUOTE5-44Table 5-8. Exposure intensity (fibers/cc) for eachlocation operation from the beginning of operations through 1982(continued)Location operationYear<50 50-59 60-63 64-67 68-70 71 72-74 75-76 77-79 80-82Screen plant-- -- -- ---- -- -- 0.5 0.5 0.5Drilling High23 23 23 239.2 9.2 9.2 0.6 0.6 0.6Low6.7 6.7 6.7 6.76.7 9.2 9.2 0.6 0.6 0.6Ore Loading High82.5 27.7 10.7 10.73.2 3.2 3.2 0.2 0.2 0.2Low24 15 9 93.2 3.2 3.2 0.2 0.2 0.2River dock High116.9 42.5 17 1717 5.1 5.1 0.5 0.5 0.5Low38 19 6.4 6.45.1 5.1 5.1 0.5 0.5 0.5Bagging plant High12.9 12.9 12.9 12.912.9 12.9 4.3 1.2 1.2 1.2Low4.6 4.6 4.6 4.64.6 4.6 4.3 1.2 1.2 1.2This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-45 From 1967 through 1982, over 4,000 air samples anal yzed for fibers by PCM analysis 1 were available to inform the exposure intensity for the 25 location operations (Table 5-8). 2 Therefore, the JEM for 1968-1982 is based on direct analytic measurements in air for each 3 location operation (Amandus et al., 1987a). With t he exception of the dry mill, no air samples 4 were available for other location operations at the mine and processing facilities prior to 1967. 5 In order to estimate exposures which occurred befor e that time, the NIOSH researchers 6 interviewed plant employees, and based estimates of exposure intensities on known changes in 7 operations over the years and professional judgment s regarding the relative intensity of exposure; 8 exposure intensity for 23 of the pre-1967 location operations was extrapolated from post-1967 9 measurements based on reasoned assumptions for each location operation (Amandus et al., 10 1987a). 11 However, the amount and quality of measurement data in the facility in earlier years was 12 much more limited (Amandus et al. 1987a). A total of 40 dust samples were taken, exclusively 13 in the dry mill, over the years 1950−1964. Using t hese measurements, much higher exposures 14 were inferred to occur prior to 1964 than those mea sured in later years. Although air sampling 15 for fibers by PCM was available beginning in 1967, average fiber concentrations (dry mill) 16 differed rather widely between limited data sets fr om different investigators up through the early 17 1970s: 1967-68, NIOSH data, 65 f/cc (n = 14); 1970 , company data, 11 f/cc (n = 15); 1971, 18 MSHA data, 31 f/cc (n = 52); 1972, MSHA and company data, 15 f/cc (n = 45). Thus, estimated 19 exposure levels continue to be uncertain during the period when fiber concentration 20 measurements by PCM became available in 1967. 21 Air samples collected by the State of Montana were available for the dry mill from 22 1956-1969, but these were analyzed for total dust, not asbestos fibers. Total dust samples 23 (collected by a midget impinger) were examined by l ight microscopy, but no distinction was 24 made between mineral dusts, debris, and asbestos fi bers. All objects were counted and reported 25 in the units of million particles per cubic foot of air (mppcf). Amandus et al. (1987a) developed 26 a relationship between total dust and asbestos fibe r counts based on comparison of 27 contemporaneous air sampling in the dry mill (see S ection 4.1.1.2). The conversion ratio of 28 4.0 fibers/cc per mppcf was used to estimate exposu re intensity for two location operations in the 29 dry mill for the years prior to 1967. 30 The exposure intensity (fibers/cc) for each of the location operations (Table 5-8) was 31 used to calculate an estimate of daily occupational exposure for each job category in the JEM 32 (Figure 5-6). For each job, the time spent at each location operation and the exposure intensity 33 for each location operation were averaged to derive an estimate of the 8-hour TWA. The 34 resulting JEM available for this assessment and pre vious epidemiologic studies of the Libby 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-46 worker cohort is based on the air concentration of fibers as enumerated by PCM which measures 1 fibers longer than 5 μ m with an aspect ratio >3:1 (i.e., the fiber size r egulated under the OSHA 2 standard [U.S. Department of Labor, 2006]). Additi onally, only fibers that are wide enough to 3
be viewed on PCM can be detected with this method. Amandus et al. (1987a) considered fibers 4
>0.44 μ m in diameter to be visible by PCM in the historica l filter analysis. More recent 5 techniques have refined the PCM method and fibers g reater than 0.25 μ m in diameter are now 6 considered PCM fibers (WHO, 1980). 7 There was one important limitation of the NIOSH JEM . In the earlier study (Amandus 8 and Wheeler, 1987), workers with “common laborer” j ob assignments and some workers with 9 unknown job assignments hired between 1935 and 1959 were assigned the relatively low 10 exposure levels estimated for the mill yard. In th e more recent study by Sullivan (2007), these 11 workers (686 from 991 hired before 1960) were assig ned the same average estimated exposure 12 intensity for all unskilled jobs during the relevan t calendar time period. The lack of information 13 on specific job assignments for these workers in th e JEM resulted in the misclassification of the 14 exposure and made exposure metrics, representative of duration of employment alone, describe 15 this large part of the cohort. For this reason and because there was little measured fiber exposure 16 data during the earlier period, difficulties were e xperienced identifying an adequate exposure17 response model fit. These difficulties are describ ed in detail in Section 5.4.3.3. As a result, the 18 IUR analyses were based on the subset of workers hi red after 1959 (i.e., on or after January 1, 19 1960) and consisted of 880 workers. Table 5-9 show s the demographic and exposure 20
characteristics of this sub-cohort. 21 In addition, re-abstracting work histories for the more recent study (Sullivan, 2007) 22 identified several job assignments not mentioned in the earlier publications. Sullivan (2007) re23 abstracted work histories by extrapolating exposure estimates for the additional job and calendar 24 time period-specific combinations based on professi onal experience and review of exposure 25 records from earlier studies of the Libby worker co hort (Amandus and Wheeler, 1987; Amandus 26 et al., 1987a, b; McDonald et al., 1986). While the Sullivan (2007) study was limited to the 27 white male workers, EPA’s analysis includes all wor kers regardless of race or gender. 28 Figure 5-6 shows a three-dimensional representation of the job-exposure matrix used by Sullivan 29 (2007) and in this assessment. Not all jobs were i ncluded; thus, the figure is not comprehensive 30
but rather illustrative. The three axes show the i ntensity of fiber exposure as an 8-hour TWA 31 (fibers/cc, vertical axis) for selected job categor ies over time (horizontal axes). For several jobs, 32 the estimated 8 hr TWA was greater than 100 fibers/ cc for the decades prior to 1963. Figure 5-6 33 shows the variability in exposures across jobs and over time. From 1967-1982, all exposure 34
measurements that inform the JEM are based on locat ion-specific air samples analyzed for fibers 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-47 by PCM. As stated above, pre-1968 exposures in the dry mill were based on measurement of 1
dust levels from 1956-1967 that were converted to P CM by Amandus et al. (1987a) and 2 extrapolated backwards in time. Pre-1968 exposures for all other locations within the JEM were 3 extrapolated from post-1967 fiber levels based on r easoned assumptions (Amandus et al., 4 1987a). 5
6
7This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-48 Table 5-9. Demographic and exposure characteristic s of the subset of the 1 Libbyworker cohort hired after 1959 2 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 Amandus and coworkers (1987a) recognized the uncert ainty in the pre-1968 exposures 6 assigned to the cohort. Although there is some unc ertainty in the dust–to-fiber conversion, this 7 conversion (4.0 fibers/cc per mppcf) was based on c ontemporaneously collected dust and fiber 8 data collected in the dry mill and only applied to the dry mill environment. Amandus and 9 coworkers considered a range of possible conversion factors (1.2-11.5 fibers/cc per mppcf). 10 Greater uncertainty may lie with the reasoned assum ptions used to extrapolate exposures to the 11 early decades for all location operations considere d. For example, there were four location 12 operations for which Amandus and coworkers estimate d a range of possible exposure intensities: 13 drilling location site, ore loading location site, river dock, and the bagging plant, where intensity 14 of exposure may vary as much as 3-fold between the low and high estimates (Table 5-8). 15 Finally, some workers were employed after 1982 thro ugh 1993 in plant close-out and clean-up 16 operations, which have not been evaluated (Sullivan , 2007). However, none of the members of 17 the post-1959 cohort (n = 880) remained employed by 1982 according to employment records. 18
19
5.4.2.4. Description of Libby Worker Cohort Work Histories 20 NIOSH staff and contractors abstracted demographic data and work history data from 21 company personnel records, including W-2 federal ta x forms. An individual’s work history was 22This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-49 determined from job change slips, which recorded ne w job assignment, date of change, and 1 change in hourly pay rate (which differed by the jo b assignment). Work history records span the 2 time period from September 1935 to May 1982. Dates of termination were unknown for 58 of 3 640 workers (9%), who left employment before Septem ber 1953. EPA adopted the assumption 4 used by NIOSH (Sullivan, 2007) that these people wo rked for 384 days, based on the mean 5 duration of employment among all workers with known termination dates before September 6 1953. The majority of workers in this cohort as a whole and among those hired on or after 7 January 1, 1960 worked at multiple jobs; many of th e workers switched jobs repeatedly. Of the 8 880 hired in 1960 or afterwards, the mean number of job changes was 5, the median was 2, and 9 the maximum number of job changes was 32 (Figure 5- 7). 10
11

12 13 Figure 5-7. Histogram showing the number of worker s who experienced 14 each incremental number of different jobs among the 880 workers hired 15 after 1959. 16 N

u

m b

e

r

o f W

o

r k

e

r

s

Number of Jobs Held by WorkerThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-50 5.4.2.5. Estimated Exposures Based on Job Exposure Matrix an d Work Histories 1 Exposure-response modeling of epidemiologic data is based on several considerations as 2 summarized by Finkelstein (1985): 3
4 After identification of an occupational hazard one of the goals of occupational 5 epidemiology is to quantify the risks by determinin g the dose-response relations for the 6 toxic agent. In many circumstances little is known about the dose received by target 7 tissues; the data available usually pertain only to exposure to various concentrations of 8 the toxic material in the workplace. The calculati on of dose requires additional 9 physiological and chemical information relating to absorption, distribution, biochemical 10 reactions, retention, and clearance. In asbestos e pidemiology the usual measure of 11 exposure is the product of the concentration of asb estos dust in the air (fibres or particles 12 per ml) and the duration of exposure to each concen tration summed over the entire 13 duration of exposure (years); this measure is the c umulative exposure. 14
15 Cumulative exposure has been the traditional method of measuring exposure in 16 epidemiologic analyses of many different occupation al and environmental exposures and was the 17 exposure metric applied to the risk of lung cancer mortality in the IRIS assessment for general 18 asbestos (U.S. EPA, 1988). Two alternative approac hes to developing exposure metrics to 19
describe the effects of concentrations of asbestos dust in the air on the risks of mortality have 20 also been proposed. The first alternative was prop osed by Jahr (1974) who studied silica21 induced pneumoconiosis and suggested that exposures to occupational dusts could be weighted 22 by the time since exposure. This yields an exposur e metric which gives greater weight to earlier 23 exposures. Berry et al. (1979) subsequently sugges ted the application of exposure metrics that 24 allowed for the clearance of dust or fibers by usin g a decay term on exposures. For the 25 evaluation of mortality risk from mesothelioma, U.S . EPA (1988) used a different exposure 26 metric than was used for lung cancer mortality whic h factored in the time since first exposure. 27 As observed in U.S. EPA (1988), it is important to note that different characterizations of 28 estimated ambient exposures may be reasonably expec ted to be associated with different 29 endpoints. 30 Most studies of asbestos-related mortality have eva luated either cumulative exposure, 31 exposure concentration, or the duration of employme nt as exposure metrics. Many studies have 32 been limited in the availability of detailed exposu re data—especially at the individual level. In 33 the Libby worker cohort data developed by NIOSH and used in this assessment, detailed work 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-51 histories, together with job-specific exposure esti mates, allowed for the reconstruction of each 1 individual’s occupational exposure experience over time to define multiple exposure metrics. 2 From this information-rich, individual-level datase t from NIOSH, EPA constructed a 3 suite of the different metrics of occupational expo sure which had been proposed in the asbestos 4
literature or used in the IRIS asbestos assessment (U.S. EPA, 1988). This suite of models was 5 defined a priori to encompass a reasonable set of p roposed exposure metrics to allow sufficient 6 flexibility in model fit to these data. These expo sure metrics were evaluated in analytic7 regression models to test which exposure metrics we re the best empirical predictors of observed 8 cancer mortality and the better fitting models were advanced for consideration as the basis of the 9 exposure-response relationship for the IUR. The ty pes of exposure metrics evaluated were 10
intended to allow for variations of the classic met ric of cumulative exposure, allowing for more 11 or less weight to be placed on earlier or later exp osures. These simulated exposure metrics were 12 derived mathematically to approximate underlying pr ocesses that are not well understood (see 13 Section 5.4.6). Thus the fit of exposure metrics i s evaluated on the basis of maximizing the 14 likelihood for the Libby worker cohort and the esti mated parameters do not necessarily have 15 biological interpretations. 16 The first exposure metric—cumulative exposure (CE) —is a simple addition of each day 17 of exposure across time (Eq. 5-1). CE has been wid ely used in modeling risk of cancer in 18 occupational epidemiology and has been used for modeling lung cancer (McDonal d et al., 2004, 19 Sullivan, 2007, Larson et al., 2010a, Moolgavkar et al., 2010) and mesothelioma (McDonald et 20 al., 2004) in the Libby worker cohort. When using this exposure metric in the risk model, all 21 exposures have equal weight regardless of when they occurred and lead to the same estimated 22 cancer risk whether exposure happened early or late r in life. 23 EPA calculated each individual’s occupational CE to Libby Amphibole asbestos over 24 time from their date of hire until the date they ce ased to be employed in the Libby operations. 25 Workers were assumed to remain at their final occup ational CE level until death or the end of the 26 follow-up period on December 31, 2006. Each worker ’s CE at any time point (daily increment) 27 since their date of hire was computed as the sum of their exposure intensity (fibers/cc) on each 28 specific occupational day (x t ) from day 1 through day k. Mathematically, this w as defined as 29
30 31

Cumulative exposure at time t k = (Eq. 5-1) 32

33
where 34 

# ∑

= k

j t j 

## x

1This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-52 x t j = the estimated job-specific exposure intensity f or the day t

j , and 1

t k = the day on which the exposure is estimated. 2

3 4 A second exposure metric—residence time-weighted (R TW) exposure—gives additional 5 weight to early exposures. By doing so, the RTW ex posure metric allows the possibility that 6 early exposures are more influential on cancer mort ality predictions in the model. Unlike many 7 chemicals that are rapidly metabolized in the body and excreted, asbestos fibers are durable and 8 some may remain in the body for years. Fibers that remain in the lung may continue to damage 9 lung cells and tissue until they are removed or cle ared (Section 3.2). Similarly, fibers that 10 translocate to the pleura may damage cells as long as they remain in this tissue. Therefore, a 11 fiber exposure may not only damage tissue during th e exposure, but fibers may remain in these 12 tissues, with cellular and tissue damage accumulati ng over time. 13 The RTW exposure metric in this assessment is somet imes called the cumulative burden, 14 or the area under the curve. A type of RTW metric was proposed for modeling of mesothelioma 15 mortality by Newhouse and Berry (1976) based on a g eneral understanding of the relationship 16 between tumor incidence rate and time to cancer (Co ok et al., 1969) as well as animal models of 17 mesothelioma (Berry and Wagner, 1969). Similar typ es of RTW metrics were applied to the 18 insulators asbestos cohort by Peto et al. (1982), d iscussed by Finkelstein (1985), and applied in 19
the derivation of the IUR in the IRIS assessment fo r asbestos (U.S. EPA, 1988). McDonald et al. 20 (2004) and Moolgavkar et al. (2010) used RTW-type m etrics for modeling mesothelioma in the 21 Libby worker cohort, and McDonald et al. (2004) app lied an RTW metric for modeling lung 22 cancer mortality in the Libby worker cohort. 23 In calculating RTW, each day’s exposure is multipli ed by the time since the exposure 24 occurred (Eq. 5-2). Residence time-weighted CE was calculated as a cumulative function of 25 each time-interval’s CE such that earlier exposures contribute greater weight. 26
27 28

Residence time-weighted CE at time t k = 

# ∑∑

= = k

j j

i t i 

## x

1 1 (Eq. 5-2) 29

30
where 31

x t i = the estimated job-specific exposure intensity f or the day t i , and 32

t k = the day on which the exposure is estimated. 33

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

— DO NOT CITE OR QUOTE 5-53 1 The CE and RTW exposure metrics result in increasin g or sustained metrics of exposure 2 across time. However, it is known that some cellul ar and genetic damage may be repaired over 3 time, which could decrease cancer risk from exposur e over time. Additionally, asbestos fibers 4 are cleared (removed) from the lung through natural processes and translocated to other tissues 5 (Section 3.2.1.1). Therefore, when considering lun g cancer, it is possible that removal of 6 asbestos fibers from the lung would reduce lung can cer risk over time. Although less is known 7 about removal of asbestos from the pleura, there ma y be clearance mechanisms operative in that 8 tissue as well (Section 3.2.1.2). As noted earlier , Berry et al. (1979) proposed the use of 9 exposure metrics based on occupational exposures wh ich addressed the issue of clearance using 10 a mathematical decay term to modify measured ambien t exposures. For mesothelioma, modeling 11 a decay term on exposure has been proposed by Berry (1999). Based on this proposal, several 12 recent papers applied a decay term to modeling meso thelioma mortality (Reid et al., 2009; Berry 13 et al., 2009; Barone-Adese et al., 2008; Gasparrini et al., 2008; Clemens et al., 2007; Hodgson et 14 al., 2005; Berry, 2004). Similarly, recent publicat ions indicate that the relative risk of lung 15 cancer due to asbestos exposure declines 15-20 year s after the cessation of exposure to asbestos 16 (Magnani et al., 2008; Hauptmann et al., 2002). 17 Mathematically allowing for the magnitude of earlie r exposures to diminish with 18 advancing time was considered to be a method of giv ing less weight in the analyses to earlier 19 exposures compared to the previous two exposure met rics. Therefore, two additional exposure 20 metrics were considered, where a decay rate was app lied to the CE and RTW exposure metrics 21 (Eqs. 5-3 and 5-4). 22 For each exposure metric, the application of a half -life was calculated by depreciating 23 each time-interval’s (t j-1 ;t j ) exposure according to a model of exponential deca y with various 24 half-lives (T 1/2 ) of 5, 10, 15, and 20 years. Note that the partic ular fiber kinetics of Libby 25 Amphibole asbestos fibers are not fully understood and the relevance of these particular half26
lives was determined from the statistical fit of th ese exposure metrics to the risk of cancer 27 mortality, rather than the biological half-life of the fibers. For a very large half-life, decay is 28 very slow, and these metrics would be very similar to the CE and RTW metrics. 29
30 31

CE with half-life at time t k = 

### ( ) 

# ( )



# ∑

=     

    

  
 

  
 

− ∗
∗ k

j j k
t T t

t
x j 1 2 1 5.0 ln exp (Eq. 5-3) 32

33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-54 RTW with half-life at time t k = 

## ( ) ( )



# ∑ ∑

= =     

    



 
 

  
 

− ∗ ∗ j

i i k t k

j T t t x i 1 2

1 1 5.0

ln exp (Eq. 5-4) 1

2 where 3

x t

j = the estimated job-specific exposure intensity f or the day t

j , and 4

t k = the day on which the exposure is estimated. 5

6
7 In addition to the exposure metrics used in the lun g cancer mortality analysis, modeling 8 of mesothelioma mortality (Section 5.4.3) included the exposure model used in the IRIS 9 assessment for asbestos (U.S., 1988), originally pr oposed in Peto et al. (1982): 10
11
12 Im = C·Q·KM (Eq. 5-5) 13
14
where 15 Im = the observed deaths from mesothelioma/person- years, 16 C = the average concentration of asbestos in the ai r, 17 KM = an estimated slope describing the relationshi p between Libby amphibole 18 exposure and mesothelioma mortality, and 19 Q = the function of the time since first exposure (t) and the duration of employment 20 (d): 21

For t ≤ 10, Q = 0 22
For 10 < t ≤ d + 10, Q = (t – 10) 3 23 For t > d + 10, Q = (t – 10) 3 – (t – 10 – d) 3

. 24
25 26 The asbestos IUR (U.S., EPA, 1988) metric was origi nally fit to aggregate cohort data 27 and was based on a function of average cohort expos ure, time since first exposure, and duration 28 of employment. The analysis here of individual-dat a for Libby Amphibole asbestos is, therefore, 29 a different application of this exposure metric, an d it’s fit to the mesothelioma mortality of the 30 Libby worker cohort is evaluated in this assessment

. 31
In addition to the use of these methods of describi ng exposure metrics representing 32 estimated ambient exposure to Libby Amphibole asbes tos dust for use in predicting the risk of 33 mortality there is the important issue of potential modifying the exposure metrics to account for 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-55 cancer latency. Without knowledge of the specific timing of etiologically relevant exposure 1 which may initiate and promote cancers ultimately r esulting in mortality, any exposure metric 2 may include exposures during some time period which do not have bearing on the risk of 3 mortality. In the absence of such information on t he specific cancer latency associated with a 4 specific exposure, Rothman (1981) suggested that th e most relevant exposure period could be 5 identified by comparing the fit of exposure metrics across multiple lag periods to allow for the 6 identification of the optimal latency period as an expression of a lag time between exposure and 7 mortality. This has since become a standard practi ce in occupational and environmental 8 epidemiology. Accordingly, exposure estimates for all exposure metrics were adjusted to 9 account for the time period between the onset of ca ncer and mortality. The lag period defines an 10 interval before death, or end of follow-up, during which, any exposure is excluded from the 11 calculation of the exposure metric. Cohort members who died or were lost within the initial 12 years of follow-up were assigned lagged exposure va lues of zero if they had not been followed 13 for longer than the lag time. The various exposure metrics were lagged at 10, 15, and 20 years to 14 account for different potential cancer latencies wi thin the limitations of the available data. While 15 mesothelioma is known to have an average latency as long as 55 years among selected 16 occupations (e.g., Bianchi and Bianchi, 2009), seve ral mesothelioma deaths in the cohort 17 occurred within 30 years from the start of the expo sure, suggesting a shorter latency period in 18 this population. Metrics without a lag were fit fo r comparison purposes but were not considered 19 to be biologically reasonable, given that the outco me under analysis is cancer mortality 20 (specifically, mesothelioma and lung cancer), for w hich latency periods of 10 years or more have 21 been established for asbestos (U.S. EPA, 1988). Co nsequently, metrics that were not adjusted by 22 lagging exposure in the final years before mortalit y (or the end of follow-up) were not 23 considered further in the development of an IUR for Libby Amphibole asbestos. 24
25 5.4.3. Exposure-Response Modeling 26 Sufficient biological information to select models for the epidemiology data on the basis 27 of biological mechanism (Section 3) is not availabl e. In this situation, EPA’s practice is to 28 investigate a range of model forms to determine how to best empirically model the exposure 29 response relationship in the range of the observed data. For Libby Amphibole asbestos, possible 30 exposure metrics were explored for model fit to the chosen models. The exposure metric options 31 were selected to provide a range of shapes that was sufficiently flexible to allow for a variety of 32 ways that time and duration might relate to cancer risk in the data being modeled. EPA then 33 evaluated how well the models and exposure metric c ombinations fit the data being modeled. 34 Metrics that did not fit the data well were rejecte d. For purposes of calculating a reasonable 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-56 upper-bound on the risk per exposure EPA accounted for uncertainty in the choice of exposure 1 metrics by using the exposure metric (among those o f reasonable fit) that estimated the highest 2 risk. This is explained in more detail below and i n Sections 5.4.4-5.4.5. 3 The risk estimates are based on epidemiological ana lysis of the primary NIOSH data 4 (Libby worker cohort). The rationale for selection of the Libby worker cohort is presented in the 5 previous section (Section 5.4.2). Analysis of this primary epidemiologic database allows the 6 comparison of multiple metrics of exposure to quant ify the exposure-response relationship. This 7 approach is intended to support the empirical repre sentation of the exposure-response 8 relationship of estimated ambient occupational expo sure to Libby Amphibole asbestos with 9 observed cancer mortality risk. The exposure-respo nse modeling may be influenced by 10 uncertainties in the magnitude and time course of t he exposure estimates and, therefore, may not 11 necessarily reflect the biologic disposition of inh aled fibers, but simply, the empirical 12 observation of estimated exposure to Libby Amphibol e asbestos, and subsequent mortality as the 13 mode of action, is not well understood (Section 5.4 .6). 14
15 5.4.3.1. Modeling of Mesothelioma Exposure-Response in the L ibby Worker Cohort 16 The background incidence of mesothelioma is extreme ly rare (Hillerdal, 1983). Since 17 there is a very low background risk, the exposure-r esponse model applied here examines the 18 relationship of the absolute risk of mesothelioma m ortality that is attributable to Libby 19 Amphibole asbestos exposure because there is not a true background risk of mesothelioma 20 mortality among people who were truly unexposed to Libby Amphibole asbestos (as opposed to 21 the relative risk model, which is used for lung can cer mortality; see Section 5.4.3.2). Poisson 22 regression models are employed here for estimating the absolute risk of mesothelioma, as the 23 Poisson distribution is an appropriate model for us e with data that are counts of a relatively rare 24 outcome, such as observed mesothelioma deaths in th e Libby worker cohort. Other analyses of 25 mesothelioma mortality in the Libby worker cohort h ave also used the Poisson model 26 (McDonald et al., 2004; Moolgavkar et al., 2010). In the Poisson model, probability of k events 27 is specified as 28
29 30

      
! (Eq. 5-6) 31
32 33 where is parameterized with the exposure metric (defined in Section 5.4.2.5). Then, life table 34 analysis is used to estimate risks in the general U .S. population for derivation of the unit risk of 35 mesothelioma mortality (Section 5.4.5.1). 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-57 Estimation of the exposure-response relationship fo r mesothelioma mortality using the 1 Poisson model was performed using a Monte Carlo Mar kov Chain (MCMC) Bayesian approach 2 with an uninformative or diffuse prior (WinBUGS Ver sion 1.4 [Spiegelhalter et al., 2003]). Use 3 of diffuse priors is a standard procedure in Bayesi an analysis, in situations like this one, when 4 there is no prior knowledge about the toxicity of L ibby Amphibole asbestos under a particular 5 model. Since this analysis focuses only on the Lib by worker cohort and does not try to factor in 6 data from other sources in estimating potency, use of a diffuse prior is considered appropriate for 7 this analysis. 8 The benefit of using the WinBUGS software is its co mputational ease and that it provides 9 a posterior distribution of β (the mesothelioma slope factor) rather than just a point estimate. A 10 diffuse (high variance) Gaussian distribution, trun cated to exclude negative parameter values, is 11 used as a diffuse prior. With such a prior, result s of MCMC analysis are expected to be similar 12 to maximum likelihood estimation in a non-Bayesian analysis. Standard practices of MCMC 13 analysis were followed for verifying convergence an d sensitivity to the choice of initial values. 14 The posterior distribution is based on three chains with a burn-in of 10,000 (i.e., the first 10,000 15 simulations are dropped so that remaining samples a re drawn from a distribution close enough to 16 the true stationary distribution to be usable for e stimation and inference) and thinning rate of 10 17 (i.e., only each 10 th simulation is used - thus reducing autocorrelation ) such that 3,000 total 18 simulations constitute the posterior distribution o f β . The mean of the posterior distribution 19 served as a central estimate, the 90% credible inte rval 1 defined the 5 th percentile and the 95 th 20 percentile of the distribution, which served as bou nds for the 95 th lower and upper one-sided 21 confidence intervals, respectively. 22 Multiple metrics of exposure (Section 5.4.2.5) as w ell as exposure intensity, duration of 23 employment, age at death or loss to follow-up, and time since first exposure were compared 24 using the Deviance Information Criterion (DIC). DI C (Spiegelhalter et al., 2002) is used in 25 Bayesian analysis and is an analogue of the Akaike Information Criterion (AIC), with smaller 26 values indicating a better statistical fit to the d ata. Use of the DIC and AIC is standard practice 27 in comparing the fit of non-nested models to the sa me dataset with the same dependent outcome 28 variable, but different independent covariates. Ac cording to Burnham and Anderson (2002), 29
‘These methods allow the data-based selection of a “best” fitting model and a ranking and 30 weighting of the remaining models in a pre-defined set.’ Because of the small number of deaths 31 from mesotheliomas in absolute terms, only uni- and bi-variate models (with age or time since 32 first exposure as the second covariate) were consid ered. Sex and race were not used as 33
covariates since all mesotheliomas were observed in men assumed to be white (Sullivan, 2007). 34

1 A credible interval is the Bayesian analogue of a confidence interval.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-58 Each exposure metric was lagged by 0, 10, 15, or 2 0 years. The use of a lag period aims to 1 account for the latency period between the onset of mesothelioma (which occurs some time 2 before clinical diagnosis) and mesothelioma mortali ty. 3
4 Results of mesothelioma mortality analysis 5 For the full Libby worker cohort (n = 1,871), the d uration of employment provided a 6 considerably better univariate model fit than the o ther possible exposure metrics, indicating that 7 this exposure metric was the best single predictor of mesothelioma mortality in the full Libby 8
worker cohort. The bivariate model which included duration of employment and age at death or 9 censoring provided the overall best fit (DIC = 196) . The inclusion of information on the 10 concentration of exposure beyond the duration of em ployment resulted in a degradation in model 11 fit (Table 5-10). The metric used in the IUR for a sbestos (U.S. EPA, 1988) (Eq. 5-5) had a 12 higher DIC of 233.7 in the analysis here. It is li kely that the poorer fit seen when using 13 information on exposure concentration is the result of the fact that duration of employment is 14 measured with comparatively little error, while der ivation of specific exposure concentrations 15 may be subject to a sizable measurement error. Mor eover, as described in Section 5.4.2.3, for 16 686 of 991 (69%) workers hired from 1935 to 1959, o nly the duration of employment was 17 known, but not the job category, and thus the same exposure concentration had been assigned to 18 653 of these workers (Sullivan, 2007). It is likel y that because of the potential for particularly 19 large exposure measurement error among more than tw o thirds of the workers hired prior to 1960 20 who were assigned the same exposure intensity, this resulted in the duration of employment 21 being the best predictor of mesothelioma mortality. Additionally, estimates of exposure intensity 22 prior to 1968 have greater uncertainty associated w ith them than more recent exposure 23
measurements which are based on fiber counts in air samples analyzed by PCM. For the 24 majority of job locations (23 of 25), no exposure m easurements were available prior to 1968, and 25 exposures were estimated based on employee intervie ws (in 1982) and what was known about 26 major changes in operations between 1935 and 1967. For two exposure locations, the dust–to27
fiber conversion ratio is based on measurements tak en in the late 1960s, so extrapolations from 28 the mid-1960s to the early 1960s is likely to be mo re certain than extrapolation further back in 29 time. The fact that the metric using only duration of employment fit best and the additional 30 incorporation of exposure intensity information wor sened the fit indicates that it is unlikely that 31 IUR estimates can be developed using the full cohor t data because exposure values were not 32 predictive of mesothelioma mortality. 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-59 Table 5-10. Comparison of univariate model fit of various exposure metrics 1 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 Since one of the mesothelioma deaths occurred less than 20 years from start of the exposure, lag 20 m etrics 5 assigned no exposure to this case which resulted in the very poor fit of exposure metrics lagged 20 ye ars. 6

b Lower DIC values represent better fits. Models wi th DIC within 10 units of the DIC of the model with the lowest 7 DIC are shown. 8 DIC = Deviance Information Criterion. 9 10 11 The DIC values for models that included lag and/or half-life adjustments to the exposure 12 metrics were not penalized in the regression analys es for including these extra parameters 13 because those factors were not represented as covar iates but rather were embedded in the 14 exposure metrics. While these results were obtaine d using each instance with lag and/or half-life 15 as a separate model fit, it may be appropriate to p enalize the DIC values from these results for 16 inclusion of these parameters. Note that if the DI C values from the lag and/or half-life models 17 were penalized, this would serve to improve the rel ative fit of the model using only duration as a 18 parameter in comparison with the lag and/or half-li fe models because the DICs for the penalized 19 models would increase while the DIC for the un-pena lized models would be unchanged. 20
21
5.4.3.2. Modeling of Lung Cancer Exposure-Response in the Li bby Worker Cohort 22 To develop an exposure-response relationship for lu ng cancer, the lung cancer mortality 23
data were modeled as a function of the historical e xposure data for the Libby worker cohort. The 24 mesothelioma mortality data were modeled to estimat e the absolute risk because it is very rare in 25 the general population (Hillerdal, 1983).. Lung ca ncer mortality does have a known background 26This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-60 risk and thus, modeling of lung cancer mortality is based on the relative risk rather than the 1 absolute risk. As such, there are different analyt ic methods available which can use information 2 on time-varying exposures. The NIOSH-developed ind ividual-level exposure data for the Libby 3 worker cohort are very detailed, with start and sto p dates for each of the workers’ jobs and 4 estimated fiber exposures for 25 specific location- operations (Amandus et al., 1987). It is, 5 therefore, important to find a model that makes efficient and effective use of these time6 dependent data. 7 The Cox proportional hazards model (Cox, 1972) is o ne of the most commonly used 8 statistical models used for the epidemiologic analy sis of survival and mortality in cohort studies 9 with extensive follow-up (Larson et al., 2010a; Moo lgavkar et al., 2010). In the Cox model, the 10 conditional hazard function, given the covariate Z, is assumed to have the form 11
12 13 

### ) exp( ) ( ) | ( 0 

### Z

t Z t T



### β λ λ = (Eq. 5-7) 14
15 16 where β is the vector of regression coefficients, λ 0 (t) denotes the baseline hazard function, and T 17 denotes transposition. One of the strengths of thi s model is that knowledge of the baseline risk 18 function is not necessary and no particular shape i s assumed for the baseline hazard; rather, it is 19 estimated nonparametrically. The contributions of covariates to the hazard are multiplicative. 20 When Z represents exposure and β T Z is small, the Cox proportional hazards model is c onsistent 21 with linearity of dose response for low doses. 22 When the proportional hazards assumption holds, it is possible to estimate the hazard 23 ratio of exposure (relative risk) without estimatin g the hazard function in the unexposed (or in 24 the lowest exposures seen within the study group) s ince this baseline hazard function drops out 25 of the calculations. The Cox proportional hazards model assumes that a function of covariates 26 (i.e., exposures) result in risks that are a consta nt multiple of the baseline hazard in unexposed 27 individuals over some time scale, typically calenda r time or age. This proportionality is assumed 28 to be constant across the range of observed exposur es, given the set of modeled covariates, and 29
can be evaluated across time. 30 The Cox proportional hazards model was chosen to mo del the lung cancer mortality data 31 for several reasons. Of primary importance is that it takes statistical advantage of the extensive 32 exposure data collected for the cohort on time-vary ing exposures to Libby Amphibole asbestos. 33
There are no other standard model formulations that allow for the analysis of time-varying 34 exposures in the manner achieved by the Cox proport ional hazards models. The exposure35 response relationship (proportional hazards ratio) determined in this model intrinsically takes 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-61 into account the effects of other causes of mortali ty that are unrelated to exposure (i.e., 1 independent censoring). Further, all comparisons a re made within the cohort by comparing the 2 mortality experience of people with different expos ures within the same cohort population. The 3 issue of competing risks that are dependent on expo sure (e.g., asbestosis or non-malignant 4 respiratory disease) is an acknowledged uncertainty for this type of analysis (see Section 5.4.6). 5 Other methods common to occupational epidemiology, such as the use of standardized 6 mortality ratios (SMRs), typically rely upon compar isons of the mortality experience in an 7 exposed population group compared to that in the ge neral population. However, the comparison 8 population may not always be appropriate due to dif ferences in general health status (e.g., the 9 healthy worker effect) and differences in exposure to other risk factors for a specific disease 10 (e.g., smoking history). The lack of comparability between the study population and the 11 comparison population can lead to confounding by ot her measured or unmeasured characteristics 12 which may be statistically associated with both the exposure of interest and the endpoint. The 13 Cox proportional hazards model controls for such po tentially confounding characteristics by 14 using a comparison group from within the study popu lation (i.e., internal controls). Internal 15 controls are a statistically appropriate comparison group because they are expected to be more 16 similar in potentially confounding characteristics to the remainder of the cohort, thereby 17 controlling for both measured and unmeasured confou nding and helping ensure that comparisons 18 are more statistically valid. 19
20 Results of lung cancer mortality analysis 21 As described in the previous section, quantitative exposure-response relationships for 22 lung cancer mortality were evaluated using the Cox proportional hazards model. Cox models of 23 this type require the specification of a time scale . Age is typically the time-related variable, with 24 the strongest relationship to cancer mortality, and was used as the timescale in these analyses. 25 Use of age as the time scale in a time-varying Cox proportional hazards model controls for age 26 as a risk factor by design rather than by parametri c modeling and effectively rules out age as a 27 potential confounder. Individual covariates availa ble to EPA in the complete analytic dataset 28 compiled from the NIOSH data were evaluated for the ir ability to explain the lung cancer 29 mortality. These included sex, race, birth year, a ge at hire, and various exposure-related 30 variables including TWA workplace intensity of expo sure in fibers/cc, job type, and the start and 31 stop date of each different job. These data allowe d for the computation of cumulative exposure, 32 cumulative exposure with application of a half-life , and residence time-weighted cumulative 33 exposure (with and without application of a half-li fe). Each exposure metric was also lagged by 34 0, 10, 15, or 20 years. The use of a lag period ai ms to account for the latency period between the 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-62 onset of lung cancer (which occurs some time before clinical diagnosis) and lung cancer 1 mortality. 2 All lung cancer mortality analyses were conducted u sing SAS software version 9.1 (SAS, 3 Cary, NC). There are two forms of the Cox model co nsidered in this evaluation: the standard 4 model, which allows only time-independent covariate s, and the extended Cox model, which 5 allows for time-dependent variables. Both forms of the Cox model rely on the assumption that 6 the hazards are proportional. 7 Standard Cox proportional hazard models assess the risk of exposures at one particular 8 point in time (e.g., death or loss to follow-up). While an individual’s exposures may have 9 changed over time, the only exposure value included in the analysis is the value of the exposure 10 metric at the time of the event (i.e., death) or ce nsoring at the end of the mortality follow-up 11 period on December 31, 2006. Since Cox proportiona l hazard models rely on the assumption 12 that the hazard rate among the exposed is proportio nal to the hazard rate among the unexposed, it 13 is important to test the model against this assumpt ion. Therefore, analyses of standard Cox 14 models (based on a single summary exposure value fo r each person) tested this assumption using 15
a Wald test on the model interaction term between t he Libby Amphibole asbestos exposure 16 metric and the time scale (i.e., age). As a genera l rule, a nonzero slope that is either increasing o r 17 decreasing indicates a violation of the proportiona l hazards assumption. Wald tests for the 18 complete cohort showed that the interaction term wa s a statistically significant predictor of lung 19 cancer mortality (p < 0.05) and was interpreted as evidence that the hazards did not remain 20 proportional over time. The cause of the lack of p roportionality is unknown, but several likely 21 explanations are discussed in Section 5.4.3.3. 22 Having observed that the proportional hazards assum ption was violated when using the 23 time-independent exposure data, the EPA fit a varia tion of the extended Cox proportional 24 hazards model (Kleinbaum, 1996; Tableman and Kim, 2 004), which included both time25 independent factors such as sex, race, and date of birth, as well as time-dependent measures of 26 Libby Amphibole asbestos exposure over the entire t ime course of each individuals’ lifetime 27
from their date of hire until death or loss to foll ow-up. This method also allows for control of 28 potential confounding by age by design rather than through multivariate covariate modeling. 29 EPA’s analyses of time-dependent exposure data also checked the proportionality 30 assumption for the Libby worker cohort and again fo und that it failed to remain proportional 31 across the time scale based on age given the modele d covariates. While this methodology of 32 using the extended Cox proportional hazards model d oes not have as strong a dependence on the 33 assumption of truly proportional hazards (Kleinbaum , 1996; Tableman and Kim, 2004) it is still 34 preferable to have the proportionality assumption h old. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-63 1
5.4.3.3. Summary of Analysis of Libby Worker Cohort 2 Several possible explanations exist for the finding that duration of employment was the 3 best fitting exposure metric for mesothelioma morta lity, as well as the finding of the lack of 4 proportionality of hazards in the lung cancer morta lity modeling. 5
6 1) Duration of employment, but not job category, was k nown for 686 of 991 (69%) workers 7 hired from 1935 to 1959. Without knowledge of the job category, the same exposure 8 concentration had been assigned to 653 of these wor kers, likely resulting in a particularly 9 large measurement error for exposure in approximate ly one third of the total cohort of 10 1,871 workers. This is a very likely explanation f or the superior fit for duration of 11 employment in modeling of mesothelioma mortality re lative to the other exposure 12 metrics based on measured exposures. Assigning the same exposure concentration to so 13 many of the workers hired before 1960, regardless o f job, likely resulted in significant 14 exposure misclassification. Random error in exposu re measurements generally attenuates 15 the strength of epidemiologic associations between exposure and observed effect, 16 weakening the predictive ability of any of the expo sure-based metrics compared to 17 duration of employment, which was more accurately d etermined for all workers in the 18 cohort. 19

2) Even where the job category was identified, few exp osure data exist prior to 1968. For 20 the majority of job locations (23 of 25), no exposu re measurements were available prior 21 to 1967, and so exposures were estimated based on e mployee interviews (conducted in 22 1982) to determine what was known about major chang es in operations between 1935 23 and 1967. For two job locations, dust–to--PCM extr apolations are based on 24 measurements taken in the late 1960s, so extrapolat ing from the mid-1960s to the early 25 1960s is likely to be more certain than extrapolati ng further back in time. Random error 26 in these exposure measurements would also generally attenuate the strength of 27 association between exposure and observed effect du ring the earlier years of mine 28 operation and, thus, a greater degree of measuremen t error in the earlier years could have 29 resulted in the lack in proportionality of the haza rd ratios for lung cancer over time. A 30 greater degree of measurement error in the earlier years could also provide an explanation 31 for the worse fit of the mesothelioma models that i ncorporated these exposure measures. 32

3) Another explanation for the lack of proportional ha zards in modeling lung cancer 33 mortality may be that this cohort has an anomalous age structure due to the hiring of 34 much older individuals during the time of the Secon d World War. Among those workers 35 in the cohort hired prior to 1960, 9% were older th an 50 years, and 22% were older than 36 40 years. Among those workers hired in 1960 or aft erwards, only 4% were older than 50 37 years, and 14% were older than 40 years. Older wor kers differ from younger workers in 38 several potentially important ways that could alter their response to exposures. Older 39 workers were born in a different era, with differen t nutritional and public health standards 40 which may influence mortality patterns. 41This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-64 4) The lack of proportional hazards in modeling lung c ancer mortality may also be a 1 reflection of confounding or effect modification, w hich can change in magnitude over 2 time. The most likely candidate for confounding or effect modification is smoking. 3 NIOSH records show that of the 1,871 workers in the full Libby workers cohort, 1,121 4 workers (60%) were missing smoking status data, whi le 750 (40%) had data with values 5 ‘S’ (Smoker), ‘Q’ (Former Smoker), or ‘N’ (Non-smok er). Given this high percentage of 6 missing values, EPA did not consider these smoking data to be adequate for use in the 7 evaluation of confounding or effect modification. 8

Smoking rates, over time, among the sub-cohort of workers hired after 1959 are likely to 9 have been more similar since smoking rates change m ore slowly over shorter periods of 10 time than over longer ones. This restriction in ti me period of hiring would also result in 11 less variation by birth year cohort, which is stron gly related to smoking patterns as people 12 of different generations developed different smokin g rates. Thus, this restriction in the 13 time period of hiring may make the cohort members m ore similar to each other thereby 14 reducing the potential impact of any smoking-relate d confounding. Further discussion of 15 the relevance of smoking can be found in the sectio n on uncertainties (Section 5.4.6). 16

17 When the assumption of proportionality is not met, the potential influence of 18 confounding factors in the full-cohort analysis is of concern. Additionally, the lack of job 19 category information for 69% of the workers hired p rior to 1960 and greater measurement error 20 in early exposures may result in significant random exposure measurement error, which may bias 21 the observed exposure-response relationships toward s the null. 22 Although duration of employment was the best exposu re metric for modeling 23 mesothelioma mortality in the full cohort, it made quantitatively estimating an exposure-response 24 relationship difficult. In addition, violation of the underlying statistical assumptions adversely 25 impacted modeling of lung cancer mortality in the f ull cohort. Therefore, EPA chose to 26 undertake a sub-cohort analysis. 27 In particular, because uncertainty in retrospective assessment of workplace exposures is 28 reduced in the later years, EPA decided to analyze a sub-cohort of all the workers with as late a 29 starting employment date as possible, while still m aintaining a sufficient number of lung cancer 30 and, especially, mesothelioma mortalities. The las t of the 686 workers with only duration of 31 employment available as an exposure metric was hire d in 1959, and so EPA developed a sub32 cohort analysis by dividing the total cohort into those hired prior to 1960 (n = 991) and those 33 hired after 1959 (n = 880). This cut point roughly divided the cohort in half. For the sub-cohort 34 of those workers hired after 1959, there were suffi cient numbers of both mesothelioma and lung 35 cancer mortalities to apply the Poisson and Cox pro portional hazards model, correspondingly. 36 EPA initially examined the fit of these models usin g several exposure metrics to predict 37 mortality from mesothelioma and found that in this sub-cohort, the exposure metrics that 38This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-65 included information on exposure concentration prov ided superior statistical fits to the exposure 1 metrics based only on employment duration. In this same sub-cohort, the assumptions of the 2 Cox proportional hazards model were also satisfied for the modeling of time-varying exposure. 3 While it is generally true that the use of more dat a is an advantage in statistical analyses 4 because it allows for the computation of more stati stically precise effect estimates, this advantage 5 in precision may be offset by a negative impact on the accuracy of the effect estimate if an 6 increase in sample size is accompanied by greater e xposure misclassification or other biases. 7 EPA’s use of the sub-cohort analysis is based on th e belief that it is important to accurately 8 estimate the true underlying exposure-response rela tionships by relying on the most accurate 9 exposure data (see also Section 5.4.6 on uncertaint ies). 10
11
5.4.3.4. Analysis of Sub-cohort of Employees Hired After 195 9 12 5.4.3.4.1. Results of analysis of mesothelioma mor tality in the sub-cohort 13 Of the 880 workers hired after 1959, 230 (26%) had died by December 31, 2006. The 14
number of mesothelioma deaths in the sub-cohort is seven (2 deaths coded in ICD-10 and 5 15 deaths coded in ICD-9), and the mesothelioma death rate of 24.7 per 100,000 person-years for 16
the sub-cohort is similar to the mesothelioma death rate of 26.8 per 100,000 person-years for the 17 full cohort (18 mesothelioma deaths), with a differ ence of less than 10%. 18 Table 5-11 shows the relative fit of various exposu re metrics for mesothelioma mortality 19 in the sub-cohort hired after 1959, including only those exposure metrics whose information 20 weight was greater than 0.01. Information weights are computed from the reported DICs 21 (Burnham and Anderson, 2002). As discussed below, metrics with higher DICs and lower 22 information weights are unlikely to provide a good fit and are thus not included in Table 5-11. 23 Information weights are commonly used in Bayesian a nalyses. Information weights can be 24 computed by first assessing the differences between the best DIC and each of the others ( ∆ DIC i ) 25
26 27

1 1 1 exp exp 2 2

R

i i i

r DIC w DIC DIC

=     = − ∆ − ∆        



## ∑ (Eq. 5-8) 28

29 Table 5-11. Comparison of model fit of exposure me trics for mesothelioma 30 mortality in the sub-cohort hired after 1959 a,b . Only the model fits with 31 information weights greater than 0.010 are shown 32 33 Exposure metric Lag(yr) DIC Information Weight CE with 5yr 1⁄2 life 15 70.6 0.428This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-66 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 Lower DIC values represent better fits. 2

b Since one of mesothelioma deaths occurred in less than 20 years from start of the exposure, lag 20 me trics assigned 3 no exposure to this case and the very poor fit of l ag 20 metrics is a result. 4 DIC = Deviance Information Criterion. 5 6 7 The other exposure metrics that were fit included those metrics used in the full cohort 8 analysis (duration of employment, time since first exposure, age at death or censoring, RTW 9 metrics, CE with lag metrics and IRIS IUR (1988) me tric), but all of them fit worse than any of 10 the metrics in Table 5-11, irrespective of possible penalization for extra parameters as discussed 11 in the analysis of the full cohort. The two metric s with cumulative exposure lagged 15 and 10 12 years, both with 5-year half-life, providing the tw o best fits as indicated by their lower DIC 13 values and higher information weights (Table 5-11). Cumulative exposures lagged 10 or 15 14 years, both with 10-year half-life, provided the ne xt two best fits according to DIC values, but 15 models including each of these metrics exhibited no ticeably lower information weights than the 16
best metric. All metrics in Table 5-11 contain a d ecay term and have the same number of 17 parameters in their corresponding model, allowing f or a direct comparison of the DIC values 18 (DICs are similar to AICs in what is considered an important difference) and information 19 weights. 20 It is important to note that the suite of exposure metrics which were applied in this 21 assessment to modeling mesothelioma mortality encom pass the range of choices described in 22 the asbestos literature including CE, RTW and decay metrics as well as the IRIS IUR(1988) 23
metric. 24This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-67 In the sub-cohort hired after 1959, the DIC value f or mesothelioma using the IRIS IUR 1 (1988) metric (Eq. 5-5) is substantially higher (DI C=98.4) than for any of the metrics in 2 Table 5-11. This indicates that the IRIS IUR (1988 ) metric does not provide as good a fit for the 3 Libby Amphibole asbestos worker cohort, using the e stimated historical exposure levels, as the 4 other metrics in Table 5-11. Setting the exponents in the IRIS IUR (1988) metric to the values of 5 2 and 4, as suggested by Nicholson et al. (1980), d id not improve the fit of the metric to the 6 Libby Amphibole asbestos worker cohort data (result s not shown). A substantial difference of 7 this analysis from the IRIS IUR (1988) modeling is that this analysis is based on individual-level 8 data, whereas IRIS IUR (1988) application was to ag gregate data. Also, cohorts used in IRIS 9 IUR (1988) did not include cohorts exposed to Libby amphibole asbestos and Libby Amphibole 10 asbestos may be different from other types of asbes tos. Alternately, the relative fit of this model 11 may have been affected by uncertainties in the esti mated exposure described in detail in 12
Section 5.4.6. 13 Next, EPA considered which covariates should be add ed to the model with the exposure 14 metric that provided the best fit. The addition of covariates ‘age at death or censoring’ and ‘time 15 since first exposure’ did not improve the fit, as m easured by DIC (results not shown). 16 As described in Section 5.4.1.5, only metrics with non-zero lag were retained for 17 derivation of unit risks. Table 5-12 shows slopes and credible intervals for all retained metrics 18 from Table 5-11. The units of the slopes are fiber s/cc-year. These slopes and credible intervals 19 represent calendar year continuous environmental ex posure as described above and define the 20 ‘Exposed Hazard Rate’ in the life-table procedure w hen multiplied by the exposure level (see 21 Appendix G for details). 22 Based on the results from the exposure metric with the lowest DIC (cumulative exposure 23 with a 5-year half-life for decay and a 15-year lag for cancer mortality latency), the slope was 24
2.06 x 10 -4 per fiber/cc-year based on a 365-day calendar year and the 95% upper bound on the 25 slope was 3.43 x 10 -4 per fiber/cc-year. This point estimate and 95% up per bound represent the 26 relative risk (including statistical uncertainty wi thin the exposure metric) of mesothelioma 27 mortality observed from exposure to Libby Amphibole asbestos fibers in the worker cohort for 28 this exposure metric. Issues related to uncertaint y in the choice of exposure metric are described 29
further in the section on the derivation of the com bined IUR of mesothelioma and lung cancer 30 (Section 5.4.5.3). 31This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-68 Table 5-12

. Mesothelioma mortality exposure metrics fits, slo pes, and 1 credible intervals 2 3 Exposure metric Lag years DIC Slope x10 - 5 90% 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 5.4.3.4.2. Results of the analysis of the lung can cer mortality in the sub-cohort 6 EPA based its final analyses for lung cancer mortal ity on the subset of workers hired after 7 1959. Thus, this analysis is based on 32 deaths fr om lung cancer 2 (ICD-8: two deaths with the 8 code 162.1; ICD-9: one death with the code 162.2, 2 0 deaths with the code 162.9; ICD-10: nine 9 deaths with the code C349) out of 230 deaths that o ccurred in sub-cohort of 880 workers. 10 All multivariate Cox proportional hazards models wi th time-varying exposures were 11 initially fit, using one exposure metric at a time, to the sub-cohort hired after 1959 employing, 12 with covariates for sex, race, and date of birth. Lung cancer mortality was modeled using 13 cumulative exposure and residence-time-weighted exp osure, where each metric was potentially 14 modified by four different half-lives (5, 10, 15, o r 20 years). Each of these exposure metrics was 15 also evaluated with four different lag period to al low for cancer latencies of 0, 10, 15, or 20 16 years. The lag period is defined as immediately pr ior to observed cancer death, where exposure 17 is not considered to be causally related to mortali ty. In all, 40 exposure-response multivariate 18 models were evaluated for the adequacy of the expos ure metric to fit the epidemiologic data. 19 Each exposure metric and the comparative model fit statistics are presented in Table 5-13. 20 The assumptions of the Cox proportional hazards mod els were re-evaluated for the sub21 cohort. Restricting the cohort addressed each of t he previously listed potential explanations for 22

2 Note that in the full cohort, it was unclear wheth er there were cases of tracheal cancer included in the definition of lung cancer as many of the recorded ICD codes on de ath certificates did not provided sufficient detail to distinguish tracheal cancer cases from lung cancer cases. Howe ver, among the sub-cohort of workers hired after 19 59, 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.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-69 Table 5-13. Model fit comparison for different exp osure metrics and lung 1 cancer mortality associated with Libby Amphibole as bestos, controlling for 2 age, gender, race, and date of birth 3 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 0.000 9 CE 10 361.073 CE 5yr 1⁄2 life 10 358.502 0.0075 0.001 0
CE 15 363.124 CE 15yr 1⁄2 life 10 358.777 0.0084 0.00 15 CE 20 364.964 CE 20yr 1⁄2 life 10 359.122 0.0098 0.00 22 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 0.01 88 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 0.121 5 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 0.290 8
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 10 364.4 77 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 20 364.58 8 0.0969 0.4815 RTW 15yr 1⁄2 life 0 362.714 RTW 20yr 1⁄2 life 20 364.62 8 0.0985 0.5763 5This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-70 Table 5-13. Model fit comparison for different exp osure metrics and lung cancer mortality associated with Libby Amphibole as bestos, 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 20 364.6 62 0.0998 0.5909
RTW 15yr 1⁄2 life 15 365.001 CE 20yr 1⁄2 life 20 364.70 3 0.1014 0.5530 RTW 15yr 1⁄2 life 20 364.662 RTW 10yr 1⁄2 life 20 364.7 19 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 20 364.83 1 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 15 364.9 62 0.1124 0.8173
RTW 5yr 1⁄2 life 0 361.593 CE 20 364.964 0.1125 0.820 4 RTW 5yr 1⁄2 life 10 363.224 RTW 15 364.990 0.1136 0.8 809 RTW 5yr 1⁄2 life 15 364.768 RTW 15yr 1⁄2 life 15 365.00 1 0.1141 0.9100
RTW 5yr 1⁄2 life 20 364.831 RTW 20yr 1⁄2 life 15 365.01 1 0.1146 0.9599 1 CE: Cumulative exposure with or without exponential decay modeled with different half-lives. RTW: Res idence2 time weighted exposure with or without exponential decay with different half-lives. AIC: Akaike Infor mation 3 Criterion. 4 5 6 the lack of hazard proportionality (see Section 5.4 .3.3). First, measurement error for exposures 7 is likely to have been smaller after 1959 for sever al reasons. One reason is that the 686 workers 8 for which job category information was missing duri ng part or all of their employment were 9 removed from the analysis. Also, beginning in 1968 , fiber concentrations by PCM analysis of 10 site-specific air samples were available for all lo cation operations to inform the JEM. Prior to 11 1968, the exposure intensity for 23 of 25 location operations was estimated based on reasoned 12 assumptions informed by employee interviews in the early 1980s. It is likely the uncertainty of 13 these reasoned assumptions increased over time, mak ing the earliest exposure estimates (1940s 14 and 1950) less certain than those only a few years before fiber count data were available. 15 Finally, between 1956 and 1967, dust-to-PCM extrapo lation data were used to estimate 16 exposures in the dry mill based on measurements tak en in the late 1960s. Although there is some 17 uncertainty in the conversion ratio selected by Ama ndus and coworkers (1987a), conversions 18 from dust to fiber counts is likely to be less unce rtain than extrapolations further backwards in 19 time to the 1950s and 1940s, where only one air sam ple for dust was available in 1944. Thus, 20This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-71 the potential attenuation effect of non-differentia l measurement error is likely to be reduced by 1 examining the post-1959 cohort alone compared to th e entire cohort. 2 In addition, by focusing on the more homogeneous ag e distribution of workers hired after 3 1959, concerns about differential cancer mortality latency were diminished. Third, smoking 4 rates among this more narrowly defined sub-cohort a re likely to have been more homogeneous, 5 and thus, restricting analysis to this sub-cohort w ould help to limit any potential confounding due 6 to smoking. Finally, when the extended Cox proport ional hazards model was applied to the sub7 cohort hired post-1959, there was no evidence to re ject the hypothesis of proportionality, and the 8 exposure models demonstrated adequate fits to the d ata, with statistically significant effect 9 estimates. In each of the Cox proportional hazards model analyses with time-varying 10 exposures—across all the exposure metrics and acros s all the lag lengths—no violations of the 11 assumption of proportionality of hazards were found

. 12 As the exposure-response models cannot strictly be considered to be nested, a standard 13
measure of fit called the Akaike Information Criter ion (AIC [Burnham and Anderson, 2002]) 14 was used for comparison of goodness of fit across m odels based on the same data set. In their 15 text on model selection, Claeskens and Hjort (2008) state that ‘for selecting a model among a list 16 of candidates, Akaike’s information criterion (AIC) is among the most popular and versatile 17 strategies.’ Smaller AIC values generally indicate a better fitting model relative to larger AIC 18 values. While large differences in AIC values can reveal important differences in model fit, 19
small differences are less conclusive. Nonetheless the model yielding the smallest AIC ‘is 20 judged the best one’ (Claeskens and Hjort, 2008) an d it is a common practice in environmental 21 epidemiology to select the single model with the be st statistical fit among the models that were 22 evaluated. However, models differing in AIC by 2 u nits or less may not always be meaningfully 23
differentiated from each other on the basis of AIC (Burnham and Anderson, 2002). 24 Table 5-13 shows the models and exposure metrics or dered by fit. Of interest is whether 25 there are models with distinct exposure metrics tha t adequately fit these data (as measured by 26 statistical significance of the model p-value) and then, a measure of relative weight among these 27 adequately fitting models. Of the 40 exposure-resp onse metrics, 14 demonstrated an adequate fit 28 to the data as measured by the overall model fit, w ith the likelihood ratio test being statistically 29 significant (p < 0.05), as well as having statistic ally significant exposure metrics (p < 0.05). 30 However, note that only the nine models that demons trated adequate model and exposure metric 31 fit and incorporated a lag period to account for lu ng cancer mortality latency were advanced for 32 potential use in developing a unit risk. While met rics that did not include an adjustment for lag 33 on the exposure metric to account for cancer mortal ity latency were fit to these data for the sake 34 of completeness, they were dropped from further con sideration because they implicitly assume 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-72 no passage of time between the initiation of cancer , subsequent promotion of that cancer, and 1 mortality. 2 Several general patterns were discernable with resp ect to which exposure metrics best 3 predicted lung cancer mortality when comparing AICs for relative model fit. The data show that 4 lagging exposure by 10 years best predicts lung can cer mortality compared to other lags. This 5 trend is seen across both the cumulative exposure w ithout decay and the various half-life 6 cumulative exposure metrics where a 10-year lag of exposure best predicts lung cancer mortality 7 for all cumulative exposure metrics compared to oth er lags; metrics with 15-year lags were 8 generally the next best in terms of fit. Another c lear conclusion is that the models that included 9 residence time-weighted exposure metrics, regardles s of half-life or lag, were less suitable than 10 the models that employed cumulative exposure and it s variants. 11 Among the 40 exposure metric models that were evalu ated, the exposure model with the 12 lowest AIC value was for cumulative exposure with a 10-year half-life for decay and a 10-year 13 lag for cancer mortality latency and had a model p- value of 0.0071 (Table 5-13). This 14 multivariate model controlled for age, gender, race , and date of birth. The extended Cox model 15 with time-varying exposures estimated a slope (beta ) of 1.26 x 10 -2 per fiber/cc-year based on a 16 365-day calendar year 3 and the 95 th percentile upper bound on this parameter was 1.88 x 10 -2 per 17 fiber/cc-year. The p-value for the Libby Amphibole asbestos regression coefficient (slope) was 18 <0.001 indicating that this parameter was statistic ally significantly greater than zero. 19 According to the model results presented in Table 5 -13, there were other exposure 20 metrics that predicted lung cancer mortality and ex hibited statistically significant effect 21
estimates. Several other metrics were considered t o fit nearly as well as the model with the 22
smallest AIC since their AIC values were within two units of the exposure model with the lowest 23 AIC, a proximity that can be considered to be a ran ge that cannot clearly differentiate between 24 models (Burnham and Anderson, 2002). As each of th e other exposure metrics was based on a 25 different reorganization of the same exposure data, the different slopes are not directly 26 comparable, but all adequately fitting lagged model s also produce statistically significant slopes 27 for the exposure-response relationship ( p < 0.05). Of particular note are the results of th e 28 cumulative exposure model, with a 10-year lag for l atency, but without a decay function, since it 29 showed the lowest AIC among non-decay models. 30 The AIC values for models that included lag and/or half-life adjustments to the exposure 31 metrics were not penalized in the regression analys es for using these extra parameters because 32 these factors were not represented as covariates bu t rather were embedded in the computation. 33

3 The two-sided 90% confidence interval is (6.00 x 1 0 -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 a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-73 While these results were obtained using each instan ce of lag and/or half-life terms in separate 1 model fit, it may be appropriate to mathematically penalize the AICs for inclusion of these 2 additional parameters. AIC values, as typically co mputed by regression software, include the 3 addition of a penalty for model complexity as measu red by the number of parameters that are fit 4 in the regression model (thereby increasing the AIC ). In the AIC calculations presented in Table 5 5-13 the models are treated as having the same numb er of parameters since each model 6 represents the same exposures in a different way bu t with a single exposure parameter in the 7 regression models and are, therefore, equally penal ized in software’s AIC calculation. However, 8 an argument can be made that exposure metrics, whic h do not include a decay function with their 9 half-life term, are implicitly more parsimonious (s impler) because they do not include decay. 10 Statistical adjustment for this type of model compl exity is not straightforward, as an exposure 11 metric without decay can also be thought of as an e xposure metric with a sufficiently long decay 12 (since human life is finite). In this sense, if th e models without decay are indistinguishable from 13 models with a sufficiently long half-life, then the re is no difference in the inherent complexity. 14 If the decay model fits were penalized for the incl usion of the decay function in the computation 15 of the exposure metric then with such an adjustment , the relative fit of the CE models would 16 improve as compared to the values in Table 5-13 (AI Cs are generally penalized 2 units for each 17 additional parameter). However, the difference betw een the lowest AIC and the AIC of the CE 18 model without decay exceeds any reasonable penalty, and thus the model results for cumulative 19 exposure with a 10-year half-life for decay and a 1 0-year lag for cancer mortality latency would 20 still have the lowest AIC regardless of whether a p enalty was applied. 21 Table 5-14 displays the lagged exposure-response mo dels and metrics with adequate 22 model fit (p < 0.05) to the epidemiologic data that were further considered. Table 5-14 shows 23 slopes and confidence intervals for all retained me trics from Table 5-13. The units of the slopes 24 are fibers/cc-year. These slopes and confidence in tervals represent calendar year continuous 25 environmental exposure as described above and defin e the ‘Exposed Hazard Rate’ in the life26 table procedure when multiplied by the exposure lev el (see Appendix G for details). 27
28 5.4.3.4.3. Summary of results of the analysis of t he lung cancer mortality in the sub-cohort 29 As presented in Table 5-14 the CE model with 10 yea r half-life and lag provided an 30 adequate fit to the data (p < 0.05) and had the low est AIC value. The cumulative exposure 31 model with a 10-year lag also yielded a statistical ly adequate fit to these data (p < 0.05), as did 32 several decay models with a 15-year lag. These res ults demonstrate reasonable uncertainty in the 33 metric of exposure such that no single exposure mod el can be definitively selected based on 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-74 goodness of fit alone. Uncertainty in the metric o f exposure is addressed in Section 5.4.5.3 as 1 IUR is based on the plausible upper bound of the ef fect estimate. Based on the results from the 2 Table 5-14. Lung cancer mortality exposure metrics fits, slopes, and 3 confidence intervals 4 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 (0.0 063, 0.0189) CE 5yr 1⁄2 life 10 358.502 0.0179 0.0055 0.0010 (0.00 89, 0.0269) CE 15yr 1⁄2 life 10 358.777 0.0106 0.0033 0.0015 (0.0 052, 0.0160) CE 20yr 1⁄2 life 10 359.122 0.0095 0.0031 0.0022 (0.0 044, 0.0146) CE 5yr 1⁄2 life 15 359.910 0.0155 0.0052 0.0032 (0.00 69, 0.0241) CE 10yr 1⁄2 life 15 360.543 0.0115 0.0043 0.0079 (0.0 044, 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 (0.0 031, 0.0163) CE 20yr 1⁄2 life 15 361.533 0.0087 0.0039 0.0254 (0.0 023, 0.0151)

6 7 lowest AIC multivariate model (i.e., cumulative ex posure with a 10-year half-life for decay and 8 a 10-year lag for cancer mortality latency), the sl ope was 1.26 x 10 -2 per fiber/cc-year based on a 9 365-day calendar year and the 95% upper bound on th e slope was 1.88 x 10 -2 per fiber/cc-year. 10 This point estimate and 95% upper bound represent t he relative risk (including statistical 11 uncertainty within exposure metric) of lung cancer mortality observed from exposure to Libby 12 Amphibole asbestos fibers in the worker cohort for this exposure metric. Issues related to 13 uncertainty in the choice of exposure metric are de scribed further in the section on the derivation 14 of the combined IUR of mesothelioma and lung cancer (Section 5.4.5.3). 15
16 5.4.3.4.4. Sensitivity analysis of the influence o f high exposures in early 1960s on the model 17 fit 18 As discussed in Section 5.4.2.5, the comparison of model fit between various exposure 19 metrics is an empirical process and does not reflec t either a specific biological or other factor as 20 an underlying cause for model fit. Although data d o not exist to evaluate biological bases for 21 model fit, other potential factors can be explored where data allow. For example, because of 22 concerns that very high (>100 fibers/cc) early (196 0-1963) 8-hour time-Libby Amphibole 23 asbestos TWA exposures may have influenced the rela tive fit of the various exposure metrics, 24 EPA conducted a sensitivity analysis of the impact of removing the 59 workers with such 25This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-75 exposures on the relative model fit. It is importa nt to note that excluding these workers left only 1 5 mesothelioma deaths and 28 lung cancer deaths amo ng 821 workers. The follow-up time for 2 the 880 member sub-cohort was 28,354 person-years ( mean 32.2), while excluding the 59 highly 3 exposed workers resulted in 26,009 person-years (me an 31.7). The reduction in cancer-specific 4 deaths and the abbreviation in the range of exposur es leads to a diminished ability to differentiate 5 the relative fit (i.e. smaller AIC/DIC differences) between the various exposure metrics due to 6 reduced statistical power. 7 For modeling mesothelioma mortality, the absolute a nd relative differences in fit 8 diminished with the exclusion of these highly expos ed workers, but the observation that exposure 9 metrics including decay fit better than exposure me trics without decay was unchanged. 10 Moreover, the relative fit of the metrics remained almost unchanged (Table 5-15). Among this 11 abbreviated data set, the relative fit of the best of cumulative exposure metrics including decay 12
was at least three units lower DIC than the best of cumulative exposure metrics not involving 13 decay (Table 5-15). Abbreviated dataset DIC for mo del used in IRIS IUR (1988) was 69.4. 14
15 Table 5-15. Relative fit of mesothelioma exposure metrics for full and 16 abbreviated data sets. Only the best of non-decay m etrics is shown. DIC 17 cannot be compared across columns as they are based on different numbers 18 of observations. 19 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 22This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-76 For modeling lung cancer mortality, among this abbr eviated data set, based on AIC 1 comparisons, there was no clear distinction between the fit of the same exposure models that fit 2
the sub-cohort of workers hired after 1959 and incl uded highly exposed workers during 1960- 3 1963 (Table 5-16). 4
5 Table 5-16. Sensitivity analysis of model fit comp arison for different 6 exposure metrics and lung cancer mortality associat ed with Libby 7 Amphibole Asbestos controlling for age, gender, rac e, and date of birth. 8 Excludes n = 59 workers who had job codes with expo sure intensity greater than 9 100 f/cc during 1960-63. Lung cancer models presen ted include those with 10 statistically significant multivariate model p-valu e and non-zero lag in exposure. 11 AIC cannot be compared across columns as they are b ased on different numbers 12 of observations 13 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 CE: Cumulative Exposure with or without exponential decay modeled with different 1⁄2 lives. 16 AIC: Akaike Information Criterion. 17 18 19 20 5.4.3.4.5. Additional analysis of the potential fo r confounding of lung cancer results by 21 smoking in the sub-cohort of workers hired after 19 59 22 In the full cohort analysis, the proportional haza rd assumption was not found to hold and 23 it was possible that one of the reasons for this fa ilure was the presence of confounding by 24This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-77 smoking which altered the proportionality of the ha zard rate in the exposed workers compared to 1 the baseline hazard rate over time. By restricting the dates of hire in the sub-cohort, those 2 workers in the sub-cohort may be made more similar to each other in ways that would reduce the 3 potential for confounding by smoking and, in this s ub-cohort, the proportional hazards 4 assumption was found to hold thus reducing concern regarding confounding by smoking. 5 As an additional check on the potential for confou nding, a new method was evaluated to 6 test for confounding by smoking in occupational coh orts which do not have data on smoking. 7 Confounding, which can bias observed results when t here is an uncontrolled variable which is 8 correlated with both explanatory variable and outco me variable, is a distinct concept from effect 9 measure modification (i.e. synergy) which might ref lect different observed effects of exposure to 10 Libby amphibole asbestos among smokers as compared to non-smokers. The extent of effect 11
measure modification cannot be assessed without ade quate data on smoking; however, the issue 12
is discussed in Section 5.4.6. 13 A method has been described by Richardson (2010) to determine if an identified 14 exposure relationship with lung cancer is confounde d by unmeasured smoking in an occupational 15 cohort study. Richardson (2010) demonstrated that an exposure of interest (i.e., Libby 16 Amphibole asbestos) can be used to predict an outco me other than lung cancer such as chronic 17 obstructive pulmonary disease (COPD), which is know n to be caused by smoking, but not 18 thought to be related to the exposure of concern. 4 If a positive relationship is identified where no 19 causal association is suspected, this would suggest that smoking and the exposure metric (Libby 20 Amphibole asbestos) were positively correlated and that the identified exposure-response 21 relationship was, in fact, confounded by smoking. EPA implemented this methodology to model 22 the potential effects of Libby Amphibole asbestos o n the risk of COPD mortality on the sub23 cohort of workers hired after 1959. Using the expo sure metric defined as cumulative exposure 24 with a 10-year lag, the extended Cox model with tim e-varying exposures estimated a slope (beta) 25 for COPD of -0.056 per fiber/cc-year based on a 365 -day calendar year. The p-value for the 26 coefficient (slope) was 0.102 indicating that this parameter was not statistically significantly 27 different from zero. Using the exposure metric def ined as cumulative exposure with a 10-year 28 half-life for decay and a 10-year lag for cancer la tency, the extended Cox model with time29 varying exposures estimated a slope (beta) of -0.13 5 per fiber/cc-year based on a 365-day 30 calendar year. The p-value for the coefficient (sl ope) was 0.116 indicating that this parameter 31 was not statistically significantly different from zero. 32

4 Richardson (2010) cited articles by Rushton (2007a , b) with possible associations between asbestos an d COPD which, if true, would have explained a positive ass ociation 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 a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-78 Summarizing these findings, EPA used the method des cribed by Richardson (2010) to 1 evaluate whether exposures to Libby Amphibole asbes tos predicted mortality from COPD as an 2 indication of potential confounding by smoking and found a non-significant negative 3 relationship, which was inconsistent with confoundi ng by smoking in the sub-cohort of workers 4
hired after 1959. 5
6 5.4.4. Exposure Adjustments and Extrapolation Meth ods 7 The estimated exposures based on job exposure matr ix and work histories are discussed 8 in Section 5.4.2.5. Note that all slopes presented with units of fibers/cc-years are for calendar 9 year and not for occupational year. 10
11 5.4.5. Inhalation Unit Risk (IUR) of Cancer Mortal ity 12
The derivation of the unit risk estimates, defined as the lifetime risk of mortality from 13 either mesothelioma or lung cancer from chronic inh alation of Libby Amphibole asbestos at a 14 concentration of 1 fiber/cc of air, is presented in the following subsections. Note that all slopes 15 are presented as per fiber/cc-year for a 365-day ca lendar year rather than for an occupational 16 year. Also, note that while the slopes are not adj usted for differences in breathing rates and the 17 number of hours of exposure in an occupational (eig ht-hour) day as compared to a whole 18 (24-hour) day, the central risk and unit risk estim ates do incorporate this adjustment. 19
20
5.4.5.1. Unit Risk Estimates for Mesothelioma Mortality 21 The increased risk of mesothelioma mortality attrib utable to continuous fiber exposure 22 was estimated using a life-table procedure based on the general U.S. population. The life-table 23 procedure involved the application of the estimated Libby Amphibole asbestos-specific toxicity 24 to a structured representation of the general U.S. population in such a manner as to yield 25 age-specific risk estimates for mesothelioma mortal ity in the absence of exposure to Libby 26 Amphibole asbestos and in the presence of exposure to Libby Amphibole asbestos. Baseline all27 cause mortality rates were included in the life-tab le in such a way as to enable computation of 28 the specific absolute risk of mesothelioma mortalit y while accounting for other competing causes 29 of mortality (Appendix G). For each age-interval i n the life-table, the effect estimates of the 30 Poisson model analysis (the absolute risk) was used to estimate mesothelioma mortality at a 31 particular exposure level. These age-specific abso lute risks can then be summed over a lifetime. 32 Different exposure levels are evaluated to ascertai n what magnitude of exposure would be 33 expected to produce 1% absolute risk of mesotheliom a mortality. By this method, the 34 exposure-response relationship determined in the Li bby worker cohort is used to estimate 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-79 mesothelioma mortality in the general U.S. populati on that would be expected from continuous 1 lifetime environmental exposure to various concentr ations of Libby Amphibole asbestos. Details 2 of this life-table methodology are shown in Appendi x G. 3 Assuming no background risk for mesothelioma, extra risk is the same as absolute risk. 4 Absolute risk estimates were calculated using the e ffect estimates derived from the modeling of 5 the mesothelioma mortality risk and a life-table an alysis program that accounts for competing 6
causes of death. 5 The unit risk of mesothelioma is computed using t he 95% upper bound to 7 estimate an upper bound for extra risk of mesotheli oma due to Libby Amphibole asbestos 8 exposure. The upper bound calculation is specific to the exposure metric parameters; the effect 9 of metric uncertainty in these values is discussed below in Section 5.4.5.3. As this human health 10
assessment derived a combined IUR for both mesothel ioma and lung cancer mortality, an interim 11 value based on the central effect estimate (rather than the upper bound) is also computed to avoid 12 statistical concerns regarding the combination of u pper bounds. Details are shown in 13 Section 5.4.5.3. This assessment does not directly apply life-table calculations to estimate partial 14 lifetime risk scenarios; the use of the IUR for par tial lifetime extrapolations is discussed in 15
Section 5.4.5.4. 16 U.S. age-specific all-cause mortality rates from t he 2010 National Vital Statistics Report 17 for deaths in 2007 among all race and gender groups combined (NCHS, 2010) were used to 18 specify the all-cause background mortality rates (R o ) in the life-table analysis. The risk with 19 exposure (R x ) was computed up to age 85 years, 6 assuming continuous environmental exposure 20 to Libby Amphibole asbestos. Conversions between o ccupational Libby Amphibole asbestos 21 exposures and continuous environmental asbestos exp osures were made to account only for 22 differences in the amount of air inhaled per day du ring a higher effort occupational shift 23 (8 hours; 10 m 3 ) compared to a standard 24-hour (20 m 3 ) day (U.S. EPA, 1994), as results were 24 already based on a 365-day calendar year. The comp utation of the unit risk involved three steps. 25
As the estimated absolute risk of mesothelioma mort ality was based on the exposure experiences 26 of an occupational cohort and most directly applica ble to adult exposures, the first step was to 27 compute the unit risk for adults. This was achieve d by initiating exposure at age 16 years and 28 maintaining continuous exposure throughout the rema inder of life while allowing for the 29

5 This program is an adaptation of the approach prev iously used by the Committee on the Biological Effe cts of Ionizing Radiation (BEIR, 1988). Compared to life -table methods based on full life exposures from bi rth, the method used here yielded unit risk differences betw een full life exposure to scaled adult-only exposur e between -3% to -2% for the mesothelioma mortality unit risks fo r the two mesothelioma models in Appendix G. A spr eadsheet containing the extra risk calculation for the deriv ation of the LEC 01 for mesothelioma mortality is presented in Appendix G.
6 Note that 85 years is not employed here as an aver age lifespan but, rather, as a cut-off point for th e life-table analysis, which uses actual age-specific mortality rates.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-80 incremental mathematical decay of previously accumu lated exposure. 7 An adjustment was also 1 made in the life-table for the lag period, so that the age-specific risk calculations began at 16 + 2 (the length of the lag period) years of age. The s tandard assumption used by EPA is that the 3 average lifetime spans 70 years. Since the adult-o nly unit risk excluded the first 16 years, the 4 adult-only unit risk based on 54 years was then res caled for an entire life of continuous exposure 5 by multiplying the interim value for adult-only exp osures by 70/54 to cover the childhood years 6 (<16 years) to compute the “adult-based” unit risk. After re-scaling, the resulting “adult-based” 7 lifetime unit risk estimate (in contrast to the uns caled “adult-only-exposure” unit risk estimate 8 obtained from the life-table calculations) may be p rorated for less-than-lifetime exposure 9
scenarios in the same manner as would be used for a n “adult-based” unit risk estimate derived 10 from a rodent bioassay (Section 5.4.5.4). 11
Consistent with the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), the 12 same data and methodology were also used to estimat e the exposure level (EC x ; “effective 13 concentration”) and the associated 95% lower confid ence limit of that exposure level (LEC x ) 14 corresponding to an absolute risk of 1% (x = 0.01). A 1%-risk level is commonly used for the 15 determination of the point of departure (POD) for l ow-dose extrapolation from epidemiological 16 data, and the LEC value corresponding to that risk level was used as the actual POD. 17 The modeling analysis presented above showed that metrics including lag and half-life 18 parameters provided the best empirical fit to the L ibby worker sub-cohort data. Although there 19 is uncertainty in applying these models for occupat ional mortality to estimation of risks for 20 different exposure levels and time patterns (Sectio n 5.4.6), following the recommendations of the 21 Guidelines for Carcinogen Risk Assessment, (U.S. EPA, 2005a), a linear low-dose extrapolation 22
below the POD was used because the mode of action f or Libby Amphibole asbestos for 23 mesothelioma is largely unknown. Using the results of the cumulative exposure model with 24 best-fitting lag and decay parameters, the LEC 01 for the adult-only exposures was determined to 25 be 0.245 fibers/cc, which yielded an adult-based un it risk of mesothelioma mortality of 0.041 26 (POD of 1% divided by the LEC 01 ), which when scaled by 70/54 to encompass the whol e 27 lifespan, yielded a lifetime unit risk of 0.053 per fiber/cc. The value of the risk corresponding to 28 the measure of central tendency involves EC 01 rather than LEC 01 . The EC 01 for the adult-only 29 exposures was determined to be 0.406 per fiber/cc, which when divided into a POD of 1% 30 yielded an adult-based central estimate for mesothe lioma mortality of 0.025, which when scaled 31 by 70/54 to encompass the whole lifespan, yielded a lifetime central estimate of 0.032 per 32
fiber/cc. 33

7 Exposures in the life tables were computed at the mid-point of each age interval and appropriately la gged.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-81 The mesothelioma unit risks for model results prese nted in Table 5-12 and discussed in 1 Section 5.4.3.4.1, are presented in Table 5-17. Al l of the metrics in Table 5-17 are cumulative 2 exposure (CE) metrics lagged 10-15 years (the fit o f 20-year lag models was much worse since 3
one of seven mesothelioma deaths occurred before 20 years; lags longer than 15 years are 4 possible, and this is an uncertainty described in S ection 5.4.6). Issues related to uncertainty in 5 the choice of exposure metric are described further in the section on the derivation of the 6 combined IUR of mesothelioma and lung cancer (Secti on 5.4.5.3). 7
8
Table 5-217

. Mesothelioma mortality exposure metrics unit risk s 9 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 5.4.5.1.1 Adjustment for mesothelioma underascertai nment 13 For mesothelioma, the undercounting of cases (under ascertainment) is a particular 14 concern given the limitations of the ICD classifica tion systems used prior to 1999. In practical 15 terms, this means that some true occurrences of mor tality due to mesothelioma are missed on 16
death certificates and in almost all administrative databases such as the National Death Index. 17 Even after introduction of special ICD code for mes othelioma with introduction of ICD-10 in 18 1999, detection rates are still imperfect (Pinhiero et al., 2004; Camidge et al., 2006) and the 19 reported numbers of cases typically reflect an unde rcount of the true number. Kopylev et al. 20 (2011) reviewed the literature on this underascerta inment and developed methods to account for 21 the likely numbers of undocumented mesothelioma dea ths. 22 Because the analysis of mesothelioma mortality was based on absolute risk, it was 23 possible to compensate for mesothelioma underascert ainment in Libby worker cohort. The 24 mesothelioma mortality unit risk was adjusted, foll owing the analysis of Kopylev et al. (2011). 25This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-82 With the adjustment factor of 1.39 (see Table 3 in Kopylev et al., 2011), the adjusted 1 mesothelioma central risk (based on the EC 01 ), corresponding to the best-fit metric, was 0.044 2 per fiber/cc, and adjusted mesothelioma mortality u nit risk was 0.074 per fiber/cc. This 3 adjustment factor can be used because the number of peritoneal mesotheliomas is partly known 4 and, therefore, the approach following Selikoff and Seidman (1992) provides the most 5 appropriate estimate of the number of mesothelioma mortality cases. 6 Mesothelioma mortality-adjusted unit risks are list ed in Table 5-18 along with their 7 information weights. 8
9 Table 5-18. Adjusted for underascertainment unit r isks for the sub-cohort 10 hired after 1959 corresponding to the different met rics 11 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 5.4.5.2. Unit Risk Estimates for Lung Cancer Mortality 15 As with mesothelioma mortality, the increased risk of lung cancer mortality attributable 16 to continuous fiber exposure was estimated using a life-table procedure based on the general U.S. 17 population. The life-table procedure involved the application of the estimated Libby Amphibole 18 asbestos-specific toxicity to a structured represen tation of the general U.S. population in such a 19 manner as to yield age-specific risk estimated for lung cancer mortality in the absence of 20 exposure to Libby Amphibole asbestos and in the pre sence of exposure to Libby Amphibole 21 asbestos. Baseline all-cause mortality and lung ca ncer-specific mortality rates were included in 22 the life-table in such a way as to enable computati on of the specific extra risk of lung cancer 23 mortality while accounting for other competing caus es of mortality (Appendix G). For each 24 age-interval in the life-table, the effect estimate s of the Cox proportional hazards model analysis 25This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-83 (the relative risk) were used to estimate extra lun g cancer mortality at a particular exposure level, 1 given the background risk. These age-specific extr a risks can then be summed over a lifetime. 2 Different exposure levels are evaluated to ascertai n what magnitude of exposure would be 3 expected to produce 1% extra risk of lung cancer mo rtality. By this method, the exposure4 response relationship estimated in the worker cohor t is used to estimate lung cancer mortality in 5 the general U.S. population that would be expected from continuous lifetime environmental 6 exposure to various concentrations of Libby Amphibo le asbestos under the assumption of a 7 10-year latency for cancer mortality and a 10-year half-life decay of the exposure metric. Details 8 of the life-table methodology are shown in Appendix G. 9 Extra risk is defined as equaling (R x -R o )/(1-R o ), where, R x is the lifetime lung cancer 10 mortality risk in the exposed population and R o is the lifetime lung cancer mortality risk in an 11 unexposed population (i.e., the background risk). Extra risk estimates were calculated using the 12
effect estimates derived from the multivariate mode ling of the lung cancer mortality risk and a 13 life-table analysis program that accounts for compe ting causes of death. 8 The unit risk of lung 14 cancer mortality is computed using the 95% upper bo und to estimate an upper bound for extra 15 risk of lung cancer mortality due to Libby Amphibol e asbestos exposure. As this human health 16
assessment derived a combined IUR of mesothelioma a nd lung cancer mortality, an interim value 17 based on the central effect estimate (rather than t he upper bound) is also computed to avoid 18 statistical concerns regarding the combination of u pper bounds. Details are shown in 19
Section 5.4.5.3. 20 U.S. age-specific all-cause mortality rates from t he 2010 National Vital Statistics Report 21 NVSR 58(19) 2010 for deaths in 2007 among all race and gender groups combined (NCHS, 22 2010) were used to specify the all-cause background mortality rates (R o ) in the life-table 23 analysis. Cause-specific background mortality rate s for cancers of the lung, trachea, and 24 bronchus were obtained from a Surveillance, Epidemi ology, and End Results (SEER) report on 25 mortality during 2003-2007 (SEER Table 15.10, age-s pecific U.S. death rates). 26 The risk with exposure (R x ) was computed up to age 85 years 9 assuming continuous 27 environmental exposure to Libby Amphibole asbestos using the life-table analysis (see 28 Tables G-3 and G-4 in Appendix G for details). Con versions between occupational exposures 29

8 This program is an adaptation of the approach prev iously used by the Committee on the Biological Effe cts of Ionizing Radiation (BEIR, 1988). Compared to life table methods based on full life exposures from bir th, the method used here yielded unit risk differences betw een full life exposure to adult-only exposure betwe en -18% to +6% for the lung cancer mortality unit risks for th e two lung cancer models in Appendix G. A spreadsh eet illustrating the extra risk calculation for the der ivation of the LEC01 for lung cancer mortality is p resented in Appendix G.
9 Rates above age 85 years are not included because cause-specific disease rates are less stable for th ose ages. Note that 85 years is not employed here as an average li fespan 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 a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-84 and continuous environmental exposures to Libby Amp hibole asbestos were made to account 1 only for differences in the amount of air inhaled p er day during a higher effort occupational shift 2 (8 hours; 10 m 3 ) compared to a standard 24-hour (20-m 3 ) day (U.S. EPA, 1994), as results were 3 already based on a 365-day calendar year. The comp utation of the unit risk involved three steps. 4 As the estimated RR of lung cancer mortality was ba sed on the exposure experiences of an 5 occupational cohort and most directly applicable to adult exposures, the first step was to compute 6 the unit risk for adults. This was achieved by ini tiating exposure at age 16 years and maintaining 7 continuous exposure throughout the remainder of lif e while allowing for the incremental 8 mathematical decay of previously accumulated exposu re depending on the particular exposure 9
metric. 10 An adjustment was also made in the life-table forthe lag period, so that the age10 specific risk calculations began at (16 + lag) year s of age. The standard assumption used by EPA 11 is that the average lifetime spans 70 years. Since the “adult-only-exposure” unit risk excluded 12 the first 16 years, the “adult-only-exposure” unit risk based on 54 years was then rescaled for an 13 entire life of continuous exposure by multiplying t he interim value for adult-only exposures by 14 70/54 to cover the childhood years (<16 years) to c ompute the “adult-based” unit risk. After re15 scaling, the resulting “adult-based” lifetime unit risk estimate (in contrast to the unscaled “adult16 only-exposure” unit risk estimate obtained from the life-table calculations) may be prorated for 17 less-than-lifetime exposure scenarios in the same m anner as would be used for an “adult-based” 18 unit risk estimate derived from a rodent bioassay. 19 Although there is uncertainty in applying these mod els for occupational mortality to the 20 estimation of risks for different exposure levels a nd time patterns (Section 5.4.6), following the 21
recommendations of the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), a linear 22 low-dose extrapolation below the POD was used becau se the mode of action for Libby 23 Amphibole asbestos for lung cancer is undetermined. The nine exposure-response models 24 retained from Table 5-13 all had reasonably similar goodness of fits. No single model stands out 25 as clearly statistically superior; however, there i s a range of quality of fit within the set that cou ld 26 be considered adequate. The lung cancer mortality unit risks are shown in Table 5-19. 27 Using the results of the exposure model with the lo west AIC value (i.e., cumulative 28 exposure with a 10-year half-life for decay and a 1 0-year lag for cancer latency) alone, the LEC 01 29 Table 5-39. Subset of lung cancer models with lagg ed exposures that yielded 30 statistically significant model fit (p < 0.05) and exposure metric fit (p < 0.05) 31 to the epidemiologic data 32 33

Exposure metric Lag AIC Exposure
p -value Central risk estimate

(based on EC 01 ) Unit risk

(based on LEC 01 )

10 Exposures in the life tables were computed at the mid-point of each age interval and appropriately la gged.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-85 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 for the adult-only exposures was determined to be 0 .333 fibers/cc which yielded an adult-based 3 unit risk of lung cancer mortality of 0.0300 (POD o f 1% divided by the LEC 01 ) which when 4 scaled by 70/54 to encompass the whole lifespan yie lded a lifetime unit risk of 0.0389 per 5 fiber/cc. The value of the risk that would corresp ond to the measure of central tendency involves 6
EC 01 rather than LEC 01 . The EC 01 for the adult only exposures was determined to be 0.499 per 7 fiber/cc which when divided into a POD of 1% yielde d an adult-based central estimate for lung 8 cancer mortality of 0.0200 which when scaled by 70/ 54 to encompass the whole lifespan yielded 9 a lifetime central estimate of 0.0260 per fiber/cc. 10 Using the results of the exposure model based on cu mulative exposure with a 10-year lag 11 for cancer latency, the LEC 01 for the adult-only exposures was determined to be 0 .191 fibers/cc 12 which yielded an adult-based unit risk of lung canc er mortality of 0.0524 (POD of 1% divided by 13
the LEC 01 ) which when scaled by 70/54 to encompass the whole lifespan yielded a lifetime unit 14 risk of 0.0679 per fiber/cc. The EC 01 for the adult only exposures was determined to be 0.325 15 per fiber/cc which when divided into a POD of 1% yi elded an adult-based central estimate for 16 lung cancer mortality of 0.0308 which when scaled b y 70/54 to encompass the whole lifespan 17 yielded a lifetime central estimate of 0.0399 per f iber/cc. 18 The resulting unit risks in Table 5-19 ranged from 0.0260 to 0.0679 fibers/cc. This 19 shows that the unit risk (i.e., 0.0389 per fiber/cc ) based on the exposure metric with the lowest 20 AIC value (i.e., cumulative exposure with a 10-year half-life for decay and a 10-year lag for 21 cancer latency) is in the center of this range and is thus statistically robust. However, because 22 this estimate is in the middle of the range it does not capture the uncertainty across metrics with 23 similar goodness of fit. As noted (Section 5.4.2.2 ), an argument can be made that the CE metric 24 with a 10 year lag and no half-life is implicitly m ore parsimonious (simpler) because it was not 25 explicitly adjusted to include decay, although this metric is mathematically equivalent to CE 26This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-86 metric with 10 year lag and an infinitely long deca y half-life. Conceptually, the AIC values are 1 penalized for increased model complexity (thereby i ncreasing the AIC). The AIC for the CE 2 models may reasonably be thought to be somewhat low er than through the standard calculation 3 of AIC. However, such a hypothesized adjustment wo uld not alter the identification of the 4 exposure model with the lowest AIC value which rema ins the CE model with a 10 year lag and a 5 10 year half-life. Nonetheless, the CE metric with a 10 year lag does fit these data, is a simpler 6 and more straightforward metric and has an extensiv e tradition of use in the epidemiologic 7 literature and in the practice of risk assessment. 8 Issues related to uncertainty in the choice of expo sure metric are described in the section 9
on the derivation of the combined IUR of mesothelio ma and lung cancer below. 10
11
5.4.5.3. IUR Derivation for Combined Mesothelioma and Lung C ancer Mortality 12 Before risks can be combined, it is important to un derstand several concepts that are 13 pertinent to the evaluation and comparison of the c ancer-specific mortality unit risks that will be 14 combined. First, there is statistical uncertainty in the potency estimate within the exposure 15 response model defined by each exposure metric. Th is within metric uncertainty is accounted 16 for by the 95 th CI around the potency estimates (see Tables 5-6 an d 5-8). Next, there is 17 uncertainty in the choice of metrics for developing IUR (called cross-metric uncertainty, 18 described below). Finally, when unit risks corresp onding to metrics are chosen accounting for 19 uncertainty, these are statistically combined into IUR. Details are provided below. 20 For this assessment, EPA obtained the best availab le demographic, exposure, and vital 21 status data from NIOSH. Subsequently, the best-fit ting statistical models were identified, which 22 were then applied to derive central estimates of th e lifetime mesothelioma and lung cancer 23 mortality risk in the general population exposed t o a continuous concentration of 1 fiber/cc of 24 Libby Amphibole asbestos. Then, the individual exp osure metric-specific risks were calculated 25 as the statistical (95%) upper confidence bounds on these central estimates. Use of the upper 26 confidence bound accounts for uncertainty in the ef fect estimate for each metric—otherwise 27 referred to as the within-metric uncertainty. 28 Another source of uncertainty is the choice of the appropriate exposure metric among a 29 set of results that appear to fit the data similarl y well. This uncertainty is referred to as the 30 between-metric or cross-metric uncertainty. For th e Libby worker cohort data, the best-fit 31 (lowest information criterion values) metrics lead to estimates of risks that are more like mid32 range estimates among the other metrics (Tables 5-1 1 and 5-13) with sufficiently close 33 information criterion values, rather than upper bou nd estimates. While the lung cancer unit risk 34 computed from the model with the lowest AIC appears to be robust, Table 5-19 shows that there 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-87 is a range of possible unit risk values from the se t of models with adequate fit (as measured by a 1 statistically significant p-value for the exposure metric term) and similar goodness of fit. 2 Likewise, for mesothelioma mortality, among the mod els with adequate fit shown in Table 5-17, 3 there is a range of possible unit risk values. 4 The IUR should be a reasonable upper bound on the e xtra risk. As is clear from 5 Tables 5-11 and 5-13 in the preceding sections, the unit risks based on the metrics with the 6 lowest information criterion values provide a lower estimate of cancer mortality risk than some 7 other similarly fitting metrics. While the models with the lowest information criterion values 8 have the greatest statistical support, other models that yield higher unit risks are also statisticall y 9 plausible. This assessment selected the upper boun d unit risk among the plausible exposure 10 metrics (regardless of the small residual differenc es in quality of fit) to account for cross-metric 11 uncertainty. Because there were few metrics with u nit risks higher than the best fitting metric’s 12 unit risk for each cancer mortality endpoint, this method effectively selects the highest unit risk 13 among those considered for each cancer mortality en dpoint. 14 Once the cancer-specific mortality unit risks are s elected, the two are then combined. 15 Because each of the unit risks is itself an upper b ound estimate, summing such upper bound 16 estimates across mesothelioma and lung cancer morta lity is likely to overstate the overall risk. 17 Therefore, following the recommendations of the Guidelines for Carcinogen Risk Assessment 18 (U.S. EPA, 2005a), a statistically appropriate uppe r bound on combined risk was derived in order 19 to gain an understanding of the overall risk of mor tality resulting from mesothelioma and from 20 lung cancers. It is important to note that this es timate of overall potency describes the risk of 21 mortality from cancer at either of the considered s ites and is not just the risk of both cancers 22 simultaneously. 23
Because the estimated risk for both mesothelioma an d lung cancer mortality were derived 24 using Poisson and Cox proportional hazards models, correspondingly, it follows from statistical 25 theory that each of these estimates of risk is appr oximately normally distributed. For 26 independent normal random variables, a standard dev iation for a sum is easily derived from 27 individual standard deviations, which are estimated from confidence intervals: standard 28 deviation = (unit risk – central risk) / Z 0.95 , where Z 0.95 is a standard normal quantile equal to 29 1.645. For normal random variables, the standard d eviation of a sum is the square root of the 30 sum of the squares of individual standard deviation s. 31 The upper bound among the mesothelioma mortality u nit risks was 0.122 per fiber/cc. 32 The upper bound among the computed lung cancer mort ality unit risks was 0.0680 per fiber/cc. 33
The central estimate of risk was 0.075 for mesothel ioma mortality per fiber/cc and 0.0399 per 34 fiber/cc for lung cancer mortality (Tables 5-7 and 5-9). 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-88 In order to combine the unit risks, one first obta ins an estimate of standard deviation of 1
the sum of the individual unit risks as 2
3 4 Sqrt { [[(0.122 - 0.075) / 1.645]^2 +(0.068 - 0.039 9) / 1.645 ]^2 } = 0.033 per fiber/cc (Eq. 5-9) 5 6 7 Then, the combined central estimate of risk of mort ality from either mesothelioma or lung cancer 8 is 0.0399 + 0.075 = 0.115 per fiber/cc, and the com bined IUR is 0.115 + 0.033*1.645 = 0.169 9 per fiber/cc. 10 Selecting the upper bound unit risk estimates for use in combining unit risks accounts for 11 many potential uncertainties. It accounts for unce rtainty in the effect estimate (i.e., the within12 metric uncertainty) and the uncertainty attributabl e to the choice of exposure metric (i.e., the 13 cross-metric uncertainty). The combined IUR from t he best fitting mesothelioma and lung 14 cancer mortality models (using two different model selection criteria) can be computed for 15 comparison with Tables 5-7 and 5-9, respectively, b y the same steps as above, and the results are 16
shown in Table 5-20. 17 Compared to the combined IUR from the best fitting exposure models, the EPA’s 18 selected combined IUR of mesothelioma and lung canc er mortality accounts for both the 19 demonstrated cross-metric uncertainty as well as se veral additional potential uncertainties, which 20
could have resulted in underestimates of the mesoth elioma and lung cancer mortality risks from 21 the epidemiologic data. These additional uncertain ties are discussed in Section 5.4.6. The IUR 22 value of 0.169 per fiber/cc accounts for important quantitative uncertainties in the selection of 23 the specific exposure metric that may have remained in an IUR that might have been based on 24 the best fitting exposure models alone. 25
26 5.4.5.3.1. Comparison with other published studies of Libby Workers Cohort 27 For lung cancer, two alternative analytic approache s to the use of EPA’s extended Cox 28 proportional hazards models could have been used fo r 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 analys es as does the length of mortality follow-up. 31 Each of the two approaches has two options to estim ate the slope of the exposure-response 32 relationship in place of the regression slope estim ated from the Cox proportional hazards model 33 and follow through with the same life-table procedu re to calculate the unit risk of lung cancer 34 mortality. 35
36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-89 Table 5-20. Reasonable upper bound and lowest info rmation criteria 1 estimates of central risks and unit risks, per fibe r/cc, for mesothelioma 2 mortality, lung cancer mortality, and the IUR for t he combined mortality 3 risk from mesothelioma and lung cancer 4 5

Model Mesothelioma Lung cancer Combined mesothelioma and lung cancer

Central estimate Unit risk Central

estimate Unit

risk Central estimate IUR
Reasonable upper bound a 0.075 0.122 0.040 0.068 0.115 0.169

Lowest information criteria b 0.044 0.074 0.026 0.040 0.070 0.103 6

a For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential d ecay half7 life of 5 years and a 15-year lag. For lung cancer , the selected model parameterized exposure as cumu lative 8 exposure without decay and a 10-year lag. 9

b For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential d ecay 10 half-life of 5 years and a 10 -year lag. For lung cancer, the selected model parameterized exposure a s cumulative 11 exposure with exponential decay half-life of 10 yea rs and a 10-year lag. 12 13 14 The first approach would be to use the published ca tegorical results based on Sullivan 15 (2007). The first option in this approach was for EPA to estimate a slope to those categorical 16 data. The second option was to use the slope estim ated in a published re-analysis of categorical 17 data of Sullivan (2007) cohort by Berman and Crump (2008). The second approach would be to 18 use the published regression results of other resea rchers who modeled the underlying continuous 19 data. The first option in this approach was to use the slope estimated by Larson et al. (2010a). 20 The second option was to use the slope estimated by Moolgavkar et al. (2010). 21 For comparison purposes, the lung cancer unit risk from these alternatives are computed, 22 however, as all analyses are based upon different s ubsets of the Libby workers cohort and used 23 different analytic methods, the results are not nec essarily interchangeable. Table 5-21 24 summarizes lung cancer risks derived from these stu dies. 25 The first alternative analytic approach to estimati ng the extra risk from a linear regression 26 of individual mortality data was to use a standard technique used in EPA cancer risk assessments 27 (U.S. EPA, 2005a) when individual-level data are no t available. This approach used a weighted 28 linear regression of standardized rate ratio (SRR) estimators for lung cancer mortality in white 29 males, as calculated in the NIOSH cohort analysis ( Sullivan, 2007), with categorical cumulative 30 exposure and a 15-year lag. The Sullivan (2007) an alysis was based only on those who have not 31 died or been lost to follow-up before January 1, 19 60 (in contrast to employment beginning after 32 January 1, 1960) because the NIOSH software program (LTAS) used for this analysis only has 33 statistics on external comparison rates for asbesto sis (one of the primary outcomes of interest in 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-90 the Sullivan [2007] analysis) beginning in 1960. T he SRR analysis involves internal 1 comparisons of lung cancer mortality rates in the l owest exposure category to the lung cancer 2 mortality rates in the higher exposure categories. The weights used for the SRRs were the 3 inverses of the variances. Midpoints of the exposu re intervals were used, and for the unbounded 4 interval, the midpoint was assumed to be twice the starting point of that interval. 5
6 Table 5-24. Lung cancer regression results from dif ferent analyses of 7 cumulative exposure in the cohort of workers in Lib by, MT. All analyses 8 used NIOSH-collected exposure data, but used differ ent cohort definitions, 9 lengths of follow-up, and lengths of exposure lags to account for cancer 10 latency. 11 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., 2010 a Full cohort, White males 95/1662 1.69 0.011

Berman and Crump, 2008 a Full cohort, White males 93/1672 3.96 0.079

Larson et al., 2010 Full cohort 98/1862 1.61 0.010 13

a Re-analysis of Sullivan (2007) 14 15 16 Using this approach, a regression coefficient of 4. 2 x 10 -3 per fiber/cc-year (standard 17 error [SE] = 7.7 x 10 -4 per fiber/cc-year, p = 0.03) was obtained from the weighted linear 18 regression of the categorical SRR results. Because the data from Sullivan (2007) were already 19 adjusted for the length of an occupational year (24 0 days) to the length of a calendar year 20 (365 days), only the standard adjustment for inhale d air volume was performed. The 21 concentration estimate obtained using this regressi on modeling and the life-table analysis 22 procedure was LEC 01 = 0.272 fibers/cc resulting in the lung cancer uni t risk of 0.0368 per 23
fiber/cc. 24 Berman and Crump (2008) re-analyzed Sullivan (2007 ) data except they used lag of 10 25 years. They fit IRIS IUR (1988) lung cancer model to aggregate data using extra multiplicative 26
parameter α . In this model, the relative risk at zero exposur e is α rather than 1. With α = 1, their 27
model does not fit and with α estimated, the fit was satisfactory. Berman and Cr ump (2008) 28This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-91 chose central estimate of slope from the fit with α estimated, but constructed “informal” 90% 1 confidence interval by the union of two confidence intervals (this upper bound is shown in 2 Table 5-21). This was done to address uncertainty in estimated parameter α , similar to what is 3 done in this assessment with estimated lag and deca y. Note also, that Berman and Crump (2008) 4 also provide an uncertainty factor to adjust for se veral sources of uncertainty in exposures, 5 resulting in upper bound risk of 0.3162. 6 The second alternative analytic approach to estimat ing the extra risk of lung cancer from 7 a Cox regression with time-dependent covariates of individual mortality data was to use the 8 results published by Larson et al. (2010a), with cu mulative exposure and a 20-year lag. This 9 analysis of lung cancer mortality was based on the full cohort of 1,862 workers updated until 10 2006 and using the same model form as the current E PA analysis (the extended Cox proportional 11 hazards model). Larson et al. (2010a) reported a r egression coefficient of 1.06 x 10 -3 per 12 fiber/cc-year (SE = 3.1 x 10 -4 per fiber/cc-year, p = 0.0006). 11 EPA assumed that the cumulative 13 exposures reported by Larson et al. (2010) were bas ed on years of occupational exposure 14 (240 days per year) during a 365-day calendar year. In order to account for exposure on every 15 day of the year for a calculation of unit risk, adj ustment for exposures during the length of an 16 occupational year (240 days) to the length of an ca lendar year (365 days) and the adjustment for 17 the volume of inhaled air were performed to match E PA’s analyses. The concentration estimate 18 obtained using the Larson et al. (2010) regression modeling and the life-table analysis procedure 19
was LEC 01 = 1.26 fibers/cc resulting in a lung cancer unit r isk of 0.0103 per fiber/cc. 20 Moolgavkar et al. (2010) also used the Cox model wi th time-dependent covariates for 21 analysis of the Sullivan (2007) cohort with a 15-ye ar lag. The parameter in this study estimates 22 (1.11 x 10 -3 per fiber/cc-year (SE = 2.5 x 10 -4 per fiber/cc-year) are very close to Larson et al. 23 (2010a), and, therefore, the lung cancer unit risk based on their analysis would be very close to 24 Larson et al. (2010a). Comparison with McDonald et al. (2004) is difficult, since their definition 25 of lung cancer (ICD-9 160-165) is much more expansi ve than other researchers’ definitions; 26 however, the parameter estimate resulting from the Poisson analysis by McDonald et al. (2004) 27
was 3.6 x 10 -3

. 28 EPA based their analyses on the exposures that occ urred after 1959, while the Sullivan 29 (2007), Larson et al. (2010a), and Moolgavkar et al . (2010) analyses were based on the cohort 30 including those hired before 1960, and McDonald et al. (2004) included only workers hired 31 before 1964. As explained in detail in the discuss ion (Section 5.4.6) on uncertainty in the 32 exposure assessment, there were only several measur ements from the 1950s and one from 1942, 33

11 Note that EPA results based on the sub-cohort hire d after 1959 were from the same model form but base d on the cumulative exposure with a 10-year lag and had a sl ope of 5.81 x 10 -3 per fiber/cc-year (SE = 2.48 x 10 -3 per fiber-cc/year, p = 0.018).This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-92 and most of the exposure estimation for the early y ears of the cohort’s experience were based on 1
estimates of the ratio of dust to fibers estimated in the late 1960s and extrapolated backwards in 2 time for several decades. Moreover, 686 workers hi red before 1960 (not necessarily short-term) 3 did not have exposure measurement assigned to them at all, leading to much larger measurement 4 error. These limitations in the underlying exposur e assessment for the years prior to 1968 likely 5 resulted in exposure measurement error that could h ave attenuated the analytic regression results, 6 thereby yielding a smaller effect estimate for the whole cohort compared to the sub-cohort hired 7 after 1959. It appears the differences in results are mostly attributable to the time periods of 8 analysis and corresponding to the time period measu rement errors rather than the analytic 9 approach. The small discrepancy between observed l ung cancer deaths between this assessment 10 and Larson et al. (2010a), described in Section 4.1 .1.1, is unlikely to play a role in the difference 11 between risk estimates. Moreover, for the sub-coho rt hired after 1959, all deaths are included in 12 the Larson et al. (2010a) lung cancer-counting rule s. 13 Neither of the approaches used by McDonald et al. ( 2004), Sullivan (2007), nor Larson et 14 al. (2010a) could have been appropriately used for the unit risk of mesothelioma as they are not 15 based on absolute risk metrics of association, and the current assessment considered the relevant 16
metric of association to be the absolute risk. Ber man and Crump (2008) did not evaluate risk of 17 mesothelioma. Moolgavkar et al. (2010) used an abso lute risk model for mesothelioma. These 18 results are summarized in Table 5-22. The upper bo und results for the full cohort presented by 19 Moolgavkar et al. (2010) are about 80% of the IRIS IUR (1988) estimate of mesothelioma slope 20 factor in a similar RTW-type metric, leading to app roximately 80% estimate of the mesothelioma 21 unit risk, as dependence is linear in the mesotheli oma slope factor (Eq. 5-5). This is very close 22 to this assessment’s estimate based on sub-cohort, which is also about 80% of the IRIS IUR 23 (1988) estimate of mesothelioma risk. Duration of employment is the best metric for the full 24 cohort, and it does not support dose-response estim ation. 25
26This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-93 Table 5-225 Mesothelioma regression results from di fferent analyses of 1 cumulative exposure in the cohort of workers in Lib by, MT. All analyses 2 used NIOSH-collected exposure data, but used differ ent cohort definitions, 3 lengths of follow-up, and lengths of exposure lags to account for cancer 4 latency. 5 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-195 9; White males 15/1672 No estimates of absolute risk

Moolgavkar et al.. 2010 a Full cohort; White males 15/1662 Upper Bound ≈ 0.13 Central ≈ 0.08
Larson et al., 2010 Full Cohort 19/1862 No estimate s of absolute risk
Berman and Crump, 2008 a No estimates provided 7

a Re-analysis of Sullivan (2007) 8 9 10 5.4.5.4. Applications of the Combined Mesothelioma and Lung Cancer Mortality IUR to 11 Partial Lifetime Environmental Exposure Scenarios 12 In the application of the IUR, scenarios other than lifetime environmental exposure are 13
often of interest to risk assessors. The life-tabl e analysis in the (general) IRIS IUR for asbestos 14 (U.S. EPA, 1988) predicts risk increases as the age of the first exposure decreases. The authors 15 of that analysis recommended the life-tables in tha t analysis be consulted when assessing partial 16 lifetime exposures (U.S. EPA, 1986). In 2009, EPA (OSWER) provided guidance for 17 calculating risk estimates for less-than-lifetime e xposures based on the source life-table analysis 18 (U.S. EPA, 2009). The age-at-onset of exposure and duration-dependent unit risks reflect the 19
influence of the time cubed function in the mesothe lioma model (eq. 5-5) (U.S. EPA, 1986, 20 2009) used in the 1986 assessment. Because the tim e-cubed model, or parameterization of 21 exposure metrics, did not fit the data for mesothel ioma mortality from exposure to the Libby 22 Amphibole asbestos, the approach to estimating risk of partial life exposure recommended by 23 EPA when applying the general IRIS IUR for asbestos (U.S. EPA, 1988) is not appropriate when 24 applying the Libby amphibole asbestos-specific IUR. 25
Thus this assessment recommends that estimates of t he risks of less-than-lifetime 26 exposures be computed by simple calculations of ave rage lifetime exposure concentration 27 multiplied by IUR. This recommendation is consiste nt with standard Superfund guidance (U.S. 28This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-94 EPA, 1986b), where exposures are estimated, average d across a lifetime exposure, and the IUR 1 simply applied to calculate excess cancer risk (U.S . EPA, 2005). 2
3 5.4.6. Uncertainties in the Cancer Risk Values 4 It is important to consider the discussion of uncer tainties in the derivation of the 5 mesothelioma and lung cancer mortality risks in thi s assessment in the context of uncertainties in 6
animal-based health assessments. This assessment d oes not involve extrapolation from high 7
dose in animals to low dose in humans. The current assessment is based on a well-documented 8 and well-studied cohort of workers with adequate ye ars of follow-up to evaluate mesothelioma 9 and lung cancer mortality risks with PODs within th e range of the data. The discussions below 10 explore uncertainty in the derivation of the IUR in order to provide a comprehensive and 11 transparent context for the resulting cancer mortal ity risk estimates. 12
13

5.4.6.1. Sources of Uncertainty 14 Sources of uncertainty in this assessment include: 15
16 1) Uncertainty in low-dose extrapolation, 17 2) Uncertainty in exposure assessment, including analy tical measurements uncertainty, 18 3) Uncertainty in model form, 19 4) Uncertainty in selection of exposure metric, 20 5) Uncertainty in assessing mortality corresponding to the cancer endpoints, 21 6) Uncertainty in control of potential confounding in modeling lung cancer mortality, 22 7) Uncertainty due to potential effect modification, 23 8) Uncertainty due to length of follow-up, 24 9) Uncertainty in use of life-tables to calculate canc er mortality unit risks, 25 10) Uncertainty in combining of mortality risks to deri ve a composite cancer mortality IUR, 26 11) Uncertainty due to extrapolation of findings in adu lts to children. 27
28 1) Uncertainty in low-dose extrapolation 29 A major source of uncertainty in quantitative cance r risk assessments generally derives 30 from extrapolating from high doses in animals to lo w doses in humans. Compared to 31 assessments based on animal data, the uncertainty f rom low-dose extrapolation in this 32 assessment employing occupational epidemiology data is considered to be somewhat reduced for 33 the following reasons. The NIOSH worker cohort dev eloped by Sullivan (2007) includes 410 34 workers employed less than 1 year among the 880 wor kers hired on or after January 1, 1960. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-95 Although short-term workers, on average, experience a mean exposure intensity per day worked 1 greater than workers employed more than a year (Sul livan, 2007), the cohort nevertheless 2 includes many short-term workers with relatively lo w cumulative occupational exposures. 3 Further, inclusion of salaried workers in the NIOSH cohort (Sullivan, 2007) adds many workers 4 with lower workplace exposure. Thus, while occupat ional exposure concentrations may be 5 generally higher than typical ongoing environmental concentrations, the exposures in this 6 occupational database may be representative of non- occupational exposures. 7 While many occupational epidemiology studies are ba sed on relatively high exposure 8 levels that are beyond the range of common environm ental exposures, many in the Libby 9 workers cohort experienced exposures that were near or below the PODs derived from the 10 life-table analysis. The POD for the selected lung cancer mortality exposure metric was 11
0.191 fibers/cc. The POD for the selected mesothel ioma mortality exposure metric was 12 0.106 fibers/cc. Among the workers hired after 195 9 who had at least 1 year of occupational 13 exposure (n = 470), there were 19 (4%) with exposur es to average occupational concentrations of 14 less than 0.3 fibers/cc, including one out of 20 lu ng cancer deaths (5%) in these workers. 15 Although data might have been modeled all the way d own to a very low level, the 16 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) recommends defining a POD for 17 low-exposure extrapolation in order to increase the stability of the IUR estimate at lower 18 exposures, where fewer cancers might be expected. Thus, the uncertainty associated with 19 low-dose extrapolation is somewhat mitigated since the linear extrapolations from the dose 20 associated with the POD from the life-table analyse s of each cancer endpoint was encompassed 21 within the observed data range. Nonetheless, some uncertainty remains in the extrapolation from 22 occupational exposures to lower environmental expos ures when using a POD. 23
24 2) Uncertainty in exposure assessment 25 Accurate exposure assessment is generally consider ed to be a major challenge for 26 occupational epidemiologic studies and is a challen ge that is well recognized by the NIOSH 27 investigators (Amandus et al., 1987a). As stated p reviously in Section 5.4.3.3, while it is 28 generally true that the use of more data is an adva ntage in statistical analyses because it allows 29 for the computation of more statistically precise e ffect estimates, this advantage in precision may 30 be offset by a negative impact on the accuracy of t he effect estimate if an increase in sample size 31 is accompanied by greater exposure misclassificatio n or other biases. Therefore, EPA decided to 32 base this Libby Amphibole asbestos-specific human h ealth risk assessment upon the mortality 33 experience of workers hired on or after January 1, 1960. EPA’s use of the sub-cohort analysis is 34 based on the belief that it is important to accurat ely estimate the true underlying 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-96 exposure-response relationships by relying on the m ost accurate exposure data. The use of this 1 sub-cohort greatly reduces the uncertainty in expos ure error compared to evaluations based on 2 the entire cohort. More specifically, 3
4 a) Job assignment was unknown for 686 of the 991 worke rs employed from 1935 to 5 1959 (69% of the cohort for this time period). Alm ost all of these workers were 6 assigned the same exposure concentration for all ye ars without job category 7 information. Examination of the post-1959 cohort r emoves this significant source of 8 exposure misclassification. 9

b) Using the more recently hired cohort minimizes the uncertainty in estimated worker 10 exposures based on the job exposure matrix, which w as informed by air sampling 11 data available in 1956 and later years. Although t here are still uncertainties in the 12 task-specific exposure estimates from 1960-1967, un certainty in the assessment of 13 earlier exposure levels is considerably greater. 14

c) Exposure measurements were collected from the area samples and represented 15 exposures for all the workers with the same job cod e. Statistically, this causes 16 Berkson-type measurement error effect which is desc ribed later in this section. 17

18 As the EPA exposure-response modeling for mesotheli oma and lung cancer mortality is 19 based on the post-1959 sub-cohort, the remaining di scussion of uncertainty in exposure 20
measurement will address these data. 21
22 Sources of uncertainty in job history information 23 Worker exposures for the EPA exposure-response mode ling were calculated based on job 24 histories and the JEM from 1960 through 1982 (Figur e 5-6). Overall, there is little uncertainty in 25 the job history information. Regarding exposure es timation for the occupational cohort, the 26 NIOSH investigators (Amandus et al., 1987a) conduct ed a detailed retrospective exposure 27 assessment to estimate the individual worker exposu res. NIOSH used extensive occupational 28 exposure data to construct the time-specific job ex posure matrix, spanning decades (Amandus, 29 1987a). These data were re-abstracted from the wor kers’ employment records for quality 30 assurance (Sullivan, 2007). NIOSH records on work histories and job-specific exposure 31 extended from the 1930s through May 1982. But, the vermiculite mining and milling operation 32 continued on for several years, and some workers we re retained through 1993 for plant close-out 33 activities. However, none of the members of the po st-1959 cohort (n = 880) were employed as 34 of the 1982 employment records. Of the employees s till working in 1982 (n = 167), all were 35 hired prior to 1960. 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-97 Sources of uncertainty in exposure intensity for th e identified location operations 1 The available exposure data that inform the JEM inc lude over 4,000 air samples, the 2 majority of which were collected after 1967 (Table 4-1). All of the job location exposure 3 estimates (Table 5-8) from 1968-1982 were directly informed from air samples collected on 4 membrane filters and analyzed for fibers by PCM. T he availability of site- and task-specific air 5 samples for these years provides a good basis for t he exposure estimates. However, there are 6 some uncertainties in estimating asbestos exposures using air samples analyzed by PCM. 7
8 9 1) PCM analysis does not determine the mineral or chem ical make-up of the fiber: The 10 PCM method defines and counts fibers based on the s ize (aspect ratio and length) of the 11 particle without regard for the material that makes up the fiber being viewed. The PCM 12 method was developed for use in occupational enviro nments where asbestos was present, 13 and the nature of the fibers should be further eval uated to confirm the fibers viewed 14 under PCM are asbestos. McGill University research ers evaluated the fibers collected on 15 membrane filters in the early 1980s and confirmed t he presence of asbestos fibers in the 16 tremolite-actinolite solution series consistent wit h the Libby Amphibole asbestos 17 (McDonald et al., 1986). NIOSH researchers confirm ed the presence of tremolite 18 asbestos in bulk dust samples but not in air sample s from the facility (Amandus et al. 19 (1987a). Although less specific to fibers, 60-80% of the airborne dust in the mills in 20 1968 was tremolite, further supporting the presence of asbestos in the air (based on State 21 of Montana air sampling, and x-ray diffraction anal ysis by the Public Health Service) 22 [PHS correspondence, October 17, 1968]. However, a lthough the presence of mineral 23 fibers in the actinolite-tremolite series was confi rmed in the work environment, it is 24 possible that there were also fibers counted by PCM from other materials (such as textiles 25 from clothes and packaging materials). Therefore, it is unknown from these data what 26 proportion of the counted PCM fibers were mineralog ically asbestos, or other materials 27 present in the workplace. 28

2) PCM defines fibers as particles with an aspect rati o greater than 3:1: Th e re is an 29 ongoing debate in the literature on asbestos toxici ty regarding the influence of aspect 30 ratio on relative toxicity. Specifically, in minin g environments, it has been speculated 31 that a larger proportion of low aspect ratio fibers from mineral dusts may significantly 32 impact the apparent cancer potency of the measured PCM fibers in those environments 33 (IRIS IUR, 1988, Berman, 2010). There are few data available to understand fiber 34 morphology and fiber aspect ratios in the Libby coh ort working environment. 35 Considering the post-1959 cohort, PCM fiber size di stribution and aspect ratio data only 36 exist for a set of eight air samples (599 fibers) c ollected from the wet mill and screening 37 operations and analyzed by the NIOSH researchers (A mandus et al. 1986a). For these air 38 samples, over 96% of the fibers viewed by PCM had a n aspect ratio greater than 10:1 39 (Table 4-2, Amandus et al., 1987a). However, becau se these samples were provided by 40 the company in the early 1980s, they do not represe nt conditions in the old wet mill or 41 dry mill operations, which were significantly dusti er environments (Amandus et al., 42 1987a). It is possible that prior to industrial hy giene (IH) modifications in 1974, the dry 43This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-98 and old wet mills generated proportionally more mineral dusts t han screening and new 1 wet mill operations after IH modifications. No dat a are available for the mining 2 environment, which would also be expected to genera te a range of mineral dusts. 3 Therefore, there is a significant uncertainty about the size and aspect ratio of fibers 4 included in PCM fiber counts for the majority of th e post-1960 workers cohort. 5

3) The resolution of visible PCM fibers: Current analytical instruments used for PCM 6 analysis have resulted in a standardization of mini mum fiber width considered visible by 7 PCM between 0.2 and 0.25 μm. Historical PCM analys is (1960s and early 1970s) 8 generally had less resolution, and fibers with mini mum widths of 0.4 or 0.44 μm were 9 considered visible by PCM (Skinke, 1980; Amandus et al., 1987a). McDonald et al. 10 (1986) compared fibers viewed by PCM and TEM and es timated that approximately 1/3 11 of the total fibers could be viewed by the optical microscope. Because 38% of the fibers 12 were <5 μm in length, this implies approximately 30 % were not viewable by optical 13 microscopy for other reasons, such as width. Howev er, it is unknown what proportion of 14 that 30% would be viewed with the minimum width res olution of 0.25 μm for later 15 optical microscopy. It is likely that early PCM co unts were underestimated relative to the 16 later data for the cohort, but by less than a facto r of 2. 17

18 Prior to 1968, no air sampling data were available for 23 of the 25 job location operations 19 (Table 4-2), and the exposure estimates were extrap olated from later air sampling data. 20 Amandus et al. (1987) recognized there is significa nt uncertainty in the extrapolation of available 21 air sampling data to previous time periods. The re searchers took into account major changes in 22 operations and interviewed employees in the early 1 980s regarding previous years of operation. 23 The assumptions used to make these extrapolations a re clearly stated for each of the plant 24 operations. For four operations, high and low esti mates of pre-1968 exposures were provided 25 based on different sets of exposure assumptions (Ta ble 5-8). For ore loading, there were 26 negligible differences in the exposure estimates fo r the period from 1960-1967 (10.7 versus 27 9 fibers/cc). For drilling, the river dock, and th e bagging plant, there were 3.4-, 2.6-, and 2.8- 28 fold differences, respectively, between the high an d low estimates of exposure between 1960 and 29
1968. 30 Dry mill exposures between 1960 and 1968 were infor med by air sampling for total dust 31 collected in the dry mill facility from 1956-1969 ( where total dust was collected by midget 32 impingers). Amandus et al. (1987a) derived a conve rsion factor of 4.0 fibers/cc per mppcf to 33 apply to the two location operations in the dry mil l during these years. There was a range of 34 conversion factors considered for the dry mill depe nding on how the dust and fiber air samples 35 (PCM) were grouped and averaged (1.2 to 11.5 fibers /cc per mppcf). A subset of dust and fiber 36 samples available over the same time period (1967-1 968) resulted in a ratio 8.0 fibers/cc per 37 mppcf. In contrast, a ratio of 1.9 fibers/cc resul ted when total dust samples from 1969 were 38This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-99 compared with fiber samples from 1970. However, bo th of these subsets had limited numbers of 1 samples available. Therefore, the conversion facto r of 4.0 fibers/cc per mppcf was selected 2 based on using the maximum samples available over a time period when the dry mill exposures 3 were considered similar: dust samples (1965-1969) a nd fiber samples (1967-1971). This 4
conversion factor is within a factor of two of the similar datasets considered. 5
6 Sources of uncertainty in the calculation of the jo b exposure matrix (JEM) 7 The exposures in the job exposure matrix (Figure 5- 6) were calculated from the exposure 8 intensities of the various task-specific exposure i ntensities shown by job location operation 9 (Table 5-8). The uncertainties in the exposure int ensity for the job location operations will 10 impact the JEM. Additionally, for each of the job categories in the JEM, NIOSH researchers 11 defined which tasks (job location operations) were conducted and for what proportion of the 12 work day. A TWA exposure for each job category acr oss time was calculated based upon these 13 assumptions and the task-specific exposure estimate s. There is a measure of uncertainty in these 14 assumptions for each job category. Additionally, t here is inter-individual variation within the job 15 categories. These uncertainties are common to expo sure reconstruction for epidemiological 16
cohorts. 17
18 Uncertainty in the exposure metric 19 The PCM measurement is the available exposure metri c for analysis of Libby worker 20 cohort at this time. Currently, there is no optima l choice of the best dose metric for asbestos in 21 general and in particular for Libby amphibole asbes tos, even if TEM-based dose-response JEM 22 were available. Uncertainties related to PCM analy tical method are discussed in Section 2.3. 23 Briefly, PCM cannot distinguish between asbestos an d non-asbestos material, or differentiate 24 between specific types of asbestos. Further, due to limitations of this methodology, PCM does 25 not take into account fibers smaller than 5 μm in l ength. 26
27 Evaluation of the effects of uncertainties in expos ure measurement 28 An understanding of the effects of exposure measure ment error on the risks estimated 29 from epidemiologic analyses is important to place t hese possible exposure measurement errors in 30 context. The effect of exposure measurement error on estimates of the risk of mesothelioma or 31 lung cancer mortality attributable to exposure depe nds upon the degree to which that error may 32
be related to the likelihood of mesothelioma or lun g cancer mortality. Exposure measurement 33 error that is similar in pattern among workers who died of lung cancer to exposure measurement 34 error in people who did not die of lung cancer is a non-differential exposure measurement error. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-100 Differential exposure measurement error that is ass ociated with the outcome can cause bias in an 1 effect estimate towards or away from the null, whil e non-differential exposure error typically 2 results in bias towards the null (Rothman, 1998). From the above evaluation of uncertainties, 3 there is no indication that the uncertainties in jo b history information, exposure estimates for 4 specific tasks, or calculation of the JEM would be differential based on the cancer health 5 outcome data. Therefore, these uncertainties are c onsidered non-differential, and the general 6 result is likely to be an attenuation in risk estim ates towards the null (that is, the addition of 7 random noise to a clear signal tends to reduce the clarity of the observed signal and the 8 avoidance of random noise – here from poor quality exposure measurements, results are a 9 stronger observed signal). 10 Generally speaking, if the exposure concentrations estimated by NIOSH were 11 systematically too high, then the associated risks of exposure estimated in the regression analysis 12 would be low since the same actual risk would be sp read across a larger magnitude of exposure. 13 Similarly, if the exposure concentrations estimated by NIOSH were systematically too low, then 14 the associated risks of exposure estimated in the r egression analysis would be too high. From the 15 above evaluation, the majority of the sources of un certainty are not systematic. There are a few 16 areas of uncertainty that may be classified as bias ed: 17
18 19 1) High- and low-exposure estimates for four job locat ion operations were provided 20 between 1960 and 1967. Amandus et al. (1987a) choo se the high estimates of 21 exposure for these job location operations when cal culating the JEM. Therefore, 22 there will be a bias towards the high end for the j ob categories informed by these 23 data. There was a 1.1- to 3.4-fold difference betw een the high and low estimates. 24 This difference will be less pronounced where these exposure concentrations are 25 averaged with other job location operations in the JEM and across multiple jobs for 26 the majority of the workers (Figure 5-7). 27

2) Current PCM analysis would count more fibers relati ve to early PCM methods based 28 on minimum fiber width resolution. For example, Am andus et al. (1987a) used a 29 minimum width cutoff of 0.44 in their review of PCM fibers in the 1980s, which may 30 have resulted in as much as a 2-fold underestimate compared to current PCM 31 methods with a width resolution of 0.25 μm. Additi onally, as PCM methodology has 32 developed over time, it is unknown when PCM results from company records would 33 be considered relatively standard to a minimum widt h resolution between 0.2 and 34 0.25 μm. Also, prior to standardization of PCM to 0.25-μm minimum width, there 35 was inter-laboratory variability as well. Therefor e, the size distribution of PCM 36 fibers (e.g., minimum width) reported in the JEM ma y have changed over time. 37 Although theoretically a systematic bias, given the years for which PCM data are 38 available, this is likely an insignificant effect. 39This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-101 3) Asbestos was a contaminant of vermiculite that was the primary object of production. 1 Mine, old dry mill, and wet mill ambient air may ha ve contained material other than 2 asbestos that could have contributed to PCM fiber c ount. The exposures in the old 3 dry and wet mills and mine location may have includ ed a greater proportion of dust to 4 fibers than tasks using the ore and refined vermicu lite after the new wet mill became 5 operational. It is possible there is a systematic over-count of fibers in the dusty 6 environment due to interference from mineral fragme nts. This likely impacts the 7 exposure intensity for 23 of 25 job location operat ions within the mine and old dry 8 mill. Estimated exposures from job categories that include these operations may be 9 biased upwards. 10

11
Non-differential measurement error in a continuous exposure can be of the classical or 12 Berkson type and typically arises in environmental and occupational settings as a mixture of the 13 two forms (Zeger et al., 2000). Classical error oc curs when true exposures are measured with 14 additive error (Carroll et al., 2006) and the avera ge of many replicate measurements, conditional 15 on the true value, equals the true exposure (Armstr ong, 1998). This error is statistically 16 independent of the true exposure that is being meas ured and attenuates true linear effects of 17 exposure, resulting in effect estimates in epidemio logic studies that are biased towards the null 18 (Zeger et al., 2000; Armstrong, 1998; Heid et al., 2004). Such errors occur when the mean 19 values of multiple local air samples are used. 20 Berkson measurement error is independent of the su rrogate measure of exposure (Heid et 21 al., 2004; Berkson, 1950) and is present when the a verage of individuals’ true exposures, 22 conditional on the assigned measurement, equals the assigned measurement. Berkson 23
measurement error can arise from the use of local a rea mean sampled exposures to represent the 24 individual exposures of people in that area—even wh en the estimated area mean is equal to the 25 true underlying mean (i.e., no classical error). E xamples of random variability in personal 26 behavior that may produce Berkson-type error in per sonal exposure estimates include the volume 27 of air breathed per day among the workers and the e ffectiveness of individuals’ nasal filtration at 28 removing contaminants. In general, Berkson error i s not thought to bias effect estimates but 29
rather increases the standard errors of effect esti mates (Zeger et al., 2000). However, some 30 epidemiologic studies have suggested that Berkson e rror can produce a quantitatively small bias 31 towards the null in some analyses (Burr, 1988; Reev es et al., 1998; Kim et al., 2006; Bateson and 32 Wright, 2010). 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-102 Exposure to other kinds of asbestos and residential exposure 1 Another source of uncertainty in the estimation of exposures in the Libby workers cohort 2 is the potential contribution of non-occupational o r residential exposures as well as exposures to 3 other kinds of asbestos in employment before or aft er working in Libby. 4 Many of the workers resided in Libby, MT, before an d/or after their employment at the 5 mining and milling facilities ended. The vermiculi te from the mine had been used at numerous 6 sites around the town, including baseball fields ar ound the expansion plant and as filler in 7 gardens (U.S. EPA, 2001, 2010). Exposure to asbest os could have occurred among individuals 8 outside of the workplace, particularly through acti vities with the potential of stirring up dirt or 9
other materials that had been mixed with the vermic ulite (Weis, 2001). The results of 10 community sampling indicated that even 10 years aft er mill operations ceased during some 11 activities, asbestos fiber concentrations in the ai r could exceed OSHA standards established for 12 the protection of workers (Weis, 2001). 13 Therefore, the workers’ actual personal exposures a s the sum of occupational and 14 non-occupational exposures are likely to have been underestimated by the use of estimated 15 Libby-related occupational exposure alone. The dif ficulty stems from the lack of data on 16 residential exposures and lack of information on pr e- and post-employment residence of the 17 Libby workers. Non-occupational exposures were lik ely to have been smaller in magnitude than 18 the occupational exposures, but workers may have li ved in and around Libby, MT, for many 19 more years than they were exposed occupationally. The impact of residential exposure could be 20 more prominent for workers with lower occupational exposure who resided in Libby for a long 21 time. Whitehouse et al. (2008) has reported severa l cases of mesothelioma among residents of 22 the Libby, MT region who were not occupationally ex posed. However, since the report by 23 Whitehouse et al. (2008) details only the cases and does not define or enumerate the population 24 from which those cases were derived, computed relat ive risks from non-occupational exposures 25 were not available. ATSDR (2000) reported higher r elative risks of mesothelioma among the 26 population of Libby, MT, including former workers r esiding in Libby, but did not provide 27 relative risk for non-occupational exposure. Inste ad, the ATSDR report on mortality (2000) 28 grouped cases among the former workers with non-occ upationally exposed cases. Therefore, it 29 is not clear what the magnitude of the contribution of workers’ non-occupational exposures were 30
to their overall risk. 31 Some of the occupational workers with lower exposur es, such as short-term workers, may 32 have either been high school or college students wo rking during the summer or may have been 33 transient workers who may not have stayed for a lon g time in Libby. Sullivan (2007) analyzed 34 differences between short and long term workers and reported little difference between the 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-103 groups except for age at hire. As the shorter term workers were younger on average, this 1 supported the suggestions that some of the short te rm workers may have been college students 2 working during the summer. It is not clear that th is population of short-term workers is well 3 defined; however, it is possible that transient wor kers could potentially have been exposed to 4 other kinds of asbestos or other lung carcinogens i n their non-Libby occupational career, which 5 might have affected their pre- and post-Libby risk profile for asbestos exposure. While their 6 occupational histories are unknown, it is very unli kely that they include exposures of the 7 magnitude that were encountered in the Libby mine a nd mill. This type of underestimation of 8 exposure related to unknown occupational histories outside of Libby could potentially lead to 9 overestimation of regression slopes for occupationa l exposures in Libby and, hence, somewhat 10 overestimated risks, although the magnitude of this overestimation is unquantifiable. 11 Counterbalancing any upward bias on the estimated e ffects of occupational exposures due to 12 non-occupational exposures is the downward bias fro m random exposure measurement error 13 with lower occupational exposure affected dispropor tionately. 14
15 Conclusion regarding uncertainty in exposure assess ment 16 Overall, there were likely to be multiple sources of uncertainty attributable to exposure 17 measurement error. It is possible that systematic error may have been introduced into the 18 exposure intensities assigned to several of the job location operations discussed above. In each 19 case, these errors in estimating exposures were ove restimates. The magnitude of the potential 20 overestimates of drilling and dry and old wet mill exposures is uncertain. The dust-to-fiber 21 conversion ratio applied to the dry mill during 196 0-1967 could be an over or underestimate by 22
as much as 2-fold. Random error in the measurement of dust or fibers would likely have 23 produced an underestimation of risk. There is no k nown bias in the assumptions to extrapolate 24 exposure to pre-1968 location operations outside of the dry mill, and random bias would also 25 likely have produced an underestimation of risk. 26
27 3) Uncertainty in model form 28 For mesothelioma mortality, the Poisson model is co mmonly used for rare outcomes and 29 has been applied by McDonald et al. (2004) and Mool gavkar et al. (2010) to model 30 mesothelioma risk in the Libby worker cohort. For lung cancer mortality, the Cox proportional 31
hazards model is a well-established method that is commonly used in cohort studies, including 32 by Larson et al. (2010a) and Moolgavkar et al. (201 0) for the Libby worker cohort, because this 33 type of survival analysis takes into account differ ences in follow-up time among the cohort. 34 Larson et al. (2010a) conducted Poisson regression analyses and reported that their lung cancer 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-104 results using this different model form were simila r to those from their extended Cox 1 proportional models, but those results were not sho wn. 2
Both of these model forms allow for the evaluation and control of important potential 3 confounding factors such as age, sex, and race, and allow for the modeling of exposure as a 4 continuous variable. Both model forms yielded expo sure-response results with good fit to the 5 occupational exposure data. The default assumption of the extended Cox proportional hazards 6
model as well as the Poisson model is that all cens oring (due to death or loss to follow-up) is 7 assumed to be independent of exposure to the Libby Amphibole asbestos (e.g., death in an 8 automobile accident or moved to Canada). However, exposure to Libby Amphibole asbestos 9 may be causing deaths from other causes such as asb estosis or non-malignant respiratory disease 10 (Larson et al., 2010a), which is referred to as dep endent censoring. The concern is that the 11 observation of lung cancer mortality may be preclud ed by mortality from other causes. 12 In the cohort of 880 workers hired after 1959, 32 d ied of lung cancer, while 10 died of 13 asbestosis and 21 died of non-malignant respiratory disease. The mean length of follow-up from 14
the date of hire until death for the workers who di ed of lung cancer was 24.9 years. However, 15 the mean length of follow-up for the workers who di ed of asbestosis or non-malignant 16 respiratory disease was 30.4 years, so it does not appear that early deaths from other causes 17 associated with exposure to the Libby Amphibole asb estos (Larson et al., 2010a) would have 18 precluded many cases of lung cancer. This implies that any potential bias in the lung cancer risk 19 estimates due to dependent competing risks is small

. 20 With respect to mesothelioma mortality, it should b e noted that the exposure-response 21 modeling is limited by the number of deaths. Howev er, dependent censoring, as described 22 above, is not accounted for in the Poisson model an d likely causes a downward bias in the 23 estimation of risk. The mean length of follow-up f or the workers who died of mesothelioma was 24 30.1 years, and there is some evidence that early d eaths from other exposure-related causes 25 precluded an individual’s risk of death from mesoth elioma; only lung cancer exhibited a shorter 26 average follow-up time compared to mesothelioma, an d in 419 cases of mesothelioma, 27 mesothelioma and lung cancer were never co-identifi ed (Roggli and Vollmer, 2008). 28
29 4) Uncertainty in selection of exposure metric 30 There is uncertainty about what metric should be us ed for modeling exposure to Libby 31 Amphibole asbestos. The previous IRIS IUR assessme nt for asbestos (1988) found that 32 cumulative exposure with a 10-year lag was the best metric for lung cancer mortality, and a more 33 complicated model (Eq. 5-5) based on average cohort exposure intensity, average cohort time 34 since first exposure, and average duration of emplo yment was the best metric for mesothelioma 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-105 mortality. This assessment evaluated these models, but also models that include unlagged and 1 lagged cumulative exposure with and without a half- life of various lengths, and residence-time 2 weighted exposure with and without a half-life. In the analysis of comparative model fit, lagged 3 cumulative exposure with a half-life provided the b est fits for both mesothelioma and lung 4 cancer mortality associated with Libby Amphibole as bestos. However, evaluation of 20-year lag 5 and longer lag times for mesothelioma was not possi ble, as the earliest mesothelioma death 6 happened less than 20 years from the start of the e xposure, and, hence, exposure was zeroed out, 7 and the fit of any model with 20-year lag was very poor. Latency time for mesothelioma may be 8 as long as 60-70 years (e.g., Bianchi and Bianchi, 2009), so the precise lag time is uncertain. 9 In evaluating the data on lung fiber burden, Berry et al. (2009) estimated the range of the 10 half-life for crocidolite to be between 5 and 10 ye ars. That range is consistent with the finding of 11 a 5-10 year half-life with 10-15 years lag that pro vided the best fit to the Libby workers cohort 12 mesothelioma mortality data. Similarly, recent pub lications indicate that the relative risk of lung 13 cancer due to asbestos exposure declines 15-20 year s after the cessation of exposure to asbestos 14 (Hauptmann et al., 2002; Magnani et al., 2008). Th e marginally best fit for the Libby workers 15 cohort lung cancer mortality data was for CE models with a 5-20-year half-life and 10-year lag. 16 However, the precise lag and half-life times are so mewhat uncertain. Sensitivity analysis that 17 excluded people with high exposure during 1960-1963 (Section 5.4.5.3) provides further 18 evidence that distinguishing between various lag an d decays may be difficult with these data. A 19 limitation of this sensitivity analysis is the decr ease in the number of cases, especially for 20 mesothelioma. Resolving this uncertainty will requ ire longer follow-up time that would allow 21 for a sub-cohort analysis of workers hired in 1967 or afterwards (when exposure estimates began 22 to be based on PCM measurements) until a sufficient number of cases would be available for 23 additional analysis. 24 These simulated decay models were derived mathemati cally to approximate underlying 25 biological processes that are not well understood, and their better fit is based on maximizing the 26 likelihood for the workers cohort and may not neces sarily apply to the environmental exposure 27 patterns. Nonetheless, while the mode of action fo r carcinogenicity is unknown, the models 28 incorporating a half-life in the exposure metric we re clearly preferable for mesothelioma 29 mortality, and the goal of the regression modeling effort was to identify the best fitting exposure 30 model for the Libby worker cohort. 31 The selection of the exposure metric is a source o f cross-metric variability discussed in 32 Section 5.4.5.3, and the IUR incorporates this vari ability. The difference between this value and 33 the value derived from the best fitting exposure mo del describes the quantitative uncertainty, 34
which is less than 2-fold. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-106 5) Uncertainty in assessing of mortality correspondin g to the cancer-specific endpoints 1 As well established in the literature, mortality ra tes calculated from death certificates are 2 lower than the true rate of death due to both lung cancer, and to a larger degree, mesothelioma 3 (lung cancer sensitivity: ranging from 86% in an as bestos cohort [Selikoff and Seidman, 1992] to 4 95% in general [Percy et al., 1981]; mesothelioma s ensitivity: ranging from 40% for ICD-9 5 [Selikoff and Seidman, 1992] to about 80% for ICD-1 0 [Camidge et al., 2006; Pinhiero et al., 6 2004]). This underestimation of the true rate will result in a lower estimated risk compared with 7
that which would be estimated based on the true rat e. The underestimation of risk is much more 8 pronounced for the absolute risk model (mesotheliom a) than for the relative risk model (lung 9 cancer). Misdiagnosis rates would need to be quite disparate in the cohort and the comparison 10 population to impact relative risks, and this is un likely for internal controls that were used in the 11 lung cancer analysis using the Cox model. Therefor e, EPA considered use of a procedure to 12 adjust risks for mesothelioma, but not for lung can cer—underascertainment (Section 5.4.5.1.1). 13 This procedure makes certain assumptions, in partic ular, that an adjustment factor derived for the 14 full cohort applies to the sub-cohort hired after 1 959, and that the rate of misdiagnosis of 15 peritoneal mesotheliomas has not improved recently and that the proportion of peritoneal 16
mesotheliomas in the cohort is estimated from the a vailable information on the type of 17
mesothelioma in one-third of mesothelioma cases. H owever, overall uncertainty in this 18 adjustment is low, and the application of the adjus tment reduces the bias associated with the 19 diagnostic underascertainment. 20 The endpoint for both mesothelioma and lung cancer was mortality, not incidence. The 21 latter is generally desirable, but median survival with lung cancer and, especially, mesothelioma 22 is not very long, so uncertainty related to the end point being death and not incidence is low. 23 There is evidence that other cancer endpoints may a lso be associated with exposure to the 24
commercial forms of asbestos. IARC concluded that there was sufficient evidence in humans 25 that commercial asbestos (chrysotile, crocidolite, amosite, tremolite, actinolite, and 26 anthophyllite) was causally associated with lung ca ncer and mesothelioma, as well as cancer of 27 the larynx and the ovary (Straif et al., 2009). Am ong the entire Libby workers cohort, only 28 2 deaths were found to be due to laryngeal cancer, and there were no deaths from ovarian cancer 29 among the 24 deaths of 84 female workers. The lack of sufficient number of workers to estimate 30 risk of ovarian cancer is an uncertainty in an over all cancer health assessment. 31 The remaining uncertainties attributed to assessing mortality corresponding to the cancer 32 endpoints are considered to be low. 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-107 6) Uncertainty in control of potential confounding in modeling lung cancer 1 It is well known that smoking is a strong independ ent risk factor for lung cancer and may 2 have a synergistic effect with asbestos exposure (W raith and Mengersen, 2007). In contrast, 3 smoking is not considered a risk factor for mesothe lioma (Mossman et al., 1990; Selikoff and 4 Lee, 1978). 5 As an important potential confounder of the lung ca ncer mortality analysis, the possible 6 effect of smoking on the estimated risk of lung can cer mortality associated with exposure to 7 Libby Amphibole asbestos needs to be evaluated to t he fullest extent possible. This 8
consideration was discussed in Amandus and Wheeler (1987) and in Section 4.1.1.3 . 9 Additionally, WR Grace instituted a smoking ban on the property in 1979 (Peacock, 10 2003). However, it is unclear how the ban on smoki ng at work relates to smoking patterns 11 outside of the work environment. Moreover, since o nly about 30% of the sub-cohort was still 12 employed in 1979 and all of the post-1959 cohort ha d been terminated by May of 1982, the 13 impact of a workplace smoking ban on cohort smoking history is unlikely to be a significant 14 factor but may explain the higher proportion of for mer smokers in the Amandus and Wheeler 15 (1987) data. 16 Without high-quality individual-level data on smoki ng that could be used to control for 17 potential confounding, it is still possible to comm ent upon the likelihood and potential magnitude 18 of confounding and the impact any confounding would be expected to have on the lung cancer 19 mortality risk estimates. Confounding can be contr olled for in a number of ways including by 20 modeling and by restriction. Restriction of the st udy population can reduce any potential 21 confounding by making the resulting population more similar. For instance, there can be no 22 confounding by gender when a study population is re stricted to only men. This assessment 23 restricted the study population to those workers hi red after 1959. Smoking habits have changed 24 over time, and it can reasonably be assumed that th e range of smoking habits among those hired 25 after 1959 is less variable than that among the who le cohort, particularly because of the narrower 26 range of birth cohorts represented in this sub-coho rt. This should have the effect of reducing 27 some of the potential for confounding. 28 Additionally, the extended Cox proportional hazards models controlled for date of birth, 29 which effectively controls for any secular trends i n confounders over time (Tableman and Kim, 30 2004). Amandus and Wheeler (1987) cite data from t he U.S. Public Health Service (1979) 31 showing a steady decrease in the prevalence of curr ent smoking from 52.9% in 1964 when the 32 U.S. Surgeon General’s report on smoking was releas ed to 42.3% in 1970 and 37.5% in 1978 33 (U.S. Surgeon General, 1990). If current smoking w ere a meaningful confounder, such a 34 reduction in smoking rates over time should have pr oduced a noticeable distortion in the 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-108 proportionality of the hazards as the magnitude of confounding by smoking changes with 1 smoking prevalence. No violation of the proportion al hazards assumption was observed in the 2 context of the Cox model; hence, there is no eviden ce of confounding by smoking in the analyses 3
of workers hired after 1959. 4 Lastly, a method has been described by Richardson ( 2010) to determine if an identified 5 exposure relationship with lung cancer is confounde d by unmeasured smoking in an occupational 6 cohort study. EPA implemented this methodology to model the potential effects of Libby 7 Amphibole asbestos on the risk of COPD mortality on the sub-cohort of workers hired after 1959 8 (see Section 5.4.3.4.4). Summarizing these finding s, EPA used the method described by 9 Richardson (2010) to evaluate whether exposures to Libby Amphibole asbestos predicted 10 mortality from COPD as an indication of potential c onfounding by smoking and found a non11 significant negative relationship, which was incons istent with confounding by smoking. 12
13 7) Uncertainty due to potential effect modification 14 Among the 32 deaths from lung cancer in workers hir ed after 1959 that were used to 15 estimate the unit risk of lung cancer mortality (se e Section 5.4.5.2), data on smoking listed 16 as 16 smokers, 4 as former smokers and 12 of the 32 had m issing data. Thus, data to support an 17 estimate of the risk of Libby Amphibole asbestos am ong known non-smokers were not available. 18 It is theoretically possible that the risk of lung cancer mortality estimated in this 19 assessment is a reflection of a positive synergy be tween smoking and asbestos, and that the 20 adverse effect of Libby Amphibole asbestos among th e potentially non-smoking workers has 21 been overestimated. The unit risk of the lung canc er estimate herein and the combined 22 mesothelioma and lung cancer mortality IUR would th en be health protective for any population 23 that had a lower prevalence of smoking than that of the Libby worker cohort. 24
25 8) Uncertainty due to length of follow-up 26 There is some potential uncertainty regarding the length of follow-up for cancer 27 mortality, even more so with the restriction of the cohort to those workers hired after 1959. The 28 hire dates among this subset of the cohort ranged f rom January 1960 to November 1981 (the 29 mean date of hire was May 1971). Follow-up continu ed until the date of death or 30 December 31, 2006, whichever occurred first. There fore the range of follow-up was from 25 to 31 46 years, with a mean of more than 35 years. 32 However, for mesothelioma mortality, the length of the latency period is considerably 33 longer. Suzuki (2001) reviewed 1517 mesothelioma c ases from 1975 through 2000 and was able 34 to estimate the latency for 800. Suzuki (2001) rep orted 17% of cases had a latency of less than 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-109 30 years with 52% of cases with a latency of less t han 40 years. Bianchi and Bianchi (2009) 1 estimated the mesothelioma latency in 552 cases and reported mean latency periods of 35 years 2 among insulators, 46 years among various industries , and 49 years among shipyard workers. 3 The effect of insufficient length of follow-up for mesothelioma mortality would be to 4 underestimate the risk of exposure since there woul d be workers who may eventually die of 5
mesothelioma that are not counted in this assessmen t. As the risk of mesothelioma mortality is 6 evaluated as an absolute risk, the unit risk of mes othelioma mortality may reasonably be 7 expected to rise with time moderated by the increas e in person-years of follow-up. According to 8 the results of Suzuki (2001) and of Bianchi and Bia nchi (2009), a mean length of follow-up of 9 35 years may only have captured half of all eventua l mesothelioma mortality cases among the 10 Libby workers hired after 1959. If this were so, t hen the unit risk of mesothelioma mortality 11 could be larger than was estimated from existing da ta, depending on the relationship between the 12 number of additional deaths and increase in person- years. 13
14 9) Uncertainty in use of life-tables to calculate can cer mortality IUR 15 The life-table procedure computes the extra risk o f death from birth up to 85 years of age, 16 in part, because this is how national cancer incide nce and mortality rate data that are one basis of 17 the life-tables are made available (see 2003-2007 S EER Table 15.10, age-specific U.S. death 18 rates). Because the prevalence of cancer mortality is a function of increasing age, this cut-off at 19 age 85 ignores a small additional risk of lung canc er mortality among a small percentage of 20 people who have the higher background risk. This h as the effect of slightly underestimating the 21
IUR that would be derived if the life-table were ex tended for an additional period of time, 22 accounting for longer life spans. Extension of the life-table analysis to people over the age of 85 23 requires an additional assumption. Assuming that h aving attained the age of 85 years, the 24 additional life expectancy is 5 years, then the lun g cancer mortality unit risk based on the LEC 01 25 would be somewhat larger—on the order of 5-10%—slig htly more than the additional 26 mesothelioma mortality risk if the life-tables were extended. 27
28 10) Uncertainty in combining of risk for composite can cer IUR 29 For the purpose of combining risks, it is assumed that the unit risks of mesothelioma and 30 lung cancer mortality are normally distributed. Si nce risks were derived from a large 31 epidemiological cohort, this is a reasonable assump tion supported by the statistical theory, and 32 uncertainty related to it is low. 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-110 11) Uncertainty in extrapolation of findings in adults to children 1 The analysis of lung cancer mortality specifically tested and confirmed the assumption 2 that the relative risk of exposure is independent o f age within the age range of the occupational 3 sub-cohort hired after 1959. However, no comparabl e data are available to estimate the lifetime 4 risk from early life exposures. The life-table pro cedure is conducted so as to initiate exposure at 5 age 16 to represent adult exposures. Then, the adu lt-only-exposure IUR estimates derived from 6 the life-table analysis need to be re-scaled to a 7 0-year lifespan in order to yield the standard 7 lifetime IUR, allowing risk estimate calculations i nvolving less-than-lifetime exposure scenarios, 8 in the standard manner. After re-scaling, the resu lting “adult-based” IUR estimate (in contrast to 9 the unscaled “adult-only-exposure” IUR estimate obt ained from the life-table calculations) can 10 be used seamlessly by the end-user in the same mann er as for an adult-based IUR estimate 11 derived from a rodent bioassay. Lack of published information on risks associated with Libby 12 Amphibole asbestos-specific exposure during childho od is the uncertainty associated with the 13 proposed extrapolation. If such information is sub sequently published, the extrapolation 14 procedure can be updated. As per the EPA Cancer Gu idelines (U.S. EPA, 2005b), when the 15 information regarding early-life susceptibility to cancer is not available, the linear low-dose 16 extrapolation approach will be used. 17
18
5.4.6.2. Summary 19 In the discussion of the overall uncertainty in the IUR, it is important to distinguish 20 between uncertainty that encompasses both the direc tion and the magnitude from uncertainty 21
with known directional effects on the IUR but of un known magnitude. In this summary, only the 22 latter uncertainties, which may result in underesti mated or overestimated risk, are listed below. 23 Uncertainties that are not thought to alter the est imated magnitude of the risk in a systematic 24 direction are not included in this summary. 25 The sources of uncertainty that could lead to a lik ely underestimation of the cancer risk 26 value include the following: 27
28 29

• Use of historical PCM exposure measurements . As asbestos was a contaminant of 30 vermiculite that was the primary object of producti on, mine and dry and old wet mill 31 ambient air may have contained material other than asbestos that could have contributed 32 to fibers counted by PCM. Therefore, it is possibl e that exposure estimates for some, or 33 possibly a large portion of the cohort, are overest imated, and, therefore, the resulting IUR 34 may be underestimated

. 35

• Measurement error in exposure assessment and assign ment . This assessment showed 36 that unit risk results from analysis of the lung ca ncer mortality in the full cohort 37This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-111 (Sullivan, 2007; Larson et al., 2010a) compared to the sub-cohort hired after 1959 may 1 have been attenuated as much as 2-6 times (see earl ier section on statistical uncertainty). 2 By excluding those cohort members hired before 1960 on whom there was insufficient 3 work history information to estimate their exposure s, the unit risk for lung cancer was 4 less attenuated due to exposure measurement error. However, exposure measurements 5 from the 1960s are also imperfect and include a les ser degree of exposure measurement 6 error, which could have led to underestimated risk even in the sub-cohort hired after 7 1959. 8

• Limited length of follow-up. Absolute risk is used for mesothelioma; therefore, the unit 9 risk of mesothelioma mortality could be larger than was estimated from existing data, 10 depending on the relationship between the number of additional deaths and an increase in 11
person-years. 12

• Use of life-tables to calculate the IUR based on ca ncer mortality. The lung cancer 13 mortality unit risk based on the LEC 01 would be somewhat larger, about 5-10%, and the 14 mesothelioma unit risk would be slightly less (abou t 3%) than that if the life-tables were 15 extended from 85 to 90 years to account for longer life spans. 16

• Small number of women and ovarian cancer. While asbestos is causally associated with 17 increased risks of ovarian cancer (Straif et al., 2 009), there were only 84 women in the 18 whole cohort, and there were no deaths from ovarian cancer among 24 total deaths. To 19 the extent that there was an increased risk of ovar ian cancer in the Libby workers cohort 20 due to inhalation exposures that was unobserved, th en the IUR would be somewhat 21 underestimated. However, it was not possible to es timate the magnitude of this 22 underestimation on the total cancer risk. 23

• Dependent competing risks . Competing risk of mortality from other diseases related to 24 exposure may have resulted in underestimates of the risk of mortality from either 25 mesothelioma or lung cancer. The mean length of fo llow-up for the Libby workers who 26 died of mesothelioma was to 30.1 years, and evidenc e exists (Suzuki, 2001; Bianchi and 27 Bianchi, 2009) that early deaths from other exposur e-related causes could have precluded 28 an individual’s risks of death from mesothelioma. However, it was not possible to 29 estimate the magnitude of this effect on the total cancer risk. 30

31 The sources of uncertainty that could lead to a lik ely overestimation of the cancer risk 32 value include the following: 33
34

• Potential residual confounding and effect modificat ion. The unit risk of lung cancer 35 mortality estimated herein, and the combined mesoth elioma and lung cancer mortality 36 IUR, would over-estimate the risk in any population that had a lower prevalence of 37 smoking than that of the Libby worker cohort. Sinc e the Libby worker cohort had a large 38 prevalence of smokers and no known non-smokers deve loped lung cancer, it is also 39 possible that estimated risk for lung cancer is act ually risk for an interaction of lung 40This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 5-112 cancer and smoking and effects of smoking and asbes tos are known to be between 1 additive and multiplicative (Section 4). 2

• The resolution of visible PCM fibers in 1960s and 1 970s and use of TEM-based PCMe

. 3 It is likely that early PCM counts were underestima ted relative to the later PCM 4 measurements. Since PCMe counts fibers that corres pond to modern PCM, using PCMe 5 for risk derived partially in old PCM fibers could lead to overestimation of mortality risk. 6This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 1 6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND 1 EXPOSURE RESPONSE 2

Libby A mphibole asbestos, a contaminant of vermiculite mined near Libby, MT, is a 3 complex mixture of amphibole fibers

— both mineralogically and morphologically ( Section 2.2 ). 4 The mixture primarily includes tremolite, winchite

, and richt e rite fibers. These fibers exhibit a 5 range of morphologies from asbestiform to cleavage fragments (Meeker et al

. , 2003). Given th e 6 exposure potential to Libby A mphibole asbestos

— and its unique mineral composition

— a hazard 7
characterization and cancer dose

- response assessment is presented. 8
As discussed in Section 1

, there is currently no RfC for asbestos and the U.S. EPA IRIS 9
inhala tion unit risk ( IUR ) for asbestos i s based on a synthesis of 14 epidemiologic studies that 10 included occupational exposure to chrysotile, amosite

, or mixed mineral fiber s (chrysotile, 11 amosite, and crocidolite) ( U.S. EPA, 198 6 ). There is uncertainty in applying the resulting IUR to 12
environments and minerals that are not included in the studies considered for the asbestos IUR 13
derivation ( U.S. EPA

, 1988 )

. Published mortality studies on the Libby, MT worker cohort have 14
become av ailable since the derivation of the IRIS asbestos IUR ( i.e., McDonald et al

. , 1986 a , 15
2004 ; Amandus and Wheeler, 1987 ; Sullivan, 2007 ; Larson et al., 2010 a ; Moolgavkar et al., 16
2010 ). This assessment document s non

- cancer and cancer health effects from inhal ation 17 exposure to Libby Amphibole asbestos. Data are not available to support de rivation of either an 18
RfD or a cancer slope factor following oral exposures to Libby Amphibole asbestos

. 19
20
6.1. HUMAN HAZARD POTENTIAL 21

6.1.1. Exposure 22 Libby A mphibole asbestos was not mined for industrial or commercial use

, but it was a 23 contaminant of vermiculite mined near Libby, MT (Section 2) . Vermiculite from the Libby , MT 24 mine was used commercially from the 1920s to 1990, and it comprised approximately 80% of 25
the total vermiculite sold in this country until that point. Vermiculite wa s most notably used as 26 attic insulation, as a soil amender for gardening

, and in the manufacturing of gypsum wallboard. 27 The exposure potential to Libby A mphibole asbestos includes historical exposures (both 28 occupational and community) ; as well as the potential for ongoing exposures to waste materials

, 29
contaminated soils and vegetation, and consumer products (e.g.

, vermiculite attic insulation )

. 30 There are many ways in which workers and residents in Lib by, MT

, and the surrounding 31 communities may have been exposed while the mining and milling operations were active

. 32 Historical routes of exposure include ( 1) occupational exposure; ( 2) take

- home exposure for 33 household contacts of the workers (including children) ; ( 3) dust/fiber emissions to the 34 community from the milling and exfoliating facilities; ( 4) distribution of waste material into the 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 2 community as fill (including yards and recreational areas); ( 5) use of vermiculite attic insulation 1 in homes; ( 6) use of vermiculite in gardening/horticulture ; and ( 7) children playing in the waste 2 rock piles (stoner rock) (ATSDR

, 2001)

. Other than documentation of dust and fiber exposure 3 levels for mine and mill workers, there are few data to inform the levels of ex posure to 4 household contacts and community members during mine and mill operation s . Although no 5 historical exposure measurements are available from the homes of the workers, the EPA has 6 conducted some sampling to determine exposure levels from vermiculite and waste materials that 7 remain in the community ( U.S. EPA

, 20 0 1 ; Appendix B ). These data provide information useful 8

to understand what historical exposures might have been for similar activities. 9 Outside of Libby, MT, m illed vermiculite ore was shipp ed to 271 domestic sites that 10
served as processing facilities ( GAO

- 08

- 71 )

. These sites included exfoliation plants (e.g. for the 11 production of vermiculite insulation) as well as non

- exfoliation facilities (e.g.

, production of 12 gypsum wallboard )

. The vermiculite ore was exfoliated by heat

- induced expansion result ing in 13
vermiculite produced for commercial purposes

. Both the commercial vermiculite and the waste 14
rock (i.e. residual wast e rock from exfoliation) were contaminated with Libby A mphibole 15
asbe stos fibers. Potential exposure route s in these communities located around the country 16 parallel the exposures in Libby, MT

, including occupational exposure s , take

- home exposures 17 from workers, and children playing in the piles of waste rock near the facility (ATSDR

, 2008 )

. 18
W aste materials (expanded vermiculite and waste rock) from some of these facilities w ere also 19
used for fill in local communities , potentially creating additional exposure pathway s based on an 20
ATSDR review of 28 facilities , and a survey of the Western Minerals Plant, MN ( ATSDR

, 2001, 21 2008)

. Few historical sampl es that could be used to quantify the exposure potential for workers 22
or for the surrounding communities are available from these facilities . Air modeling conducted 23
for one exfoliating facility in Minnesota does provide support for the potential of dust/fiber 24 emissions from exfoliating plants to impact ambient air quality in the vicinity of the plant 25 (ATSDR

, 2005). 26
While t he mine was active, t here was exposure potential to commercial products 27 containing vermiculite from Libby

, MT

, especially in gardening soils and vermiculite attic 28
insulation

. No studies have evaluate d the potential for consumer exposure when vermiculite 29 from Libby, MT

, was employed as a soil amender, but air sampling at one facility where this was 30 produced ( O.M. Scott facility in Marysville

, OH ) demonstrate d that workers handling this 31 material during manufacture were expos ed to asbestos fibers (Lockey et al., 1984)

. There is 32

p otential for exposure for homes that contain vermiculite attic insulation from Libby, MT

, as 33
residents and workers enter attics for various uses, repairs

, and renovations and for using 34 firewood from Libby, MT area (Section 2.3.3)

. 35
36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 3 6.1.2. Fiber Toxicokinetics 1 There is no specific information available at the time of this assessment on the fiber 2 toxicokinetics of Libby A mphibole asbestos . However

, as a mineral fiber

, the characteristic s that 3 define the deposition, clearance

, and translocation of other amphibole fibers might apply to 4
Lib by A mphibole asbestos . As discussed in Section 3

, the specific fiber dimensions and density 5 of Libby Amphibole asbestos will determine the probable pattern of deposition in the respiratory 6

tra ct and other tissues (e.g., pleura, peritoneum) . Bas ed on the fiber

- size profile of airbo rn e 7 Libby Amphibole asbestos fibers, deposition is expected throughout the respiratory tract 8 including the a l veolar regions. Less is known about mineral fiber translocation to other target 9
tissue s in general, and

, to date

, no studies have specifically examined t ranslocation following 10 exposure to Libby Amphibole asbestos. 11 As with other mineral fibers, clearance is likely to occur via the mucociliary apparatus in 12
the upper respiratory tract and the mucociliary escalator for those fibers deposited in the trachea 13
and bronchiol e s. This cleara n ce is enhanced by macrophage action, which may transport some 14 of the fibers from the alveolar sac to the mucociliary system. Fibers may also be dissolved in 15 lung fluids or through the more aggressive action of alveolar macrophages . In general

, 16

a mphibole asbestos is considered more persistent and less likely to dissolve than other natural 17
fibers. However, no data are available for Libby Amphibole asbestos specifically

, and it is 18
unknown if Libby Amphibole asbestos fibers would split or break in the pulmonary compartment 19
as has been shown with some amphibole fibers (e.g.

, ferroactinolite) (Coffin et al

., 1983). 20 Any fibers deposited in the respiratory tract and not cleared via the mucociliary system , 21

or which are not dissolved

, can remain in the lung or can be transport ed to other tissues. 22 Although data specific to Libby Amphibole asbestos are not yet available, other asbestos fiber 23
types may translocate from the lung via macrophage action and transport through the lymph 24 system, or direct migration may occur through tissues from the mechanical action of the lung. 25
P leural and peritoneal effects documented in Libby Amphibole asbestos

- exposed individuals 26 support the potential for translocation of Libby Amphibole asbestos into the pleura. 27
28
6.1.3. Noncancer H ealth E ffects in Humans and Laboratory Animals 29

Noncancer health effects identified in humans include effects on the res piratory system 30 (e.g., lung function, pleural and parenchymal effects) following inhalation exposure to Libby 31 Amphibole asbestos , as well as increased m ortality from non

- cancer causes

. Five cohort 32 mortality studies of Libby miners identified increased risk of mortality from non

- cancer causes, 33 including nonmalignant respiratory disease (McDonald et al

., 1986a ; Amandus and Wheeler 34
1987 ; McDonald et al

., 2004 ; Sullivan, 2007; Larson et al

., 2010 a ) and cardiovascular disease 35 (Larson et. al., 2010 a ). Studie s of respiratory system effects comprise six studies in community 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 4 members living in Libby, MT (Weill et al., 201 0 ; Peipins et al.

, 2003; Peipins et al., 2004 ; 1
Whitehouse 2004 ; Muravov et al., 2005; Vinikoor et al., 2010 ) , two studies among Libby miners 2 (Mc Donald et al., 1986b; Amandus et al., 1987 b ), and two studies among Marysville workers 3 (Lockey et al., 1984; Rohs et al., 2008). Among the studies in community members, all but one 4 (Vinikoor et al., 2010) included former vermiculite facility workers , and none of the six provided 5 quantitative exposure information

. The four studies conducted in two occupational cohorts 6 (McDonald et al., 1986b; Amandus et al., 1987 b ; Lockey et al., 1984; Rohs et al., 2008) did 7 provide quantitative exposure estimates, and we r e considered suitable for exposure

- response 8 analysis to support RfC derivation

. Each of the four studies demonstrated an increased risk of 9 pleural anomalies in workers

. 10 Although laboratory animal data and experimental data on toxicity mechanisms are 11 limited for Libby Amphibole asbestos, the existing data are consistent with the health effects 12 observed in both Libby workers and community members. Animal studies have demonstrated 13 increased collagen deposition consistent with fibrosis following intratracheal instillation of Libby 14 Amphibole asbestos fibers in C57Bl6 mice ( sex unspecified, Putnam et al., 2008; male and 15 female, Smartt et al., 2009) and increased markers of pulmonary inflammation in a rat model for 16
human cardiovasc ular disease (Shannahan et al., 2011) . Pulmonary fibrosis, inflammation, and 17 granulomas were observed after tremolite inhalation exposure in SPF male Wi star rats 18 (Bernstein et al., 2003, 2005) and intratracheal instillation in male albino Swiss mice (Sahu et al., 19 1975). Davis et al. (1985) also reported pulmonary effects after inhalation exposure to tremolite 20
in SPF male Wistar rats including increases in peribronchiolar fibrosis, alveolar wall thickening, 21
and interstitial fibrosis. 22
23
6.1.4. Carcinogenicity in H umans and Laboratory Animals 24 Libby A mphibole asbestos fibers are associated with lung cancer and mesothelioma in 25 workers from the Libby vermiculite mining and milling operations ( Larson et al., 2010 a ; 26 Moolgavkar et al.

, 2010 ; Sullivan

, 2007; McDonald et al.

, 19 86a ; 200 4 ; Amandus et al.

, 198 8; 27
Amandus and Wheeler

, 1987 ; NIOSH

, 1986 )

. No other occupational cohort with exposures to 28 Libby A mphibole asbestos has been studied with respect to mortality risks

. Whitehouse et al. 29 (2008) documented 11 meso thelioma cases in non

- workers exposed to Libby Amphibole in 30 Libby, MT. Increased lung cancer and mesothelioma deaths are also reported for worker cohorts 31 exposed to other forms of amphibole fibers ( amosite and crocidolite ) (de Klerk, 1989; 32
Seidman et al

. , 1986; Henderson and Enterline, 1979). These findings are consistent with the 33 increased cancers reported for communities exposed to various rocks and soils containing 34
tremolite fibers

, which are a component of Libby A mphibole asbestos ( Baris

, 1987 ; Yazici glu

, 35
1976 ; Yaziciglu et al.

, 1973; Langer et al

., 1987 ; Baris et al

., 1979 ; Sichletides et al

. , 1992 ; 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 5 Hasanoglu et al., 2006 ). Although potency, fiber dimension

, and mineralogy differ between 1 amphiboles, these studies are supportive of the hazard identifi cation of Libby A mphibole 2
asbestos fibers described in this document

. 3 Although there is a limited laboratory animal database , the studies that are available 4 support the carcinogenicity of Libby A mphibole asbestos fibers. Smith (1978) demonstrated 5
mesoth el iomas in hamsters given a single intrapleural injection of Libby A mphibole asbestos 6
material (Table 4

- 16 ) . Tremolit e , which comprises approximately 6% o f the fiber mixture in 7 Libby A mphibole asbestos

, is also carcinogenic in studies in rats, hamsters, and mice, resulting 8 in pleural mesothelioma, peritoneal mesothelioma

, and lung cancer depending on the route of 9

exposure ( T able 4

- 17 ) (Davis et al

. , 1985; Davis et al., 1991; Stanton, 1981; Roller et al., 1996 ; 10
Bernstein et al

. , 2003, 2005). Although compari ng the potency of the tremolite used in these 11
studies is difficult given the lack of information on fiber characteristics and other study 12 limitations, these results show an increased risk for lung cancer and mesothelioma following 13 exposure to tremolite asbestos. 14
15
6.1.5. Susceptible P opulations 16 Certain populations could be more susceptible than the general population to adverse 17 health effects from exposure to Libby Amphibole asbestos

. In general

, factors that may 18
contrib u te to increased susceptibility from envi ronmental exposures include lifestage, gender, 19 race/ethnicity, genetic polymorphisms, health status

, and lifestyle. However, little data exist to 20 address the potential of increased susceptibility to cancer from exposure to the Libby Amphibole 21
asbestos

. 22 Most occupational studies of workers exposed to Libby Amphibole asbestos have 23 examined the effects only in men because this group represents the vast majority of workers in 24 these settings (Amandus and Wheeler, 1987; Amandus et al., 1987 c , 1988; McDonald et al., 25
198 6a , 1986b, 2004; Sullivan, 2007; Mool g avkar et al., 2010 ). The analysis presented here 26
include s all workers, but there were few women and , consequently, no determination can be 27 made regarding increased susceptibility to lung cancer or mesothelioma. G ender

- related 28 differences in exposure patterns, physiology, and dose

- response are some of the factors that may 29
contribute to gender

- related differences in ri sk from asbestos exposure (Smith, 2002)

. The 30
limited data available from community

- based studies (ATSDR, 2000) do not provide a basis for 31 drawing conclusions regarding gender

- related differences in carcinogenic effects from Libby 32 Amphibole asbestos

. Raci al diversity among workers exposed to Libby Amphibole asbestos is 33
also limited

, and data on ethnic groups absent, precluding the ability to examine racial and 34 ethnicity

- related differences in the mortality risks within the Libby worker cohort

. Finally , th e 35 potential modifying effects of genetic polymorphisms, pre

- existing health conditions, nutritional 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 6 status, and other lifestyle factors have not been studied sufficiently to determine their possible 1 contribution to variation in risk in the population

. 2
3
6.1.6. Mod e - of

- Action Information 4 Limited information is available on the biological effects that may contribute to a specific 5
mode of action. Laboratory animal studies of mice (Putnam et al., 2008; Smartt et al., 2009)

, 6 hamsters (Smith

, 1978) or rats (Shannahan et al., 2011) exposed to Libby Amphibole asbestos 7
suggest a n inflammatory response similar to that observed with other mineral fibers; however, no 8 inhalation studies have been performed. In vivo studies in rats, hamsters

, or mice expose d to 9 tremolite (McConnell et al., 1983a; Davis et al., 1991; Smith et al., 1979; Wagner et al., 1982; 10
Stanton et al., 1981; Roller et al., 1996

, 1997) show results similar to other amphibole asbestos 11
fibers including lung cancer and mesothelioma , although again with limited inhalation studies 12 (Davis et al., 1985; Bernstein et al., 2003; 2005). In vitro studies demonstrate that the uptake of 13 Libby Amphibole asbestos fibers by macrophage

, mesothelial

, and lung epithelial cell line s may 14
lead to an in crease in oxidative stress as measured by reactive oxygen species production

, gene 15 expression changes ( Blake et al.

, 2007 ; Hillegass et al., 2010 )

, or genotoxicity (Pietruska et al., 16 2010)

. Th us the available data indicate that Libby A mphibole asbestos induce s biological 17 responses similar to other forms of asbestos such as oxidative stress , chronic inflammation

, 18 genotoxicity

, and increased cell proliferation . These mechanisms are potentially related to the 19
adverse health effects of asbestos fibers and o ne or mo re of these activities may be relevant to 20 the carcinogenicity of Libby Amphibole asbestos fibers. However, data are not sufficient to 21 establish a mode of action for Libby Amphibole asbestos. 22
23
6.1.7. Weight

- of

- Evidence Descriptor for Cancer Hazard 24 Libby A mphibole asbestos is carcinogenic to humans through inhalation exposure based 25 on the framework provided in the Guidelines for C arcinogen R isk A ssessment ( U.S. EPA, 2005). 26 Epidemiologic evidence provides a n association between exposure to Libby A mphibole a sbestos 27 fibers in the workplace and the mortality from lung cancer and mesothelioma ( Larson et al., 28
2010 a ; Sullivan

, 2007; McDonald et al.

, 198 6a , 2002, 2004; Amandus et al.

, 198 8; Amandus and 29
Wheeler

, 1987 ; NIOSH

, 1986 )

. Although limited, the available chronic laboratory animal study 30 involving exposure to Libby Amphibole asbestos supports these findings (Smith

, 1978). 31 As specified in the Guidelines for Carcinogen Risk Assessment , the descriptor 32 ―carcinogenic to humans‖ is appropriate when there is convincing epidemiologic evidence of a 33
causal association between human exposure and cancer (U.S. EPA, 2005)

. As described in 34
Section 4.1

, the evidence of both lung cancer and mesothelioma in the Libby worker cohort is 35
consistent over time. Additionally, this association and a convincing exposure

- re sponse 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 7 relationship are observed whe n examining both workers with a minimum tenure (McDonald et 1 al., 2004; Amandus and Wheeler, 1987), as well as when including workers regardless of 2 duration of employment (Sullivan, 2007). 3
4
6.2. EXPOSURE

- RESPONSE 5

This assessment contains a derivation of a RfC for noncancer effects and an IUR for cancer 6 based on epidemiologic data. It does not contain an RfD or oral cancer slope factor. 7
8 6.2.1. Noncancer/Inhalation 9 Libby Amphibole asbestos has been shown to cause localized pleural thickening, diffuse 10 pleural thickening, and parenchymal changes in exposed populations

. The study of Marysville 11 workers ( Lockey et al., 1984 and the follow

- up by Rohs et al., 2008) was selected as the 12 principal study due to the availabil ity of quantitative exposure information and information on 13 important covariates, increased risk of pleural and parenchymal effects, at lower cumulative 14 exposure levels (compared to the Libby worker cohort

— McDonald et al., 1986b; Amandus et 15 al., 1987 ), t he length of follow

- up, use of more recent 2000 ILO diagnostic criteria, and the 16 availability of detailed exposure and health outcome information for each worker. The outcomes 17 examined in this cohort included localized and diffuse pleural thickening, as wel l as parenchymal 18 changes. L ocalized pleural thickening was selected as the critical effect due to its higher 19 prevalence relative to the other outcomes, minimal adversity (compared with other effects) , and 20 specificity for durable mineral fiber exposure

. F or RfC derivation, analyses focused on the 21 subcohort of Marysville workers described by Rohs et al. (2008) who started employment after 22 1972, due to the greater certainty in exposure assessment in this group. 23 Benchmark dose modeling, with a BMR of 10% ext ra risk, was used to derive the POD. 24 A log

- logistic regression model was used to estimate the exposure

- response relationship for 25 Libby Amphibole asbestos and localized pleural thickening. Cumulative exposure with a lag of 26 10 years was selected as the exp osure metric, based on evidence for biological latency and model 27 fit considerations. A background rate of localized pleural thickening of 1% was assumed. The 28 resulting BMD under these modeling assumptions was 0. 2543 fibers

- yr/cc; the corresponding 29
BMDL o f 0. 076 fibers

- yr/cc is the POD for RfC derivation. The RfC is obtained by first 30 dividing the POD by a total uncertainty factor of 100 (10 for interspecies variability and 10 for 31
database deficiencies ) : 0. 076 fibers

- yr/cc x 1/100 = 0.00 0 76 fibers

- yr/cc. The RfC is for 32 continuous exposure (24 hours/day, 365 days/year, with exposure beginning at birth and 33 continuing for 70 years), adjusting for the 1 0

- year lagged exposure, is 0. 00 0 76 fibers

- yr/cc x 34 1/(70

- 10) years = 1.27 x 10 - 5 or rounded to one significant digit, 1 x 10 - 5 fibers/cc. Table 5

- 6 35 shows estimated RfCs for less than lifetime exposure scenarios. M odeling was also conducted in 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 8 the full cohort of workers described in Lockey at al. (1984) and Rohs et al. (2008). These 1 analyses used a different mod eling approach, due to the wider range of exposures and time since 2 first exposure (T). A modified Michaelis

- Menten model provided the best fit to the full cohort 3 data, which incorporated T via the plateau term for the model. For a T of 30 years, the BMD and 4 BMDL corresponding to a 10% extra risk of localized pleural thickening were 0.1384 and 0.0541 5
fibers

- yr/cc, respectively. These are quite similar to the values obtained in the analysis for the 6
RfC , and provide important support for the selected modeli ng approach. 7 Confidence in the principal study is considered medium

- to

- high. The data used are 8 human, epidemiological data rather than animal bioassays , and the principal study is conducted 9 in a population of occupationally exposed workers with long

- term, relatively low intensity 10

exposures. Confidence in the database is low

- to

- medium. The database contains long

- term 11 mortality and morbidity studies in humans exposed via inhalation to Libby Amphibole asbestos, 12
but there is a lack of data on effec ts other than in the respiratory system and uncertainties in time 13 from first exposure . Therefore, confidence in the RfC is medium reflecting medium

- to

- high 14 confidence in the principal study and low

- to

- medium confidence in the database. 15 It is important t o consider the sources of uncertainties in the derivation of the RfC for 16 Libby Amphibole asbestos

. These include: 17 Measurement error in exposure assessment and assignment. Th e estimated exposure for 18
each individual relied on self

- reported employment hist ory, which may be subject to recall error. 19 Only data from post

- 1972 were used for RfC derivation, based on lack of measured exposure 20 prior to this date; however, for certain occupations/tasks, no measurements were available until 21
1974 (Lockey, 1980). T he re is also uncertainty in the post

- 1972 data regarding asbestos content 22 in other ore sources (Virgin i a , South Carolina and South Africa) . Although Libby Amphibole 23 asbestos was not used in the facility after 1980, industrial hygiene measurements collected after 24 1980 showed low levels of fibers in the facility. However, because the concentration of fibers in 25 the workplace was near background after 1980, this exposure makes only a small contribution to 26 an individual’s cumulative exposure estimate. Similarly , any exposure to Libby Amphibole 27
asbestos outside of the workplace is not likely to contribute significantly to cumulative 28

exposure

— only ~ 10% of w orkers reported bringing raw vermiculite home , and the majority 29 showered and changed clothes before leaving t he work place

. 30
R adiographic a ssessment of localized p leural t hickening

. Conventional radiographs

, 31
rather than the more sensitive high

- resolution computed tomography

, were used to determine the 32
health outcome. Discrete pleural thickening may be difficul t to detect, leading to the potential 33
for outcome misclassification. However, uncertainty in the prevalence of localized pleural 34 thickening in each individual is considered minimal due to the use of a team of highly qualified 35 chest radiologists evaluating the radiographic films and the use of consensus diagnosis. 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 9 Length of follow

- up. Time from first exposure to x

- ray was 23.2

- 32.7 years in th e 1
recommended sub

- cohort. However, it is likely that the prevalence of localized pleural 2 thickening may continue to show some increase with increased follow

- up . In this case, the 3 modeling approach may not accurately reflect the exposure

- response relation ship that would be 4 seen with a longer follow

- up time

. Note that the likelihood that prevalence of localized pleural 5 thickening may further beyond 30 years after first exposure is a principal rationale cited for the 6
selectio n of a database UF of 10 for RfC derivation

. 7

B ackground rate of localized pleural thickenin g. In the derivation of the RfC, a 8 background rate of 1% for localized pleural thickening was used. Previous studies have reported 9

a range of prevalence estimates (0.02% to 3.9%) in populations n ot known to be occupationally 10 exposed to asbestos. However, in statistical modeling of the Marysville sub

- cohort, uncertainty 11 in the background rate of localized pleural thickening is very low. Both the fixed and estimated 12 values are in the range of esti mates above and the difference in the POD when the background 13 rate is fixed at 1% versus when it is estimated (estimated background rate of 3.76%) is less than 14
3%. 15
Model Form. A number of model forms were explored in the initial stages of analysis 16 (Appe ndix E ), before selecting the log

- logistic model. The log

- logistic model is appropriate and 17 widely used for epidemiologic data, and is easily interpreted. Further, BM C s and BM C Ls 18
estima ted from other candidate models for the sub

- cohort, as well as those obtained in modeling 19 from the full cohort, were in a similar range to the selected model. A second model

- based 20 uncertainty is the choice of lag for cumulative exposure. The RfC derivation is based on the 21 exposure lagged by 10 ye ars, since this lag yielded the lowest AIC. However, if other lags (with 22 similar AICs) are used, the difference in POD may fluctuate to be approximately 8% higher or 23 approximately 40% lower. However, as the RfC is rounded to one significant digit, the ch oice of 24 lag does not affect the estimated RfC. 25
26
6.2.2. Cancer/Inhalation 27

6.2.2.1. Background and Methods 28 The most appropriate data set for deriving quantitative cancer risk estimates based on 29 Libby Amphibole asbestos exposure in humans is the cohort of workers employed at the 30 vermiculite mining and milling operation near Libby, MT ( see Section 4.1). No data were 31 available pertaining to cancer incidence or mortality in the Marysville, OH cohort, and mortality 32 and exposure data for other populations exposed to Li bby Amphibole asbestos are very limited. 33 Whitehouse et. al. (2008) provided detailed information on 11 mesothelioma cases among non

34
workers , but this information could not be used in exposure

- response analyses for this risk 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 10 assessment, as there is no quan titative exposure information for these cases and no information 1

on the population from which these cases arose. 2
The Libby worker cohort has been the focus of two epidemiologic investigations by 3 NIOSH scientists. A database created by NIOSH in the 1980s contain s demographic data, work 4 history, and vital status at the end of 1981 for 1

, 881 workers at the vermiculite mine, mill, and 5 processing plant in Libby, MT (see Section 4.1.1.1) . Vital status follow

- up was completed by 6 NIOSH through 2006 using the National Death Index (NDI

- Plus; Bilgrad, 1995) . Nearly 54% of 7 workers in the cohort (n = 1,009) had died by December 31, 2006. 8 EPA does not have sufficient biological information to select models for the 9 epidemiology data on the basis of biological mech anism (Section 5 ). In this situation, EPA’s 10 practice is to investigate a range of model forms to determine how to best empirically model the 11 exposure response relationship in the range of the observed data. In this case, possible exposure 12
metrics were ex plored for model fit to the chosen models. The exposure metric options were 13 selected to provide a range of shapes that was sufficiently flexible to allow for a variety of ways 14 that time and duration might relate to cancer risk in the data being modeled. EPA then evaluated 15 how well the models and exposure metric combinations fit the data being modeled. Metrics that 16 did not fit the data well were rejected. For purposes of calculating a reasonable upper

- bound on 17 the risk per exposure EPA accounted for unce rtainty in the choice of exposure metrics by using 18 the exposure metric (among those of reasonable fit) that estimated the highest risk. This is 19 explained in more detail below and in Sections 5.4.3

- 5.4.5. However, there are other 20
uncertainties in the mode ling of the epidemiological data that may impact the IUR and these are 21
described in detail in Section 5.4.6. 22 Finkelstein (1985) provides an excellent summary of exposure

- response modeling of 23 epidemiologic data: ―After identification of an occupational haza rd one of the goals of 24 occupational epidemiology is to quantify the risks by determining the dose

- response relations for 25 the toxic agent. In many circumstances little is known about the dose received by target tissues; 26 the data available usually pertain o nly to exposure to various concentrations of the toxic material 27 in the workplace. The calculation of dose requires additional physiological and chemical 28 information relating to absorption, distribution, biochemical reactions, retention, and clearance. 29
In asbestos epidemiology

, the usual measure of exposure is the product of the concentration of 30 asbestos dust in the air ( fib er s or particles per m L ) and the duration of exposure to each 31 concentration summed over the entire duration of exposure (years); this measure is the 32 cumulative exposure.‖ 33 Cumulative exposure has been the traditional method of measuring exposure in 34 epidemiologic analyses of many different occupational and environmental exposures and was the 35 exposure metric applied for to the risk of lung cancer mortality in the general asbestos evaluation 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 11 in the 1988 U.S. EPA. Two alternative approaches to developing exposure metrics to describe 1 the effects of air concentrations of asbestos dust in the air on the risks of mortality have also been 2 proposed . The first alternative was proposed by Jahr (1974) who studied silica

- induced 3 pneumoconiosis. He also suggested that exposures to occupational dusts could be weighted by 4 the time since exposure yielding an exposure metric which gives greater weight to e arlier 5 exposures. Berry et al. (1979) subsequently suggested the application of exposure metrics that 6
allowed for the clearance of dust or fibers by using a decay term on exposures . For the 7 evaluation of mortality risk from mesothelioma, U.S. EPA (1988) used a different exposure 8
metric than was used for lung cancer mortality which factored in the time since first exposure. It 9 is important to note that different characterizations of ambient exposures may be reasonably 10 expected to be associated with differ ent endpoint s (i.e. lung cancer or mesothelioma)

. 11
Most studies of asbestos

- related mortality have evaluated either cumulative exposure, 12

exposure concentration, or the duration of employment as exposure metrics . Many studies have 13
been limited in the availability of detailed exposure data

— especially at the individual level. In 14 the Libby worker cohort data developed by NIOSH and used by the EPA in this assessment, 15 detailed work histories, together with job

- specific exposure estimates, allowed for the 16

r econstruction of each individual’s occupational exposure experience over time to define 17 multiple exposure metrics. From this information

- rich individual

- level dataset from NIOSH , the 18
EPA constructed a suite of the different metrics of occupational exposur e which had been 19 proposed in the asbestos literature or used in the U.S. EPA health assessment on general asbestos 20 exposures ( U.S. EPA, 1988). This suite of models was defined a priori to encompass a 21 reasonable set of proposed exposure metrics to allow su fficient flexibility in model fit to these 22
data. These exposure metrics were evaluat ed in analytic

- regression models to test which 23 exposure metrics were the best empirical predictors of observed cancer mortality and the better 24 fitting models were advanced for consideration as the basis of the exposure

- response relationship 25
for the IUR . The types of exposure metrics evaluated were intended to allow for variations of 26 the classic metric of cumulative exposure, allowing for more or less weight to be placed on 27 earlier or later exposures. These simulated exposure metrics were derived mathematically to 28 approximate underlying processes that are not well understood, and their fit is evaluated on the 29
basis of maximizing the likelihood for the workers cohort and est imated parameters does not 30 necessarily have biological interpretation (see Section 5.4.2.5 for details )

. 31
E xposure estimates for all exposure metrics were adjusted to account for the time period 32 between the onset of cancer and mortality . The lag period de fines an interval before death, or 33
end of follow

- up, during which, any exposure is excluded from the calculation of the exposure 34
metric. Modeling of mesothelioma mortality included two additional exposure metrics : duration 35 of exposure and the exposure metric including a cubic function of time ( s ee Eq. 5

- 5)

, originally 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 12 proposed in Peto et al. (1982) and employed in derivation of the IUR for asbestos ( U.S. EPA, 1 1988). 2 Analyses of mesothelioma mortality were conducted using a Poisson model w ith a 3 Markov chain Monte Carlo ( MCMC) Bayesian approach, whereas analyses of lung cancer 4 mortality were conducted using the Cox proportional hazards model with time

- varying 5

exposures. There was one important limitation of the NIOSH JEM. 686 w orkers with ―common 6 laborer‖ job assignments and some workers with unknown job assignments hired between 1935 7 and 1959 were assigned the same average estimated exposure intensity for all unskilled jobs 8 during the relevant calendar time period. That aspect of the JEM resulted in the misclassification 9 of the exposure and made cumulative exposure metrics represent duration for the large part of 10
the cohort. For th is reason and because there was little measured fiber exposure data during the 11 earlier period, identifyi ng an adequate exposure

- response model fit was unsuccessful

. T he t wo 12 biggest problems were that the duration of employment was the best

- fitting metric for modeling 13
mesothelioma and that the Cox model assumptions were violated in modeling lung cancer 14

morta lity ( Section 5.4. 3 .3 ) . As a result

, EPA developed a sub

- cohort analysis by dividing the 15
whole cohort into two groups: those hired prior to 1960 and those hired after 1959. This 16
removed all 686 cohort members with missing job category information and les sened the effect 17 of estimates of early exposures where no air sampling data were available. For the sub

- cohort of 18 those hired after 1959, those two biggest problems were resolved: the assumptions of Cox 19
models were satisfied

, and a lagged cumulative exposure with a decay (rather than duration of 20 exposure, as for the full cohort ) w as the best

- fitting metric for mesothelioma. 21 Of the 880 workers hired after 1959, 230 (26%) had died by December 31, 2006. The 22
number of mesothelioma deaths in the sub

- coho rt is relatively small (n = 7, 2 deaths coded in 23
ICD

- 10 and 5 deaths coded in ICD

- 9), but it was very similar rate of 24.7 per 100,000 person

24 years vs. 26.8 per 100,000 person

- years for the full cohort (18 mesothelioma deaths), a 25
difference of less than 10 %. 26
27
6.2.3. Modeling of Mesothelioma Exposure

- Response 28

A Poisson model is employed for estimating the absolute risk of mesothelioma following 29 exposure to Libby Amphibole asbestos , as the Poisson distribution is an appropriate model for 30
use with data that are coun ts of a relatively rare outcome, such as observed mesothelioma deaths 31 in the Libby worker cohort. Estimation of the exposure

- response relationship for mesothelioma 32 using the Poisson model was performed in WinBUGS software by a MCMC Bayesian approach 33
with an uninformative prior. The model was run to fit the mortality data to exposure data for 34 various exposure metrics described above. To comparatively evaluate how much better one 35 model fits than another, Deviance Information Criterion (DIC) was used

. DIC is used in 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 13 Bayesian analysis and is an analogue of AIC (Akaike Information Criterion [Burnham and 1 Anderson, 2002] ). Use of the DIC and AIC is standard practice in comparing the fit of 2

non

- nested models to the same dataset with the same dependent outco me variable but different 3 independent covariates. 4 Two cumulative exposure metrics with decay provided two best m odel fit s. Both metrics 5
had a common 5

- year half life , with lag times of either 1 0 or 1 5 years

. In the sub

- cohort hired 6 after 1959, the DIC value for mesothelioma using the IRIS IUR ( U.S. EPA, 1988) metric 7 (Eq. 5

- 5) is substantially higher (DIC = 98.4) than for any of the metric s in Table 5

- 11 , where the 8
low est DIC is 70.6

. This difference of over 2 0 DIC units, is an indication that the model used for 9 mesothelioma in the EPA (1988) IUR derivation (eq. 5

- 5) , does not fit these data from the Libby 10 work cohort, compared to other exposure metrics presented (Table 5

- 11 ). It should be noted that 11 the data modeled here are very different from th e data on which the IRIS assessment for asbestos 12 ( U.S EPA, 1988) is based

— and one does not necessarily expect the same model to f it different 13
datasets

—

this is why EPA goes through a process to determine the best fitting model in each 14

case. One difference with IRIS IUR ( U.S. EPA, 1988) modeling is that this analysis is based on 15
individual

- level data, whereas IRIS IUR ( U.S. EPA, 1988) application was to aggregate data. 16 Also, cohorts used in IRIS IUR (U.S. EPA , 1988 ) did not include cohorts expose d to Libby 17 amphibole asbestos and Libby amphibole asbestos may be different from other types of asbestos. 18 Alternately, the relative fit of this model may have been affected by uncertainties in the 19 estimated exposure described in detail in Section 5.4.6. 20
A s it is less likely that exposure during the last few years before death were contributory 21 to the development of the cancer, the zero lag metrics were dropped from further consideration

. 22 All eight models retai ned for derivation of IUR include a decay half

- life in the exposure metric. 23
For the sub

- cohort hired after 1959, the best fitting exposure metric was cumulative exposure 24 with a 5 year half

- life and a 15 year lag time with central estimate for the β of 2.07E

- 4 with 95% 25
UCL 3.42E

- 4. 26
27
6.2.4. Unit Risk Estimates for Mesothelioma Mortality 28

The increased risk of mesothelioma mortality attributable to continuous fiber exposure 29 was estimated using a life

- table procedure based on the general U.S

. population

. The life

- table 30 procedure involve d the application of the estimated Libby Amphibole asbestos toxicity to a 31 structured representation of the general U.S

. population in such a manner as to yield age

- specific 32
risk estimate s for cancer mortality in the presence or absence of exposure to Libby Amphibole 33
asbestos ( Section 5.4.5 ; Appendix G )

. 34
A default linear low

- dose extrapolation below the point of departure (POD) was used 35 because the mode of action by which Libby Amphibole asbestos causes mesothelioma is largely 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 14 unknown. The lower limit on the effective concentration (LEC 01 ) was determined to be 1
0.245 fibers/cc

, which yielded a unit risk for mesothelioma mortality of 0.053 per fiber/cc (POD 2 of 1% divided by the LEC 01 ). 3
The value of the effective concentration that would correspond to the measure of central 4 tendency is the EC 01 . This value is used in the derivation of a combined risk of mesothelioma 5 and of lung cancer. The EC 01 was determined to be 0. 406 per fiber/cc, which when divided into 6
a POD of 1% and s caled (by 70/54) to encompass the whole lifespan

, gives a lifetime central 7
estimate value of 0.032 per fiber/cc. 8 For mesothelioma, the undercounting of cases (underascertainment) is a particular 9 concern given the limitations of the ICD classification syste ms used prior to 1999 . In practical 10 terms, this means that some true occurrences of mortality due to mesothelioma are missed on 11
death certificates and in almost all administrative databases such as the National Death Index. 12 Even after introduction of spe cial ICD code for mesothelioma with introduction of ICD

- 10 in 13 1999, detection rates are still imperfect (Pinhiero et al., 2004; Camidge et al., 2006) and the 14 reported numbers of cases typically reflect an undercount of the true number

. Kopylev et al. 15 (201 1) reviewed the literature on this underascertainment and developed methods to account for 16 the likely numbers of undocumented mesothelioma deaths. 17 To compensate for mesothelioma underascertainment attributable to ICD coding , t he 18 mesothelioma mortality unit risk was further adjusted following the analysis of Kopylev et al. 19 (2011). The adjusted mesothelioma central (MLE) risk, corresponding to the best

- fit metric, was 20 0.044 per fiber/cc

, and the adjusted mesothelioma mortality unit risk was 0.074 per fiber/cc. The 21 adjusted mesothelioma mortality unit risks from all eight exposure parameterization models with 22 adequate fit produced a range of unit risk values (Table 5

- 14 ) from 0.044 to 0.122. The second 23 best fit metric, cumulative exposure with 10 year lag and 5

- year decay, corresponded to the 24 highest unit risk (details are in Section 5.4.5.1.1)

. 25
26
6.2.5. Modeling of Lung Cancer Exposure

- Response 27

All multivariate extended Cox models were fit to the sub

- cohort hired after 1959 with 28 covariates for sex, race, a nd date of birth, and exposure . Exposure for each of the 40 exposure 29 parameterizations was calculated independently and fit of these exposure metrics was evaluated 30

one at a time. As the exposure

- response models cannot strictly be considered to be nested, a 31
standard measure of fit

, the AIC (Burnham and Anderson, 2002)

, was used for comparison of 32
model fit with smaller values of AIC

, indicating better goodness of fit. Of the 40 exposure

33 response metrics, 14 demonstrated an adequate fit to the data as measured by the overall model 34 fit with the likelihood ratio test (p < 0.05) as well as having statistically significant exposure 35 metrics (p < 0.05). However, only the nine models that demonstrated adequate model and 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 15 exposure metric fit and incorporated a lag period to account for cancer latency were considered 1 further in the development of the IUR (Table 5

- 1 9 ). 2 Lagging exposure by 10 years was a better predictor of lung cancer mortality compared 3 to other lags. As it is less likely that exposure during the last few years before death were 4 contributory to the development of the cancer, the zero lag metrics were dropped from further 5
consideration. T he residence time

- weighted cumulative exposure, both with and without decay 6 of the exposure metric, d id not fit these lung cancer mortality data well compared to the other 7
models (Table 5

- 13 ) ; t his form of exposure metric does not demonstrate evidence of an empirical 8 fit to these epidemiologic data. 9 The model with the smallest AIC was for cumulative exposure with a 10

- year half

- life for 10 decay and a 10

- year lag for cancer latency. This multivariate model controlled for age, gender, 11 race, and date of birth within the occupational cohort. The extended Cox model with 12
time

- varying exposures estimated a slope (beta) of 1.26 × 10 - 2 per fiber/cc

- year based on a 13
365

- day year

, and the 95 th percentile upper bound was 1.88 × 10 - 2 per fiber/cc

- year

. The p

- value 14 for the Libby A mphibole asbestos regression coefficient (slope) was < 0.00 1. The slopes and 15
confidence interval for the other exposure metrics

, which had similar fits to these data are 16 reported in Table 5

- 14

. U ncertainty in the choice of the exposure metric (cross

- metric 17 uncertainty) is considered in the derivation of the final unit risk (see details in Sec tion 5.4.5.3 )

, 18 representing the range of unit risks that are derived from these similarly fitting m etric s. The 19 model results which were ultimately selected to reflect the upper

- bound among the range of 20
results were based on the cumulative exposure with a 10

- year lag exposure metric ( CE10 )

. The 21
extended Cox model with time

- varying exposures estimated a slope (beta) of 5.28 × 10 - 3 per 22
fiber/cc

- year based on a 365

- day year , and the 95 th percentile upper bound was 1.00 × 10 - 2 per 23
fiber/cc

- year

. 24
25
6.2.6. Unit Risk Estimates for Lung Cancer Mortality 26

The increased risk of lung cancer mortality attributable to continuous fiber exposure was 27 estimated using a life

- table procedure based on the general U.S. population. The life

- table 28 procedure involved the application of the estimate d Libby Amphibole asbestos

- specific toxicity 29 to a structured representation of the general U.S. population in such a manner as to yield age

30 specific risk estimated for cancer mortality in the presence or absence of exposure to Libby 31 Amphibole asbestos ( Section 5.4.5 ; Appendix G ). 32 The nine exposure

- response models retained in Table 5

- 1 8 all had reasonably similar 33 goodness of fits. No single model stands out as clearly statistically superior; however

, there is a 34 range of quality of fit within the set that could be considered to have adequate fit . The lung 35 cancer mortality unit risks are shown in Table 5

- 1 9

. 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 16 Using the results of the exposure model with the lowest AIC value (i.e.

, cumulative 1 exposure with a 10

- year half

- life for decay and a 10

- year lag fo r cancer latency) alone, the LEC 01 2
for the adult

- only exposures was determined to be 0.333 fibers/cc

. This yield s an adult

- based 3
unit risk of lung cancer mortality of 0.0300 (POD of 1% divided by the LEC 01 )

. This estimate 4
was then scaled by 70/54 to enco mpass the whole lifespan; it yielded a lifetime unit risk of 5 0.0389 per fiber/cc

. The value of the concentration that would correspond to the measure of 6 central tendency was based on the EC 01 rather than LEC 01
. The EC 01 for the adult

- only 7

exposures was determined to be 0.499 per fiber/cc

, which

, when divided into a POD of 1%

, 8 yielded an adult

- based central estimate for lung cancer mortality of 0.0200

. This estimate was 9
then scaled by 70/54 to encompass the whole lifespan to , yield ed a lifetime central e stimate of 10
0.026 0 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 LEC 01 for the adult

- only exposures was determined to be 0. 191 fibers/cc

, 13 yield ing an adult

- based unit risk of lu ng cancer mortality of 0.0524 (POD of 1% divided by the 14
LEC 01 ) . W

hen scaled by 70/54 to encompass the whole lifespan , it yielded a lifetime unit risk of 15
0.0 679 per fiber/cc

. The value of the risk that would correspond to the measure of central 16 tendency involves the EC 01 rather than the LEC 01
. The EC 01 for the adult

- only exposures was 17
determined to be 0. 325 per fiber/cc

, which

, when divided into a POD of 1%

, yielded an 18
adult

- based central estimate for lung cancer mortality of 0.0308

. This estimate was t hen scaled 19 by 70/54 to encompass the whole lifespan to , yield ed a lifetime central estimate of 0.0 399 per 20
fiber/cc. 21 The resulting unit risks in Table 5

- 1 9 ranged from 0.0260 to 0.0679 fibers/cc. This 22 shows that the unit risk (i.e.

, 0.0389 per fiber/cc ) based on the exposure metric with the lowest 23 AIC value (i.e.

, cumulative exposure with a 10

- year half

- life for decay and a 10

- year lag for 24 cancer latency) is in the center of this range and is thus statistically robust. However, because 25
this estimate is in the middle of the range it does not capture the uncertainty across metrics with 26 similar goodness of fit. The model results which were ultimately selected to reflect the upper

27 bound among the range of results were based on the CE10 exposure metric. T he CE metric with 28 a 10 year lag does fit these data, is a simpler and more straightforward metric and has an 29 extensive tradition of use in the epidemiologic literature and in the practice of risk assessment. 30
31
6.2.7. IUR Derivation Based on Combined Mesothelioma and Lung Cancer Mortality from 32 Exposure to Libby Amphibole Asbestos 33

When risks are combined, it is important to understand several concepts that are pertinent 34 to the evaluation and comparison of the cancer

- specific mortality unit risks that will be 35
combined. First, there is statistical uncertainty in the potency estimate within the exposure 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 17 response model defined by each exposure metric. This within metric uncertainty is accounted 1 for in the confidence interval on slope. Next, there is an uncertainty in the choice of metrics for 2 developing IUR ( cross

- metric uncertainty). Finally, when unit risks corresponding to metrics are 3 chosen accounting for uncertainty, these are statistically combined into IUR. Details are 4 provided in Section 5.4.5.3. 5
Table 6

- 1 shows cancer

- specific unit risks as well as combined risk of mesothelioma and 6
lunch cancer. The IUR value of 0.169 per fiber/cc accounts for important quantitative 7
uncertainties in the selection of the specific exposure metric that may have rema in ed in an IUR 8 that might have been based on the best fitting exposure model s alone. Additional uncertainties 9
are discussed in detail in Section 5.4.6. 10
11
Table 6

- 1

. Reasonable upper bound and lowest informati on criteria 12 estimates of central risks and unit risks, per fibers/cc, for mesothelioma 13 mortality, lung cancer mortality, and the IUR for the combined mortality 14 risk fr om mesothelioma and lung cancer 15 16

Model Mesothelioma Lung cancer Combined mesothelioma

a nd lung cancer

Central estimate Unit risk Central

estimate Unit

risk Central estimate IUR Reasonable upper bound a 0.075 0.122 0.040 0.068 0.115 0.169

Lowest information criteria b 0.044 0.074 0.026 0.040 0.070 0.103 17

a For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay half

18 life of 5 years and a 15

- year lag. For lung cancer, the selected model parameterized exposure as cumulative 19 exposure without decay and a 10 yea r lag. 20

b For mesothelioma, the selected model parameterized exposure as cumulative exposure with exponential decay 21 half

- life of 5 years and a 10

- year lag. For lung cancer, the selected model parameterized exposure as cumulative 22 exposure with exponential d ecay half

- life of 10 years and a 10

- year lag. 23 24 25 6.2.7.1. Comparison with Other Published Studies of Libby Workers Cohort 26 For lung cancer, t wo alternative analytic approaches to the use of EPA’s extended Cox 27 proportional hazards models could have been used for the calculation of a unit risk of lung 28 cancer mortality. All of the choices are based on different analyses of the Libby worker cohort ; 29 however, inclusion criteria differ among the analyses as does the length of mortality follow

- up. 30 Each of the two approaches has two options to estimate the slope of the exposure

- response 31 relationship in place of the regression slope estimated from the C ox proportional hazards model 32 and follow through with the same life

- table procedure to calculate the unit risk of lung cancer 33 mortality

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

— DO NOT CITE OR QUOTE 6

- 18 The first approach would be to use the published categorical results based on Sullivan 1 (2007) . The first option in thi s approach was for EPA to estimate a slope to those categorical 2
data . The second option was to use the slope estimated in a published re

- analysis of categorical 3 data of Sullivan (2007) cohort by Berman and Crump (2008). The second approach would be to 4

u se the published regression results of other researchers who modeled the underlying continuous 5 data. The first option in this approach was to use the slope estimated by Larson et al. ( 2010 a )

. 6 The second option was to use the slope estimated by Moolgavkar et al. (2010). 7 For comparison purposes, the lung cancer unit risk from these alternatives are computed, 8 however, as all analyses are based upon different subsets of the Libby workers cohort and used 9 different analytic methods, the results are not necessa rily interchangeable. Table 6

- 2 summarizes 10 lung cancer risks derived from these studies. 11
12
Table 6

- 2

. Lung cancer regression results from different analyses of 13 cumulative exposure in the cohort of workers in Libby, MT. All analyses 14 used NIOSH

- collected exposure data, but used different cohort definitions, 15 le ngths of follow

- up, and lengths of exposure lags to account for cancer 16 latency 17 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 0.03 7

Moolgavkar et al.

, 2010 a Full cohort, White males 95/1662 1.69 0.01 1

Berman and Crump

, 2008 a Full cohort, White males 93/1672 3.96 0.079 b

Larson et al.

, 2 010 Full cohort 98/1862 1.61 0.010 19

a Reanalysis of Sullivan (2007) 20

b Obtained from confidence interval that also a ddress es model uncertainty (details in Section 5 .4.5.3.1 )

. 21 22 For mesothelioma, n either of the approaches used by McDonald et al. (2004), Sullivan 23 (2007), nor Larson et al. (2010 a ) could have been appropriately used for the unit risk of 24 mesothelioma as they are not based on absolute risk metrics of association, and the current 25
assessment considered the relevant metric of assoc iation to be the absolute risk. Berman and 26 Crump (2008) did not evaluate risk of mesothelioma. Moolgavkar et al. (2010) did use an 27
absolute risk model for mesothelioma. These results are summarized in Table 6

- 3. The upper 28This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 19 bound results for the full cohort presented by Moolgavkar et al. (2010) are about 80% o f the EPA 1
IRIS (198 8 ) estimate of mesothelioma slope factor in a similar RTW

- type metric, leading to 2 approximately 80% estimate of the mesothelioma unit risk, as dependence is linear in the 3 mesothelioma slope factor (Eq. 5

- 5). This is very close to this a ssessment’s estimate based on 4 the analyzed sub

- cohort , which is also about 80% of the EPA IRIS (198 8 ) estimate of 5 mesothelioma risk. Duration of employment is the best metric for the full cohort, and it does not 6 support dose

- response estimation. 7
8
Table 6

- 3

. Mesothelioma regression results from different analyses of 9 cumulative exposure in the cohort of workers in Libby, MT. All analyses 10 used NIOSH

- collected e xposure data, but used different cohort definitions , 11 lengths of follow

- up, and lengths of exposure lags to account for cancer 12 latency 13 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 coho rt; still employed post

- 1959; White males 15/1672 No estimates of absolute risk
Moolgavkar et al.

, 2010 a Full cohort; White males 15/1662 Upper Bound ≈ 0.13 Central ≈ 0. 0 8

Larson et al

., 2010 Full Cohort 19/1862 No estimates of absolute risk Berman and Crump

, 2008 a No estimates provided 15

a Reanalysis of Sullivan (2007). 16 17 18 6.2.8. Uncertainty in the Cancer Risk Values 19

I t is important to consider the uncertainties in the derivation of the mesothelioma and 20 lung cancer mortality risks in this assessment in the context of uncertainties in animal

- based 21
health assessment s

. This assessment does not involve extrapolation from high dose in animals to 22
low dose in humans. The current assessment is based on a well

- docume nted and well

- studied 23 cohort of workers with adequate years of follow

- up to evaluate mesothelioma and lung cancer 24 mortality risks with PODs within the range of the data . The discussions in Section 5.4.6 explore 25 uncertainty in the derivation of the IUR in order to provide a comprehensive and transparent 26 context for the resulting cancer mortality risk estimates. 27This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 20 The summary below include s likely one

– sided uncertaint ies (biases) associated with the 1
derivation of the IUR in orde r to provide a context for the resulting cancer risk estimates. 2 The sources of uncertainty that could lead to a likely underestimation of the cancer risk 3 value include the following: 4
5 6 Use of historical PCM exposure measurements . As asbestos was a contaminant of 7 vermiculite that was the primary object of production, mine and dry and old wet mill 8 ambient air may have contained material other than asbestos that could have contributed 9 to fibers counted by PCM. Therefore, it is pos sible that exposure estimates for some or 10 possibly a large portion of the cohort are overestimated

, and , therefore, the resulting IUR 11 may be underestimated

. 12

Measurement error in exposure assessment and assignment

. R esults from the analys e s of 13 the lung cancer mortality in the full cohort compared to the sub

- cohort hired after the 14 1959 sub

- cohort were attenuated as much as 2

- 6 times. Job assignment s were unknown 15 for 686 of the 991 workers employed from 1935 to 1959 (69% of the cohort for th is time 16 period). The majority of these workers were assigned the same exposure concentration 17 for all years without job category information. Examination of the post

- 1959 cohort 18 removes this significant source of exposure misclassification. 19

Limited l ength of follow

- up. The IUR for mesothelioma mortality could be larger than 20 was estimated from existing data, since latency of mesothelioma can be as long as 21 60 years. The maximum length of follow

- up was 46 years in this cohort. The magnitude 22 of underes timation would depend on the relationship between the number of additional 23 deaths and the increase in person

- years. 24

Use of life

- tables to calculate the IUR based on cancer mortality

. The life

- table 25 procedure computes the extra risk of death from birth up to 85 years of age. T his cut

- off 26 at age 85 ignores a small additional risk of lung cancer mortality among a small 27 percentage of people who have a higher background risk because of the increase in lung 28 cancer risk that is seen with increasing age. The lun g

- cancer mortality unit risk based on 29 the LEC 01 would be somewhat larger

, on the order of 5

- 10%

. On the other hand, t he 30 additional mesothelioma mortality risk

, if the life

- tables were extended to account for 31 longer life spans

, would be about 3% 32

Small num ber of women and ovarian cancer

. While asbestos is ca u sally associated with 33 increased risks of ovarian cancer (Straif et al., 2009), there were only 84 women in the 34 whole cohort, and there were no deaths from ovarian cancer among 24 total deaths. The 35 lac k of observed ovarian cancer in this cohort may be a function of the limited number of 36 female deaths in the cohort allowing for the possibility that exposure to LAA could result 37 in increased risk of ovarian cancer

. However, it was not possible to estimate the 38 magnitude of this underestimation on the total cancer risk. 39

D ependent competing risks. Competing risk of mortality from other diseases related to 40 exposure may have resulted in underestimates of the risk of mo rtality from either 41This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 21 mesothelioma or lung cancer. The mean length of follow

- up for the Libby workers who 1 died of mesothelioma was to 30.1 years, and evidence exists ( Suzuki

, 2001 ; Bianchi and 2 Bianchi

, 2009) that early deaths from other exposure

- related cau ses could have precluded 3 an individual’s risks of death from mesothelioma. However, it was not possible to 4 estimate the magnitude of this effect on the total cancer risk. 5

6
The s ource of uncertainty that could lead to a li k el y ove restimation of the cancer risk 7
value : 8
9 Effect modification. It is theoretically possible that the risk of lung cancer mortality 10 estimated in this assessment is a reflection of a positive synergy between smoking and 11 asbestos, and that the adverse effect of Libby A mphibole asbestos among the potentially 12 nonsmoker workers has been overestimated

. The IUR for lung cancer mortality 13 estimate d herein, and the combined lung cancer and mesothelioma IUR would then be 14 health protective for any population that had a lower prevalence of smoking than that of 15 the Libby worker cohort. 16

17
6.3. APPLICATION OF THE L IBBY AMPHIBOLE ASBES TOS RFC AND IUR 18

6.3.1. Sites and Materials 19 This Libby A mp hibole asbestos specific assessment is based on the evaluation of worker 20
cohort s , exposed to asbestos from a single mine in Libby, M T , and is intended to allow for 21 estimates of the risk due to exposure to the asbestos fibers from that mine, or exposures to 22 asbestos fibers that arise from the management or use of the vermiculite ore and exfoliated 23
vermiculite from this mine. Th erefore

, it is appropriate to apply the L ibby Amphibole asbestos

24 specific RfC and/or IUR to sites which are believed to have been contaminated by these 25 materials when assessing risk from the amphibole fibers present from this contamination. This 26 may inclu de sites where the ore was shipped or handled, where the vermiculite was exfoliated 27 and further processed, facilities which in other ways shipped or handled the exfoliated 28 vermiculite, where products containing the raw or exfoliated vermiculite were presen t, the 29 consumer products themselves (e.g.

, VAI) and any waste streams from the above processes 30
which contain vermiculite and the related LAA

- fibers . The assessment was derived from PCM 31 measurements taken at the Libby occupational sites and the mixture of minerals found in those 32 measurements. It does not estimate the risk attributable to specific subsets of those fibers 33 whether based on size, shape, or mineral composition other than the limitations on size and shape 34 reflected in the PCM methodology and cou nting rules. As detailed in Section 2

, the amphibole 35 asbestos present in the mine, ore and expanded vermiculite , do not fit cleanly into a single 36 category of nomenclature for amphibole minerals. Most Libby Amphibole fibers are classified 37This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 22 as winchite (84% ), with lesser amounts of richterite (11%) and tremolite (6%), based on the 1 nomenclature proposed by Leake et al. (1997) (Meeker et al., 2003). There are also trace 2 amounts of magnesioriebeckite, edenite, and magnesio

- arfvedsonite present in Libby Amphibo le 3
asbestos (Meeker et al., 2003)

. W ithin the 30 samples taken from the mine the proportion of 4 these minerals differed between samples (Meeker et al., 2003) and the relative proportions of 5 these species may have varied over time (as ore from different loc ations were processed)

. This 6

assessment estimate s the risk of exposure to the varying range of mineral fiber mixtures that 7 result from material originating from the geological deposit, recognizing there is variation and 8 uncertainty as to variations in the exposure to the underlying cohort and complex variation in 9 settings to which th ese estimate s will be applied. 10
11

6.3.2. Exposure Units for Libby Amphibole Asbestos 12

As with the IRIS assessment for asbestos (U.S. EPA, 1988) , the RfC and IUR specific to 13 Libby A mphibole asbestos are presented here as f ibers

- yr /cc exposure, where exposure 14 measurements are based on analysis of air filters by Phase Contrast Microscopy (PCM). For 15 example, a nalysis of mortality data presented in Section 5 for both mesothelioma and lu ng 16

cancer used the available measurements of worker exposure for the facilities in Libby, MT. 17 From 1968 forward, exposure measurements were available for all work environments as fiber 18 counts by PCM analysis. Between 1960 and 1968, fiber counts were esti mated

, and a dust

- to

19 fiber conversion ratio was applied to exposures in the dry mill to generate estimated fiber 20 exposures in PCM units. Therefore

, the IUR is given in units based on PCM fiber analysis and is 21 intended to estimate the risk per exposure to PCM

- countable Libby amphibole asbestos fibers

. 22 Early PCM analytical techniques did not have the same resolution as current analytical 23
methods

, and it is understood that PCM data for the majority of the exposures characterized for 24 the Libby workers would likely have a width resolution of 0.4

- 0.44 μm (Skinke, 1980 ; Amandus 25 et al., 1987a ; WHO

, 1980). Therefore

, as with the IRIS assessment f or asbe stos ( U.S. EPA, 26
198 8 ), t he dimensions of the PCM fibers for the Libby A m phibole asbestos unit risk are defined 27 as fibers ≥5 μm in length with an aspect ratio of 3:1 or greater and a width >0.4 μm. 28
E nvironmental air sampling for asbestos is now often analyz ed by transmission electron 29 microscopes (TEM) to confirm th at th e fibers viewed are asbestos

, and often it is used to identify 30 the mineralogy of the fiber. Although some historical data do exist providing TEM analysis of 31
airborne fibers from the Libby , MT mill operation (McDonald et al., 1986a ; Langer et al., 1974)

, 32 these data are not sufficient to provide an alternative set of exposure measurements in TEM units 33
for the Libby worker cohort , or provide a PCM to TEM conversion across the various work 34
environments

. 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE 6

- 23 D ifferent sampling environments and varied site conditions may pose the potential for 1
airborne fibers from various materials. Because of that, it is expected that for many 2
environmental risk assessments conducted now and in the near future

, measures of exposure may 3 be done with methods such as TEM and then adjusted through fiber

- counting rules to estimate 4
the number of PCM

- countable asbestos fibers. Site

- specific environmental conditions should be 5 considered in determining how to best iden tify PCM

- countable asbestos fibers in relevant air 6 samples for exposure assessments used in conjunction with this health assessment to yield 7
estimates of risk

. 8
9
6.3.3. Applications to E arly L ifetime and P artial L ifetime E nvironmental E xposure 10 Scenarios for IUR 11

Th e life

- table analysis in the IRIS assessment for asbestos ( U.S. EPA

, 1988) predicts risk 12 increases as the age of first exposure decreases. It was recommended that th is life

- table analysis 13 be consulted when assessing partial lifetime exposures ( U.S. EPA, 1988 ). In 2009, EPA 14

p rovided guidance for calculating risk estimates for l ess

- than

- lifetime exposures based on the 15
1988 life

- table analysis ( U.S. EPA, 2009). The age

- at

- onset of exposure and duration

- dependent 16
unit risks reflect the influence of the time cubed function in the mesothelioma model (eq. 5

- 5 ) 17 ( U.S. EPA

, 1986 ; 1988; 2009) used in the 198 8 assessment

. Because th e time

- cubed model, or 18 parameterization of exposure metrics

, did not fit the data for mesotheli oma mortality from 19
exposure to Libby Amp hibole asbestos, the a pproach to estimating risk of partial life exposure 20 recommended by EPA when applying the IRIS IUR for asbestos ( U.S. EPA, 1988) is not 21 appropriate when applying the Libby A mphibole asbestos

- specific IUR. 22
T he Libby A mphibole asbestos

- s pecific unit risk derived in this assessment is a combined 23 risk of lung cancer and mesothelioma, each with its own adjustment for uncertainty in metrics

. 24
T he life table analyses for Libby A mphibole asbestos do not predict greater risk from early

- life 25

expo sures. Thus this assessment recommends that estimates of the risks of less

- than

- lifetime 26 exposures be computed by simple calculations of average lifetime exposure concentration 27 multiplied by IUR. This recommendation is consistent with st andard Superfund guidance 28 (U.S. EPA, 1986b), where exposures are estimated, averaged across a lifetime exposure, and the 29 IUR simply applied to calculate excess cancer risk (U.S. EPA, 2005 ). The weight of evidence 30 does not support a mutagenic mode of action for Libby Amphibole asbestos carcinogenicity. 31 Therefore, according to EPA’s Supplemental Guidance for Assessing Susceptibility from Early

32 Life Exposure to Carcinogens (U.S. EPA, 2005b), th e application of the Age

- Dependent 33 Adjustment Factors (ADAFs) is not recommended

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

7

- 1 DRAFT

— DO NOT CITE OR QUOTE 7. REFERENCES

AAP (American Academy of Pediatrics). 2003. Asbestos. In: Pediatric Environmental Health (Etzel RA, ed.). Elk Grove, IL:American Academy of Pediatrics.

Acheson ED, Gardner MJ, Pippard EC, Grime LP. 1982. Mortality of two groups of women who manufactured gas masks from chrysotile and crocidolite asbestos: a 40

- year follow

- up. Br J Ind Med 39:344

- 348.

Addison J, McConnell EE. 2008. A review of carcinogenicity studies of asbe stos and non

- asbestos tremolite and other amphiboles. Regul Toxicol Pharmacol S187

- S199.

Al

- Qahtani M, Morris B, Dawood S, Onerheim R. 2007. Malignant mesothelioma of the tunica vaginalis. Can J Urol 14:3514

- 3517.

Alfonso HS, Fritschi L, de Klerk NH, Ambr osini G, Beilby J, Olsen N, Musk AW. 2005. Plasma concentrations of retinol, carotene, and vitamin E and mortality in subjects with asbestosis in a cohort exposed to crocidolite in Wittenoom, Western Australia. J Occup Environ Med 47:573

- 579.

----- 2005. Plasma retinol, carotene and vitamin E concentrations and lung function in a crocidolite

- exposed cohort from Wittenoom, Western Australia: a cohort study. Nutr J 4:16.

Alfonso HS, Fritschi L, de Klerk NH, Ambrosini GL, Beilby J, Olsen N, Musk AW. 2006. Pl asma vitamin concentrations and incidence of mesothelioma and lung cancer in individuals exposed to crocidolite at Wittenoom, Western Australia. Eur J Cancer Prev 15:290

- 294.

Amandus HE, W heeler R, Jankovic J, Tucker J. 1987. The morbidity and mortality of vermiculite miners and millers exposed to tremolite

- actinolite: Part I. Exposure estimates. Am J Ind Med 11:1

- 14.

Amandus HE, Wheeler R. 1987. The morbidity and mortality of vermiculite miners and millers exposed to tremolite

- actinolite: Part II. Mortali ty. Am J Ind Med 11:15

- 26.

Amandus HE, Althouse R, Morgan WK, Sargent EN, Jones R. 1987 a . The morbidity and mortality of vermiculite miners and millers exposed to tremolite

- actinolite: Part III. Radiographic findings. Am J Ind Med 11:27

- 37.

American Thora cic Society (ATS) (1986) The diagnosis of nonmalignant diseases related to asbestos. Am Rev Respir Disease 134:363

- 368.

American Thoracic Society (ATS) (2004) Diagnosis and initial management of nonmalignant diseases related to asbestos. Am J Respir Crit Care Med 170:691

- 715.

Amin MB. 2005. Selected other problematic testicular and paratesticular lesions: rete testis neoplasms and pseudotumors, mesothelial lesions and secondary tumors. Mod Pathol 18:S131

- S145.

Anderson BA, Dearwent SM, Durant JT, Dyken J J, Freed JA, Moore SM, Wheeler JS. 2005. Exposure pathway evaluations for sites that processed asbestos

- contaminated vermiculite. Int J Hyg Environ Health 208:55

- 65.

Anderson HA, Lilis R, Daum SM, Fischbein AS, Selikoff IJ. 1976. Household

- contact asbest os neoplastic risk. Ann N Y Acad Sci 271:311

- 323.

Anderson, H; Lilis, R; Daum, S; et al. (1979) Household exposure to asbestos and risk of subsequent disease, pp 145

- 156, in Dusts and Disease: Proceedings of the Conference on Occupational Exposures to Fib rous and Particulate Dust and Their Extension into the Environment, R Lemen and J Dement, eds. Society for Occupational and Environmental Health. Pathotox Publishers, Park Forest South, Illinois.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 2 DRAFT

— DO NOT CITE OR QUOTE Anderson HA, Lilis R, Daum SM, Selikoff IJ. 1979. Asbestos is among household contacts of asbestos factory workers. Ann N Y Acad Sci 330:387

- 399.

Andrion A, Bosia S, Paoletti L, Feyles E, Lanfranco C, Bellis D, Mollo F. 1994. Malignant peritoneal mesothelioma in a 17

- year

- old boy with evidence of previous exposur e to chrysotile and tremolite asbestos. Hum Pathol 25:617

- 622.

Armstrong B. 1998. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med 55:651

- 656.

Armstrong, BG; McDonald, JC; Sebastien, P; et al. (1988) Radiological changes in vermiculite workers exposed to tremolite. Ann Occup Hyg 32:469

- 474.

Ascoli V, Carnovale

- Scalzo C, Nardi F, Efrati C, Menegozzo M. 2003. A one

- generation cluster of malignant mesothelioma within a family reveals expos ure to asbestos

- contaminated jute bags in Naples, Italy. Eur J Epidemiol 18:171

- 174.

Asgharian B, Menache MG, Miller FJ. 2004. Modeling age

- related particle deposition in humans. J Aerosol Med 17:213

- 224.

Athanasiou K, Constantopoulos SH, Rivedal E, Fitzg erald DJ, Yamasaki H. 1992. Metsovo

- tremolite asbestos fibres: in vitro effects on mutation, chromosome aberration, cell transformation and intercellular communication. Mutagenesis 7:343

- 347.

ATSDR (Agency for Toxic Substances and Disease Registry). Heal th Consultation: Mortality from Asbestosis in Libby, Montana. 12

- 12

- 2000.

----- . Year 2000 Medical Testing of Individuals Potentially Exposed to Asbestiform Minerals Associated with Vermiculite in Libby, Montana. A Report to the Community. 8

- 23

- 2001.

A TSDR (Agency for Toxic Substances and Disease Registry). 2001. Toxicological profile for asbestos. Atlanta GA. Available at http://www.atsdr.cdc.gov/toxprofiles/tp61.html

. ATSDR (Agency for To xic Substances and Disease Registry). 2002. Libby tests: lung abnormality rates high. Hazardous Substances & Public Health 12.

ATSDR (Agency for Toxic Substances and Disease Registry. Vermiculite Consumer Products. 03

- 0463. 1

- 1

- 2003.

ATSDR (Agency for T oxic Substances and Disease Registry). Health Consultation: Western Minerals Products Site. 10

- 10

- 2003.

----- . Summary report: exposure to vermiculite from Libby, Montana, at 28 processing sites in the United States. 2008.

Baris I, Artvinli M, Sahin A A. 1979. Environmental Mesothelioma in Turkey. Ann N Y Acad Sci 330:423

- 432.

Baris I, Simonato L, Artvinli M, Pooley F, Saracci R, Skidmore J, Wagner C. 1987. Epidemiological and environmental evidence of the health effects of exposure to erionite fibres : a four

- year study in the Cappadocian region of Turkey. Int J Cancer 39:10

- 17.

Barone

- Adesi F, Ferrante D, Bertolotti M, Todesco A, Mirabelli D, Terracini B, Magnani C. 2008. Long

- term mortality from pleural and peritoneal cancer after exposure to asbest os: Possible role of asbestos clearance. Int J Cancer 123:912

- 916.

Bateson TF, Schwartz J. 2008. Children's response to air pollutants. J Toxicol Environ Health A 71:238

- 243.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 3 DRAFT

— DO NOT CITE OR QUOTE Bateson TF, Wright JM. 2010. Regression calibration for classical exposure meas urement error in environmental epidemiology studies using multiple local surrogate exposures. Am J Epidemiol 172:344

- 352.

Becklake MR, Case BW. 1994. Fiber burden and asbestos

- related lung disease: determinants of dose

- response relationships. Am J Respir Crit Care Med 150:1488

- 1492.

Becklake, MR (1994) Symptoms and pulmonary functions as measures of morbidity. Ann Occup Hyg 38:569

- 580.

Becquemin MH, Swift DL, Bouchikhi A, Roy M, Teillac A. 1991. Particle deposition and resistance in the noses of adults and children. Eur Respir J 4:694

- 702.

Behling CA, Wolf PL, Haghighi P. 1993. AIDS and malignant mesothelioma

-- is there a connection? Chest 103:1268

- 1269.

Bennett WD, Messina MS, Smaldone GC. 1985. Effect of exercise on deposition and subsequent retention of inhaled particles. J Appl Physiol 59:1046

- 1054.

Bennett WD, Zeman KL, Kim C. 1996. Variability of fine particle deposition in healthy adults: effect of age and gender. Am J Respir Crit Care Med 153:1641

- 1647.

Bennett WD, Zeman KL, Kim C, Mascarella J. 1997. Enhanced deposition of fine particles in COPD patients spontaneously breaking at rest. Inhal Toxicol 9:1

- 14.

Bennett WD, Zeman KL, Jarabek AM. 2003. Nasal contribution to breathing with exercise: effect of race and gender. J Appl Physiol 95:497

- 50 3.

Bennett WD, Zeman KL. 2004. Effect of body size on breathing pattern and fine

- particle deposition in children. J Appl Physiol 97:821

- 826.

----- 2005. Effect of race on fine particle deposition for oral and nasal breathing. Inhal Toxicol 17:641

- 648.

Bennett WD, Zeman KL, Jarabek AM. 2008. Nasal contribution to breathing and fine particle deposition in children versus adults. J Toxicol Environ Health A 71:227

- 237.

Berkson J. 1950. Are there two regressions? J Am Stat Assoc 45:164

- 180.

Berman DW. 201 0. Comparing milled fiber, Quebec ore, and textile factory dust: has another piece of the asbestos puzzle fallen into place? Crit Rev Toxicol 40:151

- 188.

Berman DW, Crump KS. 2008. Update of potency factors for asbestos

- related lung cancer and mesotheliom a. Crit Rev Toxicol 38 ( Suppl

. 1 ) :1

- 47.

Bernstein D, Rogers R, Smith P. 2005. The biopersistence of Canadian chrysotile asbestos following inhalation: final results through 1 year after cessation of exposure. Inhal Toxicol 17:1

- 14.

Bernstein DM, Mast R, An derson R, Hesterberg TW, Musselman R, Kamstrup O, Hadley J. 1994. An experimental approach to the evaluation of the biopersistence of respirable synthetic fibers and minerals. Environ Health Perspect 102 Suppl 5:15

- 18.

Bernstein DM, Chevalier J, Smith P. 2003. Comparison of Calidria chrysotile asbestos to pure tremolite: inhalation biopersistence and histopathology following short

- term exposure. Inhal Toxicol 15:1387

- 1419.

Bernstein DM, Rogers R, Smith P. 2003. The biopersistence of Canadian chrysotile a sbestos following inhalation. Inhal Toxicol 15:1247

- 1274.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 4 DRAFT

— DO NOT CITE OR QUOTE Bernstein DM, Rogers R, Smith P, Chevalier J. 2006. The toxicological response of Brazilian chrysotile asbestos: a multidose subchronic 90

- day inhalation toxicology study with 92

- day recovery to as sess cellular and pathological response. Inhal Toxicol 18:313

- 332.

Berry G, Wagner JC. 1969. The application of a mathematical model describing the times of occurrence of mesotheliomas in rats following inoculation with asbestos. Br J Cancer 23:582

- 586.

B erry G, Gilson JC, Holmes S, Lewinsohn H, Roach S. 1979. Asbestosis: a study of dose

- response relationships in an asbestos textile factory. Br J Ind Med 36:98

- 112.

Berry G. 1999. Models for mesothelioma incidence following exposure to fibers in terms of timing and duration of exposure and the biopersistence of the fibers. Inhal Toxicol 11:111

- 130.

Berry G, Newhouse ML, Wagner JC. 2000. Mortality from all cancers of asbestos factory workers in east London 1933

- 80. Occup Environ Med 57:782

- 785.

Berry G, de Klerk NH, Reid A, Ambrosini GL, Fritschi L, Olsen NJ, Merler E, Musk AW. 2004. Malignant pleural and peritoneal mesotheliomas in former miners and millers of crocidolite at Wittenoom, Western Australia. Occup Environ Med 61:e14.

Berry G, Pooley F, Gibbs A, Harris JM, McDonald JC. 2009. Lung fiber burden in the Nottingham gas mask cohort. Inhal Toxicol 21:168

- 172.

Bertolotti M, Ferrante D, Mirabelli D, Botta M, Nonnato M, Todesco A, Terracini B, Magnani C. 2008. [Mortality in the cohort of the asbestos c ement workers in the Eternit plant in Casale Monferrato (Italy)]. Epidemiol Prev 32:218

- 228.

Betti M, Neri M, Ferrante D, Landi S, Biava A, Gemignani F, Bertolotti M, Mirabelli D, Padoan M, Ugolini D, Botta M, Bonassi S, Magnani C, Dianzani I. 2009. Poole d analysis of NAT2 genotypes as risk factors for asbestos

- related malignant mesothelioma. Int J Hyg Environ Health 212:322

- 329.

Bianchi AB, Mitsunaga SI, Cheng JQ, Klein WM, Jhanwar SC, Seizinger B, Kley N, Klein

- Szanto AJ, Testa JR. 1995. High frequency of inactivating mutations in the neurofibromatosis type 2 gene (NF2) in primary malignant mesotheliomas. Proc Natl Acad Sci U S A 92:10854

- 10858.

Bianchi C, Bianchi T. 2008. Susceptibility and resistance in the genesis of asbestos

- related mesothelioma. In dian J Occup Environ Med 12:57

- 60.

----- 2009. Malignant pleural mesothelioma in Italy. Indian J Occup Environ Med 13:80

- 83.

Bianchi C, Giarelli L, Grandi G, Brollo A, Ramani L, Zuch C. 1997. Latency periods in asbestos

- related mesothelioma of the pleura. Eur J Cancer Prev 6:162

- 166.

Bignon J, Monchaux G, Sebastien P, Hirsch A, Lafuma J. 1979. Human and experimental data on translocation of asbestos fibers through the respiratory system. Ann N Y Acad Sci 330:745

- 750.

Bilgrad R. 1997. National Death Index Plus : Coded Causes Of Death Supplement to the National Death Index User's Manual. Hyattsville, MD:Centers for Disease Control and Prevention.

Blake DJ, Bolin CM, Cox DP, Cardozo

- Pelaez F, Pfau JC. 2007. Internalization of Libby amphibole asbestos and ind uction of oxidative stress in murine macrophages. Toxicol Sci 99:277

- 288.

Blake DJ, Wetzel SA, Pfau JC. 2008. Autoantibodies from mice exposed to Libby amphibole asbestos bind SSA/Ro52

- enriched apoptotic blebs of murine macrophages. Toxicology 246:172

- 179

.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 5 DRAFT

— DO NOT CITE OR QUOTE Bocchetta M, Di R, I, Powers A, Fresco R, Tosolini A, Testa JR, Pass HI, Rizzo P, Carbone M. 2000. Human mesothelial cells are unusually susceptible to simian virus 40

- mediated transformation and asbestos cocarcinogenicity. Proc Natl Acad Sci U S A 97:1 0214

- 10219.

Boettcher AL. 1966. Vermiculite, hydrobiotite, and biotite in the Rainy Creek Igneous complex near Libby, Montana. Caly Minerals 6:283

- 297.

Boettcher C, Stark H, vanHeel M. 1996. Stacked bilayer helices: A new structural organization of amphi philic molecules. Ultramicroscopy 62:133

- 139.

Bohnker, BK; Muller, JG, Philippi, AF, et al. (2005) Trends in pleural radiographic findings in the Navy asbestos medical surveillance program (1990

- 1999). Military Medicine 170:375

- 380.

Bourbeau, J, Ernst, P, Chrome, J, Armstrong, B, and Becklake, M (1990) The relationship between respiratory impairment and asbestos

- related pleural abnormalities in an active work force. Am Rev Respir Dis 142:837

- 842.

Broaddus, VC, Everitt, JI, Black, B, and Kane, A (2011) Non

- neoplastic and neoplastic pleural endpoints following fiber exposure

Brody AR, Hill LH, Adkins B, Jr., O'Connor RW. 1981. Chrysotile asbestos inhalation in rats: deposition pattern and reaction of alveolar epithelium and pulmonary macrophages. Am Rev Resp ir Dis 123:670

- 679.

Brown JS, Bennett WD. 2004. Deposition of coarse particles in cystic fibrosis: model predictions versus experimental results. J Aerosol Med 17:239

- 248.

Brown JS, Zeman KL, Bennett WD. 2001. Regional deposition of coarse particles and ventilation distribution in healthy subjects and patients with cystic fibrosis. J Aerosol Med 14:443

- 454.

Brown, JS; Wilson, WE; and Grant, LD (2005) Dosimetric comparisons of particle deposition and retention in rats and humans. Inhal Toxicol 17:355

- 385.

Burnham KP, Anderson DR. 2002. Model Selection and Multimodel Inference: A Practical Information

- Theoretic Approach. 2nd ed.

Burr D. 1988. On errors

- in

- variables in binary regression

- Berkson Case. J Am Stat Assoc 83:739

- 743.

Camidge DR, Stockton DL, Bai n M. 2006. Factors affecting the mesothelioma detection rate within national and international epidemiological studies: insights from Scottish linked cancer registry

- mortality data. Br J Cancer 95:649

- 652.

Carbone M, Albelda SM, Broaddus VC, Flores RM, Hi llerdal G, Jaurand MC, Kjaerheim K, Pass HI, Robinson B, Tsao A. 2007. Eighth international mesothelioma interest group. Oncogene 26:6959

- 6967.

Carroll RJ, Ruppert D, Stefanski LA. 2006. Measurement Error in Nonlinear Models: A Modern Perspective. 2nd ed

. London:Chapman & Hall.

Case BW, Kuhar M, Harrigan M, Dufresne A. 1994. Lung fiber content of American children aged 8

- 15 years; preliminary findings. Ann Occup Hyg 38 (Suppl 1):639

- 645.

Case, BW; Dufresne, A; McDonald, AD; et al.(2000) Asbestos fiber type and length in lungs of chrysotile textile and production workers; Fibers longer than 18 μm. Inhal Toxicol 12:411

- 418.

Castellan, RM; Sanderson, WY; and Peterson, MR (1985) Prevalence of radiographic appearance of pneumoconiosis in an unexposed blue c ollar population. Am Rev Respir Dis 131:684

- 686.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 6 DRAFT

— DO NOT CITE OR QUOTE Castranova V, Pailes W, Judy D, Blake T, Schwegler

- Berry D, Jones W. 1996. In vitro effects of large and small glass fibers on rat alveolar macrophages. J Toxicol Environ Health 49:357

- 369.

Cazzadori A, Malesani F, Romeo L. 1992. Malignant pleural mesothelioma caused by non

- occupational childhood exposure to asbestos. Br J Ind Med 49:599.

Cerny FJ. 1987. Breathing pattern during exercise in young black and Caucasian subjects. J Appl Physiol 62:2220

- 2223

. Cheng JQ, Lee WC, Klein MA, Cheng GZ, Jhanwar SC, Testa JR. 1999. Frequent mutations of NF2 and allelic loss from chromosome band 22q12 in malignant mesothelioma: evidence for a two

- hit mechanism of NF2 inactivation. Genes Chromosomes Cancer 24:238

- 242.

Chhajed PN, Bubendorf L, Hirsch H, Boehler A, Weder W, Tamm M. 2006. Mesothelioma after lung transplantation. Thorax 61:916

- 917.

Churg A. 1982. Fiber counting and analysis in the diagnosis of asbestos

- related disease. Hum Pathol 13:381

- 392.

----- 1988. Chrysotile, tremolite, and malignant mesothelioma in man. Chest 93:621

- 628.

----- 1994. Deposition and clearance of chrysotile asbestos. Ann Occup Hyg 38:625.

Churg A, Vedal S. 1994. Fiber burden and patterns of asbestos

- related disease in workers with he avy mixed amosite and chrysotile exposure. Am J Respir Crit Care Med 150:663

- 669.

Churg A, Warnock ML. 1980. Asbestos fibers in the general population. Am Rev Respir Dis 122:669

- 678.

Churg A, Wright JL, Gilks B, Depaoli L. 1989. Rapid short

- term clearanc e of chrysotile compared with amosite asbestos in the guinea pig. Am Rev Respir Dis 139:885

- 890.

Churg A, Brauer M, Keeling B. 1996. Ozone enhances the uptake of mineral particles by tracheobronchial epithelial cells in organ culture. Am J Respir Crit Car e Med 153:1230

- 1233.

Chuwers P, Barnhart S, Blanc P, Brodkin CA, Cullen M, Kelly T, Keogh J, Omenn G, Williams J, Balmes JR. 1997. The protective effect of beta

- carotene and retinol on ventilatory function in an asbestos

- exposed cohort. Am J Respir Crit C are Med 155:1066

- 1071.

Claeskens G, Hjort NL. 2008. Model Selection and Model Averaging. UK: Cambridge University Press.

Clement PB, Young RH, Scully RE. 1996. Malignant mesotheliomas presenting as ovarian masses. A report of nine cases, including two primary ovarian mesotheliomas. Am J Surg Pathol 20:1067

- 1080.

Clements M, Berry G, Shi J, Ware S, Yates D, Johnson A. 2007. Projected mesothelioma incidence in men in New South Wales. Occup Environ Med 64:747

- 752.

Coffin DL, Palekar LD, Cook PM. 1983. Co rrelation of in vitro and in vivo methods by means of mass dose and fiber distribution for amosite and fibrous ferroactinolite. Environ Health Perspect 51:49

- 53.

Coin PG, Roggli VL, Brody AR. 1992. Deposition, clearance and translocation of chrysotile asb estos from peripheral and central regions of the rat lung. Environ Res 58:97

- 116.

----- 1994. Persistence of long, thin chrysotile asbestos fibers in the lungs of rats. Environ Health Perspect 102 Suppl 5:197

- 199.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 7 DRAFT

— DO NOT CITE OR QUOTE Condie LW. 1983. Review of published stud ies of orally administered asbestos. Environ Health Perspect 53:3

- 9.

Constantopoulos SH, Goudevenos JA, Saratzis N, Langer AM, Selikoff IJ, Moutsopoulos HM. 1985. Metsovo lung: pleural calcification and restrictive lung function in northwestern Greece. En vironmental exposure to mineral fiber as etiology. Environ Res 38:319

- 331.

Cook PJ, Doll R, Fellingham SA. 1969. A mathematical model for the age distribution of cancer in man. Int J Cancer 4:93

- 112.

Cook PM, Olson GF. 1979. Ingested mineral fibers: elim ination in human urine. Science 204:195

- 198.

Cooper GS, Stroehla BC. 2003. The epidemiology of autoimmune diseases. Autoimmun Rev 2:119

- 125.

Cooper SP, Fraire AE, Buffler PA, Greenberg SD, Langston C. 1989. Epidemiologic aspects of childhood mesothelioma . Pathol Immunopathol Res 8:276

- 286.

Cotran, RS; Kumar, V; and Collins, T (1999) Robbins pathologic basis of disease, 6th ed. 732

- 734. Philadelphia, WB Saunders Company.

Cox DR. 1972. Regression models and life tables (with discussion). Journal of the Royal Statistical society, Series B 34:187

- 220.

Cristaudo A, Foddis R, Vivaldi A, Buselli R, Gattini V, Guglielmi G, Cosentino F, Ottenga F, Ciancia E, Libener R, Filiberti R, Neri M, Betta P, Tognon M, Mutti L, Puntoni R. 2005. SV40 enhances the risk of malignant mesothelioma among people exposed to asbestos: a molecular epidemiologic case

- control study. Cancer Res 65:3049

- 3052.

Crump, KS, and Howe, R (1985) A review of methods for calculating statistical confidence limits in low dose extrapolation. In: Clayson, D., Krewski, I., Munro (eds). 1985. Toxicological Risk Assessment, volume 1, pp 187

- 203. Boca Raton, Fl, CRC Press.

Cugell, DW; and Kamp, DW (2004) Asbestos and the pleura: a review. Chest 125:1103

- 1117.

Cullen MR, Barnett MJ, Balmes JR, Cartmel B, Redlich CA, Brodkin CA, Barnhart S, Rosenstock L, Goodman GE, Hammar SP, Thornquist MD, Omenn GS. 2005. Predictors of lung cancer among asbestos

- exposed men in the {beta}

- carotene and retinol efficacy trial. Am J Epidemiol 161:260

- 270.

Cullen RT, Mill er BG, Davis JM, Brown DM, Donaldson K. 1997. Short

- term inhalation and in vitro tests as predictors of fiber pathogenicity. Environ Health Perspect 105 Suppl 5:1235

- 1240.

Cunningham HM, Pontefract RD. 1974. Placental transfer of asbestos. Nature 249:177

- 178.

Dai YT, Yu CP. 1998. Alveolar deposition of fibers in rodents and humans. J Aerosol Med 11:247

- 258.

Davis JM. 1989. Mineral fibre carcinogenesis: experimental data relating to the importance of fibre type, size, deposition, dissolution and migratio n. IARC Sci Publ33

- 45.

----- 1994. The role of clearance and dissolution in determining the durability or biopersistence of mineral fibers. Environ Health Perspect 102 Suppl 5:113

- 117.

Davis JM, Addison J, Bolton RE, Donaldson K, Jones AD, Miller BG. 1985 . Inhalation studies on the effects of tremolite and brucite dust in rats. Carcinogenesis 6:667

- 674.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 8 DRAFT

— DO NOT CITE OR QUOTE Davis JM, Addison J, McIntosh C, Miller BG, Niven K. 1991. Variations in the carcinogenicity of tremolite dust samples of differing morphology. Ann N Y Ac ad Sci 643:473

- 490.

Davis LK, Martin TR, Kligler B. 1992. Use of death certificates for mesothelioma surveillance. Public Health Rep 107:481

- 483.

Dawson A, Gibbs AR, Pooley FD, Griffiths DM, Hoy J. 1993. Malignant mesothelioma in women. Thorax 48:269

- 274

. Dement JM, Harris RL. Estimates of pulmonary and gastrointestinal deposition for occupational fiber exposure. NTIS PB80

- 149644. 1

- 1

- 1979. US HEW Contract 78

- 2438.

DHHS (US Department of Health and Human Resources). Reducing the Health Consequences of Smoking: 25 Years of Progress: A Report of the Surgeon General: 1989 Executive Summary. DHHS Publication No. (CDC) 89

- 8411. 1

- 1

- 1989.

Dianzani I, Gibello L, Biava A, Giordano M, Bertolotti A, Betti M, Ferrante D, Guarrera S, Betta GP, Mirabelli D, Matullo G, Magnani C. 2006. Polymorphisms in DNA repair genes as risk factors for asbestos

- related malignant mesothelioma in a general population study. Mutation Research

- Fundamental and Molecular Mechanisms of Mutagenesis 599:124

- 134.

Ding M, Dong Z, Che n F, Pack D, Ma WY, Ye J, Shi X, Castranova V, Vallyathan V. 1999. Asbestos induces activator protein

- 1 transactivation in transgenic mice. Cancer Res 59:1884

- 1889.

DiPaolo JA, DeMarinis AJ, Doniger J. 1983. Asbestos and benzo(a)pyrene synergism in the t ransformation of Syrian hamster embryo cells. Pharmacology 27:65

- 73.

Dodson RF, Atkinson MAL. 2006. Measurements of asbestos burden in tissues. Ann NY Acad Sci 1076: 281

- 291.

Dodson RF, Williams MG, Jr., Corn CJ, Brollo A, Bianchi C. 1990. Asbestos conte nt of lung tissue, lymph nodes, and pleural plaques from former shipyard workers. Am Rev Respir Dis 142:843

- 847.

Dodson RF, O'sullivan M, Corn CJ. 1996. Relationships between ferruginous bodies and uncoated asbestos fibers in lung tissue. Arch Environ Health 51:462

- 466.

Dodson RF, O'Sullivan MF, Huang J, Holiday DB, Hammar SP. 2000. Asbestos in extrapulmonary sites: omentum and mesentery. Chest 117:486

- 493.

Dodson RF, Huang J, Bruce JR. 2000. Asbestos content in the lymph nodes of nonoccupationally ex posed individuals. Am J Ind Med 37:169

- 174.

Dodson RF, O'Sullivan MF, Brooks DR, Bruce JR. 2001. Asbestos content of omentum and mesentery in nonoccupationally exposed individuals. Toxicol Ind Health 17:138

- 143.

Dodson RF, Atkinson MA, Levin JL. 2003. As bestos fiber length as related to potential pathogenicity: a critical review. Am J Ind Med 44:291

- 297.

Dodson RF, O'sullivan M, Brooks DR, Hammar SP. 2003. Quantitative analysis of asbestos burden in women with mesothelioma. Am J Ind Med 43:188

- 195.

Dodso n RF, Graef R, Shepherd S, O'sullivan M, Levin J. 2005. Asbestos burden in cases of mesothelioma from individuals from various regions of the United States. Ultrastruct Pathol 29:415

- 433.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 9 DRAFT

— DO NOT CITE OR QUOTE Dopp E, Yadav S, Ansari FA, Bhattacharya K, von RU, Rauen U, Rodels perger K, Shokouhi B, Geh S, Rahman Q. 2005. ROS

- mediated genotoxicity of asbestos

- cement in mammalian lung cells in vitro. Part Fibre Toxicol 2:9.

Dresler CM, Fratelli C, Babb J, Everley L, Evans AA, Clapper ML. 2000. Gender differences in genetic

susce ptibility for lung cancer. Lung Cancer 30:153

- 160.

Driscoll KE, Maurer JK, Higgins J, Poynter J. 1995. Alveolar macrophage cytokine and growth factor production in a rat model of crocidolite

- induced pulmonary inflammation and fibrosis. J Toxicol Environ H ealth 46:155

- 169.

Driscoll KE, Carter JM, Howard BW, Hassenbein DG, Pepelko W, Baggs RB, Oberdorster G. 1996. Pulmonary inflammatory, chemokine, and mutagenic responses in rats after subchronic inhalation of carbon black. Toxicol Appl Pharmacol 136:372

- 38 0.

Driscoll KE, Deyo LC, Carter JM, Howard BW, Hassenbein DG, Bertram TA. 1997. Effects of particle exposure and particle

- elicited inflammatory cells on mutation in rat alveolar epithelial cells. Carcinogenesis 18:423

- 430.

Dufresne A, Perrault G, Yamato H , Masse S, Begin R. 1999. Clearance of man made mineral fibres from the lungs of sheep. Occup Environ Med 56:684

- 690.

Dumortier P, Coplu L, de M, V, Emri S, Baris I, De VP. 1998. Assessment of environmental asbestos exposure in Turkey by bronchoalveolar lavage. Am J Respir Crit Care Med 158:1815

- 1824.

Duncan KE, Ghio AJ, Dailey LA, Bern AM, Gibbs

- Flournoy EA, Padilla

- Carlin DJ, Roggli VL, Devlin RB. 2010. Effect of size fractionation on the toxicity of amosite and Libby amphibole asbestos. Toxicol Sci 11 8:420

- 434.

Ehrlich R, Lilis R, Chan E, Nicholson WJ, Selikoff IJ. 1992. Long term radiological effects of short term exposure to amosite asbestos among factory workers. Br J Ind Med 49:268

- 275.

Epler GR, Fitz Gerald MX, Gaensler EA, Carrington CB. 1980. Asbestos

- related disease from household exposure. Respiration 39:229

- 240.

Ewing WM, Hays SM, Hatfield R, Longo WE, Millette JR. 2010. Zonolite attic insulation exposure studies. Int J Occup Environ Health 16(3):279

- 2 90.

Falson C. 1985. Mesothelioma or ov arian carcinoma? A case report. S Afr Med J 68:676

- 677.

Fattman CL, Tan RJ, Tobolewski JM, Oury TD. 2006. Increased sensitivity to asbestos

- induced lung injury in mice lacking extracellular superoxide dismutase. Free Radic Biol Med 40:601

- 607.

Ferrante D, Bertolotti M, Todesco A, Mirabelli D, Terracini B, Magnani C. 2007. Cancer mortality and incidence of mesothelioma in a cohort of wives of asbestos workers in Casale Monferrato, Italy. Environ Health Perspect 115:1401

- 1405.

Finkelstein MM. 1985. A study of dose

- response relationships for asbestos associated disease. Br J Ind Med 42:319

- 325.

Finkelstein MM, Dufresne A. 1999. Inferences on the kinetics of asbestos deposition and clearance among chrysotile miners and millers. Am J Ind Med 35:401

- 412.

Flynn JJ, Kardos S, Yan L. 2004. Overstating the consequences of radiographic abnormalities. Environ Health Perspect 112:A84

- A85.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 10 DRAFT

— DO NOT CITE OR QUOTE Foddis R, De RA, Broccoli D, Bocchetta M, Stekala E, Rizzo P, Tosolini A, Grobelny JV, Jhanwar SC, Pass HI, Testa JR, Carbone M. 2002. SV40 infection induces telomerase activity in human mesothelial cells. Oncogene 21:1434

- 1442.

Frank AL, Rohl AN, Wade MJ, Lipkin LE. 1979. Biological activity in vitro of chrysotile compared to its quarried parent rock (platy serpentine). J Environ Pathol Toxicol 2:1041

- 1046.

Franko A, Dodic

- Fikfak M, Arneric N, Dolzan V. 2007. Glutathione S

- transferases GSTM1 and GSTT1 polymorphisms and asbestosis. Journal of Occupational and Environmental Medicine 49:667

- 671.

Franko A, Dolzan V, Arneric N, Dodic

- F ikfak M. 2008. The influence of genetic polymorphisms of GSTP1 on the development of asbestosis. J Occup Environ Med 50:7

- 12.

Fukagawa NK, Li M, Sabo

- Attwood T, Timblin CR, Butnor KJ, Gagne J, Steele C, Taatjes DJ, Huber S, Mossman BT. 2008. Inhaled asbe stos exacerbates atherosclerosis in apolipoprotein E

- deficient mice via CD4+ T cells. Environ Health Perspect 116:1218

- 1225.

GAO (United States Government Accountability Office). EPA's Cleanup of Asbestos in Libby, Montana, and Related Actions to Address Asbestos

- Contaminated Materials. GAO

- 03

- 469. 10

- 1

- 2003.

----- . Hazardous Materials: EPA May Need to Reassess Sites Receiving Asbestos

- Contaminated Ore from Libby, Montana, and Should Improve It's Public Notification Process. 10

- 1

- 2007.

----- . Status of EPA's Efforts to Assess Sites That May Have Received Asbestos

- Contaminated Ore from Libby, Montana. GAO

- 09

- 6R. 3

- 10

- 2009.

----- . EPA's Asssessment of Sites That May Have Received Asbestos

- Contaminated Ore from Libby, Montana. GAO

- 09

- 7SP. 3

- 10

- 2009.

Gao F , Koenitzer JR, Tobolewski JM, Jiang D, Liang J, Noble PW, Oury TD. 2008. Extracellular superoxide dismutase inhibits inflammation by preventing oxidative fragmentation of hyaluronan. J Biol Chem 283:6058

- 6066.

Gasparrini A, Pizzo AM, Gorini G, Seniori CA , Silvestri S, Ciapini C, Innocenti A, Berry G. 2008. Prediction of mesothelioma and lung cancer in a cohort of asbestos exposed workers. Eur J Epidemiol 23:541

- 546.

Germani D, Belli S, Bruno C, Grignoli M, Nesti M, Pirastu R, Comba P. 1999. Cohort morta lity study of women compensated for asbestosis in Italy. Am J Ind Med 36:129

- 134.

Germine M. 1986. Asbestos in play sand. N Engl J Med 315:891.

Ghio AJ, Kennedy TP, Whorton AR, Crumbliss AL, Hatch GE, Hoidal JR. 1992. Role of surface complexed iron in

ox idant generation and lung inflammation induced by silicates. Am J Physiol 263:L511

- L518.

Gilmartin D. The serratus anterior muscle on chest radiographs. Radiology 1979; 131:629

– 635

Graham DR, Chamberlain MJ, Hutton L, King M, Morgan WK. 1990. Inhaled par ticle deposition and body habitus. Br J Ind Med 47:38

- 43.

Green, GM (1973) Alveolobronchiolar transport mechanisms. Arch Inter Med 131:109

- 114.

Griffis LC, Pickrell JA, Carpenter RL, Wolff RK, McAllen SJ, Yerkes KL. 1983. Deposition of Crocidolite asbest os and glass microfibers inhaled by the Beagle dog. Am Ind Hyg Assoc J 44:216

- 222.

Grundy GW, Miller RW. 1972. Malignant mesothelioma in childhood. Report of 13 cases. Cancer 30:1216

- 1218.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 11 DRAFT

— DO NOT CITE OR QUOTE Guinee DG, Jr., Travis WD, Trivers GE, De B, V, Cawley H, Welsh JA , Bennett WP, Jett J, Colby TV, Tazelaar H, . 1995. Gender comparisons in human lung cancer: analysis of p53 mutations, anti

- p53 serum antibodies and C

- erbB

- 2 expression. Carcinogenesis 16:993

- 1002.

Gunter, ME, and Sanchez, MS. 2009. Amphibole forensics: Using the composition of amphiboles to determine their source, the Lilbby, Montana, example. Amer Mineralogist 94:837

- 840.

Gwinn MR, and Vallyathan V. 2006. Respiratory burst: role in signal transduction in alveolar macrophages. J Toxicol Environ Health B Crit Rev 9: 27

- 39.

Halpern MT, Gillespie BW, Warner KE. 1993. Patterns of absolute risk of lung cancer mortality in former smokers. J Natl Cancer Inst 85:457

- 464.

Hamilton RF, Jr., Holian A, Morandi MT. 2004. A comparison of asbestos and urban particu late matter in the in vitro modification of human alveolar macrophage antigen

- presenting cell function. Exp Lung Res 30:147

- 162.

Hammond EC, Selikoff IJ, Seidman H. 1979. Asbestos exposure, cigarette smoking and death rates. Ann N Y Acad Sci 330:473

- 490.

Hannahan D, and Weinberg RA. 2011. Hallmarks of Cancer: The Next Generation. Cell 144: 646

- 674.

Hansen J, de Klerk NH, Musk AW, Hobbs MS. 1998. Environmental exposure to crocidolite and mesothelioma:
exposure

- response relationships. Am J Respir Crit Care Med 157:69

- 75.

Hasanoglu HC, Bayram E, Hasanoglu MD, Demirag F. 2008. Orally ingested chrysotile asbestos affects rat lungs and pleura. Arch Environ Occup Health 63:71

- 75.

Haque AK, Kanz MF. 1988. Asbestos bodies in children's lungs. An association with sudden infant death syndrome and bronchopulmonary dysplasia. Arch Pathol Lab Med 112:514

- 518.

Haque AK, Vrazel DM. 1998. Transplacental transfer of asbestos in pregnant mice. Bull Environ Contam Toxicol 60:620

- 625.

Haque AK, Kanz MF, M ancuso MG, Williams GM, Dodson RF. 1991. Asbestos in the lungs of children. Ann N Y Acad Sci 643:419

- 429.

Haque AK, Mancuso MG, Williams MG, Dodson RF. 1992. Asbestos in organs and placenta of five stillborn infants suggests transplacental transfer. Envi ron Res 58:163

- 175.

Haque AK, Vrazel DM, Burau KD, Cooper SP, Downs T. 1996. Is there transplacental transfer of asbestos? A study of 40 stillborn infants. Pediatr Pathol Lab Med 16:877

- 892.

Haque AK, Vrazel DM, Uchida T. 1998. Assessment of asbestos bur den in the placenta and tissue digests of stillborn infants in South Texas. Arch Environ Contam Toxicol 35:532

- 538.

Haque AK, Ali I, Vrazel DM, Uchida T. 2001. Chrysotile asbestos fibers detected in the newborn pups following gavage feeding of pregnant mi ce. J Toxicol Environ Health A 62:23

- 31.

Harris RL, Jr., Fraser DA. 1976. A model for deposition of fibers in the human respiratory system. Am Ind Hyg Assoc J 37:73

- 89.

Harris RL, Jr., Timbrell V. 1975. The influence of fibre shape in lung deposition

- mat hematical estimates. Inhaled Part 4 Pt 1:75

- 89.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 12 DRAFT

— DO NOT CITE OR QUOTE Hart JF, Ward TJ, Spear TM, Crispen K, Zolnikov TR. 2007. Evaluation of asbestos exposures during firewood

- harvesting simulations in Libby, MT, USA

-- preliminary data. Ann Occup Hyg 51:717

- 723.

Hasanoglu HC, Yildirim Z, Ermis H, Kilic T, Koksal N. 2006. Lung cancer and mesothelioma in towns with environmental exposure to asbestos in Eastern Anatolia. Int Arch Occup Environ Health 79:89

- 91.

Hauptmann M, Pohlabeln H, Lubin JH, Jockel KH, Ahrens W, Bruske

- Hohlfe ld I, Wichmann HE. 2002. The
exposure

- time

- response relationship between occupational asbestos exposure and lung cancer in two German

case

- control studies. Am J Ind Med 41:89

- 97.

Hawthorne FC. 1981. Amphibole Spectoscopy. In: Amphiboles and Other Hydrous Pyriboles MineralogySpringer.

Hei TK, Piao CQ, He ZY, Vannais D, Waldren CA. 1992. Chrysotile fiber is a strong mutagen in mammalian cells. Cancer Res 52:6305

- 6309.

Hei TK, Xu A, Huang SX, Zhao Y. 2006. Mechanism of fiber carcinogenesis: from reactive r adical species to silencing of the beta igH3 gene. Inhal Toxicol 18:985

- 990.

Heid IM, Kuchenhoff H, Miles J, Kreienbrock L, Wichmann HE. 2004. Two dimensions of measurement error: classical and Berkson error in residential radon exposure assessment. J Exp o Anal Environ Epidemiol 14:365

- 377.

Hein MJ, Stayner LT, Lehman E, Dement JM. 2007. Follow

- up study of chrysotile textile workers: cohort mortality and exposure

- response. Occup Environ Med 64:616

- 625.

Heller DS, Gordon RE, Westhoff C, Gerber S. 1996. As bestos exposure and ovarian fiber burden. Am J Ind Med 29:435

- 439.

Heller DS, Gordon RE, Clement PB, Turnnir R, Katz N. 1999. Presence of asbestos in peritoneal malignant mesotheliomas in women. Int J Gynecol Cancer 9:452

- 455.

Heller DS, Gordon RE, Katz N . 1999. Correlation of asbestos fiber burdens in fallopian tubes and ovarian tissue. Am J Obstet Gynecol 181:346

- 347.

Hesterberg TW, Barrett JC. 1985. Induction by asbestos fibers of anaphase abnormalities: mechanism for aneuploidy induction and possibly carcinogenesis. Carcinogenesis 6:473

- 475.

Hesterberg TW, Hart GA, Chevalier J, Miiller WC, Hamilton RD, Bauer J, Thevenaz P. 1998. The importance of fiber biopersistence and lung dose in determining the chronic inhalation effects of X607, RCF1, and chrys otile asbestos in rats. Toxicol Appl Pharmacol 153:68

- 82.

Hesterberg TW, Chase G, Axten C, Miller WC, Musselman RP, Kamstrup O, Hadley J, Morscheidt C, Bernstein DM, Thevenaz P. 1998. Biopersistence of synthetic vitreous fibers and amosite asbestos in the rat lung following inhalation. Toxicol Appl Pharmacol 151:262

- 275.

Hillegass JM, Shukla A, Macpherson MB, Lathrop SA, Alexeeva V, Perkins TN, van d, V, Vacek PM, Gunter ME, Mossman BT. 2010. Mechanisms of oxidative stress and alterations in gene expressi on by Libby six

- mix in human mesothelial cells. Part Fibre Toxicol 7:26.

----- 2010. Mechanisms of oxidative stress and alterations in gene expression by Libby six

- mix in human mesothelial cells. Part Fibre Toxicol 7:26.

Hillerdal G. 1983. Malignant mesot helioma 1982: review of 4710 published cases. Br J Dis Chest 77:321

- 343.

Hillerdal, G (1994) The human evidence: parenchymal and pleural changes. Ann Occup Hyg 38:561

- 567.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 13 DRAFT

— DO NOT CITE OR QUOTE Hirvonen A, Pelin K, Tammilehto L, Karjalainen A, Mattson K, Linnainmaa K. 1995. I nherited GSTM1 and NAT2 defects as concurrent risk modifiers in asbestos

- related human malignant mesothelioma. Cancer Res 55:2981

- 2983.

Hirvonen A, Saarikoski ST, Linnainmaa K, Koskinen K, Husgafvel

- Pursiainen K, Mattson K, Vainio H. 1996. Glutathione S

- t

ransferase and N

- acetyltransferase genotypes and asbestos

- associated pulmonary disorders. J Natl Cancer Inst 88:1853

- 1856.

Hirvonen A, Tuimala J, Ollikainen T, Linnainmaa K, Kinnula V. 2002. Manganese superoxide dismutase genotypes and asbestos

- associated pulmonary disorders. Cancer Lett 178:71

- 74.

Hodgson JT, McElvenny DM, Darnton AJ, Price MJ, Peto J. 2005. The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050. Br J Cancer 92:587

- 593.

Holt PF. 1981. Transport of inhaled dust to extrapulmonary sites. J Pathol 133:123

- 129.

----- 1982. Translocation of asbestos dust through the bronchiolar wall. Environ Res 27:255

- 260.

----- 1983. Translocation of inhaled dust to the pleura. Environ Res 31:212

- 220.

Horton K, Kapil V, Larson T, Mu ravov O, Melnikova N, Anderson B. 2006. A review of the federal government's health activities in response to asbestos

- contaminated ore found in Libby, Montana. Inhal Toxicol 18:925

- 940.

Hosmer, DW jr, and Lameshow, S (2000) Applied logistic regression, s econd edition, New York, John Wiley & Sons, Inc.

Huncharek M. 2002. Non

- asbestos related diffuse malignant mesothelioma. Tumori 88:1

- 9.

ICRP (International Commission on Radiological Protection). 1995. Human Respiratory Tract Model for Radiological Prote ction. ICRP 66 ed.Elsevier.

ILSI (International Life Sciences Institute). Similarities and differences between children and adults: Implications for risk assessment. Guzelian PS, Henry CJ, and Olin SS. 1

- 1

- 1992. Washington DC, ILSI Press.

Inase N, Takay ama S, Nakayama M, Miura H, Kimula Y. 1991. Pleural mesothelioma after neighborhood exposure to asbestos during childhood. Jpn J Med 30:343

- 345.

International Labor Office. 1981. International classification of radiographs of the pneumoconioses. Med Radi ogr Photogr 57:2

- 17.

International Labour Organization (ILO) (2000) Guidelines for the use of ILO international classification of radiographs of pneumoconiosis. Revised edition, 2000. International Labour Office, Geneva, Switzerland.

Isaacs KK, Martonen TB. 2005. Particle deposition in children's lungs: theory and experiment. J Aerosol Med 18:337

- 353.

ISO (International Organization for Standardization). 1995. Ambient air

– determination of asbestos fibres

– direct transfer transmission electron microsc opy method. ISO 10312:1995. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=18358

. Jackson JH, Schraufstatter IU, Hyslop PA, Vosbeck K, Sauerheber R, Weitzman SA, Cochrane CG. 1987. Role of oxidants in DNA damage. Hydroxyl radical mediates the synergistic DNA damaging effects of asbestos and cigarette smoke. J Clin Invest 80:1090

- 1095.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 14 DRAFT

— DO NOT CITE OR QUOTE Jakobsson, J, Strömberg, U, Albin, M, Wel inder, H, and Hagmar, L (1995) Radiological changes in asbestos cement workers. Occup Environ Med 52:20

- 27).

Jahr J. 1974. Dose

- response basis for setting a quartz threshold limit value. Arch Environ Health 29:338

- 40.

Jarabek AM, Asgharian B, Miller FJ. 2005. Dosimetric adjustments for interspecies extrapolation of inhaled poorly soluble particles (PSP). Inhal Toxicol 17:317

- 334.

Järvholm, B (1992) Pleural plaques and exposure to asbestos: a mathematical model. Inter J of Epidem 21:1180

- 1184.

Jones, AD; McMillan, CH; Johnston, AM; et al. (1988) Pulmonary clearance of UICC amosite fibers inhaled by rats during chronic exposure at low concentration. Br J Ind Med 45:300

- 304.

Jaurand MC, Bastie

- Sigeac I, Magne L, Hubert

- Habart M, Bignon J. 1983. Studies on in vitro chrysotile

- pleural mesothelial cell interaction: morphological aspects and metabolism of benzo

- 3,4

- pyrene. Environ Health Perspect 51:159

- 165.

Jung M, Davis WP, Taatjes DJ, Churg A, Mossman BT. 2000. Asbestos and cigarette smoke cause increased DNA strand breaks and necrosis in bronchiolar epithelial cells in vivo. Free Radic Biol Med 28:1295

- 1299.

Kamp DW, Weitzman SA. 1999. The molecular basis of asbestos induced lung injury. Thorax 54:638

- 652.

Kamp DW, Graceffa P, Pryor WA, Weitzman SA. 1992 . The role of free radicals in asbestos

- induced diseases. Free Radic Biol Med 12:293

- 315.

Kane MJ, Chahinian AP, Holland JF. 1990. Malignant mesothelioma in young adults. Cancer 65:1449

- 1455.

Karlson EW, Mandl LA, Aweh GN, Grodstein F. 2003. Coffee consu mption and risk of rheumatoid arthritis. Arthritis Rheum 48:3055

- 3060.

Kelly J, Pratt GC, Johnson J, Messing RB. 2006. Community exposure to asbestos from a vermiculite exfoliation plant in NE Minneapolis. Inhal Toxicol 18:941

- 947.

Kilburn KH, Lilis R, An derson HA, Boylen CT, Einstein HE, Johnson SJ, Warshaw R. 1985. Asbestos disease in family contacts of shipyard workers. Am J Public Health 75:615

- 617.

Kim CS, Kang TC. 1997. Comparative measurement of lung deposition of inhaled fine particles in normal subjects and patients with obstructive airway disease. Am J Respir Crit Care Med 155:899

- 905.

Kim HM, Yasui Y, Burstyn I. 2006. Attenuation in risk estimates in logistic and Cox proportional

- hazards models due to group

- based exposure assessment strategy. Ann Occup Hyg 50:623

- 635.

Kimizuka G, Ohwada H, Hayashi Y. 1987. Co

- carcinogenic effect of asbestos and benzo(a)pyrene in the lung of hamster. Acta Pathol Jpn 37:465

- 474.

Kitamura F, Araki S, Suzuki Y, Yokoyama K, Tanigawa T, Iwasaki R. 2002. Assessment of the mutations of p53 suppressor gene and Ha

- and Ki

- ras oncogenes in malignant mesothelioma in relation to asbestos exposure: a study of 12 American patients. Ind Health 40:175

- 181.

Klaasen C. 1986. Toxic Responses of the Respiratory System. In: Toxico logy: The Basic Science of Poisons (Casarett, Doull, eds.).McGraw

- Hill, 343.

Klein C, Hurlbut Jr CS. 1977. Manual of Mineralogy.John Wiley and Sons.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 15 DRAFT

— DO NOT CITE OR QUOTE Kleinbaum DG. 1996. Survival Analysis: A Self

- Learning Text. New York:Springer

- Verlag.

Kleymenova EV, Bia nchi AA, Kley N, Pylev LN, Walker CL. 1997. Characterization of the rat neurofibromatosis 2 gene and its involvement in asbestos

- induced mesothelioma. Mol Carcinog 18:54

- 60.

Kliment CR, Englert JM, Gochuico BR, Yu G, Kaminski N, Rosas I, Oury TD. 2009. O xidative stress alters syndecan

- 1 distribution in lungs with pulmonary fibrosis. J Biol Chem 284:3537

- 3545.

Kohyama N, Suzuki Y. 1991. Analysis of asbestos fibers in lung parenchyma, pleural plaques, and mesothelioma tissues of North American insulation w orkers. Ann N Y Acad Sci 643:27

- 52.

Kopylev L, Sullivan PA, Vinikoor LC, Bateson TF. 2011. Monte Carlo analysis of impact of underascertainment of mesothelioma cases on underestimation of risk. Open Epidemiology Journal 4:45

- 53.

Koskinen K, Pukkala E, Reijula K, Karjalainen A. 2003. Incidence of cancer among the participants of the Finnish Asbestos Screening Campaign. Scand J Work Environ Health 29:64

- 70.

Kouris, SP, Parker, DL, Bender, AP, Williams, AN (1991) Effects of asbestos

- related pleural diseas e on pulmonary function. Scand J Work Environ Health 17:179

- 183.

Krasuski P, Poniecka A, Gal E. 2002. The diagnostic challenge of peritoneal mesothelioma. Arch Gynecol Obstet 266:130

- 132.

Kroczynska B, Cutrone R, Bocchetta M, Yang H, Elmishad AG, Vacek P, Ramos

- Nino M, Mossman BT, Pass HI, Carbone M. 2006. Crocidolite asbestos and SV40 are cocarcinogens in human mesothelial cells and in causing mesothelioma in hamsters. Proc Natl Acad Sci U S A 103:14128

- 14133.

Kroschwitz JI, Seidel A. 2010. Kirk

- Othmer Encyclopedia of Chemical Technology.John Wiley and Sons.

Krowke R, Bluth V, Merker JH, Neubert D. 1983. Placental transfer and possible teratogenic potential of a sbestos in mice. Teratology 32: 26

- 27A.

Kuwano K, Kunitake R, Kawasaki M, Nomoto Y, Hagimoto N, Nakanishi Y, Hara N. 1996. P21Waf1/Cip1/Sdi1 and p53 expression in association with DNA strand breaks in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 154:477

- 483.

Lamphear BP, Buncher CR. 1992. Latent period for malignant mesothelioma of o ccupational origin. J Occup Med 34:718

- 721.

Landi MT, Dracheva T, Rotunno M, Figueroa JD, Liu H, Dasgupta A, Mann FE, Fukuoka J, Hames M, Bergen AW, Murphy SE, Yang P, Pesatori AC, Consonni D, Bertazzi PA, Wacholder S, Shih JH, Caporaso NE, Jen J. 2008. G ene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS ONE 3:e1651.

Langer AM, Nolan RP, Constantopoulos SH, Moutsopoulos HM. 1987. Association of Metsovo lung and pleural mesothelioma with exposur e to tremolite

- containing whitewash. Lancet 1:965

- 967.

Langseth H, Kjaerheim K. 2004. Ovarian cancer and occupational exposure among pulp and paper employees in Norway. Scand J Work Environ Health 30:356

- 361.

Langseth H, Johansen BV, Nesland JM, Kjaerheim K. 2007. Asbestos fibers in ovarian tissue from Norwegian pulp and paper workers. Int J Gynecol Cancer 17:44

- 49.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 16 DRAFT

— DO NOT CITE OR QUOTE Larsen ES. 1942. Alkalic rocks of Iron Hill, Gunnison County, Colorado. US Geological Society Professio nal Paper 197

- A, 1

- 64.

Larson TC, Me yer CA, Kapil V, Gurney JW, Tarver RD, Black CB, Lockey JE. 2010. Workers with Libby amphibole exposure: retrospective identification and progression of radiographic changes. Radiology 255:924

- 933.

Larson TC, Antao VC, Bove FJ. 2010. Vermiculite worker m ortality: estimated effects of occupational exposure to Libby amphibole. J Occup Environ Med 52:555

- 560.

Leake B. 1978. Nomenclature of amphiboles. Mineralogical Magazine. 533

- 563.

Leake BE, Woolley AR, Arps CES, Birch WD, Gilbert MC, Grice JD, Hawthorne FC, Kato A, Kisch HJ, Krivovichev VG, Linthout K, Laird J, Mandarino J, Maresch WV, Nickel EH, Rock NMS, Schumacher JC, Smith DC, Stephenson NCN, Ungaretti L, Whittaker EJW, Youzhi G. 1997. Nomenclature of amphiboles: Report of the Subcommittee on Amphibo les of the International Mineralogical Association Commission on New Minerals and Mineral Names. Mineralogical Magazine 61:295

- 321.

Leake, BE; Wooley, AR; Arps CES; et al (1997) Nomenclature of the amphiboles: Report of the subcommittee on amphiboles of th e International Mineralogical Association, commission on new minerals and mineral names. American Mineralogist 82:1019

- 1037.

Leake, BE; Wooley, AR; Birch, WD; et al. (2004) Nomenclature of amphiboles: Additions and revisions to the International Mineralog ical Association’s amphibole nomenclature. American Mineralogist 89:883

- 887.

Leanderson P, Soderkvist P, Tagesson C, Axelson O. 1988. Formation of 8

- hydroxydeoxyguanosine by asbestos and man made mineral fibres. Br J Ind Med 45:309

- 311.

Lee PN. 2001. Re lation between exposure to asbestos and smoking jointly and the risk of lung cancer. Occup Environ Med 58:145

- 153.

Lentz TJ, Rice CH, Succop PA, Lockey JE, Dement JM, LeMasters GK. 2003. Pulmonary deposition modeling with airborne fiber exposure data: a study of workers manufacturing refractory ceramic fibers. Appl Occup Environ Hyg 18: 278

- 2 88.

Levin JL, McLarty JW, Hurst GA, Smith AN, Frank AL. 1998. Tyler asbestos workers: mortality experience in a cohort exposed to amosite. Occup Environ Med 55(3):155

- 60.

Levresse V, Renier A, Fleury

- Feith J, Levy F, Moritz S, Vivo C, Pilatte Y, Jaurand MC. 1997. Analysis of cell cycle disruptions in cultures of rat pleural mesothelial cells exposed to asbestos fibers. Am J Respir Cell Mol Biol 17:660

- 671.

Li FP, Loki ch J, Lapey J, Neptune WB, Wilkins EW, Jr. 1978. Familial mesothelioma after intense asbestos exposure at home. JAMA 240:467.

Li FP, Dreyfus MG, Antman KH. 1989. Asbestos

- contaminated nappies and familial mesothelioma. Lancet 1:909

- 910.

Lieben J, Pistawka H. 1967. Mesothelioma and asbestos exposure. Arch Environ Health 14:559

- 563.

Lilis, R, Miller A, Godbold, J, Chan, E, Benkert S, and Selikoff, IJ (1991) The effect of asbestos

- induced pleural fibrosis on pulmonary function: quantitative evaluatio n. Ann NY Acad Sci 643:162

- 168.

Lin F, Liu Y, Liu Y, Keshava N, Li S. 2000. Crocidolite induces cell transformation and p53 gene mutation in BALB/c

- 3T3 cells. Teratog Carcinog Mutagen 20:273

- 281.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 17 DRAFT

— DO NOT CITE OR QUOTE Ling SM, Fried LP, Garrett E, Hirsch R, Guralnik JM, Hochbe rg MC. 2000. The accuracy of self

- report of physician diagnosed rheumatoid arthritis in moderately to severely disabled older women. Women's Health and Aging Collaborative Research Group. J Rheumatol 27:1390

- 1394.

Lippmann M. 1990. Effects of fiber chara cteristics on lung deposition, retention, and disease. Environ Health Perspect 88:311

- 317.

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.

Lockey JE. 1985. Pulmonary hazards associated with vermiculite exposure. University of Cincinnati.

Lockey, JE; LeMasters, GK; Levin, L; Rice, C; Yiin, J; Reutman, S; and Papes, D (2002) A longitudinal study of chest radiographic changes of workers in the refractory ceramic fiber industry. Chest 121:2044

- 2051.

Lohani M, Dopp E, Becker HH, Seth K, Schiffmann D, Rahman Q. 2002. Smoking enhances asbestos

- induced genotoxicity, relative involvement of chro mosome 1: a study using multicolor FISH with tandem labeling. Toxicol Lett 136:55

- 63.

Lohani M, Yadav S, Schiffmann D, Rahman Q. 2003. Diallylsulfide attenuates asbestos

- induced genotoxicity. Toxicol Lett 143:45

- 50.

Loli P, Topinka J, Georgiadis P, Dusins ka M, Hurbankova M, Kovacikova Z, Volkovova K, Wolff T, Oesterle D, Kyrtopoulos SA. 2004. Benzo[a]pyrene

- enhanced mutagenesis by asbestos in the lung of lambda

- lacI transgenic rats. Mutat Res 553:79

- 90.

Lowers HA, Bern AM, 2009. Particle size characteriz ation of water

- elutriated Libby Amphibole 2000 and RTI International amosite, USGS Open File Report 2009

- 1242.

Lund LG, Williams MG, Dodson RF, Aust AE. 1994. Iron associated with asbestos bodies is responsible for the formation of single strand breaks in phi X174 RFI DNA. Occup Environ Med 51:200

- 204.

Magee F, Wright JL, Chan N, Lawson L, Churg A. 1986. Malignant mesothelioma caused by childhood exposure to long

- fiber low aspect ratio tremolite. Am J Ind Med 9:529

- 533.

Magnani C, Dalmasso P, Biggeri A, I valdi C, Mirabelli D, Terracini B. 2001. Increased risk of malignant mesothelioma of the pleura after residential or domestic exposure to asbestos: a case

- control study in Casale Monferrato, Italy. Environ Health Perspect 109:915

- 919.

Magnani C, Ferrante D, Barone

- Adesi F, Bertolotti M, Todesco A, Mirabelli D, Terracini B. 2008. Cancer risk after cessation of asbestos exposure: a cohort study of Italian asbestos cement workers. Occup Environ Med 65:164

- 170.

Malorni W, Iosi F, Falchi M, Donelli G. 1990. O n the mechanism of cell internalization of chrysotile fibers: an immunocytochemical and ultrastructural study. Environ Res 52:164

- 177.

Marsella JM, Liu BL, Vaslet CA, Kane AB. 1997. Susceptibility of p53

- deficient mice to induction of mesothelioma by croc idolite asbestos fibers. Environ Health Perspect 105 Suppl 5:1069

- 1072.

Marsh GM. 1983. Critical review of epidemiologic studies related to ingested asbestos. Environ Health Perspect 53:49

- 56.

Martensson G, Larsson S, Zettergren L. 1984. Malignant mesoth elioma in two pairs of siblings: is there a hereditary predisposing factor? Eur J Respir Dis 65:179

- 184.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 18 DRAFT

— DO NOT CITE OR QUOTE Mayall FG, Jacobson G, Wilkins R. 1999. Mutations of p53 gene and SV40 sequences in asbestos associated and

non

- asbestos

- associated mesotheliomas. J C lin Pathol 52:291

- 293.

Mayne ST, Redlich CA, Cullen MR. 1998. Dietary vitamin A and prevalence of bronchial metaplasia in asbestos

- exposed workers. Am J Clin Nutr 68:630

- 635.

McConnell EE, Rutter HA, Ulland BM, Moore JA. 1983. Chronic effects of dietary exposure to amosite asbestos and tremolite in F344 rats. Environ Health Perspect 53:27

- 44.

McConnell EE, Shefner AM, Rust JH, Moore JA. 1983. Chronic effects of dietary exposure to amosite and chrysotile asbestos in Syrian golden hamsters. Environ Health Perspect 53:11

- 25.

McDonald JC, Armstrong BG, Edwards CW, Gibbs AR, Lloyd HM, Pooley FD, Ross DJ, Rudd RM. 2001. Case

- referent survey of young adults with mesothelioma: I. Lung fibre analyses. Ann Occup Hyg 45:513

- 518.

McDonald JC, Edwards CW, Gibbs AR, Lloyd HM, Pooley FD, Ross DJ, Rudd RM. 2001. Case

- referent survey of young adults with mesothelioma: II. Occupational analyses. Ann Occup Hyg 45:519

- 523.

McDonald JC, Harris J, Armstrong B. 2002. Cohort mortality study of vermiculite miners exposed to fibrous tremolite: an update. Ann Occup Hyg 46:93

- 94.

McDonald JC, Harris J, Armstrong B. 2004. Mortality in a cohort of vermiculite miners exposed to fibrous amphibole in Libby, Montana. Occup Environ Med 61:363

- 366.

McDo nald JC, Harris JM, Berry G. 2006. Sixty years on: the price of assembling military gas masks in 1940. Occup Environ Med 63:852

- 855.

McDonald JC, McDonald AD, Armstrong B, Sebastien P. 1986. Cohort study of mortality of vermiculite miners exposed to trem olite. Br J Ind Med 43:436

- 444.

McDonald JC, Sebastien P, Armstrong B. 1986. Radiological survey of past and present vermiculite miners exposed to tremolite. Br J Ind Med 43:445

- 449.

McDonald, JC, McDonald, AD, Sebastien, P, and Moy, K (1988) Health of ve rmiculite miners exposed to trace amounts of tremolite. Brit J Ind Med 44:630

- 634.

Meek ME, Grasso P. 1983. An investigation of the penetration of ingested asbestos into the normal and abnormal intestinal mucosa of the rat. Food Chem Toxicol 21:193

- 200.

Meeker GP, Bern AM, Brownfield IK, Lowers HA, Sutley SJ, Hoefen TM, Vance JS. 2003. The composition and morphology of amphiboles from the Rainy Creek complex, near Libby, Montana 1. American Mineralogist 88:1955

- 1969.

Merchant JA. 1990. Human epidemiolog y: a review of fiber type and characteristics in the development of malignant and nonmalignant disease. Env Health Persp 88:287

- 293.

Metintas M, Metintas S, Hillerdal G, Ucgun I, Erginel S, Alatas F, Yildirim H. 2005. Nonmalignant pleural lesions due to e nvironmental exposure to asbestos: a field

- based, cross

- sectional study. Eur Respir J 26:875

- 880.

Miller, JA, Zurlo, JV (1996) Asbestos plaques in a typical veteran’s hospital population. Am J Ind Med 30:726

- 729.

Miserocchi G, Sancini G, Mantegazza F, Chi appino G. 2008. Translocation pathways for inhaled asbestos fibers. Environ Health 7: 1

- 8

.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 19 DRAFT

— DO NOT CITE OR QUOTE Mishra A, Liu JY, Brody AR, Morris GF. 1997. Inhaled asbestos fibers induce p53 expression in the rat lung. Am J Respir Cell Mol Biol 16:479

- 485.

Moatamed, F, Locke y, JE, and Parry, WT (1986) Fiber contamination of vermiculites: a potential occupational and environmental health hazard. Environ Research 41:207

- 218.

Moolgavkar SH, Turim J, Alexander DD, Lau EC, Cushing CA. 2010. Potency factors for risk assessment at Libby, Montana. Risk Anal 30:1240

- 1248.

Morgan A, Talbot RJ, Holmes A. 1978. Significance of fibre length in the clearance of asbestos fibres from the lung. Br J Ind Med 35:146

- 153.

Mossman BT, Eastman A, Landesman JM, Bresnick E. 1983. Effects of croci dolite and chrysotile asbestos on cellular uptake and metabolism of benzo(a)pyrene in hamster tracheal epithelial cells. Environ Health Perspect 51:331

- 335.

Mossman BT, Eastman A, Bresnick E. 1984. Asbestos and benzo[a]pyrene act synergistically to induce squamous metaplasia and incorporation of [3H]thymidine in hamster tracheal epithelium. Carcinogenesis 5:1401

- 1404.

Mossman BT, Cameron GS, Yotti LP. 1985. Cocarcinogenic and tumor promoting properties of asbestos and other minerals in tracheobronchial ep ithelium. Carcinog Compr Surv 8:217

- 238.

Mossman BT, Bignon J, Corn M, Seaton A, Gee JB. 1990. Asbestos: scientific developments and implications for public policy. Science 247:294

- 301.

Mossman BT, Lounsbury KM, Reddy SP. 2006. Oxidants and signaling by mitogen

- activated protein kinases in lung epithelium. Am J Respir Cell Mol Biol 34:666

- 669.

Mossman BT, Borm PJ, Castranova V, Costa DL, Donaldson K, Kleeberger SR. 2007. Mechanisms of action of inhaled fibers, particles and nanoparticles in lung and card iovascular diseases. Part Fibre Toxicol 4:4.

Muravov OI, Kaye WE, Lewin M, Berkowitz Z, Lybarger JA, Campolucci SS, Parker JE. 2005. The usefulness of computed tomography in detecting asbestos

- related pleural abnormalities in people who had indeterminate chest radiographs: the Libby, MT, experience. Int J Hyg Environ Health 208:87

- 99.

Muscat JE, Huncharek M. 1996. Dietary intak e and the risk of malignant mesothelioma. Br J Cancer 73:1122

- 1125.

Musk AW, de Klerk NH, Ambrosini GL, Eccles JL, Hansen J, Olsen NJ, Watts VL, Lund HG, Pang SC, Beilby J, Hobbs MS. 1998. Vitamin A and cancer prevention I: observations in workers previou sly exposed to asbestos at Wittenoom, Western Australia. Int J Cancer 75:355

- 361.

Musk AW, de Klerk NH, Reid A, Ambrosini GL, Fritschi L, Olsen NJ, Merler E, Hobbs MS, Berry G. 2008. Mortality of former crocidolite (blue asbestos) miners and millers at Wi ttenoom. Occup Environ Med. 65(8):541

- 3.

Myojo T, Takaya M. 2001. Estimation of fibrous aerosol deposition in upper bronchi based on experimental data with model bifurcation. Ind Health 39:141

- 149.

NAS (National Academy of Sciences). 2006. Asbestos: Sele cted Cancers. Washington, DC:National Academies Press.

NCI (National Cancer Institute). Surveillance, Epidemiology and End Results. Fast Stats: Statistics Stratified by Cancer Site. National Cancer Institute . 4

- 15

- 2009. 3

- 12

- 2010.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 20 DRAFT

— DO NOT CITE OR QUOTE Nelson A, Mendoza T, H oyle GW, Brody AR, Fermin C, Morris GF. 2001. Enhancement of fibrogenesis by the p53 tumor suppressor protein in asbestos

- exposed rodents. Chest 120:33S

- 34S.

Nelson HH, Kelsey KT. 2002. The molecular epidemiology of asbestos and tobacco in lung cancer. O ncogene 21:7284

- 7288.

Neri M, Ugolini D, Dianzani I, Gemignani F, Landi S, Cesario A, Magnani C, Mutti L, Puntoni R, Bonassi S. 2008. Genetic susceptibility to malignant pleural mesothelioma and other asbestos

- associated diseases. Mutat Res.

Newhouse ML, Berry G, Wagner JC, Turok ME. 1972. A study of the mortality of female asbestos workers. Br J Ind Med 29:134

- 141.

Newhouse ML, Berry G. 1976. Predictions of mortality from mesothelial tumours in asbestos factory workers. Br J Ind Med 33:147

- 151.

Ni Z, Li u Y, Keshava N, Zhou G, Whong W, Ong T. 2000. Analysis of K

- ras and p53 mutations in mesotheliomas from humans and rats exposed to asbestos. Mutat Res 468:87

- 92.

Nicholson WJ, Rohl AN, Weisman I, Selikoff IJ. 1980. Environmental asbestos concentrations i n the United States. IARC Sci Publ823

- 827.

Niggli FK, Gray TJ, Raafat F, Stevens MC. 1994. Spectrum of peritoneal mesothelioma in childhood: clinical and histopathologic features, including DNA cytometry. Pediatr Hematol Oncol 11:399

- 408.

NIOSH (2008) NIO SH Current Intelligence Bulletin: Asbestos fibers and other elongated mineral particles: State of the science and roadmap for research. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupationa l Safety and Health.

NIOSH (National Institute for Occupational Safety and Health). 1995. Report to Congress on Workers' Home Contamination Study Conducted Under the Workers' Family Protection Act (29 U.S.C. 671a). Publication No. 95

- 123.

NIOSH (National Institute for Occupational Safety and Health). 2003. Asbestos and other fibers by PCM. NIOSH publication number 2003

- 154. ( www.cdc.gov/niosh/docs/2003

- 154/pdfs/7400.pdf )

NIOSH (National Institute for Occupational Safety and Health). 2011. Asbestos fibers and other elongate mineral particles: state of the science and roadmap for research. Current Intelligence Bulletin 62. DHHS (NIOSH) Publication Number 2011

– 159. http://www.cdc.gov/niosh/docs/2011

- 159/

. Noonan CW. 2006. Exposure matrix development for the Libby cohort. Inhal Toxicol 18:963

- 967.

Noonan CW, Ward TJ. 2007. Environmental tobacco smoke, woodstove heating and risk of asthma symptoms. J Asthma 44:735

- 738.

Noonan CW, Pfau JC, Larson TC, Spence MR. 2006. Nested case

- control study of autoimmune disease in an asbestos

- exposed population. Environ Health Perspect 114:1243

- 1247.

NRC (National Research Council) (1983) Risk ass essment in the federal government: managing the process. Washington, DC: National Academy Press.

NRC (National Research Council). Pesticides in the diets of infants and children. 1993. National Academy Press.

NTP (National Toxicology Program). 1983. NTP Lifetime Carcinogenesis Studies of Amosite Asbestos (CAS No. 12172

- 73

- 5) in Syrian Golden Hamsters (Feed Studies). Natl Toxicol Program Tech Rep Ser 249:1

- 81.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 21 DRAFT

— DO NOT CITE OR QUOTE ----- 1985. NTP Toxicology and Carcinogenesis Studies of Chrysotile Asbestos (CAS No. 12001

- 29

- 5) in F344/N Rats (Feed Studies). Natl Toxicol Program Tech Rep Ser 295:1

- 390.

----- 1988. NTP Toxicology and Carcinogenesis Studies of Crocidolite Asbestos (CAS No. 12001

- 28

- 4) In F344/N Rats (Feed Studies). Natl Toxicol Program Tech Rep Ser 280 :1

- 178.

----- 1990. NTP Toxicology and Carcinogenesis Studies of Amosite Asbestos (CAS No. 12172

- 73

- 5) in F344/N Rats (Feed Studies). Natl Toxicol Program Tech Rep Ser 279:1

- 341.

----- 1990. NTP Toxicology and Carcinogenesis Studies of Tremolite (CAS No. 1 4567

- 73

- 8) in F344/N Rats (Feed Studies). Natl Toxicol Program Tech Rep Ser 277:1

- 183.

----- . 1990. Lifetime carcinogenesis studies of chrysotile asbestos (CAS No. 12001

- 29

- 5) in Syrian golden hamsters (feed studies). Technical Report No. 277 / NIH Publica tion No. 90

- 2502.

Nuorva K, Makitaro R, Huhti E, Kamel D, Vahakangas K, Bloigu R, Soini Y, Paakko P. 1994. p53 protein accumulation in lung carcinomas of patients exposed to asbestos and tobacco smoke. Am J Respir Crit Care Med 150:528

- 533.

Oberdorster G. 1994. Macrophage

- Associated Responses to Chrysotile. Ann Occup Hyg 38:601

- 615.

Oberdorster G, Oberdorster E, Oberdorster J. 2005. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Perspect 113:823

- 839.

O gden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. 2006. Prevalence of overweight and obesity in the United States, 1999

- 2004. JAMA 295:1549

- 1555.

Okayasu R, Wu L, Hei TK. 1999. Biological effects of naturally occurring and man

- made fibres : in vitro cytotoxicity and mutagenesis in mammalian cells. Br J Cancer 79:1319

- 1324.

Oldham MJ, Mannix RC, Phalen RF. 1997. Deposition of monodisperse particles in hollow models representing adult and child

- size tracheobronchial airways. Health Phys 72:8 27

- 834.

Oldham PD. 1965. On Estimating the Arithmetic Means of Lognormally

- Distributed Populations. Biometrics 21:235

- 239.

Oliver, LC, Eisen, EA, Greene, R, and Sprince, NL (1998) Asbestos

- related pleural plaques and lung function. Am J Ind Med 14:649

- 656

. Olofsson K, Mark J. 1989. Specificity of asbestos

- induced chromosomal aberrations in short

- term cultured human mesothelial cells. Cancer Genet Cytogenet 41:33

- 39.

Omenn GS, Goodman GE, Thornquist MD, Balmes J, Cullen MR, Glass A, Keogh JP, Meyskens FL, Jr., Valanis B, Williams JH, Jr., Barnhart S, Cherniack MG, Brodkin CA, Hammar S. 1996. Risk factors for lung cancer and for intervention effects in CARET, the Beta

- Carotene and Retinol Efficacy Trial. J Natl Cancer Inst 88:1550

- 1559.

OSHA (Occupational S afety and Health Administration). 1983. Quantitative risk assessment for asbestos related cancers. OSHA contract no. J

- 9

- F

- 2

- 0074. Docket no. H033 C. exhibit no. 84

- 392.

Oshimura M, Hesterberg TW, Tsutsui T, Barrett JC. 1984. Correlation of asbestos

- indu ced cytogenetic effects with cell transformation of Syrian hamster embryo cells in culture. Cancer Res 44:5017

- 5022.

Otsuki T, Maeda M, Murakami S, Hayashi H, Miura Y, Kusaka M, Nakano T, Fukuoka K, Kishimoto T, Hyodoh F, Ueki A, Nishimura Y. 2007. Immuno logical effects of silica and asbestos. Cell Mol Immunol 4:261

- 268.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 22 DRAFT

— DO NOT CITE OR QUOTE Oyan K. EPA Cleans up Vermiculite at Libby Elementary School. Flathead Beacon . 2

- 29

- 2008. 4

- 15

- 2010.

Padilla

- Carlin DJ, Schladweiler MCJ, Shannahan JH, Kodavanti UP, Nyska A, Burgoon LD , Gavett SH. Pulmonary inflammatory and fibrotic responses in Fischer 344 rats after intratracheal instillation exposure to Libby amphibole. JTEH (accepted pending?).

Panduri V, Surapureddi S, Soberanes S, Weitzman SA, Chandel N, Kamp DW. 2006. P53 media tes amosite asbestos

- induced alveolar epithelial cell mitochondria

- regulated apoptosis. Am J Respir Cell Mol Biol 34:443

- 452.

Paris, C, Marin, A, Letourneux, M;, and Wild, P (2008) Modelling prevalence and incidence of fibrosis and pleural plaques in asbes tos

- exposed populations for screening and follow

- up: a cross

- sectional study. Environ Health 7:30

- 38.

Paris, C, Thierry S, Brochard, P, Letourneux, M, et al. (2009) Pleural plaques and asbestosis: dose

- and time

- response relationships based on HRCT data. Eur Respir J 34:72

- 79.

Park SH, Aust AE. 1998. Participation of iron and nitric oxide in the mutagenicity of asbestos in hgprt

- , gpt+ Chinese hamster V79 cells. Cancer Res 58:1144

- 1148.

Patel JD. 2005. Lung cancer in women. J Clin Oncol 23:3212

- 3218.

Pedotti P, Cardillo M, Rossini G, Arcuri V, Boschiero L, Caldara R, Cannella G, Dissegna D, Gotti E, Marchini F, Maresca MC, Montagnino G, Montanaro D, Rigotti P, Sandrini S, Taioli E, Scalamogna M. 2003. Incidence of cancer after kidney transplant: resul ts from the North Italy transplant program. Transplantation 76:1448

- 1451.

Peipins LA, Lewin M, Campolucci S, Lybarger JA, Kapil V, Middleton D, Miller A, Weis C, Spence M, Black B. 2004. Overstating the Consequences

- Peipins et al.'s response. Environ He alth Perspect 112:A84

- A85.

----- 2004. Radiographic Abnormalities

- Response from Peipins et al. 2004. Environ Health Perspect 112:A83.

Peipins LA, Lewin M, Campolucci S, Lybarger JA, Miller A, Middleton D, Weis C, Spence M, Black B, Kapil V. 2003. Radiog raphic abnormalities and exposure to asbestos

- contaminated vermiculite in the community of Libby, Montana, USA. Environ Health Perspect 111:1753

- 1759.

Percy C, Stanek E, III, Gloeckler L. 1981. Accuracy of cancer death certificates and its effect on cance r mortality statistics. Am J Public Health 71:242

- 250.

Pershouse M, Heivly S, Girtsman T. 2006. The role of SV40 in malignant mesothelioma and other human malignancies. Inhal Toxicol 18:995

- 1000.

Peto J, Seidman H, Selikoff IJ. 1982. Mesothelioma mortali ty in asbestos workers: implications for models of carcinogenesis and risk assessment. Br J Cancer 45:124

- 135.

Peto R, Darby S, Deo H, Silcocks P, Whitley E, Doll R. 2000. Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of na tional statistics with two case

- control studies. BMJ 321:323

- 329.

Pfau JC, Sentissi JJ, Weller G, Putnam EA. 2005. Assessment of autoimmune responses associated with asbestos exposure in Libby, Montana, USA. Environ Health Perspect 113:25

- 30.

Pfau JC, Per shouse M, Putnam EA. 2006. Conference summary. Directions and needs in asbestos research: new insights. Inhal Toxicol 18:919

- 923.

Pfau JC, Sentissi JJ, Li S, Calderon

- Garciduenas L, Brown JM, Blake DJ. 2008. Asbestos

- induced autoimmunity in C57BL/6 mice. J Immunotoxicol 5:129

- 137.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 23 DRAFT

— DO NOT CITE OR QUOTE Phalen RF, Oldham MJ. 2001. Methods for modeling particle deposition as a function of age. Respir Physiol 128:119

- 130.

Phalen RF, Oldham MJ, Beaucage CB, Crocker TT, Mortensen JD. 1985. Postnatal enlargement of human tracheobr onchial airways and implications for particle deposition. Anat Rec 212:368

- 380.

Phalen RF, Oldham MJ, Nel AE. 2006. Tracheobronchial particle dose considerations for in vitro toxicology studies. Toxicol Sci 92:126

- 132.

Phalen RF, Oldham MJ, Wolff RK. 200 8.The relevance of animal models for aerosol studies. J. Aerosol Med Pulm Drug Deliv 21:113

- 124.

Pietruska JR, Kane AB. 2007. SV40 oncoproteins enhance asbestos

- induced DNA double

- strand breaks and abrogate senescence in murine mesothelial cells. Cancer R es 67:3637

- 3645.

Pietruska JR, Johnston T, Zhitkovich A, Kane AB. 2010. XRCC1 Deficiency Sensitizes Human Lung Epithelial Cells to Genotoxicity by Crocidolite Asbestos and Libby Amphibole. Environ Health Perspect.

----- 2010. XRCC1 deficiency sensitizes h uman lung epithelial cells to genotoxicity by crocidolite asbestos and Libby amphibole. Environ Health Perspect 118:1707

- 1713.

Pinheiro GA, Antao VC, Bang KM, Attfield MD. 2004. Malignant mesothelioma surveillance: a comparison of ICD 10 mortality data wi th SEER incidence data in nine areas of the United States. Int J Occup Environ Health 10:251

- 255.

Pinkerton KE, Brody AR, Miller FJ, Crapo JD. 1989. Exposure to low levels of ozone results in enhanced pulmonary retention of inhaled asbestos fibers. Am Rev Respir Dis 140:1075

- 1081.

Pinsky PF, Freedman M, Kvale P, Oken M, Caporaso N, Gohagan J. 2006. Abnormalities on chest radiograph reported in subjects in a cancer screening trial. Chest 130:688

- 693.

Pira E, Pelucchi C, Buffoni L, Palmas A, Turbiglio M, Ne gri E, Piolatto PG, La VC. 2005. Cancer mortality in a cohort of asbestos textile workers. Br J Cancer 92:580

- 586.

Polissar L, Severson RK, Boatman ES, Thomas DB. 1982. Cancer incidence in relation to asbestos in drinking water in the Puget Sound region. Am J Epidemiol 116:314

- 328.

Poser I, Rahman Q, Lohani M, Yadav S, Becker HH, Weiss DG, Schiffmann D, Dopp E. 2004. Modulation of genotoxic effects in asbestos

- exposed primary human mesothelial cells by radical scavengers, metal chelators and a glutathione precursor. Mutat Res 559:19

- 27.

Pott F, Huth F, Friedrichs KH. 1974. Tumorigenic effect of fibrous dusts in experimental animals. Environ Health Perspect 9:313

- 315.

Pott F, Ziem U, Reiffer FJ, Huth F, Ernst H, Mohr U. 1987. Carcinogenicity st udies on fibres, metal compounds, and some other dusts in rats. Exp Pathol 32:129

- 152.

Pott F, Roller M, Ziem U, Reiffer FJ, Bellmann B, Rosenbruch M, Huth F. 1989. Carcinogenicity studies on natural and man

- made fibres with the intraperitoneal test in ra ts. IARC Sci Publ173

- 179.

Price B. 2004. Radiographic abnormalities and asbestos exposure: Libby, Montana. Environ Health Perspect 112:A82

- 3, author.

----- 2007. Exposure to airborne amphibole structures and health risks: Libby, Montana. Regul Toxicol Pha rmacol 52:S97

- S109.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 24 DRAFT

— DO NOT CITE OR QUOTE Putnam EA, Smartt A, Groves A, Schwanke C, Brezinski M, Pershouse MA. 2008. Gene expression changes after exposure to six

- mix in a mouse model. J Immunotoxicol 5:139

- 144.

R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3

- 900051

- 07

- 0, http://www.R

- project.org.

Rahman I, MacNee W. 1999. Lung glutathione and oxidative stress: implications in cigarette smoke

- induced airway disease. Am J Ph ysiol 277:L1067

- L1088.

Ramos

- Nino ME, Haegens A, Shukla A, Mossman BT. 2002. Role of mitogen

- activated protein kinases (MAPK) in cell injury and proliferation by environmental particulates. Mol Cell Biochem 234

- 235:111

- 118.

Rasch EK, Hirsch R, Paulose

- Ram R, Hochberg MC. 2003. Prevalence of rheumatoid arthritis in persons 60 years of age and older in the United States: effect of different methods of case classification. Arthritis Rheum 48:917

- 926.

Rasmussen RE, Mannix RC, Oldham MJ, Phalen RF. 1994. Effe cts of nitrogen dioxide on respiratory tract clearance in the ferret. J Toxicol Environ Health 41:109

- 120.

Reeves GK, Cox DR, Darby SC, Whitley E. 1998. Some aspects of measurement error in explanatory variables for continuous and binary regression models . Stat Med 17:2157

- 2177.

Regnis JA, Zeman KL, Noone PG, Knowles MR, Bennett WD. 2000. Prolonged airway retention of insoluble particles in cystic fibrosis versus primary ciliary dyskinesia. Exp Lung Res 26:149

- 162.

Reid A, Berry G, de KN, Hansen J, Heywor th J, Ambrosini G, Fritschi L, Olsen N, Merler E, Musk AW. 2007. Age and sex differences in malignant mesothelioma after residential exposure to blue asbestos (crocidolite). Chest 131:376

- 382.

Reid A, Heyworth J, de Klerk NH, Musk B. 2008. Cancer inciden ce among women and girls environmentally and occupationally exposed to blue asbestos at Wittenoom, Western Australia. Int J Cancer 122:2337

- 2344.

Reid A, Heyworth J, de KN, Musk AW. 2008. The mortality of women exposed environmentally and domestically to blue asbestos at Wittenoom, Western Australia. Occup Environ Med 65:743

- 749.

Reid A, Schneider

- Kolsky ME, O'Donnell CJ. 2008. Comparison of computed radiography and multi

- detector computed tomography in the detection of post mortem metacarpal index. Foren sic Sci Int 177:192

- 198.

Reid A, Berry G, Heyworth J, de Klerk NH, Musk AW. 2009. Predicted mortality from malignant mesothelioma among women exposed to blue asbestos at Wittenoom, Western Australia. Occup Environ Med 66:169

- 174.

Reif AE, Heeren T. 1999. Consensus on synergism between cigarette smoke and other environmental carcinogens in the causation of lung cancer. Adv Cancer Res 76:161

- 186.

Reiss B, Tong C, Telang S, Williams GM. 1983. Enhancement of benzo[a]pyrene mutagenicity by chrysotile asbestos in rat liver epithelial cells. Environ Res 31:100

- 104.

Rendall R, Du Toit R. 1994. The Retention and clearance of glass fibre and different varieties of asbestos by the lung. Ann Occup Hyg 38:757

- 761.

Rey F, Boutin C, Steinbauer J, Viallat JR, Alessandro ni P, Jutisz P, Di GD, Billon

- Galland MA, Hereng P, Dumortier P, . 1993. Environmental pleural plaques in an asbestos exposed population of northeast Corsica. Eur Respir J 6:978

- 982.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 25 DRAFT

— DO NOT CITE OR QUOTE Richardson DB. 2010. Occupational exposures and lung cancer: adjustment for unmeasured confounding by smoking. Epidemiology 21:181

- 186.

Ries L, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto BA, Feuer EJ, Altekruse SF, Lewis DR, Clegg L, Eisner MP, Reichman M, Edwards BK. SEER Cancer Statistics Review, 1975

- 2005. National Cancer Institute (NCI). 2008.

Rogan, WJ, Gladen, BC, Ragan, NB, Anderson, HA (1987) US prevalaence of occupational pleural thickening: a look
at x

- rays from the first national health and nutrition examination survey. Am J Epidemiol 12 6:893

- 900.

Rogan, WJ, Ragan, NB, and Dinse, GE (2000). X

- ray evidence of increased asbestos exposure in the US population from NHANES I and NHANES II, 1973

- 1978. Cancer Causes and Control 11:441

- 449.

Roggli VL, Vollmer RT. 2008. Twenty

- five years of fiber analysis: what have we learned? Hum Pathol 39:307

- 315.

Roggli VL, Vollmer RT, Butnor KJ, Sporn TA. 2002. Tremolite and mesothelioma. Ann Occup Hyg 46:447

- 453.

Roguin A, Ben

- Shahar M, Ben

- Dror G, Cohen I, Hazani E. 1994. [Malignant mesothelioma in f amilies of asbestos workers]. Harefuah 126:702

- 4, 764.

Rohs AM, Lockey JE, Dunning KK, 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.

Roller M, Pott F, Kamino K, Althoff GH, Bellmann B. 1996. Results of current intraperitoneal carcinogenicity studies with mineral and vitreous fibres. Exp Toxicol Pathol 48:3

- 12.

----- 19 97. Dose

- response relationship of fibrous dusts in intraperitoneal studies. Environ Health Perspect 105 Suppl 5:1253

- 1256.

Rom WN, Hammar SP, Rusch V, Dodson R, Hoffman S. 2001. Malignant mesothelioma from neighborhood exposure to anthophyllite asbestos. Am J Ind Med 40:211

- 214.

Rosenstock, L; and Hudson, LD (1987) The pleural manifestations of asbestos exposure. Occup Med 2:383

- 407.

Rosler JA, Woitowitz HJ, Lange HJ, Woitowitz RH, Ulm K, Rodelsperger K. 1994. Mortality rates in a female cohort following asbestos exposure in Germany. J Occup Med 36:889

- 893.

Ross, M, Nolan, RP, Langer, AM, and Cooper, WC (1993) Health effects of mineral dusts other than asbestos. In Mineralogical Society of America Reviews in Minearology 28:545

- 554.

Rothman KJ. 1981. Ind uction and latent periods. Am J Epidemiol 114:253

- 259.

Rothman KJ. 1986. Significance questing. Ann Intern Med 105:445

- 447.

Roushdy

- Hammady I, Siegel J, Emri S, Testa JR, Carbone M. 2001. Genetic

- susceptibility factor and malignant mesothelioma in the Cappadocian region of Turkey. Lancet 357:444

- 445.

Rudd, RM (1996) New developments in asbestos

- related pleural disease. Thorax 51:210

- 216.

Rushton L. 2007. Occupational causes of chr onic obstructive pulmonary disease. Rev Environ Health 22:195

- 212.

----- 2007. Chronic obstructive pulmonary disease and occupational exposure to silica. Rev Environ Health 22:255

- 272.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 26 DRAFT

— DO NOT CITE OR QUOTE Saegusa J, Kawano S, Koshiba M, Hayashi N, Kosaka H, Funasaka Y, Kumaga i S. 2002. Oxidative stress mediates cell surface expression of SS

- A/Ro antigen on keratinocytes. Free Radic Biol Med 32:1006

- 1016.

Sahu AP, Dogra RK, Shanker R, Zaidi SH. 1975. Fibrogenic response in murine lungs to asbestos. Exp Pathol (Jena) 11:21

- 24.

Sakellariou K, Malamou

- Mitsi V, Haritou A, Koumpaniou C, Stachouli C, Dimoliatis ID, Constantopoulos SH. 1996. Malignant pleural mesothelioma from nonoccupational asbestos exposure in Metsovo (north

- west Greece): slow end of an epidemic? Eur Respir J 9:1 206

- 1210.

Sanchez, MS, Gunter, ME, and Dyar, MD (2008) Characterization of historical amphibole samples from the former vermiculite mine near Libby, Montana, USA. Eur J of Minerology 20:1043

- 1053.

Sanden A, Jarvholm B, Larsson S. 1993. The importance of lung function, non

- malignant diseases associated with asbestos, and symptoms as predictors of ischaemic heart disease in shipyard workers exposed to asbestos. Br J Ind Med 50:785

- 790.

Sarin L, Sanchez VC, Yan A, Kane AB, Hurt RH. 2010. Selenium

- carbon bif unctional nanoparticles for the treatment of malignant mesothelioma. Adv Mater 22:5207

- 5211.

SAS Institute. SAS Software Release 9.1. 2002.

Savastano L, Bonacci S, Saracino V, Longo M. 2004. [The association of lung cancer with asbestos and tobacco smoking]. Clin Ter 155:69

- 74.

Scapoli L, Ramos

- Nino ME, Martinelli M, Mossman BT. 2004. Src

- dependent ERK5 and Src/EGFR

- dependent ERK1/2 activation is required for cell proliferation by asbestos. Oncogene 23:805

- 813.

Schiffman MH, Pickle LW, Fontham E, Za hm SH, Falk R, Mele J, Correa P, Fraumeni JF, Jr. 1988. Case

- control study of diet and mesothelioma in Louisiana. Cancer Res 48:2911

- 2915.

Schiller

- Scotland CF, Hlawa R, Gebhart J. 1994. Experimental data for total deposition in the respiratory tract of children. Toxicol Lett 72:137

- 144.

Schneider J, Grossgarten K, Woitowitz HJ. 1995. [Fatal pleural mesothelioma diseases caused by familial household contacts with asbestos fiber dust]. Pneumologie 49:55

- 59.

Schneider J, Rodelsperger K, Pohlabeln H, Woitow itz HJ. 1996. [Environmental and indoor air exposure to asbestos fiber dust as a risk and causal factor of diffuse malignant pleural mesothelioma]. Zentralbl Hyg Umweltmed 199:1

- 23.

Schneider J, Straif K, Woitowitz HJ. 1996. Pleural mesothelioma and hous ehold asbestos exposure. Rev Environ Health 11:65

- 70.

Schneider J, Rodelsperger K, Bruckel B, Kayser K, Woitowitz HJ. 1998. Environmental exposure to tremolite asbestos: pleural mesothelioma in two Turkish workers in Germany. Rev Environ Health 13:213

- 220

. Schneider U, Maurer RR. 1977. Asbestos and embryonic development. Teratology 15:273

- 279.

Schumpert JC, Noonan CW, Sylvester J, Vanek D, Ward T, Holian A. 2006. Patterns of asthma symptoms and perceptions of harm from seasonal atmospheric events in rura l Western Montana. Int J Occup Environ Health 12:52

- 58.

Schwartz AG, Cote ML, Wenzlaff AS, Van DA, Chen W, Ruckdeschel JC, Gadgeel S, Soubani AO. 2009. Chronic obstructive lung diseases and risk of non

- small cell lung cancer in women. J Thorac Oncol 4:291

- 299.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 27 DRAFT

— DO NOT CITE OR QUOTE Schwartz, DA; Fourtes, LJ; Galvin, JR; Burmeister, LF; Schmidt, LE; Leistikow, BN; Lamarte, FR; and Merchant, JA (1990). Asbestos

- induced pleural fibrosis and impaired lung function. Amer. Rev Respir. Dis 141:321

- 326.

Sebastien P, Janson X, Gaudi chet A, Hirsch A, Bignon J. 1980. Asbestos retention in human respiratory tissues: comparative measurements in lung parenchyma and in parietal pleura. IARC Sci Publ237

- 246.

Sebastien P, McDonald JC, McDonald AD, Case B, Harley R. 1989. Respiratory cancer in chrysotile textile and mining industries: exposure inferences from lung analysis. Br J Ind Med 46:180

- 187.

Seidman H, Selikoff IJ, Hammond EC. 1979. Short

- term asbestos work exposure and long

- term observation. Ann N Y Acad Sci 330:61

- 89.

Seidman H, Se likoff IJ, Gelb SK. 1986. Mortality experience of amosite asbestos factory workers: dose

- response relationships 5 to 40 years after onset of short

- term work exposure. Am J Ind Med 10:479

- 514.

Sekido Y, Pass HI, Bader S, Mew DJ, Christman MF, Gazdar AF, Mi nna JD. 1995. Neurofibromatosis type 2 (NF2) gene is somatically mutated in mesothelioma but not in lung cancer. Cancer Res 55:1227

- 1231.

Selevan SG, Kimmel CA, Mendola P. 2000. Identifying critical windows of exposure for children's health. Environ Heal th Perspect 108 Suppl 3:451

- 455.

Selikoff IJ, Lee DHK. 1978. Asbestos and Disease. New York:Academic Press.

Selikoff IJ, Hammond EC. 1979. Asbestos and smoking. JAMA 242:458

- 459.

Selikoff IJ, Seidman H. 1992. Use of death certificates in epidemiological studies, including occupational hazards: variations in discordance of different asbestos

- associated diseases on best evidence ascertainment. Am J Ind Med 22:481

- 492.

Senyigit A, Babayigit C, Gokirmak M, Topcu F, Asan E, Coskunsel M, Isik R, Ertem M. 2000 . Incidence of malignant pleural mesothelioma due to environmental asbestos fiber exposure in the southeast of Turkey. Respiration 67:610

- 614.

Shah KV. 2004. Causality of mesothelioma: SV40 question. Thorac Surg Clin 14:497

- 504.

Shannahan JH, Schladweiler MC, Richards JH, Ledbetter AD, Ghio AJ, Kodavanti UP. 2010. Pulmonary oxidative stress, inflammation, and dysregulated iron homeostasis in rat models of carciovascular disease. J Toxicol Environ Health A. 73:641

- 56.

Shannahan JH, Schladweiler MCJ, Padill a

- Carlin DJ, Nyska A, Richards JH, Ghio AJ, Gavett SH, Kodavanti UP. 2011. The role of cardiovascular disease

- associated iron overload in Libby amphibole

- induced pulmonary injury and inflammation. Inhal Toxicol (accepted).

Shannahan JH, Ghio AJ, Schladwei ler MCJ, McKee JK, Richards JH, Gavett SH, and Kodavanti UP. 2011. The role of iron in Libby amphibole

- induced lung injury and inflammation. Inhal Toxicol. (accepted).

SHORT RH. 1952. Aspects of comparative lung growth. Proc R Soc Lond B Biol Sci 140:432

- 441.

Shtol' AV, Plotko EG, Seliankina KP. 2000. [Children's health and environmental air pollution with dust containing asbestos]. Med Tr Prom Ekol10

- 13.

Shukla A, Gulumian M, Hei TK, Kamp D, Rahman Q, Mossman BT. 2003. Multiple roles of oxidants in the pathogenesis of asbestos

- induced diseases. Free Radic Biol Med 34:1117

- 1129.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 28 DRAFT

— DO NOT CITE OR QUOTE Shukla A, Lounsbury KM, Barrett TF, Gell J, Rincon M, Butnor KJ, Taatjes DJ, Davis GS, Vacek P, Naka yama KI, Nakayama K, Steele C, Mossman BT. 2007. Asbestos

- induced peribronchiolar cell proliferation and cytokine production are attenuated in lungs of protein kinase C

- delta knockout mice. Am J Pathol 170:140

- 151.

Sichletides L, Daskalopoulos E, Tsarou V , Pnevmatickos T, Chloros D, Vamvalis C. 1992. Five cases of pleural mesothelioma with endemic pleural calcifications in a rural area in Greece. Med Lav 83:326

- 329.

Smartt AM, Brezinski M, Trapkus M, Gardner D, Putnam EA. 2009. Collagen accumulation over time in the murine lung after exposure to crocidolite asbestos or Libby amphibole. Environ Toxicol.

Smith CM, Kelsey KT, Wiencke JK, Leyden K, Levin S, Christiani DC. 1994. Inherited glutathione

- S

- transferase deficiency is a risk factor for pulmonary asb estosis. Cancer Epidemiol Biomarkers Prev 3:471

- 477.

Smith DD. 2002. Women and mesothelioma. Chest 122:1885

- 1886.

Smith W. Experimental studies on biological effects of tremolite talc on hamsters. Goodwin, Aurel. Proceedings of the Symposium on Talc, Was hington, DC . 1974.

Smith WE. Final report on biologic tests of samples 22260p5 and 22263p2. 1978.

Smith WE, Hubert DD, Sobel HJ, Marquet E. 1979. Biologic tests of tremolite in hamsters. Dusts Disease335

- 339.

Smith WE, Hubert DD, Sobel HJ. 1980. Dim ensions of fibres in relation to biological activity. IARC Sci Publ357

- 360.

Solomon, A (1991) Radiological features of asbestos

- related visceral pleural changes. Am J Ind Med 19:339

- 355.

Spiegelhalter D, Best NG, Carlin BP, Van Der Linde A. 2002. Bayesia n measures of model complexity and fit. Journal of the Royal Statistical society, Series B 64:583

- 639.

Spiegelhalter D, Thomas A, Best NG. WinBUGS Version 1.4 User Manual. WinBugs . 2003.

Spina M, Sandri S, Serraino D, Gobitti C, Fasan M, Sinicco A, Gara velli PL, Ridolfo A, Tirelli U. 1999. Therapy of

non

- small

- cell lung cancer (NSCLC) in patients with HIV infection. GICAT. Cooperative Group on AIDS and Tumors. Ann Oncol 10 Suppl 5:S87

- S90.

Srebro SH, Roggli VL. 1994. Asbestos

- related disease associated with exposure to asbestiform tremolite. Am J Ind Med 26:809

- 819.

Stanton MF, Wrench C. 1972. Mechanisms of mesothelioma induction with asbestos and fibrous glass. J Natl Cancer Inst 48:797

- 821.

Stanton MF, Layard M, Tegeris A, Miller E, May M, Morgan E, Smith A. 1981. Relation of particle dimension to carcinogenicity in amphibole asbestoses and other fibrous minerals. J Natl Cancer Inst 67:965

- 975.

Stayner, L.; Steenland, K; Dosemeci, M; and Hertz

- Picciotto, I (2003) Attenuation of exposure

- response curv es in occupational cohort studies at high exposure levels. Scan J Work Environ Health 29:317

- 324.

Straif K, brahim

- Tallaa L, Baan R, Grosse Y, Secretan B, El GF, Bouvard V, Guha N, Freeman C, Galichet L, Cogliano V. 2009. A review of human carcinogens

-- p art C: metals, arsenic, dusts, and fibres. Lancet Oncol 10:453

- 454.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 29 DRAFT

— DO NOT CITE OR QUOTE Stucker I, Boffetta P, Antilla S, Benhamou S, Hirvonen A, London S, Taioli E. 2001. Lack of interaction between asbestos exposure and glutathione S

- transferase M1 and T1 genotypes in lung carcinogenesis. Cancer Epidemiol Biomarkers Prev 10:1253

- 1258.

Sturm R. 2009. A theoretical approach to the deposition of cancer

- inducing asbestos fibers in the human respiratory tract. Open Lung Cancer J 2:1

- 11.

Sullivan PA. 2007. Vermiculite, respiratory disease, and asbestos exposure in Libby, Montana: update of a cohort mortality study. Environ Health Perspect 115:579

- 585.

Suzuki K, Hei TK. 1996. Induction of heme oxygenase in mammalian cells by mineral fibers: distinctive effect of reactive oxygen species. Carcinogenesis 17:661

- 667.

Suzuki Y, Kohyama N. 1991. Translocation of inhaled asbestos fibers from the lung to other tissues. Am J Ind Med 19:701

- 704.

Suzuki Y, Yuen SR. 2001. Asbestos tissue burden study on human malignant mesothelioma. Ind Health 39:150

- 160.

----- 2002. Asbestos fibers contributing to the induction of human malignant mesothelioma. Ann N Y Acad Sci 982:160

- 176.

Suzuki Y, Yuen SR, Ashley R. 2005. Short, thin asbestos fibers contribute to the development of h uman malignant mesothelioma: pathological evidence. Int J Hyg Environ Health 208:201

- 210.

Tableman M, Kim JS. 2003. Survival Analysis Using S.Chapman and Hall/CRC.

Tan RJ, Fattman CL, Watkins SC, Oury TD. 2004. Redistribution of pulmonary EC

- SOD after ex posure to asbestos. J Appl Physiol 97:2006

- 2013.

The Vermiculite Association. What is vermiculite? The Vermiculite Association . 2010. 4

- 8

- 0010.

Thurneysen C, Opitz I, Kurtz S, Weder W, Stahel RA, Felley

- Bosco E. 2009. Functional inactivation of NF2/mer lin in human mesothelioma. Lung Cancer 64:140

- 147.

Tillman, BN. Atlas of the Human Body, Clinical Edition; Springer

- Verlag, Berlin Heidelberg 2005. English Translation version, Mud puddle Books, 2007.

Topping DC, Nettesheim P. 1980. Two

- stage carcinogene sis studies with asbestos in Fischer 344 rats. J Natl Cancer Inst 65:627

- 630.

Tossavainen A, Karjalainen A, Karhunen PJ. 1994. Retention of asbestos fibers in the human body. Environ Health Perspect 102, supplement 5:253

- 255.

Toyooka S, Kishimoto T, Date H. 2008. Advances in the molecular biology of malignant mesothelioma. Acta Med Okayama 62:1

- 7.

Trosko JE, Yotti LP, Warren ST, Tsushimoto G, Chang C. 1982. Inhibition of cell

- cell communication by tumor promoters 9. Carcinog Compr Surv 7:565

- 585.

Truhaut R, Chouroulinkov I. 1989. Effect of long

- term ingestion of asbestos fibres in rats. IARC Sci Publ127

- 133.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 30 DRAFT

— DO NOT CITE OR QUOTE Ugolini D, Neri M, Ceppi M, Cesario A, Dianzani I, Filiberti R, Gemignani F, Landi S, Magnani C, Mutti L, Puntoni R, Bonassi S. 2008. Genetic susce ptibility to malignant mesothelioma and exposure to asbestos: The influence of the familial factor. Mutat Res 658:162

- 171.

U

. S

. Department of Labor. Asbestos. Occupational Safety and Health Standards . 1

- 1

- 2006. 2

- 27

- 2007.

U

. S

. DHHS (U.S.Department of He alth and Human Services). National Vital Statistics Report: Deaths: final Data for 2007. Vol 58 (19). 1

- 1

- 2010. US DHHS.

U

. S

. EPA (U.S. Environmental Protection Agency). 1980. Distribution of orally administered chrysotile in newborn baboon body. Healt h Effects Research Laboratory, Office of Research and Development. EPA

- 600/1

- 80

- 002. Research Triangle Park, NC.

----- . 1985. Drinking Water Crite ria Document for Asbestos.

----- . 1986. Guidelines for carcinogen risk assessment. Federal Register 51(185 ):33992

- 34003.

----- . 1986. Guidelines for the health risk assessment of chemical mixtures. Federal Register 51 (185):34014

- 34025.

----- . 1986. Guidelines for mutagenicity risk assessment. Federal Register 51(185):34006

- 34012.

----- . 1986. Airborne A sbestos Health Assessment Update (1986a ). EPA/600/8

- 84/003F.

----- . 1988. Recommendations for and documentation of biological values for use in risk assessment. EPA 600/6

- 87/008, NTIS PB88

- 179874/AS.

----- . 1991. Health Assessment Document for Ver miculite. EPA/600/8

- 91/037.

----- . (1991) Guidelines for developmental toxicity risk assessment. Federal Register 56(234):63798

- 63826. Available from: <http://www.epa.gov/iris/backgr d.htm>.

----- . 1993

. Asbestos: Carcinogenicity Assessment for Lifetime E xposure. US EPA.

----- . 1994. Interim policy for particle size and limit concentration issues in inhalation toxicity: notice of availability. Federal Register 59(206):53799.

----- . 1994. Methods for derivation of inhalation reference concentrations a nd application of inhalation dosimetry. EPA/600/8

- 90/066F.

----- . 1994. Peer review and peer involvement at the U.S. Environmental Protection Agency. Signed by the U.S. EPA Administrator Carol M. Browner, dated June 7, 1994.

----- . 1995. Use of the be nchmark dose approach in health risk assessment. EPA/630/R

- 94/007.

----- . 1996. Proposed guidelines for carcinogen risk assessment. Federal Register 61(79):17960

- 18011.

----- . (1996) Guidelines for reproductive toxicity risk assessment. Federal Register 61(212):56274

- 56322.

----- . (1998) Guidelines for neurotoxicity risk assessment. Federal Register 63(93):26926

- 26954.

----- . (2000a) Science policy council handbook: risk characterization. Office of Science Policy, Office of Research and Development, Was hington, DC; EPA 100

- B

- 00

- 002.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 31 DRAFT

— DO NOT CITE OR QUOTE ----- . (2000b) Benchmark dose technical guidance document [external review draft]. Risk Assessment Forum, Washington, DC; EPA/630/R

- 00/001.

----- . (2000c) Supplementary guidance for conducting for health risk assessment of chemical mixtures. Risk Assessment Forum, Washington, DC; EPA/630/R

- 00/002. Available from: <http://www.epa.gov/iris/backgr d.htm>.

----- . (2000d) Sampling and analysis of consumer garden products that contain vermiculite. Office of Pesticides and Toxic S ubstances, Washington, DC; EPA/744

- R

- 00

- 010.

----- . 2001. EPA Fact Sheet: Libby Schools Update. Fact Sheet Number 5.

----- . 2001. Asbestos Removal at Local Schools. Fact Sheet Number 6.

----- . 2001. Progress Polluti on Report: Libby Asbestos.

----- . 2001. Progress Pollution Report: Libby Asbestos 09/26/2001.

----- . 2001. EPA's Actions Concerning Asbestos

- Contaminated Vermiculite in Libby, Montana. 2001

- S

- 7.

----- . 2002. Past Exposure at Schools. Fact Sheet Number 7.

----- . (2002a) A review of the reference dose and reference concentration processes. Risk Assessment Forum, Washington, DC; EPA/630/P

- 02/0002F.

----- . 2004. Dosimetry of inhaled particles in the respiratory tract. National Center for Environmental Assessment. EPA/600/P

- 99/002bF. Rese arch Triangle Park, NC. Air Quality Criteria for Particulate Matter.

----- . 2005. Aging and toxic response: issues relevant to risk assessment. EPA/630/P

- 03/004A.

----- . 2005. Guidelines for carcinogen risk assessment and supplemental guidance for asse ssing susceptibility from early

- life exposure to carcinogens. Federal Register 70 (66):17765

- 17817.

----- . (2005a) Guidelines for carcinogen risk assessment. Risk Assessment Forum, Washington, DC; EPA/630/P

- 03/001B.

----- . (2005b) Supplemental guidance for assessing susceptibility from early

- life exposure to carcinogens. Risk Assessment Forum, Washington, DC; EPA/630/R

- 03/003F.

----- . 2006. Phase 2 Study Data Summary Report for Libby, Montana Environmental Monitoring for Asbestos Evaluation of Exposure to Airborne Asbestos Fibers During Routine and Special Activities.

----- . (2006a) Science policy council handbook: peer review. Third edition. Office of Science Policy, Office of Research and Development, Washington, DC; EPA/100/B

- 06/002. Available from: <http://www.epa.gov/iris/backgr d.htm>.

----- . (2006b) A Framework for Assessing Health Risk of Environmental Exposures to Children. National Center for Environmental Assessment, Washington, DC, EPA/600/R

- 05/093F. Available from: <http://cfpub.epa.gov/nce a/cfm/recordisplay.cfm?deid=158363>.

----- . 2008. A framework for assessing health risk of environmental exposures to children. EPA/600/R

- 05/093F.

----- . ( 2009) Benchmark Dose Software (BMDS) Version 2.1 User’s Manual. Version 2.0. Developed for: Unit ed States Environmental Protection Agency, Office of Environmental Information, Washington, DC, developed by Systems Engineering Center (SEC), Arlington, VA. Doc No.: 53

- BMDS

- RPT

- 0028. April 30, 2009.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 32 DRAFT

— DO NOT CITE OR QUOTE ----- . 2010. Libby Asbestos Superfund Site. US EPA . 1

- 1

- 2008.

----- . 2010. Vermiculite. US EPA.

----- . 2010. Record of Decision for Libby Asbestos Superfund Site: The Former Export Plant Operable Unit 1. US EPA.

----- . 2010. Particle size distribution data for Libby Amphiboles structures observed in a ir at the Libby Asbestos Superfund Site.

U

. S

. PHS (U.S.Public Health Service). 1990. The Health Benefits of Smoking Cessation: A Report of the Surgeon General. DHHS Publication Number (CDC) 90

- 8416.

Valavanidis A, Balomenou H, Macropoulou I, Zarodimos I. 1996. A study of the synergistic interaction of asbestos fibers with cigarette tar extracts for the generation of hydroxyl radicals in aqueous buffer solution. Free Radic Biol Med 20:853

- 858.

Valavanidis A, Vlachogianni T, Fiotakis K. 2009. Tobacco smok e: involvement of reactive oxygen species and stable free radicals in mechanisms of oxidative damage, carcinogenesis and synergistic effects with other respirable particles. Int J Environ Res Public Health 6:445

- 462.

Vasama

- Neuvonen K, Pukkala E, Paakkulai nen H, Mutanen P, Weiderpass E, Boffetta P, Shen N, Kauppinen T, Vainio H, Partanen T. 1999. Ovarian cancer and occupational exposures in Finland. Am J Ind Med 36:83

- 89.

Vaslet CA, Messier NJ, Kane AB. 2002. Accelerated progression of asbestos

- induced me sotheliomas in heterozygous p53+/

- mice. Toxicol Sci 68:331

- 338.

Vinikoor LC, Larson TC, Bateson TF, Birnbaum L. 2010. Exposure to asbestos

- containing vermiculite ore and respiratory symptoms among individuals who were children while the mine was active i n Libby, Montana. Environ Health Perspect 118:1033

- 28.

Voisin C, Marin I, Brochard P, Pairon JC. 1994. Environmental airborne tremolite asbestos pollution and pleural plaques in Afghanistan. Chest 106:974

- 976.

Wagner JC, Sleggs CA, Marchand P. 1960. Diffuse pleural mesothelioma and asbestos exposure in the North Western Cape Province. Br J Ind Med 17:260

- 271.

Wagner JC, Chamberlain M, Brown RC, Berry G, Pooley FD, Davies R, Griffiths DM. 1982. Biological effects of tremolite. Br J Cancer 45:3 52

- 360.

Walker C, Everitt J, Barrett JC. 1992. Possible cellular and molecular mechanisms for asbestos carcinogenicity. Am J Ind Med 21:253

- 273.

Wanner A, Salathe M, O'Riordan TG. 1996. Mucociliary clearance in the airways. Am J Respir Crit Care Med 154: 1868

- 1902.

Ward E, Jemal A, Cokkinides V, Singh GK, Cardinez C, Ghafoor A, Thun M. 2004. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin 54:78

- 93.

Ward TJ, Spear T, Hart J, Noonan C, Holian A, Getman M, Webber JS. 2006. Tr ees as reservoirs for amphibole fibers in Libby, Montana. Sci Total Environ 367:460

- 465.

Ward TJ, Noonan CW, Hooper K. 2007. Results of an indoor size fractionated PM school sampling program in Libby, Montana. Environ Monit Assess 130:163

- 171.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 33 DRAFT

— DO NOT CITE OR QUOTE Wassermann M, Wassermann D, Steinitz R, Katz L, Lemesch C. 1980. Mesothelioma in children. IARC Sci Publ253

- 257.

Webber JS, Getman M, Ward TJ. 2006. Evidence and reconstruction of airborne asbestos from unconventional environmental samples. Inhal Toxicol 18:969

- 973.

Webber JS, Blake DJ, Ward TJ, Pfau JC. 2008. Separation and characterization of respirable amphibole fibers from Libby, Montana. Inhal Toxicol 20:733

- 40.

Weis C. July 9 memorandum from Christopher P. Weis, EPA, to Paul Peronard, EPA. Persona l communication.

----- December 20 memorandum from Christopher P. Weis, EPA, to Paul Peronard, EPA. Personal communication.

Wheeler J. Location of vermiculite expansion plants across the United States. Personal communication.

Wheeler, MW (2005) Benchmark do se estimation using SAS®. 2005. SUGI 30, paper 201

- 30.

Whitehouse AC. 2004. Asbestos

- related pleural disease due to tremolite associated with progressive loss of lung function: serial observations in 123 miners, family members, and residents of Libby, Mo ntana. Am J Ind Med 46:219

- 225.

Whitehouse AC, Black CB, Heppe MS, Ruckdeschel J, Levin SM. 2008. Environmental exposure to Libby Asbestos and mesotheliomas. Am J Ind Med 51:877

- 880.

WHO (World Health Organization). Manual of the International Statistica l Classification of Diseases, Injuries, and Causes of Death. Fifth Revision. 1

- 1

- 1938. Geneva, World Health Organization.

----- . Manual of the International Statistical Classification of Diseases, Injuries, and Causes of Death. Sixth Revision. 1

- 1

- 194 8. Geneva, World Health Organization.

----- . Manual of the International Statistical Classification of Diseases, Injuries, and Causes of Death. Seventh Revision. 1

- 1

- 1957. Geneva, World Health Organization.

----- . Manual of the International Statistica l Classification of Diseases, Injuries, and Causes of Death. Eighth Revision. 1

- 1

- 1967. Geneva, World Health Organization.

----- . Manual of the International Statistical Classification of Diseases, Injuries, and Causes of Death. Ninth Revision. 1

- 1

- 19 77. Geneva, World Health Organization.

----- . International Statistical Classification of Diseases and Related Health Problems. Tenth Revision. 1

- 1

- 1992. Geneva, World Health Organization.

Wignall BK, Fox AJ. 1982. Mortality of female gas mask assembl ers. Br J Ind Med 39:34

- 38.

Witschi, HP, Last, JA. 1996. In Casarett and Doull’s Toxicology: The Basic Science of Poisons, Fifth Edition. Klassen CD, editor. McGraw

- Hill Companies, Inc.

Wraith D, Mengersen K. 2007. Assessing the combined effect of asbes tos exposure and smoking on lung cancer: a Bayesian approach. Stat Med 26:1150

- 1169.

Wright RS, Abraham JL, Harber P, Burnett BR, Morris P, West P. 2002. Fatal asbestosis 50 years after brief high intensity exposure in a vermiculite expansion plant. Am J Respir Crit Care Med 165:1145

- 1149.This document is a draf t for review purposes only and does not constitute Agency policy.

7

- 34 DRAFT

— DO NOT CITE OR QUOTE Wylie AG, Bailey KF, Kelse JW, Lee RJ. 1993. The importance of width in asbestos fiber carcinogenicity and its implications for public policy. Am Ind Hyg Assoc J 54:239

- 252.

Wylie AG, Skinner HC, Marsh J, Snyder H, Garz ione C, Hodkinson D, Winters R, Mossman BT. 1997. Mineralogical features associated with cytotoxic and proliferative effects of fibrous talc and asbestos on rodent tracheal epithelial and pleural mesothelial cells. Toxicol Appl Pharmacol 147:143

- 150.

Wyli e, AG, and Verkouteren, JR (2000) Amphibole asbestos from Libby, Montana: Aspects of nomenclature. Amer Mineralogist 85:1540

- 1542.

Yano E, Wang ZM, Wang XR, Wang MZ, Takata A, Kohyama N, Suzuki Y. 2009. Mesothelioma in a worker who spun chrysotile asbesto s at home during childhood. Am J Ind Med 52:282

- 287.

Yates, DH, Browne, K, Stidolph, PN, and Neville, E (1996) Asbestos

- related bilateral diffuse pleural thickening: natural history of radiographic and lung function abnormalities. Am J Respir Crit Care Med 153:301

- 306.

Yegles M, Saint

- Etienne L, Renier A, Janson X, Jaurand MC. 1993. Induction of metaphase and anaphase/telophase abnormalities by asbestos fibers in rat pleural mesothelial cells in vitro. Am J Respir Cell Mol Biol 9:186

- 191.

Yu, CP, Asgharian, B, Yen, BM. 1986. Impaction and sedimentation deposition. Am Ind Hyg Assoc J 47:72

- 77.

Yu CP, Zhang L, Oberdorster G, Mast RW, Glass LR, Utell MJ. 1994. Clearance of refractory ceramic fibers (RCF) from the rat lung: development of a model. Environ Res 65:243

- 253. (for clearance section).

Yu CP, Ding YJ, Zhang L, Oberdorster G, Mast RW, Maxim LD, Utell MJ. 1997. Retention modeling of refractory ceramic fibers (RCF) in humans. Regul Toxicol Pharmacol 25:18

- 25.

Zanella CL, Posada J, Tritton TR, Mossman BT. 1996. Asbestos causes stimulation of the extracellular signal

- regulated kinase 1 mitogen

- activated protein kinase cascade after phosphorylation of the epidermal growth factor receptor. Cancer Res 56:5334

- 5338.

Zanella CL, Timbli n CR, Cummins A, Jung M, Goldberg J, Raabe R, Tritton TR, Mossman BT. 1999. Asbestos

- induced phosphorylation of epidermal growth factor receptor is linked to c

- fos and apoptosis. Am J Physiol 277:L684

- L693.

Zeger SL, Thomas D, Dominici F, Samet JM, Schwar tz J, Dockery D, Cohen A. 2000. Exposure measurement error in time

- series studies of air pollution: concepts and consequences. Environ Health Perspect 108:419

- 426.

Zeren EH, Gumurdulu D, Roggli VL, Zorludemir S, Erkisi M, Tuncer I. 2000. Environmental ma lignant mesothelioma in southern Anatolia: a study of fifty cases. Environ Health Perspect 108:1047

- 1050.

Zerva LV, Constantopoulos SH, Moutsopoulos HM. 1989. Humoral immunity alterations after environmental asbestos exposure. Respiration 55:237

- 241.

Zhan g L, Asgharian B, Anjilvel S. 1996. Inertial and interceptional deposition of fibers in a bifurcating airway. J Aerosol Med 9:419

- 430.

Zhao X

- H, Jia G, Liu Y

- Q, Liu S

- W, Yan L, Jin Y, Liu N. 2006. Association Between Polymorphisms of DNA Repair Gene XRCC 1 and DNA Damage in Asbestos

- Exposed Workers. Biomedical and Environmental Sciences 19:232

- 238.

Zhou Y, Su WC, Cheng YS. 2007. Fiber deposition in the tracheobronchial region: experimental measurements. Inhal Toxicol 13: 1071

- 1078.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 PUBL IC

COMMENTS AND DISPOSI TIONThis 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 DISTRI BUTION DATA FOR LIBBY AMPHIBOLE STRUCTURES OBSERVED IN AIR AT THE LIBBY ASBESTO S SUPERFUND SITEPARTICLE SIZE DISTRIBUTION DATA FOR LIBBY AMPHIBOLE STRUCTURES OBSERVED IN AIR AT THE LIBBY ASBESTO S SUPERFUND SITE

July 14, 2010

Prepared by: U.S. Environmental Protection Agency Region 8 Denver, CO

With Technical Assistance from:

SRC, Inc. Denver, CO1 PARTICLE SIZE DISTRIBUTION DATA FOR LIBBY AMPHIBOLE STRUCTURES OBSERVED IN AIR AT THE LIBBY ASBESTO S 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 varyi ng levels of a form of asbestos referred to as Libby Amphibole (LA). In 1999, EPA Region 8 init iated 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 ma y 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 im pacted 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 Li bby 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 performe d a number of investigations to characterize the nature and extent of LA c ontamination 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 si ze of the inhaled asbestos partic les, these investigations have included the measurement of the dimensions (le ngth and width) of LA particles observed in samples collected from the Libby site.

The purpose of this report is to summarize size dist ribution data for LA particles that have been observed in air samples collected at the site, a nd to utilize these data to make comparisons between various subsets of the data to determin e 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 Libb y site. Table 1 provides an overview of the sampling programs that have gene rated these data. The raw data for the air samples included in this assessmen t 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 modi fications 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 lengt h, 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 distribu tions of two different sets of LA particles is through a graph that plots the cumulative distribution function (C DF) for each particle set. This graphical format shows the fraction of all pa rticles 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 compar e 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 sim ilar 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 consultati on with the analytical laboratory before entry of the data into the databa se. 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 st ructures are included in Table 1. Of these structures, 25% have undergone data validation in accord with standard site-wid e operating procedures (USEPA 2008b) to ensure that data for length, width, partic le type, and mineral clas s 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 th e database updated as appropriate.

3.2 Consolidated Data Set

Originally, most samples of air at Libby were an alyzed 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 and3 LB-000031 (see Attachment B ) were created to document the historic modifications and instructions that laborat ories have followed throughout the Libby program.

Figure 3-1 presents the partic le size distributions for 29,504 LA particles observed to date 1 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 us ing 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 se t of particles with an aspect ratio of 5:1 or

more.

3.3 Frequency of Complex Structures

Asbestos particles occur not onl y as fibers but also in more complex structures including bundles, clusters, and matrix complexes. The fre quency of these structure types in air samples from Libby are summarized below:

Type 2 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 struct ures 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 ai r 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 importa nt differences in size distributions between operable units, sampling locations (indoor, outdoo r), 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 st ructure 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 stra tified by analysis method (ISO vs AHERA)

Figure 3-2 is a comparison of diffe rent structure types (fiber, bundl es, 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 fibe r length in matrix fi bers is obscured by the matrix particle. In contrast, the length and th ickness 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 distribu tions of LA at different operabl e units (OUs) at the site. As seen, there appears to be little differen ce in structures from the different OUs.

Figure 3-4 shows the distribution of structure sizes for different t ypes of air samples. Samples have been placed into three gr oups: ambient air, indoor ABS, a nd outdoor ABS. As shown, the length and width distributions fo r indoor and outdoor ABS samples ar e 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 rela tively uncertain because of the small number (136) of particles that constitute the ambient air data set.

Figure 3-5 compares the size dist ributions for samples using dire ct and indirect preparation methods. As shown, there is little difference in the distributions or either length of width, suggesting that preparation me thod does not have a significant impact on particle size.

Figure 3-6 compares the particle si ze distributions as a f unction of analytical counting rules. As shown, the length and width distributions for par ticles analyzed using AHERA rules tend to be somewhat right-shifted relative to the distributions fo r particles analyzed using ISO 10312 rules. This apparent difference might be related either to differenc es 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 Superf und site. Most (about 80%) LA particles are fibers, with less than 20% complex structures (b undles, clusters, or matrices). LA particle5 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 typi cally 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 Standa rdization (ISO). Ambient Air – Determination of Asbestos Fibres – Direct-Transfer Tr ansmission Electron Microscopy Method. ISO 10312:1995(E).

USEPA. 2000. Sampling and Quality Assurance Project Plan Revision 1 for Libby, Montana. Environmental Monitoring for Asbestos. Baseli ne Monitoring for Source Area and Residential Exposure to Tremolite-Actinolite Asbestos Fibe rs. 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 fo r 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 Invest igation, Contaminant Screening Study, Libby Asbestos Si te, Operable Unit 4. Report pr epared by U.S. Environmental Protection Agency Region 8, with techni cal support from CDM. April 30, 2002.

USEPA. 2003. Final Sampling and Analysis Plan Addendum, Post Clean-up Evaluation Sampling, Contaminant Screening Study, Libby As bestos Site, Operable Unit 4. Report prepared by U.S. Environmental Protection Agen cy Region 8, with tech nical support from CDM and Syracuse Research Corporation. December 1, 2003.

USEPA. 2005. Supplemental Reme dial Investigation Quality Assurance Project Plan for Libby, Montana. Revision 1. U.S. Environmenta l Protection Agency Region 8. August 5, 2005.

USEPA. 2006. Sampling and Analysis Plan fo r 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 Syracu se 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 Pl an 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 Modi fication to Laboratory Activ ities LB-000031A. Requested by Lynn Woodbury of Syracuse Resear ch Corporation. January 18, 2008.

USEPA. 2008b. Standard Operating Proce dure 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 suppor t 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 Gr oundwater. Prepared by U.S. Environmental Protection Agency Region 8, with technical suppo rt from Syracuse Research Corporation and NewFields Boulder LLC. July 2, 2008.

USEPA. 2008d. Final Sampling and Analysis Pl an Libby Public Schools – Stationary Air Sample Collection Libby Asbestos Site Li bby, Montana. Prepared by U.S. Dept. of Transportation and CDM Federal Programs Cor p. with technical support from Syracuse Research Corporation. December 5, 2008.

USEPA. 2009a. Remedial Invest igation for Operable Unit 3 Libby Asbestos Superfund Site. Phase III Sampling and Analysis Plan. Prepar ed 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. w ith technical support from SRC, Inc. July 17, 2009.ProgramProgram DateRangeSampling andAnalysis Plan (s)Number of LAStructures(a)Phase 1Table 1. Air Sample Collection ProgramsInitial investigation sampling to assess natureand extent of potential contamination.Includes source areas (e.g., screening plant,export plant), commercial buildings, andProgram DescriptionDec 1999 - present USEPA 2000 328Phase 1RJun 2000 - present USEPA 2000 18,525Phase 2Mar - Nov 2001 USEPA 2001 867Phase 2RApr 2008 - Nov2009 1,717CSSA 2003 O 2006S A 20023residential 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 iCSSApr 2003 - Oct 2006 USEPA 2002 3SQAPPJun 2005 - Oct 2006 USEPA 2005 1,456Ambient Air(AA)Oct 2006 – Jun 2008USEPA 2006,USEPA 2007a 136Contaminant 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 inOU6. Samples represent long-term(continuous 5-day) collection periods.OU4 Indoor/Outdoor ABSJul 2007– Jun 2008USEPA 2007b,USEPA 2007c 5,603IndoorSchoolsDec 2008 USEPA 2008d 2OutdoorSchoolsJul - Sept 2009 USEPA 2009b 5Phase 2Sampling to assess exposures during indoorABS (passive & active activities) andoutdoor 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 andmaintenance staff.Ambient air sampling. Samples represent long-term (continuous 5-day) collection(OU3)July - Oct 2008 USEPA 2008c 67Phase 3(OU3)Aug - Nov 2009 USEPA 2009a 59Clean-upEvaluationNov 2003 - Feb 2004 USEPA 2003 5OtherAug 2001 - present various 731Sampling to monitor air and dust levels after completion of clean-up activities at 31properties.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 constructionLA structure counts are based on a download of Libby2DB performed on 12-8-09 and the Libby OU3 DB on 2-9-10.ProgramLA Structures Description(a) Restricted to LA structures recorded in accordance with a 5:1 aspect ratio rule.Other1A9 AIRS Site (418 Mineral Ave)BN17 BNSFCR3 Cumulative Risk StudyDM1 Demolition Sampling from 2006 onlyE11 BNSF Rail Yard Exclusion ZonesEP104 Export PlantFC184 Flower CreekFL146WR G (Fl it )WR Grace (Flyway site)SL266 Stimson LumberFigure 3-1. Particle Size Distributions of LA Particles in Libby Air Samples

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Length

All Air Samples

0 1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF Width

All Air Samples

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% 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Length

All Air Samples

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) Width

All Air Samples

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio Aspect Ratio

All Air Samples

TEM Results_12-8-09_V4.xls, ALL51Figure 3-2. Particle Size Distributions of LA Particles in Libby Air Samples by Structure Type

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Fibers

Matrices

Bundles

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF Fibers

Matrices

Bundles

Data are based on a download of Libby 2DB perfo rmed 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 su ffficient number of structures. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Fibers

Matrices

Bundles

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) Fibers

Matrices

Bundles

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio Fibers

Matrices

Bundles

TEM Results_12-8-09_V4.xls, ALL_StrucTypeFigure 3-3. Particle Size Distributions of LA Particles in Libby Air Samples by Operable Unit (OU)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) OU1

OU2

OU3

OU4

OU5

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF OU1

OU2

OU3

OU4

OU5

Data are based on a download of Libby 2DB perfo rmed 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 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) OU1

OU2

OU3

OU4

OU5

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) OU1

OU2

OU3

OU4

OU5

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio OU1

OU2

OU3

OU4

OU5

TEM Results_12-8-09_V4.xls, OUFigure 3-4. Particle Size Distributions of LA Particles in Libby Air Samplesby Air Type

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Indoor ABS

Outdoor Ambient Air

Outdoor ABS

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF Indoor ABS

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 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Indoor ABS

Outdoor Ambient Air

Outdoor ABS

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) Indoor ABS

Outdoor Ambient Air

Outdoor ABS

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio Indoor ABS

Outdoor Ambient Air

Outdoor ABS

TEM Results_12-8-09_V4.xls, AA_ABSFigure 3-5. Particle Size Distributions of LA Particles in Libby Air Samples by Preparation Method

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Direct

Indirect

0 2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF Direct

Indirect

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 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) Direct

Indirect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) Direct

Indirect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio Direct

Indirect

TEM Results_12-8-09_V4.xls, PrepFigure 3-6. Particle Size Distributions of LA Particles in Libby Air Samples by Analysis Method

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) ISO

A HER A

0 2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CDF ISO

AHERA

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 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Length (um) ISO

A HER A

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.01 0.1 1 10CDF

Width (um) ISO

AHERA

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.1 1 10 100CDF

Aspect Ratio ISO

A HER A

TEM Results_12-8-09_V4.xls, MethodAPPENDIX 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 fileCounting 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 dis p erse matrix MD11 (F) Y **recorded using ISO structure type, assumed to be dis p erse matrix MF (F) Y **recorded using ISO structure type, assumed to be matrix w/fiber p rotrusio n B B

C C **recorded using AHERA structure type, assumed to be com p act cluste r 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 com p act 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 10312APPENDIX B

LIBBY-SPECIFIC LABORATO RY MODIFICATION FORMS

LB-000016 LB-000031Lab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 TE M-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 hist oric 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 requi rements in relation to project-specific issues. Negative implications - comparisons of the Total # of LA structures bet ween historical results and current results may be biased (high or low) due to differences in recording ru les with regard to aspect ratio criteria. Positive implications - consistency in procedures between and within project laboratorie s 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 – Atta ch 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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 TE M-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-000016ALab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 pr ocedures provided in SOP EPA-Libby-08. Secondary filters will be analyzed according to the ISO counting rules for this project. Calcula tions 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 100 th 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 ma y 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 bein g recorded, unless identified as a “close call” (see LB000066 for details). This project-specific modificati on 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 met hod 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 otherw ise or specifically requested otherwise. Thus, fibers (either individual fibers or fibers within disperse matri ces or clusters) shall only be recorded if the length is greater than or equal to 0.5 um and the aspect ratio is gr eater than or equal to the appropriate criterion. Bundles shall only be recorded if they contain individual constituen t 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 concentrati on 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 in to an adjacent grid opening, providing that it does not intersect a non-counting grid bar, the en tire 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 laborator ies (primarily EMSL and RESI) were following the ISO method with regard to structure recording (i.e., recording only those st ructures 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 minimu m length or aspect ratio. This recording rule change enabled data users to gain a better understanding of the dimension a ttributes 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 st ructures 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 th e raw structure data, as entered from the laboratory bench sheet, into the Libby site database.

Although it is uncertain exactly when the recording rules ch anged after the Phase 2 Inves tigation, based on analyst interviews, project laboratories reverted back to following th e ISO method (i.e., recording only those structures meeting an aspect ratio of greater than or equal to 5:1) beginni ng 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 crite rion of greater than or equal to 3:1).

Laboratory modifications LB-000016B thr ough 16F (provided as Attachment B) document the historical laboratory and analyst-specific deviations in recording/counting rules fo r ISO based on analyst interviews conducted in August and September 2006.

Beginning August 29, 2006, all project laboratories began utiliz ing an aspect ratio criterion of 3:1, unless specifically requested otherwise.

Preparation techniques and recording rules were further refine d 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-inves tigative 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 anal yses 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 reco rding 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 COUNTI NG RULES PRIOR TO AUGUST 2006 (LB-000016B through 16F)

LB-000016B - Batta LB-000016C - EMSL LB-000016D - Hygeia

LB-000016E - MAS LB-000016F - RESILab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 TE M-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 Mi croscopy Services Company: Batta Laboratories, Inc. Date: September 26, 2006

Description of Modification: Laboratory-specific clarificat ion of potential inconsistenc ies among analysts when record ing structures using ISO 10312, as modified by LB-000016.

Reason for Modification: To document potential past diff erences between analysts and l aboratories in recording stru ctures using ISO 10312, as modified by LB-000016, and provide a hist orical timeline of potential changes in aspect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): All I ndividual(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 – Atta ch legible copies of approved form w/ all associated raw data packages

Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000016BLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 analyzi ng 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 cont ained PCME structures.

Starting September 11, 2006 (beginning wi th 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” wa s assigned to both primary and total structure columns.

Section 4B – Recording Dimensions for Di sperse Clusters (CD) and Matrices (MD)

Dimensions for disperse clusters (CD) and matrices (MD) were measured at tw o perpendicular directions across the structure. The longest length wa s defined first, and then the widt h perpendicular to the length.

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

Before March 2006, structures intersec ting the adjacent grid bars on the lower left corner were counted and those intersecting the adjacent bars on the uppe r right were rejected. After March 2006, this practice was corrected to comply with ISO rules: only those fiber s interesting or touching the lower ri ght corner grid bars were counted and others were rejected.

Section 4D – Recording Structures Crossing Multiple Grid Openings

ISO rules on structures touchi ng grid bars were followed.

Section 4E – Recording Structures Cro ssing 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 contact s 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 TE M-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 clarificat ion of potential inconsistenc ies among analysts when record ing structures using ISO 10312, as modified by LB-000016.

Reason for Modification: To document potential past diff erences between analysts and l aboratories in recording stru ctures using ISO 10312, as modified by LB-000016, and provide a histor ical timeline for each Libby laborato ry of potential changes in aspect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): A ll 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000016CLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 A nalytical began analyzing Libby samples), all analysts interviewed applied a 5:1 aspect ratio and the counting rules outlined in the ISO me thod. Sometime during the spring of 2001, verbal directions were given to record all structures, regardless of aspec t 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 Di sperse 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 ca lled for recording asbestos st ructures 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 Cro ssing 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 contact s 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 TE M-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 clarificat ion of potential inconsistenc ies among analysts when record ing structures using ISO 10312, as modified by LB-000016.

Reason for Modification: To document potential past diff erences between analysts and l aboratories in recording stru ctures using ISO 10312, as modified by LB-000016, and provide a histor ical timeline for each Libby laborato ry of potential changes in aspect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): All Indi vidual(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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000016DLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 utiliz ing an aspect ratio criterion of 5:1 or greater, unless the project-specific requirements stated otherwise. For example, all SQAPP samp les submitted (first job su bmitted in June 20, 2005) and all settled dust samples (SRC-Libby-07 method; first j ob 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 counti ng rules mentioned in LB-000016 and clarifications stated below:

1. Hygeia recorded no asbestos det ected grid opening as “NSD” inst ead 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 stoppi ng rules or target analytical sensit ivity 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 struct ures were still recorded if the analysts thought it was necessary and marked as su ch 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 Di sperse Clusters (CD) and Matrices (MD)

All analysts in compliance.

Section 4C – Recording Structures Crossing Non-Countable Grid Bars

All structures intersecting countable grid bars (botto m/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-c ountable grid bars were recorded and indicated as noncountable on the data sheet and EDD. Inte rsecting grid bar was also indicated on the EDD comment field as crossing E, W, S, N grid bar, i.e. “Non-countab le; 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 Cro ssing 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 contact s 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 TE M-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 clarificat ion of potential inconsistenc ies among analysts when record ing structures using ISO 10312, as modified by LB-000016.

Reason for Modification: To document potential past diff erences between analysts and l aboratories in recording stru ctures using ISO 10312, as modified by LB-000016, and provide a histor ical timeline for each Libby laborato ry 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000016ELab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 (Mik e Mount, Kevin Simpson, Mehrdad Mota medi and Will Stark) performing ISO 10312 analyses of Libby samples. The first set of Libby IS O samples (dust samples) were received on 04/03/03. At this time analyst performing analysis we re Will Stark, Ding Qian and Jayme Ca llan. All ISO samples were counted and recorded for asbestos structures utiliz ing an aspect ratio criter ion of 5:1 or greater, unl ess the project-specific requirements stated otherwise. SQAPP samples and settled dust samples for ISO 10312 s ubmitted 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-as bestos structures (NAM) t hat he deemed significant. However, when NAM structures were recorded, they were always noted as non-countable st ructures (i.e., placed a “0” in the Total column).

Section 4B – Recording Dimensions for Di sperse 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 reco rds asbestos structures t hat are deemed non-countable du e 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 record s the entire length of a stru cture that extends into an adjacent grid opening.

Section 4E – Recording Structures Cro ssing Non-Countable and Countable Grid Bars

All analysts in compliance. MAS reco rds the entire length of a structure t hat 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 contact s 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 TE M-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 clarificat ion of potential inconsistenc ies among analysts when record ing structures using ISO 10312, as modified by LB-000016.

Reason for Modification: To document potential past diff erences between analysts and l aboratories in recording stru ctures using ISO 10312, as modified by LB-000016, and provide a histor ical timeline for each Libby laborato ry of potential changes in aspect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): A ll 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000016FLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 t he Libby Project in November, 1999. Analysts applied an aspect ratio of 5:1 to the c ountable structures unless proj ect specific requirements st ated otherwise. The samples with a prefix of “SQ-“ or “2-“ were ana lyzed with an aspect ratio of 3:1 as requested. Curr ent aspect ratio rules are outlined in LB-000053 and are delineated by the classifi cation 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 struct ures if the analyst determined the stru cture 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 stru ctures such as gypsum or gla ss was removed after August, 2006.

Section 4B – Recording Dimensions for Di sperse 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 Cro ssing 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 contact s 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 Transmissi on 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 requi rements in relation to project-specific issues. Negative implications - comparisons of the Total # of LA structures bet ween historical results and current results may be biased (high or low) due to differences in recording ru les with regard to aspect ratio criteria. Positive implications - consistency in procedures between and within project laboratorie s 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 – Atta ch 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000031ALab 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 modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 co ntains loose debris, it will be prepared indirectly according to procedures provided in SOP EPA-Libby-08. Secondary filters will be analyzed according to th e 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 dat a from samples that otherwise would be rejected.

3. Stopping Rule Clarification:

For this project, a maximum of ten grid openings w ill be read unless specifically instructed otherwise.

4. Abundant Chrysotile Modification:

If abundant chrysotile is present, the chrysotile count may be terminated in accordance with th e 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 “Str ucture Type” designations on the EPA Region 8 spreadsheet: Fiber F Bundle B Cluster C Matrix M

If the analyst determines that additional information is n eeded 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 det ected) will be used to document when no structures are detected for the grid opening.

d. Recording rules will be as described in the AHERA met hod 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 otherw ise or specifically requested otherwise. Thus, fibers shall only be recorded if the aspect ratio is greater than or equal to the appropriate crite rion. Bundles, clusters, and matrices shall only be recorded if they contain individual c onstituent 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 anLab Modification Form Revision 10 (9-11-07) exposed termination) should be recorded for informationa l purposes, but identified as non-countable, to ensure they are excluded from structure density and concentration cal culations. These non-countable structures will be denoted with a zero in the Total column.

f. If a structure originates in one grid openi ng and extends into an adjacent grid op ening, 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 w ill be recorded. It is not appropriate to record all substructures as in the ISO 10312 method. Structure dimensi ons may be recorded in microns (μm) or screen units provided that the scaling factors are recorded. See Attach ment 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 fo r 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 laborat ories (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 minimu m length or aspect ratio. This recording rule change enabled data users to gain a better understanding of the dimension a ttributes 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 ch anged after the Phase 2 Inves tigation, 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) be ginning approximately in late 2001 to early 2002.

Laboratory modifications LB-000031B thr ough 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, 200 6, all project laboratories were instru cted to utilize an aspect ratio criterion of 5:1 for AHERA/ASTM analyses, unless sp ecifically requested otherwise.

Beginning April 24, 2007, all project la boratories were instructed to utilize an aspect ratio criterion of 3:1 for AHERA/ASTM analyses of “investigative” samples. Instructi on 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 arrange ment 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 av erage width. The aspect ratio of the overall structure is not a factor. At least three individual sub-structures in parallel arrange ment 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 mustbe 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 RECORD ING 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 - RESILab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 clarificat ion of potential inconsist encies among analysts when re cording structures using AHERA/ASTM, as modified by LB-00 0017, LB-000017A, and LB-000031.

Reason for Modification: To document potential past di fferences 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 as pect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): All I ndividual(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 – Atta ch legible copies of approved form w/ all associated raw data packages

Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000031XLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 requi rements 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 neces sary 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 fo llowing 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 ParticleLab 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 clusterLab 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 micronLab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 clarificat ion of potential inconsist encies among analysts when re cording structures using AHERA/ASTM, as modified by LB-00 0017, LB-000017A, and LB-000031.

Reason for Modification: To document potential past di fferences 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 as pect 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000031CLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 great er than or equal to 5:1. Sometime in the spring of 2001, analysts received verbal direction to record structures regardless of aspect ra tio. In late 2001 or early 2002, project analysts reverted back to 5:1. EMSL analyst intervie ws were conducted in Augu st and September 2006. On August 29, 2006, all laboratories were instru cted to use 5:1 for AH ERA/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 struct ure 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 stru cture 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 widthLab 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 micronsLab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 clarificat ion of potential inconsist encies among analysts when re cording structures using AHERA/ASTM, as modified by LB-00 0017, LB-000017A, and LB-000031.

Reason for Modification: To document potential past di fferences 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 as pect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one): All Indi vidual(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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r 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-000031DLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 Sept ember 16, 2002. Two analysts, Kyeong Corbin and Quynh Trieu, were involved in analyzing all Li bby project samples. All AHERA samples were analyzed according to the AHERA counting rules (aspect ratio 5:1 or greater) and any project-s pecific 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 ru les mentioned in LB-000017, LB-000017a, and LB-000031 and clarifications stated below:

1. Hygeia recorded no asbestos det ected grid opening as “NSD” as expressed in the AHERA instead of “ND” as requested by project modification during th e period of 6/1/02 to 11/ 30/02. The Lab Mod LB000023 was filed.

2. As results of August 6, 2002 tele conference, the following instructi ons were given to the analysts:

• Do not count NAM structures. At times, NAM stru ctures were still recorded if the analyst thought it was necessary and marked as such on the dat a 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 te leconference, the following instruct ions were given to the analysts:

• Measure only visible portion of the fiber or bundle sti cking out of the matrix. (Prior to this date, any matrix components that were embedded in opaque ma trix 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 indica ted as such on the dat a sheet and on the EDD.)

• Intersection is defined as stat ed on the AHERA, i.e. Non-paralle l toughing or crossing of fibers, with the projection having an aspect ratio 5:1 or greater. (If <5:1 pr ojection, it was considered no intersection.)

4. As results of September 9 and S eptember 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/l ongest sub-structure’s dimension;

• Do not use F/m, B/m, or C/m classifi cation; just record it as M, matrix.

5. Stopping rules for chrysotile stru ctures were changed from 100 structur es 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 contact s 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 clarificat ion of potential inconsist encies among analysts when re cording structures using AHERA/ASTM, as modified by LB-00 0017, LB-000017A, and LB-000031.

Reason for Modification: To document potential past di fferences 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 as pect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one) : All Individual(s) Material s 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000031ELab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 (Mik e Mount, Kevin Simpson, Mehrdad Mota medi, 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 a nalysis were Will Stark, Ding Qian a nd Kevin Simpson. AHERA/ASTM method dust samples were counted and recorded for asbestos structures utiliz ing 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 rule s, 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 Moun t and Mehrdad Motamedi, recorded asbestos fibers and bundles with visible ends as a matrix st ructure instead of a fiber or bundle.Lab Modification Form Revision 10 (9-11-07) Instructions to Requester: E-mail form to contact s 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 clarificat ion of potential inconsist encies among analysts when re cording structures using AHERA/ASTM, as modified by LB-00 0017, LB-000017A, and LB-000031.

Reason for Modification: To document potential past di fferences 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 as pect ratio recording rules.

Potential Implications of this Modification: None.

Laboratory Applicability (circle one) : All Individual(s) Reservoi rs 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 – Atta ch legible copies of approved form w/ all associated raw data packages
Permanent (Complete Proposed Modification Se ction) 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 fo r direction on selecting data quality indicators:

Not Applicable Reject Low Bi as 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-000031FLab Modification Form Revision 10 (9-11-07) DATA QUALITY INDICATOR DEFINITIONS

Reject - Samples associated with this modi fication form are not useable. The conditions outlined in the modification form adversely effect the asso ciated sample to such a degree that the data are not reliable.

Low Bias - Samples associated with this modification form are useable, but results are likel y to be biased low. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated low.

Estimate - Samples associated with this modification fo rm are useable, but resu lts should be considered approximations. The conditions outlined in the modification fo rm suggest that associated sample data are reliable, but estimates.

High Bias - Samples associated with this modification form are useable, but results are likel y to be biased high. The conditions outlined in the modification form suggest that associated sample dat a are reliable, but estimated high.

No Bias - Samples associated with this modification form ar e useable as reported. T he 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 t he Libby Project in November, 1999. Analysts applied an aspect ratio of 5:1 to the countabl e structures unless project specif ic requirements stated otherwise.

DEVIATIONS FROM LAB MODIFICATIONS LB-000017, LB-000017A, LB-000031

All analysts followed AHERA/ASTM method recording rule s, as well as the guidance in the above laboratory modifications, except as noted below:

 Analysts at Reservoirs hav e historically characterized a fiber with bot h ends visible and matrix in the middle as a matrix, whereas LB-000031 designates th is type of structure as a fiber.

 Samples counted in 1999 and 2000 were recorded on historic al laboratory bench sheets and are identified in LB000001 through LB-000014.

 In August, 2002 the project no longer required that NAM st ructures be recorded. On occasion, analysts recorded NAM structures when the anal yst found the structure not able. 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 ob scured by matrix or a fiber with less than the required aspect ratio, were recorded and designated as non-c ountable 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

- 1129C

- 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 p lant 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 Afr ica 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 wit h 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 pyr oxene. 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 th e principle amphibole in the Vermiculite Mountain deposit based on chemical investigation referencing the cl assification system of Leake et al. [1997] and optical properties. Meeker et al. [2003] investigated amphibole types from the mine complex using e lectron 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 Amph iboles is roughly winchite, richterite, tremolite, magnesio

- riebeckite, magnesio

- arfvedsonite, and edenite [84:11:6:<1:<1:<1]. The O

. M

. ScottC

- 3

facility received ore from the Ve rmiculite 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 [pyro xenite] 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 Pala bora 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 fiber s 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 mic roscopy 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 prefere ntially mined because of better commercial properties compare d 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 sa mples from the Louisa deposit. The chrysotile was reported a s fibers and bundles while the amphibole s w ere 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 fragme nts having low aspect ratio s . 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 widelyC

- 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 condu cted 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 ord er 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 b ulk 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 migh t 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 [ exact ly 2.0 gms] were mixed with 18 gms of analytical silica sand and placed in a fluidized bed asbestos segregator vessel to load 25mm
M CE air sampling filters [0.8 μ pore size]. The fluidized bed asbestos segregator was run for 3
minutes to load the filter cassettes with suf ficient 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 Microscop y equivalent [PCMe] fibers were counted for each of the ore/vermiculite samples as described in Appendix B . A total of 1.0 mm 2 area or a total of 200
asbestos structures were counted to achieve the desired analytical sensitivity [1/g; 1.5E+4]

. Energy dis persive 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 vermiculi te 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
fibe rs 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 mm 2 ]

.

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 facilityC

- 6

after 1980 when only ore from Virginia, Palabora, and South Carolina was used (A ppendix F ) is consistent with the findings of low fiber counts for the Virginia and Palabora materials. In addition, n umerous 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 : A 0

- 1 B 2 C 5 T 8 O 22 [OH] 2 where A, B, C, and T represent the various cations. The modern classification system of amphi boles 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 c ations 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 f

irst 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 ca tions
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 oxy gens. Next the sites are filled up by assigning cations to them subsequently, specifically:
T: Si

4+ , Al 3+ , and Ti 4+

C: Al 3+ and Ti 4+ [only after the T sites are filled first] and then Mg 2+ , Fe 2+ ,Fe 3+

, and then Mn 2+

. B: Any remaining Mg 2+ , Fe 2+ , and Mn 2+ [after the C sites are filled], all Ca 2+ , 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, Fe 2+ ] 4 [Al, Fe 3+ ]Si 8 O 22 [ OH] 2 Richterite, NaCaNa [Mg, Fe 2+ , Mn, Fe 3+ ] 5 Si 8 O 22 [OH] 2 Tremolite, Ca 2 Mg 5 Si 8 O 22 [OH] 2 Magnesio

- riebeckite Na 2 [Mg 3 , Fe 3+ 2 ]Si 8 O 22 [OH] 2 Magnesio

- arfvedsonite NaNa 2 [Mg 4 ,Fe 3+ ]Si 8 O 22 [OH] 2C

- 7

Edenite NaCa 2 Mg 5 Si 7 AlO 22 [OH] 2

Libby amphibole is characterized by a low amount of Al in the T site [and a correspondingly high Si content] s o according to Leake’s classification, if the Si [expressed as

a toms per formula unit, a pfu] 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 appr oach was described by Meeker et al. [ 2003 ] for the Rainy Creek complex.

EDS spectra [TEM/EDS] were co llected 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 Afric a ore sample. EDS spectra were collected for one of the bundles. The chemical formula of serpentine is Mg 3 Si 2 O 5 [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 O 2

- . The calculated cation numbers will be affected if a significant quantity of O 2

- 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 re port all Fe was assumed to be
Fe 2+ . A routine for calculating the ratio of Fe 2+ to Fe 3+ 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 si te. 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.

Appl ying 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. Virgin i a Expanded and South African ore both contained amphibole fibers characteristic of non

- Libby [Na and K negative] in the tremolite seri es.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 f or 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 expandedC

- 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 ra nge for LA fibers and were
consistent with fiber size distributions for soil a ctivity

- 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 da ta, 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

Fi gure C

- 2

. Fiber size Distribution of LA amphibolesC

- 11

R eferences

Bern, A.G., Brownfield, I. and Meeker, G.P. 2002. Preparation and analysis of soil samples for amphibole asbestos content by sc anning electron microscopy and energy dispersive spectroscopy. USGS Open File R eport. December 18, 2002. p p

. 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. p p

. 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 forensic s: 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 South eastern 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. I SO Metho d 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 Surre y GU@ 8XG. Report #609

- 01591. p p

. 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. p p

. 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.

Lang er, 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 u ltramafic 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. 20 03. 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

- 98 0.

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 Assessmen t. 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. p p

. 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 c rocidolite 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,
Gov erneur, New York) used was not fibrous. Instead, the material was crystalline, as this form
was a common contaminan t 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

- 3 1 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 Leng th interval a
<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

a Average 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/m 3 (~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 rat io >3:1. The r esults 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 ( Table s 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) a 23.0 (21.4

– 24.2) 13.4 (9.7

– 18.9)

– Irregular alveolar wall thickening (SD) b 35.2 (27.7

– 41.0) 27.7 (20.8

– 35.4)

– Interstitial fibrosis (SD) b 0 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 8Mesotheliomas 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., 199 1) 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 a fter cessation of tremolite exposure i n 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 obje cts 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 (Bernstei n 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: Bern stein 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 e t 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 bur den 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 expo sure, 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 p oints (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. Thi s 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 Amph ibole 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 th e 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 expose d to Lucifer Yellow stain to further analyzeTable 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 i solated 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 stand ard RNA. Gene expression results were analyzed by GO Miner, and genes exhibiting at least 1.25

- fold up

- or down

regulation 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 o pposed 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 are as 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 asb estos 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 wer e 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 c hanges at the pleura of the lungs that were indicative of potential mesotheliomawere 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, crocidol ite

- 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 dem onstrated 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 Co l3A1 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 asbestos 1 to 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 Lib by 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 o bserved 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 post

exposure. 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
homeost asis 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 mi ce 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 acut e 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 n o 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 ass ays 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

c haracteristics 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 anti gens. Theseautoantibodies 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 surfac e 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 ly mph 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 p articles 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 mo nths 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 Sa mple 60, Sample 63, and saline, respectively ( Table D

- 6 ). Pleural fibrosis was observed 3 months postexposure, 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 rating a,b 0 (n = 10) 3.3 (n = 10) 3.6 (n = 10)

Total tumors/animals c 8/59 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

a As analyzed in first group sacrificed (between 41 and 92 days post exposure).
b Rating for pleural adhesions: 0 = no adhesions; 1 = minimal adhesions; 4 = extensive adhesions.

c These include adrenal adenomas, adrenal adenocarcinomas, lymphoma, pulmonary adenocarcinoma, adrenal and salivary carcinoma, mesothelioma, rhabdomyosarcoma, hepatom a, thyroid carcinoma, subcutaneous carcinoma, and malignant melanoma. Source: Smith (1978).

The Smith et al. (1979) study was designe d 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 d iameter) (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 furt her 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 a nd 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 t he 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 fib ers 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 dete rmined 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
l imited infor mation 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 pap ers 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 injecti on into Syrian hamsters of 10

- or 25

- mg fiber samples

Sample Average

length a (μm) Average

diameter a (μm) Tumors/survivors at 10 mg b Tumors/survivors at 25 mg b

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

a Although 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.
b Numerator = 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 t remolite, 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 usi ng 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 prese ntation 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 pleur al 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 ani mals was not provided, no statistical analysis can be performed. Both studies demonstrate that intrapleural injections of Libby Amphibole asbestos 4 leads to an increase in pleural fibrosis and mesothelioma in hamsters compared to controls or animals inject ed 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 mecha nisms 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 consi stent 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, an d Korea) using SPF Sprague

- Dawley (n = 48) and Wistar (n = 32) rats, then followed up with a range of in vitro test s 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 quantitativ e 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 h ad 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 t he 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 non

fibrous particles (×10 4 ) Total no. of

fibers (×10 4 ) No. of fibers >8 μm

long (×10 3 ) <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, t he 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 (× 10 5 ) No. of fibers ≥8

μm long, <0.25 μm diameter a (× 10 5 ) No. of particles

in 1 mg injected dust (× 10 5 ) 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 S tanton 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 f ibers 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 partic les 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 asb estos 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 follow ing a li fetime (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 c haracteristics 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 mesot helioma 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 anim als (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 w ith two forms of asbestos (crocidolite [25 or 50 μg/mL] and Libby Amphiboleasbestos obtained from Site No. 30, Libby, MT [25 or 50 μg/mL]) and ultrafine particulate

matter (PM 2.5 [particulate matter 2.5 microns diameter or less] [50 or 100 μg/mL]). Limit ed 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 hou rs 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 10 6 macrophages were co

- cultured with autologous lymphocytes (1 10 6 cells) 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 wa s assayed for cytokines at day 11, and Hamilton et al. (2004) found that pretreatment with either asbestos or PM 2.5 significantly upregulated both T H1 and T H2 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 PM 2.5 pretreatment. The author s 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 PM 2.5 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 mi ne (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/cm

2 ), after which the cells were analyzed by transmission electron microscopy to measure
internalization. The r esults 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/cm 2 . This same concentration (62.5 μg/cm 2 ) was selected for the remaining studies because cell viability was not decreased at this concentration for theLibby 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 crocid olite, 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 un less 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 dic hlorofluorescein 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/cm 2 ), as early as 1 hour post exposure at the highest dose ( p < 0.05), a s 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/cm 2 ) 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 decrea se 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, m ost 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 toincreased 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

- hydroxy

2‘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 diffe rent 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 t o equal concentrations of crocidolite, Libby Amphibole asbestos, and wollastonite (62.5 μg/cm 2 , 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

treatme nt). 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 crocidoli te 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 pl ay 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 di rectly 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 hour s, 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 inthe unexposed controls an d 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 hum an 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) follo wing 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

- 10μ g/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

- 1 1

. Micronuclei induction was measured following treatment of cells by controls as desc ribed above, and by 5μg/cm 2 fibers or TiO 2 particles for
24 hrs. Following treatment, cells were fixed, permeabilized, and blocked before exposed to
anti

- centromere ant ibodies, 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

com pared to control. Wollastonite and titanium dioxide did not induce micronuclei in either cell type. Crocidolite and Lib by Amphibole asbestos induced dose

- dependent increases in micronuclei formation in both cell types but with an increase in proportion o f 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, cro cidolite 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 rela ted 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 distribut ion 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/cm

2 ) 71.5 ± 3.4% 86.0 ± 1.2% a

Crocidolite (5 μg/cm 2 ) 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 micro arrays (analysis with GeneSifter) following exposure to
15×10 6 μm

2 /cm 2 Libby Amphibole asbestos 5 as compared to the non

- pathogenic control (75×10 6 μm

2 /cm 2 glass 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 comp ared 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 d escribed based on viability assays as either the non

- toxic (15×10 6 μm 2 /cm 2 ) or the toxic dose (75×10 6 μm 2 /cm 2 ).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 adhe sion and motility and only one gene related to reactive oxygen species processing. Follow

- up studies at both the  ̳non

- toxic dose‘ (15×10 6 μm 2 /cm 2 ) and the  ̳toxic dose‘ (75×10 6 μm 2 /cm 2 ) 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 Amphibol e 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 followi ng 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) fluorescenc e 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). Ox idative 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 75×10 6 μm 2 /cm 2 levels of Libby Amphibole asbestos, with a gradual recovery up to 48hrs in LP9/TERT

- 1 cells (HK NM

- 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 oxidativ e 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 expre ssion 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 primaryhuman 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 wat er 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/cm 2 of 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/cm 2 AM2.5 or LA2.5 only. At 4h post

- exposure, fibers were mainly localized on the periphery of the

ce ll 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/cm 2 ) of both amosite a nd 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 fluor escence 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 w ere 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 betwee n 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 foll owing 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 μ g/cm 2 ). 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/cm 2 AM, LA, AM2.5 or LA2.5 or to 26.4 μg/cm 2 LA2.5 only. The results show a pr o

- 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/cm 2

amphibole asbestos for 24 hr a

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

a All 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

- bicar bonate

dithionite (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 table s at the end of this section. In general, all fibrous tremolite samples were shown to be carcinogenic, with those contain ing 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/cm 3 ) (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 meas ured bymethylene 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 con trol. 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 stu dy 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, therefor e, 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, howev er, 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 erioni te, 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 crocidol ite 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 epi thelial (HTE) and rat pleural mesothelial (RPM) cells with a colony

- forming efficiency assay. HTE cells are used because the y 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 examine d in vitro for

cytotoxic (HTE cells) and proliferative effects (RPM cells)

Sample Description (% of sample) Surface area (mm 2 /g) Fibers/μg Fibers ≥5μm/μg

FD14 Talc (37), tremolite (35), serpentine

(15), other (<2), unknown (12) 6.2 ± 0.2 2.5 × 10 3 0.8 × 10 3

SI57 Talc (60), tremolite (12), unknown

(21), other (4), anthophyllite (3), quartz (1) 4.9 ± 0.2 1.1 × 10 4 4.8 × 10 3

CPS183 Talc (50), quartz (12), unknown

(28), tremolite (4), other (4), anthophyllite (3) 4.9 ± 0.4 1.1 × 10 4 9.2 × 10 3

NIEHS crocidolite Riebeckite (100) 10.3 ± 1.3 5.3 × 10 5 3.8 × 10 5
NIEHS chrysotile Chrysotile (100) 25.4 ± 0.5 5.3 × 10 4 3.4 × 10 4

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/cm 2 ) show elevated colonies in HTE cells following exposures to both asbestos fibers ( p < 0.05) at the lowest concentrations, while sig nificant decreases were observed for both asbestos fibers at the higher concentrations (0.5 μg/cm 2

, p < 0.05) (Wylie et al., 1997). No proliferation was observed for either chrysotile or crocidolite asbestos fibers in RPM cells, but cytotoxicity was observ ed at concentrations greater than 0.05 μg/cm 2 ( 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 lea ds 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 res ults 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 fo r the slight cytotoxicity of FD14 at 2.6 fibers/cm 2 . 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 thesurface area, which demonstrates the impact of the dose metric on data. Analyzing the data for

cytotoxicity and proliferation based on the exposure measurement demonstrated differ ences 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 usefu l 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 apparatu s 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 mutagenici ty 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. typhimu rium 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 r evertants 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/cm 2 ) in Sy rian 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 (Athanasi ou 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/cm 2 ) ( 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/cm 2 ) Micronuclei incidence/1,000 cells Chromosomal aberrations (including

chromatid gaps, breaks, isochromat id breaks, and chromosome type)

0 17 3

0.5 31 a 4

1.0 70 b 12 c

2.0 205 b 9 a

3.0 Not tested 13 c

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/cm

2 tremolite. A statistically significant dose

- dependent increase in levels of micronuclei was demonstrated following tremolite exposure at concentrations as low as
0.5 μg/cm

2 ( 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 co mmunication following exposure to 0

– 4.0 μg/cm 2 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 diamet er (arithmetic mean) with the number of fibers per microgram of sample equal to 1.05 × 10 5 . 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 chromoso me 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 cy totoxicity analysis for mutagenicity was performed by exposing 1 × 10 5 A(L) cells to a range of concentrations of fibers as measured by weight (0

– 400 μg/mL or 0

– 80 μg/cm 2 ) for 24 hours at 37°C. CD59 mutant induction showed a
dose

- dependent increase in mu tation 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 thro ugh 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 characteristi cs (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 197 2 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 respo nse 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 covar iates 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 logisti c 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 (Ho smer

- 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: t ime 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

-- p

value=0.3206. Therefore, only exposure (CHEEC) was included i n further analyses. See Table E

- 1.2

TABLE E

- 1 Evaluatio n 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 prev alence 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 estim ated 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 inve stigated 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 m odel 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 assumpti on 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 whic h 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 exp ected 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 57.43

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 disc rete 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 Hosmer

Lemeshow 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

Michaelis

Menten± ln(CHEEC) P(CE) = bkg + (Plateau

– bkg)*CE / [exp(

- a)+CE] 75.8 0.5413 0.3330 0.13424

Figure E

- 1. Observed prevalence of discrete pleural thickening and estimated probability of discrete pleural thickening

DERIVATION OF THE CA NDIDATE POD AND RFC FOR DISCRETE PLEURAL THICKENING USING THE 3

- PARAMETER LOG

- LOGISTIC MODEL The candidate POD is 0.0757 fibers

- yr/cc, the BMDL 10 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. S TATISTICAL 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 x

rays of former workers in the Marysvill e 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 o f 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
years5

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 si gnificant 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 havi ng 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 pleura l 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 pot ential 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 cons iderations 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 th e 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 mod el 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 expo sure 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 a s 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 x

ray was a potentially significant explanatory variable with p = 0. 14. It should be noted that this6

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 exp osure 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 plots8

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 backgr ound 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

- bk g)*k1*T]

- Plateau = MIN[1, bkg +(1

- bkg)*k1*T 2 ]

- Plateau = MIN[1, bkg +(1

- bkg)*k1*T 3 ]

- 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 t he 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 li kelihood (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: BMD T = 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%

e xtra 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+0 6 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 0.031 5 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

-- 0.02 41

-- 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(BM D70) 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 la g 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 fro m 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 c hanged 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 set14

Derivation of the POD and Rf C for Discrete Pleural Thickening Using the Cumulative Normal Michaelis

- Mente n 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 BMDL 10 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 divide d 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 BMDL 10 with T = 40 years of 0.0134 fibers

- yr/cc calculated with the Cumulative Normal Michaelis

- Menten model. The BMDL 10 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 la g 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 r esults 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
exposu re of 70 years.15

TABLE E

- 4 Sensitivity analysis of RfC based on alternative modeled values of T

T he 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 wa s 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 disc rete 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 Micha elis

- 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

- 0616

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 19721

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 452672

Introduction

This project builds on the previous work of Dr. James Lockey et al. investigating possible effects of exposures to dust containing Li bby amphiboles at a plant in Marysville, Ohio 1,2 . The data used in the original exposure reconstruction and as reported in the published manuscripts, was based on the exposures measurements available at that time 1 . 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 fr om 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 s tep, 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 m

easurements 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 th e 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 backgro und 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 a llowed 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 pe r 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

concentra tion 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 d uplicate 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 condu cted 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 rec ord 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 we re each numbered starting from 1. The document ID variable states EPA, DOJ or UC followed by the document number. Record IDs were generated4

by using a unique identifier like a sample number for each document. If a unique identifier was unable to be disc erned 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 bel ow 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 Micros oft 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 appropriat e 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, s ampling 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 b ased 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,

sam ple 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 data sets 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 eac h 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 was5

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 amphibo le 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, fi bers 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 tr ends 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

sour ces: 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 18 0 (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 t he 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 gener ally 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, the6

workers‟ recollections supported the findings from the IH data demonstrati ng 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 mark ed 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. Hig h

- 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

r ecorded 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 m easurements 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 documentati on 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 e stimates 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) report 3,4

. Post 1980 usage included A frican/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 ear lier, 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 vermic ulite, 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 use d 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 demonstrat e 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 measurement s 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

e xposure 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 d ue 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 a vailable; 1957 to 1971, when no IH measurements were available. The exposure estimation process is described below, first for Trionizing where8

vermiculite was expanded and then for other departments where either no or expanded vermiculite was used.

Tr ionizing 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 f rom 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 g eneral 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 follow ed:

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 c hosen 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 perio d 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 ea ch 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 exposur e 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) wi thin 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 out doors 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 I H 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 perce nt 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 desc ribed 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 19 94. 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 “pl ant 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 q ualitative 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 trion izing, 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
exposur e 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 trionizin g 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 ex pander, 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 u nexpanded 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 departm ent 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 work ed:

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 minu te 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 3 rd fifteen minute break was provided when working longer than eight hours in a day.11

Employees in so me 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 hou rs 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 wo rk 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 packa ging 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 sam e 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 was12

accepted as provided for the development of a cumulative human equivalent exposure concentration (CHEEC) for the Marysville occupational cohort.

5.1 Seasonal sche dule 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 su pplied 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

o ccupational 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 maint enance, 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 starti ng 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 depar tment 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 Correcti on Factor usually used with an occupational study is 5 days/7 days x 10

m 3 /20 m

3

UC Procedure

CHEEC= ( Exposure Est year

- dept

- season 1 x Correction Factor season 1 x Seasonal Duration Factor) + ( Exposure Est year

- dept

- season 2 x Correction Factor season 2 x Seasonal Duration Factor) + ... ( Exposure Est year

- dept

- season x x Correction Factor season 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 = 1.2 5 m 3 /hr x 12 hrs = 15 m 3

Breathing rate, not working = 0.625 m 3 /hr x 12 hrs = 7.5 m 3

Total breathing rate = 15 + 7.5 = 22.5 m 3 /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 s ummer vacation

(92

– 14) x 5/7 = 55.714286 days
Breathing rate, working = 1.25 m 3 /hr x 8 hrs = 10 m 3

Breathing rate, not working = 0.625 m 3 /hr x 16 hrs = 10 m 3

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, 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 m 3 /hr x 12 hrs = 15 m 3

Breathing rate, working, 8 hr day = 1.25 m 3 /hr x 8 hrs = 10 m 314

Breathing rate, not working = 0.625 m 3 /hr x 16 hrs = 10 m 3

Total breathing rate, 12 hour work day = 15 + 7.5 = 22.5 m 3 /day
Total breathing rate, 8 hr work day = 10 + 10 = 20 m 3 /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 m 3 /hr x 8 hrs = 10 m 3

Breathing rate, not working = 0.625 m 3 /hr x 16 hrs = 10 m 3

Total breathing rate = 10 + 10 = 20 m 3 /day

Correction Factor Spring = 107.321429/151.25 x 10/20 = 0.354782

Sum mer
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 m 3 /hr x 8 hrs = 10 m 3

Breathing rate, not working = 0.625 m 3 /hr x 16 hrs = 10 m 3

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 days15

Breathing rate, working, 8 hr day = 1.25 m 3 /hr x 8 hrs = 10 m 3

Breathing rate, not working = 0.625 m 3 /hr x 16 hrs = 10 m 3

Total breathing rate = 10 + 10 = 20 m 3 /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. 2 These tables describe each subject‟s identification number, job start and stop date, date of radiogr aph, 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 q uantitative 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 provide d 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 f

or 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 archi ved 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 incre ased 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 decisi ons 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 v iewed 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 clean up. 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 testin g 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 non

expander 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 quanti tative 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. Us e 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 Facili ty 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) 91420

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 memo22

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 m aint (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 m aint (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
Backgro und* 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, Warehouse23This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 1 APPENDIX G. EXTRA RISK AND UNIT RISK CALCULATION 1

The following tables illustrate the computational details of the unit risks for 2 mesothelioma mortality (Tables G

- 1 and G

- 2) and for lung cancer mortality (Tables G

- 3 and 3
G

- 4). Section 5.4.5.1 details the application of the life

- table methodology for mesothelioma. 4
The results of Tables G

- 1 and G

- 2 are shown in Table 5

- 1 7 and are not adjusted for the 5
underascertainment of m esothelioma described in the Section 5.4.5.1.1

. The unit risks adjusted 6
for underascertainment are shown in Table 5

- 18

. 7
8 Column Definitions for Tables G

- 1 and G

- 2 9 10 Column A: Age interval up to age 85. 11

Column B: All

- cause mortality rate for interval i (× 1 0 5 /year) (2007 data NVSR 58(19) 2010). 12

Column C: All

- cause hazard rate for interval i (h* i ) (= all

- cause mortality rate × number of 13 years in age interval). 14

Column D: Probability of surviving interval i (q i ) (= exp(−h* i )). 15

Column E: Probability of survivin g up to interval i (S i ) (S 1 = 1; S i = S i−1 × q i−1 , for i>1). 16

Column F: Lagged exposure at mid

- interval (x dose). 17

Column G: Mesothelioma mortality hazard rate in exposed people for interval. To estimate the 18 LEC 01 , i.e., the 95% lower bound on the continuous exposure giving an extra risk of 19 1%, the 95% upper bound on the regression coefficient is used. 20

Column H: All

- cause hazard rate in exposed people for interval i (h*x i ) (= h* i + (hx i − h i )). 21

Column I: Probability of surviving interval i without d ying from mesothelioma for exposed 22 people (qx i ) (= exp(−h*x i )). 23

Column J: Probability of surviving up to interval i without dying from mesothelioma for 24 exposed people (Sx i ) (Sx 1 = 1; Sx i = Sx i−1 × qx i

- 1 , for i>1). 25

Column K: Conditional probability of dying from mesothelioma in interval i for exposed people 26 (= (hx i /h*x i ) × Sx i × (1−qx i )) (Rx, the lifetime probability of dying from 27 mesothelioma for exposed people = the sum of the conditional probabilities across 28 the intervals). 29

30 31 Note that the life tables for mesothelioma mortality estimate the extra risk as the absolute 32 risk since there is no assumption of a background risk in the absence of exposure. In each of the 33 life tables, inhalation exposure commences at age 16 years and c ontinues at the same exposure 34 concentration for the duration of the life table. This allows for the computation of an 35
“adult

- only” occupational lifetime unit risk, which is then scaled by a ratio of 70:54 to account 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 2 for risk over the standard 70

- year life time. While exposure is initiated in the life table at age 1
16 years, this exposure is lagged to match the corresponding exposure

- response models, which 2 provide the hazard rates per unit of exposure. For example, in Table G

- 1, Column F shows 3 exposure lagg ed by 10 years so that no lagged exposure appears in the table until age 26 years. 4
In Table G

- 2, Column F shows exposure lagged by 15 years so that no lagged exposure appears 5 in the table until age 31 years. 6 7This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 3 Table G

- 1

. Mesot helioma extra risk calculation for environmental exposure 1 to 0.1479 fibers/cc Libby Amphibole asbestos using the metric of cumulative 2 exposure with a 10

- year exposure lag and a 5

- year half

- life of exposure, as 3 described in Section 5.4. 5

. 3 as the reasonable upper bound 4 5

A B C D E F G H I J K

A ge 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 6This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 4 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

A ge 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 R x = 0.0100 1 Absolute risk = 0.01000, exp

. level = 0.1479; Occupational lifetime unit risk = 0.0676 (Based on occupational 2 exposures beginning at age 16 years); Scaled occupational lifetime unit risk = 0.0876 (Scaled by ratio of 70:54 to 3 account for risk over 70 year lifetime). 4 5This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 5 Table G

- 2

. Mesothelioma extra risk calculation for environmental exposure 1 to 0.2446 fibers/cc Libby Amphibole asbestos using the metric of cumulative 2 exposure with a 15

- year exposure lag and a 5

- year half

- life of exposure, as 3 described in Sectio n 5.4. 5

. 3 as the lowest information criterion 4 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 6This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 6 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 R x = 0.0100 1 Absolute risk = 0.01000; exp level = 0.2446; Occupational lifetime unit risk = 0.0409 (Based on occupational 2 exposures beginning at age 16 years); Scaled occupational lifetime unit risk = 0.0530 (Scaled by ratio of 70:54 to 3 account for risk over 70 year li fetime). 4 5This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 7 The following tables show details of the computations of the unit risks for lung cancer 1 mortality (Tables G

- 3 and G

- 4). Section 5.4.5.2 details the application of the life

- table 2 methodology for lung cancer. The results of Tables G

- 3 and G

- 4 ar e shown in Table 5

- 1 9

. 3
4 Column Definitions for Tables G

- 3 and G

- 4 5 6 Column A: Age interval up to age 85. 7

Column B: All

- cause mortality rate for interval i (× 10 5 /year) (2007 data NVSR 58(19) 2010). 8

Column C: Lung cancer mortality rate for interval i (× 10 5 / year) (2003

- 2007 SEER Table 9 15.10, age

- specific U.S. death rates). 10

Column D: All

- cause hazard rate for interval i (h* i ) (= all

- cause mortality rate × number of 11 years in age interval). 12

Column E: Probability of surviving interval i (q i ) (= exp(−h* i )). 13

Column F: Probability of surviving up to interval i (S i ) (S 1 = 1; S i = S i−1 × q i−1 , for i>1). 14

Column G: Lung cancer mortality hazard rate for interval i (h i ) (= lung cancer mortality rate × 15 number of years in interval). 16

Column H: Conditional probability of dying from lung cancer in interval i (= (h i /h* i ) × S i × 17 (1−q i )), i.e., conditional upon surviving up to interval i (Ro, the background lifetime 18 probability of dying from lung cancer = the sum of the conditional probabilities 19 across the intervals). 20

Column I: Lag ged exposure at mid

- interval (x dose). 21

Column J: Lung cancer mortality hazard rate in exposed people for interval. To estimate the 22 LEC 01 , i.e., the 95% lower bound on the continuous exposure giving an extra risk of 23 1%, the 95% upper bound on the regressio n coefficient is used, i.e., 24 MLE + 1.645 × SE. 25

Column K: All

- cause hazard rate in exposed people for interval i (h*x i ) (= h* i + (hx i − h i )). 26

Column L: Probability of surviving interval i without dying from lung cancer for exposed 27 people (qx i ) (= exp(−h*x i )). 28

Column M: Probability of surviving up to interval i without dying from lung cancer for exposed 29 people (Sx i ) (Sx 1 = 1; Sx i = Sx i−1 × qx i

- 1 , for i>1). 30

Column N: Conditional probability of dying from lung cancer in interval i for exposed people 31 (= (hx i /h* x i ) × Sx i × (1−qx i )) (Rx, the lifetime probability of dying from lung 32 cancer for exposed people = the sum of the conditional probabilities across the 33 intervals). 34

35 36 Note that the life tables for lung cancer mortality estimate the extra risk as (R x - R o ) / 37 (1

- R o ), where R x is the lifetime cancer mortality risk in the exposed population and R o is the 38This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 8 lifetime lung cancer mortality risk in an unexposed population (i.e., the background risk). In 1 each of the life tables, inhalation exposure commences at age 16 years and continues at the same 2 exposure concentration for the duration of the life table. This allows for the computation of an 3
“adult

- only” occupational lifetime unit risk, which is then scaled by a ratio of 70:54 to account 4
for risk over the standard 70

- year lifetime. While exposure is initiated at age 16 years, this 5 exposure is lagged to match the correspo nding exposure

- response models, which provide the 6 hazard rates per unit of exposure. For example, in Tables G

- 3 and G

- 4, Column I shows 7 exposure lagged by 10 years so that no lagged exposure appears until age 26 years. 8This document is a draft for review purposes only and does not constitute Agency policy

.

G

- 9 DRAFT: DO NOT CITE OR QUOTE 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 0.0000This document is a draft for review purposes only and does not constitute Agency policy

.

G

- 10 DRAFT: DO NOT CITE OR QUOTE 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 describe d 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 0.0010This document is a draft for review purposes only and does not constitute Agency policy

.

G

- 11 DRAFT: DO NOT CITE OR QUOTE 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 describe d 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

R o = 0.0531 R x = 0.0625

Extra risk = 0.01001; exp level = 0.191; Occupational lifetime unit = 0.0524 (Based on occupational exposures beginning at ag e 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 OR QUOTE 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

- yea r 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 0.0000This document is a draft for review purposes only and does not constitute Agency policy

.

G

- 13 DRAFT: DO NOT CITE OR QUOTE 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 0.0004This document is a draft for review purposes only and does not constitute Agency policy

.

G

- 14 DRAFT: DO NOT CITE OR QUOTE 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

R o = 0.0531 R x = 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).