TSD for Noncancer RELs June 2008 

Air Toxics Hot Spots 

Risk Assessment Guidelines 

Technical Support Document 

For the Derivation of 

Noncancer Reference Exposure Levels 

Air Toxicology and Epidemiology Branch 

Office of Environmental Health Hazard Assessment 

California Environmental Protection Agency 

1515 Clay Street, 16th Floor 

Oakland, California 94612 

As approved by the Scientific Review Panel, June 18, 2008 

i 
TSD for Noncancer RELs June 2008 

Project Lead: Andrew G. Salmon, M.A., D. Phil. 

Reviewed by: 

Melanie A. Marty, Ph.D. 

Chief, Air Toxicology and Epidemiology Branch, OEHHA 

George V. Alexeeff, Ph.D. 

Deputy Director of Scientific Affairs, OEHHA 

OEHHA also acknowledges the following contributors: 

Amy Arcus, D.V.M. 

Robert Blaisdell, Ph.D. 

Joseph P. Brown, Ph.D. 

John Budroe, Ph.D. 

Shelley DuTeaux, Ph.D. MPH 

James F. Collins, Ph.D. 

Daryn E. Dodge, Ph.D. 

Richard Lam, Ph.D. 

Melanie A. Marty, Ph.D. 

Mark Miller, MD 

Karen Riveles, Ph.D. MPH 

Andrew G. Salmon, M.A., D.Phil. 

Kathleen Vork, Ph.D. MPH 

Bruce Winder, Ph.D. 

Jianming Yang, Ph.D. 

Air Toxicology and Epidemiology Branch 

Office of Environmental Health Hazard Assessment 

ii 
TSD for Noncancer RELs June 2008 

CONTENTS

EXECUTIVE SUMMARY........................................................................................................................ XI

1 INTRODUCTION.................................................................................................................................1

1.1 OBJECTIVE ................................................................................................................................2

1.2 LEGISLATIVE MANDATES.....................................................................................................3

1.3 SUMMARY OF THE METHODOLOGICAL CHANGES FOR DEVELOPING 

ACUTE, EIGHT-HOUR AND CHRONIC RELS .................................................................6

2 REFERENCE EXPOSURE LEVEL (REL) DEVELOPMENT PROCESS .........................................8

2.1 LIST OF SUBSTANCES CONSIDERED ..................................................................................9

2.2 HAZARD INDEX APPROACH .................................................................................................9

3 POPULATIONS OF CONCERN........................................................................................................12

3.1 CHILDREN AS A POPULATION OF CONCERN .................................................................13

3.2 DIFFERENCES BETWEEN CHILDREN AND ADULTS......................................................14

3.2.1 Pharmacokinetic Differences................................................................................... 15 

3.2.2 Pharmacodynamic Differences................................................................................ 23 

3.3 RISK ASSESSMENT CONSIDERATIONS ............................................................................24

4 REL DEVELOPMENT METHODOLOGIES....................................................................................26

4.1 CRITERIA FOR STUDIES UTILIZ ED TO IDENTIFY ADVERSE HEALTH 

EFFECTS..............................................................................................................................26

4.1.1 Selection of Key Studies.......................................................................................... 26 

4.2 WEIGHT OF EVIDENCE EVALUATIONS AND CRITERIA FOR CAUSALITY ..............30

4.2.1 Strength of Association............................................................................................ 31 

4.2.2 Consistency of Association...................................................................................... 32 

4.2.3 Temporality.............................................................................................................. 33 

4.2.4 Coherence and Biological Plausibility..................................................................... 33 

4.2.5 Dose-response .......................................................................................................... 33 

4.2.6 Specificity ................................................................................................................ 34 

4.2.7 Experimental Evidence ............................................................................................ 34 

4.3 HAZARD IDENTIFICATION..................................................................................................34

iii 
TSD for Noncancer RELs June 2008 

4.3.1 Definition of Adverse Effect.................................................................................... 34 

4.3.2 Nature of Adverse Effects........................................................................................ 36 

4.3.3 Severity of Effects.................................................................................................... 36 

4.3.4 Target Organs........................................................................................................... 37 

4.4 DOSE-RESPONSE ASSESSMENT .........................................................................................38

4.4.1 Estimation of Threshold or Low Response Concentrations..................................... 38 

4.4.2 Extrapolation and Uncertainties in the Database ..................................................... 46 

4.4.3 Types of Uncertainty and Variability....................................................................... 46 

4.4.4 Application of Mechanistic Data in Interspecies and Intraspecies 

Extrapolation............................................................................................................ 49 

4.4.5 Extrapolating from LOAELs to NOAELs............................................................... 52 

4.4.6 Extrapolating from Study Duration to REL Reference Period ................................ 54 

4.4.7 Accounting for Potentially Greater Human Susceptibility ...................................... 57 

4.4.8 Increased Susceptibility of Sensitive Individuals .................................................... 61 

4.4.9 Uncertainty Associated with Deficiencies in the Overall Database ........................ 66 

4.4.10 Summary of Uncertainty Factors............................................................................. 68 

4.5 SUPPORTING DATA...............................................................................................................68

5 ACUTE REFERENCE EXPOSURE LEVELS...................................................................................70

5.1 TIME FRAME OF INTEREST .................................................................................................70

5.2 EXPOSURE DURATION AND PATTERNS ..........................................................................70

5.2.1 Exposure Concentration Averaging Period.............................................................. 71 

5.3 HUMAN EQUIVALENT CONCENTRATION (HEC) PROCEDURE FOR ACUTE 

RELS ....................................................................................................................................73

5.4 EFFECTS OF EXPOSURE DURAT ION – SPE CIAL CONSIDERATIONS FOR 

ACUTE EFFECTS ...............................................................................................................73

5.4.1 Concentration and Time Extrapolation using Haber’s Law..................................... 73 

5.4.2 Time Extrapolation for Acute RELs Based on Developmental Studies.................. 77 

5.5 LOAEL TO NOAEL EXTRAPOLATION ...............................................................................77

5.6 PRE-EXISTING ACUTE EXPOSURE GUIDELINES............................................................79

5.6.1 The California Ambient Air Quality Standards (CAAQS)...................................... 80 

5.6.2 The Threshold Limit Value-Time Weighted Average (TLV-TWA) and 

Short-Term Exposure Limit (STEL)........................................................................ 80 

iv 
TSD for Noncancer RELs June 2008 

5.6.3 Various Emergency Exposure Guidance Levels...................................................... 81 

5.7 AREAS FOR FURTHER RESEARCH.....................................................................................82

5.7.1 Acute Toxicity Data................................................................................................. 82 

5.7.2 LOAEL to NOAEL Uncertainty Factor................................................................... 82 

5.7.3 Interspecies Uncertainty Factor ............................................................................... 82 

5.7.4 Uncertainty Factor for Database Deficiencies......................................................... 83 

5.7.5 Time Extrapolation for Acute RELs........................................................................ 83 

5.7.6 Additivity of Adverse Effects.................................................................................. 83 

6 EIGHT-HOUR REFERENCE EXPOSURE LEVELS .......................................................................84

6.1 POPULATIONS AT RISK........................................................................................................84

6.2 CHARACTERIZATION OF 8-HOUR EXPOSURES..............................................................85

6.2.1 Eight-Hour Averaging Period for Chronic Toxicity................................................ 85 

6.2.2 Eight-Hour Averaging Period for Acute Recurrent Toxicity................................... 86 

6.2.3 Eight-Hour REL Exposure Duration Adjustments for Developmental 

Toxicants.................................................................................................................. 86 

6.3 HUMAN EQUIVALENT CONCENTRATION (HEC) AND UNCERTAINTY 

FACTOR APPLICATIONS .................................................................................................87

6.4 HAZARD INDEX CALCULATION ........................................................................................87

7 CHRONIC REFERENCE EXPOSURE LEVELS..............................................................................88

7.1 PRIORITY FOR EVALUATION OF CHEMICALS ...............................................................88

7.2 EXPOSURE CONCENTRATION AVERAGING PERIOD....................................................88

7.2.1 Effects of Exposure Continuity and Duration.......................................................... 89 

7.2.2 Differences between Lifetime and Less-than-Lifetime Exposures.......................... 89 

7.3 LOAEL TO NOAEL EXTRAPOLATION ...............................................................................91

7.4 HYPERPLASIA.........................................................................................................................92

7.5 PRE-EXISTING CHRONIC EXPOSURE GUIDELINES .......................................................92

7.5.1 U.S. EPA Reference Concentrations........................................................................ 92 

7.5.2 U.S. EPA Reference Doses...................................................................................... 92 

7.5.3 Occupational Threshold Limit Values..................................................................... 93 

7.5.4 California Ambient Air Quality Standards .............................................................. 93 

REFERENCES............................................................................................................................................94

v 
TSD for Noncancer RELs June 2008 

APPENDIX A: LIST OF ACRONYMS .....................................................................................................1 

A.1 LIST OF ACRONYMS......................................................................................................................2 

APPENDIX B: ACUTE, 8-HOUR, AND CHRONIC REFERENCE EXP OSURE LEVELS 

(RELs) SUMMARY TABLE................................................................................................................1 

INTRODUCTION TO REFERENCE EXPOSURE LEVELS SUMMARY TABLE .............................2 

ACUTE, 8-HOUR, AND CHRONI C REFERENCE EXPOSURE LEVELS (RELs) 

SUMMARY TABLE..............................................................................................................3 

APPENDIX C: SUBSTANCES FOR WHICH EMISSIONS MUST BE QUANTIFIED ........................... 

APPENDIX D: INDIVIDUAL ACUTE, 8-HOUR, AND CHRONIC REFERENCE EXPOSURE 

LEVEL SUMMARIES............................................................................................................................ 

D.1 SUMMARIES USING THIS VERSION OF THE HOT SPOTS RISK ASSESSMENT 

GUIDELINES........................................................................................................................... 

D.1.A. Acetaldehyde................................................................................................................ 

D.1.B. Acrolein ........................................................................................................................ 

D.1.C. Arsenic.......................................................................................................................... 

D.1.D Formaldehyde................................................................................................................ 

D.1.E. Manganese .................................................................................................................... 

D.1.F. Mercury......................................................................................................................... 

APPENDIX E: TOXICOKINETICS ..........................................................................................................1 

E.1 APPLICATIONS OF PHARMACOKINETIC ANALYSIS AND PBPK MODELING ............2 

E.2 PUBLISHED SUMMARIES OF AGE-DEPENDENT TOXICOKINETICS.............................4 

E.2.1 Age-dependent Toxicokinetic parameters. ................................................................ 4 

E.2.2 PBPK Models of Inter-individual Variability.......................................................... 15 

E.3 OEHHA STUDIES USING PBPK MODELING AND SPECIES DIFFERENCES: ...............19 

E.3.1 Materials and Methods............................................................................................. 20 

E.3.2 Results...................................................................................................................... 24 

E.3.3 Discussion ................................................................................................................ 41 

E.3.4 Uncertainty Factor for Variability within the Human Population ........................... 46 

E.3.5 Adequacy of the UFH-k for Younger Ages – Newer Analyses............................... 47 

E.4 TOXICOKINETIC MODEL PARAMETERS FOR INDIVIDUAL CHEMICALS.................51 

E.5 TOXICOKINETICS: BERKELEY MADONNA MODEL CODES ........................................67 

E.5.1 Model Code for Furan 0-5 yr Child ......................................................................... 67 

E.5.2 Model Code for MTBE 0-6 Yr Child....................................................................... 68 

E.5.3 Model Code for PCE 0-6 yr Child ........................................................................... 71 

vi 
TSD for Noncancer RELs June 2008 

E.5.4 Model Code for BaP vapor 0-6 yr Child.................................................................. 73 

E.5.5 Model Code for NAP/NO RT 0-6 yr Child.............................................................. 76 

E.5.6 Model Code for BD/BMO RT 0-5 yr Child............................................................. 86 

E.5.7 Model Code for BD/BMO/DEB 0-5 Yr Child......................................................... 95 

E.5.8 Model Code for Styrene/SO RT (Sarangapani et al., 2002) 0-5 yr Child.............. 101 

E.5.9 Model Code for Vinyl Chloride 0-5 yr Child ........................................................ 109 

E.5.10 Model Code for TCE 0-5 yr child.......................................................................... 112 

E.5.11 Model Code for Styrene/SO (Csanady et al., 2003) 0-6 yr Child.......................... 117 

E.5.12 Model Code for DCM 0-5 yr Child ....................................................................... 121 

E.5.13 Model Code for Ethylene/Ethylene oxide 0-6 yr Child ......................................... 124 

E.5.14 Model Code for Styrene/SO RT Model of Csanady et al. (2003) Adult ............... 127 

E.5.15 Model Code for Carbon tetrachloride 0-6 yr Child................................................ 132 

E.5.16 Model Code for Toluene 0-6 yr Child ................................................................... 133 

E.5.17 Model Code for Xylene 0-6 Yr Child .................................................................... 135 

E.5.18 Model Code for Toluene-Xylene Mixed Exposure 0-6 Yr Child .......................... 137 

APPENDIX F: ESTIMATING HUMAN EQUIVALENT CONCENTRATIONS USING THE 

U.S. EPA DEFAULT APPROACH ......................................................................................................1 

F.1 ESTIMATING HUMAN EQUIVALE NT CONCENTRATIONS USING THE U.S. 

EPA DEFAULT APPROACH ...............................................................................................1 

F.1.1 Gases with Respiratory Effects.................................................................................. 1 

F.1.2 Gases with Systemic Effects...................................................................................... 3 

F.1.3 Particulates with Respiratory Effects......................................................................... 3 

F.2 HUMAN EQUIVALENT CONCENTRATION CALCULATION FOR CHILDREN..............4 

F.2.1 Respiratory Differences between Children and Adults.............................................. 4 

F.2.2 Calculation of Adult and Child HECs........................................................................ 8 

F.3 CONCLUSIONS........................................................................................................................13 

APPENDIX G: VALUE OF THE HABER’S LAW EXPONE NT (N) FOR VARIOUS GASES 

AND VAPORS FOR ACUTE RELS DEVELOPED USING OE HHA (1999) 

PROCEDURES .....................................................................................................................................1 

APPENDIX H. TARGET ORGANS OR SYST EMS USED IN ACUTE , 8-HOUR AND 

CHRONIC HAZARD INDEX CALCULATIONS...............................................................................1 

vii 
TSD for Noncancer RELs June 2008 

LIST OF FIGURES 

Figure 1-1. Public and Peer Review Process for Establishing Reference Exposure Levels.......... 5 

Figure 2-1. REL Development Process ......................................................................................... 9 

Figure 4-1 Log-probit modeling of dose-response data for silica1 .............................................. 42 

Figure 4-2. Interspecies Extrapolation......................................................................................... 50 

Figure 4-3 Intraspecies Extrapolation.......................................................................................... 52 

Figure E.3 1 Human/Animal Metric Ratios For Aliphatic Aldehydes.................................................41 

Figure F.2 1. Changes in Minute Volume/Tracheobronchial Surface Area with Age. ................10 

Figure F.2 2. Increase in Number of Alveoli from Birth to Age 8. ..............................................11 

Figure F.2 3. Decline in Minute Volume/Tracheobronchial Surface Area with Age...................12 

LIST OF TABLES 

Table 4.4.1. Possible D efault Uncertainty Fact ors used in Deriving A cute, 8-hour and 

Chronic RELs .........................................................................................................................47 

Table 4.4.2. Che micals Studied by OEHHA in PBPK Analyses, Grouped by m odeled 

UFH-k for Infants and Children (see Appendix E) ................................................................66 

Table 5.5.1. Relationship between Effect Categories and Severity..............................................79 

Table 7.2.1. Average Life-span for Humans VS. Experimental Animals ....................................90 

TABLE B.1.1. OEHHA ACUTE (A), EIGHT-HOUR (8) AND CHRONIC (C) REFERE NCE 

EXPOSURE LEVEL (REL) SUMMARY ............................................................................................3 

Table E.2.1. Compounds Showing Reduced Elimination in Infants and/or Children1.................. 5 

Table E.2.2. CYP1A2 Mediated Metabolic Partial Clearances in Healthy Volunteers................. 6 

Table E.2.3. Inter-individual Variation in Toxicokinetics of Caffeine in Healthy 

Volunteers1

.......................................................................................................................... 7 

Table E.2.4. Toxicokinetics of Caffeine: Comparisons Between Healthy Adults and 

Different Subgroups............................................................................................................ 8 

Table E.2.5. Inter-individual Variation in Toxicokinetics of Theophylline in Healthy 

Volunteers......................................................................................................................... 10 

viii 
TSD for Noncancer RELs June 2008 

Table E.2.6. Toxicokinetics of Theophylline: Comparisons between Healthy Adults and 

Different Subgroups.......................................................................................................... 11 

Table E.2.7. Inter-individual Variation in Toxicokinetics of Theobromine and 

Paraxanthine in Healthy Volunteers ................................................................................. 13 

Table E.2.8. Pathway-specific TK-UFs for Children after Oral Exposure and Neonates 

after Intravenous Exposure ............................................................................................... 14 

Table E.3.1. PBPK Model Predictions for Selected Chemicals: Low End of Range 

(1 μg/m3

 x 8 hr/d, 24 hr simulations)................................................................................ 25 

Table E.3.2. PBPK Model Predictions for Selected Chemicals: High End of Range 

(10 mg/m3 x 8 hr/d, 24 hr simulations)............................................................................. 27 

Table E.3.3. PBPK Model Predictions for Ethylbenzene with Human Age-Specific 

Regressions and Alternative Metabolic Parameters (1 μg/m3 x 8 hr/d, 24-48 hr 

simulations)....................................................................................................................... 29 

Table E.3.4. PBPK Model Predictions for Ethylbenzene with Age-Specific Parameters 

Modeling of Neonatal Rat (1 μg/m3 x 8 hr/d, 24-48 hr simulations)............................... 31 

Table E.3.5. PBPK Model Predictions for Toluene with Age-Specific Regressions 

(1 μg/m3 x 8 hr/d, 24 hr simulations)................................................................................ 32 

Table E.3.6. PBPK Model Predictions for Vinyl Chloride with Age-Specific Regressions 

(1 μg/m3 x 8 hr/d, 24 hr simulations)................................................................................ 33 

Table E.3.7. PBPK Model Predictions for Naphthalene/ Naphthalene Oxides (NPO) with 

Age-Specific Regressions (Naphthalene 1 μg/m3 x 8 hr/d, 24 hr simulations) ............... 34 

Table E.3.8. PBPK Model Predictions for Styrene/ Styrene Oxide with Age-Specific 

Regressions (1 μg/m3

 x 8 hr/d, 24 hr simulations) ........................................................... 35 

Table E.3.9. PBPK Model Predictions for Formaldehyde with Age-Specific Parameters 

from Clewell et al. 2003: Modeling of Neonatal and Immature Rat 

(1 μg/m3 x 8 hr/d, 24 hr simulations)................................................................................ 36 

Table E.3.10. PBPK Model Predictions for Formaldehyde with Age-Specific Parameters: 

Modeling of Human Children (1 μg/m3

 x 8 hr/d, 24 hr simulations). .............................. 37 

Table E.3.11. DAFs Summary Based on PBPK Modeling of Internal Dosimetry...................... 38 

Table E.3.12. Human/Rat PBPK Model Predictions for Aliphatic Aldehydes: 

(1 μg/m3

 x 8 hr/d, 24 hr simulations)................................................................................ 40 

Table E.3.13. Summary of Infants’ and Children’s PBPK Modeling by OEHHA with 

Selected Environmental Toxicants by Inhalation Exposure ............................................. 43 

ix 
TSD for Noncancer RELs June 2008 

Table E.3.14. Comparison of Slopes of Mild and Lethal Effectsa............................................... 47 

Table E.4.1. PBPK Model Parameters for Furan 0-6 Years of Age ........................................... 51 

Table E.4.2. PBPK Model Parameters for MTBE 0-6 Years of Age .......................................... 52 

Table E.4.3. PBPK Model Parameters for PCE 0-6 Years of Age .............................................. 53 

Table E.4.4. PBPK Model Parameters for BaP 0-6 Years of Age............................................... 54 

Table E.4.5. PBPK-RT Model Parameters for NAP/NO 0-5 Years of Age ................................ 55 

Table E.4.6. PBPK-RT Model Parameters for BD/BMO 0-5 Years of Age ............................... 56 

Table E.4.7. PBPK Model Parameters for BD/BMO/DEB 0-5 Years of Age............................. 57 

Table E.4.8. PBPK-RT Model Parameters for Styrene/SO 0-5 Years of Age............................. 58 

Table E.4.9 PBPK Model Parameters for Vinyl Chloride 0-5 Years of Age ............................. 59 

Table E.4.10. PBPK Model Parameters for TCE and Metabolites 0-5 Years of Age ................. 60 

Table E.4.11. PBPK Model Parameters for DCM 0-5 Years of Age .......................................... 61 

Table E.4.12. PBPK Model Parameters for Ethylene/EO 0-6 Years of Age............................... 62 

Table E.4.13. PBPK-RT Model Parameters for Styrene/SO Adult ............................................ 63 

Table E.4.14. PBPK Model Parameters for Carbon Tetrachloride 0-6 Years of Age ................ 64 

Table E.4.15. PBPK Model Parameters for Toluene 0-6 Years of Age ..................................... 65 

Table E.4.16. PBPK Model Parameters for Xylene 0-6 Years of Age ....................................... 66 

Table F.1.1. Default Lung Surface Area Estimates (U.S. EPA, 1994).......................................... 2 

Table F.1.2. Intercept and Slope Parameters for Estimating Minute Volume from Body 

Weight................................................................................................................................. 2 

Table F.1.3. Parameters for Deposition Efficiency Equation ........................................................ 4 

Table F.2.1. Relative Minute Volume (MV) to Surface Area (SA) Ratios for Pulmonary, 

Tracheobronchial, and Extrathoracic Spaces in Children................................................... 9 

Table G.1 Value of the Haber’s Law Exponent (n) for Various Gases and Vapors for 

Acute RELs Developed Using OEHHA 1999 Procedures ................................................. 2 

x 
TSD for Noncancer RELs June 2008 

Executive Summary 

This document describes the methodology used in developing acute, 8-hour and chronic 

Reference Exposure Levels (RELs) for use in risk assessments conducted under 

California’s Air Toxics Hot Spots and Toxic Air Contaminants programs. RELs are 

concentrations of a chemical at or below which adverse noncancer health effects are not 

anticipated to occur for a specified exposure duration. The basic methodology for REL 

development used previously by the Office of Environmental Health Hazard Assessment 

(OEHHA) and other agencies undertaking public health risk assessment remains 

unchanged. This consists of identification of a point of departure, such as an exposure 

level in an animal experiment or an epidemiological study at which no adverse effects (or 

at least minimal adverse effects) are observed, or a benchmark dose (a statistical estimate 

of a low response rate, typically 5%, in the dose response curve for the chemical of 

concern). Extrapolation from this point of departure to a health protective level for the 

target human population is by means of explicit models where possible, but more often 

by means of uncertainty factors. 

This document addresses the SB 25 mandate to ensure that OEHHA’s risk assessment 

methods adequately protect infants and children, and incorporates scientific advances 

since the methodologies for acute and chronic RELs were first developed. Methods are 

added for developing 8-hr RELs in addition to the traditional acute REL for infrequent 1-

hr exposures, and the chronic REL for long-term, continuous exposures. We harmonize 

the methodologies for acute, eight-hour and chronic RELs to the extent possible. This 

document also defines special procedures for derivation of RELs based on certain 

toxicological endpoints such as trigeminal nerve transmitted irritation of the eyes, nose, 

and upper airway. 

The following sections summarize the specific changes in methodology relative to the 

previous version of the Hot Spots guidelines for evaluation of noncancer health effects. 

These changes are described in detail in the main body of this report. 

Use of Specific Models Rather than Uncertainty Factors when Possible 

There are many information gaps in the data available for evaluating noncancer health 

impacts of chemicals and setting appropriate RELs. The standard approach has been to 

divide an animal no-observed-adverse-effect-level (NOAEL) by uncertainty factors (UFs) 

to help ensure that public health is protected. In recent years such techniques as the 

benchmark dose method (BMD) and physiologically based pharmacokinetic (PBPK) 

modeling have taken advantage of available data to quantitatively address uncertainties in 

the standard approach to noncancer REL derivation. OEHHA recommends the use of 

these techniques wherever possible in order to address quantitatively the adequacy of 

acute and chronic RELs to protect the health of both children and adults. However, in 

some cases the available dose-response data are not suitable for application of the 

benchmark dose approach. Furthermore, it must be noted that data are not available for 

many chemicals to use PBPK modeling. Thus, while PBPK is a useful tool, the 

xi 
TSD for Noncancer RELs June 2008 

traditional paradigm (e.g., NOAEL or BMD divided by UFs) will still be most frequently 

used. 

Value of Default Intraspecies Uncertainty Factor (UFH) 

The uncertainty factor used to account for intraspecies (inter-individual) variability in the 

human population (UFH) has previously been assigned a default value of 10. 

Investigators have proposed subdividing the intraspecies uncertainty factor into √10 for 

toxicokinetic (UFH-k) and √10 for toxicodynamic (UFH-d) subfactors. The toxicokinetic 

uncertainty factor is meant to cover differences in humans in disposition of the toxicant 

(absorption, distribution, metabolism, and elimination), while the toxicodynamic 

uncertainty factor is meant to account for differences in response at the receptor level. 

However, it appears that a default toxicokinetic value of √10 may not be adequate for all 

chemicals, routes of elimination, or for the entire population, in particular the 

subpopulation of infants. A toxicokinetic subfactor of 10 is therefore recommended to 

protect infants, unless data are available to indicate that this subpopulation is not at 

higher risk due to differences in toxicokinetics. There may also be cases where a 

toxicokinetic subfactor larger than 10 is warranted based on chemical-specific factors. 

Currently, there are scant data available to indicate whether or not the toxicodynamic 

subfactor of √10 adequately protects infants and children. It is known that the developing 

organism can be many-fold more sensitive to toxicants such as lead (Pb), mercury (Hg), 

and diethylstilbestrol (DES) than a mature organism. Differentiating the contribution of 

toxicokinetic (TK) and toxicodynamic (TD) differences is difficult. Where data indicate, 

a toxicodynamic uncertainty factor (UFH-d) larger than the traditional value of √10 may 

be used. For example, there are toxicological endpoints of particular concern for infants 

and children in terms of increased susceptibility such as neurotoxicity (OEHHA, 2001). 

Potential red flags or triggers that may indicate the need for the use of an increased UF to 

account for developmental toxicity include: neurotoxicity and neurobehavioral deficits, 

effects on the thyroid, mutagenicity, carcinogenicity, immunotoxicity, structure activity 

relationship models, endocrine agonist or antagonist activity, changes in cell 

proliferation, alteration of signal transduction and/or gene expression, or disruptions in 

maternal homeostasis. In addition, it is important to recognize impacts on the respiratory 

system by compounds that may induce or exacerbate asthma, as children are more 

impacted by asthma than adults, and allergic asthma may be viewed as resulting from 

impacts on the developing immune and respiratory systems. The assessor may increase 

the intraspecies uncertainty factor for toxicodynamics in these cases, particularly where 

no data exist for early life stage exposure. 

Additional Three-fold Database Deficiency Factor 

The United States Environmental Protection Agency (U.S. EPA) applies a general UF to 

Reference Concentrations (RfCs) and Doses (RfDs) for chemicals lacking adequate 

toxicological studies (U.S. EPA, 2002a) to account for the potential for deriving an 

under-protective RfD/RfC as a result of an incomplete characterization of the chemical’s 

toxicity. Although this was not used in the previous version of the Hot Spots guidance, 

OEHHA now recommends an additional three-fold UF to apply in developing a REL for 

chemicals with substantial toxicological data gaps, including, but not limited to, 

xii 
TSD for Noncancer RELs June 2008 

developmental toxicity. In some cases, it may be appropriate to apply a database 

deficiency factor larger than three-fold. This partially addresses the mandate under SB 

25 to ensure that acute, chronic and eight-hour RELs are protective of infants’ and 

children’s health. The need for the additional database deficiency UF will be evaluated 

on a chemical-by-chemical basis and justified in the individual REL summaries. 

Available data on mechanisms of toxicity, reactivity, potential for systemic distribution, 

and structure activity relationships, will be considered in the decision to apply the 

additional database deficiency UF. In addition, where evidence of effects at lower levels 

exists but available data are inadequate to use for a quantitative dose-response 

assessment, consideration will be given to applying a database deficiency UF. 

Use of Modified Haber’s Law for Duration Adjustments 

Previously, OEHHA adjusted the lowest observed adverse effect level (LOAEL) or no 

observed adverse effect level (NOAEL) in a study by means of the modified Haber’s Law 

procedure if the exposure duration of the study was different from the exposure duration 

of concern for an acute (1-hour) REL. OEHHA continues to recommend this adjustment, 

where appropriate, in developing acute RELs (and eight-hour RELs based on acute 

studies) for systemic toxicity and other endpoints where cumulation over time is 

anticipated. The current recommendation is to use a default value of 3 for the 

concentration exponent (n), rather than 2 as before, in the absence of compound-specific 

information. 

The most sensitive health endpoint for a number of acute RELs and some chronic RELs 

is trigeminal nerve transmitted irritation of the eyes, nose or upper respiratory system. 

Trigeminal mediated irritation is a receptor-mediated mechanism, sometimes referred to 

as the common “chemical sense.” It appears that Haber’s Law does not apply to 

trigeminal irritation effects, so OEHHA will not use Haber’s Law adjustments for 

instances in which a trigeminal mechanism for eye, nasal and respiratory irritation can be 

determined for the chemical and concentration of concern. 

U.S. EPA Human Equivalent Concentrations (HEC) Procedure and Interspecies 

Extrapolation 

OEHHA’s previous guidelines endorsed the use of the U.S. EPA Human Equivalent 

Concentration (HEC) procedure for chronic RELs. This procedure adjusts the internal 

dose in an animal inhalation study to the human equivalent dose taking into account 

differences in breathing rates, surface area of the respiratory tract and deposition. The 

U.S. EPA HEC procedure has been used in the past instead of the toxicokinetic 

component of the interspecies uncertainty factor, resulting in a total UFA of √10. 

However, the HEC procedure only adjusts the exposure and does not account for other 

aspects of kinetics such as absorption, distribution, metabolism and excretion. OEHHA 

will continue to use the U.S. EPA HEC procedure when appropriate, but will retain a 

toxicokinetic factor of 2 as part of the overall interspecies uncertainty factor (i.e., a total 

UFA of 6) when it is used, to reflect the greater uncertainty involved in this procedure 

than in full compound- and species-specific pharmacokinetic models. 

xiii 
TSD for Noncancer RELs June 2008 

Physiologically Based Pharmacokinetic Modeling (PBPK) 

PBPK modeling provides a scientific methodology for assessing interspecies differences 

and intraspecies variability. The PBPK approach requires chemical specific information 

or estimation techniques for parameters such as blood:air partition coefficients. The 

PBPK approach is potentially more accurate than other approaches such as the U.S. 

EPA’s HEC procedure because it takes into account more parameters and uses chemical 

specific information. However, it requires much more data and therefore cannot be 

applied in all cases. The PBPK approach involves greater uncertainty when required 

parameters are estimated rather than measured. Validation of PBPK models with 

independent data is required for them to be used with confidence. While in many cases 

the variability in a parameter can be adequately incorporated into the model where data 

exist to characterize the distribution of this parameter, there is still residual uncertainty. 

Sensitivity analyses should be conducted to ascertain the importance of specific variables 

in the model output and gain understanding of model uncertainty. OEHHA will use this 

methodology when possible, instead of the default application of the pharmacokinetic 

portions of the intraspecies and interspecies uncertainty factors and in preference to the 

HEC procedure. This change will apply to all three types of Reference Exposure Levels. 

Exposure Duration Adjustments for Developmental Toxicity Data 

Developmental toxicity presents difficulties as a critical endpoint for REL development. 

Developmental toxicants can act during narrow temporal windows of fetal and postnatal 

development, often with multiple and/or poorly understood mechanisms. These 

chemicals can cause very specific anatomical lesions, more general impacts such as 

growth retardation, or functional deficits (e.g., behavioral changes). The tissue dose 

during the window of vulnerability may be the critical factor rather than the total dose, or 

vice versa. The developmental studies available to determine an acute, eight-hour or 

chronic REL may provide little clue as to the mechanism, window of temporal 

vulnerability, or relative importance of total dose versus peak tissue concentration as a 

determinant of toxicity. 

In order to ensure that RELs based on developmental endpoints are protective, OEHHA 

will not use Haber’s Law to adjust the NOAEL or LOAEL in an animal developmental 

study where the exposure duration of the study and averaging time of the REL (e.g., 1 hr) 

are different. Instead, since the actual timing and duration of the sensitive period are 

unknown, the NOAEL will serve as the basis of the REL without a time extrapolation 

adjustment. This should help ensure that the REL will be protective if the tissue or air 

concentration during a particular temporal window is more of a determinant of 

developmental toxicity than total dose. 

Eight-hour Reference Exposure Levels 

OEHHA has developed a methodology for a new class of Reference Exposure Levels for 

eight-hour exposure. This new type of REL is needed for the Hot Spots Program in order 

to refine the risk assessment approach for the large number of facilities that operate and 

emit chemicals for 8 hours per day, 5 to 7 days per week and to utilize the advanced 

xiv 
TSD for Noncancer RELs June 2008 

features in air dispersion modeling. The air dispersion modeling in the Hot Spots 

Program has traditionally modeled such emissions as if they were uniformly emitted over 

24 hours a day, continuously. Advances in computer capabilities have made it feasible to 

model more accurately the ground level concentrations of these emission scenarios by 

using meteorology obtained during the time when the facilities are actually operating 

(generally daytime). The majority of the highly populated areas in California have 

significant diurnal-nocturnal meteorological differences that can affect the magnitude of 

the modeled risk and location of receptors. 

The chronic noncancer health impacts on offsite workers (individuals working at other 

worksites in areas impacted by the facility emissions) have been traditionally assessed 

with the 24-hour chronic RELs. Because offsite workers generally work 8 hours not 24, 

the eight-hour RELs will ensure a more accurate assessment of the health impacts of their 

exposures. The eight-hour RELs will also be useful for assessing the health impacts of 

exposure of children in schools. The eight-hour RELs should be protective against 

repeated daily eight-hour exposures, so the pharmacokinetics will need to be carefully 

considered when setting the standards. The accumulation of the chemical or cumulative 

injury from repeated daily exposures will need to be considered if either the total dose or 

the area under the exposure x time curve is the determinant of toxicity (reflecting a 

possible role of cumulative injury). Therefore, some, but not all eight-hour RELs will 

have the same basis as the corresponding chronic REL. 

Children’s Health in Relation to Chronic and Eight-hour RELs 

Children and infants show biochemical, physiological and behavioral differences from 

adults, which can result in higher tissue doses for some chemicals. The significance of 

the higher tissue doses can vary considerably, particularly for infants, depending on the 

mechanism of toxicity. This becomes especially important when developing RELs for 

eight-hour repeated or chronic continuous exposures. The chronic and eight-hour RELs 

are intended to be “safe levels” of lifetime or repeated eight-hour exposure to a particular 

chemical. The dose received by inhalation or by non-inhalation routes can be greater for 

infants and children than for adults in the same setting. Further, the internal dose and the 

toxicological consequences may be different for infants and children than it is for adults. 

For some chemicals it may be appropriate to use infant or child parameters for PBPK 

modeling, when PBPK modeling can be used for a chronic or eight-hour REL. For other 

chemicals, it may be more appropriate to take into consideration the greater exposure of 

children during exposure assessment as part of the total lifetime daily dose, and adult 

parameters for PBPK modeling may be the most appropriate. 

Chronic Oral Reference Exposure Levels (RELs) and Children’s Health 

Some chemicals in the Air Toxics Hot Spots Program are evaluated for multi-pathway 

exposures; in particular, non-volatile chemicals that deposit on surfaces such as soil or 

food crops, and/or bioaccumulate in breast milk. Oral RELs are used in risk assessment 

to address these pathways. Oral RELs are expressed in terms of a “safe” dose in mg/kg 

body weight (BW)-day. Thus, the higher doses in mg/kg that children receive from oral 

or dermal pathway exposure, relative to adults, can be directly assessed, when 

xv 
TSD for Noncancer RELs June 2008 

appropriate, by using the appropriate exposure variables for children (e.g., soil ingestion 

rates). Whenever possible, OEHHA will assess differences between children and adults 

using PBPK modeling when developing oral RELs. As in the case of the inhalation 

RELs, an intraspecies UF of 30 (√10 x 10) instead of 10 may be considered in some cases 

to protect children’s health when insufficient data exist for PBPK modeling. Likewise, 

an additional UF may be considered for toxicodynamic susceptibility or where there are 

substantial data gaps. 

 

xvi 
TSD for Noncancer RELs June 2008 

1 Introduction 

Hazardous substances are routinely released into the environment as a result of predictable 

continuous, intermittent or short-term emissions from facilities and predictable process upsets or 

leaks. As a result, the public living or working in communities surrounding industrial facilities is 

at risk of being exposed to airborne toxicants. Local air pollution control officers, industrial 

facility operators, and others have a need for clear guidance regarding the acute and chronic 

health effects of hazardous substances emitted into the air. 

Under the Air Toxics Hot Spots Act (Senate Bill 1731, Statutes of 1992), the Office of 

Environmental Health Hazard Assessment (OEHHA) was required to develop risk assessment 

guidelines for stationary sources of airborne toxicants. In an initial response to this mandate, 

OEHHA followed the recommendations of a then current National Academy of Sciences/ 

National Research Council review (NRC, 1994) of risk assessment practices by establishing 

uniform, science-based guidelines to be used in the derivation of acute and chronic Reference 

Exposure Levels (RELs) applicable to the general public exposed routinely to hazardous 

substances released into the environment. The products of this original guidelines development 

process were presented in the previous versions of the Technical Support Documents (TSDs) 

comprising the Air Toxics Hot Spots Risk Assessment Guidelines (OEHHA, 2000a). 

Application of these RELs in risk assessments is described in the Air Toxics Hot Spots Risk 

Assessment Guidance Manual (OEHHA, 2003). 

This document builds on that earlier effort by updating the methodology for developing RELs for 

noncancer health endpoints and incorporating additional information that has since become 

available. In particular, it will address the mandate of the Children’s Environmental Health 

Protection Act (Senate Bill 25, Escutia; Chapter 731, Statutes of 1999; “SB 25”) to specifically 

consider children in evaluating the health effects of Toxic Air Contaminants (TACs). In recent 

years, there have been growing concerns regarding children’s exposure to environmental 

chemicals; the California Legislature passed SB 25 to help address these concerns. OEHHA’s 

initial response was to develop a prioritization rationale for identifying those TACs most likely 

to show differential health impacts on infants and children, and to identify the five highest 

priority TACs as mandated by the statute (OEHHA, 2001). The present document continues the 

process by presenting revised methodology for the development of RELs for use in the Air 

Toxics Hot Spots program that takes into account possible differential impacts of TACs or other 

Hot Spots chemicals on children’s health. This document also presents updates to the 

methodology for REL development that reflect advances in the science of risk assessment since 

OEHHA’s methodologies for acute and chronic RELs were previously presented. These 

advances include issues explored in various reports by the U.S. EPA (U.S. EPA, 2000; U.S. 

EPA, 2002a; U.S. EPA, 2004; U.S. EPA, 2006a; U.S. EPA, 2006b; U.S. EPA, 2006c; U.S. EPA, 

2007b) and by committees of the National Academy of Sciences/National Research Council 

(NRC, 2001; NRC, 2007) issued since OEHHA completed development of the previous 

guidelines, as well as a variety of research papers and reviews published in the general scientific 

literature. 

Quantitative risk assessment is used to derive noncancer health values including acute, chronic 

and the newly defined eight-hour RELs. OEHHA has aimed for consistency between the 

Technical Support Document 1 June, 2008 
TSD for Noncancer RELs June 2008 

recommended methods for developing acute, eight-hour and chronic RELs, and therefore has 

consolidated the previously separate guidance for acute and chronic RELs into a single TSD 

covering all three current types of RELs. The use of benchmark dose methodology, presented as 

an option in a previous document, is now recommended as the default method when data permit. 

Additional discussion of pharmacokinetic methods for interspecies extrapolation is presented, 

and, most importantly, these guidelines respond to new scientific understanding of the diversity 

in exposure and sensitivity of the human population, especially infants and children. 

1.1 Objective 

The objective of this document is to present our revised methods for deriving acute, eight-hour 

and chronic inhalation RELs for hazardous airborne substances. RELs are used in risk 

assessments to evaluate the potential for adverse noncancer public health impacts from facility 

emissions or similar localized sources in the Air Toxics Hot Spots Program, and from 

widespread exposures in the Toxic Air Contaminants program. The REL is an exposure at or 

below which adverse noncancer health effects are not expected to occur in a human population, 

including sensitive subgroups (e.g., infants and children), exposed to that concentration for a 

specified duration. These health-based RELs are applicable to risk characterization of air 

releases, defined in Health and Safety Code (H&SC) Section 44303, as: 

“including actual or potential spilling, leaking, pumping, pouring, emitting, emptying, 

discharging, injecting, escaping, leaching, dumping, or disposing of a substance into the 

ambient air and that results from routine operation of a facility or that is predictable, 

including, but not limited to continuous and intermittent releases and predictable process 

upsets or leaks.” 

These health guidance values are designed to be protective against the noncancer health effects 

of exposure to airborne chemicals. OEHHA has also developed or endorsed cancer potency 

factors that can be used to protect the general public against the cancer causing effects of 

carcinogenic TACs (OEHHA, 2005a). In some cases, a carcinogenic chemical can cause 

noncancer health impacts and thus may have both types of health values. The acute RELs are 

designed to protect against a 1-hour exposure duration occurring infrequently (e.g., no more than 

once every two weeks) (see Section 5.4.1). Chronic RELs are designed to protect against longterm exposure for 24 hours a day, and are used in the Air Toxics Hot Spots risk assessments to 

evaluate exposures anywhere from about 9 years up to a lifetime. Chronic exposure studies are 

defined by U.S. EPA and Cal/EPA based on traditional toxicological testing, as 12% of a 

standard lifetime (e.g., for humans, 8.4 years). This document updates our procedures for acute 

and chronic RELs in order to ensure that the potentially greater vulnerability of children and 

infants is taken into consideration. In addition, OEHHA is developing eight-hour RELs, 

designed to protect against routine exposures of that duration (which approximates an average 

workday) that could occur as often as daily. 

Existing risk assessment methodologies use an uncertainty factor of 10 for intraspecies (interindividual) variability in the human population (UFH) which is assumed to protect children as 

well as other sensitive subpopulations. Such assumptions are crude but data have generally been 

lacking to use anything other than this assumption for most chemicals. This document evaluates 

available information in order to determine if more specific guidance for protecting children can 

Technical Support Document 2 June, 2008 
TSD for Noncancer RELs June 2008 

be incorporated into our standard procedures for determining noncancer guidance values 

protective of children. 

One of the challenges in developing a standard public health protective approach for acute, eighthour and chronic RELs is the variability in available data for different chemicals. Some wellstudied chemicals have human data, extensive animal data, and data necessary for 

pharmacokinetic modeling of tissue dose, while other chemicals may have limited data based on 

animal studies. OEHHA needs an approach that allows development of a public health 

protective acute, eight-hour or chronic REL accounting for the potential greater vulnerability of 

children. Child protective default procedures need to be available in the absence of chemical 

specific information. However, more scientifically sophisticated methods can be employed 

when chemical specific data are available. 

1.2 Legislative Mandates 

OEHHA is responsible for conducting health effects assessments of airborne chemicals, 

including chemicals listed under Health and Safety Code (H&SC) Section 44321, that are used 

by the California Air Resources Board in its risk management activities. As defined under the 

Air Toxics “Hot Spots” Information and Assessment Act of 1987 (Assembly Bill 2588; Chapter 

1252, Statutes of 1987; California H&SC Section 44300 et seq., as amended), a risk assessment 

includes a comprehensive analysis of the dispersion of hazardous substances in the environment 

and the potential for human exposure, and a quantitative assessment of both individual and 

population-wide health risks associated with those levels of exposure. This document establishes 

a standardized procedure for generating the health-based values (acute, eight-hour and chronic 

RELs) used for assessing noncancer risks within the risk assessment process. 

In preparing this document, OEHHA is responding to state legislation enacted in 1992 and 1999. 

SB 1731 (Chapter 1162, Statutes of 1992) required OEHHA to develop risk assessment 

guidelines for implementing the “Hot Spots” Act. The original guidelines were published as 

Technical Support Documents in 1999 – 2000 and as a condensed Guidance Manual in 2003 

(OEHHA, 2003). This revision is in response to the Children’s Environmental Health Protection 

Act (SB 25), which requires the California Environmental Protection Agency (CalEPA) to 

consider children specifically in setting Ambient Air Quality Standards (AAQS) and in 

developing criteria for Toxic Air Contaminants (TACs). SB 25 requires OEHHA to consider the 

following in its health effects assessments and recommendations: 

(1) exposure patterns among infants and children that result in disproportionately high exposure, 

(2) special susceptibility of infants and children, 

(3) effects of simultaneous exposures to compounds with the same mechanisms of action, and 

(4) any interactions of air pollutants. 

The law requires OEHHA to evaluate available information on the Ambient Air Quality 

Standards (AAQS) and to develop a list of Toxic Air Contaminants (TACs) that “may cause 

infants and children to be especially susceptible to illness”. OEHHA developed the document 

Prioritization of Toxic Air Contaminants under the Children’s Environmental Health Protection 

Act (OEHHA, 2001) to address the identification of the first five TACs with special impacts on 

Technical Support Document 3 June, 2008 
TSD for Noncancer RELs June 2008 

infants and children. That document underwent public comment and peer review by the State’s 

Scientific Review Panel (SRP) on Toxic Air Contaminants. The statute requires OEHHA to 

evaluate 15 toxic air contaminants per year to ensure that our health effects assessments are 

adequate to protect infants and children, and use these assessments to update the list of TACs 

that may cause infants and children to be especially susceptible to illness. To help meet the 

requirements of SB 1731 and SB 25, OEHHA in this document describes and evaluates the 

methodology to estimate RELs that explicitly consider infants and children, and derives such 

levels for specific chemicals. 

OEHHA and the California Air Resources Board (CARB) have set up a procedure to facilitate 

the public comment and peer review process necessary for implementation of SB 1731 and SB 

25 (Figure 1-1). This process includes internal OEHHA review, consultation with the California 

Air Pollution Control Officers Association (CAPCOA) and the California Air Resources Board 

(CARB), a public comment period, and public workshops. In addition, the Scientific Review 

Panel (SRP) on Toxic Air Contaminants, administered by the CARB, will review this document. 

OEHHA staff will respond to public comments on the changed methods and chemical-specific 

RELs, and update and revise the document as appropriate. The SRP will review the revised 

document, public comments and OEHHA’s responses, and provide scientific input, which will 

be incorporated into the final draft. 

Technical Support Document 4 June, 2008 
TSD for Noncancer RELs June 2008 

FIGURE 1-1. PUBLIC AND PEER REVIEW PROCESS FOR ESTABLISHING 

REFERENCE EXPOSURE LEVELS 

Prioritization of Compounds 

Adoption of “Technical Support Document 

for the Determination of Reference 

Exposure Levels” 

Development of “Technical Support Document” for 

the Determination of Reference Exposure Levels

Consultation with California Air Pollution Control 

Officers Association (CAPCOA)

Draft Documentation for Public Comment

Public Workshop

Review of document and response to comments by 

Scientific Review Panel on Toxic Air Contaminants

Selection of Candidate Compound

Technical Support Document 5 June, 2008 
TSD for Noncancer RELs June 2008 

1.3 Summary of the Methodological Changes for Developing Acute, Eight-hour and 

Chronic RELs 

• If sufficient data are available for REL development, then a benchmark dose approach 

will be preferred over the traditional NOAEL/UF approach, unless some specific feature 

or limitation of the data makes this impossible. 

• If sufficient data are available, PBPK modeling (Appendix E) will be used to determine 

the tissue dose in a default human model if an animal toxicity study is used. The 

relationship determined by the PBPK model will replace the pharmacokinetic portion 

(UFA-k) of the interspecies uncertainty factor (UFA). A smaller residual UFA-K of 2 may 

be used where the PBPK model is only a partial model (e.g., DAF for analogue 

chemical). In cases where a PBPK model is not available, UFA-k is assigned a default 

value of √10, and the total UFA would be 10. 

• The HEC procedure (Appendix F) may be used in place of a part of the UFA-k, leaving a 

residual value of 2 for this sub-factor (since the default value of the toxicodynamic 

component interspecies uncertainty factor, UFA-d, is √10, this results in an overall value 

of UFA= 6 when a HEC calculation is used, or 10 where no model of any kind is applied) 

(OEHHA, 2003). A modified HEC procedure using children’s physiological values may 

be used as appropriate. 

• The mechanism of toxicity and pharmacokinetics for chemicals will be evaluated when 

developing chronic and 8-hr RELs in order to help determine whether long-term total 

dose or shorter-term tissue concentration is the predominant factor for toxicity. Infants 

and children are potentially more vulnerable because of pharmacokinetic, 

pharmacodynamic, and exposure differences. Therefore if PBPK modeling is used to 

determine human equivalent concentration from an animal study, children’s or infants’ 

physiological and biochemical parameters may be used in the modeling as appropriate. 

• Since children may also be more vulnerable because of metabolism (activation or 

deactivation), excretion differences, or special sensitivities of developing organs and 

tissues, PBPK modeling may be needed to model children’s tissue doses when a worker 

study is used to determine a LOAEL or NOAEL. 

• When an uncertainty factor approach is used due to the lack of data for compoundspecific models of toxicokinetics and toxicodynamics, an overall intraspecies uncertainty 

factor (UFH) of 30 rather than 10 (toxicokinetic component, UFH-k =10; toxicodynamic 

component, UFH-d = √10) will be used as a default procedure to protect infants’ and 

children’s health, for example, in cases where differences in metabolism and excretion 

are key to the toxicological activity. For direct-acting chemicals whose site of action is 

the point of first contact, a UFH-k of √10 may be sufficient. Where significant concern for 

toxicodynamic differences larger than three-fold is present, a larger UFH-d may be 

applied, such that the total UFH could be larger than 30.. 

Technical Support Document 6 June, 2008 
TSD for Noncancer RELs June 2008 

• The LOAELs or NOAELs for RELs based on trigeminally-transmitted irritation will no 

longer be adjusted using modified Haber’s Law when the exposure duration of the study 

is different from the exposure duration for which the REL is intended to be protective. In 

other cases, where the modified Haber’s law procedure is applicable, the default value of 

the concentration exponent, used when chemical specific information is unavailable, for 

time extrapolation from longer to shorter REL averaging time will be 3, rather than 2 as 

previously. 

• In order to ensure that RELs based on developmental endpoints are protective, OEHHA 

will not use Haber’s Law to adjust the NOAEL or LOAEL in an animal developmental 

study where the exposure duration of the study and averaging time of the REL (e.g., 1 hr) 

are different. Instead, since the actual timing and duration of the sensitive period are 

unknown, the NOAEL will serve as the basis of the REL without a time extrapolation 

adjustment. 

• If the available toxicology literature does not include developmental studies for a 

chemical, or has substantial toxicological data gaps, the toxicological endpoints 

associated with the chemical, the chemical’s reactivity, mechanism of toxicity, structure 

activity relationships and other factors, will be evaluated to decide if an additional 

database deficiency factor, with a default value of 3, will be applied. A larger value may, 

in some extenuating circumstances, be applied.

Technical Support Document 7 June, 2008 
TSD for Noncancer RELs June 2008 

2 Reference Exposure Level (REL) Development Process 

Reference Exposure Levels (RELs) are concentrations or doses at or below which adverse 

noncancer health effects are not likely to occur in the general human population, including 

sensitive subpopulations, for specified exposure durations. A central assumption is that a 

population threshold exists below which adverse effects will not occur in a population; however, 

such a threshold is not observable and can only be estimated. Areas of uncertainty in estimating 

effects among a diverse human population are addressed using extrapolation and UFs. 

RELs are based on the most sensitive relevant health effect reported in the medical and 

toxicological literature (see Section 4.0). One of the issues in selecting the appropriate health 

end point for REL development is the difficulty in distinguishing “adverse” health effects from 

“precursor” effects, which may be purely biochemical changes indicative of initial events in a 

chain of consequences which might (or might not) result in disease, and “adaptive” responses. 

The latter reflect impacts of a biological process which is not of itself harmful or is within the 

capacity of normal biochemistry and physiology to accommodate without impairment of overall 

function of the organism. NRC (2007) has considered this issue and points out the need for a 

health-protective and case-specific approach to selection of endpoints, noting that the concept of 

“adversity” is actually a continuum ranging from initial events such as contact and uptake of the 

toxicant, through distribution, metabolism and contact with the target tissue, to various grades of 

precursor event leading up to frank pathological changes. They point out also that the degree of 

response over this continuum at a specific dose level may vary widely between individuals, 

depending on their age, genetic constitution, nutrition, prior health status and many other factors, 

not all of which can necessarily be predicted. OEHHA concurs in recommending a cautious 

selection of endpoint, emphasizing the importance of considering precursor events and individual 

variability; this issue is discussed further in Section 4.3.2. 

RELs are designed to protect the most sensitive individuals in the population including infants 

and children by the selection of appropriate toxicological endpoints and extrapolation models, 

and by the inclusion of margins of safety in the form of various UFs. Since uncertainty factors 

are incorporated to address data gaps and uncertainties, exposures that exceed the REL do not 

automatically indicate an adverse health impact. 

Figure 2-1 depicts the steps involved in developing RELs. A complete literature search is 

conducted for each chemical, but the chemical summaries in Appendix D may only cite those 

studies contributing to the REL development and reflecting relevant routes of exposure. After 

identifying critical studies and toxicological endpoints, we identify a point of departure on the 

dose-response curve for extrapolating to the general human population. Various procedures for 

dose and time extrapolation and use of uncertainty factors are described in Section 4. 

Technical Support Document 8 June, 2008 
TSD for Noncancer RELs June 2008 

FIGURE 2-1. REL DEVELOPMENT PROCESS 

 conduct literature search 

 ⇓ 

choose best study, emphasizing human data 

⇓ 

identify critical biological endpoint 

⇓ 

estimate threshold for effect (benchmark or NOAEL) 

⇓ 

temporal/dosimetric adjustments (time extrapolation, HEC, Children’s HEC, PBPK) 

⇓ 

account for uncertainties in data 

(extrapolating animal data to humans; occupational studies to sensitive subgroups) 

⇓ 

Reference Exposure Level (REL) 

(listed in Appendix B & Appendix D ) 

NOAEL = No-Observed-Adverse-Effect Level; HEC = Human Equivalent Concentration; PBPK = 

physiologically-based pharmacokinetic models 

2.1 List of Substances Considered 

All substances listed by the California Air Resources Board (CARB), whose emissions must be 

quantified for the Air Toxics “Hot Spots” Program, are considered for evaluation and 

development of RELs. The substances included on the Air Toxics “Hot Spots” Program List are 

those substances found on lists developed by the International Agency for Research on Cancer 

(IARC), the U.S. Environmental Protection Agency (U.S. EPA), the U.S. National Toxicology 

Program (NTP), the CARB ( i.e., the list used in the Toxic Air Contaminant Program), the 

Hazard Evaluation System and Information Service (State of California), or on the Proposition 

65 list of carcinogens and reproductive toxicants (State of California). The complete list of 

substances whose emissions must be quantified is contained in Appendix C. 

2.2 Hazard Index Approach 

The Air Toxics “Hot Spots” Program uses RELs as indicators of potential adverse noncancer 

health effects. A “hazard index” (HI) approach is used to estimate potential health effects 

resulting from hazardous substances by comparing measured or modeled exposure levels with 

RELs. (For a detailed description of this method, please refer to the document entitled The Air 

Toxics Hot Spots Program Guidance Manual for Preparation of Health Risk Assessments

(OEHHA, 2003)), which is available online at: 

http://www.oehha.ca.gov/air/hot_spots/pdf/HRAguidefinal.pdf 

Technical Support Document 9 June, 2008 
TSD for Noncancer RELs June 2008 

Cumulative exposure (to multiple chemicals and from multiple sources) has become a concern in 

recent years. For many facilities a large number of chemicals may be emitted or may be present 

in the air at the location of the individual receptor or exposed population. To assess the 

cumulative impact of several chemicals present at the same time, it is important to consider the 

interaction of effects of the toxicants. These interactions may result in an overall effect that is 

equal to, less than, or greater than predicted from the effects observed with exposures to the 

individual chemicals (Ikeda, 1988; Jonker et al., 1990; DeVito et al., 2000). The potential for 

synergistic or antagonistic interactions has only been investigated for a small fraction of the 

millions of possible combinations of chemicals with potential human exposure. Effects of 

multiple chemical exposures on human health remain an area for future study. 

One of the specific mandates of SB25 is the consideration of cumulative exposures to toxic 

chemicals, which has become a core concern for environmental justice in view of the tendency 

for disadvantaged communities to be located in areas with a high density of pollutant-emitting 

facilities, and near freeways and other heavily used traffic corridors with attendant local peak 

concentrations of mobile-source-related pollutants. They thus receive higher local pollutant 

exposures compared to the State-wide average. This adverse environmental milieu is often 

compounded by the presence of population factors tending towards more adverse health impacts, 

such as less access to health care, poor nutrition and poor housing. Cumulative exposures may 

consist of multiple sources of the same chemical impacting a single receptor, multiple chemical 

exposures from the same source, or combinations of these situations. The hazard index approach 

is useful in addressing the predicted effects of such cumulative exposures (Salmon, 2007). 

In risk assessments using the HI approach for either acute, eight-hour or chronic exposures, the 

impacts of exposures to multiple chemicals which impact the same target organ are treated as 

additive by default. The concept of a threshold underlies the assumption of additivity in 

chemical interactions. Exposure to a single chemical in the air may not result in a toxic response 

if it is below the threshold necessary to elicit a response. However, simultaneous exposure to 

two similar chemicals at sub-threshold levels may result in a toxic response. This is taken into 

account by adding together the individual ratios of the modeled concentration to the REL for all 

chemicals that impact the same target organ or system. 

This may underestimate the effect in cases in which interactions are synergistic, or overestimate 

it if the effects are either not additive or antagonistic. There are a few cases where synergism 

between different toxic chemical exposures has been identified, but most multiple exposure 

situations which have been examined quantitatively in fact do show simple additivity, at least at 

low to moderate doses. This is well illustrated by the work on PCBs (DeVito et al., 2000; 

Crofton et al., 2005), which also shows that synergism or antagonism, perhaps related to enzyme 

induction or inhibition may be observed at higher doses. Additivity was also seen for phthalates 

causing male reproductive tract malformations in rats following co-exposure to di(n-butyl) 

phthalate (DHP) and diethylhexyl phthalate (DEHP) during sexual differentiation (Howdeshell et 

al., 2007). These observations support the use of the additivity assumption by default for lowdose exposures, but also indicate the need to consider synergistic or antagonistic interactions 

between chemicals in those special cases where they have been identified. 

Another limitation of the HI approach to assess the potential for health effects is the fact that 

different RELs have vastly different cumulative uncertainty factors depending on the quality of 

Technical Support Document 10 June, 2008 
TSD for Noncancer RELs June 2008 

the data. Thus the probability of adverse health impacts when the REL or HI is exceeded may be 

quite different with different chemicals. 

For a particular target organ or system, the HI is calculated as follows (U.S. EPA, 2004): 

HI = C1 / REL1 + C2 / REL2 + . . . Ci / RELi

where for i substances with the same toxicological endpoint, 

 HI = hazard index 

 Ci = concentration for the ith substance 

RELi = REL for the ith substance 

Advances in computer capabilities and the development of software programs, such as the Air 

Resources Board’s Hot Spots Analysis Reporting Program (HARP), have made assessment of 

chemical exposure from multiple stationary facilities more practical. However, it should be 

borne in mind that both the cumulative exposures and the health impacts in children may be 

different from those in adults, because of physiological, biochemical and behavioral factors 

(Hattis, 1996a; OEHHA, 2001; Miller et al., 2002). 

Technical Support Document 11 June, 2008 
TSD for Noncancer RELs June 2008 

3 Populations of Concern 

RELs developed since the beginning of the Hot Spots Program are intended to protect the 

individuals who live or work in the vicinity of emissions of potentially toxic substances. The 

general population consists of individuals with a wide range of susceptibility. An individual’s 

current level of susceptibility may be transitory or long lasting, and innate or induced by some 

prior exposure or event. The general population includes some people who are likely to be 

especially susceptible to developing adverse effects (e.g., infants, children, the elderly, pregnant 

women and those with acute or chronic illnesses). Individuals in the general population who 

may be at greater risk for developing adverse effects following chemical exposure include: 

• those with increased exposure (e.g., infants, children, adults engaged in physical activity), 

• those undergoing physiological change (e.g., infants, children and adolescents; pregnant 

women and their fetuses), 

• individuals with impaired physiological conditions (e.g., elderly persons, persons with 

existing diseases, persons who are immunosuppressed), 

• individuals with lower levels of protective biological mechanisms due to genetic or 

epigenetic variability within the population (U.S. EPA, 1994a) and 

• members of communities impacted by multiple sources of exposure. 

Less susceptible individuals are healthy adults without any genetic or biological predisposition 

that may increase sensitivity to the chemical of concern. RELs are intended to protect both 

individuals at low risk for chemical injury as well as identifiable sensitive subpopulations 

(groups of more highly susceptible individuals) from adverse health effects in the event of 

exposure. There have been concerns that infants and children in particular may be more 

sensitive to some toxic agents, and that this difference may not have been considered sufficiently 

in setting existing health protective standards. OEHHA has therefore been mandated under SB 

25 to ensure that RELs are also protective of infants and children, and other sensitive 

subpopulations. More useful scientific information has become available in the last few years to 

evaluate the special vulnerabilities of infants and children and to thus ensure that the RELs are 

protective. 

The elderly constitute another important group where greater sensitivity to environmental 

toxicants may be anticipated. With aging, there is a general tendency for clearance mechanisms 

and other protective toxicokinetic processes to decline. There is also a tendency for reserve 

capacity, responsiveness of homeostatic mechanisms and ability to repair cellular damage to 

decline with age. Thus the elderly tend to show greater susceptibility to toxicants as a result of 

both toxicokinetic and toxicodynamic factors (ElDesoky, 2007). This has been shown as an 

important phenomenon for drugs, where adjustment of dose and even selection of therapeutic 

agents is modified for elderly patients (Ginsberg et al., 2005). The potentially enhanced 

sensitivity of older individuals has also been identified as a cause for concern, and a stimulus for 

additional research and regulatory activity, for environmental toxicants (Geller and Zenick, 

Technical Support Document 12 June, 2008 
TSD for Noncancer RELs June 2008 

2005). Although this document is primarily designed to address the mandate to consider the 

special susceptibilities of infants and children, the same principles developed here can also be 

used in identifying and responding to concerns for the enhanced sensitivity of the elderly. 

Because the true range of variability is unknown, there may be a proportion of the population for 

whom the RELs will not be fully protective. It is OEHHA’s intent that the levels will protect 

nearly all individuals, including those who are identifiable at the high end of susceptibility. 

However, they may not protect hypersensitive individuals (i.e., those who exhibit extremely rare 

or idiosyncratic responses that could not have been predicted from studying the health effects of 

the substance in animal studies, or population-based epidemiological studies of reasonable size). 

While OEHHA has attempted to identify specific sensitive subgroups for each substance from 

the literature, it is not possible to identify all conditions predisposing toward adverse health 

effects following exposure to toxic substances. Because RELs pertain to inhalation exposures, 

the lungs are often the major target organ of toxicity, and asthmatics are frequently identified as 

a sensitive subgroup. For most compounds, the range of inter-individual variability is poorly 

characterized. An exception is sulfur dioxide, which has been studied in both normal as well as 

asthmatic individuals. In a study of asthmatic subjects, there was a 7-fold distribution in the 

range of sulfur dioxide concentrations required to produce bronchoconstriction (Horstman et al., 

1986). Thus, it is reasonable to conclude that asthmatics may be at least seven times as sensitive 

to the effects of sulfur dioxide as non-asthmatic individuals. 

Hattis has presented an analysis of human variability in threshold responses in pharmacodynamic 

and toxicological studies (Hattis, 1996a; Hattis, 1996b; Hattis et al., 1999). In many cases the 

variability in response in the general human population appears to be continuous and is well 

modeled by a log-normal distribution. The magnitude of the variability depends greatly on the 

endpoint and on the slope of the dose-response curve. In their analysis, some human threshold 

responses ranged over more than three orders of magnitude. Such continuous variability is 

hypothesized to reflect cumulative influences of a number of polymorphisms. These may be at 

various genetic loci, all of which individually have a small impact on the degree of dose 

dependence of the response, or there may be several polymorphisms at a single locus, associated 

with a variety of different levels of response. In other cases the response may be primarily 

influenced by a single gene, with two relatively common variants having markedly different 

properties. In this case the observed variability may appear to be heterogeneous, and is better 

described by a bimodal distribution or separate distributions. 

3.1 Children as a Population of Concern 

In response to the potentially greater vulnerability of infants and children to chemical exposure 

compared to adults (reviewed by OEHHA 2001), revised procedures for development and 

reassessment of noncancer health standards are outlined here. These are intended to ensure that 

age-related sensitivities are taken into consideration as far as possible in the development of 

noncancer health standards. 

Many of the issues, which arise in ensuring adequate protection of children’s health, are the same 

for acute, eight-hour, and chronic RELs. In this document, our approach in addressing these 

issues will be two-fold. 

Technical Support Document 13 June, 2008 
TSD for Noncancer RELs June 2008 

1. We have developed changes in the methodology for REL derivation in order to ensure 

that these health values are protective of children’s health. 

2. We have applied the changes in the methodology to individual RELs. 

Over time, we will add new RELs, and address the adequacy of existing RELs that were 

developed with the initial methodologies. In recent years new methodologies have been 

developed to address uncertainties more quantitatively in determining health-protective levels 

and to ensure public health protection. OEHHA recommends expanding the use of techniques 

such as the benchmark dose method and physiologically based pharmacokinetic (PBPK) 

modeling wherever possible in order to improve the protection of public health with acute, eighthour and chronic RELs for all members of the population, and for infants and children in 

particular. As noted in the introduction, data gaps will prevent PBPK modeling for many 

chemicals, and in some cases, will make benchmark dose analysis difficult. Thus, the traditional 

UF approach remains an important method. 

3.2 Differences between Children and Adults 

Children’s potential vulnerability to toxic effects from chemicals can result from differences in 

exposure. For example: 

• Children breathe more than adults on a per kg body weight basis and thus inhale more of 

the pollutant. 

• For non-inhalation routes of exposure, children are also more exposed on a per kg body 

weight basis than adults because they eat more food, drink more water, have more dermal 

contact with soil, and have higher rates of inadvertent soil ingestion than adults. 

• Children may consume more of one type of food than adults and have a less diverse diet. 

Children may differ in terms of pharmacodynamics, particularly since more cell division and 

differentiation are occurring in children than in adults due to growth and organ development. 

• Undifferentiated cells may be more prone to injury than are differentiated cells. 

• Major organ development occurs in utero and during early postnatal development. 

• Disruption of migration and differentiation of one type of cell may alter that of another. 

• Some structures (e.g., the brain) continue to develop through adolescence. 

Children and particularly neonates can be quite different pharmacokinetically from adults. 

Factors that affect absorption, distribution, metabolism, and elimination (ADME) of toxicants, 

often differ by age. Such factors include: 

• Lung surface area available for absorption of gases, and airflow dynamics that alter 

deposition of inhaled particulates. 

• Activating or detoxifying enzymes such as cytochrome P450 enzymes and Phase II 

conjugating enzymes are known to be present in infants in amounts and ratios different 

from adults. Fetal forms of cytochrome P450 are present and may differ in activity 

towards many substrates compared with the adult forms. 

Technical Support Document 14 June, 2008 
TSD for Noncancer RELs June 2008 

• Consequently, xenobiotic metabolism may occur through different pathways, be 

apportioned differently between competing pathways, and occur more slowly than in 

adults. 

• Elimination may occur faster or more slowly than in adults. 

These factors influencing vulnerability in children may change rapidly and may be present only 

for a period of weeks to months. In the case of prenatal development, periods of vulnerability 

could be as short as hours or days. In neonates, the variability in developmental stage appears to 

be wide for some processes including xenobiotic metabolism and renal clearance (Islam and 

Schlipkoter, 1989). Consequently inter-individual variability in susceptibility in a particular age 

range may be much greater than in adults, and thus harder to characterize. OEHHA addressed 

some of the behavioral, physiological and biochemical differences between children and adults 

in OEHHA (2001) and in Miller et al. (2002). 

3.2.1 Pharmacokinetic Differences 

3.2.1.1 Absorption

3.2.1.1.1 Inhalation 

The lungs are the major route of entry of volatile airborne environmental pollutants and the 

majority of airborne semivolatile and nonvolatile pollutants. Exposure to environmental 

pollutants during lung development has the potential to significantly affect the overall growth 

and function of the respiratory system in children. The effects of exposure are likely to be 

different during each phase of development. Recent studies have found links between air 

pollution and preterm birth (Ritz and Yu, 1999; Sagiv et al., 2005; Wilhelm and Ritz, 2005), 

infant mortality (Loomis et al., 1999; Conceicao et al., 2001; Ha et al., 2001; Woodruff et al., 

2006), deficits in lung function growth (Kunzli et al., 1997;Galizia and Kinney, 1999; 

Gauderman et al., 2004), and possibly, development of asthma (Gauderman et al., 2005; 

McConnell et al., 2006). The lungs are structurally immature in neonates and continue to mature 

throughout childhood (Fanucchi and Plopper, 1997; Plopper and Fanucchi, 2004; Fanucchi et al., 

2006). 

Lung development is a long-term, continuous process that continues for up to eight years after 

birth. Lung function growth continues through adolescence. The development of the human 

lung can be divided into six stages: embryonic, pseudo-glandular, canalicular, saccular, alveolar, 

and vascular maturation (Zeltner and Burri, 1987). The first four stages occur prior to birth 

during fetal development. By birth the alveolar stage has begun, and the newborn infant has 

about 10 million alveoli. By approximately age eight, when adult numbers of alveoli have 

developed, there are about 300 million alveoli. Concomitantly the alveolar surface area 

increases from about 3 m2 at birth to about 75 m2 in adults leading to an air-tissue gas exchange 

area some 25-fold larger in adults (Islam and Schlipkoter, 1989). 

During the maturation time following birth, there are multifold increases in overall lung size, 

active cellular differentiation, cell division, branching morphogenesis, and alveolar formation 

(Tyrala et al., 1977; Harding et al., 2004). Episodic exposure to environmental pollutants, 

Technical Support Document 15 June, 2008 
TSD for Noncancer RELs June 2008 

specifically ozone, compromises postnatal morphogenesis of tracheobronchial airways in the 

monkey (Fanucchi et al., 2006). 

Airways change in size and shape with maturation, altering deposition patterns. In animal 

models, exposure to environmental ozone during the early postnatal period alters the 

development of the distal pulmonary airways (Fanucchi et al., 2006). Lung function also 

continues to change, increasing until late adolescence in both males and females. Chronic 

airway disease and decreased lung function in children exposed to ambient air pollution may be 

due to repeating cycles of injury and repair altering normal lung maturation (Smiley-Jewell et al., 

2000; Fanucchi et al., 2006). 

Respiratory minute ventilation is increased in infants and children, resulting in a greater exposure 

per unit time on a weight basis and per unit surface area of lung, compared to adults. For 

inhalation exposures to equivalent chemical concentrations, both indoor and outdoor, infants and 

children are at the same or greater risk of exposure based on their much higher minute ventilation 

on a body weight and lung surface area basis (i.e., mL/kg/m2/min) (Snodgrass, 1992). 

The deposition of inspired particles in the lungs is dependent on particle size and anatomical 

features of the respiratory tract. Deposition occurs primarily through impaction for coarser 

particles and through Brownian motion for finer particles. Greater fractions of inhaled particles 

less than five μm in diameter reach the distal airways compared to larger particles. Particle 

deposition tends to be greater in children because of the smaller diameters of the airways 

compared to adults, and models of particle deposition indicate larger deposition in the pulmonary 

region of 3 month old infants relative to adults (Ginsberg et al., 2004c; Snodgrass, 1992). 

3.2.1.1.2 Other Routes 

Ingestion is a major route by which infants and children are exposed to environmental chemicals 

(U.S. EPA, 2000). A number of factors may result in significant differences in the absorption of 

environmental pollutants by infants and children vs. adults. Most drugs administered by the oral 

route are absorbed into the systemic circulation by passive diffusion. The two factors most 

affecting this process are gastric pH and emptying time (Milsap and Jusko, 1994). Both 

processes vary with age from birth through infancy and childhood. At birth the gastric acidity is 

neutral (pH 6-8) due to the presence of amniotic fluid in the stomach (Avery et al., 1966). 

Following birth, gastric acid appears in the first one to two days of life and increases during the 

first weeks to months approaching adult levels by three months of age (Miller, 1941). Premature 

infants may continue to have lower gastric acidity due to immature acid secretion. The pH of the 

stomach influences the absorbed dose of ionizable chemicals, thus altering the potential dose to 

the infant (Agunod et al., 1969). 

Gastric emptying time influences the fraction of an oral dose which is absorbed into the systemic 

circulation. The gastric emptying rate in neonatal infants is variable and prolonged and is 

affected by both gestational and postnatal age (Signer, 1975; Siegel et al., 1984). Absorption 

rates for several chemicals (e.g., phenobarbital, digoxin, arabinose and xylose) increase 

throughout the first year of life. While delayed absorption seen in neonates is partially due to 

slower gastric emptying and gastrointestinal motility, other factors such as lower pancreatic 

Technical Support Document 16 June, 2008 
TSD for Noncancer RELs June 2008 

enzyme function and bile acid secretion as well as a higher gastrointestinal pH also play a role 

(Alcorn and McNamara, 2003). 

Not all absorption processes depend on passive diffusion: some toxic materials (especially 

metals) are carried by, or interfere with the operation of, physiological active transport processes, 

which show age-dependent changes. For instance, the decline in the absorption of lead by the 

gastrointestinal tract from 45% in children to 10% in adults probably reflects decreases in 

calcium receptors with age (Heath et al., 2003). 

Skin permeability and hence dermal absorption change as the skin develops. Dermal absorption 

may be significantly higher in neonates due to an immature epidermis and increased skin 

hydration. The surface area/bodyweight ratio is also much higher in infants and children than 

adults (0.067 to 0.033 m2

/kg vs. 0.025 m2/kg in adults) (Snodgrass, 1992). Severe toxicity has 

been observed in infants following topical application of hexachlorophene (Tyrala et al., 1977) 

and isopropanol (McFadden and Haddow, 1969) due to higher absorbed dose in infants relative 

to adults on a body weight basis. Alternatively, dermal administration may be the preferred 

therapeutic route in certain situations (e.g., theophylline in premature infants) 

3.2.1.2 Distribution

Our knowledge of the distribution of chemicals in the body is perhaps best developed for 

pharmaceuticals, while much less is known about chemical distribution following environmental 

exposures. For this reason, the discussion that follows relies heavily on the pharmaceutical 

literature. 

The distribution of absorbed chemicals in the infant and child is affected by a number of factors, 

primarily the concentration and types of plasma proteins and the relative size of fluid, fat and 

tissue compartments of the body (Milsap and Jusko, 1994). Total body water may be as high as 

85 percent by weight in premature infants and 78 percent in full-term neonates versus 50-60 

percent in adults (Friis-Hansen, 1961; Friis-Hansen, 1971). The percentage of body weight that 

is body water affects the volume of distribution of absorbed drugs and other chemicals. The 

apparent volume of distribution (Vd, measured in L/kg body weight) relates the amount of drug 

in the body to its plasma concentration. Chemicals that are water-soluble have higher volumes 

of distribution, while those that are lipophilic have lower volumes of distribution in infants. For 

example, gentamycin, theophylline and phenytoin show two to three-fold higher Vds in neonates 

than in adults. Conversely, the Vd in neonates for diazepam, which is more lipophilic, is only 

one-third that of the adult value (Milsap and Jusko, 1994). 

The binding of absorbed chemicals to plasma proteins depends upon the quantity of binding 

proteins available, the binding or affinity constant of the chemical for the protein(s), the number 

of available binding sites, and the presence of pathophysiological conditions, which may alter the 

binding interaction (Besunder et al., 1988). The affinity of plasma albumin for acidic drugs 

increases along with total plasma protein concentration from birth into early infancy (Morselli et 

al., 1980). The reduced plasma protein binding of drugs in newborns is probably due to lower 

total plasma protein concentration as well as such qualitative differences as persistence of fetal 

albumin with lower affinity for drugs and lower levels of γ-globulins and lipoproteins (Morselli 

et al., 1980). During early infancy plasma albumin, total protein concentrations, and α1-acid 

Technical Support Document 17 June, 2008 
TSD for Noncancer RELs June 2008 

glycoprotein are lower and don’t reach adult values until one year of age (Brodersen et al., 1983; 

Herngren et al., 1983). Three of the drugs noted above (theophylline, diazepam and phenytoin) 

all exhibit lower protein binding (1/3 to 1/10) in neonates versus adults (Morselli, 1976; Rane 

and Wilson, 1976; Morselli et al., 1980). In addition to the quantitative and qualitative 

differences in plasma proteins during early development, disturbances in acid-base balance and 

increased blood concentrations of endogenous substances such as free fatty acids and bilirubin 

can affect protein binding of drugs or the release (i.e., displacement) of bound drugs or other 

exogenous chemicals. Lower levels of albumin and elevated free fatty acids and bilirubin in the 

neonate may result in a larger Vd of and higher unbound concentrations of trichloroacetic acid 

(TCA), a metabolite of perchloroethylene (PCE) and trichloroethylene (TCE), in the blood 

(Muller et al., 1972; Ginsberg, 2000). 

For some chemicals, the route of exposure affects the form of the chemical that appears in the 

blood. This in turn affects the binding and distribution of the absorbed chemical. For example, 

manganese appears to be absorbed from the gut primarily in the divalent form, with 

approximately 80% of it subsequently bound in plasma to ß1-globulin and albumin (Foradori et 

al., 1967). These manganese-protein complexes are efficiently removed from the blood during 

the first pass through the liver and returned to the gut in bile for elimination, thus limiting their 

distribution. By contrast, the pulmonary route of exposure results in much higher blood levels of 

manganese which are more widely distributed in the body (Roels et al., 1997). In the blood, 

unbound manganese may be converted by ceruloplasmin to the trivalent cation which is then 

bound by transferrin. Transferrin-manganese complexes are much less efficiently removed by 

the liver and thus survive first pass elimination to circulate to other tissues throughout the body 

(Gibbons et al., 1976). 

Other factors that affect tissue distribution of toxicants include higher organ to body weight 

ratios in infants and children than adults, and the lack of a mature blood:brain and other 

tissue:blood barriers. Morphine is three to ten times more toxic to newborn than to adult rats due 

primarily to the higher permeability of the newborn brain to morphine (Rozman and Klaassen, 

1996), and the distribution of methylmercury to the brain is greater if exposure occurs in utero or 

neonatally (Ballatori and Clarkson, 1982). Brain size in infants and children is much greater 

than in adults relative to body weight. A newborn’s brain is one-third the size of an adult brain 

while its body weight is only about four percent that of adults (i.e., 3.0 vs. 70 kg) (Snodgrass, 

1992). Cerebral blood flow is greater per brain weight in children vs. adults. A five-year old 

child has a cerebral flow of about 71 ml/min-100 g vs. 51 ml/min-100 g in an adult (Chiron et al., 

1992). Thus not only is there a relatively greater brain mass to body weight ratio in children, but 

the brain receives a higher blood flow. 

Dosing of infants and children based on surface area instead of body weight is generally 

preferred although this applies mainly to drugs that are distributed in extracellular body water. 

Environmental chemicals, which are often lipophilic, may pose a greater risk to children due to 

the possibility of enhanced dermal uptake due to increased skin surface area and permeability. In 

addition, lipophilic agents would have decreased volumes of distribution in infants due to their 

lower body fat content compared to typical adults. 

Technical Support Document 18 June, 2008 
TSD for Noncancer RELs June 2008 

3.2.1.3 Metabolism

The development of xenobiotic metabolizing enzymes in utero and after birth affects the rates of 

activation of chemicals to toxic intermediates, and the detoxification and ultimately clearance of 

xenobiotic compounds. The most common metabolic enzymes include the so-called Phase I 

enzymes, cytochrome P450 (CYP) isoforms, which are involved primarily in oxidative reactions, 

and the Phase II enzymes involved in conjugating the xenobiotic or, more commonly, its 

metabolite with endogenous substrate rendering the molecule more amenable to excretion. 

Recent reviews have highlighted available information on the ontogeny of the Phase I enzymes 

primarily in liver tissue, the main organ of xenobiotic metabolism (Cresteil, 1998; Hines and 

McCarver, 2002). The metabolic capacity of the liver (as a function of body weight) generally 

increases rapidly to near adult levels early in life, but significant qualitative and quantitative 

differences exist in newborns and neonates. Most of the microsomal enzyme systems 

responsible for drug metabolism are present at birth and their activities increase with postnatal 

age (Morselli et al., 1980). Liver Phase I reactions (oxidation, reduction, hydroxylation) develop 

rapidly during infancy (Stromland et al., 1994). The total cytochrome P450 (CYP) content of 

human liver microsomes is unchanged from fetal life through the first year of postnatal life and is 

approximately one-third the total adult content (Streissguth et al., 1991). There are fetal 

isoforms of the CYP enzymes, and the exogenous chemical substrate kinetics of these forms are 

not well characterized. In general, the level of inducibility of fetal CYP forms is unknown 

(Pineiro-Carrero and Pineiro, 2004). The postulated development of individual cytochrome 

P450 (CYP) forms during infancy and childhood has been summarized in a previous document 

(OEHHA, 2001, see Table 5). Except for CYP3A4 and CYP3A7 these data are based on 

immunological analyses for the presence of CYP mRNA and protein rather than on the 

metabolism of specific chemical substrates. In general, three groups of CYP P450 could be 

described: 

(1) CYP3A7 and CYP4A1 present in fetal liver and active on endogenous substrates; 

(2) an early neonatal group including CYP2D6 and CYP2E1 which surge within hours of 

birth; and 

(3) a later developing neonatal group, CYP3A4, CYP2C’s, and CYP1A2 (Cresteil, 1998). 

Treluyer et al. (1997) observed that treatment of infants with barbiturates resulted in induction of 

CYP2C activity and increased metabolism of diazepam and tolbutamide. Total CYP3A protein, 

a major cytochrome P450 enzyme responsible for biotransformation of many exogenous 

compounds, is relatively constant in neonates and adults, but the isoforms change. CYP3A7 

expression peaks in the neonatal liver and declines over time to undetectable levels in adult liver; 

CYP3A7 appears to be responsible for aryl hydrocarbon hydroxylase (AHH) activity in the fetus 

(Hakkola et al., 1998). CYP3A4 is the primary adult hepatic form of the 3A series, and is very 

low in neonatal liver increasing slowly after birth; at one month there is about one-third of the 

CYP3A4 activity as an adult liver (Rice and Barone Jr., 2000). CYP2E1 is an important enzyme 

catalyzing metabolism of a number of environmental contaminants including benzene, 

trichloroethylene and toluene. Following birth hepatic CYP2E1 increases gradually reaching 

about one-third of adult levels by one year of age and is essentially 100% of adult levels by 10 

years of age (Rice, 1996). CYP2D6 levels are low in fetal hepatic tissue and rise after birth 

Technical Support Document 19 June, 2008 
TSD for Noncancer RELs June 2008 

reaching about two-thirds of the adult levels in infant’s age one month to five-year old children 

(Treluyer et al., 1991). CYP2C9 and CYP2C19, the most abundant CYP 2 enzymes in adult 

human liver, appear in the week after birth, and are about 30% of adult levels up to one year of 

age (Treluyer et al., 1997). CYP1A2 appears between one and three months of age, and reaches 

about half of adult levels at one year (Hines and McCarver, 2002). Evidence from both substrate 

activation and immunological detection indicates CYP1A1 is expressed in fetal liver, where it 

can activate such xenobiotics as benzo[a]pyrene and aflatoxin B1 (Hines and McCarver, 2002). 

CYP1A1 appears to be less important in adult liver but present in inducible form in extrahepatic 

tissues (Hakkola et al., 1998), while CYP1B1 is present in both adult and fetal extrahepatic 

tissues. Studies of pharmaceutical clearance demonstrate the ontogeny of cytochrome P450 in 

infants and children, including gender-based differences (e.g., caffeine demethylation) (Kearns et 

al., 2003). 

While there has been some study of the development of human CYP P450 enzymes and of the 

Phase II conjugation enzymes in the liver (reviewed in (Cresteil, 1998; Kearns et al., 2003; Hines 

and McCarver, 2002; McCarver and Hines, 2002)), there is less information about the timing of 

development of activity in other tissues. Watzka et al. (1999) observed sex- and age-related 

differences in CYP1A1 activity in the human brain, where enzyme activity increased 

dramatically from birth and reached adult levels by puberty. In the lung, animal studies have 

shown that exposure to environmental toxicants (sidestream tobacco smoke) can induce 

cytochrome P450 enzymes resulting in earlier activity (Gebremichael et al., 1995). The impacts 

on toxicity from activation of compounds by cytochrome P450 enzymes in early life depends on 

the rate of detoxification as well as capacity for repair. In neonatal rabbits, repair of injury to 

pulmonary Clara cells by toxicants activated by cytochrome P450 enzymes is decreased resulting 

in permanent structural changes in the adult animal (Miles, 1983). 

The activity of phase II conjugation reactions, which are usually detoxifying, is generally lower 

at birth (Goldstein, 1983). Hence, there is concern that detoxification and elimination of 

chemicals are slower in infants. Conjugation with glucuronic acid is significantly lower at birth 

with activity 2.5-fold below adult levels (Lehman and Fitzhugh, 1954). Glucuronidation 

generally matures to adult levels in two months, although glucuronidation of some drugs does 

not reach adult levels until puberty (Calabrese, 1983). Reduced glucuronidation would result in 

slower removal of aniline, N-hydroxyarylamines, phenol, and benzene metabolites in neonates. 

Acetylation and sulfation reactions are generally high in newborn infants and rapidly reach adult 

levels. Thus, neonates may conjugate drugs or environmental chemicals with sulfate rather than 

glucuronic acid (e.g., acetaminophen). Acetylation by the N-acetyltransferases and sulfation by 

sulfotransferases are generally high in newborn infants and somewhat comparable to adult levels, 

although it may vary by tissue and by specific sulfotransferase (Renwick et al., 2000;McCarver 

and Hines, 2002). Infants are less susceptible to acetaminophen acute toxicity than adults due to 

their more active sulfation (Levy et al., 1975). There are several forms of glutathione (GSH) 

sulfotransferases (GSTs) with GST-Pi prevalent in the fetus and decreasing postnatally. GSTalpha and GST-mu are detected in fetal liver and increase over the first few years of life to adult 

levels (McCarver and Hines, 2002). GST-mu is involved in arene oxide detoxification. GSTalpha is two-fold more active in children 0.5 to 4 years of age than in adults. Plasma glutathione 

(GSH) is similar in children 0.5 to four years of age and in adults (U.S. FDA, 2006). Blood 

esterase activity, which is less than half that of adults at birth, is more depressed in premature 

infants than in full-term infants and doesn’t reach the latter’s activity for ten to twelve months. 

Technical Support Document 20 June, 2008 
TSD for Noncancer RELs June 2008 

Esterase activity in newborns is two to ten-fold lower than the adult level. Low esterase activity 

coupled with lower volume of distribution may account for prolonged effect of local anesthetics 

observed during delivery (Ecobichon and Stephens, 1973). Neonates and premature infants may 

be deficient in the detoxification of organophosphorus (OP) insecticides (Augustinsson and Barr, 

1963; Cole et al., 2003; Costa et al., 2003; Costa et al., 2005). Epoxide hydrolase, important in 

detoxifying reactive epoxide metabolites, is present in fetal liver although at much reduced 

activity relative to adults (McCarver and Hines, 2002). 

As a result of differing enzyme activity, some chemicals are metabolized by wholly different 

metabolic pathways depending on age. In infants, theophylline is N-methylated to caffeine, a 

minor pathway for adults in whom the majority of theophylline dose is N-demethylated or Coxidized to monomethylxanthines or methyl-uric acid. Several studies have evaluated agerelated pharmacokinetic differences in humans using information about drug disposition 

(Renwick and Lazarus, 1998; Renwick et al., 2000; Ginsberg et al., 2002; Hattis et al., 2003). 

Calculation of internal doses as the area under the blood concentration times time curve (AUC) 

for the same doses (mg/kg) indicated that the major difference from adults occurs in preterm and 

full-term neonates and young infants (Renwick et al., 2000). Higher AUC internal doses in 

neonates and young infants versus adults were noted for seven drugs which are substrates for 

glucuronidation, one with substrate specificity for CYP1A2, and four with substrate specificity 

for CYP3A4 metabolism, and inter-individual variation in elimination by these detoxification 

pathways did not differ by age group (Renwick et al., 2000). Ginsberg et al. (2002) evaluated 

pharmacokinetic information on 45 drugs in children and adults metabolized by different 

cytochrome P450 pathways, Phase II conjugations, or eliminated unchanged by the kidney. 

These authors noted half-lives in infants three to nine-fold longer than those of adults. In 

evaluating the inter-individual variability by age, Hattis et al. (2003) noted that the largest interindividual variability occurred in the youngest children, apparently due to variability in 

development of critical metabolism and elimination pathways. Notably, these authors observed 

that for risk assessment modeling, unimodal distributions may be inadequate for young children 

and for overweight older children. 

While the variations in pharmacokinetics with age are an important consideration in risk 

assessment, additional complexity is overlain by genetic polymorphisms rendering some 

individuals more susceptible than others. Perera et al. (1999) showed that: 

1. There is significant transplacental transfer of polycyclic aromatic hydrocarbons (PAHs) 

and environmental tobacco smoke constituents from mother to fetus. 

2. PAH-DNA adducts in maternal and newborn white blood cells are increased from 

environmental exposure. 

3. The fetus is more sensitive to genetic damage than the mother. 

4. Newborns with a specific restriction fragment length polymorphism (RFLP), CYP1A1 

Msp1, had elevated numbers of adducts compared to those without the RFLP. 

The report of Canalle et al. (2004) indicated that genetic variants in xenobiotic metabolizing 

genes may play a significant role in the susceptibility to acute lymphoblastic leukemia (ALL), 

the most frequent malignancy in children. The case-control study involved 113 Brazilian 

children with ALL and 221 matched controls. Carriers of the rare GSTP1 Val allele were at 

Technical Support Document 21 June, 2008 
TSD for Noncancer RELs June 2008 

higher risk of ALL (odds ratio (OR) = 2.7; 95% CI = 1.1-6.8; p = 0.04). No difference was seen 

for GSTM1, GSTT1 or for the mutant CYP1A1*2 and CYP2E1*3 variants. However, when the 

mutant CYP variants were analyzed in combination with GSTM1 and the risk elevating GSTP1, 

the risk of ALL was increased (OR = 10.3; 95% CI = 1.0-111.8; p = 0.05) suggesting a combined 

effect. 

3.2.1.4 Excretion

Many drugs are more slowly eliminated in neonates and infants than in adults (Kearns et al., 

2003). A classic example is the toxicity associated with chloramphenicol in neonates resulting 

from a longer half-life (10 hr) compared to adults (2-5 hr) (Miles, 1983). This difference would 

also apply to some environmental contaminants in the drinking water. Differences in volume of 

distribution, metabolism, and the maturity of renal and hepatic elimination processes all play a 

role. Premature infants may have very low glomerular filtration rates (GFRs) (0.6 to 0.8 

mL/min) (Plunkett et al., 1992; Milsap and Jusko, 1994). Studies with a large number of drugs 

have shown that the large majority of these agents are more slowly eliminated in neonates and 

infants than in adults. While this may be partly due to an increased volume of distribution for 

water-soluble drugs and their metabolites, additional factors may also be involved. At birth, 

glomerular function is more developed than tubular function and this persists for six months 

(Guignard et al., 1975; Hook and Hewitt, 1977; Arant Jr., 1978). At birth, the GFR is lower (2-4 

ml/min), increases in the first few days (8-20 ml/min) and slowly increases to adult values by 8-

12 month old infants (Robillard et al., 1999; Kearns et al., 2003). Over the first two to three 

weeks of life, Guignard et al. (1975) reported a two-fold increase in insulin clearance. The GFR 

values in neonates ranged from 6.0 to 32.2 mL/min/m2 with a mean value of 10.8 ± 1.0 

mL/min/m2

, n = 22. Premature infants may have GFRs as low as 0.6 to 0.8 mL/min (Plunkett et 

al., 1992; Milsap and Jusko, 1994). Early increases in GFR are related to: increases in cardiac 

output, decreases in peripheral vascular resistance, increases in mean arterial pressure, increased 

surface area of the kidney for filtration, and increased membrane pore size (Morselli et al., 1980; 

Plunkett et al., 1992). For the first two to three months of life, GFR is lower than that of adults. 

Clearance values at 10 to 30 times lower than seen in adults have been reported for some 

compounds (e.g., aminoglycoside antibiotics) (Morselli et al., 1980; Morselli, 1989). These agerelated changes in renal function lead to decreased body clearance and prolonged blood halflives in neonates for any chemical that relies upon renal excretion for elimination. 

Studies in animals have shown that newborn and young animals have a reduced capacity to 

excrete chemicals into the bile compared to adult animals. Klaassen (1972) observed that the 

blood concentration of ouabain, a drug whose primary route of excretion is the bile, was sevenfold higher in seven-day old rats than in 39-day old rats. Ouabain is 40-fold more toxic in 

newborn than adult rats. Similarly both indocyanine green and sulfobromophthalein and their 

respective glucuronides were excreted more slowly in the bile of neonates than in adult rats 

(Klaassen, 1973). Ballatori and Clarkson (1982) found that the long half-life of methylmercury 

in neonatal rats was due to their inability to excrete the chemical in the bile, which is the main 

elimination route in adults. It is anticipated that these findings are indicative of likely 

deficiencies in the biliary excretion of certain environmental chemicals in human neonates and 

young children (Hattis, 1996a). 

Technical Support Document 22 June, 2008 
TSD for Noncancer RELs June 2008 

The use of various toxicokinetic factors in assessing children’s risk is the subject of a recent 

comprehensive review (Ginsberg et al., 2004c). These authors conclude that children are more 

toxicokinetically variable than adults because of their variable growth and maturation rates, in 

addition to the factors that create toxicokinetic variability in adults that relate to genetics, 

nutrition, disease, body composition, and prescription (and other) drug use. Children also 

present the problem of variable growth rates, which can make even small age bins (subdivisions) 

relatively heterogeneous, especially if one in concerned about protecting the tails of the 

distribution (e.g., 90th percentile). With greater variability, it is also more likely that a 

substantial fraction of a certain age group will lie outside the half-log toxicokinetic variability 

range we normally allocate to the adult defaults. 

3.2.2 Pharmacodynamic Differences 

Much more information exists for pharmacokinetics as a function of age than for 

pharmacodynamics. Many changes in pharmacodynamics occur with age and the mechanisms 

underlying toxic effects are of great interest to toxicologists, however, quantitative data are 

sparse. 

The maturation of various systems, including the immune system, the nervous system, the 

reproductive system, the digestive system, and the blood-brain barrier, reflects qualitative and 

quantitative changes in receptors with age. Age-related differences in the LD50, which reflect 

changes in the dose-response curve with age, may be related to receptor changes. Examples of 

pharmacodynamic differences in the pharmaceutical literature include development of Reye’s 

syndrome following aspirin administration in children but not adults (Belay et al., 1999), and 

greater risk for developing hypersensitivity reactions, including Stevens-Johnson syndrome, 

when treated with lamotrigine (Guberman et al., 1999). Windows of susceptibility to various 

toxicants are seen during development. In humans, autism has been associated with thalidomide 

exposure during days 20 to 24 after conception (Stromland et al., 1994). Diphenylhydantoin, an 

anticonvulsant used to treat epilepsy, may cause human malformations including craniofacial 

anomalies and growth abnormalities (Eluma et al., 1984; Orup Jr. et al., 2003). In mice it causes 

different types and frequencies of malformations depending on when during gestation it is 

administered (Eluma et al., 1984; Buehler et al., 1994). Other agents known to cause human 

developmental toxicity include aminopterin, diethylstilbestrol (DES), ethyl alcohol in alcoholic 

beverages, etretinate, isotretinoin (retinoic acid), lead, and methylmercury. 

In the case of ethyl alcohol, exposure in utero is associated with Fetal Alcohol Syndrome (FAS), 

a distinctive constellation of abnormalities at birth. In its more severe form, the syndrome is 

characterized by dysmorphogenesis of the head, heart, limbs and brain, deficits in IQ and 

memory, and behavioral problems such as hyperactivity and adolescent antisocial behavior 

(Streissguth et al., 1991). Similarly, lowered IQ and behavioral problems manifesting during 

childhood and later have also been associated with early life exposures to lead (Needleman et al., 

1990) and cigarette smoke (Williams et al., 1998; Maughan et al., 2001; Yolton et al., 2005). As 

a result, the insidious nature of exposure to toxic agents during development may become more 

evident later in life, years after cessation of the toxic exposure. Delayed neurotoxicity has been 

seen both in humans following developmental exposure to methylmercury in contaminated 

seafood, and in monkeys experimentally exposed to methylmercury in utero through puberty. 

Among humans with developmental exposure to methylmercury, the manifestations of Minamata 

Technical Support Document 23 June, 2008 
TSD for Noncancer RELs June 2008 

disease were delayed for several years in some individuals following cessation of exposure, and 

got progressively worse in others (Harada, 1995). Compared to an unexposed cohort, individuals 

exposed to methylmercury 20-30 years previously experienced significant deficits in motor 

function and impairments in visual, auditory and somatosensory systems (reviewed in Rice and 

Barone (2000). Similarly in monkeys, significant impairments of somatosensory and motor 

functions were observed up to 11 years following cessation of methylmercury exposure 

compared to controls (Rice, 1996). Developmental neurotoxicity is also a feature of human 

exposure to PCBs, as was initially noticed in children who suffered exposure to PCBs as a result 

of rice oil contamination, such as the Yusho and YuCheng incidents (Jacobson et al., 1990; Chen 

et al., 1994). Subsequent studies have shown neurotoxic impacts of a wide range of PCBs and 

related compounds, by mechanisms involving both direct impacts on the central nervous system 

and interference with neurodevelopment via disruption of the regulation and effects of thyroid 

hormones (Darras, 2008). 

While the specifics of the mechanisms underlying the toxicodynamic responses to these 

compounds have not been elucidated in most cases, the data generally indicate a significantly 

greater vulnerability of developing versus mature systems. In addition, it is important to 

recognize that the deleterious effects of exposure may not be immediately apparent, and in fact, 

may not be evident until much later in life. 

3.3 Risk Assessment Considerations 

The U.S. EPA recently developed a document entitled: A Framework for Assessing Health Risks 

of Environmental Exposures to Children (U.S. EPA, 2006c). This document presents 

considerations and questions for the risk assessor when evaluating the effects of life-stage at 

exposure on the toxic response or health outcome. A number of useful questions that the risk 

assessor should consider when evaluating the database on any chemical are discussed in the 

framework document. It should be noted that for the majority of chemicals in the Air Toxics Hot 

Spots Program, there will be limited data and this matrix of questions will likely remain 

unanswered for the risk assessor. Some of the considerations for dose-response assessment are 

noted below: 

• Are there life-stage specific outcomes in the database for the chemical, particularly those 

with adequate information to conduct a quantitative does-response assessment? 

• What is the most susceptible life-stage evident in the available data or by inference from 

other information? 

• Are there known windows of susceptibility or developmental processes likely to be 

susceptible to the agent? 

• Are there differences in the mechanism of toxicity in immature and mature animals or 

humans? 

• Are there known toxicodynamic differences by life-stage (for example, receptors, repair 

mechanisms, enzymes or processes involved in signal transduction or other key 

developmental processes)? 

Technical Support Document 24 June, 2008 
TSD for Noncancer RELs June 2008 

• Are there likely differences in toxicokinetics that can be modeled or estimated? Do those 

differences change over life-stages and how? Are there data to describe toxicokinetic 

differences coinciding with the most susceptible life-stage? 

Technical Support Document 25 June, 2008 
TSD for Noncancer RELs June 2008 

4 REL Development Methodologies 

OEHHA has developed uniform approaches for acute and chronic Reference Exposure Level 

(REL) development. The first descriptions of these approaches appeared in the Air Toxics Hot 

Spots Program Risk Assessment Guidelines, Part III Technical Support Document for the 

Determination of Noncancer Chronic Reference Exposure Levels, April 2000 (OEHHA, 2000a) 

and the Air Toxics Hot Spots Program Risk Assessment Guidelines. Part 1: The Determination 

of Acute Reference Exposure Levels for Airborne Toxicants (OEHHA, 1999). 

As noted in the Executive Summary and Introduction, OEHHA is updating these REL 

development methodologies. A complete literature search is conducted for each chemical, but 

the chemical summaries in Appendix D may only cite those studies contributing to the REL 

development and reflecting relevant routes of exposure. After identifying critical studies and 

toxicological endpoints, we identify a point of departure on the dose-response curve for 

extrapolating to the general human population. Various procedures for dose and time 

extrapolation and use of uncertainty factors are described in Section 4.4. 

4.1 Criteria for Studies Utilized to Identify Adverse Health Effects 

Although a wide variety of information may be reviewed, only certain key studies are used to 

develop RELs. The following criteria are used to determine the relevance and quality of data 

used for REL development. 

Peer reviewed papers published in the open scientific literature are the usual and preferred source 

of the data used in REL development, but other sources that may be used include government 

reports such as National Toxicology Program (NTP) studies, full laboratory reports of Good 

Laboratory Practices (GLP) compliant or otherwise well-conducted studies, and documents that 

have been reviewed by other impartial organizations but have not themselves been published in 

the literature, such as doctoral dissertations. These alternative sources are normally only used if 

they, or at least an appropriate summary of them are publicly available online or by request. 

Occasionally it may be necessary to request additional data from the author of a published study: 

these data may be used in REL development, and will be detailed in the REL summary. Review 

papers are not used for REL development as they are considered secondary sources. Studies 

involving a single chemical are given preference over those with multiple simultaneous 

exposures, especially if these are not quantified. Studies using multiple exposure doses and 

clearly indicating dose-response information are preferred, but in some cases an inadequate 

toxicology database may necessitate the use of older studies in which such information is 

unclear. Such studies are used only if there is no other relevant study available, and they are 

consistent with the general toxicology database. 

4.1.1 Selection of Key Studies 

An important step in the development of any REL is the identification of peer-reviewed research 

studies that contribute most significantly to the weight of evidence as to the degree of hazard 

presented to humans by a particular substance. The studies may involve a human population 

studied in an epidemiological, clinical, or experimental exposure setting, or they may involve 

Technical Support Document 26 June, 2008 
TSD for Noncancer RELs June 2008 

experimental studies with animals. These key studies are given greatest weight in estimating the 

dose-response relationship for adverse effects, and in identifying the nature of the critical adverse 

effect. 

4.1.1.1 Human Data

Human data are logically most relevant to assessing human health effects associated with 

chemical exposures. Principles for evaluating human exposure studies for use in determining 

health-based exposure levels have been discussed (OEHHA, 1999; OEHHA, 2000a; OEHHA, 

2003; OEHHA, 2005a). Whenever possible, RELs have been based on human data with relevant 

routes of exposure (principally inhalation). Of the 51 acute RELs originally developed, 36 were 

based on human health effects (OEHHA, 1999), while of the first 80 chronic RELs, 22 were 

based on human data (OEHHA, 2005a). 

Human studies used in assessing health effects of chemicals have included epidemiological 

studies, controlled exposure experiments, and case reports. Each of these three study types can 

provide important information needed to protect public health. When using these studies for risk 

assessment, several factors are important in evaluating their quality and in determining the level 

of certainty associated with their use. It should be noted that controlled studies of exposure in 

infants and children are rare outside of clinical trials. 

4.1.1.1.1 Epidemiological Data 

Epidemiological studies generally produce data on effects of chemical exposure to a large 

number of persons. Areas of concern when interpreting epidemiological studies include 

exposure measurement, health effects measurement, and accounting for co-variables and 

confounding variables (Rothman and Greenland, 2005). The population studied may consist of 

the general public or employees exposed in the workplace to varying concentrations of airborne 

chemicals. 

Exposure measures frequently represent the greatest weakness of available epidemiological 

studies. Continuous, long-term exposure monitoring of individual subjects is rarely available. 

Frequently it is necessary to use limited, short-term, exposure monitoring data, which in many 

cases are not specific to the individuals under study, in order to derive an estimate of what the 

individual exposures may have been. Occupational exposures may vary over time as industrial 

hygiene practices change and individuals change jobs. Also, analytical methods have changed 

and in many cases improved over the years, and earlier measurements may be much less 

accurate, or not comparable to more recent data (due to different techniques and equipment for 

trapping and quantifying, especially for particles and fibers, and recent development of 

biological monitoring methods). Thus, estimating exposure levels that existed in workplaces 

many years ago is difficult, and exposure measurements in population-based environmental 

studies may be even more problematic. Nevertheless, the degree to which air concentrations can 

be adequately estimated is critical in determining the usefulness of an epidemiological study. 

Health effect measures in epidemiological studies also frequently differ from those reported in 

experimental animal studies and must be carefully examined. Measurements of human health 

effects generally consist of recording observable effects, including clinical reports of disease or 

Technical Support Document 27 June, 2008 
TSD for Noncancer RELs June 2008 

disability. If tests are conducted specifically for a study, those tests are usually non-invasive, but 

standard clinical hematology, X-rays and other standard medical results may also be available. 

Health effects data are compared with those compiled from a non-exposed group and may be 

presented as incidence, standardized mortality ratios (SMR), odds ratio (OR) or relative risk 

(RR) ratios. Health effects with a long latency may be missed if the exposure duration or length 

of follow-up in the study is inadequate. 

For epidemiological studies to be useful, co-variables and confounding variables need to be 

controlled or removed from the study. Co-exposure to other chemicals is also an important 

concern as a potentially confounding or modifying effect. Occupational studies raise an 

additional concern in that generally healthy workers may be less sensitive to the adverse effects 

of chemical exposures than some others in the general population, including children, the elderly, 

carriers of genetic polymorphisms, which affect sensitivity, and persons with preexisting medical 

conditions. Gender-specific effects may be obscured by bias that may be present where a 

workplace is disproportionate by gender (NRC, 1986a). 

“Negative” epidemiological studies (i.e., those not presenting an outcome different from the null 

hypothesis, properly described as “non-positive”) present additional difficulties in interpretation. 

Estimating the power of the study to detect adverse effects is important to indicate the maximum 

incidence consistent with the observed null result. In addition, statistical confidence limits can 

be put around an observed null result. Although non-positive epidemiological studies may be 

useful as supporting evidence in favor of a REL derived from another data set, it is unlikely that 

such studies would themselves provide useful data for deriving a REL. 

4.1.1.1.2 Controlled Human Exposure Studies 

Controlled exposure studies have the advantages of having quantified exposure concentrations 

and of being conducted with human subjects, thus combining two important features of human 

epidemiological and animal toxicity studies (Hackney and Linn, 1983). The limitations of such 

studies include: 

(1) involve small sample sizes, 

(2) have a very short exposure duration, 

(3) use narrowly focused response measurements that might miss significant health effects and 

(4) usually only involve relatively healthy adults. 

In spite of these potential shortcomings, controlled studies in human subjects, especially in 

sensitive subpopulations such as asthmatics, are given preference over animal studies in the REL 

development process, particularly for acute RELs. Human studies were used only if they were 

consistent with the standard ethical practices of investigation at the time they were conducted. 

The preferred study is a modern, ethical study approved by an Institutional Review Board for 

Human Studies. Although data from exposure of infants and children would help OEHHA 

address its mandate to protect children’s health, controlled studies of exposures to infants and 

children are rare outside the context of clinical trials of potentially beneficial pharmaceuticals. 

Controlled exposures of children or infants to environmental chemicals are difficult to justify 

Technical Support Document 28 June, 2008 
TSD for Noncancer RELs June 2008 

ethically, even at presumably safe levels, because their lack of maturity may result in increased 

and unpredicted susceptibility; also, there are issues surrounding their free and informed consent 

with children. 

4.1.1.1.3 Case Reports 

Individual case reports of adverse effects associated with exposures to a chemical can be useful, 

especially as qualitative confirmation that effects observed and quantified in animals also occur 

in exposed humans. Multiple case histories with the same endpoint are especially relevant. 

However, these reports are generally not appropriate for quantitative analysis because of the very 

small sample size and the usually unquantified exposures (Goldstein, 1983). Only rarely would 

case reports be used as the basis for a proposed REL. 

4.1.1.2 Animal Data

Animal toxicity studies are the most widely available source of data for REL development. 

However, studies that address all the toxic endpoints of interest, which include, specifically, preand postnatal developmental toxicity as well as effects on adults, are not always available, and 

studies with the specific exposure periods of one hour, eight hours, or with chronic exposure may 

not have been done. 

Identification of the most appropriate animal species requires consideration of all available data 

relevant to prediction of human effects from animal observations. Studies of the most sensitive 

species have frequently been selected as key studies. Such an approach has the advantage of 

offering maximal protection, especially since humans may be more sensitive than laboratory 

animals in response to chemical exposure (Lehman and Fitzhugh, 1954). However, the animal 

species most sensitive to a substance is not necessarily that most similar to humans in developing 

adverse effects from a particular exposure. In general, of the animals used in laboratory studies, 

non-human primates are considered to be the most similar in response to exposures to toxic 

substances, but to date only the acute REL for hydrogen cyanide was developed using data from 

a controlled exposure of a non-human primate (the cynomolgus monkey, Macaca fascicularis). 

Such studies are relatively rare; those that are conducted typically involve less than lifetime 

exposure and a small number of individuals and thus have limited statistical power. 

Selection of the animal model and key study can be influenced by what is known about human 

health effects, and relevant areas of similarity and dissimilarity between humans and the test 

animal species (Calabrese, 1983). Comparison of human and animal pharmacokinetics and 

metabolism may be useful in selecting the relevant animal model for predicting human health 

effects. For example, hamsters and rabbits have much greater metabolic rates than monkeys 

(Plopper et al., 1983). This may increase or decrease their susceptibility to toxic substances 

relative to humans. However, in most instances it is not possible to determine which species 

responds most like humans. 

An experimental study should have a clear rationale and protocol, use Good Laboratory Practice 

(GLP) standards (or equivalent), and use appropriate analysis methods, including statistical 

analysis (U.S. FDA, 2006). Experimental study designs and criteria recommended by the NTP 

have been reviewed (Chhabra et al., 1990). However, the goal of protecting public health must 

Technical Support Document 29 June, 2008 
TSD for Noncancer RELs June 2008 

be weighed with experimental design so that important endpoints are not missed and responses 

of relevant species are not ignored. 

Previously some chronic RELs (e.g., naphthalene and 1,3-butadiene) were calculated using data 

from the standard 2-year NTP chronic toxicity/carcinogenicity study (OEHHA, 2000a). In many 

cases shorter screening studies, used for determining appropriate dose levels for the two-year 

study, are also reported and may contain additional useful information on noncancer toxic 

endpoints. In an NTP study using the inhalation route, groups of 50 rats and mice (6-8 weeks 

old) of both sexes are exposed for two years to two or three concentrations of a chemical, and the 

animals are examined for changes in approximately 30 organs. Although the NTP study has 

been considered a gold standard for assessing chronic adverse effects, the protocol specifies 

young adult (8-10 week old rats or mice) animals as the starting point, so it does not include 

exposures of fetal, neonatal, pre-pubertal or adolescent animals. Thus,the study design may miss 

or underestimate effects in young animals. Two current animal tests most relevant to children’s 

health are (1) the developmental toxicity study, in which pregnant females are exposed during 

specific periods of gestation, and (2) the two-generation reproductive study, in which parents and 

offspring are exposed to the chemical. The absence of one or both of these studies from a 

chemical’s database creates a serious data gap relating to children’s environmental health and 

may result in the application of a data deficiency uncertainty factor (UFD, typically √10, but 

possibly higher) to address a lack of developmental data (Section 4.4.9.1). 

4.2 Weight of Evidence Evaluations and Criteria for Causality 

A “weight-of-evidence” approach is generally used to describe the body of evidence on whether 

or not exposure to a chemical causes a particular effect. Under this approach, the number and 

quality of toxicology and epidemiological studies, as well as other sources of data on biological 

plausibility, are considered in making a scientific judgment (OEHHA, 2005b). OEHHA has not 

adopted a categorical ranking of the weight-of-evidence, but provides a descriptive analysis of 

strengths and uncertainties of the evidence considered as part of the toxicity review supporting 

each REL. The U.S. EPA on the other hand has used a formal scheme of this type for their RfCs 

(U.S. EPA, 1994a). 

Many of the proposed criteria for determination of causality are based on analyses of 

epidemiological studies (OEHHA, 2005b). These same criteria are however of general 

applicability to animal toxicology studies, although the degree of emphasis and extent of likely 

problems differ between these two data types. In analyzing animal studies, the nature and extent 

of the exposure and the characteristics of the exposed animals are generally well controlled. 

Under these circumstances, issues such as observation of a dose-response relationship, 

reproducibility of findings, and mechanism of action (including consideration of its relevance to 

humans) are key elements of the weight-of-evidence. On the other hand, for epidemiological 

studies the nature and extent of the exposure is often uncertain quantitatively and even 

qualitatively, and the exposed population is substantially more diverse than in a controlled 

animal experiment. Selected methodological issues that are considered in the review of the 

epidemiologic literature include: 

Technical Support Document 30 June, 2008 
TSD for Noncancer RELs June 2008 

1) the sample size of the study, which affects the power to detect an effect; 

2) the extent to which the analysis or design takes into account potential confounders, or 

other risk factors; 

3) over-adjustments for potential confounders, which would lead to underestimating effects 

of the toxin; 

4) selection bias, or whether the study groups were comparable; including consideration of 

the “healthy worker effect” and survivor bias, 

5) the potential for bias in ascertaining exposure and, in particular, nondifferential exposure 

misclassification, which biases effect size estimates towards the null. 

Criteria for evaluating associations between exposure and health effects have been recommended 

by the International Agency for Research on Cancer (IARC, 2006) 

(http://monographs.iarc.fr/ENG/Preamble/index.php), and standard epidemiology texts 

(Lilienfeld and Lilienfeld, 1980; Rothman and Greenland, 1998). Much discussion has ensued 

over the last two centuries on causal inference. Most epidemiologists utilize similar sets of 

causal guidelines, proposed by Bradford Hill (1971), which OEHHA has employed (OEHHA, 

2005b). 

It should be noted that the causal criteria are guidelines for judging whether a causal association 

exists between a factor and a disease, rather than hard-and-fast rules. Lilienfeld and Lilienfeld 

(1980) note that: 

“In medicine and public health, it would appear reasonable to adopt a pragmatic concept 

of causality. A causal relationship would be recognized to exist whenever evidence 

indicates that the factors form part of the complex of circumstances that increases the 

probability of the occurrence of disease and that a diminution of one or more of these 

factors decreases the frequency of that disease. After all, the reason for determining the 

etiological factors of a disease is to apply this knowledge to prevent the disease.” 

Commonly used causal criteria, based on those of Bradford Hill (1971), are described briefly 

below. These considerations are described in more detail in Rothman and Greenland (1998), the 

Surgeon General’s Reports on Smoking (U.S. DHHS, 2004), and OEHHA’s environmental 

tobacco smoke (ETS) document (OEHHA, 2005b). 

4.2.1 Strength of Association 

A statistically significant strong association, which is easier to detect if there is a high relative 

risk, between a factor and a disease is often viewed as an important criterion for inferring 

causality because, all other things being equal, a strong and statistically significant association 

makes alternative explanations for the disease less likely. However, as discussed in Rothman 

and Greenland (1998), the fact that a relative risk is small in magnitude does not exclude a casual 

association between the risk factor and the outcome in question. Since it is more difficult to 

Technical Support Document 31 June, 2008 
TSD for Noncancer RELs June 2008 

detect (i.e., reach statistical significance) a small magnitude risk, they are just as likely to be 

causal as larger magnitude risks. 

When assessing all evidence, it is important to consider the strength of the study design 

(particularly controlling for confounding variables, obtaining an unbiased sample, measurement 

error) and the level of statistical significance (i.e., the ability to exclude a Type I [false positive] 

error). The power of the study to detect biologically meaningful effects (i.e., the risk of a Type II 

[false negative] error) is important in considering studies that do not reach traditional (i.e., 

P<.05) statistical significance, particularly if the biological endpoint is serious. If the outcome is 

serious and the study small (i.e., low power), a larger P value (e.g., P<.10) may be adequate 

evidence for identifying an effect. 

There are a number of examples of statistically significant, small magnitude associations that are 

widely accepted as causal, such as causal links between air pollution and 

cardiovascular/pulmonary mortality and between second-hand smoke exposure and various 

cancers and heart disease. From a public health perspective, even a small magnitude increase in 

risk for a common disease can mean large numbers of people affected by the health outcome 

when exposure is frequent and widespread, as measured by the population attributable risk or 

attributable fraction. Small magnitude of association must not be confused with statistical 

significance, which is much more important. 

4.2.2 Consistency of Association 

If several investigations find an association between a factor and a disease across a range of 

populations, geographic locations, times, and under different circumstances, then the factor is 

more likely to be causal. Consistency argues against hypotheses that the association is caused by 

some other factor(s) that varies across studies. Unmeasured confounding is an unlikely 

explanation when the effect is observed consistently across a number of studies in different 

populations, or when controlling for known confounders. 

Relevant observations include similarity of effects noted in different studies. For example, if an 

effect was noted in only one of many studies of a particular strain of laboratory rodent, or in only 

one of many epidemiological studies, evidence for a causal association between the chemical 

exposure and the effect is weakened. Associations that are replicated in several studies of the 

same design and among different populations (or species for animal studies) or geographical 

regions, using different epidemiological approaches, or considering different routes or sources of 

exposure are more likely to represent a causal relationship than isolated observations from single 

studies (IARC, 2006). If there are inconsistent results among investigations, possible reasons are 

sought (such as adequacy of sample size or control group, methods used to assess exposure, 

range in levels of exposure, over-correction for known confounders), and results of studies 

judged to be rigorous are emphasized over those of studies judged to be methodologically less 

rigorous. For example, studies with the best exposure assessment are more informative for 

assessing the association than studies with limited exposure assessment, all else being equal. 

Technical Support Document 32 June, 2008 
TSD for Noncancer RELs June 2008 

4.2.3 Temporality 

Temporality means that the factor associated with causing the disease occurs in time prior to 

development of the disease. The adverse health effect should occur at a time following exposure 

that is consistent with the nature of the effect. For example, respiratory irritation immediately 

following exposure to an irritant vapor is temporally consistent, whereas effects noted years later 

may not be. On the other hand, tumors, noted immediately following exposure, might be 

temporally inconsistent with a causal relationship, but tumors arising after a latency period of 

months (in rodents) or years (in rodents or humans) would be temporally consistent. An issue of 

temporal association that is sometimes difficult to clarify is the distinction between an effect due 

to chronic exposure and an acute effect due to repeated acute exposures. It may be inappropriate 

to develop a chronic REL based on an endpoint that is essentially an acute health effect seen 

repeatedly with daily workplace exposure. 

4.2.4 Coherence and Biological Plausibility 

A causal interpretation cannot conflict with what is known about the biology of the disease. The 

availability of experimental data or mechanistic theories consistent with epidemiological 

observations strengthens conclusions of causation. For example, the presence of known 

carcinogens in tobacco smoke supports the concept that exposure to tobacco smoke could cause 

increased cancer risk. Similarly, if the mechanism of action for a toxicant is consistent with 

development of a specific disease, then coherence and biological plausibility can be invoked. It 

should be noted that our understanding of the biology of disease, and therefore biological 

plausibility, changes in light of new information which is constantly emerging from molecular 

biology (including epigenetics), and from new clinical and epidemiological investigations 

revealing effect influenced by genetic polymorphisms, pre-existing disease, and so forth. 

4.2.5 Dose-response 

A basic tenet of toxicology is that increasing exposure or dose generally increases the response 

to the toxicant. A progressively increasing response with increased exposure makes it difficult to 

argue that the factor is not associated with the disease. To argue otherwise necessitates that an 

unknown factor varies consistently with the dose of the substance and the response under 

question. While increased risk with increasing levels of exposure is considered to be a strong 

indication of causality, absence of a graded response does not exclude a causal relationship 

(IARC, 2006). The dose-response curves for specific toxic effects may be non-monotonic. For 

instance, where the dose response shows saturation in an observable or experimentally 

achievable exposure range, the effect of exposures in this range could be nearly maximal, with 

any additional exposure having little or no effect. In some instances, a response is seen strongly 

in susceptible subpopulations, and the dose-response is masked by mixing susceptible and nonsusceptible individuals in a sample. Further, there are examples of U-shaped or inverted Ushaped dose-response curves, (e.g., for endocrine disrupters) (Almstrup et al., 2002; Lehmann et 

al., 2004). Finally, timing of exposure during development may mask an overall increase in risk 

with increasing dose. 

Technical Support Document 33 June, 2008 
TSD for Noncancer RELs June 2008 

4.2.6 Specificity 

Specificity is generally interpreted to mean that a single cause is associated with a single effect. 

It may be useful for determining which microorganism is responsible for a particular disease, or 

associating a single carcinogenic chemical with a rare and characteristic tumor (e.g., liver 

angiosarcoma and vinyl chloride, or mesothelioma and asbestos). However, the concept of 

specificity is not a particularly helpful criterion when studying diseases that are multi-factorial or 

toxic substances that contain a number of individual constituents, each of which may have 

several effects and/or target sites. 

4.2.7 Experimental Evidence 

While experiments are often conducted over a short period of time or under artificial conditions 

(compared to real-life exposures), experiments offer the opportunity to collect data under highly 

controlled conditions that allow strong causal conclusions to be drawn. Experimental data that 

are consistent with epidemiological results, and vice versa, strongly support conclusions of 

causality. There are also “natural experiments” that can be studied with epidemiological 

methods, such as when exposure of a human population to a substance declines or ceases; if the 

effect attributed to that exposure decreases, then there is evidence of causality. One example of 

this is the drop in heart disease death and lung cancer risk after smoking cessation. 

4.3 Hazard Identification 

4.3.1 Definition of Adverse Effect 

The general aim in developing health-protective levels such as RELs is to define a level at which 

no impairment of the health of an exposed human is anticipated. Risk assessment guidance has 

therefore historically focused on the identification of an adverse effect as critical in determining 

health-protective levels. Thus, U.S. EPA has used a general definition of ‘adverse effect’ as 

“any effect resulting in functional impairment and/or pathological lesions that may affect the 

performance of the whole organism, or that reduce an organism’s ability to respond to an 

additional challenge” (U.S. EPA, 1994a). 

However, the definition of an “adverse effect” has proved to be a source of significant difficulty 

and controversy. Not all effects reported for a substance are necessarily considered adverse; 

some adaptive biochemical responses such as enzyme induction are not considered necessarily 

adverse, unless they are identified as precursor events consistent with the mode of action for 

more obviously adverse pathophysiological events (Sherwin, 1983; American Thoracic Society 

(ATS), 2000a).

Within the health-disease spectrum, health effects could range from mild symptoms of ill health 

to exacerbations of terminal illnesses of diverse kinds; an inordinate depletion of cell, tissue, and 

organ reserves; subclinical disease; and mortality. Reserve loss involves both reversible and 

irreversible alterations of the cell population and includes metabolic abnormalities and 

alterations of the intercellular milieu. Therefore, the earliest adverse effect is an altered ecology 

at the cellular level. Irreversible abnormalities that appear relatively minor may have a serious 

Technical Support Document 34 June, 2008 
TSD for Noncancer RELs June 2008 

impact on health by increasing susceptibility to disease in general, or by exacerbating other 

disease processes (Sherwin, 1983). 

NRC (2007) has pointed out that this continuum of responses presents a basic difficulty in 

defining any particular effect as adverse or otherwise: 

“Dividing effects into dichotomous categories of ‘adverse’ and ‘non-adverse’ is problematic. 

Adverse effects usually develop along a continuum, starting with the uptake of a toxicant, 

distribution and metabolism, contact with a target organ, biologic change, physiologic response 

and repair, and clinical disease. Thus with some doses and hosts, biologic changes occur, but 

the body has sufficient defense mechanisms for detoxification or adaptation, and there is little or 

no adverse cumulative effect, particularly at low doses. In other situations, biologic changes are 

measurable and are precursors of an adverse clinical change, so an adverse effect, or the 

precursor of an adverse effect, could be defined in terms of a chemical metabolite or biologic 

change that is an indicator of both exposure and effect. The same biologic change could have 

little impact at a small dose (and so be termed ‘non-adverse’) but produce a much larger impact 

at a greater dose or in a more vulnerable person (and thus be termed ‘adverse’).”

The U.S. EPA considers both the biological and statistical significance of effects when 

determining if the observed effect can be defined as adverse. Their determination also takes into 

account what is known about the underlying mode of action (U.S. EPA, 2002a). Biological 

significance is the determination that the observed effect (e.g., a biochemical change, a 

functional impairment, or a pathological lesion) is likely to impair the performance or reduce the 

ability of an individual to function or to respond to additional challenges from the agent. For 

some quantal endpoints (e.g., birth defects, tumors, or some discrete pathological changes), 

criteria are already established to decide the type and incidence of effects, which may be 

considered adverse, and statistical tools to support the decision. However, changes in continuous 

measures such as body weight, enzyme changes, and physiological measures, are more difficult 

to use as endpoints because the amount of change considered to be biologically significant has 

not been well defined (U.S. EPA, 2002a). 

In particular, relatively subtle alterations in such continuous measures such as cellular 

proliferation, maturation, gene activation or suppression, and altered signal transduction, can 

lead to serious outcomes in developing humans. Thus it can be difficult, but important to the 

protection of developing infants and children, to determine the biological significance of 

seemingly minor alterations in an enzyme. Some changes in enzyme activity or levels can 

produce severe effects in a developing organism if they produce a change in signal transduction. 

For example, fetal exposure to chlorpyrifos alters receptor numbers and activity in serotonergic 

neurons in adults (Aldridge et al., 2004). Also, brief inhibition of cyclooxygenase-2 during the 

perinatal period alters neurodevelopment and severely inhibits reproductive behavior in the adult 

male rat (Amateau and McCarthy, 2004). 

OEHHA therefore follows NRC (2007) in recommending a cautious and health-protective 

approach to the consideration of whether a given biological endpoint is appropriate to consider 

frankly “adverse,” or is a biologically significant precursor lesion, in which case it would be a 

suitable endpoint for consideration in a risk assessment, or is rather a non-adverse adaptive or 

incidental change. An example of the necessary decision process is shown in the determination 

Technical Support Document 35 June, 2008 
TSD for Noncancer RELs June 2008 

of a Public Health Goal for perchlorate in drinking water (Ting et al., 2006). Here the decision 

was made to use the precursor biochemical alteration of impaired iodine uptake, a known effect 

of the perchlorate ion resulting from inhibition of the active transport protein responsible for 

iodide translocation, the NIS symporter. This is clearly shown at higher doses to impact thyroid 

hormone synthesis and this in turn is known to have severe impacts on central nervous system 

development in the human fetus and infant and on IQ deficits in the human newborn. It was 

concluded that even modest impacts on the iodine uptake process had the potential to impact 

sensitive targets, such as the fetus of a woman with suboptimal iodine intake. 

4.3.2 Nature of Adverse Effects 

The toxic effects of chemicals are of varying types and degrees of severity. Following an acute 

(one-hour) exposure to a substance released into the atmosphere, effects on the upper and lower 

respiratory tract may be observed as so-called “portal of entry” effects. Toxic effects from 

airborne substances may also be due to exposure via the skin and eyes. Systemic effects, such as 

hemolysis or central nervous system injury, may result from absorption of material through the 

lungs, and, to a lesser extent, through the skin. 

Toxic effects do not have to be observed immediately to be considered due to an acute exposure, 

but may instead appear hours to days after that exposure. For example, a brief exposure to 

phosgene may result in pulmonary edema several hours later. In the case of benzene, death may 

result from leucopenia days following high-level acute exposure. 

Certain chemicals, after a single exposure, have the potential to produce delayed adverse effects. 

Often acute toxicity tests do not have a sufficient follow-up period to allow thorough assessment 

of the potential for delayed health effects from single exposures. With respect to two kinds of 

delayed effects, cancer and reproductive or developmental harm, there is more information 

available. Carcinogenicity is treated separately in risk assessment and cancer potency factors for 

carcinogens are described in the Air Toxics Hot Spots Program Risk Assessment Guidelines Part 

II: Technical Support Document for Describing Available Cancer Potency Factors (OEHHA, 

2005a). Reproductive and developmental toxicants are considered here because substantial 

research effort has been devoted toward specifically identifying such delayed effects. 

Some substances exert their toxic effects through their metabolites. For example, methylene 

chloride’s acute toxicity is mediated through its metabolite, carbon monoxide. Whenever 

possible, information on toxic metabolites is provided in the toxicity summaries. When detailed 

information is available on the relationship of dose of the parent chemical to level of metabolite 

and the metabolite level to degree of toxic response, this is taken into account in developing the 

RELs. However, RELs are always expressed in terms of the concentration of the parent 

compound, not the metabolite. 

4.3.3 Severity of Effects 

Adverse effects may occur with a range of severity from mild (sensory or subjective effects, or 

statistically significant incidence of precursor changes, which are reversible) to severe (clinically 

significant pathological changes, disabling or strongly objectionable sensory effects, persistent or 

irreversible histological or functional damage), or even to life-threatening. These effect levels 

Technical Support Document 36 June, 2008 
TSD for Noncancer RELs June 2008 

have been used in a variety of ways including in models of progressive dose-response such as 

that used in U.S. EPA’s categorical regression methodology. 

The endpoint of choice for determination of a REL, which is intended to protect the health of the 

community at large, will generally be a mild effect. However, more severe effects may be used 

if these are in fact the most sensitive endpoint (for example irreversible developmental effects), 

or if no data on mild effects are available. Under such circumstances, additional models or UFs 

may be used as described in the following section in order to provide adequate health protection 

for the majority of the exposed population. 

4.3.4 Target Organs 

The nature of the target organs or systems involved in a given toxicological response is 

important since this is considered for hazard index (HI) calculations (Section 2.2). Consideration 

of the cumulative impact of exposure to multiple chemicals is one of the requirements of SB25, 

and a key objective for environmental justice considerations. The target organs or systems are 

described by general categories that may include varied effects: categories and effects currently 

used in existing acute and chronic RELS are shown in Appendix H. For example, the target 

system, “respiratory system,” includes upper airway irritation as well as lower airway effects, 

such as bronchoconstriction. Obviously this list of specific endpoints is not exclusive, and may 

be augmented or amended as new RELs are developed. In order for the acute and chronic REL 

HI target organs to be consistent, developmental and reproductive effects, which were previously 

combined, have been separated into two categories. New target organ categories may need to be 

added, based on the toxicological data used to develop additional RELs. 

For simplicity, this approach to HI calculations assumes additivity when multiple toxicants 

impact the same organ system or biochemical target. Other possible modes include independent 

(non-additive), synergistic or antagonistic. The description of synergism or antagonism is 

difficult, and probably requires determination of joint dose-response functions on a compoundspecific basis. There are relatively few compounds for which synergism or antagonism have 

been documented. It is unclear whether this is because such interactions are genuinely 

infrequent, or because the standard toxicological screens are not designed to identify these 

effects, and also because the database on the toxicology of chemical mixtures is relatively small. 

In using the additive HI approach it is necessary to define what constitutes the “same” 

toxicological endpoint, which when impacted by multiple toxicants results in effectively 

cumulative damage (Salmon, 2007). This might be the same molecular target, the same 

physiological process, or perhaps the same anatomical unit. The traditional basis has usually 

been the anatomical unit by default, since actual mechanisms and physiological interactions 

between organs are frequently unknown. More recently, the availability of information on 

toxicological mechanisms has prompted discussion of both broader and narrower frames of 

reference. The concept of a single molecular target is attractively simple, but may be too narrow 

when multiple control or functional systems give input to a single critical system or process 

downstream from the molecular targets of various toxicants. Because the precise relative 

contributions of exposure to multiple substances that principally affect different areas of the 

same physiologic system are unknown, the assumption of additivity across a single major organ 

system may either under- or over-estimate the effects of chemical interactions in certain cases. 

Technical Support Document 37 June, 2008 
TSD for Noncancer RELs June 2008 

However, in most cases this approach provides an appropriate health protective assumption. We 

have indicated in Appendix B, Table B-1 which toxicological endpoints are relevant to the 

specific REL for each chemical. While the REL is based on the most sensitive endpoint, other 

toxicological endpoints are manifested at exposures close to that which induces the toxicological 

endpoint that serves as the basis for the REL. Therefore, some chemicals should be evaluated for 

impacts on multiple target organs or systems. In addition, predisposing conditions are known to 

increase susceptibility to some chemicals. The target organs for those predisposing conditions 

should also be included in the HI approach. 

4.4 Dose-Response Assessment 

4.4.1 Estimation of Threshold or Low Response Concentrations 

Noncancer health effects assessment has been based on the concept that a threshold 

concentration or dose exists below which no adverse effects would occur. While such thresholds 

are observed among individuals, the existence and magnitude of a population threshold below 

which no members of the population would experience adverse effects cannot be demonstrated. 

In any study, the entire population of concern is not examined; rather a sample of the population 

from which inferences are drawn is studied. Therefore, it is not possible to distinguish whether a 

concentration is truly below a population threshold level for an adverse effect or is rather a level 

associated with a relatively low incidence of adverse effects, which cannot be distinguished from 

background rates in the population. 

There may also be cases where no threshold exists in the general population for a particular 

effect. This situation may occur for responses for which there is no theoretical threshold due to 

the mechanism of toxicity. The most accepted example of this is chemical carcinogenesis, 

particularly for genotoxic carcinogens. However, there may, at least in principle, be other types 

of toxicity which do not show a threshold at any dose level. 

Even where a true threshold exists in the dose response of a particular individual to a chemical 

exposure, there may in fact be no identifiable threshold in the response of the general population. 

This may occur in the case where some individuals in a diverse population show a threshold 

whereas others do not, which is at least theoretically possible if genetic polymorphisms exist 

which inactivate a protective mechanism. However, the most likely case is where a true 

threshold in the response occurs in all individuals at low doses, but the background rate or extent 

of that toxic response in the population is already above zero due to population-wide exposure to 

that pollutant or another causative factor which produces the same end-point or disease. In this 

case, any increment in exposure to the pollutant of concern will cause an increase in the 

prevalence or severity of the disease, in spite of the existence of a threshold in the individual 

dose-response relationship. A probable example of this is seen in the neurodevelopmental 

effects of lead exposure in children, which recent risk assessments have described using linear or 

other continuous dose-response functions (Carlisle and Dowling, 2006). The data available for 

criteria pollutants such as ozone or particulate matter are consistent with linear no-threshold 

dose- response curves for cardiovascular mortality (Daniels et al., 2000; Schwartz et al., 2001; 

Schwela, 2000; Vedal et al., 2003). 

Technical Support Document 38 June, 2008 
TSD for Noncancer RELs June 2008 

Where these special cases are demonstrated to exist on the basis of population health data, or 

appear likely based on mechanistic studies, it will be appropriate to use these data to develop 

risk-based or continuous-response models to describe the population impacts of exposure to 

these pollutants, rather than relying on the threshold dose-response description to identify a 

“safe” exposure level. It should be noted that lack of a true threshold does not necessarily imply 

linearity of response at all doses (although at sufficiently low dose levels any continuous nonthreshold dose-response curve will necessarily approach linearity). Conversely, the observation 

of a non-linear dose-response curve does not necessarily imply the existence of a threshold. 

However, in the majority of cases for noncancer effects the existence of a threshold in the dose 

response is both plausible, and often, within the acknowledged limitations, demonstrable (U.S. 

EPA, 1993). Therefore, the threshold assumption is regarded as the default for noncancer risk 

assessment, and is most often used. 

Two major strategies are used for dose-response assessment methods to estimate “thresholds” of 

responses from study data. These are the benchmark dose (BMD) or benchmark concentration 

(BMC) approach and the no-observed-adverse-effect-level (NOAEL) approach. In both 

approaches, uncertainty factors (UF) are applied to account for various uncertainties in 

extrapolating from the study results to the general population. These are described in Sections 

4.4.2 through 4.4.9. 

Of the methods presented, the BMC approach is preferred. Quantal or continuous dose-response 

data for a toxicant (measured for at least two dose levels and a control) are required to estimate 

levels using the BMC method. Supporting toxicological data will not, however, always be 

sufficient to permit this level of quantification. In most cases, the method will allow 

determination of a benchmark concentration even with relatively sparse data; however, obviously 

the confidence in the result will be lower in this case. The alternative NOAEL method may give 

the appearance of providing a result more easily with poor data, but in fact the uncertainty in 

such a result can be extremely large, and the situation is not improved by the inability to quantify 

this uncertainty. OEHHA has used the BMC approach to develop two acute RELs (OEHHA, 

1999; Collins et al., 2005). More recently a number of chronic RELs have been developed using 

this approach (Collins et al., 2003; Collins et al., 2004). Based on recent experience with the 

benchmark method, new REL values will be developed using the BMC approach whenever data 

of sufficient quality to support this methodology are available. 

4.4.1.1 Use of No-Observed-Adverse-Effect-Levels (NOAEL)

A No-Observed-Adverse-Effect-Level (NOAEL) in a human or animal study may be defined as 

an exposure level with no biologically or statistically significant increase in the frequency or 

severity of adverse effects among the exposed group relative to a control group. The NOAEL 

must be tempered by appropriate statistical interpretation. A NOAEL is sometimes incorrectly 

viewed as an estimate of a threshold level for adverse effects. However, a NOAEL could be 

associated with a substantial (1-20%) but undetected incidence of adverse effects among the 

exposed experimental group or population. This is so because only a subset of individuals from 

the population has been observed, and because the experiment may not have been designed to 

observe all adverse effects associated with the substance. Therefore, one may not safely 

conclude that the study concentration or dose is not associated with any adverse effects (U.S. 

Technical Support Document 39 June, 2008 
TSD for Noncancer RELs June 2008 

EPA, 1994a). Alternatively, a NOAEL could be many-fold lower than a true population 

threshold due to study design and dose spacing (Gaylor, 1992; Leisenring and Ryan, 1992). 

The U.S. EPA (1994a) determined that a NOAEL not associated with any biological effect (a 

“no-observed-effect-level” or NOEL) identified from a study with only one dose level is 

unsuitable for derivation of an RfC for chronic exposure. Because there is a limited availability 

of multi-dose studies for the variety of chemicals considered, OEHHA may use a NOAEL 

without an associated LOAEL (lowest-observed-adverse-effect-level) identified in the same 

study (termed a free-standing NOAEL) in deriving a REL, but only if there are no other suitable 

studies, and so long as the overall health hazard data (including any case reports or studies with 

shorter durations) for that substance are consistent with the NOAEL study. 

4.4.1.1.1 Derivation of Reference Exposure Levels (RELs) Using NOAELs 

Reference Exposure Levels are derived by the application of UFs to the NOAEL for a critical 

endpoint. The application and values of UFs, which are similar for the NOAEL and BMC 

approaches, are described below. 

NOAEL / UF =REL 

Prior to the determination of a NOAEL, the literature is examined to identify the relevant 

endpoints. Toxicological endpoints are evaluated to determine the most sensitive effect 

(occurring at the lowest exposure level), and a dose-response relationship is determined. The 

most sensitive adverse effect of relevance to human health (termed the “critical effect”) is used 

as the basis of the REL, and as noted above this is usually a mild adverse effect. 

4.4.1.1.2 Use of Lowest-Observed-Adverse-Effect-Levels (LOAEL) 

A Lowest-Observed-Adverse-Effect-Level (LOAEL) may be defined as the lowest exposure 

level in a study or series of studies with a biologically and/or statistically significant increase in 

the frequency or severity of adverse effects among an exposed population relative to a control 

group. The highest exposure concentration which results in biological effects that are not 

considered adverse may be termed the lowest-observed-effect-level (LOEL); this is identical to 

the NOAEL (U.S. EPA, 1994a). If a NOAEL is not identifiable from the literature, it must be 

estimated from the lowest exposure concentration reported to produce the adverse effect; this is 

the lowest observed adverse effect level (LOAEL). An UF is applied to the LOAEL to estimate 

the NOAEL. Use of a LOAEL should be a last resort; use of the BMC methodology is 

preferable whenever possible. Where experimental data showing intermediate response rates are 

very limited, this may place constraints on the benchmark response rate and curve-fitting model 

used. However, even in these cases the overall uncertainty is likely to be both smaller and better 

quantified by the BMC methodology than by a LOAEL-based derivation. 

If there exist multiple, non-identical NOAELs and LOAELs for the same compound and critical 

effect, the study of the best quality reporting the highest value for a NOAEL (preferred) or the 

lowest value for the LOAEL is used for the development of RELs. 

Technical Support Document 40 June, 2008 
TSD for Noncancer RELs June 2008 

4.4.1.2 Benchmark Concentration

The importance of a dose-response relationship in the evaluation of effects of chemical exposure 

is well-established. The NOAEL approach, does not explicitly incorporate information on the 

shape of the dose-response curve and is highly dependent on the test doses chosen by the original 

investigators. This led to explorations of the concept that a concentration estimated to be 

associated with a predefined low risk could provide an alternative to the NOAEL (Mantel and 

Bryan, 1961; Mantel et al., 1975; Crump, 1984; Dourson, 1986; Hartung, 1987; Gaylor, 1988; 

Gaylor et al., 1998). Crump (1984) proposed the term “benchmark dose” and extensively 

evaluated this concept. In this document, the term benchmark concentration (BMC) is used since 

inhalation toxicology data are usually described in terms of air concentrations. 

The BMC method allows a mathematical and statistical approach to the calculation of RELs 

(Crump, 1984; Lewis and Alexeeff, 1989; Alexeeff et al., 1992; Alexeeff et al., 1993; Barnes et 

al., 1995; Collins et al., 2005; Starr et al., 2005). In this document, the BMC is defined as the 

95% lower confidence limit of the concentration expected to produce toxic responses in a chosen 

percentage of subjects (the benchmark response rate) exposed at this dose. A suitable 

mathematical function is fitted to the concentration versus response relationship using likelihood 

methodology. The function used is selected according to defined quality of fit criteria. The 

concentration expected to produce the benchmark response rate, and the lower confidence bound 

on that concentration are identified from the fitted curve. In the case of quantal data in an animal 

toxicity experiment, the benchmark response rate is usually selected at 5% (see Section 4.4.1.2.1

below). Other types of data, including continuous measures of toxic response, and data from 

epidemiological studies, require an appropriate benchmark response rate to be identified on a 

case-by-case basis. An example of the benchmark dose methodology is graphically depicted in 

Figure 4-1. 

Technical Support Document 41 June, 2008 
TSD for Noncancer RELs June 2008 

FIGURE 4-1 LOG-PROBIT MODELING OF DOSE-RESPONSE DATA FOR SILICA1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 2 4 6 8 10 12 14 16

Fraction Affected

Dose (mg/m3-y)

Log Dose/Probit Model with 0.95 Confidence Level 

BMDL BMD

Probit

BMD Lower Bound 

1

Data from (Hnizdo et al., 1993; Collins et al., 2005). 

In spite of its advantages, there are sources of uncertainty in the experimentally derived BMC 

value. For example, the studies used to estimate the BMC have usually been performed with 

animals rather than humans e.g., (Kuwabara et al., 2006). Also, the experimental duration of 

exposure may differ from that which is of interest for the establishment of RELs. Additionally, 

the dose of toxicant delivered to the target tissue may differ between species and among humans 

and may depend on the type of activity in which the individual is engaged. Another area of 

uncertainty is that there can be a large degree of variability in the number of people who respond 

at any exposure. For example, there may be over a 10-fold variability in the irritation threshold 

(the concentration of a substance at which irritation of the eyes, nose and/or throat is first 

detectable) for chlorine (Anglen, 1981). In order to estimate a health protective level such as a 

REL for the population of concern, the BMC is therefore modified by UFs, except where explicit 

extrapolation models are available to allow for these differences. 

BMC / UF = REL 

Most frequently, the characteristics of the BMC are chosen so that its properties are similar to 

that of the NOAEL described below. Thus, similar UFs (Table 4.4.1) are applied with both 

approaches. Specific data sets may however result in the use of UFs different from what would 

be used with a standard NOAEL, determined on a case-by-case basis; the rationale would be 

described in each toxicity summary for the individual chemicals. 

Technical Support Document 42 June, 2008 
TSD for Noncancer RELs June 2008 

4.4.1.2.1 Selection of Appropriate Benchmark Concentration Response Rate 

A response range of 1% to 5% approximates the lower limit of adverse effect detection likely to 

occur in typical human epidemiological studies, and in large laboratory animal studies the 

detectable response rate is typically in the 5 to 10% range (Gaylor, 1992). In 1995, using animal 

developmental toxicity data, the U.S. EPA concluded that a 1% response rate was likely to be too 

low to be detected and therefore too uncertain to use as a point of departure, while either 5% 

(BMC05) or 10% (BMC10) response rates were adequate for the purposes of estimating a 

benchmark concentration (Barnes et al., 1995). One reason for this conclusion was the large 

difference (29-fold) between observed NOAELs and the 1% benchmark using developmental 

toxicity data. Subsequently, the U.S. EPA (2007a ) used a 10% response rate for benchmark 

concentrations when deriving chronic inhalation reference concentrations (RfCs). More recently, 

RfC determinations for various endpoints by the U.S. EPA have used either 5% or 10% as the 

benchmark response rate, depending on the statistical uncertainty in the data (U.S. EPA, 2002a; 

U.S. EPA, 2004). OEHHA has used the 5% response rate in several chronic RELs, and showed 

that the lower 95% confidence bound on the BMC05 typically appears equivalent for risk 

assessment purposes to a NOAEL in well designed and conducted animal studies where a 

quantal measure of toxic response is reported (Lewis and Alexeeff, 1989; Alexeeff et al., 1992; 

Alexeeff et al., 1993; Barnes et al., 1995; Collins et al., 2004; Collins et al., 2005; Starr et al., 

2005; Alexeeff et al., 2006; Brown et al., 2006). Therefore, OEHHA typically uses a 5% 

response rate as the default for determination of the BMC from quantal data (i.e. the effect is 

either present or it is not) in animals (Fowles et al., 1999). 

Other response rates may be selected if the data indicate that this is appropriate. For instance, 

large epidemiological studies examining a relatively severe endpoint such as clinical disease may 

support the use of a 1% response criterion, as in the case of the chronic REL recently developed 

for respirable crystalline silica (Collins et al., 2005). In that case, the size of the epidemiological 

database was large and thus there was high confidence in the response at low exposures. In the 

case of a steep dose-response relationship, the selection of response rate is less influential on the 

final value. For acute lethality studies, 1 and 5% response rate benchmark concentrations 

differed, on average, by less than 2-fold from the respective NOAEL (Fowles et al., 1999). 

Various criteria have been proposed for selecting an appropriate benchmark response rate for 

continuous data such as body weight, blood cell numbers, and levels of enzyme activity (U.S. 

EPA, 1995; Gaylor et al., 1998; Crump, 2002; Sand et al., 2003). One criterion is statistical 

confidence, e.g., criteria based on some multiple (1.0 - 3.0) of the standard deviation of the 

reported measurements, either above or below the mean, particularly in controls or low-dose 

groups. A standard deviation of 2.33 from the mean identifies values at the first and 99th

percentiles, extreme values even if not adverse. If values greater than the 98th to 99th percentile 

are abnormal, then a concentration that changes the mean by one standard deviation yields 

roughly 10% excess risk in subjects in the abnormal range (Crump, 1995). A second criterion is 

scientific judgment as to what constitutes a biologically relevant perturbation in a measured 

parameter, such as one that exceeds the likely range of physiological compensation. Some 

clinical guidelines are generally accepted as cutoff points although they are not necessarily 

thresholds. These might include: 

Technical Support Document 43 June, 2008 
TSD for Noncancer RELs June 2008 

(1) reduction in lung function (>20% of expected forced expiratory volume (FEV)) as 

clinically significant 

(2) a carboxyhemoglobin level of 1.1 to 1.3%, and 

(3) a pesticide worker’s blood cholinesterase level less than 80% of the individual’s baseline 

level. 

The choice of an appropriate benchmark criterion for continuous data is currently based on the 

particular nature of those data, including supporting information on severity of the effect and 

possible mechanisms of repair or compensation, rather than on any overall policy-based 

guidance. In the development of the chronic REL for carbon disulfide, OEHHA used as the 

benchmark response rate a five percent reduction in peroneal motor conduction velocity 

(BMC05), a mild effect and definitely within the range of normal variation. In some cases, 

population shifts in a continuous variable such as FEV1, blood pressure, birth weight, thyroid 

hormone levels (Ting et al., 2006) or IQ (e.g. effects of lead as reported by Lanphear et al. 

(2005)) may result in pushing more individuals into a high-risk category, and thus small shifts 

can be considered adverse. 

4.4.1.2.2 Selection of Confidence Limits 

The benchmark dose or concentration is selected by fitting an assumed dose-response curve to 

the observed response data. Mathematical curve fitting of this type necessarily involves 

recognition of uncertainty and variability in the input data. Fitted curves or interpolated values 

are generally described in terms of both maximum likelihood estimates (MLE) and confidence 

bounds on these estimates. Variation around the predicted values is generally assumed to follow 

a χ

2

 (chi-squared) distribution, and the χ

2

 statistic is used as a criterion of fit quality and in 

deriving “p” values and confidence limits on estimates. The 95% lower confidence limit (LCL) 

of the concentration at the chosen benchmark response rate or level is generally used as the 

BMC, rather than the MLE. This is preferred since it takes into account sources of uncertainty 

intrinsic to the source data, including the variability of the test population and the number of 

subjects in the study. This provides an incentive for the generation and use of higher quality 

data, unlike the NOAEL/LOAEL methodology, which makes no explicit quantitative allowance 

for uncertainty in the underlying data. Use of the 95% LCL in a benchmark calculation also 

takes into account the quality of fit for the dose-response curve. The Benchmark Dose 

Workshop (Barnes et al., 1995) recommended using the 95% LCL in benchmark dose 

calculations. With robust data sets the 90, 95, and 99% LCLs are close to each other and to the 

MLE (Sand et al., 2002). 

4.4.1.2.3 Selection of Models to fit the Dose-Response Curve 

It is important to select an appropriate mathematical model for the type of data used for 

benchmark concentration calculations (Filipsson et al., 2003). The U.S. EPA’s Benchmark Dose 

Software (BMDS) contains a variety of models (U.S. EPA, 2006a). 

Technical Support Document 44 June, 2008 
TSD for Noncancer RELs June 2008 

For dichotomous data, the models include the following: 

(1) gamma distribution, (4) probit, (7) Weibull models. 

 N d), er 

for 

and

a th

mo

(2) logistic, (5) quantal linear, 

(3) multistage, (6) quantal quadratic, and 

The quantal linear and the quantal quadratic are special cases of the Weibull model in which the 

exponents are one and two, respectively. The probit and logistic models can be run using either 

the dose or the logarithm of the dose. These models are useful for data where the subjects at 

each level of exposure did or did not experience a specific adverse effect such as eye irritation, 

liver enlargement, or an impaired nervous system (based on passing or failing a specific test). 

For nested dichotomous data, such as found in animal developmental data in which individual 

offspring are nested in litters, the models available are: 

1) Logistic (logistic neste (2) NCTR (National Cent

Toxicological Research), 

(3) Rai & Van Ryzin (after the 

u ors who described the 

del). 

For continuous data such as body weight, enzyme activity level, blood cell counts, IQ, and nerve 

conduction velocity, the models available are the: 

• (1) linear, • (3) power, and 

• (2) polynomial, • (4) Hill. 

To date the models most used by OEHHA are those for dichotomous data. Usually each model 

is fit to a dose-response data set of the most sensitive endpoint available, and both the MLE and 

the lower 95% confidence bound benchmark confidence level (BMCL05) of the effective dose 

(ED05) are derived from each model. When the number of subjects is very large as in the case 

for some occupational epidemiological exposures such as respirable, crystalline silica, the MLE01

and BMCL01 (a one percent benchmark) can be determined (Collins et al., 2005). The models 

that give an acceptable fit (p ≥ 0.10 by χ

2

) are further examined. Some models may fit the entire 

range of the data equally well by the χ

2

 test, but one may be better than another in describing the 

shape of the dose-response curve at the lower end of the dose range, which is critical in defining 

a benchmark such as BMCL05. If more than one model gives an acceptable fit to the data, then 

some judgment is used in balancing a model’s goodness of fit (as possibly indicated by a much 

higher p value or as determined visually from the plotted curve) versus the level of health 

protection provided by the BMCL05 derived using that model. From the perspective of 

protecting public health, the lowest value of the BMCL05 from a model having an acceptable fit 

might be taken. However, with certain data sets, some models (including the often used logprobit model) may indicate an MLE which is very far from the BMCL value (Murrell et al., 

1998). For well-fitting models, the BMCL is seldom less than one third of the corresponding 

MLE, unless the overall precision of the data is poor. The analyst should also beware of 

attempts to fit complex models to data sets with insufficient precision to specify all the model 

parameters accurately (U.S. EPA, 2006a). Thus there must be allowance for professional 

Technical Support Document 45 June, 2008 
TSD for Noncancer RELs June 2008 

judgment by toxicologists and statisticians. These considerations are discussed in the summary 

for each REL derived by the benchmark method. 

4.4.2 Extrapolation and Uncertainties in the Database 

A BMC or observed NOAEL may be a concentration where adverse effects are observable 

rarely, or not at all, in a specific study, but this level may not be without effect among the general 

human population, which includes individuals who are more sensitive than average, or who may 

receive repeated or extended exposures. In development of a REL, systematic extrapolation 

methods must be used to relate the dose-response characteristics observed in the experimental (or 

epidemiological) data to those expected for the general human population in a community 

exposure situation. The REL must also address, and where possible quantify, uncertainties in the 

available data and variability in the target population. These issues are accounted for by means 

of explicit extrapolation models where these are available and appropriate input data can be 

obtained. Where these explicit models are unavailable, UFs have been used extensively with 

human or animal toxicity data to estimate “safe” or “acceptable” exposure levels for humans. 

Extrapolation methods are used by OEHHA in deriving RELs to account for exposure duration 

adjustments and discontinuity, interspecies differences in exposure and pharmacokinetics, and 

expected differences among members of the target human population (e.g., differences between 

adults and children). Extrapolation methods are based on identification of measurable attributes 

that are judged to be relevant to addressing an area of concern, and incorporation of these data 

into, ideally, a mechanistic model, or (failing an established mechanistic model) an empirical 

mathematical model of the exposure and toxicological response. 

4.4.3 Types of Uncertainty and Variability 

Model-based extrapolation procedures or, where these are unavailable, UFs are used by OEHHA 

in deriving RELs to account for: 

(1) the magnitude of effect observed at a LOAEL compared with a NOAEL (Dourson and 

Stara, 1983; Mitchell et al., 1993); 

(2) for chronic RELs, the potentially greater effects from a continuous lifetime exposure 

compared to a subchronic exposure (Lehman and Fitzhugh, 1954; Bigwood, 1973; 

Dourson and Stara, 1983). 

(3) the potentially greater sensitivity of humans relative to experimental animals not 

accounted for by differences in relative inhalation exposure (Vettorazzi, 1977; Dourson 

and Stara, 1983); 

(4) the potentially increased susceptibility of sensitive individuals, for example due to interindividual variability in response (Vettorazzi, 1977; Hattis, 1996a; Ginsberg et al., 2002; 

Miller et al., 2002; Dorne and Renwick, 2005a) and 

(5) other deficiencies in the study design (Lehman and Fitzhugh, 1954; Bigwood, 1973; 

Dourson and Stara, 1983; NRC, 1993; U.S. EPA, 1993). 

Technical Support Document 46 June, 2008 
TSD for Noncancer RELs June 2008 

The use of UFs for determining “safe” or “acceptable” levels has been discussed extensively in 

the toxicological literature (Vettorazzi, 1977; NRC, 1977-1987; Dourson and Stara, 1983; 

Alexeeff et al., 1989a; Alexeeff and Lewis, 1989b; U.S. EPA, 1994a; Dourson et al., 1996). 

As noted above, UFs are used when insufficient data are available to support the use of 

chemical-specific and species-specific extrapolation factors. In this document, five UFs will be 

described (see Table 4.4.1): 

(1) LOAEL uncertainty factor – UFL; 

(2) subchronic uncertainty factor – UFS; 

(3) interspecies uncertainty factor – UFA; 

(4) intraspecies uncertainty factor – UFH, and 

(5) database deficiency factor - UFD. 

Historically, UFs have most often been order-of-magnitude factors, indicating the broad level of 

uncertainty in addressing the area of concern (Dourson and Stara, 1983). More recently, 

OEHHA and the U.S. EPA have used intermediate UFs, usually having a value of 3 (the rounded 

square root of 10) in areas estimated to have less residual uncertainty (U.S. EPA, 1994a). In 

special cases, other UF values may be considered appropriate. While the actual value of √10 is 

3.16, in practice, a single intermediate UF is calculated as 3 rather than 3.16, while two such 

intermediate UFs cumulate to 10. Thus, cumulative UFs could equal 1, 3, 10, 30, 100, 300, 

1000, or 3000. 

TABLE 4.4.1. POSSIBLE DEFAULT UNCERTAINTY FACTORS USED IN 

DERIVING ACUTE, 8-HOUR AND CHRONIC RELS

Method or Factor Values Used REL types 

LOAEL uncertainty factor (UFL)

Values used: 1 NOAEL or benchmark used 

6 LOAEL, mild effect 

10 LOAEL, severe effect 

10 LOAEL, any effect 

A, 8, C 

A 

A 

8, C 

Interspecies uncertainty factor (UFA)

Values used for a 

combined interspecies 

uncertainty factor 

(UFA):

1 human observation 

√10 animal observation in nonhuman primates 

10 where no data are available on 

toxicokinetic or toxicodynamic differences 

between humans and a non-primate test 

species 

A, 8, C 

Technical Support Document 47 June, 2008 
TSD for Noncancer RELs June 2008 

TABLE 4.4.1. POSSIBLE DEFAULT UNCERTAINTY FACTORS USED IN 

DERIVING ACUTE, 8-HOUR AND CHRONIC RELS

Method or Factor Values Used REL types 

Values used for the 

toxicokinetic 

component (UFA-k) of 

the interspecies 

uncertainty factor: 

1 where animal and human PBPK models are 

used to describe interspecies differences 

2 for residual toxicokinetic differences in 

studies of non-primate species using the 

HEC approach or incomplete DAF model 

√10 non-primate studies with no chemical- or 

species-specific kinetic data 

A, 8, C 

Values used for the 

toxicodynamic 

component (UFA-d) of 

the interspecies 

uncertainty factor: 

1 where animal and human mechanistic data 

fully describe interspecies differences. 

(This is unlikely to be the case.)

2 for residual susceptibility differences where 

there are some toxicodynamic data 

√10 non-primate studies with no data on 

toxicodynamic interspecies differences 

A, 8, C 

Intraspecies uncertainty factor (UFH) 

Values used for the 

toxicokinetic 

component of the 

intraspecies 

uncertainty factor, 

(UFH-k) for systemic 

toxicants: 

1 human study including sensitive 

subpopulations (e.g., infants and children) 

1 where a PBPK model including measured 

inter-individual variability is used 

√10 for residual susceptibility differences where 

there are some toxicokinetic data (e.g., 

PBPK models for adults only) 

10 to allow for diversity, including infants and 

children, with no human kinetic data 

A, 8, C 

Values used for the 

toxicodynamic 

component of the 

intraspecies 

uncertainty factor, 

(UFH-d):

1 Human study including sensitive 

subpopulations (e.g., infants and children) 

√10 Studies including human studies with 

normal adult subjects only, but no reason to 

suspect additional susceptibility of children 

10 Suspect additional susceptibility of 

children (e.g., exacerbation of asthma, 

neurotoxicity) 

A, 8, C 

Subchronic uncertainty factor (UFS)

Technical Support Document 48 June, 2008 
TSD for Noncancer RELs June 2008 

TABLE 4.4.1. POSSIBLE DEFAULT UNCERTAINTY FACTORS USED IN 

DERIVING ACUTE, 8-HOUR AND CHRONIC RELS

Method or Factor Values Used REL types 

Values used: 1 Study duration >12% of estimated lifetime 

√10 Study duration 8-12% of estimated lifetime 

10 Study duration <8% of estimated lifetime 

C 

Database deficiency factor (UFD) 

Values used: 1 No substantial data gaps 

√10 Substantial data gaps including, but not 

limited to, developmental toxicity 

A, 8, C 

Notes for Table 4.4.1: 

A = acute REL; 8 = eight-hour REL; C = chronic REL. “Toxicodynamic” refers to the processes involved in the 

toxic action at the system, tissue or cellular level. “Toxicokinetic” refers to processes involved in deposition, 

absorption, distribution, metabolism and excretion of the toxicant. 

Individual UFs are rounded after multiplication, so two factors of √10 cumulate to 10, but one is rounded down to 3. 

Cumulative UF values are normally limited to between 1 and 3,000: if the latter value is exceeded it is generally 

taken to indicate that the source data are insufficient to support derivation of a REL. 

The table presents suggested default values in particular situations; these may be modified in either direction by 

more specific data relating to the test and target populations considered. 

4.4.4 Application of Mechanistic Data in Interspecies and Intraspecies Extrapolation 

It is necessary to determine what (if anything) is known of the mechanism of action of the toxic 

agent as a first step in evaluating which extrapolation methodologies or UFs should be applied to 

the point of departure (BMC, NOAEL or LOAEL) for the extrapolation to estimate a safe level 

for human exposure. This will determine whether there are data to support a mechanistic model, 

or if a more generic model would be applicable. If the information necessary to construct a 

model is lacking, then the UF approach is necessary. The size of the UFs used is based on 

information about variability in response to broad classes of toxic agents, tests systems and target 

populations, and is necessarily a policy choice. It may nevertheless be possible to narrow the 

bounds of the uncertainty if specific features such as the site of action (either the respiratory 

system or other point of first contact, as used in the HEC approach, or a systemic target), and the 

general type of toxic response can be identified. 

Extrapolation generally will be necessary to cover two basic areas of difference between the test 

system (e.g., animals in a toxicological experiment) and the target human population: 

a) differences in absorption, distribution, metabolism and excretion (dosimetric and 

toxicokinetic adjustments), and 

Technical Support Document 49 June, 2008 
TSD for Noncancer RELs June 2008 

b) differences between species or individuals in their sensitivity to the toxic material 

(either the original substance or a metabolite) at the site of its action 

(toxicodynamic adjustments). 

As will be described in greater detail below, both these types of difference need to be considered 

either by means of a model, or by an UF, both for extrapolation from the test species (usually a 

rodent) to the human, and to allow for the likely range of inter-individual variation among 

members of a human population which is diverse in age, sex, genetic background, health status, 

diet, and lifestyle. 

A general scheme for extrapolation between test and target species is shown Figure 4.2 below. 

FIGURE 4-2. INTERSPECIES EXTRAPOLATION 

 CA DA

Dosimetric and/or 

pharmacokinetic model 

(Animal parameters)

 CH DH

Dosimetric and/or 

pharmacokinetic model 

(Human parameters)

Model of 

toxicodynamic 

differences 

between species 

Default: 

UF= 10 

Applied 

Concentration 

Internal dose 

Test 

Species 

Human 

CA = Applied concentration (e.g., BMC, LOAEL or NOAEL) in an animal experiment. 

DA = Dose of compound or active metabolite at site of action in animal. 

DH = Similarly effective dose of compound or active metabolite at site of action in a human. 

CH = Human equivalent applied concentration. 

Technical Support Document 50 June, 2008 
TSD for Noncancer RELs June 2008 

In this diagram and that which follows, the term “model” is used in the formal sense rather than 

implying that a detailed quantitative model of the transition is actually available. In practice 

such a quantitative model is usually not available, or may be incomplete, in which case the 

uncertainty caused by this deficiency needs to be recognized by inclusion of an UF. As will be 

described in Sections 4.4.7.2.1 and 4.4.8.2.1 below, detailed models are sometimes available to 

describe interspecies and intraspecies differences in pharmacokinetics. Unfortunately at this 

time there are few cases where quantitative pharmacodynamic models are available, so these 

extrapolations almost always utilize UFs to account for pharmacodynamic differences within 

humans and between species. Model parameters may be defined as single values appropriate to 

the test species and the default human, or as distributions representing uncertainty in the values 

of these parameters. In principle, variability in the values of key parameters in the animal 

models could also be represented by distributions, although in practice such variation is usually 

small due to the standardized genotype and environment of laboratory animals. 

A similar scheme (Figure 4-3) may be applied in considering extrapolation from the default adult 

specified in the interspecies extrapolation to other specific individuals, or (when a quantitative 

model is available, by replacing defined single parameter values with distributions) to a range of 

such individuals encompassing the expected extent of variation in the target population 

(intraspecies extrapolation). 

If CH is the human equivalent concentration of an effect threshold such as the NOAEL or 

BMCL05 (adjusted for duration and for any other uncertainties), and a sufficient number of 

human cases (i), or an appropriate range of a distribution, is considered so that all but rare 

hypersensitive individuals are represented, then the REL is set at the level of the lowest 

individual equivalent concentration, or at an appropriate lower bound on the distribution of CHi

values. In order to provide a REL, which is protective of children’s health, it is necessary that at 

least some of the cases considered, or distribution values included in the models, represent 

children. 

A selection of useful model types and extrapolation procedures is given below. It should be 

noted that this selection is exemplary rather than prescriptive, and that the models used in any 

particular case will be determined by the availability of data and mechanistic information for that 

toxic agent and type of effect. 

Technical Support Document 51 June, 2008 
TSD for Noncancer RELs June 2008 

FIGURE 4-3 INTRASPECIES EXTRAPOLATION 

CH DHd

Dosimetric and/or 

pharmacokinetic model 

(Default human parameters)

CH1 DH1

Dosimetric and/or 

pharmacokinetic model 

(Human #1 parameters)

Model of toxicodynamic 

differences between 

individuals 

Etc. 

CH2 DH2

Dosimetric and/or 

pharmacokinetic model 

(Human #2 parameters)

Default: 

UF= 10 

Internal dose 

Test 

Species 

Specific 

subpopulations 

or 

individuals 

Applied 

Concentration 

CH = Human equivalent applied concentration (default human adult). 

DHd = Dose of compound or active metabolite at site of action in a default human. 

DH1 = Similarly effective dose at site of action in human #1. 

DH2 = Similarly effective dose at site of action in human #2. 

CH1 = Equivalent applied concentration in human #1 

CH2 = Equivalent applied concentration in human #2 

4.4.5 Extrapolating from LOAELs to NOAELs 

The use of the BMC methodology allows derivation of a point of departure suitable for REL 

determination even when an actual NOAEL has not been observed in the experiment. Since this 

approach uses an empirical model fit to the actual experimental data over the range of doses 

examined, it is the preferred way to address the uncertainty inherent in deriving a REL from such 

an experiment. When this model-based extrapolation is not possible due to limitations of data 

quality or reporting, an observed LOAEL may be used as the basis of the REL. The UF 

approach is then used to estimate a health-protective level. This is a last resort, when data are 

entirely unsuitable for a benchmark dose analysis (e.g., all dose groups except control show 

100% response rate). It should be recognized that use of the LOAEL methodology fails to reveal 

or quantify the actual uncertainty and variability contained in the source data, and can be 

influenced by the study design. A one-to-ten-fold uncertainty factor (UFL) has been proposed to 

Technical Support Document 52 June, 2008 
TSD for Noncancer RELs June 2008 

account for the higher health risk potentially associated with a LOAEL compared with use of a 

NOAEL (U.S. EPA, 1994a). Historically, a factor of 10 has been used in U.S. EPA and OEHHA 

assessments. This UFL is applied to estimate a threshold level (NOAEL) from the LOAEL: 

LOAEL/UFL = NOAEL 

The relationship between LOAELs and NOAELs for acute, and some chronic, exposures has 

been examined by various authors. The effectiveness of a 10-fold LOAEL to NOAEL UF was 

confirmed for several data sets with inhalation exposure (Gift et al., 1993; Kadry et al., 1995; 

Alexeeff et al., 1997; Alexeeff et al., 2002) and oral exposure (Dourson and Stara, 1983). 

Mitchell et al. (1993) evaluated the LOAEL to NOAEL ratio for 107 subchronic and chronic 

inhalation studies. They reported that 15 of the 107 studies had LOAEL to NOAEL ratios of 10 

or greater. Alexeeff et al. (2002) evaluated 215 acute inhalation studies for 36 chemicals and 

reported that the range of LOAEL to NOAEL ratios for mild effects had 90th and 95th percentiles 

of 5.0 and 6.3, respectively. In contrast, the ratio of the LOAEL for serious effects to the 

NOAEL for all effects had 90th and 95th percentiles of 12 and 40, respectively (Alexeeff et al., 

1997). Kadry et al. (1995) showed that among a small data set (four chemicals) LOAEL to 

NOAEL ratios were less than 5. However, where only a LOAEL has been observed, the 

magnitude of the difference between the observed LOAEL and the hypothetical NOAEL is 

uncertain. 

On the basis of these data and following earlier precedents, OEHHA considers a 10-fold UFL for 

extrapolation from a LOAEL to a NOAEL to be protective when applied to all types of studies. 

However, OEHHA has also attempted to delineate situations where UFs less than 10 could be 

used in the REL development process. The use of an UF less than 10 may be appropriate under 

certain circumstances, but application of UFs less than 10 has sometimes been somewhat 

subjective, and guidance as to when it is appropriate is lacking. Consequently, OEHHA has 

developed criteria for use of an intermediate UF for acute RELs (see Section 5). These criteria 

are based primarily on data from acute exposures. When the effect is of low severity, the 

exposure is likely to be relatively nearer to the NOAEL. Conversely, more severe effects 

indicate the likelihood of a higher LOAEL to NOAEL ratio. However, extending this concept to 

evaluating chronic exposures or repeated 8-hour exposures is more complicated in this case 

because multiple effects are more likely to be seen, and serious and persistent effects such as 

developmental neurotoxicity may occur at low doses. Further, the 8 hour RELS are for repeated 

exposures, and chronic RELs are for continuous exposure – the exposure does not cease, so 

effects that are of no consequence for a short period of time may indeed be adverse chronically. 

Recommended default values of UFL for acute, eight-hour and chronic REL derivations are 

therefore as follows: 

(1) Where the observed effect level used as the basis of the REL is a NOAEL or equivalent 

benchmark, the value of UFL is 1. 

(2) When the acute REL is based on a LOAEL, where the observed effect is mild for acute 

exposures (U.S. EPA grade 5 or below, Table 5.5.1), the value of UFL is 6. 

(3) When the acute REL is based on a LOAEL, where the observed effect is moderate to 

severe, the value of UFL is 10. 

Technical Support Document 53 June, 2008 
TSD for Noncancer RELs June 2008 

(4) When the chronic REL is based on a LOAEL, the value of UFL is 10; except in chemicalspecific circumstances where there is an indication that the LOAEL is closer to the 

NOAEL. One such indicator used in the previous guidance is when the percent of the 

population responding at the LOAEL is ≤ 30. 

(5) When the 8-hour REL for repeat exposures is based on a LOAEL, and the effect is 

essentially an acute response, then the guidelines for the acute REL derivation are 

followed. When the 8-hour REL for repeat exposures is based on a study where the 

effect is essentially a chronic response, the guidelines for chronic REL derivation are 

followed. 

These default values may be replaced by more specific values where appropriate data are 

available (e.g., for specific toxicological endpoints or chemical classes). However, the use of a 

LOAEL as the basis of a REL is to be avoided wherever possible, by using data sets in which a 

NOAEL is also observed or, preferably, by applying the BMC methodology to a study where a 

range of response levels with increasing dose is measured. 

4.4.6 Extrapolating from Study Duration to REL Reference Period 

The target reference period for development of a REL is one hour (single or infrequent exposure) 

for acute RELs, eight hours with potential for repeat exposures for the eight-hour RELs, and 

lifetime/annual average for the chronic RELs. Acute RELs are typically based on data from 

short-term exposures of a few minutes to a few hours, and eight-hour or chronic RELs typically 

involve data from extended repeat-dosing studies. However, the experimental duration, or 

exposure period in an animal or human study, is not generally the same as the REL reference 

period. Scaling procedures may therefore be required to extrapolate from the specific duration 

(and extent of repetition) of the studies to the REL reference period. Since these are specific to 

the type of REL being developed they are described in the subsequent sections dealing with 

procedures specific to the individual REL types. 

4.4.6.1 Eight-hour and Chronic RELs

The dose-response for most toxicological processes is assumed to follow some form of dose-time 

integral over moderate periods of exposure. For medium-term adjustments in repeat-exposure 

animal studies such as six to eight hours to twenty-four, or five days a week to seven, a simple 

concentration multiplied by time (C x T) dependence, often referred to as Haber’s Law, is 

assumed, so these results are adjusted by simple proportion, as described in Section 7.2.1. 

The default approach to extrapolating from subchronic to chronic exposures used by OEHHA 

(Section 7.2.2) for development of chronic RELs is to use a 1 to 10-fold subchronic uncertainty 

factor (UFS) for subchronic exposures (Table 4.4.1). Chronic studies in standard toxicological 

testing paradigms are those where the exposure duration is 12% or more of the expected lifetime 

of the test species, while subchronic studies are repeat-dosing studies shorter than this but longer 

than standard sub-acute protocols. The same adjustment is used for human studies where the 

average exposure duration is less than 12% of lifetime (70 years). For exposures less than 8% of 

expected lifetime a 10-fold UFS is applied, while for exposures from 8 to 12% of expected 

Technical Support Document 54 June, 2008 
TSD for Noncancer RELs June 2008 

lifetime a 3-fold UFS is applied. Where exposures are longer than 12% of expected lifetime there 

is no adjustment, i.e., UFS = 1. 

4.4.6.2 Acute RELs

For shorter study periods such as those of a few minutes to hours that are considered in 

development of acute RELs and some eight-hour RELs, the basic C x T dependence is modified 

by means of exponents. Most commonly, an expression of the form Cn x T is used to reflect acute 

toxic responses where concentration is a more important determinant of response than duration 

over the time period of the observation. Application of this modified Haber’s Law procedure in 

development of acute RELs is described in Section 5.4.1. 

4.4.6.3 Exposure Duration REL Adjustments for Developmental Toxicants

Historically, duration adjustment of inhalation exposures for developmental toxicity studies has 

not been done (U.S. EPA, 2002a). Unlike subchronic and chronic toxicity studies, in which 

months or even years of exposure may be needed before tissue damage becomes evident,

developmental toxicity is frequently the result of exposure during a small window of time during 

gestation in which exposure may only be on the order of hours during a critical stage of 

development. Because the timing and duration of the sensitive period of gestation is usually 

unknown, the standard experimental protocol is to expose pregnant animals for several hours per 

day over several days during gestation in order to increase the power of the study to detect an 

effect. As a result, time extrapolation to the REL must take into account two principal 

toxicokinetic issues to prevent, in particular, underestimation of developmental toxicity - peak 

tissue concentration and total tissue dose (e.g., area under the concentration-time curve (AUC)). 

Instances of developmental toxicants that operate predominantly by one or the other 

toxicokinetic factors have been observed. For example, prenatal exposure of mice to short, high 

exposures of ethylene oxide on day 7 of gestation was found to cause more adverse 

developmental effects than mice exposed to the same C x T multiple but at longer, lower 

exposures (Weller et al., 1999). Alternatively, pregnant rats administered all-trans-retinoic acid 

indicated that AUC, and not maximum plasma concentrations, was the most appropriate 

pharmacokinetic marker of developmental toxicity (Tzimas et al., 1997). The following 

procedures are designed to be health-protective even in the case where a developmental effect is 

the result of a possibly very brief sensitive period during a single day of exposure to the toxicant 

(U.S. EPA, 2004). 

4.4.6.3.1 Developmental REL Duration Adjustment from Shorter to Longer Exposures 

When the principal toxicokinetic process involved in the developmental toxicity of a nonaccumulating chemical is unknown, the U.S. EPA Technical Panel recommends that duration 

adjustment procedures from discontinuous to continuous exposures be based on equivalent 

multiples of concentration (C) and duration (T) for inhalation developmental toxicity studies as it 

is used for other health effects from inhalation exposure (U.S. EPA, 2002a). This C x T 

approach favors a health protective overestimation of risk when adjusting the exposure duration 

from a shorter period to a longer period of exposure, as has been shown experimentally in doserate studies of developmental toxicants (Weller et al., 1999). The pharmacokinetic basis for this 

duration adjustment assumes that the total tissue dose during a single-day exposure period is the 

Technical Support Document 55 June, 2008 
TSD for Noncancer RELs June 2008 

critical quantity in determining the level of response and ensures that the AUC, as well as the 

peak tissue level, will not be increased in the duration adjustment. Correspondingly, OEHHA 

will use this time adjustment procedure when estimating a chronic REL based on a 

developmental study. The default approach for duration adjustment of a developmental endpoint 

from discontinuous exposure to chronic continuous exposure is the same as that used for a 

chronic toxicity duration adjustment, and can be summarized as: 

CAVG = (COBS) x (H hours / 24 hours) x (D days per 7 days); 

where CAVG is the time-weighted average concentration, and COBS is the observed concentration. 

Time extrapolation to an eight-hour REL must also take into account pharmacokinetic processes 

affecting a developmental endpoint, from either a single eight-hour exposure or multiple daily 

eight-hour exposures during gestation. Thus, estimation of the eight-hour REL will also use the 

daily average C x T time adjustment when extrapolating from a shorter exposure time to an 

eight-hour REL. The same daily average C x T adjustment should also be used when an acute 

REL is based on a developmental study involving exposure of animals for less than an hour for 

one or more days during gestation. 

As more information becomes available on PBPK modeling of developmental toxicants for 

interspecies extrapolation from the exposed animal species to humans, modeling of blood and 

tissue levels may confirm the C x T adjustments on the REL exposure durations to ensure they 

do not exceed the peak tissue concentration or total tissue dose at the NOAEL. 

4.4.6.3.2 Developmental REL Duration Adjustment from Longer to Shorter Exposures 

For acute REL development, time duration adjustment will often require extrapolation from 

multi-hour exposure to the 1-hour exposure duration of the acute REL. Dose-rate exposure 

studies have shown that a C x T approach from a long exposure duration to a shorter exposure 

duration could underestimate the response of developmental toxicants (Weller et al., 1999). To 

avoid underestimation of risk when the pharmacokinetic nature of the developmental toxicant is 

unknown, OEHHA recommends no duration adjustment on the exposure concentration when 

extrapolating from a longer exposure duration per day down to a one-hour exposure. This 

procedure primarily protects against higher peak tissue concentrations that would occur if a C x 

T time adjustment was applied. Preferably, the acute studies used as the basis of an acute REL 

would be those with exposure duration nearest one hour, in order to reduce the uncertainty of this 

approach. This approach would also apply to eight-hour RELs in which the primary study used 

daily exposures greater than eight hours (i.e., no time extrapolation would be applied). 

4.4.6.3.3 Duration Adjustment for Bioaccumulating Developmental Toxicants 

An additional pharmacokinetic issue to consider involves chemicals in which discontinuous daily 

exposures may take one to two weeks of gestational exposure before tissue saturation occurs. 

Many developmental studies begin exposures following conception. Conceivably, the critical 

point of gestation for developmental effects may have passed before maximal fetal/maternal 

blood levels were attained during the exposure period. For example, the aromatic hydrocarbon 

1,2,4-trimethylbenzene shows a gradual increase in prior-to-shift blood levels (and the AUC) in 

humans over a 5-day period with daily eight-hour exposures (Jarnberg and Johanson, 1999). 

Technical Support Document 56 June, 2008 
TSD for Noncancer RELs June 2008 

Ideally, in animal studies of a bioaccumulating toxicant, maternal exposures would occur prior to 

the beginning of gestation so tissue saturation at a given exposure concentration is already 

present when development begins. Multi-generation studies often expose animals for at least 

several weeks prior to mating and could resolve this concern. In lieu of multi-generation studies 

and studies that started exposure prior to gestation, a modifying UF may be considered for those 

chemicals that slowly accumulate to maximal tissue levels during gestational exposure, to 

account for the potential underestimation of dose during the window of developmental 

susceptibility. Alternatively, a fractional adjustment of the exposure level can be made if 

sufficient pharmacokinetic data are available to identify the time to tissue saturation and tissue 

saturation levels. This pharmacokinetic adjustment would prevent exceedance of peak tissue 

levels or total tissue dose at critical time points in fetal development. 

For major bioaccumulators such as dioxins and some metals, developmental exposure studies in 

which exposure occurred only during gestation is not sufficient for establishing eight-hour or 

chronic RELs based on developmental toxicity. These types of toxicants can accumulate in body 

tissues over extended periods of time prior to gestation, leading to very high maternal body 

burdens that may be detrimental to the fetus during gestation. Lack of sufficient chronic 

exposure and multi-generation studies and lack of adequate pharmacokinetic modeling 

information that can predict body tissue burdens may require application of a modifying UF for 

pharmacokinetic deficiencies in calculating the REL. 

4.4.6.3.4 Effects of Exposure Continuity 

Acute, eight-hour and chronic RELs are intended to protect members of the general population 

from the types of exposures resulting from facility emissions or ambient levels of air pollutants. 

Such emissions may show variations diurnally, seasonally or over the long term. Modeling and 

interpretation of such exposure patterns are covered in the Exposure Assessment section of the 

Hot Spots Technical Support Documents (OEHHA, 2000b). It may also be necessary to apply 

models or adjustments to the exposures received by the subjects (animal or human) of studies 

used as the basis for derivation of acute, eight-hour or chronic RELs, where animal experiments 

or human studies involve discontinuous or repeated exposure patterns. Specific adjustment 

procedures are prescribed for derivation of RELs, which are different for the three types of REL. 

They are therefore described separately in Sections 5, 6 and 7, covering issues specific to each 

type of REL. 

4.4.7 Accounting for Potentially Greater Human Susceptibility 

4.4.7.1 Introduction

Greater sensitivity of humans compared to animal test species for a variety of toxicological 

endpoints have been shown Dourson and Stara (1983). A well-known example is teratogenesis 

by various agents including thalidomide Brown and Fabro (1983). In general, interspecies UFs 

are applied to the animal study results to account for potentially greater human susceptibility (see 

Section 4.4.7.3). However, a preferred approach to interspecies extrapolation is to employ 

chemical-specific kinetic models to assess species differences in relevant tissue dosimetry. If 

chemical specific models are not available, generic approaches such as the human equivalent 

concentration (HEC; the air concentration of an agent that induces the same magnitude of toxic 

Technical Support Document 57 June, 2008 
TSD for Noncancer RELs June 2008 

effect in humans as that seen in experimental animals) and/or an animal to human uncertainty 

factor (UFA) may be applied. As described above (Section 4.4.4), this factor may be regarded as 

consisting of toxicokinetic and toxicodynamic factors, which may be considered separately 

where explicit models are available to describe some aspects of the extrapolation, especially 

toxicokinetics. Differences in acute behavioral toxicity of toluene in rats and humans are partly 

described by a toxicokinetic model: there are residual differences in sensitivity between species 

based on the tissue dose levels which might relate to actual sensitivity differences at the cellular 

level, or to differences in the sensitivity and comparability of the tests used in the two species 

(Benignus et al., 1998; Bushnell et al., 2007). 

4.4.7.2 Kinetic Modeling in Interspecies Extrapolation

As part of the scientific basis for this update of the risk assessment guidelines, OEHHA 

conducted a pilot investigation of the application of physiologically based pharmacokinetic 

(PBPK) modeling to dosimetric adjustments in noncancer risk assessment. The aim was to 

derive alternate dosimetric adjustment factors (DAFs) or human equivalent concentration (HEC) 

factors based on metrics of internal dosimetry such as peak concentrations (Cmaxs) and areas 

under the blood or tissue concentration x time curves (AUCs). The chemicals selected for this 

pilot study were: ethylbenzene, vinyl chloride, toluene, styrene/styrene oxide, 

naphthalene/naphthalene oxides, and formaldehyde. All of these chemicals occur in outdoor and 

indoor air and have some prior PBPK model availability for rat or human. Initial comparisons 

were limited to rat/human conversions for adults and immature animals/children. In addition, 

since the overall objective is to improve the scientific basis for predictive toxicological criteria 

for air pollutants the investigation also included a series of straight chain aliphatic aldehydes: 

CH3(CH2)nCHO (n = 0 to 8). Several aliphatic aldehydes have been observed in outdoor (Uebori 

and Imamura, 2004) and indoor (Arcus et al., 1995) air sampling or are known to originate in 

building materials or furnishings. 

4.4.7.2.1 PBPK Models 

The type of PBPK model used by OEHHA is dependent on the physicochemical characteristics 

and toxicokinetic properties of the agent in question (See Appendix E for more detail; see U.S. 

EPA (2006b) for a general description of PBPK modeling). Broadly speaking, gaseous agents 

fall into one of three categories. 

• Category 1 gases are reactive gases that interact mainly at the site of contact; either the 

nasal or respiratory tracts (RT) as portals of entry. 

o For agents in Category 1, OEHHA used either a 4- compartment RT model of the 

type described by Sarangapani et al. (2004) that is similar to a 3-compartment 

default model of the RT recommended by Hanna et al. (2001), with uptake 

defined by regional mass transfer coefficients. Depending on the agent being 

studied, for some Category 1 gases, OEHHA used nasal models as described by 

Frederick et al. (1998). 

Technical Support Document 58 June, 2008 
TSD for Noncancer RELs June 2008 

• Category 2 gases tend to be less reactive and water soluble, and have effects both locally, 

on the RT, and systemically. 

o For Category 2 gases, OEHHA used RT-PBPK models of the type described by 

Sarangapani et al. (2004). These models included both RT compartments and 

body compartments for remote distribution and metabolism as recommended by 

Hanna (2001). 

• Category 3 gases are less water soluble, less reactive, and therefore scrubbed less 

efficiently in the respiratory tract, and mainly have remote systemic effects. 

o For Category 3 gases, with mainly remote effects, OEHHA used either a onecompartment or, alternatively, a two-compartment lung model as described by 

Evelo et al. (1993), consisting of a high-perfusion alveolar exchange compartment 

and a low-perfusion bronchial compartment. In some instances flow-limited 

model components may be augmented or replaced with diffusion-limited 

components based on physicochemical/kinetic properties and improved model 

performance (e.g., dioxin). 

Particle exposures are defined mainly by air concentration (μg/m3

), size distribution including 

mean mass aerodynamic diameter (MMAD, μm) and geometric standard deviation (σg), 

breathing rate, nose versus mouth contributions, and particle solubility. The prototypical human 

model is the Human Respiratory Tract Model for Radiological Protection from the International 

Commission on Radiological Protection (ICRP, 1994). This model provides tables of deposition 

fractions by RT region, age, sex, breathing rate and particle size. Computer models are available 

to predict RT clearance for a given exposure, and particle deposition and ICRP clearance 

parameters, e.g., Humorap 2 (Sanchez, 2002). A more complete deposition and clearance model 

for humans and rats is the multiple path particle deposition (MPPD) model of The Chemical 

Industry Institute of Toxicology (CIIT) and the Netherlands National Institute for Public Health 

and the Environment (RIVM) (Anjilvel and Asgharian, 1995; Brown et al., 2005; Jarabek et al., 

2005). This model provides several particle number and mass-based dose metrics, although 

mass/surface area metrics need to be derived from graphic outputs of deposition and user 

supplied regional RT surface areas (Sarangapani et al., 2003). Another advantage of this model 

is a number of built in human child parameters for different ages. However, this model is very 

complex and longer-term simulations may not run successfully. Additional particle deposition 

and clearance models may be much simpler and adequate in many instances (Snipes, 1989a; 

Snipes et al., 1989b). The main dose metric of the Snipes model is mg/lung or lung burden. 

Also Yu and Xu (1987) provide a deposition model description for humans, rats, hamsters and 

guinea pigs that may be useful in many cases. 

4.4.7.2.2 HEC Adjustment 

The development of reference exposure concentrations (RfCs) by the U.S. EPA (1994a) requires 

the conversion by dosimetric adjustment of the NOAELs and LOAELs observed in laboratory 

animal experiments to human equivalent concentrations (HECs) for ambient exposure conditions 

(U.S. EPA, 1994a). The HEC procedure estimates the concentration for human exposure, which 

would be equivalent to the animal exposure, by adjusting for differences in minute volume and 

Technical Support Document 59 June, 2008 
TSD for Noncancer RELs June 2008 

surface area of various regions of the respiratory tract between the experimental species and 

humans. The conversion of animal exposures to HECs is described in detail in Appendix F and 

involves the use of regional deposited dose ratios (RDDRs) for particles or regional gas dose 

ratios (RGDRs) for gases. Category 1 gases are highly reactive and/or soluble, and they do not 

accumulate in the blood. For these compounds, the conversion factor usually reduces to a ratio 

of alveolar ventilation (AVA) to regional surface area (RSAA) for the animal test species, divided 

by the same ratio for the human (AVH / RSAH). Adjustments for extrathoracic (ET), 

tracheobronchial (TB) and pulmonary (PU) regions or the total lung can be calculated (U.S. 

EPA, 1994a). For pulmonary exposures to a category 1 gas from rat data, adult and child 

specific dosimetric adjustment factors (DAFs) may be derived as follows: 

DAF = (AVA / RSAA) / (AVH / RSAH) 

DAF (Adult) = (120 cm3

/min/3400 cm2) / (7000 cm3/min/633,000 cm2) = 3.19 

DAF (Child) = (120 cm3/min/3400 cm2) / (914 cm3/min/21,500 cm2) = 0.83 

At the other extreme of reactivity and solubility are Category 3 gases that have predominantly 

systemic effects. In the default methodology, the average exposure concentration is adjusted 

with a RGDR that represents the ratio of the blood:air partition coefficient in experimental 

animals to that in humans [RGDR = (Hb/g)A/(Hb/g)H] (see Appendix F.1.2). Category 2 gases fall 

somewhere between categories 1 and 3 on the continuums of reactivity and solubility. They are 

moderately soluble and/or reactive and may have both local (respiratory tract) and systemic 

effects. In practice, in the absence of data sufficient to perform more sophisticated modeling, 

these compounds are treated as either Category 1 or Category 3 gases depending on their 

physicochemical properties and the data available for the specified toxicological endpoint. 

Thus, a given rat NOAEL or LOAEL concentration would be multiplied by these factors to give 

human equivalent concentrations (HECs) for adults and children, respectively. The U.S. EPA 

derives RfCs by dividing the HECs by appropriate UFs. While this is a standard methodology, it 

is obvious that no chemical-specific information, other than a broad characterization of gas 

category, is involved. The method essentially adjusts for a potential difference in absorption 

based on physiological and anatomical differences between species. This methodology is 

described in greater detail and reviewed in Appendix F which also considers extensions 

necessary to allow for human intraspecies variability, including age differences. 

4.4.7.3 Uncertainty Factor for Animal to Human Extrapolation (UFA)

Where data are insufficient to allow development of an extrapolation model, the default approach 

has been to apply a 10-fold uncertainty factor (UFA) to animal data based on an assumption that 

an average human is likely to be at most 10-fold more susceptible to the effects of the substance 

than experimental animals Table 4.4.1). This is truly an “uncertainty” factor since we are unsure 

how humans would respond, in contrast to the animals tested, to the specific chemical. 

However, the UF is based on the potential for greater sensitivity of humans and the larger surface 

area of humans compared with experimental animals (Rall, 1969; Weil, 1972; Krasovskii, 1976; 

Lewis and Alexeeff, 1989). This UF methodology is in contrast to the practice used in cancer 

Technical Support Document 60 June, 2008 
TSD for Noncancer RELs June 2008 

risk assessment where an allometric surface area correction and a 95% confidence interval of the 

slope of the dose response are used. The UF approach was used by the U.S. EPA (1994a) and 

recommended by NRC (1977-1987) for drinking water standards. Dourson and Stara (1983) 

provided limited support for the concept of a ten-fold UF. Khodair et al. (1995) showed that 

among a small data set (six chemicals) animal NOAEL to human NOAEL ratios were less than 

four. Schmidt et al. (1997) evaluated interspecies variation between human and five other 

animal species. Sixty compounds had human data that could be matched to one or more animal 

species. The animal to human ratio of 10 represented approximately the 85th percentile. 

The U.S. EPA has used human equivalent concentration (HEC) extrapolation and a 3-fold UFA

for RfC derivation (U.S. EPA, 1994a). In the U.S. EPA method, this intermediate value is 

chosen since the HEC derivation is assumed to have accounted for the toxicokinetic part of the 

difference between the species. However, this HEC extrapolation addresses only some of the 

differences; in particular, only respiratory regional exposure and deposition of the parent 

compound is considered; any differences in metabolism and elimination are ignored. The 

remaining 3-fold UF is to account for pharmacodynamic or response differences between the 

species. This modified approach was also previously used by OEHHA for derivation of chronic 

RELs where sufficient data were available. OEHHA continues to recommend the HEC 

methodology where data are insufficient to support a full PBPK model. However, it is 

recommended that the toxicokinetic part of the UFA be reduced to 2, rather than 1 to reflect the 

presence of remaining uncertainties in toxicokinetics due to metabolism and excretion. In some 

instances, it may be appropriate to retain a larger UFA, for example if differences in deposition 

between the test species and humans are known to be large. OEHHA has also examined the 

effect of child-specific parameters on the HEC calculation. 

Where both chemical- and species-specific data are unavailable, and therefore a HEC cannot be 

estimated, a 10-fold UFA is normally used. The 10-fold default UFA would only be applied after 

consideration of other factors that potentially affect the validity of the default assumption. Such 

factors include differences between humans and the test species in absorption, distribution, and 

metabolism, which would serve as a basis for predicting interspecies differences in susceptibility. 

In some cases, data may indicate that a larger UFA is appropriate. An exception is made for data 

from studies of non-human primates, where a default UFA of √10 is used because of their 

similarities to humans (See Table 4.4.1). 

4.4.8 Increased Susceptibility of Sensitive Individuals 

4.4.8.1 Introduction

RELs are intended to protect identifiable sensitive individuals from harm due to chemical 

exposure. Susceptibility to harm from chemical exposure may vary among individuals due to 

genetic and epigenetic variability within the population, resulting in lower levels of protective 

biological mechanisms. Predisposition to increased metabolic activation or to decreased 

detoxification are just two examples of how genetic variability influences response to toxicants 

(Hattis et al., 1987; Eichelbaum et al., 1992; Grandjean, 1992; U.S. EPA, 1994a; Autrup, 2000). 

Additionally, susceptibility to chemical-related health effects may vary over time for the same 

individual due to changing factors such as age, health status, and activity level. It should be 

recognized that RELs may not necessarily protect individuals who may develop an idiosyncratic 

Technical Support Document 61 June, 2008 
TSD for Noncancer RELs June 2008 

response, such as allergic hypersensitivity, that cannot be predicted from scientific investigation 

of the chemical. 

Thus, sensitive individuals may include infants, children, pregnant women and their fetuses, 

elderly persons, those with existing diseases such as lung, heart or liver disease, and persons 

engaging in physical activity (U.S. EPA, 1994a). Other factors, such as acute illness or 

immunosuppression, may cause short-term variations in individual susceptibility. Seasonal 

changes in absorption and toxicity have also been noted in laboratory animals Barton and Huster 

(1987). 

Healthy workers, the subject of most epidemiological studies, are often found to have lower rates 

of morbidity and mortality than the general population (Wen et al., 1983; Monson, 1986) 

(Rothman and Greenland, 1998, p 119). In studies of experimental animals, highly 

homogeneous (inbred), healthy strains are generally used. Such strains are likely to have much 

less variability in response than the heterogeneous human population. Chizhikov (1973) found 

that animals in poor health were more likely to experience adverse effects from chronic oral 

exposure to chemicals than were healthy animals. 

Finally, OEHHA is required to protect infants and children in developing Reference Exposure 

Levels. There are a number of differences in response to toxicant by age, which in some cases, 

increase the susceptibility of infants and children. These are described more fully elsewhere in 

Section 3.1.1 and Miller et al. (2002) and OEHHA (2001). 

4.4.8.2 Pharmacokinetic Factors in Inter-individual Variability

4.4.8.2.1 Physiologically Based Pharmacokinetic (PBPK) Models of Inter-individual Variability 

PBPK models can give useful predictions of how the body handles a particular chemical and its 

metabolites. The models address issues of internal body or tissue dosimetry, route-to-route 

extrapolation and, in some cases, interspecies extrapolation. To date, relatively few published 

models for various environmental pollutants address infant and child exposure and 

pharmacokinetics in a systematic fashion. This is parallel to the bulk of toxicity testing in 

animals, which is usually initiated in young adult animals. 

However, this issue has received more attention in recent years than previously. Several authors 

have undertaken systematic modeling studies using child-specific physiological, biochemical and 

exposure parameters for various toxicants of interest (Pelekis et al., 2001; Pelekis et al., 2003; 

Price et al., 2003; Clewell et al., 2004; Ginsberg et al., 2004b). These studies are summarized 

and evaluated in Appendix E. OEHHA has used these published results and also undertaken a 

series of original investigations (also described in detail in Appendix E) to explore both the 

feasibility of using child-specific PBPK models when the necessary supporting data are 

available, and the appropriate values for UFs or other limited analyses where the data required 

for a full chemical-specific model are not available. 

PBPK models are meant to increase the accuracy of risk assessment and inform as to the 

adequacy of the traditional NOAEL/UF approach to deriving RELs. These models are used only 

where there are adequate data available. While in many cases the variability in a parameter can 

be adequately incorporated into the model where data exist to characterize the distribution of this 

Technical Support Document 62 June, 2008 
TSD for Noncancer RELs June 2008 

parameter, there is still residual uncertainty. Further, many parameters may have limited data 

available. Sensitivity analyses should be conducted in a chemical specific PBPK modeling 

exercise to ascertain the importance of specific variables to the model output, and to gain 

understanding of model uncertainty. 

OEHHA’s approach to applying PBPK modeling to assess children’s environmental health risks 

has been similar to that of Pelekis et al. (2001). We have used a case study approach using 

published PBPK models of selected environmental toxicants and adjusting anatomical and 

physiological parameters to simulate infant and child ages from newborn to 18 years. Results are 

then compared to those using adult models. In these models, we have scaled metabolic 

parameters as a function of body weight. Where possible we have focused on dose metrics 

involving toxicologically relevant metabolites. Initial findings by this approach were presented 

at the 2001 Children’s Environmental Health Symposium (Brown, 2001). Of the seven 

chemicals studied with oral and inhalation exposures (vinyl chloride, DCM, TCE, chloroform, 

arsenic, butadiene, and naphthalene), three chemicals showed greater internal doses in children 

compared to adults: DCM, TCE, and butadiene, all via the inhalation route. 

In follow up work we have attempted to standardize the modeling approach for different 

chemicals as much as possible and focus on inhalation exposures only. For example, we have 

employed several of the age specific regressions for model parameters suggested by Price et al. 

(2003). Also in a few cases we have used more elaborate lung modeling, for example as 

proposed by Sarangapani et al. (2002) for styrene and styrene oxide, as opposed to the simpler 

lung modeling of Evelo et al. (1993) for butadiene. Two or three similar child models were used 

with differing fractional tissue flows more heavily weighted towards rapidly perfused tissues 

than in adults. Details are provided in Appendix E. 

The published studies and the OEHHA case studies of PBPK modeling show clearly that infants 

in the first year of life are likely to show increased internal dosages via the inhalation route for a 

variety of agents and their metabolites and longer clearance times (see Appendix E). It is also 

apparent that the current default intraspecies UF (UFA-k) for kinetic effects of √10 is inadequate 

to protect neonates and young infants from some chemicals, as further discussed below. 

It is worth noting that the large majority of studies and PBPK modeling exercises involve 

relatively short-term exposures that represent environmental, occupational, or therapeutic 

scenarios. Extreme situations of short-term high exposures or very long-term low exposures 

were not simulated. Despite this limitation, the results are considered indicative of the unique 

toxicokinetics of infants and children for some environmental pollutants. As such, a revised PK 

UF should be broadly applicable to acute (one-hour), eight-hour, and chronic RELs. 

4.4.8.2.2 Uncertainty Factor for Variability within the Human Population (UFH) 

Where data are insufficient to permit development of a reliable model, an intraspecies 

uncertainty factor (UFH) has traditionally been used to account for variability within the human 

population. This factor is intended to account for the greater susceptibility to chemical toxicity 

of various sensitive subpopulations, including infants and children. Previously, OEHHA has, 

like the U.S. EPA generally applied a 10-fold UFH to address variability in response among 

individual members of the general population (U.S. EPA, 1994a). 

Technical Support Document 63 June, 2008 
TSD for Noncancer RELs June 2008 

4.4.8.2.2.1 Contribution of Kinetic Factors to UFH

The variability in human response to toxicants may result from differences in toxicokinetics and 

toxicodynamics. The UFH typically used in OEHHA’s risk assessment methodology is thus 

considered to be composed of two sub-factors to allow for both toxicokinetic (UFH-k) and 

toxicodynamic (UFH-d) differences (Table 4.4.1). 

Some studies suggested that the overall 10-fold factor was reasonable to account for intraspecies 

variability in humans. Gillis et al. (1997) suggested, based on modeled intraspecies variability, 

that for chronic exposures, a 10-fold factor will protect the 85th percentile. Within this overall 

10-fold UFH, the values of the two sub-factors UFH-k and UFH-d were both assumed to be √10, 

which equals 3.16. However, more recent studies have indicated that a value higher than √10 

should be considered for the pharmacokinetic component of the intraspecies uncertainty factor 

(UFH-k), especially for substances that are bioactivated, since the enzymes involved in both Phase 

I (primarily CYP) and Phase II (numerous conjugating reactions) of xenobiotic metabolism have 

shown pronounced polymorphism in many cases (Renwick and Lazarus, 1998; Hattis et al., 

1999). 

4.4.8.2.2.2 Infants and Children 

The difference in toxicokinetics is even more pressing when considering infants and children as 

part of the affected population. As discussed in Section 3.1, it has been suggested that children 

may be both more sensitive, and more diverse, than adults, as a result of both pharmacodynamic 

and pharmacokinetic factors affecting toxicity. Several revisions in this version of OEHHA’s 

risk assessment methodology are designed to address this concern. An additional 10-fold UF 

(presumably to account for both toxicokinetic and toxicodynamic factors) has been mandated by 

Congress to specifically protect children in assessments conducted for pesticides in accordance 

with the Federal Food Quality Protection Act (FQPA), assuming infants and children are more 

sensitive than adults, unless data to the contrary exist. U.S. EPA (2002b) has developed 

guidelines for evaluating data to determine an appropriate value (generally between 1 and 10) for 

the FQPA-specified uncertainty factor. In the following discussion, the approach will be to 

determine an appropriate value to substitute for the default value for the two separate 

components of UFH, rather than to specify additional overall UFs. 

In Appendix E we have summarized the more relevant data and studies bearing on the size of the 

default UF to protect infants and children adequately from the adverse effects of toxic air 

contaminants. Obviously, these studies and data are not ideal since they rely heavily on the 

pharmacology literature where most drugs are administered orally and not by inhalation. In 

addition, drug literature frequently focuses on the parent compound rather than downstream 

metabolites, which are often of interest to environmental toxicologists due to their frequent 

involvement in toxic modes of action. Modeling of environmental toxicants also presents 

difficulties, the foremost being a lack of relevant metabolic parameters at various stages of 

human development. Infant and child metabolism of environmental agents is usually estimated 

by scaling from adult human or animal values, a limitation when there are qualitative as well as 

quantitative differences in infant vs. adult metabolism (e.g., theophylline). Table 4.4.2

summarizes the PK UF values indicated by the PBPK modeling of various test chemicals by 

OEHHA and others. Of the 25 chemicals and metabolites in this table, 13 have UFH-k greater 

Technical Support Document 64 June, 2008 
TSD for Noncancer RELs June 2008 

than √10. This results primarily from the differences in toxicokinetics between infants and 

adults, resulting in higher internal dosages of the compounds and longer clearance half-lives. 

The details of these modeling exercises are given in Appendix E (text and tables; model 

parameters; and model equations). 

4.4.8.2.2.3 Value of UFH-k to Account for Toxicokinetic Differences by Age 

Based on the limited information presently available, OEHHA thinks it is appropriate to increase 

the default UFH-k from its previous value of √10 = 3.16 in order to protect neonates and young 

infants from potential adverse effects of airborne toxicants. OEHHA will apply a UFH-k value of 

10 as a default for gases acting systemically, and for particles that involve systemic exposure via 

dissolution and absorption in the lung or via the gastro-intestinal tract. Thus, in these cases, the 

overall default intraspecies UF would be 30. Gases that act solely at the portal of entry (i.e., lung 

or upper respiratory tract for inhaled toxicants) without involvement of metabolic activation or 

other complex kinetic processes would use a UFH-k of √10. These are default values applicable 

to acute, eight-hour and chronic RELs derived from animal studies or epidemiological studies of 

healthy adult populations (e.g., workers). An exception to this procedure is when an exposure 

level is estimated from a study that includes the assessment of a sensitive human subpopulation, 

where a default UFH-k of 1 may be appropriate. 

Because the true extent of variability is frequently unknown, there may be a portion of the 

population for whom the chronic RELs will not be protective. When information defining 

susceptible individuals is available, such data will be incorporated by means of pharmacokinetic 

models or adjustment of UFs as necessary to protect those individuals. Ideally, more chemicalspecific data in sensitive subgroups would obviate the need for the use of a default UFH. 

Unfortunately, such data are rarely available for children (or even immature animals) with 

environmentally relevant toxicants. 

Technical Support Document 65 June, 2008 
TSD for Noncancer RELs June 2008 

TABLE 4.4.2. CHEMICALS STUDIED BY OEHHA IN PBPK ANALYSES, GROUPED 

BY MODELED UFH-K FOR INFANTS AND CHILDREN (SEE APPENDIX E) 

UFH-k ≤ √10 UFH-k > √10 to 9.9 UFH-k ≥ 10 

Furan MTBE 

Perchloroethylene Styrene/Styrene oxide 

Butadiene/Butadiene 

monoxide/Diepoxybutane 

Naphthalene/Naphthalene oxides Ethylene/Ethylene oxide Dichloromethane 

Carbon tetrachloride Vinyl chloride TCE and metabolites 

Chloroform Toluene Benzo[a]pyrene 

Arsenic and metabolites† m-Xylene 

Ethylbenzene* Toluene/Xylene mixtures 

1,1-Dichloroethylene‡ Isopropanol 

Benzene* 

Bromochloromethane* 

Methyl chloroform* 

Diethyl ether* 

*Note that simulation results for these chemicals are not shown in the text but are based on parameters in 

Haddad et al. (2001) and Gargas et al. (1986) using the same approach as for toluene and xylene. †Based 

on PBPK model of Yu (1999). ‡ Based on PBPK model of El-Masri et al. (1996a,b). 

4.4.8.3 Contribution of Toxicodynamic Factors to UFH

A subfactor UFH-d to account for toxicodynamic differences between individuals has generally 

been assigned a default value of √10. This assumption is consistent with the previous 

assumptions about likely human interindividual variability. However, although there are some 

specific data on individual susceptibility for pharmaceutical agents (for example, bumetanide: 

(Skowronski et al., 2001)), there is little basis other than this precedent for setting a default value 

of UFH-d that would be suitable for the kind of toxic chemicals of concern to the Air Toxics Hot 

Spots program. However, there are grounds for suspecting that the differences between infants 

or children and adults may be greater for certain endpoints, as discussed in Section 3.2.2. In 

these cases (such as chemicals causing neurotoxicity, or suspected of causing or exacerbating 

asthma) it may be appropriate to select a different, and larger, value for UFH-d on a chemicalspecific basis. Such choices will be explained and justified in the description of the individual 

RELs where they are applied. 

4.4.9 Uncertainty Associated with Deficiencies in the Overall Database 

In some cases, the database on an environmental chemical may be insufficient to be confident 

that the REL will be protective. Since this type of deficiency necessarily implies a lack of 

adequate data, it is accommodated by application of a database deficiency uncertainty factor 

(UFD), usually a value of √10 (Table 4.4.1). This is similar to the U.S. EPA modifying factor of 

1 to 10 to account for data uncertainties in their procedures for calculating RfDs (U.S. EPA, 

1993). As noted in U.S.EPA (2002a), “the database UF is intended to account for the potential 

for deriving an underprotective RfD/RfC as a result of an incomplete characterization of the 

chemical’s toxicity. In addition to identifying toxicity information that is lacking, review of 

Technical Support Document 66 June, 2008 
TSD for Noncancer RELs June 2008 

existing data may also suggest that a lower reference value might result if additional data were 

available. Consequently, in deciding to apply this factor to account for deficiencies in the 

available data set and in identifying its magnitude, the assessor should consider both the data 

lacking and the data available for particular organ systems as well as life stages.” Although this 

was not used in the previous version of the Hot Spots guidance, OEHHA now recommends an 

additional three-fold UF to apply in developing an REL for chemicals with substantial 

toxicological data gaps, including, but not limited to, developmental toxicity. In some cases, it 

may be appropriate to apply a database deficiency factor larger than three-fold. The need for the 

additional database deficiency UF will be evaluated on a chemical-by-chemical basis, and 

justified in the individual REL summaries. Examples of situations where this might be 

considered appropriate include where a structurally related chemical indicates potentially more 

toxicity for the compound of concern than has been evaluated experimentally. Thus, structureactivity analysis may be brought to bear on use of the database deficiency factor. Another 

example is where there is a metabolite for which data indicate a concern for a type or severity of 

toxic response which has not been evaluated experimentally for the parent compound. Similarly, 

this factor might be applied where a preliminary study was reported but the sample sizes used 

were too small or the number of doses used was inadequate to characterize an effect accurately. 

4.4.9.1 Database Deficiency Factor for Lack of Developmental Toxicity Data

Under SB 25, OEHHA is mandated to ensure that our health standards take into account the 

potential greater vulnerability of infants and children to chemical exposure and toxicity. Some 

chemicals can affect the developing fetus or development in infants and children. If studies in 

immature animals are lacking, it may be impossible to predict effects on developing organs and 

tissues. OEHHA will use a database deficiency factor (UFD), with a default value of between 

√10 and 10, when animal developmental studies are not available for a chemical in order to help 

ensure that RELs protect infants and children. The rationale for application of this uncertainty 

factor will be presented in the individual toxicity summary. 

4.4.9.2 Estimation of Inhalation Effects from Oral Exposure Data

Strong weight is given to inhalation exposure-based health effects data. If adequate inhalation 

data are not available, oral exposure data are also considered. Both the U.S. EPA (1994a) and 

the NRC (1986b) support route-to-route extrapolation under certain circumstances. Route-toroute extrapolation may sometimes be inappropriate (e.g., where chemicals act at the portal of 

entry). 

Use of oral exposure studies to develop RELs requires consideration of kinetic differences 

between routes, including differences in absorption across the lung versus the gastrointestinal 

tract. Wherever possible, such extrapolations should be undertaken using PBPK models which 

allow for the route-specific features of uptake and distribution of the specific chemical. 

Where data are unavailable to support this approach it may be possible to use default 

assumptions or limited data to allow for route-to-route differences, at least in simple and 

straightforward cases. While route-specific differences in absorption and potency may occur, no 

additional UF is generally applied for non-inhalation data. Instead, attempts should be made to 

adjust for absorption and other kinetic differences (e.g., first pass metabolism following oral 

Technical Support Document 67 June, 2008 
TSD for Noncancer RELs June 2008 

exposure) when possible. Owen (1990) found that the median inhalation/oral absorption 

coefficient ratio was 1.0 for 34 substances. For 32 of the substances (94%), inhalation 

absorption coefficients were at most 10-fold higher than oral absorption coefficients for the same 

substance. The two exceptions (6%) with much greater absorption by inhalation were metals 

with very low oral absorption (<1%): inhalation absorption of beryllium and elemental mercury 

was estimated to be 500-fold and 7,500-fold higher, respectively, than corresponding oral 

absorption. Fifteen substances (44%) were predicted to have greater inhalation than oral 

absorption, and 7 substances (21%) were predicted to have at least 2-fold greater inhalation than 

oral absorption. Pepelko (1987; 1991) provided additional evidence that differences between 

toxic effects following oral and inhalation exposures are generally within a 10-fold dose range. 

Inhalation and oral doses associated with a 25% additional risk of cancer (RRD(25)) were 

estimated for various chemicals. Carcinogens were more potent via oral exposure compared 

with inhalation exposure in 15 of 23 rodent data sets, and 20 oral exposure data sets (87%) 

predicted inhalation results within a 10-fold factor. Greater than 10-fold differences in potency 

were found in rats exposed to asbestos, hexavalent chromium (CrVI), hydrazine, or vinyl 

chloride. 

4.4.10 Summary of Uncertainty Factors 

A summary of UFs used for acute, eight-hour and chronic REL development is given in Table 

4.4.1. 

4.5 Supporting Data 

Summaries describing the development of the acute, eight-hour and chronic RELs for each 

chemical are found in Appendix D. In addition, a list of acronyms is provided in Appendix A. 

All toxicity summaries for the newest RELs include a discussion of the information upon which 

the calculations are based. This discussion includes the following key elements: 

1. Physical and chemical properties: Descriptions include information on volatility, reactivity, 

stability, toxic secondary compounds, flammability, density, water solubility, color, odor, 

and some additional properties. 

2. Occurrence and use: The typical major uses of the chemical are described as well as where it 

is likely to be found. If available, measured ambient air levels are provided. 

3. Routes of exposure: The routes of exposure that may lead to toxic effects are mentioned for 

each substance. Since the intent of this document is to provide information on airborne 

toxicants, the data presented focuses on inhalation exposure studies and may be 

supplemented by relevant non-inhalation toxicology studies. If inhalation data are 

unavailable or are of poor quality for a particular chemical, other routes of exposure may be 

considered for the development of RELs. For extrapolation from oral to inhalation 

exposures, ideally a PBPK model dealing with both routes is used. Failing that, methodology 

presented by U.S. EPA (1994a) should be used. 

4. Summary of toxic effects: Toxic effects are described for relevant endpoints. Where 

possible, all of the following attributes are mentioned: endpoints, test species, concentration 

Technical Support Document 68 June, 2008 
TSD for Noncancer RELs June 2008 

or dose, duration and frequency of exposure, type of effect level (such as benchmark dose or 

NOAEL), reversibility of findings, UFs applied, and RELs derived. Note: while an overview 

of the toxicity of the chemical is provided in the summary, only the papers deemed key to the 

REL are described in detail. 

5. Pharmacokinetics and metabolism: A discussion of pharmacokinetics is included if 

information is available. This may include information on absorption, distribution, 

metabolism, and excretion. The inhalation route of exposure is examined preferentially. 

Metabolites of the parent compound are also identified when known. Where data are 

available to support it, a pharmacokinetic model may be derived; if used in the derivation it is 

described in the summary. 

6. Children’s sensitivity to the chemical relative to adults: A discussion of the potential for 

infants’ and children’s differential sensitivity to the chemical is provided, and any 

adjustments to the REL to protect children’s health are described. Effects on other 

potentially sensitive subpopulations are also considered. 

7. Quality assurance measures: Weak or conflicting data are reviewed. Studies are evaluated 

for any recognized violations of sound laboratory or statistical practices. 

8. Sources of data: In the absence of well-documented experimental dose-response studies in 

humans, reliance on toxicological data from animal studies and human data from workplace 

and other exposures is appropriate. In addition, in vitro toxicity studies are sometimes 

reviewed, particularly for information on mechanism of action. 

9. Oral RELs: Substances emitted to the air may deposit on soil, water or plants with 

subsequent human exposure via non-inhalation routes. Since oral exposure is the 

predominant non-inhalation pathway, non-inhalation RELs are referred to as oral RELs. 

Where appropriate, oral RELs are included to capture the contribution of this pathway, for 

example, for the nonvolatile compounds anticipated to be present in the air adsorbed to 

particulate matter. In the absence of adequate inhalation data, oral REL data may be used in 

the development of inhalation RELs. 

Technical Support Document 69 June, 2008 
TSD for Noncancer RELs June 2008 

5 Acute Reference Exposure Levels 

This section presents methods for deriving acute (one-hour) inhalation Reference Exposure 

Levels (RELs) for toxic air contaminants (TACs). The acute REL is an exposure that is not 

likely to cause adverse effects in a human population, including sensitive subgroups (such as 

infants and children), exposed to that concentration for one hour on an intermittent basis. 

As with all health effects, certain individuals may be more susceptible to adverse health 

consequences following exposure above the acute REL. These sensitive individuals may suffer 

health effects at a lower level of exposure than the general population. For example, individuals 

with asthma, who following exposure to sulfur dioxide are likely to exhibit bronchoconstriction 

at a lower concentration than the general population, may require greater protection from this 

substance than non-asthmatic persons. Acute RELs are designed to be protective for the range of 

susceptible persons in the general population including infants and children. 

5.1 Time Frame of Interest 

In the Air Toxics Hot Spots Program, routine industrial emissions are evaluated for potential 

public health impacts. Facility emissions may fluctuate considerably, with daily and hourly 

maximum and minimum concentrations. The commonly used air dispersion models can be used 

to model concentrations hour by hour throughout a year, giving an indication of the one-hour 

maximum exposure concentrations. The hourly fluctuations are a reflection of the changing 

meteorological conditions that are included in the model. Section 5.4.1 provides more 

description of the underlying assumptions and applicability of the acute REL. 

In general, the one-hour modeled maximum concentrations in the Air Toxics Hot Spots Program

are used in a HI approach in order to evaluate “acute” exposures and potential public health 

impacts from such exposures. The HI is the ratio of the one-hour maximum modeled ground 

level concentration (GLC) to the acute reference exposure level (REL). If the ratio exceeds one, 

then the risk manager needs to consider whether risk reduction is appropriate. An exceedance of 

one does not mean adverse effects will occur. Rather, it is an indication of the erosion of the 

margin of safety for exposure to that chemical 

5.2 .Exposure Duration and Patterns 

As indicated in Section 5.1, the focus of acute RELs is on short-term exposures. A one-hour 

exposure is used as the timescale for which toxicity is assessed, which is consistent with the 

hour-by-hour monitoring or modeling that is generally conducted for facilities under the Hot 

Spots Program. Sometimes it is necessary to extrapolate from other durations of experimental 

exposure or from reports of human exposure situations, to a 1-hour exposure duration. This is 

described in Section 5.4, and is also discussed on a chemical-by-chemical basis in the toxicity 

reviews for many compounds. 

Technical Support Document 70 June, 2008 
TSD for Noncancer RELs June 2008 

5.2.1 Exposure Concentration Averaging Period 

The acute REL is a concentration that is not likely to cause adverse noncancer effects in a human 

population, including sensitive subgroups, exposed on an intermittent basis to that concentration 

for one hour. Intermittent exposure is difficult to define. The U.S. EPA views intermittent 

exposure as that lasting less than 24 hours and occurring no more frequently than monthly. This 

is, in part, based on an assumption that an acute exposure concentration is at least 10-fold higher 

than the monthly average, and the presumption that individual exposures are independent of one 

another. They point out that very few chemicals will have sufficient data to determine the safe 

“periodicity” of an acute exposure. Thus, U.S. EPA (1994b) has identified three issues to be 

addressed: length of acute exposure, periodicity of exposures, and the relationship between the 

acute exposure and the chronic background (U.S. EPA, 1994b). These will be discussed below. 

In acute toxicology experiments, the study design usually involves exposures of short duration to 

an otherwise unexposed animal. However, real world “acute” exposures occur intermittently, 

rather than as rare events in a lifetime. Thus, the typical ambient exposure scenario is not 

reflected in the standard acute toxicology experimental design. The possibility of cumulative 

effects from intermittent ambient exposure cannot be addressed in acute REL development. 

Hence, acute environmental exposures are considered by the U.S. EPA to occur no more 

frequently than monthly. The U.S. EPA also recommends that longer inter-exposure periods be 

established for chemicals with long clearance times or for those with evidence of cumulative or 

sensitizing effects. 

A related exposure issue is the fact that peak exposures are superimposed on lower long-term 

exposures to the same compound. This is also not reflected in the standard acute toxicology 

design. For some compounds this will result in an increased body burden relative to the typical 

toxicology experimental design and in a potential lowering of the acute exposure needed to 

produce an adverse effect. For sensitizers, peak exposures in occupational settings can increase 

the response to much lower levels. It is not clear whether sensitization occurs at environmental 

exposures, but it is an uncertainty. Chronic exposures to particulate matter pollution can result in 

elevated risk factors for heart disease such as atherosclerosis; peak exposures may trigger a 

cardiovascular event such as a myocardial infarction. The U.S. EPA’s approach is to assume that 

the peak exposures are at least 10 times the monthly average so that the acute exposure can be 

considered to be relatively independent of the longer-term chronic exposure to the same 

substance (U.S. EPA, 1994b). This may be generally true for specific industrial emissions (but 

not for regional air pollution). 

Despite these limitations, it is imperative to examine whether short-term exposures to peak 

concentrations might result in adverse public health impacts. OEHHA’s RELs should be 

compared to the modeled one-hour maximum (or multi-hour as noted for specific 

reproductive/developmental toxicants) concentrations used in the HI approach to risk 

assessment. OEHHA recommends that these acute RELs be used to evaluate exposures that 

occur no more frequently than every two weeks in a given year. The two-week interval was 

chosen because in most acute toxicology experiments two weeks is the duration of time an 

animal is observed for signs of adverse outcomes following exposure. 

Technical Support Document 71 June, 2008 
TSD for Noncancer RELs June 2008 

An assumption in making this recommendation is that the REL is protective of adverse health 

effects that are not cumulative. Thus, the effects of each peak exposure are independent of 

previous or subsequent peak exposures that occur as often as every two weeks. This 

recommendation is only valid for substances that do not bioaccumulate. Also, the assumption of 

independence of peak exposures is geared to typical ambient environmental exposures, and not 

occupational exposures or exposures to environmental tobacco smoke indoors, for example. 

When bioaccumulation is known to occur and body burden is associated with an adverse effect, 

or where cumulative tissue damage occurs with repeated exposures, longer inter-exposure 

periods should be specified. 

The modeled one-hour peak concentrations are typically much greater than the maximum 

average annualized concentrations used for determining chronic exposure and risk. Thus, it is 

assumed that acute exposures are independent of the long-term average exposure based on the 

modeled annualized maximum average concentration. However, under certain meteorological 

conditions (poor mixing, persistent calm winds), it is conceivable that there are many hours in a 

day or within a few days where exposures are close to the peak one-hour in any given year. 

Concentrations close to the maximum one-hour exposure may occur many times during the year 

including on consecutive days. In addition, it is conceivable that exposure concentrations close 

to the maximum may occur in consecutive hours. Currently, the local air districts, Air Board, 

and facilities do not ascertain how often exposures close to the one-hour maximum occur in a 

given day, week, month or year. This contributes to the uncertainty in evaluating the adverse 

health effects of peak one-hour exposures. 

In evaluating chemicals with developmental toxicity, we found that the standard experimental 

paradigm of repeated exposure over several days did not lend itself easily to extrapolation to a 

one-hour acute REL. Since developmental endpoints are frequently manifested in a small 

window of time during gestation, the standard protocol is to expose pregnant animals for several 

hours per day over several days during gestation in order to increase the power of the study to 

detect an effect. Issues that affect the extrapolation to one hour include not only when the 

sensitive gestational period is, but also toxicokinetic issues. Whether or not a single one-hour 

exposure could produce a reproductive or developmental adverse outcome depends on the 

toxicokinetics governing the concentration of the chemical in maternal and fetal tissues, timing 

of exposure, mechanism of action, and other factors. These issues are not easily taken into 

account in extrapolating to a one-hour acute REL. Thus, for those acute RELs addressing a 

developmental endpoint determined under our previous methodology, the REL was for the 

exposure duration chosen for a single day in the experimental protocol. In this revised 

methodology, OEHHA proposes to use the exposure concentration in a developmental toxicity 

study as the basis of the one-hour REL, regardless of the daily exposure duration in the study. 

Given the seriousness of developmental endpoints and our mandate to ensure our risk assessment 

methods adequately protect infants and children, this is justified. It is rarely clear in a 

developmental toxicity study if the toxicity depends on tissue concentration during a discrete 

time interval or on total dose over the course of exposure. This may be particularly important for 

developmental endpoints where short time periods of extreme vulnerability to toxicants may be 

accompanied by uncertainties in toxicological mechanisms. The duration of the period of 

vulnerability may itself be highly uncertain. 

Technical Support Document 72 June, 2008 
TSD for Noncancer RELs June 2008 

5.3 Human Equivalent Concentration (HEC) Procedure for Acute RELs 

When animal studies are used for acute REL development, the U.S. EPA HEC procedure 

(described in Appendix F) may be used as a partial adjustment for interspecies toxicokinetic 

differences, in which case the 10-fold interspecies UF is reduced to 6 (UFH-k = 2, UFH-d = √10). 

The modifications of the HEC procedure to account for children may also be used. These 

procedures will be used where applicable as the acute RELs are updated to reflect additional 

available research and to fulfill the mandates of SB 25 to account for potentially greater 

vulnerability of children when setting health standards. 

5.4 Effects of Exposure Duration – Special Considerations for Acute Effects 

Studies of adverse health effects associated with exposures in humans or experimental animals 

are generally conducted for time periods different from that which is of interest in the acute 

exposure scenario. Typical exposure scenarios involve several hours for human exposures and 

several daily exposures for two weeks in animals. OEHHA acute RELs, on the other hand, are 

designed to be protective for one-hour exposures (with the exception of some developmental 

toxicants where the REL is for several hour exposures). 

Acute inhalation toxicology studies (exposure duration of 8 hours or less) are preferred over 

other exposure routes. In their absence, studies using exposures of longer durations may be 

employed if appropriate (e.g., symptoms noted after short period of time; developmental 

endpoints). If inhalation toxicity data are unavailable, studies on other exposure routes may be 

used. Studies that include an adequate follow-up period (hours to days, depending on the 

chemical and endpoint) to account for delayed health effects are preferred to those that terminate 

observation immediately following exposure. In order to adjust experimental exposure durations 

to one-hour, OEHHA uses a method termed time extrapolation. 

5.4.1 Concentration and Time Extrapolation using Haber’s Law 

“Haber’s Law” states that the product of the concentration (C) and time of exposure (T) required 

to produce a specific physiologic effect is equal to a constant level or severity of response (K), or 

C * T = K (Rinehart and Hatch, 1964). When the duration of experimental exposure differs from 

the desired exposure duration for which an acute exposure level is being calculated (in this case 1 

hour), a modification of Haber’s Law is used to adjust the experimental exposure duration to the 

desired duration of the acute exposure level: 

Cn

 * T = K 

where n is a chemical-specific parameter greater than zero (ten Berge et al., 1986). When n is 

equal to one (n = 1), the toxicity of a chemical is equally dependent on changes in concentration 

and duration of exposure; when n is less than one (n < 1), the duration of exposure is a greater 

determinant of toxicity than the concentration; finally, when n is greater than one (n > 1), the 

toxicity of a chemical is determined to a greater extent by exposure concentration than by 

duration. 

Technical Support Document 73 June, 2008 
TSD for Noncancer RELs June 2008 

5.4.1.1 Value of the concentration exponent, n

Ideally, the magnitude of n should be determined for all chemicals by evaluating the 

concentration versus response relationships for several different exposure durations. However, 

this information is available for only a limited number of substances. Empirically-derived values 

of the exponent n range from 0.8-3.5 (ten Berge et al., 1986). The time-concentration-response 

relationship depends on the time-frame considered and the endpoint measured. There are usually 

multiple “n” values for a single chemical that are applicable to different response endpoints. For 

example, the “n” for irritation of ammonia is 4.6, while the “n” for lethality of ammonia is 2 (See 

Appendix G.). As concentration becomes the more important factor, the value of n will increase. 

Values of n greater than three suggest that concentration has a strong predominance over time. 

The value for the exponent n used by OEHHA in acute toxicity summaries is chosen as follows. 

First, when an empirically derived value for the exponent is available from the open literature, 

this is adopted for time extrapolation, using the modification of Haber’s Law as described above. 

Appendix G shows published or OEHHA derived values for n which were used in acute RELs 

previously developed by OEHHA (1999). 

When a derived value is not available and there are insufficient data from which to determine a 

value de novo, a default value for n must be used. The published or OEHHA derived values for 

n shown in Appendix G range from 0.8 to 4.6. The mean value in this range rounds to 2; the 

interquartile range (25%-75%) is from 1 to 2.2. Previously, the mean value of n = 2 was used by 

OEHHA (1999) when extrapolating from an exposure duration that is greater than one hour to a 

1-hour level. However, when this issue was considered by NRC (2001) they concluded that it 

would be more appropriate to use the value n = 3, which approximates the 95th percentile of the 

range of values reported by ten Berge (1986). OEHHA now therefore recommends the use of n

= 3 when extrapolating from experimental exposures greater than one hour to the 1-hour period 

of concern for the acute RELs. Use of this exponent makes concentration much more important 

than time, and is thus health-protective when extrapolating from greater than one hour exposures 

to one-hour exposures. 

When extrapolating from an experimental exposure duration of less than one hour to a 1-hour 

level, the value of n = 1 was used. Using a value of n = 1 is more health-protective than a value 

of n = 3. A value of n = 1 results in a relatively rapid decrease in the derived REL when 

extrapolations are made from shorter to longer exposures. For example, when extrapolating 

from a 30 minute exposure at the published NOAEL of 60 ppm (Purser et al., 1984) to a 60 

minute exposure for hydrogen cyanide, using n = 1 results in an extrapolated 1-hour NOAEL of 

30 ppm; when using n = 3, the extrapolated NOAEL is 48 ppm. 

In summary, the default exponents used by OEHHA in the formula Cn

 T = K for extrapolation to 

1-hour acute RELs are as follows: 

• From less than 1 hour n = 1 

• From greater than 1 hour n = 3 

Technical Support Document 74 June, 2008 
TSD for Noncancer RELs June 2008 

5.4.1.2 Haber’s Law and Irritants

The applicability or otherwise of Haber’s Law to irritants has been the subject of various studies. 

The NAS has suggested that Haber’s Law does not apply for “some irritants” (NRC, 1986a; 

NRC, 1986b; NRC, 1993). This statement is apparently based on the observation that for some 

substances, irritation appears to be solely concentration dependent. In fact, the time course of 

response to the small number of sensory irritants for which data are available suggests that 

although the response follows a modified dose/time integral relationship (like most other 

toxicities) over very short time scales of a few seconds or minutes, this relationship has reached a 

plateau where the level of response is dependent only on concentration well within the one-hour 

time scale of concern for acute REL derivation (Shusterman et al., 2006). 

Response to mild sensory irritants is detected through binding to the trigeminal nerve receptors. 

In humans, this results in a complex response including a burning sensation of the eyes, nose, 

and throat. Other notable symptoms are coughing, nasal congestion, rhinorrhea, sinus and 

Eustachian tube dysfunction, and worsening of lower airway function in some asthmatics (the socalled "naso-bronchial reflex") (Widdicombe, 1990; Raphael et al., 1991). The response in the 

rodent is simpler, consisting primarily of a reflex depression of the breathing rate. When a 

mouse is exposed to an irritant, the decrease in respiratory rate is proportional to the 

concentration of the airborne chemical. Also, a minimum respiratory rate is reached and remains 

at a plateau, or fades in response during exposure. This has been used as the basis for a bioassay 

of sensory irritant properties (Alarie, 1966), in which irritant potency is expressed as the 

concentration producing a 50% depression in respiratory rate (RD50). 

Although the receptors have not been fully characterized, receptor binding has been found to 

follow the classic Michaelis-Menten receptor kinetics. There is evidence that many chemicals 

bind to a common receptor, sometimes referred to as the common “chemical sense” receptor 

(Cometto-Muniz et al., 1997; Bryant and Silver, 2000). There is also evidence of multiple 

receptor types on the trigeminal nerve (Nielsen and Vinggaard, 1988). The irritant response can 

be described by the Michaelis-Menten equation in an animal model (Kristiansen et al., 1986; 

Nielsen and Vinggaard, 1988). Competitive agonism has been demonstrated in the mouse RD50 

bioassay (Bos et al., 1991). This is additional evidence for a receptor mediated mechanism, with 

a common receptor for these two chemicals. 

The degree of receptor binding is mediated by the tissue concentration of the toxicant, not the 

duration of exposure once equilibrium is reached, which generally occurs relatively quickly. At 

equilibrium, at a constant exposure concentration, a constant level of receptor binding would be 

expected presumably leading to a constant effect level, independent of the duration (the “T” 

factor in the Haber’s Law equation). Michaelis-Menten kinetics also predicts that receptors 

would be saturated at higher irritant chemical concentrations and therefore additional trigeminal 

transmitted irritation response with increased exposure concentration would not be expected in 

this situation. 

Thus, irritation should be more a function of the air concentration of the irritant than of the total 

dose. Often these chemicals are non-reactive volatile organic compounds (VOCs), although in 

some cases they may be reactive. This simple picture is complicated by additional factors such 

Technical Support Document 75 June, 2008 
TSD for Noncancer RELs June 2008 

as desensitization of receptors that can cause a decline in perceived irritation over time for some 

chemicals (Nielsen, 1991; Shusterman et al., 2006). 

The trigeminal system is distinct from odor perception. Persons lacking a functional sense of 

smell (anosmics) still perceive chemical irritants in a similar fashion to people with a normal 

sense of smell. Odor perception occurs at a lower threshold than irritation, and in some cases at 

a much lower threshold. In both odor perception and trigeminal irritation from a chemical, there 

can be wide variability in threshold air concentrations in the general population. 

If the irritation reaction is a function of the concentration, then the fact that children have higher 

breathing rates than adults should not influence the health impact of a particular concentration. 

There is no evidence that infants and children have different or more irritation receptors than 

adults. Therefore, OEHHA has not assumed that children are more sensitive than adults to the 

sensory effects of eye, nasal or respiratory irritants. However, it must be considered that many 

irritants, especially those that are chemically reactive, may have the potential to exacerbate or 

induce asthma, which is a special concern for children’s health. 

OEHHA will consider trigeminally-transmitted sensory irritation endpoints to be independent of 

the duration of exposure over the one-hour timescale, unless data indicate such time dependence. 

Data establishing time dependency should be in a concentration range relevant for trigeminal 

nerve transmitted effects and not at considerably higher concentrations. Higher concentrations 

may cause irritation through tissue damage, and thus show time dependence because of 

accumulating tissue damage. The National Academy of Science Subcommittee on Acute 

Exposure Guideline Levels made the same determination for their Acute Exposure Guideline 

Levels (AEGLs) (NRC, 2001). Sometimes trigeminally-transmitted irritation is difficult to 

distinguish (based on available data for a chemical) from tissue damage mechanisms, or there 

may be mixed mechanisms. Empirical determination of an “n” value, indicating that duration of 

exposure for a particular chemical influences toxicity, will be accepted as an adequate basis for 

Haber’s Law adjustments in these cases. 

5.4.1.3 Strong Irritants Causing Tissue Damage and/or Hyperplasia

Some chemicals cause irritation through tissue damage that can result in hyperplasia or other 

nasal, eye or respiratory tissue damage. The tissue damage resulting from exposure to these 

chemicals may be both time and concentration dependent and in some cases be dependent on the 

total cumulative dose or the concentration. For example, formaldehyde-induced hyperplasia 

appears to be more concentration dependent than exposure duration dependent according to an 

analysis of several studies cited in the development of OEHHA’s chronic REL (OEHHA, 

2000a). Trigeminally-transmitted irritation may occur at a lower concentration, while tissue 

damage or hyperplasia may occur at a higher concentration. Tissue damage or hyperplasia may 

also occur with longer exposure to the same concentration (Barrow et al., 1986). This is 

particularly evident with highly reactive chemicals such as chlorine which have both sensory 

irritant and direct tissue-damaging properties in the upper respiratory tract (Jiang et al., 1983; 

Bos et al., 1991). Chemicals which cause sensory irritation in the upper respiratory tract as a 

result of lower short-term exposures may also cause pathological changes in the lower 

respiratory tract, especially following more extended or more intense exposures (Shusterman, 

1999). However, persistent histological changes may not always be seen with the isolated oneTechnical Support Document 76 June, 2008 
TSD for Noncancer RELs June 2008 

hour exposures against which acute RELs are intended to be protective. In such cases, the onehour REL would likely be different from a chronic REL. 

If such tissue damage in the nose or other parts of the respiratory tract accumulates over time, 

then the toxic effect would be dependent upon both time and concentration. Such damage could 

trigger pain receptors, and needs to be distinguished from trigeminally-transmitted irritation. In 

contrast to the case of pure sensory irritation, the use of Haber’s Law where tissue damage is a 

factor in the response is appropriate. 

The higher breathing rates of children may need to be considered for chemicals causing 

cumulative damage at the point of entry, for which either total dose or AUC is the determinant of 

toxicity. An analysis of the mechanism of toxicity may help to determine whether children are 

more sensitive than adults to irritants that cause tissue damage or hyperplasia. 

5.4.2 Time Extrapolation for Acute RELs Based on Developmental Studies 

In the previous guidelines, OEHHA (1999) considered that extrapolation to one hour using 

Haber’s law was not appropriate in the case of repeated dose studies for developmental 

endpoints. OEHHA chose a single day’s exposure for each chemical (ranged from 1 to 8 hours) 

as the exposure duration for which the REL is to be applied. Thus, no time extrapolation was 

used for developmental toxicants. Several of the acute RELs derived using these earlier 

guidelines based on developmental studies have averaging times longer than one hour. These 

averaging times include six hours for benzene, carbon disulfide, EGEE, EGEEA, and EGME, 

and seven hours for carbon tetrachloride and chloroform (OEHHA, 1999). 

OEHHA has developed a different underlying methodology in the present version of these 

guidelines, which has been described in Section 4.4.6.3. As in the cases noted above, the time 

extrapolation used when deriving acute RELs will most often be from a longer experimental 

duration to a shorter one-hour reference period for the REL. In these cases, the revised 

methodology treats the experimentally applied concentration as the basis for the acute REL, i.e.

the concentration present during the experiment is not to be exceeded during any 1-hour period. 

The revised methodology will, in these cases, result in an acute REL which is numerically the 

same as that obtained by the previous method, although the previous complication of having a 

non-standard averaging time is avoided. 

5.5 LOAEL to NOAEL Extrapolation 

As noted in the general discussion of REL methodology, there are some cases where a 

benchmark concentration approach will not work because of data constraints. The studies may 

not have identified a NOAEL, but only a LOAEL, and it may be necessary to extrapolate from 

the LOAEL to a NOAEL using a default UFL of 10. We have developed criteria for when the 

UFL can be less than the default of 10. 

Following acute exposure, health effects of varying severity may be observed, depending on the 

extent of exposure, or dose, and the toxic properties of the compound. Although the relationship 

between exposure and health outcome is a continuous one, effects may be categorized into 

discrete severity levels, particularly for acute exposures (Table 5.7.1). The purpose of acute 

Technical Support Document 77 June, 2008 
TSD for Noncancer RELs June 2008 

RELs for the preparation of risk assessments under the Hot Spots Program is to evaluate impacts 

of short-term exposure from non-emergency releases. Thus the RELs are generally protective 

against mild adverse effects, although in a few cases the most sensitive endpoint, which was used 

in development of the REL, is severe (e.g., a reproductive/developmental endpoint). 

Mild effects are defined as those with severity of grade 5 or below, as described in Table 5.5.1. 

Based on an analysis of LOAELs and NOAELs reported in various acute toxicological studies, 

we found that when extrapolating from a LOAEL to a NOAEL for mild effects, UFs less than 10 

are justified (Alexeeff et al., 1997; Alexeeff et al., 2002). In the case of the mild adverse effect, 

an analysis by Alexeeff et al. (1997) of LOAEL to NOAEL ratios for over 100 datasets indicated 

that the 95th percentile of that ratio is 6.2. The distribution was skewed to the right; for some 

chemicals, a UF of 10 may not be adequate. OEHHA has chosen a UF of 6 to extrapolate from 

the LOAEL to the NOAEL where the effect is mild, based on this analysis. Recommended 

default values of UFL for acute REL derivations are therefore as follows: 

• Where the observed effect level used as the basis of the REL is a NOAEL or 

equivalent benchmark, the value of UFL is 1. 

• For a LOAEL where the observed effect is mild (for acute exposures, U.S. EPA grade 

5 or below, Table 5.5.1), the value of UFL is 6. 

• For a LOAEL where the observed effect is moderate to severe, the value of UFL is 10. 

These default values may be replaced by more specific values where appropriate data are 

available (e.g., for specific toxicological endpoints or chemical classes). However, the use of a 

LOAEL as the basis of a REL is to be avoided wherever possible, by using data sets in which a 

NOAEL is also observed or, preferably, by applying the BMC methodology to a study where a 

range of response levels with increasing dose is measured. 

Technical Support Document 78 June, 2008 
TSD for Noncancer RELs June 2008 

TABLE 5.5.1. RELATIONSHIP BETWEEN EFFECT CATEGORIES AND SEVERITY. 

Severity Level Effect Category Effect 

0 NOEL No observed effects. 

1 NOAEL Enzyme induction or other biochemical change 

(excluding signal transduction effects), consistent with 

possible mechanism of action, with no pathologic 

changes, no change in organ weights, and no 

downstream adverse developmental effects. 

2 NOAEL/LOAEL Enzyme induction and subcellular proliferation or other 

changes in organelles, consistent with possible 

mechanism of action, but no other apparent effects. 

3 NOAEL/LOAEL Hyperplasia, hypertrophy, or atrophy, but without 

changes in organ weight. 

4 NOAEL/LOAEL Hyperplasia, hypertrophy, or atrophy, with changes in 

organ weight. 

5 LOAEL Reversible cellular changes including cloudy swelling, 

hydropic change, or fatty changes. 

6 (LO)AEL Degenerative or necrotic tissue changes with no 

apparent decrement in organ function. 

7 (LO)AEL/FEL Reversible slight changes in organ function. 

8 FEL Pathological changes with definite organ dysfunction 

which are unlikely to be fully reversible. 

9 FEL Pronounced pathological change with severe organ 

dysfunction and long-term sequelae; developmental 

dysfunction including biochemical changes affecting 

signal transduction that result in developmental defects 

or dysfunction. 

10 FEL Life-shortening or death. 

(Adapted and expanded from U.S. EPA, 1994a) 

NOEL – no-observed-effect-level; NOAEL – no-observed-adverse-effect-level; LOAEL – 

lowest-observed-adverse-effect-level; AEL – adverse-effect-level; FEL – frank-effect-level. 

5.6 Pre-Existing Acute Exposure Guidelines 

Acute exposure standards have been developed by several different organizations. However, 

there are no inhalation exposure values that were derived using a consistent basis to protect the 

public from planned industrial emissions. Values designed for protection of the general public 

exist, but they are intended to address accidental releases and use methodologies that are not well 

documented. Occupational exposure guidelines are available for hundreds of substances, but 

Technical Support Document 79 June, 2008 
TSD for Noncancer RELs June 2008 

have an inconsistent basis, often have not incorporated recently available data, and are not 

designed to protect sensitive subpopulations. The existing exposure guidelines considered for 

possible relevance to OEHHA’s acute RELs are described below.

5.6.1 The California Ambient Air Quality Standards (CAAQS) 

CAAQSs are promulgated by the California Air Resources Board (CARB) based on 

recommendations from OEHHA, and are specified concentrations and durations of exposure to 

air pollutants which reflect the relationship between the intensity and composition of air 

pollution to undesirable effects. The CAAQS for a criteria air pollutant has in the past been 

adopted as the acute REL. If necessary, a one-hour value was derived using time extrapolation 

(described below). The CARB on April 28, 2005 reviewed the current one-hour ozone standard 

and left it unchanged, but promulgated a new eight-hour ozone standard. The two together are 

meant to provide adequate protection of sensitive populations including children. 

5.6.1.1 Ambient Air Quality Standards as Acute RELs

Almost all acute RELs were developed de novo. However, the California Ambient Air Quality 

Standards for Criteria Air Pollutants were reviewed. If they were found to be appropriate, they 

were adopted as the relevant acute toxicity RELs. For the six criteria air pollutants carbon 

monoxide, nitrogen dioxide, sulfates, ozone, hydrogen sulfide, and sulfur dioxide, the CAAQS 

for short-term (one-hour) exposure is used as the REL, or one-hour values were derived by 

extrapolation from the 24-hour standard. 

5.6.2 The Threshold Limit Value-Time Weighted Average (TLV-TWA) and Short-Term 

Exposure Limit (STEL) 

The TLV-TWAs and STELs are developed by the American Conference of Governmental 

Industrial Hygienists (ACGIH) and updated annually (ACGIH, 2006); similarly, the National 

Institute for Occupational Safety and Health (NIOSH) recommended exposure limits also exist 

(NIOSH, 2005). The TLV-TWA is defined as the time-weighted average concentration for a 

normal eight-hour workday and a 40-hour workweek to which nearly all workers may be 

repeatedly exposed, day after day, without adverse effect. The STEL is defined as a 15-minute 

TWA exposure which should not be exceeded at any time during the workday. 

Occupational exposure limits have sometimes been used to derive chemical exposure guidelines 

for the general public (NATICH and McCullough, 1991; Robinson and Paxman, 1992; U.S. 

EPA, 1994a). More than 600 ACGIH TLVs and NIOSH RELs are available. These values have 

been attractive because of the large number of accessible values and the concept that they are 

intended to protect a human population from inhalation exposures. However, these values are 

not designed for or recommended for protection of the general public, and in many cases may 

not prevent adverse health effects among workers (Roach and Rappaport, 1990). OEHHA has 

therefore not taken the TLV-TWAs and STELs directly into account in developing acute RELs, 

but has taken advantage of the data identified and evaluations offered by ACGIH when relevant. 

Technical Support Document 80 June, 2008 
TSD for Noncancer RELs June 2008 

5.6.3 Various Emergency Exposure Guidance Levels 

A variety of guidance levels have been developed to assist in dealing with accidental chemical 

releases. As such, these values focus on emergency planning and response, not on the routine 

emissions and exposure which are the focus of this document. Thus NRC (2001) described the 

objective of U.S. EPA’s AEGL program (see below) as “to develop guideline levels for once-ina-lifetime, short-term exposures to airborne concentrations of acutely toxic, high-priority 

chemicals.” Emergency guidelines are typically defined as predicted thresholds above which 

some level of adverse health effect is anticipated: standard margins of safety are not 

incorporated. Also, in many cases these guidelines are designed to identify tolerable conditions 

for emergency first responders such as firefighters or military personnel, rather than to protect 

the general population. Such guidance values are seldom comparable to the acute RELs, and are 

not suitable for protecting the health of the general public from routine emissions. However they 

may incorporate relevant information as to the type of effects to be expected and the dose 

response for exposure to compounds of interest. 

Emergency Exposure Guidance Levels (EEGLs) are designed to provide guidelines for military 

personnel operating under emergency conditions that are peculiar to military operations and for 

which regulatory agencies have not set standards, and are defined by the NAS as the ceiling 

concentrations of substances in air that may be judged by the Department of Defense to be 

acceptable for the performance of specific tasks during rare emergency conditions lasting for 

periods of 1 to 24 hours (NRC, 1986a). “Emergency” connotes an unexpected situation with 

potential for loss of life. The Short-term Public Emergency Guidance Level (SPEGL) is defined 

by the NAS as a suitable concentration for unpredicted, single, short-term, emergency exposure 

of the general public (NRC, 1986a). In contrast to the EEGL, the SPEGL takes into account the 

wide range of susceptibility of the general public, but it is not designed for repeated or multiple 

exposures. 

The American Industrial Hygiene Association (AIHA) has defined Emergency Response 

Planning Guidelines (ERPGs) as concentration ranges where adverse health effects could be 

observed (AIHA, 2006). ERPGs have a specific emphasis on responding to accidental releases. 

The U.S. EPA has developed Acute Emergency Guidance Levels (AEGLs) to provide 

information to incident commanders in an emergency. The NRC has published a methodology 

for developing AEGLs (NRC, 2001). As of April 2007, AEGLs for 31 chemicals have been 

finalized (U.S. EPA, 2007a). 

Technical Support Document 81 June, 2008 
TSD for Noncancer RELs June 2008 

5.7 Areas for Further Research 

5.7.1 Acute Toxicity Data 

Many chemicals lack adequate data on acute toxicity. There are approximately 450 chemicals on 

the Air Toxics Hot Spots list of substances to be quantified (Appendix C). This is the list of 

substances, which facilities must report in their emissions inventories. We have to date only 

developed acute RELs for 51 of these compounds, six of which have so far been revised 

according to these new guidelines. While not all of the 450 chemicals have reported emissions 

in California, more work needs to be done in analyzing available literature for the remaining 

compounds and in revising existing RELs to take explicit account of children’s health issues. 

5.7.2 LOAEL to NOAEL Uncertainty Factor 

The application of UFs to account for extrapolation from a LOAEL to a NOAEL warrants 

further analysis (see Section 4.4.5). When evaluating dose-response relationships, the slope of 

the dose-response curve determines the distance between the LOAEL and the NOAEL from a 

particular study. Some endpoints tend to have steep dose-response slopes and may not warrant a 

10-fold UF to extrapolate from a LOAEL to a NOAEL; other endpoints have a shallow doseresponse slope and may warrant a 10-fold (or higher) UF for extrapolating from the LOAEL to 

the NOAEL. An analysis of the distribution of the LOAEL to NOAEL ratios for 112 datasets 

(Gift et al., 1993; Kadry et al., 1995; Alexeeff et al., 1997; Almstrup et al., 2002) suggested an 

intermediate UF of 6 to extrapolate from the LOAEL to a NOAEL for mild effects. Further 

analysis of 215 data sets for 36 pollutants yielded LOAEL to NOAEL ratios of 2.0, 5.0, 6.3, and 

10.0 for the 50th, 90th, 95th, and 99th percentile, respectively (Alexeeff et al., 2002). The 90% 

confidence interval for the 95th percentile was 5.0-7.5. Thus, the LOAEL to NOAEL UF of 6 

would be protective for 95% of the responses, and a value of 10 for 99%. However, the 99th

percentile value was considered unstable. For this reason the 95th percentile value is chosen for 

extrapolation of the LOAEL to a NOAEL for mild effects. 

Little variability was noted among species, particularly at the median. This analysis is based on 

toxicity data from mild acute inhalation studies and may not be applicable to other exposure 

routes, exposure durations, or more severe toxicity. (A value of 10 should be used by default for 

effects considered severe.) In addition, this analysis did not focus specifically on children. 

5.7.3 Interspecies Uncertainty Factor 

An interspecies UF of 10 is commonly used to extrapolate from animal studies to the human 

response (UFA) (Section 4.4.7.3). The available analyses supporting use of the 10-fold 

interspecies UFA were conducted on studies of toxicity by the oral route of administration 

(Dourson and Stara, 1983). Further analysis of available data on chemicals for which there is 

both human and animal data for the same endpoints by the inhalation route of exposure is 

warranted. 

In some cases, there may be a reason that a different UF could be used. Some part of the 

uncertainty encompassed by this factor may be replaced by species-specific models of 

Technical Support Document 82 June, 2008 
TSD for Noncancer RELs June 2008 

deposition, distribution or metabolism where data exist to support these models. In certain cases 

there may be specific reasons for concluding that the toxicodynamic component of this 

variability may be smaller or larger than the √10 assumed by default. For example, in lethality 

studies, the exposure to irritant chemicals producing lung edema may have very similar doseresponse slopes because the basic loss of cellular integrity at high doses may not be a 

phenomenon that would vary substantially from one species to another. However, in general 

data on the extent of toxicodynamic differences between species are limited, although the 

situation for acute exposures may be simpler than for the case of chronic exposures. The 

existing analyses are limited in terms of toxicological endpoints examined. Interspecies 

variability may differ significantly for different toxicological endpoints. This is another area 

where more research is warranted. 

5.7.4 Uncertainty Factor for Database Deficiencies 

An additional UF may be used in cases where there are identifiable deficiencies in the data 

(Section 4.4.8.3). For example, a database deficiency factor of √10 (UFD) may be applied to 

protect developing infants and children if no developmental data are available. Judgment is still 

needed when some developmental data are available. The ideal dataset for evaluating 

developmental endpoints would include studies in two species in which exposure occurs during 

gestation and a two-generation reproductive study in each of two species. In practice such a 

large database is unusual. Available mechanistic data will be considered when deciding when to 

apply the data base deficiency factor and what its value should be. The more robust the 

database, the less likely that the factor will be needed. Other types of data deficiency besides 

developmental toxicity may also be addressed with this factor. 

5.7.5 Time Extrapolation for Acute RELs 

We have used time extrapolation with a modified Haber’s Law to extrapolate from the 

experimental duration in the acute study to an equivalent concentration for a one-hour exposure, 

for endpoints other than sensory irritation. There are empirical data for the value of n in Haber’s 

equation for some chemicals. More data would be valuable for additional chemicals. Further 

analysis of the validity of the Haber’s Law application for different toxicological endpoints 

would be useful. 

5.7.6 Additivity of Adverse Effects 

We currently use an additive approach to assess the impacts of multiple chemicals on a target 

organ (Sections 2.2; 5.3). Some interactions may be synergistic and others antagonistic. 

Additivity has generally been accepted as health-protective at low environmental concentrations. 

However, there is a need for key studies on the additivity or synergism of chemicals at low 

concentrations that act on the same target organ. Further literature evaluation would also be 

helpful to elucidate whether the additive approach is the most valid approach for all scenarios 

(DeVito et al., 2000; Crofton et al., 2005; U.S. EPA, 2007b). 

Technical Support Document 83 June, 2008 
TSD for Noncancer RELs June 2008 

6 Eight-Hour Reference Exposure Levels 

This section presents additional information for deriving eight-hour inhalation Reference 

Exposure Levels (RELs) for toxic air contaminants (TACs). Eight-hour RELs are concentrations 

at or below which adverse health effects are not likely to occur in the general human population 

with intermittent exposures of eight hours per day, up to 7 days per week. 

6.1 Populations at Risk 

The Air Toxics Hot Spots Program Guidance Manual for Preparation of Health Risk 

Assessments (OEHHA, 2003) calls for evaluation of sensitive receptors such as daycare centers 

and schools as well as offsite workers. Onsite workers are under the jurisdiction of the 

California Occupational Safety and Health Administration. Current occupational standards such 

as threshold level values (TLVs) and permissible exposure levels (PELs) are usually expressed 

as a time weighted average (TWA) over an eight-hour shift. Noncancer health impacts for 

children at schools have been evaluated using either the chronic or acute RELs. Acute RELs are 

only useful for evaluating impacts of estimated maximum one-hour air concentrations when such 

exposures occur infrequently. Exposure duration for children and offsite workers will vary, but 

an eight-hour exposure duration assumption would be reasonable, particularly if children and 

offsite workers are exposed to facility emissions at their school or place of work and not at their 

residential locations. It is not intended that applications of 8-hour RELs will be confined to five 

days per week. Many facilities operate seven days a week, and the exposed individuals include 

categories besides workers on a standard daily shift. This means that it cannot simply be 

assumed that cumulative impacts and bioconcentration issues are covered by the timing of 

exposures for a typical occupational exposure. 

Chronic RELs are designed to be protective against long term 24-hour a day exposure and thus 

may overestimate some noncancer chemical risks associated with shorter, daily exposure (e.g., 

eight-hour exposures). Alternatively, chronic RELs may underestimate the noncancer risk where 

facility operation and emissions occur only 8 hours per day but coincide with the presence of 

nearby offsite workers and attendance at daycares and schools. Many facilities operate five days 

a week, eight hours per day. Such facilities have been modeled as if the total emissions were 

occurring twenty-four hours a day, seven days a week, three hundred sixty five days a year. The 

annual average ground level concentration (GLC) could then be compared to the chronic REL to 

determine noncancer chronic health risk. This approach has the advantage of simplicity but is a 

less accurate modeling approach because of diurnal meteorological variation and non-continuous 

facility emissions. This approach assumes that a higher eight-hour pollutant concentration 

followed by a sixteen-hour period of no exposure is toxicologically equivalent to the twenty-four 

hour average concentration (with two days of non-operation). Development of eight-hour RELs 

would allow more accurate evaluation of the impacts of exposure to modeled eight-hour ground 

level concentrations by comparison with noncancer health standards specifically tailored to 

actual exposure duration. 

Technical Support Document 84 June, 2008 
TSD for Noncancer RELs June 2008 

6.2 Characterization of 8-Hour Exposures 

An eight-hour REL, designed to protect against periodic exposure that could occur as often as 

daily, may share characteristics of both acute and chronic exposure. Frequent eight-hour 

exposures to a chemical with a short half-life in the body that does not cause tissue damage or 

accumulate may resemble a series of acute exposures. The previous exposures may have little or 

no impact on the current-day exposure. In these cases, acute exposure methodology would be 

employed for derivation of the eight-hour REL. Frequent eight-hour exposures to a chemical 

that accumulates in the body, or causes cumulative tissue damage, and/or activating or 

deactivating enzyme induction is considered a chronic exposure, requiring chronic exposure 

methodology for derivation of the eight-hour REL. The REL for an eight-hour period would 

need to be adjusted in such cases to reflect cumulative dose from previous eight-hour exposures. 

Pharmacokinetic modeling may be appropriate to determine the cumulative dose from serial 

eight-hour exposures. 

6.2.1 Eight-Hour Averaging Period based on Chronic Toxicity 

In cases where the evidence shows that an eight-hour REL should be derived based on chronic 

exposure, a modification of the default approach adopted for the chronic RELs is used (see 

Section 7.2.1). The default approach to estimating an equivalent time-weighted average 

concentration (CAVG) from the observed concentration (COBS) in non-occupational, continuous 

exposure studies may be summarized as: 

 CAVG = COBS x (H hours per 8 hours) x (D days per week) 

Based on the assumption that half of the 20 m3

 of air breathed in any 24-hour period is breathed 

while active at work, the default approach to estimating an equivalent inhalation-weighted 

average concentration (CAVG) for an eight-hour period of elevated activity (such as at work) from 

the observed concentration (COBS) for continuously exposed humans or experimental animals is: 

 CAVG = COBS x (20 m3

/day total exposure / 10 m3/day occupational exposure) 

x (D days per week) 

Commonly encountered exposure scenarios in both worker studies and experimental animal 

toxicology studies involve exposures of 6 to 8 hours per day for 5 days per week. Less time 

adjustment, and associated uncertainty, occurs applying an eight-hour REL under these exposure 

scenarios relative to applying a chronic REL. 

For simplicity, it may be desirable in some cases to use the chronic REL as a health guidance 

value for repeated eight-hour exposures. This might be appropriate for substances where the 

response is concentration rather than time dependent, and for substances that accumulate in the 

body and have long internal half-lives, such as dioxins and some metals, or substances that 

demonstrate cumulative toxicity, where large pharmacokinetic uncertainties exist. Thus, in these 

cases the chronic and eight-hour REL may be the same. 

Technical Support Document 85 June, 2008 
TSD for Noncancer RELs June 2008 

6.2.2 Eight-Hour Averaging Period for Acute Recurrent Toxicity 

There are some acute RELs that have used single health studies with six- or eight-hour human 

exposures to derive NOAELs or LOAELs. It may be appropriate to use such studies to derive 

eight-hour RELs using acute REL methodology if the data show that the chemical is quickly 

eliminated and does not cause cumulative tissue damage. Similarly, intermittent exposure 

studies in experimental animals, often with daily exposures at or near six hours, may exhibit 

toxicity that reflects a daily recurrent acute effect rather than a chronic cumulative-type injury. 

In some cases, that daily recurrent acute effect may consist of sensory irritation, in which case no 

concentration adjustment is applied to extrapolate to an eight-hour REL (see Section 5.8.1). A 

cautious interpretation of such situations is necessary, however, since a number of agents shown 

to cause sensory irritation during a single one-hour (acute) exposure have also been shown to 

cause persistent (and therefore to some degree cumulative) histological damage in various parts 

of the respiratory tract following repeated (chronic) exposures. 

In cases where daily intermittent exposure shows a recurrent acute effect other than sensory 

irritation, acute REL methodology is applied for time extrapolation to an eight-hour exposure 

employing a modification of Haber’s Law as follows: 

Cn * T = K, 

In this equation, (C) is concentration, (T) is time of exposure, and n is a chemical-specific 

parameter greater than zero. When the value of n is unknown, default exponents are used by 

OEHHA for extrapolation to 8 hours. When extrapolating from an experimental exposure 

duration of less than 8 hours to an eight-hour level, the value of n=1 was used. A value of n = 1 

results in a relatively rapid decrease in the derived REL when extrapolations are made from 

shorter to longer exposures and is considered an appropriate health-protective approach. Most 

human worker and experimental animal studies with daily intermittent exposures have time 

durations at or near 6-8 hours per day, which are well-suited for extrapolating to an eight-hour 

REL. Data on experimental or workplace exposures longer than 8 hours are less likely to be 

encountered, but if this were the case a value of n = 3 would be used as for the acute RELs. 

Daily exposures considerably less than 6 hours are not as preferable and may be more practical 

for acute REL derivation. 

6.2.3 Eight-Hour REL Exposure Duration Adjustments for Developmental Toxicants 

Because the timing and duration of the sensitive period of gestation is usually unknown, time 

extrapolation to an eight-hour REL must take into account two principal toxicokinetic issues to 

prevent, in particular, underestimation of developmental toxicity - peak tissue concentration and 

total tissue dose (e.g., area under the concentration-time curve, or AUC). Additionally, for those 

developmental toxicants where there is a suspicion that the chemical or its metabolites may 

accumulate with daily eight-hour exposures, a duration adjustment from discontinuous to 

continuous exposures based on equivalent multiples of concentration (C) and duration (T) is 

recommended (See Section 4.4.6.1). This C x T approach avoids possible underestimation of 

risk when adjusting the exposure duration from a shorter period to a longer period of exposure. 

Technical Support Document 86 June, 2008 
TSD for Noncancer RELs June 2008 

As more information becomes available on PBPK modeling of developmental toxicants for 

interspecies extrapolation from the exposed animal species to humans, modeling of blood and 

tissue levels may confirm the C x T adjustments on the REL exposure durations to ensure they 

do not exceed the peak tissue concentration or total tissue dose at the NOAEL. 

For developmental studies in which the daily exposures are greater than eight-hours, a “not to be 

exceeded” health guidance is recommended in which no adjustment is applied to the duration 

with extrapolation down to 8 hours. This procedure avoids underestimation of risk when the 

pharmacokinetic nature of the developmental toxicant is unknown. 

For bioaccumulating toxicants such as dioxins and some metals, developmental exposure studies 

in which exposure occurred only during gestation is not sufficient for establishing an eight-hour 

REL based on developmental toxicity. These types of toxicants can accumulate in body tissues 

over extended periods of time prior to gestation, leading to maternal body burdens that may be 

detrimental to the fetus during gestation. Lack of sufficient chronic-exposure and multigeneration studies, and adequate pharmacokinetic modeling information that can predict body 

tissue burdens, may require application of a modifying UF for pharmacokinetic deficiencies in 

calculating the REL. 

6.3 Human Equivalent Concentration (HEC) and Uncertainty Factor Applications 

Application of HEC adjustments and UFs for eight-hour REL derivation uses the same formulae 

as are used for the acute and chronic RELs. For eight-hour RELs based on chronic effects, the 

UFs used for chronic exposure are applied; for eight-hour RELs based on acute recurrent effects, 

the UFs used for acute exposures are applied. 

6.4 Hazard Index Calculation 

In calculating the HI, the same standardized target organ categories are used for the eight-hour 

RELs as for acute and chronic RELs (see Section 4.3.4). Calculation of the HI is described in 

Section 2.2. 

Technical Support Document 87 June, 2008 
TSD for Noncancer RELs June 2008 

7 Chronic Reference Exposure Levels 

This section presents additional information for deriving chronic inhalation Reference Exposure 

Levels (RELs) for hazardous airborne substances. Chronic exposure is evaluated using ambient 

air concentrations of emitted chemicals averaged over a year. The annualized average air 

concentration forms the basis for both chronic noncancer and cancer risk evaluation. In reality, 

exposure over a 24-hour period does not occur at a continuous level. Chronic RELs are 

concentrations at or below which adverse health effects are not likely to occur in the general 

human population exposed continuously over a lifetime. 

7.1 Priority for Evaluation of Chemicals 

Chronic noncancer RELs have been developed for 80 substances as of May, 2005; these are 

described in Appendix B of this Technical Support Document and in OEHHA (OEHHA, 2000a). 

Substances were selected for chronic REL development primarily based on (1) the magnitude of 

current known emissions in California, (2) the availability of a strong scientific database on 

which to estimate a chronic REL, and (3) toxicity. We include impacts on children’s health or 

other sensitive subpopulations in prioritizing chemicals for chronic REL development. 

The amount of data and the quality of the information will ultimately determine whether a 

chronic REL can be derived for a specific chemical. Margins of safety or UFs can be used to 

address the common data gaps encountered in risk assessment, but in some cases, chronic RELs 

cannot be developed because the data are not relevant to inhalation exposure, or because too 

much uncertainty exists in the database and subsequent derivations. As more data become 

available over time, chronic RELs may be added or re-evaluated. 

Exposure above a particular chronic REL may or may not lead to the development of adverse 

health effects. Conversely, there may be individuals exhibiting idiosyncratic responses 

(unpredictable health effects) at concentrations below the chronic RELs. Health effects 

associated with individual chemicals are presented in Appendix D individual summaries of 

acute, eight-hour, and chronic RELs. 

7.2 Exposure Concentration Averaging Period 

The exposure period of concern in the development of chronic RELs is a full lifetime, which 

encompasses periods of potentially increased susceptibility to adverse health effects from 

chemical exposure, particularly during childhood and the later years of life. The chronic REL is 

intended to be protective for individuals exposed continuously over their lifetime. Scientific data 

available to assess these effects generally consist of discontinuous exposures over a shorter 

interval. In such cases, default or chemical-specific assumptions are required to estimate 

concentrations causing comparable effects if exposures were to be continued over the entire 

lifetime. 

Technical Support Document 88 June, 2008 
TSD for Noncancer RELs June 2008 

7.2.1 Effects of Exposure Continuity and Duration 

Studies of adverse health effects associated with long-term exposures of humans or experimental 

animals generally involve discontinuous exposures. Commonly encountered exposure scenarios 

involve exposures of six to eight hours per day for five days per week. OEHHA’s chronic RELs, 

however, are intended to protect the general public who could be exposed continuously. In 

practice, discontinuous facility emissions are generally adjusted to a continuous daily or annual 

average. 

The default approach adopted for the chronic RELs presented in this document to account for 

differences in effects associated with discontinuous and continuous inhalation exposures to 

substances is an equivalent time-weighted average approach. This is the same approach used in 

the derivation of U.S. EPA RfCs (U.S. EPA, 1994a). It is similar to modified Haber’s law 

approach used for acute and 8-hour RELs in the special case where n = 1. Values of n greater 

than 1 have not been shown to be applicable in chronic exposure situations, although 

toxicokinetic effects such as extensive bioaccumulation may require other types of special 

treatment. 

For non-occupational studies, the default approach for estimating an equivalent time-weighted 

average concentration (CAVG) from the observed concentration (COBS) may be summarized as: 

 CAVG = COBS x (H hours per 24 hours) x (D days per 7 days) 

For studies of occupationally exposed humans, based on the assumption that half of the 20 m3 of 

air breathed in any 24-hour period is breathed while active at work, the default approach to 

estimating an equivalent inhalation-weighted average concentration (CAVG) from the observed 

concentration (COBS) is: 

 CAVG = COBS x (10 m3

/day occupational exposure / 20 m3/day total exposure) 

x (D days per 7 days) 

7.2.2 Differences between Lifetime and Less-than-Lifetime Exposures 

Studies of adverse health effects associated with exposures of humans or experimental animals 

generally involve less-than-lifetime exposures. The OEHHA chronic RELs, however, are 

intended to protect the general public who could be exposed over their entire lifetime. In 

traditional toxicity testing paradigms, studies that expose experimental animals for at least 12% 

of the expected lifetime for the test species are considered chronic exposure studies. RELs based 

on such chronic animal studies are not adjusted for less-than-lifetime exposures. Similarly using 

this convention, chronic exposure for humans is considered to be greater than 12% of a lifetime 

of 70 years. Thus, human exposures of greater than 8 years are considered chronic exposures 

and are not adjusted either in their calculation or application. Although a potential source of 

uncertainty, this approximation appears reasonable for the majority of chemicals. 

There are certain situations, such as in cancer risk assessment, where dependence on cumulative 

dose over long periods up to and including a lifetime (subject to weighting during critical periods 

Technical Support Document 89 June, 2008 
TSD for Noncancer RELs June 2008 

early in life) may reasonably be assumed. Models of dose-time cumulation over relatively short 

timescales have been explored for various acute toxicity endpoints, and are described elsewhere 

in this document. However, for most situations involving chronic noncancer toxicity an explicit 

description of the time/dose relationship over longer intervals (including several weeks or 

months to a full lifetime) is not available. Toxicity studies tend to be conducted for specific 

periods representing subchronic, chronic and lifetime exposures, but these are seldom directly 

related to one another, and frequently report different endpoints. Subchronic exposures are those 

with duration less than 12% of expected lifetime for the test species, except in the case of mice 

and rats where the U.S. EPA has considered 13 weeks subchronic. Therefore, the default 

approach to extrapolating from subchronic to chronic exposures used by OEHHA and the U.S. 

EPA is to use a 1 to 10-fold uncertainty factor, UFS for subchronic exposures. 

The UFS to extrapolate from subchronic to chronic exposures is determined as follows: 

(1) exposures less than 8% of expected lifetime were given a 10-fold UF 

(2) exposures from 8 to <12% of expected lifetime were given a 3-fold UF, and 

(3) exposures ≥12% of expected lifetime were given a 1-fold UF. 

Average life spans assumed for humans and experimental animals are presented in Table 7.2.1. 

TABLE 7.2.1. AVERAGE LIFE-SPAN FOR HUMANS VS. EXPERIMENTAL 

ANIMALS

Species 

Approximate average 

Life-span (years)1

Subchronic exposure 

duration (weeks)2

Human 70 ≤ 364 

Baboon 55 ≤ 286 

Cat 15 ≤ 78 

Dog 15 ≤ 78 

Guinea pig 6 ≤ 31 

Hamster 2.5 ≤ 133

Mouse 2 ≤ 133

Rabbit 6 ≤ 31 

Rat 2 ≤ 13 

Rhesus monkey 35 ≤ 182 

1 U.S. EPA (1988). 

2 Subchronic exposures are usually defined as those over less than 12% of average lifetime 

(U.S. EPA, 1994a).

3 Special rule adopted by U.S. EPA that exposures of 13 weeks or less are subchronic 

regardless of the species involved (U.S. EPA, 1994a). 

Unlike the extensive exposure concentration-duration-effect analyses that have been conducted 

for acute lethality data in experimental animals, only limited work has been done to compare the 

differences between acute, sub-chronic, chronic and lifetime exposure scenarios. 

Technical Support Document 90 June, 2008 
TSD for Noncancer RELs June 2008 

Kadry and associates (1995) showed that among a small data set (6 chlorinated chemicals) 

subchronic NOAEL to chronic NOAEL ratios were less than 10. Nessel et al. (1995) reported 

that for 9 inhalation studies the mean and median subchronic NOAEL to chronic NOAEL ratios 

were 4.5 and 4.0 respectively (range = 1 to 8). However, in a study of published animal 

NOAELs for a larger group of pesticides, Nair and associates (1995) found that 19 of 148 (13%) 

of the subchronic to chronic NOAEL ratios differed by more than 10-fold. The U.S. EPA 

reported that, based on an analysis of responses to 100 substances, the subchronic to chronic 

ratios formed a distribution with a median value of 2 and an upper 95th percentile of 15; the value 

of 10 represents the 90th percentile (Swartout, 1997). This supports the selection of a default 

maximum value of 10 for the UFS. 

7.3 Human Equivalent Concentration (HEC) Procedure for Chronic RELs 

As previously noted for the acute and 8 hour RELs, the preferred method of adjustment for 

interspecies toxicokinetic differences when animal studies are used for chronic REL 

development is the application of a compound- and species-specific toxicokinetic model. 

However, there will be many case where the data to support such a model are not available. If 

no model-based correction can be developed the default UFH-k = √10 would apply. Where 

suitable parameters are available for the test species (e.g. for rats and mice), the U.S. EPA HEC 

procedure (described in Appendix F) may be used as a partial adjustment for interspecies 

toxicokinetic differences, in which case UFH-k = 2 ( thus the 10-fold overall interspecies UF is 

reduced to 6 if UFH-d has the default value of √10). The modifications of the HEC procedure to 

account for children may also be used to address concerns for human intraspecies variability and 

to fulfill the mandates of SB 25 to account for potentially greater vulnerability of children when 

setting health standards. 

7.4 LOAEL to NOAEL Extrapolation 

There are some instances where the data are not available to identify a NOAEL for a chronic 

exposure to a chemical, and are not suitable for a benchmark concentration approach. In these 

cases, we are left with a LOAEL upon which to base a chronic REL. It should be noted that use 

of a LOAEL is a last resort, as it is frequently not clear how close one is to the NOAEL for a 

particular effect. In developing chronic RELs, OEHHA will use a default value for the LOAEL 

to NOAEL UF of 10. There may be cases where a smaller value can be used where the data 

indicate that the LOAEL is fairly close to an expected NOAEL. For acute effects, OEHHA has 

used the severity of effect concept developed by U.S. EPA as an indicator of proximity to a 

NOAEL (see Section 5.5). However, this does not work well for chronic effects, where there are 

disparate types of response, which are difficult to compare. Many chronic effects could 

reasonably be considered “serious” even if they occurred rarely or to a low degree, and the 

concept of reversibility (an important criterion of severity for acute effects) is ambiguous in the 

context of continuous exposures over extended periods up to and including a complete lifetime. 

Therefore, for chronic effects, indicators of the proximity or otherwise of a LOAEL to the 

presumed NOAEL will be described on a case-by-case basis in the toxicity summary of a 

specified chemical. OEHHA may use an intermediate uncertainty factor where an effect was 

observed in ≤ 30% of subjects, since for many endpoints a low response rate suggests that the 

exposure is likely to be relatively nearer to the NOAEL. Similar arguments may be applied for 

Technical Support Document 91 June, 2008 
TSD for Noncancer RELs June 2008 

continuous endpoints, where low intensity of response and/or low frequency of responses above 

control values suggest proximity to a NOAEL. 

7.5 Hyperplasia 

The use of hyperplasia as a toxicological endpoint for setting chronic RELs should consider 

whether the hyperplasia may progress to dysplasia and neoplasia. In a chronic study, if 

hyperplasia was the most sensitive endpoint for that chemical, it was used as an endpoint for 

REL development. Hyperplasia can be seen as a normal response (e.g., to hormones), and is also 

seen in response to a number of sensory irritants which are not carcinogens. When hyperplasia 

was used as the toxicological endpoint and as a mild effect, the histological grade was low (e.g., 

one on a scale of one to five) and there was no increase in organ weight noted. 

7.6 Pre-Existing Chronic Exposure Guidelines 

Chronic exposure levels have been derived using several different approaches, but inhalation 

exposure values estimated using a consistent basis to protect the general public are only available 

for certain chemicals. The U.S. EPA RfCs, now published for 72 chemicals, are a notable 

example. Other values designed for the protection of the general public, such as the U.S. EPA 

reference doses (RfDs), are available for more chemicals but are intended primarily to deal with 

non-inhalation exposures to chemicals and are usually based on toxicity data obtained following 

oral exposure. It is likely that the oral and dermal routes would underestimate the health effects 

of inhalation exposure, unless the health effect is an identifiable systemic effect and not affected 

by first-pass metabolism. If the effect is systemic, then appropriate adjustments for absorption 

can be made. 

7.6.1 U.S. EPA Reference Concentrations 

The U.S. EPA developed an inhalation reference concentration (RfC) method (Jarabek et al., 

1989; U.S. EPA, 1994a; 2002). The inhalation RfC considers toxic effects for both the 

respiratory system (portal-of-entry) and for effects peripheral to the respiratory system (extrarespiratory effects). The RfC is comparable to earlier Acceptable Daily Intake (ADI) and RfD 

methods but addresses inhalation specific issues such as respiratory dynamics and delivered 

doses by inhalation. Dosimetry models are used to extrapolate the internal dose metric across 

species and to estimate the human equivalent concentration (HEC), as described in Appendix F. 

7.6.2 U.S. EPA Reference Doses 

The U.S. EPA developed an oral reference dose (RfD) concept in 1987 (Barnes and Dourson, 

1988). This provides a protocol for study selection, identifying NOAELs, applying UFs, and 

assessing the weight of evidence. As of September 2005, U.S. EPA RfDs were available for more 

than 350 substances (U.S. EPA, 2007a). The major limitation of these values for application to 

inhalation REL development is that they are almost entirely based on studies of exposures by 

routes other than inhalation. However, they have utility for substances treated as multi-pathway 

chemicals in the Air Toxics Hot Spots Program’s risk assessments. Additionally, route-to-route 

extrapolation is sometimes possible, depending on the compound’s toxicity and pharmacokinetics. 

Technical Support Document 92 June, 2008 
TSD for Noncancer RELs June 2008 

7.6.3 Occupational Threshold Limit Values 

Occupational exposure limits have been used to derive chemical exposure guidelines for the 

general public (NATICH and McCullough, 1991; Robinson and Paxman, 1992). As of May, 

2005, more than 600 American Conference of Governmental Industrial Hygienists (ACGIH) 

Threshold Limit Values (TLVs) (ACGIH, 2005) and National Institute for Occupational Safety 

and Health (NIOSH) Permissible Exposure Limits (PELs) (NIOSH, 2005) were available. The 

California Occupational Safety and Health Administration also has approximately 600 

occupational PELs. However, these values lack a consistent basis, are not designed for or 

recommended for protection of the general public, and in many cases may not prevent adverse 

health effects among workers (Roach and Rappaport, 1990). Occupational exposure guidelines, 

which are available for hundreds of substances, have been used in many states to derive 

inhalation exposure guidelines for the general public. These values, however, have an 

inconsistent basis, which often included risk management and feasibility considerations specific 

to industrial facilities, in addition to health-based criteria, and have not always incorporated 

recently available data. Most importantly, occupational exposure guidelines are designed to 

protect healthy adult workers, and do not allow for possibly more sensitive members of the 

general population such as children and the elderly, or those with genetically predetermined 

sensitivities. Thus, OEHHA does not use occupational guidelines for chronic RELs, which are 

intended to protect the general public. 

7.6.4 California Ambient Air Quality Standards 

California Ambient Air Quality Standards (CAAQS) are available for criteria air pollutants 

(CAPCOA, 1993). Where defined according to a basis appropriate to lifetime exposures, the 

CAAQS was adopted as the chronic inhalation REL. 

Technical Support Document 93 June, 2008 
TSD for Noncancer RELs June 2008 

References

ACGIH. (2005). Documentation of the Threshold Limit Values and Biological Exposure Indices 

Annual Reports of the Committees on TLVs® and BEIs® for Year 2005 Publication #0106A. 

Cincinnati (OH): American Conference of Governmental Industrial Hygienists, Inc. 

ACGIH. (2006). Documentation of the Threshold Limit Values and Biological Exposure Indices 

Annual Reports of the Committees on TLVs® and BEIs® for Year 2006 Publication #0106. 

Cincinnati (OH): American Conference of Governmental Industrial Hygienists, Inc. 

Agunod M, Yamaguchi N, Lopez R, Luhby AL and Glass GB (1969). Correlative study of 

hydrochloric acid, pepsin, and intrinsic factor secretion in newborns and infants. Am J Dig Dis 

14(6): 400-414. 

AIHA. (2006). Public Exposure Guidelines. American Industrial Hygiene Association. 

http://archive.orr.noaa.gov/cameo/locs/expguide.html. 

Alarie Y (1966). Irritating properties of airborne materials to the upper respiratory tract. Arch 

Environ Health 13(4): 433-49. 

Alcorn J and McNamara PJ (2003). Pharmacokinetics in the newborn. Adv Drug Deliv Rev 

55(5): 667-686. 

Aldridge JE, Seidler FJ and Slotkin TA (2004). Developmental exposure to chlorpyrifos elicits 

sex-selective alterations of serotonergic synaptic function in adulthood: Critical periods and 

regional selectivity for effects on the serotonin transporter, receptor subtypes, and cell signaling. 

Environ Health Perspect 112(2): 148-155. 

Alexeeff GV, Broadwin R, Liaw J and Dawson SV (2002). Characterization of the LOAEL-toNOAEL uncertainty factor for mild adverse effects from acute inhalation exposures. Regul 

Toxicol Pharmacol 36(1): 96-105. 

Alexeeff GV, Deng KK, Broadwin RL and Salmon AG (2006). Benchmark dose evaluation for 

human irritation [Abstract # 901]. The Toxicologist 90(1): 183. 

Alexeeff GV, Fowles JR, Hill M and Dodge D (1997). Stochastic evaluation of acute inhalation 

thresholds from reported LOAELs [Abstract #851]. The Toxicologist 36(1 Part 2): 167. 

Alexeeff GV and Lewis DC. (1989b). Factors Influencing Quantitative Risk Analysis of 

Noncancer Health Effects From Air Pollutants. Vol. 89-91.3: 1-14. Pittsburgh (PA): Air & 

Waste Management Association. 

Alexeeff GV, Lewis DC and Lipsett MJ (1992). Use of toxicity information in risk assessment 

for accidental release of toxic gases. J Hazard Mater 29(3): 387-403. 

Alexeeff GV, Lewis DC and Ragle NL (1993). Estimation of potential health effects from acute 

exposure to hydrogen fluoride using a "benchmark dose" approach. Risk Anal 13(1): 63-69. 

Technical Support Document 94 June, 2008 
TSD for Noncancer RELs June 2008 

Alexeeff GV, Lipsett MJ and Kizer KW (1989a). Problems associated with the use of 

immediately dangerous to life and health (IDLH) values for estimating the hazard of accidental 

chemical releases. Am Ind Hyg Assoc J 50(11): 598-605. 

Almstrup K, Fernandez MF, Petersen JH, Olea N, Skakkebaek NE and Leffers H (2002). Dual 

effects of phytoestrogens result in u-shaped dose-response curves. Environ Health Perspect 

110(8): 743-748. 

Amateau SK and McCarthy MM (2004). Induction of PGE2 by estradiol mediates 

developmental masculinization of sex behavior. Nat Neurosci 7(6): 643-650. 

American Thoracic Society (ATS) (2000a). What constitutes an adverse health effect of air 

pollution. Am J Respir Crit Care Med 161(2 Pt 1): 665-673. 

Anglen DM. (1981). Sensory Response of Human Subjects to Chlorine in Air. Dissertation. Ann 

Arbor (MI): University of Michigan. 

Anjilvel S and Asgharian B (1995). A multiple-path model of particle deposition in the rat lung. 

Fundam Appl Toxicol 28(1): 41-50. 

Arant Jr. BS (1978). Developmental patterns of renal functional maturation compared in the 

human neonate. J Pediatr 92(5): 705-712. 

Arcus A, Broadwin R, Lam R and Vork K. (1995). A Database of Indoor Air Volatile Organic 

Compounds and Selected Combustion Products: Construction and Development (draft). Report 

to Indoor Air Quality and Personal Exposure Assessment Section. Sacramento (CA): Air 

Resources Board, California Environmental Protection Agency. 

Augustinsson KB and Barr M (1963). Age variation in plasma arylesterase activity in children. 

Clin Chim Acta 8: 568-573. 

Autrup H (2000). Genetic polymorphisms in human xenobiotica metabolizing enzymes as 

susceptibility factors in toxic response. Mutat Res 464(1): 65-76. 

Avery GB, Randolph JG and Weaver T (1966). Gastric acidity in the first day of life. Pediatrics 

37(6): 1005-1007. 

Ballatori N and Clarkson TW (1982). Developmental changes in the biliary excretion of 

methylmercury and glutathione. Science 216(4541): 61-63. 

Barnes DG, Daston GP, Evans JS, Jarabek AM, Kavlock RJ, Kimmel CA, Park C and Spitzer 

HL (1995). Benchmark Dose Workshop: Criteria for use of a benchmark dose to estimate a 

reference dose. Regul Toxicol Pharmacol 21(2): 296-306. 

Barnes DG and Dourson M (1988). Reference dose (RfD): Description and use in health risk 

assessments. Regul Toxicol Pharmacol 8(4): 471-486. 

Technical Support Document 95 June, 2008 
TSD for Noncancer RELs June 2008 

Barrow C, Buckley L, James R, Steinhagen W and Chang J (1986). Sensory Irritation: Studies on 

correlation to pathology, structure-activity, tolerance-development, and prediction of species 

differences to nasal injury. Chapter 7. In: Toxicology of the Nasal Passages. Barrow C. 

Hemisphere. Washington (DC) & New York (NY): 101-122. 

Barton JC and Huster WJ (1987). Seasonal changes in lead absorption in laboratory rats. Environ 

Health Perspect 73: 209-214. 

Belay ED, Bresee JS, Holman RC, Khan AS, Shahriari A and Schonberger LB (1999). Reye's 

syndrome in the United States from 1981 through 1997. N Engl J Med 340(18): 1377-1382. 

Benignus VA, Boyes WK and Bushnell PJ (1998). A dosimetric analysis of behavioral effects of

acute toluene exposure in rats and humans. Toxicol Sci 43(2): 186-95. 

Besunder JB, Reed MD and Blumer JL (1988). Principles of drug biodisposition in the neonate. 

A critical evaluation of the pharmacokinetic-pharmacodynamic interface (Part I). Clin 

Pharmacokinet 14(4): 189-216. 

Bigwood EJ (1973). The acceptable daily intake of food additives. CRC Crit Rev Toxicol 2(1): 

41-93. 

Bos PM, Zwart A, Reuzel PG and Bragt PC (1991). Evaluation of the sensory irritation test for 

the assessment of occupational health risk. Crit Rev Toxicol 21(6): 423-450. 

Bradford Hill AB (1971). Chapter XXIV: Statistical Evidence and Inference. In: Principles of 

Medical Statistics. Oxford University Press. Oxford (UK): 309-323. 

Brodersen R, Friis-Hansen B and Stern L (1983). Drug-induced displacement of bilirubin from 

albumin in the newborn. Dev Pharmacol Ther 6(4): 217-229. 

Brown JP (2001). Physiological and Anatomical Parameters for Infants and Children's PBPK 

Modeling. Children's Environmental Health--Developing a Risk Assessment Framework.Office 

of Environmental Health Hazard Assessment, California Environmental Protection Agency. 

Monterey (CA), April. 

Brown JP, Collins JF, Salmon AG, Marty MA and Alexeeff GV (2006). Use of benchmark dose 

methodology on human non-cancer data to develop protective criteria for child exposures to 

arsenic [Abstract # 2185]. The Toxicologist 90(1): 448. 

Brown JS, Wilson WE and Grant LD (2005). Dosimetric comparisons of particle deposition and 

retention in rats and humans. Inhal Toxicol 17(7-8): 355-385. 

Brown NA and Fabro S (1983). The value of animal teratogenicity testing for predicting human 

risk. Clin Obstet Gynecol 26(2): 467-77. 

Bryant B and Silver W (2000). Chemesthesis: The common chemical sense. In: The 

Neurobiology of Taste and Smell (2nd Edition). Finger T., Silver W. and Restrepo D. WileyLiss. NY: 73-100. 

Technical Support Document 96 June, 2008 
TSD for Noncancer RELs June 2008 

Buehler BA, Rao V and Finnell RH (1994). Biochemical and molecular teratology of fetal 

hydantoin syndrome. Neurol Clin 12(4): 741-748. 

Bushnell PJ, Boyes WK, Shafer TJ, Bale AS and Benignus VA (2007). Approaches to 

extrapolating animal toxicity data on organic solvents to public health. Neurotoxicology 28(2): 

221-6. 

Calabrese EJ (1983). Principles of Animal Extrapolation. Environment Science and Technology. 

New York: Wiley. 

Canalle R, Burim RV, Tone LG and Takahashi CS (2004). Genetic polymorphisms and 

susceptibility to childhood acute lymphoblastic leukemia. Environ Mol Mutagen 43(2): 100-109. 

CAPCOA. (1993). The Air Toxics Hot Spots Program Revised 1992 Risk Assessment Guidelines. 

Cameron Park (CA): California Air Pollution Control Officers’ Association Toxics Committee 

CARB. (1990). Proposed Identification of Vinyl Chloride as a Toxic Air Contaminant. 

Technical Support Document. Part C. California Air Resources Board. 

Carlisle J and Dowling K (2006). Child-specific health guidance for lead. Abstract #2185, p 

448. The Toxicologist. 

Chen YC, Yu ML, Rogan WJ, Gladen BC and Hsu CC (1994). A 6-year follow-up of behavior 

and activity disorders in the Taiwan Yu-cheng children. Am J Public Health 84(3): 415-21. 

Chhabra RS, Huff JE, Schwetz BS and Selkirk J (1990). An overview of prechronic and chronic 

toxicity/carcinogenicity experimental study designs and criteria used by the National Toxicology 

Program. Environ Health Perspect 86: 313-321. 

Chiron C, Raynaud C, Maziere B, Zilbovicius M, Laflamme L, Masure MC, Dulac O, 

Bourguignon M and Syrota A (1992). Changes in regional cerebral blood flow during brain 

maturation in children and adolescents. J Nucl Med 33(5): 696-703. 

Chizhikov VA (1973). [Effect of harmful substances on animals with weakened health under 

chronic feeding conditions][Russian]. Gig Sanit 38(7): 7-12. 

Clewell HJ, Gentry PR, Covington TR, Sarangapani R and Teeguarden JG (2004). Evaluation of 

the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. 

Toxicol Sci 79(2): 381-393. 

Cole TB, Jampsa RL, Walter BJ, Arndt TL, Richter RJ, Shih DM, Tward A, Lusis AJ, Jack RM, 

Costa LG and Furlong CE (2003). Expression of human paraoxonase (PON1) during 

development. Pharmacogenetics 13(6): 357-364. 

Collins JF, Alexeeff GV, Lewis DC, Dodge DE, Marty MA, Parker TR, Budroe JD, Lam RH, 

Lipsett MJ, Fowles JR and Das R (2004). Development of acute inhalation reference exposure 

levels (RELs) to protect the public from predictable excursions of airborne toxicants. J Appl 

Toxicol 24(2): 155-166. 

Technical Support Document 97 June, 2008 
TSD for Noncancer RELs June 2008 

Collins JF, Salmon AG, Brown JP, Lewis DC, Dodge DE, Marty MA and Alexeeff GV (2003). 

Use of benchmark concentration methodology in risk assessment for air toxics [Abstract]. The 

Toxicologist 72: 142. 

Collins JF, Salmon AG, Brown JP, Marty MA and Alexeeff GV (2005). Development of a 

chronic inhalation reference level for respirable crystalline silica. Regul Toxicol Pharmacol 

43(3): 292-300. 

Cometto-Muniz JE, Cain WS and Hudnell HK (1997). Agonistic sensory effects of airborne 

chemicals in mixtures: Odor, nasal pungency, and eye irritation. Percept Psychophys 59(5): 665-

674. 

Conceicao GM, Miraglia SG, Kishi HS, Saldiva PH and Singer JM (2001). Air pollution and 

child mortality: A time-series study in Sao Paulo, Brazil. Environ Health Perspect 109 (Suppl 3): 

347-350. 

Costa LG, Richter RJ, Li WF, Cole T, Guizzetti M and Furlong CE (2003). Paraoxonase (PON 

1) as a biomarker of susceptibility for organophosphate toxicity. Biomarkers 8(1): 1-12. 

Costa LG, Vitalone A, Cole TB and Furlong CE (2005). Modulation of paraoxonase (PON1) 

activity. Biochem Pharmacol 69(4): 541-550. 

Cresteil T (1998). Onset of xenobiotic metabolism in children: Toxicological implications. Food 

Addit Contam 15 (Suppl): 45-51. 

Crofton KM, Craft ES, Hedge JM, Gennings C, Simmons JE, Carchman RA, Carter Jr. WH and 

DeVito MJ (2005). Thyroid-hormone-disrupting chemicals: Evidence for dose-dependent 

additivity or synergism. Environ Health Perspect 113(11): 1549-1554. 

Crump KS (1984). A new method for determining allowable daily intakes. Fundam Appl Toxicol 

4(5): 854-871. 

Crump KS (1995). Calculation of benchmark doses from continuous data. Risk Anal 15: 78-89. 

Crump KS (2002). Critical issues in benchmark calculations from continuous data. Crit Rev 

Toxicol 32(3): 133-153. 

Csanady GA, Kessler W, Hoffmann HD and Filser JG (2003). A toxicokinetic model for styrene 

and its metabolite styrene-7,8-oxide in mouse, rat and human with special emphasis on the lung. 

Toxicol Lett 138(1-2): 75-102. 

Daniels MJ, Dominici F, Samet JM and Zeger SL (2000). Estimating particulate matter-mortality 

dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US 

cities. Am J Epidemiol 152(5): 397-406. 

Darras VM (2008). Endocrine disrupting polyhalogenated organic pollutants interfere with 

thyroid hormone signalling in the developing brain. Cerebellum. 

Technical Support Document 98 June, 2008 
TSD for Noncancer RELs June 2008 

DeVito MJ, Menache MG, Diliberto JJ, Ross DG and Birnbaum LS (2000). Dose-response 

relationships for induction of CYP1A1 and CYP1A2 enzyme activity in liver, lung, and skin in 

female mice following subchronic exposure to polychlorinated biphenyls. Toxicol Appl 

Pharmacol 167(3): 157-172. 

Dorne JL and Renwick AG (2005a). The refinement of uncertainty/safety factors in risk 

assessment by the incorporation of data on toxicokinetic variability in humans. Toxicol Sci 

86(1): 20-26. 

Dourson ML (1986). New approaches in the derivation of acceptable daily intake (ADI). Comm 

Toxicol 1: 35-48. 

Dourson ML, Felter SP and Robinson D (1996). Evolution of science-based uncertainty factors 

in noncancer risk assessment. Regul Toxicol Pharmacol 24(2 Pt 1): 108-120. 

Dourson ML and Stara JF (1983). Regulatory history and experimental support of uncertainty 

(safety) factors. Regul Toxicol Pharmacol 3(3): 224-238. 

Ecobichon DJ and Stephens DS (1973). Perinatal development of human blood esterases. Clin 

Pharmacol Ther 14(1): 41-47. 

Eichelbaum M, Kroemer HK and Mikus G (1992). Genetically determined differences in drug 

metabolism as a risk factor in drug toxicity. Toxicol Lett 64-65 (Spec No): 115-122. 

El-Masri HA, Constan AA, Ramsdell HS, and Yang RSH (1996a). Physiologically based 

pharmacodynamic modeling of an interaction threshold between trichloroethylene and 1,1-

dichloroethylene in Fischer 344 rats. Toxicol Appl Pharmacol 141: 124-132. 

El-Masri HA, Tessari JD, and Yang RSH (1996b). Exploration of an interaction threshold for the 

joint toxicity of trichloroethylene and 1,1-dichloroethylene:utilization of a PBPK model. Arch 

Toxicol 70: 527-539. 

ElDesoky ES (2007). Pharmacokinetic-pharmacodynamic crisis in the elderly. Am J Ther 14(5): 

488-98. 

Eluma FO, Sucheston ME, Hayes TG and Paulson RB (1984). Teratogenic effects of dosage 

levels and time of administration of carbamazepine, sodium valproate, and diphenylhydantoin on 

craniofacial development in the CD-1 mouse fetus. J Craniofac Genet Dev Biol 4(3): 191-210. 

Evelo CT, Oostendorp JG, ten Berge WF and Borm PJ (1993). Physiologically based 

toxicokinetic modeling of 1,3-butadiene lung metabolism in mice becomes more important at 

low doses. Environ Health Perspect 101(6): 496-502. 

Fanucchi MV and Plopper CG (1997). Pulmonary Developmental Responses to Toxicants. In: 

Comprehensive Toxicology Roth R. Pergamon. New York: Vol. 8: 203-220. 

Technical Support Document 99 June, 2008 
TSD for Noncancer RELs June 2008 

Fanucchi MV, Plopper CG, Evans MJ, Hyde DM, Van Winkle LS, Gershwin LJ and Schelegle 

ES (2006). Cyclic exposure to ozone alters distal airway development in infant rhesus monkeys. 

Am J Physiol Lung Cell Mol Physiol 291(4): L644-L650. 

Filipsson AF, Sand S, Nilsson J and Victorin K (2003). The benchmark dose method--review of 

available models, and recommendations for application in health risk assessment. Crit Rev 

Toxicol 33(5): 505-542. 

Foradori AC, Bertinchamps A, Gulibon JM and Cotzias GC (1967). The discrimination between 

magnesium and manganese by serum proteins. J Gen Physiol 50(9): 2255-2266. 

Fowles JR, Alexeeff GV and Dodge D (1999). The use of benchmark dose methodology with 

acute inhalation lethality data. Regul Toxicol Pharmacol 29(3): 262-278. 

Frederick CB, Bush ML, Lomax LG, Black KA, Finch L, Kimbell JS, Morgan KT, 

Subramaniam RP, Morris JB and Ultman JS (1998). Application of a hybrid computational fluid 

dynamics and physiologically based inhalation model for interspecies dosimetry extrapolation of 

acidic vapors in the upper airways. Toxicol Appl Pharmacol 152(1): 211-231. 

Friis-Hansen B (1961). Body water compartments in children: Changes during growth and 

related changes in body composition. Pediatrics 28: 169-181. 

Friis-Hansen B (1971). Body composition during growth. In vivo measurements and biochemical 

data correlated to differential anatomical growth. Pediatrics 47(1 Suppl 2): 264-274. 

Galizia A and Kinney PL (1999). Long-term residence in areas of high ozone: Associations with 

respiratory health in a nationwide sample of nonsmoking young adults. Environ Health Perspect 

107(8): 675-679. 

Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D, Berhane K, McConnell R, Kuenzli N, 

Lurmann F, Rappaport E, Margolis H, Bates D and Peters J (2004). The effect of air pollution on 

lung development from 10 to 18 years of age. N Engl J Med 351(11): 1057-1067. 

Gauderman WJ, Avol E, Lurmann F, Kuenzli N, Gilliland F, Peters J and McConnell R (2005). 

Childhood asthma and exposure to traffic and nitrogen dioxide. Epidemiology 16(6): 737-743. 

Gaylor D, Ryan L, Krewski D and Zhu Y (1998). Procedures for calculating benchmark doses 

for health risk assessment. Regul Toxicol Pharmacol 28(2): 150-164. 

Gaylor DW (1988). Applicability of cancer risk assessment techniques to other toxic effects. 

Toxicol Ind Health 4(4): 453-459. 

Gaylor DW (1992). Incidence of developmental defects at the no observed adverse effect level 

(NOAEL). Regul Toxicol Pharmacol 15(2 Pt 1): 151-160. 

Gebremichael A, Chang AM, Buckpitt AR, Plopper CG and Pinkerton KE (1995). Postnatal 

development of cytochrome P4501A1 and 2B1 in rat lung and liver: Effect of aged and diluted 

sidestream cigarette smoke. Toxicol Appl Pharmacol 135(2): 246-253. 

Technical Support Document 100 June, 2008 
TSD for Noncancer RELs June 2008 

Geller AM and Zenick H (2005). Aging and the environment: a research framework. Environ 

Health Perspect 113(9): 1257-62. 

Gibbons RA, Dixon SN, Hallis K, Russell AM, Sansom BF and Symonds HW (1976). 

Manganese metabolism in cows and goats. Biochim Biophys Acta 444(1): 1-10. 

Gift JS, Webb CK and Jarabek AM (1993). Suitability of LOAEL-to-NOAEL 10-fold 

uncertainty factor for health assessments of inhaled toxicants [Abstract # 475]. The Toxicologist 

13: 140. 

Gillis CA, Keenan RE, Carlson-Lynch HL and Price PS (1997). Characterization of the interindividual (UFH) factors: Alternative models and approaches [Abstract #1053]. The Toxicologist 

36: 207. 

Ginsberg G (2000). Xenobiotic Metabolism and Chemical Fate From Early Childhood Onward. 

Children's Environmental Health Symposium.Office of Environmental Health Hazard 

Assessment, California Environmental Protection Agency. Oakland (CA), May 2, 2000. 

Ginsberg G, Hattis D, Russ A and Sonawane B (2004b). Physiologically based pharmacokinetic 

(PBPK) modeling of caffeine and theophylline in neonates and adults: Implications for assessing 

children's risks from environmental agents. J Toxicol Environ Health A 67(4): 297-329. 

Ginsberg G, Hattis D, Russ A and Sonawane B (2005). Pharmacokinetic and pharmacodynamic 

factors that can affect sensitivity to neurotoxic sequelae in elderly individuals. Environ Health 

Perspect 113(9): 1243-9. 

Ginsberg G, Hattis D, Sonawane B, Russ A, Banati P, Kozlak M, Smolenski S and Goble R 

(2002). Evaluation of child/adult pharmacokinetic differences from a database derived from the 

therapeutic drug literature. Toxicol Sci 66(2): 185-200. 

Ginsberg G, Slikker Jr. W, Bruckner J and Sonawane B (2004c). Incorporating children's 

toxicokinetics into a risk framework. Environ Health Perspect 112(2): 272-283. 

Goldstein BD (1983). Toxic substances in the atmospheric environment. A critical review. J Air 

Pollut Control Assoc 33(5): 454-467. 

Grandjean P (1992). Individual susceptibility to toxicity. Toxicol Lett 64-65 (Spec No): 43-51. 

Guberman AH, Besag FM, Brodie MJ, Dooley JM, Duchowny MS, Pellock JM, Richens A, 

Stern RS and Trevathan E (1999). Lamotrigine-associated rash: risk/benefit considerations in 

adults and children. Epilepsia 40(7): 985-991. 

Guignard JP, Torrado A, Da Cunha O and Gautier E (1975). Glomerular filtration rate in the first 

three weeks of life. J Pediatr 87(2): 268-272. 

Ha EH, Hong YC, Lee BE, Woo BH, Schwartz J and Christiani DC (2001). Is air pollution a risk 

factor for low birth weight in Seoul? Epidemiology 12(6): 643-648. 

Technical Support Document 101 June, 2008 
TSD for Noncancer RELs June 2008 

Hackney JD and Linn WS (1983). Controlled clinical studies of air pollutant exposure: 

Evaluating scientific information in relation to air quality standards. Environ Health Perspect 52: 

187-191. 

Haddad S, Beliveau M, Tardif R and Krishnan K (2001). A PBPK modeling-based approach to 

account for interactions in the health risk assessment of chemical mixtures. Toxicol Sci 63(1): 

125-131. 

Hakkola J, Tanaka E and Pelkonen O (1998). Developmental expression of cytochrome P450 

enzymes in human liver. Pharmacol Toxicol 82(5): 209-217. 

Hanna LM, Lou SR, Su S and Jarabek AM (2001). Mass transport analysis: Inhalation RfC 

methods framework for interspecies dosimetric adjustment. Inhal Toxicol 13(5): 437-463. 

Harada M (1995). Minamata disease: Methylmercury poisoning in Japan caused by 

environmental pollution. Crit Rev Toxicol 25(1): 1-24. 

Harding R, Pinkerton K and Plopper CG (2004). The Lung: Development, Aging and the 

Environment. London (UK); San Diego (CA): Elsevier Academic Press. 

Hartung R (1987). Dose-Response Relationships. In: Toxic Substances and Human Risk. Tardiff 

R. G. and Rodricks J. V. Plenum Press. New York: 29-46. 

Hattis D (1996a). Variability in susceptibility--how big, how often, for what responses to what 

agents? Environ Toxicol Pharmacol 2: 135-145. 

Hattis D (1996b). Human interindividual variability in susceptibility to toxic effects: From 

annoying detail to a central determinant of risk. Toxicology 111(1-3): 5-14. 

Hattis D, Banati P and Goble R (1999). Distributions of individual susceptibility among humans 

for toxic effects. How much protection does the traditional tenfold factor provide for what 

fraction of which kinds of chemicals and effects? Ann N Y Acad Sci 895: 286-316. 

Hattis D, Erdreich L and Ballew M (1987). Human variability in susceptibility to toxic 

chemicals--a preliminary analysis of pharmacokinetic data from normal volunteers. Risk Anal 

7(4): 415-426. 

Hattis D, Ginsberg G, Sonawane B, Smolenski S, Russ A, Kozlak M and Goble R (2003). 

Differences in pharmacokinetics between children and adults--II. Children's variability in drug 

elimination half-lives and in some parameters needed for physiologically-based pharmacokinetic 

modeling. Risk Anal 23(1): 117-142. 

Heath LM, Soole KL, McLaughlin ML, McEwan GT and Edwards JW (2003). Toxicity of 

environmental lead and the influence of intestinal absorption in children. Rev Environ Health 

18(4): 231-250. 

Technical Support Document 102 June, 2008 
TSD for Noncancer RELs June 2008 

Herngren L, Ehrnebo M and Boreus LO (1983). Drug binding to plasma proteins during human 

pregnancy and in the perinatal period. Studies on cloxacillin and alprenolol. Dev Pharmacol Ther 

6(2): 110-124. 

Hines RN and McCarver DG (2002). The ontogeny of human drug-metabolizing enzymes: Phase 

I oxidative enzymes. J Pharmacol Exp Ther 300(2): 355-360. 

Hnizdo E, Murray J, Sluis-Cremer GK and Thomas RG (1993). Correlation between radiological 

and pathological diagnosis of silicosis: An autopsy population based study. Am J Ind Med 24(4): 

427-445. 

Hook JB and Hewitt WR (1977). Development of mechanisms for drug excretion. Am J Med 

62(4): 497-506. 

Horstman D, Roger LJ, Kehrl H and Hazucha M (1986). Airway sensitivity of asthmatics to 

sulfur dioxide. Toxicol Ind Health 2(3): 289-298. 

Howdeshell KL, Furr J, Lambright CR, Rider CV, Wilson VS and Gray LE, Jr. (2007). 

Cumulative effects of dibutyl phthalate and diethylhexyl phthalate on male rat reproductive tract 

development: altered fetal steroid hormones and genes. Toxicol Sci 99(1): 190-202. 

IARC. (2006). IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. 

Preamble. International Agency for Research on Cancer 

http://monographs.iarc.fr/ENG/Preamble/index.php. 

ICRP (1994). Human Respiratory Tract Model for Radiological Protection. International 

Commission on Radiological Protection. Publication 66 Annals of the ICRP. Tarrytown (NY): 

Elsevier Science Inc. 24(1-3). 

Ikeda M (1988). Multiple exposure to chemicals. Regul Toxicol Pharmacol 8(4): 414-421. 

IRDC. (1985). Three Acute Inhalation Toxicity Studies of Arsine on Rats (Final Report) 

Report No. 533-001, 533-002, and 533-003. Mattawan (MI): International Research & 

Development Corporation. 

Islam MS and Schlipkoter HW (1989). Reversible fraction of airway resistance in healthy 

children of areas with different levels of atmospheric pollutants. Exp Pathol 37(1-4): 23-26. 

Jacobson JL, Jacobson SW and Humphrey HE (1990). Effects of exposure to PCBs and related 

compounds on growth and activity in children. Neurotoxicol Teratol 12(4): 319-26. 

Jarabek AM, Asgharian B and Miller FJ (2005). Dosimetric adjustments for interspecies 

extrapolation of inhaled poorly soluble particles (PSP). Inhal Toxicol 17(7-8): 317-334. 

Jarnberg J and Johanson G (1999). Physiologically based modeling of 1,2,4-trimethylbenzene 

inhalation toxicokinetics. Toxicol Appl Pharmacol 155(3): 203-214. 

Technical Support Document 103 June, 2008 
TSD for Noncancer RELs June 2008 

Jiang X, Buckley L and Morgan K (1983). Pathology of toxic responses to the RD50 

concentration of chlorine gas in the nasal passages of rats and mice. Toxicol Appl Pharmacol 

71(2): 225-236. 

Jonker D, Woutersen RA, van Bladeren PJ, Til HP and Feron VJ (1990). 4-Week oral toxicity 

study of a combination of eight chemicals in rats: Comparison with the toxicity of the individual 

compounds. Food Chem Toxicol 28(9): 623-631. 

Kadry AM, Khodair AJ, Skowronski GA and Abdel-Rahman MS (1995). Evaluation of the 

current method for extrapolating subchronic and chronic test results for estimation of lifetime 

exposure levels [Abstract #180]. The Toxicologist 15(1): 33. 

Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS and Kauffman RE (2003). 

Developmental pharmacology--Drug disposition, action, and therapy in infants and children. N 

Engl J Med 349(12): 1157-1167. 

Khodair AJ, Kadry AM, Skowronski GA and Abdel-Rahman MS (1995). Comparison of animal 

and human data to estimate the no adverse effect level (NOAEL) in humans [Abstract #179]. The 

Toxicologist 15(1): 33. 

Klaassen CD (1972). Immaturity of the newborn rat's hepatic excretory function for ouabain. J 

Pharmacol Exp Ther 183(3): 520-526. 

Klaassen CD (1973). Comparison of the toxicity of chemicals in newborn rats to bile ductligated and sham-operated rats and mice. Toxicol Appl Pharmacol 24(1): 37-44. 

Krasovskii GN (1976). Extrapolation of experimental data from animals to man. Environ Health 

Perspect 13: 51-58. 

Kristiansen U, Hansen L, Nielsen GD and Holst E (1986). Sensory irritation and pulmonary 

irritation of cumene and n-propanol: Mechanisms of receptor activation and desensitization. Acta 

Pharmacol Toxicol (Copenh) 59(1): 60-72. 

Kunzli N, Lurmann F, Segal M, Ngo L, Balmes J and Tager IB (1997). Association between 

lifetime ambient ozone exposure and pulmonary function in college freshmen--results of a pilot 

study. Environ Res 72(1): 8-23. 

Kuwabara Y, Alexeeff G, Broadwin R and Salmon A (2006). Evaluation of the RD50 for 

determining acceptable levels of exposure to airborne sensory irritants. [Abstract #882]. The 

Toxicologist 90(1): 179. 

Lanphear BP, Hornung R, Khoury J, Yolton K, Baghurst P, Bellinger DC, Canfield RL, Dietrich 

KN, Bornschein R, Greene T, Rothenberg SJ, Needleman HL, Schnaas L, Wasserman G, 

Graziano J and Roberts R (2005). Low-level environmental lead exposure and children's 

intellectual function: an international pooled analysis. Environ Health Perspect 113(7): 894-9. 

Lehman AJ and Fitzhugh OG (1954). 100-Fold margin of safety. Assoc Food Drug Off USQ 

Bull 18: 33-35. 

Technical Support Document 104 June, 2008 
TSD for Noncancer RELs June 2008 

Lehmann KP, Phillips S, Sar M, Foster PM and Gaido KW (2004). Dose-dependent alterations in 

gene expression and testosterone synthesis in the fetal testes of male rats exposed to di (n-butyl) 

phthalate. Toxicol Sci 81(1): 60-68. 

Leisenring W and Ryan L (1992). Statistical properties of the NOAEL. Regul Toxicol Pharmacol 

15(2 Pt 1): 161-171. 

Levy G, Khanna NN, Soda DM, Tsuzuki O and Stern L (1975). Pharmacokinetics of 

acetaminophen in the human neonate: Formation of acetaminophen glucuronide and sulfate in 

relation to plasma bilirubin concentration and D-glucaric acid excretion. Pediatrics 55(6): 818-

825. 

Lewis DC and Alexeeff GV (1989). Quantitative Risk Assessment of Noncancer Health Effects 

for Acute Exposure to Air Pollutants. 82nd Annual Meeting of the Air and Waste Management 

Association.Air and Waste Managment Association. pp. 89-91. Anaheim (CA). 

Lilienfeld AM and Lilienfeld DE (1980). Foundations of Epidemiology, 2nd Edition. New 

York: Oxford University Press. 

Loomis D, Castillejos M, Gold DR, McDonnell W and Borja-Aburto VH (1999). Air pollution 

and infant mortality in Mexico City. Epidemiology 10(2): 118-123. 

Mantel N, Bohidar NR, Brown CC, Ciminera JL and Tukey JW (1975). An improved MantelBryan procedure for "safety" testing of carcinogens. Cancer Res 35(4): 865-872. 

Mantel N and Bryan WR (1961). "Safety" testing of carcinogenic agents. J Natl Cancer Inst 27: 

455-470. 

Maughan B, Taylor C, Taylor A, Butler N and Bynner J (2001). Pregnancy smoking and 

childhood conduct problems: A causal association? J Child Psychol Psychiatry 42(8): 1021-

1028. 

McCarver DG and Hines RN (2002). The ontogeny of human drug-metabolizing enzymes: Phase 

II conjugation enzymes and regulatory mechanisms. J Pharmacol Exp Ther 300(2): 361-366. 

McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F, Gilliland F, Kunzli N, Gauderman J, 

Avol E, Thomas D and Peters J (2006). Traffic, susceptibility, and childhood asthma. Environ 

Health Perspect 114(5): 766-772. 

McFadden SW and Haddow JE (1969). Coma produced by topical application of isopropanol. 

Pediatrics 43(4): 622-623. 

Miles M (1983). Pediatric Pharmacokinetics. In: Applied Clinical Pharmacokinetics. Mungall D. 

McGraw-Hill. New York: 367-388. 

Miller MD, Marty MA, Arcus A, Brown J, Morry D and Sandy M (2002). Differences between 

children and adults: Implications for risk assessment at California EPA. Int J Toxicol 21(5): 403-

418. 

Technical Support Document 105 June, 2008 
TSD for Noncancer RELs June 2008 

Miller RA (1941). Observations on the gastric acidity during the first month of life. Arch Dis 

Child 16: 22-30. 

Milsap RL and Jusko WJ (1994). Pharmacokinetics in the infant. Environ Health Perspect 102 

(Suppl 11): 107-110. 

Mitchell WA, Gift JS and Jarabek AM (1993). Suitability of LOAEL to NOAEL 10-fold 

uncertainty factor for health assessments of inhaled toxicants [Abstract #475]. The Toxicologist 

13: 140. 

Monson RR (1986). Observations on the healthy worker effect. J Occup Med 28(6): 425-433. 

Morselli PL (1976). Clincial pharmacokinetics in neonates. Clin Pharmacokinet 1(2): 81-98. 

Morselli PL (1989). Clinical pharmacology of the perinatal period and early infancy. Clin 

Pharmacokinet 17 (Suppl 1): 13-28. 

Morselli PL, Franco-Morselli R and Bossi L (1980). Clinical pharmacokinetics in newborns and 

infants. Age-related differences and therapeutic implications. Clin Pharmacokinet 5(6): 485-527. 

Muller G, Spassovski M and Henschler D (1972). Trichloroethylene exposure and 

trichloroethylene metabolites in urine and blood. Arch Toxikol 29(4): 335-340. 

Murrell JA, Portier CJ and Morris RW (1998). Characterizing dose-response I: Critical 

assessment of the benchmark dose concept. Risk Anal 18(1): 13-26. 

Nair RS, Sherman JH, Stevens MW and Johannsen FR (1995). Selecting a More Realistic 

Uncertainty Factor: Reducing Compounding Effects of Multiple Uncertainties. St. Louis (MO): 

Monsanto Company. Unpublished report. 

NATICH and McCullough M. (1991). National Air Toxics Information Clearinghouse Database 

Report on State, Local and EPA Air Toxics Activities: Final. Research Triangle Park (NC): 

Office of Air Quality Planning and Standards, United States Environmental Protection Agency, 

National Air Toxics Information Clearinghouse. 

Needleman HL, Schell A, Bellinger D, Leviton A and Allred EN (1990). The long-term effects 

of exposure to low doses of lead in childhood. An 11-year follow-up report. N Engl J Med 

322(2): 83-88. 

Nielsen GD (1991). Mechanisms of activation of the sensory irritant receptor by airborne 

chemicals. Crit Rev Toxicol 21(3): 183-208. 

Nielsen GD and Vinggaard AM (1988). Sensory irritation and pulmonary irritation of C3-C7 nalkylamines: Mechanisms of receptor activation. Pharmacol Toxicol 63(4): 293-304. 

NIOSH. (2005). NIOSH Pocket Guide to Chemical Hazards. Washington (DC) U.S. Department 

of Health and Human Services, National Institute for Occupational Safety and Health. 

http://www.cdc.gov/niosh/npg/. 

Technical Support Document 106 June, 2008 
TSD for Noncancer RELs June 2008 

NRC. (1977-1987). Drinking Water and Health. 1-7. Washington (DC): National Research 

Council, National Academy Press. 

http://darwin.nap.edu/openbook.php?record_id=1780&page=905. 

NRC. (1986a). Criteria and Methods for Preparing Emergency Exposure Guidance Level 

(EEGL), Short Term Public Emergency Guidance Level (SPEGL) and Continuous Exposure 

Guidance Level (CEGL) Documents. Washington (DC): Committee on Toxicology, National 

Research Council, National Academy Press. 

NRC (1986b). Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking 

Water Limits. In: Drinking Water and Health. National Research Council, National Academy 

Press. Washington (DC): Vol. 6: 457. 

NRC. (1993). Guidelines for Developing Community Emergency Exposure Levels for Hazardous 

Substances. Washington (DC): Committee on Toxicology, National Research Council, National 

Academy Press. 

NRC. (1994). Science and Judgement in Risk Assessment. Washington (DC): Committee on Risk 

Assessment of Hazardous Air Pollutants, National Research Council, National Academy Press. 

http://www.nap.edu/books/030904894X/html/index.html. 

NRC. (2001). Standing Operating Procedures for Developing Acute Exposure Guideline Levels 

for Hazardous Chemicals. Washington (DC): Subcommittee on Acute Exposure Guideline 

Levels, National Research Council, National Academy Press. 

NRC. (2007). Scientific Review of the Proposed Risk Assessment Bulletin from the Office of 

Management and Budget. Washington (DC): National Academy Press. 

OEHHA. (1999). The Air Toxics Hot Spots Program Risk Assessment Guidelines. Part I: 

Technical Support Document for Determination of Acute Reference Exposure Levels for 

Airborne Toxicants. Air Toxicology and Epidemiology Section, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency. 

http://www.oehha.ca.gov/air/acute_rels/acuterel.html#download. 

OEHHA. (2000a). The Air Toxics Hot Spots Program Risk Assessment Guidelines Part III: 

Technical Support Document for the Determination of Noncancer Chronic Reference Exposure 

Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard 

Assessment, California Environmental Protection Agency. 

http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf. 

OEHHA. (2000b). The Air Toxics Hot Spots Program Risk Assessment Guidelines. Part IV: 

Technical Support Document for Exposure Assessment and Stochastic Analysis. Air Toxicology 

and Epidemiology Section, Office of Environmental Health Hazard Assessment, California 

Environmental Protection Agency. 

http://www.oehha.ca.gov/air/hot_spots/finalStoc.html#download. 

OEHHA. (2001). Prioritization of Toxic Air Contaminants Under the Children’s Environmental 

Health Protection Act. Air Toxicology and Epidemiology Section, Office of Environmental 

Technical Support Document 107 June, 2008 
TSD for Noncancer RELs June 2008 

Health Hazard Assessment, California Environmental Protection Agency. 

http://www.oehha.ca.gov/air/toxic_contaminants/pdf_zip/SB25%20TAC%20prioritization.pdf. 

OEHHA. (2003). The Air Toxics Hot Spots Program Guidance Manual for Preparation of 

Health Risk Assessments. August. Air Toxicology and Epidemiology Section; Office of 

Environmental Health Hazard Assessment, California Environmental Protection Agency. 

http://www.oehha.ca.gov/air/hot_spots/pdf/HRAguidefinal.pdf. 

OEHHA. (2005a). The Air Toxics Hot Spots Program Risk Assessment Guidelines. Part II: 

Technical Support Document for Describing Available Cancer Potency Factors. Air Toxicology 

and Epidemiology Section, Office of Environmental Health Hazard Assessment, California 

Environmental Protection Agency. 

http://www.oehha.ca.gov/air/hot_spots/pdf/May2005Hotspots.pdf. 

OEHHA. (2005b). Proposed Identification of Environmental Tobacco Smoke as a Toxic Air 

Contaminant. Part B: Health Effects. Air Toxicology and Epidemiology Section, Office of 

Environmental Health Hazard Assessment, California Environmental Protection Agency. 

http://www.oehha.ca.gov/air/environmental_tobacco/2005etsfinal.html. 

Orup Jr. HI, Holmes LB, Keith DA and Coull BA (2003). Craniofacial skeletal deviations 

following in utero exposure to the anticonvulsant phenytoin: Monotherapy and polytherapy. 

Orthod Craniofac Res 6(1): 2-19. 

Owen BA (1990). Literature-derived absorption coefficients for 39 chemicals via oral and 

inhalation routes of exposure. Regul Toxicol Pharmacol 11(3): 237-252. 

Pelekis M, Gephart LA and Lerman SE (2001). Physiological-model-based derivation of the 

adult and child pharmacokinetic intraspecies uncertainty factors for volatile organic compounds. 

Regul Toxicol Pharmacol 33(1): 12-20. 

Pelekis M, Nicolich MJ and Gauthier JS (2003). Probabilistic framework for the estimation of 

the adult and child toxicokinetic intraspecies uncertainty factors. Risk Anal 23(6): 1239-1255. 

Pepelko WE (1987). Feasibility of route extrapolation in risk assessment. Br J Ind Med 44(10): 

649-651. 

Pepelko WE (1991). Effect of exposure route on potency of carcinogens. Regul Toxicol 

Pharmacol 13(1): 3-17. 

Perera FP, Jedrychowski W, Rauh V and Whyatt RM (1999). Molecular epidemiologic research 

on the effects of environmental pollutants on the fetus. Environ Health Perspect 107 (Suppl 3): 

451-460. 

Phalen RF, Oldham MJ, Beaucage CB, Crocker TT and Mortensen JD (1985). Postnatal 

enlargement of human tracheobronchial airways and implications for particle deposition. Anat 

Rec 212(4): 368-380. 

Pineiro-Carrero VM and Pineiro EO (2004). Liver. Pediatrics 113(4 Suppl): 1097-1106. 

Technical Support Document 108 June, 2008 
TSD for Noncancer RELs June 2008 

Plopper CG and Fanucchi MV (2004). Development of Airway Epithelium. In: The Lung: 

Development, Aging and The Environment Harding R., Pinkerton K. and Plopper C. G. Elsevier 

Academic Press. San Diego (CA). 

Plopper CG, Mariassy AT, Wilson DW, Alley JL, Nishio SJ and Nettesheim P (1983). 

Comparison of nonciliated tracheal epithelial cells in six mammalian species: Ultrastructure and 

population densities. Exp Lung Res 5(4): 281-294. 

Plunkett LM, Turnbull D and Rodericks JV (1992). Differences Between Adults and Children 

Affecting Exposure Assessment. In: Similarities and Differences Between Children and Adults: 

Implications for Risk Assessment. Guzelian P. S., Henry C. J. and Olin S. S. ILSI Press. 

Washington (DC): pp. 79-94. 

Price K, Haddad S and Krishnan K (2003). Physiological modeling of age-specific changes in 

the pharmacokinetics of organic chemicals in children. J Toxicol Environ Health A 66(5): 417-

433. 

Purser DA, Grimshaw P and Berrill KR (1984). Intoxication by cyanide in fires: a study in 

monkeys using polyacrylonitrile. Arch Environ Health 39(6): 394-400. 

Rall DP (1969). Difficulties in extrapolating the results of toxicity studies in laboratory animals 

to man. Environ Res 2(5): 360-367. 

Rane A and Wilson JT (1976). Clinical pharmacokinetics in infants and children. Clin 

Pharmacokinet 1(1): 2-24. 

Raphael G, Meredith S, Baraniuk J and Kaliner M (1991). Nasal reflexes. In: Rhinitis (2nd 

Edition). Settipane G. OceanSide Publications. Providence (RI): 135-142. 

Renwick AG, Dorne JL and Walton K (2000). An analysis of the need for an additional 

uncertainty factor for infants and children. Regul Toxicol Pharmacol 31(3): 286-296. 

Renwick AG and Lazarus NR (1998). Human variability and noncancer risk assessment--An 

analysis of the default uncertainty factor. Regul Toxicol Pharmacol 27(1): 3-20. 

Rice D and Barone Jr. S (2000). Critical periods of vulnerability for the developing nervous 

system: Evidence from humans and animal models. Environ Health Perspect 108(Suppl 3): 511-

533. 

Rice DC (1996). Evidence for delayed neurotoxicity produced by methylmercury. 

Neurotoxicology 17(3-4): 583-596. 

Rinehart WE and Hatch T (1964). Concentration-time product (Ct) as an expression of dose in 

sublethal exposures to phosgene. Am Ind Hyg Assoc J 25: 545-553. 

Ritz B and Yu F (1999). The effect of ambient carbon monoxide on low birth weight among 

children born in Southern California between 1989 and 1993. Environ Health Perspect 107(1): 

17-25. 

Technical Support Document 109 June, 2008 
TSD for Noncancer RELs June 2008 

Roach SA and Rappaport SM (1990). But they are not thresholds: A critical analysis of the 

documentation of Threshold Limit Values. Am J Ind Med 17(6): 727-753. 

Robillard J, Guillery E and Petershack J (1999). Renal Function During Fetal Life. In: Pediatric 

Nephrology. Barratt T. M., Avner E. and Harmon W. Lippincott Williams & Wilkins. Baltimore 

(MD): 21-37. 

Robinson JC and Paxman DG (1992). The role of threshold limit values in U.S. air pollution 

policy. Am J Ind Med 21(3): 383-396. 

Rothman KJ and Greenland S (1998). Modern Epidemiology. Philadelphia (PA): Lippincott, 

Raven Publishers. 

Roels H, Meiers G, Delos M, Ortega I, Lauwerys R, Buchet JP and Lison D (1997). Influence of 

the route of administration and the chemical form (MnCl2, MnO2) on the absorption and cerebral 

distribution of manganese in rats. Arch Toxicol 71(4): 223-230. 

Rothman KJ and Greenland S (2005). Causation and causal inference in epidemiology. Am J 

Public Health 95 (Suppl 1): S144-S150. 

Rozman KK and Klaassen CD (1996). Absorption, Distribution, and Excretion of Toxicants. In: 

Casarett and Doull's Toxicology; The Basic Science of Poisons. Klaassen C. D. McGraw-Hill. 

New York: p 104. 

Sagiv SK, Mendola P, Loomis D, Herring AH, Neas LM, Savitz DA and Poole C (2005). A 

time-series analysis of air pollution and preterm birth in Pennsylvania, 1997-2001. Environ 

Health Perspect 113(5): 602-606. 

Salmon A (2007). Standard risk assessment procedures: Are they adequate to account for 

cumulative risks? International Journal of Toxicology in press. 

Sanchez G. (2002). Humorap: A Mathematica package for solving the ICRP 66 and 78 

compartmental models and more. http://web.usal.es/~guillerm/. 

Sand S, Filipsson AF and Victorin K (2002). Evaluation of the benchmark dose method for 

dichotomous data: Model dependence and model selection. Regul Toxicol Pharmacol 36(2): 184-

197. 

Sand SJ, von Rosen D and Filipsson AF (2003). Benchmark calculations in risk assessment using 

continuous dose-response information: The influence of variance and the determination of a cutoff value. Risk Anal 23(5): 1059-1068. 

Sarangapani R, Gentry PR, Covington TR, Teeguarden JG and Clewell 3rd. HJ (2003). 

Evaluation of the potential impact of age- and gender-specific lung morphology and ventilation 

rate on the dosimetry of vapors. Inhal Toxicol 15(10): 987-1016. 

Technical Support Document 110 June, 2008 
TSD for Noncancer RELs June 2008 

Sarangapani R, Teeguarden JG, Cruzan G, Clewell 3rd. HJ and Andersen ME (2002). 

Physiologically based pharmacokinetic modeling of styrene and styrene oxide respiratory-tract 

dosimetry in rodents and humans. Inhal Toxicol 14(8): 789-834. 

Sarangapani R, Teeguarden JG, Gentry PR, Clewell 3rd. HJ, Barton HA and Bogdanffy MS 

(2004). Interspecies dose extrapolation for inhaled dimethyl sulfate: A PBPK model-based 

analysis using nasal cavity N7-methylguanine adducts. Inhal Toxicol 16(9): 593-605. 

Schmidt CW, Gillis CA, Keenan RE and Price PS (1997). Characterizing inter-chemical 

variation in the interspecies uncertainty factor (UFA) [Abstract #1057]. The Toxicologist 36(1 Pt 

2): 208. 

Schwartz J, Ballester F, Saez M, Perez-Hoyos S, Bellido J, Cambra K, Arribas F, Canada A, 

Perez-Boillos MJ and Sunyer J (2001). The concentration-response relation between air pollution 

and daily deaths. Environ Health Perspect 109(10): 1001-6. 

Schwela D (2000). Air pollution and health in urban areas. Rev Environ Health 15(1-2): 13-42. 

Sherwin RP (1983). What is an adverse health effect? Environ Health Perspect 52: 177-182. 

Shusterman D (1999). Upper and lower airway sequelae of irritant inhalations. Clin Pulm Med 

6(1): 18-31. 

Shusterman D, Matovinovic E and Salmon A (2006). Does Haber's Law apply to human sensory 

irritation? Inhal Toxicol 18(7): 457-471. 

Siegel M, Lebenthal E and Krantz B (1984). Effect of caloric density on gastric emptying in 

premature infants. J Pediatr 104(1): 118-122. 

Signer E (1975). Gastric emptying in newborns and young infants. Measurement of the rate of 

emptying using indium-113m-microcolloid. Acta Paediatr Scand 64(3): 525-530. 

Skowronski GA, Seide M and Abdel-Rahman MS (2001). Oral bioaccessibility of trivalent and 

hexavalent chromium in soil by simulated gastric fluid. J Toxicol Environ Health A 63(5): 351-

62. 

Smiley-Jewell SM, Liu FJ, Weir AJ and Plopper CG (2000). Acute injury to differentiating Clara 

cells in neonatal rabbits results in age-related failure of bronchiolar epithelial repair. Toxicol 

Pathol 28(2): 267-276. 

Snipes MB (1989a). Long-term retention and clearance of particles inhaled by mammalian 

species. Crit Rev Toxicol 20(3): 175-211. 

Snipes MB, McClellan RO, Mauderly JL and Wolff RK (1989b). Retention patterns for inhaled 

particles in the lung: Comparisons between laboratory animals and humans for chronic 

exposures. Health Phys 57 (Suppl 1): 69-77; discussion pp. 77-78. 

Technical Support Document 111 June, 2008 
TSD for Noncancer RELs June 2008 

Snodgrass WR (1992). Physiological and Biochemical Differences Between Children and Adults 

as Determinants of Toxic Response to Environmental Pollutants. In: Similarities and Differences 

Between Children and Adults: Implications For Risk Assessment. Guzelian P. S., Henry C. J. 

and Olin S. S. ILSI Press. Washington (DC): 35-42. 

Starr TB, Goodman JI and Hoel DG (2005). Uses of benchmark dose methodology in 

quantitative risk assessment. Regul Toxicol Pharmacol 42(1): 1-2. 

Streissguth AP, Aase JM, Clarren SK, Randels SP, LaDue RA and Smith DF (1991). Fetal 

alcohol syndrome in adolescents and adults. JAMA 265(15): 1961-1967. 

Stromland K, Nordin V, Miller M, Akerstrom B and Gillberg C (1994). Autism in thalidomide 

embryopathy: A population study. Dev Med Child Neurol 36(4): 351-356. 

Swartout JC (1997). Exposure-duration uncertainty factor for the RfD [Abstract #1060]. The 

Toxicologist 36: 209. 

ten Berge WF, Zwart A and Appelman LM (1986). Concentration-time mortality response 

relationship of irritant and systemically acting vapours and gases. J Hazard Mater 13: 301-309. 

Ting D, Howd RA, Fan AM and Alexeeff GV (2006). Development of a health-protective 

drinking water level for perchlorate. Environ Health Perspect 114(6): 881-6. 

Treluyer JM, Gueret G, Cheron G, Sonnier M and Cresteil T (1997). Developmental expression 

of CYP2C and CYP2C-dependent activities in the human liver: In-vivo/in-vitro correlation and 

inducibility. Pharmacogenetics 7(6): 441-452. 

Treluyer JM, Jacqz-Aigrain E, Alvarez F and Cresteil T (1991). Expression of CYP2D6 in 

developing human liver. Eur J Biochem 202(2): 583-588. 

Tyrala EE, Hillman LS, Hillman RE and Dodson WE (1977). Clinical pharmacology of 

hexachlorophene in newborn infants. J Pediatr 91(3): 481-486. 

Tzimas G, Thiel R, Chahoud I and Nau H (1997). The area under the concentration-time curve of 

all-trans-retinoic acid is the most suitable pharmacokinetic correlate to the embryotoxicity of this 

retinoid in the rat. Toxicol Appl Pharmacol 143(2): 436-444. 

U.S. DHHS. (2004). The Health Consequences of Smoking; A Report of the Surgeon General. 

Washington (D.C.) Office on Smoking and Health, United States Department of Health and 

Human Services. http://www.cdc.gov/tobacco/sgr/sgr_2004. 

U.S. EPA. (1988). Recommendations for and Documentation of Biological Values for Use in 

Risk Assessment. EPA/600/6-87-008. Washington (DC): United States Environmental Protection 

Agency. 

U.S. EPA. (1993). Reference Dose (RfD): Description and Use in Health Risk Assessments. 

Background Document1A. March 15. 2005. United States Environmental Protection Agency. 

http://www.epa.gov/iris/rfd.htm. 

Technical Support Document 112 June, 2008 
TSD for Noncancer RELs June 2008 

U.S. EPA. (1994a). Methods for Derivation of Inhalation Reference Concentrations and 

Application of Inhalation Dosimetry. EPA/600/8-90/066F. Washington (DC): Office of Research 

and Development, United States Environmental Protection Agency. 

http://www.epa.gov/iris/subst/0364.htm. 

U.S. EPA. (1994b). Methods for Exposure-Response Analysis and Health Assessment for Acute 

Inhalation Exposure to Chemicals. Development of the Acute Reference Exposure (Draft). 

Research Triangle Park (NC): Office of Health and Environmental Assessment, United States 

Environmental Protection Agency. 

U.S. EPA. (1995). The Use of the Benchmark Dose Approach in Health Risk Assessment. 

EPA/630/R-94/007. Washington (DC): Risk Assessment Forum, United States Environmental 

Protection Agency. http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=42601. 

U.S. EPA. (2000). Strategy for Research on Environmental Risks to Children. EPA/600/R00/068. Washington (DC): Office of Research and Development, United States Environmental 

Protection Agency. http://www.epa.gov/nceawww1/pdfs/stra4resrch.pdf. 

U.S. EPA. (2002a). A Review of the Reference Dose and Reference Concentration Process. 

EPA/630/P-02/002F. Washington (DC): Risk Assessment Forum, United States Environmental 

Protection Agency. http://www.epa.gov/iris/RFD_FINAL%5B1%5D.pdf. 

U.S. EPA (2002b). Determination of the Appropriate FQPA Safety Factor(s) in Tolerance 

Assessment. February 28, 2002. 

U.S. EPA. (2004). An Examination of EPA Risk Assessment Principles and Practices. 

EPA/100/B-04/001. Washington (DC): Office of the Science Advisor, United States 

Environmental Protection Agency. http://www.epa.gov/OSA/pdfs/ratf-final.pdf. 

U.S. EPA (2006a). Benchmark Dose Software (BMDS) Tutorial. United States Environmental 

Protection Agency. http://www.epa.gov/ncea/bmds_training/methodology/model.htm. June 29, 

2006. 

U.S. EPA. (2006b). Approaches for the Application of Physiologically Based Pharmacokinetic 

(PBPK) Models and Supporting Data in Risk Assessment. EPA/600/R-05/043F. National Center 

for Environmental Assessment, Office of Research and Development, United States 

Environmental Protection Agency. 

http://oaspub.epa.gov/eims/xmlreport.display?deid=157668&z_chk=33346&format=print. 

U.S. EPA. (2006c). A Framework for Assessing Health Risks of Environmental Exposures to 

Children. Washington (D.C.): Office of Research and Development, United States 

Environmental Protection Agency. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=150263. 

U.S. EPA (2007a). Integrated Risk Information System (IRIS) Database. United States 

Environmental Protection Agency. http://www.epa.gov/IRIS/. 

Technical Support Document 113 June, 2008 
TSD for Noncancer RELs June 2008 

U.S. EPA (2007b). Acute Exposure Guideline Levels. United States Environmental Protection 

Agency. http://www.epa.gov/oppt/aegl/pubs/compiled.pdf. May 14, 2007. 

U.S. FDA. (2006). Redbook 2000 Toxicological Principles for the Safety Assessment of Food 

Ingredients. Office of Food Additive Safety, U.S. Food and Drug Administration. 

http://www.cfsan.fda.gov/~redbook/red-toca.html. 

Uebori M and Imamura K (2004). Analysis of aliphatic and aromatic carbonyl compounds in 

ambient air by LC/MS/MS. Anal Sci 20(10): 1459-1462. 

Vedal S, Brauer M, White R and Petkau J (2003). Air pollution and daily mortality in a city with 

low levels of pollution. Environ Health Perspect 111(1): 45-52. 

Vettorazzi G (1977). Safety Factors and Their Application in the Toxicological Evaluation. In: 

The Evaluation of Toxicological Data for the Protection of Public Health: Proceedings of the 

International Colloquium, Luxembourg, December 1976. Commission of the European 

Communities, Pergamon Press. Oxford, [Eng.]; New York: 207-223. 

Watzka M, Bidlingmaier F, Schramm J, Klingmuller D and Stoffel-Wagner B (1999). Sex- and 

age-specific differences in human brain CYP11A1 mRNA expression. J Neuroendocrinol 

11(12): 901-905. 

Weil CS (1972). Statistics vs safety factors and scientific judgment in the evaluation of safety for 

man. Toxicol Appl Pharmacol 21(4): 454-463. 

Weller E, Long N, Smith A, Williams P, Ravi S, Gill J, Henessey R, Skornik W, Brain J, 

Kimmel C, Kimmel G, Holmes L and Ryan L (1999). Dose-rate effects of ethylene oxide 

exposure on developmental toxicity. Toxicol Sci 50(2): 259-270. 

Wen CP, Tsai SP and Gibson RL (1983). Anatomy of the healthy worker effect: A critical 

review. J Occup Med 25(4): 283-289. 

Widdicombe JG (1990). Nasal pathophysiology. Respir Med 84 Suppl A: 3-9; discussion 9-10. 

Wilhelm M and Ritz B (2005). Local variations in CO and particulate air pollution and adverse 

birth outcomes in Los Angeles County, California, USA. Environ Health Perspect 113(9): 1212-

1221. 

Williams GM, O'Callaghan M, Najman JM, Bor W, Andersen MJ, Richards D and U C (1998). 

Maternal cigarette smoking and child psychiatric morbidity: A longitudinal study. Pediatrics 

102(1): e11. 

Woodruff TJ, Parker JD and Schoendorf KC (2006). Fine particulate matter (PM2.5) air 

pollution and selected causes of postneonatal infant mortality in California. Environ Health 

Perspect 114(5): 786-790. 

Technical Support Document 114 June, 2008 
TSD for Noncancer RELs June 2008 

Yolton K, Dietrich K, Auinger P, Lanphear BP and Hornung R (2005). Exposure to 

environmental tobacco smoke and cognitive abilities among U.S. children and adolescents. 

Environ Health Perspect 113(1): 98-103. 

Yu CP and Xu GB (1987). Predictive models for deposition of inhaled diesel exhaust particles in 

humans and laboratory species. Res Rep Health Eff Inst(10): 3-22. 

Yu D (1999). A physiologically based pharmacokinetic model of inorganic arsenic. Regul 

Toxicol Pharmacol 29: 128-141. 

Zeltner TB and Burri PH (1987). The postnatal development and growth of the human lung. II. 

Morphology. Respir Physiol 67(3): 269-282. 

Technical Support Document 115 June, 2008