A Collaborative Project of The Mickey Leland National Urban Air Toxics Research Center and The National Center for Health Statistics pot

The National Health and Nutrition Examination SurveysNHANES Volatile Organic Compound Dataset: An Introduction to the Project and Analyses of the Relationship between Personal Exposures

The National Health and Nutrition Examination Surveys

(NHANES) Volatile Organic Compound Dataset:

An Introduction to the Project and Analyses of the Relationship between Personal Exposures to VOCs and Behavioral,

Socioeconomic, and Demographic Characteristics

A Collaborative Project of The Mickey Leland National

Urban Air Toxics Research Center and The National

Center for Health Statistics

NUMBER 16

2009

ABOUT THE NUATRC

The Mickey Leland National Urban Air Toxics Research Center (NUATRC or the Leland Center) was established in 1991 to develop and support research into potential human health effects of exposure to air toxics in urban communities Authorized under the Clean Air Act Amendments (CAAA) of 1990, the Center released its first Request for Applications

in 1993 The aim of the Leland Center since its inception has been to build a research program structured to investigate and assess the risks to public health that may be attributed to air toxics Projects sponsored by the Leland Center are designed to provide sound scientific data useful for researchers and for those charged with formulating environmental regulations.

The Leland Center is a public-private partnership, in that it receives support from government sources and from the private sector Thus, government funding is leveraged

by funds contributed by organizations and businesses, enhancing the effectiveness of the funding from both of these stakeholder groups The U.S Environmental Protection Agency (EPA) has provided the major portion of the Center’s government funding to date, and a number of corporate sponsors, primarily in the chemical and petrochemical fields, have also supported the program.

A nine-member Board of Directors oversees the management and activities of the Leland Center The Board also appoints the thirteen members of a Scientific Advisory Panel (SAP) who are drawn from the fields of government, academia and industry These members represent such scientific disciplines as epidemiology, biostatistics, toxicology and medicine The SAP provides guidance in the formulation of the Center’s research program and

conducts peer review of research results of the Center’s completed projects.

The Leland Center is named for the late United States Congressman George Thomas

“Mickey” Leland from Texas who sponsored and supported legislation to reduce the problems of pollution, hunger, and poor housing that unduly affect residents of low-income urban communities.

This project has been funded wholly or in part by the United States Environmental Protection Agency under assistance agreement X83234601 The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of

The National Health and Nutrition Examination Surveys (NHANES) Volatile Organic Compound Dataset: An Introduction to the Project and Analyses of the Relationship between Personal

Exposures to VOCs and Behavioral,

Socioeconomic, and Demographic Characteristics

A Collaborative Project of The Mickey Leland National Urban Air Toxics

Research Center and The National Center for Health Statistics

2 NUATRC RESEARCH REPORT NO 16

TABLE OF CONTENTS

3 3 3 3 3 4 4 4 4 4 4 5 5 6 6 7 7

9 9 19 31 41

BACKGROUND AND PURPOSE

THE MICKEY LELAND NATIONAL URBAN AIR TOXICS RESEARCH CENTER (NUATRC)

THE NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEYS (NHANES)

THE NUATRC-NCHS COLLABORATION: THE VOC PROJECT

PURPOSE OF THIS REPORT

THE VOC PROJECT

OBJECTIVE

VOC MEASUREMENT

REVIEW OF LABORATORY ANALYSES

QUALITY CONTROL AND QUALITY ASSURANCE PROCEDURES

BLOOD LEVEL VOCS

PUBLIC RELEASE OF THE VOC DATASET

ANALYSIS OF THE NHANES VOC DATASET

CONCLUSION

REFERENCES

ACKNOWLEDGMENTS

ABBREVIATIONS

JOURNAL MANUSCRIPT REPRINTS

DISTRIBUTIONS OF PERSONAL VOC EXPOSURES: A POPULATION-BASED ANAYSIS (JIA, ET AL)

PREDICTORS OF PERSONAL AIR CONCENTRATIONS OF CHLOROFORM AMONG U.S ADULTS IN NHANES 1999-2000

(RIEDERER, ET AL)

DEMOGRAPHIC, RESIDENTIAL, AND BEHAVIORAL DETERMINANTS OF ELEVATED EXPOSURES TO BENZENE,

ETHYLBENZENE, AND XYLENES AMONG U.S POPULATION: RESULTS FROM 1999-2000 NHANES (SYMANSKI, ET AL) CHARACTERIZING RELATIONSHIPS BETWEEN PERSONAL EXPOSURES TO VOCS AND SOCIOECONOMIC,

DEMOGRAPHIC, BEHAVIORAL VARIABLES (WANG, ET AL)

NUATRC RESEARCH REPORT NO 16

BACKGROUND AND PURPOSE

THE MICKEY LELAND NATIONAL URBAN AIR TOXICS

RESEARCH CENTER (NUATRC)

The Clean Air Act Amendments of 1990 established a

control program for sources of 187 “hazardous air

pollutants,” or “air toxics” that may pose a risk to public

health With the passage of these amendments, Congress

established the NUATRC to develop and direct an

environmental health research program that would promote

a better understanding of the risks posed to human health

by the presence of these toxic chemicals in urban air.

Established as a public/private research organization, the

NUATRC's research program is developed with guidance

from a Scientific Advisory Panel composed of scientific

experts from academia, industry, and government and seeks

to fill gaps in scientific data NUATRC-funded research is

intended to assist policy makers in the evaluation and

promulgation of sound environmental health decisions

The NUATRC accomplishes its research mission by

sponsoring research on human health effects of air toxics at

universities and research institutions, by supporting

periodic workshops to share the current science on air

toxics, and by publishing NUATRC-funded study results in

its “NUATRC Research Reports,” thereby contributing

meaningful and relevant data to the peer-reviewed

literature.

THE NATIONAL HEALTH AND NUTRITION EXAMINATION

SURVEYS (NHANES)

The National Health Survey Act, passed in 1956,

authorized a continuing survey of the Nation's health to

provide current statistical data on the effects of illness and

disability in the US To comply with the Act, the National

Center for Health Statistics (NCHS) conducted three

National Health Examination Surveys in the 1960s In 1970,

a nutrition component was added to the survey, and,

between 1971 and 1994, NCHS conducted four National

Health and Nutrition Examination Surveys (NHANES).

These surveys were designed to capture specific

consecutive time periods, usually of six years' duration, and

data were released for three or six-year periods In these

surveys, data on individuals were typically collected by at

least three approaches: through direct interview, physical

examination, and by clinical testing and measurement

With the inception of the 1999 NHANES, the survey

became a continuous annual event It now collects data

from a representative sample of the US population each year About 5,000 randomly selected subjects per year are chosen, aged from birth onward, from 15 different locations across the nation Participants provide demographic and health data and undergo physical examinations to assess their current health status For this purpose, fully equipped Mobile Examination Centers (MECs) are transported to data collection sites, referred to as “stands,” so that medical personnel can conduct the exams on-site in a standardized manner

THE NUATRC-NCHS COLLABORATION: THE VOC PROJECT

The NUATRC submitted a proposal in 1997 to the NCHS for a collaborative project that would measure personal exposures to volatile organic compounds (VOCs) among a representative subgroup of participants in NHANES 1999-

2001 The collaborative project was designed to provide a profile of VOC exposures experienced by US adults during their daily activities The NHANES-VOC project was a data- gathering effort; the data are available on the NCHS website,

as described below

To encourage wide use of the dataset for new research projects and scientific publications, the NUATRC released a Request For Applications (RFA) in 2006 entitled:

“Relationship between Personal Exposures to VOCs and Behavioral, Socioeconomic, and Demographic Characteristics: Analysis of the NHANES VOC Project Dataset.” Manuscripts written by the project grantees, based

on their research under this program, are reproduced in this report.

PURPOSE OF THIS REPORT

This report is intended to inform the research community about the NUATRC- and NCHS-funded VOC database so that it can be accessed for future data mining activities It also features the analyses of four investigators funded by NUATRC to analyze the dataset; their work highlights the utility of the dataset in understanding the national distribution of personal exposures to VOCs and determinants of these exposures Their work can be used by other investigators to generate hypotheses about potentially significant exposure sources and pathways for VOCs in the general US population.

3

4 NUATRC RESEARCH REPORT NO 16

THE VOC PROJECT

OBJECTIVE

The NUATRC proposed a project that would collect

personal exposure data on specific VOCs in a representative

subset of NHANES participants Such data would provide

information on the distribution of personal exposures to

these hazardous air pollutants in the US population If such

an effort were continued, it would provide valuable

information on trends over time of these exposures and also

help evaluate impact of regulations to control these

hazardous air pollutants.

The NUATRC proposal was accepted by NCHS, and the

Collaborative NCHS-NUATRC VOC Project (VOC Project)

became a three-year component of the NHANES survey

during the period 1999-2001 The aim of the project was to

collect personal exposure data about specific VOCs in a

representative subset of NHANES participants between the

ages of 20 and 59 years The target sample size for the VOC

Project was 1,000 participants over the three-year period.

Personal exposure data were obtained for periods of 48 to

72 hours, using small lightweight passive sampling badges

that subjects wore from the time they left the MECs until

they returned to the MEC 48 to 72 hours later Eligible

participants were recruited after completion of their

physical examinations Activity data for the exposure

periods were collected from participants by means of a

questionnaire administered at the end of the exposure

periods when the participants returned to the MEC The

participants also provided information about household

characteristics at that time.

VOC MEASUREMENT

The VOCs measured in the personal exposure study

included: benzene, chloroform, ethylbenzene,

tetrachloroethene, toluene, trichloroethene, o-xylene,

m-p-xylene, 1,4-dichlorobenzene, and methyl tert-butyl ether

(MTBE).

The VOC passive exposure monitor (or badge) used

in the study was the 3M Organic Vapor Monitor (Model

3520, 3M Company, St Paul MN) All VOC analyses

were performed in accordance with methods described

in the 3M publication: “Organic Vapor Monitor

Sampling and Analysis Guide- October 1998.”

(http://multimedia.3m.com/mws/mediawebserver?66666U

uZjcFSLXTtlX&6OXMtEVuQEcuZgVs6EVs6E666666 )

Extraction efficiencies were determined in accordance with

the 3M procedures Method detection limits were

determined for each compound based on the standard laboratory methods A Gas Chromatograph/Mass Spectrometer was used for analyses Laboratory procedures and equipment standards followed accepted USEPA protocols.

REVIEW OF LABORATORY ANALYSES

During the three-year project period, two different laboratory contractors performed the badge analyses in two different time periods Exposure data for the first year and a half of the project was analyzed by Clayton Laboratories, and for the remainder of the project, by the Environmental and Occupational Health Sciences Institute (EOHSI) laboratory of the University of Medicine and Dentistry, New Jersey (UMDNJ), both contractors to the Leland Center Prior to approving the release of the VOC Project data set, NCHS scientists conducted a review of the procedures followed by the two laboratory groups in order to assess the compatibility of the approaches taken by the two laboratories and the reasonableness of the data produced for the project Although the methods used by the two contract laboratories differed from those used at NCHS, the results were judged to be comparable after the review was completed.

QUALITY CONTROL AND QUALITY ASSURANCE PROCEDURES

Laboratory procedures and equipment standards followed accepted USEPA protocols For Quality Assurance purposes, 10 percent of samples were split and analyzed independently by the NUATRC contractor laboratory and

an outside laboratory The analyses of these paired samples were conducted at the two laboratories concurrently The results were evaluated for consistency and accuracy Quality Control procedures during the VOC Project included the collection and analysis of the following samples from each of the stands: two field blanks, one positive control, two duplicate pairs, and one office air sample.

BLOOD LEVEL VOCS

A subset of VOC Project participants also took part in a related NHANES component, sponsored by the Centers for Disease Control's (CDC) Center for Environmental Health (CEH) That component collected data on blood-level VOCs and home drinking water VOCs Those study subjects were asked to bring samples of home drinking water to the MEC when they returned at the end of their exposure periods The goal of the CEH Project was to characterize the

NUATRC RESEARCH REPORT NO 16

distributions of blood and water VOCs and to investigate

possible relationships between them

PUBLIC RELEASE OF THE VOC DATASET

After the three-year data collection period for the VOC

Project ended, a Workshop was held to review the project

data Participants included a panel of six researchers with

significant experience in conducting and evaluating

community studies of environmental health effects (Edo

Pellizzari of Research Triangle Institute, Paul Feder of

Battelle, David Ashley of CDC, Thomas Stock of the

University of Texas School of Public Health, Martin Harper

of CDC, and Edward Avol of the University of Southern

California Keck School of Medicine), NCHS scientists and

staff, and NUATRC staff.

At the conclusion of the Workshop, the Panel

recommended that the 1999-2000 VOC Project dataset be

released on the NCHS web site as part of the 1999-2000

NHANES data release Data for ten VOCs were released in

April 2005: benzene, chloroform, ethylbenzene,

tetrachloroethylene, trichloroethylene, toluene,

m-p-xylene, o-m-p-xylene, 1,4 dichlorobenzene, and MTBE The

website for the1999-2000 NHANES dataset is:

http://www.cdc.gov/nchs/nhanes/nhanes99_00.htm.

The 2001 VOC Project dataset could not be publicly

released because of the small size, and the risk of disclosure

of individual information or identities in a one-year dataset.

The three-year 1999-2001 VOC Project was released for use

in the Research Data Center in 2007.

The Research Data Center at NCHS was established to

assist researchers whose projects require access to data that

are confidential in nature, or might lead to the disclosure of

confidential information or individual identities These

researchers are asked to submit proposals to the Research

Data Center, describing their projects If their proposals are

approved, the staff will then prepare a dataset created for

the particular project, while maintaining strict

confidentiality, and can provide statistical programming

and consulting expertise to facilitate the data analysis for

the project There are fees associated with using the

Research Data Center.

The Research Data Center is located at the NCHS

headquarters office in Hyattsville, Maryland Researchers

may work onsite at the headquarters or may access their

data at a remote site Another option is to carry out the

research at a Census Research Data Center The web site

address for this Center is:

http://www.cdc.gov/nchs/r&d/rdc.htm

ANALYSIS OF THE NHANES VOC DATASET

To encourage wide use of the dataset for new research projects and scientific publications, the NUATRC released

an RFA in 2006 entitled: “Relationship between Personal Exposures to VOCs and Behavioral, Socioeconomic, and Demographic Characteristics: Analysis of the NHANES VOC Project Dataset.”

In November 2006, the NUATRC awarded four one-year contracts A condition of the award was that each investigator was to prepare a manuscript based on the project and submit it to a peer-reviewed publication Grants were awarded to the following investigators:

• Stuart Batterman, Environmental Health Sciences, School

of Public Health, University of Michigan, Ann Arbor, Michigan

• P Barry Ryan, Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, Georgia

• Elaine Symanski, Division of Epidemiology and Disease Control, University of Texas School of Public Health, Houston, Texas

• Sheng-Wei Wang, Institute of Environmental Health, Taiwan (formerly of Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey)

In conformance with award requirements, each of these investigators published their findings in the peer-reviewed literature, and these publications (through agreement with the respective journals) are reprinted in the pages that follow

Briefly, Drs Jia, D'Souza, and Batterman (2008) characterized distributions of personal exposures to ten of the VOCs measured in the 1999-2000 NHANES This study provides graphs and tables that illustrate the national exposure distribution and compares the NHANES results to studies assessing VOC exposures among different populations According to the Jia et al analyses, participants' exposures to VOCs vary dramatically They identified four groups of possible emission sources: gasoline vapors and exhaust; tap water disinfection products; cleaning products; and gasoline additive (MTBE) They identified several methodological issues, and suggested that complete models for the distribution of VOC exposures require an approach that combines standard and extreme value distributions and carefully identifies outliers Drs Riederer, Bartell, and Ryan (2009) found that 8 of 10

US adults were exposed to detectable levels of chloroform.

5

6 NUATRC RESEARCH REPORT NO 16

Significant predictors of personal exposure to chloroform

included: demographic (age, race/ethnicity) and housing

characteristics (type of home, chloroform concentration in

home tap water), and personal exposure microevents

(leaving home windows open, visiting a pool) Reported

showering activity was not a significant predictor of

personal air chloroform in the study The authors argued

that NHANES measurements likely underestimated true

inhalation exposures since subjects did not wear sampling

badges while showering or swimming, and because of

possible undersampling by the passive monitors

Drs Symanski, Stock, Tee, and Chan (2009) investigated

the relationship of socioeconomic, behavioral,

demographic, and residential characteristics to personal

exposures to benzene, toluene, ethylbenzene, and xylenes

(BTEX) compounds among a subsample of the NHANES

participants Geometric mean (GM) levels were

significantly higher for males for all compounds except

toluene For benzene, GM levels were elevated among

smokers and Hispanics Regression analyses suggested that

the presence of an attached garage (for BTEX), having

windows closed in the home during the monitoring period

(for benzene and toluene), pumping gasoline (for toluene,

ethylbenzene and xylenes), or using paint thinner, brush

cleaner, or stripper (for xylenes) resulted in higher

exposures in the general population The results of these

analyses confirmed findings of previous studies

Drs Wang, Majeed, Chu, and Lin (2009) found that

different subsets of behavioral, socioeconomic, and

demographic variables were significant exposure

predictors, depending upon the nature of the VOCs.

Sociodemographic factors (e.g., race/ethnicity and family

income) were generally found to influence personal

exposures to three chlorinated compounds: chloroform,

1,4-dichlorobenzene, and tetrachloroethane For the BTEX

compounds, housing characteristics (e.g., leaving windows

open and having an attached garage), and personal activities

related to the use of fuels or solvent-related products had a

significant influence on exposures Differences in BTEX

exposures were also found in relation to gender due to

differences in time spent at work/school and outdoors The

investigators presented a variety of statistical analysis

techniques for resolving challenges and limitations of the

dataset, including dealing with issues of outliers,

collinearity, and interaction effects.

CONCLUSION

A number of VOCs are among the air toxics listed in the

1990 Clean Air Amendments Many of these compounds were known to be present in both indoor and outdoor air, but had not been monitored among the general population Information on levels of exposure to these compounds was essential to determine the need for regulatory mechanisms

to reduce the levels of hazardous air pollutants to which the general public is exposed The NUATRC therefore embarked on a project with the NCHS to develop a profile

of VOC exposures encountered by US adults in their daily activities

The NUATRC-NCHS collaborative project provides valuable data, revealing a national distribution of personal exposures to VOCs, which can be used to compare how exposures in individual communities relate to the national distribution Because the NHANES characterized national- level VOC exposures using a population-based sampling strategy, the results represent non-occupational VOC exposures throughout the US The results of the four NUATRC grant recipients can be used by other investigators

in generating hypotheses about potentially significant exposure sources and pathways for VOCs in the general US population The results may also help in developing approaches for minimizing VOC exposures and reducing environmental health risks in the general population Other investigators are encouraged to access the dataset for future data mining activities.

2000 NHANES J Toxicol Environ Health A 72(14):915-24.

Wang SW, MA Majeed, PL Chu, HC Lin 2009 Characterizing Relationships between Personal Exposures

to VOCs and Socioeconomic, Demographic, Behavioral

Variables Atmos Environ 43:2296-2302.

NUATRC RESEARCH REPORT NO 16

ACKNOWLEDGMENTS

The NUATRC wishes to express its sincere appreciation

to the recipients of its NHANES VOC Project grants, Dr.

Stuart Batterman at University of Michigan, Drs Barry Ryan

and Anne Riederer at Emory University, Dr Elaine

Symanski at the University of Texas, and Dr Sheng-Wei

Wang at Institute of Environmental Health in Taiwan as

well as their research teams The NUATRC also thanks Drs.

Thomas Stock and Maria Morandi, who developed the

original study design and questionnaire for the Pilot Study

and Dr Clifford Weisel of EOHSI, who supervised the

analysis of badge samples We also thank Brenda Gehan,

NUATRC Project Coordinator; Clifford Johnson, Director of

NHANES; Susan Schober, Senior Epidemiologist, NCHS;

David Lacher, Medical Officer, NCHS; Lester Curtin, Senior

Mathematical Statistician, NCHS; and NUATRC Scientific

Advisory Panel, whose expertise, diligence, and patience

have facilitated the successful completion of this report.

ABBREVIATIONS

BTEX benzene, ethylbenzene, toluene, and xylene

Sciences Institute

NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination

Surveys NUATRC National Urban Air Toxics Research Center

UMDNJ University of Medicine and Dentistry, New

Jersey

VOC Project Collaborative NCHS-NUATRC VOC Project

7

Distributions of personal VOC exposures: A population-based analysis

Chunrong Jia, Jennifer D'Souza, Stuart Batterman *

University of Michigan, Ann Arbor, MI 48109-2029, USA

Article history:

Received 19 November 2007

Accepted 10 February 2008

Available online 1 April 2008

© 2008 Elsevier Ltd All rights reserved

* Corresponding author Tel.: +1 734 763 2417

E-mail address:StuartB@umich.edu(S Batterman)

0160-4120/$ – see front matter © 2008 Elsevier Ltd All rights reserved

Pollutant distributions used in exposure and risk analyses are usually derived from empirical data, and measurements using personal monitoring are considered to be the best approximations to actual exposure ( NRC,1991 ) While personal monitoring has been used for many pollutants, e.g., particulate matter, nitrogen oxides and volatile organic compounds (VOCs), previous studies have not used a population-based sample, and thus are not necessarily representative

of a broad population In addition, the databases underlying many studies used to estimate distributions may be unavailable, inconsistent

in quality, and difficult to understand Indeed, it is a mammoth task to design, recruit, monitor, quality-assure and evaluate a population- based program, especially for large regions like the U.S Importantly, if the assumed pollutant distribution is not representative, then pre dictions may not reflect true exposures, and conclusions regarding exposures and risks may be erroneous.

The objective of this study is to characterize the distributions of personal exposures to VOCs in the U.S measured in the 1999–2000

1 Introduction

Information regarding the distribution of pollutant concentrations

is used to answer many important questions in exposure and risk

assessment, such as ‘What is the variability of the exposure

es-timates?’( US EPA, 1992 ), and ‘How many individuals have exposure

over a given risk-based threshold?’ In the context of risk management,

this information is needed to apportion emission sources and, more

generally, to evaluate policies and interventions: ‘What fraction of

exposure is due to occupational exposure, traffic, indoor and other

sources?’ and: ‘Would controlling emission sources in residential

garages significantly reduce benzene exposure?’ ( Batterman et al.,

2007; Loh et al., 2007 ) The availability, and then the form and

parameterization of distributions are critical assumptions that

deter-mine the answers to such questions While the use of “standard”

distributions has been encouraged when feasible ( Finley and

Paus-tenbach, 1994 ), typical statistical measures of central tendency and

dispersion, such as means, medians and standard deviations, and the

common assumption of log-normality may inadequately describe the

true distribution Probabilistic methods, which use probability

distributions instead of point estimates to represent the range of

possible exposures, are potentially more representative of actual

Information regarding the distribution of volatile organic compound (VOC) concentrations and exposures isscarce, and there have been few, if any, studies using population-based samples from which representativeestimates can be derived This study characterizes distributions of personal exposures to ten different VOCs inthe U.S measured in the 1999–2000 National Health and Nutrition Examination Survey (NHANES) PersonalVOC exposures were collected for 669 individuals over 2–3 days, and measurements were weighted to derivenational-level statistics Four common exposure sources were identified using factor analyses: gasoline vaporand vehicle exhaust, methyl tert-butyl ether (MBTE) as a gasoline additive, tapwater disinfection products, andhousehold cleaning products Benzene, toluene, ethyl benzene, xylenes chloroform, and tetrachloroethenewere fit to log-normal distributions with reasonably good agreement to observations.1,4-Dichlorobenzene andtrichloroethene were fit to Pareto distributions, and MTBE to Weibull distribution, but agreement was poor.However, distributions that attempt to match all of the VOC exposure data can lead to incorrect conclusionsregarding the level and frequency of the higher exposures Maximum Gumbel distributions gave generallygood fits to extrema, however, they could not fully represent the highest exposures of the NHANESmeasurements The analysis suggests that complete models for the distribution of VOC exposures require anapproach that combines standard and extreme value distributions, and that carefully identifies outliers This isthe first study to provide national-level and representative statistics regarding the VOC exposures, and itsresults have important implications for risk assessment and probabilistic analyses

Jia et al: Reprinted from Environment International, 34(7), Jia C, J D'Souza, and S Batterman, “Distributions of Personal VOC Exposures:

A Population-based Analysis,” 922-931, 2008, with permission from Elsevier.

National Health and Nutrition Examination Survey (NHANES) This

population-based survey represents what is believed to be the largest

study of VOC exposures in a community setting The behavior of the

full range of the measurements is described using common statistical

distributions We use correlations and factor analyses to identify

related VOCs and possible sources, and compare measurements to

risk-based levels We then fit extreme concentrations to the maximum

Gumbel distribution, and address the issue of outliers We conclude by

contrasting the NHANES measurements with several other recent

studies of personal VOC exposures.

2 Methods

2.1 NHANES

NHANES was designed primarily to assess the health and nutritional status of

adults and children in the U.S through interviews and physical examinations Surveys

were conducted periodically from 1971 to 1994, and became continuous in 1999 The

current NHANES (also known as continuous NHANES) was initiated in 1999 and uses a

2-year survey cycle In the overall NHANES 1999–2000 sample, there were 9965

participants (5161 adults and 4804 children≤18 years of age) Participants were

sampled through a stratified, multistage probability sampling scheme (CDC, 2006a,b)

Initially, counties (or blocks of counties) were selected Within counties, groups of

blocks (household clusters) were chosen Letters were sent to selected households

within those blocks, informing them of the study, after which NHANES staff visited the

households and one or more participants were interviewed from the household Five

sub-populations were over-sampled to ensure sufficient sample size, specifically,

low-income persons, adolescents 12–19 years, persons ≥60 years of age, African Americans,

and Mexican Americans The 1999–2000 survey was the first to measure personal

exposure to VOCs A sub-sample of 851 adults (ages 20–59 years) of the overall NHANES

sample was selected to participate in these measurements The sub-sample is based on

a one-fourth sample from 1999 and a one-third sample from 2000, and was designed to

be nationally representative

2.2 VOC sampling and analysis

Personal VOC exposures were collected on the adult sub-sample selected from the

NHANES sample There were no additional exclusion criteria Participants were

instructed to wear badge-type passive exposure monitors (3M 3520 OVM, 3M Co., St

Paul, MN) for 48–72 h Additionally, participants were administered a short

questionnaire regarding the length of time they wore their badge and 30 other

questions on factors potentially related to VOC exposures, e.g., contact with dry cleaning,

tobacco smoke and gasoline vapor over the past several days These questions were not

included in the larger NHANES survey

VOC badges were chemically desorbed and analyzed by gas chromatography/mass

spectrometry (GC/MS, HP 5890/5972 MSD, EnviroQuant ChemStation, Hewlett-Packard,

Palo Alto, CA) following well-defined protocols and QA/QC protocols (CDC, 2006c;

Weisel et al., 2005a; Chung et al., 1999a,b) VOCs included benzene, toluene, ethyl

benzene, m,p-xylene, o-xylene (i.e., BTEX compounds), chloroform, trichloroethene

(TCE), tetrachloroethene (PERC), 1,4-dichlorobenzene (p-DCB) and methyl tert-butyl

ether (MTBE) (CDC, 2006c) Properties and method detection limits (MDLs) of these

compounds are summarized inTable 1, and the MDLs determined byWeisel et al.(2005a)were applied in this paper

2.3 Data acquisition and cleaningData were extracted from the 1999–2000 NHANES databases, maintained at theCenter for Disease Control and Prevention's (CDC) website (www.cdc.gov/nchs/about/major/nhanes/lab99_00.htm) The original dataset contained 851 cases (individuals)and 53 variables, which included the participant's identification number, concentra-tions and detection status of the ten VOCs, sampling information (including number ofhours the badge was worn), house characteristics, and participant activities The datasetalso contain sampling variables specific to the VOC dataset, which represent theinfluence of the observation in extrapolating to the national level, and which accountfor the clustering in the data These variables allow the results to be generalized to theU.S civilian non-institutionalized population Due to the clustering, the total variancealso includes intra-cluster correlation, since observations within a cluster tend to besimilar Not accounting for the clustering gives incorrect variance estimates and inflatedsignificance

Of the 851 cases, 182 were non-respondents and were excluded from furtheranalyses Two cases with excessively long sampling periods (5.7 and 7.9 days,participants #578 and #468, respectively) were excluded An initial screening analysisidentified two outliers (participants #3852 and #4076) with extremely high concentra-tions of BTEX (N2000 μg m− 3of ethyl benzene and xylenes for #3852, andN6000 μg m− 3

of toluene for #4076) These two cases were excluded Thefinal dataset included 665participants

2.4 Data analysis

As simple indicators of exposure, we defined two new variables: BTEX as the sum ofthefive BTEX components; and TVOC10as the sum of the ten VOCs measured inNHANES The sums also used one-half of the MDLs for non-detects Analysis startedwith basic descriptive statistics, including sample size, detection frequency (DF),average, standard deviation and percentiles Spearman rank correlation coefficientswere calculated to investigate the relationship among pairs of VOCs using the weighteddataset The statistical significance of the correlations was determined for each VOC pair

as the minimum p-value from two linear regressions of each VOC on the other, alsousing the weights as well as appropriate variance estimates This procedure was usedfor |r|N0.4, and coefficients were considered significant for p≤0.05 These statisticswere generated by SAS-callable SUDAAN (release 9.0, Research Triangle Institute,Research Triangle Park, NC, U.S.) and the survey procedures in SAS 9.1 (SAS Institute Inc.,Cary, NC, U.S.), which contain algorithms that properly weight cases and account for thenon-random and clustered sampling of the NHANES data Factor analysis was used tohelp identify common VOC sources and to identify a subset of four VOCs with varyingproperties and different sources for further analysis in the present paper (Supplementalmaterials give results for all ten VOCs) This analysis used log-transformed unweighteddata as full concentrations of most compounds were roughly log-normally distributed(see results), and varimax rotations Our analysis focused on the larger factor loadings,typicallyN0.6 These analyses used SAS 9.1

Tofit distributions of the full range of concentrations and extreme values, wesynthesized a derived dataset (n = 14,898) in which cases were repeated with thefrequency of repetitions based on the case weights This approach yields valid statisticswhen the variance and correlation among variables was unimportant, e.g., univariateanalyses Distributions werefitted by maximum likelihood estimation (Thompson,1999a) using a sample sizeN10,000 to achieve a high level of reliability in distributional

Table 1

Physical and chemical properties and method detection limits (MDLs) of the 10 VOCs

VOC Abbreviation Chemical

p-Xylene C8H10 106-42-3 106.2 13.3 138.4 1.4 0.65 NA NA NAo-Xylene o-Xylene C8H10 95-47-6 106.2 −25.2 144.4 0.85 0.29 NA NA NA1,4-Dichlorobenzene p-DCB C6H4Cl2 106-46-7 147.0 53.0 174.1 0.91 0.43 2.2 NA 800Chloroform Chloroform CHCl3 67-66-3 119.4 −63.5 61.2 0.42 0.28 0.3 2.3 × 10− 5 NATrichloroethene TCE C2HCl3 79-01-6 131.4 −84.8 87.0 0.44 0.24 NA NA NATetrachloroethene PERC C2Cl4 127-18-4 165.8 −22.4 121.3 0.42 0.22 1.1 NA NAMethyl tert-butyl ether MTBE C5H12O 1634-04-4 88.2 −108.6 55.2 0.68 0.38 NA NA 3000CAS=Chemical Abstracts Service, MW=molecular weight, MP=melting point, and BP=boiling point are all from the CRC handbook (Lide, 2005) RfC=Reference concentration, UnitRisk=carcinogenic slope factor

aBased on 48-hour samples

b FromUS EPA (2007)showing the high unit risk estimate for benzene (low estimate is 2.2 × 10− 6)

attribution (Haas, 1997) Goodness-of-fit was evaluated using Anderson–Darling (A–D),

Kolmogorov–Smirnov (K–S), and Chi-square (χ2

) tests, and by visually examiningprobability plots and histograms The A–D test served as the primary criterion since it is

suitable forfitting distributions with extreme tails, and thus appropriate for the

extrema emphasized here Smaller A–D statistics indicate better fits The other tests

help to confirm or improve the selection These analyses primarily used Crystal Ball

(Decisioneering, Inc., Denver, CO, U.S.)

To test whether the highest concentrationsfit a maximum Gumbel distribution, a

form used in several earlier air pollution analyses (Roberts, 1979a,b), we used a

relatively simple procedure (Barnett, 1975) in which each ordered extreme value Ciis

plotted against quantity−ln[−ln(Pv)], where Pvis:

Pv¼ r  0:44ð Þ= N þ 0:12ð Þ ð1Þ

and where r = the reverse rank of Ci, and N = the number of the extreme values A good

fit (e.g., R2near unity) to the linear regression line confirms the appropriateness of this

distribution This analysis was performed for the top decile among all participants

(n = 64–65 cases after eliminating missing data), and also for the top 5% of

concentrations that exceeded MDLs (n = 11–30 cases, depending on the VOC)

3 Results

3.1 Descriptive analysis

Descriptive statistics for the NHANES 1999–2000 VOC data are given inTable 2

(Supplementary materials give the complementary unweighted analysis in Table S1)

Most of the VOCs had detection frequencies (DF) exceeding 60%, except for TCE

(DF = 23%) and MTBE (DF = 28%) Concentrations varied widely, reflected in large

standard deviations and skewness coefficients Chloroform's range was more restricted

(bMDL to 54 µg m− 3) In most cases, statistics obtained using weighted and unweighted

approaches were similar (Tables 2and S2) although p-DCB and MTBE show several

differences at the higher concentrations, e.g., the weighted 75th and higher percentile

concentrations were much lower than the unweighted data for p-DCB, showing the

importance of using population-based statistics

Of the ten VOCs, four had reference concentrations related to non-cancer toxicity

and two had cancer-slope factors listed in the US EPA IRIS database (US EPA, 2007)

(toxicity information for other VOCs is available elsewhere, but we restricted analyses to

the IRIS list, which is peer-reviewed and widely accepted) To identify those individuals

with high exposures to certain VOCs, we calculated the fraction with exposures thatexceeded the reference concentration or excess lifetime cancer risk levels of 10− 4, 10− 5and 10− 6, with the strong assumption that the short-term NHANES measurement wasrepresentative of long term exposures Nearly all (N99%) of the measurements fell belowthe reference concentrations A few (b1%) of the benzene and ethyl benzenemeasurements exceeded reference concentrations However, 77 and 10% of the NHANESmeasurements exceeded benzene concentrations that correspond to lifetime individualrisks of 10− 5and 10− 4(1.3 and 12.8 µg m− 3, respectively) (the upper bound cancer-slopefactor in IRIS was used for benzene) For chloroform, 86 and 16% exceeded these risklevels (0.4 and 4.4μg m− 3, respectively) However, because benzene's MDL (typically1.1μg m− 3) corresponds to a risk level of 8.6 × 10− 6, and chloroform's MDL (0.4μg m− 3)corresponds to 9.7 × 10− 6, the statistics for the 10− 5risk level (and lower) may not bemeaningful Still, statistics for the higher exposures are significant and striking — thereare few other environmental pollutants that yield≥10− 4 risks in 10–16% of thepopulation The median risks for benzene and chloroform (2.2 × 10− 5and 2.3 × 10− 5,respectively) also are very similar to predictions based on microenvironmental con-centrations and time activity patterns (Loh et al., 2007), although the fraction of theNHANES subjects with risks≥10− 4for these compounds appears to exceed the upperrange of predictions This suggests that a full range distribution provides a poorfit toextrema, which deserves special attention since these extrema represent the mostexposed individuals In the following, we discuss the major VOC groups and individualcompounds

3.1.1 BTEX compoundsUnsurprisingly, thefive BTEX compounds were detected in nearly every sample(DF = 66 for benzene to DF = 96% for m,p-xylene) Toluene and m,p-xylene had thehighest concentrations among the ten VOCs (medians of 17.4 and 6.5μg m− 3,respectively), and toluene was the predominant VOC component among the tenVOCs for most (55%) participants BTEX comprised the majority of TVOC10(averagepercentage of BTEX:TVOC10= 67 ± 25%) BTEX compounds often arise as a group,primarily from evaporated gasoline and vehicle exhaust However, toluene and xylenealso have many separate and indoor sources, e.g., paints, solvents, and cigarette smoke.Many studies have detected and reported high concentrations of the BTEX compounds(Raw et al., 2004; Saarela et al., 2003; Mohamed et al., 2002; Clayton et al., 1999).3.1.2 Chlorinated compounds

The four chlorinated compounds in the NHANES dataset had lower detectionfrequencies (23–79%) than the BTEX compounds Typically, outdoor levels of these

Table 2

Descriptive statistics of weighted data including the ten VOCs plus BTEX and TVOC10

VOC Missing DF Mean SD GM GSD Skewness Min 25th Median 75th 90th 95th 99th Max

(%) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3) (μg m− 3)Benzene 21 65.5 5.3 7.0 3.2 2.6 4.3 0.7 1.4 2.8 5.8 13.5 18.7 32.6 119.5Toluene 30 93.6 36.4 107.3 17.5 2.8 10.3 1.7 9.2 17.4 29.9 59.8 98.3 331.1 1610.8Ethyl benzene 26 93.0 8.4 41.3 2.9 3.2 17.7 0.1 1.3 2.6 5.2 14.2 25.2 110.9 837.1m,p-Xylene 22 95.9 18.8 43.2 7.2 3.5 5.8 0.2 3.3 6.5 14.6 38.7 69.8 233.0 728.7o-Xylene 22 92.5 6.5 14.5 2.8 3.2 6.6 0.1 1.3 2.4 4.9 14.1 26.4 62.5 202.3BTEX 15 97.6 74.4 153.0 36.5 2.9 6.6 0.8 18.6 33.2 66.6 152.8 285.3 784.4 1966.2p-DCB 24 62.9 27.3 120.7 3.2 5.7 11.5 0.3 0.9 1.7 9.2 34.8 142.1 490.8 2235.6Chloroform 17 79.3 2.7 4.5 1.4 3.0 4.4 0.2 0.6 1.1 3.0 5.9 12.1 25.4 53.9TCE 24 22.9 3.4 22.7 0.4 3.4 11.0 0.1 0.2 0.3 0.5 1.2 7.4 75.5 327.3PERC 26 69.0 5.2 31.2 1.0 4.1 16.1 0.1 0.4 0.7 2.4 6.6 18.5 76.8 659.1MTBE 24 27.8 5.2 15.6 1.4 4.1 7.9 0.4 0.5 0.6 5.5 10.7 21.3 50.0 181.7TVOC10 13 99.2 117.3 200.9 61.9 2.9 5.3 0.6 31.1 55.6 106.0 273.4 382.8 1206.4 2276.1

Spearman rank correlation coefficients for the 10 VOCs using the weighted data, with statistically significantly coefficients (pb0.05) in bold

VOCs Toluene Ethyl benzene m,p-Xylene o-Xylene p-DCB Chloroform TCE PERC MTBE

compounds are low, and exposure occurs mostly from indoor or especially occupational

sources Due to the differences among these compounds, each is discussed separately

• p-DCB levels were surprisingly high and showed tremendous variability (median =

1.7μg m− 3, average = 27μg m− 3, maximum = 2236 µg m− 3), possibly due to the use of

mothballs, air fresheners and other deodorants (Sack et al., 1992; Wallace et al., 1987)

p-DCB was the predominant VOC in 15% of the exposure measurements, and for these

133 participants, the median concentration was high, 61.7 µg m− 3

• Chloroform was found in most samples (79%) at a median concentration of 1.1μg m− 3

Chloroform along with bromodichloromethane, dibromochloromethane and

bromo-form, are trihalomethanes (THMs) that are often formed as water disinfection

by-products when chlorine is added to water, and that can be released to indoor air

when chlorinated tap water is used (Weisel et al., 1999)

• Tetrachloroethylene (PERC) was found in 69% of samples with a median concentration

of 0.7μg m− 3 PERC is a component of dry-cleaningfluids, and high concentrations

might result from wearing freshly dry-cleaned clothes or visiting a dry cleaner Two

measurements were extremely high (659 and 490 µg m− 3for participants #9751 and

#130), more thanfive times higher than the next measurement It is puzzling,

however, that these two participants did not report dry-cleaning exposure, breathing

fumes from or using dry-cleaningfluid or spot remover Subject #9751 spent an

unusually large amount of time at work/school (mean = 9.4 h day− 1) Subject #130

worked with paint thinners, brush cleaners, or strippers as well as glues, adhesives,

hobbies or crafts, and also reported having new carpet installed in the past 6 months,

and possibly the high exposure might be explained by“exposure to solvents” that this

individual reported

• Trichloroethylene (TCE) was detected in relatively few cases (DF = 23%) However, the

top ten highest concentrations exceeded 300 µg m− 3 TCE has many industrial

applications, e.g., it has been commonly used as a degreasing solvent, but residential

uses are limited Some exposure can occur from vapor intrusion into buildings from

contaminated sub-soils and from other environmental sources, but the high

concentrations suggest more immediate contact with solvents

3.1.3 MTBEThis gasoline additive was detected in 28% of the measurements, with six very highconcentrations (98–182 µg m− 3) It was the predominant VOC in 5% (n = 45) of thesubjects where the median level was 23 µg m− 3 MTBE has been used in gasoline inselected areas in the U.S since 1979, though it is now being phased out No other usesare likely to lead to public exposure Thus, MTBE should not be detected in areas whereMTBE is not in gasoline, and MTBE should be a unique tracer for gasoline vapor in areaswhere this compound is in gasoline This suggests that a MTBE will have a bimodaldistribution in the NHANES data which combines these two areas, as shown later Thegeographic location of participants is not available for NHANES 1999–2000 (in contrast

to earlier data) because the sample size is much smaller, estimates by geographic regionare less stable, and the risk of identifying subjects is greater

3.2 Correlations and factor analyses

As expected, the BTEX compounds were strongly correlated (rN0.60,Table 3), andthe correlations between ethyl benzene, p,m-xylene and o-xylene were especially high(0.92≤r≤0.95) The latter three compounds co-exist in gasoline, as well as in otherproducts where they are called“mixed xylenes” (ATSDR, 2005) Chlorinated compoundsTCE and PERC showed moderate correlation (r = 0.41) Correlations among other VOCswere weak Weighted and unweighted ( Table S3) correlation matrices were similar.The factor analysis identified three factors that explained 67% of the total variancewhen an eigenvalue cut-off of 1.0 was used, but results obtained unreasonablyassociated MTBE, the gasoline tracer, with the chlorinated solvents TCE and PERC Wethen used a four factor analysis with a lower eigenvalue cut-off (0.8), which resolvedthis issue This analysis explained 76% of the variance Factor 1 included the BTEXcompounds in which the mixed xylenes had very high loadings (N0.9), following fromthe correlations and showing that these VOCs nearly always occur together Tolueneand benzene had lower loadings (0.73 and 0.79, respectively), indicating that otherfactors contribute to these compounds Factor 2 included TCE and PERC, which aremainly used in dry-cleaning products Factor 3 contained p-DCB, a deodorant foundespecially in toilets, and chloroform, a water disinfection byproduct, thus this factorlikely reflects exposures in bathrooms Factor 4 contained only MTBE (loading of 0.83).These factors varied slightly depending on whether or not the data were log-trans-formed The factor analyses helped confirm to identification of the major VOC groupsand the likely sources of exposure For further analysis in the present paper, we selectedone compound from each four factors, specifically, benzene, PERC, chloroform andMTBE (Supplemental materials show the other VOCs)

3.3 Probability and frequency distributions 3.3.1 Full distributions

Frequency distributions show“heavy” tails for the BTEX compounds, and high,narrow peaks at low concentrations with only a very few high observations for TCE,PERC and MTBE (Fig S1) The latter three compounds were detected least frequently(i.e., many values were below MDLs), and their median concentrations were the lowestamong the ten VOCs in NHANES Bimodal distributions were observed for MBTE andchloroform For MTBE, over 70% of measurements were below the MDL, which formed amode around 0.7 µg m− 3; the second but smaller mode occurred around 7 µg m− 3 Thelower mode of the bimodal distribution reflects MDLs obtained for those studyparticipants living in areas where MTBE is not used, as well as those living in MTBE-useareas but who have very low exposure to gasoline vapors The upper mode reflectsMTBE-exposed participants living in MTBE-use areas For chloroform, the lower mode

at about 1μg m− 3may reflect both background levels and perhaps an erroneously lowMDL (stated as 0.4μg m− 3); the upper mode near 4μg m− 3may reflect individuals

Table 4

Identification and parameters of best-fit distributions

VOCs Bestfits Distribution parameters Goodness-of-fit

testsLocation Scale Shape A–D p-valueBenzene Log-normal 4.95 5.89 – 150.1 b0.005

Log-normal distribution is not the bestfit for p-DCB, TCE and MTBE, but estimated

parameters for this distribution as well as the best-fit distributions are shown

Fig 1 A Observed cumulative frequency distribution for measurements, andfitted (log-normal) cumulative probability distribution for benzene concentrations B Probability plotsfor maximum Gumbel type distributionfitting both the top 5 and 10% of measurements Points show individual measurements; lines show fitted distribution based on linear

having higher exposure to chloroform Each NHANES measurement (all VOCs) is

assigned a unique MDL, which depends on the averaging time

Of the candidate distributions, log-normal distributions had the bestfit to all VOCs

except for p-DCB and TCE, which were assigned the Pareto distribution, and MTBE, which

was assigned the Weibull distribution (Table 4) MTBE is a special case of a mixed

distribution since, as just discussed, concentrations outside the MTBE-use area reflect

MDLs, which in turn reflect the small amount of variation in the time that the badge was

exposed In the MTBE-use area, distributions would be expected to be roughly log-normal,

paralleling benzene which also arises from gasoline-related sources However, as noted,

the MTBE distribution cannot be cleanly split since information on the locations of

participants is unavailable These distributions were selected using all observations

(n = 665) and the A–D test; the K–S and χ2tests gave similar results However,

goodness-of-fit tests usually rejected the candidate distributions, a typical result for environmental data,

in part due to anomalies and measurement errors (Ott, 1995)

Fitted and measured cumulative frequency distributions are compared for the four

VOCs inFigs 1–4( Fig S2 shows similar plots for all ten VOCs, BTEX and TVOC10)

Agreement was considered“good” if fitted quantiles were within ±20% of the

observations Most concentrations below the 20th percentile were underestimated;

however, these measurements usually fell below MDLs and risk-based values (Table 1)

Otherwise,fits varied by VOC and percentile The BTEX compounds and chloroform

generally showed good agreement with log-normal distributions, although ethyl

benzene and xylenes showed moderate differences, e.g., 65–80th percentiles were

overestimated by 20–30%, and 99th percentile concentrations were underestimated by

13–60% Other compounds showed poor agreement, e.g., 95th to 99th percentile

concentrations measurements were underestimated by 30–65% for chloroform, TCE

and PERC; overestimated for MTBE by 7–35%; and hugely overestimated for p-DCB

(factor of 2–28) Fits for TCE and MTBE were also poor at intermediate percentiles

Log-normal distributions for p-DCB, TCE and MTBE, e.g.,Fig 4B, demonstrated poorfits that

were clearly worse than the selected Pareto and Weibull distributions The composite

variables, BTEX and TVOC10, closelyfit log-normal distributions, probably because these

summations of VOCs tended to“average-out” disparities

While log-normal distributions provided moderately goodfits to most compounds,

both low and high concentrations were underestimated, and the middle range was

overestimated Geometric means were very close to medians for BTEX compounds,

moderately higher for chlorinated compounds, and much higher for p-DCB and MTBE

(Table 2) The highest concentrations (≥95th percentile) were significantly

under-predicted The geometric standard deviationsσgranged from 2.6 (benzene) to 5.7 DCB), showing considerable variation and no clear groupings (Table 2) None of thecandidate distributionsfit p-DCB, TCE and MTBE, compounds with low detectionfrequencies (63, 23 and 28%, respectively) As elaborated in the Discussion, we speculatethat these measurements reflect multiple circumstances: non-detects; moderateconcentrations due to local but dispersed sources; and very high concentrations due

(p-to some unusual contact or exposure situation

3.3.2 Extreme distributionsFitted and observed maximum Gumbel distributions for concentrations exceedingthe 90th and 95th percentile concentrations are shown for benzene, PERC, chloroformand MTBE inFigs 1–4, respectively;fitting results, e.g., goodness-of-fit as R2

• Benzene, 1 measurement (119 µg m− 3, subject #5359,Fig 1B) This individualreported using household disinfectants, degreasing cleaners or furniture polish

• Toluene, 6 measurements (1611, 1551, 1399, 1267, 797 and 668 µg m− 3for subjects

#4879, #8631, #2037, #4479, #2002, and #1002, respectively) All of these subjectsreported at least one of the following activities: pumping gasoline into a car; near asmoking person forN10 min; and breathing fumes or using gasoline Note that at theonset, we deleted two cases with still higher toluene levels (1352 and 6280 µg m− 3forparticipants #3852 and #4076)

• Ethyl benzene measurement, 1 measurement (837 µg m− 3, subject #4514)

• m,p-Xylene measurement, 1 measurement (729 µg m− 3, subject #8801)

• o-Xylene, 3 measurements (202, 173 and 129 µg m− 3for subjects #8801, #4514 and

#8110, respectively) Subjects #4514 and #8801 reported being near a smoker forN10 min Subject #8110 reported pumping gasoline into a car

• PERC, 2 measurements (659 and 490 µg m−3for subjects #9751 and #130, respectively,

Fig 2B) These subjects did not report any contact with dry-cleaning products.Fig 2 Observed andfitted distributions for PERC Otherwise asFig 1

fitted distributions for chloroform No outliers are removed from the maximum Gumbel distribution Otherwise as

• p-DCB, 4 measurements (2236, 2227, 1511, 1152 µg m− 3for subjects #3294, #8172,

#7929 and #9158, respectively) Three of these subjects reported deodorizer use (not

#3294)

• MTBE, 6 measurements (182,170, 159, 155,126 and 98 µg m− 3for subjects #6514, #2031,

#1551, #4350, #1002 and #7949, respectively,Fig 4B) Five of these subjects (not #7949)

reported pumping gasoline into a car, or breathing fumes or using gasoline

Chloroform and TCE did not show obvious outliers Interestingly, only four subjects

had multiple outliers (#1551 for toluene and MTBE; #2002 for toluene and MTBE;

#4514 for ethyl benzene and o-xylene; #8801 for m,p-xylene and o-xylene) In

addition, subject #5359, who had a high benzene exposure, had the second highest

chloroform concentration Several of these concentrations are extremely high and

indicate the presence of very strong and local sources, e.g., p-DCB concentrations of

N1000 μg m− 3are likely due to the use of mothballs and deodorizers If such exposures

are infrequent, then the calculated lifetime exposures and risks may not be excessive

Unfortunately, the NHANES dataset does not allow an estimate of the frequency of such

events BTEX and TVOC10did not show outliers other than the two cases (subjects

#4076 and #3852) removed at the onset

After removing these measurements, thefit of the Gumbel distribution improved

considerably, and most R2

values exceeded 0.75 with the exceptions of toluene and TCE

Results for chloroform and TCE were unchanged since no data were removed (the

rationale and approach to such selective data censoring is discussed in the Discussion)

Still, the top decile may not represent true extrema, especially if many measurements

fall below MDLs Thus, we refit the Gumbel distribution to the top 5% of the data that

exceeded MDLs This improvedfit for all VOCs, especially for TCE for which the R2

jumped to 0.88 Removal of outliers further improvedfits, giving R2

≥0.85 for all VOCsexcept MTBE Removing the top 8 MTBE measurements (2 additional points, rather than

just the 6 noted earlier) improved MTBEs R2

to 0.87 Thus, with appropriate delineation

of extrema and exclusion of outliers, extreme values can be closely modeled

4 Discussion

Measurements of environmental pollutants such as those in the

NHANES VOC exposure database re flect multiple circumstances that

may be classi fied into four groups based on the capabilities of the monitoring method: (1) values falling below method detection limits (MDLs), which are frequently assigned an estimated or imputed value, e.g., 1/2 MDL; (2) detections or “traces” exceeding MDLs but still below quantitation limits (e.g., 10 σ), that can only be imprecisely deter- mined; (3) values within the normal linear range of the instrument; and (4) “over-range” measurements that are likely to be under-re- ported due to saturation or other non-linear effects Reported mea- surements are also prone to errors in the collection, analysis, data entry and other factors Measurements also may be classi fied into four groups with respect to the phenomena that underlie the pollution or the pollutant “event” during the measurement period: (1) an absence

of the pollutant; (2) generally low or “background levels” that arise due to contributions from distant or “regional” emission sources; (3) moderate-to-high concentrations from “local” or strong emission sources that are well-dispersed; and (4) occasional very high con- centration “hits” yielding “extrema” due to “near-field” impacts, ex- ceptionally strong sources, or a combination of moderately-to-strong sources and unfavorable dispersive conditions For pollutants where MDLs are low, measurements often re flect contributions from both background and local sources A conceptual understanding of these groupings and at least some quanti fication of the applicable con- centration ranges, which do not have precise boundaries, are necessary

to properly interpret measurements and distributions, including the identi fication of outliers We note that few laboratories or investigators report performance measures that include limit of quantitation and linear dynamic range Also, all VOC sampling techniques have limitations, and partial saturation of the adsorbents in the passive samplers used in NHANES will reduce their sampling uptake rate atFig 4 Observed andfitted distributions for MTBE “Fitted Distribution1” is Weibell distribution, the best fit “Fitted Distribution2” is log-normal, shown for comparison Otherwise asFig 1

Table 5

Parameters of extreme distributions, including slopes, intercepts (IC), and R2from Eq 1

outliersremovedWith outliers Without outliers With outliers Without outliers

long averaging times and at high concentrations, leading to negative

biases at high concentrations or sampling times ( Jia et al., 2007 ).

4.1 Probability distributions

In general, pollutant concentrations and exposures are random in

nature as they depend upon a number of variable factors, e.g.,

emis-sion rates, microenvironmental characteristics, time activity budgets,

and human activities Often, basic information regarding VOC

concentrations or exposures is neither available, generalizable, nor

certain This is in strong contrast to distributions of other variables

used in exposure and risk calculations, e.g., dosimetric parameters

(e.g., body weight, intake rate) and time activity durations ( Sexton

et al., 1992; Finley et al., 1994 ), which are well-characterized and easily

bounded Notably, the variation in concentrations or exposures can

dwarf the variation in other parameters (with the possible exception

of toxicity parameters like cancer-slope factors) Further variation in

results of measurement programs can be caused by a host of factors,

including sampling and analysis methods, sampling time, study

popu-lation, season, weather, etc.

4.1.1 Full distributions

Early studies of probability distributions focused on ambient

mea-surements of criteria pollutants in cities, e.g., carbon monoxide ( Ott,

1979 ) and sulfur dioxide ( Berger et al., 1982 ), and concentrations at all

averaging times were usually found to approximate log-normal

distributions ( Larsen, 1969; Ott, 1995 ) In workplace settings, 8-hour

time-weighted average concentrations also have been frequently

represented using log-normal distributions ( Nicas and Jayjock, 2002 ).

Relatively few studies have examined distributions of VOC

concentra-tions in non-occupational settings Log-normal distribuconcentra-tions were

assigned to ten VOCs measured in 427 indoor air samples collected in

residences in Denver, Colorado ( Foster et al., 2003 ) Gamma

distribu-tions provided the best fit to concentrations of 28 VOCs (including 11

aldehydes) measured in 1417 Japanese homes ( Park and Ikeda, 2004 ).

A recent U.S review reported the log-normal distribution as the best

fit for 9 VOCs in most microenvironments, and the Gamma

dis-tribution for chloroform in dining rooms ( Loh et al., 2007 ) As in the

ambient, workplace and indoor studies, log-normal distributions

provided only an approximate fit, at best, for most of the ten VOCs

examined in the present study The fit was not always very good,

especially for the less frequently detected compounds, and statistical

tests of agreement usually failed.

The practice of fitting and analyzing distributions of air pollutant

concentrations has not become routine practice in exposure

assess-ment To determine the underlying distribution, measurements are

generally matched to theoretical distributions using three steps:

selection of a candidate distribution; estimation of its parameters; and

assessment of the goodness-of- fit ( US EPA, 1997 ) Despite the

avail-ability of automated software that can rapidly perform such analyses

(e.g., Crystal Ball, Decisioneering, Inc., Denver, CO, USA; @Risk,

Palisade Corporation, Ithaca, NY, USA; Risk Solver, Frontline Systems,

Inc., Incline Village, NV, USA), it appears that the most common

approach continues to be the assumption of log-normality Thus,

medians or geometric means are used as a measure of central

ten-dency, and data are log-transformed for statistical inference testing.

These statistics give little if any information regarding extrema, and the

log-normal distributions rarely meet goodness-of- fit criteria.

4.1.2 Extreme distributions

As we noted at the onset, few studies have used a sampling design

or attained a sample size that is suf ficient to characterize population

exposure Importantly, extrema can only be derived from large studies.

Extreme values generally do not follow the distribution derived

from the full range of the data In many cases, a particular distribution

or several distributions may reasonably approximate the middle 80%

of the values; however, it may be inappropriate for the top 5 or 10% of the data ( Haas, 1997 ) Extreme concentrations of air pollutants were found to follow the Gumbel distribution when the full range was log- normally distributed ( Singpurwalla, 1972 ) We recently found that Gumbel distributions were appropriate for the top decile concentra- tions of 23 VOCs and carbonyls measured in Michigan, U.S ( Le et al.,

2007 ) The present study con firms that Gumbel distributions can be used to describe the extreme values (e.g., top 10th or 5th percentiles)

of personal VOC exposures, with the caveat that a small number of outliers will still exceed the fitted distribution As noted by Ott (1995) , the upper tail of a distribution re flects a stochastic process, and it is insensitive to the type of the hypothetical distributions, regardless the original distribution producing the tail Thus, a variety of distributions can fit the extreme values equally well While the NHANES VOC extrema were well fit by the Gumbel distribution, we found that Gamma and Weibull distributions were selected for the top 10th percentile data, and Gamma and Beta distributions for the top 5th percentile data on the basis of A –D tests (data not shown) One advantage of the Gumbel distribution, however, is that its linear plot helps in the identi fication of outliers.

Our experience analyzing the NHANES data provides guidance in fitting extrema First, a large sample is required, and it is advantageous

if most measurements exceed MDLs Possibly 5% or fewer of the observed values above MDLs may be considered extrema Second, distribution fitting cannot depend solely on goodness-of-fit tests, but also on subjective judgment Third, while the Gumbel (and other) distributions are extreme value distributions, they may not fit outliers; thus, these points must still be identi fied and removed, and an iterative approach may be the best option Such data censoring also may be necessary to improve model fit for both full and extreme value distributions Such actions often and justi fiably are criticized as

“cherry picking” We recognize the uncertainty of the data, and believe that most of the deleted values represent unusual cases However, relatively common situations such as refueling a vehicle, smoking, and wearing freshly dry-cleaned clothes need more investigation to see if they can produce the very high measurements encountered Still, the 24 censored measurements, plus the 2 censored cases representing 20 additional measurements, represent a very small percentage (0.7%) of the 6600 VOC measurements in NHANES For most of these measurements, our initial examination of the NHANES survey data did not show anything unusual, though this investigation is ongoing Unfortunately, in a study design like NHANES, follow-up interviews or repeated measurements to try to understand the exposure source and the reliability of the measure- ment are not possible.

4.2 Comparison of NHANES and other exposure estimates For some years, it has been known that exposure estimates derived using personal sampling often exceed exposures based on indoor monitoring, which in turn exceed measurements using outdoor or ambient monitoring This can apply to VOCs ( Sexton et al., 2004; Edwards et al., 2005 ), as well as other pollutants, e.g., particulate matter ( Wallace, 2000 ) While this “personal pollution cloud” or

“Linus effect” (after the comic strip character) is becoming better recognized, its strength and variability among individuals have not been quanti fied Due to its significance, NHANES measurements should only be compared to other studies that use personal sampling For VOCs, these include the Total Exposure Assessment Method (TEAM) studies in the 1980s ( Wallace, 2001 ), the National Human Exposure Assessment Survey (NHEXAS) in the late 1990s, and more recently, the Relationships of Indoor, Outdoor and Personal Air (RIOPA) study However, these (and other mostly smaller) studies are not necessarily representative of the U.S population, and none used a population-based sampling strategy Thus, these comparisons may re flect local or regional differences in VOC exposure.

We selected three U.S studies that measured personal VOC

exposures that were more or less contemporaneous with NHANES.

These were conducted in Minnesota (MN) by Sexton et al (2004) , in

Maryland (MD) by Payne-Sturges et al (2004) , and in New Jersey,

Texas and California (NJ/TX/CA; Weisel et al., 2005b ) We also included

the slightly earlier (mid-1990s) NHEXAS study ( Clayton et al., 1999 ).

percentile for NJ/TX/CA) concentrations reported in these studies.

Measurements from all studies show the very strong effect of

non-normality, e.g., means are typically 2 to 3 times higher than medians

(the NJ/TX/CA study shows a 30-fold difference for p-DCB) Largely

due to the in fluence of high concentrations (including potential

outliers), and to an extent due to the limited sample sizes (especially

in the MD study), it is clear that averages do not provide robust

measures of central tendency Thus, the following discussion

emphasizes non-parametric statistics.

Of the four reported VOCs, median concentrations in NHEXAS

signi ficantly exceeded those in the more recent studies This is

unsurprising given the general downward trend in indoor and

outdoor VOC concentrations ( Hodgson and Levin, 2003 ) In the three

other studies, medians and upper percentile statistics were similar to

NHANES Only three compounds showed sizable differences:

• p-DCB: In MN, levels were very low (examining 50th and 90th

per-centiles), about 4 to 6 times lower than the NHANES data In NJ/TX/

CA, medians were comparable to NHANES, but the 95th percentile

concentration was extremely high (314 µg m− 3), twice that in

NHANES (95th percentile concentration is 142 µg m− 3, Table 2 ).

• TCE: NHANES data showed a median TCE level 1.7–2.6 times higher

than those in the three other studies.

• MTBE: In MD, the median MTBE level was nearly 5 times higher than

the NHANES results, while the 90th percentile concentration was 6

times higher The NJ/TX/CA statistics were 3 to 4 times higher In

cases, these studies emphasized highly traf fic-exposed individuals,

moreover, MTBE may be widely used in these study areas in

comparison to NHANES, which included areas where it was not

used After censoring non-detected MTBE measurements, the

NHANES data gave a of 6.2 µg m− 3, just slightly lower than levels in

the NJ/TX/CA and MD studies Note that this comparison is

mean-ingful only if it is assumed that all or most measurements in MTBE

usage areas would result in detections, which did occur for the other

gasoline components (MTBE was not reported in the MN study).

This comparison reveals several important findings First, larger

though localized studies can give statistics that are representative or

nearly so, judged on the basis of their similarity to the NHANES data, which is population-based and thus should be representative This mainly applies to the BTEX compounds that are ubiquitous Second, there is a need for additional and probably improved measurements of chlorinated compounds, especially since some or much of the inter- study variation seems likely to arise from MDL effects (lower MDLs are needed) Finally, as noted earlier, when a pollutant like MTBE is used in only a subset of the region studied, the resulting statistics and derived distributions may not be reliable or nationally representative 4.3 Importance and applications

The analysis of the NHANES data suggests that representing the full range of VOC exposures requires a combined approach, namely, a log- normal (or other) distribution may be used for low to moderately high concentrations, and an extreme value distribution for the very highest ( ≥95th percentile) concentrations It is the highest concentrations and exposures that may need control or mitigation, or drive policies to this effect, thus these values require further attention Also, the shift from deterministic to probabilistic analyses, such as Monte Carlo methods, requires appropriate distributions of exposure parameters ( US EPA,

1995 ), and fitting and assigning probability distribution is a first and critical step ( Haas, 1997; Hamed and Bedient, 1997; Thompson, 1999b ) Log-normal distributions are not always the first choice, and several VOCs appear to follow other distributions All of the full distributions, that is, those that attempt to match all of the data, are likely to lead to the wrong conclusions concerning the level and frequency of extrema 4.4 Study limitations

We could not stratify the data to isolate regions where MTBE is used in gasoline, and thus a single distribution very poorly described MTBE concentrations There are no replicates in the NHANES dataset, uncertainty estimates for individual datum, or opportunities to further investigate outliers Exposure assumptions were simpli fied, i.e., short- term NHANES measurements were extrapolated to estimate lifetime exposures without adjustment for trends and uncertainties We also note that the risk levels and reference concentrations used are pro- tective guidelines, not standards As concentrations of many VOCs are decreasing, the fitted distributions and other statistics in the present paper will likely need updates in future years Our identi fication of the factors that explain the variation in the dataset is tentative, and might change with additional information Finally, it should be recognized that due to correlations among VOCs, univariate analyses cannot be

Table 6

Results from selected studies of personal exposure to VOCs in the U.S since 1990, and comparison to NHANES

Study area NHANES RIOPA NHEXAS Minneapolis, MN South Baltimore, MD

U.S Elizabeth, NJ; Houston, TX;

used to represent VOC mixtures, which represent a challenging public

health issue ( US EPA, 2000; ATSDR, 2000 ).

5 Conclusions

This study explored the distribution of personal exposure

mea-surements of VOCs, and its findings are relevant to health risk

as-sessment and risk management It is the first study to characterize

VOC exposures at the national level using a population-based

sampling strategy, thus, results should be broadly representative of

non-occupational VOC exposures throughout the U.S Eight of the ten

VOCs monitored using personal sampling of 669 individuals in the

NHANES dataset were detected in most samples Exposures among

study participants showed tremendous variability, ranging from

below method detection limits to as high as 6280 μg m− 3for

indi-vidual compounds and 14,287 μg m− 3as the sum of the ten VOCs in

the NHANES dataset Correlations and factor analysis identi fied four

groups of possible emission sources: gasoline vapors and exhaust; tap

water disinfection products; cleaning products, and gasoline additive

(MTBE) Log-normal distributions were assigned to benzene, toluene,

ethyl benzene, xylenes, chloroform and PERC with moderate-to-good

agreement to observations Different distributions were assigned to

p-DCB and TCE (Pareto distributions) and MTBE (Weibull distribution),

all with considerably poorer fit Extrema were fit to the maximum

Gumbel distribution, and reasonable agreement was found for most

compounds, especially after censoring outliers and de fining extrema

as the top 5% of measurements above MDLs The dataset contained a

small fraction ( b1%) of extremely high concentrations, considered to

be outliers as they did fit neither the full nor extreme value

dis-tributions The NHANES exposure database suggests that log-normal

distributions are not always the first choice for distributions, and that

none of standard distributional forms provided a close match to the

levels and frequencies of the highest exposure concentrations that

pose the greatest risks.

Acknowledgement

This work was performed under the support of the Mickey Leland

National Urban Air Toxics Research Center, Grant RFA 2006-01,

entitled “The relationship between personal exposures to VOCs and

behavioral, socioeconomic, demographic characteristics: analysis of

the NHANES VOC project dataset ”

Appendix A Supplementary data

Supplementary data associated with this article can be found, in

the online version, at doi:10.1016/j.envint.2008.02.002

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Predictors of personal air concentrations of chloroform among US adults in NHANES 1999–2000

ANNE M RIEDERERa, SCOTT M BARTELLa,b AND P BARRY RYANa

aDepartment of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

bProgram in Public Health, University of California, Irvine, California, USA

Keywords: chloroform personal air inhalation risk.

© 2009 Nature Publishing Group All rights reserved 1559-0631/09/$32.00www.nature.com/jes

Riederer et al: Reprinted by permission from Macmillan Publishers Ltd: Journal of Exposure Science & Environmental

Epidemiology, 19(3): 248-259, 2009.

Volunteer studies suggest that showering/bathing with chlorinated tap water contributes to daily chloroform inhalation exposure for the majority of US adults.We used data from the 1999–2000 US National Health and Nutrition Examination Survey (NHANES) and weighted multiple linear regression to test the hypothesis that personal exposure microevents such as showering or spending time at a swimming pool would be significantly associated with chloroform levels in 2–3 day personal air samples The NHANES data show that eight of 10 US adults are exposed to detectable levels of chloroform Median (1.13 µg/m3), upper percentile (95th, 12.05 µg/m3), and cancer risk estimates were similar to those from recent US regional studies Significant predictors of log personal air chloroform in our model (R2=0.34) included age, chloroform concentrations in home tap water, having no windows open

at home during the sampling period, visiting a swimming pool during the sampling period, living in a mobile home/trailer or apartment versus living in a single family (detached) home, and being Non-Hispanic Black versus Non-Hispanic White, although the race/ethnicity estimates appear influenced by several outlying observations Reported showering activity was not a significant predictor of personal air chloroform, possibly due to the wording of th e NHANES shower question The NHANES measurements likely underestimate true inhalation exposures since subjects did not wear sampling badges while showering or swimming, and because of potential undersampling by the passive monitors Research is needed to quantify the potential difference Journal of Exposure Science and Environmental Epidemiology (2009) 19, 248–259; doi:10.1038/jes.2008.7; published online 12 March 2008

Introduction

Chloroform is a colorless, volatile liquid that is sparingly soluble

in water and moderately lipophilic (Lide, 1996) Natural

sources including sea water and soil processes account for 90%

of emissions (Keene et al., 1999) while anthropogenic sources

include releases from drinking water and wastewater treatment,

certain industrial processes, cooling towers, and swimming

pools (McCulloch, 2003) Most chloroform in the environment

partitions to air, with the global average atmospheric

concentration estimated to be 73 ng/m3 (McCulloch, 2003).

In mammals, inhaled chloroform is metabolized in the liver, kidney, and nasal mucosa to trichloromethanol, which degrades to phosgene (US Environmental Protection Agency (EPA, 2001a)) Phosgene reacts with nucleophilic groups on enzymes and proteins to form cytotoxic adducts (EPA, 2001a) There is no current evidence of long-term bioaccu- mulation in humans (EPA, 2001a) Although an inhalation reference concentration has not been published, EPA published an oral reference dose of 0.01 milligrams per kilogram body weight per day (mg/kg-d) based on animal evidence of hepatotoxicity (EPA, 2007) EPA classifies chloroform as a probable human carcinogen based on animal studies showing that inhalation or ingestion at cytotoxic doses produces hepatic and renal neoplasia (EPA, 2001a) EPA has published an inhalation unit risk of 2.3 × 10-5 per µg/m3 and estimated air concentrations of 4 µg/m3,

4 × 10-1 µg/m3, and 4 × 10-2 µg/m3 at the 1 in 104, 1 in

105, and 1 in 106 cancer risk levels, respectively (EPA, 2007) The California Environmental Protection Agency (CalEPA) published an inhalation unit risk of 5.3 × 10-6 per µg/m3

(CalEPA, 2002) There is limited evidence for mutagenicity

or reproductive effects at doses below those causing systemic toxicity (EPA, 2001a).

Chlorinated water is thought to be the primary source of non-occupational chloroform exposure among US adults (Nieuwenhuijsen et al., 2000; Wallace, 2001) Chloroform is

The present work was performed at the Department of Environmental

and Occupational Health, Rollins School of Public Health, Emory

University.

1 Abbreviations: AER, air exchange rate; CalEPA, California

Environmental Protection Agency; CDC, US Centers for Disease

Control and Prevention; CI, confidence interval; DHHS, US

Depart-ment of Healtha nd Human Services; NHANES, US National Health

and Nutrition Examination Survey; RfD, reference dose; TEAM, Total

Exposure Assessment Methodology; EPA, US Environmental Protection

Agency

2 Address all correspondence to: Dr Anne M Riederer, Department of

Environmental and Occupational Health, Rollins School of Public Health,

Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA Tel.:

þ404 712 8458 Fax: þ404 727 8744 E-mail: arieder@sph.emory.edu

Received 26 October 2007; accepted 24 January 2008; published online

12 March2008

formed in treated water by the reaction of chlorine with

humic acids and other organic material Concentrations vary

by region, day, and time withreported levels ranging from

below detection to maximum values of 100–200 mg/l

(Clayton et al., 1999; Backer et al., 2000; Kerger et al.,

2000; Lynberg et al., 2001; Gordon et al., 2006) Bench-scale

experiments have shown that heating tap water gradually (as

in a hot water heater) or boiling can affect point of use levels.

Weisel and Chen (1994) recorded up to twofold increases in

tap water chloroform after heating from 25–651C for 30 min,

presumably from increased formation reactions among free

chlorine and dissolved organic constituents Krasner and

Wright (2005) on the other hand hypothesized that

simultaneous formation and volatilization were responsible

for the 34% decrease they observed in tap water chloroform

after boiling for one minute.

Chloroform’s volatility and ubiquitous presence in tap

water may help explain why it is frequently detected in

personal air (e.g., air sampled from the breathing zone of

subjects) and indoor air at concentrations 10–100-fold higher

than outdoor levels The EPA TEAM (Total Exposure

Assessment Methodology) studies showed consistently higher

levels in personal than outdoor air in 24-h samples collected

from over 1,500 subjects in four states (Wallace, 1987).

Recently, Weisel et al (2005) measured higher concentrations

in 48-hsamples of personal air (adult median 1.04 mg/m3)

and indoor air (median 0.92 mg/m3) than colocated outdoor

samples (median 0.17 mg/m3) from 300 homes in Los

Angeles, Elizabeth, and Houston Other US researchers

have found similar ratios of personal to indoor and outdoor

levels (Clayton et al., 1999; Payne-Sturges et al., 2004;

Sexton et al., 2004a).

While these studies illustrate the greater exposure potential

of personal and indoor air versus outdoor air, less is known

about which activities and microenvironments contribute the

largest fraction of daily inhalation intake US adult volunteer

studies point to showering and/or bathing with chlorinated

tap water as a major contributor to daily inhalation

exposures Gordon et al (2006) found a 440-fold increase

in bathroom air chloroform after subjects took hot showers

in their study of household water use activities by seven

volunteers Kerger et al (2000) found that bathroom air

chloroform increased 3 and 1 mg/m3 during showering and

bathing respectively for each mg/l chloroform in water Using

a mass balance approach, water use data, and exposure

factor assumptions including 1 mg/l tap water chloroform,

McKone (1987) estimated that showering contributes up to

50% of lifetime chloroform inhalation exposures for the

average US adult versus spending time in the bathroom or

remainder of the house Additionally, recent biomarker

studies of US adults show that showering/bathing is

significantly associated withincreases in breathand/or blood

chloroform, while other household water use activities such

as washing dishes or clothes are not (Weisel et al., 1999;

Backer et al., 2000; Lynberg et al., 2001; Nuckols et al., 2005; Xu and Weisel, 2005) Swimming in chlorinated pools

is also associated withelevated biomarker concentrations though most studies have been conducted outside the United States (Lindstrom et al., 1997; Le´vesque et al., 2000; Erdinger et al., 2004; Caro and Gallego, 2007).

We used multiple linear regression to investigate the major predictors of chloroform in personal air in the NHANES 1999–2000 VOC (volatile organic compound) Subsample (US Centers for Disease Control and Prevention (CDC, 2007a)) The NHANES data, which include chloroform concentrations in personal air and household tap water in addition to socioeconomic data and information on activity patterns, provide a unique opportunity to evaluate predictors

of inhalation exposures in a nationally representative sample.

We hypothesized that personal exposure microevents such as showering/bathing and/or spending time at a pool would be significantly associated withch loroform concentrations in personal air while associations with other exposure factors would not We also compared personal air levels to EPA’s inhalation unit risk values to evaluate the distribution of cancer risk at the national level and among key subgroups.

Methods

NHANES Data Collection Detailed methods are available at the NHANES website (CDC, 2001) Briefly, a random subsample of subjects aged 20–59 was recruited to participate in the VOC study during the NHANES medical examination Consenting subjects wore passive VOC exposure badges (3Mt Organic Vapor Monitor 3520, 3M Corporation, St Paul, MN, USA) continuously for 46–76 hafter th e examination Subjects were instructed to wear it on the upper chest, leave it on a bedside table or clipped to a nearby lampshade while sleeping, and leave it in an adjacent room while showering since humidity affects readings Subjects were also asked to record hours spent indoors at home, indoors at work/school, and outdoors using an activity log, and instructed to collect a tap water sample from a bathtub or an outside faucet in an NHANES-provided container (CDC, 2001) When subjects returned their samples, an NHANES interviewer adminis- tered a brief questionnaire to collect information on VOC exposure-related activities (CDC, 2001) Home examiners interviewed and collected samples from subjects who could not return to the trailer within 46–76 h; samples collected outside this window were considered invalid.

Samples were analyzed at CDC or contract laboratories Badge measurements below the analytical detection limit were replaced withth e detection limit, adjusted for badge wearing minutes, divided by O2 (CDC, 2005a) Water measurements below detection were replaced withth e detection limit divided by O2 Although badge field

Journal of Exposure Science and Environmental Epidemiology (2009) 19(3) 249

duplicates, field blanks, and positive controls were collected,

results for these quality control samples were not available in

the NHANES public release data.

In addition to tap water chloroform, we considered 30

NHANES variables potential predictors of chloroform

inhalation exposure Of these, 17 were from the VOC

Questionnaire (CDC, 2001), eight from the Demographic

Questionnaire (CDC, 2005b), and three from the Housing

Characteristics Questionnaire (CDC, 2005c) Another, body

mass index, was recorded during the NHANES examination

(CDC, 2005d) We considered the variable indicating

whether subjects participated in morning, afternoon or

evening examination sessions (CDC, 2005d) a proxy for

the time of day subjects began wearing the badge.

We downloaded the relevant data sets from the NHANES

website (CDC, 2005a, d–f, 2007a, b) and used the NHANES

VOC Subsample weights (WTSVOC2Y) as well as the

stratum (SDMVSTRA) and cluster (SDMVPSU) variables

available in the NHANES 1999–2000 demographic data for

weighted statistical analyses Certain NHANES data were

updated after their initial public release; all used in the

present study were updated as of June 2007.

Variable Recodes

We preserved the NHANES categorical variable groupings

but recoded them so the group with the highest weighted

frequency in the VOC subsample was the reference group.

Minor recodes included combining the ‘‘something else’’ and

‘‘dorm’’ responses to the NHANES type of home question

into one category and transforming badge wearing minutes

to hours We treated household income (INDHHINC) as a

continuous variable using the NHANES numerical

cate-gories (1–11) instead of their corresponding income ranges.

NHANES included two additional income categories (12,

4$20,000 and 13, o$20,000) to minimize refused/don’t

know responses We recoded Category 12 responses as

missing; this affected 3.2% of subjects There were no

Category 13 responses.

We developed a new occupation variable to identify

subjects withworkplace exposure potential NHANES

Question OCD230 asked subjects the industry they worked

in while Question OCD240 asked the type of work they

performed We created a variable (‘‘occupation’’) withfour

response categories: 0 F other; 1 F food preparation/store/

restaurant; 2 F manufacturing (paper, chemicals, food,

electrical/transport equipment); 3 F construction, and; 4 F

no industry/job recorded We considered Categories 1–3 to

have workplace exposure potential based on information

from the 11th Report on Carcinogens (US Department of

Healthand Human Services (DHHS, 2005)) and industries

reporting 410,000 lb annual chloroform releases during

1999–2000 to the EPA Toxic Release Inventory Program

(EPA, 2001b, 2002) We considered other industries/jobs

(Category 0) to have limited exposure potential.

Category 1 includes subjects who reported ‘‘retail-food stores’’ or ‘‘retail-eating/drinking places’’ in response to Question OCD230, as well as subjects who reported ‘‘cooks’’

or ‘‘miscellaneous food preparation/service’’ to Question OCD240 We assumed these workers would spend part of the day in a kitchen around water use activities If a subject said s/he worked in food preparation but as a waitress/waiter, we coded her/him as Category 0, assuming s/he spent less time around water than cooks or dishwashers for example Category 2 includes workers in food/kindred products, paper products/printing/publishing, chemicals/petroleum/coal pro- ducts, or transportation equipment industries Textile/appa- rel/furnishings machine operators were also included in Category 2 since one author (A Riederer) observed extensive water use on visits to US textile mills in the 1990s, and since the textile response category to Question OCD230 applied to finished products which we assumed do not require as much water to manufacture as unfinished cloth Category 3 includes subjects who reported working in construction (Question OCD230) and/or in construction trades (Question OCD240).

Exploratory Data Analysis

Of the 851 subjects selected, 669 completed the VOC sampling protocol Subsample weights were adjusted by CDC for non-response, and to matchprojected Census 2000 counts, and sum to 150,249,991 (CDC, 2006) We calculated weighted response frequencies and 95% confidence intervals (95% CIs) using PROC SURVEYFREQ in SAS 9.1 (SAS Institute, Cary, NC, USA) We also conducted exploratory analysis on the weighted and unweighted continuous variables Distributions of raw and log-transformed data were visually evaluated for normality and outliers Variables withh istograms appearing righ t-skewed were log-trans- formed for the regressions We evaluated colinearity between continuous predictors using simple scatter plots Last, we calculated weighted cumulative percentiles of personal air chloroform and 95% CIs for the percentile estimates using the DESCRIPT procedure in SUDAAN 9.0.0 (Research Triangle Institute, ResearchTriangle Park, NC, USA) Regression Modeling and Diagnostics

We conducted weighted regression modeling in SUDAAN PROC REGRESS, using the NHANES fill-in values for measurements below detection Model building was con- ducted by first performing univariate regressions of log- transformed chloroform badge concentrations on each of the

31 initial predictors We also included a quadratic term for badge wearing time to account for potential non-linearity in response Predictors with p-values of 0.2 or less were retained for the multivariable analysis These were assigned a random number and added one-by-one in ascending order to a multivariable model fitted using PROC REGRESS Pre- dictors with P r0.2 were retained in eachsubsequent step.

250 Journal of Exposure Science and Environmental Epidemiology (2009) 19(3)

We fit the final model and manually removed predictors with

p40.05 until all remaining predictors had P r0.05, our

criterion for statistical significance.

We evaluated model assumptions of normality and

homoscedasticity by examining plots of predicted values

versus residuals as well as histograms and normal probability

plots of residuals Model fit was evaluated using the R2

statistic Following Korn and Graubard (1998), we

exam-ined partial regression plots to identify potentially influential

observations then compared parameter estimates in the full

model versus a model witheachinfluential observation

excluded Influential observations were excluded one at a

time in these analyses.

Cancer Risk Estimates

We estimated lifetime excess cancer risk for individual

subjects by multiplying her/his badge concentration by

EPA’s chloroform inhalation unit risk (EPA, 2005) This

method estimates an individual’s upper-bound risk of

developing cancer over a lifetime (70 years) of exposure at

the measured concentration We estimated population risk in

units of excess cancer cases by multiplying eachsubject’s

individual excess risk by her/his NHANES sample weight,

then summing across the total population or subgroup To

evaluate the distribution of risk burden within subgroups, we

calculated the weighted percent of each subgroup at the Z1

in 104, 1 in 106–1 in 105, and r1 in 106individual risk levels.

We considered subgroups withhigher proportions of people

at the Z1 in 104risk level to bear a greater cancer burden

than subgroups with fewer at that level For comparison, we

repeated these calculations using the CalEPA inhalation unit

risk (CalEPA, 2002).

Results

Weighted Detection and Response Frequencies

Chloroform was measured at levels at or above detection

limits in 77.2% of badge and 80.1% of water samples.

Measurements were below detection in 20.0% of badge and

15.3% of water samples, while 2.8 and 4.6% of badge and

water samples respectively were missing One water

measure-ment exceeded the upper bound of the calibrated range of the

analytical method but by o20% thus we included it in our

regressions; excluding it did not change statistical outcomes.

Table 1 shows weighted response frequencies and

descrip-tive statistics for the regression predictors Missing responses

ranged from 0–4.2% while refusals or ‘‘don’t know’’ (not

shown) accounted for o1% of responses Household income

(not shown) was missing for 8.4% of subjects, while the three

most commonly reported categories were $25,000–34,999

(11.7%), $55,000–64,999 (10.4%), and Z$75,000 (22.7%).

Most subjects (48.8%) participated in the morning

NHANES examination A majority reported wearing the

badge at all times (88.2%) and taking a hot shower for

Z 5 min (85.9%) Half (55.4%) reported having windows open at home, and/or breathing fumes from/using air fresheners/room deodorizers (47.4%) and/or disinfectant/ degreasing cleaners (39.5%) Less than a third responded yes

to other chloroform-related items on the VOC naire Only 8.8% reported visiting a pool The median badge wearing hours was 53.6 and no subject wore her/his badge o28 h Median hours spent indoors at home, indoors at work/school, and outdoors were 29.9, 7.8 and 5.7, respectively Median chloroform in water was 13.7 ng/ml (7.0–19.3 ng/ml, 95% CI) while the 95th percentile was 74.7 ng/ml (50.6–112.9 ng/ml, 95% CI).

Question-Distribution of Personal Air Chloroform Figure 1 shows the weighted cumulative distribution of personal air chloroform in NHANES 1999–2000 Median and 95thpercentile levels were 1.13 mg/m3(0.93–1.39 mg/m3, 95% CI) and 12.05 mg/m3 (8.12–13.54 mg/m3, 95% CI), respectively The maximum concentration was 53.9 mg/m3 Figure 1 also shows the detection limits and the EPA- estimated air concentrations at the 1 in 105 and 1 in 104cancer risk levels Detection limits varied for eachbadge depending on wearing duration, withth ose worn longer having lower limits than those worn for shorter periods All measurements at or below the 1 in 105risk level (0.4 mg/m3), corresponding to 13% of US adults, were below detection All measurements at or above 0.55 mg/m3 were in the detectable range Approximately 59% (6% of US adults)

of values in the 0.41–0.55 mg/m3range were below detection while 41% (4% of US adults) of values in this range were detectable The majority (62%) of US adults had measure- ments at the 1 in 105–1 in 104risk level while 19% had values exceeding the 1 in 104risk level.

Significant Predictors of Personal Air Chloroform Predictors eliminated by the univariate screen included: wore badge at all times, education, body mass index, new carpets, hours indoors at work/school, hours outdoors, took hot shower for Z5 min, in dry cleaning shop/drycleaned clothes, near wood-burning, breathed fumes from/used dry cleaning fluid/spot remover, and breathed fumes from/used glues/ adhesives hobbies/crafts Predictors eliminated during multi- variable modeling included: badge wearing hours, examina- tion session, gender, occupation, income, wear respirator at work, wear gloves at work, number of rooms in the home, hours indoors at home, use home water treatment devices, store paints/fuels inside home, and breathe fumes from/use paint, disinfectant/degreasing cleaners, and air fresheners/ room deodorizers.

Diagnostic plots suggested that model assumptions of normality and homoscedasticity were valid A maximum of

13 parameters were estimable in the final fitted model Table 2 summarizes the regression coefficients (bs) for predictors

Journal of Exposure Science and Environmental Epidemiology (2009) 19(3) 251

Table 1 Weighted response frequencies and descriptive statistics of chloroform inhalation exposure predictors in the NHANES 1999–2000 VOC Subsample.

percentile

Range

0¼ morning 48.8 Time-activity patterns

Race/ethnicity (RIDRETH1) ¼ missing/don’t know F Personal exposure microevents

1¼ Mexican American 2¼ Other Hispanic

7.3 7.8

% Missing % Yes % No

4¼ Non-Hispanic Black 11.7

1¼ mobile home/trailer 6.9 Breathe fumes from/use:

2¼ 1 family, detach ed 3¼ 1 family, attach ed

61.5 6.1

1 ¼ private/public water 83.9 New carpets home/work past 6 months (VTQ070) 3.1 18.3 78.6

significant at the a ¼ 0.05 level in the final model (multiple

R2¼ 0.34) Chloroform in home tap water was a significant predictor of log personal air chloroform with a coefficient of 0.016 (P o0.0001) Having no windows open at home (b ¼ 0.413, P ¼ 0.0007) and visiting a swimming pool (b ¼ 0.523, P ¼ 0.0102) were also associated withelevated levels Certain home types were associated with elevated levels relative to the reference group (single family, detached): mobile home/trailer (b ¼ 0.684, P ¼ 0.0204), apartment (b ¼ 0.507, P ¼ 0.0045), and dormitory/something else (b ¼ 0.580, P ¼ 0.0118) Removal of one influential observa- tion reduced the dormitory/something else coefficient by 28% and increased the P-value to 0.0762.

Certain race/ethnicity groups were also associated with elevated levels compared to the reference group (Non- Hispanic White): Other Hispanic (b ¼ 0.535, P ¼ 0.0460), and Non-Hispanic Black (b ¼ 0.260, P ¼ 0.0437) Removal

of three influential observations changed the P-values in the Non-Hispanic Black category to 0.0528, 0.0588, and 0.0609, respectively, but did not change the coefficients by 410% Removal of two others changed the P-values for the Other Hispanic category to 0.0645 and 0.0651, respectively, but did not change the coefficient by 47% Removal of another lowered the Other Hispanic category coefficient by 12% and increased the P-value for the Non-Hispanic Black category

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 80 85 90 95

Personal air chloroform ( µg/m3)

1 in 105risk level 1 in 104risk level

below analytical detection limit

59% of values 0.41– 0.55

detection limit

Figure 1 Weighted cumulative percentiles of personal air chloroform among US adults (age 20–59) in NHANES 1999–2000 (dotted lines denote weighted lower and upper 95% confidence intervals for percentile estimates; dark shading indicates values above EPA 1 in 104cancer risk level, light shading indicates values between 1 in 105and 1

in 104risk levels).

Journal of Exposure Science and Environmental Epidemiology (2009) 19(3) 253