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

NUMBER 16 2009 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 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 not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use 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 TABLE OF CONTENTS 3 3 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 4 4 4 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 9 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 19 (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) 31 CHARACTERIZING RELATIONSHIPS BETWEEN PERSONAL EXPOSURES TO VOCS AND SOCIOECONOMIC, DEMOGRAPHIC, BEHAVIORAL VARIABLES (WANG, ET AL) 41 NUATRC RESEARCH REPORT NO 16 The Mickey Leland National Urban Air Toxics Research Center and The National Center for Health Statistics 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) 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 19992001 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 datagathering 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 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 NUATRC RESEARCH REPORT NO 16 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 The Relationship between Personal Exposures to VOCs and Behavioral, Socioeconomic, and Demographic Characteristics 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-pxylene, 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 The Mickey Leland National Urban Air Toxics Research Center and The National Center for Health Statistics 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-pxylene, o-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 NUATRC RESEARCH REPORT NO 16 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 of 10 US adults were exposed to detectable levels of chloroform The Relationship between Personal Exposures to VOCs and Behavioral, Socioeconomic, and Demographic Characteristics 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, 1,4dichlorobenzene, 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 nationallevel 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 REFERENCES Jia C, J D'Souza, and S Batterman 2008 Distributions of Personal VOC Exposures: A Population-based Analysis Environ Int 34(7): 922-931 Riederer AM, SM Bartell and PB Ryan 2009 Predictors of Personal Air Concentrations of Chloroform among US Adults in NHANES 1999-2000 J Expo Sci Environ Epidemiol 19(3):248-259 Symanski E, TH Stock, PG Tee, W Chan 2009 Demographic, Residential, and Behavioral Determinants of Elevated Exposures to Benzene, Toluene, Ethylbenzene, and Xylenes among the US Population: Results from 19992000 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 The Mickey Leland National Urban Air Toxics Research Center and The National Center for Health Statistics ACKNOWLEDGMENTS ABBREVIATIONS 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 BTEX CAAA CDC CEH EOHSI NUATRC RESEARCH REPORT NO 16 benzene, ethylbenzene, toluene, and xylene Clean Air Act Amendments Centers for Disease Control Center for Environmental Health Environmental and Occupational Health Sciences Institute EPA Environmental Protection Agency GM geometric mean MTBE methyl tert-butyl ether MEC mobile examination center NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination Surveys NUATRC National Urban Air Toxics Research Center RFA Request for Applications SAP Scientific Advisory Panel UMDNJ University of Medicine and Dentistry, New Jersey VOC volatile organic compound VOC Project Collaborative NCHS-NUATRC VOC Project 908 27.2 30.0 24.8 18.0 Weighted % 48.5 51.5 15.1 68.3 12.0 4.6 83.0 4.6 12.4 19.6 27.0 53.4 23.7 20.7 26.7 22.3 6.6 67.3 32.7 183 171 152 130 n 285 351 225 260 124 19 446 118 72 197 160 279 187 130 150 108 61 447 162 17.40 (15.29–19.51) 16.16 (12.57–19.75) 17.83 (13.65–22.01) 19.82 (12.70–26.93) Toluene 4.20 (2.86–5.55)a 2.99 (2.36–3.61) 3.03 (1.88–4.19) 4.34 (1.08–7.59) 15.80 (10.51–21.10) 16.94 (11.71–22.17) 17.97 (13.99–21.96) 17.43 (12.39–22.47) 25.97 (13.33–38.60) 16.75 (13.78–19.73) 19.09 (13.64–24.54) 2.94 (2.39–3.48)a 3.90 (2.61–5.20) 16.08 (11.88–20.28) 18.01 (14.78–21.25) 17.83 (13.76–21.89) 3.45 (2.38–4.52) 3.28 (2.11–4.45) 3.13 (2.30–3.96) 2.71 (2.17–3.24) 4.48 (1.49–7.47) 3.89 (2.93–4.86) 3.15 (2.30–4.00) 3.01 (2.28–3.74) 17.07 (14.10–20.04) 20.95 (12.53–29.37) 19.47 (9.05–29.88) 18.05 (14.13–21.97) 17.61 (13.85–21.38) 16.69 (13.86–19.53) 16.58 (1.34–31.82) 3.64 (2.62–4.66)a 2.85 (2.33–3.36) 3.05 (2.47–3.62) 4.77 (3.04–6.49) 3.88 (1.61–6.15) 18.99 (14.79–23.19) 16.24 (13.57–18.90) 3.03 (2.42–3.65) 3.46 (2.55–4.36) 2.99 (2.15–3.83) 3.39 (2.16–4.63) Benzene Satherthwaite adjusted F statistic from the univariate regression analyses, p < 05 a Age (yr) 20–29 30–39 40–49 50–59 Gender Male Female Race/ethnicity Hispanic Non-Hispanic White Non-Hispanic Black Other Country of birth United States Mexico Other Education Less than high school High school More than high school Family income $0–19,999 $20,000–34,999 $35,000–64,999 $65,000 and over Missing Serum cotinine level ≤15 ng/ml >15 ng/ml Characteristic 2.63 (1.93–3.32) 3.60 (2.00–5.20) 2.96 (1.66–4.26) 2.49 (1.58–3.41) 2.90 (2.24–3.56) 2.88 (1.75–4.01) 5.10 (1.63–8.57) 3.34 (1.74–4.95) 3.10 (2.52–3.68) 2.71 (1.87–3.55) 2.84 (2.03–3.65) 3.15 (2.08–4.21) 3.49 (1.29–5.68) 3.69 (2.10–5.29) 2.91 (1.98–3.83) 2.49 (1.84–3.14) 2.27 (0.10–4.44) 3.46 (2.65–4.26)a 2.49 (1.68–3.31) 2.55 (2.08–3.02) 3.16 (1.72–4.59) 3.02 (2.21–3.82) 3.03 (1.76–4.31) Ethylbenzene 6.83 (4.95–8.71) 8.42 (4.24–12.61) 7.48 (3.69–11.27) 6.00 (3.51–8.49) 6.84 (5.61–8.07) 7.56 (4.08–11.04) 14.10 (3.05–25.15) 8.28 (4.14–12.42) 7.37 (5.74–9.01) 6.91 (4.53–9.29) 6.90 (4.84–8.95) 8.50 (5.30–11.69) 9.95 (2.80–17.09) 10.02 (5.70–14.33) 7.05 (4.62–9.47) 6.36 (4.48–8.24) 6.08 (−0.56–12.72) 8.78 (6.43–11.13)a 6.11 (4.04–8.17) 6.44 (5.17–7.70) 7.70 (4.43–10.98) 7.41 (5.05–9.77) 7.80 (4.10–11.50) m,p-Xylene TABLE Weighted Geometric Mean Levels of BTEX for U.S Adults for Various Demographic Subgroups, NHANES 1999–2000, VOC Subsample (n = 636 Adults) Downloaded By: [Francis A Countway] At: 15:00 October 2009 2.66 (1.99–3.33) 3.08 (1.86–4.30) 2.73 (1.61–3.85) 2.53 (1.51–3.54) 2.59 (2.13–3.05) 2.94 (1.76–4.11) 4.78 (1.49–8.07) 2.97 (1.67–4.26) 2.96 (2.37–3.54) 2.65 (1.88–3.43) 2.69 (2.00–3.38) 3.10 (2.02–4.18) 3.44 (1.03–5.85) 3.34 (2.02–4.66) 2.82 (1.99–3.65) 2.32 (1.80–2.83) 2.15 (−0.15–4.44) 3.36 (2.49–4.24)a 2.34 (1.70–2.98) 2.56 (2.11–3.02) 2.90 (1.89–3.90) 2.74 (1.97–3.50) 3.07 (1.79–4.35) o-Xylene VOC EXPOSURE IN THE U.S ADULT POPULATION 909 TABLE Final Regression Models of Key Determinants of Personal Exposure to BTEX Regression coefficient (95% CI) Downloaded By: [Francis A Countway] At: 15:00 October 2009 Determinant (p valuea) Benzene Home with attached garage vs no attached garage (p = 0067) Any windows open in the home vs no windows open (p = 0140) Cotinine level >15 ng/ml vs ≤15 ng/ml (p = 0185) Toluene Home with attached garage vs no attached garage (p = 0051) Gas stove vs electric stove (p = 0205) Any windows open in the home vs no windows open (p = 0011) Pumping gas vs not pumping gas (p = 0013) Ethylbenzene Home with attached garage vs no attached garage (p = 0207) Pumping gas vs not pumping gas (p = 0072) o-Xylene Home with attached garage vs no attached garage (p = 0063) Pumping gas vs not pumping gas (p = 0194) Breathing fumes vs not breathing fumes from furniture polish (p = 0088) Breathing fumes vs not breathing fumes from paint thinner, brush cleaner or stripper (p = 0010) m,p-Xylene Home with attached garage vs no attached garage (p = 0030) Pumping gas vs not pumping gas into a car or motor vehicle (p = 0393) Breathing fumes vs not breathing fumes from furniture polish (p = 0039) Breathing fumes vs not breathing fumes from paint thinner, brush cleaner or stripper (p = 0058) a b Multiplicative factorb (95% CI) 0.329 (0.107, 0.551) −0.420 (−0.741, −0.099) 0.309 (0.060, 0.557) 1.39 (1.11, 1.73) 0.66 (0.48, 0.91) 1.36 (1.06, 1.74) 0.384 (0.136, 0.633) −0.288 (−0.524, −0.052) −0.420 (−0.640, −0.202) 0.226 (0.105, 0.347) 1.46 (1.15, 1.88) 0.75 (0.59, 0.95) 0.66 (0.53, 0.82) 1.25 (1.11, 1.41) 0.325 (0.058, 0.592) 0.343 (0.109, 0.577) 1.38 (1.06, 1.80) 1.41 (1.12, 1.78) 0.378 (0.126, 0.631) 0.323 (0.061, 0.586) −0.379 (−0.646, −0.112) 1.46 (1.13, 1.88) 1.38 (1.06, 1.80) 0.68 (0.52, 0.89) 0.818 (0.397, 1.240) 2.27 (1.49, 3.46) 0.433 (0.174, 0.692) 0.318 (0.018, 0.617) 1.54 (1.19, 2.0) 1.37 (1.02, 1.85) −0.438 (−0.711, −0.166) 0.65 (0.49, 0.85) 0.769 (0.262, 1.275) 2.16 (1.30, 3.58) The p value associated with the Satherwaite adjusted F-statistic in the final model Fold range change in geometric mean levels (μg/m3) cleaner, or furniture stripper Individuals who used a gas rather than electric stove during the monitoring period experienced lower toluene exposures Breathing fumes from or using furniture polish decreased personal exposures to o-xylene and m,p-xylene While there were slight differences in the magnitude of regression estimates, the same factors were identified as predictors of o-xylene and m,p-xylene personal exposures DISCUSSION This study afforded a unique opportunity to use information collected as part of the National Health and Nutrition Examination Survey to identify influential determinants of exposures to selected VOC in a population-based sample of adults aged 20 to 59 yr in the United States GM levels of VOC exposures were examined on the basis of sociodemographic characteristics No clear patterns emerged on the basis of age, education, or income level, although study participants who did not provide information on family income appeared to have higher BTEX exposures as compared to any other group Results comparing GM levels suggest that personal VOC exposures were significantly higher among males for all compounds except toluene The U.S EPA TEAM studies reported mixed results for benzene and o-xylene exposures between males and females depending upon the region studied in the United States, the year of data collection, and the time of sample collection (i.e., overnight or during the day) (Wallace, 1987) Further, increased exposure to benzene (but not toluene, ethylbenzene, or xylenes) was reported among males in a recent study of nonsmoker volunteers from four Australian cities (Hinwood et al., 2007) In contrast, it is also interesting to note that in a multivariate analysis that controlled for several covariates including air levels and body mass index using the NHANES VOC subsample, Lin et al (2008) reported higher blood levels of BTEX in women as compared to men, although such differences were not significant Downloaded By: [Francis A Countway] At: 15:00 October 2009 910 E SYMANSKI ET AL Benzene exposures were higher among Hispanics compared to other ethnic/racial groups While Arif and Shah (2007) recently reported differences in VOC exposures by race/ethnicity using a subset of VOC study participants, their geometric mean values were consistently only about one-third as large as those reported in this investigation among racial/ethnic groups or overall (across groups) in another study that also relied on the same database (Jia et al., 2008) While it is expected that disadvantaged populations may be exposed disproportionately to environmental contaminants, relatively little has been reported on differences in VOC exposures across such subpopulations The earliest evaluation looking at differences in VOC exposures between ethnic/racial groups was made in the California TEAM studies in which equivocal results were reported between Hispanics and non-Hispanics for selected compounds depending on where the participants resided and on whether the samples were collected during the day or night (Wallace et al., 1988) About a decade later the NHEXAS Region Pilot Study reported higher (albeit nonsignificant) benzene exposure levels for Hispanic Whites and other minorities as compared to nonminorities (Pellizzari et al., 1999) Our study confirms previous findings from studies that were more regional in scope or that relied on convenience samples, but allows for broader inferences to the general U.S adult population regarding effects of specific behaviors and activities that contribute to elevated VOC exposures For example, individuals living in homes with an attached garage experienced increases in exposures to BTEX, which is consistent with studies that showed that vehicle emissions infiltrate from an attached garage indoors into the home (Thomas et al., 1993; Graham et al., 2004; Batterman et al., 2007) Exposures to benzene and toluene, both of which have known indoor VOC sources, were lower for individuals whose homes had windows open during the monitoring period An analysis of personal exposures to 14 VOC among 70 nonsmoking adults in Minneapolis-St Paul (Sexton et al., 2007) also suggested that being in or near a garage significantly increased exposure to BTEX, and opening windows at home ≥6 h/d decreased VOC exposure to most compounds It was also found that pumping gas into a car or motor vehicle increased exposures to toluene, ethylbenzene, and xylene but was not an important determinant of benzene exposure In the aforementioned study in Minnesota (Sexton et al., 2007), factors related to exposure to gasoline were unrelated to increases in personal BTEX exposure Among the five VOC exposures examined in this study, geometric mean levels were higher for current smokers as compared to nonsmokers for benzene alone This is in agreement with the results of the NHANES VOC analysis reported by Lin et al (2008) Likewise, in the multivariate analysis, current smoking was an important predictor of population-based VOC exposure for benzene only, which had an approximately equal effect on increasing personal exposure as living in a home with an attached garage While tobacco smoke remains a predominant source of exposure to benzene among smokers (Edwards et al., 2001), the importance of smoking as a key determinant of population-based exposure to BTEX has likely diminished in the United States as the prevalence of smoking has declined over the past 25 yr (Gregg et al., 2005) Our results also indicated more than a twofold difference in exposures to xylenes due to contact with paint thinners, brush cleaner, or furniture stripper While this result was expected given the chemical constituents of these products, our regression results also produced findings of lower exposures to xylenes associated with the use of furniture polish, as well as lower exposures to toluene with the use of a gas rather than electric stove To explore the possibility of influential observations on these unanticipated findings, sensitivity analyses were conducted by calculating the difference between the observed and predicted values from the final models and rerunning the regression analyses excluding the upper and lower tails (i.e., omitting values below the 1st and above the 99th percentiles of the distributions of the residuals) However, the associations between xylenes (both analytes) and furniture polish, as well as between toluene and use of a gas or electric stove, remained significant and of a similar magnitude and direction as compared to results from the original final models It is likely, therefore, that one or more unmeasured factors are confounding the observed associations that were detected Despite the obvious advantages of NHANES with regard to national representativeness, a relatively small number of persons participated in the VOC subsample Thus, the capacity of the data to allow for the simultaneous evaluation of multiple predictors is limited (Riederer et al., 2009) because the number of parameters that can be estimated is restricted by the number of primary sampling units and primary clusters Analysis of subdomains within any single wave of NHANES and thus with the 1999–2000 NHANES VOC subsample as well is also constrained by sample size (CDC, 2006) As such, our evaluation of differences among demographic subgroups was limited, as was our regression analyses that only examined main effects Another possible limitation relates to differences among study participants in terms of the duration of the monitoring period as well as the length of time that the monitor was worn, although those effects were minimized in our analyses by excluding data on individuals who wore their badges less than 75% of the time Moreover, the monitoring period among the study participants who met the criteria for inclusion in our study ranged from 43 to 76 h (92% wore the monitor the entire period) and thus these differences in averaging times were not likely to have adversely affected our results Findings were also restricted by the nature of the questionnaire data that were collected on factors potentially related to VOC exposures in that the majority of the responses were dichotomous (“Yes” versus “No”), which may have introduced some error (i.e., residual confounding) in the effects of those variables included in the final models Because of multicollinearity between sociodemographic variables and the VOC questionnaire data, it was not possible to evaluate the effects of demographic factors Downloaded By: [Francis A Countway] At: 15:00 October 2009 VOC EXPOSURE IN THE U.S ADULT POPULATION and household or individual behaviors simultaneously in our regression models Finally, building regression models was problematic because existing statistical software that accounts for a complex survey design (like SUDAAN) does not have the capacity to run model selection algorithms An attempt was made to apply two different strategies for model building to facilitate comparisons in the results that may have been influenced by the selection of the algorithm used to produce the final models However, the ad hoc all possible regression procedure that was developed did not distinguish among the regression models that were evaluated and therefore such a comparison could not be made While the backward elimination approach that was applied in the current investigation has been used in recent studies of NHANES data (Calafat et al., 2008a, 2008b), other approaches have been applied as well For example, in a study to examine predictors of chloroform exposure from the NHANES VOC substudy (Riederer et al., 2009), a forward approach was applied by adding variables one at a time, with the order determined randomly Since our goal was to identify the most important predictors of VOC exposure among adults in the United States, a change in estimate approach was not applied since focus was not restricted to a single determinant (Greenland, 1989) Statistical techniques were also applied for dealing with potential issues associated with multiple comparisons, which have not been applied in previous regression analyses of NHANES data In conclusion, this is the first study to examine risk factors for higher BTEX exposures among a nationally representative population-based study of U.S adults Significant differences in exposures were found across selected demographic groups and on the basis of behavioral and household characteristics, which were generally consistent with findings reported in studies that relied on convenience samples or targeted and regionally focused population samples These results update the earlier U.S EPA TEAM studies carried out nearly 30 yr ago and should provide a baseline for examining determinants of VOC exposure in the future Further studies are needed to verify our findings regarding key determinants that influence population-based VOC exposures in the 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http://www.epa.gov/air/caa/caa112.txt Wallace, L A., Pellizzari, E D., Hartwell, T D., Sparacino, C., Whitmore R., Sheldon, L., Zelon, H., and Perritt, R 1987 The TEAM (Total Exposure Assessment Methodology) Study: Personal exposures to toxic substances in air, drinking water, and breath of 400 residents of New Jersey, North Carolina, and North Dakota Environ Res 43:290–307 Wallace, L A 1987 The total exposure assessment methodology (TEAM) study: Summary and analysis: Volume Washington, DC: Office of Research and Development, U.S Environmental Protection Agency Wallace, L A., Pellizzari, E., Hartwell, T D., Whitmore, R., Zelon, H., Perritt, R., and Sheldon, L 1988 The California TEAM study: Breath concentrations and personal exposures to 26 volatile compounds in air and drinking water of 188 residents of Los Angeles, Antioch, and Pittsburg, CA Atmos Environ 22:2141–2163 Wallace, L A., Pellizzari, E., and Hartwell, T D 1985 Personal exposures, indoor–outdoor relationship and breath levels of toxic air pollutants measured for 355 persons in New Jersey Atmos Environ 19:1651–1661 Weisel, C P., Zhang, J., Turpin, B J., Morandi, M T., Colome, S., Stock, T H., Spektor, D M., Korn, L., Winer, A., Alimokhtari, S., Kwon, J., Mohan, K., Harrington, R., Giovanetti, R., Cui, W., Afshar, M., Maberti, S., and Shendell, D 2005 Relationship of indoor, outdoor and personal air (RIOPA) study: Study design, methods and quality assurance/control results J Expos Anal Environ Epidemiol 15:123–137 Atmospheric Environment 43 (2009) 2296–2302 Contents lists available at ScienceDirect Atmospheric Environment j o u r n a l h o m e p a g e : w w w e l s ev i e r c o m / l o c a t e / a t m o s e n v Characterizing relationships between personal exposures to VOCs and socioeconomic, demographic, behavioral variables Sheng-Wei Wang a,*,1, Mohammed A Majeed a,b , Pei-Ling Chu c, Hui-Chih Lin d a University of Medicine and Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School, NJ, USA Department of Natural Resources & Environmental Control (DNREC), State of Delaware, USA c Novo Nordisk Inc., Princeton, NJ, USA d Department of Marketing & Distribution Management, The Overseas Chinese Institute of Technology, Taiwan b A R T I C L E I N F O Article history: Received September 2008 Received in revised form 21 January 2009 Accepted 25 January 2009 Keywords: Volatile organic compounds Personal exposures Time-activity patterns Socio-demographic factors NHANES A B S T R A C T Socioeconomic and demographic factors have been found to significantly affect time-activity patterns in population cohorts that can subsequently influence personal exposures to air pollutants This study investigates relationships between personal exposures to eight VOCs (benzene, toluene, ethylbenzene, o-xylene, m-,p-xylene, chloroform, 1,4-dichlorobenzene, and tetrachloroethene) and socioeconomic, demographic, time-activity pattern factors using data collected from the 1999–2000 National Health and Nutrition Examination Survey (NHANES) VOC study Socio-demographic factors (such as race/ethnicity and family income) were generally found to significantly influence personal exposures to the three chlorinated compounds This was mainly due to the associations paired by race/ethnicity and urban residence, race/ethnicity and use of air freshener in car, family income and use of dry-cleaner, which can in turn affect exposures to chloroform, 1,4-dichlorobenzene, and tetrachloroethene, respectively For BTEX, the traffic-related compounds, housing characteristics (leaving home windows open and having an attached garage) and personal activities related to the uses of fuels or solvent-related products played more significant roles in influencing exposures Significant differences in BTEX exposures were also commonly found in relation to gender, due to associated significant differences in time spent at work/ school and outdoors The coupling of Classification and Regression Tree (CART) and Bootstrap Aggregating (Bagging) techniques were used as effective tools for characterizing robust sets of significant VOC exposure factors presented above, which conventional statistical approaches could not accomplish Identification of these significant VOC exposure factors can be used to generate hypotheses for future investigations about possible significant VOC exposure sources and pathways in the general U.S population 2009 Elsevier Ltd All rights reserved Introduction Volatile Organic Compounds (VOCs) are common air pollutants that can be found in both indoor and outdoor environments There are numerous sources of VOCs including gasoline, solvents, paints, and consumer products such as air fresheners, cleaning supplies, dry-cleaned clothing, building or furnishing materials, and so on (USEPA, 2007) In the literature, several VOC exposure monitoring studies have reported that personal and indoor air concentrations of VOCs are higher than outdoor ones, and that the factors of indoor * Corresponding author IEH, 7F, No 17, Xuzhou Rd., Taipei 100, Taiwan Tel.: 886-2-33668107; fax: 886-2-33668114 E-mail addresses: shengwei@ntu.edu.tw, mumumi@gmail.com (S.-W Wang) Institute of Environmental Health, National Taiwan University, Taipei, Taiwan sources and personal activity can contribute significantly to personal exposures (Adgate et al., 2004; Serrano-Trespalacios et al., 2004; Sexton et al., 2004) Further, socioeconomic and demographic factors have been found to significantly affect time-activity patterns in population cohorts (McCurdy and Graham, 2003) It is important to know if significant different time-activity patterns defined by socioeconomic and demographic attributes also correlate with significant different VOC exposures Edwards et al (2006) reported the relationships between VOC exposures and socio-demographic factors, time-activity patterns in the European exposure study, EXPOLIS (Jurvelin et al., 2001) Sexton et al (2007) and Liu et al (2007) reported the relationships between VOC exposures and timeactivity patterns for selected adult populations in different urban areas of the U.S However, besides time-activity patterns, the impacts of socioeconomic and demographic factors on personal 1352-2310/$ – see front matter 2009 Elsevier Ltd All rights reserved doi:10.1016/j.atmosenv.2009.01.032 Wang et al: Reprinted from Atmospheric Environment, 43, Wang SW, MA Majeed, PL Chu, HC Lin, “Characterizing Relationships between Personal Exposures to VOCs and Socioeconomic, Demographic, Behavioral Variables,” 2296-2302, 2009, with permission from Elsevier S.-W Wang et al / Atmospheric Environment 43 (2009) 2296–2302 exposures to VOCs have not been adequately evaluated for the general U.S population The 1999–2000 National Health and Nutrition Examination Survey (NHANES) VOC project dataset (CDC, 2006a) provides an excellent and unique data source to correlate personal exposures to VOCs with socioeconomic, demographic, housing, and time-activity factors for the general U.S population The objectives of the current study were to (1) examine the relationships between VOC exposures and socio-demographic, lifestyle (i.e housing and time-activity) variables, and (2) to characterize significant VOC exposure factors among these variables for a large population-based sample of the general U.S population by analyzing the 1999–2000 NHANES VOC data Materials and methods 2.1 Data source The aims of the 1999–2000 NHANES VOC study were to characterize exposures to VOCs in the general U.S population and determine predictors of exposure This was the first time that NHANES included personal exposure measurements for VOCs Participants were a representative sub-sample of NHANES subjects between the ages of 20 and 59 years Personal air measurements were available for ten VOCs: benzene, chloroform, 1,4-dichlorobenzene (PDB), ethylbenzene, methyl tertiary-butyl ether (MTBE), tetrachloroethene (PERC), toluene, trichloroethylene (TCE), oxylene, and m-,p-xylene Information about individual demographic, socioeconomic status, residences, as well as time and activity data for the exposure period, were also available for this population subset The time and activity data collected via the special designed questionnaire can help identify possible sources of exposures and characterize personal activities that might contribute to exposure The 1999–2000 NHANES study uses a stratified, multistage probability sample of the non-institutionalized US civilian population Detailed information about the study design and operation of NHANES can be found in the analytical and reporting guidelines of the NHANES (CDC, 2006b) Participants of the 1999–2000 NHANES VOC project were asked to wear passive personal monitors (3 M Organic Vapor Monitors) for a period of 48–72 h for measuring personal exposures to ten VOCs (CDC, 2006a) On their return, a short exposure questionnaire was administered to participants to assess personal activities and exposures related to VOC measurements The collected personal air samples were analyzed via GC-MS Table summarizes the numbers of available Table Overview of personal air measurements in the NHANES 1999–2000 VOC dataset for benzene, chloroform, ethylbenzene, tetrachloroethene, toluene, trichloroethene, oxylene, m,p-xylene, 1,4-dichlorobenzene, and methyl tert-butyl ether (MTBE) VOC Na Percentage of measurements at or above limit of detectionb Geometric mean (mg mÀ3) Geometric standard deviation (ug mÀ3) benzene chloroform ethylbenzene tetrachloroethene toluene trichloroethylene o-xylene m,p-xylene 1,4-dichlorobenzene MTBE 647 651 642 642 638 644 646 646 644 644 77.43 86.02 97.51 71.18 94.98 30.75 94.89 97.06 77.64 36.18 1.26 0.76 2.50 0.32 13.96 0.03 2.12 6.15 1.61 0.11 10.61 9.31 4.27 19.11 5.04 16.47 5.44 4.91 24.33 23.91 a N: number of total available measurements If the measurement was below the limit of detection, the concentration was reported as the limit of detection divided by the square root of b 2297 measurements of the ten VOCs, percentages of measurements at or above limits of detection (LODs), as well as their geometric means and geometric standard deviations TCE and MTBE were excluded from the data analysis, since less than 40% of the available measurements were above the respective LODs for both chemicals For the remaining eight VOCs, if the measurement was below the LOD, the concentration reported as the LOD divided by the square root of was used for data analysis Socioeconomic and demographic attributes of the participants were extracted from the full survey data of 1999–2000 NHANES including: age, gender, education, race/ethnicity, and poverty income ratio (i.e ratio of family income to poverty threshold) A smoking status variable was also assigned to each of the participants based on their measured serum cotinine levels Smoker or Environmental Tobacco Smoke (ETS) were assigned to those participants whose serum cotinine levels were greater than 14 ng mlÀ1, and the others as non-smokers (Lin et al., 2008) The collected exposure questionnaire data provided participants’ responses to 30 questions (i.e 30 variables) related to housing characteristics as well as time and activity patterns of participants during the exposure monitoring period Two variables (wearing the exposure badge at all times and hours badge not worn) were excluded from the data analysis, since they were used to perform the data cleaning procedure for the corresponding personal air measurements Therefore, there are total of 34 variables in the socioeconomic, demographic, housing, and time-activity factors for examining their relationships with VOC exposures 2.2 Statistical analyses The NHANES data were collected based on a complex sampling design with sampling weights for generating national estimates In the current study, we conducted unweighted statistical analyses, since the analysis results were not used for the estimation of population parameters that can be generalized as national estimates Instead, they were used from the exploratory perspective for identifying significant VOC exposure factors, which can be used to generate hypotheses about possible significant exposure sources and pathways in future studies In order to reveal the impacts of socioeconomic and demographic factors on VOC exposures, univariate analyses were conducted first for examining group differences of personal exposures stratified by socioeconomic, demographic, and smoking attributes Student’s t-test was used to examine differences between two groups The Bonferroni adjustment was used to examine differences for multiple comparisons However, when all of the predictor variables (including socioeconomic, demographic, housing, and time-activity factors) were involved in data analysis, several characteristics of the NHANES VOC dataset need to be recognized First, the dataset include a large number of variables, which are of disparate type (i.e continuous and categorical) Second, high correlations may exist among exposure factors (collinearity), as well as non-linear and interaction effects between exposure factors for influencing VOC exposures These characteristics make it difficult to perform data analysis using conventional statistical techniques The approaches of Classification and Regression Tree (CART) and Bootstrap Aggregating (Bagging) were used in the current study for resolving above challenges in analyzing the NHANES VOC dataset CART was used to explore potential non-linear and interaction effects among exposure factors on personal exposures to VOCs The CART models are comprised of a collection of rules that partition the space of dependent variable as a function of predictor variables (Breiman et al., 1984) The rules are constructed by a recursive partitioning procedure using a ‘‘training dataset’’ containing values of dependent and predictor variables The over-fitting of CART 2298 S.-W Wang et al / Atmospheric Environment 43 (2009) 2296–2302 model can be prevented through K-fold cross-validation (CV) as follows: (1) randomly split the training dataset into K subsets (typically K ¼ 10 as used in the current study) of approximately equal size; (2) leave out each subset in turn, construct a CART model using the remaining subsets, and repeat K times; (3) identify the optimal CART model by selecting the one with the best predictive performance on the observations that were left out in the construction of the model The CART method has been used to characterize associations of biomarkers of exposure with environmental, dietary, demographic, and activity variables for benzene and lead (Roy et al., 2003) The Bagging algorithm (Breiman, 1996) was used to resolve the issue of collinearity and obtain a data-driven importance measure of predictor variable It used bootstrapping to generate multiple training sets The base algorithm (such as CART) was then used to create a different base model instance for each bootstrapped training set Combining multiple instances of the same model type can reduce the variance and drastically improve predictive performance The best enhancement by Bagging is when the model instances are very different from each other There are two parameters required to be determined for conducting the Bagging analysis: the probability (P) used for generating the bootstrapped samples and the number of times (N) for performing bootstrapping If we use small numbers of P (such as 0.1, or 0.2), the size of bootstrapped samples would be too small and the constructed tree models would not be robust On the contrary, large numbers of P (such as 0.8, or 0.9) would result in similar bootstrapped samples, which could not provide the instability needed by the Bagging approach Through the iterative searching process, we found the optimal parameter value of P as 0.3, since the constructed ‘‘Bagging Trees’’ had the best performance in predicting the personal air concentrations of the eight VOCs through the cross-validation procedure For developing the ‘‘importance measure’’ of predictors, we counted the number of times out of the N constructed optimal CART models that identified this variable as the primary predictor The ‘‘importance measure’’ provides a quantitative scale about the significance of a predictor contributing to the predictive performance on the response variable The higher the counts, the more significant the predictor is for determining personal exposure The parameter of N should be large enough for producing stable Bagging analysis results We set N as 1000 in the current study, and identified the predictors with more than 50 counts of importance measure as significant exposure factors, which is equivalent to the statistical significance level of 0.05 Before conducting univariate, CART, and Bagging analyses, the data cleaning procedure was performed for excluding the personal air measurements collected with significantly less sampling time based on the questionnaire responses to ‘‘wearing the exposure badge at all times’’ and ‘‘hours badge not worn’’ Only a small percentage of participants (less than 5%) were excluded Natural logarithmic transformation was applied to the measured personal air concentrations, since the distributions were skew to the right Outliers were also identified through normal probability plots of the log-transformed data and then excluded for data analysis The software packages including MATLAB, R, and SAS were used to perform data analyses in this study Results and discussion 3.1 Univariate analyses of VOC exposures vs socio-demographic factors 3.1.1 Age and gender The effect of age was only revealed in chloroform exposures with significantly negative correlation, indicating that young participants had higher chloroform exposures Significant differences in personal exposures to benzene, ethylbenzene, o-xylene, and m-,p-xylene were observed between males and females, with males having higher exposures than females (see Table 2) Edwards et al (2006) reported similar findings of gender differences in exposures to traffic-related aromatics (i.e ethylbenzene, o-xylene, and m,p-xylene) in the EXPOLIS study However, Edwards et al (2006) did not find significant gender differences in benzene exposures Schweizer et al (2007) reported that men spent less time in home than women, and men tended to work away from home in the EXPOLIS study Further, Graham and McCurdy (2004) suggested using age and gender as ‘‘first-order’’ attributes to identify statistically significant different cohorts with respect to the time spent indoors, outdoors, and in-vehicles by analyzing the USEPA Consolidated Human Activity Database (CHAD) In this study, we found that males spent significantly more time at work/ school than females (male mean: 10.37 h, female mean: 7.57 h, pvalue: 0.0001); males also spent significantly more time outdoors than females (male mean: 10.40 h, female mean: 6.94 h, p-value < 0.0001) during the exposure monitoring period This finding might suggest that males could spend more time in commuting to work, resulting in elevated exposures to traffic-related aromatics Significant gender differences were not observed in exposures to toluene and the three chlorinated chemicals (chloroform, PERC, and PDB) 3.1.2 Race/ethnicity Significant differences were observed in exposures to benzene and the three chlorinated chemicals (chloroform, PERC, and PDB) among different race/ethnicity groups (see Table 3) For benzene, Mexican Americans had higher exposures than both non-Hispanic whites and blacks Churchill et al (2001) reported that Mexican Americans were less likely to have elevated blood benzene levels than non-Hispanic whites from their analysis on the NHANES-III blood VOC data However, as pointed out by Lin et al (2008), the blood–air relationships of BTEX were influenced by factors such as age, gender, BMI, and smoking Thus, higher benzene exposures would not necessarily correspond to higher benzene blood levels For chloroform, Non-Hispanic blacks had higher exposures than non-Hispanic whites and Mexican Americans Churchill et al (2001) reported a similar finding that non-Hispanic blacks were more likely to have elevated chloroform blood levels than nonHispanic whites in the NHANES-III blood VOC study Churchill et al (2001) also indicated the protective effect of rural residence due to the increased well-water use for less exposure to chlorine-treated water By examining the questionnaire responses to ‘‘description of street where you live’’, we found that non-Hispanic whites had significantly higher proportion of rural residence than nonHispanic blacks (white proportion: 0.20, black proportion: 0.08, p-value: 0.001), thus resulting in lower chloroform exposures Table Gender differences in personal VOC exposures (ug mÀ3) Chemical Male Female p-value Mean benzenea toluene ethylbenzenea o-xylenea m,p-xylenea chloroform tetrachloroethene 1,4-dichlorobenzene N Mean N 6.33 28.19 6.54 6.46 19.43 2.65 7.71 30.11 295 282 289 288 290 296 292 291 4.67 23.42 3.99 3.84 11.44 2.90 3.23 43.22 352 347 347 352 352 355 350 350