Ethnicity, housing and personal factors as determinants of VOC exposures

Atmospheric Environment 43 (2009) 2884–2892

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Ethnicity, housing and personal factors as determinants of VOC exposures Jennifer C. D’Souza, Chunrong Jia, Bhrarmar Mukherjee, Stuart Batterman* University of Michigan, Ann Arbor, MI 48109-2029, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 June 2008 Received in revised form 7 March 2009 Accepted 12 March 2009

Previous studies investigating effects of personal, demographic, housing and other factors on exposures to volatile organic compounds (VOC) have focused on mean or median exposures, and generally not the high exposures that are of great interest. This paper identifies determinants of personal VOC exposures on a quantile-specific basis using a nationally representative sample. The NHANES 1999–2000 VOC dataset was merged with personal, demographic, housing, smoking and occupation variables. Bivariate analyses tested for differences in geometric means and quantiles across levels of potential exposure determinants. Multivariate sample-weighted ordinary least-squares (OLS) and quantile regression (QR) models were then used to adjust for covariates. We identify a number of exposure determinants, most of which varied by exposure quantile. The most striking finding was the much higher exposures experienced by Hispanics and Blacks for aromatic VOCs (BTEX: benzene, toluene, ethylbenzene and xylenes), methyl tert-butyl ether (MTBE), and 1,4-dichlorobenzene (DCB). Exposure to gasoline, paints or glues, and having a machine-related occupation also were associated with extremely high BTEX and MTBE exposures. Additional determinants included the presence of attached garages and open windows, which affected exposures of BTEX (especially at lower quantiles) and MTBE (especially at higher quantiles). Smoking also increased BTEX exposures. DCB was associated with air freshener use, and PERC with dry-cleaned clothing. After adjusting for demographic, personal and housing factors, age and gender were not significant predictors of exposure. The use of QR in conjunction with OLS yields a more complete picture of exposure determinants, and identifies subpopulations and heterogeneous exposure groups in which some individuals experience very elevated exposures and which are not well represented by changes in the mean. The high exposures of Hispanics and Blacks are perplexing and disturbing, and they warrant further investigation. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Benzene Chloroform Distributions Exposure Minority NHANES Quantile regression Volatile organic compounds

1. Introduction Volatile organic compounds (VOCs) are emitted, often as mixtures, in many microenvironments and are present in virtually all indoor and outdoor settings (e.g., Wallace, 2001; Weisel et al., 2005a; Jia et al., 2008a,b). Median personal exposures to several VOCs have been associated with excess lifetime cancer risks in the 104–105 range, considerably exceeding the U.S. guideline (Loh et al., 2007). Identifying the sources and factors associated with VOC exposures is a prerequisite for reducing exposures and risks. Many sources have been identified (Wallace et al., 1987; Sack and Steele, 1992; Jones, 1999; Wallace, 2001). Factors known to influence exposures include: urban environments and housing characteristics such as having an attached garage (Adgate et al., 2004; Batterman et al., 2007; Jia et al., 2008b; Dodson et al., 2008); activities such as smoking, certain hobbies and the use of specific * Corresponding author. Tel.: þ1 734 763 2417. E-mail address: [email protected] (S. Batterman). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.03.017

consumer products (Wallace et al., 1989; Heavner et al., 1995; Edwards et al., 2005; Kwon et al., 2007); and social and demographic factors influencing time-activity patterns (Graham and McCurdy, 2004; Schweizer et al., 2007) that in turn affect exposures (Edwards et al., 2006). Previous studies examining VOC exposures have several limitations. First, most have focused on mean or median exposures, however, higher exposures require attention since they are more likely to lead to adverse health effects and because the underlying risk profile may differ (Edwards et al., 2005). Second, the techniques commonly used to identify exposure determinants raise statistical issues, e.g., ordinary least squares (OLS) regression imposes normality requirements, potentially causing biases given that VOC exposures remain right-skewed even after log-transformation (Brown et al., 1994). Such problems are exacerbated with extreme values and outliers that are common in exposure data (Jia et al., 2008c). Third, analyses of exposure determinants inevitably exclude important factors, e.g., residence location, activities and weather, and available information lacks pertinent details,

J.C. D’Souza et al. / Atmospheric Environment 43 (2009) 2884–2892

2. Methods 2.1. Data NHANES 1999–2000 measured exposures on adults aged 20–59 years in 1999 and 2000 to ten VOCs: benzene, toluene, ethylbenzene, o-xylene, m,p-xylene, methyl tert-butyl ether (MTBE), chloroform (CF), tetrachloroethene (PERC), trichloroethene (TCE), and 1,4-dichlorobenzene (DCB). Participants were instructed to wear passive exposure monitors (3M 3520 Organic Vapor Monitors) when they left the mobile examination center, which they returned 2–3 days later at which time a short survey was administered regarding activities potentially related to exposures. VOCs were measured by GC/MS and selected-ion-monitoring mode (CDC, 2006b); a second laboratory used GC/MS in scan mode (Weisel et al., 2005b). Of the 851 participants, 182 were non-respondents. We removed respondents with questionable or invalid measurements: two with extremely high exposures (2210 mg m3 of ethylbenzene in one case, 6280 mg m3 of toluene in the second); two with excessively long sampling periods (>6 days; their VOC data were missing anyway); seven with short exposures; and 12 due to missing data for all VOCs. The final dataset had 646 respondents. In addition to those in the VOC survey, 5 demographic, 15 housing, 3 occupation and 21 personal variables in other NHANES datasets were extracted (see Supplemental materials). Five composite variables were derived to improve balance of the responses, e.g., exposures to dry-cleaned clothing/dry-cleaning and stain removal products were combined. (Component variables were not used further.) For occupation, subjects’ current occupation was used, and job categories were classified into six groups: cleaning (building services, etc.); health-care; food service/preparation; construction; machine-related (vehicle and machine operators, mechanics, etc.); and lastly and used as a referent group, all other occupations (46.5% of which were office or sales) and unemployed (40.1% of the group). Many observations of income were missing (17.5% of respondents). These were replaced by multiple imputation. Results using imputed and actual datasets did not differ substantially. Year of home construction (24.9% missing) was not used as similar variables were available, e.g., whether the home was built <5 years ago, and years lived in the home. Most other variables had few missing observations (<2%); missing values were coded as not having occurred.

weights were used for calculating percentiles and means. Unadjusted associations between VOCs and each factor were examined by comparing percentiles across levels of each variable, and then tested using QR. Differences in geometric means were tested using weighted linear regressions with each variable modeled as a categorical variable – this is equivalent to a t-test for 2-level variables, and ANOVAs for variables with 3 levels. As noted, because logtransformed distributions remained skewed, these comparisons may have been influenced by outliers. Each variable was further investigated using multivariate linear (OLS) and QR (Neter et al., 1992; Koenker and Bassett, 1978). Since benzene, toluene, ethylbenzene, m,p-xylene and o-xylene (BTEX) often have similar sources (Edwards et al., 2006), these VOCs were summed, log-transformed and tested together. Results for the separate and summed BTEX compounds were similar, thus results are reported for the sum (exceptions are noted). Variables in OLS models were selected using both forward and backward stepwise selection. A few variables with strong theoretical support were retained. Since social and demographic variables were of special interest, age, gender, race/ethnicity, education and annual household income were forced into the final models. When appropriate, interaction terms were tested, but none were found to be statistically significant. Model evaluation used R2, effect size, and significance. The QR models used the variables determined in the final OLS models. QR is similar to linear regression, but differences of the weighted absolute residuals from specified quantiles (rather than the sum of squared residuals from the mean) are minimized, and the estimated coefficients represent the change in the quantile per unit change of the variable (rather than the change in the mean). Analogous to a mean being defined as the solution to minimizing the sum of squared residuals, the median can be viewed as minimizing absolute residuals with extensions to quantiles by asymmetrically weighting residuals based on the chosen quantile (Koenker and Bassett, 1978). Examination of QR coefficients allows insight into possibly varying exposure relationships. Two contrasting examples from our analyses are given. Fig. 1 displays OLS and QR results showing the change in log-BTEX exposures for Hispanics relative to non-Hispanic Whites. Positive OLS and QR coefficients indicate increased exposure among Hispanics; a zero coefficient (b ¼ 0) indicates no effect; and negative coefficients indicate decreased exposure. Effects at the 90th percentile are large 2

Coefficient (Δ log (BTEX))

e.g., job classification and occupational exposures. Fourth, most studies have been conducted in localized areas, and the ability to generalize findings is unknown. Lastly, while the most useful and generalizable approach to characterize exposures uses populationbased samples (Wallace, 2001) and personal measurements (NRC, 1991), few such studies have been undertaken. This paper has the objective of identifying personal, housing, social and demographic factors associated with VOC exposures in a nationally representative sample. We use the NHANES 1999–2000 VOC dataset (CDC, 2004, 2006a; Jia et al., 2008c) to identify factors associated with both typical and ‘‘high-end’’ exposures. Quantile regression (QR) is used to help to address distributional concerns and to handle heterogeneous distributions (Koenker and Bassett, 1978; Cade and Noon, 2003). No prior applications of this powerful technique in the exposure field have been identified.

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1

0

-1 0.2

0.4

0.6

0.8

1

Quantile 2.2. Statistical methods Statistical analyses used log-transformed exposures (Jia et al., 2008c). National Center for Health Statistics (NCHS) sampling

Fig. 1. Adjusted QR and OLS model results for BTEX exposure and Hispanic ethnicity: (referent ¼ non-Hispanic White). The connected dots show QR coefficients and the shaded area is the corresponding 95% confidence interval. The solid horizontal line is the OLS estimate and the dashed horizontal lines are the corresponding confidence interval.

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3. Results

Coefficient (Δ log (CF))

1

Exposures to toluene, ethylbenzene, m,p-xylene and o-xylene were nearly universal with 93–96% of respondents showing detectable levels (Table 1). Benzene, CF, PERC and DCB exposures were also common (63–80% detection), while MTBE and TCE detections were infrequent (23–28%). In large part, the detection statistics reflect the method detection limits (MDLs) in the NHANES measurements, e.g., using a more sensitive method, TCE was found in over half of 159 Michigan homes tested (Jia et al., 2006, 2008a). Toluene showed the highest median exposure (17.1 mg m3), followed by m,p-xylene (6.5 mg m3). OLS and QR results for ethnicity, housing and personal factors are summarized in Tables 2–4, respectively. Full results for the models are provided as Supplemental materials. The following discusses each VOC in turn.

0

-1

-2 0.2

0.4

0.6

0.8

1

Quantile

3.1. BTEX

Fig. 2. Adjusted QR and OLS model results for chloroform (CF) exposure and wellwater use.

and exceed the upper confidence limits of the OLS model, which shows only a modest effect. Overall, Hispanics experience greater BTEX exposures than Whites, and differences increase at higher exposures. Fig. 2 shows results for log-chloroform exposures contrasting well-water versus water from other sources. Here, QR coefficients are relatively constant across the quantiles, indicating a ‘‘location shift,’’ i.e., uniformly higher exposure among non-wellwater users. The first example shows the additional information extracted by QR analyses; the second that results are comparable in the case of a location shift. To investigate effects of each factor on the full exposure distribution, QR models were examined graphically and compared with OLS estimates for each VOC. Several upper quantile estimates were unstable, especially for sparsely populated cells, and these results should be interpreted cautiously (noted in the text). Both OLS and QR models used sampling weights, however, only the former adjusted for the clustered sample design. Unfortunately, QR models for clustered data have not been well developed (Mechta¨talo et al., 2008). While the 95% confidence interval (CI) obtained by QR may overstate significance levels, results still illustrate the factor– exposure relationship. To help identify outliers and influential observations, QR and OLS models were run both with and without sampling weights. Data were analyzed using SUDAAN 9.0, survey procedures in SAS 9.1.3, the experimental Proc Quantreg for QR (Dec. 2005 release), and the resampling method (200 iterations) for calculating CIs. Proc MI and MIanalyze were used for imputation.

BTEX are ubiquitous VOCs, emitted from volatilized gasoline, vehicle exhaust, paints, solvents, adhesives and many other sources, and thus BTEX exposures were expected to be associated with many personal and housing factors. In OLS models adjusted for age, gender, annual household income and education, log-BTEX exposures were strongly associated with: personal and housing factors, including attached garage; years lived in home (or home built <5 years ago); type of street; exposures to cigarette smoke, gasoline, and paints/ glues; and occupation. These along with demographic factors were forced into the final model, which explained 24.4% of the variance. Often, OLS and QR results diverged as some factors affected specific quantiles, rather than the full distribution, as described below. 3.1.1. Upper quantile effects Hispanics had the highest BTEX exposures (medians of 36.5, 33.2, and 29.5 mg m3 for Hispanics, non-Hispanic Whites, and nonHispanic Blacks, respectively). In the fully adjusted OLS and QR models (which included demographic, housing, and personal factors), Hispanics exposures remained higher than Whites (e.g., bmean ¼ 0.31 or 1.4 times (e0.31 ¼ 1.4)), and differences were larger at higher quantiles (e.g., b0.95 ¼ 1.33 or 3.8 times; Table 2; Fig. 1). Although not always statistically significant, similar effects were seen for the individual BTEX compounds. Fewer years lived at home (especially at upper exposure quantiles) and more rooms in the home were associated with significant and/or large increases in BTEX exposure (Table 3). Fewer years lived at home was also associated with newer homes (p < 0.0001). Newer homes can have higher concentrations as they are both more air-tight (Jones, 1999) and contain VOC-emitting materials (e.g., paint and caulks; Park and Ikeda, 2006).

Table 1 Summary of the VOC and BTEX measurements in NHANES. Excludes outliers and non-respondents. Italics indicate percentiles below method detection limits (MDLs). VOC

N

N missing

Below MDL (%)

Geometric mean

Minimum

10th

25th

50th

75th

90th

95th

99th

Maximum

0.7 1.7 0.1 0.2 0.1 0.8 0.4 0.2 0.3 0.1 0.1

1.0 5.0 0.8 1.7 0.7 10.2 0.4 0.3 0.6 0.2 0.2

1.4 9.2 1.3 3.3 1.3 18.6 0.5 0.6 0.9 0.4 0.2

2.8 17.1 2.6 6.5 2.3 33.1 0.6 1.1 1.7 0.7 0.3

5.7 29.7 5.2 14.0 4.9 65.4 5.5 3.0 8.8 2.3 0.5

12.6 55.3 12.3 38.2 13.4 138.3 10.8 5.9 32.9 5.7 1.2

17.8 92.6 25.2 80.6 26.4 285.3 21.5 12.1 144.2 18.5 7.4

32.6 331.1 110.9 233.0 62.5 784.4 50.0 25.4 490.8 76.8 95.6

119.5 1610.8 837.1 728.7 202.3 1966.2 181.7 53.9 2235.6 659.1 327.3

(mg m3) Benzene Toluene Ethylbenzene m,p-xylene o-xylene BTEX MTBE Chloroform (CF) 1,4-Dichlorobenzene (DCB) Tetrachloroethene (PERC) Trichloroethene (TCE)

638 629 633 637 637 644 635 642 635 633 635

8 17 13 9 9 2 11 4 11 13 11

34.6 6.3 6.8 4.2 7.3 – 72.5 20.6 37.6 31.4 77.3

3.1 17.3 2.9 7.2 2.8 36.1 1.4 1.4 3.1 1.0 0.4

J.C. D’Souza et al. / Atmospheric Environment 43 (2009) 2884–2892 Table 2 Adjusted OLS and QR results for ethnicity (referent ¼ non-Hispanic White). Statistically significant (p < 0.05) results in bold. VOC

OLS

25th

50th

75th

90th

95th

Hispanic

BTEX MTBE DCB CF

0.31 0.40 0.72 0.17

0.03 n/a 0.37a 0.14

0.21 n/a 0.88 0.15

0.37 0.79 1.14 0.31

1.10 1.19 1.27 0.33

1.33 1.43 1.48 0.23

Black

BTEX MTBE DCB CF

0.03 0.31 0.89 0.24

0.24 n/a 0.31a 0.20

0.02 n/a 0.46 0.30

0.03 0.66 2.10 0.46

0.04 0.92 2.12 0.13

0.34 0.88 1.64 0.40

a

40th percentile.

BTEX exposures were elevated for machines/motor vehiclerelated occupations (median and 90th percentile concentrations of 62.6 and 316 mg m3, respectively, compared to 31.5 and 105 mg m3 for the reference), and differences increased at high quantiles (Table 4). These occupational groups had a modest sample size (n ¼ 88). BTEX exposures have been associated with vehicle operation and repair (Jo and Song, 2001; Wilson et al., 2007). The QR results show a subset of workers who are much more exposed than most in their occupational group, which is easily explained by heterogeneous exposures within the group, and which is especially likely for the broad job categories used in NHANES. For exposures to paints/glues and gas/fuels, QR models also showed large differences at the upper quantiles (Table 4), again indicating a highly exposed subset. 3.1.2. Lower quantile effects Participants living in a house with an attached garage had higher BTEX exposure (medians of 38.4 versus 32.0 mg m3; p ¼ 0.002), and after adjustment the ‘‘garage effect’’ was slightly greater at both lower and upper quantiles, but only significant for the lower quantile (Table 3). QR results showed some variation for individual compounds: o-xylene also had smaller effects at upper quantiles; benzene and toluene had larger effects at upper quantiles; ethylbenzene and m,p-xylene had consistent effects across quantiles. The garage effect has been shown in indoor sampling by Jia et al. (2008b) where attached garages increased median ethylbenzene and toluene concentrations by 1.3 and 18.8 mg m3, respectively, and by Dodson et al. (2008) where mean BTEX levels increased by 2–6-fold. The personal measurements taken in NHANES show similar, but smaller changes, reflecting the contributions from a number of exposure sources in addition to garages.

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Exposure to tobacco smoke and/or being a smoker was associated with higher BTEX exposure (median of 35.7 versus 31.3 mg m3; means test p ¼ 0.01). After adjustment, effects diminished at higher quantiles (Table 4), and slightly differed for benzene (location shift) and o-xylene (upper quantile effect). Tobacco smoke is a wellknown VOC source (Charles et al., 2008) and, as seen for attached garages, the ‘‘smoking effect’’ was strongest at lower quantiles. Again, the effect size was diminished in the presence of other strong BTEX sources. Exposure to tobacco smoke was a combined variable, and possibly being a smoker or being exposed at work might cause different impacts. Those reporting opened windows had lower BTEX exposures (median 29.3 versus 38.4 mg m3; means test p ¼ 0.01). The effect remained after controlling for other factors, though it was less pronounced at upper quantiles (Table 3). Significant effects were seen for BTEX and for benzene, toluene, and m,p-xylene separately. Window opening can increase air exchange and thus lower indoor VOC concentrations (Wallace et al., 2002). In Michigan homes, BTEX levels decreased significantly in summer (but not winter) when windows were opened (Jia et al., 2008b). Seasonal effects could not be investigated in NHANES. 3.2. MTBE MTBE’s major use was as an oxygenate and octane booster in gasoline (phased out in 2006). MTBE was detected in only 28% of samples. We focused on exposures above the 75th percentile, i.e., values above MDLs. High concentrations may occur while refueling vehicles, in vehicle cabins (Lioy et al., 1994), and in residences with attached garages (Dodson et al., 2008). Ambient and indoor information on MTBE levels is limited. Given MTBE’s use in gasoline, we forced attached garage, exposure to gasoline/fuels, and living on a commercial street/highways into the models, as well as the demographic factors. The OLS model explained 13% of the variation. 3.2.1. Upper quantile effects As seen for BTEX, MTBE exposures were highest among Hispanics (75th and 90th percentile concentrations of 6.7 and 16.7 mg m3, respectively) compared to non-Hispanic Whites (4.7 and 10.0 mg m3). Blacks had slightly elevated exposures (75th and 90th percentile concentrations of 6.9 and 9.0 mg m3, respectively.) QR and bivariate analyses gave similar results. Across all quantiles, exposures for Hispanics were 2.5–4 times higher than those of non-Hispanic Whites; exposures of Blacks were 1.5–2.5 times higher. Race/ethnicity effects increased at upper quantiles (Table 2). Attached garages were

Table 3 Adjusted OLS and QR results for housing factors. Otherwise as Table 2. VOC

OLS

25th

50th

75th

90th

95th

Attached Garage

BTEX MTBE PERC

0.42 0.61 0.39

0.52 n/a 0.36b

0.19 n/a 0.27

0.30 0.83 0.35

0.42 1.20 0.65

0.49 1.16 0.20

Commerical street/Highway vs. Rural/Residential

BTEX MTBE DCB PERC BTEX DCB CF PERC

0.03 0.47 0.53 0.71 0.45 0.09 0.44 0.37

0.25 n/a 0.81a 0.94b 0.43 0.22a 0.35 0.25b

0.04 n/a 0.71 0.64 0.32 0.21 0.51 0.41

0.15 0.65 0.47 0.81 0.36 0.05 0.56 0.41

0.01 0.12 0.27 0.56 0.36 0.39 0.50 0.61

0.16 0.38 0.35 0.45 0.26 0.52 0.63 0.95

BTEX CF PERC TCE

0.13 0.48 0.27 0.04

0.10 0.38 0.12b n/a

0.07 0.54 0.18 n/a

0.06 L0.50 L0.68 0.04

0.23 0.79 0.09 0.13

0.31 0.67 0.03 0.37

Open Windows

Years lived in home Well

a b

40th percentile. 30th percentile.

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3.3. 1,4-Dichlorobenzene

Table 4 Adjusted OLS and QR results for personal factors. Otherwise as Table 2.

Job Machine-Related

Health Exposure to Smoking Paints/glues Gas/fuels Pool Mothballs, crystals Air Freshener Dry-cleaning, stain remover a b

VOC

OLS

25th

50th

75th

90th

95th

BTEX MTBE TCE CF

0.55 0.06 0.34 0.58

0.33 n/a n/a 0.02

0.39 n/a n/a 0.42

0.64 0.12 0.26 1.25

0.58 0.49 1.61 0.62

0.75 1.10 2.17 0.43

BTEX BTEX BTEX MTBE CF DCB TCE DCB PERC

0.26 0.21 0.17 0.01 0.57 0.76 0.68 0.08 0.62

0.37 0.06 0.06 n/a 0.46 1.07a n/a 0.07a 0.53b

0.08 0.05 0.12 n/a 0.47 1.08 n/a 0.01 0.69

0.22 0.68 0.27 0.07 0.68 0.83 1.35 0.13 0.89

0.08 0.88 0.36 0.33 0.54 0.76 2.00 0.79 1.41

0.09 0.64 0.35 0.61 0. 94 2.32 2.26 0.69 1.27

40th percentile. 30th percentile.

associated with elevated MTBE exposures, e.g., 90th percentile exposures were 18.7 and 7.9 mg m3 with and without a garage, respectively. These differences were maintained after adjustment for other factors (e.g., b0.90 ¼ 1.2 or 3-fold increase; Table 3). Exposure to gasoline/fuels also elevated exposures (90th percentile exposure of 14.1 compared to 7.8 mg m3), a result unchanged after controlling for demographic, housing and personal factors (Table 4). Like BTEX, exposure increased strongly at higher quantiles for individuals reporting exposure to gas/fuels, reflecting MTBE’s specificity to gasoline. Higher household income was associated with higher exposures, e.g., incomes $20,000 had 75th and 90th percentile exposures of 1.2 and 7.0 mg m3, respectively; incomes from $21,000 to $54,000 had 4.3 and 9.5 mg m3, and incomes $55,000 had 7.0 and 14.3 mg m3. The income-MTBE association suggests increased vehicle-related exposure, however, this cannot be tested directly with the data available in NHANES.

DCB is used in indoor products such as air fresheners, deodorizers and mothballs/crystals (ATSDR, 2006). Use of these products and ventilation were expected to be exposure determinants, and the following variables were forced into the models: open windows, new carpet in past 6 months, exposure to moth repellents, use of disinfectants or degreasers, and air freshener use. The final OLS model explained 12.7% of the variance. 3.3.1. Upper quantile effects Race/ethnicity was strongly related to DCB exposure. Exposures of Hispanics (median of 4.8 mg m3) and Blacks (3.4 mg m3) greatly exceeded that of non-Hispanic Whites (1.4 mg m3), and differences increased dramatically at upper quantiles, e.g., 90th percentile concentrations for Hispanics and Blacks exceeded 100 mg m3 compared to 17.4 mg m3 for non-Hispanic Whites. These effects were confirmed by both OLS and QR models (Table 2). Exposure to mothballs, crystals or flakes greatly increased DCB concentrations, e.g., 50th and 90th percentile exposures were 6.3 and 188 mg m3, respectively, compared to 1.7 and 32.1 mg m3 for those without exposure. Although effects were consistent and large, neither OLS nor QR models showed that this activity was statistically significant, probably due to the few subjects with this exposure (n ¼ 23). Air freshener use increased high-end DCB exposures, e.g., 90th percentile exposures were 40.3 and 30.7 mg m3 with and without fresheners, respectively (Table 4 and Fig. 4). 3.3.2. Lower quantile effects Opened windows lowered DCB exposures, although again effects were not significant (Table 3). DCB exposure increased for individuals living on a commercial street for the mean and lower quantiles, possibly from industrial emissions (ATSDR, 2006), although outdoor levels generally fall far below indoor levels (Sexton et al., 2004; Jia et al., 2008b). 3.4. Chloroform

3.2.2. Lower quantile effects Living on a commercial street or highway increased MTBE exposure for primarily the lower quantiles (OLS results were significant, QR results were not; Table 3 and Fig. 3). Proximity to highways has been associated with elevated MTBE in ambient and residential air (Kwon et al., 2006). 2

Chloroform is a byproduct of water disinfection using chlorine. Because exposure was expected from water sources and water use, the final model included water source, taking a hot shower for 5 min, being near a swimming pool, occupation, housing and the aforementioned demographic factors. The OLS model explained 24% of the variance. Personal factors were significant at the upper quantiles, while most of the housing factors showed a location shift,

1

Coefficient ( Δ log (DCB))

Coefficient (Δ log (MTBE))

2

0

1

0

-1 0.65

0.75

0.85

0.95

1

Quantile

-1 0.2

0.4

0.6

0.8

1

Quantile Fig. 3. Adjusted QR and OLS model results for MTBE exposure and street type (commercial street/highway versus residential/rural).

Fig. 4. Adjusted QR and OLS model results for DCB exposure and air freshener use.

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e.g., opened windows and well-water use, both of which lowered exposures (Table 3). The effect of opened windows has been shown by Wallace et al. (1989) and Jia et al. (2008b). 3.4.1. Upper quantile effects Unadjusted analyses indicated that CF exposures were significantly higher among Hispanics (median and 90th percentile levels of 2.0 and 6.2 mg m3, respectively) and Blacks (1.9 and 12.3 mg m3) compared to non-Hispanic Whites (1.0 and 5.3 mg m3; p ¼ 0.03), however, these differences were not significant in multivariate models (Table 2). After adjustment, individuals in the youngest age group (20–30 yr) had the highest exposures, and effects were slightly more pronounced at upper quantiles. The type of home made the biggest difference in CF exposures. Participants living in detached homes had significantly lower exposure (mean and 90th percentile concentrations of 0.9 and 4.3 mg m3, respectively) compared to apartments and other housing types (2.1 and 11.8 mg m3), probably reflecting widespread use of chlorinated water in urban areas, compared to domestic well-water which is rarely chlorinated. This is reinforced by the finding that well-water users had lower CF exposure (median of 0.5 mg m3) than those using other water sources, e.g., city water (1.3 mg m3; means test p ¼ 0.02); this cross-quantile effect represents a location shift in the QR analysis, as discussed earlier (Table 3 and Fig. 2). Gas stoves were associated with lower CF exposures, an unexpected result, although the effect was small. This association may reflect housing characteristics and geographic location (Eisner and Blanc, 2003). Older homes tend to be less air-tight, which would decrease CF. In this population, gas stoves were more likely in both newer and older homes (built after 1990 or before 1949; c2 ¼ 12.2; p ¼ 0.02). However, this result should be interpreted cautiously as 160 subjects (20%) did not know when their home was built. Swimming pool visits increased CF exposure by 1.6–2.6 times. Chloroform levels can be very high in and near pools (e.g., Hinwood et al., 2006). Because the samplers worn by NHANES participants could not get wet, exposures while swimming are not reflected in the dataset. Longer showers (5 min) were not associated with exposure, although high CF levels have been associated with showers (Xu and Weisel, 2005). Possibly the NHANES variable did not capture the variability in showering exposure to chloroform, or perhaps subjects did not always bring the sampler into the bathroom while showering. CF emissions and exposure may also result from dishwashing, laundry and other household activities (Nuckols et al., 2005) but descriptors of these activities were unavailable. 3.5. Tetrachloroethene Because PERC is used in dry-cleaning and is often found in drinking water, exposure to dry-cleaning and well-water use was forced into the final models. The OLS model explained 14.4% of the variance. Few demographic factors were associated with PERC in either OLS or QR models (Table 2). 3.5.1. Upper quantile effects Exposure to dry-cleaning was associated with very high exposures (median and 90th percentile of 1.9 and 23.4 mg m3, respectively, compared to 0.7 and 4.1 mg m3 for unexposed individuals), and effect sizes increased with exposure (Table 4). Dry-cleaning has long been recognized as the dominant PERC exposure source for most individuals (Wallace et al., 1985). Exposures decreased with opened windows (median and 90th percentile of 0.6 and 4.2 mg m3, respectively) compared to unopened windows (1.1 and 8.7 mg m3); reductions were largest at the top quantiles (Table 3).

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3.5.2. Lower quantile effects Individuals living in a home with an attached garage had higher PERC exposures (median and 90th percentile exposures of 0.8 and 8.7 mg m3, respectively) compared to those without garages (0.7 and 4.7 mg m3); differences were significant at the low and central quantiles (Table 3). In addition to dry-cleaning solvents, PERC is a constituent of some vehicle-related products, hobby and crafts goods (e.g., fabric adhesive), and construction products (e.g., sealants, adhesives; NLM, 2007). Storing these products in garages may account for their association with garages. Jia et al. (2008b) found elevated levels indoors with attached garages, though the effect was not significant. Because effects were significant at only lower quantiles, other factors appear to cause high PERC exposures. Living on a commercial street/highway increased exposures at lower and central quantiles, possibly reflecting industrial and urban sources (Kwon et al., 2006; Adgate et al., 2004; Table 3). 3.6. Trichloroethene TCE was detected for only a small fraction (23%) of participants, thus low percentile results are uninformative. TCE has been used as a metal degreaser, paint solvent and, less frequently, a dry-cleaning solvent, and it is a widespread contaminant in drinking water. Accordingly, exposure to paints/glues, well-water, dry-cleaning, occupation and shower variables were forced into the final models. However, few factors were associated with TCE, and the final OLS model explained only 5% of the variance, and thus only an abbreviated description is provided. Demographic factors were not significantly associated with the mean or most quantiles of TCE exposure, except that exposure among males (90th percentile concentrations of 2.8 mg m3) exceeded that of females (0.9 mg m3). Machine-related occupations boosted exposures (90th percentile levels of 11.4 mg m3) compared to other occupations/unemployed (1.0 mg m3). The QR models showed this difference at upper quantiles (e.g., b0.95 ¼ 2.17 or 8.8 times). Occupation was statistically significant for only the mean, although results at higher percentiles were large and approached significance. TCE’s use as degreaser in machine-related occupations explains this relationship. Exposures increased strongly with the use of mothballs/flakes at several of the upper quantiles (e.g., b0.80 ¼ 2.26 or a nearly 10-fold increase), however, the sample size was small (n ¼ 23). Excluding the mothballs/flakes variable, effect of dry-cleaning increased though it remained statistically insignificant. Use of paints/glues/paint thinners did not show a large or significant effect, but use of paint thinners/brush cleaners (excluding exposure to paints, glues and adhesives) had a large but statistically insignificant effect on the mean and upper quantile exposures. The sample size of this group was small (n ¼ 37). TCE concentrations in residences and outdoors are often similar, and typical levels range from 0.03 to 0.08 mg m3 (Rosenbaum et al., 1999; Hodgson and Levin, 2003; Jia et al., 2008b). Indoor levels have been associated with recent renovation and the presence of an attached garage (Jia et al., 2008b). The high MDL in NHANES data (w0.44 mg m3) may obscure identification of TCE determinants. 4. Discussion Personal exposures across individuals tend to be highly variable, given the many microenvironments, sources, and habits of people that influence exposure. For most VOCs, personal exposures typically exceed indoor concentrations, which in turn exceed ambient concentrations. Exposure levels seen in NHANES are comparable to those in other contemporary but smaller personal monitoring studies, as discussed by Jia et al. (2008c), and many of the identified

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demographic, personal and housing determinants are also shared by the indoor studies. Some of the key determinants of VOC exposure include the following:  Race/ethnicity had among the greatest and strongest effect. As compared to non-Hispanic Whites, Hispanics and Blacks had elevated exposures, especially at upper quantiles, for BTEX, MTBE and DCB. With the exception of DCB, exposures of Hispanics exceeded those of Blacks.  Attached garages increased exposures of BTEX, MTBE and PERC, likely due to emissions from cars, fuel containers, and other materials stored inside that migrate into the occupied portion of the house (Batterman et al., 2007). The low quantile effects for BTEX and PERC indicate this is the major exposure source for a subset of the population. For MTBE, upper quantile effects were observed.  Opened windows reduced exposures of many VOCs, showing that ventilation diluted levels from indoor VOC sources. Effects were slightly more pronounced at lower quantiles of BTEX and DCB exposures, relatively even across CF quantiles, and at upper PERC quantiles.  Several occupations were associated with higher exposures, especially at the upper quantiles: machine-related occupations showed elevated BTEX, MTBE and TCE; and health-related jobs showed elevated CF.  Several personal factors, including a number of specific sources, were identified: BTEX and MTBE were associated with exposure to gasoline/fuels; BTEX with paints/glues and smoking; DCB with moth repellents and air freshener use; PERC with dry-cleaning and stain removers; and CF with the city water and pool exposures. These are consistent with known sources. Most of the personal factors showed upper quantile effects, indicating that these are strong or primary exposure sources for some of the population. After adjustment for personal and housing factors, age and gender generally were not associated with exposures (exceptions were BTEX, o-xylene; m,p-xylene, ethylbenzene and benzene). Less education and low household income were weakly associated with higher exposures to BTEX compounds. The most striking demographic determinant – the association of Hispanic and Black race/ ethnicity with much higher exposures of BTEX, MTBE and PERC – was maintained even after adjustment for personal and housing factors. Upper quantile differences often were large, e.g., at the 95th percentile, Hispanics had BTEX exposures that were elevated 3-fold (Fig. 1). For PERC, differences were weaker, but Hispanics still tended to have greater PERC exposure. Why is race/ethnicity so strongly associated with exposure? First, we confirmed results by rerunning analyses after identifying and removing influential outliers, and also after omitting sample weights. While effect sizes were slightly reduced, differences remained. Thus, all indications are that this race/ethnicity effect is real. High VOC exposure in minority populations has been shown in indoor and personal monitoring (Adgate et al., 2004; Sexton et al., 2005; Sax et al., 2006; Arif and Shah, 2007), although these studies were not designed to test such effects or to be nationally representative. Many environmental justice studies (EJ) have concluded that minorities have elevated exposures because they live in more polluted cities or in areas of a city that are more polluted, however, these studies do not use personal exposure measurements, and much of the ethnicity effect in EJ studies disappears after adjustments for social characteristics, e.g., education and home ownership (Ash and Fetter, 2004). In contrast, this effect persisted in the NHANES dataset. Elevated exposures among minorities might occur for many reasons: differences in residence location (urban/

rural) and proximity to VOC sources (e.g., busy roads, gas stations); unknown occupational and/or smoking exposures; use of older and/or higher emitting vehicles; different behaviors and/or timeactivity patterns (e.g., longer commutes); high-emitting products in the home or hobbies (e.g., engine repair); and modifying factors that increase exposure. Unfortunately, little information on these factors is available in NHANES 1999–2000. Integrated exposure measurements, as used in NHANES, will increase with the concentration, duration and/or frequency of exposures. The behaviors and microenvironments frequented by Hispanics and Blacks that increase VOC exposure deserve further examination. Are Hispanics exposed to motor vehicles (a major source of BTEX compounds) more frequently or somehow more intensely than non-Hispanic Whites? Do they use more highemitting products, as suggested anecdotally for Hispanics for air fresheners in motor vehicles (Elliott and Loomis, 2008)? Are there other relevant activities associated with minority groups? Which microenvironments account for the bulk of exposure, and are there other environments, not examined, that cause high exposure? We know, for example, a greater fraction (14%) of Hispanics worked over 35 h/week as compared to non-Hispanic White and Blacks (6–7%, p ¼ 0.13), which may intensify occupational exposures. For individuals with multiple jobs, we could only account for the main occupation (provided in NHANES), but part-time jobs might account for a disproportionate share of exposure. Ash and Fetter (2004) have suggested that minority populations have less access to information regarding the health effects of pollutants and lower average wealth than whites, even when incomes are similar, which could affect housing choices. These authors also suggest that racism in the housing or credit markets could constrain housing choices. Race/ethnicity appears to affect exposure more than most of the personal, occupational, and housing-related factors identified. We saw one additional puzzling relationship. The use of furniture polish (n ¼ 85) was associated with lower levels of BTEX and MTBE. Furniture polish is a formulation of highly volatile paraffin, mineral, naphtha and ‘‘lemon’’ oils that contain many hydrocarbons (e.g., distilled alkanes, cycloalkanes, and aromatic compounds), but very little benzene or other NHANES target compounds. Possibly these VOCs may have partially saturated the adsorbent sampler, and the reduced sampling rate then biased concentrations downwards. Analytical interferences (e.g., high baseline) may have yielded a similar bias. This is speculative and also requires further investigation. While most of the personal and housing-related exposure determinants identified are consistent with known sources or influences, the models explained only a fraction of the variance, and relatively few demographic, housing and personal factors attained statistical significance. This is unsurprising given the range of personal exposures, the many factors affecting exposure, and the crude descriptors, e.g., ‘‘opened windows’’ is at best a rough indicator of a building’s air exchange rate. More refined analyses might include variables not in NHANES, for example: ambient concentrations; location/classification of homes and workplaces as urban, rural, traffic-exposed, etc; characterization of important emission sources; air exchange rates; house size; number of occupants; recent renovations; and meteorological variables (Johnson et al., 2004; Wallace et al., 2002; Jia et al., 2008a; Park and Ikeda, 2006; Ho et al., 2004; Schlink et al., 2004; Kwon et al., 2006). In addition, time-activity pattern information could be enhanced to address time spent in vehicles, washing dishes and clothes, and working (Wallace et al., 1989; Edwards et al., 2006). It is also important to determine those behaviors that affect exposure. Such information should improve model fit and predictive ability, and it would strengthen linkages with exposure sources. In addition, sample size did not allow investigation of most interactions. Finally, our risk

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profiles for highly exposed populations must be considered preliminary, and further information is needed to understand the specific activities leading to high exposures, and to develop strategies for reducing exposures. The QR analyses revealed important distinctions not shown by OLS analyses or other tests of central tendency. The QR results fell into three patterns. First, some factors caused a general location shift, e.g., an ‘‘across-the-board’’ change in CF exposures for the use of city water (Fig. 2). In such cases, OLS and QR results were similar. More commonly, factors caused larger differences at upper exposure percentiles, e.g., dry-cleaning with PERC exposure and air freshener use with DCB (Figs. 1 and 4). This pattern suggests that the factor is a principal exposure determinant. The third and opposite pattern was a greater change at lower exposure quantiles, often without significant effects at upper quantiles. This occurred for opened windows with DCB exposure, and street type with MTBE (Fig. 3). This pattern was relatively rare. It suggests that the factor alone is generally not the prime determinant of high exposures, but that it may be influential for individuals without elevated exposures. Because the NHANES 1999–2000 VOC data is a populationbased sample, the identified exposure determinants should be generalizable to the US population. The QR models, not previously demonstrated in an exposure application, supplement simpler OLS models and provide information across the exposure. 5. Conclusions This analysis confirms many previous reports regarding the sources and factors that affect VOC exposures. The QR models indicate that highly exposed individuals often have a different ‘‘risk factor’’ profile than those with lower exposures. This information is important since identifying and controlling factors that affect primarily the mean or median exposures may not be an effective strategy for highly exposed groups. We also confirm, perhaps the first time in a robust manner, that demographic factors including being Hispanic and Black are strong exposure determinants. Acknowledgements 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. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atmosenv.2009.03.017. References Adgate, J.L., Eberly, L.E., Stroebel, C., Pellizari, E., Sexton, K., 2004. Personal, indoor, and outdoor VOC exposures in a probability sample of children. Journal of Exposure Analysis and Environmental Epidemiology 14, S4–S13. Arif, A.A., Shah, S.M., 2007. Association between personal exposure to volatile organic compounds and asthma among US adult population. International Archives of Occupational and Environmental Health 80, 711–719. Ash, M., Fetter, T.R., 2004. Who lives on the wrong side of the environmental tracks? Evidence from the EPA’s risk-screening environmental indicators model. Social Science Quarterly 85, 441–462. ATSDR, 2006. Toxicological Profile for Dichlorobenzenes. U.S. Department of Health and Human Services, Atlanta, GA. Batterman, S., Jia, C., Hatzivasilis, G., 2007. Migration of volatile organic compounds from attached garages to residences: a major exposure source. Environmental Research 104, 224–240.

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