Our objective was to compare polycyclic aromatic hydrocarbon (PAH) exposure estimates based on survey, biological monitoring, and geographic information system (GIS) methods. The 304 participants in this study supplied a urine sample and completed questionnaires about exposure to potential PAH sources. We assayed urine samples for 1-hydroxypyrene-O-glucuronide (1-OHPG), the major metabolite of pyrene, a common PAH. We used a GIS to estimate traffic exhaust exposure using vehicle count data at the residence and workplace. The five subjects who reported smoking during the 48-hour period had median 1-OHPG concentrations 10-fold that of nonsmokers (1.6 versus 0.16 pmol/mL; P = 0.01). Among nonsmokers, those who reported eating grilled, roasted, or broiled meat had significantly higher 1-OHPG concentrations than those who did not reported eating meat prepared by these methods (0.25 versus 0.06 pmol/mL; P = 0.02). Nonsmokers who reported traveling on roads for ≥3 hours during the 48-hour period also had significantly higher 1-OHPG levels than those who traveled <3 hours (0.23 versus 0.11 pmol/mL; P = 0.03). 1-OHPG levels were also correlated with hours of secondhand smoke exposure among nonsmokers (P = 0.04). In this study, 1-OHPG urine concentrations were not associated with self-reported exposures to cooking smoke, wood burning, or traffic levels near the home or to traffic density or urban/rural status determined using a GIS. Self-reported indicators of residential proximity to high traffic volume were, however, associated with GIS traffic density measures. (Cancer Epidemiol Biomarkers Prev 2006;15(7):1376–81)

There has been growing interest in evaluating exposure to vehicle exhaust for epidemiologic studies of lung cancer (1, 2), childhood cancer (3-6), breast cancer (7), asthma (8-10), respiratory symptoms (11), all cause mortality (12), and birth outcomes (13-16). These studies often rely on exposure estimates derived from surveys or a geographic information system (GIS). It is not clear, however, how well these measures capture personal exposures to vehicle exhaust or how they relate to each other. Vehicle exhaust is a major contributor to the concentration of polycyclic aromatic hydrocarbons (PAH) in urban areas and near major roads (17, 18). Several PAHs are classified as probable human carcinogens based on evidence from occupational and laboratory animal studies (19, 20), making these compounds of primary interest for epidemiologic studies of cancer and exposure to vehicle exhaust. PAHs are formed during incomplete combustion of organic compounds; therefore, exposure occurs not only from vehicle exhaust but also from a variety of sources, including diet and tobacco smoke (21).

Personal air monitoring of women in New York City found that pyrene had the highest mean concentration among the PAHs measured (22). Personal air monitoring for PAHs is difficult and expensive and does not capture exposure from dietary sources, making development of a good biological exposure marker highly desirable. 1-Hydroxypyrene-O-glucuronide (1-OHPG) is the major urinary metabolite of pyrene, a common PAH (23). Elevated 1-OHPG levels have been measured in steel plant and waste incineration workers exposed to PAHs (24, 25). Because 1-OHPG is more sensitive than other exposure biomarkers, it has been proposed as a biomarker of exposure to environmental PAHs in air (26).

In this cross-sectional study, we determined 1-OHPG concentrations for 24-hour urine samples from a subset of women enrolled in the California Teachers Study cohort. The main objective of our study was to determine whether 1-OHPG urine concentrations were related to self-reported sources of PAH exposure or GIS-based estimates of traffic density. If so, this marker could be used to estimate PAH exposures in larger epidemiologic studies.

Study Population

Our study population consisted of 304 women participating in a measurement substudy of the California Teachers Study cohort. The California Teachers Study cohort includes 133,479 women who were active or retired public school teachers or administrators enrolled in the State Teachers Retirement System in 1995 (27).

The measurement substudy, conducted in 2000, included a random sample of 528 California Teachers Study participants who were ≤85 years old when the cohort was originally established (i.e., at baseline) and who resided in the substudy area (i.e., western Alameda, Santa Clara, San Mateo, Santa Cruz, Monterey, or northern San Benito counties in California). Forty-four (8%) of these women were not contacted because they had died, moved out of the substudy area, or could not be located. Of the 484 women invited to participate, 328 (68%) agreed, 138 refused, and 18 were not interviewed for other reasons. Most of the 18 not interviewed were too ill (n = 9). Reasons for not interviewing the remaining 9 were varied and included passive refusals (i.e., showed initial interest but failed to follow-through with the interview), personal/family issues that interfered with completing the interview, and scheduling problems that precluded interviews from taking place before the end of the study period. Of the 328 participants, 304 (93%) provided a 24-hour urine specimen. Our analysis is based on these 304 women, 124 of whom were urban and 180 of whom were rural residents. The use of human subjects in this study was reviewed by the California Health and Human Services Agency, Committee for the Protection of Human Subjects and found to be in compliance with their ethical standards as well as with the U.S. Code of Federal Regulations, Title 45, Part 46 on the Protection of Human Subjects.

Survey Data

We conducted in-person interviews, covering a variety of topics, including general exposure information and information on selected exposures within the 24 hours preceding urine collection. A self-administered questionnaire collected exposure information during the 24-hour urine collection period. The information we obtained from participants included cigarette smoking; exposure to secondhand tobacco smoke; time spent traveling on roads; traffic levels on their street of residence; presence of a highway within four blocks of their home; use of wood-burning stoves; use of cooking methods that produce smoke, including grilling, frying, or sautéing; and consumption of meat prepared by methods that generate the highest PAH levels (grilling, broiling, or roasting). The in-person interviews covering the 24 hours preceding urine collection used a more detailed questionnaire with meat photographs that was developed to assess exposure to PAHs based on the degree of doneness (28). Subjects also provided their residential address and workplace address if they were currently employed outside their homes.

Biological Monitoring Data

We gave each participant a collection kit and instructed her to collect all urine produced in the 24-hour period starting immediately following the in-person interview. We asked them to use cold packs, placed around the outside of the jug, to keep the sample chilled and, if willing, to refrigerate the samples during the 24-hour collection period. At the end of the 24 hours, interviewers collected each sample and stored it in a −20°C freezer until it was transported to our main laboratory (within 2 weeks) where it was thawed, aliquoted, and frozen at −70°C.

We assayed the urine samples for 1-OHPG, a major urinary metabolite of pyrene. 1-OHPG concentrations were quantified using immunoaffinity chromatography specific for PAH metabolites followed by synchronous fluorescence spectroscopy (23). 1-OHPG levels were determined in pmol/mL. The reporting limit for 1-OHPG was 0.05 pmol/mL urine. We did not adjust for creatinine concentration because the samples were 24-hour voids, making normalization for hydration unnecessary when examining chemical exposures (29, 30). The concentration of 1-OHPG in urine is an indicator of recent exposure to PAHs through all routes. The half-life for urinary excretion of 1-OHPG ranges from 6 to 35 hours and peak urine concentration occurs a few hours following exposure (31).

GIS Data and Analysis

We used a GIS to address-geocode residences and workplace information to spatially referenced road networks (32, 33). The geocoded residential locations were also assigned urban or rural classifications based on U.S. Census data (34). To estimate traffic density around each location, we used traffic-count data for 2000, available from the Highway Performance and Monitoring System of the U.S. Department of Transportation (35). These data provide the annual average daily traffic (AADT) for all highways and most major roads in the state. The AADT represents the average number of vehicles per day traveling in both directions on a road segment. The road segments are spatially referenced in a GIS database. AADT is based on rotating traffic counts conducted every 3 years; during noncount years, the AADT is estimated using traffic trends for that location. The AADT is not typically measured on smaller, residential roads with low traffic volumes; therefore, values for these roads are not included in the database. For our analyses, we set the traffic volumes assigned for these small roads to zero.

For each residence and workplace, we calculated the vehicle miles of travel (VMT) within a 500-foot radius of the residence, for each attributed road segment, by multiplying the AADT by the road segment length within the buffer (Fig. 1). We used a 500-foot radius because this approximates the distance from a road at which pollutant concentrations are the highest based on available monitoring data (36-38). We calculated traffic density by summing the VMT for all road segments and dividing by the buffered area (0.028 mi2). The resulting units for traffic density are VMT/d/mi2.

Figure 1.

Method used to estimate traffic density (VMT/d/mi2) near a residence.

Figure 1.

Method used to estimate traffic density (VMT/d/mi2) near a residence.

Close modal
\[\mathrm{TD}\ =\ \frac{{\sum}(\mathrm{AADT}\ {\times}\ L)}{A_{\mathrm{B}}}\]

where TD is traffic density (vehicles × miles/d/mi2), AADT is annual average daily traffic (vehicles/d), L is length of road segment (miles), and AB is buffered area (mi2).

Statistical Analysis

We used nonparametric tests for statistical comparisons because the distributions of both traffic density and 1-OHPG were highly skewed. All analyses were done using SAS version 8 (SAS Institute, Inc., Cary, NC). We evaluated the relationship of survey and GIS-based exposure measures to 1-OHPG levels using both continuous and categorical variables. For continuous variables, we used the Spearman rank correlation coefficient (PROC CORR SPEARMAN); for categorical variables, we used the Kruskal-Wallis one-way ANOVA by ranks (PROC NPAR1WAY). We also used the Kruskal-Wallis one-way ANOVA by ranks to compare the distribution of GIS-based traffic measures with self-reported traffic levels.

We combined information from the personal interview and self-administered questionnaire to estimate potential PAH exposure during a 48-hour period, including the day before and day of urine collection. We also evaluated data from the self-administered questionnaire covering only the 24-hour urine collection period.

The age of study participants ranged from 27 to 80 years (mean, 51 years). Two hundred fifty-nine (85%) of the 304 participants were non-Hispanic white, 3 (1%) were black, 18 (6%) were Hispanic, 13 (4%) were Asian, and 11 (4%) were classified as other/unknown race/ethnicity. The distributions of these characteristics among the 156 women who were eligible for the study but did not participate were not different. The participation rate was higher among rural residents (75%) than among urban residents (57%).

Table 1 shows the distributions of 1-OHPG levels in urine and residential and workplace traffic density exposures. Forty-five percent of the subjects had 1-OHPG levels below the reporting limit of 0.05 pmol/mL urine. The mean ± SD concentration was 0.58 ± 1.2 pmol/mL urine. The 75th percentile and maximum values of 1-OHPG were 0.51 and 8.9 pmol/mL, respectively. Thirty-four percent of the participants' residences and 29% of their workplaces did not have a road with measured traffic within 500 feet and were therefore assigned traffic density values of zero. The traffic density distributions appeared log normal with a wide range of values. The median traffic density was 10,986 VMT/d/mi2 for residences and 38,717 VMT/d/mi2 for workplaces. The maximum traffic density was 687,250 VMT/d/mi2 for residences and 1,499,208 VMT/d/mi2 for workplaces.

Table 1.

Distributions of 1-OHPG levels (pmol/mL) in urine and traffic density (VMT/d/mi2) within 500 feet of location

Value1-OHPG (pmol/mL)Residential traffic (VMT/d/mi2)Workplace traffic (VMT/d/mi2)
n 304 304 221 
Mean (SD) 0.58 (1.2) 43,675 (87,877) 87,948 (178,705) 
Minimum ≤0.05* 
% Minimum 45 34 29 
25th Percentile 0.05 
50th Percentile 0.18 10,986 38,717 
75th Percentile 0.51 54,577 108,368 
90th Percentile 1.4 112,057 187,549 
Maximum 8.9 687,250 1,499,208 
Value1-OHPG (pmol/mL)Residential traffic (VMT/d/mi2)Workplace traffic (VMT/d/mi2)
n 304 304 221 
Mean (SD) 0.58 (1.2) 43,675 (87,877) 87,948 (178,705) 
Minimum ≤0.05* 
% Minimum 45 34 29 
25th Percentile 0.05 
50th Percentile 0.18 10,986 38,717 
75th Percentile 0.51 54,577 108,368 
90th Percentile 1.4 112,057 187,549 
Maximum 8.9 687,250 1,499,208 
*

The reporting limit for 1-OHPG was 0.05 pmol/mL.

Table 2 provides the results from categorical comparisons of the survey-based exposure data with 1-OHPG levels. Subjects who reported smoking in the last 48 hours had median 1-OHPG concentrations (1.61 pmol/mL) that were 10 times higher than those of nonsmokers (0.16 pmol/mL) and the difference was statistically significant (P = 0.01). Because there were only five subjects who reported smoking, further comparisons between PAH exposures and 1-OHPG values were limited to nonsmokers (n = 299). Nonsmokers who ate grilled, broiled, or roasted meat during the 48-hour period had levels of 1-OHPG significantly higher (P = 0.02), with a median concentration (0.25 pmol/mL) more than four times higher, than those who did not eat meat prepared by these methods (0.06 pmol/mL). Subjects that traveled on roads by car, bus, or bike or on foot for ≥3 hours during the 48-hour period also had 1-OHPG concentrations (0.23 pmol/mL) significantly higher (P = 0.03) than those who traveled on roads for <3 hours (0.11 pmol/mL). Median 1-OHPG values did not differ by exposure to secondhand tobacco smoke, cooking by sautéing, frying, or grilling, or living on a street with heavy or moderate traffic or within four blocks of a highway. The results were similar using only exposure information from the 24 hours coinciding with urine collection (data not shown).

Table 2.

Categorical comparison of survey-based exposure estimates and biological monitoring results using Kruskal-Wallis one-way ANOVA by ranks

Exposure category*Subjects (n)Median 1-OHPG (pmol/mL)P
Smoked 1.61 0.01 
Did not smoke 299 0.16  
Among nonsmokers only (n = 299)    
Exposed to secondhand smoke 24 0.25 0.20 
Not exposed to secondhand smoke 275 0.16  
Ate grilled meat 134 0.25 0.02 
Did not eat grilled meat 165 0.06  
Cooked by grilling, etc. 129 0.21 0.67 
Did not cook by grilling, etc. 170 0.13  
Wood burning in home 23 0.25 0.89 
No wood burning 276 0.16  
Traveled on roads ≥3 hours 88 0.23 0.03 
Traveled on roads <3 hours 211 0.11  
Heavy/moderate traffic on street 54 0.25 0.18 
Light traffic on street 244 0.12  
Lived within four blocks of highway 97 0.10 0.33 
Did not live within four blocks of highway 202 0.19  
Exposure category*Subjects (n)Median 1-OHPG (pmol/mL)P
Smoked 1.61 0.01 
Did not smoke 299 0.16  
Among nonsmokers only (n = 299)    
Exposed to secondhand smoke 24 0.25 0.20 
Not exposed to secondhand smoke 275 0.16  
Ate grilled meat 134 0.25 0.02 
Did not eat grilled meat 165 0.06  
Cooked by grilling, etc. 129 0.21 0.67 
Did not cook by grilling, etc. 170 0.13  
Wood burning in home 23 0.25 0.89 
No wood burning 276 0.16  
Traveled on roads ≥3 hours 88 0.23 0.03 
Traveled on roads <3 hours 211 0.11  
Heavy/moderate traffic on street 54 0.25 0.18 
Light traffic on street 244 0.12  
Lived within four blocks of highway 97 0.10 0.33 
Did not live within four blocks of highway 202 0.19  
*

For the 24 hours preceding and 24 hours coinciding with urine collection.

Table 3 presents the results from categorical comparisons of GIS-based traffic exposure estimates and 1-OHPG levels. Among nonsmokers, 1-OHPG concentrations did not differ between urban and rural residents. Nor did 1-OHPG concentrations differ between categories of residential and workplace traffic density. Subjects in the highest traffic density exposure category (≥100,000 VMT/d/mi2) had a median 1-OHPG concentration at the reporting limit (0.05 pmol/mL). The results were similar for residential traffic density when restricted to participants that did not report a work address and were likely to spend the most time at home. We obtained similar results with respect to urban or rural status and workplace traffic density levels when limited to respondents who did not report smoking, eating grilled, broiled, or roasted meat, or traveling ≥3 hours on roads (data not shown).

Table 3.

Categorical comparison of GIS-based exposure estimates and biological monitoring results using Kruskal-Wallis one-way ANOVA by ranks

Exposure categorySubjects (n)Median 1-OHPG (pmol/mL)P
Among nonsmokers only (n = 299 for residential and 217 for workplace)    
Urban residence 122 0.16 0.84 
Rural residence 177 0.17  
Residential traffic density* ≥100,000 36 0.05 0.12 
Residential traffic density* 1-99,999 160 0.21  
Residential traffic density103 0.11  
Workplace traffic density* ≥100,000 56 0.05 0.38 
Workplace traffic density* 1-99,999 98 0.21  
Workplace traffic density63 0.28  
Exposure categorySubjects (n)Median 1-OHPG (pmol/mL)P
Among nonsmokers only (n = 299 for residential and 217 for workplace)    
Urban residence 122 0.16 0.84 
Rural residence 177 0.17  
Residential traffic density* ≥100,000 36 0.05 0.12 
Residential traffic density* 1-99,999 160 0.21  
Residential traffic density103 0.11  
Workplace traffic density* ≥100,000 56 0.05 0.38 
Workplace traffic density* 1-99,999 98 0.21  
Workplace traffic density63 0.28  
*

Traffic density (VMT/d/mi2) within 500 feet of location.

No measured roads within 500 feet.

Table 4 shows the correlations between continuously measured exposure measures and 1-OHPG levels. The number of cigarettes smoked and number of meals eaten with grilled, broiled, or roasted meat during the 48-hour period were both positively correlated (P < 0.05) with 1-OHPG levels. Although these correlations were statistically significant, the correlation coefficients were not large (r = 0.13 and 0.16, respectively). The number of meals cooked by sautéing, frying, or grilling during the 48-hour period was not related to 1-OHPG levels. When we used continuous, instead of categorical, variables, the results were different for exposure to secondhand smoke and time spent traveling on roads. Hours of exposure to secondhand tobacco smoke was positively correlated with 1-OHPG levels (P < 0.05), although the median 1-OHPG levels were not different when compared as a categorical variable (Table 2). The number of hours spent traveling on roads over the last 48 hours was not significantly correlated with 1-OHPG concentrations (P = 0.10), but the median levels were different when compared categorically. The traffic densities (VMT/d/mi2) within 500 feet of the subjects' residences or workplaces were also not correlated with 1-OHPG levels. The relationship between continuous measures of traffic density and 1-OHPG concentrations did not change substantially when we excluded smokers and those who consumed grilled meats.

Table 4.

Spearman rank correlation coefficients for continuous exposure measures and 1-OHPG levels (pmol/mL) in urine

48-h Exposure measureReported rangeSubjects (n)Spearman correlation coefficientP
Meals eaten with grilled meat 0-4 300 0.16 0.01 
No. cigarettes smoked 0-40 304 0.13 0.02 
Hours of secondhand smoke exposure 0-16 304 0.12 0.04 
Hours of travel on roads 0-8 304 0.09 0.11 
Meals cooked by grilling, etc. 0-3 301 0.04 0.47 
Residential traffic density* 0-687,250 304 −0.04 0.52 
Workplace traffic density* 0-1,499,208 221 −0.09 0.17 
48-h Exposure measureReported rangeSubjects (n)Spearman correlation coefficientP
Meals eaten with grilled meat 0-4 300 0.16 0.01 
No. cigarettes smoked 0-40 304 0.13 0.02 
Hours of secondhand smoke exposure 0-16 304 0.12 0.04 
Hours of travel on roads 0-8 304 0.09 0.11 
Meals cooked by grilling, etc. 0-3 301 0.04 0.47 
Residential traffic density* 0-687,250 304 −0.04 0.52 
Workplace traffic density* 0-1,499,208 221 −0.09 0.17 
*

Traffic density (VMT/d/mi2) within 500 feet of a location.

Table 5 presents the correspondence between GIS-based residential traffic density values and self-reported data on traffic exposure. Subjects who reported living on streets with heavy to moderate traffic had mean traffic density values almost four times higher than those who reported living on streets with light traffic. Respondents that reported living within four blocks of a highway also had much higher mean traffic density values as did those who lived in urban areas compared with rural locations. These results were all highly statistically significant (P = 0.0001) and illustrate the large differences in traffic density between self-reported exposure categories.

Table 5.

Comparison of survey responses and traffic density values at residence using Kruskal-Wallis one-way ANOVA by ranks

Exposure category of residenceData sourceSubjects (n)Mean traffic density* (VMT/d/mi2)P
Heavy/moderate traffic Survey 55 78,237 0.0001 
Light traffic  248 19,858  
Highway within four blocks Survey 99 64,790 0.0001 
No highway  205 14,569  
Urban residence GIS 124 61,620 0.0001 
Rural residence  180 9,779  
Exposure category of residenceData sourceSubjects (n)Mean traffic density* (VMT/d/mi2)P
Heavy/moderate traffic Survey 55 78,237 0.0001 
Light traffic  248 19,858  
Highway within four blocks Survey 99 64,790 0.0001 
No highway  205 14,569  
Urban residence GIS 124 61,620 0.0001 
Rural residence  180 9,779  
*

Within 500 feet of residence.

In this study, 1-OHPG levels in urine were related to self-reported exposure to tobacco smoke. We observed much higher 1-OHPG levels among the five smokers. It has been estimated that smoking a pack of cigarettes per day doubles the dose of carcinogenic PAHs a person receives (39). A previous study in Korea found that 1-OHPG concentrations in urine were significantly correlated with cigarette smoking (40). Two studies that measured 1-hydroxypyrene in urine also noted higher levels in smokers than in nonsmokers (41, 42). In the present study, among nonsmokers, the median 1-OHPG levels between those exposed and not exposed to secondhand smoke were not different, but the number of hours of exposure to secondhand smoke was significantly correlated with 1-OHPG concentrations.

1-OHPG levels were related to eating meat prepared using methods (such as grilling, broiling, or roasting) that lead to charring. Previous studies of urinary 1-OHPG concentrations and dietary PAH exposures also found higher levels of 1-OHPG postconsumption compared with preconsumption (43, 44). For adult nonsmokers, it has been estimated that diet accounts for 96% of the potential dose of carcinogenic PAHs (39). Dietary and nondietary (soil and dust) ingestion also accounted for the majority of measured PAH exposure in a recent study of nine young children (45). Barbequed and charbroiled meats have the highest measured PAH levels in food (46). However, due to greater consumption volumes, grains are thought to be the largest contributor of PAHs in the average western diet (47, 48). Our study was not designed to estimate PAH exposures from the intake of grains or foods other than meat during the relevant exposure period for the excretion of PAH metabolites in urine. Additionally, we only obtained information on the doneness of meats consumed by participants during the in-person interviews for the 24-hour period before urine collection. Our results, and those of others, show that the collection of information about the doneness of grilled foods should be considered an important component for future studies (28).

Although previous monitoring studies have identified vehicle traffic as a major contributor to PAH concentrations in outdoor air, we did not see a relationship between survey- or GIS-based measures of proximity to traffic and urinary 1-OHPG levels. A previous study of toll station workers in Taiwan found that 1-OHPG concentrations increased in a dose-response pattern with traffic counts (49). Higher 1-hydroxypyrene levels have also been observed among people occupationally exposed to traffic exhaust, including toll booth attendants, bus mechanics, and waste collection workers (50, 51). However, in this study, participants who spent the most time traveling on roads did have significantly higher 1-OHPG levels than those who spent less time traveling on roads. A previous study found that the concentrations of several measured pollutants, including benzene, carbon monoxide, and particulate matter, were considerably higher inside vehicles traveling along California roads than at ambient monitoring sites (52). In Baltimore, gasoline and diesel vehicles contributed an estimated 16% to 26% of the total PAHs found in urban air (53). Measured levels of PAHs in outdoor air were higher in close proximity to a bus terminal and along bus routes in Massachusetts (54). In Boston, ∼46% of PAH mass measured in outdoor air was attributable to primary vehicle emissions near a busy road (55). A study in Denmark found that traffic contributed 60% to 90% of PAHs to outdoor air near a busy street was and 40% to city background air (56). In a study of PAH concentrations measured in indoor air, traffic was also the major outdoor emission source, but the effects of cooking and smoking were also significant (57). Long travel time on roads had a similar influence on 1-OHPG concentrations as exposure to secondhand smoke in this study. Exposure to secondhand smoke ranks third as a major preventable cause of death behind only smoking and alcohol (58). Further research on the health effects of long travel times on busy roads is warranted.

A study of Korean children found a correlation (r = 0.8) between 1-OHPG concentrations and an index of particulate matter pollution across six regions varying in levels of urban development (59). Two studies have observed significantly higher levels of 1-hydroxypyrene in the urine of residents from more urban areas (60, 61), but another study did not find such a trend among children (62). PAH concentrations measured in homes of nonsmokers were also attributed mainly to vehicle emissions in an urban area of Germany (63). We did not observe a difference in 1-OHPG levels between urban and rural residents in our study despite a fairly large difference in the traffic density levels between the two areas. The urban areas included in our study, however, are better described as suburbs, with a population density lower than that often associated with more urban areas.

There are several limitations to our study. 1-OHPG urine concentrations represent exposure during the previous 6 to 35 hours (31). For this study, urine samples were collected over a 24-hour period and our exposure information from the survey covered the 24-hour urine collection period and the preceding 24 hours. Therefore, we might have missed some relevant exposure information, and for some of the information collected, we might not have allowed sufficient time for excretion of this PAH metabolite. This study was cross-sectional with results based on only one measurement and did not allow for the evaluation of intraindividual variation. There are other urinary metabolites that may be potentially useful for determining exposure to traffic exhaust, but these were not measured in this study. A significant correlation was observed between urinary 2-naphthol and ambient particulate matter, suggesting that this may be a good biomarker for inhalation exposure to PAHs (64). Urinary hydroxymetabolites of phenanthrene have been proposed as specific markers of occupational exposure to diesel exhaust but may not be sensitive enough for ambient exposure levels (50).

We did not have information on some potentially important sources of outdoor PAH emissions, including neighborhood wood burning, off-road diesel equipment, and industrial combustion sources (53). A recent study found that roadside PAH concentrations were significantly associated with large diesel vehicle counts but not with total vehicle traffic counts (38). The traffic count data we used in this study did not differentiate between gasoline- and diesel-powered vehicles. Our traffic density measure also did not consider factors that influence the dispersion of pollutants, including wind speed and direction. Due to limitations in the accuracy of the road networks used in this study (65), we were unable to evaluate the relationship between traffic density within a smaller radius (e.g., 150 feet) and 1-OHPG levels. To better estimate PAH inhalation exposures, more detailed emission data are needed in future studies.

Individual metabolic variation, which our analysis did not account for, is likely to be a more important issue. In a previous study, 1-OHPG levels varied 8-fold among individuals exposed to the same amount of PAHs by ingestion (44). Based on metabolic rate variations, the differences in PAH air concentrations in our study were probably too small to detect. Results from a breast cancer study showed that PAH DNA adduct levels were not related to evaluated exposure measures, suggesting that adduct formation may be more closely related to individual metabolism than to dose (15). Glutathione S-transferase (GSTM1) genotype has been shown to influence the association between PAH exposure and 1-OHPG levels in urine (25, 40). Several genetic polymorphisms, including a cytochrome P450 variant (CYP1A1), glutathione S-transferases (GSTT1 and GSTM1), N-acetyltransferase (NAT2), and an epoxide hydrolase (mEH3), have also been related to variation in 1-hydroxypyrene levels in urine (42, 66-69).

In this study, self-reported exposure to cigarette smoke, consumption of grilled, broiled, or roasted meat, and time spent traveling on roads were significant predictors of urinary 1-OHPG levels, suggesting that information for these factors collected from individual surveys is valuable in estimating PAH exposures. The results from our comparison of GIS-based traffic density and self-reported traffic levels also show the utility of self-reported data for characterizing proximity to vehicle traffic. The GIS- and survey-based methods we used for estimating proximity to traffic did not predict urinary 1-OHPG concentrations, however. Higher workplace traffic density levels and the association of 1-OHPG levels with self-reported time spent traveling on roads suggest that future studies need to consider these factors in addition to residential traffic density in future GIS-based studies.

Based on our results, 1-OHPG seems to be an effective biomarker for integrating PAH exposures from both inhalation and ingestion. Over 45% of all subjects had 1-OHPG urine concentrations below the detection limit and levels among most nonsmokers that did not eat grilled, broiled, or roasted meat were near the detection limit, indicating that PAH exposures from ambient air were low in this study. Information on key genetic polymorphisms should also be incorporated into future studies using 1-OHPG as a biomarker of exposure to PAHs.

Grant support: National Cancer Institute grants R01 CA77398, U01 CA81789, and P30 ES03819.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Christine Collins for specimen collection and survey data coordination, Valerie Lee for data management, Theresa Saunders for article preparation, and the study participants for their time and commitment to the study.

1
Nyberg F, Gustavsson P, Jarup L, et al. Urban air pollution and lung cancer in Stockholm.
Epidemiology
2000
;
11
:
487
–95.
2
Okona-Mensah KB, Battershill J, Boobis A, Fielder R. An approach to investigating the importance of high potency polycyclic aromatic hydrocarbons (PAHs) in the induction of lung cancer by air pollution.
Food Chem Toxicol
2005
;
43
:
1103
–16.
3
Raaschou-Nielsen O, Hertel O, Thomsen BL, Olsen JH. Air pollution from traffic at the residence of children with cancer.
Am J Epidemiol
2001
;
153
:
433
–43.
4
Reynolds P, Von Behren J, Gunier RB, et al. Traffic patterns and childhood cancer incidence rates in California, United States.
Cancer Causes Control
2002
;
13
:
665
–73.
5
Crosignani P, Tittarelli A, Borgini A, et al. Childhood leukemia and road traffic: a population-based case-control study.
Int J Cancer
2004
;
108
:
596
–9.
6
Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Hertz A. Residential exposure to traffic in California and childhood cancer.
Epidemiology
2004
;
15
:
6
–12.
7
Lewis-Michl EL, Melius JM, Kallenbach LR, et al. Breast cancer risk and residence near industry or traffic in Nassau and Suffolk Counties, Long Island, New York.
Arch Environ Health
1996
;
51
:
255
–65.
8
Brauer M, Hoek G, Van Vliet P, et al. Air pollution from traffic and the development of respiratory infections and asthmatic and allergic symptoms in children.
Am J Respir Crit Care Med
2002
;
166
:
1092
–8.
9
Kim JJ, Smorodinsky S, Lipsett M, et al. Traffic-related air pollution near busy roads: the East Bay Children's Respiratory Health Study.
Am J Respir Crit Care Med
2004
;
170
:
520
–6.
10
Gauderman WJ, Avol E, Lurmann F, et al. Childhood asthma and exposure to traffic and nitrogen dioxide.
Epidemiology
2005
;
16
:
737
–43.
11
van Vliet P, Knape M, de Hartog J, et al. Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways.
Environ Res
1997
;
74
:
122
–32.
12
Hoek G, Brunekreef B, Goldbohm S, Fischer P, van den Brandt PA. Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study.
Lancet
2002
;
360
:
1203
–9.
13
Wilhelm M, Ritz B. Local variations in CO and particulate air pollution and adverse birth outcomes in Los Angeles County, California, USA.
Environ Health Perspect
2005
;
113
:
1212
–21.
14
Santodonato J. Review of the estrogenic and antiestrogenic activity of polycyclic aromatic hydrocarbons: relationship to carcinogenicity.
Chemosphere
1997
;
34
:
835
–48.
15
Gammon MD, Santella RM, Neugut AI, et al. Environmental toxins and breast cancer on Long Island. I. Polycyclic aromatic hydrocarbon DNA adducts.
Cancer Epidemiol Biomarkers Prev
2002
;
11
:
677
–85.
16
Bonner MR, Han D, Nie J, et al. Breast cancer risk and exposure in early life to polycyclic aromatic hydrocarbons using total suspended particulates as a proxy measure.
Cancer Epidemiol Biomarkers Prev
2005
;
14
:
53
–60.
17
Bostrom CE, Gerde P, Hanberg A, et al. Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air.
Environ Health Perspect
2002
;
110
Suppl 3:
451
–88.
18
Sapkota A, Buckley TJ. The mobile source effect on curbside 1,3-butadiene, benzene, and particle-bound polycyclic aromatic hydrocarbons assessed at a tollbooth.
J Air Waste Manag Assoc
2003
;
53
:
740
–8.
19
U.S. Environmental Protection Agency. Integrated Risk Information System (IRIS) [Web page; accessed 2003 Dec 10]. Washington (DC): U.S. Environmental Protection Agency; 94. Available from: http://www.epa.gov/iris/index.html.
20
IARC. IARC monographs programme on the evaluation of carcinogenic risks to humans [Web page; accessed 2003 Dec 10]. Lyon (France): IARC; 87. Available from: http://monographs.iarc.fr/.
21
Agency for Toxic Substances and Disease Registry. Toxicological profile for polycyclic aromatic hydrocarbons. Atlanta (GA): Research Triangle Institute; 1995.
22
Tonne CC, Whyatt RM, Camann DE, Perera FP, Kinney PL. Predictors of personal polycyclic aromatic hydrocarbon exposures among pregnant minority women in New York City.
Environ Health Perspect
2004
;
112
:
754
–9.
23
Strickland PT, Kang D, Bowman ED, et al. Identification of 1-hydroxypyrene glucuronide as a major pyrene metabolite in human urine by synchronous fluorescence spectroscopy and gas chromatography-mass spectrometry.
Carcinogenesis
1994
;
15
:
483
–7.
24
Kang D, Rothman N, Cho SH, et al. Association of exposure to polycyclic aromatic hydrocarbons (estimated from job category) with concentration of 1-hydroxypyrene glucuronide in urine from workers at a steel plant.
Occup Environ Med
1995
;
52
:
593
–9.
25
Lee J, Kang D, Lee KH, et al. Influence of GSTM1 genotype on association between aromatic DNA adducts and urinary PAH metabolites in incineration workers.
Mutat Res
2002
;
514
:
213
–21.
26
Strickland P, Kang D. Urinary 1-hydroxypyrene and other PAH metabolites as biomarkers of exposure to environmental PAH in air particulate matter.
Toxicol Lett
1999
;
108
:
191
–9.
27
Bernstein L, Allen M, Anton-Culver H, et al. High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States).
Cancer Causes Control
2002
;
13
:
625
–35.
28
Sinha R, Rothman N. Exposure assessment of heterocyclic amines (HCAs) in epidemiologic studies.
Mutat Res
1997
;
376
:
195
–202.
29
Alessio L, Berlin A, Dell'Orto A, Toffoletto F, Ghezzi I. Reliability of urinary creatinine as a parameter used to adjust values of urinary biological indicators.
Int Arch Occup Environ Health
1985
;
55
:
99
–106.
30
Boeniger MF, Lowry LK, Rosenberg J. Interpretation of urine results used to assess chemical exposure with emphasis on creatinine adjustments: a review.
Am Ind Hyg Assoc J
1993
;
54
:
615
–27.
31
Strickland P, Kang D, Sithisarankul P. Polycyclic aromatic hydrocarbon metabolites in urine as biomarkers of exposure and effect.
Environ Health Perspect
1996
;
104
Suppl 5:
927
–32.
32
Geographic Data Technology. Dynamap/2000 [data file]. Lebanon (NH): Geographic Data Technology, Inc.; 2000.
33
Navigational Technologies. NAVSTREETS [data file]. Chicago (IL): Navigational Technologies; 2002.
34
U.S. Bureau of the Census. Census of population and housing, 1990: modified age/race, sex and Hispanic origin (MARS) state and county file [data file]. Washington (DC): U.S. Bureau of the Census; 1992.
35
U.S. Department of Transportation, Office of Highway Policy Information. Highway Performance and Monitoring System [data file]. Washington (DC): U.S. Department of Transportation; 2003.
36
Monn C, Carabias V, Junker M, et al. Small-scale spatial variability of particulate matter <10 μM (PM10) and nitrogen dioxide.
Atmos Environ
1997
;
31
:
2243
–7.
37
Zhu Y, Hinds W, Kim S, Sioutas C. Concentration and size distribution of ultrafine particles near a major highway.
J Air Waste Manag Assoc
2002
;
52
:
1032
–42.
38
Levy JI, Bennett DH, Melly SJ, Spengler JD. Influence of traffic patterns on particulate matter and polycyclic aromatic hydrocarbon concentrations in Roxbury, Massachusetts.
J Expo Anal Environ Epidemiol
2003
;
13
:
364
–71.
39
Menzie CA, Potocki BB, Santodonato J. Exposure to carcinogenic PAHs in the environment.
Environ Sci Technol
1992
;
26
:
1278
–84.
40
Hong YC, Leem JH, Park HS, et al. Variations in urinary 1-hydroxypyrene glucuronide in relation to smoking and the modification effects of GSTM1 and GSTT1.
Toxicol Lett
1999
;
108
:
217
–23.
41
Jongeneelen FJ, Bos RP. Excretion of pyrene and hydroxypyrene in urine.
Cancer Lett
1990
;
51
:
175
–9.
42
Yang M, Jang JY, Kim S, et al. Genetic effects on urinary 1-hydroxypyrene levels in a Korean population.
Carcinogenesis
2003
;
24
:
1085
–9.
43
Buckley TJ, Lioy PJ. An examination of the time course from human dietary exposure to polycyclic aromatic hydrocarbons to urinary elimination of 1-hydroxypyrene.
Br J Ind Med
1992
;
49
:
113
–24.
44
Kang DH, Rothman N, Poirier MC, et al. Interindividual differences in the concentration of 1-hydroxypyrene-glucuronide in urine and polycyclic aromatic hydrocarbon-DNA adducts in peripheral white blood cells after charbroiled beef consumption.
Carcinogenesis
1995
;
16
:
1079
–85.
45
Wilson NK, Chuang JC, Lyu C, Menton R, Morgan MK. Aggregate exposures of nine preschool children to persistent organic pollutants at day care and at home.
J Expo Anal Environ Epidemiol
2003
;
13
:
187
–202.
46
Kazerouni N, Sinha R, Hsu CH, Greenberg A, Rothman N. Analysis of 200 food items for benzo[a]pyrene and estimation of its intake in an epidemiologic study.
Food Chem Toxicol
2001
;
39
:
423
–36.
47
Dennis MJ, Massey RC, Cripps G, et al. Factors affecting the polycyclic aromatic hydrocarbon content of cereals, fats and other food products.
Food Addit Contam
1991
;
8
:
517
–30.
48
Phillips DH. Polycyclic aromatic hydrocarbons in the diet.
Mutat Res
1999
;
443
:
139
–47.
49
Lai CH, Liou SH, Shih TS, et al. Urinary 1-hydroxypyrene-glucuronide as a biomarker of exposure to various vehicle exhausts among highway toll-station workers in Taipei, Taiwan.
Arch Environ Health
2004
;
59
:
61
–9.
50
Kuusimaki L, Peltonen Y, Mutanen P, Peltonen K, Savela K. Urinary hydroxy-metabolites of naphthalene, phenanthrene and pyrene as markers of exposure to diesel exhaust.
Int Arch Occup Environ Health
2004
;
77
:
23
–30.
51
Tsai PJ, Shih TS, Chen HL, et al. Urinary 1-hydroxypyrene as an indicator for assessing the exposures of booth attendants of a highway toll station to polycyclic aromatic hydrocarbons.
Environ Sci Technol
2004
;
38
:
56
–61.
52
California Environmental Protection Agency, Air Resources Board, Research Division. Measuring concentrations of selected air pollutants inside California vehicles. Sacramento (CA): California Environmental Protection Agency; 1998.
53
Larsen RK III, Baker JE. Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere: a comparison of three methods.
Environ Sci Technol
2003
;
37
:
1873
–81.
54
Levy JI, Houseman EA, Spengler JD, Loh P, Ryan L. Fine particulate matter and polycyclic aromatic hydrocarbon concentration patterns in Roxbury, Massachusetts: a community-based GIS analysis.
Environ Health Perspect
2001
;
109
:
341
–7.
55
Dunbar JC, Lin CI, Vergucht I, Wong J, Duran JL. Estimating the contributions of mobile sources of PAH to urban air using real-time PAH monitoring.
Sci Total Environ
2001
;
279
:
1
–19.
56
Nielsen T, Jorgensen H, Larsen J, Poulsen M. City air pollution of polycyclic aromatic hydrocarbons and other mutagens: occurrence, sources and health effects.
Sci Total Environ
1996
;
189–90
:
41
–9.
57
Dubowsky SD, Wallace LA, Buckley TJ. The contribution of traffic to indoor concentrations of polycyclic aromatic hydrocarbons.
J Expo Anal Environ Epidemiol
1999
;
9
:
312
–21.
58
Glantz SA, Parmley WW. Passive smoking and heart disease. Epidemiology, physiology, and biochemistry.
Circulation
1991
;
83
:
1
–12.
59
Kang D, Lee KH, Lee KM, et al. Design issues in cross-sectional biomarkers studies: urinary biomarkers of PAH exposure and oxidative stress.
Mutat Res
2005
;
592
:
138
–46.
60
Adonis M, Martinez V, Riquelme R, et al. Susceptibility and exposure biomarkers in people exposed to PAHs from diesel exhaust.
Toxicol Lett
2003
;
144
:
3
–15.
61
Yang M, Kim S, Lee E, et al. Sources of polycyclic aromatic hydrocarbon exposure in non-occupationally exposed Koreans.
Environ Mol Mutagen
2003
;
42
:
250
–7.
62
van Wijnen JH, Slob R, Jongmans-Liedekerken G, van de Weerdt RH, Woudenberg F. Exposure to polycyclic aromatic hydrocarbons among Dutch children.
Environ Health Perspect
1996
;
104
:
530
–4.
63
Fromme H, Lahrz T, Piloty M, et al. Polycyclic aromatic hydrocarbons inside and outside of apartments in an urban area.
Sci Total Environ
2004
;
326
:
143
–9.
64
Kang JW, Cho SH, Kim H, Lee CH. Correlation of urinary 1-hydroxypyrene and 2-naphthol with total suspended particulates in ambient air in municipal middle-school students in Korea.
Arch Environ Health
2002
;
57
:
377
–82.
65
Wu J, Funk TH, Lurmann FW, Winer AM. Improving spatial accuracy of roadway networks and geocoded addresses.
Trans GIS
2005
;
9
:
585
–601.
66
Kuljukka-Rabb T, Nylund L, Vaaranrinta R, et al. The effect of relevant genotypes on PAH exposure-related biomarkers.
J Expo Anal Environ Epidemiol
2002
;
12
:
81
–91.
67
Kim YD, Lee CH, Nan HM, Kang JW, Kim H. Effects of genetic polymorphisms in metabolic enzymes on the relationships between 8-hydroxydeoxyguanosine levels in human leukocytes and urinary 1-hydroxypyrene and 2-naphthol concentrations.
J Occup Health
2003
;
45
:
160
–7.
68
Hansen AM, Wallin H, Binderup ML, et al. Urinary 1-hydroxypyrene and mutagenicity in bus drivers and mail carriers exposed to urban air pollution in Denmark.
Mutat Res
2004
;
557
:
7
–17.
69
Nerurkar PV, Okinaka L, Aoki C, et al. CYP1A1, GSTM1, and GSTP1 genetic polymorphisms and urinary 1-hydroxypyrene excretion in non-occupationally exposed individuals.
Cancer Epidemiol Biomarkers Prev
2000
;
9
:
1119
–22.