Warning: mkdir(): Permission denied in /home/virtual/lib/view_data.php on line 81

Warning: fopen(upload/ip_log/ip_log_2024-11.txt): failed to open stream: No such file or directory in /home/virtual/lib/view_data.php on line 83

Warning: fwrite() expects parameter 1 to be resource, boolean given in /home/virtual/lib/view_data.php on line 84
Occupational exposure to polycyclic aromatic hydrocarbons in Korean adults: evaluation of urinary 1-hydroxypyrene, 2-naphthol, 1-hydroxyphenanthrene, and 2-hydroxyfluorene using Second Korean National Environmental Health Survey data


Your browser does not support the NLM PubReader view.

Go to this page to see a list of supporting browsers.

Occupational exposure to polycyclic aromatic hydrocarbons in Korean adults: evaluation of urinary 1-hydroxypyrene, 2-naphthol, 1-hydroxyphenanthrene, and 2-hydroxyfluorene using Second Korean National Environmental Health Survey data

Article information

Ann Occup Environ Med. 2023;35.e6
Publication date (electronic) : 2023 March 24
doi : https://doi.org/10.35371/aoem.2023.35.e6
Dong Hyun Hongorcid_icon, Jongwon Jungorcid_icon, Jeong Hun Joorcid_icon, Dae Hwan Kimorcid_icon, Ji Young Ryuorcid_icon
Department of Occupational and Environmental Medicine, Inje University Haeundae Paik Hospital, Busan, Korea.
Correspondence: Ji Young Ryu. Department of Occupational and Environmental Medicine, Inje University Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Korea. lyou77@paik.ac.kr
Received 2022 October 21; Revised 2023 January 20; Revised 2023 February 28; Accepted 2023 March 01.

Abstract

Background

Polycyclic aromatic hydrocarbons (PAHs) are occupational and environmental pollutants generated by the incomplete combustion of organic matter. Exposure to PAHs can occur in various occupations. In this study, we compared PAH exposure levels among occupations based on 4 urinary PAH metabolites in a Korean adult population.

Methods

The evaluation of occupational exposure to PAHs was conducted using Second Korean National Environmental Health Survey data. The occupational groups were classified based on skill types. Four urinary PAH metabolites were used to evaluate PAH exposure: 1-hydroxypyrene (1-OHP), 2-naphthol (2-NAP), 1-hydroxyphenanthrene (1-OHPHE), and 2-hydroxyfluorene (2-OHFLU). The fraction exceeding the third quartile of urinary concentration for each PAH metabolite was assessed for each occupational group. Adjusted odds ratios (ORs) for exceeding the third quartile of urinary PAH metabolite concentration were calculated for each occupational group compared to the “business, administrative, clerical, financial, and insurance” group using multiple logistic regression analyses.

Results

The “guard and security” (OR: 2.949; 95% confidence interval [CI]: 1.300–6.691), “driving and transportation” (OR: 2.487; 95% CI: 1.418–4.364), “construction and mining” (OR: 2.683; 95% CI: 1.547–4.655), and “agriculture, forestry, and fisheries” (OR: 1.973; 95% CI: 1.220–3.191) groups had significantly higher ORs for 1-OHP compared to the reference group. No group showed significantly higher ORs than the reference group for 2-NAP. The groups with significantly higher ORs for 1-OHPHE than the reference group were “cooking and food service” (OR: 2.073; 95% CI: 1.208–3.556), “driving and transportation” (OR: 1.724; 95% CI: 1.059–2.808), and “printing, wood, and craft manufacturing” (OR: 2.255; 95% CI: 1.022–4.974). The OR for 2-OHFLU was significantly higher in the “printing, wood, and craft manufacturing” group (OR: 3.109; 95% CI: 1.335–7.241) than in the reference group.

Conclusions

The types and levels of PAH exposure differed among occupational groups in a Korean adult population.

Keywords: Polycyclic aromatic hydrocarbons; Occupations; Occupational exposure; Epidemiology

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are chemical pollutants in which 2 or more aromatic rings are bonded.1 PAHs are produced when organic substances are incompletely combusted, and are carcinogenic, particularly in the lungs, skin, and bladder.2 Exposure can occur both occupationally and environmentally. The International Agency for Research on Cancer (IARC) has identified PAH occupational exposure levels for some occupations, such as coal-gasification workers, as carcinogenic to humans.3 Environmental exposure to PAHs can occur via tobacco smoke, food (e.g., grilled meats, fried foods, and grains), ambient air (e.g., indoor heating, cooking, exhaust fumes, and wildfires), water, and soil.3

PAH exposure can also occur in occupations other than those identified by the IARC. Several studies have examined PAH exposure in various occupations. The Australian Work Exposures Study reported that the proportion of workers exposed to PAHs was highest in the agriculture sector, followed by public administration and safety, accommodation and food services, and mining.4 In a Canadian study, by industry, PAH exposure was highest in restaurants, automobile maintenance, gasoline stations, and public administration workers (including firefighters).5 By occupation, cooks, chefs, automobile mechanics, and firefighters comprise the workers most exposed to PAHs.5 Koh et al.6 evaluated PAH occupational exposure in Korea based on urinary 1-hydroxypyrene (1-OHP) levels. Exposure was highest in construction and mining, fisheries, transport, sales, and metal-machinery-part workers.

Although the above studies analyzed PAH exposure in a variety of occupations, they had some limitations. Biological markers were not examined in the Australian and Canadian studies, so they were restricted to evaluating the actual exposure levels of workers to PAHs. In the Korean study, the only PAH biomarker used was 1-OHP; because workers may be exposed to PAHs other than pyrene depending on their occupation, urinary 1-OHP may not accurately reflect PAH exposure. Moreover, previous studies used the standard classification of occupations, which is limited by the fact that skill level is the principle criterion (such that work type and environment can differ within the same occupational group). By supplementing these points, in this study, PAH exposure levels were compared among occupations based on 4 urinary metabolites of PAHs.

METHODS

Study participants

The Korean National Environmental Health Survey (KoNEHS), conducted by the National Environmental Research Institute, is a national survey that monitors concentrations of hazardous environmental chemicals and their effects on the Korean population.7 We used data from the second KoNEHS (2012–2014). The survey covered adults aged > 19 years in 16 regions of Korea; based on data from the 2010 population and housing census, a multi-stage stratified cluster sampling design was adopted.7 In total, 6,478 participants were enrolled, distributed evenly among 400 districts.7 Interviews were conducted and biological samples were collected.7

Classification of occupations

The KoNEHS includes details of participants’ current occupation; the data were classified according to the 7th Korean Standard Classification of Occupations (KSCO), We re-coded the data according to the Korean Employment Classification of Occupations 2018 (KECO) and classified occupations based on sub-major (2-digit code) occupational groups of KECO. Because skill level is the principle classification criterion for the major (1-digit code) KSCO occupational groups, tasks and working environment may differ between 2 occupations classified into the same major group. Skill type, which reflects the actual work content, is also considered in the sub-major group in KSCO, but several occupations are classified into different major and sub-major groups despite having the same skill type. In addition, as there are 53 sub-major groups in KSCO, the sample sizes of each are too small to perform meaningful analyses. In contrast, KECO considers skill type before skill level for both the major and sub-major groups, so that occupations, for which the tasks, work environments and chemical exposure at work are similar, are classified into the same major and sub-major groups. Additionally, there are only 35 sub-major groups, such that there are more samples in each group compared with the KSCO.

In this study, among the KECO sub-major groups, those with small sample sizes were combined with other groups likely to have similar occupational environments, to create new larger groups. For example, the group, “business, administrative, clerical, financial, and insurance” was formed by combining “business, administrative and clerical works” with “financial and insurance works.” In this manner, the number of occupational groups was reduced from 35 to 26.

Analysis of urinary PAH metabolite concentrations

We used 4 urinary PAH metabolites to evaluate exposure to PAHs: 1-OHP, 2-naphthol (2-NAP), 1-hydroxyphenanthrene (1-OHPHE), and 2-hydroxyfluorene (2-OHFLU). The KoNEHS analyses of urinary PAH metabolites were conducted as follows.8 Urine samples were collected into sterile containers, and immediately refrigerated and shielded from light. After transfer to the laboratory, the samples were stored at −20°C until analysis. The urinary PAH metabolites were hydrolyzed with β-glucuronidase/acryl sulfatase, derivatized with bis (trimethylsilyl) trifluoroacetamide, and analyzed by gas chromatography–mass spectrometry. Concentrations were obtained from a calibration curve obtained using the standard addition method. The coefficient of determination (R2) of the calibration curve was > 0.995. The limit of detection (LOD) was 0.015 μg/L for 1-OHP, 0.05 μg/L for 2-NAP, 0.047 μg/L for 1-OHPHE, and 0.04 μg/L for 2-OHFLU. LOD/√2 was used to replace values below the LOD. Urinary PAH metabolite concentrations were adjusted based on urinary creatinine concentrations. Urinary creatinine was measured using a colorimetric method, the ADVIA 1800 instrument (Siemens, Washington, D.C., USA) and a creatinine reagent (Siemens). Participants whose urinary creatinine concentrations did not fall within the reference range (0.3–3.0 g/L) were excluded from the study. The urinary PAH metabolites were classified into 2 groups based on the third quartile of the urinary concentration of each metabolite.

Variables

Age, sex, body mass index, smoking status, drinking status, and region were used as covariates. Smoking status was divided into 3 categories (non-smoker, former smoker, and current smoker), and drinking status was divided into 2 categories (drinker and non-drinker). Region was classified into 3 categories (urban, rural, and coastal area).

Statistical analyses

As the KoNEHS used a multi-stage cluster sampling design, we applied strata, cluster, and sampling weights in our analyses, which were conducted using SPSS software (version 25.0 for Windows; IBM Corp., Armonk, NY, USA).9 Due to the skewed distributions of all urinary PAH metabolite concentrations, the concentrations were log-transformed.9 To examine the demographic characteristics of the subjects, we estimated means for continuous variables and percentages for categorical variables. The distributions of demographic variables among occupational groups were examined. Since there are no reference ranges of PAH metabolites to determine high level exposure to PAHs, we used the third quartile of each PAH metabolite’s distribution in general population as a cutoff value for high level exposure to PAHs. The fraction exceeding the third quartile of urinary concentration for each PAH metabolite was calculated for each occupational group, and compared among them using odds ratios (ORs) and 95% confidence intervals (CIs) calculated via multiple logistic regression, with adjustment for demographic variables. The “business, administrative, clerical, financial, and insurance” group was used as the reference group because the workers therein were expected to have less occupational exposure to PAH sources, such as coal tars, bitumens, and diesel exhaust. A p-value < 0.05 was considered significant.

Ethics statement

This study was approved by the Institutional Review Board of Inje University Haeundae Paik Hospital (2022-09-018).

RESULTS

Table 1 shows the sub-major occupational groups of KECO and the occupational groups of our study. Excluding “housewives” and “students, unemployed, and social service agents” groups, the estimated percentage was highest in the “business, administrative, clerical, financial, and insurance” group (9.2%) and second highest in the “sales” group (6.9%). Table 2 shows the estimated means and distributions of the demographic variables. The estimated proportion of male and female were similar (49.2% for male and 50.8% for female), and the estimated prevalence of current smokers was 21.5%. Most participants lived in urban areas (93.1%). Table 3 shows the distribution of demographic characteristics among occupational groups. The proportion of men exceeded 50% in 18 of the 26 groups, and was highest in the “information, communications-related installation, maintenance, and manufacturing” (100%) and “construction and mining” (96.1%) groups. Excluding homemakers, the group with the highest percentage of women was “cooking and food service” (79.9%). The proportion of current smokers was highest in the “police, firefighters, prison officers, and military servicemen” (57.5%), “construction and mining” (56.0%), and “electricity and electronics installation, maintenance, and manufacturing” (50.6%) groups, while the “housewives” (3.5%), “education, law, social welfare, and religious” (10.1%), and “cooking and food service” (11.6%) groups had the lowest percentages of current smokers. The drinking rate was highest in the “chemistry and environmental installation, maintenance, and manufacturing” group (91.7%). The proportion living in urban areas exceeded 90% in almost all occupations, except for “agriculture, forestry, and fisheries” (57.3%), and “information and communications-related installation, maintenance, and manufacturing” (89.0%).

Occupational groups and their sample sizes

Demographics in the Second KoNEHS

Distribution of demographic characteristics among occupational groups

Table 4 shows percentages below LODs, estimated means and distributions of urinary PAH metabolites. Table 5 shows the fractions exceeding the third quartile of urinary concentrations for each PAH metabolite, and the adjusted ORs for each occupational group. The groups with the largest fractions exceeding the third quartile of the 1-OHP level were “construction and mining” (47.8%), “food manufacturing” (41.4%), “guard and security” (41.3%), “driving and transportation” (40.3%), and “agriculture, forestry, and fisheries” (40.2%). The adjusted ORs were significantly higher in the “guard and security” (OR: 2.949; 95% CI: 1.300–6.691), “driving and transportation” (OR: 2.487; 95% CI: 1.418–4.364), “construction and mining” (OR: 2.683; 95% CI: 1.547–4.655), and “agriculture, forestry, and fisheries” (OR: 1.973; 95% CI: 1.220–3.191) groups than the reference group.

Estimated means and distributions of urinary polycyclic aromatic hydrocarbon metabolites

Fractions exceeding the third quartiles of urinary concentrations for each polycyclic aromatic hydrocarbon metabolite and adjusted ORs for each occupational group

The “driving and transportation” group had the largest fraction exceeding the third quartile of urinary 2-NAP concentration (43.0%), followed by the “information, communications-related installation, maintenance, and manufacturing” group (42.1%). No group had significantly higher ORs than the reference group, while the “researchers and engineers” (OR: 0.432; 95% CI: 0.236–0.792), “health and medical” (OR: 0.311; 95% CI: 0.114–0.848), “food manufacturing” (OR: 0.140; 95% CI: 0.027–0.729), and “routine manufacturing” (OR: 0.346; 95% CI: 0.131–0.915) group had significantly lower ORs than the reference group.

The “cooking and food service” group had the highest fraction (39.4%) exceeding the third quartile for 1-OHPHE. The groups with significantly higher ORs were “cooking and food service” (OR: 2.073; 95% CI: 1.208–3.556), “driving and transportation” (OR: 1.724; 95% CI: 1.059–2.808), and “printing, wood, and craft manufacturing” (OR: 2.255; 95% CI: 1.022–4.974). The “chemistry and environmental installation, maintenance, and manufacturing” group had a significantly lower OR (OR: 0.187; 95% CI: 0.042–0.837).

The group with the largest fraction exceeding the third quartile for 2-OHFLU was “construction and mining” (56.6%), followed by the “printing, wood, and craft manufacturing” (50.9%) and “electricity and electronics installation, maintenance, and manufacturing” (49.8%) groups. Workers employed in “printing, wood, and craft manufacturing” had a significantly elevated OR (OR: 3.109; 95% CI: 1.335–7.241) compared to the reference group.

DISCUSSION

In this study, the fraction exceeding the third quartile of urinary concentration for PAH metabolites differed among occupational groups. Among the groups with significantly higher ORs than the reference group, the “cooking and food service,” “driving and transportation,” “construction and mining,” and “agriculture, forestry, and fisheries” groups were also reported to have high proportions of workers exposed to PAHs in previous studies.456

The “cooking and food service” group displayed high excess fractions of 1-OHP and 1-OHPHE, with higher ORs in both cases compared to the reference group (especially significant for the latter). Many studies have identified PAH emissions, including phenanthrene and benzo[a]pyrene, from commercial-kitchen exhaust systems.101112 In particular, Masuda et al.13 reported that the proportion of phenanthrene in the PAHs of cooking exhaust gas was much higher than that of urban air and exhaust gas from an incinerator. A Chinese study reported that the sources of PAHs in the air in commercial kitchens were cooking-oil fumes and cooking practice.14 When cooking oil is heated, PAHs in the oil evaporate into the air, and are pyrolyzed and resynthesized into smaller PAHs.15 Regarding cooking practice, certain cooking methods (i.e., frying and broiling), high cooking temperatures, and high fat content of foods are associated with more PAH emissions.1416 According to Pan et al.,17 the average benzo[a]pyrene concentrations in kitchens and dining areas in Chinese restaurants were 6.9 and 1.1 ng/m3, respectively, which exceeds the target value of 1 ng/m3 in ambient air set by the EU.18 Oliveira et al.19 reported significantly elevated levels of urinary monohydroxyl-PAHs, including 1-OHPHE and 1-OHP, in grill workers during workdays.

Motor vehicles are a major source of PAHs in cities.2021 Incomplete combustion by motor vehicle engines, particularly heavy-duty and diesel engines, produces large amounts of PAHs.22 Several studies have reported that naphthalene, phenanthrene, fluorene, and pyrene are the major PAHs from diesel-engine exhaust,232425 which accords with the high excess fractions and ORs for all 4 PAH metabolites in our “driving and transportation” group. In particular, the urinary 1-OHP and 1-OHPHE levels of this group were significantly higher compared to the reference group. The interior of a vehicle can become contaminated with PAHs mainly by infiltration of outside pollutants, mostly from the exhausts of vehicles and, to a lesser degree, home heating exhaust systems and resuspended soil.26 Several studies reported high benzo[a]pyrene concentrations inside vehicles (> 1 ng/m3), particularly during the winter.272829 For example, the 24-hour exposure amount for benzo[a]pyrene among taxi drivers in Genoa, Italy was significantly higher (1.22–1.4 ng/m3) than that of a control group (0.16 ± 0.12 ng/m3).28 Considering that Korean taxi drivers’ average driving time is 10.2 hours per day,30 significant PAH exposure can be expected in this group. Indeed, some studies showed that urinary 1-OHP levels in taxi and bus drivers were higher than in controls.313233

In this study, the “construction and mining” group exhibited high excess fractions of all 4 PAH metabolites, and urinary 1-OHP levels differed significantly between this group and the reference group. Construction workers, such as roofers and road pavers, can be exposed to PAHs while handling bitumens and coal tar.343536 Moreover, construction and mining workers may be exposed to PAHs from exhaust generated by construction and mining machinery.373839

Our “agriculture, forestry, and fisheries” group exhibited high PAH levels. The excess fractions and ORs for 1-OHP, 1-OHPHE, and 2-OHFLU were high in this group; in particular, there was a significant difference in urinary 1-OHP concentrations compared to the reference group. The Australian Work Exposures Study4 suggested that incinerating agricultural waste and exhaust fumes from agricultural equipment, such as lawn mowers, could lead to PAH exposure among farmers. Furthermore, a variety of PAHs (mainly low molecular weight ones) are released during open burning of agricultural residues; among particulate PAHs, phenanthrene and fluorene were the most common.40 PAHs generated by open burning of crop residues increase atmospheric PAH concentrations.4142 Regarding PAH exposure in fisheries, urinary 1-OHP levels were higher in ship-engine-room workers, especially those with oil-contaminated skin, compared to a control group.4344

In this study, urinary PAH metabolite levels were elevated in the “guard and security” and “printing, wood, and craft manufacturing” groups, neither of which has been well-studied. In particular, the excess fractions and ORs for 1-OHP, 2-NAP, and 2-OHFLU were high in the “guard and security” occupation group; however, we could not find any previous studies on this group. Considering that this group consisted mainly of building concierges (61 of 67 group members), PAH exposure may have been caused by proximity to vehicle exhausts in building parking lots or on the roadside.45

Our “printing, wood, and craft manufacturing” group included various occupations, such as woodworking and printing; as the sample size for each individual occupation was small, false-positives may have arisen. Some studies have shown that ink and wood processing are associated with exposure to PAHs. In one study, PAHs such as benzo[a]pyrene and phenanthrene were detected in newspaper ink; these can penetrate through the skin to cause genotoxicity.46 During woodworking, various PAHs are generated from incomplete combustion of wood, resulting in exposure among woodworkers.4748 Another study showed that concentrations of phenanthrene and fluorene from wood burning were higher compared with other sources, such as plastics and paper.49 These findings may explain the large excess fractions of all 4 PAH metabolites in this occupation group seen in our study, as well as the significant differences in urinary 1-OHPHE and 2-OHFLU levels compared to the reference group. Therefore, potential PAH exposure should be monitored continuously in these workers.

The excess fraction of 1-OHPHE in our “chemistry and environmental installation, maintenance, and manufacturing” group was low, and was significantly lower compared to the reference group. The 1-OHPHE was analyzed for 20 subjects in this group and the group comprised 3 minor occupational groups (3-digit code): 2 in the petroleum and chemical material processing machine-operator group, 14 in the chemical, rubber, and plastic production machine-operator group, and 4 in the water treatment and recycling machine operator group. The average urinary 1-OHPHE concentrations in these 3 minor groups were 0.1359, 0.1218, and 0.0913 µg/g Cr, respectively. The geometric mean urinary 1-OHPHE level for all participants in this study was 0.1239 µg/g Cr; thus, the concentrations in petroleum and chemical material processing-machine operators exceeded the overall mean. However, due to the small sample sizes, it was difficult to conduct analyses for these minor groups.

The “food manufacturing,” “health and medical,” “routine manufacturing,” “education, law, social welfare, and religious,” and “researchers and engineers” groups in this study had the smallest 2-NAP excess fractions; in all of these groups (except “education, law, social welfare, and religious” group), the levels of urinary 2-NAP were significantly lower compared to the reference group. Urinary 2-NAP is a naphthalene metabolite; as naphthalene exists mostly in the gaseous phase, it could be used as an indicator of airborne PAH exposure.50 Therefore, our results imply that workers in the groups listed above may be less exposed to airborne PAHs than other workers.

Our study analyzed 4 urinary PAH metabolites and showed that types and levels of PAH exposure differed among various occupations. This implies that analyses of various PAH metabolites is needed when evaluating PAH exposure. Furthermore, as we classified occupational groups based on the sub-major groups of KECO, occupations with similar tasks and work environments could be classified into the same groups.

Our study had several limitations. First, in several occupational groups the number of samples was insufficient. Additionally, due to small numbers of samples, some occupations that were expected to have high PAH exposure levels were combined, so could not be evaluated individually (e.g., traffic police and firefighters, which were combined to create the group “police, firefighters, prison officers, and military servicemen”). Second, due to the short half-lives of PAHs, the sampling performed in KoNEHS may not accurately reflect occupational exposure. Third, the KoNEHS measured only 4 PAH metabolites, and our study could evaluate only exposure to those PAHs. Forth, information related to PAH exposure by general environment or local air condition are lack in KoNEHS data. Although we adjusted for regional classification as a demographic factor in the analyses, it may be too broad to clearly reflect regional differences of PAH exposure by general environment or local atmospheric condition.

CONCLUSIONS

This study found that the types and levels of PAH exposure differed among occupations in the Korean adult population. Further studies are merited for validation, as well as appropriate protective measures for workers in those occupations.

Acknowledgements

This study used data from the Second Korean National Environmental Health Survey (2012–2014), which was conducted by National Institute of Environmental Research. The Authors gratefully acknowledge their effort.

Notes

Competing interests: The authors declare that they have no competing interests.

Authors contributions:

  • Conceptualization: Ryu JY.

  • Data curation: Hong DH, Jo JH, Jung J.

  • Formal analysis: Hong DH, Ryu JY.

  • Investigation: Hong DH, Ryu JY.

  • Methodology: Jo JH, Kim DH.

  • Writing - original draft: Hong DH, Ryu JY.

  • Writing - review & editing: Ryu JY, Kim DH.

Abbreviations

BMI

body mass index

CI

confidence interval

IARC

International Agency for Research on Cancer

KECO

Korean Employment Classification of Occupations

KoNEHS

Korean National Environmental Health Survey

KSCO

Korea Standard Classification of Occupations

LOD

limit of detection

OR

odds ratio

PAH

polycyclic aromatic hydrocarbon

2-NAP

2-naphthol

1-OHP

1-hydroxypyrene

1-OHPHE

1-hydroxyphenanthrene

2-OHFLU

2-hydroxyfluorene

References

1. Harrison RJ. Chemicals. In : LaDou J, Harrison RJ, eds. CURRENT Diagnosis & Treatment: Occupational & Environmental Medicine 6th edth ed. New York, NY, USA: McGraw Hill; 2021. p. 505–508.
2. Boffetta P, Jourenkova N, Gustavsson P. Cancer risk from occupational and environmental exposure to polycyclic aromatic hydrocarbons. Cancer Causes Control 1997;8(3):444–472. 9498904.
3. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Some non-heterocyclic polycyclic aromatic hydrocarbons and some related exposures. IARC Monogr Eval Carcinog Risks Hum 2010;92:1–853. 21141735.
4. Driscoll TR. The Australian Work Exposures Study (AWES): Polycyclic Aromatic Hydrocarbons Canberra, Australia: Safe Work Australia; 2014.
5. CAREX Canada. PAHs occupational exposures Updated 2021. Accessed July 19, 2022. https://www.carexcanada.ca/profile/polycyclic_aromatic_hydrocarbons-occupational-exposures/ .
6. Koh DH, Park JH, Lee SG, Kim HC, Choi S, Jung H, et al. Comparison of polycyclic aromatic hydrocarbons exposure across occupations using urinary metabolite 1-hydroxypyrene. Ann Work Expo Health 2020;64(4):445–454. 32064494.
7. Yoo JY, Choi WH, Jeon HR, Joo YK, Lee CW. Guidelines for Using Raw Data for Korean National Environmental Health Survey (KoNEHS): The Second Stage (2012–2014) Incheon, Korea: Korean National Institute of Environmental Research; 2017.
8. Kim SJ, Baek YW, Kwon YM, Choi WH, Yoo SD, Choi KH. The Second Korean National Environmental Health Survey Manual for the Analysis of Environmentally Harmful Substances in Biological Samples: Organic Compounds Incheon, Korea: Korean National Institute of Environmental Research; 2015.
9. Lee TW, Kim DH, Ryu JY. Association between urinary polycyclic aromatic hydrocarbons and hypertension in the Korean population: data from the Second Korean National Environmental Health Survey (2012-2014). Sci Rep 2020;10(1):17142. 33051516.
10. Chen JW, Wang SL, Hsieh DP, Yang HH, Lee HL. Carcinogenic potencies of polycyclic aromatic hydrocarbons for back-door neighbors of restaurants with cooking emissions. Sci Total Environ 2012;417-418:68–75. 22245161.
11. Chen Y, Ho KF, Ho SS, Ho WK, Lee SC, Yu JZ, et al. Gaseous and particulate polycyclic aromatic hydrocarbons (PAHs) emissions from commercial restaurants in Hong Kong. J Environ Monit 2007;9(12):1402–1409. 18049780.
12. Li CT, Lin YC, Lee WJ, Tsai PJ. Emission of polycyclic aromatic hydrocarbons and their carcinogenic potencies from cooking sources to the urban atmosphere. Environ Health Perspect 2003;111(4):483–487. 12676603.
13. Masuda M, Wang Q, Tokumura M, Miyake Y, Amagai T. Risk assessment of polycyclic aromatic hydrocarbons and their chlorinated derivatives produced during cooking and released in exhaust gas. Ecotoxicol Environ Saf 2020;197110592. 32298857.
14. Zhu L, Wang J. Sources and patterns of polycyclic aromatic hydrocarbons pollution in kitchen air, China. Chemosphere 2003;50(5):611–618. 12685737.
15. Moret S, Conte LS. Polycyclic aromatic hydrocarbons in edible fats and oils: occurrence and analytical methods. J Chromatogr A 2000;882(1-2):245–253. 10895949.
16. Yao Z, Li J, Wu B, Hao X, Yin Y, Jiang X. Characteristics of PAHs from deep-frying and frying cooking fumes. Environ Sci Pollut Res Int 2015;22(20):16110–16120. 26066859.
17. Pan CH, Chan CC, Wu KY. Effects on Chinese restaurant workers of exposure to cooking oil fumes: a cautionary note on urinary 8-hydroxy-2′-deoxyguanosine. Cancer Epidemiol Biomarkers Prev 2008;17(12):3351–3357. 19064550.
18. EC-European Commission. Directive 2004/107/EC of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air. Off J Eur Union L 2005;23:3–16.
19. Oliveira M, Capelas S, Delerue-Matos C, Morais S. Grill workers exposure to polycyclic aromatic hydrocarbons: Levels and excretion profiles of the urinary biomarkers. Int J Environ Res Public Health 2020;18(1):230. 33396787.
20. Kwon HO, Choi SD. Polycyclic aromatic hydrocarbons (PAHs) in soils from a multi-industrial city, South Korea. Sci Total Environ 2014;470-471:1494–1501. 24011990.
21. Liu Y, Yan C, Ding X, Wang X, Fu Q, Zhao Q, et al. Sources and spatial distribution of particulate polycyclic aromatic hydrocarbons in Shanghai, China. Sci Total Environ 2017;584-585:307–317. 28117160.
22. Barakat AO. PAHs and petroleum markers in the atmospheric environment of Alexandria City, Egypt. Water Air Soil Pollut 2002;139(1):289–310.
23. de Souza CV, Corrêa SM. Polycyclic aromatic hydrocarbons in diesel emission, diesel fuel and lubricant oil. Fuel 2016;185:925–931.
24. He C, Ge Y, Tan J, You K, Han X, Wang J. Characteristics of polycyclic aromatic hydrocarbons emissions of diesel engine fueled with biodiesel and diesel. Fuel 2010;89(8):2040–2046.
25. Zheng X, Wu Y, Zhang S, Hu J, Zhang KM, Li Z, et al. Characterizing particulate polycyclic aromatic hydrocarbon emissions from diesel vehicles using a portable emissions measurement system. Sci Rep 2017;7(1):10058. 28855647.
26. Romagnoli P, Balducci C, Cecinato A, L’Episcopo N, Gariazzo C, Gatto MP, et al. Fine particulate-bound polycyclic aromatic hydrocarbons in vehicles in Rome, Italy. Environ Sci Pollut Res Int 2017;24(4):3493–3505. 27878483.
27. Karageorgou K, Manoli E, Kouras A, Samara C. Commuter exposure to particle-bound polycyclic aromatic hydrocarbons in Thessaloniki, Greece. Environ Sci Pollut Res Int 2021;28(42):59119–59130. 32506397.
28. Piccardo MT, Stella A, Redaelli A, Balducci D, Coradeghini R, Minoia C, et al. Personal daily exposures to benzo(a)pyrene of taxi drivers in Genoa, Italy. Sci Total Environ 2004;330(1-3):39–45. 15325156.
29. Fromme H, Oddoy A, Piloty M, Krause M, Lahrz T. Polycyclic aromatic hydrocarbons (PAH) and diesel engine emission (elemental carbon) inside a car and a subway train. Sci Total Environ 1998;217(1-2):165–173. 9695180.
30. Jang JH, Kang SW, Lee MK. A study on the taxi driver’s working environment and improvement plan: focusing on long-term driving and health. Labor J 2020;(5):1–239.
31. Brucker N, Moro AM, Charão MF, Durgante J, Freitas F, Baierle M, et al. Biomarkers of occupational exposure to air pollution, inflammation and oxidative damage in taxi drivers. Sci Total Environ 2013;463-464:884–893. 23872245.
32. Petchpoung K, Kaojarern S, Yoovathaworn K, Sura T, Sirivarasai J. The influence of metabolic gene polymorphisms on urinary 1-hydroxypyrene concentration in Thai bus drivers. Environ Toxicol Pharmacol 2011;31(1):160–164. 21787681.
33. Chuang CY, Chang CC. Urinary 1-hydroxypyrene level relative to vehicle exhaust exposure mediated by metabolic enzyme polymorphisms. J Occup Health 2007;49(2):140–151. 17429172.
34. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Bitumens and bitumen emissions, and some N- and S-heterocyclic polycyclic aromatic hydrocarbons. IARC Monogr Eval Carcinog Risks Hum 2013;103:1–342.
35. Toraason M, Hayden C, Marlow D, Rinehart R, Mathias P, Werren D, et al. DNA strand breaks, oxidative damage, and 1-OH pyrene in roofers with coal-tar pitch dust and/or asphalt fume exposure. Int Arch Occup Environ Health 2001;74(6):396–404. 11563602.
36. Heikkilä P, Riala R, Hämeilä M, Nykyri E, Pfäffli P. Occupational exposure to bitumen during road paving. AIHA J (Fairfax, Va) 2002;63(2):156–165. 11975651.
37. Seidel A, Dahmann D, Krekeler H, Jacob J. Biomonitoring of polycyclic aromatic compounds in the urine of mining workers occupationally exposed to diesel exhaust. Int J Hyg Environ Health 2002;204(5-6):333–338. 11885357.
38. Zhang Q, Yang L, Ma C, Zhang Y, Wu L, Mao H. Emission characteristics and chemical composition of particulate matter emitted by typical non-road construction machinery. Atmos Pollut Res 2020;11(4):679–685.
39. Cui M, Chen Y, Li C, Yin J, Li J, Zheng J. Parent and methyl polycyclic aromatic hydrocarbons and n-alkanes emitted by construction machinery in China. Sci Total Environ 2021;775144759.
40. Kakareka SV, Kukharchyk TI. PAH emission from the open burning of agricultural debris. Sci Total Environ 2003;308(1-3):257–261. 12738218.
41. Lai CH, Chen KS, Wang HK. Influence of rice straw burning on the levels of polycyclic aromatic hydrocarbons in agricultural county of Taiwan. J Environ Sci (China) 2009;21(9):1200–1207. 19999966.
42. Zhang H, Hu D, Chen J, Ye X, Wang SX, Hao JM, et al. Particle size distribution and polycyclic aromatic hydrocarbons emissions from agricultural crop residue burning. Environ Sci Technol 2011;45(13):5477–5482. 21615081.
43. Moen BE, Nilsson R, Nordlinder R, Ovrebø S, Bleie K, Skorve AH, et al. Assessment of exposure to polycyclic aromatic hydrocarbons in engine rooms by measurement of urinary 1-hydroxypyrene. Occup Environ Med 1996;53(10):692–696. 8943834.
44. Nilsson R, Nordlinder R, Moen BE, Øvrebø S, Bleie K, Skorve AH, et al. Increased urinary excretion of 8-hydroxydeoxyguanosine in engine room personnel exposed to polycyclic aromatic hydrocarbons. Occup Environ Med 2004;61(8):692–696. 15258276.
45. Li H, Zeng X, Zhang D, Chen P, Yu Z, Sheng G, et al. Occurrence and carcinogenic potential of airborne polycyclic aromatic hydrocarbons in some large-scale enclosed/semi-enclosed vehicle parking areas. J Hazard Mater 2014;274:279–286. 24797904.
46. Paschke M, Hutzler C, Brinkmann J, Henkler F, Luch A. Polycyclic aromatic hydrocarbons in newspaper inks: migration, metabolism, and genotoxicity in human skin. Polycycl Aromat Compd 2015;35(1):32–40.
47. Bruschweiler ED, Danuser B, Huynh CK, Wild P, Schupfer P, Vernez D, et al. Generation of polycyclic aromatic hydrocarbons (PAHs) during woodworking operations. Front Oncol 2012;2:148. 23087908.
48. Huynh C, Schüpfer P, Boiteux P. Occupational exposure to Polycyclic Aromatic Hydrocarbons in wood dust. J Phys Conf Ser 2009;151012004.
49. Cheng K, Li JY, Wang Y, Ji WW, Cao Y. Characterization and risk assessment of airborne polycyclic aromatic hydrocarbons from open burning of municipal solid waste. Front Environ Sci 2022;10861770.
50. Rappaport SM, Waidyanatha S, Serdar B. Naphthalene and its biomarkers as measures of occupational exposure to polycyclic aromatic hydrocarbons. J Environ Monit 2004;6(5):413–416. 15152308.

Article information Continued

Copyright © 2023 Korean Society of Occupational & Environmental Medicine
Korean Society of Occupational & Environmental Medicine
https://creativecommons.org/licenses/by-nc/4.0/

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 1

Occupational groups and their sample sizes

Sub-major groups (2-digits) in KECO 2018 Occupational groups in this study
Occupational group titles Estimated percentage (%) Numbera Occupational group titles Estimated percentage (%) Numbera
Business, administrative, and clerical works 7.8 405 Business, administrative, clerical, financial, and insurance 9.2 489
Financial and insurance works 1.3 84
Managers (executive and director) 2.5 144 Managers 2.5 144
Humanities and social sciences researchers 0.1 4 Researchers and engineers 3.8 165
Natural and bioscience researchers 0.1 7
Information and communications researchers 1.3 49
Construction and mining researchers 0.6 29
Manufacturing researchers 1.7 76
Education 3.9 206 Education, law, social welfare, and religious 5.5 309
Law 0.1 8
Social welfare and religious works 1.5 95
Police, firefighters, and prison officers 0.5 19 Police, firefighters, prison officers, and military servicemen 0.6 23
Military servicemen 0.1 4
Health and medical works 1.8 87 Health and medical 1.8 87
Art, design, and broadcasting works 1.0 46 Art, design, broadcasting, sports, and recreation 1.2 56
Sports and recreation works 0.2 10
Beauty works 0.7 46 Beauty, tour, accommodation, nursing, and parenting 2.1 147
Tour and accommodation works 0.6 31
Nursing and parenting works 0.7 70
Cooking and food service works 2.4 187 Cooking and food service 2.4 187
Guard and security works 0.9 67 Guard and security 0.9 67
Cleaning and other service works 2.1 189 Cleaning and other services 2.1 189
Sales works 6.9 397 Sales 6.9 397
Driving and transportation works 4.3 236 Driving and transportation 4.3 236
Construction and mining works 2.4 152 Construction and mining 2.4 152
Machine installation, maintenance, and manufacturing works 1.8 99 Machine installation, maintenance, and manufacturing 1.8 99
Metal and material installation, maintenance, and manufacturing works 0.8 44 Metal and material installation, maintenance, and manufacturing 0.8 44
Electricity and electronics installation, maintenance, and manufacturing works 2.0 83 Electricity and electronics installation, maintenance, and manufacturing 2.0 83
Information, communications-related installation, maintenance, and manufacturing works 0.2 11 Information, communications-related installation, maintenance, and manufacturing 0.2 11
Chemistry and environmental installation, maintenance, and manufacturing works 0.4 23 Chemistry and environmental installation, maintenance, and manufacturing 0.4 23
Textile and apparel manufacturing works 0.7 47 Textile and apparel manufacturing 0.7 47
Food manufacturing works 0.6 51 Food manufacturing 0.6 51
Printing, wood, and craft manufacturing works 0.9 46 Printing, wood, and craft manufacturing 0.9 46
Routine manufacturing works 0.8 53 Routine manufacturing 0.8 53
Agriculture, forestry, and fisheries 4.5 559 Agriculture, forestry, and fisheries 4.5 559
Not classified in KECO 41.7 2,814 Housewives 24.3 1,862
Students, unemployed, and social service agents 17.4 952
Total 6,478 Total 6,478

KECO: Korean Employment Classification of Occupations.

aUnweighted sample size.

Table 2

Demographics in the Second KoNEHS

KoNEHS (2012–2014) Estimated mean ± SE or unweighted sample size (estimated %)
Age (years) 46.3 ± 0.4
Sex
Male 2,774 (49.2)
Female 3,704 (50.8)
BMI (kg/m2) 24.1 ± 0.1
Smoking
Non-smoker 4,259 (62.7)
Ex-smoker 1,058 (15.8)
Current smoker 1,161 (21.5)
Drinking
No drinking 2,654 (35.3)
Drinking 3,824 (64.7)
Region
Urban 5,765 (93.1)
Rural 485 (5.8)
Coastal 228 (1.1)

KoNEHS: Korean National Environmental Health Survey; SE: standard error; BMI: body mass index.

Table 3

Distribution of demographic characteristics among occupational groups

Occupational group Sex (male)a Age (years) BMI (kg/m2) Smoking (current smoker)a Drinkinga Region (urban)a
Business, administrative, clerical, financial, and insurance 235 (56.0) 39.6 ± 0.6 24.2 ± 0.2 109 (27.0) 375 (79.7) 457 (95.6)
Managers 109 (82.0) 48.4 ± 1.0 24.9 ± 0.4 43 (32.4) 109 (79.9) 127 (93.0)
Researchers and engineers 153 (92.1) 38.8 ± 0.8 25.1 ± 0.3 55 (29.2) 140 (83.4) 154 (95.9)
Education, law, social welfare, and religious 95 (36.6) 39.6 ± 0.9 23.2 ± 0.3 31 (10.1) 195 (65.3) 283 (93.3)
Police, firefighters, prison officers, and military servicemen 22 (95.2) 38.9 ± 2.4 24.7 ± 0.6 12 (57.5) 20 (90.4) 21 (93.1)
Health and medical 26 (33.9) 36.2 ± 1.4 22.8 ± 0.5 11 (13.3) 57 (65.7) 85 (98.7)
Art, design, broadcasting, sports, and recreation 32 (59.4) 36.5 ± 1.4 24.5 ± 0.7 15 (32.2) 44 (82.8) 51 (91.5)
Beauty, tour, accommodation, nursing, and parenting 27 (24.3) 47.0 ± 1.4 24.1 ± 0.3 16 (15.1) 76 (50.9) 134 (95.0)
Cooking and food service 28 (20.1) 47.3 ± 1.3 24.6 ± 0.4 22 (11.6) 114 (65.2) 167 (92.2)
Guard and security 64 (91.2) 54.0 ± 3.4 23.8 ± 0.4 21 (31.9) 42 (72.6) 63 (93.4)
Cleaning and other services 58 (31.7) 57.8 ± 1.5 24.7 ± 0.3 29 (16.3) 95 (52.0) 178 (96.4)
Sales 210 (60.3) 44.5 ± 0.8 24.7 ± 0.2 95 (30.1) 274 (72.5) 367 (95.5)
Driving and transportation 218 (96.0) 48.2 ± 0.9 25.1 ± 0.3 100 (47.4) 172 (73.6) 219 (92.6)
Construction and mining 141 (96.1) 48.9 ± 1.1 24.6 ± 0.3 69 (56.0) 114 (80.3) 134 (93.2)
Machine installation, maintenance, and manufacturing 83 (88.7) 41.4 ± 1.1 23.8 ± 0.4 37 (37.3) 80 (75.8) 93 (92.9)
Metal and material installation, maintenance, and manufacturing 39 (91.7) 43.0 ± 1.9 24.1 ± 0.5 17 (39.7) 37 (83.5) 42 (92.2)
Electricity and electronics installation, maintenance, and manufacturing 72 (90.0) 38.6 ± 1.6 24.4 ± 0.6 31 (50.6) 68 (76.0) 74 (90.3)
Information and communications-related installation, maintenance, and manufacturing 11 (100.0) 43.6 ± 3.2 23.4 ± 0.6 5 (44.8) 8 (79.5) 10 (89.0)
Chemistry and environmental installation, maintenance, and manufacturing 15 (69.3) 43.4 ± 2.2 25.1 ± 0.4 8 (40.0) 21 (91.7) 20 (91.3)
Textile and apparel manufacturing 14 (51.1) 51.1 ± 1.7 24.8 ± 0.6 9 (32.5) 26 (68.8) 44 (97.7)
Food manufacturing 16 (35.3) 48.1 ± 3.0 24.7 ± 0.7 6 (14.4) 37 (79.4) 42 (91.6)
Printing, wood, and craft manufacturing 35 (82.1) 47.0 ± 1.5 24.2 ± 0.5 14 (38.6) 32 (77.1) 38 (91.4)
Routine manufacturing 12 (30.6) 46.2 ± 2.3 24.2 ± 0.5 8 (17.5) 34 (65.7) 49 (91.6)
Agriculture, forestry, and fisheries 310 (58.0) 61.5 ± 0.9 24.6 ± 0.2 103 (23.5) 281 (53.0) 295 (57.3)
Housewives 4 (0.4) 52.0 ± 0.5 24.0 ± 0.1 70 (3.5) 767 (44.3) 1,720 (94.8)
Students, unemployed, and social service agents 745 (73.6) 42.3 ± 1.0 23.6 ± 0.2 225 (22.9) 606 (69.9) 898 (96.6)
Total 2,774 (49.2) 46.3 ± 0.4 24.1 ± 0.1 1,161 (21.5) 3,824 (64.7) 5,765 (93.1)

Values are presented as number (estimated %) or mean ± standard error.

BMI: body mass index.

aUnweighted sample size.

Table 4

Estimated means and distributions of urinary polycyclic aromatic hydrocarbon metabolites

Metabolite Numbera LOD (μg/L) Percentage below LOD (%) Estimated GM (95% CI) (μg/g Cr) Estimated percentile (μg/g Cr)
5th 25th 50th 75th 95th
1-OHP 6,418 0.015 2.8 0.1986 (0.1909–0.2067) 0.0601 0.1280 0.2009 0.3121 0.6517
2-NAP 6,410 0.050 1.0 3.0728 (2.9276–3.2255) 0.5429 1.3281 2.8019 7.4000 20.6370
1-OHPHE 6,413 0.047 21.0 0.1239 (0.1194–0.1286) 0.0410 0.0777 0.1214 0.1934 0.3809
2-OHFLU 6,397 0.040 7.3 0.3666 (0.3490–0.3852) 0.0923 0.1883 0.3097 0.7073 2.0524

LOD: limit of detection; GM: geometric mean; CI: confidence interval; 1-OHP: 1-hydroxypyrene; 2-NAP: 2-naphthol; 1-OHPHE: 1-hydroxyphenanthrene; 2-OHFLU: 2-hydroxyfluorene.

aUnweighted sample size.

Table 5

Fractions exceeding the third quartiles of urinary concentrations for each polycyclic aromatic hydrocarbon metabolite and adjusted ORs for each occupational group

Occupational groups 1-OHP 2-NAP 1-OHPHE 2-OHFLU
Numbera % Q3 (95% CI) ORb 95% CI for OR Numbera % Q3 (95% CI) ORb 95% CI for OR Numbera % Q3 (95% CI) ORb 95% CI for OR Numbera % Q3 (95% CI) ORb 95% CI for OR
Business, administrative, clerical, financial, and insurance 420 20.1 (15.6–25.5) Ref. Ref. 420 27.2 (22.2–32.9) Ref. Ref. 421 19.6 (15.3–24.8) Ref. Ref. 420 30.0 (24.4–36.2) Ref. Ref.
Managers 136 25.5 (18.2–34.4) 1.335 0.730–2.440 136 24.2 (16.0–35.0) 0.679 0.313–1.473 136 23.8 (15.8–34.2) 1.208 0.681–2.144 136 29.0 (19.6–40.7) 0.743 0.248–2.222
Researchers and engineers 148 18.0 (11.9–26.4) 1.141 0.634–2.052 148 16.4 (10.8–24.0) 0.432 0.236–0.792 148 22.4 (15.3–31.6) 1.551 0.915–2.630 147 26.1 (18.8–35.1) 0.709 0.327–1.540
Education, law, social welfare, and religious 269 19.9 (14.5–26.7) 1.226 0.758–1.982 270 14.0 (9.7–19.9) 0.683 0.370–1.260 269 14.0 (8.6–21.9) 0.733 0.389–1.384 268 12.5 (8.4–18.1) 0.549 0.284–1.064
Police, firefighters, prison officers, and military servicemen 21 17.4 (6.2–40.3) 0.732 0.227–2.357 20 32.8 (13.1–61.2) 0.850 0.184–3.922 21 15.3 (4.6–40.3) 0.717 0.181–2.841 21 45.3 (23.4–69.2) 0.926 0.128–6.716
Health and medical 77 18.8 (9.8–32.9) 1.048 0.394–2.786 76 9.8 (4.8–18.7) 0.311 0.114–0.848 77 25.1 (14.5–40.0) 1.542 0.694–3.424 77 13.1 (7.1–23.1) 0.367 0.104–1.295
Art, design, broadcasting, sports, and recreation 50 31.8 (17.7–50.4) 2.058 0.810–5.229 50 32.0 (18.8–48.9) 1.363 0.557–3.338 50 27.6 (14.8–45.5) 1.657 0.671–4.090 49 26.5 (15.2–42.0) 0.700 0.171–2.863
Beauty, tour, accommodation, nursing, and parenting 115 18.8 (11.5–29.3) 0.718 0.360–1.435 115 18.8 (11.1–30.1) 0.610 0.312–1.191 115 22.6 (13.8–34.7) 0.923 0.434–1.963 115 25.0 (15.0–38.7) 1.180 0.550–2.534
Cooking and food service 158 33.1 (23.9–43.8) 1.705 0.952–3.054 157 20.2 (12.6–30.7) 0.861 0.434–1.707 158 39.4 (29.6–50.2) 2.073 1.208–3.556 157 15.8 (10.0–24.0) 0.748 0.379–1.475
Guard and security 58 41.3 (25.7–58.8) 2.949 1.300–6.691 60 37.0 (22.4–54.4) 1.712 0.682–4.297 58 20.4 (9.6–38.2) 0.889 0.369–2.141 57 35.7 (20.8–53.9) 1.450 0.461–4.559
Cleaning and other services 144 32.4 (23.6–42.7) 1.362 0.816–2.273 146 24.8 (17.4–34.2) 0.789 0.413–1.508 144 31.2 (22.2–41.8) 1.178 0.678–2.046 144 26.4 (17.8–37.3) 1.135 0.534–2.412
Sales 336 26.9 (21.3–33.4) 1.365 0.861–2.165 337 26.7 (21.4–32.7) 0.811 0.499–1.319 336 25.9 (20.4–32.4) 1.299 0.868–1.945 336 31.0 (25.0–37.6) 0.884 0.461–1.694
Driving and transportation 217 40.3 (31.7–49.7) 2.487 1.418–4.364 219 43.0 (34.8–51.6) 1.398 0.842–2.322 217 32.3 (24.8–40.9) 1.724 1.059–2.808 217 47.0 (38.4–55.8) 1.391 0.718–2.693
Construction and mining 139 47.8 (37.1–58.6) 2.683 1.547–4.655 139 39.7 (29.9–50.5) 0.724 0.368–1.424 139 34.9 (26.0–44.9) 1.663 0.946–2.924 138 56.6 (46.1–66.5) 1.485 0.642–3.433
Machine installation, maintenance, and manufacturing 91 23.0 (14.7–34.1) 1.206 0.598–2.430 90 33.0 (23.0–44.9) 1.100 0.544–2.224 91 25.2 (16.5–36.4) 1.501 0.822–2.740 91 31.4 (21.9–42.7) 0.606 0.258–1.422
Metal and material installation, maintenance, and manufacturing 43 25.0 (14.1–40.4) 1.406 0.649–3.044 43 27.2 (14.7–45.0) 0.762 0.244–2.379 43 14.1 (6.6–27.5) 0.689 0.290–1.640 43 37.7 (23.1–55.0) 1.181 0.321–4.348
Electricity and electronics installation, maintenance, and manufacturing 77 33.8 (21.1–49.3) 1.783 0.826–3.849 77 34.2 (20.6–51.1) 0.711 0.264–1.920 77 25.1 (14.0–40.8) 1.312 0.647–2.661 77 49.8 (35.1–64.6) 1.210 0.478–3.064
Information, communications-related installation, maintenance, and manufacturing 11 29.8 (9.7–62.5) 1.710 0.316–9.242 11 42.1 (15.5–74.2) 1.647 0.095–28.575 11 NA NA NA 11 44.8 (17.3–75.9) 1.409 0.572–3.468
Chemistry and environmental installation, maintenance, and manufacturing 20 17.0 (6.2–38.9) 0.665 0.179–2.467 20 35.4 (15.0–62.9) 1.074 0.369–3.128 20 5.2 (1.2–19.8) 0.187 0.042–0.837 20 48.9 (24.9–73.4) 2.108 0.607–7.315
Textile and apparel manufacturing 41 24.5 (11.5–44.9) 0.921 0.366–2.318 40 36.8 (19.9–57.7) 1.467 0.429–5.013 41 26.2 (13.0–45.6) 1.027 0.422–2.496 40 33.4 (16.7–55.6) 1.233 0.284–5.352
Food manufacturing 43 41.4 (22.2–63.7) 2.702 0.939–7.774 43 5.6 (1.3–21.3) 0.140 0.027–0.729 43 32.7 (18.1–51.6) 1.646 0.695–3.899 43 26.3 (10.9–50.9) 1.482 0.374–5.875
Printing, wood, and craft manufacturing 43 30.3 (18.1–45.9) 1.557 0.690–3.514 44 38.4 (23.3–56.2) 1.372 0.707–2.661 43 36.8 (22.8–53.5) 2.255 1.022–4.974 43 50.9 (35.2–66.4) 3.109 1.335–7.241
Routine manufacturing 48 20.7 (10.4–36.8) 0.833 0.354–1.959 48 13.9 (5.7–30.3) 0.346 0.131–0.915 48 13.6 (6.4–26.7) 0.482 0.196–1.183 48 18.1 (7.9–36.1) 0.467 0.196–1.114
Agriculture, forestry, and fisheries 470 40.2 (34.2–46.5) 1.973 1.220–3.191 471 29.0 (23.6–35.1) 0.849 0.530–1.359 470 36.3 (30.4–42.6) 1.382 0.905–2.111 470 32.2 (26.4–38.6) 1.287 0.716–2.313
Housewives 1,496 23.3 (20.5–26.4) 0.922 0.603–1.410 1,494 18.2 (15.7–20.9) 0.866 0.564–1.330 1,496 30.1 (27.2–33.2) 1.153 0.794–1.674 1,494 11.8 (9.5–14.5) 0.787 0.425–1.459
Students, unemployed, and social service agents 833 18.0 (14.4–22.3) 0.987 0.663–1.469 836 28.6 (24.8–32.8) 1.429 0.924–2.210 834 17.9 (14.4–22.0) 0.990 0.695–1.409 832 22.6 (19.0–26.6) 0.728 0.435–1.219
Total 5,504 25.0 (23.1–27.0) 5,510 25.0 (23.3–26.8) 5,506 25.0 (23.1–27.1) 5,494 25.0 (23.1–27.0)

Bold indicates statistically significant results (p < 0.05).

1-OHP: 1-hydroxypyrene; 2-NAP: 2-naphthol; 1-OHPHE: 1-hydroxyphenanthrene; 2-OHFLU: 2-hydroxyfluorene; % Q3: fraction exceeding the third quartile; OR: odds ratio; CI: confidence interval; NA: not available.

aUnweighted sample size.

bAdjusted for age, sex, body mass index, smoking status, drinking status, and region.