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Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model.

ABSTRACT: It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons (PAHs), a widely found air pollutant in both indoor and outdoor air. This study presents the first application of the Monte Carlo population exposure assessment model to quantify the effects of different intervention strategies on inhalation exposure to PAHs and the associated lung cancer risk. The method was applied to the population in Beijing, China, in the year 2006. Several intervention strategies were designed and studied, including atmospheric cleaning, smoking prohibition indoors, use of clean fuel for cooking, enhancing ventilation while cooking and use of indoor cleaners. Their performances were quantified by population attributable fraction (PAF) and potential impact fraction (PIF) of lung cancer risk, and the changes in indoor PAH concentrations and annual inhalation doses were also calculated and compared. The results showed that atmospheric cleaning and use of indoor cleaners were the two most effective interventions. The sensitivity analysis showed that several input parameters had major influence on the modeled PAH inhalation exposure and the rankings of different interventions. The ranking was reasonably robust for the remaining majority of parameters. The method itself can be extended to other pollutants and in different places. It enables the quantitative comparison of different intervention strategies and would benefit intervention design and relevant policy making.

SUBMITTER: Zhou B

PROVIDER: S-EPMC3885750 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model.

Zhou Bin B Zhao Bin B

PloS one 20140108 1

It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons (PAHs), a widely found air pollutant in both indoor and outdoor air. This study presents the first application of the Monte Carlo population exposure assessment model to quantify the effects of different intervention strategies on inhalation exposure to PAHs and the associated lung cancer risk. The method was applied to the population in Beijing, China, in the ...[more]

PMID: 24416436

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Project description:BackgroundRefined coal tar sealant (RCTS) emulsions are used to seal the surface of asphalt pavement. Nine of the 22 polycyclic aromatic hydrocarbons (PAHs) evaluated in this study are classified as known, probable, or possible human carcinogens. Exposure assessment research for RCTS workers has not been published previously.ObjectivesThe overall objective of this study was to develop a representative occupational exposure assessment of PAH exposure for RCTS workers based on worksite surveys. The specific aims were to: 1) quantify full-shift airborne occupational exposures to PAHs among RCTS workers; 2) quantify workers' dermal exposures to PAHs; 3) quantify biomarkers of PAH exposure in workers' urine; 4) identify specific job titles associated with RCTS exposure; and 5) apply these results to a biological exposure index to assess risk of potential genotoxicity from occupational exposures.MethodsA total of twenty-one RCTS workers were recruited from three companies. Personal and area air samples were collected using a modification of NIOSH Method 5515. Dermal exposure was assessed by hand and neck wipes before and after shifts. Twenty-two PAHs were quantified via gas chromatography-mass spectrometry selected ion monitoring. Internal dose was estimated by quantifying select PAH metabolites in pre- and post-shift urine samples using on-line solid phase extraction-high performance liquid chromatography-tandem mass spectrometry.ResultsPAH levels in the worker breathing zones were highest for naphthalene, acenaphthene, and phenanthrene, with geometric means of 52.1, 11.4, and 9.8 μg/m3, respectively. Hand wipe levels of phenanthrene, fluoranthene and pyrene were the highest among the 22 PAHs with geometric means of 7.9, 7.7, and 5.5 μg/cm2, respectively. Urinary PAH biomarkers for naphthalene, fluorene, phenanthrene, and pyrene were detected in all workers and were higher for post-shift samples than those collected pre-shift. Urinary concentrations of the metabolite 1-hydroxypyrene were greater than the American Conference of Governmental Industrial Hygienists (ACGIH) Biological Exposure Index (BEI) for this metabolite in 89 percent of post-shift samples collected on the final day of the work week or field survey. Statistically significances were found between concentrations of fluorene, naphthalene, and phenanthrene in the breathing zone of workers and their corresponding urinary PAH biomarkers. Workers were placed in two work place exposure groups: applicators and non-applicators. Applicators had higher total PAH concentrations in personal breathing zone (PBZ) air samples than non-applicators and were more likely to have post-shift hand wipe concentrations significantly higher than pre-shift concentrations. Concentrations of post-shift urinary biomarkers were higher, albeit not significantly, for applicators than non-applicators.ConclusionsThe exposure results from RCTS worker samples cannot be explained by proximal factors such as nearby restaurants or construction. Air and skin concentration levels were substantially higher for RCTS workers than previously published levels among asphalt workers for all PAHs. PAH profiles on skin wipes were more consistent with RCTS sealant product than air samples. Last day post-shift urinary concentrations of 1-hydroxypyrene greatly exceeded the ACGIH BEI benchmark of 2.5 μg/L in 25 of 26 samples, which suggests occupational exposure and risk of genotoxicity. When pyrene and benzo[a]pyrene were both detected, concentration ratios from personal exposure samples were used to calculate the adjusted BEI. Concentrations of 1-hydroxypyrene exceeded the adjusted BEIs for air, hand wipes, and neck wipes in most cases. These results indicate the need to increase safety controls and exposure mitigation for RCTS workers.

Project description:IntroductionWhen it is not possible to capture direct measures of occupational exposure or conduct biomonitoring, retrospective exposure assessment methods are often used. Among the common retrospective assessment methods, assigning exposure estimates by multiple expert rater review of detailed job descriptions is typically the most valid, but also the most time-consuming and expensive. Development of screening protocols to prioritize a subset of jobs for expert rater review can reduce the exposure assessment cost and time requirement, but there is often little data with which to evaluate different screening approaches. We used existing job-by-job exposure assessment data (assigned by consensus between multiple expert raters) from a large, population-based study of women to create and test screening algorithms for polycyclic aromatic hydrocarbons (PAHs) that would be suitable for use in other population-based studies.MethodsWe evaluated three approaches to creating a screening algorithm: a machine-learning algorithm, a set of a priori decision rules created by experts based on features (such as keywords) found in the job description, and a hybrid algorithm incorporating both sets of criteria. All coded jobs held by mothers of infants participating in National Birth Defects Prevention Study (NBDPS) (n = 35,424) were used in developing or testing the screening algorithms. The job narrative fields considered for all approaches included job title, type of product made by the company, main activities or duties, and chemicals or substances handled. Each screening approach was evaluated against the consensus rating of two or more expert raters.ResultsThe machine-learning algorithm considered over 30,000 keywords and industry/occupation codes (separate and in combination). Overall, the hybrid method had a similar sensitivity (87.1%) as the expert decision rules (85.5%) but was higher than the machine-learning algorithm (67.7%). Specificity was best in the machine-learning algorithm (98.1%), compared to the expert decision rules (89.2%) and hybrid approach (89.1%). Using different probability cutoffs in the hybrid approach resulted in improvements in sensitivity (24-30%), without the loss of much specificity (7-18%).ConclusionBoth expert decision rules and the machine-learning algorithm performed reasonably well in identifying the majority of jobs with potential exposure to PAHs. The hybrid screening approach demonstrated that by reviewing approximately 20% of the total jobs, it could identify 87% of all jobs exposed to PAHs; sensitivity could be further increased, albeit with a decrease in specificity, by adjusting the algorithm. The resulting screening algorithm could be applied to other population-based studies of women. The process of developing the algorithm also provides a useful illustration of the strengths and potential pitfalls of these approaches to developing exposure assessment algorithms.

Dataset Information

Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model.

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Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model.

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