Enhancing the power of biomarkers: a polycyclic aromatic hydrocarbon case study in using biomarkers, emissions data and air quality modeling

Abstract

Biomonitoring data provide a direct way to link the human exposure to environmental contaminants. However, these data do not reveal how various exposure routes or media contribute to the body burden of a specific chemical. Polycyclic aromatic hydrocarbons (PAH) are hazardous air pollutants formed during incomplete combustion and are detected in food, air, and soil. These chemicals readily enter the human body through inhalation and ingestion and are detected in urine as hydroxylated metabolites. Recent work by one of us and colleagues at the University of California, Davis revealed that modeled national data for PAH from outdoor and indoor emissions and food intake had the range of cumulative distribution as urinary PAH metabolite concentrations among 2003-2004 National Health and Nutrition Examination Survey (NHANES) participants. Building on that work, this project compares an individual’s urinary PAH concentration from the 2001–2006 NHANES survey with several routes of exposure including food intake, indoor sources, and outdoor emissions. Food intake and indoor sources are estimated through each individual’s questionnaire responses. Outdoor emissions of PAH are estimated from daily PM2.5 exposure estimates and air toxic emission data from US EPA National Air Toxic Assessment (NATA). The Hierarchical-Bayesian estimates of PM2.5 developed by USEPA were augmented to estimate daily outdoor concentrations of PAH at the census tract level. Multivariate linear regression models (MLRM) will be used to track the direct relationship between an individual’s urinary PAH biomarkers and the various exposure pathways. This effort demonstrates the power of combining air emissions data and NHANES results together will spatially detailed models to elucidate exposure routes that could not otherwise be quantified.