Background: Exposure to polycyclic aromatic hydrocarbon (PAH) has been linked to various adverse health outcomes. Personal PAH exposures are usually measured by personal monitoring or biomarkers, which are costly and impractical for a large population. Modeling is a cost-effective alternative to characterize personal PAH exposure although challenges exist because the PAH exposure can be highly variable between locations and individuals in non-occupational settings. In this study we developed models to estimate personal inhalation exposures to particle-bound PAH (PB-PAH) using data from global positioning system (GPS) time-activity tracking data, traffic activity, and questionnaire information. Methods: We conducted real-time (1-min interval) personal PB-PAH exposure sampling coupled with GPS tracking in 28 non-smoking women for one to three sessions and one to nine days each session from August 2009 to November 2010 in Los Angeles and Orange Counties, California. Each subject filled out a baseline questionnaire and environmental and behavior questionnaires on their typical activities in the previous three months. A validated model was used to classify major time-activity patterns (indoor, in-vehicle, and other) based on the raw GPS data. Multiple-linear regression and mixed effect models were developed to estimate averaged daily and subject-level PB-PAH exposures. The covariates we examined included day of week and time of day, GPS-based time-activity and GPS speed, traffic- and roadway-related parameters, meteorological variables (i.e. temperature, wind speed, relative humidity), and socio-demographic variables and occupational exposures from the questionnaire. Results: We measured personal PB-PAH exposures for 180 days with more than 6 h of valid data on each day. The adjusted R2of the model was 0.58 for personal daily exposures, 0.61 for subject-level personal exposures, and 0.75 for subject-level micro-environmental exposures. The amount of time in vehicle (averaging 4.5% of total sampling time) explained 48% of the variance in daily personal PB-PAH exposure and 39% of the variance in subject-level exposure. The other major predictors of PB-PAH exposures included length-weighted traffic count, work-related exposures, and percent of weekday time. Conclusion: We successfully developed regression models to estimate PB-PAH exposures based on GPS-tracking data, traffic data, and simple questionnaire information. Time in vehicle was the most important determinant of personal PB-PAH exposure in this population. We demonstrated the importance of coupling real-time exposure measures with GPS time-activity tracking in personal air pollution exposure assessment. © 2012 Wu et al.; licensee BioMed Central Ltd.