Gas/particle distribution of polycyclic aromatic hydrocarbons in coupled outdoor/indoor atmospheres

Concentrations of polycyclic aromatic hydrocarbons (PAHs), PM2.5, and organic and elemental carbon (OC and EC) were measured in 48 h integrated samples collected in the indoor and outdoor air in Los Angeles, CA, Houston, TX, and Elizabeth, NJ from July 1999 to June 2000. The objective of the study was to evaluate the hypothesis that outdoor air pollution contributed strongly to indoor air pollution. The measured partition coefficients of PAHs, Kp,meas, in the individual samples were well correlated with the compounds’ sub-cooled liquid vapor pressure, pLo. Values of Kp,meas varied by about two orders of magnitude for any given value of vapor pressure. These variations in gas/particle partitioning of PAHs were higher than the estimated systematic and random error of Kp,meas and are related to the aerosol characteristics and sampling conditions. Stepwise multiple linear regression analysis (MLR) of the pooled data, which included pLo at 25°C, temperature, fOC and fEC as independent variables, explains 84.5% of the variability of the partition coefficients. This is higher than the explained variance when pLo is used as a single parameter (77.5%). The relative importance of each variable for prediction of PAH partition coefficient is determined by partial coefficients of determination. Vapor pressure at 25°C (RpoL2=0.84) and temperature (RT2=0.21) are the two most important predictors followed by fEC (RfEC2=0.12) and fOC (RfOC2=0.038). Both EC and OC carbon are important predictors of gas/particle partitioning of PAHs, with EC being a better predictor. Because EC is highly correlated with (and is a good tracer of) primary combustion-generated OC, this result suggests that PAHs more readily sorb on combustion-generated aerosol containing EC. Enrichment of the indoor aerosol in non-combustion OC suggests that sorption of PAHs is more important in the indoor air compared to the outdoor air. The MLR developed in this work will improve prediction of gas/particle partitioning of PAHs in indoor and outdoor air.