Delfino et al. biomarkers of inflammation may be attrib­ uted to other unmeasured factors or chance. We then tested two­ pollutant regression models that included both outdoor PM0.25 mass and total PAHs to assess whether PAHs explained the nominal association with mass. We found that IL­6 and sTNF­RII associa ­ tions with mass were completely confounded by PAHs in that the regression coefficient for mass decreased to just below zero and the regression coefficient for PAHs was nearly unchanged (Figure 2A,B). We found a sim­ ilar effect for hopanes, which confounded the nominal association of PM0.25 mass with sTNFR­II (Figure 2C). The variance inflation factor was < 3.5 for exposures, thus showing little evidence of multicollinearity. Transition metals were not associated with the biomarkers [see Supplemental Material, Table 2 (doi:10.1289/ehp.0901407)]. As previously shown (Delfino et al. 2009), the analysis of the relation of erythrocyte anti­ oxidant enzymes (Cu,Zn­ SOD and GPx­ 1) to air pollutants among all 60 subjects showed regression coefficients were largely negative, suggesting inverse associations, but most upper confidence limits crossed 1.0 (see Supplemental Material, Table 3 (doi:10.1289/ehp.0901407)]. The exploratory analysis showed that among seven subjects previously identified as a “posi­ tive responder group” (Delfino et al. 2009), we found largely positive associations of Cu,Zn­ SOD and GPx­1 with air pollutants, and lower confidence limits were > 1.0 for out­ door PM0.25 mass and several other exposures. In the 53 subjects previously identified as a “negative responder group,” we found inverse associations of Cu,Zn­SOD and GPx­1 with indoor and outdoor total, LMW, MMW, and HMW PAHs and with hopanes (all markers of exposures linked to primary combustion). Indoor WSOC was inversely (p < 0.07) associ­ ated with Cu,ZnS­OD , but we found no other associations with SOA markers in the negative responder group. Confidence limits were wider for GPx1­ than for Cu,ZnS­OD . Discussion To our knowledge, this is the first report from a panel cohort study to show associations of circulating biomarkers of response in human subjects to specific PM organic compound classes. The measured chemicals serve as indi­ cators and tracers for air pollutant sources and for classes of chemicals with the potential for redox activity in the body. Our prior work has focused on carbonaceous aerosols that pro­ vided some differentiation between POAs and SOAs by showing associations of biomarkers of inflammation with primary PM2.5 organic carbon (OC) but not secondary PM2.5 OC (a marker of SOAs) (Delfino et al. 2008, 2009). In the present analysis, we found the stron­ gest biomarker associations with air pollutant variables for all molecular weight classes of PAHs and specific source markers of vehicular emissions (hopanes) measured in PM0.25 with GC/MS. Furthermore, two­pollutant mod ­ els of the relation between the biomarkers of systemic inflammation and both total PAHs and PM0.25 mass showed that mass associa­ tions were completely explained by PAHs. Given the results of the chemical mass balance analysis [see Supplemental Material, Chemical mass balance (CMB) model (doi:10.1289/ ehp.0901407) and Arhami et al. 2010], we infer that the confounding of nominal asso­ ciations between biomarkers and PM0.25 mass by PAHs was through a common set of sources. PAHs likely serve here as a surrogate for redox­ active PM chemical components as evidenced in experimental models (Riedl and Diaz­ Sanchez 2005). For example, PAHs from diesel exhaust particles and oxidized derivatives of PAHs such as quinones lead to the generation of reactive oxygen species and subsequent oxidant injury and inflammatory responses, including the expression of nuclear transcription factor­ κB (NFκB) (Riedl and DiazS­anchez 2005). NF κB increases the tran­ scription of cytokines and acute­ phase proteins that are predictive of coronary artery disease risk (Pai et al. 2004). PAHs can induce oxida­ tive stress responses after biotransformation to quinones by cytochrome P450 1A1 (Bonvallot et al. 2001), perhaps after delivery from the lungs to systemic targets. In the Los Angeles Basin, most outdoor PAHs in PM0.25 are expected to be from mobile sources (Schauer et al. 1996), and the CMB exposure correlations are consistent with this expectation. PAHs were also correlated with source markers of vehicular emissions (hopanes). Hopanes are the most unambiguous source marker of traffic emissions. However, the moderate but not strong correlation between hopanes and PAHs suggests that the measured PAHs include a different subset of mobile sources than that of hopanes. This may in part be due to the variability in PAHs rela­ tive to hopanes by combustion­related prob ­ lems in the vehicle fleet (Lough et al. 2007). Overall, the associations of biomarkers with PAHs and hopanes suggest that our previous findings of positive associations of biomarkers with PM2.5, EC, and primary OC (Delfino et al. 2009) were due to PM of mobiles­ource origin. PAHs are found in greater concentra­ tions in the quasiU­FP range compared with larger particles (Ntziachristos et al. 2007), and this has been hypothesized to explain enhanced prooxidative and proinflammatory effects of urban UFPs in the lungs and peripheral target organs of rodents (Araujo et al. 2008). The increased biological potency of UFPs may be related to the content of organic chemicals that have the capacity to reduce oxygen, such as quinones and nitro­PAHs , for which PAHs may act, in part, as a surrogate (Ntziachristos et al. 2007) or as a source after biotransfor­ mation. From the present results we infer that, although PAHs may have an effect by 0.6 0.4 )L 0.2 m / g p (-6 0.0 L I –0.2 –0.4 760 300 200 )L 100 I ( 0 I sTN–100 –200 –300 m m / / g g p p R R - - F F 300 200 )L 100 ( I I 0 sTN–100 –200 –300 PM0.25 alone PM0.25 with PAH PAH alone PAH with PM0.25 PM0.25 alone PM0.25 with PAH PAH alone PAH with PM0.25 PM0.25 alone PM0.25 with hopanes Hopanes alone Hopanes with PM0.25 Figure 2. Associations of circulating biomarkers of inflammation with outdoor PM0.25 mass coregressed with outdoor total PAHs and hopanes in PM0.25. (A) IL-6, PAHs, and PM0.25. (B) sTNF-RII, PAHs, and PM0.25. (C) sTNF-RII, hopanes, and PM0.25. Expected change in the biomarker (adjusted coefficient and 95% CI) corre- sponds to an IQR increase in the air pollutant concentration (see Table 2), adjusted for temperature. ovlume 118 | number 6 | June 2010 • Environmental Health Perspectives