mixed model assumptions and the presence of influential observations. We found four influential high outliers for IL6­ > 10 pg/mL that were reset to 10 pg/mL (upper limit of its standard curve) to obtain more representative estimates of association. In a model for 5d­ay average PM0.25, including the outliers resulted in an association of 0.41 pg/mL [95% con­ fidence interval (CI), 0.00–0.82] per inter­ quartile change in PM0.25 of 7.37 µg/m3, whereas resetting them to 10 pg/mL resulted in an association of 0.26 pg/mL (95% CI, –0.06 to 0.57). It is important that in the previous analysis of PM0.25 mass (Delfino et al. 2009), the associations with 1­ day and 3­day average PM 0.25 were stronger and had narrower 95% CIs than did the 5d­ay average for both IL6­ and sTNFR­II. In exploratory analyses, we retested models for erythrocyte antioxidant enzymes (Cu,Zn­SOD and GPx­1 ) from our previ­ ous publication (Delfino et al. 2009). Random slopes and individual autoregres­ sive models showed small, highly influential subject clusters (seven subjects) with posi­ tive associations between air pollutants and antioxidant enzymes, whereas most of the Table 5. Associations of biomarkers of systemic effect with indoor and outdoor 5-day average PM0.25 mass and organic components [regression coefficient (95% CI)]. Air IL-6 sTNF-RII pollutant (pg/mL) (pg/mL) PM0.25 mass Indoor 0.05 (–0.12 to 0.22) 18 (–61 to 97) Outdoor 0.26 (–0.06 to 0.57) 125 (–40 to 289) WSOC Indoor –0.11 (–0.30 to 0.08) 15 (–77 to 108) Outdoor –0.08 (–0.27 to 0.10) 63 (–19 to 145) PAHs Total Indoor 0.25 (0.07 to 0.43)** 119 (16 to 223)* Outdoor 0.27 (0.10 to 0.44)** 135 (45 to 225)** LMW Indoor 0.30 (0.10 to 0.50)** 115 (–2 to 233) Outdoor 0.22 (0.05 to 0.39)* 109 (19 to 200)* MMW Indoor 0.28 (0.07 to 0.48)** 138 (22 to 254)* Outdoor 0.30 (0.12 to 0.48)** 143 (47 to 238)** HMW Indoor 0.18 (0.02 to 0.35)* 91 (1 to 181)* Outdoor 0.26 (0.07 to 0.44)** 137 (39 to 234)** Hopanes Indoor 0.22 (0.04 to 0.39)* 107 (10 to 204)* Outdoor 0.06 (–0.08 to 0.20) 89 (26 to 151)** n-Alkanes Indoor 0.01 (–0.03 to 0.06) –6 (–27 to 16) Outdoor 0.009 (–0.03 to 0.05) 14 (–6 to 34) Organic acids Indoor –0.05 (–0.22 to 0.12) –36 (–109 to 37) Outdoor –0.22 (–0.39 to –82 (–164 to 1) –0.06)** Regression coefficients and 95% CIs are for the expected change in the biomarker among 60 subjects associated with an IQR change in the air pollutant (see Table 2), adjusted for temperature. *p < 0.05, **p < 0.01. 0.6 0.4 ) 0.2 L m / (pg 0 6 - L I –0.2 –0.4 –0.6 remaining 53 subjects showed inverse associa­ tions. Details of these clusters and their inter­ pretation are presented elsewhere (Delfino et al. 2009). We present these data­driven results with the new air pollutant exposure data primarily in the Supplemental Material, Table 3 (doi:10.1289/ehp.0901407). Results Table 2 provides descriptive statistics for the measured exposures. Seasonal differences were greatest for MMW PAHs, HMW PAHs, and na­lkanes, which were higher in the cool sea­ son, and for WSOC, which was higher in the warm season, as expected because of photo­ chemistry. Indoor/outdoor ratios were close to 1.0 for PAHs and hopanes, and indoor– outdoor correlations were strong (median R was 0.60 for PAH species and 0.74 for hopane species) (Arhami et al. 2010). This suggests high penetration of these outdoor PM0.25 components into indoor environ­ ments and that measured indoor components were largely of outdoor origin. On the other hand, indoor/outdoor ratios were high for n­alkanes and n­alkanoic acids, with gener ­ ally low indoor/outdoor correlation coeffi­ cients (Arhami et al. 2010). This suggests that indoor sources influenced the indoor levels of na­lkanes and na­lkanoic acids. Table 3 shows a correlation matrix for measured outdoor organic components. We found moderate to strong correlations between PM0.25 mass, PAHs, and hopanes. We also found small negative correlations of these spe­ cies with organic acids and small positive cor­ relations with WSOC, suggesting that POA and SOA concentrations are relatively inde­ pendent of each other at the study sites. To further improve our understanding of the clearly positive associations of biomarkers with summed PAH compounds presented Biomarkers and particle components below, we used the chemical mass balance model (CMB) source apportionment esti­ mates from Arhami et al. (2010) to evalu­ ate the possible sources of PAHs. We briefly summarize methods and source apportion­ ment results in the Supplemental Material, Chemical mass balance (CMB) model (doi:10.1289/ehp.0901407). Table 4 shows a correlation matrix for the relation of PAHs to the CMB­estimated sources. Strong correla ­ tions are seen for total PAHs with vehicular emission sources, whereas the apportioned mass from other sources shows weak to correlations. In the mixed­model regression analy ­ ses, we found positive associations of cir­ culating biomarkers of inflammation (IL­6 and sTNF­RII) with organic components (Table 5, Figure 1). We found the strongest associations with biomarkers for both indoor and outdoor PAHs, including LMW, MMW, and HMW PAHs. The next strongest asso­ ciations were for hopanes. Indoor but not outdoor hopanes were associated with IL­6 , whereas both indoor and outdoor hopanes were associated with sTNFR­II. Outdoor WSOC (a marker of SOAs) was positively associated with sTNFR­II, but confidence limits crossed 1.0 (p < 0.14), and we found no other associations with SOA markers. The outdoor organic acids (another marker of SOAs) showed a pattern oppo­ site to that of the POA markers, with largely negative regression coefficients in relation to biomarkers of inflammation. To assess whether this was due to inverse correlations with PAHs, we coregressed outdoor total PAHs with outdoor organic acids. We found that associations with PAHs and with organic acids decreased in magnitude to small degrees when coregressed, suggesting that the negative regression coefficients for organic acids with Environmental Health Perspectives • volume 118 | number 6 | June 2010 759 Outdoor Indoor 400 350 300 250 )L 200 /gm 150 p II( 100 -FR 50 N sT 0 –50 –100 –150 –200 PM0.25 Mass PAH Hopanes WSOC Organic acids POA components SOA components and tracers and tracers PM0.25 Mass PAH Hopanes WSOC Organic acids POA components SOA components and tracers and tracers Figure 1. Associations of biomarkers with 5-day average outdoor and indoor community PM0.25 mass, and markers of POAs and SOAs. (A) IL-6. (B) sTNF-RII. Expected change in the biomarker (adjusted coefficient and 95% CI) corresponds to an IQR increase in the air pollutant concentration (see Table 2), adjusted for temperature.