Leitte et al. associations with particle number concentra- tion for particles > 100 nm. Andersen et al. (2008) investigated the association between short-term exposure to size-segregated parti- cles and hospital admissions due to respiratory diseases in Copenhagen among the elderly (age > 65 years). They reported associations between total particle number concentrations and respiratory disease admissions (RR = 1.04; 95% CI, 1.00–1.07) comparable with the associations of PNC100–300 and PNC300–1,000 that we found. Particle surface area concentrations also were adversely associated with total respiratory ERV, in agreement with Sager and Castranova (2009). This would be expected, as we derived particle surface area concentrations from par- ticle number concentrations. Toxicologic studies report that surface area plays an impor- tant role in determining the biological activity of smaller particles, as they occupy less vol- ume, resulting in a larger number of particles with a greater surface area per unit mass and an increased potential for biological interac- tion and absorption of chemical compounds (Oberdörster et al. 2005). However, it is not yet possible to measure particle surface area directly on a continuous scale. Adverse associations between total respi- ratory ERV and both particle number and surface area concentrations remained after adjustment for particle mass (PM10). In two studies of respiratory health outcomes and air pollution, associations with particle number concentrations diminished after controlling for PM2.5 (Andersen et al. 2008; Halonen et al. 2008). Unfortunately, we did not have PM2.5 data available. NO2 was associated with total respiratory ERV, in agreement with other studies (Peel et al. 2005; Stieb et al. 2009; Tolbert et al. 2007). NO2 itself has adverse health effects at high concentrations (> 200 gμ/m 3) (WHO 2005), but such levels are rare in Beijing. NO2 originates mainly from combustion processes and traffic, which are major sources of air pollution in Beijing (Sun et al. 2004), and NO2 is often correlated with other pollutants. Traffic-related air pollution has been associ- ated with respiratory diseases in several studies (Brunekreef et al. 2009). In our two-pollutant models, associations with NO2 were most consistent. When controlling for NO2, asso- ciations with 100–300 nm stayed moderately significant (p < 0.09). A pollutant that exhibits a relatively strong association in a multipol- lutant model may be acting as a surrogate for an unmeasured or poorly measured pollutant (Peel et al. 2005). In addition, associations with pollutants based on single-pollutant mod- els may be attributable to an association with another pollutant that is correlated with the measured pollutant. Typically, epidemiologic studies (including ours) cannot verify to what extent associations with NO2 are attributable to NO2 itself or to other pollutants correlated with NO2. Therefore, we cannot rule out the possibility that observed associations for NO2 were due to NO2-correlated air pollution. W e g a t h e r e d d a i l y c o n c e n t r a t i o n s of SO2, NO2, and PM10 from the Beijing Environmental Protection Bureau, but PM2.5 concentrations were not available. Because of size limitations, we did not investigate associations with disease-specific outcomes. Associations within population subgroups, such as the elderly or children, and disease spe- cific analyses have suggested variation in effects and susceptibilities (Halonen et al. 2008). Associations between cardiovascular diseases and particle mass concentrations were not in the scope of our study, but are under investi- gation. Guo et al. (2009) reported that PM2.5, NO2, and SO2 concentrations were associated with hospital ERV for cardiovascular diseases in Beijing. Exposure assessment and misclassifica- tion of exposure is a well-recognized limi- tation of epidemiologic time-series studies. Measurements from only one station were Table 4. Overview of RRs (95% CIs) between respiratory ERV and an IQR increment of air pollutant while controlling for NO2 or PM10. a While controlling While controlling Pollutant Time delay (days) IQRb for NO2 for PM10 NO2 3 40 — 1.07 (1.01–1.13)* 4 40 — 1.07 (1.01–1.14)* 5 40 — 1.08 (1.01–1.15)* PNC50–100 2 3,600 1.06 (0.99–1.14) 1.07 (1.00–1.15)* 3 3,600 1.06 (0.98–1.16) 1.08 (1.00–1.17)* PNC100–300 2 4,400 1.08 (1.00–1.17) 1.10 (1.02–1.19)* 3 4,400 1.06 (0.97–1.16) 1.11 (1.02–1.21)* PSC50–100 2 60 1.06 (0.99–1.14) 1.07 (1.01–1.15)* 3 60 1.07 (0.98–1.16) 1.09 (1.01–1.17)* PSC100–300 2 440 1.07 (0.99–1.16) 1.10 (1.02–1.19)* 3 440 1.05 (0.95–1.15) 1.10 (1.01–1.20)* aFor a complete table, see Supplemental Material, Table 3 (doi:10.1289/ehp.1002203). Estimates were calculated using cumulative effects models representing time-delayed effects with moving averages up to 6 days (mean of the same day and 5 previous days) and including both pollutants with the same lag, for example, same-day total particle number concentration and same-day NO2 concentration. bUnits for IQR: NO2 (µg/m3); PNCx (1/cm3); PSCx (µm2/cm3). *p < 0.05 (p-values for the hypothesis that the corresponding parameter is zero). used for the particle size distribution data, and we were not able to assess spatial variation in particle number concentrations. Our meas- urement site may be considered as an urban background station because of its location (20 m height and > 500 m from a major road). Average particle number size distributions at a PKU measurement site and another regional measurement site, located approximately 50 km south of the PKU, were similar (Yue et al. 2009), confirming that our measurement site may be considered as an urban background station. In Beijing, concentrations of fine PM (PM2.5 and PM10) are evenly distributed over the urban area, including the Haidian dis- trict (Cheng et al. 2007; Wang et al. 2009). In urban areas, differences of absolute par- ticle number concentrations between different measurement sites can be great (Krudysz et al. 2009), with the largest variations in ultrafine (< 100 nm) and coarse (2,500–10,000 nm) particles and more homogenous spatial dis- tributions of accumulation mode particles (100–2,000 nm) (Monn 2001). The low cor- relation between NO2—as an urban aver- age, representing traffic exhaust levels and the main source of UFP—and UFP might suggest low correlation between UFP at the meas- urement site and UFP levels elsewhere in the city. On the other hand, moderate correla- tions between UFP and NO2 were observed for subgroups, for example, seasons or particle subfractions of UFP. Personal levels of UFP can differ substantially, such as in proximity to traffic (Kaminsky et al. 2009). However, Buzorius et al. (1999), Cyrys et al. (2008), and Puustinen et al. (2007) showed that daily temporal correlations of particle number con- centration time series between different moni- toring sites are high. The authors concluded that using one carefully chosen monitoring site is a reasonable approach to characterize exposure of particle number concentrations in epidemiologic time-series studies. When associations are estimated by Poisson regres- sion model in time-series studies, Lipfert and Wyzga (1995) argued that exposure misclas- sification will reduce the precision of effect estimates, resulting in wider CIs, but will not bias estimates. On the other hand, bias away from the can occur in time-series studies of multiple pollutants, such as this one, when measurement errors of different pollutants are correlated (Zeger et al. 2000). The air pollution mixture in Beijing is dif- ferent from that of Western cities. The main sources of particulate air pollution in Beijing are coal burning, traffic, and dust from long- range transport (Sun et al. 2004). The mean particle number concentrations in urban areas in Europe or North America are 60–80% of the Beijing values, and particulate mass con- centrations in Beijing are about three times higher than in Europe (Wehner et al. 2008). 512 ovlume 119 | number 4 | April 2011 • Environmental Health Perspectives