one paper on a rare health end point (hospital admissions for headache); a total of 34 papers were included in the review. All these studies had adjusted for major confounders: seasonal- ity and nonlinear function of temperature and relative humidity. For the cohort studies, we excluded two papers on birth outcomes. Meta-analysis. For the time-series studies, we calculated pooled fixed and random effects relative risk (RR) estimates for all health end points for which estimates from at least three different studies were available for the same age group and for different cities. We report random effect estimates as significant hetero- geneity was observed (p < 0.05) among indi- vidual estimates for some end points. In case of no heterogeneity, fixed and random effect estimates are similar, so we report random effect estimates for all end points for reasons of consistency. If estimates for multiple lags were reported, we used the estimate discussed by the author, as indicated in APED as “selected” lag. If multiple risk estimates were available from the same city, we only included the most recent estimate, and if the study area was part of a larger administrative area included in another paper (e.g., the Netherlands rather than Amsterdam), we included results for the larger area only. Finally, we excluded city- specific estimates for which PM10 was partly derived from BS. We calculated summary fixed and random effects estimates using the metan procedure in STATA (version 11.2; StataCorp, College Station, TX, USA), as described by Harris et al. (2008). In order to calculate pooled estimates and compare estimated effects for BS and PM per mass unit, we converted RRs for BS to RRs for EC using the average conversion factor (10 μg/m 3 BS = 1.1 μg/m 3 EC) or the range of conversion factors from individual studies (i.e., 0.5–1.8) for sensitivity analysis. We expressed pooled effect estimates per 10 gμ/m 3 (for BS and PM10) or 1 gμ/m 3 (for PM2.5 and EC). To compare effects based on comparable contrasts, we calculated the aver- age ratio of the interquartile ranges (IQRs) for PM mass and BCP and compared it with the ratio of RR–BCP:RR–PM mass. We could not use study-specific IQRs to estimate pooled effects because IQRs were not avail- able for all studies. Exposure contrast in BCP compared with PM mass. We identified studies that simulta- neously measured PM mass and BCP concen- trations ≤ 50 m from busy roads (as defined as such by the authors) and at background loca- tions and calculated the ratio between these concentrations. To calculate the roadside increment (which we define as the difference between traffic and background concentra- tions) for PM2.5 and EC, we averaged meas- urements at different traffic locations within the same study area to derive a single value Environmental Health Perspectives • volume 119 | number 12 | December 2011 for each study area, and we converted BS and Abs concentrations to EC using the 10 gμ/m 3 BS to 1.1 μg/m 3 EC conversion factor (or a range of conversion factors) as described above. We then divided the area-specific aver- age difference between traffic and background EC concentrations by the corresponding aver- age difference between traffic and background PM2.5 concentrations to estimate the percent- age of EC in the roadside increment of PM2.5 Comparison of estimated health benefits of traffic abatement measures using PM2.5 or BCP. To illustrate the potential implications of using BCP as an air quality indicator, we estimated the health benefits of a traffic abate- ment measure for populations living along busy roads based on both PM2.5 mass and BCP. We used the average and 95% confi- dence interval (CI) of the percentage EC in the roadside increment in PM2.5 (calculated as described above) to estimate the health ben- efits of a hypothetical traffic abatement policy measure resulting in a 1-μg/m 3 reduction in PM2.5 mass. This approach assumes that the reduction in BCP resulting from traffic abate- ment will be proportional to the decrease in PM mass by the percentage of EC in the roadside increment for PM mass, an assump- tion that will not hold for all policies. We estimated the increase in life expec- tancy that would result from such a traffic abatement policy using life table calculations, as described by Miller and Hurley (2003), for a hypothetical population of 500,000 people 18–64 years of age, distributed in age categories comparable to the 2008 Dutch population. We estimated the effects on this population for a lifetime. Results Health effects of BCP compared with PM mass. Studies on BCP and PM10. Most papers con- cerned time-series studies on PM10 and BS (as a measure of BCP) conducted in Europe. We Black carbon as additional indicator present random effects estimates for the per- cent change in each outcome with a 10-gμ/m 3 increase in PM10 or BS in Table 1. Information and effect estimates for all individual studies, and tests of heterogeneity and fixed effects esti- mates for studies included in meta-analyses, are reported separately for each outcome in Supplemental Material, Tables B1–B10 (http://dx.doi.org/10.1289/ehp.1003369). Single-city estimates for the percent change in all-cause mortality with a 10-μg/m 3 increase in BCP and PM10 are also presented in Figure 1. Available data were dominated by estimates from the APHEA (Air Pollution and Health–A European Approach) study (Analitis et al. 2006; Atkinson et al. 2001; Katsouyanni et al. 2001; Le Tertre et al. 2002). For most outcomes, pooled effects esti- mates for a 10-μg/m 3 increase in exposure are larger for BS than for PM10, especially for mortality and hospital admissions for car- diovascular causes (Table 1). However, the average ratio of the IQRs for PM10:BS [1.7; see Supplemental Material, Tables B1–B10 (http://dx.doi.org/10.1289/ehp.1003369)] was consistent with the ratios of RR for BS:PM10 (e.g., 0.90/0.60 = 1.5 for cardio- vascular mortality in Table 1), which suggests that effect estimates expressed for a similar increase in concentration (IQR) would be more or less equivalent. When we used a 10-to-1.1 conversion factor to transform the estimated effect of a 10-gμ/m 3 increase in BS to the estimated effect for a 1-gμ/m 3 increase in EC, the pooled random effect estimate for all-cause mortality changed from 0.68% (95% CI: 0.31, 1.06) to 0.62% (i.e., 0.68/1.1; 95% CI: 0.26, 0.96). When study-specific conver- sion factors were used, estimated effects for a 1-μg/m 3 increase in EC ranged from 0.38% to 1.36% (for conversion factors of 1.8 and 0.5, respectively), which suggests that the effect of a 1-gμ/m 3 increase in EC on all-cause mortality is at least eight times larger than References (Supplemental Material table)a B, D, E, H (B1) A, B, H (B2) A, B, H (B3) B, C, G (B4) C (B5) B, C (B6) B, C (B7) B, F (B8) F (B9) B, F (B10) 1693 Table 1. Summary of pooled random effects estimates for PM10 and BS from time-series studies. End point Mortality All causesb Cardiovascular Respiratory Hospital admissions All respiratory (≥ 65 years)c Asthma + COPD (≥ 65 years) Asthma (0–14 years) Asthma (15–64 years) Cardiac (all ages) Cardiac (≥ 65 years) IHD (≥ 65 years) No. of estimates 7 7 7 6 5 5 5 4 4 5 Percent change per 10-μg/m3 increase (95% CI) PM10 0.48 (0.18, 0.79)* 0.60 (0.23, 0.97)* 0.31 (–0.23, 0.86) 0.70 (0.00, 1.40)* 0.86 (0.03, 1.70)* 0.69 (–0.74, 2.14) 0.77 (–0.05, 1.61) 0.51 (0.04, 0.98)* 0.67 (0.28, 1.06)* 0.68 (0.01, 1.36)* BS 0.68 (0.31, 1.06)* 0.90 (0.40, 1.41)* 0.95 (–0.31, 2.22) –0.06 (–0.53, 0.41) 0.22 (–0.73, 1.18) 1.64 (0.28, 3.02)* 0.52 (–0.50, 1.55) 1.07 (0.27, 1.89)* 1.32 (0.28, 2.38)* 1.13 (0.72, 1.54)* Abbreviations: COPD, chronic obstructive pulmonary disease; IHD, ischemic heart disease. aSee Supplemental Material, Tables B1–B10 (http://dx.doi.org/10.1289/ehp.1003369), for specific studies. References: A, Analitis et al. (2006); B, Anderson et al. (2001); C, Atkinson et al. (2001); D, Hoek et al. (2000); E, Katsouyanni et al. (2001); F, Le Tertre et al. (2002); G, Prescott et al. (1998); H, Zeghnoun et al. (2001). bIncludes cardiovascular and respiratory mortality. cIncludes asthma and COPD. *p < 0.05.