showed evidence of a healthy worker survivor effect for lung cancer. Average exposure was not significantly associated with lung cancer risk. Estimated trucking industry EC exposures declined considerably over the course of the study and also varied based on location, job, and the period of diesel forklift use. Using model- based data to estimate trucking industry exposure trends (Davis et al. 2011), the median EC exposure over a shift for LH drivers in 1971–1980, 1981–1990, and 1991–2000 was 5.88 gμ/m 3, 4.26 gμ/m 3, and 2.01 μg/m 3, respectively. For P&D drivers, in warm weather (windows open), estimated median levels were 9.59 μg/m 3, 6.97 μg/m 3, and 2.77 μg/m 3, respectively, and in cold weather (windows closed), corresponding median levels were 4.15 μg/m 3, 2.95 μg/m 3, and 1.64 μg/m 3, respectively. In the 1980s, the estimated median EC exposure for dockworkers working with diesel forklifts was 29.86 gμ/m 3, whereas for propane forklifts it was 1.43 μg/m 3. Therefore, average exposure intensity is unlikely to be an accurate surrogate for cumulative exposure and pulmonary dose of particulate over time in the study cohort. Exposure to vehicle exhaust particulate was estimated based on the assessment of EC mass in PM1.0. We conducted a source appor- tionment study using particle-phase organic molecular markers in personal and work area samples at a freight terminal in an urban area in 2003 (Sheesley et al. 2008a). These data indicated that for the LH drivers, P&D drivers, dockworkers, mechanics, and in the terminal yard and in an urban background site, most (≥ 80%) of the EC was from diesel exhaust sources, with a smaller percentage from spark ignition vehicles. These findings are consistent with other source apportion- ment studies indicating that diesel vehicles significantly contributed to EC in the United States during the period of the study, particu- larly in urban areas (Schauer 2003). A unique feature of this study was the quantitative approach linking historical esti- mates of EC with job title and trucking ter- minal location information from work history records. We conducted a national exposure assessment at representative work locations in the participating companies. Statistical Table 5. Lung cancer hazard per 1,000 µg/m3-months associated with cumulative EC for exposure with no lag, a 5-year lag, and a 10-year lag, excluding mechanics. Coefficient (SE) Relative 1,000 µg/m3-months hazard No lag 5-year lag 10-year lag 0.0345 (0.0349) 0.0665 (0.0379) 0.0849 (0.0501) 1.04 1.07 1.09 95% CI 0.97, 1.11 0.99, 1.15 0.99, 1.20 Adjusted for race, census region, calendar year of fol- low-up, and duration of employment. For the no lag, 5-year lag, and 10-year log, p = 0.32, 0.08, and 0.09, respectively. exposure models were developed that identified determinants of EC exposure, and these factors were used to estimate exposure nationally and historically (Davis et al. 2011). In contrast, pre- vious studies of this industry estimated expo- sures based on diesel vehicle miles, emission rates, and fuel efficiency factors (Bailey et al. 2003; Steenland et al. 1998). We conducted analyses with and without mechanics because we had less confidence in the historical extrapolation of their EC expo- sures since their job duties changed over time as major truck repairs were outsourced. Additionally, the patterns and potentially the composition of exposures for this job group were different from the others (McDonald et al. 2011). Misclassification of exposure would make it more difficult to detect a rela- tionship between EC and lung cancer mortal- ity with mechanics included. In addition, we hypothesized that because of their intermittent exposure, mechanics have less exposure to fresh particles that may be more hazardous than aged particles, thereby reducing their lung cancer risk. Short-lived reactive oxygen species (ROS) present in traffic-related PM and ultrafine par- ticles resulting in DNA damage is a mechanism whereby traffic-related exposures may increase lung cancer risk. ROS activity has been found to be associated with exposure to traffic-related PM and ultrafine particles, potentially explain- ing the greater risk in persons such as drivers and dockworkers who are in jobs with more continuous exposures to fresh exhaust (de Kok et al. 2006; Li et al. 2003). Our results are consistent with a large body of literature that supports a relation- ship between diesel exhaust exposure and lung cancer risk in occupationally exposed work- ers (Diesel Working Group 1995; Office of Research and Development 2002). These stud- ies have not included historical quantitative estimates of exposure. The positive association between cumulative EC exposure and lung can- cer estimated in the present analysis is consistent with our previous assessments of lung cancer mortality by job title in this cohort (Garshick et al. 2008) and is similar to results from a case– control study conducted by Steenland et al. (1990) in the same industry in 1982–1983. Further, our findings are consistent with an analysis that demonstrated a linear relationship between PM mass and lung cancer mortality based on estimates of PM mass exposure from particulate air pollution, second hand smoke, and active smoking (Pope et al. 2011). Several other studies have measured diesel exhaust exposures and estimated lung cancer mortality in miners. A study of potash min- ers exposed to diesel exhaust with cumulative exposure based on occupational measurements was suggestive of a positive exposure–response relationship (Neumeyer-Gromen et al. 2009). Attfield et al. (2012) reconstructed historical Environmental Health Perspectives • volume 120 | number 9 | September 2012 Lung cancer and elemental carbon EC exposures over a 30–50 year period for a cohort mortality study of 12,315 U.S. non- metal miners. In comparison to the trucking industry, the duration of underground mine exposure was relatively short (a mean of 8 years compared with a mean of 21.6 years in the present study) but more intense, with mean EC exposures of 128 μg/m 3 com- pared with the historical trucking industry’s lower cross-shift estimates noted previously. Cumulative exposure quartiles reported by Attfield et al. (2012) for underground miners ranged from < 108 gμ/m 3-years (equivalent to 1,296 μg/m 3-months) to ≥ 946 μg/m 3-years (equivalent to 11,352 μg/m 3-months) in a 15-year exposure lag model, which over- laps with our estimates for trucking industry cumulative EC. For example, for the 10-year lag exposures excluding the mechanics, the 75th percentile was 1,305 gμ/m 3-months and the maximum was 10,341 μg/m 3-months (Table 3). We converted the 10-year lag regression coefficient (indicating risk per 1,000 μg/m 3-months) for trucking industry EC exposure in Table 5 to the same units used in Attfield et al. (2012) by multiply- ing by 12 months. This calculation resulted in an HR of 2.77 (95% CI: 0.85, 9.00) per 1,000 μg/m 3-years. Attfield et al. (2012) found that the corresponding HR, based on a 15-year lag, was 4.06 per 1,000 gμ/m 3-years (95% CI: 2.11, 7.83), indicating overlap between the relative risks estimated for the two study cohorts. As mentioned above, there is no one expo- sure metric unique to diesel exhaust; therefore, we selected EC mass in PM1.0 as our marker. Although our source apportionment analyses support this choice, using EC also incorporates exposure to other mobile sources (Sheesley et al. 2008b). In addition, it is also possible that other parameters related to vehicle exhaust particulate matter are relevant to the assess- ment of health risk, such as particle number or surface area (Wittmaack 2007). As the biologic mechanisms are not known, we do not propose that EC serves as a lung carcinogen, but serves as a marker of exposure to components associ- ated with combustion sources. An additional limitation is the lack of exposure information before employment in the four participating companies. In a sur- vey mailed in 2003 to a sample of active and retired workers (Jain et al. 2006), workers may have had up to 10 additional years of trucking industry exposure, thereby underestimating actual exposure and potentially reducing appar- ent risk associated with estimated exposure. Potential overestimation of exposures using our exposure model for workers at very small terminals with little trucking activity would have a similar effect. Additionally, our cumula- tive exposure metric has the implicit assump- tion that effects of long-term low-intensity 1305