Davis et al. mortality among 55,000 trucking industry workers who were employed in 1985 at these four trucking companies with mortality fol- low-up through 2000. We obtained detailed work history records to link the subjects to exposures that included a chronological series of work locations, job titles, and years worked in each job. For a description of the various jobs in this occupational cohort, see Supplemental Material, Table 1 (doi:10.1289/ ehp.1002981). The exposure monitoring plan is described in detail elsewhere (Davis et al. 2006; Smith et al. 2006). Briefly, concentrations of vehicle exhaust markers (EC and organic carbon) in PM1 and in PM ≤ 2.5mμ in aerodynamic diameter (PM2.5) were simultaneously meas- ured for individual workers, indoor work areas, truck cabs, and ambient background conditions at the 36 trucking terminals. EC levels were determined using the NIOSH (National Institute for Occupational Safety and Health 1998) 5040 method, whereby PM1 was collected on a 22-mm quartz tis- sue filter, preceded by a precision machined cyclone separator (SCC1.062 Triplex; BGI, Inc., Waltham, MA) to remove particles > 1 mμ in aerodynamic diameter. Because we were interested in vehicle exhaust from die- sel-fueled sources, EC in PM1 was chosen as our primary marker of vehicle exhaust (Fraser et al. 2003; Kleeman et al 2000; Riddle et al. 2008; Schauer 2003; Sheesley et al. 2009). A more detailed discussion of the choice of EC as a surrogate marker of diesel exhaust is provided in the Supplemental Material (doi:10.1289/ehp.1002981). Baseline exposure model. We used struc- tural equation modeling (SEM) techniques (Sanchez et al. 2005) to predict shift-specific personal EC levels for the on-site terminal workers, including dockworkers (who load and unload cargo), mechanics, clerks, and hostlers (on-site drivers that move trailers using small specialized tractor units). In brief, personal job-specific exposures were predicted by smok- ing status and work area exposures (R2 = 0.64); work area exposures were predicted by ter- minal-specific characteristics, ventilation, job location in the terminal, and matching back- ground exposures from the area around the terminals (R2 = 0.64); and background expo- sures were predicted by local weather charac- teristics, proximity to a major road, industrial land use characteristics around the terminal, and regional location within the United States (R2 = 0.51) (Davis et al. 2006) [for additional details on the structure of the exposure model, see Supplemental Material, Table 2, Figure 2 (doi:10.1289/ehp.1002981)]. The modeling approach was validated using additional expo- sure data collected during a series of six repeat site visits conducted after the initial 36 termi- nal sampling trips (Davis et al. 2009). 998 Separate exposure models were constructed for drivers who worked off-site delivering and picking up freight (Davis et al. 2007), includ- ing local pickup-and-delivery (P&D) drivers and long-haul (LH) drivers whose exposures could not be modeled explicitly within the SEM because of the dynamic nature of their exposures. Driver EC exposures were mod- erately correlated with background EC lev- els measured at their home terminals, with stronger correlations for local P&D drivers (r = 0.4–0.5; p < 0.01) than for LH drivers (r = 0.2–0.4; p < 0.01). Measured EC levels inside the driver cabs were also significantly higher when the windows were predicted to be open versus shut [p < 0.05; for further detail on window status, see Supplemental Material (doi:10.1289/ehp.1002981)]. Differences in EC measurements across driver groups (P&D vs. LH) and by driver smoking status were not statistically significant. Spatial extrapolation approach. For the present study, we used the SEM exposure model and characterization of driver expo- sures described above (Davis et al. 2007) to extrapolate EC exposures to workers at the 103 additional large terminals in the epidemi- ologic cohort that were not part of the original exposure assessment. The SEM used for this purpose did not include smoking or relative humidity because these data were not available for the entire cohort of workers and termi- nals. However, excluding these variables did not appreciably affect model estimates. Using input data from each of the terminals and the estimated coefficients from the modified SEM, we extrapolated exposures spatially across the entire cohort for the year 2000 and calculated a monthly exposure estimate for each job and terminal combination. The year 2000 was cho- sen as the base year for the exposure extrapola- tion efforts because it represents the final year of follow-up in our epidemiologic cohort. To extrapolate driver exposures spatially across the cohort, we constructed scaling fac- tors that related measured driver EC to model- based background predictions. Specifically, we determined ratios of median driver to terminal background EC for each driver type. This ratio resulted in a multiplier of 2.1 for LH driv- ers and hostlers, indicating that exposures for these drivers were typically 2.1 times higher than terminal background conditions. For P&D drivers, we calculated separate multipli- ers for warm- and cold-weather conditions to account for the impact of open cab windows in the truck cabs that were not air conditioned. The multiplier for P&D driver exposures to background levels was 2.3 (window open) in warm-weather conditions (> 10°C) and 2.0 for colder temperatures (window shut). Temporal extrapolation approach. To account for changes in job-related exposure characteristics over time, we conducted a comprehensive historical review of work prac- tices in the industry, including the introduction of diesel-fueled vehicles across job groups and companies [see Supplemental Material, Table 3 (doi:10.1289/ehp.1002981)]. Although LH drivers began operating diesel-fueled trucks in the 1950s, the P&D fleet was predominantly gasoline-powered until the 1970s and 1980s. Such changes may not have affected drivers directly but would have affected the composi- tion of highway and road emissions, which increased EC exposures as the whole industry changed truck engine types over a short period. Diesel forklifts were in operation on the ter- minal docks for only a relatively brief period of time during the 1980s at three of the four companies; otherwise, gasoline- or propane- powered forklifts were used. Propane-powered forklifts predominated after diesel forklifts were phased out in the early 1990s. We derived job-specific historical multipli- ers to extrapolate baseline exposure model esti- mates derived for 2000 to earlier periods based on EC exposure data from a study of unionized trucking industry workers conducted in the late 1980s (Zaebst et al. 1991). Specifically, we used the structure of our exposure model along with historical input data on the model covari- ates to predict job-specific EC exposures for workers in the earlier study; we used the ratio of our model-based predictions to the meas- ured values reported by Zaebst et al. (1991) to adjust for changes in work-related conditions over time. In addition, because forklifts used during the TrIPS exposure assessment were powered by propane fuel only, we used data from Zaebst et al. (1991) to develop fuel use multipliers by comparing EC concentrations from propane with concentrations related to diesel- and gasoline-powered forklifts. There is no widespread national database for ambient EC or PM1 (either current or his- torical) that could be used to adjust for the indirect effect of changes in ambient EC levels over time on work-related EC exposures across the cohort. For this reason, we explored trends in a surrogate EC marker known as the coeffi- cient of haze (COH). COH represents an early air pollution monitoring technique developed in 1953 and widely employed in the 1960s and 1970s (U.S. Environmental Protection Agency 1983). The COH measurement method has been shown to be strongly predic- tive of EC (R2 = 0.94) (Cass et al. 1983), and COH has recently been used to characterize changes in diesel-related PM exposure condi- tions over time (Davis et al. 2010; Kirchstetter et al. 2008). Although COH measurements away from traffic reflect a variety of air pol- lution sources, diesel dominates COH levels observed near highways and roads. Most historical COH data have been lost because of changes in data storage methods. However, we obtained monthly COH averages ovlume 119 | number 7 | July 2011 • Environmental Health Perspectives