Predicting changes in PM exposure over time at U.S. trucking terminals using structural equation modeling techniques.

This study analyzes the temporal variability of occupational and environmental exposures to fine particulate matter in the U.S. trucking industry and tests the predictive ability of a novel multilayer statistical approach to occupational exposure modeling using structural equation modeling (SEM) techniques. For these purposes, elemental carbon mass in PM<1 microm at six U.S. trucking terminals were measured twice during the same season up to 2 years apart, observing concentrations in the indoor loading dock (median EC: period 1 = 0.65 microg/m(3); period 2 = 0.94 microg/m(3)) and outdoor background location (median EC: period 1 = 0.46 microg/m(3); period 2 = 0.67 microg/m(3)), as well as in the truck cabs of local drivers while on the road (median EC: period 1 = 1.09 microg/m(3); period 2 = 1.07 microg/m(3)). There was a general trend toward higher exposures during the second sampling trips; however, these differences were statistically significant in only a few cases and were largely attributable to changes in weather patterns (wind speed, precipitation, etc.). Once accounting for systematic prediction errors in background concentrations, the SEM approach provided a strong fit for work-related exposures in this occupational setting.