Predicting Changes in PM Exposure Over Time at U.S. Trucking Terminals Using Structural Equation Modeling Techniques

Abstract

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 μm 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 μg/m³; period 2 = 0.94 μg/m³) and outdoor background location (median EC: period 1 = 0.46 μg/m ³ ; period 2 = 0.67 μg/m³), as well as in the truck cabs of local drivers while on the road (median EC: period 1 = 1.09 μg/m³; period 2 = 1.07 μg/m³). 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.

Keywords:

• diesel exhaust
• elemental carbon
• PM
• structural equation model
• trucking industry

ACKNOWLEDGMENTS

We would like to acknowledge the contribution of the other members of the Trucking Industry Particle Study: Douglas W. Dockery and Frank E. Speizer, and to thank Kevin Lane and Jonathan Natkin for excellent field work and technical data support. We would also like to thank LaMont Byrd and the International Brotherhood of Teamsters Safety and Health Department, and the participating companies. This work was supported by NIH/NCI R01 CA90792 and HEI 4705-RFA03-1/04-1.

Notes

^A Significant differences (p < 0.05) in median values using Wilcoxon Ranksum nonparametric comparison test.

^A Significant differences (p < 0.05) in median values using Wilcoxon Ranksum nonparametric comparison test.

^A Significant differences (p < 0.05) in median values using Wilcoxon Ranksum nonparametric comparison test.

^A Terminal size and industrial land use around a terminal did not change across the two periods.

^B Significant differences (p < 0.05) in median values using Wilcoxon Ranksum nonparametric comparison test.

^A Actual values represent the observed medians for each trip.

^A Actual values represent the observed medians for each trip.