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

Mary E Davis1, Francine Laden, Jaime E Hart

  • 1Department of Urban and Environmental Policy and Planning, Tufts University, Medford, Massachusetts 02155, USA. mary.davis@tufts.edu

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Truck drivers face varying fine particulate matter exposures. A new statistical model using structural equation modeling (SEM) accurately predicts these occupational exposures, considering environmental factors.

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Area of Science:

  • Environmental Health
  • Occupational Health
  • Exposure Science

Background:

  • Occupational and environmental exposures to fine particulate matter (PM2.5) are a concern in the U.S. trucking industry.
  • Temporal variability in these exposures can impact driver health and requires accurate assessment.
  • Existing exposure modeling techniques may not fully capture the complexities of real-world occupational settings.

Purpose of the Study:

  • To analyze the temporal variability of occupational and environmental exposures to fine particulate matter (PM2.5) among U.S. truck drivers.
  • To evaluate the predictive capability of a novel multilayer statistical approach, specifically structural equation modeling (SEM), for occupational exposure modeling.
  • To identify key factors influencing exposure levels, including environmental and weather-related variables.

Main Methods:

  • Elemental carbon (EC) mass concentrations in PM2.5 were measured at six U.S. trucking terminals (indoor loading dock, outdoor background, truck cabs) over two sampling periods, up to two years apart.
  • Structural Equation Modeling (SEM) was employed to develop a multilayer statistical model for predicting occupational exposures.
  • Statistical analyses were conducted to assess temporal variability, significance of differences between sampling periods, and the influence of weather patterns.

Main Results:

  • Median EC concentrations varied across locations: indoor loading dock (0.65–0.94 µg/m³), outdoor background (0.46–0.67 µg/m³), and truck cabs (1.09–1.07 µg/m³).
  • A general trend of higher exposures was observed during the second sampling period, though statistically significant differences were infrequent and often linked to weather variations.
  • The SEM approach demonstrated a strong fit for predicting work-related exposures after adjusting for background concentration prediction errors.

Conclusions:

  • Occupational and environmental exposures to fine particulate matter in the trucking industry exhibit temporal variability, influenced by factors like weather.
  • The developed SEM approach shows promise as a robust tool for modeling and predicting occupational exposures in this sector.
  • Further research can refine this approach to better account for dynamic environmental influences on worker exposure.