Modeling Particle Exposure in U.S. Trucking Terminals

Details

• Personal Author:
  Davis ME ; Garshick E ; Hart JE ; Laden F ; Smith, Thomas J.
• Description:
  Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the US trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor "background" location. Using the SEM method, we predict: 1) personal exposures as a function of work related exposure and smoking status; 2) work related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and 3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling. [Description provided by NIOSH]
• Subjects:
  Environmental Exposure Occupational Health Particulate Matter Risk Assessment Risk Factors Safety Vehicle Emissions
• Keywords:
  Carbon Diesel Emissions Diesel Exhaust Environmental Exposure Exhaust Gases Exposure Levels Indoor Environments Models Monitoring Systems Outdoors Particulates Risk Factors Trucking

  More +
• ISSN:
  0013-936X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Massachusetts ; OSHA Region 1
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  40
• Issue:
  13
• NIOSHTIC Number:
  nn:20055149
• Citation:
  Environ Sci Technol 2006 Jul; 40(13):4226-4232
• Contact Point Address:
  M. E. Davis, Department of Environmental Health, Harvard School of Public Health
• Email:
  medavis@hsph.harvard.edu
• CAS Registry Number:
  Carbon (CAS RN 7440-44-0)
• Federal Fiscal Year:
  2006
• Performing Organization:
  Harvard School of Public Health
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Environmental Science and Technology
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:b608caa17503263101fdc5d90b500e3840aaf5c5ff51f23bdfe88e5346e04ac85ce9efe051bb09f7f171b49fe72de9509ec62aa3325fb9177c83da6febb21f07
• Download URL:
  https://stacks.cdc.gov/view/cdc/217511/cdc_217511_DS1.pdf
• File Type:
  [PDF - 95.43 KB ]