Modeling of In-vehicle PM(2.5) Exposure Using the Stochastic Human Exposure and Dose Simulation Model

Area of Science:

• Environmental Health Sciences
• Exposure Science
• Air Quality Modeling

Background:

• In-vehicle exposure to fine particulate matter (PM2.5) is a critical component of total personal exposure.
• Existing models like the Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) have limitations in accurately estimating in-vehicle PM2.5 concentrations.
• Factors such as location, vehicle type, and ambient PM2.5 levels influence the ratio of in-vehicle to fixed-site monitor concentrations.

Purpose of the Study:

• To identify and assess factors influencing in-vehicle PM2.5 exposure.
• To review and evaluate the current SHEDS-PM methodology for in-vehicle PM2.5 estimation.
• To explore and propose alternative modeling approaches for more accurate in-vehicle exposure assessment.

Main Methods:

• Reviewed the SHEDS-PM model, which uses linear regression based on ambient PM2.5 concentrations from fixed-site monitors or air quality models.
• Applied SHEDS-PM to estimate PM2.5 exposure for a sample population in Wake County, NC.
• Explored an alternative approach using dispersion modeling for near-road PM2.5 and a mass balance model for in-vehicle concentrations.

Main Results:

• In-vehicle PM2.5 exposure can constitute up to 50% of total exposure for certain individuals, particularly commuters.
• The ratio of in-vehicle to ambient PM2.5 concentrations exhibits significant variability based on various factors.
• The study highlights the substantial contribution of in-vehicle time to overall PM2.5 exposure.

Conclusions:

• In-vehicle exposure is a significant determinant of total PM2.5 exposure, necessitating accurate modeling.
• Recommendations are provided for enhancing the input data for the existing SHEDS-PM model.
• Implementation of an alternative dispersion and mass balance modeling approach is suggested for improved accuracy.