This study presents a modeling framework to quantify the complex roles that traffic, seasonality, vehicle characteristics, ventilation, meteorology, and ambient air quality play in dictating in-vehicle commuter exposure to CO and PM 2.5 . For this purpose, a comprehensive one-year monitoring program of 25 different variables was coupled with a multivariate regression analysis to develop models to predict in-vehicle CO and PM 2.5 exposure using a database of 119 mobile tests and 120 fume leakage tests. The study aims to improve the understanding of in-cabin exposure, as well as interior-exterior pollutant exchange. Model results highlighted the strong correlation between out-vehicle and in-vehicle concentrations, with the effect of ventilation type only discerned for PM 2.5 levels. Car type, road conditions, as well as meteorological conditions all played a significant role in modulating in-vehicle exposure. The CO and PM 2.5 exposure models were able to explain 72 and 92% of the variability in measured concentrations, respectively. Both models exhibited robustness and no-evidence of over-fitting.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Waste Management and Disposal
- In-vehicle exposure
- Multivariate analysis