Determinants of in-cabin exposure to vehicle-induced emissions -

Abstract

Vehicular exhaust is a major air pollution source in urban areas and contributes a large portion of carbon monoxide (CO) and fine particulate matter (PM₂.₅) present in outdoor air that can flow into enclosed micro-environments. Occupants of vehicles are at highest risk of exposure to CO and PM₂.₅ due to their proximity to the exhaust of other vehicles. Attempts at interpreting the high levels of traffic emissions inside the vehicle attributed the problem to a large array of factors including ventilation setting, weather conditions, roadway type, vehicle characteristics and self pollution. However, several determinants remained scarcely, superficially or not yet studied such as out-vehicle sample intake location, indoor to outdoor difference in temperature, pressure and humidity levels and self pollution potential. Also, multivariate regression models reported in the literature on in-vehicle exposure to CO could explain at best 69percent of CO variability inside a car cabin. Hence, the current work aims at improving the understanding of in-cabin exposure to CO and PM₂.₅, and self pollution potential inside vehicles. For this purpose, field testing was conducted using six different vehicles and involving the monitoring of in- and out-vehicle CO and PM₂.₅ concentrations and 25 different potential determinants including air quality, meteorological, temporal, vehicle and traffic related variables. Monitoring data from a total of 119 mobile tests, 120 fume leakage tests, and 36 stationary tests were coupled with mathematical and regression modeling analysis to estimate in-cabin fume leakage rates inside self polluting vehicles and develop models of in-cabin air pollutant concentrations. Air pollution levels were unexpectedly higher in new vehicles compared to old vehicles, with in-cabin air quality most correlated to that of out-vehicle air near the front windshield. Self-pollution was observed at variable rates in three of the six tested vehicles. Significant correlations