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Vehicle-induced emissions constitute a major source of air pollutants, particularly in urban areas, where heavy traffic is common occurrence. Contaminated air can flow into enclosed micro-environments, including vehicle compartments. Among various exhaust emissions, carbon monoxide (CO) was the first indicator examined in passenger compartments. This paper presents a critical review of worldwide research work conducted to characterize CO exposure inside vehicles. Measurement methodologies for field testing are presented alongside impacts of various factors on in-vehicle CO exposure, including outdoor CO levels, roadway type, ventilation mode, weather conditions, and vehicle characteristics. Results of in-vehicle CO exposure measurements in various cities are compared. Modeling efforts to characterize in-vehicle CO exposure and relate it to potential explanatory factors are also discussed. Based on the review findings, limitations and future needs are defined.

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In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO X ), carbon monoxide (CO), fine particulate matter (PM 10 ) and sulfur dioxide (SO 2 )) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO X , CO, PM 10 and SO 2 , respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.Implications: In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM 10 , and SO 2 .