Since the transportation sector is highly responsible for U.S. fuel consumption and emissions, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Also, high emitting vehicles need to be considered in the modeling of mobile-source emissions, because they contribute to a large portion of the total emissions, although they comprise a small portion of the vehicle fleet. In the context of this research, the thesis quantifies the environmental impacts of roadway grades and proposes a procedure that can enhance the screening of high emitting vehicles.

First, the study quantifies the environmental impacts of roadway grades. Although roadway grades are known to affect vehicle fuel consumption and emission rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this study addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emission rates using the INTEGRATION microscopic traffic simulation software. The energy and emission impacts are quantified for various cruising speeds, under stop and go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant with increases in fuel consumption and emission rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to various forms of advanced traffic management systems.

Second, the study proposes a new procedure for estimating vehicle mass emissions from remote sensing device measurements that can be used to enhance HEV screening procedures. Remote Sensing Devices (RSDs) are used as supplementary tools for screening high emitting vehicles (HEVs) in the U.S. in order to achieve the National Ambient Air Quality Standards (NAAQS). However, tailpipe emissions in grams cannot be directly measured using RSDs because they use a concentration-based technique. Therefore, converting a concentration measurement to mass emissions is needed. The research combines the carbon balance equation with fuel consumption estimates to make the conversion. In estimating vehicle fuel consumption rates, the VT-Micro model and a Vehicle Specific Power (VSP)-based model (the PERE model) are considered and compared. The results of the comparison demonstrate that the VSP-based model under-estimates fuel consumption at 79% and produces significant errors (R2 = 45%), while the VT-Micro model produces a minimum systematic error of 1% and a high degree of correlation (R2 = 87%) in estimating a sample vehicle's (1993 Honda Accord with a 2.4L engine) fuel consumption. The sample vehicle was correctly identified 100%, 97%, and 89% as a normal vehicle in terms of HC, CO, NOX emissions, respectively, using its in-laboratory measured emissions. Its estimated emissions yielded 100%, 97%, and 88% of correct detection rates in terms of HC, CO, NOX emissions, respectively. The study clearly demonstrates that the proposed procedure works well in converting concentration measurements to mass emissions and can be applicable in the screening of HEVs and normal emitting vehicles for several vehicle types such as sedans, station wagons, full-size vans, mini vans, pickup trucks, and SUVs.