Vehicle emissions are a major source of particulate matter (PM2.5) in urban areas with emissions from on-road vehicles significantly impacting human health and the environment. Emission simulators and near-road ambient studies are used to estimate PM2.5 exposure; however, studies are emerging that emission simulators underestimate the vehicle emitted PM2.5 observed near-road. First, a statistical model examination of the relationship between weather parameters, traffic data, and the near-roadway PM2.5 yielded R2< 0.24 indicating that something other than traffic and weather data was needed to better predict near-road PM2.5; such as the gas-particle (G/P) partitioning of the organic PM2.5. The underestimation is due to emission simulators treating all PM2.5 as non-volatile and not accounting for the G/P partitioning of organics. Next, this dissertation describes a PM2.5 correction factor (CF) to account for G/P partitioning of organics emitted from on-road gasoline and diesel vehicles. The CF accounts for sampling dilution and temperature, ambient temperature, background PM2.5, distance from the vehicle, and the vehicle’s initial reactive organic gas (ROGi) concentration and elemental carbon to organic carbon (EC:OC). Using the CF, a look-up table and four Random Forest (RF) models were created. In building the RF it was found that generally the ambient temperature, vehicle’s EC:OC and ROGi concentration were the most important variables in predicting the CF. Implementing the CF with emission simulators and/or dispersion models would allow for a more realistic PM2.5 concentration thereby improving our understanding of how vehicle emissions affect human health, air quality, and the environment. Additionally, a case study is included within that evaluates the impacts of exposure-based routing in a Southern California disadvantaged community and demonstrates how the CF can be applied. Results indicated that re-routing heavy-duty diesel trucks along “low exposure routes” (LER) could reduce inhaled PM2.5 by 14+% depending on meteorological and traffic conditions. The reduction in PM2.5 inhalation could increase by an additional 50+% by selecting LER that are over 10m from the sensitive populations, and when accounting the CF.