The transportation sector has a significant impact on the environment both nationally and globally since it is a major vehicle fuel consumption and emissions contributor. These emissions are considered a major environmental threat. Consequently, decision makers desperately need tools that can estimate vehicle emissions accurately to quantify the impact of transportation operational projects on the environment. Microscopic fuel consumption and emission models should be capable of computing vehicle emissions reliably to assist decision makers in developing emission mitigation strategies. However, the majority of current state-of-the-art models suffer from two major shortcomings, namely; they either produce a bang-bang control system because they use a linear fuel consumption versus power model or they cannot be calibrated using publicly available data and thus require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in state-of-the-art emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not result in a bang-bang control and can be calibrated using publicly available vehicle and road pavement parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive Power-Based Fuel consumption and Emission Model (VT-CPFEM). The study proposes two square root models where the first model structure is a cubic polynomial function that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the cubic function of the VT-CPFM fuel estimates with a linear speed term. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. Moreover, the model is tested and compared with existing models to demonstrate the robustness of the model. Furthermore, the performance of the model was further investigated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the efficacy of the model in replicating empirical observations reliably and simply with only two parameters.