The rapid growth of traffic congestion has led to an increased level of emissions and energy consumption in urban areas. Well designed infrastructure and traffic controllers along with more efficient vehicles and policy measures are required to mitigate congestion and thus reduce transport emissions. In order to evaluate how changes in the traffic system affect energy use and emissions, traffic analysis tools are used together with emission models. In large urban areas emission models mainly rely on aggregated outputs from traffic models, such as the average link speed and flow. Static traffic models are commonly used to generate inputs for emission models, since they can efficiently be applied to larger areas with relatively low computational cost. However, in some cases their underlying assumptions can lead to inaccurate predictions of the traffic conditions and hence to unreliable emission estimates. The aim of this paper is to investigate and quantify the errors that static modeling introduces in emission estimation and subsequently considering the source of those errors, to suggest and evaluate possible solutions. The long analysis periods that are commonly used in static models, as well as the static models' inability to describe dynamic traffic flow phenomena can lead up to 40 % underestimation of the estimated emissions. In order to better estimate the total emissions, we propose the development of a post processing technique based on a quasi-dynamic approach, attempting to capture more of the excess emissions created by the temporal and spatial variations of traffic conditions