Markov chain traffic assignment (MCTA) is a relatively recent approach for modeling traffic conditions in a road network. It results in traffic density measurements that express congestion levels in the network at the road level. While the stochastic approach is powerful analytically, the resulting traffic density does not readily lend itself to emissions estimation. Typically, average-speed emission models are utilized to estimate emissions on an aggregate network level. However, while this is easily done in user equilibrium (UE) models using the Bureau of Public Roads (BPR) relationship to derive vehicles’ average speed from traffic flow, no such relationship exists for traffic density. We highlight the mathematical challenge here and propose an approach to estimate vehicles’ average speed based on traffic density obtained from MCTA. The average speeds are then used to estimate vehicles’ Greenhouse Gas emissions using average-speed emission estimation models. This approach can be applied on a road level or aggregated to a network level. We utilize the BPR relationship to build a linear piece-wise approximation function and illustrate its use through two widely used average-speed emission estimation models: TRANSYT-7F and COPERT v5.