This　paper　presents　a　macroscopic　dynamic　modeling　framework　for　road　traffic　emissions　in　an　urban　city　that　has　numerous　central　business　districts　(CBDs).　The　framework　combines　a　two-dimensional　(2D)　continuum　dynamic　traffic　assignment　(DTA)　model　that　captures　macroscopic　features　of　traffic　flow　and　a　microscopic　instantaneous　emission　model　associated　with　the　instantaneous　speed　and　acceleration　of　traffic　flow.　A　heterogeneous　traveling　population　continuously　distributed　in　space　is　classified　according　to　the　trip　destinations　chosen　by　travelers.　Each　copy　of　flow　tends　to　select　an　optimal　route　with　the　minimum　cost　to　destination　by　means　of　instantaneous　traffic　information.　For　each　copy　of　flow,　an　unstructured　finite　volume　method　as　well　as　a　fast　sweeping　method　is　designed　to　solve　the　proposed　model.　In　order　to　validate　the　feasibility　of　the　model　and　algorithm,　a　case　study　is　conducted　as　an　example　to　study　the　macroscopic　characteristics　of　traffic　flow　and　predict　traffic-related　pollutants　like　NOx,　VOC,　CO2　and　PM　in　urban　areas.　©　2022　Elsevier　B.V.