Traffic　flow　prediction　and　control　in　the　active　traffic　control　system　is　considered　as　one　of　the　most　critical　issues　in　Intelligent　Transportation　Systems　(ITS).　Among　the　proposed　AI-based　approaches,　Deep　Learning　(DL)　has　been　largely　applied　while　showing　better　performances.　This　research　improves　macroscopic　traffic　flow　model　METANET　by　establishing　a　graph　convolution　neural　network　(GCN)　to　explicitly　and　more　precisely　incorporate　microscopic　traffic　flow　dynamics.　The　microscopic　emission　model　utilizes　the　feature　extraction　function　of　GCN　to　reduce　the　complexity　of　measuring　the　environmental　profits　for　the　whole　traffic　network.　By　introducing　the　GCN　model　to　facilitate　the　aggregation　of　vehicle　information,　the　proposed　framework　reduces　the　computational　burden　and　obtains　better　optimization　performance.　The　designed　algorithms　are　tested　on　a　microscopic　simulation　platform　based　on　field　data.　The　results　demonstrate　that　the　proposed　control　method　produce　a　more　robust　and　smooth　traffic　flow　environment,　which　leads　to　improved　traffic　efficiency　and　overall　carbon　emissions　of　the　road　network.