Global　warming　caused　by　greenhouse　gas　(GHG)　is　receiving　increasingly　attention　from　all　over　the　world,　and　urban　transportation　is　a　significant　source　of　greenhouse　gas　and　pollutant　emission.　However,　the　research　on　traffic　state　of　urban　road　networks　(URNs)　based　on　sparse　floating　vehicle　data　(FVD)　is　insufficient.　Therefore,　we　mainly　utilize　big　data　techniques　to　explore　the　congestion　and　pollutant　emission　of　URN　with　FVD.　Firstly,　the　location　of　vehicles　is　identified　and　matched　with　the　URN.　We　then　grid　the　FVD　and　city　maps　to　more　accurately　identify　areas　of　congestion　and　emission　in　later　section.　Following　this,　we　use　the　congestion　index　and　K-means　clustering　algorithm　to　evaluate　the　traffic　state　over　time,　pollutant　emission　is　calculated　based　on　emission　calculation　standards　and　carbon　emission　is　estimated　by　using　the　fuel　consumption-speed　model.　The　results　indicate　that　congestion　and　emission　are　very　severe　during　peak　hours　(e.g.,　8:00　a.m.),　particularly　in　some　transportation　hub　areas,　such　as　high-speed　rail　stations.　During　off-peak　hours　(e.g.,　11:00　p.m.),　congestion　and　emission　are　relatively　lower.　The　negative　correlation　between　congestion　index　and　emission　is　also　revealed.　This　study　provides　some　practical　approaches　to　more　accurately　estimate　the　overall　urban　traffic　state　by　using　sparse　traffic　data,　and　may　offer　support　to　urban　traffic　managers　in　managing　traffic　congestion　and　pollutant　emissions.