In　order　to　improve　the　forecasting　accuracy　of　atmospheric　pollutant　concentration,　a　prediction　model　of　atmospheric　PM2.5　and　nitrogen　dioxide　(NO2)　concentration　based　on　support　vector　regression　(SVR)　is　established.　Quantum-behaved　particle　swarm　optimization　(QPSO)　algorithm　is　used　to　select　the　optimal　parameters　influencing　the　performance　of　SVR.　And　in　order　to　improve　the　problem　that　the　fixed　SVR　model　is　difficult　to　adapt　to　the　highly　nonlinear　process,　a　simple　online　SVR　based　on　re-modeling　method　is　proposed　instead　of　the　fixed　one.　According　to　hourly　PM2.5　and　NO2　concentrations　and　meteorological　conditions　from　May　2014　to　April　2015　in　Wanliu　Monitoring　Station　of　Beijing　in　China,　the　experiment　is　carried　out　based　on　the　data　of　3　months.　Meanwhile,　PM2.5　concentration　is　predicted　by　three　different　prediction　methods,　including　the　recursive　prediction　method,　direct　prediction　method,　and　online　direct　prediction　method.　The　results　show　that　the　online　direct　prediction　method　is　the　most　accurate　in　the　three　prediction　methods.　In　addition,　compared　with　original　particle　swarm　optimization　(PSO)　algorithm,　QPSO　algorithm　is　tested　more　efficiently　for　the　improvement　of　global　search　ability　and　robustness　during　the　procedure　of　parameter　selection.　Moreover,　the　hybrid　QPSO-SVR　model　proposed　in　this　paper　has　higher　prediction　accuracy　and　less　computational　time　compared　with　the　PSO-SVR　model,　genetic　algorithm　(GA)-SVR　model,　and　grid　search　(GS)-SVR　model,　which　indicates　reliability　and　effectiveness　of　the　QPSO-SVR　model　in　prediction　of　these　two　pollutant　concentrations.