In　recent　years,　air　quality　has　become　a　severe　environmental　problem　in　China.　Since　bad　air　quality　brought　significant　influences　on　traffic　and　people＇s　daily　life,　how　to　predict　the　future　air　quality　precisely　and　subtly,　has　been　an　urgent　and　important　problem.　In　this　paper,　a　Spatio-Temporal　Extreme　Learning　Machine　(STELM)　method　is　proposed　for　air　quality　prediction.　STELM　considers　temporal　and　spatial　characteristics　of　air　quality　data　and　related　meteorological　data,　constructs　a　prediction　model　based　on　ELM,　and　realizes　air　quality　prediction　with　more　than　80%　precision.　A　prototype　system　is　implemented　and　the　experiments　on　practical　air　quality　data　in　Beijing　validate　the　effectiveness　of　our　method　and　system.