Air　quality　is　currently　arousing　drastically　increasing　attention　from　the　governments　and　populace　all　over　the　world.　In　this　paper,　we　propose　a　heuristic　recurrent　air　quality　predictor　(RAQP)　to　infer　air　quality.　The　RAQP　exploits　some　keymeteorology-　and　pollution-related　variables　to　infer　air　pollutant　concentrations　(APCs),　e.g.　the　fine　particulatematter　(PM2.5).　It　is　natural　that　the　meteorological　factors　and　APCs　at　the　current　time　have　strong　influences　on　air　quality　the　next　adjacent　moment,　that　is　to　say,　there　exist　high　correlations　between　them.　With　this　consideration,　applying　simple　machine　learners　to　the　current　meteorology-　and　pollution-related　factors　can　reliably　predict　the　air　quality　indices　at　a　time　later.　However,　owing　to　the　nonlinear　and　chaotic　reasons,　the　above　correlations　decline　with　the　time　interval　enlarged.　In　such　cases,　it　fails　to　forecast　the　air　quality　after　several　hours　by　only　using　simplemachine　learners　and　the　current　measurements　of　meteorology-　and　pollution-related　variables.　To　solve　the　problem,　our　RAQP　method　recurrently　applies　the　1-h　prediction　model,　which　learns　the　current　records　of　meteorology-　and　pollution-related　factors　to　predict　the　air　quality　1　h　later,　to　then　estimate　the　air　quality　after　several　hours.　Via　extensive　experiments,　results　confirm　that　the　RAQP　predictor　is　superior　to　the　relevant　state-of-the-art　techniques　and　nonrecurrent　methods　when　applied　to　air　quality　prediction.