In　this　paper,　we　propose　a　new　stacked　selective　ensemble-backed　predictor　(SSEP)　to　forecast　the　concentration　of　PM2.5　based　on　the　impact　of　measurements　of　the　known　air　pollutants　and　meteorological　data　on　the　unknown　PM2.5　concentration　over　the　following　48　h.　It　was　found　that　a　single　learner　cannot　validly　uncover　and　model　the　relationship　between　the　future　PM2.5　concentration　and　the　current　and　historical　meteorological　and　pollutant　data,　mainly　because　any　individual　learner　has　limitations,　especially　facing　to　highly　complex　and　ever-changing　environmental　problems,　such　as　PM2.5　prediction.　Ensemble　methods,　which　are　widely　acknowledged　to　yield　strong　generalization　ability　by　boosting　weak　learners,　are　used　in　this　paper　to　solve　the　aforesaid　challenge.　Our　solution,　aligned　with　an　analysis　of　influencing　factors　on　the　future　PM2.5　concentration,　generates　multiple　component　learners　for　aggregation　by　introducing　three　types　of　diversities.　Then,　we　adopt　a　pruning　method　to　remove　the　negative　component　learners　in　each　diverse　type　according　to　dynamic　thresholds,　which　are　determined　by　jointly　considering　the　performance　of　each　individual　learner　and　the　correlations　between　each　pair　of　learners.　A　stacking　technique　is　finally　applied　to　the　selected　positive　component　learners　to　forecast　the　PM2.5　concentration　in　the　future.　Thorough　experiments　demonstrate　the　superiority　of　our　proposed　SSEP　in　contrast　to　relevant　state-of-the-art　models　when　applied　to　PM2.5　prediction.