As　one　of　the　by-products　of　the　municipal　solid　waste　incineration　(MSWI)　process,　dioxin　(DXN)　is　not　only　difficult　to　detect　but　also　potential　harm　to　humans　and　the　environment.　The　article　proposes　a　method　for　detecting　DXN　emissions.　It　addresses　the　challenge　of　poor　generalization　performance　in　detection　models　due　to　the　dynamic　nature　of　the　MSWI　process.　Firstly,　the　method　constructs　a　historical　soft　sensor　model　and　a　drift　detection　model　based　on　historical　samples.　Secondly,　it　assesses　online　samples　for　drift　detection.　When　drift　is　detected,　it　calculates　a　distance　threshold　to　prune　the　ensemble　model.　Subsequently,　it　reconstructs　new　ensemble　sub-models　and　integrates　them　with　the　historical　model　to　form　a　preliminary　online　ensemble　model.　Finally,　it　conducts　local　pruning　and　reconstruction　on　each　sub-model,　refining　the　final　online　ensemble　model　based　on　weighted　posterior　information.　The　efficacy　of　this　approach　is　validated　using　synthetic,　benchmark,　and　real　DXN　datasets　from　an　MSWI　plant　in　Beijing.　　©　2025　IEEE.