The　concentration　of　carbon　monoxide　(CO)　emissions　is　intricately　linked　to　the　operational　status　and　combustion　efficiency　of　municipal　solid　waste　incineration　(MSWI)　processes,　which　are　characterized　by　complex,　dynamic,　and　time-varying　behaviors.　In　order　to　tackle　the　challenge　of　predicting　CO　emissions,　this　article　introduces　a　novel　method　based　on　nested　dual-window　drift　detection　(NDWDD).　Initially,　a　typical　sample　pool　(TSP)　is　generated　using　the　$k$　-means　algorithm.　An　offline　prediction　model　combining　long　short-term　memory　(LSTM)　with　a　feature　space　drift　detection　model　based　on　robust　principal　component　analysis　(RPCA)　is　then　developed.　The　control　limit　for　error　space　prediction　accuracy　is　set　using　the　fast　Hoeffding　drift　detection　method　(FHDDM).　The　NDWDD　employs　a　unique　combination　of　external　feature　space　drift　detection　and　nonparametric　drift　detection　within　the　internal　error　space,　using　a　nested　mechanism　to　enhance　detection　efficiency　and　reduce　the　influence　of　inherent　noise　factors　in　industrial　processes.　Finally,　the　dual-space　drift　sample　collection　facilitates　updates　to　the　TSP,　historical　prediction　models,　RPCA　model,　and　FHDDM　control　limits.　Experimental　results　from　a　Beijing　MSWI　power　plant　demonstrate　that　the　proposed　method　can　predict　CO　emissions　both　robustly　and　effectively.