The　concentration　of　pollutant　emissions　during　the　municipal　solid　waste　incineration　(MSWI)　process　has　a　significant　global　impact　on　the　atmospheric　environment.　Developing　effective　pollutant　emission　models　to　support　optimization　for　emission　reduction　is　a　critical　challenge　that　must　be　addressed.　To　address　the　challenges　of　high　uncertainty　and　poor　interpretability　in　pollutant　emission　concentration　models　for　the　MSWI　process,　this　article　proposes　a　novel　method　for　modeling　multi-pollutant　emission　concentrations　using　a　virtual-real　data-driven　method.　First,　a　whole-process　numerical　simulation　model　for　the　MSWI　process　is　developed　using　a　multi-software　coupling　strategy.　Virtual　simulation　mechanism　dataset　under　diverse　operating　conditions　is　generated　through　a　combination　of　orthogonal　experimental　design　and　implementation.　Subsequently,　to　tackle　the　challenge　of　limited　sample　size　resulting　from　the　high　cost　of　simulation,　virtual　sample　generation　(VSG)　is　utilized　to　enhance　the　dataset.　Finally,　a　virtual-real　data-driven　multi-pollutant　emission　concentration　model　is　developed,　leveraging　the　Interval　Type-2　Fuzzy　Broad　Learning　System　(IT2FBLS)　and　the　Linear　Regression　Decision　Tree　(LRDT)　algorithm　with　a　main-compensation　mechanism.　The　proposed　methodology　is　validated　using　data　from　an　MSWI　power　plant　in　Beijing.　©　2025