The　modeling　data　used　for　pollutant　emission　concentration　in　the　municipal　solid　waste　incineration　(MSWI)　is　time-varying　due　to　the　concept　drift　phenomenon,　which　is　caused　by　factors　such　as　fluctuations　in　waste　composition,　equipment　wear　and　repair,　and　seasonal　changes.　Thus,　it　is　necessary　to　identify　new　samples　that　can　represent　the　concept　drift　for　pollutant　measurement　model　updating.　However,　the　existing　methods　are　limited　by　the　modeling　samples　true　values,　which　are　difficult　to　be　effectively　applied　to　industrial　processes.　Thus,　a　semi-supervised　concept　drift　detection　method　by　combining　sample　output　space　and　feature　space　is　proposed.　Firstly,　unsupervised　mechanism　based　on　principal　component　analysis　(PCA)　is　used　in　the　sample　feature　space　to　identify　concept　drift　samples.　Then,　semi-supervised　mechanism　based　on　temporal-difference　(TD)　learning　is　used　in　the　sample　output　space　to　label　the　pseudo-true　value　for　the　identified　concept　drift　samples.　Further,　the　Page-Hinkley　detection　method　is　used　to　confirm　the　concept　drift　samples.　Finally,　the　new　samples　obtained　by　the　above　steps　are　combined　with　historical　samples　to　update　the　measurement　model.　The　simulation　results　based　on　synthetic　and　real　industrial　process　data　sets　show　that　the　proposed　method　has　better　performance　than　the　existing　methods.　Moreover,　the　cost　of　sample　annotation　is　effectively　reduced　and　the　drift　adaptability　of　the　measurement　model　is　enhanced.　Copyright　©　2022　Acta　Automatica　Sinica.　All　rights　reserved.