Data-driven　models　(DDMs)　are　widely　developed　for　monitoring　wastewater　treatment　processes　(WWTPs).　However,　DDMs,　derived　from　invalid　or　noisy　datasets,　may　fail　to　capture　the　dominant　features　of　WWTPs　and　further　result　in　inferior　monitoring　results.　To　solve　this　issue,　a　dynamic-static　model　is　designed　to　monitor　WWTPs.　Primarily,　the　operational　status　of　WWTPs　is　divided　by　a　receding　condition　partition　strategy,　which　can　prevent　the　mutual　interference　of　fluctuations　among　different　operational　conditions.　As　to　the　operational　conditions　without　invalid　datasets,　the　dynamic　features　of　WWTPs　are　extracted　by　a　dynamic　intelligent　model　(DIM).　DIM　is　built　using　an　interval　type-2　fuzzy　neural　network　to　mimic　the　dynamic　relationship　between　process　variables　accurately.　Meanwhile,　the　unreliable　results　caused　by　invalid　datasets　are　compensated　by　a　static　statistical　model.　This　model　is　developed　to　describe　static　properties　using　historical　datasets,　which　are　copied　to　execute　the　monitoring　of　WWTPs　with　a　similarity　discrimination　mechanism.　Finally,　the　proposed　dynamic-static　model　is　validated　by　several　experiments　in　terms　of　total　nitrogen　removal　under　multiple　operational　conditions.　The　experimental　results　illustrate　that　the　proposed　model　can　ensure　continuous　and　reliable　monitoring　of　WWTPs.