A new perspective on real-time monitoring and function interpretation of nitrification based on machine learning and...

The stability of nitrification driven by nitrifying bacteria is susceptible to fluctuations in operating parameters. The traditional physicochemical parameters cannot directly reveal the metabolic activity of microorganisms; it remains essential to integrate direct characterization of microbial metabolic functional status for real-time monitoring of the variation during the nitrification process. Therefore, this study proposes a method that combines the proton-transfer-reaction time-of-flight mass spectrometry with machine learning. Based on 28,396 groups of mass spectrometry and water quality parameter data collected during the operation of the air-lift reactor, a real-time monitoring and analysis system centered on the mass-to-charge ratio (m/z) signals of microbial volatile organic compounds (mVOCs) during nitrification was constructed. XGBoost model demonstrates excellent analytical capabilities for key process parameters (test set R²: 0.96 for temperature, 0.95 for DO, 0.99 for pH) and specific characteristic m/z signals, particularly Mz42 and Mz87, show strong correlations with ΔNH₄⁺-N (R² up to 0.83). This study further revealed the specific association between characteristic m/z signals and microbial metabolic states: an increase in Mz42 can indicate inhibition of nitrification (decrease in Nitrospira) and proliferation of Thiothrix, while Mz45 can effectively reflect Sphaerotilus proliferation. This study provides a new technical path for the paradigm shift of wastewater treatment monitoring from the traditional "water quality parameter monitoring" to "metabolic state monitoring".