Digital twins for wastewater systems are normally fitted once and then frozen. That is a reasonable simplification while process conditions, sensor behaviour and weather patterns remain close to the training period, but it can fail silently when they move. This paper reports RT-ReGenTwin, an online digital twin that learns continuously from public sensor telemetry and reports threshold-exceedance risk as a calibrated probability rather than as a point forecast. Fifteen-minute water-quality records covering 2026-03-13T05:00 to 2026-09-09T05:00 were obtained from two continuous monitoring stations of the United States Geological Survey on effluent-influenced reaches downstream of large municipal treatment works, with matched hourly rainfall from Open-Meteo. Two further stations were probed and not used, one because it reports no continuous water-quality determinand and one because it had no current record. Turbidity, dissolved oxygen and specific conductance were used as receiving-water surrogates for solids loading, oxygen-demand effects and effluent fraction. The twin couples an adaptive random-forest regressor with drift detectors and an adaptive conformal layer. Across four station–determinand combinations, empirical coverage was closer to the nominal 0.90 level for the online calibrator than for the frozen reference in all four cases. Only one of the four combinations recorded any threshold exceedance during the window, so the probabilistic scores and the dispatch analysis rest on that stream alone; for it, coverage was 0.898 versus 0.595 and the Brier score 0.0472 versus 0.0345. Risk-triggered regenerative-capacity dispatch avoided 337 of 590 threshold-exceedance hours at a duty cycle of 0.163, compared with 414 avoided under continuous operation. The results support calibrated risk reporting for streaming environmental monitoring, while the receiving-water surrogates and dispatch simulation should not be interpreted as direct permit-compliance measurements or a treatment experiment.