Uncovering hidden industrial wastewater discharges in urban rivers via deep learning-enhanced fiber-optic distributed...

Illicit wastewater discharges from concealed outfalls threaten urban river ecosystems, often evading conventional monitoring. This study introduces an intelligent framework that integrates distributed acoustic sensing (DAS) with a Residual Network (ResNet) deep learning model to overcome this challenge. By deploying a fiber-optic sensing cable as a dense acoustic sensor array in a Chinese river, we trained a ResNet model to distinguish discharge signals from complex ambient river noise (including vessel traffic and hydrodynamics), achieving over 92% identification accuracy. This system successfully located a concealed, submerged textile factory outfall with meter-scale precision, demonstrating its capability to intercept high-risk pollutants. Analysis of the outfall's acoustic signature revealed continuous, low-frequency (<10 Hz) discharge patterns, including three brief interruptions attributed to routine equipment inspections. The re-initiation of discharge after these interruptions produced a transient energy peak consistent with jet flow pressure dynamics, showcasing distinct frequency characteristics between transient and steady-state responses. This work demonstrates that fusing DAS with deep learning offers a powerful, data-driven solution for regulators to detect, locate, and characterize hidden pollution sources, ultimately advancing environmental risk control in complex aquatic environments.