Academic Journal
Uncovering hidden industrial wastewater discharges in urban rivers via deep learning-enhanced fiber-optic distributed acoustic sensing.
| Τίτλος: | Uncovering hidden industrial wastewater discharges in urban rivers via deep learning-enhanced fiber-optic distributed acoustic sensing. |
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| Συγγραφείς: | Chen Z; School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China., Zhang CC; School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China. Electronic address: zhang@nju.edu.cn., Rossi M; Faculty of Engineering, LTH, Lund University, Lund, SE-22100, Sweden., Wang Z; School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China., Zhao M; School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China., Wei G; Suzhou NanZee Sensing Technology Ltd., Suzhou, 215123, China., Shi B; School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China. Electronic address: shibin@nju.edu.cn. |
| Πηγή: | Environmental research [Environ Res] 2026 Oct 01; Vol. 307, pp. 125534. Date of Electronic Publication: 2026 Aug 21. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0953 (Electronic) Linking ISSN: 00139351 NLM ISO Abbreviation: Environ Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2000- > : Amsterdam : Elsevier Original Publication: New York, Academic Press. |
| Ιατρικοί όροι (MeSH): | Rivers*/chemistry , Environmental Monitoring*/methods , Wastewater*/analysis , Deep Learning* , Acoustics* , Fiber Optic Technology*, China |
| Περίληψη: | 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. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Deep learning; Distributed acoustic sensing (DAS); Environmental monitoring; Fiber-optic sensing; Illicit wastewater discharge; Pollution source tracking |
| Substance Nomenclature: | 0 (Wastewater) |
| Entry Date(s): | Date Created: 20260821 Date Completed: 20260912 Latest Revision: 20260912 |
| Update Code: | 20260913 |
| DOI: | 10.1016/j.envres.2026.125534 |
| PMID: | 42628670 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1096-0953 |
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| DOI: | 10.1016/j.envres.2026.125534 |