Models and information and communication technology (ICT) can assist in the effective supervision of urban receiving water bodies and drainage systems. Single model-based decision tools, e.g., water quality models and the pollution source identification (PSI) method, have been widely reported in this field. However, a systematic pathway for environmental decision support system (EDSS) construction by integrating advanced single techniques has rarely been reported, impeding engineering applications. This paper presents an integrated supervision framework (UrbanWQEWIS) involving monitoring-early warning-source identification-emergency disposal to safeguard the urban water quality, where the data, model, equipment and knowledge are smoothly and logically linked. The generic architecture, all-in-one equipment and three key model components are introduced. A pilot EDSS is developed and deployed in the Maozhou River, China, with the assistance of environmental Internet of Things (IoT) technology. These key model components are successfully validated via in situ monitoring data and dye tracing experiments. In particular, fluorescence fingerprint-based qualitative PSI and Bayesian-based quantitative PSI methods are effectively coupled, which can largely reduce system costs and enhance flexibility. The presented supervision framework delivers a state-of-the-art management tool in the digital water era. The proposed technical pathway of EDSS development provides a valuable reference for other regions.
Keywords: Anomaly detection; Data-driven model; Drainage system; Early warning; Pollution source identification; Smart city; Urban water environment; Water quality.
Copyright © 2023 Elsevier Ltd. All rights reserved.