IoT-Based Digital Twin for Freshwater Pollution Monitoring: A Use Case of River Derwent

This paper presents an IoT-enabled digital twin framework designed to enhance real-time monitoring and management of freshwater pollution. The framework integrates distributed IoT sensors to collect high-resolution data on dissolved oxygen (DO), temperature, and pollution dynamics, enabling the timely detection of ecological stressors. A four-layer architecture, comprising device, virtualisation, aggregation, and service layers, facilitates scalable data processing, visualisation, and stakeholder engagement. Key contributions include empirical validation of the IoT-enabled digital twin framework to monitor fresh water pollution, a modular system for anomaly detection and historical trend analysis, and actionable insights derived from spatially distributed edge nodes. The results highlight the inverse correlation between temperature and DO levels, disrupted during storm events by abrupt oxygen crashes (e.g. below 4 mg/l) and the critical role of edge node deployment in the river bed. The digital twin monitoring, recollection, and predictive modules collectively support proactive water quality management, identifying pollution gradients and seasonal patterns. Testing and evaluation on the Derwent river shows the system’s practical efficacy in reducing environmental risks and informing sustainable interventions.