We are pleased to inform that the article “On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants” has been published in Water as part of the Special Issue Smart Urban Water Networks and is available online:
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The article is authored by researchers of UCAM and TelLab:
- Ramón Martínez, Juan M. Navarro: Research Group in Advanced Telecommunications (GRITA), Universidad Católica de Murcia (UCAM)
- Nuria Vela, Abderrazak el Aatik: Applied Technology Group to Environmental Health, Universidad Católica de Murcia (UCAM)
- Eoin Murray, Patrick Roche: Research & Development, T.E. Laboratories Ltd. (TelLab).
The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.