Evaluation of the IoT Device for Nitrate and Nitrite Long-Term Monitoring in Wastewater Treatment Plants

Water pollution is an issue of global concern that requires continuous monitoring to maintain the integrity of surface and groundwater. This growing concern about environmental contamination has prompted the development and use of physical and chemical systems to control water quality in situ in most ecosystems, including wastewater treatment plants (WWTPs). In most water reuse projects, effluents must meet applicable water quality standards, regardless of whether they are discharged to surface or groundwater bodies, for irrigation or industrial reuse. In this article, an evaluation of the performance of the Internet-of-Things (IoT) device for long-term monitoring and detection of nitrate and nitrite in wastewater is presented. This device is integrated in the IoT system with these main components: an ion chromatography (IC) sensor to provide accurate data, together with mechanical elements for water sample acquisition and electronic circuits for the control of the whole monitoring process, a communication module to send real-time information, and a cloud software platform to store, analyze, and visualize the obtained water quality metrics. This evaluation was carried out by comparing traditional laboratory results with data captured by the remote device during a long-term measurement campaign at three WWTPs located in Murcia, Spain. The results show a satisfactory validation against standardized laboratory values, demonstrating the long-term measurement capability of the system. Specifically, the coefficient of determination of the regression between laboratory and device procedures reaches 96.7% on average in the three WWTPs for both nitrate and nitrite and influent and effluent streams.

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Publication Details

Journal
IEEE Sensors Journal
Published
2024-12-16
DOI
https://doi.org/10.1109/jsen.2024.3512355
Citations
3
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.60

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article

Evaluation of the IoT Device for Nitrate and Nitrite Long-Term Monitoring in Wastewater Treatment Plants

Juan M. Navarro, Abderrazak El Aatik, Nuria Vela, Antonio Pita et al.
3 citations
IEEE Sensors Journal
Water Quality Monitoring Technologies
0.60
article

Evaluation of the IoT Device for Nitrate and Nitrite Long-Term Monitoring in Wastewater Treatment Plants

Juan M. Navarro, Abderrazak El Aatik, Nuria Vela, Antonio Pita, Ramón Martínez
article en
3 citations

Abstract

Water pollution is an issue of global concern that requires continuous monitoring to maintain the integrity of surface and groundwater. This growing concern about environmental contamination has prompted the development and use of physical and chemical systems to control water quality in situ in most ecosystems, including wastewater treatment plants (WWTPs). In most water reuse projects, effluents must meet applicable water quality standards, regardless of whether they are discharged to surface or groundwater bodies, for irrigation or industrial reuse. In this article, an evaluation of the performance of the Internet-of-Things (IoT) device for long-term monitoring and detection of nitrate and nitrite in wastewater is presented. This device is integrated in the IoT system with these main components: an ion chromatography (IC) sensor to provide accurate data, together with mechanical elements for water sample acquisition and electronic circuits for the control of the whole monitoring process, a communication module to send real-time information, and a cloud software platform to store, analyze, and visualize the obtained water quality metrics. This evaluation was carried out by comparing traditional laboratory results with data captured by the remote device during a long-term measurement campaign at three WWTPs located in Murcia, Spain. The results show a satisfactory validation against standardized laboratory values, demonstrating the long-term measurement capability of the system. Specifically, the coefficient of determination of the regression between laboratory and device procedures reaches 96.7% on average in the three WWTPs for both nitrate and nitrite and influent and effluent streams.

IEEE Sensors JournalVol. 25(4)
Universidad Católica de Murcia (ES)
Ministerio de Ciencia, Innovación y Universidades, European Commission, Comunidad Autónoma de la Región de Murcia, LIFE programme
Clean water and sanitation
Openalex Percentile: Top 32%
Water Quality Monitoring Technologies
0.60
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