Water pollution continues to pose a significant environmental and public health challenge in many developing nations, where insufficient monitoring and slow detection of contamination often exacerbate waterborne diseases, ecosystem deterioration, and economic setbacks. This study presents the development and preliminary deployment of a low-cost, data-driven internet of things (IoT) system for monitoring water quality. It enables the near real-time measurement of temperature, pH, and electrical conductivity (EC) in surface waters. A specialized IoT sensor kit was assembled using commercially available components and installed at the Opa Reservoir, Obafemi Awolowo University (OAU), Ile-Ife, Nigeria. Data collection occurred over a four-day timeframe, utilizing a LoRaWAN communication system with remote cloud-based visualization and data storage. The findings of this pilot study indicated that the system consistently transmitted water quality information at three-minute intervals, showcasing the feasibility of IoT solutions for continuous monitoring. The 4-day dataset analysed showed that temperature, pH and EC ranged from 24.24 to 26.67 °C; 5.92 to 7.69; and 149.33 to 192.37 µS/cm with mean ± SD values of 25.23 ± 0.47 °C; 7.25 ± 0.29; and 172.18 ± 7.76 µS/cm, respectively. The study emphasizes potentials for expanding the system, incorporating advanced sensors for measuring more water parameters, and transitioning to AI-based predictive analytics, thereby aiding decision-making for water resource managers. This research presents the IoT-based monitoring of temperature, electrical conductivity, and pH in surface water resources of economic importance in Africa. The proof-of-concept study also demonstrates a replicable model for sustainable hydroinformatics and water quality management in low- and middle-income countries.
Gizaw et al. (Thu,) studied this question.
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