Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River

Monitoring water quality in the Amazon River remains a significant challenge due to limited accessibility, high sediment loads, intermittent connectivity, and the lack of continuous data in remote regions. This study presents the development and experimental validation of an IoT-based system for real-time water quality monitoring. The platform integrates an STM32WL-based embedded architecture with multiparameter sensing, LoRaWAN communication, and configurable monitoring strategies to enable autonomous operation in dynamic environments. The system was validated through a comparative study involving 698 manually collected samples over eight months and 49,570 automated measurements collected during a three-month field deployment. The evaluation considered measurement consistency, variability and operational autonomy based on CONAMA Resolution No. 357/2005. The results showed good agreement between manual and automated measurements, with MAE/RMSE values of 0.18/0.20 °C for water temperature, 0.36/0.44 for pH, and 12.99/20.09 NTU for turbidity. Additionally, the energy analysis demonstrated autonomous operation under variable solar irradiance, achieving self-sufficiency under typical conditions and maintaining operation for up to 4.9 days without solar input. Taken together, the study provides a robust and scalable framework for continuous monitoring in sediment-rich tropical river systems.