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Monitoring Water Quality: Suggestions and Prospects
Summary
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Water quality is one of the most important variables affecting human life. Usually, it is necessary to evaluate the water's quality right away. If the area to be investigated is large, testing at several places will be necessary. Repeatedly evaluating the water quality will be challenging and time-consuming. A real-time monitoring system is required to protect the water and check its condition to prevent contamination. The water quality is tracked and shown using environmental sensors. LoRa technology (Long Range) refers to a group of wide-area communication technologies with enhanced obstacle occlusion, longer signal propagation distances, and the Node-RED application. It entails monitoring and collecting information on variables including air pollution, turbidity, pH, electrical conductivity, and climate that have an impact on water quality. The study's microcontroller processes the sensor data before wirelessly sending it to the database structure, where it is shown on the Node-RED display. A Node-RED dashboard and real-time water quality monitoring are features of the IoT-based monitoring system. Future directions will concentrate on satellite technology, machine learning for emerging contaminants (like microplastics), and integrated watershed management. Water quality monitoring is moving toward real-time, datadriven approaches using IoT, AI, and smart sensors, enabling faster response to pollution. However, this requires better integration of chemical and biological data, stakeholder involvement, and standardized protocols for diverse contaminants.
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