IoT-Based Real-Time Air and Water Quality Monitoring System Using ESP 32 and ThingSpeak

Authors:
P. Srinivasan, J. Joe Fredlin, M. Shankara Subramanian, R. Aadi Shankar

Addresses:
Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

Abstract:

Being conscious of the environment is more important than ever in the modern, fast-paced world.  In most cases, centralised systems are the ones that inform us about the safety of our surroundings; yet, clean air and Water are essential for a healthy life.  To bridge that gap, the ESP32 microcontroller is utilized to develop a real-time monitoring system that is straightforward, low-cost, and uncomplicated.  The system utilizes sensors for measuring air quality, including the MQ2, MQ7, and MQ135; water quality sensors, such as those for turbidity and pH, are also employed.   The ESP32 collects data from these sensors and sends it to a local web dashboard for real-time viewing. Additionally, it sends the data to ThingSpeak for cloud-based monitoring.  Through the use of your mobile device or computer, this technology provides instantaneous information on indoor air pollution, drinking water contamination, and environmental changes occurring in local water bodies.   In addition to number interpretation, we also provide fundamental interpretation, such as determining whether the readings are safe or indicate cause for alarm.  Environmental sensing should be simplified to make it more accessible to everyone.  In settings where the cleanliness of the air and Water is of utmost importance, such as households, farms, small enterprises, and schools, this method is effective.

Keywords: Internet of Things; ThingSpeak; PH Sensors; Environmental Monitoring; Highly Accurate; Detecting Smoke; Harmful Gases; Water Turbidity; PH levels; Web Dashboard.

Received on: 28/08/2024, Revised on: 07/11/2024, Accepted on: 15/12/2024, Published on: 07/03/2025

DOI: 10.69888/FTSIN.2025.000369

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 1, Pages: 49-58

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