Journal of Electrical Engineering and Automation
IRO Journals
Volume 7 — Issue 2 — June 2025

Environmental Air Pollution and Water Quality Systems in Educational Institutions

https://doi.org/10.36548/jeea.2025.2.008
Sampath P.
Department of Electronics and Communication Engineering, Dr NGP Institute of Technology, Coimbatore, Tamil Nadu, India
Ellakkiah S.
Department of Electronics and Communication Engineering, Dr NGP Institute of Technology, Coimbatore, Tamil Nadu, India
Kavinmathy G M.
Department of Electronics and Communication Engineering, Dr NGP Institute of Technology, Coimbatore, Tamil Nadu, India
Logeshwaran D.
Department of Electronics and Communication Engineering, Dr NGP Institute of Technology, Coimbatore, Tamil Nadu, India
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How to Cite

P., Sampath, Ellakkiah S., Kavinmathy G M., and Logeshwaran D. 2025. “Environmental Air Pollution and Water Quality Systems in Educational Institutions”. Journal of Electrical Engineering and Automation 7 (2): 192-203. https://doi.org/10.36548/jeea.2025.2.008.
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Keywords

— Real-Time Monitoring
— IoT Connectivity
— Environmental Sustainability
— Data Analytics
— Pollution Detection
Published: 22-07-2025

Abstract

An intelligent environmental monitoring system receives real-time conditions through continuous interpretation of several environmental parameters to provide dependable results alongside ongoing updates. The data communication through IoT connectivity creates a smooth transmission to central platforms which enables remote observation along with preventive decision-making to boost health and environmental sustainability. The system operates over extended periods by using automated analytics that track environmental changes with data-driven evaluation methods. It provides an accessible platform that helps users track essential parameters effectively, supporting public security and healthcare initiatives. The system enables environmental management alongside public health due to its feature set, which includes early alerts, quick responses, and strategic decision-making abilities. It requires the integration of machine learning and improved data analytics to advance its prediction functions.

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