AirGuard: Smart Air Quality Monitoring and Analysis Using MATLAB and Simulink

Air pollution is a growing concern in urban and industrial areas, directly affecting public health and environmental sustainability. Traditional monitoring systems often provide limited data and lack predictive capabilities. This project focuses on the design and simulation of a smart air quality monitoring and analysis system using MATLAB and Simulink. The system models pollutant levels such as carbon monoxide (CO), nitrogen dioxide (NO₂), and particulate matter (PM2.5), integrating sensor inputs and environmental variables to assess air quality in real time.

The proposed model incorporates data acquisition from simulated sensors, filtering algorithms to process noisy signals, and a classification module that categorizes air quality into standard health indices (e.g., good, moderate, unhealthy). Predictive modeling is also included to forecast pollution trends based on traffic density, weather conditions, and industrial activity. MATLAB and Simulink provide a platform to simulate sensor behavior, data processing, and predictive analytics, enabling comprehensive evaluation of system performance.

The primary objective of this project is to develop a reliable and scalable air quality monitoring solution that supports public health initiatives and environmental management. The implementation demonstrates how simulation tools can enhance pollution tracking, provide early warnings, and guide policy decisions for cleaner and healthier urban environments.

Members:
Baes, Kurt Samuel P.
Perez, Ronalyn
Precilla, Jesselle Cristine A.
Sala, Justine Philip

Видео AirGuard: Smart Air Quality Monitoring and Analysis Using MATLAB and Simulink канала Rionel Caldo