Innovative Air Quality Monitoring with K-Means Algorithm and Smart Sensor Solutions

Air pollution is a growing concern worldwide, with detrimental effects on human health and living conditions. The increasing interest in air quality assessment has led to numerous projects initiated by civil society and individuals aiming to locally evaluate air quality, especially in heavily polluted regions like northern India. The work is based on the implementation of a real-time atmospheric air quality monitoring system that integrates multiple sensors to measure parameters such as CO2, SO2, NO, NH3, O3, PM2.5, PM10, temperature, humidity, and dissolved oxygen. The sensor data is processed by a microcontroller, utilizing both Arduino and motherboard components, with results displayed on an LCD screen and transmitted to a laptop. To enhance the analysis of collected data, the K-Means clustering algorithm is employed to identify patterns and classify air quality conditions. This approach allows for the categorization of pollution levels, providing valuable insights into air quality trends. The proposed calibration method demonstrates the system's effectiveness, showcasing its ability to achieve sensitivity and granulometric resolution comparable to more sophisticated instruments. This comprehensive system bridges the gap between technical precision and social relevance, offering a powerful tool for monitoring and disseminating crucial air quality information to end-users. The integration of K-Means clustering further enriches the system's capability to provide actionable insights for addressing air pollution challenges.