SMART AIR QUALITY CHCKER FOR ASTHAMA AND HEART RELATED PATIENT

Air pollution has emerged as a critical health threat in modern society, especially for vulnerable groups such as individuals suffering from asthma, chronic respiratory conditions, and heart-related diseases. Continuous exposure to elevated levels of particulate matter, toxic gases, and other airborne pollutants can severely impact their health, often leading to emergency situations. The Smart Air Quality Checker developed in this project addresses this urgent need for personalized and real-time environmental monitoring.         This system successfully combines low-cost air quality sensors, an ESP32-based microcontroller, wireless communication modules, and a user-friendly alert mechanism to provide accurate, real-time tracking of pollutants such as PM2.5, PM10, CO, NO₂, and environmental conditions like temperature and humidity. The data collected is processed to compute the Air Quality Index (AQI), which is then used to evaluate the level of health risk for the user. When pollutant levels cross predefined safety thresholds, the system immediately notifies the user through local (buzzers, LEDs, displays) and remote (mobile app, cloud dashboard) alerts.      Through this project, we have demonstrated that accessible technology can be harnessed effectively to protect and improve the quality of life for individuals at risk due to poor air quality. The Smart Air Quality Checker represents a step toward proactive healthcare and environmental responsibility, contributing to both personal well-being and public health awareness.  

[1]. P. Gupta et al., “IoT-Based Air Quality Monitoring System for Asthma Patients,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 12, no. 4, pp. 67-78, Apr. 2024. [2]. A. Sharma et al., “Machine Learning Approaches for Air Quality Prediction in IoT-Based Systems,” International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), vol. 11, no. 2, pp. 45-59, Feb. 2023. [3]. R. Verma et al., “Fire and Smoke Detection Using IoT and AI-Based Systems,” International Journal of Smart Sensor Technologies (IJSST), vol. 9, no. 3, pp. 112-126, July 2023. [4]. S. Kumar and D. Patel, “A Predictive Model for Fire Hazard Detection Using IoT and Machine Learning,” International Journal of Emerging Technologies in Computational Intelligence (IJETCI), vol. 8, no. 5, pp. 88- 101, Oct. 2022. [5]. T. Roy et al., “Air Pollution Monitoring and Prediction Using IoT and Deep Learning,” Journal of Environmental Monitoring and Analytics (JEMA), vol. 15, no. 1, pp. 55-72, Mar. 2023. [6]. M. Iqbal et al., “Smart Healthcare Monitoring System for Respiratory Patients Using IoT and Cloud Computing,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 4, pp. 1203-1215, Apr. 2022. [7]. L. Chen and J. Wang, “An AI-Based System for Air Quality Prediction and Alert Generation,” Sensors and Actuators B: Chemical, vol. 347, pp. 78-95, Jan. 2023. [8]. S. Ahmed et al., “IoT-Based Real-Time Fire Detection and Air Quality Monitoring System,” International Journal of Internet of Things and Smart Sensors (IJITSS), vol. 10, no. 2, pp. 30-44, Aug. 2023. [9]. H. Tanaka et al., “Predicting Environmental Hazards Using AI-Driven IoT Systems,” Proceedings of the 2022 International Conference on Intelligent Systems and Networks (ICISN), pp. 200- 215, Dec. 2022. [10]. J. Miller, “A Review of IoT-Enabled Air Pollution and Fire Detection Systems,” Journal of Smart Cities and Internet of Things, vol. 7, no. 3, pp. 15-29, June 2021.