Real Time Air Quality Monitoring And Health Alert App Using AI

Air pollution is an acute and long-term problem of the health of the population, especially in populous and densely populated areas with traffic jams, which requires intelligent and real-time monitoring solutions. The present study suggests a Real-Time Air Quality Monitoring and Health Alert Application based on AI tools which is expected to transform the continuous monitoring of the environment into real-time and personalized health information. The main task is to ensure that a single system is created that would not only forecast air quality changes at high time resolutions but would also provide timely health warnings depending on the vulnerability and exposure conditions of the particular person. The suggested framework integrates information of air quality sensors on the basis of IoT, meteorological data, traffic and demographic data. The models used are advanced machine learning and deep learning models, such as ensemble models and temporal networks, to predict the pollutant concentrations and assign the air quality level in real time. Explainable AI methods are integrated with the aim of increasing the level of transparency and interpretability of predictions. The experimental findings indicate that it has a reliable performance in real-time predictions, better performance in responding to spikes in pollution, and the effective identification of high-risk areas and populations. The system manages to combine predictive air quality analytics and user-friendly health communication. To sum up, the suggested AI-based application can provide a scalable, interpretable, and proactive environmental health management solution to enable smart city efforts and information-based decisions about health to the people.