REAL TIME AIR QUALITY PREDICTION AND ANOMALY DETECTION

When it comes to addressing the growing concerns about environmental health and safety, real-time air quality forecast and anomaly detection are essential. This study describes a robust system that effectively tracks, analyses, and forecasts air quality parameters in real-time using a combination of cutting-edge sensors, sophisticated data analytics, and machine learning algorithms. Utilising a wide range of environmental sensors, the system continuously monitors pollutants such as carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter (PM2.5 and PM10), and ozone. Modern data analytics techniques are then used to process and analyse the gathered data. A machine learning platform at the centre of the system makes use of time-series forecasting methods to forecast air quality conditions. Additionally, it uses anomaly detection algorithms to identify anomalous trends or abrupt spikes in pollutant concentrations, which may indicate operational issues or environmental dangers. Environmental agencies and public health experts can take timely action and make informed judgements thanks to these skills. In addition, the system has an easy-to-use interface that provides both specialists and non-experts with real-time updates and visual representations of air quality statistics. Using IoT technology increases the system’s expandability to larger regions and guarantees effective data flow. By providing consistent and accurate air quality information, this technique not only supports urgent mitigation measures but also facilitates strategic environmental and public health planning. Predictive analytics and proactive surveillance are intended to improve environmental governance and public health.