A Python-Based Framework for Interactive Real-Time Air Quality Monitoring and Visualization

Abstract: Air pollution has emerged as a major global challenge, severely impacting human health, environmental quality, and climate stability. Rapid industrialization, urban expansion, and increased vehicular emissions have contributed to rising levels of pollutants, including particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3). Monitoring air quality in real-time is essential for timely interventions, public health advisories, and informed policy-making. This research presents a comprehensive Python-based system for real- time air quality monitoring and visualization. The system fetches live data from the OpenAQ API, with a simulation fallback to ensure continuous operation during data unavailability. Key pollutants are monitored, logged in CSV files for historical analysis, and visualized using interactive multi-line graphs. The approach enables dynamic observation of pollution trends, identification of peak pollution periods, and facilitates informed decision-making to mitigate environmental and health risks.

Keywords: Air Pollution, Real-Time Monitoring, Python, Data Visualization, PM2.5, PM10, CO, NO2, O3, Environmental Health, Urban Air Quality, Public Health, Pollution Trends.