Inspecting Satellite Data for Surface Air Quality Monitoring and, Prediction of Surface AQI using ML MODELS and Prophet.

Rapid urbanization and industrialization have caused Indian urban regions to suffer severe air pollution issues; insufficient ground monitoring data and incomplete air pollution source characterization impedes putting policy measures to overcome this issue, remote sensing and GIS can tackle this hurdle to some extent. The necessity of monitoring and preserving air quality has now become an essential priority in many industrial and urban areas today. The air quality is skeptically affected due to multiple forms of pollution caused by fossil fuel burning, stubble burning, electricity, etc. The accumulating deposition of harmful gases is creating a serious threat to the quality of life in urban areas and their surroundings. The quality of air is affected by multi-dimensional factors including location, time, and meteorological factors. This study focuses on the current and future advancing techniques of monitoring and predicting air quality for Ahmedabad city by scrutinizing the use of remote sensing data for surface air quality monitoring which includes acquiring the satellite image data using python for NO2 and SO2, correlating ground and satellite data. And using ground data, predicting air quality index using machine learning tools like Random Forest Regressor, Adaboost Regressor, and times series model Prophet.