Combating Respiratory Health Issues with Intelligent NO2 Level Prediction from Sentinel 5P Satellite

The ongoing issue of air quality in Delhi, situated in India, is a significant concern. The urban region has experienced numerous notable air pollution events in recent years, leading to unfavourable health outcomes and economic losses. Nitrogen dioxide (NO2) is a prevalent air pollutant in Delhi, recognised for its adverse impact on human health, encompassing respiratory disorders, cardiovascular illnesses, and carcinogenicity. The current investigation utilises the satellite data from Sentinel 5P to monitor Delhi's nitrogen dioxide (NO2) levels. Then, a deep learning algorithm is utilised to forecast the concentrations of NO2 within the urban locality. Our investigation results exhibit our model's effectiveness in precisely predicting NO2 levels in Delhi. This implies that the model has the potential to be efficiently employed in augmenting air quality management approaches within the urban area. The findings of our study hold considerable implications for air quality management in urban regions, encompassing Delhi and other cities worldwide. Using satellite data and machine learning methodologies has facilitated improved accuracy in monitoring and forecasting air quality. Data can facilitate developing and implementing effective air quality regulations to protect public health and the environment.