Impact of Air Quality and Its Effects on Lungs Health: A novel Hybrid Approach for Quality Assessment

This work examines the association between the prevalence of respiratory disease and exposure to airborne pollutants, specifically the effect of air pollution on lung health. The hybrid method approach was proposed in this work, where data from environmental monitoring stations were combined with those from urban and rural pollution sources. Advanced deep learning models, such as convolution Neural Networks(CNNs) and ResNet-50, were used to assess these effects. The CNNs were able to extract the features of vapor patterns and dispersion of PM2.5, while the ResNet-50 identified intricate patterns relating air quality with lung health outcomes with a predictive accuracy of 92% in lung risk scoring. Synthetic data augmentation, based on Air Quality Index (AQI) readings, simulated scenarios of declining lung health to enhance model robustness. A multimodal analysis integrated image data with AQI sensor readings and user-reported health metrics, providing comprehensive insights. Results indicated significant correlations between air pollution levels (notably PM2.5 and PM10) and lung health decline. Personalized health recommendations were delivered through real-time lung risk scoring, accessible via a mobile application that provided instant notifications on air quality and health risks. This research shows the potential of deep learning and multi-modal approaches to solutions of public health challenges related to air pollution, and future efforts concentrate on enhancing the interpretability of models and incorporating wearable device data for personal health monitoring.