A Survey on Air Pollution using Deep Learning

Air pollution remains a significant environmental and public health issue, with particulate matter being a major factor in declining air quality. This research is an attempt to examine the various methodologies for monitoring, classifying, and forecasting air quality using statistical approaches, time-series analysis, and machine learning techniques. Traditional models such as regression analysis, ARIMA, and time-series decomposition have been commonly employed for air quality evaluation. However, advancements in artificial intelligence (AI) have introduced more precise predictive models, including support vector machines (SVM), deep convolutional neural networks (DCNN), and long short-term memory (LSTM) networks. Furthermore, the impact of meteorological factors on pollutant dispersion and the effectiveness of urban greening strategies in reducing air pollution are explored. The findings indicate that hybrid AI models, which combine deep learning with statistical techniques, demonstrate superior predictive capabilities, presenting a promising approach for real-time air quality monitoring and informed decision-making