A survey on predicting and controlling air pollution using Machine learning and deep learning techniques

Since air pollution has a significant negative influence on public health, early warning systems must be able to make precise long-term predictions about air quality. Predicting air quality has garnered a lot of interest, combining computer science, statistics, and environmental science. The Paper reviews a number of studies on air pollution, its effects on mental health, and the use of machine learning methods for air quality monitoring and prediction. Some of the studies investigate the connection between mental health outcomes and air pollution. They draw attention to the substantial influence that air pollutants, especially PM2.5, have on mental health conditions like depression and psychotic episodes. Machine learning techniques are used in many research to forecast pollution levels and air quality. To predict air quality indicators and particular pollutant concentrations, this research uses a variety of models, including Support Vector Machines (SVM), Random Forests, Neural Networks, and ensemble techniques like XGBoost. Innovative methods of environmental monitoring, such as the application of AI, IoT, and computer vision technologies, are the subject of certain studies. These technologies are used in urban settings to identify and categorize different types of pollution, including visual pollution. The combined findings of these studies highlight the substantial harm that air pollution causes to the general public, especially to mental health, and show how cutting-edge technologies can be used to monitor, forecast, and control air quality in metropolitan settings