Predicting Particulate Matter Values in Metropolitan Areas Using Machine Learning

Atmospheric particulate matter (PM), such as PMIO and PM2.5, significantly impacts ecosystems, climate, and human health. Previous studies have shown that black carbon, a type of PM2.5, significantly contributes to global warming. To this end, this study introduces a set of advanced neural network models aimed at improving air quality predictions within ur-ban environments, specifically targeting PM2.5 and PMIO. By harnessing data from Chile's National Air Quality Information System (SINCA), we employ a diverse array of neural networks, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), a hybrid CNN-LSTM model called ConvLSTM, transformers, and XGBoost models. Our empirical results show that these models are adept at forecasting air pollution trends, with CNN in particular outperforming all other models, providing crucial insights for real-time environmental monitoring. Our innovative approach enables policymakers and public health officials to take timely and effective measures to combat urban air pollution, thereby enhancing the overall quality of life in metropolitan areas.