Air Pollution Data

Air pollution data analysis focuses on understanding and predicting pollutant concentrations (e.g., NOx, ozone, PM2.5) to inform public health and environmental policy. Current research emphasizes the application of advanced machine learning techniques, such as deep learning models (including LSTMs, GRUs, and physics-informed deep learning), to improve forecasting accuracy and spatial resolution of pollution predictions. These efforts are complemented by the development of open-source tools to democratize access to data and analytical capabilities. Ultimately, improved data analysis contributes to more effective strategies for mitigating air pollution and its associated health and environmental impacts.