Air Quality Forecasting

Air quality forecasting aims to predict future pollutant levels, enabling proactive measures to mitigate health risks and environmental damage. Current research heavily utilizes machine learning, particularly deep learning architectures like LSTMs, GRUs, and Graph Neural Networks, often integrating diverse data sources such as satellite imagery, meteorological forecasts, and ground-level sensor readings to improve prediction accuracy and spatial resolution. This field is crucial for public health, informing individual decisions and policy interventions, and also advances our understanding of complex atmospheric processes through the development and application of sophisticated spatiotemporal models.