Air Pollution

Air pollution research focuses on accurately monitoring and predicting pollutant levels, particularly in urban areas, to mitigate its significant health and environmental impacts. Current research employs diverse machine learning models, including random forests, gradient boosting, neural networks (like LSTMs), and Gaussian processes, often integrating data from various sources such as low-cost sensors, satellite imagery, meteorological forecasts, and even web search queries. These advancements improve the spatial and temporal resolution of pollution monitoring, enabling more precise identification of hotspots and improved forecasting capabilities. The resulting insights inform public health interventions, urban planning strategies, and the development of more effective pollution control policies.