Pollution Level

Research on pollution levels, primarily focusing on air quality, aims to accurately monitor, predict, and understand the spatial and temporal dynamics of pollutants to mitigate their harmful effects on human health and the environment. Current research employs diverse machine learning models, including Random Forests, XGBoost, LSTMs, and Transformers, often integrating data from satellite imagery, meteorological forecasts, and ground-based monitoring stations to improve prediction accuracy and spatial coverage. These advancements are crucial for informing public health interventions, developing effective pollution control strategies, and enhancing our understanding of pollution's complex interactions with various environmental and societal factors.