Air Quality

Air quality research focuses on accurately monitoring and predicting pollutant concentrations, primarily PM2.5, ozone, and nitrogen dioxide, to mitigate their adverse health and environmental impacts. Current research employs diverse machine learning models, including deep learning architectures like transformers, graph neural networks, and recurrent neural networks (RNNs such as LSTMs), often combined with wavelet transforms or physics-based constraints to improve accuracy and handle data sparsity and missing values. These advancements are crucial for developing effective air quality management strategies, informing public health interventions, and enabling more precise, cost-effective monitoring systems, particularly in low-resource settings. The integration of low-cost sensors and satellite data with sophisticated algorithms is a key trend driving progress in this field.