Scientists of SACAQM publish two papers in international journals.

Scientists of SACAQM publish two papers in international journals

Researchers affiliated with SACAQM initiative have recently published two peer-reviewed papers in international journals, reflecting the consortium’s active contribution to air-quality research.

The first publication is a review paper titled "Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks", providing a comprehensive overview of recent advances in data-driven and machine-learning approaches for air-quality monitoring and forecasting. https://doi.org/10.1016/j.scitotenv.2025.180593

The second publication is an original research article titled “Spatiotemporal Graph Neural Networks for PM2.5 Concentration Forecasting”. This study investigates the use of spatiotemporal graph neural networks (ST-GNNs) to forecast PM2.5 concentrations. https://doi.org/10.3390/air4010002

Together, these publications highlight SACAQM’s commitment to high-quality, peer-reviewed research and to advancing innovative, data-driven solutions for air-quality management.