A Graph-Based System for PM_2.5 Prediction

Air pollution poses a global challenge, carrying serious risks to both human health and the environment. Fine particulate matter (PM_2.5) is a key factor contributing to respiratory and cardiovascular diseases. Therefore, accurate PM_2.5 prediction is essential for analyzing pollution trends, safeguarding public health, guiding environmental planning, and shaping policy decisions. In this study, we conducted extensive experiments to enhance PM_2.5 prediction and developed a robust prediction system. Our system incorporates multiple components, including AirBox and EPA data preprocessing, data fusion, feature engineering, feature selection, and the proposed prediction model, DCRNN-GS. Designed for iterative multi-step forecasting, our model utilizes data from the previous 24 h to predict PM_2.5 levels for the next 24 h. Experimental results demonstrate that our system surpasses state-of-the-art methods in PM_2.5 prediction performance.