Time-Series Data-Driven PM2.5 Forecasting: From Theoretical Framework to Empirical Analysis

PM2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM2.5 prediction models to support decision-making and reduce risks. This review comprehensively explores the progress of PM2.5 concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, and future development directions. This article obtained data on 2327 journal articles published from 2014 to 2024 from the WOS database. Bibliometric analysis shows that research output is growing rapidly, with China and the United States playing a leading role, and recent research is increasingly focusing on data-driven…