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

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 292 - 292

Published: Feb. 28, 2025

PM2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need develop accurate prediction models support decision-making reduce risks. This review comprehensively explores progress of concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, future development directions. article obtained on 2327 journal articles published from 2014 2024 WOS database. Bibliometric analysis shows that research output growing rapidly, with China United States playing leading role, recent increasingly focusing data-driven methods such as learning. Key sources include ground monitoring, meteorological observations, remote sensing, socioeconomic activity data. Deep (including CNN, RNN, LSTM, Transformer) perform well capturing complex temporal dependencies. With its self-attention mechanism parallel processing capabilities, Transformer particularly outstanding addressing challenges long sequence modeling. Despite these advances, integration, model interpretability, computational cost remain. Emerging technologies meta-learning, graph neural networks, multi-scale modeling offer promising solutions while integrating into real-world applications smart city systems can enhance practical impact. provides informative guide for researchers novices, providing understanding cutting-edge methods, systematic paths. It aims promote robust efficient contribute global management protection efforts.

Language: Английский

Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models

AppliedMath, Journal Year: 2024, Volume and Issue: 4(4), P. 1428 - 1452

Published: Nov. 25, 2024

Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), analyze historical data from monitoring stations predict future PM2.5 levels. The results reveal decreasing trend levels 2019 mid-2024, suggesting effectiveness of policies implemented by City Council. However, observed interannual fluctuations peaks indicate need for continuous policy adjustments address specific events seasonal variations. comparison local those European Union underscores importance greater coherence alignment optimize outcomes. Predictions made with Prophet–LSTM provide solid foundation planning decision making, enabling urban managers design more effective strategies. not only provides detailed understanding pollution patterns, but also emphasizes adaptive environmental citizen participation improve air quality. findings this work can be great assistance policymakers, providing basis research actions quality Madrid. effectively captured both trends spikes predictions indicated general downward concentrations across most districts Madrid, significant reductions areas such as Chamartín Arganzuela. approach improves accuracy long-term capturing short-term dependencies, making it robust solution management complex environments, like suggest that Council are having positive impact

Language: Английский