Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis
PM<sub>2.5</sub> in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM<sub>2.5</sub> prediction models to support decision-making and reduce risks. This review comprehensively explores the p...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/3/292 |
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| author | Chunlai Wu Ruiyang Wang Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_facet | Chunlai Wu Ruiyang Wang Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_sort | Chunlai Wu |
| collection | DOAJ |
| description | PM<sub>2.5</sub> in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM<sub>2.5</sub> prediction models to support decision-making and reduce risks. This review comprehensively explores the progress of PM<sub>2.5</sub> 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 methods such as deep learning. Key data sources include ground monitoring, meteorological observations, remote sensing, and socioeconomic activity data. Deep learning models (including CNN, RNN, LSTM, and Transformer) perform well in capturing complex temporal dependencies. With its self-attention mechanism and parallel processing capabilities, Transformer is particularly outstanding in addressing the challenges of long sequence modeling. Despite these advances, challenges such as data integration, model interpretability, and computational cost remain. Emerging technologies such as meta-learning, graph neural networks, and multi-scale modeling offer promising solutions while integrating prediction models into real-world applications such as smart city systems can enhance practical impact. This review provides an informative guide for researchers and novices, providing an understanding of cutting-edge methods, practical applications, and systematic learning paths. It aims to promote the development of robust and efficient prediction models to contribute to global air pollution management and public health protection efforts. |
| format | Article |
| id | doaj-art-b75ef34a8ac14c7cb06b0da991deb86e |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-b75ef34a8ac14c7cb06b0da991deb86e2025-08-20T02:11:15ZengMDPI AGAtmosphere2073-44332025-02-0116329210.3390/atmos16030292Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical AnalysisChunlai Wu0Ruiyang Wang1Siyu Lu2Jiawei Tian3Lirong Yin4Lei Wang5Wenfeng Zheng6School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of KoreaDepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USASchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaPM<sub>2.5</sub> in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM<sub>2.5</sub> prediction models to support decision-making and reduce risks. This review comprehensively explores the progress of PM<sub>2.5</sub> 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 methods such as deep learning. Key data sources include ground monitoring, meteorological observations, remote sensing, and socioeconomic activity data. Deep learning models (including CNN, RNN, LSTM, and Transformer) perform well in capturing complex temporal dependencies. With its self-attention mechanism and parallel processing capabilities, Transformer is particularly outstanding in addressing the challenges of long sequence modeling. Despite these advances, challenges such as data integration, model interpretability, and computational cost remain. Emerging technologies such as meta-learning, graph neural networks, and multi-scale modeling offer promising solutions while integrating prediction models into real-world applications such as smart city systems can enhance practical impact. This review provides an informative guide for researchers and novices, providing an understanding of cutting-edge methods, practical applications, and systematic learning paths. It aims to promote the development of robust and efficient prediction models to contribute to global air pollution management and public health protection efforts.https://www.mdpi.com/2073-4433/16/3/292PM<sub>2.5</sub>deep learningbibliometric analysisLSTMtransformertime-series |
| spellingShingle | Chunlai Wu Ruiyang Wang Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis Atmosphere PM<sub>2.5</sub> deep learning bibliometric analysis LSTM transformer time-series |
| title | Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis |
| title_full | Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis |
| title_fullStr | Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis |
| title_full_unstemmed | Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis |
| title_short | Time-Series Data-Driven PM<sub>2.5</sub> Forecasting: From Theoretical Framework to Empirical Analysis |
| title_sort | time series data driven pm sub 2 5 sub forecasting from theoretical framework to empirical analysis |
| topic | PM<sub>2.5</sub> deep learning bibliometric analysis LSTM transformer time-series |
| url | https://www.mdpi.com/2073-4433/16/3/292 |
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