Regional PM2.5 pollution forecasting using a hybrid model based on multi-scales feature fusion and deep learning algorithms

Yong Zhang, Wenya Zhang, Bo Wu and Weichen Yi

PLOS ONE, 2025, vol. 20, issue 10, 1-18

Abstract: The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutants from January 1, 2021, to December 31, 2023 in the Chengdu-Chongqing urban agglomeration, along with concurrent surface meteorological data, are used and builds multi-scales feature fusion regional pollution prediction network (MSFRPM) based on a multi-input-multi-output deep learning framework. This model can simultaneously forecast PM2.5 concentrations for all cities in the region. The results show that the annual and seasonal prediction evaluation metrics of the MSFRPM model are significantly better than those of the baseline models. This can be attributed to the ability of the MSFRPM model to effectively capture the temporal dependency of historical PM2.5, the complex nonlinear relationships between other pollutants and meteorological factors within cities, and the multi-scales spatiotemporal dependencies of PM2.5 transport between cities in the urban agglomeration. In 2023, the Chengdu-Chongqing urban agglomeration experienced 15 days of mild regional pollution, 21 days of moderate pollution, and 2 days of severe pollution, with moderate pollution being the dominant type of PM2.5 pollution. Seasonally, regional PM2.5 pollution in the Chengdu-Chongqing urban agglomeration is mainly concentrated in the winter. The MSFRPM model assesses that the interannual and seasonal assessments of regional PM2.5 pollution in the Chengdu-Chongqing urban agglomeration in 2023 are generally consistent with actual observations. Accurate prediction of regional PM2.5 pollution is of great significance for the coordinated management and early warning of regional pollution.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333489 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 33489&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0333489

DOI: 10.1371/journal.pone.0333489

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().