Work

Air quality prediction model based on deep learning hybrid framework

Year: 2026

Type: article

Abstract: As modernization and industrialization continue to accelerate, air pollution has become an increasingly pressing problem. Air quality prediction is considered an essential technical support for air pollution prevention and control. To achieve more accurate predictions for urban air pollution, we propose a hybrid model called CBLA, which consists of three parts: one-dimensional Convolutional Neural Networks (1D-CNNs), Bidirectional Long Short-Term Memory network (BiLSTM), and attention mechanism. Firstly, 1D-CNNs extract the deep features of the original data. Secondly, BiLSTM mines time-series features for initial prediction. Finally, the attention mechanism captures the effect of characteristic conditions on PM2.5 concentration at different times to further optimize the model. The eXtreme Gradient Boosting (XGBoosting) tree is used to integrate the preliminary prediction results and meteorological data to improve prediction accuracy further. We conducted extensive experimental evaluations using Beijing's air quality and meteorological datasets, which showed that the CBLA model has excellent performance and model expression power.

Source: Scientific Reports

DOI: 10.1038/s41598-026-37896-y

Authors: Chao Yin, Weidong Li, Tongfang Li, Sihao Yuan, Dan Tang, Xiaogang Dong

Institutions: Jiujiang University, Hunan University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $2,690