Development of a data-driven three-dimensional PM2.5 forecast model based on machine learning algorithms

• Published in: Environmental technology & innovation Vol. 37; p. 103930
• Main Authors: Han, Zizhen, Guan, Tianyi, Wang, Xinfeng, Xin, Xin, Song, Xiaomeng, Wang, Yidan, Dong, Can, Ren, Pengjie, Chen, Zhumin, Ren, Shilong, Zhang, Qingzhu, Wang, Qiao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2025
• Subjects: aerosols; air quality; altitude; China; data collection; environmental technology; human health; inventories; Machine learning; meteorological data; Multi-source data; ozone; PM2.5; pollution; Prediction; satellites; spatial distribution; Three dimension; China; Three dimension; Multi-source data; PM2.5; Prediction; Machine learning
• ISSN: 2352-1864; 2352-1864
• DOI: 10.1016/j.eti.2024.103930

Fine particle matter (PM2.5) pollution is a global environmental problem and has significant impacts on air quality and human health. Accurate prediction is crucial for mitigating PM2.5 pollution and reducing its environmental and health impacts. However, the current data-driven PM2.5 prediction model does not fully consider the vertical distribution pattern and the contribution of source emissions to achieve a broader and more accurate prediction of PM2.5. This study introduces a novel approach to predict three-dimensional (3D) air quality at a high spatial-temporal resolution, with multi-source data and machine learning algorithms. Specifically, we developed a two-stage 3D PM2.5 prediction model by standardizing and integrating meteorology data, anthropogenic emission inventory data, air quality monitoring data, and satellite remote sensing data into a 3D dataset. In the first stage, we used random forest (RF) models to estimate the spatial-temporal distributions of aerosol optical depth (AOD) and ozone (O3) density. In the second stage, we further used these estimations to predict hourly PM2.5 concentrations at both the surface and altitude levels with another RF model. To enhance the prediction performance, dynamic corrections were implemented to the predicted PM2.5 concentrations. Using this model, we predicted PM2.5 concentrations for the next 72 hours and validated the spatial-temporal fluctuations against monitoring data across Shandong Province, China. Furthermore, we assessed the contribution of local emissions and evaluated the air quality improvement resulting from local emission reduction measures. Our findings confirm the capability of the data-driven machine learning model for 3D air quality prediction on a regional scale, emphasizing the importance of regional emission control to improve local air quality. [Display omitted] •Three-dimensional hourly PM2.5 for the next 72 hours was predicted by a two-stage random forest model.•Air quality, meteorology, emission, remote sensing data of AOD and O3 density were integrated for prediction.•The data-driven models perform well in different spatial locations and within the future period.•The PM2.5 forecast model can be used to assess local emission contributions and reduction effects.