Application and Performance Comparison of Tree Model in PM2.5 Concentration Prediction

DOI

10.2991/978-2-38476-585-0_32How to use a DOI?

Keywords

Air Pollution; PM2.5; RF; XGBoost; LightGBM

Abstract

With the acceleration of urbanization and the increase of industrial emissions, air pollutants have posed an increasingly serious threat to human health and environmental safety. This study uses an air pollution dataset collected from Southeast Asian countries, which contains multiple pollutant concentration indicators. Three tree planting models were developed to perform regression prediction on PM2.5 concentration: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The relationships among variables were explored by cleaning the original data set and combining visualization. In the model results, all three models achieved good predictive capabilities, but the RF performed the best. The findings demonstrate that the tree model performs well when the data scale is medium and the feature correlation is poor. This paper further analyzes the possible reasons for the performance differences of the model and points out the limitations of the current research, such as insufficient feature dimensions and inadequate parameter tuning of the model. Finally, this paper puts forward improvement suggestions, including introducing larger-scale data, adopting deep learning methods and combining with spatial visualization platforms, to enhance the accuracy and practical application value of air pollution prediction.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pengzhou Xu
PY  - 2026
DA  - 2026/06/18
TI  - Application and Performance Comparison of Tree Model in PM2.5 Concentration Prediction
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 271
EP  - 279
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_32
DO  - 10.2991/978-2-38476-585-0_32
ID  - Xu2026
ER  -