Predicting PM2.5 in Urban and Suburban Beijing: A Comparative Study of Random Forest and Linear Regression Models

Zhuoyang Zhou

2025

Abstract

This study evaluates the performance of multiple linear regression (MLR) and random forest regression (RFR) models in predicting PM2.5 concentrations across twelve air quality monitoring stations in Beijing, China, using hourly meteorological and pollution data from 2013 to 2017. The analysis reveals that RFR significantly outperforms MLR, with R² values improving from 0.11 to 0.22 (MLR) to 0.29–0.41 (RFR), demonstrating better handling of non-linear interactions. However, both models exhibit critical limitations, particularly in predicting extreme pollution events (PM2.5 > 300 µg/m³), where systematic underprediction occurs. Geographical disparities in model accuracy are evident, with suburban stations (e.g., Dingling, Huairou) exhibiting lower errors than urban-industrial sites (e.g., Dongsi, Aotizhongxin), likely due to the complexity of emission sources and microclimates. Dew point temperature emerges as the most influential predictor, while precipitation shows limited impact. These findings underscore the challenges in air quality forecasting and advocate for localised, hybrid modelling approaches integrating real-time emission data to enhance predictive reliability for public health applications.

Paper Citation

in Harvard Style

Zhou Z. (2025). Predicting PM2.5 in Urban and Suburban Beijing: A Comparative Study of Random Forest and Linear Regression Models. In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-774-0, SciTePress, pages 331-336. DOI: 10.5220/0013825300004708

in Bibtex Style

@conference{iampa25,
author={Zhuoyang Zhou},
title={Predicting PM2.5 in Urban and Suburban Beijing: A Comparative Study of Random Forest and Linear Regression Models},
booktitle={Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA},
year={2025},
pages={331-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013825300004708},
isbn={978-989-758-774-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA
TI - Predicting PM2.5 in Urban and Suburban Beijing: A Comparative Study of Random Forest and Linear Regression Models
SN - 978-989-758-774-0
AU - Zhou Z.
PY - 2025
SP - 331
EP - 336
DO - 10.5220/0013825300004708
PB - SciTePress