Estimating and characterizing spatiotemporal distributions of elemental PM2.5 using an ensemble machine learning approach in Taiwan
Journal
Atmospheric Pollution Research
Journal Volume
16
Journal Issue
5
Start Page
102463
ISSN
1309-1042
Date Issued
2025-05
Author(s)
Huang, Chun-Sheng
Lo, Kang
Wu, Yee-Lin
Wang, Fu-Cheng
Shiu, Yi-Shiang
Chen, Chu-Chih
Lin, Yuan-Chien
Kuo, Cheng-Pin
Liao, Ho-Tang
Lin, Tang-Huang
Abstract
This paper presents an ensemble machine learning approach that combines Generalized Additive Model (GAM) with eXtreme Gradient Boosting (XGBoost) to estimate and characterize the spatiotemporal distributions of elemental PM2.5 in Taiwan. Daily field measurements of 12 PM2.5 elemental components were collected from 28 air quality monitoring stations between June 2021 and May 2022. Time-variant meteorological factors and time-invariant land-use patterns were incorporated as predictors. Results showed that the ensemble model effectively captured spatial variations in elemental PM2.5 levels, as demonstrated by the identification of numerous time-invariant features using Shapley additive explanations analysis. A comparative analysis was conducted with a model using only XGBoost, which outperformed the ensemble model with higher cross-validated R2 and lower prediction errors. While the XGBoost-only model is recommended for exposure prediction, the ensemble model offers superior interpretability for investigating air pollution sources and aids in formulating air quality strategies from a spatial perspective.
Publisher
Elsevier BV
Type
journal article