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Nationwide Machine Learning-Ensemble PM_2.5 Mapping Prediction and Forecasting Models in South Korea with High

Seoyeong Ahn¹, Ayoung Kim², Yeonseung Chung³

¹Department of Information Convergence Engineering, Pusan National University, Busan 46241, South Korea.

Environment & Health (Washington, D.C.)

|August 21, 2025

Summary

This study developed advanced machine learning models for nationwide particulate matter (PM2.5) prediction and forecasting in South Korea. It also introduced a novel method for assessing health risk estimation and selecting the best models.

Keywords:

Fine particulate matter (PM2.5)Forecasting models Mapping prediction models PM2.5-related Health risk estimation and Machine learning algorithms

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Area of Science:

Environmental Science
Data Science
Public Health

Background:

Limited nationwide machine learning models exist for both PM2.5 mapping and forecasting.
Previous studies focused on prediction accuracy, neglecting PM2.5 health risk estimation assessment.

Purpose of the Study:

To develop nationwide PM2.5 mapping and forecasting models using machine learning in South Korea.
To propose a method for assessing statistical properties of PM2.5 health risk estimation for model selection.

Main Methods:

Utilized three machine learning algorithms and an ensemble method for PM2.5 prediction.
Employed satellite-driven data for spatial prediction (1 km²) and two-day forecasting (2015-2022).
Conducted simulation studies to evaluate PM2.5 health risk estimation properties.

Main Results:

Ensemble spatial prediction model achieved superior performance (0.956 test R²).
Mapping models demonstrated low average % bias (1.403%-1.787%) for PM2.5-mortality risk.
Forecasting models achieved a best R² of 0.904.

Conclusions:

Developed effective machine learning models for spatial PM2.5 prediction and forecasting in Korea.
Proposed a concurrent approach for PM2.5 risk estimation and model selection.