Machine learning-based quantification and separation of emissions and meteorological effects on PM2.5 in Greater Bangkok

Machine learning-based quantification and separation of emissions and meteorological effects on PM2.5 in Greater Bangkok

Abstract This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM2.5 over Greater Bangkok (GBK). Six ML models namely random fores...

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Bibliographic Details
Main Authors: Nishit Aman, Sirima Panyametheekul, Ittipol Pawarmart, Di Xian, Ling Gao, Lin Tian, Kasemsan Manomaiphiboon, Yangjun Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Himawari-8
Explainable machine learning
Meteorological normalization
PM2.5 mapping
Hurst exponent
SHAP
Online Access:https://doi.org/10.1038/s41598-025-99094-6
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https://doi.org/10.1038/s41598-025-99094-6

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