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Modelling daily fine particulate matter (PM2.5) and wildfire effects using machine learning and deep learning in a cold urban region
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DOI：10.1016/j.apr.2026.103099.png)
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Abstract
En 中文
• Three ML/DL models predicted wildfire-related PM2.5 levels • Wildfires increased specific day PM2.5 up to 4000% above baseline levels • XGBoost outperformed Random Forest and LSTM models • Supports targeted health protection during wildfire PM2.5 exposure
Keywords:
Machine learning
PM2.5
air pollution
wildfire
time series analysis
multivariate analysis
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