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A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan by Chau-Ren Jung; Wei-Ting Chen; Li-Hao Young; Ta-Chih Hsiao is a scholarly article available to read on EtoBox.

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Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008–2010 and 2017–2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2 ) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temper

Author

Chau-Ren Jung; Wei-Ting Chen; Li-Hao Young; Ta-Chih Hsiao

Publisher

Elsevier BV

Published

2023

Language

EN