Constructing transferable and interpretable machine learning models for black carbon concentrations

Constructing transferable and interpretable machine learning models for black carbon concentrations

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual s...

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Bibliographic Details
Main Authors: Pak Lun Fung, Marjan Savadkoohi, Martha Arbayani Zaidan, Jarkko V. Niemi, Hilkka Timonen, Marco Pandolfi, Andrés Alastuey, Xavier Querol, Tareq Hussein, Tuukka Petäjä
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:Environment International
Subjects:
BC estimation
Virtual sensors
Relative importance
Neural network
SHAP
Traffic emission
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412024000357

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http://www.sciencedirect.com/science/article/pii/S0160412024000357

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