Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor va...

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Bibliographic Details
Main Authors: Tan Mi, Die Tang, Jianbo Fu, Wen Zeng, Michael L. Grieneisen, Zihang Zhou, Fengju Jia, Fumo Yang, Yu Zhan
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Geoscience Frontiers
Subjects:
Aerosol optical depth
Dataset shift
Spatiotemporal Distribution
Air quality monitoring
Multiple imputation by chained equations
Online Access:http://www.sciencedirect.com/science/article/pii/S1674987123001536

Internet

http://www.sciencedirect.com/science/article/pii/S1674987123001536

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