Application of Machine Learning for the in-Field Correction of a PM(2.5) Low-Cost Sensor Network

Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM(2.5) from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM(2.5) LCSs from July 2017 to December 2018. Three candidate models w...

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Detalles Bibliográficos
Autores principales: Wang, Wen-Cheng Vincent, Lung, Shih-Chun Candice, Liu, Chun-Hu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506620/
https://www.ncbi.nlm.nih.gov/pubmed/32899301
http://dx.doi.org/10.3390/s20175002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506620/
https://www.ncbi.nlm.nih.gov/pubmed/32899301
http://dx.doi.org/10.3390/s20175002

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