Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 10
Articles | Volume 15, issue 10
https://doi.org/10.5194/amt-15-3261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-3261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 10
Research article
 | 
01 Jun 2022
Research article |  | 01 Jun 2022

Machine learning techniques to improve the field performance of low-cost air quality sensors

Tony Bush, Nick Papaioannou, Felix Leach, Francis D. Pope, Ajit Singh, G. Neil Thomas, Brian Stacey, and Suzanne Bartington

Viewed

Total article views: 4,107 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,003 1,016 88 4,107 72 71
  • HTML: 3,003
  • PDF: 1,016
  • XML: 88
  • Total: 4,107
  • BibTeX: 72
  • EndNote: 71
Views and downloads (calculated since 29 Oct 2021)
Cumulative views and downloads (calculated since 29 Oct 2021)

Viewed (geographical distribution)

Total article views: 4,107 (including HTML, PDF, and XML) Thereof 4,124 with geography defined and -17 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
Poor air quality is a human health risk which demands high-spatiotemporal-resolution monitoring data to manage. Low-cost air quality sensors present a convenient pathway to delivering these needs, compared to traditional methods, but bring methodological challenges which can limit operational ability. In this study within Oxford, UK, we develop machine learning methods to improve the quality of low-cost sensors for NO2, PM10 (particulate matter) and PM2.5 and demonstrate their effectiveness.
Read more