Articles | Volume 14, issue 8
Atmos. Meas. Tech., 14, 5637–5655, 2021
https://doi.org/10.5194/amt-14-5637-2021
Atmos. Meas. Tech., 14, 5637–5655, 2021
https://doi.org/10.5194/amt-14-5637-2021

Research article 18 Aug 2021

Research article | 18 Aug 2021

Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability

Peer Nowack et al.

Viewed

Total article views: 1,260 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
868 374 18 1,260 11 19
  • HTML: 868
  • PDF: 374
  • XML: 18
  • Total: 1,260
  • BibTeX: 11
  • EndNote: 19
Views and downloads (calculated since 22 Dec 2020)
Cumulative views and downloads (calculated since 22 Dec 2020)

Viewed (geographical distribution)

Total article views: 1,127 (including HTML, PDF, and XML) Thereof 1,123 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Jan 2022
Download
Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.