Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/amt-19-603-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Collection:
https://doi.org/10.5194/amt-19-603-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Research article
 | 
23 Jan 2026
Research article |  | 23 Jan 2026

Enhancing Accuracy of Indoor Air Quality Sensors via Automated Machine Learning Calibration

Juncheng Qian, Thomas Wynn, Bowen Liu, Yuli Shan, Suzanne E. Bartington, Francis D. Pope, Yuqing Dai, and Zongbo Shi

Viewed

Total article views: 10,986 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
9,295 1,463 228 10,986 368 255 241
  • HTML: 9,295
  • PDF: 1,463
  • XML: 228
  • Total: 10,986
  • Supplement: 368
  • BibTeX: 255
  • EndNote: 241
Views and downloads (calculated since 26 Aug 2025)
Cumulative views and downloads (calculated since 26 Aug 2025)

Viewed (geographical distribution)

Total article views: 10,986 (including HTML, PDF, and XML) Thereof 10,822 with geography defined and 164 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 20 Oct 2026
Download
Short summary
We developed a multi-stage AutoML (Automated Machine Learning) calibration framework to improve low-cost indoor PM2.5 sensor accuracy. Using chamber tests with varied emission sources, the method corrected drift, humidity effects, and non-linear responses, raising R2 above 0.9 and halving RMSE (Root Mean Square Error). The approach enables reliable, scalable indoor air quality monitoring for research and public health applications.
Read more
Share
Collection
Low-cost sensors for the measurement of atmospheric...