Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 9
Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-5161-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Collection:
https://doi.org/10.5194/amt-12-5161-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 9
Research article
 | 
26 Sep 2019
Research article |  | 26 Sep 2019

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Tongshu Zheng, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson

Viewed

Total article views: 7,810 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,519 2,136 155 7,810 574 181 165
  • HTML: 5,519
  • PDF: 2,136
  • XML: 155
  • Total: 7,810
  • Supplement: 574
  • BibTeX: 181
  • EndNote: 165
Views and downloads (calculated since 01 Mar 2019)
Cumulative views and downloads (calculated since 01 Mar 2019)

Viewed (geographical distribution)

Total article views: 7,810 (including HTML, PDF, and XML) Thereof 6,887 with geography defined and 923 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Apr 2026
Download
Short summary
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.
Read more
Share
Collection
Low-cost sensors for the measurement of atmospheric...