Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
AMT | Articles | Volume 12, issue 9
Atmos. Meas. Tech., 12, 5161–5181, 2019
https://doi.org/10.5194/amt-12-5161-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 5161–5181, 2019
https://doi.org/10.5194/amt-12-5161-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

Viewed

Total article views: 2,656 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,947 684 25 2,656 84 19 19
  • HTML: 1,947
  • PDF: 684
  • XML: 25
  • Total: 2,656
  • Supplement: 84
  • BibTeX: 19
  • EndNote: 19
Views and downloads (calculated since 01 Mar 2019)
Cumulative views and downloads (calculated since 01 Mar 2019)

Viewed (geographical distribution)

Total article views: 1,946 (including HTML, PDF, and XML) Thereof 1,904 with geography defined and 42 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

No saved metrics found.

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

Latest update: 05 Jun 2020
Publications Copernicus
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.
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline...
Citation