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

Journal metrics

IF value: 3.668
IF3.668
IF 5-year value: 3.707
IF 5-year
3.707
CiteScore value: 6.3
CiteScore
6.3
SNIP value: 1.383
SNIP1.383
IPP value: 3.75
IPP3.75
SJR value: 1.525
SJR1.525
Scimago H <br class='widget-line-break'>index value: 77
Scimago H
index77
h5-index value: 49
h5-index49
Definitions
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
AMT | Articles | Volume 12, issue 9
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.
AMT | 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 et al.

Viewed

Total article views: 3,245 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,360 854 31 3,245 112 21 25
  • HTML: 2,360
  • PDF: 854
  • XML: 31
  • Total: 3,245
  • Supplement: 112
  • BibTeX: 21
  • EndNote: 25
Views and downloads (calculated since 01 Mar 2019)
Cumulative views and downloads (calculated since 01 Mar 2019)

Viewed (geographical distribution)

Total article views: 2,501 (including HTML, PDF, and XML) Thereof 2,455 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 27 Jan 2021
Publications Copernicus
Download
PDF XML
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
Citation
Atmospheric Measurement Techniques

An interactive open-access journal of the European Geosciences Union

All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.
Contact
|
Imprint
|
Data protection