Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 8
Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5535-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5535-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 8
Research article
 | 
13 Aug 2021
Research article |  | 13 Aug 2021

Data imputation in in situ-measured particle size distributions by means of neural networks

Pak Lun Fung, Martha Arbayani Zaidan, Ola Surakhi, Sasu Tarkoma, Tuukka Petäjä, and Tareq Hussein

Viewed

Total article views: 2,077 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,373 636 68 2,077 62 66
  • HTML: 1,373
  • PDF: 636
  • XML: 68
  • Total: 2,077
  • BibTeX: 62
  • EndNote: 66
Views and downloads (calculated since 18 Mar 2021)
Cumulative views and downloads (calculated since 18 Mar 2021)

Viewed (geographical distribution)

Total article views: 2,077 (including HTML, PDF, and XML) Thereof 1,976 with geography defined and 101 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 2024
Download
Short summary
Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. However, due to the instrumental insufficiency and inversion limitations, the raw dataset contains missing gaps or negative values, which hinder further analysis. With a merged particle size distribution in Jordan, this paper suggests a neural network method to estimate number concentrations at a particular size bin by the number concentration at other size bins.
Read more