Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/amt-18-2201-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2201-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Research article
 | 
16 May 2025
Research article |  | 16 May 2025

Comparison of methods for resolving the contributions of local emissions to measured concentrations

Taylor D. Edwards, Yee Ka Wong, Cheol-Heon Jeong, Jonathan M. Wang, Yushan Su, and Greg J. Evans

Viewed

Total article views: 3,087 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,246 642 199 3,087 142 213
  • HTML: 2,246
  • PDF: 642
  • XML: 199
  • Total: 3,087
  • BibTeX: 142
  • EndNote: 213
Views and downloads (calculated since 17 Oct 2024)
Cumulative views and downloads (calculated since 17 Oct 2024)

Viewed (geographical distribution)

Total article views: 3,087 (including HTML, PDF, and XML) Thereof 3,055 with geography defined and 32 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 20 May 2026
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
We tested a variety of scientific measurements and algorithms for distinguishing the amounts of air pollution that were emitted by a nearby polluter from background pollution that was already in the air. The results show that machine learning and other statistical algorithms produced accurate estimates of this background pollution. These findings help scientists and regulators to understand where pollution comes from and to improve measurements of pollution from sources like traffic.
Read more
Share