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

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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.
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