Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 14
Articles | Volume 16, issue 14
https://doi.org/10.5194/amt-16-3547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 14
Research article
 | 
25 Jul 2023
Research article |  | 25 Jul 2023

Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Blake Actkinson and Robert J. Griffin

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Latest update: 13 Dec 2024
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Short summary
Data collected using air quality instrumentation deployed on automobiles and driven repeatedly in Houston neighborhoods are analyzed using a novel machine learning technique. The aim is to separate large plumes from the rest of the data in order to identify the sources of the highest levels of the pollutants. The number and nature of these plumes are characterized spatially and can be linked to emissions from different types of motor vehicles.
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