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

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dc.citation.firstpage3547en_US
dc.citation.issueNumber14en_US
dc.citation.journalTitleAtmospheric Measurement Techniquesen_US
dc.citation.lastpage3559en_US
dc.citation.volumeNumber16en_US
dc.contributor.authorActkinson, Blakeen_US
dc.contributor.authorGriffin, Robert J.en_US
dc.date.accessioned2024-05-08T18:56:11Zen_US
dc.date.available2024-05-08T18:56:11Zen_US
dc.date.issued2023en_US
dc.description.abstractMobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.en_US
dc.identifier.citationActkinson, B., & Griffin, R. J. (2023). Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise. Atmospheric Measurement Techniques, 16(14), 3547–3559. https://doi.org/10.5194/amt-16-3547-2023en_US
dc.identifier.digitalamt-16-3547-2023en_US
dc.identifier.doihttps://doi.org/10.5194/amt-16-3547-2023en_US
dc.identifier.urihttps://hdl.handle.net/1911/115685en_US
dc.language.isoengen_US
dc.publisherCopernicus Publicationsen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDetecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noiseen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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