Articles | Volume 16, issue 14
https://doi.org/10.5194/amt-16-3547-2023
https://doi.org/10.5194/amt-16-3547-2023
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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Cited articles

Actkinson, B.: bactkinson/Anomaly_Analysis: AMT Preprint Submission (AMT), Zenodo [code], https://doi.org/10.5281/zenodo.7700290, 2023a. 
Actkinson, B.: bactkinson/Plume_Detection_with_DBSCAN: Plume Detection with DBSCAN – R Shiny App (AMT), Zenodo [code], https://doi.org/10.5281/zenodo.7700300, 2023b. 
Actkinson, B.: DBSCAN Plume Detection Tool, shinyapps.io [code], https://bactkinson.shinyapps.io/plume_detection_with_dbscan/ (last access: 6 March 2023), 2023c. 
Actkinson, B. and Griffin, R.: Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01, Zenodo [data set], https://doi.org/10.5281/zenodo.6473859​​​​​​​, 2022​​​​​​​. 
Actkinson, B., Ensor, K., and Griffin, R. J.: SIBaR: a new method for background quantification and removal from mobile air pollution measurements, Atmos. Meas. Tech., 14, 5809–5821, https://doi.org/10.5194/amt-14-5809-2021, 2021. 
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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.