Preprints
https://doi.org/10.5194/amt-2023-50
https://doi.org/10.5194/amt-2023-50
14 Mar 2023
 | 14 Mar 2023
Status: this preprint is currently under review for the journal AMT.

Detecting plumes in mobile air quality monitoring time series with Density-based Spatial Clustering of Applications with Noise

Blake Actkinson and Robert Griffin

Abstract. Mobile 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.

Blake Actkinson and Robert Griffin

Status: open (until 19 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Blake Actkinson and Robert Griffin

Blake Actkinson and Robert Griffin

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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.