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

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 separ...

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Veröffentlicht in:Atmospheric measurement techniques Jg. 16; H. 14; S. 3547 - 3559
Hauptverfasser: Actkinson, Blake, Griffin, Robert J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Katlenburg-Lindau Copernicus GmbH 25.07.2023
Copernicus Publications
Schlagworte:
Air monitoring
Air pollution
Air quality
Algorithms
Anomalies
Censuses
Clustering
Datasets
Density
Emission analysis
Environmental monitoring
Global positioning systems
GPS
Land use
Machine learning
Methods
Monitoring
Nitrogen dioxide
Noise monitoring
Outdoor air quality
Plumes
Pollutants
Spatial variations
Technology application
Time series
Unsupervised learning
Vehicle emissions
ISSN:1867-8548, 1867-1381, 1867-8548
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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.
AbstractList 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.
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.
Audience Academic
Author Actkinson, Blake
Griffin, Robert J.
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SubjectTerms Air monitoring
Air pollution
Air quality
Algorithms
Anomalies
Censuses
Clustering
Datasets
Density
Emission analysis
Environmental monitoring
Global positioning systems
GPS
Land use
Machine learning
Methods
Monitoring
Nitrogen dioxide
Noise monitoring
Outdoor air quality
Plumes
Pollutants
Spatial variations
Technology application
Time series
Unsupervised learning
Vehicle emissions
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Title Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
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