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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Bibliographic Details
Published in:	Atmospheric measurement techniques Vol. 16; no. 14; pp. 3547 - 3559
Main Authors:	Actkinson, Blake, Griffin, Robert J.
Format:	Journal Article
Language:	English
Published:	Katlenburg-Lindau Copernicus GmbH 25.07.2023 Copernicus Publications
Subjects:	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
Online Access:	Get full text
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