Detection of Outliers and Extreme Events of Ground Level Particulate Matter Using DBSCAN Algorithm with Local Parameters

The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter...

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Veröffentlicht in:Water, air, and soil pollution Jg. 233; H. 6; S. 203
Hauptverfasser: Aslan, Meryem Ezgi, Onut, Semih
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.06.2022
Springer
Springer Nature B.V
Schlagworte:
air
Air pollution
Algorithms
Analysis
Atmospheric Protection/Air Quality Control/Air Pollution
Climate Change/Climate Change Impacts
Clustering
Control charts
Data analysis
Diameters
Earth and Environmental Science
Environment
Environmental monitoring
Forecasting
Forecasts and trends
Ground level
Human error
human health
Hydrogeology
Identification methods
Methods
Noise
Outdoor air quality
Outlier analysis
Outliers (statistics)
Particulate emissions
Particulate matter
particulates
Pollutants
prediction
Process controls
Sensors
soil pollution
Soil Science & Conservation
Spatial data
Suspended particulate matter
water
Water Quality/Water Pollution
Air pollution
DBSCAN
Particulate matter (PM)
Noise
Extreme events
Outlier analysis
ISSN:0049-6979, 1573-2932
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Beschreibung
Zusammenfassung:The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter concentrations. The data includes considerable amount of abnormal measurements. To perform an eligible analysis and prediction, a proper outlier analysis process is essential. However the studies focused on outlier identification in PM data are relatively few. This paper focuses on finding outliers and extreme events in ground level PM10 (particles smaller than or equal to 10 μm in diameter) data using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The results show the effectiveness of the method to identify noise and extreme events.
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ISSN:0049-6979
1573-2932
DOI:10.1007/s11270-022-05679-6