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| Titel |
Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment |
| VerfasserIn |
F. Salimi, Z. Ristovski, M. Mazaheri, R. Laiman, L. R. Crilley, C. He, S. Clifford, L. Morawska |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 21 ; Nr. 14, no. 21 (2014-11-12), S.11883-11892 |
| Datensatznummer |
250119154
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| Publikation (Nr.) |
 copernicus.org/acp-14-11883-2014.pdf |
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| Zusammenfassung |
| Long-term measurements of particle number size distribution (PNSD) produce a
very large number of observations and their analysis requires an efficient
approach in order to produce results in the least possible time and with
maximum accuracy. Clustering techniques are a family of sophisticated
methods that have been recently employed to analyse PNSD data; however,
very little information is available comparing the performance of different
clustering techniques on PNSD data. This study aims to apply several
clustering techniques (i.e. K means, PAM, CLARA and SOM) to PNSD data, in
order to identify and apply the optimum technique to PNSD data measured at
25 sites across Brisbane, Australia. A new method, based on the Generalised
Additive Model (GAM) with a basis of penalised B-splines, was proposed to
parameterise the PNSD data and the temporal weight of each cluster was also
estimated using the GAM. In addition, each cluster was associated with its
possible source based on the results of this parameterisation, together with
the characteristics of each cluster. The performances of four clustering
techniques were compared using the Dunn index and Silhouette width
validation values and the K means technique was found to have the highest
performance, with five clusters being the optimum. Therefore, five clusters
were found within the data using the K means technique. The diurnal
occurrence of each cluster was used together with other air quality
parameters, temporal trends and the physical properties of each cluster, in
order to attribute each cluster to its source and origin. The five clusters
were attributed to three major sources and origins, including regional
background particles, photochemically induced nucleated particles and
vehicle generated particles. Overall, clustering was found to be an
effective technique for attributing each particle size spectrum to its source
and the GAM was suitable to parameterise the PNSD data. These two techniques
can help researchers immensely in analysing PNSD data for characterisation
and source apportionment purposes. |
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