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Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Citation

Salimi, F and Ristovski, Z and Mazaheri, M and Laiman, R and Crilley, LR and He, C and Clifford, S and Morawska, L, Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment, Atmospheric Chemistry and Physics, 14 pp. 11883-11892. ISSN 1680-7316 (2014) [Refereed Article]


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Author(s) 2014. Licensed under Creative Commons Attribution 3.0 Unported (CC BY 3.0) http://creativecommons.org/licenses/by/3.0/

DOI: doi:10.5194/acp-14-11883-2014

Abstract

ong-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.

Item Details

Item Type:Refereed Article
Keywords:particle number size distribution, clustering
Research Division:Earth Sciences
Research Group:Atmospheric Sciences
Research Field:Atmospheric Aerosols
Objective Division:Environment
Objective Group:Air Quality
Objective Field:Antarctic and Sub-Antarctic Air Quality
Author:Salimi, F (Dr Farhad Salimi)
ID Code:104343
Year Published:2014
Web of Science® Times Cited:13
Deposited By:Menzies Institute for Medical Research
Deposited On:2015-11-10
Last Modified:2015-12-18
Downloads:235 View Download Statistics

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