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

Salimi, Farhad, Ristovski, Zoran, Mazaheri, Mandana, Laiman, Rusdin, Crilley, Leigh R., He, Congrong, Clifford, Sam, & Morawska, Lidia (2014) 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.

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Abstract

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 which 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 spectra 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.

Impact and interest:

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ID Code: 82005
Item Type: Journal Article
Refereed: Yes
Additional Information: Published by Copernicus Publications on behalf of the European Geosciences Union.
Keywords: air quality, air pollution, Particle Number Size Distribution, Source Appointment, Atmospheric aerosols
DOI: 10.5194/acp-14-11883-2014
ISSN: 1680-7324
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > ATMOSPHERIC SCIENCES (040100)
Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > ATMOSPHERIC SCIENCES (040100) > Atmospheric Aerosols (040101)
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ENVIRONMENTAL SCIENCE AND MANAGEMENT (050200) > Environmental Monitoring (050206)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ENVIRONMENTAL ENGINEERING (090700) > Environmental Engineering not elsewhere classified (090799)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Institutes > Institute of Health and Biomedical Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2014 The Authors
Deposited On: 24 Feb 2015 23:27
Last Modified: 27 Feb 2015 10:39

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