Cluster Analysis of Rural, Urban, and Curbside Atmospheric Particle Size Data

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• 作者：David C. S. Beddows ; Manuel Dall’ ; Osto ; Roy M. Harrison
• 刊名：Environmental Science & Technology
• 出版年：2009
• 出版时间：July 1, 2009
• 年：2009
• 卷：43
• 期：13
• 页码：4694-4700
• 全文大小：345K
• 年卷期：v.43,no.13(July 1, 2009)
• ISSN：1520-5851

文摘

Particle size is a key determinant of the hazard posed by airborne particles. Continuous multivariate particle size data have been collected using aerosol particle size spectrometers sited at four locations within the UK: Harwell (Oxfordshire); Regents Park (London); British Telecom Tower (London); and Marylebone Road (London). These data have been analyzed using k-means cluster analysis, deduced to be the preferred cluster analysis technique, selected from an option of four partitional cluster packages, namely the following: Fuzzy; k-means; k-median; and Model-Based clustering. Using cluster validation indices k-means clustering was shown to produce clusters with the smallest size, furthest separation, and importantly the highest degree of similarity between the elements within each partition. Using k-means clustering, the complexity of the data set is reduced allowing characterization of the data according to the temporal and spatial trends of the clusters. At Harwell, the rural background measurement site, the cluster analysis showed that the spectra may be differentiated by their modal-diameters and average temporal trends showing either high counts during the day-time or night-time hours. Likewise for the urban sites, the cluster analysis differentiated the spectra into a small number of size distributions according their modal-diameter, the location of the measurement site, and time of day. The responsible aerosol emission, formation, and dynamic processes can be inferred according to the cluster characteristics and correlation to concurrently measured meteorological, gas phase, and particle phase measurements.