Abstract
Clustering air mass trajectories is used to identify source regions of certain chemical species. Current clustering methods only use the trajectory coordinates as clustering variables, and as such, are unable to differentiate between similar shaped trajectories that have different source regions and/or seasonal differences. This can lead to a higher variance in the chemical composition within each cluster and loss of information. We propose an adaptive K-means clustering algorithm that uses both the trajectory variables and the associated chemical value. We show, using carbon monoxide data from the Cape Verde for 2007, that our method produces a far more informative clustering than the existing standard method, whilst achieving a lower level of subjectivity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Borge, R., Lumbreras, J., Vardoulakis, S., Kassomenos, P., Rodríguez, E.: Analysis of long-range transport influences on urban PM10 using two-stage atmospheric trajectory clusters. Atmospheric Environment 41, 4434–4450 (2007)
Brankov, E., Rao, S.T., Porter, P.S.: A trajectory-clustering-correlation methodology for examining the long-range transport of air pollutants. Atmospheric Environment 32, 1525–1534 (1998)
Cape, J.N., Methven, J., Hudson, L.E.: The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland. Atmospheric Environment 34, 3651–3663 (2000)
Dorling, S.R., Davies, T.D., Pierce, C.E.: Cluster analysis: a technique for estimating the synoptic meteorological controls on air and precipitation chemistry–Results from Eskdalemuir, South Scotland. Atmospheric Environment. Part A. General Topics 26, 2583–2602 (1992)
Eneroth, K., Kjellström, E., Holmén, K.: A trajectory climatology for Svalbard; investigating how atmospheric flow patterns influence observed tracer concentrations. Physics and Chemistry of the Earth, Parts A/B/C 28, 1191–1203 (2003)
Jorba, O., Pérez, C., Rocadenbosch, F., Baldasano, J.M.: Cluster analysis of 4-day back trajectories arriving in the Barcelona area, Spain, from 1997 to 2002. Journal of Applied Meteorology 43, 887–901 (2004)
Kassomenos, P., Vardoulakis, S., Borge, R., Lumbreras, J., Papaloukas, C., Karakitsios, S.: Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories. Theoretical and Applied Climatology 102, 1–12 (2010)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Moody, J.L., Galloway, J.: Quantifying the relationship between atmospheric transport and the chemical composition of precipitation on Bermuda. Tellus 40, 463–479 (1988)
Moy, L.A., Dickerson, R.R., Ryan, W.F.: Relationship between back trajectories and tropospheric trace gas concentrations in rural Virginia. Atmospheric Environment 28, 2789–2800 (1994)
SPSS Inc.: PASW 13 Modeler for Windows. Rel 13.0.0.366. SPSS Inc.,Chicago (2009)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, London (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mace, A., Sommariva, R., Fleming, Z., Wang, W. (2011). Adaptive K-Means for Clustering Air Mass Trajectories. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-23878-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23877-2
Online ISBN: 978-3-642-23878-9
eBook Packages: Computer ScienceComputer Science (R0)