Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring
Dipartimento di Scienza e Alta Tecnologia, Università Degli Studi dell’Insubria, Via Valleggio, 11, Como 22100, Italy
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Academic Editor: Robert W. Talbot
Atmosphere 2016, 7(12), 155; https://doi.org/10.3390/atmos7120155
Received: 27 October 2016
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Revised: 28 November 2016
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Accepted: 30 November 2016
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Published: 2 December 2016
Atmospheric monitoring produces huge amounts of data. Univariate and bivariate statistics are widely used to investigate variations in the parameters. To summarize information graphs are usually used in the form of histograms or tendency profiles (e.g., variable concentration vs. time), as well as bidimensional plots where two-variable correlations are considered. However, when dealing with big data sets at least two problems arise: a great quantity of numbers (statistics) and graphs are produced, and only two-variable interactions are often considered. The aim of this article is to show how the use of multivariate statistics helps in handling atmospheric data sets. Multivariate modeling considers all the variables simultaneously and returns the main results as bidimensional graphs that are easy-to-read. Principal Component Analysis (PCA; the most known multivariate method) and multiway-PCA (Tucker3) are compared from methodological and interpretative points of view. The article demonstrates the ability to emphasize different information depending on the data handling performed. The results and benefits achieved using a more complex model that allows for the simultaneous consideration of the entire variability of the system are compared with the results provided by the simpler but better-known model. Atmospheric monitoring (SO2, NOx, NO2, NO, and O3) data from the Lake Como Area (Italy) since 1992 to 2007 were chosen for consideration for the case study.
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Keywords:
PCA; Tucker3; atmospheric monitoring; tropospheric pollutants; multivariate analysis; multiway analysis
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MDPI and ACS Style
Giussani, B.; Roncoroni, S.; Recchia, S.; Pozzi, A. Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring. Atmosphere 2016, 7, 155. https://doi.org/10.3390/atmos7120155
AMA Style
Giussani B, Roncoroni S, Recchia S, Pozzi A. Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring. Atmosphere. 2016; 7(12):155. https://doi.org/10.3390/atmos7120155
Chicago/Turabian StyleGiussani, Barbara, Simone Roncoroni, Sandro Recchia, and Andrea Pozzi. 2016. "Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring" Atmosphere 7, no. 12: 155. https://doi.org/10.3390/atmos7120155
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