Multivariate Chemometric Analysis of a Polluted River of a Megalopolis


A chemometrical study regarding a 10-years water quality monitoring plan at 15 sampling points along a section of the Reconquista River and its stream channels, which embraces 21 campaigns, is presented. The original data were pre-treated in order to eliminate missing data and outliers, obtaining a final data matrix of 270 samples containing 26 physical-chemistry variables each. Multivariate statistical methods like multi curve resolution, canonical correlation analysis and factor analysis methods, as well as current univariate statistics were applied. The interpretation was simplified when variables were separated in groups containing environmentally and chemically related variables instead of analyzing them all together. These methods have shown that the presence of metals likely come from at least 3 different type of sources. Although the stream channels arriving to the main river course are highly polluted, their flow rates are so low that do not significantly decrease its water quality. They mainly contribute to the high levels of biochemical-oxygen demand and chemical-oxygen demand as well as nitrogen-content species. Furthermore, regarding metals, the pollutants coming from the upstream of the river is higher than those introduced by all channels.

Share and Cite:

A. García-Reiriz, J. Magallanes, M. Vracko, J. Zupan, S. Reich and D. Cicerone, "Multivariate Chemometric Analysis of a Polluted River of a Megalopolis," Journal of Environmental Protection, Vol. 2 No. 7, 2011, pp. 903-914. doi: 10.4236/jep.2011.27103.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] World Bank, “Environment Matters,” Annual Review 2003, Water Supply and Sanitation, p. 23.
[2] Aguas Argentinas, Obras Sanitarias de Buenos Aires, Instituto Ringuelet y Servicio de Hidrografía Naval, “Calidad de las Aguas de la Franja Costera Sur del Río de la Plata, (San Fernado-Magdalena),” Consejo Permanente para el monitoreo de la Calidad de Aguas de la franja Costera Sur del Río de la Plata (ed.), Secretaría de Obras Públicas, Dirección Nacional de Gestión de los Recursos Hídricos, anexos I y II, Buenos Aires, 1997.
[3] G. Kiely, “Ingeniería Ambiental,” McGraw Hill, Boston, 1998.
[4] D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte and L. Kaufman, “Chemometrics: A Textbook,” Elsevier, Amsterdam, 1988.
[5] D. L. Massart, B. G. M. Vandeginst, L. M. C. Buydens, S. De Jong, P. J. Lewi and J. Smeyers-Verbeke, “Hanbook of Chemometrics and Qualimetrics,” Elsevier, Amsterdam, 1997.
[6] J. W. Einax, “Chemometrics in Environmental Che- mistry-Applications,” Springer-Verlag, Berlin, 1995.
[7] J. W. Einax, H. W. Zwanziger and S. Geiss, “Che- mometrics in Environmental Analysis,” VCH-Weinheim, Germany, 1997.
[8] S. Verboven and M. Hubert, “Libra: A Matlab Library for Robust Analysis,” Chemometrics and Intelligent Laboratory Systems, Vol. 75, No. 2, 2005, pp. 127-136. doi:10.1016/j.chemolab.2004.06.003
[9] M. Felipe-Sotelo, L. Gustems, I. Hernández, M. Terrado and R. Tauler, “Investigation of Geographical and Temporal Distribution of Tropospheric Ozone in Catalonia (North-East Spain) during the period 2000-2004 Using Multivariate Data Analysis Methods,” Atmospheric Environment, Vol. 40, No. 38, 2006, pp. 7421-7436. doi:10.1016/j.atmosenv.2006.07.013
[10] H. Hotelling, “Relations between Two Sets of Variates,” Biometrika, Vol. 28, No. 3-4, 1936, pp. 321-377.
[11] R. Tauler, “Multivariate Curve Resolution Applied to Second Order Data,” Chemometrics and Intelligent Laboratory Systems, Vol. 30, No. 1, 1995, pp.133-146. doi:10.1016/0169-7439(95)00047-X
[12] R. Tauler, A. Smilde and B. Kowalski, “Selectivity, Local Rank, 3-Way Data Analysis and Ambiguity in Multivariate Curve Resolution,” Journal of Chemometrics, Vol. 9, No. 1, 1995, pp. 31-58. doi:10.1002/cem.1180090105
[13] T. Jolliffe, “Principal Component Analysis,” Springer- Verlag, New York, 2002, p. 26.
[14] M. Terrado, D. Barceló and R. Tauler, “Quality Assessment of the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) Method for the Investigation of Environmental Pollution Patterns,” Environmental Science & Technology, Vol. 43, No. 14, 2009, pp. 5321-5326. doi:10.1021/es803333s
[15] C. J. F. Ter Braak, “Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis,” Ecology, Vol. 67, No. 5, 1986, pp. 1167-1170.
[16] S. Becker, “Mutual Information Maximization: Models of Cortical Self-Organization Network,” Computation in Neural Systems, Vol. 7, No. 1, 1996, pp. 7-31.
[17] W. Stum and J. Morgan, “Aquatic Chemistry. Chemical Equilibria and Rates in Natural Waters,” 3rd Edition, John Wiley & Sons, Inc., New York, 1995.
[18] G. Nader, “Modelización del Transporte de Metales en el río Reconquista (entre ex ruta 8 y Panamericana): Etapas de Conceptualización, Formulación y Calibración,” Ph.D. Dissertation, Universidad Nacional De San Martín, San Martín, 2009.
[19] G. Cappari, “Evaluación de la Calidad de Agua Superficial del Río Reconquista a la Altura del Partido de San Martín Mediante Estudios Ecotoxicológicos en dos Oportunidades de Muestreo (2007-2008) y Caracte- rización de los Sedimentos Obtenidos en el Mismo Tramo de la Cuenca,” Ph.D. Dissertation, Universidad Nacional De San Martín, San Martín, 2008.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.