Abstract
The paper studies trend associations between atmospheric pollutants and meteorological variables time series of Mexico City Metropolitan Area (MCMA) by applying the Moving Approximation Transform (MAP). This recently introduced technique measures and visualizes associations of the dynamics between different time series in the form of an association network. The paper studies associations between 5 atmospheric pollutants (SO2, O3, NO2, NOx and PM2.5) and 7 meteorological variables (mean wind velocity, minimum, average and maximum values of both temperature and relative humidity) measured daily during one year in three meteorological stations located in different zones of MCMA. These associations were studied for 4 seasons characterized by different meteorological conditions. For considered stations atmospheric pollutants and meteorological variables for different seasons positive and negative associations have been found and explained.
Chapter PDF
Similar content being viewed by others
References
Roberts, S.: Biologically Plausible Particulate Air Pollution Mortality Concentration-Response Functions. Environ. Health Perspect. 112(3), 309–313 (2004)
Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H.R., Sunyer, J., et al.: Estimating the Exposure–Response Relationships between Particulate Matter and Mortality within the APHEA Multicity Project. Environ. Health Perspect. 113, 88–95 (2005)
Galán, I., TobÃas, A., Banegas, J.R., Aránguez, E.: Short-Term Effects of Air Pollution on daily Asthma Emergency Room Admissions. Eur. Respir. J. 22, 802–808 (2003)
Heo, J.K., Kim, D.S.: A New Method of Ozone Forecasting using Fuzzy Expert and Neural Network Systems. Sci. Total Environ. 325, 221–237 (2004)
Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., Cawley, G.: Extensive Evaluation of Neural Network Models for the Prediction of NO2 and PM10 Concentrations, compared with a Deterministic Modelling System and Measurements in Central Helsinki. Atm. Env. 37, 4539–4550 (2003)
Chaloulakou, A., Saisana, M., Spyrellis, N.: Comparative Assessment of Neural Networks and Regression Models for Forecasting Summertime Ozone in Athens. Sci.Tot. Environ. 313, 1–13 (2003)
Liu, Z., Lai, Y.C., Lopez, J.M.: Noise-induced Enhancement of Chemical Reactions in Nonlinear Flows. Chaos. 12(2), 417–425 (2002)
Cheng, W., Kuo, Y., Lin, P., Chang, K., Chen, Y., Lin, T., Huang, R.: Revised Air Quality Index Derived from an Entropy Function. Atmos. Environ. 38, 383–391 (2004)
Dillner, A.M.: A Quantitative Method for Clustering Size Distributions of Elements. Atm. Env. 39, 1525–1537 (2005)
Hyvönen, S., Junninen, H., Laakso, L., Dal Maso, M., Grönholm, T., Bonn, B., Keronen, P., Aalto, P., Hiltunen, V., Pohja, T., Launiainen, S., Hari, P., Mannila, H., Kulmala, M.: A Look at Aerosol Formation Using Data Mining Techniques. Atmos Chem. Phys. Discuss. 5, 7577–7611 (2005)
Molina, M., Molina, L.: Megacities and Atmospheric Pollution. J. Air Waste Manage. Assoc. 54, 644–680 (2004)
Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Panova, A.: Association Networks in Time Series Data Mining. In: NAFIPS 2005 Soft Computing for Real World Applications, Ann Arbor, Michigan, USA, pp. 754–759 (2005)
Edgerton, S.A., Bian, X., Doran, J.C., Fast, J.D., Hubbe, J.M., Malone, E.L., Shaw, W.J., Whiteman, C.D., Zhong, S., Arriaga, J.L., Ortiz, E., Ruiz, M., Sosa, G., Vega, E., Limon, T., Guzman, F., Archuleta, J., Bossert, J.E., Elliot, S.M., Lee, J.T., McNair, L.A., Chow, J.C., Watson, J.G., Coulter, R.L., Doskey, V.: Particulate Air Pollution in Mexico City: A Collaborative Research Project. J. Air Waste M. A. 49(10), 1221–1229 (1999)
Harrison, R., Deacon, A., Jones, M., Appleby, R.: Sources and Processes Affecting Concentrations of PM10 and PM2.5 Particulate Matter in Birmingham (U.K). Atm. Env. 31(24), 4103–4117 (1997)
Chow, J.C., Watson, J.G., Edgerton, S.A., Vega, E.: Chemical Composition of PM2.5 and PM10 in Mexico City During Winter 1997. Sci. Tot. Environ. 287, 177–201 (2002)
Tai, A., Mickley, L., Jacob, D.: Correlations Between Fine Particulate Matter (PM2.5) and Meteorological Variables in the United States: Implications for the sensitivity of PM2.5 to Climate Change. Atm. Env. 44, 3976–3984 (2010)
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
Almanza, V., Batyrshin, I. (2011). On Trend Association Analysis of Time Series of Atmospheric Pollutants and Meteorological Variables in Mexico City Metropolitan Area. In: MartÃnez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-21587-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21586-5
Online ISBN: 978-3-642-21587-2
eBook Packages: Computer ScienceComputer Science (R0)