Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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A methodological approach to the environmental Umbria data
Marta Bosi, Monica Franzese, Cristiano Trani, Sandra Verduci

Last modified: 2018-06-20

Abstract


Environment often plays a direct or indirect decisive role on people’s health; it’s extremely complex determining the resultant effect that different pollutants in air, water, soil can have as consequence on the overall environmental pollution. Analysis illustrated in this work, based on Arpa Umbria’s databases (e.g., [1,2,3]), carries out on a significant cartographic representation structured in a 5Kmx5Km reticular units, so as to reflect the  presence of environmental pollutants in a geographical area in terms of overall environmental impact. For this purpose it was constructed a composite indicator capable of measuring environmental pollution, synthesising the information provided by the sampling of parameters, on the basis of simple indicators calculated for each parameter and appropriately weighted applying a Probabilistic Principal Components Analysis(e.g., [4,5]).

References


  1. E. Bartolucci, M. Bussettini, N. Calace, L. D’Aprile, M. Fratini, M. Guerra, L. Marangio, G. Pirani, A. Vecchio: Protocollo for setting background values for the inorganic substancesin groundwater. ISPRA- Inter-service Department for the environmental emergencies-  Contaminated sites sector, 2009
  2. Kathryn Z. Guyton, Karen A. Hogan, Cheryl Siegel Scott, Glinda S. Cooper, Ambuja S. Bale, Leonid Kopylev, Stanley Barone Jr., Susan L. Makris, Barbara Glenn, Ravi P. Subramaniam, Maureen R. Gwinn, Rebecca C. Dzubow, and Weihsueh A. Chiu, Human Health Effects of Tetrachloroethylene: Key Findings and Scientific Issues, <<Enviromental Health Perspectives>>, volume 122, number 4, 2014, pp 325-334
  3. Lorenzo Benini, Lucia Mancini, Simone Manfredi, Serenella Sala, Erwin M. Schau, Rana Pant: Normalisation method and data for Environmental Footprints. European Commission, JRC Technical Report,, 2014
  4. Tipping, M. E., & Bishop, C. M.: Probabilistic principal component analysis, Journal of the Royal Statistical Society, 61, 611–622, 1999
  5. Roweis, S.: EM algorithms for PCA and sensible PCA (Technical Report). California Institute, 1997

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