AIR POLLUTION – MONITORING MODELLING AND HEALTH

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244 Air Pollution – Monitoring, Modelling and Health [24] Kupper H, Lombi E, Zhao F J, Wieshammer G, McGrath S P., Cellular compartimentation of nickel in the hyperaccumulators Alyssum lesbiacum, Alyssum bertolonii and Thlaspi goesingense. Journal of Experimental Botany; 2001 (52), 2291-2300. [25] Leblond. S. Etude pluridisciplinaire du transfert des métaux de l’atmosphère vers les mousses (Scleropodium purum (Hedw.) Limpr.) : Suivi sur un site rural (Vouzon, France). Thèse de doctorat en Chimie de la Pollution Atmosphérique et Physique de l’Environnement, Université Paris 7 - Denis Diderot 2004. 212p. [26] Maatoug M. Détection de la pollution de l’air d’origine routière par certaines espèces végétales bioaccumulatrices de quelques métaux lourds (Pb, Zn, Cu). Revue pollution atmosphérique 2007 ; 196 : 385-394. [27] Maatoug M. Cartographie de la pollution atmosphérique par le plomb d’origine routière à l’aide de transplantation d’un lichen bioaccumulateur Xanthoria parietina dans la ville de Tiaret (Algérie). Revue pollution atmosphérique 2010 ; 205 : 93-101. [28] Madany IM, Ali SM, Akhter MS. Assessment of lead in roadside vegetation in Bahrain. Environment International 1990 (16): 123-126. [29] Ouzounidou G, Ciamporova M, Moustakas M, Karataglis S.,. Responses of Maize (Zea- Mays L) Plants to Copper Stress .1. Growth, Mineral-Content and Ultrastructure of Roots. Environmental and Experimental Botany; 1995 (35), 167-176. [30] Ouzounidou G, Moustakas M, Eleftheriou E P.,. Physiological and ultrastructural effects of cadmium on wheat (Triticum aestivum L) leaves. Archives of Environmental Contamination and Toxicology; 1997 (32),154-160. [31] Peulon V., Le dépérissement des arbres en ville. Edition du STU, Ministère de l’Equipement (France) 1988, 61p. [32] Semadi .A et Deruelle .S Détection la pollution plombique à l’aide de transplants lichéniques dans la région de Annaba (Algérie). Revue pollution atmosphérique Octobre-Décembre 1993 : 86-101. [33] Vazquez M D, Poschenrieder C, Barcelo J., Ultrastructural Effects and Localization of LowCadmium Concentrations in Bean Roots. New Phytol; 1992 (120), 215-226.

11 Developing Neural Networks to Investigate Relationships Between Air Quality and Quality of Life Indicators Kyriaki Kitikidou and Lazaros Iliadis Democritus University of Thrace, Department of Forestry and Management of the Environment and Natural Resources, Orestiada Greece 1. Introduction Quality of life (QOL) is an integral outcome measure in the management of diseases. It can be used to assess the results of different management methods, in relation to disease complications and in fine-tuning management methods (Koller & Lorenz, 2003). Quantitative analysis of quality of life across countries, and the construction of summary indices for such analyses have been of interest for some time (Slottje et al., 1991). Most early work focused on largely single dimensional analysis based on such indicators as per capita GDP, the literacy rate, and mortality rates. Maasoumi (1998) and others called for a multidimensional quantitative study of welfare and quality of life. The argument is that welfare is made up of several distinct dimensions, which cannot all be monetized, and heterogeneity complications are best accommodated in multidimensional analysis. Hirschberg et al. (1991) and Hirschberg et al. (1998) identified similar indicators, and collected them into distinct clusters which could represent the dimensions worthy of distinct treatment in multidimensional frameworks. In this research effort we have considered the role of air quality indicators in the context of economic and welfare life quality indicators, using artificial neural networks (ANN). Therefore in this presentation we have obtained the key variables (life expectancy, healthy life years, infant mortality, Gross Domestic Product (GDP) and GDP growth rate) and developed a neural network model to predict the air quality outcomes (emissions of sulphur and nitrogen oxides). Sustainability and quality of life indicators have been proposed recently by Flynn et al. (2002) and life quality indices have been used to estimate willingness to pay (Pandey & Nathwani, 2004). The innovative part of this research effort lies in the use of a soft computing machine learning approach like the ANN to predict air quality. In this way, we introduce the reader to a technique that allows the comparison of various attributes that impact the quality of life in a meaningful way.

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