Air Qaulity as an Integrating Factor for the Greek and Turkish Cypriot Communities
Within the project "Assessment of Ambient Air Quality in Cyprus" the objectives of this study were to determine the spatial and temporal distribution of the air pollutants over the whole island of Cyprus and to find out the sources of airborne particulate matter (PM10). The spatial pollutants distribution was performed by diffusive sampling at 250 sites over one year for the components NO2, SO2 and Benzene. The measurements had been carried out in the two regions of Greek and Turkish Cypriot Communities with active participation of both communities and interactive exchange, interpretation and discussion of results. For the evaluation and depiction a Neural Network simulation tool was used with the input parameters emissions inventory and population density and trained by the results of diffusive sampling. To investigate the particulate matter situation in Cyprus, PM10 samplings were carried out at different points in both communities at numerous urban and rural sites. It can be recognised that at all traffic and at some residential and urban background sites the actual PM10 EU limit values have been exceeded. Also Sahara dust events could be detected and identified. Within this study the origin of the PM10 load was investigated by factor analysis. At traffic sites PM10 load could be allocated to traffic activities, exhaust emissions, vehicle and road abrasion and re-suspended soil dust originally coming from fields or unpaved roads, and sea salt components. The comparison of the PM10 concentrations in Cyprus cities with values of other European cities demonstrates the PM10 problem in Cyprus due to the dry mediterranean climate and therefore underlines the necessity of abatement strategies. Thus, the assessment of air quality problems served as an integrating factor for the people of Greek and Turkish Cypriot regions in Cyprus since the air does not recognise any boarders.
Keywords: Cyprus, PM, NO2, SO2, VOC, Ozone, spatial distribution, neural network, temporal variation, continuous monitoring