Titanium dioxide is the most important photocatalysts used for purifying applications. If a TiO2- containing material is left outdoors as a form of flat panels, it is activated by sunlight to remove harmful NOx gases during the day. The photocatalytic efficiency of a TiO2-treated mortar for removal of NOx was investigated in the frame of this work. For this purpose a fully equipped monitoring system was designed at a pilot site. This system allows the in situ evaluation of the de-polluting properties of a photocatalytic material by taking into account the climatologic phenomena in street canyons, accurate measurements of pollution level and full registration of meteorological data The pilot site involved three artificial canyon streets, a pollution source, continuous NOx measurements inside the canyons and the source as well as background and meteorological measurements. Significant differences on the NOx concentration level were observed between the TiO2 treated and the reference canyon. NOx values in TiO2 canyon were 36.7 to 82.0% lower than the ones observed in the reference one. Data arising from this study could be used to assess the impact of the photocatalytic material on the purification of the urban environment.

Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network's weights after the training procedure (i.e., the percentage of influence-PI, the weight product-WP, and the partial derivatives-PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables' importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.

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Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

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ABSTRACTΤhe extreme weather conditions in Middle East Area led to the construction of tightly sealed, air conditioned buildings, characterized by indoor air quality deterioration. This study presents the results of chemical characterization of outdoor and indoor PM 2.5 and PM 10 in Doha city, over a two-month period including normal days and dust events, aiming at identifying the factors affecting the indoor air of an office building. The WHO guideline values were exceeded in 100% of the outdoor measurements. 49% of the days of the sampling campaign were characterized as non-dusty (PM 10 < 200 µg m ). The contribution of both dust and anthropogenic emissions sources is depicted in particles' mass and chemical composition. The elevated -especially outdoor-levels of carbonate carbon indicate the presence of crustal matter originating from the surrounding crustal material. OC/EC values reveal possible combined contribution from secondary organic aerosol, trafficrelated sources and re-suspended dust. The influence of anthropogenic emissions is implied by the predominance of nitrate and sulfate ions, which constitute a substantial percentage of the particle mass. The crustal origin of particles is also depicted in metals. However, the higher enrichment factor values which may imply anthropogenic activities of both the outdoor and indoor environment were determined sequentially for Cd, Pb, As, Cu and Zn, suggesting the role of infiltration. Concluding, the indoor to outdoor relationship is significantly influenced by particles infiltration and penetration into the building mainly through the ventilation system and to a lesser extent, through windows or cracks in the building envelope. Although the low indoor to outdoor ratio underlies the predominance of outdoor levels compared to the indoor ones, there is positive correlation between indoor and outdoor PM, during the days that the building was open to the public and employees.

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