Browsing by Author "Lanzafame, Rosario"

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    Measurement and Modeling of Ground-Level Ozone Concentration in Catania, Italy using Biophysical Remote Sensing and GIS
    (Research India Publications, 2017) Famoso, Fabio; Wilson, Jeffrey S.; Monforte, Pietro; Lanzafame, Rosario; Brusca, Sebastian; Lulla, Vijay
    This experimental study examined spatial variation of ground level ozone (O3) in the city of Catania, Italy using thirty passive samplers deployed in a 500-m grid pattern. Significant spatial variation in ground level O3 concentrations (ranging from 12.8 to 41.7 g/m3) was detected across Catania’s urban core and periphery. Biophysical measures derived from satellite imagery and built environment characteristics from GIS were evaluated as correlates of O3 concentrations. A land use regression model based on four variables (land surface temperature, building area, residential street length, and distance to the coast) explained 74% of the variance (adjusted R2) in measured O3. The results of the study suggest that biophysical remote sensing variables are worth further investigation as predictors of ground level O3 (and potentially other air pollutants) because they provide objective measurements that can be tested across multiple locations and over time.
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    Measurement and Modeling of Ground-Level Ozone Concentration in Catania, Italy using Biophysical Remote Sensing and GIS
    (Research India Publications, 2017) Famoso, Fabio; Wilson, Jeffrey S.; Monforte, Pietro; Lanzafame, Rosario; Brusca, Sebastian; Lulla, Vijay; Geography, School of Liberal Arts
    This experimental study examined spatial variation of ground level ozone (O3) in the city of Catania, Italy using thirty passive samplers deployed in a 500-m grid pattern. Significant spatial variation in ground level O3 concentrations (ranging from 12.8 to 41.7 g/m3) was detected across Catania’s urban core and periphery. Biophysical measures derived from satellite imagery and built environment characteristics from GIS were evaluated as correlates of O3 concentrations. A land use regression model based on four variables (land surface temperature, building area, residential street length, and distance to the coast) explained 74% of the variance (adjusted R2) in measured O3. The results of the study suggest that biophysical remote sensing variables are worth further investigation as predictors of ground level O3 (and potentially other air pollutants) because they provide objective measurements that can be tested across multiple locations and over time.
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    A site selection model to identify optimal locations for microalgae biofuel production facilities in sicily (Italy)
    (Research India Publications, 2017) Brusca, Sebastian; Famoso, Fabio; Lanzafame, Rosario; Messina, Michele; Wilson, Jeffrey S.
    The lack of sustainability and negative environmental impacts of using fossil fuel resources for energy production and their consequent increase in prices during last decades have led to an increasing interest in the development of renewable biofuels. Among possible biomass fuel sources, microalgae represent one of the most promising solutions. The present work is based on the implementation of a model that facilitates identification of optimal geographic locations for large-scale open ponds for microalgae cultivation for biofuels production. The combination of a biomass production model with specific site location parameters such as irradiance, geographical constraints, land use, topography, temperatures and CO2 for biofuels plants were identified in Sicily (Italy). A simulation of CO2 saved by using the theoretical biofuel produced in place of traditional fuel was implemented. Results indicate that the territory of Sicily offers a good prospective for these technologies and the results identify ideal locations for locating biomass fuel production facilities. Moreover, the research provides a robust method that can be tailored to the specific requirements and data availability of other territories. © Research India Publications.