Yazar "Ozcan, H. Kurtulus" seçeneğine göre listele

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    Heavy metal amounts in soil and sediments of surface water sources in the industrial regions of Istanbul
    (Destech Publications, Inc, 2007) Ozbas, Emine Elmaslar; Ozcan, H. Kurtulus; Balkaya, Nilgun; Demir, Goksel; Bayat, Cuma
    Today, heavy metal-oriented soil pollution composes a substantial problem, especially in regions with dense industrial activities. Industrial wastes may also be introduced to superficial waters either directly or indirectly, forming a dense accumulation of heavy metal in aquatic sediments. Istanbul exhibits a high density of industrial activities. In this study, soil and sediment samples were taken from 4 superficial water resources in the dense industrial region that borders Istanbul. In the first section of the study, the heavy metal concentrations (Cu, Ni, Zn, Pb, Cd) in these samples were determined and compared with each other. In the second section of the study, after the comparison with similar studies in the literature, an assessment was made.
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    Heavy metal concentrations of atmospheric ambient deposition dust in Istanbul-Bosphorus Bridge tollhouses
    (Destech Publications, Inc, 2007) Ozcan, H. Kurtulus; Demir, Goksel; Nemlioglu, Semih; Sezgin, Naim; Bayat, Cuma
    Air Pollution emissions due to motor vehicle-related traffic on motorways are sent into the atmosphere. Along with many pollutants from motor vehicles because of fossile fuel usage, heavy metals are also emitted as particles with exhausted gas. Heavy metals are one of the important parameters among environmental pollution sources. Heavy metals from motor vehicles may be found as PM in motorway atmosphere and be involved in street dust by depositing on roads as atmospheric precipitates. Bridge tollhouses are one of the most closely encountered environments of human beings with this kind of dust. In this study, time-related changes of atmospheric precipitate depositing per unit area were determined on breathing height of tollhouse workers, as well as concentrations of lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), and nickel (Ni) were measured. The study area was Istanbul Bosphorus Bridge, which is one of the busiest points of the world (referred to as the junction point of Asia and Europe) after it was merged to international D-100 motorway. According to the results obtained from the samples collected in ten different times during thirteen months, average lead concentration was found to be 1454.65 mg/kg dry soil, where the concentrations of copper, zinc, cadmium, and nickel were 399.12, 2034.78, 24.37, and 140.92 mg/kg dry soil, respectively.
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    Modeling of Methane Distribution in a Landfill Using Genetic Algorithms
    (Mary Ann Liebert, Inc, 2009) Ozcan, H. Kurtulus; Balkaya, Nilgun; Bilgili, Erdem; Demir, Goksel; Ucan, O. Nuri; Bayat, Cuma
    Landfill gas (LFG) results from the biologic decomposition of municipal waste and consists of mostly methane (CH4) and carbon dioxide (CO2), as well as trace amounts of a variety of other compounds. In this study, the major landfill gas emissions produced in Istanbul Hasdal landfill were investigated and modeled. In the investigated area, CH4, CO2, and O-2 measurements were made for 3.5 years in order to monitor long-term variations. In addition, a supervised algorithm for the evaluation of CH4 concentration using a genetic algorithm (GA) was developed and applied to real data. The model and the actual measurement results were compared and statistically evaluated. It was observed that the long term changes of the CH4 concentrations can be estimated effectively by the GA model structure. A correlation with 0.86 value was ascertained between the actual values and model results. The results of the study indicated that the GA can be used in modeling landfill gases generated in solid waste deposition areas.
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    Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks
    (Science China Press, 2007) Ozcan, H. Kurtulus; Bilgil, Erdem; Sahin, Ulku; Ucan, O. Nuri; Bayat, Cuma
    Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.