Cuma Bayat

Determination of heavy metal concentrations in street dusts in Istanbul E-5 highway

Components and quantity of street dust are environmental pollution indicators especially in big cities. Street dust is generally composed of car exhaust gas originated particles and wind-transported particles. Heavy metals, which are found in street dust, such as Pb, Cu, Mn, Zn, Cd and Ni are significant for environmental pollution. According to the kind of vehicle in traffic, quantity and type of heavy metals vary in street dust. The use of leaded gasoline gives a boost to the importance of lead level especially in street dust even at the start of 21st century. These metals possess bioaccumulation property, and the possibility of the amount of these metals reaching a critical value and threatening human health increases the importance of this issue. In this study, street dusts have been collected from E-5 Highway from Topkapi to Avcilar regions that spans about 18 km in Istanbul, Turkey, and Pb, Cu, Mn, Zn, Cd and Ni concentrations have been detected in street dust. Twenty-two street dust samples were taken from a total of 22 different points at previously decided 14 main areas. Analyses were conducted using Leeds Public Analyst method. According to the results of this study, Pb, Cu and Zn concentrations in E-5 Highway between Topkapi and Avcilar region in Istanbul were higher than maximum concentration levels of these heavy metals in normal soil. This situation indicates that there is heavy metal pollution in the inspected area in E-5 Highway in Istanbul.

Hydrogels with Acid Groups for Removal of Copper(II) and Lead(II) Ions

Acrylamide-maleic acid (AAM-MA) hydrogels having high acid group content prepared with different maleic acid ratios were used for the removal of Cu(II) and Pb(II) ions from aqueous solutions in competitive and noncompetitive conditions. The effects of pH, time, and initial metal ion concentration on the metal ion adsorption capacity were investigated. The adsorption isotherm models were applied on experimental data and it is shown that the Freundlich equation was the best model for Cu(II) ion while the Langmuir isotherm model was the best one for Pb(II) ion. The stability constants of acrylamide-maleic acid hydrogel-Cu(II) and Pb(II) complexes were also determined by van den Berg/Ruzic transformation, and K values obtained were 1.60 × 103 and 1.81 × 103 for Cu(II) and Pb(II) ions, respectively. The experiments under competitive conditions showed that the hydrogels prefered Pb(II) ion and this preference increased with increasing of carboxylic acid group content (AGC) of polymers. It is stated that these hydrogels can be regenerated efficiently (>95%) and used repeatedly.

Assessment of particulate matter in the urban atmosphere: size distribution, metal composition and source characterization using principal component analysis

In this study, the size distribution of airborne particles and related heavy metals Co, Cd, Sn, Cu, Ni, Cr, Pb and V in two urban areas in Istanbul: Yenibosna and Goztepe, were examined. The different inhalable particles were collected by using a cascade impactor in eight size fractions (<0.4 μm, 0.4-0.7 μm, 1.1-2.1 μm, 2.1-3.3 μm, 3.3-4.7 μm, 4.7-5.8 μm, 5.8-9 μm and >9 μm) for six months at each station. Samples were collected on glass fiber filters and filters were extracted and analyzed using ICP-MS. Log-normal distributions showed that the particles collected at the Yenibosna site have a smaller size compared to the Goztepe samples and the size distribution of PM was represented the best by the tri-modal. The average total particle concentrations and standard deviations were obtained as 67.7 ± 17.0 μg m(-3) and 82.1 ± 21.2 μg m(-3), at the Yenibosna and Göztepe sites, respectively. The higher metal rate in fine and medium coarse PM showed that the anthropogenic sources were the most significant pollutant source. Principal component analysis identified five components for PM namely traffic, road dust, coal and fuel oil combustion, and industrial.

A New Approach to Prediction of SO2 and PM10 Concentrations in Istanbul, Turkey: Cellular Neural Network (CNN)

This article describes the application of a cellular neural network (CNN) to model air pollutants. In this study, forthcoming daily and hourly values of particulate matter (PM10) and sulphur dioxide (SO2) were predicted. These air pollutant concentrations were measured at four different locations (Yenibosna, Sarachane, Umraniye and Kadikoy) in Istanbul between 2002 and 2003. Eight different meteorological parameters (temperature, wind speed and direction, humidity, pressure, sunshine, cloudiness, rainfall) recorded at Florya and Goztepe meteorological stations were used to model inputs. First, the results of CNN prediction and statistical persistence method (PER) were compared. Then, CNN and PER outputs were correlated with real time values by using statistical performance indices. The indices of agreement (d) for daily mean concentrations were found using CNN and PER prediction models: 0.71–0.80 and 0.71–0.78 for PM10, and 0.81–0.84 and 0.77–0.82 for SO2 in all air quality measurement stations, respectively. From these values, CNN prediction model are concluded to be more accurate than PER, which is used for comparison. In hourly prediction of mean concentrations with CNN, d value is found as 0.78 and 0.92 for PM10 and SO2, respectively. Thus, it was concluded that CNN-based approaches could be promising for air pollutant prediction.