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.     

Soil lead (Pb) in residential transects through Lubbock,Texas: a preliminary assessment

Residential lead (Pb) contamination, resulting from decades-long use of leaded gasoline and lead-based paint, is likely to be present in soils in most urban areas. A screening level sampling effort demonstrated that Lubbock, Texas, USA, like other cities of its age and size, has areas of elevated soil Pb. This effort was based on soil sampling performed on residential, commercial and thoroughfare properties. The focus of this study was to investigate that component of soil contamination due to combustion of leaded gasoline. Soils were collected from the 1–2 cm surface layer from street-side property borders, well away from buildings that might lead to soil contamination from leaded paint chips. All samples were analyzed for Pb after a 1 M HNO₃ mild extraction to determine the amount of bioavailable Pb. Two of three transects through the city demonstrated significant trends of decreasing Pb concentrations with distance from the city center, paralleling a decrease in developed property age. Peak soil Pb concentrations outside city development was 4.9 ± 0.6 mg/kg while the median concentration for the city was 35.4 mg/kg. Peak soil Pb concentrations in the city center ranged from 90.0 to 174.0 mg/kg and decreased exponentially to 6.0–9.0 mg/kg at the furthest terminus of the residential transects.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Manual Material Handling Assessment Among Workers of Iranian Casting Workshops

Manual material handling (MMH) tasks can be found in most workplaces and they may constitute a risk factor for work-related musculoskeletal disorders (WMSDs). This study was conducted to determine the prevalence of WMSDs and to compare MMH loads with the acceptable weight and force limits among Iranian casting workers. Data were collected from 50 workers of casting workshops who performed MMH tasks. The Nordic musculoskeletal disorders questionnaire and the Snook tables were used as tools for data collection. Hand/ wrist symptoms were the most prevalent problems among the workers (84%). The results of the Snook tables showed that the loads in lifting (84%), lowering (86%), carrying (66%), pushing with initial (43%) and sustained force (59%), and pulling tasks with initial (48%) and sustained force (93%) exceeded recommended limits. WMSDs occurred in high rates among the workers and, thus, ergonomics interventions should focus on decreasing WMSDs and redesigning MMH tasks.     

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 G?ztepe 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.     

Simultaneous determination of binary solution of triphenylmethane dyes in complex matrices onto magnetic amino-rich SWCNT using second-order calibration method

This study suggested a new method for simultaneous quantification of two dyes in complex matrices using second-order data by spectrophotometry. Second-order data was generated simply without any expensive instrument using two independent variables including wavelength and the monotonic addition of sodium dodecyl sulfate. Solid-phase extraction (SPE) based on amino-rich magnetic single-walled carbon nanotube as an adsorbent was employed prior to second-order data generation. SPE optimization was performed by Box-Behnken design, and parameters and their interaction which were dependent on the simultaneous extraction of dyes were examined. Competitive Langmuir and Freundlich isotherms for a binary system and individual dyes could all represent the equilibrium data well. The second-order data was processed by parallel factor analysis (PARAFAC and PARAFAC2) and multivariate curve resolution-alternating least squares (MCR-ALS). Figures of merit of the model including a limit of detection of 3.0 and 2.5 ng mL^?1 for crystal violet and malachite green, respectively, were estimated using the MCR-ALS method. The combination of the second-order calibration and SPE presents an easy and versatile method for determination of the mixture of two dyes in the presence of uncalibrated interferences in environmental water, synthetic, and fish samples with the recoveries of 94–104.     

Improving Estimation of Specific Surface Area by Artificial Neural Network Ensembles Using Fractal and Particle Size Distribution Curve Parameters as Predictors

Specific surface area (SSA) is one of the principal soil properties used in modeling soil processes. In this study, artificial neural network (ANN) ensembles were evaluated to predict SSA. Complete soil particle-size distribution was estimated from sand, silt, and clay fractions using the model by Skaggs et al. and then the particle-size distribution curve parameters (PSDCPs) and fractal parameters were calculated. The PSDCPs were used to predict 20 particle-size classes for a soil sample’s particle size distribution. Fractal parameters were calculated by the model of Bird et al. In addition, total soil-specific surface area (TSS) was calculated using the above 20 size classes. Pedotransfer functions were developed for SSA and TSS using ANN ensembles from 63 pieces of SSA data taken from the literature. Fractal parameters, PSDCPs, and some other soil properties were used to predict SSA and TSS. Introducing fractal parameters and PSDCPs improved the SSA estimations by 12.5 and 11.1 %, respectively. The improvements were even better for TSS estimations (27.7 and 27.0 %, respectively). The use of fractal parameters as estimators described 44 and 92.8 % of the variation in SSA and TSS, respectively, while PSDCPs explained 42 and 6.6 % of the variation in SSA and TSS, respectively. The results suggested that fractal parameters and PSDCPs could be successfully used as predictors in ANN ensembles to predict SSA and TSS.     

Comparison the effects of bioaugmentation versus biostimulation on marine microbial community by PCR–DGGE: A mesocosm scale

In order to better understand the effects of biostimulation and bioaugmentation processes on a marine microbial community, three different mesocosm experiments were planned. Natural seawater(10.000 L) was artificially polluted with crude oil(1 L) and(1) inorganic nutrients(Biostimulating Mesocosm, BM),(2) inorganic nutrients and an inoculum of Alcanivorax borkumensis SK2(Single Bioaugmentation Mesocosm, SBM),(3) inorganic nutrients and inoculums of A. borkumensis SK2 and Thalassolituus oleivorans MIL-1(Consortium Bioaugmentation Mesocosm, CBM). During the experimental period(20 days), samples were taken from each mesocosm and the community structure was analyzed by PCR–DGGE. The 16 S r RNA gene DGGE banding patterns and sequence analysis demonstrated that biostimulation had the lowest effect on microbial biodiversity in the mesocosms; however, the biodiversity of the marine microbial community dramatically decreased in the CBM(Shannon index was 0.6 in T3). The community structures among the three mesocosms were also markedly different,and major bacteria derived from DGGE bands were related to uncultured Gamma Proteobacteria. The biodegradation results show that the Single Bioaugmentation Mesocosm(SBM) system had the highest percentage of degradation(95%) in comparison to the BM mesocosm(80%) and CBM(70%).