Application of multivariate statistical approach to identify heavy metal sources in bottom soil of the Seyhan River (Adana), Turkey

In this study, freshly deposited soils were sampled from the Seyhan River (Turkey) from the exit of the Seyhan Dam to the Adana exit. Heavy metal contents were measured with X-ray fluorescence spectrometer. Multivariate statistical approach is used to identify the sources of heavy metals and other elements in soil samples. Considering the size of anomalies, metals are ranked as Co>Pb>Cr>Zn>Al. Based on the hierarchical cluster analysis results, three clusters were observed. P, Mg, Ti, Fe, Ca, Na, K, Al, Si, and Nb form the first cluster, Zn, Sr, Pb, and Cr associated as the second cluster, and Ba and Co form the third cluster. Three factors computed from principal component analysis are explained with a cumulative variance of 95%. The first factor is defined with “high background lithogenic factor” Co, the second factor with “local industrial factor” Pb, Cr, Ba, and Mg, and the third factor with “natural factor” Cr and Pb.     

Heavy metal contents of organically produced, harvested, and dried fruit samples from Kayseri, Turkey

Organically produced, harvested, and dried fruit samples bought at organic markets in Kayseri, Turkey have been analyzed for their trace element contents. In the determinations, flame atomic absorption spectrometer (FAAS) was used. This pilot study is the first to be performed for organically produced, harvested, and dried fruit samples from Kayseri, Turkey. The copper, iron, manganese, and zinc concentrations were found to be 1.6–15.5, 10.3–144, 23.0–211, and 23.3–91.6 μg/g, respectively. The cobalt, lead, cadmium, chromium, and nickel concentrations in all analyzed organic fruit samples were below the quantification limits of FAAS. SRM 1570A spinach leaves and SRM 1515 apple leaves were used to check the accuracy of the procedure. The results for the dried fruit samples found in this work were compared with the values from some studies from the world. The results found in the presented work may also be useful for future studies about organically produced, harvested, and dried fruit samples.     

Trace metal levels and some chemical parameters in herby cheese collected from south eastern Anatolia-Turkey

Due to the consumption of the herby cheese in south eastern Anatolia-Turkey is at high ratio, chemical parameters such as dry matter, pH, acidity, aCl, protein, fat and trace metal contents in 50 different herby cheeses produced at this region have been determined in the presented study. The content of lead, copper, cobalt, nickel, chromium, cadmium, and iron have been analyzed by flame atomic absorption spectrometer in air/acetylene flame after combination ash digestion method and wet digestion method. The mean concentration of lead, copper, cobalt, nickel, chromium and cadmium in our samples are higher than literature values except iron.     

Variation of heavy metal and micro and macro element concentrations of bread and durum wheats and their relationship in grain of Turkish wheat cultivars

The 64 varieties displayed a large variation for all mineral elements, investigated Fe, Mn and Zn, ranging from 24.2 to 43.1 mg/kg, 27.6 to 64.8 mg/kg and 10.4 to 38.2 mg/kg, respectively. The mean Ca, K, Mg, Na, P and S concentrations in wheat rain varieties amounted to 378, 4,266, 1,183, 317, 3,513 and 1,542 mg/kg, respectively. Ca, K, Mg, Na, P and S contents of wheat varieties changed at the levels between 266 and 531 mg/kg, 3,029 and 5,566 mg/kg, 972 and 1,525 mg/kg, 277 and 368 mg/kg, 2,422 and 4,610 mg/kg and 1,241 to 2,052 mg/kg, respectively. The concentrations of Al, Cu, Fe, Mn and Zn of durum wheat samples were found at high levels. The mean Al, Cr, Mo, Ni, Cu, Fe, Mn and Zn concentrations in durum wheat varieties amounted to 10.93, 0.47, 0.72, 0.72, 5.37, 34.9, 37.28 and 20.88 mg/kg, respectively.     

Evaluation of metal contents of household detergent samples from Turkey by flame atomic absorption spectrometry

The concentrations of cadmium, copper, chromium, cobalt, iron, lead, manganese, nickel, and zinc in detergent samples from Kayseri, Turkey were determined by flame atomic absorption spectrometry. HClO₄ (10 mL)/HNO₃ (10 mL) mixture was used for the digestion of household detergent samples. The correctness of the analytical procedures was checked with standard addition–recovery tests in different detergent samples for the investigated metal ions. The concentration ranges of the elements in the detergent samples were found as 17.2–60.1, 11.1–40.1, 2.5–32.3, 8.1–10.5, 7.2–21.6, 9.8–17.9, 1.7–3.8, 12.5–22.5, and 2.0–5.8 μg/g for iron, manganese, zinc, copper, lead, cobalt, cadmium, nickel, and chromium, respectively. The values found in this work were compared with some other studies around the world conducted on detergent samples.     

The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂?=?400 mg/L, Fe⁺²?=?40 mg/L, pH?=?3, irradiation time?=?150 min, and temperature?=?30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R ²?=?0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.     

Investigating motivating factors for sound hospital waste management

Sustainable management of hospital waste requires an active involvement of all key players. This study aims to test the hypothesis that three motivating factors, namely, Reputation, Liability, and Expense, influence hospital waste management. The survey for this study was conducted in two phases, with the pilot study used for exploratory factor analysis and the subsequent main survey used for cross-validation using confirmatory factor analysis. The hypotheses were validated through one-sample t tests. Correlations were established between the three motivating factors and organizational characteristics of hospital type, location, category, and size. The hypotheses were validated, and it was found that the factors of Liability and Expense varied considerably with respect to location and size of a hospital. The factor of Reputation, however, did not exhibit significant variation. In conclusion, concerns about the reputation of a facility and an apprehension of liability act as incentives for sound hospital waste management, whereas concerns about financial costs and perceived overburden on staff act as disincentives.

Implications: This paper identifies the non economic motivating factors that can be used to encourage behavioral changes regarding waste management at hospitals in resource constrained environments. This study discovered that organizational characteristics such as hospital size and location cause the responses to vary among the subjects. Hence a policy maker must take into account the institutional setting before introducing a change geared towards better waste management outcomes across hospitals. This study covers a topic that has hitherto been neglected in resource constrained countries. Thus it can be used as one of the first steps to highlight and tackle the issue.     

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0–30cm, 30–60cm, 60–90cm and 90–120cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.     

Application of Tabu Search Algorithm With a Coupled AnnAGNPS‐CCHE1D Model to Optimize Agricultural Land Use1

Abstract: A principal contributor to soil erosion and nonpoint source pollution, agricultural activities have a major influence on the environmental quality of a watershed. Impact of agricultural activities on the quality of water resources can be minimized by implementing suitable agriculture land‐use types. Currently, land uses are designed (location, type, and operational schedule) based on field study results, and do not involve a science‐based approach to ensure their efficiency under particular regional, climatic, geological, and economical conditions. At present, there is a real need for new methodologies that can optimize the selection, design, and operation of agricultural land uses at the watershed scale by taking into account environmental, technical, and economical considerations, based on realistic simulations of watershed response. In this respect, the present study proposes a new approach, which integrates computational modeling of watershed processes, fluvial processes in the drainage network, and modern heuristic optimization techniques to design cost effective land‐use plans. The watershed model AnnAGNPS and the channel network model CCHE1D are linked together to simulate the sediment and pollutant transport processes. Based on the computational results, a multi‐objective function is set up to minimize soil losses, nutrient yields, and total associated costs, while the production profits from agriculture are maximized. The selected iterative optimization algorithm uses adaptive Tabu Search heuristic to flip (switching from one alternative to another) land‐change variables. USDA’s Goodwin Creek experimental watershed, located in Northern Mississippi, is used to demonstrate the capabilities of the proposed approach. The results show that the optimized land‐use design with BMPs using an integrated approach at the watershed level can provide efficient and cost‐effective conservation of the environmental quality by taking into account both productivity and profitability.