Solid phase extraction of Cu(II), Ni(II), Pb(II), Cd(II) and Mn(II) ions with 1-(2-thiazolylazo)-2-naphthol loaded Amberlite XAD-1180

A new method for separation and preconcentration of trace amounts of Cu(II), Ni(II), Pb(II), Cd(II) and Mn(II) ions in various matrices was proposed. The method is based on the adsorption and chelation of the metal ions on a column containing Amberlite XAD-1180 resin impregnated with 1-(2-thiazolylazo)-2-naphthol (TAN) reagent prior to their determination by flame atomic absorption spectrometry (FAAS). The effect of pH, type, concentration and volume of eluent, sample volume, flow rates of sample and elution solutions, and interfering ions have been investigated. The optimum pH for simultaneous retention of all the metal ions was 9. Eluent for quantitative elution was 20 ml of 2 mol l(-1) HNO(3). The optimum sample and eluent flow rates were found as 4 ml min(-1), and also sample volume was 500 ml, except for Mn (87% recovery). The sorption capacity of the resin was found to be 0.77, 0.41, 0.57, and 0.30 mg g(-1) for Cu(II), Ni(II), Cd(II), and Mn(II), respectively. The preconcentration factor of the method was 200 for Cu(II), 150 for Pb(II), 100 for Cd(II) and Ni(II), and 50 for Mn(II). The recovery values for all of the metal ions were > or = 95% and relative standard deviations (RSDs) were < or = 5.1%. The detection limit values were in the range of 0.03 and 1.19 microg l(-1). The accuracy of the method was confirmed by analysing the certified reference materials (TMDA 54.4 fortified lake water and GBW 07605 tea samples) and the recovery studies. This procedure was applied to the determination of Cu(II), Ni(II), Pb(II), Cd(II) and Mn(II) in waste water and lake water samples.     

River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.     

Properties of steel foundry electric arc furnace dust solidified/stabilized with Portland cement

Electric arc furnace dust from steel production is generated in considerable amounts worldwide and needs to be treated as hazardous waste. The aim of this study was to investigate the properties of electric arc furnace dust solidified/stabilized by using Portland cement. Mortar and paste samples were prepared with varying waste-to-binder ratios between 0% and 90%. A comprehensive experimental program was designed including XRF characterization, setting time, unconfined compressive strength, and toxicity characteristics leaching procedure (TCLP), synthetic precipitation leaching procedure (SPLP), and acid neutralization capacity (ANC) tests. The results were evaluated in order to determine if the solidified /stabilized product can be disposed of at a landfill site with domestic waste or at a segregated landfill. The effect of using sand on S/S performance was also investigated. The results indicated that the solidification /stabilization process using PC helps the heavy metals to be bound in the cement matrix, but the TCLP leaching results exceeded the EPA landfilling limits. The SPLP leaching results conformed to the limits implying that the waste or S/S products can be disposed of at a segregated landfill; however the low ANC of the S/S products reveals that there may be leaching in the long-term. The sand used in the mortar samples adversely affected the S/S performance, causing higher heavy metal leaching levels, and lower pH levels in the leachate after the TCLP extraction than those measured in the leachate of the paste samples.     

Precipitable water modelling using artificial neural network in Çukurova region

Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Çukurova region, south of Turkey. We applied Levenberg–Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova region. Precipitable water was the output. Correlation coefficient (R²) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.     

Modeling the Formation of Chlorination By-Products During Enhanced Coagulation

Because of increasing need to balance health risks for pathogen control and disinfection by-products (DBP) formation in drinking water supplies, water utilities are forced to closely examine and optimize their disinfection practices. This research was designed to investigate the effects of independent variables of dissolved organic carbon (DOC), ferric chloride dosage, chlorine dose, and reaction time on trihalomethanes (THMs) formation in Terkos Lake Water (TLW) of Istanbul City. A statistically-based empirical model was developed for predicting THM formation during enhanced coagulation. The R ² and F value of model were 0.762 and 460, respectively. The model was found to be statistically significant for all four variables, and model predictions appear to be most accurate for this study. A multiple linear model exhibited the best fit of data. It was observed that THM formation depended primarily on DOC removal. Model calibration, testing and validation were accomplished by using independent data set.     

Formation of Disinfection Byproducts (DBPs) in Surface Water Sources: Differential Ultraviolet (UV) Absorbance Approach

Chlorination for drinking water forms various disinfection byproducts (DBPs) of trihalomethanes (THMs) and haloacetic acids (HAAs). Chlorination has been attributed to the destruction of activated aromatic sites of the natural organic matter (NOM) predominantly at electron rich sites. Experiments with Istanbul surface waters showed that the magnitude of the decrease in the ultraviolet (UV) absorbance at 272 nm (UV₂₇₂) was an excellent indicator of destruction of these sites by chlorine. The main objective of the present study is to develop the differential UV₂₇₂ absorbance (ΔUV₂₇₂) related models for the prediction of the formation of THM, HAA, and their species in raw water samples from Terkos, Buyukcekmece, and Omerli lakes under different chlorination conditions. Significant factors affecting DBP formation in the raw waters were identified through numerical and graphical techniques. The R² values of the models varied between 0.94 and 0.97, indicating excellent predictive ability for THM₄ and HAA₉ in the raw waters. The models were validated using additional data. The results of this study concluded that addition of ΔUV₂₇₂ parameter into THM₄ and HAA₉ models make the prediction of these DBP compounds more precisely than those of DBP models developed in the past. A better understanding of these modeling systems will help the water treatment plant operators to minimize the DBP formation, providing a healthier and better drinking water quality as well as identifying strategies to improve water treatment and disinfection processes.     

Multi-pathway risk assessment of trihalomethanes exposure in Istanbul drinking water supplies

The lifetime cancer risk and the hazard index of trihalomethanes (THMs) through oral ingestion, dermal absorption, and inhalation exposure from tap water of 15 districts in Istanbul are estimated. The most dominant THM compounds are chloroform, bromodichloromethane (BDCM), and dibromochloromethane (DBCM) in Istanbul tap water. The results indicate that within three different pathways, Istanbul residents had a higher cancer risk through oral ingestion than through the other two pathways. The lifetime cancer risks of oral ingestion for total THMs was highest in Esenyurt district, while the lowest lifetime cancer risk for total THMs was in Basaksehir district. The lifetime cancer risks of chloroform, BDCM, and DBCM from tap water of all 15 districts were higher than 10(-6), the negligible risk level defined by the USEPA. Among the 15 districts, people living in Esenyurt have the highest risk of cancer due to the THM exposure through the multi-pathways, mainly because of the exposure to BDCM and DBCM. The total cancer risk analysis concluded that each year approximately 5 of the 8 million Istanbul residents could get cancer from the daily intake of tap water.     

Spatial and Seasonal Variations of Disinfection Byproducts (DBPs) in Drinking Water Distribution Systems of Istanbul City,Turkey

This study presents the seasonal and spatial variations of trihalomethanes (THMs) and haloacetic acids (HAAs) in 30 sampling points within three water distribution systems of Istanbul City, Turkey. The effects of surface water quality, seasonal variation, and species differences were examined. The occurrence of chlorinated THMs and HAAs levels was considerably lower in the system in which raw water is subjected to pre-ozonation versus pre-chlorination. Seasonal analysis of the data indicated that the median concentration of four THMs (THM₄) was higher than nine HAAs (HAA₉) concentrations in all three distribution systems sampling points. For all distribution systems monitored, the highest median THM₄ and HAA₉ concentrations were observed in the spring and summer season, while the lowest concentrations of these disinfection byproduct (DBP) compounds were obtained in the fall and winter period. Due to the higher level of bromide in supplying waters of these two systems, moderate levels of brominated DBP species have been observed in the Kagithane and Buyukcekmece distribution systems districts. In fact, Spearman partial correlations (Spearman rank correlation coefficients [rs]) tend to be higher among analogues in terms of number and types of substituent, especially TCAA with TCM (rs 0.91), and DBAA with DBCM (rs 0.90). In contrast, the hydraulic (residence time and flow rate) and chemical mechanisms (hydrolysis, volatilization, and adsorption) affect the fate and transport of DBPs in distribution systems. Seasonal and spatial variations of DBPs presented in this study have important implications on regulatory issues and from an epidemiological point of view.