Removal of pesticides from secondary treated urban wastewater by reverse osmosis

Environmental Science and Pollution Research - The residues of pesticides that reach water resources from agricultural activities in several ways contaminate drinking water resources and threaten...     

A revised stratigraphic framework for Later Cenozoic sequences in the northeastern Mediterranean region

This study describes the lithostratigraphic character of mid-Cenozoic (Oligocene–Pliocene) sequences in different parts of the northeastern Mediterranean area and offers a detailed stratigraphic correlation for this region. The sequences concerned are drawn from the Camardi area (south–central Anatolia), the Adana Basin, the Misis Mountains and the Kyrenia Range (northern Cyprus) and the submerged Florence Rise (west of Cyprus). The stratigraphic relationships identified here indicate the following: (a) Following the middle Eocene (Lutetian) regression there was uplift throughout the entire region; (b) Episodes of fluvial and lacustrine deposition in intramontane settings ensued in most of this region during the late Eocene/early Miocene interval; (c) Following a regionally extensive phase of tectonic compression, major marine transgression commenced in the late Oligocene in northern Cyprus and in the early Miocene in adjacent southern Turkey, with the exception of the Ecemis Fault Zone where continental deposition continued; (d) These Oligo-Miocene transgressive sequences comprise a broadly diachronous complex of both shallow and deeper marine facies, including reefal carbonates, littoral clastics, basinal shales and fan-turbidites; (e) Deeper marine Miocene facies persisted longer in the Misis area and in northern Cyprus; (f) A regional regression occurred throughout most of the area during the late Serravallian to Tortonian interval and is marked by the abrupt, locally discordant appearance of extensive shallow marine, deltaic and fluvial deposits; (g) Continued regression in the Messinian led to the formation of significant evaporite deposits in the western and southern parts of the region, but localized uplift of the Misis area is attested by the initial deformation of the Neogene rocks there and the absence of Messinian sediments from this area; (h) In the Pliocene there was extensive emergence of the northern parts of the region interrupted by brief marine incursions. The present-day drainage pattern was established at this time; (i) Marine conditions persisted longer in northern Cyprus, where emergence occurred only in the latest Pliocene.     

A chemical substitution study for a wet processing textile mill in Turkey

Wet processing textile industry has many different processing stages (dyeing, sizing, de-sizing, scouring, softening, etc.). Many chemicals currently used in the wet processing textile industry affect the amount and the type of waste produced and their influence on the aquatic life of the receiving stream. One of the critical steps in pollution prevention studies is auditing the use of chemicals and making the necessary chemical substitutions. This chemical substitution study was conducted on one of the major textile factories in Turkey with a capacity of 20,000 tons of denim fabric per year. During this study, chemical consumption level, receipts applied, environmentally problematic and alternative chemicals were examined. Integrated Pollution Prevention and Control (IPPC) Reference Document on Best Available Techniques (BAT) for the Textiles Industry was accepted as main reference document and also related case studies were examined. According to the study, over 70% reduction in sulphide, which is very toxic to aquatic life, was achieved by replacing sulphur dyestuff with low sulphide content. By replacing an alternative complexing agent, the mill not only prevented the 3100 kg/month COD load to the wastewater treatment plant (WWTP), but also obtained more biodegradable wastewater generated during production. On the other hand, some of the chemical substitution options were on progress or dropped.     

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.     

Water quality of Mediterranean coastal plains: conservation implications from the Akyatan Lagoon,Turkey

The water quality of the Akyatan Lagoon was characterized using hydrochemical methodology. The lagoon is located on the Mediterranean coast and is the largest wetland ecosystem in Turkey. In addition, the lagoon is classified as a hyper-salinity wetland. Water samples were collected monthly between December 2007 and November 2008. Eleven stations within the lagoon were determined, and triplicate grab samples were obtained from each station to characterize water quality as follows: T °C, pH, total alkalinity (TAlk), dissolved oxygen (DO), total dissolved solids (TDS), salinity, electrical conductivity (EC), and main anions, including chloride (Cl^?), nitrates (NO₃ ^?), and sulfate (SO₄ ^2?). Results from selected stations indicated varying TDS, EC, salinity, and Cl^? concentrations, from 20,892 to 175,824 mg/L, from 35.7 to 99.6 mS/cm, from 22.3 to 71.0 ppt, and from 14,819 to 44,198 mg Cl^?/L, respectively. Data indicated that the spatial distribution of water quality parameters was significantly affected by freshwater input via the constructed drainage channels which collect water from a catchment area and discharge water into the lagoon as a point source, thus preventing drainage water to reach the lagoon as a nonpoint source.     

Monitoring spatiotemporal variations of diel radon concentrations in peatland and forest ecosystems based on neural network and regression models

Concentrations of outdoor radon-222 (²²²Rn) in temperate grazed peatland and deciduous forest in northwestern Turkey were measured, compared, and modeled using artificial neural networks (ANNs) and multiple nonlinear regression (MNLR) models. The best-performing multilayer perceptron model selected out of 28 ANNs considerably enhanced accuracy metrics in emulating ²²²Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest ²²²Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1?+?2.5 Bq?m^?3 h^?1) of ²²²Rn in the forest was three times smaller than that (15.8?+?9.7 Bq?m^?3) of ²²²Rn in the peatland. Mean ²²²Rn level had negative and positive relationships with air temperature and relative humidity, respectively.     

Adoption of European Union's IPPC Directive to a textile mill: Analysis of water and energy consumption

The present study was undertaken as part of the first application and evaluation of the BREF (Best Available Techniques; BAT Reference Document) Textile Document within the context of the European Union's Integrated Pollution Prevention and Control (IPPC) Directive to a large scale textile mill in Turkey. The BAT requirements for the denim manufacturing textile mill were selected in cooperation with the factory management. Detailed mass balance calculations were conducted to evaluate the overall effect of the selected BAT options. The initial findings indicated that the adoption of the selected BAT options resulted in considerable savings in water and energy consumption in the mill. Besides the installation of flow meters and use of semi-counter current rinsing in the most water-intensive processes, minimization of wash waters in the water softening plant, reuse of the concentrate stream from the reverse osmosis plant and compressor cooling waters provided a 29.5% reduction in the total specific water consumption of the mill, reaching the lower limits suggested by the BREF Textile Document. In terms of energy consumption, use of waste heat from finishing wastewater streams in heating up the wash waters, heat insulation and maintenance applications in addition to BAT measures taken for water minimization reduced specific energy consumption by 9% achieving the limits set by the BREF Textile Document.     

Using interactive multimedia training in TQEM systems and applications

Corporate America will spend $52.2 billion on formal training in 1995,¹ of which 72 percent will be spent on training staff salaries. The classroom has been the main training format for decades. Within the last decade, however, training videos and audio tapes have captured some of the training market. However, none of these tools has the capability to be as effective as interactive multimedia training. Computer-based interactive multimedia training (IMT) is emerging as a powerful tool in the training field. The ability to provide consistent delivery on a timely basis without impacting work schedules makes this tool very attractive. Although IMT is not a solution to all training problems, it has been shown to increase the effectiveness of training significantly when used appropriately. This article introduces the concept of IMT and its application to TQEM.