Inventory of emissions from major air pollutant categories in Turkey

In this study, an inventory of air pollutant emission estimates from major air polluting sources in Turkey for period between 1985 and 2005 with 5-year intervals were estimated. Inventory covers anthropogenic sources of five major air pollutants of particulate matter, sulfur dioxide, carbon monoxide, nitrogen oxides and non-methane volatile organic compounds. Their break-down with respect to main activity sectors were shown and their distribution by the largest industrial source categories were worked out as annual estimates. This inventory and its analysis point to serious environmental implications of air pollutants and a need to develop a policy plan for reducing these emissions.     

Effect of diosmin on lipid peoxidation and organ damage against subacute deltamethrin exposure in rats

Environmental Science and Pollution Research - The aim of this study was to investigate the protective efficacy of diosmin against subacute deltamethrin exposure. For this purpose, 40 male Wistar...     

Robust optimization evaluation of reliance on locally produced foods

While local food production may be beneficial in terms of developing the local economy and reducing greenhouse gases from transportation, sustainability strategies focused on local food production may generate their own risks due to yield variability. We have developed a robust optimization (RO) model to determine the minimum amount of land (cropland and pasture) required to grow food items that would satisfy a local population’s (accounting for gender and age) calorie and nutrient needs. This model has been applied to Boone County, Missouri, which has a population of approximately 170,000. Boone County is 1790 km², with 16% of the land defined as cropland and 30% defined as pasture. The model includes 27 nutrients from 17 potential foods that could be produced: six fruits and vegetables, five grains and six animal-sourced foods. Yield estimates are based on the predominate methods of agriculture in the USA. We first run our model assuming no variability, using the midpoint yield estimates. Then, to quantify uncertainty in yield for different food types, we use historical yield data over 10 years to estimate this variability and run our RO model under these variability estimates. We compare the two model results to illustrate the impact of data uncertainty on meeting sustainable local food for communities. Solutions suggest that nutrition needs can be met for the Boone County population within the land area defined.     

Amygdalin in bitter and sweet seeds of apricots

Hydrogen cyanide (HCN) poisoning due to amygdalin (AMY) in apricot seeds is one of the public health issues in Turkey. The aim of this study was to investigate the AMY content of 13 different apricot seeds including bitter and sweet ones, and which are either sulfurized or roasted. The AMY content was determined by a high-performance liquid chromatography. Release of HCN was predicted and total amount of seeds which can cause poisoning was calculated. The mean AMY content of bitter seeds was 26 ± 14 mg g^?1 and that of sweet seeds was 0.16 ± 0.09 mg g^?1. The consumption of small amounts of bitter seeds may cause cyanide poisoning.     

Workplace violence: a study of Turkish workers

This research was conducted to address the experience of workplace violence of Turkish workers from different sectors and to investigate the impact of the exposed violence on their psychological well-being. Data were collected anonymously with printed questionnaires from the volunteer participants and depended on self-reporting. The response rate was 79.0% (1708/2161). The prevalence of workplace violence was found to be 44.8%. The most common type was verbal violence together with mobbing (bullying). Victims of physical violence were mostly males, whereas females were found to be victims of verbal, psychological and sexual violence. Most cases did not result in legal action and the victims remained silent. Psychological well-being of exposed workers in terms of depression, anxiety and stress seemed to deteriorate. Workplace violence remains a silent epidemic in Turkey. Preventive measures against workplace violence and social support for violated workers do not exist.     

Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun,Turkey: association between garbage disposal facility and metallic accumulation

Environmental Science and Pollution Research - The objective of this paper was to stress the possible potential toxic element (PTE) accumulation in the surface sediments of the Çavu?lu...     

Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon

The main objective of this study is to generate a knowledge base which is composed of user-defined variables and included raster imagery, vector coverage, spatial models, external programs, and simple scalars and to develop an expert classification using Landsat 7 (ETM+) imagery for land cover classification in a part of Trabzon city. Expert systems allow for the integration of remote-sensed data with other sources of geo-referenced information such as land use data, spatial texture, and digital elevation model to obtain greater classification accuracy. Logical decision rules are used with the various datasets to assign class values for each pixel. Expert system is very suitable for the work of image interpretation as a powerful means of information integration. Landsat ETM data acquired in the year 2000 were initially classified into seven classes for land cover using a maximum likelihood decision rule. An expert system was constructed to perform post-classification sorting of the initial land cover classification using additional spatial datasets such as land use data. The overall accuracy of expert classification was 95.80%. Individual class accuracy ranged from 75% to 100% for each class.     

Air Quality Management in Izmir Region of Turkey as Required by Clean Air Plans

Future (24 h later) daily ground level SO₂ concentration in Istanbul was modeled and predicted using a new and powerful technique, Artificial Neural Networks (ANN) in the case of meteorological parameters as input variables. Results show that the trend of SO₂ from higher values in winter to lower values in spring and summer, and again to higher values towards winter can be correctly represented by the neural networks. The model better predicted the lower SO₂ values in spring and summer seasons when compared to higher values in winter season because of the pattern distribution in training data sets. Beside the amount of the database, the more the variation of the values of the parameters in their own ranges, the more the network learns the database. As a result of this study, considerably successful results were obtained when considering the complex and nonlineer structure of the atmosphere, which is the source of the database.     

Analysis of Heavy Metals in the Endometrial Tissue Using Electron Microscope

Objective This study was done to estimate whether heavy metals in the air may affect endometrial chemical composition. Materials and Methods A total of 30 albino Wistar rats were employed and randomly divided into three groups. The rats of Group 1 and 2 were acutely and sub-chronically exposed to the gas form of heavy metals, respectively. Group 3 was used as a control group. Endometrial tissue worth of heavy metals of the groups was measured in through using scanning electron microscope. Results A statistically no significant difference was found for endometrial chemical composition of all of the heavy metals (chrome, manganese, iron, cobalt, nickel, zinc and lead) between group 1 and 2 (p > 0.05). On the other hand, there was statistically significant difference for all of the heavy metals between group 1 and group 3 (p < 0.05), while there was statistically significant difference for chrome, manganese, iron, cobalt and nickel (p < 0.05), but zinc and lead (p > 0.05) between group 2 and 3. Conclusion Air pollutants of Pb and Zn resulting mostly from combustion of fossil fuels and certain special industrial process in Kirikkale may be a risk factor for the high pregnancy loses by changing endometrial homeostasis.     

Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks

Suspended sediment concentration (SSC) is generally determined from the direct measurement of sediment concentration of river or from sediment transport equations. Direct measurement is very costly and cannot be conducted for all river gauge stations. Therefore, correct estimation of suspended sediment amount carried by a river is very important in terms of water pollution, channel navigability, reservoir filling, fish habitat, river aesthetics and scientific interests. This study investigates the feasibility of using turbidity as a surrogate for SSC as in situ turbidity meters are being increasingly used to generate continuous records of SSC in rivers. For this reason, regression analysis (RA) and artificial neural networks (ANNs) were employed to estimate SSC based on in situ turbidity measurements. The SSC was firstly experimentally determined for the surface water samples collected from the six monitoring stations along the main branch of the stream Harsit, Eastern Black Sea Basin, Turkey. There were 144 data for each variable obtained on a fortnightly basis during March 2009 and February 2010. In the ANN method, the used data for training, testing and validation sets are 108, 24 and 12 of total 144 data, respectively. As the results of analyses, the smallest mean absolute error (MAE) and root mean square error (RMSE) values for validation set were obtained from the ANN method with 11.40 and 17.87, respectively. However these were 19.12 and 25.09 for RA. It was concluded that turbidity could be a surrogate for SSC in the streams, and the ANNs method used for the estimation of SSC provided acceptable results.