Mapping water chemical variables with spatially correlated errors

One of the most important considerations in many environmental studies is need to allow for correlations among the variables. Monitoring and analyzing relationships between chemical environmental parameters using spatial correlation based regression modelling is the main motivation of this applied study. For this purpose, some noticeable environmental parameters of data sets obtained from two lakes have been considered and the concentrations of chemical variables such as cadmium and nitrate have been appraised by a regression-based geostatistical methodology. The modelling procedure consists of two stages. In the first stage, spatial variables are analyzed via multi-linear regression and some relationships are provided. Next, by using the spatial auto-correlations of the residuals, a type of regression-based kriging procedure is applied. The capacity of the model for appraising the water chemical variables is also tested and performance comparisons with ordinary kriging are conducted. Finally, the applications showed that analyzing water chemical variables with spatially correlated errors is a convenient and applicable approach for assessing the environmental systems.     

Effects of climate change on surface water management of Seyhan basin,Turkey

The goal of this study was to set-up the basis for climate change adaptation of water resources management policies in Seyhan River basin. The first priority was to identify the balances between water resources and water users with respect to existing and planned projects. In this respect various aspects of Seyhan basin were evaluated, including evaluation of existing water resources, determination of water demand of existing and planned projects, and water resources supply-demand characteristics. The global climate change model was downscaled to the basin scale, the results were associated with hydrometeorological monitoring network and finally the impact of climate change on surface water resources and demands were determined for specific projection years. Water resources management scenarios were developed to evaluate adaptation alternatives to climate change scenarios at the basin level. It was determined that even though there was no water stress in Seyhan basin in 2010, many parts of the basin were expected to suffer significant shortages over the coming years.     

Characterization of lead-resistant river isolate Enterococcus faecalis and assessment of its multiple metal and antibiotic resistance

Contamination of surface waters has a direct impact on the public health of entire communities. Microorganisms inhabiting contaminated surface waters have developed mechanisms of coping with a variety of toxic metals and drugs. Investigations were carried out to isolate and identify lead-resistant bacteria from the river K?z?l?rmak along the city of K?r?kkale, Turkey. Of the 33 lead-resistant isolates, one isolate with a minimal inhibitory concentration of 1,200 mg?L^?1 was isolated and identified as Enterococcus faecalis by using biochemical tests and 16S rRNA sequencing. Lead-resistant E. faecalis isolate was found out to be resistant to other heavy metals like aluminum, lithium, barium, chromium, iron, silver, tin, nickel, zinc, and strontium and to drugs like amikacin, aztreonam, and gentamicin. E. faecalis harbored four plasmids with the molecular sizes of 1.58, 3.06, 22.76, and 28.95 kb. Plasmid profile analyses of cured derivatives revealed that the lead resistance ability of E. faecalis was still existing despite the elimination of all the plasmids. Moreover, the antibiotic resistance pattern of the cured derivatives did not demonstrate any change from the parental strain. Our findings indicated that the lead resistance genes of E. faecalis were located on the chromosomal DNA rather than the plasmid.     

Applying linear analysis methods to GIS-supported procedures for preventing traffic accidents: Case study of Konya

This study develops methods to obtain maps to determine traffic Hot Spots in Konya, Turkey, by applying linear analysis supported by Geographical Information Systems (GIS).Hot Spot analysis is known method but the study differs from former researches at the point of determining of risky zones, classification and illustration of them on the maps with the different accident parameters. The aim is not to contribute another Hot Spot analysis using a number of statistical methods, but to determine the Highest Potential Hot Spots (HPHS), which are inter-sectional clusters, and to use different parameters, such as number of accidents, number of fatalities and injured, and number of accidents with only financial loss.In addition, apart from classical illustrational techniques, Hot Pieces (HPCS) on roads divided into 1 km segments are shown by their grading according to their numerical values. Hence, thematic illustration distinguishes them from others.Another aspect of this study is that, besides investigation of Hot Spots by means of data of accidents of previous years’, Probable Hot Spots (PRHS) were illustrated and highly potential Hot Spots were determined. These latter are candidates for Hot Spots in the near future. Thus, premature accidents can be anticipated easily.The main intention of this study is to emphasize the importance of using criteria, other than total accident number, to illustrate intersection Hot Spots and to constitute a model of accident severity and variety. It is anticipated that the results obtained from highway accidents data will guide improvement of the route segments.     

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.     

Effects of probiotic bacteria on the bioaccessibility of aflatoxin B1 and ochratoxin A using an in vitro digestion model under fed conditions

In the present study, we aimed at determining the release of aflatoxin B₁ (AFB₁) and ochratoxin A (OTA) from different food products in the gastro-intestinal tract in the absence and presence of probiotics, a possible adsorbent. The average bioaccessibility of AFB₁ and OTA without probiotics was about 90%, and 30%, respectively, depending on several factors, such as food product, contamination level, compound and type of contamination (spiked versus naturally contaminated). The six probiotic bacteria showed varying binding capacity to AFB₁ and OTA depending on the bacterial strain, toxin studied, type of food and contamination level. A reduction to a maximum of 37% and 73% as observed for the bioaccessibility of AFB₁ and OTA in the presence of probiotic bacteria, respectively. This is the first report on the effect of probiotic bacteria on reducing the fraction of mycotoxins available for absorption in the gastrointestinal tract from different food products.     

Development of new monthly global and diffuse solar irradiation estimation methodologies and comparisons

In assessing and deciding the prediction schemes of solar irradiation countrywide, better the accuracy means better the management of energy transition toward renewables. Consequently, the present study is on the development of new models to make the most accurate possible estimations of the global and diffuse solar irradiation based on ground measurements. Such analysis produces the most accurate estimations for the input of solar energy systems. This is utmost significant for deciding the investments on solar energy systems and their design periods. Turkey is a high-potent country whose solar energy market has been growing rapidly. She doesn’t have adequate reliable measurement network and there is no estimation methodology developed for each and every point within its territory. Moreover, installing such a measurement system network doesn’t seem to be economically feasible and technically possible, inter alia. Accordingly, this study defines a methodology to make the most accurate estimations of monthly mean daily solar irradiation on horizontal surface and its diffuse and beam components. For the global and diffuse estimations, new methodologies in linear and quadratic forms are developed, compared, and discussed. The comparison is applied by using mean bias error and root mean square error statistical comparison methods. The measured data values used for modeling and comparisons are provided from the State Meteorological Service of Turkey responsible authority for solar irradiation measurements. The results revealed that the methodologies explained in this study give very high accurate values of total solar irradiation on a horizontal surface and its diffuse component.     

Heavy metal content and distribution in surface sediments of the Seyhan River, Turkey

Chemical fractionation of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) was studied using a modified three-step sequential procedure to assess their impacts in the sediments of the Seyhan River, Turkey. Samples were collected from six representative stations in two campaigns in October 2009 and June 2010, which correspond to the wet and dry seasons, respectively. The total metal concentrations in the sediments demonstrated different distribution patterns at the various stations. Cadmium was the only metal that was below detection at all stations during both sampling periods. Metal fractionation showed that, except for Mn and Pb, the majority of metals were found in the residual fraction regardless of sampling time, indicating that these metals were strongly bound to the sediments. The potential mobility of the metals (non-residual fractions) is reflected in the following ranking: Pb > Mn > Zn > Cu > Ni > Cr in October 2009 and Mn > Pb > Zn > Cu > Ni > Cr in June 2010. The second highest proportion of metals was bound to organic matter/sulfides, originating primarily from anthropogenic activities. Non-residual metal fractions for all stations were highest in June 2010, which may be linked to higher organic matter concentrations in the sediment samples with 1.40% and 15.1% in October 2009 and June 2010, respectively. Potential sediment toxicity was evaluated using the Risk Assessment Code (RAC). Based on RAC classification, Cd and Cr pose no risk, Cu and Ni pose low risk, Pb and Zn were classified as medium risk metals, while the environmental risk from Mn was high. In addition, based on the sediment quality guidelines (SQG), the Seyhan River can be classified as a river with no, to moderate, toxicological risks, based on total metal concentrations.     

The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle?

Regional Environmental Change - Climate change and a growing population around the Mediterranean Rim are increasing the need for water and, consequently, the pressure on resources in terms of both...