Geostatistical analysis of the relationship between heavy metals in drinking water and cancer incidence in residential areas in the Black Sea region of Turkey.

In the study described in this article, the authors examined the relationship between heavy metals in the drinking water and cancer densities in residential areas. The Turkish cities of Trabzon, Rize, and Giresun in the eastern Black Sea region were chosen as the study areas. Cancer registry data, population information, heavy metal chemical analysis results for drinking water, and other spatial information for the region were collected in a database designed in GIS. Information on a total of 13,012 registered cancer cases from the years 2000-2007 was obtained from a cancer record center and depicted spatially on a map. The incidence values explaining cancer density in residential units were calculated. Chemical analyses were then conducted to determine the presence of 17 different heavy metals by collecting a total of 541 drinking water samples. It was determined that among the 17 analyzed heavy metals, beryllium, nickel, antimony, and molybdenum had a significant relationship with cancer incidence values in the residential units.

Geo-relationship between cancer cases and the environment by GIS: a case study of Trabzon in Turkey.

Cancer is an important health issue in Turkey because it ranks as the second cause of death in the country. Examination of the relationships between the distribution of cancer cases and geo-environmental factors is significant in determining the causes of cancer. In this study, GIS were used to provide data about the distribution of cancer types in Trabzon province, Turkey. To determine the cancer occurrence density, the cancer incidence rates were calculated according to local census data, then a cancer density map was produced, and correlations between cancer types and geographical factors were examined.

Using landsat data to determine land use/land cover changes in Samsun, Turkey.

The rapid industrialization and urbanization of an area require quick preparation of actual land use/land cover (LU/LC) maps in order to detect and avoid overuse and damage of the landscape beyond sustainable development limits. Remote sensing technology fits well for long-term monitoring and assessment of such effects. The aim of this study was to analyze LU/LC changes between 1980 and 1999 in Samsun, Turkey, using satellite images. Three Landsat images from 1980, 1987 and 1999 were used to determine changes. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Images were classified into six LU/LC types; urban, agriculture, dense forest, open forest-hazelnut, barren land and water area. It is found that significant changes in land cover occurred over the study period. The results showed an increase in urban, open forest/hazelnut, barren land and water area and a decrease in agriculture and dense forest in between 1980 and 1999. In this period, urban land increased from 0.77% to 2.47% of the total area, primarily due to conversions from agricultural land and forest to a lesser degree. While the area of dense forest decreased from 41.09% to 29.64% of the total area, the area of open forest and hazelnut increased from 6.73% to 11.88%.

Detection of current and potential hazelnut plantation areas in Trabzon, North East Turkey using GIS and RS.

Monitoring agricultural products requires the periodic determination of land cover and the production of land use policies in an optimum way. The hazelnut is one of the important Turkish agricultural exports and Turkey provides 77% of the world's hazelnuts. In Turkey, hazelnut production exceeds the demand; new regulations have been enacted to create new land use policies. By putting into practice regulations restricting hazelnut plantation areas, a more efficient and productive hazelnut harvest policy could be created. Therefore, more information on existing land cover is required to determine optimum (or ideal) potential hazelnut areas (PHA) and to forecast future crop production. The principle aim of this study is to create a methodology for determining existing PHA, using Geographic information system (GIS) and remote sensing (RS) techniques regarding to support hazelnut policy developers and economists. This study was basically carried out in the province of Trabzon, which is one of the most important hazelnut production areas in Turkey. Landsat ETM+ image was used to generate a current land cover classification. Using the supervised classification method, overall accuracy was determined to be 84.7%. Suitable hazelnut areas were determined according to criteria settled by government regulations.