Ph.D. Tezi Görüntüleme | |||||||||||||||||||||
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Summary: In this study, a surface water quality research was conducted from the source to the stream mouth in the Stream Harsit having a catchment area of 3280 km2, the biggest sub-watershed of the Eastern Black Sea Basin, during the period of March 2009 and February 2010. The sampling was fortnightly conducted on ten monitoring stations along the main branch. In situ measurements (water temperature, pH, dissolved oxygen, electrical conductivity and turbidity) and on laboratory analysis (suspended sediment, total hardness, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen, total nitrogen, total Kjeldahl nitrogen, orthophosphate phosphorus, chemical oxygen demand, total organic carbon, methylene blue active substances, aluminum, manganese, total iron and total chromium) were conducted. It was focused on the temporal variations of the surface water quality along the stream, and the effects of the anthropogenic activities on this quality were examined. The Stream Harsit was classified according to the Turkish Water Pollution Control Regulation, and the quality and safety of the water used for drinking purposes were evaluated according to the national and international directives and guidelines. It was determined that there was a need to the treatment except for nitrate nitrogen, ammonium nitrogen, chemical oxygen demand, total organic carbon, manganese and aluminum. The Stream Harsit collecting the waste waters from the locations on the course had III. Class water quality with the values of 0.021 mg/L for the nitrite nitrogen, 1.437 mg/L for the ammonium nitrogen, 0.307 mg/L for the orthophosphate phosphorus and 0.619 mg/L for the methylene blue active substances in the waste water discharge point for the city of Gumushane. After the discharge point, a remarkable improvement was observed in the stream water quality due to the joining of the tributaries, and the hydraulic residence times of the dam reservoirs.Finally, the suspended sediment concentration (SSC) was estimated based on turbidity, total iron and total chromium using several Regression Analysis (RA) and Artificial Neural Networks (ANNs) method. The ANNs method used for the estimation of the SSC yielded better results than the RA, and provided acceptable results. Key Words: Suspended Sediment, Wastewater, Stream Harsit, Water Quality, Neural Networks
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