Hydrogeochemical and Geophysical Investigation of the Istanbul Tuzla–Icmeler Spring Area for Environmental and Land Use Planning Purposes

The spring waters of Tuzla–Icmeler are on the Marmara Sea coast in Tuzla town of Istanbul city. The springs discharge a natural sodium chloride mineral water that consumed for ages for therapeutic purposes attributed to their chemical properties. Development of springs commenced during the Ottoman times and a surface collection structure was built at the discharge point of the main spring. Two deep wells were drilled to tap mineral water within the past decades. The bottled water of these springs is also sold for a couple of years and its consumption as a beverage is increasing. The geochemical properties of these springs were investigated by several researchers in the past. This study comprises geochemical and geophysical measurements performed between July 2001 and July 2002 in order to construct a conceptual hydrogeological model for environmental and land use planning purposes. The seasonal evaluation of Tuzla–Icmeler (mineral spring) shows that the chemical properties fluctuate from the beginning of summer until the beginning of winter. This indicates that the overdraft of water during the summer season causes the movement and mix of normal groundwater with the mineralized groundwater. As a result, mixing of less mineralized groundwater decreases the salinity of mineralized groundwater. Using the site-specific hydrogeological, geochemical and geophysical data, zones of protection areas were delineated in order to prevent a possible pollution access to the springs and surroundings from nearby dockyards, dwellings and vehicle traffic. For this purpose, a new land use plan was proposed using the existing settlement sustainability plans.     

Tradeoffs between environmental goals and urban development: the case of nitrogen load from the Stockholm County to the Baltic Sea

Urban dwellers depend on the generation of ecosystem services for their welfare. The city of Stockholm is growing, and a 25% increase in population is projected by 2030. The effects of urban development were estimated through the quantification of nitrogen (N) leakage to the Baltic Sea under two urban development scenarios. We found that total net N load will increase by 6% or 8%, depending on which growth scenario is applied, and population increase by itself will contribute at least 15% of the point source N leakage. Technical improvements in sewage treatment could, according to our results, decrease total N load to the Baltic Sea by 4%. Based on our results, we conclude that proactive measures such as spatial urban planning can provide a constructive tool for sustainable urban development on regional as well as national and international scales, depending on geographical context as well as the ecosystem services' scale of operation.     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.

     

Solid waste composition and COVID-19-induced changes in an inland water ecosystem in Turkey

Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste...