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Öğe Creation of carbon footprint originating from road transportation in Turkey and digital mapping of it(2023) Ceylan Demirel, Şeyma; Polat Bulut, AybenIn this study, the carbon footprint created by the greenhouse gases originating from road transportation in Turkey was calculated. In emission calculations, the methodology recommended by the Intergovernmental Panel on Climate Change and determined by the tier-1 and tier-2 approaches was used. As a result of the study, it was observed that the CO2 emission, which was 95,689 GgCO2 in 2018 according to the tier 1 method, decreased to 92,424 GgCO2 in 2020, and the CO2 emission, which was 417,359 GgCO2 in 2018 in the tier-2 method, decreased to 404,631 GgCO2 in 2020. Among the fuels used, it was determined that the diesel fuel type had the highest CO2 emission in both methods. Among the provinces, it was determined that Istanbul, Ankara and Izmir have the highest CO2 emissions, respectively. CO2 emissions were calculated for each province and presented visually on maps prepared using the ARCGIS method.Öğe Creation of carbon footprint originating from road transportation in Turkey and digital mapping of it(Inderscience, 2023) Bulut, Polat, Ayben; Demirel, Ceylan, ŞeymaIn this study, the carbon footprint created by the greenhouse gases originating from road transportation in Turkey was calculated. In emission calculations, the methodology recommended by the Intergovernmental Panel on Climate Change and determined by the tier-1 and tier-2 approaches was used. As a result of the study, it was observed that the CO2 emission, which was 95,689 GgCO2 in 2018 according to the tier 1 method, decreased to 92,424 GgCO2 in 2020, and the CO2 emission, which was 417,359 GgCO2 in 2018 in the tier-2 method, decreased to 404,631 GgCO2 in 2020. Among the fuels used, it was determined that the diesel fuel type had the highest CO2 emission in both methods. Among the provinces, it was determined that Istanbul, Ankara and Izmir have the highest CO2 emissions, respectively. CO2 emissions were calculated for each province and presented visually on maps prepared using the ARCGIS method.Öğe Determining the water footprint of sunfower in Turkey and creating digital maps for sustainable agricultural water management(Springer, 2023) Bulut, Polat, AybenWith the rapid population growth, global warming and increasing urbanization in recent years, existing water resources are rapidly depleted and polluted. As a result of uncon scious consumption and pollution of water resources, studies on the sustainable manage ment of water have gained momentum. In recent years, the concept of water footprint has attracted attention in terms of the sustainability of water resources. The concept of water footprint refers to the amount of water required throughout the production of any service or product. In this study, the green, blue and total water footprint sizes of the sunfower in Turkey in 2017–2021 were determined and calculated as 0.803 billion m3 , 2.656 billion m3 and 3.460 billion m3 , respectively. The region with the highest sunfower production and the largest sunfower water footprint was determined as the Marmara-Thrace region, and the province as Tekirdağ. The main reason for the high water footprint of the sunfower in Tekirdağ is the highest sunfower production in the province. For efcient and sustainable use of water, the blue water footprint should be low and the green water footprint high. Thus, when Turkey is evaluated, it has been determined that the highest green water foot print for sunfower is in the Black Sea region. Therefore, it seems possible for Turkey to reduce the blue water footprint of sunfowers by focusing on sunfower production in the Black Sea region.Öğe Groundwater potential assessment based on GIS?based Best–Worst Method (BWM) and Step?Wise Weight Assessment Ratio Analysis (SWARA) Method(Springer, 2023) Karakuş, Can BülentIn this study, the most suitable areas in terms of groundwater potential within the borders of the adjacent area of Sivas Municipality (Sivas/Turkey) were determined with the help of Geographic Information System (GIS)-based Best–Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) methods. Slope, drainage density, Topographic Position Index (TPI), lineament density, lithology, soil types, land use, geomorphology, and rainfall criteria were selected to determine groundwater potential areas. These criteria were weighted with the help of BWM, SWARA, and BWM-SWARA methods and the Groundwater Potential Index (GPI) was calculated according to the weighted linear combination method. According to the calculated GPI values, the groundwater potential of the study area was represented as “excellent,” “very good,” “good,” “moderately good,” “low,” and “very low.” According to all three methods, areas in the “excellent” class constituted 10.99%, 8.40%, and 11.16% of the study area, respectively, while areas in the “very low” class covered 8.33%, 7.98%, and 9.04% of the study area, respectively. The linear correlation coefcient (R2 ) values of the BWM, SWARA, and BWM-SWARA methods were calculated as 0.80, 0.82, and 0.75, respectively, while the area under the curve (AUC) values were determined as 0.83, 0.79, and 0.81, respectively. These results showed that the accuracy of the model was “very good” overall. As a result, groundwater potential mapping created for the study area will contribute to better development of groundwater resources and water management planning.Öğe Gis?multi criteria decision analysis?based land suitability assessment for dam site selection(Springer, 2022) Karakuş, Can Bülent; Yıldız, SayiterIn this study, the appropriate areas were determined to select the most suitable dam sites within the borders of Sivas/Turkey with the help of Geographic Information System (GIS) according to the Analytical Hierarchy Process (AHP) method, which is one of the multi-criteria decision-making (MCDM) methods. Nine criteria (elevation, slope, distance to roads, rainfall, lineament density, distance to residential areas, land use/land cover, soil types and stream density) were used for dam site selection. The CR (Consistency) value was calculated as 0.054 for the criteria considered in the selection of the dam site within the scope of the AHP method, and this value showed that the results obtained were consistent and acceptable. The suitability categories revealed by the dam site selection suitability map created with the method were represented by 5 different classes “very high (12.70%),” “high (20.63%),” “medium (25.43%),” “low (25.11%)” and “very low (16.12%).” Most of the dams currently operating in Sivas province (64.63%) were in the "high" and "medium" level of suitability, while the majority of the planned dams (57.14%) were represented by the "low" class of suitability. The dam site selection suitability mapping obtained as a result of the study is a very important tool in terms of providing resource data to decision makers for regional water resources management and sustainable development.Öğe Index-based evaluation of the relationship between bioclimatic comfort levels and air quality levels of particles and sulfur dioxide in Şanlıurfa Province (Turkey)(Springer, 2022) Doğan, Tuba; Karakuş, Can Bülent; Aksoy, İbrahim EtemThe aim of this study is (i) to reveal the bioclimatic comfort zones depending on the Discomfort Index (DI) in Şanlıurfa province with the help of geographic information system (GIS), and (ii) to determine the relationship between bioclimatic comfort levels and Air Quality Index (AQI) levels in the Şanlıurfa city. For all analyzes made in the study, annual and monthly average values of meteorological (temperature, relative humidity, wind speed) and air pollutant parameters (for PM10 and SO2) between the years 2006–2021 were used. In this context, meteorological parameters, air pollutant parameters, temporal changes of DI and AQI (for PM10 and SO2) parameters were determined by Mann-Kendal (MK) trend analysis and the relationships between all these parameters were determined by Pearson correlation analysis. The most suitable (21≤DI<24) months in terms of bioclimatic comfort in Şanlıurfa province were June and September. In the Şanlıurfa city, annual and monthly average AQIPM10 values were generally in the “good” and “moderate” class, while AQISO2 values were in the “good” class in all years and all months. While the annual average temperature values showed a statistically signifcant increase, the annual average wind speed and PM10 and AQIPM10 values showed a statistically signifcant decrease. There was a negative “weak” correlation (r= −0.028) between DI and AQIPM10, and a positive “moderate” correlation between DI and AQISO2 (r=0.449; p<0.05). In addition, correlations between DI, PM10, and SO2 were signifcant at the p<0.05 level.
