İklim ve Deniz Bilimleri Lisansüstü Programı - Doktora

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  • Öge
    Van Gölü'nün sedimentolojik-stratigrafik evrimi ve göl seviyesi değişimleri
    (Avrasya Yer Bilimleri Enstitüsü, 2017-12-19) Damcı, Emre ; Çağatay, Memet Namık ; 601062008 ; İklim ve Deniz Bilimleri Anabilim Dalı ; Climate and Marine Sciences
    Van Gölü konumu gereği iklime duyarlı bir bölgede, Doğu Anadolu yüksek platosu, üzerinde yer almaktadır. Arap plakasının Avrasya levhasıyla çarpışması sonucunda platonun yükselmesi yaklaşık 12 Myıl'da başlamıştır. Göl; batıda ve kuzeyde Kuvaterner volkanları, güneyde Paleozoyik Bitlis Masifi ile çevrilidir. Doğuda ise yığışım karmaşığına ait birimler ve Plio-Kuvaterner çökelleri yer almaktadır. Ayrıca bölge, Kuzey Atlantik, Sibirya yüksek basınç ve orta enlem subtropikal yüksek basınç sistemlerinin kesiştiği bir noktada, Doğu Anadolu ve Akdeniz arasındaki iklime hassas bir bölgede yer almaktadır. Bulunduğu bu konum itibari ile iklim çalışmaları açısından önem arz etmesinin yanında gölün varvlı çökelleri ile paleoiklim, tektonik ve volkanoloji çalışamaları bakımından da bulunduğu bölgenin karmaşık yapısına ışık tutması açısından ilgi odağı olmuştur. Van Gölü Havzası, doğu-batı uzanımlı Muş depresyonunda Arap, Avrasya levhalarının çarpışması ile şekillenmiştir. Paleotektonik olarak sınıflandırılan bu birimlerin üzerine Miyosen-Kuvaterner yaşlı Neotektonik döneme ait birimler gelmektedir. Alt Miyosen-Pliyosen'de denizin çekilmesi ile bölge aşınma etkisinde kalmış ve bu dönemde genç volkanizma ürünleri bölge yüzeyini kaplamaya başlamıştır. Temelde yer alan birimlerin üzerine, göl içerisinde uyumsuzlukla gelen Pleyistosen-Kuvaterner çökelleri gelmektedir. Bu birimler içerisinde ise, Kuvaterner'de aktif olan Nemrut, Süphan ve İncekaya volkanlarına ait tefra birimleri yer almaktadır. Gölün oluşum yaşı volkanizma ve jeolojik çalışmaların yanı sıra ICDP PALEOVAN projesi kapsamında yapılan çalışmalar ile GÖ 600 kyıl olduğu ve Nemrut Volkanının faaliyete geçmesi ile volkanik bir set gölü olarak oluştuğu anlaşılmaktadır. Yapılan çalışmalarda, KB-GD sıkışma rejimi altında doğrultu atımlı fayların da etkisiyle şekillenen, başlangıçta Muş Havzası ile bağlantısı olan göl, ilk önce tatlı su gölü olarak oluşmuş, daha sonra Nemrut Volkanı'nın faaliyetleri sonucu gölün dışarı akışı kesilerek, volkanizma, kayaç ayrışması ve buharlaşmanın etkisi ile bugünkü kapalı, acı-alkali göl halini almıştır. Van Gölü, 607 km3 hacim, 3570 km2'lik alan ve 450 metrelere varan derinliği ile Türkiye'nin en büyük gölüdür. Van Gölü, 450 m derinliğindeki Tatvan Havzası, 260 m derinliğinde Kuzey Havzası (NB), batıda kuzeydoğudaki kuzey-kuzeydoğu yönünde yönelim gösteren Ahlat (AR) ve Kuzey Sırtı, kuzeydeki Erciş Körfezi ve Erek Fanı, doğuda Doğu Şelfi ve Doğu Fanı gibi birkaç morfolojik bölgeden oluşmaktadır. Bu tez çalışmasında, gölün sedimanter, tektonik ve paleoiklimsel evrimini anlamak için, göl içerisinde piston karot ve karotlu sondaj çalışmaları yanında sismik yansıma ve batimetri verileri toplanmış ve yorumlanmıştır. Tez çalışması kapsamında değerlendirilen 4-5 m uzunluğundaki piston karotlar Van İlinin batısında Doğu şelfi olarak adlandırılan bölgeden alınmıştır. Sondaj karotları ise ICDP PALEOVAN projesi kapsamında gölün kuzeybatısında yer alan NB olarak adlandırılan bölgede 245m su derinliğinden ve AR olarak adlandırılan bölgede 375m su derinliğinden alınmıştır. Göl tabanı çökellerinden alınan NB ve AR karotların uzunluğu göl tabanından itibaren sırası ile 145 m ve 220 m'dir. Alınan bu çökel karotlarda, çok sensörlü karot tarayıcı (Multi Sensor Core Logger – MSCL) ile yapılan ölçümlerden, karot boyunca p-dalgası hızı, manyetik geçirgenlik, gama yoğunluk ve özdirenç gibi fiziksel özellikleri hesaplanmıştır. P-dalga hızları ve gama yoğunlukları kullanılarak üretilen yapay sismogramlar yardımıyla çökel karotları ile sismik kesitler deneştirilmiş ve gölün sedimentolojik ve tektonik evrimi ile ilişkilendirilmeye çalışılmıştır. μ-X-Ray Florescene (XRF) tarayıcı ile çökel karotların derinlik boyunca element analizleri yapılarak Ca/Fe, Ca/Ti gibi iklimden etkilenen element oranları ile göl su seviyesi ve iklim değişimleri ile ilişkisi çalışılmıştır. Bunun yanı sıra K, Zr, Na, gibi elementlerin gama yoğunluk ve manyetik geçirgenlik verilerinin birlikte değerlendirilmesi ile çökel istifteki volkanizma ürünü çökelimlerin tespitine yönelik çalışmalar yapılmıştır. XRF tarayıcıdan elde edilen radyografi görüntülerinden tez çalışması kapsamında MATLAB yazılımı kullanılarak geliştirilen algoritma ile mevsimsel değişimlere bağlı olarak çökelen laminaların sayımı yapılarak, yıllık çökelimi oluşturan varvların kalınlığı hesaplanmış ve karot boyunca, yine karot çökelleri içerisinden alınan bitki parçası örneklerinden yaptırılan radyokarbon ve tefra örneklerinden alınan 40Ar/39Ar tarihlendirme analizleri ile, karot istiflerininin kronolojisi oluşturulmuştur. Bu kronoloji Grönland buzul karotları (NGRIP, intGRIP) ve denizel izotop dönemleri (MIS) ile desteklenmiştir. Kronolojiye bağlı jeokimyasal-sedimentolojik verilerin spektral sinyal analizi yapılarak, buradan iklime ait döngüsellikler belirlenmiş ve geçmiş iklim modellemesi yapılmıştır. Spektral analizin yanı sıra değişik periyotlarda çökel kalınlıkları grafiklenerek çökelim hızındaki farklılıklardan kurak/soğuk ve nemli/ılıman iklim dönemlerinin belirlenmesine çalışılmıştır. Oluşturulan bu iklim modelinin, tez çalışması kapsamında değerlendirilen diğer verilerden elde edilen bulgular ile karşılaştırılması yapılarak, göl su seviyesi değişimleri ve bu değişimin üzerinde iklimin etkisi tartışılmıştır. Verilerin değerlendirilmesi sonucu oluşturulan iklim modeli ve göl su seviyesi değişimleri, Van Gölü'ndeki ve literatürdeki diğer çalışmaların sonuçları ile karşılaştırılmıştır. Toplanan sismik yansıma ve batimetri verilerinden gölün batimetri haritası oluşturulmuş. Sismik yansıma verilerinin yorumlanması ile göl çökellerinin GÖ 600 kyıl'dan bu yana Tatvan Havzası'nda ve yaklaşık GÖ 90 kyıl'dan bu yana Kuzey Havzası'nda süreklilik gösterdiği sonucuna ulaşılmıştır. Çok kanallı sismik yansıma kesitleri ile oluşturulan yaş modeli korelasyonu ile günümüz göl su seviyesinin yaklaşık 200 m altında D1 deltasının GÖ 20-17 kyıl, 105m altında D1a deltasının G.Ö 65-60 kyıl, 150m altında D2 deltasının GÖ 115-106 kyıl, 225 m altında D3 deltasının GÖ 166-142 kyıl, 300 m altında D4 deltasının GÖ 195-169 kyıl, 375m altında D5 deltasının GÖ 270-234 kyıl ve 450m altında D6 deltasının 400-350 kyıl aralığında çökeldikleri belirlenmiştir. Yüksek çözünürlükte sismiklerde ise, günümüz göl su seviyesinin 60 m altında GÖ 4-3 kyıl göl seviyesine ait klinoform ile, 30 m, 20 m ve 15 m altında tarihlendirilemeyen; ancak GÖ 3 kyıl'dan sonrasında, olasılıkla Yunan Karanlık veya Demir Çağı Soğuk Dönemi, Avrupa Karanlık Çağı ve Küçük Buzul Çağı dönemine ait olan düşük göl seviyesine ait delta ve kıyı düzlükleri (taraçalar) tanımlanmıştır. Van Gölü içerisinde sualtı kanal sistemleri, Doğu Şelfi'nde ve Erciş Boğazı'nda oldukça gelişmiş bir yapı göstermektedir. Kara üzerinde yer alan akarsu drenaj sistemi ile bağlantılı olarak göl içerisinde 100 m'den daha fazla su derinliklerine kadar uzanırlar. Genel olarak kanallar 50 m kadar derin ve 500 m genişliğindedir. Şelf üzerinde ve eğimli alanlarındaki sualtı kanal sistemleri, Van Gölü su seviyesinin günümüze kıyasla, G.Ö 15 kyıl'da 200m ve Younger Dryas (YD) döneminde 70m gibi göl su seviyesinin daha düşük olduğu dönemlerde gelişmiştir. Varv kalınlıklarından yıllık çökelim hızları hesaplanmıştır. Kuzey Havzada göl çökelleri türbidit ve tefra ardalanmalı şekilde çökelmiştir. Burada tefra ve kütle akması birimleri hesaba katılmadan elde edilen Holosen ve en geç Pleyistosen dönemindeki çökelme hızları; 5,8-12,65 kyıl arasında 0,43 mm/yıl ile en düşük, 12,65-13,89 kyıl arasında 3,72 mm/yıl ile en yüksek çökelme hızlarına sahip dönemlerdir. Holosen'deki ortalama çökelim hızı ise 1,54 mm/yıl'dır. Kuzey Havzası'ndaki çökelim hızı, Tatvan Havzasındaki ve Doğu Deltasındaki çökelimden kabaca iki kat daha hızlıdır. Gerek AR sondajından gerekse kısa karotlardan elde edilen çökelim hızları yaklaşık 0,4-0,7 mm/yıl arasındadır. Varv kalınlıklarının spektral dekompozisyonu ile iklimsel periyodların belirlenmesi ve bu periyodlar ile oluşturulan geçmiş iklim modellerinin literatürde yer alan Bond, Heinrich (H), Dansgaard-Oeschger (DO), güneş aktivitesi minimumları ve tarihsel olaylar ile uyumu Van Gölü'ndeki çökelimin iklimsel dinamikler ile uyumlu olduğunu göstermektedir. Aynı zamanda bu uyum, birim zamanda çökel istifin kalınlığının bilinmesi ile geçmiş iklim modelinin oluşturulabileceğini göstermiştir. İklim modelinden elde edilen sonuçlara göre; GÖ 82-81 kyıl, GÖ 71-69 kyıl, GÖ 62-60 kyıl, GÖ 58-GÖ 55 kyıl, GÖ 38-35kyıl, GÖ 18-16 kyıl, GÖ 5-4,8 kyıl, 4,4-4,2 kyıl, GÖ 2,7-2,6 kyıl, GÖ 1,6-1,2 kyıl (MS 450-800), 0,7-0,45 kyıl (MS 1300-1550), MS 1790-1840 ve MS 1880-1950 göreceli en soğuk dönemler; GÖ 87-82 kyıl, GÖ 65-62 kyıl, GÖ 47-43 kyıl, GÖ 32-30 kyıl, GÖ 22-21 kyıl, GÖ 8-5 kyıl, GÖ 4,8-4,4 kyıl, GÖ 3,2-3,15 kyıl, GÖ 2,6-2,5 kyıl, GÖ 1,8-1,65 kyıl (MS 200-450), GÖ 1,2-0,95 kyıl (MS 800-1050), MS 1550-1790, MS 1840-1880 ve MS 1950'den günümüze göreceli sıcak dönemler olarak iklim modelinden elde edilmiştir. Tektonik rejimin sürekliliği bölgede sıkışan havzaların daralarak D-B ve KB-GD doğrultulu kıvrımlanmasına ve volkanizma ile beraber havzaların bölünmesine sebep olmuştur. Van Gölü içerisinde yer alan Kuzey Sınır Fayı (KSF) boyunca oluşan AR ve Kuzey Sırt (KS), gölün kuzeyinde yer alan havza ile Tatvan Havzası'nı birbirinden ayırmaktadır. Muş Havzası ile Van Gölü Havzası'nı ise, Üst Miyosen'de Avrasya Levhasının altına dalan okyanus kabuğunun kopması ile açılan pencereden giren magma sonucu Kuvaterner başlarında geliştiği öne sürülen Nemrut Volkanı ayırmaktadır. Havzayı güneyden sınırlayan Güney Sınır Fayı (GSF) boyunca benzer özellikteki volkanik kraterlerin yer alması, magmanın yüzeye ulaşmasında bu fayın etkisinin olduğunu göstermektedir. KB-GD yönlü sıkışma rejimi altında, genel olarak KB-GD yönlü normal fay sistemi gelişmiştir. Bu fay sistemi boyunca karbonat kayaç birimlerinin yer aldığı Adilcevaz ve Tatvan açıklarında bikarbonatça zengin yer altı suyu ile beslenerek, sismik kesit ve batimetri verilerinde de açık bir şekilde gözlemlenen, boyları 30 m'ye varan mikrobiyolitler oluşmuştur. Gölün güney şelfinde, sismik ve batimetri verilerinden KB-GD doğrultulu düşey bileşene sahip sol yanal atımlı bir fay belirlenmiştir. Bu fay büyük olasılıkla 9 Kasım 2011'de meydana gelen Mw= 5,7 büyüklüğündeki Edremit depreminin kırığıdır. Ayrıca Erciş Boğazı'ndan güneye yönelen kanal sistemlerinin de fay kontrollü geliştiği anlaşılmıştır. Erciş Boğazı'nda yer alan sismik kesitte gözlenen fayın düşey atım hızı 0,43 mm/yıl olarak hesaplanmıştır.
  • Öge
    Urmia lake desiccation as a new source of dust in themiddle east: Investigation of the anthropogenic impactsand climatic factors on drying up of urmia lake
    (Eurasia Institute of Earth Sciences, 2020-01-30) Ghale, Yusuf Alizade Govarchin ; Ünal, Alper ; 601142005 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri Anabilim Dalı
    In recent decades, some of the world's water bodies, such as the Aral Sea, Dead Sea, Lake Poopó, Lake Eyre and Lake Mead have been shrinking mostly due to human-induced activities and climate change. Desertification and salinization caused by the drying up of these lakes have led to soil degradation and dust storms, which have negative impacts on people's health and the environment as well. Urmia Lake, the largest inland lake of Iran has lost most of its water surface area over the past 2 decades mainly due to agricultural development and decreasing precipitation in the basin. As a result, more than 90% of the water surface area of this unique hypersaline lake has changed to saline body. Preparing a comprehensive rehabilitation plan for Urmia Lake is essential. A critical step for preparing such a plan is quantifying the water budget (balance) changes and real factors that cause the decline in the water level of Urmia Lake. On the other hand, understanding the dynamics of Land Use Land Cover (LULC) is one of the main issues to have a sustainable water management system in Urmia Lake Basin. Remote sensing technology and Geographical Information Systems (GISs) can help to determine the systematic and random changes over time to provide an environmental management system, prevent environmental degradation and act correctly and effectively. Due to the lack of detailed studies in these fields, the main objectives of this thesis are; 1- Monitoring the long-term salinization progress, land cover changes and development of irrigated lands in Urmia Lake Basin between 1975 and 2019. This study used hydroclimatic data, Landsat satellite images, and image processing techniques in conjunction with field survey data to analyze the interaction between agricultural lands and Urmia Lake ecosystem. Understanding the interaction between agricultural lands and Urmia Lake ecosystem helps to determine a lake recovery plan and a sustainable water resources management system in the northwestern Iran. In this section, Maximum Likelihood Classification (MLC) method was used to monitor Land Use Land Cover (LULC) changes and agricultural development in the basin. Vegetation changes and salinization progress were investigated using Normalized Difference Vegetation Index (NDVI) and soil Salinity Index (SI), respectively. 2- Determining the underlying factors for the drying up of Urmia Lake based on drought analysis and water budget equation. In this section, a multidisciplinary and comprehensive investigation were performed on Urmia Lake to propose a new hypothesis and method to separate the effects of climate change from anthropogenic factors in the shrinkage of Urmia Lake. Standardized Precipitation Index (SPI) derived from precipitation data of meteorological stations around Urmia Lake was analyzed to identify drought condition and its effects on the Urmia Lake desiccation. 3-Investigating the impacts of Urmia Lake desiccation on the local and regional aerosol pollution in the northwestern Iran and eastern Turkey using hourly ground observation PM10 data of 2 air quality stations located in the northwestern Iran (Urmia and Tabriz) and 5 stations located in the eastern Turkey (Hakkari, Van, Agri, Igdir and Kars), meteorological data of MERRA-2, Aerosol Optical Depth (AOD) data of Terra MODIS and Aqua MODIS and Ultraviolet Aerosol Index data of Ozone Monitoring Instrument (OMI) observed between 2010 and 2017. Contrary to studies that emphasize the role of climatic factors in the shrinkage of Urmia Lake, the hypothesis of this study states that the role of anthropogenic factors in the dryness of the lake is more important than climatic factors and lake bed can be known as a new source of dust in the Middle East. This study shows how urbanization, land use change and agricultural expansion over time have led to disruption of water cycle and gradual drying up of Urmia Lake. Misguided water and agricultural policies along with climate change have had negative impacts on Urmia Lake environment leading up to salinization, desertification and air pollution as well. The results of this study indicated that the water surface area of Urmia Lake decreased from 5982 km2 in 1995 to 4058 km2 in 2006. In other words, slightly over one third of the lake dried up during the period of 1995 until 2006. Nearly 90% of the total area dried up by 2014. The water surface area has increased after 2014 due to increase in precipitation and releasing more water from dams to the lake. The input runoff has decreased extremely especially in the years between 1998 and 2002. In general, moderate drought condition has occurred in the basin between 2003 and 2010, but 2003, 2004 and 2007 could be considered as years with normal condition. As a result of the dramatic decrease in input runoff into the lake, the area of Urmia Lake has decreased extremely. Water balance changes of Urmia Lake from 1985 to 2010 and SPI results indicated that Urmia Lake has been affected by both anthropogenic and climatic factors. However, human impacts on the lake and its basin are more excessive than climatic factors. Decreased rivers discharge into the lake could be known as the main factor in decline of the volume of Urmia Lake. Droughts have intensified and accelerated the shrinkage of Urmia Lake. In addition, with the overuse of groundwater and surface water resources and developing of agricultural lands and mismanagement of water resources, the water input into Urmia Lake has decreased significantly. Despite previous studies which assumed groundwater output from Urmia Lake is negligible, the results of this study indicated a significant output of groundwater from the lake. This factor can be one of the reasons of groundwater salinization in regions close to Urmia Lake. The final results of this study indicated that major changes in the variables, which reduced the volume of Urmia Lake, started since 1998. Anthropogenic and climate change have roughly 80% and 20% effects on Urmia Lake shrinkage in period 1998– 2010, respectively. The area of irrigated lands was estimated about 4850 km2 in 2018, which indicated a decrease by 12% comparing with its maximum area (5525 km2 in 2011) in the study period. Inverse correlation between shrinking Urmia Lake and expanding the area of salt and salt affected lands is concerning. The final results of land cover change in the basin indicated that the current conditions of Urmia Lake (2018–2019) is very similar to its conditions in 2013 and lake recovery is possible. Based on the results of this study, PM10 concentration in Urmia and Igdir stations were higher than other stations. At both stations, the annual mean PM10 concentration significantly increased after 2013, while, no significant changes were observed at other stations. The maximum annual mean concentrations of PM10 at Urmia (104.247 μg/m3) and Igdir (129.757 μg/m3) were observed in 2015 and 2017, respectively. The analysis of monthly and seasonal PM10 variations indicated that winter accounts for high level PM10 concentration in the northwestern Iran and eastern Turkey. The results indicated that AOD values in the northwestern Iran and eastern Turkey were strongly correlated. In total, 129 days with mean AOD values more than 1 were observed in box covering all parts of the lake between 2010 and 2017, which indicated the severity of aerosol pollution and dust emission from the dried bottom of the lake. The daily mean AOD in the west part of the lake and east part of the lake was 0.348 and 0.508, respectively. The extensive of salinization and desertification in the eastern part of the lake and prevailing wind direction from west to east can be the reasons of high level AODs in this part. Restoration of Urmia Lake is the only solution of the environmental problem in the northwestern Iran. Compromise with climate change and providing a sustainable water resources management system under a changing climate can be the most effective ways to revive the lake. Teaching the local people to understand the importance of water, training water saving skills, improvement of agricultural methods, agricultural water management and cultivation of low-water-use crops can play positive role in the lake rehabilitation. People should be discouraged from growing crops that consume a lot of water and they should be courage to grow crops which modify the unfair effects of soil texture. It seems better to release more water from dams to the lake and stop the establishment of new dams and wells in the basin.
  • Öge
    Quantification of the impact of uncertainty in emissions on air quality model estimates
    (Eurasia Institute of Earth Sciences, 2019-12-20) Özdemir, Ümmügülsüm Alyüz ; Ünal, Alper ; 601122007 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri
    The Air Quality Model, especially Chemical Transport Model, prediction represents mean concentration over the entire grid volume. Predictions of CTMs may differ from observations due to four reasons; 1) inherent or stochastic variability in the observations, 2) errors in model physics and chemistry assumptions, 3) errors due to uncertainties in model input variables, and 4) numerical errors. Here, variability is a description of the range of spread of the values, and it is often expressed by statistical metrics such as variance and standard deviation. Therefore, inherent uncertainty can be considered as variability. Uncertainty refers to lack of knowledge regarding the true value of a quantity. Uncertainty can be reduced or eliminated with more or better data, where variability cannot be reduced. Among the four reasons of uncertainty, provided above, inputs are regarded to have the largest levels of uncertainty. The aim of this study is to evaluate and quantify the contribution of uncertainties in input dataset to AQM estimates. For this purpose, it is necessary to define the problem that poor performance of the model is caused mostly by unfit data. In literature, models perform poor in the Eastern European countries. However, a more detailed study is needed to say that this poor performance is mostly due to model inputs. Because, as it is known, the poor performance of the models may also have other reasons. In the first part of this study, inter-model variability is defined quantitatively by participating in an international project. In the second part of the study, contribution of uncertainties to this problem is quantified by being part of a national project. In the second part, a sample of the solution is presented which includes development of country specific emission factors and compiling a probabilistic emission inventory. As a part of an international project (AQMEII-3), 12 modelling groups were cooperated from different countries of Europe and conducted 18 model runs on Europe domain (covers 34 Europe countries) for 2010 by using 7 different AQMs, 3 meteorology models and 2 emission inventories. This study, for the first time in Turkey, contributed to AQMEII-3 which is organized by the joint leading of U.S. EPA and European JRC. One of the most important benefits of this project is that the model results of all groups can be reached through a common platform. In this dissertation, performance metrics were calculated and mapped for each of 1431 stations of Europe, and for each model for evaluation of model performances. Taylor diagrams were also used for seasonal evaluation. Up to now, there are several air quality modelling studies for Turkey, however they are developed for a specific city or region of Turkey for a timescale starting from days to a few months, or by using just one type of AQM. Thanks to its wide coverage domain (Europe continent) and multi-model contributions from AQMEII-3 project, this study looks to the problem from a large perspective in order to define the problem and recommends a solution by representing a sample of the solution. Thus, an inventory study was conducted to overcome this problem by adopting a deep statistical approach which is not encountered in Turkish inventory studies yet. To this end, country specific EFs are calculated for the energy production industry of Turkey, an inventory has been created for the energy production industry of the Marmara Region. Monte Carlo and Bootstrap approaches are used for uncertainty calculations at these stages. According to results of modelling part of this dissertation, correlations between models and PM10 observations are 8% less in Eastern European countries when compared to Western European countries. BIAS of Eastern European countries is 2.5-fold of Western European countries, when all countries are considered. RMSE of Eastern countries is 90% more than Western countries average, where MAE is 99% and MNE is 25% more. From these results it is clear that, model predictions are significantly beyond the observations in Eastern European countries. Turkey, which is located in the Eastern Europe, has one of the worst results calculated by all models. All models predict PM10 concentrations with an average of -40 ug/m3 BIAS in stations of Turkey, where it is the worst value within 34 countries of Europe considered in this study. Moreover, models predict close to each other but quite far from the observations in 80% of the stations. MAE is over 20 ug/m3 in 80% of all stations in Turkey. Remaining 20% of the stations encounters 18 over 101, mostly in Istanbul and some other big cities. In fact, when the results of the models are examined, it is seen that models generally make better predictions in big cities compared to the small cities. This may be due to the fact that inventory compilers have more information on emission sources in large cities. In seasonal evaluation, it is seen that emissions in Winter cannot be well predicted, but in Summer it is relatively better predicted. This difference can be caused by inadequate representation of increased emissions (in the model inputs) in Winter months from residential heating and traffic emissions when compared to other months. In this case, it would not be unreasonable to suspect that the inputs to the models significantly affect predictions. Model inputs are considered as a reason for poor model predictions in this study. However, problems caused by the model itself or erroneous measurements, or combination of all, may also cause this. In this study, problems due to the model itself are out of consideration since 6 different AQMs were used by 13 modelling groups where same models were also considered by different groups. The fact that all models give close CDFs in Western Europe despite they have different modelling configurations, where they are not close to observations in Eastern Europe countries even in same models, shows that problems in the models are not dominant in prediction errors. Since the number of observation stations included in the scope of this study is very high, measurement errors are not considered to be predominant in poor model estimates. Also, systematic errors are not thought to occur at all stations at the same time. The quality of an emission inventory that will be used in air quality modelling is associated with its low-level uncertainty and adequate coverage of the sources. Emission inventories approach to the ultimate result as in-situ measurements and full activity data are available. In this study, in-situ measurements were conducted within the scope of the national KAMAG project in order to generate country-specific EFs, and an emission inventory was prepared in the light of the most consistent information possible. Besides, official emission measurement reports (EMRs), whose reliability is controversial as they were prepared by the companies under authorization of the emission emitting plants, were also used for comparison with in-situ EFs. Country-specific dust, CO, SO2, NO, NO2 and NOx EFs are calculated in this part of the study for each of coal combusting large wet/dry bottom boilers, coal combusting large size fluid bed boilers, coal combusting large wet and dry bottom boilers, natural gas combusting medium size boilers and gaseous fuels combusting gas turbines. EFs are typically assumed to be representative of an average emission rate from a population of pollutant sources in a specific category. However, there may be uncertainty in the average emissions from population because of three reasons: random sampling error, measurement errors, or when the sample population is not representative for EF development. First two factors typically lead to imprecision in the estimate of the population average. The third factor may lead to possible biases or systematic errors in the estimated average. In order to avoid errors, it is important to understand and account for the uncertainty in the inventory. In the relevant part of this study, a probabilistic emission inventory is developed by considering statistical analysis of variability and uncertainty. The development of a consistent procedure for the uncertainty evaluation is still a challenge for the scientific community. In this study a deep uncertainty analysis technique is applied in EF development, which is including Monte Carlo method and Bootstrap simulation. The uncertainty analysis described in this study can be used as a basis for developing probabilistic emission inventories. When the probability range of emissions to be given as input to air quality is known, it is possible to determine the probability of the model result. Thus, for example, the probability of achieving an air quality management goal can also be calculated. In statistics, sampling error is a type of error caused by investigating a small part of the population rather than examining the whole population. It is calculated by the difference of a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter. Since uncertainty is expressed as lack of knowledge regarding to true value of a quantity, random sampling error can be represented by a sampling distribution. In order to calculate uncertainty of EFs, a distribution is fitted (F^) to the EF dataset (x) where actual underlying distribution (F) is unknown. The goodness-of-fit is evaluated by some techniques. Then Monte Carlo method is applied in order to generate random datasets from assigned distribution, F^. In Bootstrap simulation part of the study, each of the alternative probability models generated by Monte Carlo approach (Bootstrap replicates) are simulated to develop a reasonably stable characterization of the percentiles of the distribution. Then parameters, θ^*, are estimated. In this study, uncertainty in the estimate of θ is reflected by dispersion of θ^*, which also gives random sampling error. A confidence interval for a statistic is a measure of the lack of knowledge regarding the true value of the statistic. The θ^* data is sorted then, in order to calculate confidence interval for the fitted cumulative distribution function. Consequently, the results are compared to the original dataset by generating probability bands. Then results are compared to EMEP and EPA EFs. At the end, dust EFs obtained from in-situ measurements are significantly lower than the literature for coal combusting plants. The reason of these large differences between in-situ measurements and literature EFs may be due to wide usage of dust abatement technologies in Turkish energy production plants. CO and SO2 EFs are significantly larger than EMR, EMEP and EPA EFs in large coal combusting plants and in plants combusting gaseous fuels with gas turbines. But in all EFs, uncertainty is low when compared to EMEP EFs. Country specific NOx EFs are generally larger than all other studies and range of confidence interval is narrow when compared to them. This situation indicates low uncertainty in in-situ EFs. Since each stack measurement may differentiate from the real value due to variations in operating conditions, the overall uncertainty of the emission factors can also be referred as "uncertainty due to variability". After calculating country specific EFs, next step is preparing an emission inventory for power plants of Marmara region and comparing it with the existing emission inventories. The most common emission inventories currently used by CTMs are the TNO-MACC and EDGAR-HTAP emission inventories. These two inventories are mainly used in AQMEII-3 models. EDGAR-HTAP emission inventory contains much more plants (34 plants) than TNO-MACC (19 plants) but is still far from the actual number of power plants (57 plants) that considered in this study for Marmara region of Turkey. Furthermore EDGAR-HTAP emission inventory has more plants than TNO-MACC in all regions of Turkey. From this point of view, it is clear that EDGAR-HTAP emission inventory is more inclusive than TNO-MACC emission inventory in Turkey in terms of number of plants. Also, it is more inclusive in Eastern Anatolian regions of Turkey where TNO-MACC emission inventory has almost no plants for public electricity and heat production sector. There are missing plants in EDGAR-HTAP and TNO-MACC emission inventories where there some unidentified plants in those emission inventories. As a result of emission inventory calculations, NOx emissions calculated in this study is 93,000 ton/year with lower CI as 69,000 ton/year and upper CI as 114,000 ton/year. When same emission inventory is calculated with EMEP EFs 60,000 ton/year with lower CI as 33,000 and upper CI as 90,000 ton/year. The inventory compiled by this study beyond the upper CI of EMEP and it is considerably larger than TNO (24,000 ton/year) and EDGAR-HTAP (42,000 ton/year). SO2 emissions are calculated as 152,379 tonne/year in this study. Same activity data is used in calculation of EMEP emission inventory and resulted 170,596 tonne/year, because in-situ SO2 EF was smaller than EMEP EF for coal combustion plants. It is 69,000 ton/year in TNO and 125,00 ton/year in EDGAR-HTAP emission inventory. 4 large lignite combustion plants, which are not included in the TNO inventory, have resulted in 73,500 tons less SO2 emissions in TNO emission inventory when compared to this study. 1000 tonnes of SO2 emissions is also not included in the TNO inventory due to about 40 missing natural gas incineration plants. Uncertainty range of NOx emission inventory of this study is between 26 (lower bound of CI) to 23% (upper bound of CI). When same emission inventory is compiled with EMEP EFs, overall uncertainty range is 45 (lower) to 48% (upper). As it is clear, country specific EFs decrease uncertainty when compared to usage of EFs from literature. This situation is dominant in NOx emission inventory than SO2 and CO emission inventories, because number of natural gas combusting power plants are large (48 over 57 plants in Marmara region). TNO and EDGAR HTAP emission inventories are out of the uncertainty range of this study which proves their inadequacy for representing emissions of power plants in Marmara region. Generally, the data on energy facilities is among the most easily accessed by inventory compilers. Such large differences in emissions from power plants reinforce doubts about the reliability of the entire TNO-MACC and EDGAR-HTAP emission inventories. In this case, it is quantifically proved that poor emission inventories are primarily responsible for the poor air quality predictions in Turkey, and most probably in all Eastern European countries. No matter how many and high-quality measurements are conducted, no matter how good models are used, it is not possible for air quality models to predict accurate results without a good emission inventory. Therefore, consistent, low uncertainty and comprehensive emission inventories should be compiled for the Eastern European countries, including Turkey. Development country specific EFs is the preliminary step of emission inventory development. Access to activity data used in these studies should be facilitated in order to make room for calculation of the representative EFs easily.
  • Öge
    Statistical Challenges İn Paleoclimatology: İndependent Component Analysis Of Lake Hazar And Lake Van Data, And A Bayesian Test For 4.2 Ka Bp Event
    (Eurasia Institute of Earth Sciences, 2018-10-19) Ön, Zeki Bora ; Özeren, Mehmet Sinan ; 601132003 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri
    There are numerous statistical and numerical methodological problems of paleoclimate studies. In this study, I offer solutions for two problems of paleoclimatology in three different studies. It is a well known fact that, each geochemical measurement and especially each micro-X-ray fluorescence (μ-XRF) measurement through a sediment core is a reflection of different independent processes, i.e. an indirect indicator of paleoenvironments. That's why most studies present μ-XRF measurements as elemental ratios, in order to eliminate a possible dependence upon a single profile. Some studies use second order statistical methods, such as principal component analysis, to eliminate dependence, however there are systematic problems of second order statistical methods as is used in these studies. In order to overcome this issue, we offer an almost well-defined signal processing technique, independent component analysis of geochemistry data gathered from paleoclimate archives. Accordingly, we propose data based models of paleo-precipitation and paleo-temperature for the studied regions. In the first study (Chapter 2), a 3.5 m long piston core (Hz11-P03) has been recovered from Lake Hazar and it is used for multiproxy measurements. μ-XRF, magnetic susceptibility (MS) and stable isotope (δ 18 O and δ 13 C) measurements have been carried out for 3 mm, 1 cm and 3 cm resolutions, respectively. A Bayesian age-depth model according to six radiocarbon dates shows that Hz11-P03 represents the last 17.3 ka BP. We apply independent component analysis on Lake Hazar μ-XRF data (namely, Ca, Fe, K, Mn, Sr and Ti counts). By the measure of distance correlation of resulting independent components with the analyzed data and other regional well-defined paleorecords, we select two independent components as proxies of temperature (Hz-ic5) and precipitation (Hz-ic4) of the region. According to the results, the region was wet/cold during 17.3 ka BP and 14.8 ka BP and wet/warm during the Bølling-Allerød period. According to the age model, there is a hiatus at the Younger Dryas period. At the start of the Holocene, temperatures rose gradually and reached the Holocene "normals" around 8 ka BP. During that period, it was wet. Between 8 ka BP and 5 ka BP, it was warm but exceptionally dry. Between 5 ka BP and 3.5 ka BP, it was warm/wet. After 3.5 ka BP within the oscillations there are abrupt cold/dry phases around 3.5 ka BP, 2.8 ka BP and 1.8 ka BP. In the second study (Chapter 3), ICA method is applied to previously published data from Lake Van, which span the last 250 ka BP. The data used through ICA were element concentrations of Ca, Fe, K, Mn, Si from XRF measurement, TOC and CaCO 3 content and B* (color reflectance) of the Ahlat Ridge sediment record. The analysis is based on applying the algorithm several times by changing the initial random unit vector and clustering the possible independent components through average–link agglomeration, which make it different and innovative than Lake Hazar study. Appropriate components are selected by mutual information method. Accordingly, we claim that Van-IC8 is a proxy for temperature variability for the region, by its similarity with Greenland δ 18 O data and (Van-IC7) is a proxy for precipitation variability for the region, by its similarity with B* (Van-IC7) data. The results reveal that, temperature of the region follows the Northern Hemisphere records, i.e. warm during interglacials, cold during stadials with abrupt warming episodes. On the other hand, precipitation record shows that, it was not dry, or at least as much wet as today, during the LGM and at the end of penultimate glacial as previous studies claim. It was previously proposed that an abrupt climatic change around 4.2 ka BP was the cause of the collapse of the Akkadian Empire. Afterwards, many geological studies arose, which claim to support the climatic deterioration hypothesis. In the third study (Chapter 4), we apply a Bayesian test on the records from Eastern Mediterranean and Arabian Peninsula which claim to show an abrupt climatic change around 4.2 ka BP. To do this, time series are reconstructed using "unaffected" ones in a fully Bayesian framework by the Bayesian structural time series method and then a Bayesian hypothesis test is applied on the results. Our results show that some studies which have previously been cited to support the abrupt 4.2 ka BP event hypothesis hold true, we also show that in a number of other studies, there is no statistically significant abrupt climatic change effect.
  • Öge
    Understanding The Sources And The Extend Of Atmospheric Particulate Matter Problem Over Turkey Using Mesoscale Chemical Transport Model
    (Eurasia Institute of Earth Sciences, 2018-12-21) Baykara, Metin ; Ünal, Alper ; 601132002 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri
    Turkey, with a population climbing to 80 million, has its own challenging air pollution issues, especially particulate matter pollution. Local emission sources are generally the main contributors of particulate matter levels due to their nature. Among these local emission sources, residential heating is one of the emission sectors that contribute to emissions of harmful air pollutants in highly populated urban areas. As the capital of Turkey's industry, megacity Istanbul has been experiencing air pollution problems that has reached to significant levels since 1980's, in which the pollutant concentrations have exceeded the air quality standards for several times. In Istanbul, local anthropogenic sources comprise nearly 60% of particulate matter levels. According to the air quality monitoring report of the Ministry of Environment and Urbanization, the daily mean particulate matter (<10 μg, PM10) concentrations exceeded the limit with more than 100 μg/m3 at several provinces in winter of 2015 in Istanbul. Representation of major emission sources such as road transportation and residential heating are crucial for the air quality modeling and policy making. Modeling concentrations of particulate matter have a number of important roles, some of which are complementary to measurement. These roles include assessing concentrations at locations without monitors and answering questions such as how will particulate matter levels change in the future. Results of modeling studies can be directly compared to the appropriate ambient air quality standards because all relevant sources of pollution in the modeling domain are included in this type of model. The US EPA Community Multiscale Air Quality (CMAQ v5.2) model, a three-dimensional Eulerian atmospheric chemistry and transport model, was used to evaluate the air quality of Turkey, focusing on Marmara Region and Istanbul for the winter of 2015 using three-level nested domains with an up to date spatially distributed high-resolution emissions inventory based on local activity data. Emissions is one of the two main inputs of CMAQ model. In order to process the high-resolution emissions inventory used in this thesis, a regional emission model, called DUMANpy, similar to Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System of the US EPA was adapted and customized to create temporally and spatially distributed emission for Turkey. One of the main purpose of DUMANpy is to convert the resolution of the emission inventory data to the resolution needed by an air quality model. Other main input of CMAQ model is the meteorology input because sophisticated air quality models require meteorological fields and incorporate complex chemical reaction schemes. The meteorological modeling inputs are important due to complications caused by complex terrain conditions, where measurement is not an option. The meteorological inputs for air quality modeling were generated using the Weather Research and Forecasting (WRF v3.8.1) model. CMAQ model results showed that using high-resolution emissions for the residential heating sector significantly improve the spatial distribution and concentration of air pollutants (SO2, PM10, PM2.5) for Istanbul. Air quality model simulations with our high-resolution emissions underestimated PM10 concentrations throughout the study episode on average by only 4.16% with a mean bias of 2.23 μg/m3 while base inventory underestimated PM10 concentrations on average by 35.1% with a mean bias of 18.91 μg/m3. Results show that our spatially distributed high-resolution emissions inventory produces more realistic results for Istanbul during wintertime when residential heating has the most influence on local air pollution. These results show the necessity and importance of high-resolution local emissions for anthropogenic emissions sectors for urban areas which in turn would help improve our understanding and extend of the air pollution problem in Turkey.