Development of Grid e-Infrastructure in South-Eastern Europe

Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure. It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe. From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries. During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries. Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure. In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.Comment: 22 pages, 12 figures, 4 table

A Comprehensive Emission Inventory of Bbiogenic Volatile Organic Compounds in Europe: Improved Seasonality and Land-cover

Biogenic volatile organic compounds (BVOC) emitted from vegetation are important for the formation of secondary pollutants such as ozone and secondary organic aerosols (SOA) in the atmosphere. Therefore, BVOC emission are an important input for air quality models. To model these emissions with high spatial resolution, the accuracy of the underlying vegetation inventory is crucial. We present a BVOC emission model that accommodates different vegetation inventories and uses satellite-based measurements of greenness instead of pre-defined vegetation periods. This approach to seasonality implicitly treats effects caused by water or nutrient availability, altitude and latitude on a plant stand. Additionally, we test the influence of proposed seasonal variability in enzyme activity on BVOC emissions. In its present setup, the emission model calculates hourly emissions of isoprene, monoterpenes, sesquiterpenes and the oxygenated volatile organic compounds (OVOC) methanol, formaldehyde, formic acid, ethanol, acetaldehyde, acetone and acetic acid. In this study, emissions based on three different vegetation inventories are compared with each other and diurnal and seasonal variations in Europe are investigated for the year 2006. Two of these vegetation inventories require information on tree-cover as an input. We compare three different land-cover inventories (USGS GLCC, GLC2000 and Globcover 2.2) with respect to tree-cover. The often-used USGS GLCC land-cover inventory leads to a severe reduction of BVOC emissions due to a potential miss-attribution of broad-leaved trees and reduced tree-cover compared to the two other land-cover inventories. To account for uncertainties in the land-cover classification, we introduce land-cover correction factors for each relevant land-use category to adjust the tree-cover. The results are very sensitive to these factors within the plausible range. For June 2006, total monthly BVOC emissions decreased up to −27% with minimal and increased up to +71% with maximal factors, while in January 2006, the changes in monthly BVOC emissions were −54 and +56% with minimal and maximal factors, respectively. The new seasonality approach leads to a reduction in the annual emissions compared with non-adjusted data. The strongest reduction occurs in OVOC (up to −32 %), the weakest in isoprene (as little as −19 %). If also enzyme seasonality is taken into account, however, isoprene reacts with the steepest decrease of annual emissions, which are reduced by −44% to −49 %, annual emissions of monoterpenes reduce between −30 and −35 %. The sensitivity of the model to changes in temperature depends on the climatic zone but not on the vegetation inventory. The sensitivity is higher for temperature increases of 3K (+31% to +64 %) than decreases by the same amount (−20 to −35 %). The climatic zones “Cold except summer” and “arid” are most sensitive to temperature changes in January for isoprene and monoterpenes, respectively, while in June, “polar” is most sensitive to temperature for both isoprene and monoterpenes. Our model predicts the oxygenated volatile organic compounds to be the most abundant fraction of the annual European emissions (3571–5328 Gg yr−1), followed by monoterpenes (2964–4124 Gg yr−1), isoprene (1450–2650 Gg yr−1) and sesquiterpenes (150–257 Gg yr−1). We find regions with high isoprene emissions (most notably the Iberian Peninsula), but overall, oxygenated VOC dominate with 43–45% (depending on the vegetation inventory) contribution to the total annual BVOC emissions in Europe. Isoprene contributes between 18–21 %, monoterpenes 33–36% and sesquiterpenes contribute 1–2 %.We compare the concentrations of biogenic species simulated by an air quality model with measurements of isoprene and monoterpenes in Hohenpeissenberg (Germany) for both summer and winter. The agreement between observed and modelled concentrations is better in summer than in winter. This can partly be explained with the difficulty to model weather conditions in winter accurately, but also with the increased anthropogenic influence on the concentrations of BVOC compounds in winter. Our results suggest that land-cover inventories used to derive tree-cover must be chosen with care. Also, uncertainties in the classification of land-cover pixels must be taken into account and remain high. This problem must be addressed together with the remote sensing community. Our new approach using a greenness index for addressing seasonality of vegetation can be implemented easily in existing models. The importance of OVOC for air quality should be more deeply addressed by future studies, especially in smog chambers. Also, the fate of BVOC from the dominant region of the Iberian Peninsula should be studied more in detail

Hydrological cycle in the Danube basin in present-day and XXII century simulations by IPCCAR4 global climate models

We present an intercomparison and verification analysis of 20 GCMs (Global Circulation Models) included in the 4th IPCC assessment report regarding their representation of the hydrological cycle on the Danube river basin for 1961–2000 and for the 2161–2200 SRESA1B scenario runs. The basin-scale properties of the hydrological cycle are computed by spatially integrating the precipitation, evaporation, and runoff fields using the Voronoi-Thiessen tessellation formalism. The span of the model- simulated mean annual water balances is of the same order of magnitude of the observed Danube discharge of the Delta; the true value is within the range simulated by the models. Some land components seem to have deficiencies since there are cases of violation of water conservation when annual means are considered. The overall performance and the degree of agreement of the GCMs are comparable to those of the RCMs (Regional Climate Models) analyzed in a previous work, in spite of the much higher resolution and common nesting of the RCMs. The reanalyses are shown to feature several inconsistencies and cannot be used as a verification benchmark for the hydrological cycle in the Danubian region. In the scenario runs, for basically all models the water balance decreases, whereas its interannual variability increases. Changes in the strength of the hydrological cycle are not consistent among models: it is confirmed that capturing the impact of climate change on the hydrological cycle is not an easy task over land areas. Moreover, in several cases we find that qualitatively different behaviors emerge among the models: the ensemble mean does not represent any sort of average model, and often it falls between the models’ clusters

Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010

In recent decades, naturally growing mosses have been used successfully as biomonitors of atmospheric deposition of heavy metals and nitrogen. Since 1990, the European moss survey has been repeated at five-yearly intervals. In 2010, the lowest concentrations of metals and nitrogen in mosses were generally found in northern Europe, whereas the highest concentrations were observed in (south-)eastern Europe for metals and the central belt for nitrogen. Averaged across Europe, since 1990, the median concentration in mosses has declined the most for lead (77%), followed by vanadium (55%), cadmium (51%), chromium (43%), zinc (34%), nickel (33%), iron (27%), arsenic (21%, since 1995), mercury (14%, since 1995) and copper (11%). Between 2005 and 2010, the decline ranged from 6% for copper to 36% for lead; for nitrogen the decline was 5%. Despite the Europe-wide decline, no changes or increases have been observed between 2005 and 2010 in some (regions of) countries

Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on European Crop Calorie Supply

Even if global warming is kept below +2°C, European agriculture will be significantly impacted. Soil degradation may amplify these impacts substantially and thus hamper crop production further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on calories supply from ten major crops and vulnerability to soil degradation in Europe using crop modelling. The Environmental Policy Integrated Climate (EPIC) model together with regional climate projections from the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX) were used for this purpose. A robustly positive calorie yield change was estimated for the EU Member States except for some regions in Southern and South-Eastern Europe. The mean impacts range from +30 Gcal ha–1 in the north, through +25 and +20 Gcal ha–1 in Western and Eastern Europe, respectively, to +10 Gcal ha–1 in the south if soil degradation and heat impacts are not accounted for. Elevated CO2 and increased temperature are the dominant drivers of the simulated yield changes in high-input agricultural systems. The growth stimulus due to elevated CO2 may offset potentially negative yield impacts of temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability ranging from 0 to 80 Gcal ha–1 due to insufficient compensation for nutrient depletion and this might undermine climate benefits in many regions, if not prevented by adaptation measures, especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop intensification are about two to fifty times higher than climate change impacts

Web-Based Visualization of Very Large Scientific Astronomy Imagery

Visualizing and navigating through large astronomy images from a remote location with current astronomy display tools can be a frustrating experience in terms of speed and ergonomics, especially on mobile devices. In this paper, we present a high performance, versatile and robust client-server system for remote visualization and analysis of extremely large scientific images. Applications of this work include survey image quality control, interactive data query and exploration, citizen science, as well as public outreach. The proposed software is entirely open source and is designed to be generic and applicable to a variety of datasets. It provides access to floating point data at terabyte scales, with the ability to precisely adjust image settings in real-time. The proposed clients are light-weight, platform-independent web applications built on standard HTML5 web technologies and compatible with both touch and mouse-based devices. We put the system to the test and assess the performance of the system and show that a single server can comfortably handle more than a hundred simultaneous users accessing full precision 32 bit astronomy data.Comment: Published in Astronomy & Computing. IIPImage server available from http://iipimage.sourceforge.net . Visiomatic code and demos available from http://www.visiomatic.org

Investigation Of Agricultural Damages Caused By Air Pollution Over Europe By Using Wrf/cmaq Modelling System

Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, Yüksek LisansM.Sc. (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, YAŞAR BURAK ÖZTANERThe population of Europe, including non-EU countries located in continental Europe, is estimated to be around 740 million, which corresponds to 10% of the world's population (United Nations-UN, 2015). Wheat production in between 1996-2014 in Europe is 133.9 million tons (Mt). This corresponds to 21% of world's wheat production (FAO, 2015). In addition, because of Industrial Revolution in Europe an increasing trend in air pollution and pollutants that persists up to present day can be observed. This increase in air pollution is the cause of critical environmental impacts. Even though there are various studies in Europe about impacts of ozone on human health, not many studies exist to investigate ozone's impact on agriculture. Besides the negative impact on human health, exposure to high concentrations of ozone is a threat to food security and agricultural activities. Elevated O3 concentrations and changes in the concentrations affect plant life functions such as photosynthesis, transpiration, and gas exchanges. It has been found by many scientific studies that ground-level ozone exposure reduces photosynthesis of crops since it damages substomatals apoplast, cell membranes and walls. Decreased photosynthesis result in low growth rates in terms of volume or biomass. In Europe and United States of America (USA), various observational and experimental studies conducted on this subject. These studies resulted in different empirical ozone exposure equations for different parts of the world. Agricultural production losses can be calculated because of these equations. In Europe, AOT40 (cumulative summation of differences in high ozone concentrations over 40 ppb) is a widely used method which is a product of experimental studies conducted in Europe. However, in USA, W126 method (summation of weighted ozone concentrations in day light time by using sigmoidal distribution equation) is being widely used. Other than these two methods there are many other methods used around the world to calculate agricultural production loss due to ozone impacts. Some of these methods are daily summation of difference of threshold values (SUM-X method) or daily mean calculation (M-X method). There are several studies from different parts of the world that were conducted on the impacts of ozone on agricultural crops (i.e., wheat, soybean, rice, potato), their yield losses, and relative yield losses. In a study by USEPA, a 10% crop loss due to ozone was observed in agricultural production in USA. A similar study for the Europe found that the loss was around 5% in Europe. Tropospheric ozone as a regional and global threat to plants threatens our current and future food security. In literature, there are studies conducted on impacts of ozone on agricultural productions for different regions in the world. Even though these studies can show the local loss, they fail to perform well for regional impacts. For this reason, some scientific studies focused on quantifying the impact of ozone pollution on crops using regional or global atmospheric models. Low spatial resolution of global models affects the level of representation of results. Spatial resolution is better in regional studies compared to global ones, however, there are studies utilizing this higher resolution to calculate agricultural production losses. In a study, in India, conducted on impacts of ozone on wheat production loss using WRF/Chem regional chemical transportation model it was found that wheat production loss was 5 Mt for 2005. In a similar study, Eta-CMAQ regional chemical transport model was used to estimate the soybean loss in USA (2005), and found that amount of loss was in range of 1.7-14.2 %. Due to regional changes in ozone concentrations, working with a regional chemistry model yields better results for the calculation of agricultural production loss. In global models, there are many uncertainties due to low resolutions. In this study, WRF/CMAQ modeling system with three different ozone crop exposure indices (AOT40, W126, and M7) was used to estimate wheat production loss in Europe. Growing season was selected as May – July for wheat in Europe. European Environmental Agency (EEA) AirBase database ozone observations were used to calculate mean ozone values for growing season of years 2008 to 2012. The highest growing season average (45.6 ppb) was found in 2009. Averages for other years are as follows, 33.28 ppb for 2008, 29.29 ppb for 2010, 39.12 ppb for 2011, and 30.42 for 2012. This is the reason behind the selected study period growing season (May-July) of 2009. Country based total wheat production data for 2009 were obtained from Food and Agriculture Organizations (FAO). Spatial distribution of country based total wheat production data was performed by using gridded global wheat production map (for year 2000) from studies of Monfreda et al. (2008) and Ramankutty et al. (2008). For each grid cell countries contain a total value was found. These totals then divided by number of grid cells countries contain and grid cell ratios were calculated. These ratios were multiplied with total wheat production data of FAO 2009 and spatially distributed. This created map then remapped according to model area and resolution. In this study, modeling method is WRF / CMAQ modeling system with 30 km spatial resolution. As Meso-scale Atmosphere Circulation Model, WRF-ARW 3.6 (Weather Research and Forecast-Advanced Research WRF) was used with 35 horizontal levels, and with 191 cells in east-west and 159 cells in north-south direction. Also, 0.75 degree ECWMF Era-Interim Reanalysis data was used to prepare initial and boundary conditions of the model. For land-use, MODIS-30 20-class data was prepared. DUMANv2.0 emission model (developed by Istanbul Technical University, Eurasia Institute of Earth Science) was used for emission modeling. Inputs of emission model were anthropogenic, biogenic, and fire emissions. Anthropogenic emissions are created from TNO-2009 database by using DUMANv2.0 with CB05-AERO5 chemical mechanism. MEGAN v2.10 biogenic emission model was used for biogenic emissions. Fire emissions were calculated by data obtained from GFASv1.0 satellite dataset. CMAQv4.7.1 model with CB05-AERO5 chemical mechanism was used for chemical transportation modeling. WRF outputs were converted into M3MODEL structure by using MCIP (Meteorology-Chemistry Interface Processor). ICON (Initial Cond.) and BCON (Boundary Cond.) were used to create initial and boundary conditions. Inputs for these modules were obtained from ECMWF – MACC 3-hour model output with spatial resolution of 80-100 km. Open sky photolysis data were prepared with JPROC (Photolysis Rate Processor). Ozone variable was obtained from CMAQv4.7.1 model and applied to three ozone exposure indices. Gridded map of wheat production map of 2009 were multiplied with these values, thus calculated the wheat loss in each cell. Total economic loss was calculated by multiplication of calculated production loss and FAO 2009 country based wheat production price index. In order to calculate economic loss between countries, each country's 2009 GDP was normalized. The highest wheat loss was found in Russia (7.14 Mt - 11.6% and 17.3 Mt – 28%) by AOT40 and M7 methods while W126 method found the highest loss in Italy (1.54 Mt-24%). Following countries generally have higher wheat loss in every method, Turkey (6.8 Mt), France (3.47 Mt), Germany (2.45 Mt), and Egypt (5.54 Mt). According to the regional results the highest loss was found in South (8.3 Mt – 61%) and East (12.8 Mt – 37%) Europe, the lowest loss was found in Northern European countries (2.2%- 0.65Mt). Greatest losses were found in M7 method while W126 method has the lowest loss values. This provides a range (min-max) for ozone caused wheat loss in Europe. The highest economic loss was in Russia with 2.23 billion American Dollar (USD). Turkey ($2.24 bn), Italy ($1.64 bn), and Egypt ($1.59 bn) were other countries with high economic loss, right after Russia. Eastern Europe has the highest regional economic losses with ($1.6 bn) USD and Southern Europe ($2.8 bn). The lowest economic loss was in Northern Europe ($0.01 bn). Reason behind the high wheat loss values in Southern and Eastern Europe region is due to ozone precursor transport from Middle – Western European region via southerly – easterly meteorological systems. This causes higher ozone concentrations in Southern and Eastern Europe and affect wheat loss. Emission regulations should be more focused and applied in Middle – Western European countries.Avrupa nüfusu – Avrupa Birliği üyesi olmayana ama kıtasal Avrupa'da yer alan ülkelerle birlikte – 740 milyon civarındadır. Bu dünya nüfusunun %10'una denk gelmektedir (United Nations-UN, 2015). Ayrıca, Avrupa'nın 1996 – 2014 yılları arası toplam buğday üretim miktarı ortalaması 133.9 milyon metrik ton olduğu görülmektedir. Dünya buğday üretiminin %21'ne karşılık gelmektedir (FAO,2015). Buna ek olarak, Endüstri Devrimi'nin Avrupa'da gerçekleşmesinin bir sonucu olarak, bölgenin hava kirliliğinde ve kirletici emisyonlarında günümüze kadar bir artış gözlemlenmiştir. Bu artış beraberinde ciddi çevresel etkileri getirmektedir. Avrupa'da insan sağlığı üzerine yapılan çeşitli çalışmalar ile ozon etkisi tespit edilse de,tarım üzerine odaklanmış çok fazla çalışma bulunmamaktadır. Yüksek Ozon konsantrasyonuna maruziyet, insan sağlığına olan zararlı etkilerinin yanı sıra gıda güvenliğine ve tarımsal aktivitelere ciddi etkileri gözlemlenmiştir. Yüksek ozon konsantrasyonu ve ozon konsantrasyonundaki değişimler, fotosentez, terleme ve gaz alışverişi gibi bitki yaşam fonksiyonlarını ciddi şekilde etkilemektedir. Literatürde birçok çalışma, yüksek ozon konsantrasyonundan dolayı bitkilerin alt stoama çeperinin, hücre zarı ve duvarlarının zarar gördüğünü göstermiştir. Bu zarar fotosentez hızını düşürmektedir. Bu durum bitki büyümesi hacim ve kütle olarak azalmasına neden olmaktadır. Avrupa'da ve Amerika Birleşik Devletleri'nde (USA) bir çok farklı deneysel ve gözlemsel çalışmalar yapılmıştır. Bu çalışmalar sayesinde dünyanın farklı bölgelerinde daha iyi çalıştığı düşünülen ampirik ozon maruziyet denklemleri üretilmiştir. Tarımsal üretim kayıpları bu ve bunun gibi denklemler sayesinde hesaplanabilmektedir. Avrupa'da yapılan deneysel çalışmalar neticesinde AOT40 – 40 ppb'den yüksek ozon konsantrasyonlarınının farkının kümülatif toplamları – yöntemi yaygın olarak kullanılmaktadır. USA'da ise W126 yöntemi USEPA tarafından önerilmektedir. W126 yöntemi sigmoidal dağılım fonksiyonunu kullanarak ozon konsantrasyonlarına ağırlık ataması yapıp gün ışığı süresince olan toplamı almaktadır. Bu iki yöntem dışında belirli eşik değerlerinin farkının günlük toplam şeklinde hesaplanması (SUM-X yöntemi) veya günlük ortalama şeklinde hesaplanması (M-X yöntemi) gibi bir çok yöntemde ozondan kaynaklı tarımsal üretim kaybının hesaplanmasında dünya çapında kullanılmaktadır. Dünyanın çeşitli yerlerinde yapılan çalışmalar ozonun buğday, soya fasulyesi, pirinç patates gibi tarım ürünlerinin üretiminde ve veriminde kayıplar olduğu söylemiştir. USEPA tarafından 1996 yılında USA için yapılan bir çalışmada yüksek ozon konsantrasyonuna maruz kalması sebebiyle tarımsal üretimde %10 için kayıp olduğu tespit edilmiştir. Benzer bir çalışma bu kaybın Avrupa %5 civarında olduğu göstermektedir. Küresel ve bölgesel bir problem olarak ozon, bitkiler üzerindeki bu etkisi sebebiyle günümüzdeki ve gelecekteki gıda güvenliğini tehdit etmektedir. Literatürde dünyanın çeşitli bölgelerinde ozonun çeşitli tarım ürünleri üzerindeki etkisini ölçümler ile inceleyen çalışmalar mevcuttur. Bu çalışmalar lokal kaybı gösterse de bölgesel etkiyi göstermekte zayıftır. Bu yüzden bölgesel veya küresel olarak modelleme yöntemi ile üretim kaybı hesaplayan bilimsel çalışmalar literatürde bulunmaktadır. Küresel model yaklaşımı yapılan çalışmaların yersel çözünürlüklerinin düşük olması, elde edilen sonuçların temsiliyetini etkilemektedir. Bölgesel çalışmalarda ise çözünürlük iyi olmasına rağmen, tarım üretim kaybı hesabı hemen hemen hiç bir çalışma da hesaplanmamıştır. WRF/Chem bölgesel kimyasal taşınım modeli ile Hindistan için yapılan bir çalışmada 2005 yılında ozondan kaynaklı buğday üretim kaybı 5 milyon metrik ton olarak hesaplanmıştır. Yine benzer bir çalışmada Eta-CMAQ modeli ile USA'daki soya fasulyesi üretim kaybı 1.7 – 14.2 % olarak hesaplanmıştır. Ozonun bölgesel değişimi sebebiyle bölgesel kimyasal model ile çalışmak hesaplanan tarımsal üretim kaybındaki belirsizliği azalmaktadır. Küresel modellerde yüzeyin tanımlanması, yersel çözünürlüğün düşük olması gibi birden çok belirsizlik söz konusudur. Bu çalışmada WRF/CMAQ model sistemi ile Avrupa'daki Buğday üretim kaybının üç farklı ozon maruziyet denklemi (AOT40, W126 ve M7) kullanılarak belirlenmiştir. Bunu için öncelikle buğday bitkisini büyüme mevsimi (Avrupa için Mayıs – Temmuz ) literatüre göre tespit edilmiştir. Avrupa Çevre Ajansı (Europen Enviromental Agency - EEA) AirBase veri tabanı ozon gözlemleri 2008 -2012 yılları büyüme mevsimleri ortalamaları hesaplanmış ve incelenmiştir. En yüksek buğday büyüme mevsimi ortalaması (45.6 ppb) 2009 yılında bulunmuştur. Bu değer 2008 yılında 33.28 ppb, 2010 yılında 29.29 ppb, 2011 yılında 39.12 ve 2012 yılında 30.42 ppb olarak hesaplanmıştır. Bu yüzden çalışma dönemi olarak 2009 büyüme mevsimi (Mayıs - Temmuz) seçilmiştir. Çalışmada Food and Agriculture Organizations (FAO)'dan seçilen yıl 2009 için ülke bazlı toplam buğday üretim verisi temin edilmiştir. Ülke bazlı toplam buğday üretim verilerinin yersel dağılımı ise Monfreda vd. (2008) ve Ramankutty vd. (2008) çalışmalarında yayınlanan küresel ve gridlenmiş 2000 yılı için buğday üretim haritası kullanılarak yapılmıştır. Bunun için ülkelere düşen her grid hücresinin ülke bazlı toplamı alınmıştır. Hesaplanan toplamlar, grid hücrelerindeki değerlere bölünerek her bir hücrenin oranı belirlenmiştir. Bu oranlar FAO 2009'dan temin edilen toplam buğday üretim verisi ile çarpılarak 2009 yılı FAO buğday üretim verileri yersel olarak dağıtılmıştır. Hazırlanan harita, model alanı ve çözünürlüğüne göre yeniden haritalandırılmıştır. Çalışmada modelleme yöntemi olarak WRF / CMAQ model sistemi 30 km yersel çözünürlükle kurgulanmıştır. Mezo-ölçek Atmosfer Sirkülasyon Modeli olarak WRF-ARW 3.6 (Weather Research and Forecast-Advanced Research WRF) modeli, düşeyde 35 seviye, doğu-batı yönünde 191 ve kuzey-güney yönünde 159 hücre ile çalıştırılmıştır. Ayrıca 0.75 derece ECWMF Era-Interim Reanalysis verisi modelin başlangıç ve sınır koşullarının hazırlanması için kullanılmıştır. Yüzey kullanımı için MODIS-30s 20-Sınıf verisi hazırlanmıştır. Emisyon modellemesi, İTÜ Avrasya Yer Bilimleri Enstitüsü tarafından geliştirilen DUMANv2.0 modeli kullanılarak yapılmıştır. Emisyon modeline insan kaynaklı, biyojenik ve yangın emisyonları girdi olarak verilmiştir. İnsan kaynaklı emisyonlar, TNO-2009 veri tabanından elde edilmiş ve DUMANv2.0 tarafından CB05-AERO5 kimyasal mekanizmasına göre işlenmiştir. Biyojenik emisyonlar için MEGAN v2.10 kullanılmıştır. Yangın emisyonları ise literatürde yer alan ve GFASv1.0 uydu veri setinden elde edilen bilgilerle hesaplanmıştır. Kimyasal Taşınım modeli olarak CMAQv4.7.1 modeli CB05-AERO5 kimyasal mekanizmasına göre çalıştırılmıştır. İlk olarak WRF çıktıları MCIP (Meteorology-Chemistry Interface Processor) kullanılarak M3MODELs yapısına çevrilmiştir. ICON (Initial Cond.) ve BCON (Boundary Cond.) modülleri kimyasal başlangıç ve sınır koşullarını oluşturmak için çalıştırılmıştır. Bu modüllere girdi bilgisi ECMWF – MACC 3 saatlik model (yersel çözünürlüğü 80-100 km) çıktılarından sağlanmıştır. JPROC (Photolysis Rate Processor) ile açık gökyüzü şartlarındaki fotoliz bilgisi hazırlanmıştır. CMAQv4.7.1 modelinden ozon değişkeni temin edilmiş ve belirlenen üç ozon maruziyet denklemlerine uygulanmıştır. Hazırlanan 2009 yılı için gridlenmiş buğday üretim haritası ile çarpılarak her hücredeki buğday kaybı hesaplanmıştır. Bu kayıplar ile FAO'dan 2009 yılı için alınan ülke bazlı buğday üretici fiyat indeksi çarpılarak her bir ülkenin toplam ekonomik kaybı hesaplanmıştır. Ülkeler arası ekonomik kaybı hesaplayabilmek için her ülkenin 2009 yılı için GDP'si ile normalize edilerek yorumlanmıştır. Buna göre, en yüksek buğday kaybı AOT40 ve M7 yöntemleri ile Rusya'da (7.14 Mt - %11.6 ve 17.3 Mt %28), W126 yöntemi ile İtalya'da (1.54 Mt-%24) hesaplanmıştır. Genel olarak kaybın tüm yöntemlerde yüksek görüldüğü diğer ülkeler, Türkiye (6.8 Mt), Fransa (3.47 Mt), Almanya (2.45 Mt) ve Mısır (5.54 Mt)'dır. Bölgesel olarak kayıplar incelendiğinde ise tüm yöntemler içinde en yüksek Güney (8.3 Mt - %61) ve Doğu (12.8 Mt – %37 ) Avrupa'da, en düşük bölge ise kuzey Avrupa ülkeleri (%2.2- 0.65Mt) olduğu belirlenmiştir. En yüksek hesaplanan kayıplar M7 yönteminde, en düşük kayıplar ise W126 yöntemi ile yapılan hesaplamada bulunmuştur. Bu sonuç Avrupa'da ozondan kaynaklı buğday kaybı hakkında bir aralık (minimum - maksimum) sunmaktadır. En yüksek ekonomik kayıp Rusya'da 2.23 Milyar Amerikan Doları (USD) olarak hesaplanmıştır. Turkiye ($2.24 Milyar), Italya ($1.64 Milyar), Mısır ($1.59 Milyar) Rusya'yı takip etmektedir. Hesaplanan ekonomik kayıplara göre, en yüksek kayıplar Doğu ($1.6 Milyar) ve Güney ($2.8 Milyar) Avrupa ülkelerinde, en düşük ekonomik zarar yine Kuzey Avrupa ülkelerinde ($0.01 Milyar) görülmüştür. Güney ve Doğu Avrupa'da bu derece yüksek kayıpların çıkması, Merkez ve Batı Avrupa ülkelerindeki endüstriden kaynaklı ozon öncül kirleticilerin güney ve doğu yönlü meteorolojik sistemlerle taşınmasıdır. Bu sebeple Avrupa'nın güneyinde ve doğusunda ozon yüksektir ve buğday kaybı bundan dolayı daha yüksek hesaplanmıştır. Emisyon kontrolleri Batı ve Merkez Avrupa ülkelerinde daha yoğun şekilde uygulanmalıdır.M.Sc.Yüksek Lisan