Konu "Air pollution" ile Avrasya Yer Bilimleri Enstitüsü'a göz atma
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Ögeİstanbul'daki Evsel Isınma Kaynaklı Emisyonların Cmaq Hava Kalitesi Modeli Kullanılarak İncelenmesi(Eurasia Institute of Earth Sciences, 2016-07-07) Öksüz, Elvin ; Ünal, Alper ; 601121007 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri Anabilim DalıIstanbul is the most populated city of Turkey as well as Europe. The population is over than 14 million. The city is economical center of the country. Labour and social opportunities makes the city attractive to live and this situation causes inevitable increasing on urbanization of the province. According to authorities, it is expected that the population will be over 16 million in 2030. Due to high population, house holding is also increasing. Distribution of buildings is expending over the city. Residential heating is the main requirement of the people in cold, winter season. By the high population and urbanization, residential heating emissions significantly affects air pollution over the city. Results of many epidemiological studies proves that air pollution causes negative impact on cardiovascular and respiratory system, serious diseases such as cancer and hearth attack. Especially for sensitive people such as elders, children, babies or pregnant the effects may be higher and vitally important. This study aims to examine residential heating impact over Istanbul city by atmospheric modelling. For this purpose WRF (Weather Research and Forecasting) meteorology model and CMAQ (Community Multiscale Air Quality) chemistry and transport model was applied. The first step was preparing emission inventory as input of the model. More complete and current emission inventory provides more trustable outputs. Residential heating emissions are generated with activity data and emission factor. The calculated emissions are also compared with TNO and EMEP emissions. Another purpose of this study was developing region specific emission factors of residential heating for Istanbul. The main fuels which are commonly used in the city are determined and combustion system is analysed. Residential heating is commonly supplied from natural gas and solid fuels such as coals and wood. The coals are classified as domestic and import coal. The fuels were burned in conventional stoves that is commonly used individual combustion system in Istanbul and pollutant concentrations are measured. The measurements for solid fuels were continuous and the concentration values of each pollutants are reported minutely. For natural gas, individual combustion system was combi and concentrations were measured instantaneously. Combustion systems, burning efficiency and calorific values of the fuels are essential for burning regime and pollutant concentrations. Moreover, fuel consumption per unit time is a critical parameter for emission factor calculation. By considering all these parameters and concentrations emission factors are calculated for each fuels and pollutants. The main pollutants of this source are SOx, NOx, CO, PM10. Moreover, uncertainties of region specific emission factors that are calculated with continuous measurements are evaluated for solid fuels. Statistical methods are used in order to quantify the factors. Both parametric and non-parametric bootstrapping techniques applied and many distribution fitting models and related diagnostics were applied in the study. The emissions via using calculated region specific emission factors and WRF meteorological model outputs were used as input of CMAQ. The study episode was three months that is from December 1, 2009 to February 30, 2010. As reference case CMAQ model is applied with TNO inventory and then the model is run for the same episode with new emission inventory that is updated with calculated residential emissions. The difference of concentrations between two model outputs provide to understand contribution of the revised residential emissions over the city. The days and hours that have maximum concentration differences are determined as giving the highest response to the new inventory.
ÖgeUnderstanding 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 BilimleriTurkey, 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.
ÖgeWrf/cmaq Modelleme Sistemi İle Hava Kirliliğinden Kaynaklanan Avrupa'daki Tarımsal Zararın İncelenmesi(Eurasia Institute of Earth Sciences, 2016-05-02) Öztaner, Yaşar Burak ; Ünal, Alper ; 601131005 ; Climate and Marine Sciences ; İklim ve Deniz Bilimleri Anabilim DalıThe 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.