Yurdanur Sezginer Unal

Analysis of surface ozone and nitrogen oxides at urban, semi-rural and rural sites in Istanbul, Turkey

Ozone (O(3)) mixing ratios were measured at three different sites (urban/traffic, semi-rural and rural/island) in Istanbul from September 2007 to December 2009 in order to determine the diurnal, monthly and seasonal variations of O(3) and nitrogen oxides (NO(x)) and to study the local and regional impacts. This is the first study that evaluates the O(3) levels in semi-rural and rural sites in Istanbul in addition to the urban sites. The diurnal O(3) variations are generally characterized by afternoon maxima (64 ppb at the urban, 80 ppb at the semi-rural and 100 ppb at the rural site) and the nighttime minimum being more pronounced at the polluted urban site. The monthly mean O(3) mixing ratios start to increase in March, reaching their maximum values in August for the urban (~25 ppb) and semi-rural sites (30 ppb). However, at the rural site, the monthly mean O(3) levels reach their maximum value in June (35 ppb). The O(3) mixing ratios for weekends were higher than those on weekdays at each site by up to 28%, possibly due to changes in VOC sensitivity and reduction in NO(x) levels. In order to better understand and characterize the relationship between air masses and O(3) levels, cluster analysis was applied to the back-trajectories calculated by the HYSPLIT model for the semi-rural site. The analyses clearly showed that major transport is characterized by northern and western clusters, particularly from the Eastern Europe and the Mediterranean region, as well as recirculation over Istanbul due to high pressure systems leading to accumulated levels of O(3). The results clearly suggest that extended measurement networks from urban to rural sites should be considered for a more comprehensive evaluation of O(3) levels.

Summer heat waves over western Turkey between 1965 and 2006

Global warming is one of the greatest environmental, economic, and social threats in the world. There are many assessments to estimate climate variability over many regions. A change in the Earth’s surface temperature leads to increase in extreme temperature events, which are harmful to the ecosystem, and moreover, they create danger on human health. In this study, we have selected the western part of Turkey as the study area, since climate change projections for Turkey point out that the highest temperature change can be expected on this region during summer, and the Turkish population is very dense here to be affected by extreme events. We have used apparent temperatures to define the heat waves which we have determined their frequencies for the summer months (June–August) of 1965–2006. Since the regional comparisons of station results are intended, we selected the 90th percentile value for each station as a threshold value to be used in the delineation of heat waves. Then, the number of heat waves is determined by imposing the constraint that apparent temperatures stay above the threshold value at least for three consecutive days. Then, the changes in the number of hot days and heat waves and also their durations are analyzed by using the linear least square method. We have found that the number of hot days, heat waves, and heat wave durations is increased between 1965 and 2006 on the western part of Turkey. Additionally, their rate of change is larger within the last decade and extremes are frequently observed after 1998. Regional distributions show that the tendency of the number of heat wave events increases towards the southern latitudes of the domain. Moreover, we investigated the relationship between the number of hot days and the sea surface temperatures of the Mediterranean Sea and Black Sea. Correlation analyses are carried out by the number of hot days and averaged sea surface temperatures on the regions of the western, central, and eastern Mediterranean Sea and the Black Sea. It is found that the number of hot days of west Turkey is better correlated with the sea surface temperatures averaged over eastern Mediterranean and Black Seas. The number of heat waves is found significantly correlated with the fire occurrences for most of the stations.

The influence of meteorological conditions and stringent emission control on high TSP episodes in istanbul

Istanbul has faced serious air pollution problems since the mid-80s. This is mainly due to particulate air pollution coming from poor quality lignite in areas, which are heavily populated and industrialized. As a consequence of severe air pollution problems, stringent control on the emissions in the city started in the year of 1994. In this work, in order to study the relationship between emissions and meteorological conditions, an assessment of air pollution episodes and air pollution potential in the city is presented for the terms at the changed emission schedule as the influence of an emission reduction strategy. The influence of meteorological conditions on the TSP (total suspended particulates) levels is considered for two consecutive winter periods. On this occasion, the city has faced different TSP levels and episode characteristics depending on stringent emission reductions covering the banned, poor-quality lignite and fuel switching. For this purpose, climatological conditions and air quality analyses were performed.

Factors Influencing the Variability of SO2 Concentrations in Istanbul

ABSTRACT The correlation between sulfur dioxide (SO2) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993–1994 and 1994–1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO2 levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous day SO2 concentrations as independent variables. Results suggest that the most important parameters, highly correlated with SO2 concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.

72hr forecast of wind power in Mani̇sa, Turkey by using the WRF model coupled to WindSim

Wind power forecasting has recently become important to fulfill the increasing demand on energy usage. Two main approaches are applied to the wind power forecasting which can vary from 6 hours to 48 hours. One way is to model the atmosphere dynamically and the other method is to analyze wind speed and direction statistically. Although dynamical models forecast better than statistical models, since the former solves the problem physically, statistical models can be preferable when short term forecasting is needed due to their quick response time. Most of the currently available wind power forecasting systems analyzes the effect of wind field on wind power based on numerical weather prediction models. However, the resolution of these models is not sufficient enough when the scale of the turbines on the wind farms is considered. It is possible to overcome this problem by using computational fluid dynamics (CFD) models, which can provide both linear and nonlinear solutions on the turbine scale in terms of both wind speed and wind power forecasting. In this study, the WRF model is used for 72-hour wind speed and direction forecasting. The initial and boundary conditions of the model are provided by ECMWF operational forecasting data with the resolution of 0.25 degree. The WRF model is downscaled to 1 km resolution over Manisa Soma wind farm and 72-hour forecasts for each day of 2010 are accomplished. WindSim uses wind speed and direction values, which are solved on the nearest grid point of the WRF model to the location of a wind turbine, to simulate high-resolution wind speed values for 72hours. These WRF to WindSim coupled model results are compared to the wind power observations. As a result, we found that daily wind power generation errors per turbine vary between 90kW and 200kW for the seasons of spring, summer, and fall, whereas the error is about 150-350kW for winter. We also compared the errors of 24 hourly model outputs and we found that there is no significant difference among the first, the second, and the third 24 hourly forecasts. We finally applied model output statistics to the WRF to WindSim coupled model results in order to minimize their errors.

Daily peak ozone forecast in Istanbul

In this study, we attempt to develop an ozone forecast model using two different approaches. The first approach is to use a multiple linear regression method and the second is to use a feed-forward artificial neural network. Models are developed for the ozone period of April through to September of the years 2002 and 2003 and verified for May to August 2004. In both models, 19 predictors are used. Calculated agreement indices (AI) for the model development period are 0.82 for the linear regression model and 0.88 for the artificial neural network model. On the other hand, AI values decrease to 0.53 and 0.64 for the validation period. Poor performance of the models in the validation phase might be due to the different maximum daily ozone averages of these two periods. While the average of maximum ozone values is 61.1 μg m−3 in the model development phase, it is 42.2 μg m−3 in the model validation phase.