Evaluation of air pollution for Sanliurfa with Chimere model

Air pollution, affecting all developed and developing countries, is a global problem that may cause serious and fatal consequences not only in health but also in environment. It is seen that air pollution may result in catastrophic consequences with London episode in December 1952 by reason of the fact that thousands of people got sick and died within four days. Nowadays, however, increasing urbanization, the number of motor vehicles, human-induced factors such as fuels used for heating play a major role in the increase of air pollution. Many governments and national and international institutions have made regulations towards controlling air pollution caused by irrepressible increase of industry and population growth. It is aimed to protect all living creatures and environment from effects of air pollution by means of these regulations controlling air pollution emissions. Air quality standards are determined by a number of organization and institutions like U.S. EPA, WHO, EU Air Quality Framework and additional directives, World Bank. Effect of meteorological parameters is pretty significant in terms of distribution and transport of pollutants because the environment they release is atmosphere. Air pollution in a region also depends on topographical factors except anthropogenic contaminants. The information of how and which pollutans reached to region can be acquired by examining meteorological parameters and topography together. In this way, it can be known which weather conditions affect air pollution in advance. Thus, a control mechanism can be developed and can be taken precaution. Air pollution in Turkey came to the fore with air pollution episodes in big cities such as Istanbul, Ankara and İzmir where demand increased to oil and coal by population growth and rapid urbanization in 1950s. Air pollution in Turkey is still a struggling issue as in many country with population increase and rapid urbanization and industrialization. Principally in winter months, episode days can occur with heating and meteorological conditions. Air pollution has begun to become a problem with increasing urbanization and industry in developing cities. Air quality also has been changed in Şanlıurfa with increase of its population and industry day by day. The most important factors affect air quality of Southeastern Anatolia are human based pollutants by transportation and industry, heating and dust transportation occurs naturally. Southeastern Anatolia Project (GAP) which has been implemented most actively among regional development plan and programs prepared up to today, is the most extensive and highest cost project in Turkish Republic history. Şanlıurfa which is the most crowded ninth city of Turkey represents one of the most important cities of GAP because of its overpopulation and surface area. Şanlıurfa has rapidly become significance in that history of religions and belief tourism in world culture. Consequently, air quality has important in terms of living's health, tourism and agricultural activities in the city. Increasing population and pollutants due to composed from industry and motor vehicles induce to lower air quality. In addition, meteorological parameters and dust clouds flitting from Syria in additional to pollutant sources in region cause to form episode periods. In this study, episode periods will be analysed in winter and summer season throughout 2014 and will be compared with observation data of air quality and outcomes of CHIMERE model. Episode periods will be investigated together with synoptic charts thus correlation of air pollution with meteorological parameters will be studied as seasonal. In addition to meteorological parameters and pollution sources in the region, dust clouds from time to time due to the geographical location of Syria is experiencing can cause episodes. In this study, two different episode periods belong two different season as winter and autumn to investigate pollution days and sources in detail in Şanlıurfa. The reason choosing two different season is to examine dust transformation effect which is common in autumn and spring months and to reveal how much atmospheric conditions affect of city's pollution in winter months. Therefore, the relationship between pollutants and meteorological parameters will be clearly come to light and analysing of episode periods will be gotten easy. In addition, performance of CHIMERE model is revealed by simulated pollution situation in episode periods with CHIMERE chemistry model. Also, having two different season is advantageous in evaluation of model performance. The study also is aimed to exhibit to what extent episode periods remain in effect of dust transformation, local pollutant sources and meteorological parameters by searching detailed pollution situations. For this purpose, dust transformation effect is revealed with back trajectory analysis using NOAA Hysplit model outputs for these periods. Proportion of atmospheric conditions in pollution situations is studied to determine with synoptic maps, graphs of meteorological parameters and upper atmosphere maps provided by WRF. Air pollution data is provided by Şanlıurfa Air Quality Monitoring Station which is established by Republic of Turkey Ministry of Environment and Urbanisation. Hourly and daily data consist from PM10 and SO2 pollutants. Meteorological parameters as temperature, wind speed and wind direction also is provided from same station. Episode periods is selected according to EU limit values. The purpose of considering EU limit values is the plan of having same limit values in Turkey by 2019 year. Thus, it will be apparent how going has Şanlıurfa in pollution situation in general. Study also becomes more of an issue by being the first study which execute air pollution model for Şanlıurfa. Pearson correlation coefficient is accounted for PM10 and SO2 data to test model outputs and observation data. Also, time series are given in graphic. Thus, model's performance is seen more clearly. Model considerably underestimated pollutant observation concentrations. However, model relatively captured high concentration values in Şanlıurfa when examining Turkey outputs in general. For PM10 concentrations, pearson correlation coefficient is accounted as r=0.3 between CHIMERE model outputs and observation data considering 8-10 February hourly data. The signal of correlation coefficient shows direction of relationship as numerical value shows relationship's strength. Accordingly, it can be said there is a weak relationship between observation data and model forecasts for PM10 concentrations. For SO2 concentrations, pearson correlation coefficient is accounted as r=0.82 between CHIMERE model outputs and observation data considering from 8 February 00Z during 12 hour data. Even if it is difficult to deduce a certain result due to inadequate data, it can be said that model represents SO2 concentrations better in comparison with PM10 concentrations. For PM10 concentrations, pearson correlation coefficient is accounted as r= -0.064 between CHIMERE model outputs and observation data considering 11-12 October hourly data. Hence, it can be said there is too weak relationship between observation data and model forecasts for PM10 concentrations. This result which is especially seen in dust transport term shows that model is weaker in dust transport terms. In addition, this result for episode periods is radically overlaps other studies' outcomes. For SO2 concentrations, pearson correlation coefficient is accounted as r= 0.31 between CHIMERE model outputs and observation data considering 11-12 October hourly data. It can be sait that model represents SO2 data better in comparison with PM10 data like in winter period. It is aimed that the study demonstrate to guide and to bring light for a similar issue in the future. CHIMERE model's outputs substantially underestimated observation data especially for PM10. The most important cause of this result can be said to not specialised the WRF and CHIMERE model according to study area. Furthermore, many parameters which are given in CHIMERE model's general features in this study are run according to CHIMERE model's algorithm adoption. CHIMERE model which is especially initiated for Europe based studies can be understood for parameterization importance in model by running for other regions the model. It is obvious that adoption based parameters will be problem for model run. Model is quite underestimated observation data in episode periods for PM10 concentrations. It becomes more of an issue to run model providing meteorological data and emission data for study region.