Evaluation and analysis of environmental noise pollution in the city of Erzurum, Turkey

Noise pollution and its influence on the environment and life quality of human beings is a hot topic in scientific research. A study has been carried out to determine the level of noise and its sources in the city of Erzurum located in the east of Turkey. Noise measurements were taken in the morning, at noon and in the evening to determine noise pollution all over the city, particularly motorway transportation noise. In order to determine the sensitivity of the city people to noise pollution, standard survey studies were also conducted. According to the results, the noise level in Erzurum city centre exceeded allowed values at 96 of 126 measurement points. From the measured noise values, a map of noise pollution was created for Erzurum. Proposed solutions are set out.     

Interaction between air pollution and meteorologicalparameters in Erzurum, Turkey

In recent years, the rapid increase in population density has caused increases in the consumption of fuel, and the outdoor air quality has deteriorated in the crowded urban areas of Turkey. Erzurum, a city in the eastern part of Turkey, is influenced by air pollutants such as SO₂ and suspended particles. It is known that, in general, the air pollution concentrations have a close relationship with meteorological factors. In this study, the relationship between outdoor air quality data and meteorological factors, such as wind speed, rainfall, temperature, sunshine hours and relative humidity, is statistically analysed, using the code SPSS. According to the results obtained through multiple linear regression analysis, there are moderate levels of correlation between SO₂ and particle concentrations and meteorological factors in Erzurum.     

A mathematical modeling of sulphur dioxide pollution in Erzurum City

A non-linear simple air-quality model was developed by applying the continuity equation for the air control volume over Erzurum city center and tested using daily average values of SO2 and meteorological data obtained during the winter seasons in Erzurum, Turkey from 1994 to 1998. Model parameters are estimated by non-linear regression analysis. Agreement between model predictions and measured data was found very satisfactory with standard deviations less than 20 microg/m3.     

Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters in the urban area are important due to health impact. Artificial intelligent techniques are successfully used in modelling of highly complex and non-linear phenomena. In this study, adaptive neuro-fuzzy logic method has been proposed to estimate the impact of meteorological factors on SO2 and total suspended particular matter (TSP) pollution levels over an urban area. The model forecasts satisfactorily the trends in SO2 and TSP concentration levels, with performance between 75-90% and 69-80 %, respectively.     

Air pollution in cities

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Air pollution in cities is a serious environmental problem – especially in the developing countries. The air pollution path of the urban atmosphere consists of emission and transmission of air pollutants resulting in the ambient air pollution. Each part of the path is influenced by different factors. Emissions from motor traffic are a very important source group throughout the world. During transmission, air pollutants are dispersed, diluted and subjected to photochemical reactions. Ambient air pollution shows temporal and spatial variability. As an example of the temporal variability of urban air pollutants caused by motor traffic, typical average annual, weekly and diurnal cycles of NO, NO₂, O₃ and O_x are presented for an official urban air-quality station in Stuttgart, southern Germany. They are supplemented by weekly and diurnal cycles of selected percentile values of NO, NO₂, and O₃. Time series of these air pollutants give information on their trends. Results are discussed with regard to air pollution conditions in other cities. Possibilities for the assessment of air pollution in cities are shown. In addition, a qualitative overview of the air quality of the world's megacities is given.     

Air pollution modelling of the Calabrian pine forests withered by the Yatagan thermal power plant, Turkey

The emissions from the chimney of the Yatagan thermal power plant have caused a 3050-hectare forest to wither. Such air pollutants as sulfur dioxide, nitrogen oxides and fly ash are carried towards the Bencikdagi Calabrian pine forests on the prevailing north-east winds. To study these effects, the Q-Basic computer program developed for the Gaussian puff model was used with meteorological data provided by the Yatagan Meteorological Office and the pollutant loads formed by the thermal power plant. In the grid system on the map showing the surroundings of the thermal power plant, the concentrations of the pollutants in each grid square were determined. The Yatagan plant has three units containing 9000 mg/m3 sulfur dioxide, 1800 mg/m3 nitrogen oxides and 680 mg/m3 fly ash in its chimneys. The minimum pollutant emissions were found to be 100 μg/m3 SO₂, 20 μg/m3 NO_x2, and 77 μg/m3 NO_x     

Air pollution in a convective boundary layer

Detailed profiles of the concentration and the concentration flux for pollutants released continuously from a point source near the ground are presented. Although there are no observational data of the concentration flux and the covariance of temperature and concentration, the distributions of concentration and eddy diffusivity derived from this study are in good agreement with those of laboratory experiments. This study also shows that the covariance of temperature and concentration is important in producing a countergradient concentration flux in a convective boundary layer.     

Air pollution and inversion features in Istanbul

Air pollution in the Istanbul metropolitan area arises from three significant sources: industrial and residential fossil fuels and road traffic. In winter this combination pushes air pollution to levels that are dangerous to humans and to plant life. As far as fossil fuels are concerned, total suspended particulates (TSP) and sulphur dioxide are the chief pollutants. In order to investigate the problem, five-year winter-season monthly pollution concentration records are used, starting from 1989. Classical statistical methods, such as frequency distribution functions, are examined and found to have exponential (SO₂), logarithmic normal (TSP) and Weibull (inversion height) distributions. A simple concept of 'pollutant polygon' is presented in order to make detailed interpretations of the relationship between the monthly averages of SO₂ and TSP. These levels were affected by the weak dispersion conditions in the lower atmosphere over the city.     

Hierarchical space-time modelling of PM10 pollution

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.