Noise pollution is a type of environmental pollution threatening the health of community and living organisms. Research show that both short and long-term exposure to noise pollution weakens hearing, increases blood pressure leading to cardiovascular disorders, causes sleep and mood disorders and changes behavior patterns. Urbanism and vehicles have increased noise pollution. Nowadays, noise pollution studies is an important subject in environmental studies. This paper attempts to investigate noise pollution in Tehran considering the population growth and large contributing of vehicles to this type of pollution. The study areas consisted of high traffic areas of Tajrish, Tohid, Hemmat and Hakim Freeways. In a field study, the noise level was measured via sound meter and the noise map was generated based on geostatistical methods via GIS software. For this purpose, the factors influencing noise level (e.g. traffic, road width, slope, and residential or administrative-commercial land use) were surveyed and recorded for each point and their local and time dependencies were computed via SPSS. The results indicated that by a large margin, traffic was the most influential factor on noise level, followed by residential areas, road width, slope, administrative-commercial land use, and green space. According to the noise pollution map, Hemmat Freeway had the highest level of noise pollution, respectively followed by Hakim Freeway, Tohid, and Tajrish areas. It appears that the freeway function of Hemmat and Hakim areas and their relatively longer distance from residential areas has significantly influenced the noise pollution level. In most streets of the area, newly constructed, multi-story buildings prevent the roads from being widened, however it is proposed to prevent the construction of tall buildings around the main streets and maintain standard distances between buildings and main streets or freeways.
In this paper, we did unit ranking by determining the best and the worst rank that each unit could have. By applying the optimistic Model, we determine the best rank of each unit, then by using the Pessimistic model, we determine the worst rank of that unit. The highest rank of each unit is displayed by r°b and the lowest rank is displayed by r°w.
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