عنوان به زبان ديگر :
Determination of Spatial Variation of CO and PMi0 Air Pollutants, Using GIS Techniques (Case study : Tehran, Iran)
چكيده لاتين :
The growth of technology and industry has caused air pollution in many countries all over the world. Since air pollution has a direct effect on the health of human beings, animals and plants, much attention have been paid by decision makers, experts and researchers to this problem. Iran , especially Tehran city, is not immune to this phenomenon as well; and huge financial, physical and social losses are occured each year.
There are many different methods for estimating air pollution. Some of these include: Proximity, Interpolation, LUR, Diffusion and so on, which each of these methods includes some advantages and disadvantages.
This study tried to determine spatial and temporal changes of the air pollution. To achievethis, interpolation method was applied. Moreover, to specify the factors that had effects on air pollution, another method- namely, land use regression (LUR)- was used.
Since the most important pollutants of the city are CO and PM10, using the Statistic MSE (Mean Square Error), different interpolation methods were first compared to produce air quality maps. Then the air quality maps for these two pollutants were produced for all days during 2004-2005 by optimum interpolation method. Also all of the produced maps were classified based on Air Quality Index(AQI).
Air pollution is associated with different factors such as topography, climate, population, transportation system and industry. To determine the most significant factors LUR method was used. Then, using the same method, modeling of the mentioned pollutants in spring was conducted.
LUR method consists geospatial information system and multivariate regression methods. In this study, the chosen locations for measuring the pollutants were the same locations where the station were situated. Then traffic volume, land use, population, elevation and distance of the stations from neighborhood roads were measured in different buffers. Finally, by using Multivariate, Backward, Forward and Stepwise regression methods, the relationships among the selected pollutants (as dependent variables) and the other variables (as independent ones) were obtained.
The results showed that the best method for estimating CO pollutants is Cokriging method with three secondary parameters- namely temperature, speed and wind direction. However, Spline method for PMjo pollutants yielded the better results.
Also, results demonstrated that in December and April CO volume and in March and July PMi0 volume were at their highest levels. Also it was found that the most CO pollutant volume were seen in 6, 7, 11, 12 district; while the most PM10 pollutant volume belonged to the 9, 10, 16, 17, 18 district.
Results of LUR model demonstrated that the most important factor affecting CO pollutant was the volume of traffic. The most important factor affecting the PM]0 was the existence of industrial locations and the final R2 of CO and PM,0 Pollutants models were 0.47 and 0.62 respectively.
