Zoning of Particulate Matters (PM) Pollution Using Local Statistical Models in GIS (Case Study: Tehran Metropolisies)

Nowadays, various pollutants have appeared in the air due to the human and biological activities. They threaten citizens’ lives especially in metropolises. These pollutions have direct effect on the health of citizens. Tehran, as the capital city and a metropolis, is engaged constantly with these risks. In recent years, one of the greatest threats for Tehran was suspended particles with a diameter of 2.5 microns, which causes most unhealthy days in recent years. The particles may be of natural origin (e.g., pollen, protozoa, fungi, plant fibers, and dust caused by volcanic activity) or human (e.g., combustion fumes, smoke, metal oxides, salts, oil or tar droplets, silicates, and metal thick smoke). Health studies have shown a significant association between exposure to dust and premature death from heart and lung diseases. In this respect, pollutant concentrations become a major challenge for management in Tehran. The status of the spatial distribution of pollution emissions enables managers to take appropriate actions proportionate to the dangers and risks and reduce risks. In other hand, measuring the concentration of pollutants is costly and performed for points. But, it is necessary that measuring these data where use of them for regional analysis and so then, generalization and distribution of these data to study on city area. Generally both methods are for spatial interpolation and dispersion models to identify and zoning pollutants. In recent years, for development of statistical and geostatistical models, it was available and used multiple spatial interpolation model. Linear interpolation methods use known values around unknown values and estimate these values, but effect of known values on unknown values cause to divide interpolation methods in two broad categories of totally and regionally parts. Frist part, a sheet gives fitness by total dates and in second method it takes place by near points. In Spatial studies, we faced with data that are shifting. They sifted by moving of a region to others. However, in this method of modeling, studied parameters are under independent variables that changed in regions. In these situations if use of statically methods ultimate matrix weight or Final dependence for each independent variable seem values same and, means that in totally method where consider total connect regions equal by each parameter, that in many cases are different with reality and dependency sifting with changing in locations. In contrast, in regionally method considered a limited area around any sample and estimated weight and relationship between independent and dependent variable(s). In this situation, weights and Dependency ratios are not constant and changed for regionally. So, our observe are very similar and those that are far of each other show Higher spatial dependence. This study used of a Geographically Weighted Regression method for zoning pollutants PM2.5 that is one of local methods. In this method use of land Use, population, elevation, main roads, freeways, temperature, wind and direction speed and Pollutants concentration as input data to model. In the general approach by concentration and know values and a Geographically Weighted Regression estimated weighted matrix and after applied This matrix to the Grid, Evaluated amounts of concentration. Finally, by Kriging model and concentration, PM2.5 concentrations fitted on Tehran. Finally, this research leads to produce the map of PM2.5 on the city of Tehran, which is useful to identify the risk areas in the city and applying measures to reduce pollution in these areas. Comparing these map produced with the observed data and reviewing statistical parameters such as coefficient of determination (R2=0.75-0.80) and root mean square error (RMSE=7.1-8.5) showed that the proposed model has high ability in estimation the concentration in various areas in the city of Tehran.