Document Type : Original Article
Department of Environment, Islamic Azad University, Isfahan (Khorasgan) branch, Isfahan, Iran
The rapid growth of technology has led to an increase in air pollution in most countries of the world. One of the most serious problems that metropolitan cities such as Esfahan encounter is air pollution. The most important pollutants that should be mentioned are PM, O3, SO2, CO and NOX. The main objective of this study is to analyze the land use effects and other effective parameters such as traffic on the air quality of Esfahan and evaluating the spatial dispersion of PM, O3, SO2, CO and NOX. LUR offers an improved level of detail at which pollution variability is observed. Numerous studies have shown that land use regression (LUR) models can be applied to obtain accurate, small-scale air pollutant concentrations without a detailed pollutant emission inventory.
Materials and methods:
Land use regression modelling is used as a useful method for estimating changes in the concentrations of air pollutants in cities. Thus, LUR predicts the concentrations of pollution based on surrounding land use and traffic characteristics within circular areas (buffers) as predictors of measured concentrations. Moreover, the enhancement of geographic information system (GIS) techniques has contributed to the dissemination of the LUR method. Since the air pollution is in relation to factors such as population, traffic, land use, height, road length and public transportation as the most effective factors in producing these pollutants have prepared using ArcGIS 10.2 and modeled by LUR method. The regression model was run using SPSS 19.
Results and discussion:
With the usage of the LUR method, the most important and effective factors could be determined and modelled. It should be mentioned that among different types of land uses, residential areas and industrial regions cause the maximum effects on air pollution.
The results of the LUR model have revealed that traffic volume, population and land use are the most important factor affected on pollutants production.