Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city

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.


  1. Alesheikh, A.A., Gharagouzlou, A., Sajadian, M. 2012. Study of Air Polution Resulting from the Transportation Traffic in Tehran Metropolis by Using LUR Model Combined With GIS And Emission Factors. Geographical Journal Of Chashmandaz-E-Zagros. 4(11), 143-158.
  2. Alikhah, M. and Foroutan, A.L., 2013, The usage of classification methods for land use mapping in the Hablehrood area, The fourth conference on human and environment, Hamedan, 41-47.
  3. Barati Ghahfarokhi, S., Soltani koupaei, S., Khajeddin, S.J., Rayegani, B., 2009. Land use change detection in Ghaleshahrokh using remote sensing technology. Journal of Water and Soil Science. 47, 439-465.
  4. Beelen, R., Hoek, G., Vienneau, D., 2013. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – the ESCAPE project. Atmos. Environ. 72, 10-23.
  5. Ghanbari Fard, R., Safavi A.A., Setoodeh, P., 2017. Effect of traffic flow modelling on air pollution in Shiraz city. Environmental Sciences. 15(1), 157-174.
  6. Hennig, F., Sugiri, D., Tzivian, L., Fuks, K., Moebus, S., Jockel, K., Vienneau, D., Kuhlbusch , A.J., Hoogh, K., Memmesheimer, M., Jakobs , H., Quass, U., Hoffmann, B., 2016. Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area. Atmosphere. 7(48), 1-19.
  7. Hosseiniebalam, F. and Ghaffarpasand, O. 2015. The effects of emission sources and meteorological factors on sulphur dioxide concentration of Great Isfahan, Iran. Atmospheric Environment. 100, 94-101.
  8. Jerrett, M., Aram, A., Kanaroglou, P., Beckerman, B., Potoglon, D., Sahsuvaroglu, T., Morrison, J., Giovis, C., 2005. A Review and Evaluation of Intra Urban Air Pollution Exposure Models. Journal of Exposure Analysis and Environmental. 15, 185-204.
  9. Kassomenos, P.A., Kelessis, A., Petrakakis, M., Zoumakis, N., Christidis, T., Paschalidoua, A.K. 2012. Air quality assessment in a heavily polluted urban Mediterranean environment through air quality indices. Ecological Indicators. 18, 259–268.
  10. Khedmatgozar Dolati, S.M., 2011. Land use mapping using the principal component analysis of satellite images in Shafarood area, master thesis, Natural resources faculty of Gilan University.
  11. Lee, M., Brauer, M., Wong, P., Tang, R., Tsui, T. H., Choi, C., Barratt, B. 2017. Land use regression modeling of air pollution in high density high rise cities: A case study in Hong Kong. Science of the Total Environment. 592, 306-315.
  12. Matkan, A.A., Shakiba, A., Pourali, H., Baharlou, I., 2010. The usage of LUR in estimating CO and PM10 pollutants (The case study of Tehran city), Geomatica conference, Tehran, 57-72.
  13. Miller, K.A., Siscovick, D.S., Sheppard, L., Shepherd, K., Sullivan, J.H., Anderson, L., Kaufiman, J.D., 2007. Long term Exposure to Air Pollution and Incidence of Cardiovascular Events in Women. New England Journal of Medicine. 356, 447-458.
  14. Mohammadi, A. and Rahimi, S. 2013. The effect of land use pattern on the spatial distribution of pollutants. research and urban planning. 14, 123-142.
  15. Moore DK, Jerrett M, Mack WJ, Kunzli N. 2007. A Land Use Regression Model for Predicting Ambient Fine Particulate Matter across Los Angeles, Journal of Environmental Monitoring, 9: 246-252
  16. Muttoo, S., Ramsay, L., Brunekreef, B., Beelen, R., Meliefste, K., Naidoo, R. N. 2018. Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa. Science of the Total Environment. 610-611, 1439–1447.
  17. Naughton, O., Donnelly, A., Nolan, P., Pilla, F., Misstear, B. D., Broderick, B. 2018. A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach. Science of the Total Environment. 630, 1324–1334.
  18. Shalaby, A. and Tateishi, R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography. 27(1), 28-41.
  19. Sahsuvaroglu, T., Arain, A., Kanaroglou, P., Finkelstein, N., Newbold, B., Jerrett, M., Beckerman, B., Brook, J., Finkelstein, M., Gilbert, N.L., 2016. A Land Use Regression Model for Predicting Ambient Concentrations of Nitrogen Dioxide in Hamilton, Ontario, Canada, J. Air & Waste Manage. Assoc. 56:1059–1069.