Analysis and zoning of air pollution in urban landscape using different models of spatial analysis (Case study: Tehran)

Document Type : Research Paper


1 Faculty of Agriculture and Natural Resources, Lorestan University, Iran

2 Faculty of Environment, College of Engineering, University of Tehran, Iran


In this study, spatial zoning models were compared to evaluate the concentrations of PM 2.5 on a large scale in the urban landscape of Tehran. The spatial analysis of PM 2.5 concentration was conducted based on the data from twenty-four stations that measure and monitor the air in Tehran. Three interpolation models were used to assess the air pollution status via Arc GIS 10.6.1 software: universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW). The root mean square error (RMSE) and correlation coefficient (R2) were applied to compare the spatial models and select the best model. Standardized root-mean-square error (RMSE) was used to select the best conditions for running the OK and UK models. The results showed that the southern and central regions of Tehran had high concentrations of PM 2.5, and the annual mean of all the stations exceeded the EPA standard (15 μ/m3). According to the annual average, station 16 had the highest concentration of PM2.5 (112.75 μ/m3). The results of RMSE showed that the OK model was more suitable than the others for the spatial zoning of air pollution in the urban landscape (RMSE=9.322).


Main Subjects

[1] Tainio, M., Sofiev, M., Hujo, M., Tuomisto, J. T., Loh, M., Jantunen, M. J., Porvari, P. (2009). Evaluation of the European population intake fractions for European and Finnish anthropogenic primary fine particulate matter emissions. Atmospheric environment, 43(19), 3052-3059.
[2] Sosa, B. S., Porta, A., Lerner, J. E. C., Noriega, R. B., Massolo, L. (2017). Human health risk due to variations in PM10-PM2. 5 and associated PAHs levels. Atmospheric environment, 160(2), 27-35.
[3] Jiang, N., Yin, S., Guo, Y., Li, J., Kang, P., Zhang, R., Tang, X. (2018). Characteristics of mass concentration, chemical composition, source apportionment of PM2. 5 and PM10 and health risk assessment in the emerging megacity in China. Atmospheric pollution research, 9(2), 309-321.
[4] Pio, C., Alves, C., Nunes, T., Cerqueira, M., Lucarelli, F., Nava, S., Karanasiou, A. (2020). Source apportionment of PM2. 5 and PM10 by Ionic and Mass Balance (IMB) in a traffic-influenced urban atmosphere, in Portugal. Atmospheric environment, 223(22), 117-132.
[5] Basu, N., Lanphear, B. P. (2019). The challenge of pollution and health in Canada. Canadian journal of public health, 110(2), 159-164.
[6] Feng, C., Li, J., Sun, W., Zhang, Y., Wang, Q. (2016). Impact of ambient fine particulate matter (PM 2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China. Environmental health, 15(1), 17-28.
[7] Yin, P., Brauer, M., Cohen, A., Burnett, R. T., Liu, J., Liu, Y., Zhou, M. (2017). Long-term fine particulate matter exposure and nonaccidental and cause-specific mortality in a large national cohort of Chinese men. Environmental health perspectives, 125(11), 102-117.
[8] Sicard, P., Khaniabadi, Y. O., Perez, S., Gualtieri, M., De Marco, A. (2019). Effect of O 3, PM 10 and PM 2.5 on cardiovascular and respiratory diseases in cities of France, Iran and Italy. Environmental science and pollution research, 26(31), 32645-32665
[9] Janssen, N. A. H., Fischer, P., Marra, M., Ameling, C., Cassee, F. R. (2013). Short-term effects of PM2. 5, PM10 and PM2. 5–10 on daily mortality in the Netherlands. Science of the total environment, 463(9), 20-26.
[10] Kollanus, V., Prank, M., Gens, A., Soares, J., Vira, J., Kukkonen, J., Lanki, T. (2017). Mortality due to vegetation fire–originated PM2. 5 exposure in Europe—assessment for the years 2005 and 2008. Environmental health perspectives, 125(1), 30-37.
[11] Ciarelli, G., Colette, A., Schucht, S., Beekmann, M., Andersson, C., Manders-Groot, A., Adani, M. (2019). Long-term health impact assessment of total PM2. 5 in Europe during the 1990–2015 period. Atmospheric environment: 12(3), 100-132.
[12]. Wang, Y., Shi, L., Lee, M., Liu, P., Di, Q., Zanobetti, A., Schwartz, J. D. (2017). Long-term exposure to PM2. 5 and mortality among older adults in the southeastern US. Epidemiology, 28(2), 189- 207.
[13]. Yazdi, M. D., Wang, Y., Di, Q., Zanobetti, A., Schwartz, J. (2019). Long-term exposure to PM2. 5 and ozone and hospital admissions of Medicare participants in the Southeast USA. Environment international, 130(21), 104-119.
[14] Maji, K. J., Arora, M., Dikshit, A. K. (2018). Premature mortality attributable to PM2. 5 exposure and future policy roadmap for ‘airpocalypse’affected Asian megacities. Process safety and environmental protection, 118(9), 371-383.
[15] Liang, F., Xiao, Q., Gu, D., Xu, M., Tian, L., Guo, Q., Liu, Y. (2018). Satellite-based short-and long-term exposure to PM2. 5 and adult mortality in urban Beijing, China. Environmental pollution, 242(11), 492-499
[16] Yorifuji, T., Kashima, S., Tani, Y., Yamakawa, J., Doi, H. (2019). Long-term exposure to fine particulate matter and natural-cause and cause-specific mortality in Japan. Environmental epidemiology, 3(3), 111-131.
[17] Ansari, M., Ehrampoush, M. H. (2019). Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran, Iran. Environmental research, 170(21), 141-150.
[18] Liu, M., Zhou, G., Saari, R. K., Li, S., Liu, X., Li, J. (2019). Quantifying PM2. 5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm. ISPRS journal of photogrammetry and remote sensing, 158(1), 90-98.
[19] Shukla, K., Kumar, P., Mann, G. S., Khare, M. (2020). Mapping spatial distribution of particulate matter using Kriging and inverse distance weighting at supersites of megacity Delhi. Sustainable cities and society, 54(8), 101-118.
[20] Hinojosa-Baliño, I., Infante-Vázquez, O., Vallejo, M. (2019). Distribution of PM2. 5 air pollution in Mexico City: Spatial analysis with land-use regression model. Applied sciences, 9(14), 29-36.
[21] Ehrampoush, M. H., Jamshidi, S., Zare Sakhvidi, M. J., Miri, M. (2017). A Comparison on Function of Kriging and inverse distance weighting models in PM10 zoning in Urban Area. Journal of environmental health and sustainable development, 2(4), 379-387.
[22] Kim, S. Y., Yi, S. J., Eum, Y. S., Choi, H. J., Shin, H., Ryou, H. G., Kim, H. (2014). Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities. Environmental health and toxicology, 29(1), 112-134.
[23] Sampson, P. D., Richards, M., Szpiro, A. A., Bergen, S., Sheppard, L., Larson, T. V., Kaufman, J. D. (2013). A regionalized national universal kriging model using Partial least squares regression for estimating annual PM2. 5 concentrations in epidemiology. Atmospheric environment, 75(5), 383-392.
[24] Habibi, R., Alesheikh, A. A., Mohammadinia, A., Sharif, M. (2017). An assessment of spatial pattern characterization of air pollution: A case study of CO and PM2. 5 in Tehran, Iran. ISPRS international journal of Geo-information, 6(9), 270-284.
[25] Pardakhti, A., Ebrahimi Qadi, M. (2019). Introduction and application of new GIS_AQI model: Integrated pollution control in Tehran. Pollution, 5(4), 789-801.
[26] Haghparast, M., Haji Seyed Mirza Hosseini, S. A., Mansouri, N., Ghodousi, J. (2019). Prediction of air pollution index by the GIS tools during cold seasons in the commercial zones of Tehran. Environmental energy and economic research, 3(3), 241-260.
[27] Xu, S., Zou, B., Lin, Y., Zhao, X., Li, S., Hu, C. (2019). Strategies of method selection for fine-scale PM2. 5 mapping in an intra-urban area using crowdsourced monitoring. Atmospheric measurement techniques, 12(5), 114-128.
[28] Cao, R., Li, B., Wang, Z., Peng, Z. R., Tao, S., Lou, S. (2020). Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2. 5 concentrations. Environmental pollution, 42(5), 135-149.
[29] Wang, Y., Bechle, M. J., Kim, S. Y., Adams, P. J., Pandis, S. N., Pope III, C. A., Marshall, J. D. (2020). Spatial decomposition analysis of NO2 and PM2. 5 air pollution in the United States. Atmospheric environment, 12(2), 117-128.
[30] Zhang, T., Liu, P., Sun, X., Zhang, C., Wang, M., Xu, J., Huang, L. (2020). Application of an advanced spatiotemporal model for PM2. 5 prediction in Jiangsu Province, China. Chemosphere, 246(2), 125-139.
[31] Yang, W., Wang, G., Bi, C. (2017). Analysis of longrange transport effects on PM2. 5 during a short severe haze in Beijing, China. Aerosol and air quality Research, 17(21), 1610-1622.
[32] Norpoor, A., Feyz, M. A. (2014). Determination of the Spatial and Temporal Variation of SO2, NO2 and Particulate Matter Using GIS Techniques and Estimation of Concentration Modeling with LUR Method (Case Study: City of Tehran), Journal of environmental studies, 40(3), 723-738.
[33] Berman, J. D., Breysse, P. N., White, R. H., Waugh, D. W., Curriero, F. C. (2015). Evaluating methods for spatial mapping: Applications for estimating ozone concentrations across the contiguous United States. Environmental technology and innovation, 3(2), 1-10.
[34] Halek, F., Kavousi-Rahim, A. (2014). GIS assessment of the PM 10, PM 2.5 and PM 1.0 concentrations in urban area of Tehran in warm and cold seasons. The international archives of photogrammetry, Remote sensing and spatial information sciences, 40(2), 141-153.